Electroencephalography (EEG)-based emotion recognition is an established and increasingly important technology for affective computing, mental health assessment (MHA), brain–computer interfaces (BCIs), and human–machine interaction (HMI). Despite significant advances, many EEG-based emotion recognition systems (ERSs) continue to rely substantially on predefined frequency bands and handcrafted spectral features, which may not fully capture the complex neurophysiological dynamics underlying emotional processing. This editorial argues that the next generation of EEG-based emotion recognition should move beyond conventional frequency-band analysis toward representation-driven and physiologically grounded paradigms that link neural dynamics to meaningful affective and physiological representations. We discuss major limitations of traditional EEG processing pipelines, including sensitivity to inter-subject variability, limited temporal modeling, poor cross-dataset generalization, and inadequate interpretability. We further outline emerging directions based on deep neural representations, self-supervised learning, multimodal integration, and physiologically informed modeling. A conceptual framework is proposed in which EEG signals are transformed into semantically meaningful neural representations that preserve temporal dynamics and neurophysiological relevance. Such a transition may support more robust ERSs with improved cross-subject adaptation, explainability, and translational potential. Moving beyond frequency-band-centered analysis may therefore represent an important step toward reliable, generalizable, and interpretable EEG emotion recognition technologies for future biomedical and neuroengineering applications.

Purpose: Magnetoencephalography (MEG) is a brain imaging method with high temporal and acceptable spatial resolution, achieved by recording neural magnetic fields. The data quality of this imaging method is compromised due to reasons such as the failure of one or more sensors. This study aims to explore the efficiency of the various data reconstruction techniques in MEG for recovering poor-quality or missing channel data.

Materials and Methods: We compared three surface reconstruction methods (Mean, Median, and Trimmed mean), two partial differential equations (modified Poisson and Diffusion equation), and a Finite Element-based interpolation method using data from 11 young adults (aged 30±12). Using varying levels of simulated data loss (2%, 5%, 11%, and 16%), we assessed each method in terms of time taken for reconstruction, R-squared, root mean squared error (RMSE), and signal-to-noise ratio (SNR) compared to a reference signal. Statistical tests (P-value < 0.05) were used to analyze the relationships between the mentioned evaluation criteria. Generalized Linear Models revealed that surface reconstruction methods and finite-element interpolation outperformed partial differential equations.

Results: The Trimmed mean method achieved the highest R-squared (0.882 ± 0.0610) and lowest RMSE (0.0155 ± 0.00904) with a reconstruction time of 9.5154 microseconds for a 500-millisecond epoch of MEG channel data.

Conclusion: The surface reconstruction methods can recover the noisy or lost signal in MEG with a suitable error and required time. These findings support the use of robust statistical strategies for improving MEG signal quality, especially in high-density sensor arrays.

Purpose: The present study aimed to assess the Entrance Skin Doses (ESD) at the thyroid gland region and evaluate the local Diagnostic Reference Level (DRL) in panoramic radiography in a city (Yasuj, Iran).

Materials and Methods: In the current study, 31 patients (17 women and 14 men) with a mean age of 33.90±16.49 years were included. To assess the ESD values, 3 thermoluminescence dosimeters (TLD-100) were attached to the thyroid gland region for each patient. The DRLs were estimated as the third quartile of the ESD values. The ESD variations among the different genders (men and women), devices, and age groups (children [5-10 years], adolescents [11-19 years], and adults [˃19 years]) were calculated. T-test, one-way ANOVA, and Bonferroni's post hoc test were used for parametric tests; Kruskal-Wallis and Spearman's correlation coefficient were used for the non-parametric tests.

Results: Mean ESD and DRL values were obtained at 72±21 µGy and 0.091±0.02 mGy, respectively. For ESDs, there was no significant difference between different genders (men: 76±20 µGy and women: 69±23 µGy) as well as among the three investigated devices (P-value˃ 0.05). The ESD values of children were significantly lower than adolescent and adult patients (P-value<0.001); however, there was no statistically significant difference between the adolescent and adult patients (P-value =0.057).

