Purpose: Interoceptions are a combination of sensation, integration, and interpretation of internal bodily signals. Interoceptions are bidirectionally related to the human being mental and physiological health, and well-being. Sleep and different interoceptive modalities are proven to share common relations.
Heartbeat Evoked Potential (HEP) is known as a robust readout to interoceptive processes. In this study, we focused on the relation between HEP modulations and sleep-related disorders.
Materials and Methods: We investigated four different sleep-related disorders, including insomnia, rapid eye movement behavior disorder, periodic limb movements and nocturnal frontal lobe epilepsy, and provided HEP signals of multiple Electroencephalogram (EEG) channels over the right hemisphere to compare these disorders with the control group. Here, we investigated and compared the results of 35 subjects, including seven subjects for the control group and seven subjects for each of above-mentioned sleep disorders.
Results: By comparing HEP responses of the control group with sleep-related patients’ groups, statistically significant HEP differences were detected over right hemisphere EEG channels, including FP2, F4, C4, P4, and O2 channels. These significant differences were also observed over the grand average HEP amplitude activity of channels over the right hemisphere in the sleep-related disorders as well.
Conclusion: Our results between the control group and groups of patients suffering from sleep-related disorders demonstrated that during different stages of sleep, HEPs show significant differences over multiple right hemisphere EEG channels. Interestingly, by comparing different sleep disorders with each other, we observed that each of these HEP differences’ patterns over specific channels and during certain sleep stages bear considerable resemblances to each other.

Purpose: In recent years, the use of Steady-State Visual Evoked Potentials (SSVEPs) in Brain-Computer Interface (BCI) systems has dramatically increased across several fields, such as rehabilitation, cognitive impairment, and brain disease or disorder detection, as well as artificial limbs, wheelchairs, and biomechanical systems. In this study, a novel method is proposed to help scientists develop more efficient BCI systems for Machine Learning Operations (MLOps). This study proposed a state-of-the-art method for detecting SSVEP-based stimulation frequencies with statistical models to design an optimal BCI system.
Materials and Methods: In this study, the Canonical Correlation Analysis (CCA) method has been implemented to extract features from the accessible-to-the-public Tsinghua University Benchmark dataset. A limited number of subjects are being studied. After completing feature selection methods and selecting the best subset of features using a specified feature selection method, the classification of the best features using machine learning-based classification methods has been completed. Furthermore, it is assumed that scientists will design and implement a system specifically for subjects. Models work for subjects independently. However, because model training is subject-specific, we must execute the proposed methods on each subject separately.
Results: The findings indicate that the novel suggested BCI system achieves an average accuracy of 83±9% in stimulation detection, which is higher than that of the traditional CCA approach with an accuracy of 80±11% (p<0.05).
Conclusion: Based on the findings, we demonstrated an increase in accuracy with the novel method. It was also discovered that by using the proposed techniques, it is possible to keep MLOps systems as an advantage.

Purpose: Interoceptions are a combination of sensation, integration, and interpretation of internal bodily signals. Interoceptions are bidirectionally related to the human being mental and physiological health, and well-being. Sleep and different interoceptive modalities are proven to share common relations.
Heartbeat Evoked Potential (HEP) is known as a robust readout to interoceptive processes. In this study, we focused on the relation between HEP modulations and sleep-related disorders.
Materials and Methods: We investigated four different sleep-related disorders, including insomnia, rapid eye movement behavior disorder, periodic limb movements and nocturnal frontal lobe epilepsy, and provided HEP signals of multiple Electroencephalogram (EEG) channels over the right hemisphere to compare these disorders with the control group. Here, we investigated and compared the results of 35 subjects, including seven subjects for the control group and seven subjects for each of above-mentioned sleep disorders.

