Purpose: In this study, the fracture resistance of prosthetic screws was tested using abutments made of titanium, zirconia, and polyether ether ketone (PEEK) on dental implants.

Materials and Methods: From Straumann AG in Basel, Switzerland, dental implants with specified dimensions and prosthetic screws were purchased. Three different materials (Ti, Zr, and PEEK) were evaluated as abrasives. The implant-abutment units were subjected to a constant vertical force using a Universal Testing Machine (UTM) until the prosthetic screw broke. The force at the screw fracture site was measured, and one-way ANOVA and Tukey's post-hoc tests were used to statistically analyze the data.

Results: For Titanium, Zirconia, and PEEK abutments, the mean (±standard deviation) fracture resistance was 475±35 N, 430±40 N, and 390±30 N, respectively. A substantial difference in fracture resistance was found between the various abutment materials according to the one-way ANOVA (F (2,87) = 26.37, p<.001). Zirconia shown much stronger fracture resistance than PEEK (p <0.05) and Titanium abutments demonstrated significantly higher resistance than both Zirconia and PEEK (p <0.01), according to post-hoc tests.

Conclusion: For Titanium, Zirconia, and PEEK abutments, the mean (±standard deviation) fracture resistance was 475 35 N, 430 40 N, and 390 30 N, respectively. A substantial difference in fracture resistance was found between the various abutment materials according to the one-way ANOVA (F(2,87) = 26.37, p.001). Zirconia shown much stronger fracture resistance than PEEK (p .05) and Titanium abutments demonstrated significantly higher resistance than both Zirconia and PEEK (p .01), according to post-hoc tests.

Skin Cancer (SC) is a significant problem for public health on a global scale. Its early identification is essential for improving patient prognostics. However, there are substantial problems in this field with regard to the dearth of trained specialists and medical equipment. Deep learning-based approaches have significantly improved Skin Cancer Detection (SCD) as compared to traditional Machine Learning (ML) tasks and have attained high performance. Deep learning (DL) methods used for automated SCD have been popular in this domain. Several DL techniques have been put forth as of late to accomplish Federated Learning (FL) based SCD. There are several steps in the SCD employing deep learning and the FL model. Initially, primary sources and standardised databases are used to gather images of SC from a variety of patients. The next step is data cleaning, which includes noise reduction, resizing, and contrast enhancement. Additionally, the affected malignant section is segmented using edge-based, region-based, and morphological-based segmentation techniques. Following the extraction of features from the photos, deep learning approaches with FL assistance are used to classify the images. Last but not least, the FL-aided deep learning techniques categorise the image as malignant and non-cancerous. This review, which was undertaken by pulling data from 100 papers published between the years 2019 and 2023, provides a thorough statistical analysis. Finally, this survey will be beneficial for SCD researchers.

Purpose: To evaluate the antibacterial efficacy of different concentrations of natural cold-pressed flaxseed oil when used as an intra-canal medicament against Enterococcus faecalis.

Materials and methods: The antibacterial efficiency of flaxseed oil against E. faecalis was assessed in two sections using different concentrations. Both sections were compared to calcium hydroxide and tricresol formalin. The first section was on the agar, using two methods: agar diffusion and vaporization. The second section is on the extracted roots contaminated with E. faecalis for 21 days to form biofilms, confirmed by SEM examination, and includes two different methods: direct contact and vaporization. Bacterial swabs were collected before and after medication throughout two-time periods (3 and 7 days). The canal contents were swabbed using paper points kept for 1 minute in the root canal, and the collected samples were diluted and cultivated on plates containing blood agar. Survival fractions were determined by calculating the number of colony-forming units on culture medium after 24 hours.

The oil's minimum inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) against E. faecalis were determined using the micro-broth dilution method.

The active components in flaxseed oil were evaluated using GC-MS and HPLC analysis.

Results: The tested oil demonstrated antibacterial efficacy against E. faecalis in different concentrations and levels. The MBC was 22.5 µl/ml. Tricresol formalin induced powerful antibacterial action, while calcium hydroxide exhibited less effective antibacterial action as compared to flaxseed oil. Flaxseed oil contains numerous biologically active components.

Conclusion: Flaxseed oil exhibits strong antibacterial activity when evaluated against E. faecalis biofilm that has been cultivated in root canals.

Background: Patients with diabetes are more likely to develop polyhydramnios. The rate of Polyhydramnios among diabetic patients is ascending when contrasted with non-diabetic patients.

Objective: To compare the amniotic fluid index of diabetics and non-diabetics using sonography.

Methods: 200 people participated in a case-control study, 100 of whom were diabetic and the other 100 were non-diabetic. Toshiba XARIO XG was used in the study at the university ultrasound clinic in Green Town. It has a convex probe of 3.5-7.5 MHz frequency. All patients with diabetes and gestational diabetes of age 18-45 years are included during 2^nd & 3^rd trimesters. Any underlying pathologies like hypertension, multiple gestations were excluded in this study. SPSS version 25.0 was utilized for the analysis of the data.

 Results: The mean amniotic fluid index in diabetics and non-diabetics was 21.19 and 13.20 respectively.  In both diabetics and non-diabetics, the amniotic fluid index was found to be statistically significant (p=0.000). The chi-square analysis shows a significant association between AFI category and diabetes status. With the Diabetic group having a higher proportion of cases with Polyhydramnios AFI category and a lower proportion of cases with Normal AFI category compared to the Non-diabetic group. The mean estimated fetal weight in diabetics and non-diabetics was 1341.64 and 1372.53 respectively. Result shows that there was no significant difference in the estimated weight of the fetus between diabetic and non-diabetic females (p=0.088).

Conclusions: Study concluded that diabetes during pregnancy is associated with a significant increase in amniotic fluid levels, leading to a higher likelihood of polyhydramnios.

Purpose: This research aimed to evaluate how different concentrations of MgO filler particles influence on the hardness, surface roughness and SEM investigation of VerSiltal 50 silicone elastomeric materials that vulcanize at room temperature.

Material and methods Using different weight percentages of MgO powder, 60 samples were created (0%, 0.5%, and 1% by wt.). The analysis made use of thirty samples from each group. Tests for surface roughness and surface hardness were performed on two experimental groups that contained 0.5% and 1% weight of MgO filler. Descriptive statistics and analysis of variance with multiple comparison tests were used to evaluate the data, and significance was indicated by a P value < 0.05. Scanning electron microscopy was used to measure the surface topography (SEM). Energy dispersive X-ray spectroscopy (EDS) can be used to determine the distribution of Magnesium Oxide within the VST-50 silicone matrix.

Result: surface roughness and hardness increased as the percentage of MgO increased from 0.5 wt. % to 1 wt. %, compared with those in the control group. The Sem test showed a good dispersion of the micro fillers and incorporation within the polymeric matrix of silicone. It showed that there was a slight little agglomeration of micro filler as filler loading increased.

Conclusion: Compared to the control group, the means for surface roughness and hardness increased significantly in the 0.5 and 1 wt.%  MgO experimental groups.

As we embrace 2024, the field of translational medicine stands at an exciting crossroads where innovation, collaboration, and cutting-edge technology converge. Among the most powerful tools driving this transformation is preclinical imaging, a discipline that continues to redefine our ability to understand disease mechanisms and accelerate the development of effective therapies.

Preclinical imaging offers an unparalleled window into the living body—enabling researchers to monitor biological processes at anatomical, cellular, and molecular levels in real time. From high-resolution micro-ultrasound and MRI to PET, SPECT, and CT, these imaging modalities are integral to visualizing disease progression, evaluating therapeutic responses, and ultimately bridging the bench-to-bedside gap in translational research.

Building on the success of previous years, the TPCF (Tehran University of Medical Sciences Preclinical Core Facility) has taken a bold step forward in 2024 by expanding its flagship event into The 1st International Tehran Preclinical Imaging Conference (TPIC 2024). This evolution from symposium to conference reflects the growing impact of preclinical imaging and our community’s commitment to advancing translational science.

TPIC 2024 serves as a dynamic platform for researchers, clinicians, engineers, and industry partners to exchange knowledge, present novel findings, and explore emerging imaging technologies that are shaping the future of biomedical research. With a robust scientific program, including keynote lectures, oral and poster presentations, and hands-on workshops, the conference highlights both foundational innovations and practical applications in drug discovery, disease modelling, and therapy development.

We are proud that Frontiers in Biomedical Technology (FBT) continues its tradition of supporting scientific dissemination by publishing the abstracts from TPIC 2024. This compilation represents the dedication, creativity, and rigor of a global community united by a shared vision: to harness the power of imaging for better health outcomes.

We invite all attendees, contributors, and readers to explore these abstracts and join us in celebrating the progress and promise of preclinical imaging. For more information about TPIC 2024, including the full program and future initiatives, please visit our website at www.TPCF.ir.

Together, let us continue to illuminate the path of translational medicine—one image at a time.

In the past, heavy drinking was often linked to fatty liver. The prevalence of non-alcoholic fatty liver disease (NAFLD), which affects people who do not consume alcohol, has garnered a lot of attention in the last 20 years. Nearly all fatty liver diseases are now the leading cause of liver disease in industrialized nations. Fatty liver has traditionally been defined as having a hepatic fat content of more than 5% of liver weight. Several medical issues, including those caused by medications, poor diet, and infections, may lead to fatty infiltration of the liver. Modern scientific understanding, however, attributes fatty liver in most individuals to either being overweight or obese or to drinking too much alcohol. This research proposes a stacked ensemble approach to detect NAFLD efficiently and achieves 95.9% correct classification accuracy. It also compares the proposed method with other basic and boosting machine learning approaches. To improve machine learning for trustworthy and reliable NAFLD screening and diagnosis, we apply explainable AI methods to the ensemble model to identify the most influential features and patterns for NAFLD predictions.