Conclusion: Compared to national/international, the DRL value in our study was relatively low; patient doses can be decreased in the panoramic examinations by increasing the knowledge of health workers of the radiation parameters, specifically operators.

Purpose: The initial evaluation of trauma poses a formidable and time-intensive challenge. This study aims to scrutinize the diagnostic efficacy and utility of integrating machine learning models with radiomics features for the identification of blunt traumatic kidney injuries in abdominal CT images.

Materials and Methods: This investigation involved the collection of 600 CT scan images encompassing individuals with varying degrees of kidney damage resulting from trauma, as well as images from healthy subjects, sourced from the Kaggle dataset. An experienced radiologist performed the segmentation of axial images, and radiomics features were subsequently extracted from each region of interest. Initially, 30 machine learning models were deployed, with a final selection narrowed down to three models: Light Gradient-Boosting Machine (LGBM), Ridge Classifier, and Adaptive Boosting (AdaBoost). The performance of these chosen models was subjected to a more comprehensive examination.

Results: The AdaBoost model exhibited notable performance in diagnosing mild kidney injury, achieving accuracy and sensitivity rates of 93% and 94%, respectively. Furthermore, for severe kidney injury, the AdaBoost model demonstrated a remarkable sensitivity of 96% and an accuracy of 97%. The Area Under the Curve (AUC) values for this model were also calculated, yielding values of 92.91% and 97.04% for mild and severe renal injuries, respectively.

Conclusion: The artificial intelligence models employed in this study hold significant potential to enhance patient care by providing valuable assistance to radiologists and other medical professionals in the diagnosis and staging of trauma-related kidney injuries. These models offer the capability to prioritize positive studies, expedite evaluations, and accurately identify more severe injuries that may necessitate immediate intervention.

Purpose: Evaluating the effects of different surface treatments on the zirconia surface and resin cement adhesive strength.

Materials and Methods: Using an STL file, 60 monolithic zirconia discs (Vita YZ HT) with dimensions of 10 mm in diameter and 2 mm in height were produced. They were machined, and sintered, and the surface was smoothed using 600, 800, and 1200 grit aluminum oxide (Al₂O₃) 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 after thermocycling. The Scanning Electron Microscope (SEM) has been utilized to analyze the morphological alterations of a specimen from every group. The results were statistically analyzed using a two-way ANOVA and a post-hoc Tukey's test (P < 0.05).

Results: The data analysis revealed that airborne particle abrasion with 50-µm Al₂O₃ produced the greatest shear bond strength values that were recorded at 128.933 ± 2.764Mpa. At 50.933 ± 9.573 Mpa, the control group's results were the lowest. There was a statistically significant increase in the shear bond strength values (p<0.05) when 50-µm Al₂O₃ was utilized in airborne particle abrasion.

Conclusion: Surface treatments increased the adhesive strength between zirconia and resin cement, and airborne particle abrasion with 50-µm Al₂O₃ was shown to be a useful technique for bond strength enhancement.

Purpose: The prevalence of the Second Distal Canal (SDC) in the Mandibular First Molar (MFM) was assessed using histological and Cone Beam Computed Tomography (CBCT) studies.

Materials and Methods: This study included a total of 348 extracted MFM, which were divided into 6 groups according to gender and age. All the teeth were viewed by one CBCT device, and then the teeth were histologically examined. The images were evaluated, and the obtained results were analyzed by SPSS software using the Chi-square test. The comparison was considered statistically significant at p<0.05.

Results: The prevalence rate of the canals was 171 (49.13%), males and females were 89 and 82, respectively, and according to the level of sectioning, they were found to be 43.94% of all the sections, with the lowest 16.11% and 15.62% at level 5 (coronal level) and level 1 (apical level) when viewed only histologically, being 110, while when viewed histologically and by CBCT, there were 61 teeth for the SDC, respectively, which was more in younger than older patients with no statistically significant difference.

Conclusion: The prevalence rate of the SDC seen by CBCT was 17.32%, while histologically it was 49.13%, respectively. Clinically, many canals cannot be detected because of their small size, which cannot be seen radiographically.