Results: By comparing HEP responses of the control group with sleep-related patients’ groups, statistically significant HEP differences were detected over right hemisphere EEG channels, including FP2, F4, C4, P4, and O2 channels. These significant differences were also observed over the grand average HEP amplitude activity of channels over the right hemisphere in the sleep-related disorders as well.
Conclusion: Our results between the control group and groups of patients suffering from sleep-related disorders demonstrated that during different stages of sleep, HEPs show significant differences over multiple right hemisphere EEG channels. Interestingly, by comparing different sleep disorders with each other, we observed that each of these HEP differences’ patterns over specific channels and during certain sleep stages bear considerable resemblances to each other.

Purpose: In contemporary time, breast cancer has been extensively found among women at a global rate of 24.2% as of 2018. This exposes the significant and imperative need for detecting masses and micro calcifications in the breast to avoid death rates. The existing histopathological images have gained a golden standard considering they would afford reliable results. However, these images have been entangled with various complexities including insufficient image contrast, noise, and misdiagnosis. These pitfalls might negatively impact the detection rate for which automatic recognition has become vital. With the momentous evolvement of Machine Learning (ML) and Deep Learning (DL), various researchers have endeavoured to consider ML and DL for accomplishing this prediction. However, they need to improve in accuracy rate due to ineffective feature extraction, and most studies averted to consider segmentation. Hence, this study regards accomplishing a high classification and segmentation process.
Materials and Methods: The study proposes Modified Weight Updated Convolutional Block-U-Net (MWu-Conv-U-Net) to handle the image dimensions optimally. In this case, U-Net based model is regarded by improvising it with the inclusion of an additional convolutional layer in individual encoder-decoder. Further, the study proposes an Optimized Weight Updated eXtreme Gradient Boosting-Support Vector Machine (OWu-XGBoost-SVM) for determining the optimal gradient, which would eventually enhance the prediction rate.
Results: The overall performance is assessed through performance metrics to confirm its effectiveness in classifying and segmenting the Breast Cancer Histopathological (BACH) image dataset. Comparison is undertaken with conventional systems in accordance with metrics (recall, F1-score, precision, and accuracy). The results revealed the efficacy of the proposed system with 99% accuracy, 99% F1-score, 99% precision, and 99% recall.
Conclusion: High accuracy procured through the analysis reveals its efficacy and hence it could be applicable for real-time execution for assisting medical experts in early breast cancer prognosis.

Purpose: In this paper, we propose a secure wearable and portable device aiming to (i) monitor physical activity in medical and clinical rehabilitation, (ii) evaluate the subject's movements in sports environments, and (iii) monitor wellness indicators.
Materials and Methods: The innovative low-power custom circuit can acquire, pre-process, digitalize, and transmit EMG signals to a Raspberry PI4 device via a low-energy Bluetooth module. The Raspberry PI4 is a hub that sends data to a cloud-based system for remote monitoring and processing. The best cybersecurity practices have been implemented: firewall, anti-DDoS and SELinux.
Results: We have assessed the system's vulnerability before and after the system's hardening. SELinux makes the system safer and prevents unauthorized access to patients' data health by tampering with the devices.
Conclusion: Adopting IoT systems in telemedicine requires greater attention to cybersecurity. It is necessary to implement security mechanisms to guarantee the privacy of patient's health data.

Purpose: In recent years, the Electroencephalography (EEG)-based Brain-Computer Interface (BCI) appli-cation has been growing rapidly and has emerged as a technology with high translational potential since it allows disabled people to translate human intentions into control signals and to interact with the external environment without any kinesthetic movement. Individuals with significant health problems can benefit from this technology to improve their independence and facilitate participation in activities, thus improving general well-being and preventing impairments. The rapid advances in technology have led to optimal innovation in the context of wearable health monitoring and remote control solutions. Many wearable devices for capturing EEG signals in daily life have recently been released on the market. Our paper aims to present a wearable de-vice-based system for real-time and remote EEG monitoring, to describe the proposed system modules and the signal processing algorithms, to explore the functionalities of this wearable EEG solution, and to suggest its potential application for daily brain data recordings in the home en-vironment.
Materials and Methods: The validity of the Emotiv Epoc+ device in the continuous and real-time EEG signals acquisition and monitoring was demonstrated by means of preliminary test measurements during a resting state paradigm performed by a healthy control subject.
Results: Our findings confirmed the Epoc+ reliability for event-related brain potential research and in measuring acceptable spectral EEG data.
Conclusion: The described approach, focusing on the emerging area of remote monitoring sensors and wearable device applications, might be leveraged to measure complex health outcomes in non-specialist and remote settings.