Purpose: Denture stomatitis, poor oral health, and angular cheilitis can all result from bacterial and fungal colonization. As a result, denture cleaners have been suggested to preserve the longevity of partially removable dentures and the health of the oral mucosa. The purpose of the present study was to investigate the impact of ozone water on Polymethyl Methacrylate (PMMA) by studying wettability, Ultraviolet (UV) absorption, and surface topography following soaking for 10 and 20 minutes at a 2 mg/l concentration.

Materials and Methods: A sixty-disc-shaped sample of polymethacrylate material (Ivoclar Vivadent) was fabricated for the wettability and UV absorption tests, and three bar-shaped samples of polymethacrylate material (Ivoclar Vivadent) were fabricated for the surface topography. Three groups were created: the first was the control group (immersion of samples in distal water). Second group (immersion of samples in 2 mg/l of ozone water solution for 10 min), and third group (immersion of samples in 2 mg/l of ozone water solution for 20 min). The contact angle (a wettability parameter) on the surfaces of the samples was measured after each storage period. The UV absorption test was assessed using a spectrophotometer; ANOVA was used to perform statistical analysis on the data at level 0.0.5; and surface topography was evaluated using Scanning Electron Microscopy (SEM).

Results: Based on the findings of this research, there was no statistically significant difference between the experimental and control groups when testing wettability and UV absorption. There is no change in surface topography when assessed by SEM.

Conclusion: This research concluded that the samples prepared from PMMA material can be safely soaked in an ozone water solution without compromising their properties.

Purpose: The purpose of this study was to create an Intelligence System (IS) to analyze the Electroencephalogram (EEG) characteristics of patients with mild Traumatic Brain Injury (mTBI) and healthy volunteers. Generally, mTBI research demonstrates that patients suffer from Working Memory (WM). The frontal cortex is involved in the clinical physiology of mTBI and is crucial for delayed memory.

Materials and Methods: The Frontal-Medial Theta (FMT) is one of the most critical factors in mTBI verification. The oscillatory strength of FMT (4-8Hz) over the Frontal-Medial Cortex (FMC) or Supplementary Motor Area (SMA) and the medial-Sensory Motor Cortex (mSMC) is associated with efficient WM performance. The designed IS accesses the FMT of mTBI and healthy subjects by FCz and Cz electrodes placed in FMC or SMA and mSMC, respectively. The Multi-level Discrete Wavelet Transformation (MDWT) of EEG (FCz and Cz) is suggested here to investigate the mTBI. The FMT rhythms of EEG of FCz and Cz channels are extracted through 3-level-DWT. Then, 1768 features [712 features of healthy subjects + 1056 features of mTBI patients] for both the FCz and Cz electrodes were calculated via their FMT using eight statistical feature computations.

Results: The study found that the FMT strength of FCz and Cz electrodes is similar. The Bagging Classifier achieved 83.3333% accuracy with the 20-fold validation for the FCz electrode.

Conclusion: The strength of the FMT-FCz and FMT-Cz electrodes is approximately the same, and both are equally crucial to investigating mild Traumatic Brain Injury.

Purpose: One of the indirect methods that has been proposed as a way of the detection of anemia is blood attenuation in non-contrast Computed Tomography (CT) scans. Some indices of non-contrast CT scans have been studied as a clue. Most known of such indices include aortic blood density and the difference between blood density and aortic wall density. In the current study, we aimed to evaluate the left ventricle blood attenuation and its relation to patients’ hemoglobin levels.

Materials and Methods: A total of 523 patients who underwent non-contrast chest CT scan with available hemoglobin levels within 48 hours of interval from CT scan acquisition were recruited for this study. Left ventricle blood attenuation was measured and the correlation with hemoglobin levels was evaluated.

Results: There was found to be a linear correlation between blood attenuation in the left ventricle and hemoglobin levels (r=0.33). Our results showed that the highest level of accuracy for diagnosis of anemia is in the Hounsfield Unit of 37.5 for women and 38.5 for men (with 68% sensitivity and 60% specificity) which can be regarded as a reliable threshold.

Conclusion: It can be concluded that the attenuation of the blood in the left ventricle can potentially be a hint for anemia and further evaluation for Hb levels.

Purpose: Timely detection of breast cancer is essential for improving treatment outcomes, particularly in the field of oncology. Several diagnostic techniques are available, and one promising approach is the use of Quantum Dots (QDs) for accurate and early detection. This study focuses on the utilization of cadmium selenium QDs with and without silver coating, which can modulate the transfer intensity of light sources.

Materials and Methods: Cadmium selenium QDs with silver coating (CdSe@Ag₂S) were synthesized and characterized. These QDs were then mixed with blood samples containing different concentrations of hemoglobin to simulate breast cancer conditions. The mixture was injected into phantom vessels representing breast tissue, and the transmitted light intensity was measured using a power meter. The light source used operated in the near-infrared range at a wavelength of 635 nm.

Results: The transmitted light intensity from vessels containing normal hemoglobin concentration was measured at 5.24 mW. However, in cancerous breast tissue, the intensity decreased to 4.56 mW and 3.34 mW for two and four times the hemoglobin concentrations, respectively. When the CdSe QDs were combined with different hemoglobin concentrations, the intensities of transmitted light were found to be 3.14 mW, 2.26 mW, and 1.22 mW for normal, twice, and four times the concentration of hemoglobin in turn. Furthermore, when the test was conducted using CdSe@Ag₂S QDs, the intensities of transmitted light were 1.83 mW, 2.52 mW, and 3.31 mW for the same hemoglobin concentrations, respectively.

Conclusion: This study concludes that the combination of different hemoglobin concentrations with QDs enables the differentiation between healthy and cancerous blood, enabling the early detection of breast cancer during its initial stages of development. Early detection of breast cancer has significant potential for improving treatment outcomes in the field of oncology.

Background: Three-dimensional echocardiography (3DE) allows simultaneous evaluation of the entire left ventricular (LV) volume, motion, and mechanical dyssynchrony. This study aimed to provide valuable data on the feasibility and reliability of 3DE in assessing LV dyssynchrony in healthy individuals.

Methods: One hundred healthy volunteers, including both genders, with mean age, weight, and BMI of 39.64±10.21 years, 76.57±14.65 kg, and 27.59±4.3 kg.m^-2, respectively, without evidence of structural heart or chronic disease, were included. 3DE examinations were conducted using a 4-chamber view and the full-volume method for all volunteers. Dyssynchrony was automatically quantified as the systolic dyssynchrony index (SDI) for selected LV segments using Q-lab software. The standard deviation (SD) of the time to attain minimum systolic volume was considered as SDI. This time length was expressed as % R-R to compensate for the variability of heart rate and increase reproducibility. Consequently, a single SDI (global SDI) was available for quantifying the degree of LV dyssynchrony by comparing all segments.

Results: According to echocardiographic findings, the mean global LV-SDI, apical SDI, basal SDI, and mid SDI were 28.68±15.48 msec, 26.16±27.47 msec, 24.41±14.35 msec, and 22.07±18.24 msec, respectively. After correction for RR duration, the mean global LV-SDI was 3.49±1.97%, apical SDI: 3.21±3.58%, basal SDI: 2.97±1.82%, and mid SDI: 2.68±2.19%.

Conclusion: 3DE proves to be a useful tool for evaluating LV dyssynchrony. The data provided include age- and sex-related changes in total and regional SDI in healthy volunteers, serving as a suitable reference for further investigations into LV dyssynchrony changes.

Introduction: The use of CT scan in diagnosis is increasing significantly. The purpose of This study is evaluating the normal brain and chest Computed Tomography (CT) scans at six medical imaging departments in Tehran and evaluate the radiation dose and estimate the cancer risk associated with these normal CT scans.

Materials and methods:  The data of 1080 patients between 20 to 50 years old referred to the six medical imaging centers in Tehran hospitals (1^st January to 30^th March) were collected. Patients were categorized into six groups according to their ages. In this study, a radiologist helped us in interpreting the CT scan images. The DRL was assessed and the BEIR VII model was used to estimate the radiation cancer.

Results: Among the 1080 patients, 642 (59%) were males and the average age of the patients was 45.05 ± 22.60 years. Brain CT scans in 65% cases and chest CT scan in 52% were reported normal. The third quartile of CTDI_vol, DLP, and ED values in the brain and chest scans were calculated and introduced as local DRL values. These values were determined as 22.13, 428.58, and 0.65 for CTDI_vol, DLP, and ED values in the brain scan, and 5, 187.35, and 3.71 in the chest scan. The highest risk of cancer incidence in the brain scan was related to leukemia with a value of 0.73 per 100000 exposures, followed by thyroid with a value of 0.62 in women aged 20-25 years. And in the chest scan, the highest risk of cancer incidence was related to breast cancer with a value of 22.4 per 100000 followed by lung cancer with a value of 19.02 in the same age group.

Conclusion: As the age decreases, the risk of cancer increases, therefore, by optimizing the radiation dose and avoiding CT scans without indications, the risk of cancer can be significantly reduced.

Purpose: At resting state, the human brain releases cycles of Electroencephalography (EEG), which has been proven aberrant in persons with schizophrenia. Deep learning methods and patterns found in EEG of brain activity are helpful features for verifying schizophrenia. The proposed study demonstrates the applicability of alpha-EEG rhythm in a Gated-Recurrent-Unit-based deep learning model for studying schizophrenia.