Purpose: Silicone elastomers are extensively used in the fabrication of maxillofacial prostheses, yet their characteristics are far from optimal. Key factors necessitating the frequent re-fabrication of these prostheses include their limited lifespan and the deterioration of both their color and mechanical properties. This study aims to evaluate the impact of copper oxide nanoparticles (CuO NPs) on the mechanical properties of VST 50F maxillofacial silicone.

Materials and Methods: A total of 120 specimens were prepared, with 30 specimens for each test. Five mechanical tests—tear strength, tensile strength, percentage of elongation, Shore A hardness, and surface roughness—were conducted with two experimental groups and a control group for each test.

Results: Statistical analysis revealed a significant increase in tear strength for both experimental groups (P < 0.05). Specifically, Group B showed a 12.5% increase with a mean value of 23.89 kN/m, while Group C demonstrated a 21.57% (25.81 kN/m) increase compared to the control group (21.23 kN/m). Tensile strength also improved, with Group B exhibiting a 13.9% increase with a mean value of 4.655 Mpa and Group C exhibiting a 14.0% (4.660 Mpa) increase compared to the control group (4.08 Mpa). Additionally, Shore A hardness increased by 7.98% (33.34 IU) for Group B and 14.58% (35.37 IU) for Group C, alongside a significant decrease in the percentage of elongation—Group B decreased by 3.36% (864.80) and Group C by 3.80% (860.86) compared to the control group (894.87). However, there was a non-significant increase in surface roughness (P > 0.05). Group B displayed a 4.48% (0.303 µm) increase, while Group C showed a 5.17% (0.305 µm) increase compared to the control group (0.290 µm).

Conclusion: The results of this study suggest that CuO NPs can improve silicone elastomers' shortcomings; however, further studies are required to fully comprehend these NPs' long-term effects and how they affect other silicone material physical properties.

Purpose: Posterior oblique beams are increasingly common in radiotherapy techniques. Radiation beams traversing through the treatment couch would be attenuated and cause under-dosage in the tumor region. The attenuation of an IGRT carbon fiber Couch for different angles, energies, field sizes, measurement points, couch regions, and the ability of the Eclipse treatment planning system to predict doses were investigated.

Materials and Methods: The Varian Vital Beam linear accelerator (Varian Medical Systems) and the Exact IGRT couch top, a new device for patient positioning, were utilized. The couch coefficient was calculated using in-air gantry angle measurements to determine the most attenuation angle. the attenuation measurements were performed in three measurement points of an inhomogeneous PMMA phantom using a farmer ionization chamber for three energies with six field sizes in three regions of an IGRT couch at the most attenuation gantry angle.

Results: The results of the couch coefficient measurements demonstrated that in three regions of the IGRT couch at an angle of 130˚, the photon beam was most attenuated. The difference between the calculated dose by the Eclipse treatment planning system and the experimental dose measurements was 1.855%. The difference is less than 3% and the attenuation was within the allowable range for treatment.

Conclusion: The IGRT treatment couch in posterior oblique gantry angles decreased the dose in the measurement points due to gantry angle, field size, energy, and couch region. The Eclipse treatment planning system can sufficiently predict the tumor dose distribution.

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.

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: The rise in coronavirus cases has led to an increased reliance on CT scans, which are known for delivering higher radiation exposure. This study aims to estimate organ doses (ODs) and effective doses (EDs) to evaluate the lifetime attributable risks (LARs) of cancer occurrence and mortality among patients. A total of 600 patients, either confirmed or suspected to have COVID-19, were included in this investigation.

Materials and Methods: To assess patient doses and associated cancer risks, dosimetric parameters such as DLP, volumetric CTDI, and scan length were utilized. The ImPACT CT dosimetry software was employed to calculate ODs and EDs.

Results: For female patients, the mean ED was recorded as 2.36 ± 0.48 mSv based on ICRP Report 103 and 1.2 ± 0.28 mSv based on ICRP Report 60. In male patients, these values were 2.31 ± 0.53 mSv and 1.21 ± 0.45 mSv, respectively. The mean LAR for all cancer rates in males was found to be 14.79 ± 4.85 per 100,000 individuals, while for cancer mortality, it was 8.59 ± 2.42 per 100,000 individuals. For females, the LARs were higher, at 23.37 ± 9.59 for incidence and 12.61 ± 3.89 for mortality per 100,000 individuals.

Conclusion: The findings indicate that chest CT scans are associated with significant radiation exposure and potential cancer risks. Therefore, it is essential to optimize CT protocols according to the ALARA principle to minimize radiation-induced risks while maintaining diagnostic effectiveness during the ongoing pandemic.

Purpose: Polymethyl methacrylate resins are a class of materials used widely in dental practices. However, reinforcement of PMMA resins is the most important issue for the fabrication of overdentures/dentures with long-term durability. In the current study, the mechanical properties (in terms of tensile, bending, and impact properties) and applicability of the heat-polymerized Polymethyl methacrylate denture base materials were investigated by incorporating the synthesized sol-gel derived nano-porous silica aerogel.

Materials and Methods: Polymethyl methacrylate overdenture base materials were prepared by direct mixing of liquid MMA component with the desired weight contents of the synthesized sol-gel derived silica aerogel (i.e., 0, 2.5, 5, 7.5, and 10 wt%) followed by thermal polymerization. The mechanical properties of the prepared nano-composites were investigated in terms of tensile, impact, and bending strength, and the experimental results were also compared with those commercial Triplex Hot acrylic base overdenture resin.

Results: The experimental results revealed that there is a meaningful correlation (P<0.05) between silica aerogel weight content and mechanical properties of the as-prepared silica aerogel- Poly-methyl methacrylate composites. Compared with commercial Triplex Hot base composite, fabricated Poly-methyl methacrylate base denture composites containing only 7.5 wt% of silica aerogel show higher mechanical properties. At optimum concentration, the bending, impact, and flexural properties of the composites reached 16.3 MPa, 22.9 MPa, and 38.1 Kj. m^-2, respectively.

Conclusion: The experimental results revealed that nano-porous silica aerogel is a promising alternative candidate to be used as an efficient filler in the fabrication of overdentures. Fabricated nano-composites using silica aerogel show higher mechanical properties, compared to fabricated commercial base composites. Furthermore, denture/overdentures had better fracture toughness by using nano-porous silica aerogel, and no more space is needed.

Purpose: Laryngeal cancer is a critical health issue, often treated using advanced radiation therapy techniques such as Intensity-Modulated Radiation Therapy (IMRT). The gamma index is a widely used metric for quality assurance in radiotherapy, assessing the agreement between planned and delivered dose distributions.

This study aims to evaluate the feasibility and accuracy of laryngeal IMRT treatment plans using three gamma analysis algorithms and varying evaluation parameters, including Dose Difference (DD%), and Distance-To-Agreement (DTA).

Materials and Methods: IMRT treatment plans for laryngeal cancer were generated using the Prowess Panther V5.5 Treatment Planning System (TPS). Patient-Specific Quality Assurance (PSQA) was conducted using EBT3 Gafchromic films scanned at 350 dpi and analyzed with RIT Complete 6.11 software. Dose distributions were evaluated using three gamma analysis algorithms under 3%/3 mm criteria and low-dose thresholds of 15%. Calibration of the films was performed to ensure accurate dose measurements.

Results: Gamma Passing Rates (GPR) for the laryngeal IMRT plans demonstrated high accuracy, with over 90% of pixels passing the criteria in most cases. Composite gamma analysis showed 53.89% of pixels meeting both DD and DTA criteria simultaneously, while individual evaluation revealed the impact of stricter thresholds on GPR. Subtraction analysis identified dose discrepancies, emphasizing the need for accurate calibration.

Conclusion: This study highlights the effectiveness of gamma analysis in ensuring the accuracy of IMRT treatment plans for laryngeal cancer. The findings underscore the importance of rigorous PSQA, parameter optimization, and advanced algorithms to enhance treatment precision.