Materials and Methods: This study suggests Rudiment Densely-Coupled Convolutional Gated Recurrent Unit (RDCGRU) for the EEG rhythm (gamma, beta, alpha, theta, and delta) based diagnoses of schizophrenia. The model includes multiple 1-D-Convolution (Con-1-D) folds with steps greater than 1, which enables the model to programmatically and effectively learn how to reduce the incoming signal. The Con-1-D layers and numerous Gated Recurrent Unit (GRU) layers comprise the Exponential-Linear-Unit activation function. This powerful activation function facilitates in-deep-network training and improves classification performance. The Densely-Coupled Convolutional Gated Recurrent Unit (DCGRU) layers enable RDCGRU to address the training accuracy loss brought on by vanishing or exploding gradients. This makes it possible to develop intense, deep versions of RDCGRU for more complex problems. The sigmoid activation function is implemented in the digital classifier's output nodes.

Results: The RDCGRU framework performs efficiently with alpha-EEG rhythm (88.06%) and harshly with delta-EEG rhythm (60.05%). The research achievements: In EEG rhythm-based schizophrenia verification, GRU cells with the RDCGRU deep learning model performed better with alpha-EEG rhythm.

Conclusion: The α-EEG rhythm is a crucial component of the RDCGRU deep learning model for studying schizophrenia using EEG rhythms. In our investigation of RDCGRU deep learning architectures, we noticed that Con-1-D layers connected special learning networks function well with the α-EEG rhythm for the EEG rhythm-based verification of schizophrenia.

Abstract

Purpose: Magnetoencephalography is the recording of magnetic fields resulting from the activities of brain neurons and provides the possibility of direct measurement of their activity in a non-invasive manner. Despite its high spatial and temporal resolution, magnetoencephalography has a weak amplitude signal, drastically reducing the signal-to-noise ratio in case of environmental noise. Therefore, signal reconstruction methods can be effective in recovering noisy and lost information.

Materials and Methods: The magnetoencephalography signal of 11 healthy young subjects was recorded in a resting state. Each signal contains the data of 148 channels which were fixed on a helmet. The performance of three different reconstruction methods has been investigated by using the data of adjacent channels from the selected track to interpolate its information. These three methods are the surface reconstruction methods, partial differential equations algorithms, and finite element-based methods. Afterward to evaluate the performance of each method, R-square, root mean square error, and signal-to-noise ratio between the reconstructed signal and the original signal were calculated. The relation between these criteria was checked through proper statistical tests with a significance level of 0.05.

Results: The mean method with the root mean square error of 0.016  0.009 (mean  SD) at the minimum time (3.5 microseconds) could reconstruct an epoch. Also, the median method with a similar error but in 5.9 microseconds with a probability of 99.33% could reconstruct an epoch with an R-square greater than 0.7.

Conclusion: The mean and median methods can reconstruct the noisy or lost signal in magnetoencephalography with a suitable percentage of similarity to the reference by using the signal of adjacent channels from the damaged sensor.

Purpose:  The human brain is comprised of distinct regions, each contributing uniquely to behavioral control. The execution of even basic tasks necessitates synchronized activities among multiple brain regions. Fundamental cognitive functions hinge on the capacity to retain and flexibly manipulate information, a key role ascribed to working memory (WM). This study seeks to enhance our understanding of the neural mechanisms underlying WM and elucidate the coordinated neural activities spanning various brain regions.

Materials and Methods: To achieve this objective, the invasively recorded electrophysiological activities from medial temporal (MT) cortex of human using high number of electrodes were analyzed. The subjects did a verbal working memory task including three phases: encoding, maintenance and retrieval.

Phase synchronization between electrode signals in common frequency rhythms determined by phase locking value (PLV) was used to create brain network graphs.

Results: This study validates prior findings on neural synchronization in the hippocampus, entorhinal cortex, and amygdala during WM within the theta, alpha, and beta bands. Analysis of Phase Locking Value (PLV) dynamics during encoding and maintenance, reveals strong modulation in the theta, alpha and beta rhythm. Notably, PLV of theta oscillation between channels within posterior hippocampal region was significantly reduced during maintenance. Conversely, PLV of theta-alpha rhythms between anterior hippocampal region (AHL) and amygdala/entorhinal cortex was significantly increased by WM.

 Conclusion:

This study, for the first time demonstrates the networks involved in WM within MT areas in the human brain. These findings underscore the frequency-specific intricacies in WM modulation, providing valuable insights into neural coordination during specific processing stages.

Purpose: Understanding neural mechanisms is critical for discerning the nature of brain disorders and enhancing treatment methodologies. Functional Magnetic Resonance Imaging (fMRI) plays a vital role in gaining this knowledge by recording various brain regions. In this study, our primary aim was to categorize visual objects based on fMRI data during a natural scene viewing task. We intend to elucidate the challenges and limitations of previous models in order to produce a generalizable model across different subjects using advanced deep learning methods.

Materials and Methods: We've designed a new deep learning model based on transformers for processing fMRI data. The model includes two blocks, the first block receives fMRI data as input and transforms the input data to a set of features called fMRI space. Simultaneously a visual space is extracted from visual images using a pre-trained inceptionv3 network. The model tries to construct the fMRI space similar to the extracted visual space. The other block is a fully connected (FC) network for object recognition based on fMRI space. Using transformer capabilities and an overlapping method, the proposed architecture accounts for structural changes across different voxel sizes of the subjects' brains.

Results: A unique model was trained for all subjects with different brain sizes. The results demonstrated that the proposed network achieves an impressive similarity correlation between visual space and fMRI space around 0.84 for train and 0.86 for test dataset. Furthermore, the classification accuracy was about 70.3%. These outcomes underscored the effectiveness of our fMRI transformer network in extracting features from fMRI data.

Conclusion: The results indicated the potential of our model for decoding images from the brain activities of new subjects. This unveils a novel direction in image reconstruction from neural activities, an area that has remained relatively uncharted due to its inherent intricacies.

Purpose: Nowadays, detecting brain tumors is a crucial application. If a tumor is discovered later on, the medical issues are significant. Therefore, early diagnosis is essential. Magnetic Resonance Imaging (MRI) is the most recent detection, diagnosis, and assessment technology.

Materials and Methods: In this study, MRI images are segmented before input to a pulse-coupled neural network model to identify the existence of a tumor in the brain picture. The doctor may turn to this model for assistance if there are more input MRI brain pictures. This work preprocesses the images using normalization smoothing with linear filter and adaptive histogram. Statistical and Local Binary Patterns (LBP) features are extracted from the preprocessed images to perform the classification process. The Deep Convolutional Network (DCNN) is used to segment the image. The Pulse Coupled Neural Networks (PCNN) categorize the input images as normal and tumor.

Results: Accuracy, sensitivity, specificity, and precision are the various metrics evaluated. This work achieves 99.35 accuracies, 99.78 sensitivity, 98.45 specificities, and 97.61 precision. This work is compared with previous implementations to measure performance.

Conclusion: The comparison analysis improves tumor segmentation and classification accuracy. The suggested method yields great outcomes.

Purpose: The incorporation of Nanoparticles (NPs) in Computed Tomography (CT) imaging significantly enhances the contrast, clarity, and sensitivity of CT scans, leading to a substantial improvement in the accuracy and reliability of diagnostic information obtained from the images. The objective of the current research was to investigate the application of gold (Au) NPs in enhancing the imaging capabilities of Breast Cancer (BC) cells.

Materials and Methods: Au NPs were synthesized by loading Trastuzumab (TZ) on PEGylated Au NPs. Firstly, Au NPs were produced and coated with PEG-SH to form PEG-Au NPs. Next, TZ was coupled with OPSS-PEG-SVA to enable its attachment to the PEG-Au NPs. The resulting NPs were characterized for their structure, size, and morphology using standard analytical techniques. To assess the potential of the developed NPs for CT scan imaging of BC cells, SKBr-3 cells were treated with Au NPs and TZ-PEG-Au NPs. Additionally, the cytotoxicity of the NPs was evaluated with the MTT technique.

Results: The SEM and TEM analyses revealed that the synthesized NPs exhibited a spherical shape and displayed a relatively uniform size distribution (approximately 45 nm). The results showed that the developed Au NPs have acceptable biocompatibility and superior X-ray attenuation properties compared to a commonly used contrast agent.

Conclusion: Based on our results, it can be concluded that the proposed TZ- Polyethylene Glycol-Au NPs are suitable for CT imaging of BC cells.

Purpose: In this research, using the Geant4 software toolbox and metamaterials as a neutron shield, it was tried to introduce the proper metamaterial for this matter.

Materials and Methods: Boron Neutron Capture Therapy )BNCT( treatment is one of the most significant approaches used to treat brain tumors. The neutron source that is the main part of the BNCT method is produced by protons irradiation of ⁷Li converter. The brain tumor tissue, which contains a high concentration of ¹⁰B, is exposed to thermal neutron energy that is moderated by shield material. The dose of alpha particles that produced by the neutron decay of ¹⁰B in tumor tissue can be calculated by changing the metamaterial thickness. The best thickness of metamaterial for minimizing the radioactive elements production in brain tumor is calculated using the Geant4 toolkit.

Results: Waveguide Core (WC( metamaterial with 10 cm thickness is suitable for neutron moderation. The secondary elements produced in brain tumors is less than other thickness that is calculated by taking into account the alpha spectrum in tumor tissue. The alpha spectrum was calculated by the interaction of neutron spectrum released by the WC metamaterial.