Purpose: This study aims to explore the effect of mean dose constraint in optimization shells on the reduction of normal lung dose in lung Stereotactic Body Radiation Therapy (SBRT) plans.

Materials and Methods: This study investigated 28 VMAT-based lung SBRT plans optimized with three artificial shells, which were re-generated with the same setup and an additional mean dose constraint besides the maximum dose limit. Dosimetric measurements of target volume and Organs At Risk (OARs) were compared between the original plans and re-generated ones using the Wilcoxon signed-rank test at 5% level significance (two-tailed).

Results: Replanning resulted in slight improvements in some parameters, such as R50% and the Gradient Measure (GM), which were reduced by 1.3% and 1.0%, respectively, with p < 0.05. However, there were slight increases in other parameters, such as D2cm and the maximum target dose, though these increases were not statistically significant. The Conformity Index (CI) remained nearly identical, with a mean value of 0.99 ± 0.03 for both original and re-optimized plans. Similarly, the V105% values were consistent, with mean values of 0.02 ± 0.13% in both groups. The parameters for dose deposited in normal lung tissue showed statistically significant reductions ranging from 1.0% to 1.7%. In addition, the mean dose to the spinal cord, esophagus, and skin was slightly reduced, but the mean dose to the heart showed a slight increase.

Conclusion: The study found that adding mean dose constraints to optimization shells in lung SBRT plans can reduce normal lung dose while maintaining dose conformity to the target. However, there may be slight changes in some OARs such as the spinal cord, esophagus, and skin. These changes were not statistically significant.

Purpose: The objective of this study is to develop a hybrid model combining K-Nearest Neighbor (K-NN) and Bidirectional Convolutional Long Short-Term Memory (BiConvLSTM) for effective detection of lung cancer nodules in chest CT images.

Materials and Methods: A dataset of 200 high-resolution CT images (100 nodular, 100 healthy controls) was utilized. Preprocessing steps, including wavelet-based noise reduction and GAN-enhanced contrast refinement, ensured standardized image quality. Spatial features were initially extracted using K-NN, optimized across multiple values of k. Temporal dependencies were processed through BiConvLSTM, supported by residual pathways to preserve information integrity. Evaluation metrics and advanced optimizers (Adam, Nadam, and AdamW) were compared to assess performance.

Results: The hybrid model achieved peak accuracy (97.2% ± 1.1) with k = 7, while k-fold cross-validation with k = 5 maintained strong performance. AdamW optimizer outperformed others in generalization metrics and Dice similarity (0.93), while Nadam demonstrated faster convergence, reducing training epochs by 20%. Metrics such as AUROC (0.98) and the Jaccard index confirmed the robustness of the model compared to conventional architectures like U-Net. Additionally, the statistical significance of the proposed model was validated with a P-value of 1.2E-5.

Conclusion: This proposed hybrid AI model addresses challenges in nodular identification and provides a scalable, reliable solution for lung cancer detection in clinical imaging workflows.

Purpose: This study aims to optimize neural networks for diagnosing and predicting lung cancer stages, leveraging advanced recurrent neural network architectures such as Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks.

Materials and Methods: The research utilizes a comprehensive dataset of clinical data and high-resolution imaging to develop novel Deep Neural Networks (DNNs). These networks are optimized using sophisticated algorithms like Stochastic Gradient Descent with Momentum (SGDM) and Adaptive Moment Estimation (Adam). Transfer Learning (TL) techniques and big data analytics are integrated to enhance model precision and reliability. Additionally, early stopping is employed to prevent overfitting, k-fold cross-validation (k=5) is used to ensure robust model evaluation, and L2 regularization is applied to improve generalization by penalizing large weights.

Results: The optimized GRU and LSTM networks demonstrate superior performance in detecting lung cancer stages, significantly improving diagnostic accuracy and prognostic predictions. The models effectively handle long-term dependencies and mitigate the vanishing gradient problem, common in simple RNNs. Using k-fold cross-validation (k=5), the optimized GRU network with SGDM achieved the following metrics in Fold 3: Test Loss of 0.0531, Test Accuracy of 0.97, Sensitivity (Recall) of 0.97, Precision of 0.98, and an F1 Score of 0.975.

Conclusion: The study's findings underscore the potential of integrating advanced neural network models into clinical practice, revolutionizing AI-driven medical diagnostics and elevating the standard of patient care.

Purpose: The hippocampus is a crucial brain region responsible for memory, spatial navigation, and emotion regulation. Precise hippocampus segmentation from Magnetic Resonance Imaging (MRI) scans is vital in diagnosing various neurological disorders. Traditional segmentation methods face challenges due to the hippocampus's complex structure, leading to the adoption of deep learning algorithms. This study compares four deep learning frameworks to segment hippocampal parts, including concurrent, separated, ordinal, and attention-based strategies.

Materials and Methods: This research utilized 3D T1-weighted MR images with manually delineated hippocampus head and body labels from 260 participants. The images were randomly split into five folds for experimentation, each time one of those designated as the test set and the rest as the training set.

Results: The findings indicate that both the concurrent and separated frameworks perform better than the ordinal and attention-based frameworks regarding the Dice and Jaccard coefficients. In head segmentation, the separated framework had a Dice similarity of 0.8748, a Jaccard similarity of 0.7794, and a Hausdorff distance of 5.4160. In body segmentation, the concurrent framework had a Dice similarity of 0.8616, a Jaccard similarity of 0.7591, and a sensitivity of 0.8437. Statistical results from the one-way ANOVA test showed a significant difference in performance for the body part (P-value=0.008), but not for the head region (P-value=0.652) between concurrent and separated frameworks. Comparing the concurrent with ordinal and attention-based frameworks showed a significant difference in both body and head regions (P-value<0.001 for both comparisons).

Conclusion: Researchers must consider the differences between various frameworks while selecting a segmentation method for their specific task. Understanding the strengths and weaknesses of every framework is essential for deciding on the top-rated segmentation approach for precise applications.

Purpose: Lung cancer is the most common and deadliest type of cancer that is the cause of one million deaths around the world every year. Due to the present level of medical research, identifying lung tumors on chest Computed Tomography (CT) images has become a significant process in modern medicine. Enhancing treatment and reducing lung cancer mortality can be achieved by promptly identifying and accurately diagnosing suspected malignant lung tumors. While many deep learning algorithms have been developed recently for the classification of lung cancer, achieving high accuracy in lung cancer classification is still a challenge. An advanced deep learning technique is designed to boost the effectiveness of early lung cancer diagnosis.

Materials and Methods: In this research, we have proposed an enhanced ensemble deep-learning model for lung cancer classification and segmentation. Initially, we carried out an extensive pre-processing process, including image resizing, noise reduction, and contrast enhancement, to enhance the image quality. The problem of small sample size is addressed by applying conventional data augmentation techniques like flipping, rotating, zooming, and shearing. Next, seven statistical features are retrieved using Improved Empirical Wavelet Transforms (IEWT). After feature extraction, the Enhanced ResNeXt model is used to classify lung cancer into normal, malignant, and benign classes. The interested region of the lung tumor is segmented using the Modified ShuffleNetV2 model. Individuals are classified into normal, malignant, and benign categories based on the presence of lung cancer. The experiments are performed on the benchmark datasets LIDC-IDRI and IQ-OTH/NCCD.

Results: For lung cancer classification, the proposed Enhanced ResNeXt model achieves an exceptional model accuracy of 99.43% for the IQ-OTH/NCCD dataset and 99.37% for the LIDC-IDRI dataset. Furthermore, the proposed Modified ShuffleNetV2 model effectively segments lung tumor regions, achieving an Intersection over Union (IOU) of 98.43% and a Dice Similarity Coefficient (DSC) of 97.24% on the LIDC-IDRI dataset, and an IOU of 97.05% and DSC of 96.23% on the IQ-OTH/NCCD dataset, demonstrating its robustness and accuracy in delineating tumor boundaries. The expected outcomes show that the accuracy and efficiency of our proposed ensemble deep learning model outperform other CNNs.