Conclusion: The dose of alpha and secondary particles was obtained by the calculation of numbers and energy of these particles in brain tumors. The number of radioactive elements produced in the tumor tissue, as well as the most effective thickness of proper metamaterial to reduce the dose of secondary particles indicated that the WC metamaterial with a thickness of 17 cm is the best material for reducing radiation of neutron source that is produced by 35 MeV proton irradiation of ⁷Li neutron converter.

Lung cancer is a deadly disease which has high occurrence and death rates, worldwide. Computed Tomography (CT) imaging is being widely used by clinicians for detection of lung cancer. Radiomics extracted from medical images together with Machine Learning (ML) platform has given encouraging results in lung cancer diagnosis. Therefore, this study is proposed with the aim to efficiently apply and evaluate radiomics and ML techniques to classify pulmonary nodules in CT images. Lung Image Data Consortium is utilized in which nodules are given malignancy score 1 through 5 i.e. benign through malignant. Three scenarios are created randomly using these groups: G54 Vs G12, G543 Vs G12, and G54 Vs G123. Radiomics are extracted using Shape, Gray Level Co-occurrence Method, Gray Level Difference Method, and Gray Level Run Length Matrix along with Wavelet Packet Transform. To select a relevant set of features, four techniques i.e. Chi-square test, Analysis of variance, boosted ensemble classification tree and bagged ensemble classification tree are applied. The classification of nodule into benign or malignant is evaluated by using six models of Support vector machine. The results, in Scenario 1, show that MGSVM+Chi-square yields the best outcome compared to rest of the models with 75.3% accuracy, 77.9% sensitivity and 71.5% specificity. In Scenario 2, QSVM+Chi-square yields the best outcome compared to rest of the models with 74.7% accuracy, 70.3% sensitivity and 77.4% specificity. And in Scenario 3, CSVM+BACET yields comparatively better results with70.3% accuracy, 70.6% sensitivity and 62.1% specificity.

Purpose: There is a growing interest in the clinical application of new PET radiopharmaceuticals. This study focuses on using ⁶⁴Cu-DOTA-Trastuzumab for Positron Emission Tomography–Computed Tomography (PET/CT) imaging in gastric cancer patients. It aims to enhance the understanding of its bio-kinetic distribution and absorbed dose for safe and practical application in nuclear medicine.

Materials and Methods: The study was conducted at the Agricultural, Medical, and Industrial Research School (AMIRS), where ⁶⁴Cu was produced and purified. The radiopharmaceutical ⁶⁴Cu-DOTA-Trastuzumab was prepared, and three patients with confirmed Human Epidermal growth factor Receptor 2 (HER2)-positive gastric cancer underwent PET/CT scans at 1, 12 and 48 hours post-injection. Images were gained using a Discovery IQ PET/CT system and analyzed for an SUV. Bio-distribution was modeled using a two-exponential function, and absorbed doses were calculated using IDAC-Dose 2.1 software. CT doses were also evaluated.

Results: The study found that post-injection imaging at 12 hours or more provided superior image quality. The liver exhibited the highest cumulative activity, followed by the spleen and other organs. The effective dose estimates for ⁶⁴Cu-DOTA-Trastuzumab were within acceptable limits. CT dose calculations revealed that sensitive organs received higher doses.

Conclusion: This study successfully assessed the bio-kinetic distribution and absorbed dose of ⁶⁴Cu-DOTA-Trastuzumab in gastric cancer patients, demonstrating its safety and potential for clinical use. The optimal timing for PET/CT imaging and dosimetry data can inform clinical decision-making. Further research is warranted to explore the therapeutic potential of ⁶⁴Cu-DOTA-Trastuzumab and to establish clinical guidelines for its use.

Purpose: The present study aimed to evaluate the effectiveness of incorporating of nanohydroxyapatite in to hydrogen peroxide bleaching material on color, microhardness and morphological features of dental enamel.

Materials and Methods: 33 sound maxillary first premolar were used for the study. Enamel blocks (7mm× 5mm×3mm) were prepared from the middle third of buccal halves of each tooth. Each dental block was embedded in self-curing acrylic resin with exterior enamel surface exposed for various applications. The dental blocks were  randomly divided into three groups (n=11) according to the bleaching technique. The groups were designed as follows: control; hydrogen peroxide (HP) and hydrogen peroxide with nanohydroxyapatite (HP-nHAp) groups. Color measurements and microhardness tests were conducted before and after treatment. one sample represented each group was selected for morphological analysis.

Results: The  results showed  that both HP and HP-nHAp groups induced color changing. Enamel microhardness loss of HP group was significantly higher than that of HP-nHAp and control groups. The enamel morphological changes was only observed in HP group.

Conclusion: nHAp could significantly reduce the enamel microharness loss caused by HP while preserving enamel surface morphological features without affecting bleaching efficacy.

Purpose: Diabetes, resulting from insufficient insulin production or utilization, causes extensive harm to the body. The conventional diagnostic methods are often invasive. The classification of diabetes is essential for effective management. The progression in research and technology has led to additional classification approaches. Machine Learning (ML) algorithms have been deployed for analyzing the huge dataset and classifying diabetes.

Materials and Methods: The classification and the regression of diabetic and non-diabetic are performed using the XGBoost mechanism. On the other hand, the proposed class-centric Focal XG-Boost is applied to elevate the model performance by measuring the similarity among the features. The prediction of the model is based on the classification and regression rates of diabetic and non-diabetic individuals, which are anticipated using applicable and effectual metrics to estimate their working performance.

The dataset used in the Class-Centric Focal XG Boost model is attained using the Arduino Uno Kit. The data collection is done under a sampling rate of 100 Hz. The data are gathered from Bharati Hospital Pathology Laboratories, located in Pune.

Results: The inclusive outcomes of the proposed model with their appropriate Exploratory Data Analysis (EDA) among classification and regression, with the suitable dataset used in the study are exemplified.

Conclusion: The proposed Class-Centric Focal XG Boost model has numerous advantages and is less delicate to the hyperparameters than the conventional XGBoost algorithm. As a part of the real-time application of the Class-Centric Focal XG Boost model, the model can be utilized in other communicable and communicable disease classification and detection.

Purpose: The goal of biomedical researchers is to overcome harmful bacteria' resistance to antibiotics by developing new active chemicals quickly, cheaply, easily, and environmentally.

Materials and Methods: In this instance, iron nanoparticles (Fe-NPs) were created using a green method by mixing an equal volume of Zingiber officinale aqueous extract with a 100 ml solution of ferrous chloride tetrahydrate (FeCl2.4H2O (0.4 M)). The distinctive dark brown color shift of the mixture indicated the production of Fe-NPs.

Results: This suggests that the nanocomposite was created and the reaction occurred. FT-IR, TEM, and UV-Vis spectroscopy were utilized to analyze phytosynthesized Fe-NPs. Overall, the phytosynthesized Fe-NPs show activities that enable their use in various biomedical and biotechnological applications. Additionally, the antimicrobial effect was investigated against Gram-negative bacteria (Escherichia coli).

Conclusion: The antibacterial activity of E. coli was determined, and the highest zone of inhibition was observed at the concentration of 100 μg/mL.

Purpose: People with Down Syndrome must be served special because they have an intellectual disability with abnormality in memory and learning, so, creating a model for DS recognition may provide safe services to them, using the transfer learning technique can improve high metrics with a small dataset, depending on previous knowledge, there is no available Down syndrome dataset, one can use to train.

Materials and Methods: A new dataset is created by gathering images, two classes (Down=209 images, non-Down=214 images), and then expanding this dataset using Augmentation to be the final dataset 892 images (Down=415images, Non-Down=477 images. Finally, using a suitable training model, in this work, Xception and Resnet models are used, the pretrained models are trained on Imagenet dataset which consists of (1000) classes.

Results: By using Xception model and Resnet model, it concluded that when using Resnet model the accuracy = 95.93% and the loss function =0.16, while by using Xception model, the accuracy =96.57% and the loss function =0.12.

Conclusion: A transfer learning is used, to overcome the suitability of dataset size and minimize the cost of training, and time processing the accuracy and loss function is good when using Xception model, in addition, the Xception metrics are the best by comparing with the previous studies.

Purpose: Lung cancer treatment often involves radiotherapy, which can lead to an increased risk of secondary cancers in sensitive organs and Organs At Risk (OARs). Understanding this risk is crucial for optimizing treatment strategies and minimizing long-term adverse effects. The objective of this study is to estimate the Secondary Cancer Risks (SCRs) in sensitive organs and OARs using radiation-induced cancer risk prediction models, specifically the Biological Effects of Ionizing Radiation (BEIR) VII model and the International Commission on Radiological Protection (ICRP) model.

Materials and Methods: The radiotherapy dosimetric data of 30 lung cancer patients were collected all of whom underwent Computed Tomography (CT) scans. The PCRT-3D Treatment Planning System (TPS) was used for the treatment planning process. The risks were calculated based on the dose distribution in the target volume. The models for Excess Absolute Risk (EAR) and Excess Relative Risk (ERR) values (per 100,000 person-year) were utilized to estimate SCRs in planning target volume, OARs, and sensitive organs.

Results: The results indicate that, according to the BEIR VII model, the estimated EAR of cancer per 100,000 person-years was 38.39 in the heart, 35.83 in the esophagus, 5.49 in the contralateral lung, 2.17 in the liver, and 3.41 in the pancreas. Conversely, using the ICRP model, the EAR was calculated to be 58.73 in the heart, 38.78 in the esophagus, 20.48 in the contralateral lung, 3.49 in the liver, and 5.44 in the pancreas. These findings suggest that lung cancer patients treated with 3DCRT exhibit relatively high SCRs in the heart, esophagus, and contralateral lung organs in both models.

Conclusion: In this study, SCRs in a range of organs in lung cancer patients treated with 3DCRT were quantified. Our findings revealed that there were comparatively high SCRs in the heart in 3DCRT of lung cancer patients. Based on the findings of the current investigation, the ICRP model SCRs are greater in comparison to the BEIR VII model. These findings underscore the importance of considering SCRs in treatment planning and highlight the need for further research to optimize radiation therapy strategies and minimize long-term risks for lung cancer patients.

Purpose: In this work, nanocomposite with different weight ratios reduce graphene oxide/copper doping-anatase (rGO/Cu-TiO₂) has been successfully prepared using the photolysis method to evaluate the role of rGO/Cu in photovoltaic properties performance application as a photoanodes.

Materials and Methods: The X-Ray Diffraction (XRD), Raman spectrum, and X-Ray Photoelectron (XPS) results analysis confirmed successfully incorporating rGO/Cu in the TiO₂ crystal structure. Transmission Electron Microscopy (TEM) reveals the formation of spherical agglomeration nanoparticles with a size approximately equal to 18nm.

Results: The current density–voltage curves (J-V) and Intensity-Modulated Photocurrent Spectroscopy (IMPS) showed that the incorporation of rGO sheets enhances the ability of N3 loading of (rGO/Cu-TiO₂) photoanodes with faster charge transfer.

Conclusion: Our results illustrate that optimal Cu and rGO can increase the efficiency of dye-sensitized solar cells (4.56%) by 8.2% higher than TiO₂ DSSCs (3.52%).

Purpose: In this study, we propose a novel generalizable hybrid underlying mechanism m for mapping Human Pose Estimation (HPE) data to muscle synergy patterns, which can be highly efficient in improving visual biofeedback.

Materials and Methods: In the first step, Electromyography (EMG) data from the upper limb muscles of twelve healthy participants are collected and pre-processed, and muscle synergy patterns are extracted from it. Concurrently, kinematic data are detected using the OpenPose model. Through synchronization and normalization, the Successive Variational Mode Decomposition (SVMD) algorithm decomposes synergy control patterns into smaller components. To establish mappings, a custom Bidirectional Gated Recurrent Unit (BiGRU) model is employed. Comparative analysis against popular models validates the efficacy of our approach, revealing the generated trajectory as potentially ideal for visual biofeedback. Remarkably, the combined SVMD-BiGRU model outperforms alternatives.

Results: the results show that the trajectory generated by the model is potentially suitable for visual biofeedback systems. Remarkably, the combined SVMD-BiGRU model outperforms alternatives. Furthermore, empirical assessments have demonstrated the adept ability of healthy participants to closely adhere to the trajectory generated by the model output during the test phase.

Conclusion: Ultimately, the incorporation of this innovative mechanism at the heart of visual biofeedback systems has been revealed to significantly elevate both the quantity and quality of movement.

Purpose: This study aimed to investigate the biological effects of photon radiation and its potential for cancer treatment through targeted radiation therapy by studying direct and indirect DNA damage induced by 15, 30, and 50 keV photon radiation using Geant4-DNA Monte Carlo simulations.

Materials and Methods: Two spherical cells (C and C₂) and their cell nucleus were modeled in liquid water. An atomic DNA model constructed in the Geant4-DNA Monte Carlo simulation toolkit, containing 125,000 chromatin fibers, was placed inside the nucleus of the C₂ cell. The number of direct and indirect single-strand breaks (SSBs), double-strand breaks (DSBs), and hybrid double-strand breaks (HDSB) in the C₂ cell caused by 15, 30, and 50 keV photons were calculated for N₂←CS, N₂←Cy, N₂←C, and N₂←N Target←Source combinations, at the distances of 0, 2.5, and 5 μm between two cells.

Results: Low energy (15 keV) photons emitted within the cell surface and the cell cytoplasm resulted in the highest DNA damage, producing markedly higher SSBs, DSBs, and HDSBs compared to the whole cell and the nucleus sources across 0-5 μm target distances. Increasing the photon energy to 30 and 50 keV showed 81-96% reduced DNA damage. Additionally, the 2.5 μm target distance decreased DSBs up to 53%.

Conclusion: Based on the results, 15 keV photons are more effective for the inhibition or control of cancer cells.

Purpose: Integrating magnetic Nanoparticles (NPs) into contrast-enhanced Magnetic Resonance (MR) imaging can significantly improve the resolution and sensitivity of the resulting images, leading to enhanced accuracy and reliability in diagnostic information. The present study aimed to investigate the use of targeted trastuzumab-labeled iron oxide (TZ-PEG-Fe₃O₄) NPs to enhance imaging capabilities for the detection and characterization of Breast Cancer (BC) cells.

Materials and Methods: The NPs were synthesized by loading Fe₃O₄NPs with the monoclonal antibody TZ. Initially, Fe₃O₄ NPs were produced and subsequently coated with Polyethylene Glycol (PEG) to form PEG- Fe₃O₄ NPs. The TZ antibody was then conjugated to the PEG- Fe₃O₄ NPs, resulting in TZ-PEG-Fe₃O₄ NPs. The resulting NPs were characterized using standard analytical techniques, including UV-Vis spectroscopy, FTIR, SEM, TEM, VSM, and assessments of colloidal stability.

Results: Analyses indicated that the targeted TZ-PEG-Fe₃O₄ NPs exhibited a spherical morphology and a relatively uniform size distribution, with an average diameter of approximately 60 nm. These results confirmed the successful synthesis and controlled fabrication of the Fe₃O₄ NPs, which is crucial for developing effective Contrast Agents (CAs) for medical imaging applications. Additionally, the study confirmed the biocompatibility and magnetic properties of the synthesized TZ-PEG-Fe₃O₄ NPs.

Conclusion: The findings suggest that the developed targeted TZ-PEG-Fe₃O₄ NPs have significant potential as effective CAs for MR imaging of BC cells.

Objective:

Disease development or viruses that invade our bodies can be monitored using computed tomography imaging tools. However, it is not sufficient to reach the level of lung damage in COVID-19 patients through automated detection. Firstly, 100 patients were recruited between September 29 2020 and July 10, 2022, of whom tested positive for COVID-19 and CT images were collected, then composite technique is implemented to extract the percentage of lung damage of covid 19 patients.

Methods:

 In this study, a new approach was presented for improving CT images of the lung and specifying further lesions. This will help calculate the extent of damage without human intervention. The structure of the proposed technique draws upon four phases (data collection, improving, segmentation and extraction lung damage region and evaluation).

Results:

 The results revealed an effective method for quickly and practically calculating the percentage of lung damage. The convergence between manual evaluation, which represents the evaluation of the radiologist, and automatic evaluation, which is the result of implementing the proposed method, is clear, and this confirms the possibility of using it as an alternative in the absence of a specialist doctor. The difference in the arithmetic mean between it and the evaluation of the first specialist was equal to 3.5%, and the second was 10%. In addition, according to the results presented, the age group between (30-60) years is the most affected by the Corona virus.

Conclusions:

This method is An effective tool for assessment the percentage of lung damage of COVID19 quickly and practically. Where, Lung damage can be evaluated without human intervention It can be invested in telemedicine and emergency cases at the absence of a specialist doctor.

Purpose: The concrete construction of the musculoskeletal modeling is efficiently performed using information obtained from patients rather than collected from cadavers. In this study, we have endeavored to propose an automated technique that calculates the skeletal muscle pennation angle of patient ultrasound images and compares it with manual evaluations of the same images.

Materials and Methods: The proposed technique consists of three steps after the process of collecting the data from 30 volunteers of different muscles of the upper and lower limb. The first step is to improve the contrast in the image and identify the important details in the image through the use of two methods that depend on a fuzzy inference system, and this step is considered essential to prepare the image in the next step. The Hough Transform was used to follow the muscle fibers and draw them as lines, this is the second step. The third step is to calculate the angle and compare it with the manual evaluation that was done depending on the ultrasound machine options.

Results: The results reveal that there is a slightly difference between manual and automated evaluations of pennation angle for biceps (upper limb muscle) and gastrocnemius (lower limb muscle) as 8.6% and 0.45% respectively. Furthermore, the manual assignment of pennation angles is significantly slower, taking minutes, while the automated approach takes only a few seconds. Automated measurements take 85% more time compared to manual measurements.

Conclusion: There is no significant difference between measurements based on t-test. In future work, we aspire to a wider application of this technique to other muscles in the body and to activate it as an option available in the ultrasound device.

Background and Objective: Orthodontic archwires play an important part in the demineralization of enamel during orthodontic treatment. Dental caries is thought to be caused by the adhesion and colonization of mutans streptococci on these surfaces, followed by the formation of pathogenic plaque. This research was conducted with the purpose of testing and comparing the adhesion of mutans streptococci to a variety of aesthetic archwires as well as a conventional archwire made of NiTi.

Methods: four types of nickel titanium archwires with round cross section 0.016 inch were used in the current study, one type uncoated NiTi and three types of coated NiTi (rhodium, gold and flexyblue). After two hours of agitation in 2 ml of sterile UWS, 5 pieces of each archwire incubated in a streptococcus mutans suspension for 5-, 90-, and 180-minutes time interval. Bacterial adhesion was assessed by a microbial culture technique and the amount of bacterial adhesion were counted by colony forming unit.

Results: there was no statistically significant differences in mutans streptococci adhesion among archwires at (90 and 180 minutes), However, at 5 minutes, the differences between gold and uncoated NiTi and rhodium and uncoated NiTi were statistically significant.

Conclusion: Clinical use of esthetic-coated archwires may provide the same risks for bacterial adhesion compared with uncoated conventional archwires and increased mutans streptococci adhesion was significantly related to longer incubation time.