Conclusion: The proposed models beat existing CNN-based models in terms of speed and number of training parameters, which means that using CT scan images to diagnose lung cancer automatically is a suitable option and a strong selection for extensive use in medical environments.

Purpose: Computed Tomography (CT) is a widely used medical imaging technique that employs continuous X-ray radiation. However, compared to other imaging modalities, CT scanning delivers relatively high radiation doses, potentially causing tissue damage and increasing cancer risk. This study aims to assess the radiation dose received by patients during abdominal-pelvic CT examinations, compare it with existing studies, and propose methods to minimize patient dose, thereby reducing associated cancer risks.

Materials and Methods: This study evaluated radiation doses to the liver, stomach, bladder, left kidney, and right kidney in six patients scanned with a Siemens CT scanner. Dosimetry was performed using TLD-GR200 thermoluminescent dosimeters.

Results: Results show that the bladder received the highest average dose, followed by the stomach, liver, left kidney, and right kidney, in that order. Notably, the mean dose to the stomach exceeded that of the bladder, indicating a heightened risk to the stomach. The organ doses measured in this study were consistent with values reported in prior studies and within the Diagnostic Reference Levels (DRLs) recommended by the International Commission on Radiological Protection (ICRP). However, given the stochastic nature of radiation risks, which lack a threshold and can manifest even at doses below 10 mSv, continued efforts to optimize patient dose are imperative.

Conclusion: This study highlights that the bladder receives the highest radiation dose, while the kidneys receive the lowest. The observed organ doses align with ICRP recommendations and prior research. Nonetheless, minimizing radiation exposure remains critical to enhance patient safety without compromising diagnostic efficacy.

Purpose: This paper aims to thoroughly investigate non-invasive techniques employed in monitoring Intracranial Pressure (ICP) accompanied by a thorough exploration of the research endeavors in this specialized domain.

Materials and Methods: A thorough review of papers sourced from PubMed was conducted to explore the latest methods utilized in monitoring Intracranial Pressure (ICP). A search was carried out for review papers, systematic reviews, books and documents, and meta-analysis using the term “non-invasive AND intracranial pressure AND monitoring” yielding 82 results. The study systematically examined past and recent literature, focusing prominently on newer techniques. The gathered data from these sources was diligently incorporated into the paper, providing updated insights into ICP monitoring methods. These techniques were then analyzed and compared to highlight their advantages and disadvantages, based on their applications.

Results: The review focused on several key non-invasive methods for Intracranial Pressure (ICP) monitoring, notably encompassing Imaging techniques such as CT and MRI, Electroencephalogram (EEG), Near-Infrared Spectroscopy (NIRS), Optic Nerve Sheath Diameter (ONSD) measurement, and Transcranial Doppler (TCD) Ultrasound. These modalities were examined for efficacy and feasibility in non-invasively assessing and monitoring ICP and compared accordingly.

Conclusion: While invasive methodologies, particularly the intraventricular catheter, are commonly favored in clinical settings for Intracranial Pressure (ICP) monitoring due to their accuracy, non-invasive alternatives gain traction, especially when employing invasive techniques isn't viable. The emergence of non-invasive methods marks a significant stride in ICP monitoring. Despite their relatively lower familiarity in clinical practice, these non-invasive approaches present notable advantages, notably enhanced safety and a reduced risk of infection. Their growing significance lies in offering feasible options when invasive monitoring poses challenges, thus expanding the scope and safety of ICP monitoring beyond conventional invasive methods.

Purpose: 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).

Materials and Methods: A systematic search was done in PubMed/MEDLINE, Scopus, WoS, 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 functional Magnetic Resonance Imaging (fMRI), Diffusion Tensor Imaging (DTI), and Positron Emission Tomography (PET), enhance diagnostic accuracy by visualizing neural activity and structural integrity. Cell therapy, particularly with Mesenchymal Stem Cells (MSCs), promotes axon regeneration and reduces inflammation. BCIs offer the 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 hopeful avenues for improving the diagnosis and treatment of BSCI. However, further rigorous research is needed to validate the ability 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 develop the quality of life for those affected by BSCI.