Background: The prevalence of coronavirus has increased the use of CT scans, a high-exposure imaging technique. This study was designed to estimate organ dose and effective dose to investigate the lifetime attributable risks (LARs) of cancer incidence and mortality in COVID-19 patients. 600 patients who had COVID-19 or were suspected of having it, were included in the current study.

Methods: Dosimetric parameters such as dose length product (DLP), volumetric CT dose index (CTDI_V), and scan length, were used to estimate the patient’s dose and cancer risk. The ImPACT CT dosimetry software was also used to calculate organ doses and effective doses. The cancer risk was calculated using the National Academy of Sciences' Biologic Effects of Ionizing Radiation (BEIR VII) report.

Results: For females, the mean effective dose based on International Commission Radiation Protection 103 (ICRP103) and ICRP 60 was 2.36 ± 0.48 mSv and 1.2 ± 0.28 mSv, respectively. For males, this parameter was 2.31 ± 0.53 mSv and 1.21 ± 0.45 mSv based on ICRP103 and ICRP60, respectively. For males, the mean LAR of all cancer incidence and cancer mortality was 14.79 ± 4.85 and 8.59 ± 2.42 per 100000 people, respectively. For females, these parameters were 23.37 ± 9.59 and 12.61± 3.89 per 100000 people, respectively.

Conclusion: Chest CT scan examination connected with a non-considerable radiation dose and risk of cancer. So according to the ALARA principle, CT protocol must be optimized to limit radiation-induced risk.

Purpose: Recent years have witnessed a significant amount of research into laser ablation in liquids due to the many potential applications of laser microprocessing of materials, including the synthesis of nanomaterials and nanostructures. This study aims to explore the antibacterial effects of cadmium oxide on both positive and negative bacteria.

Materials and Methods: Pulsed Laser Ablation (PLA) in liquid is a straightforward and environmentally friendly technical technique that works in water or other organic liquids under ambient conditions, in contrast to other, usually chemical methods. Here, 30 milliliters of deionized water were used to create Cadmium Oxide (CdO) nanoparticles using the PLA method. UV-vis spectrometry, FT-IR, X-ray spectroscopy, and FESEM were utilized to analyze the products' morphology, spectral content, and particle size.

Results: The produced nanocomposites were tested as antibacterial agents against gram-negative bacteria (Proteus mirabilis and Escherichia coli) as well as gram-positive bacteria (Staphylococcus aureus and Enterococcus faecalis). The MIC results on the MTP plate with the synthesized Cd NPS compound were as follows: Proteus mirabilis (6.25%), Escherichia coli (12.5%), Enterococcus faecalis (12.5%), and Staphylococcus aureus (12.5%). These findings indicate that the synthesized nanoparticles exhibit comparable antibacterial activity against both Gram-positive and Gram-negative bacteria.

Conclusion: The synthesized nanoparticles made of CdO demonstrated notable antibacterial activity that was effective against all of the bacteria that were tested in the experiments. This indicates that the CdO nanocomposite has the potential to inhibit the growth of a wide variety of bacterial strains.

Purpose: The process of making a decision based on available sensory information is called “Perceptual Decision Making”. The manner in which this decision is made has a direct impact on a person's social and personal relationships. Despite numerous studies in the field of perceptual decision making, there is still no robust system that can recognize people's perceptual decisions objectively. To this aim, this study aims to examine the relationship between EEG signals and perceptual decision making in healthy individuals.

Materials and Methods: The research employs an online EEG dataset based on visual stimuli, including faces and cars, obtained from 16 participants. Since there is no binary decision-making mode in the brain and there is an uncertainty in which each option has a special weight in decision-making and finally the option that passes a threshold is selected, this research has tried to incorporate this uncertainty into the final model to improve perceptual decision recognition system performance. For this purpose, a fuzzy radial basis function (FRBF) network was utilized.

Results: After extracting 26 features from the preprocessed EEG signals, Friedman’s non-parametric statistical analysis was performed, revealing that differences in the coherence of stimulus representations have a greater impact on an individual's decision-making process than spatial prioritization. Then, FRBF network classifier, with the extracted features from TP9 and TP10 channels as input, achieved an accuracy of 90.3% in classifying the test data as either a "face" or "car".

Conclusion: The classification accuracy results showed that the proposed method is an effective procedure for recognition of human decisions.

Purpose: The phantom-less patient-specific quality assurance (PSQA) for intensity‐modulated radiotherapy (IMRT) plan verification has been exploited recently. The aim of this study was the feasibility of the PSQA of the plan based on a log file and onboard detector for prostate patients in helical tomotherapy.

Method: For 15 prostate patients, the quality assurance (QA) of the helical tomotherapy plan was performed using the Delta4 phantom and Cheese phantom to evaluate the spatial dose distribution and point dose, respectively. These parameters were also reconstructed by delivery analysis (DA) software using the measured leaf open times (LOTs). The gamma analysis and relative dose difference were used to compare the measured and reconstructed dose with the calculated values. Then, using the relative discrepancy, the log file and onboard detector data were compared with the expected data to assess machine performance.

Results: The mean relative dose difference was within 1.3% among the measurement, reconstruction, and calculation. The results of statistical analysis and p-value showed there is no statistically significant difference in determining the dose difference between the DA-based and conventional QA methods. The gamma values of 3%/3mm, 3%/2mm, 2%/3mm, 2%/2mm, 2%/1mm, and 1%/1mm for the DA-based QA method were the same as the measurement QA method. However, the gamma values of 3%/1mm, 1%/3mm, and 1%/2mm were comparable. The mean percentage difference LOTs was 0.07%, and most differences occurred in very low and some high LOTs. The relative difference was lower than 2.30% for the couch speed, couch movement, monitor unit, and rotation per minute (RPM) gantry between the log file and expected data.

Conclusion: The DA software is an efficient alternative to the measurement-based PSQA method. However, the accuracy of the DA software requires further investigations for gamma analysis at strict criteria. The very low and high LOTs may lead to the dose discrepancy. The tomotherapy machine can accurately implement the planned parameters.

Abstract

Purpose: The objective of this paper is studying the feasibility of using effective connectivity (Granger causality) obtained from resting-state functional magnetic resonance imaging (rs-fMRI) data and stacked autoencoder for diagnosing autism spectrum disorder (ASD) and comparing the results with those obtained using functional connectivity (Pearson correlation coefficient).

Background: ASD affects the normal development of the brain in the field of ​​social interactions and communication skills. Because diagnosing ASD using behavioral symptoms is a time-consuming subjective process which needs exact collaboration of the ASD subject or his/her relatives, in recent years diagnosing ASD using resting-state functional neuroimaging modalities like rs-fMRI, has been taken into consideration.

Materials and Methods: We used rs-fMRI data and compared the use of functional and effective connectivity features using autoencoder to classify people with ASD from healthy subjects. We used ABIDE dataset and have divided the brain into 100 regions using the Harvard-Oxford Atlas. We calculated the Pearson correlation coefficient in classification using functional connectivity, and we calculated the granger causality in classification using effective connectivity. We used a stacked autoencoder to reduce the dimension of feature-space and a multi-layered perceptron (MLP) neural network as classifier in both classifications. To the best of our knowledge this is the first time that granger causality and stacked autoencoder have been used to diagnose ASD together.

Results: We achieved accuracy of 67.8%, sensitivity of 68.5% and specificity of 66.6% in classification using functional connectivity, and we achieved accuracy of 67.6%, sensitivity of 73.1% and specificity of 60.8% in classification using effective connectivity. 

Conclusion: Although the accuracy obtained using the functional and effective connectivity are almost similar, the sensitivity is notably higher using effective connectivity. Since sensitivity is more important than specificity in the medical diagnosis, it seems that using effective connectivity features may outperform the ASD diagnosis in practice.

Purpose: Identification and categorization of brain tumors is a cyclical process in which tumor components are assessed and suggestions for therapy are made based on their classifications. Many imaging techniques are used for this work. Because MRI provides better soft tissue than CT, and MRI does not involve radiation. The currently available manual method is inefficient and hence we provide an advanced method by using the deep learning concepts.

Materials and Methods: This MRI creates detailed images of our body's organs and tissues by using a computer's radio wave and an attracting field. Deep Learning (DL), a subset of Machine Learning (ML), is helpful for the categorization and identification of issues. This project uses one dataset consisting of three categories (Meningioma, Glioma, Pituitary.

Results: In this work, the first stage is pre-processing concerning two datasets. Later involves detection by using a Convolution Neural Network algorithm (CNN). The suggested CNN performs admirably, with the greatest overall accuracy for the datasets coming in at 94.3% and 96.1%. The final results demonstrate the model's capability for brain tumor classification and detection problems.

Conclusion: The proposed system helps to automatically differentiate between the types of Tumors from the normal, future it can be improved to analyze the brain tumor and classification, which will be more useful in the treatment. A few more sectors of artificial intelligence can also be incorporated along with the proposed system to increase the standard of the proposed system.

Purpose: Evaluating the impact of various surface treatments on the adhesive strength between resin cement and zirconia surface.

Material and methods: Using an STL file, 60 monolithic zirconia discs (Vita YZ HT) with dimensions of 10 millimeters in diameter and 2 millimeters in height were produced. They were machined, sintered, and the surface was smoothed using 600, 800, and 1200 grit aluminum oxide paper. Four groups were created based on the surface treatment applied to the discs: no treatment (control), sandblasting, potassium hydrogen difluoride and Zircos-E solution. Resin cement cylinders (Panavia V5; Kuraray Noritake) were applied on zirconia discs using a custom mold. The shear bond strength was assessed subsequent to thermocycling. The scanning electron microscope (SEM) has been utilised to analyse the morphological alterations of a specimen from every group. A post-hoc Tukey's test (P < 0.05) and a two-way ANOVA were used to statistically analyse the data.

Results: The data analysis showed that the maximum shear bond strength values, measured at 128.933 ± 2.764Mpa, were obtained via airborne particle abrasion with 50-µm Al2O3. The values obtained by the control group were the lowest, at 50.933 ± 9.573 Mpa. The use of 50-µm Al2O3 in airborne particle abrasion caused a significant increase in shear bond strength values (p<0.05).

Conclusion: The adhesive strength between zirconia and resin cement was improved by surface treatments, and airborne particle abrasion with 50-µm Al2O3 was shown to be an effective way to increase bond strength.

Purpose: Dental caries can emerge anywhere in the mouth particularly in the interior of the cheeks and the gums. Some of the indications are patches on the inner lining of the mouth, along with bleeding, toothache, numbness and an unusual red and white staining. Hence, it is important to predict the presence of cavity at an early stage. The currently available manual method is inefficient and hence we provide an advanced method by using the deep learning concepts.

Materials and Methods: In this work, different types of algorithms such as Res Net, Deeper Google Net and mini VGG Net are to be used to predict the class of cavity at an early stage.

Results: A comparison between the accuracy of three different algorithms is given in this paper. Thus, by using efficient deep learning algorithms, it will be able to predict the presence of cavity and the class of cavity at an early stage and take necessary steps to overcome it.

Conclusion: In this work, a comparison between three different algorithms is given and proved that the efficient algorithm is the inception algorithm among the other algorithms and achieve an accuracy of about 98%, which is suitable for use in hospitals.

Abstract

Purpose: The dose of Computed tomography (CT) scan exams consists of a large proportion of all medical imaging modalities’ dose burdens. There are different methods to measure and describe radiation in CT. A standardized way is to measure the Computed Tomography Dose Index (CTDI). However, due to the increase in the detector system size along the z-axis in new CT scanners generations, new measurement methods are described in the American Association of Physicists in Medicine-Task Group No.111(AAPM-TG111). This study aims to estimate the equilibrium dose and compare it with the dose displayed in the volume computed tomography dose index (CTDI_vol) at the end of each exam. Eventually, the effective dose was calculated for both methods.

Material and Methods: Using standard phantom of polymethylmethacrylate (PMMA) and pencil ionization chamber, the values of CTDI₁₀₀, ( CTD₁₀₀), CTDI_vol, cumulative dose, equilibrium dose, and effective dose were calculated.

Results: Six protocols performed in two centers and the results indicated that the measurements with a standard CT dosimetry phantom, was varied between average equilibrium dose and CTDI_vol and the discrepancies ranged between 26% to 35%.

Conclusion: the CTDI_Vol is not suitable to evaluate the radiation dose at the end of each scan and the use of an equilibrium dose for dosimetry of new systems is recommended.

Keywords: Multidetector computed tomography, Equilibrium dose, Computed tomography volume dose index, AAPM-TG 111, Radiation dosimetry

Background: For whole-body (WB) kinetic modeling based on a typical positron emission tomography (PET) scanner, a multipass multibed scanning protocol is necessary because of the limited axial field of view. Such a protocol introduces loss of early dynamics of the time-activity curve (TAC) and sparsity in TAC measurements, inducing uncertainty in parameter estimation when using prevalent least squares estimation (LSE) (i.e., common standard) especially for kinetic microparameters.

Purpose: We developed and investigated a method to estimate microparameters enabling parametric imaging, by focusing on general image qualities, overall visibility, and tumor detectability, beyond the common standard framework for fitting of data and parameter estimation.

Methods: Our parameter estimation method, denoted parameter combination-driven estimation (PCDE), has two distinctive characteristics: 1) improved probability of having one-on-one mapping between early and late dynamics in TACs (the former missing from typical protocols) at the cost of the precision of the estimated parameter, and 2) utilization of multiple aspects of TAC in selection of best fits. To compare the general image quality of the two methods, we plotted tradeoff curves for the normalized bias (NBias) and the normalized standard deviation (NSD). We also evaluated the impact of different iteration numbers of the ordered-subset expectation maximization (OSEM) reconstruction algorithm on the tradeoff curves. In addition, for overall visibility, a measure of the ability to identify suspicious lesions in WB (i.e., global inspection), the overall signal-to-noise ratio (SNR) and spatial noise (NSD_spatial) were calculated and compared. Furthermore, the contrast-to-noise ratio (CNR) and relative error of the tumor-to-background ratio (RE_TBR) were calculated to compare tumor detectability within a specific organ (i.e., local inspection). Furthermore, we implemented and tested the proposed method on patient datasets to further verify clinical applicability.

Results: With five OSEM iterations, improved general image quality was verified in microparametric images (i.e., reduction in overall NRMSE: 57.5, 71.1, and 56.1 [%] in the K₁, k₂, and k₃ images, respectively). The overall visibility and tumor detectability were also improved in the microparametric images. (i.e., increase in overall SNR: 0.2, 4.1, and 2.4; decrease in overall NSD_spatial: 0.2, 5.4, and 4.1; decrease in RE_TBR for a lung tumor: 17.5, 82.2, and 68.4 [%]; decrease in RE_TBR for a liver tumor: 255.8, 1733.5, and 80.3 [%], in K₁, k₂, and k₃ images, respectively; increase in CNR for a lung tumor: 1.3 and 1.0; increase in CNR for a liver tumor: 1.2 and 9.8, in K₁ and k₃ images, respectively). In addition, with five OSEM iterations, the differences in macroparametric images of the two methods were insignificant (i.e., overall NRMSE difference was within 10 [%]; differences in overall SNR, overall NSD_spatial, and CNRs for both tumors were within 1.0; and the difference in RE_TBR was within 10 [%] except for an exceptional case). For patient study, improved overall visibility and tumor detectability were demonstrated in micoparametric images.

Conclusions: The proposed method provides improved microkinetic parametric images compared to common standard in terms of general image quality, overall visibility, and tumor detectability.

Purpose: This topic focuses on a comprehensive evaluation of various diffusion tensor imaging (DTI) estimation methods, such as linear least squares (LLS), weighted linear least squares (WLLS), iterative re-weighted linear least squares (IRLLS) and non-linear least squares (NLS). The article will explore how each method performs in terms of accuracy, efficiency in estimating the diffusion tensor and robustness against noise.

Materials and Methods:  The study compares the methods using simulated diffusion-weighted MRI data. Time complexity and performance were evaluated across key metrics such as TRMSE, RMSE, MSD and ΔSNR.

Results: The results of the study demonstrate that LLS and IRLLS consistently outperform other methods in terms of TRMSE, MSD and SNR, particularly in high-noise scenarios. NLS performs best in reducing RMSE but high noise causes it to fit to noise, so it is not robust. WLLS showed the weakest performance across all metrics.

Conclusion: LLS and IRLLS provide a balance between accuracy and computational efficiency, making them practical for use in DTI analysis.

Purpose: One of the increasing neurological disorders is Alzheimer's, which progressively weakens brain cells and leads to critical cerebral impairments like memory loss. The present diagnostic techniques comprise PET scans, MRI scans, CSF biomarkers, and others that frequently need manual power and time-consuming process which might not offer appropriate results. This emphasizes the requirement for more precise and potential diagnostic solutions.

Materials and Methods: The proposed model utilizes AI-based Deep Learning (DL) techniques for effective multi-class classification of AD such as Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), Mild Cognitive Impairment (MCI), Cognitive Normal (CN) and Alzheimer’s Disease (AD) using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The proposed study utilizes Tri Branch Attention Network (TBAN) with Unified Component Incorporation (UCI) by capturing both spatial and channel attention information, by replacing the Squeeze and Excitation (SE) component in the conventional EfficientNet model and helps in addressing the concerns associated to imbalanced spatial feature distribution in images. Further, the incorporation of the proposed TBAN module in the Conv Layer helps, not only in terms of capturing the long-term dependence between the different channels of the network but also helps in retaining the specific location information to enhance the performance of the model. Similarly, the proposed UCI which is used in the MBConv layer deals with regularization, as the accuracy of the model can be dropped due to unbalanced regularization, hence the incorporation of UCI advocates strong regularization for combatting the concerns associated with overfitting and aids in providing better accuracy.

Results: Eventually, the proposed framework is evaluated with different metrics and the accuracy value obtained by the proposed model is 0.95. Likewise, precision, recall, and F1 scores gained by the proposed work are 0.95, 0.95, and 0.95.

Conclusion: The proposed research resolves significant gaps in the present diagnostic practices by implementing emerged AI techniques to improve the efficacy and accuracy of Alzheimer's diagnosis by medical imaging. Through enhancing the abilities of early detection, this proposed model holds the prospective to majorly affect treatment tactics for people affected with Alzheimer's. Finally, it led to better patient consequences and life quality.

Purpose

In epilepsy pre-surgical evaluations, semi-automated quantitative analysis of 18F-FDG brain PET images is a valuable adjunct to visual assessment for localizing seizure onset zones. This study investigates how adjusting image reconstruction parameters can enhance the accuracy of these quantitative results.

Materials and Methods

A total of 234 reconstruction parameters were applied to 18F-FDG brain PET images of a focal epilepsy patient. The parameters encompassed the 3D-Ordered-Subset Expectation Maximization image reconstruction method with resolution recovery (HD) and without (non-HD), various numbers of iterations and subsets (#it×sub), pixel sizes, and Gaussian filters. The accuracy errors were determined using the relative difference percentage (RD%) in measured SUV_max and the absolute Z-scores compared to reference values derived from the normal database reconstruction set serving as the benchmark.