Injectable hydrogels are gaining popularity due to their diverse biomedical applications, including tissue engineering, dermal fillers, drug delivery, and regenerative medicine. An ideal injectable hydrogel should have suitable physicochemical properties for in situ injection into the body, be non-toxic and biocompatible, and should not initiate any adverse reactions. Also, the injectable hydrogel should be porous and made of highly interconnected networks to facilitate the movement of nutrients for better integration into the surrounding tissues. Injectable hydrogels offer unique advantages such as minimally invasive delivery and targeted administration, making them promising candidates for various medical interventions. The preparation of injectable hydrogels involves physical or chemical crosslinking methods, and various stimuli-responsive injectable hydrogels are available for clinical use. Optimising their composition, distribution, and dosing regimens is key to enhancing patient outcomes and expanding the clinical utility of injectable hydrogels shortly.  There are various injectable hydrogel products in the market, and a few of them are in different phases of clinical trials for treating several diseases and conditions. Examples of commonly used injectable hydrogels available in the market include hyaluronic acid, calcium phosphate, collagen, fibrin, and thermoresponsive hydrogels. Furthermore, ongoing clinical trials investigating novel injectable hydrogels such as BioSentry tract sealant system, PROMGEL-OA, HYADD, PAAG-OA, NOLTREX, AQUAMID, GELSTIX, and REACT are discussed in this review, providing valuable insights into the landscape of injectable hydrogel research and development. In this review, we primarily focus on injectable hydrogels under various stages of clinical trials for future addition to the clinical setting, and some of the products approved for clinical use. It also examines the current clinical landscape of injectable hydrogels for biomedical applications, highlighting their versatility in tissue engineering, drug delivery, and wound healing. While approved products promise conditions like osteoarthritis, ongoing clinical trials underscore the need for further research to assess safety, efficacy, and long-term outcomes, suggesting significant potential for transformative advancements in healthcare.

Theranostics is emerging as a powerful modality in precision oncology, integrating diagnostic imaging with targeted therapies to enable more effective and individualized cancer management. In parallel, artificial intelligence (AI) and digital twin (DT) technologies are increasingly being explored as enabling frameworks for advancing research, education, and clinical decision support. AI facilitates a range of quantitative and workflow-driven tasks, including organ and lesion segmentation, longitudinal lesion matching and tracking, and absorbed dose estimation, while also contributing to evidence generation, implementation, and evaluation processes. Complementing this, DTs integrate multimodal images, pharmacokinetic models, molecular characteristics, and clinical data to create dynamic, patient-specific representations of disease and treatment response. Together, these technologies improve treatment response and outcome prediction, enhance treatment planning, and support more adaptive and data-informed disease management strategies. In the near term, clinical practitioners and trainees must learn to effectively supervise AI systems, understand algorithmic limitations, and ensure their safe and effective use within clinical workflows. Over time, DT-enabled environments may support immersive, simulation-based learning with continuous feedback and exposure to complex or rare clinical scenarios, reshaping professional training. More broadly, the convergence of AI and DT technologies is driving an evolution in the structure of oncology practice itself. Alongside the four established clinical specialties medical oncology, radiation oncology, surgical oncology, and nuclear oncology a complementary role is emerging: computational oncology. These clinical and computational roles operate synergistically within an integrated health system, advancing data-driven and patient-centered precision oncology.

Abstract

Purpose: This case report aims to describe a new treatment for severe inflammatory external Root Resorption (RR) using calcium hydroxide at different intervals, supplemented by X-ray imaging follow-up.

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. 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.

Results: 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: We demonstrated that our method, which involves using calcium hydroxide as an antimicrobial and anti-resorptive agent with X-ray imaging follow-up, can effectively result in complete treatment.