Results

The study revealed that reconstructed images with 5mm or 8mm Full width at half maximum (FWHM) Gaussian filters yielded RD% values above 5% for SUV_max and Z-scores, indicating potential inaccuracy with higher values of post-smoothing filters. The recommended reconstruction sets with RD% values below 5% for both HD and non-HD images were those with a 3mm FWHM Gaussian filter and higher (#it×sub), specifically (5×21, 8×21), (5×21, 6×21), and (7×21, 8×21) for pixel sizes of 1.01 mm, 1.35 mm, and 2.03 mm, respectively.

Conclusions

The findings underscore the significant impact of altering the image reconstruction sets on the SUV_max and Z-scores. Furthermore, the inconsistent fluctuations of Z-scores emphasize the importance of using standard image reconstruction sets to ensure accurate and reliable quantitative outcomes in epilepsy pre-surgical evaluations.

Novel antimicrobials are needed now more than ever as antibiotic resistance to dangerous bacteria and the number of contagious diseases are on the rise. Nanoparticles (NPs) are innovative and promising therapeutic agents as they possess distinctive physiochemical characteristics and can hinder the growth of microorganisms. As such, their potential use as antimicrobials has garnered significant research interest. Researchers have, similarly, trained their sights on nanotechnology, which is the study and development of materials, tools, and systems that have physical, chemical, and biological characteristics that are more unique than those found in larger systems, due to its potential applications and advantages over existing conventional materials, especially in the dental and medical industries. Therefore, a better understanding of the science behind nanotechnology is needed to comprehend how these materials can be used in everyday life. Zein-coated magnesium oxide nanoparticles (MgO NPs) have recently shown significant promise as a powerful antibacterial compound that can be combined with other materials to fabricate a variety of brand-new dental formulations.

Purpose: The objective of this paper is to review the non-invasive methods for ICP monitoring and the research conducted in the field.

Materials and Methods: A comprehensive literature search was conducted on NIH and PubMed, and papers highlighting the newer methods used in Intracranial Pressure monitoring were reviewed and the related data was included in the paper.

Results:  The prominent methods of non-invasive ICP monitoring reviewed were: Imaging (CT and MRI), Electroencephalogram (EEG), Near-Infrared Spectroscopy (NIRS), Optic Nerve Sheath Diameter (ONSD), and Transcranial Doppler (TCD) Ultrasound.

Conclusion: While invasive methods for ICP monitoring are preferred over non-invasive methods in a clinical setting, with the intraventricular catheter being the gold standard for ICP monitoring, many non-invasive methods for ICP monitoring are considered, especially in settings where invasive ICP monitoring is not possible. The use of non-invasive methods represents an advancement in the field of ICP monitoring. Although not very well known in a clinical setting, non-invasive methods offer more safety and carry a lesser risk of infection.

Background: This review aims to synthesize current literature on recent advances in the diagnosis and treatment of Brain and spinal cord injuries (SCIs), focusing on molecular imaging, cell therapy, brain-computer interfaces (BCIs), and craniosacral therapy (CST).

Methods: A systematic search was conducted in PubMed/MEDLINE, Scopus, Web of Science, Cochrane Library, and Google Scholar to identify relevant articles published between 2015 and 2025. Keywords included "Brain Injury," "Spinal Cord Injury," "Molecular Imaging," "Cell Therapy," "Brain-Computer Interface," and "Craniosacral Therapy."

Results: Molecular imaging techniques, such as fMRI, DTI, and PET, enhance diagnostic accuracy by visualizing neural activity and structural integrity. Cell therapy, particularly with mesenchymal stem cells (MSCs), shows promise in promoting axon regeneration and reducing inflammation. BCIs offer potential for restoring motor function and enhancing neural plasticity. The evidence for CST is mixed, with some studies suggesting benefits in pain relief and cognitive improvement, while others raise concerns about methodological limitations.

Conclusion: Recent advances in molecular imaging, cell therapy, and BCIs offer promising avenues for improving the diagnosis and treatment of BSCI. However, further rigorous research is needed to validate the efficacy of these approaches and to address ethical considerations. While CST has gained attention as a complementary therapy, more high-quality studies are required to determine its effectiveness. This review highlights the need for interdisciplinary collaboration to translate scientific discoveries into clinical practice and to improve the quality of life for individuals affected by BSCI.

Abstract

Objective: To evaluate the effectiveness of polymer-based shields containing boron compounds for radiation protection in medical centers, focusing on their performance against neutron and gamma radiation.

Methods: A comprehensive literature review was conducted using databases including PubMed, Scopus, Web of Science, and Embase. Studies published from 2010 to February 2025 were included. The search strategy employed keywords related to polymer-based shields, boron compounds, and radiation protection in medical settings.

Results: Boron-containing polymers demonstrated significant potential for radiation shielding, particularly against neutrons. Nanocomposites incorporating high-Z elements showed improved gamma radiation attenuation. Hexagonal boron nitride (h-BN) nanocomposites exhibited superior neutron absorption properties. Epoxy-based composites with various nanoparticles showed enhanced protection against both neutron and gamma radiation. Recycled high-density polyethylene (R-HDPE) composites containing gadolinium oxide demonstrated promising thermal neutron shielding capabilities.

Conclusion: Polymer-based shields containing boron compounds offer lightweight, flexible, and effective alternatives to traditional shielding materials. These materials show particular promise in medical applications, potentially improving safety for both patients and healthcare providers. However, challenges remain in optimizing material composition, thickness, and long-term stability for practical implementation in clinical settings.

Radiopharmaceuticals are combinations of two main components, a pharmaceutical component that targets specific moieties, and a radionuclide component that acts through spontaneous degradation for diagnostic, therapeutic purposes, or both simultaneously known as theranostics. By combining diagnostic and therapeutic methods, radiotheranostics play an important role in reducing radiation dosages for patients, increasing treatment effectiveness, controlling side effects, improving patient outcomes, and reducing overall treatment costs. Despite the diagnostic and therapeutic roles, radiopharmaceuticals are beneficial for assessing prognosis, disease progression and possibility of recurrences, treatment planning strategies, and assessing response to treatment. The most incredible role of radiopharmacy is establishing new radiopharmaceuticals to better target and tolerated agents for imaging and treatment in a clinic. These approaches are supported by nuclear medicine non-invasive procedures. It is crucial for radiopharmaceuticals that drug delivery occurs in a highly selective and sensitive manner to minimize the potential radiation risk to patients. This report will provide an overview of the recent progress in radiopharmaceuticals for diagnosis and therapy, including the latest radiotheranostic tracers, key concerns within the field, and future trends and prospects. Additionally, the available and useful radiopharmaceuticals are categorized into separate tables based on their specific characteristics. Presenting information in table format enhances organization and makes the data more understandable and accessible for users. This structured approach allows users to quickly locate relevant information, compare different radiopharmaceuticals, and grasp essential details at a glance. By utilizing tables, we ensure that critical information is not only easy to read but also effectively highlights the unique attributes of each radiopharmaceutical, ultimately improving the decision-making process for healthcare professionals.

Purpose: Artificial intelligence (AI) techniques have been extensively utilized for diagnosing and prognosis of several diseases in recent years. This study identifies, appraises and synthesizes published studies on the use of AI for the prognosis of COVID-19.

Method: Electronic search was performed using Medline, Google Scholar, Scopus, Embase, Cochrane and ProQuest. Studies that examined machine learning or deep learning methods to determine the prognosis of COVID-19 using CT or chest X-ray images were included. Polled sensitivity, specificity area under the curve and diagnostic odds ratio were calculated. 

Result: A total of 36 articles were included; various prognosis-related issues, including disease severity, mechanical ventilation or admission to the intensive care unit and mortality, were investigated. Several AI models and architectures were employed, such as the Siamense model, support vector machine, Random Forest , eXtreme Gradient Boosting, and convolutional neural networks. The models achieved 71%, 88% and 67% sensitivity for mortality, severity assessment and need for ventilation, respectively. The specificity of 69%, 89% and 89% were reported for the aforementioned variables.

Conclusion: Based on the included articles, machine learning and deep learning methods used for the prognosis of COVID-19 patients using radiomic features from CT or CXR images can help clinicians manage patients and allocate resources more effectively. These studies also demonstrate that combining patient demographic, clinical data, laboratory tests and radiomic features improves model performances.

Purpose: This case report aimed to describe a treatment for severe inflammatory external root resorption (RR).

Materials and Methods: A 13-year-old boy reported the avulsion of his upper left central incisor. The tooth had been avulsed four months prior and was replanted forty minutes later by an emergency service. The canal was thoroughly irrigated with 2% sodium hypochlorite and then filled with calcium hydroxide of a creamy consistency as an intracanal medication due to its antimicrobial properties, using lentulo spirals. The calcium hydroxide was left inside the canal for a month.

Results: Following the diagnosis, treatment involved conventional endodontic therapy with calcium hydroxide dressings, and the root canal was definitively filled after radiographic control of the resorption. At the 6- and 12-month follow-ups, clinical and radiographic examinations revealed no signs or symptoms of any abnormalities. The resorption process had halted, and the radiograph showed the reappearance of the normal lamina dura, indicating successful therapy.

Conclusion: This case report details the treatment of severe external inflammatory RR in a tooth undergoing orthodontic treatment. Successful tooth replantation depends on the effective implementation of the recommended therapy. However, when inflammatory external RR occurs, appropriate endodontic treatment is necessary to eliminate necrotic tissue and bacteria, along with the use of calcium hydroxide dressings.