Purpose: Parkinson's disease is a neurodegenerative disorder that affects the basal ganglia of the brain, which plays an important role in movement. Basal Ganglia-Thalamic network model including Subthalamic nucleus, Globus Pallidus externa, Globus Pallidus interna and Thalamus neurons. Optogenetics is a combination of optical and genetic tools used to stimulate basal ganglia neurons by light-sensitive ion channels (opsins) to eliminate the pathological effects of Parkinson's disease.
Materials and Methods: To analyze the effect of optogenetic stimulation on Parkinsonian nervous systems, two complete models of BG and RT (including STN, GPe, Gpi, and TH neurons) have been selected and developed for Parkinson’s disease and to apply three and four-state optogenetic stimulation. For this purpose, ChETA, ChRwt, and NpHR opsins have been selected in three-state and four-state stimulations and different stimulation conditions according to different parameters in both models have been investigated.
Results: To evaluate the performance of two models for each gene in three- and four-state stimulation conditions with different values of basic parameters, the value of error index is calculated and stimulation conditions that created an error index equal to zero have been introduced as optimal conditions. Based on the results, frequencies of 20 and 200 Hz in the four-state ChRwt model and frequency of 80 Hz in the three-state ChETA model have been introduced as optimal genes, frequencies, and models. To verify the developed model, the obtained results have been compared with the results of experimental studies.
Conclusion: In optimal conditions, STN provides excitatory input and GPe provide appropriate inhibitory input to GPi, and GPi can provide appropriate inhibitory input to TH, and as a result, its function improves and pathological effects of Parkinson's disease disappear. The response of GPe neurons is consistent with the experimental results and the response of other neurons is also similar to the response of GPe neurons.

Purpose: This study aimed to investigate the effect of CAREdose4D on the dose and image quality in Brain Computed Tomography (CT).
Materials and Methods: Noise, Signal-to-Noise Ratio (SNR), and Contrast-to-Noise Ratio (CNR) for Gray Matter (GM), White Matter (WM), Cerebrospinal Fluid (CSF), and skull bones were investigated in brain CT scans of 60 patients. In addition, a phantom study was conducted to examine the effect of CAREdose 4D on the same subject in the brain, chest, abdomen, and pelvic CT protocols. Volume CT dose index (CTDIvol) and Dose Length Product (DLP) were recorded for each scan. Data were analyzed by T-test and Mann-Whitney statistical test with a significance level of less than 0.05.
Results: The following results were obtained in active and passive modes of CAREdose 4D in brain CT of patients, respectively: CTDIvol, 15.76±3.94 and 16.96±2.14 mGy (p<0.05); DLP, 253.81±84.69 and 252.73±43.26 mGy.cm (p>0.05). There was no significant difference between SNRs and noise of various tissues of the brain (p>0.05) but CNR difference for gray matter, white matter, and cerebrospinal fluid (CSF) was significant (p<0.05). In the phantom study, SNR decreased in the active status of CAREdose 4D for the head in sequential and spiral modes, Chest, abdomen and pelvis by 7%, 84%, 45%, 20%, and 22%, respectively.
Conclusion: CAREdose 4D reduces the dose without having an adverse effect on noise and SNR in brain CT scans. It is recommended newbies and untrained technicians to use CAREdose 4D.

Purpose: Urine volume and urine conductivity monitoring allow better care for urinary tract infection disease. Urine volume and conductivity involve electrical bioimpedance change at the lower abdomen. In previous studies, bioimpedance has been only used for estimating the volume, and the estimation error significantly increases when the conductivity changes.
Materials and Methods: In this work, the neuron network technique is proposed to determine both the volume and the conductivity based on the measured bioimpedance data on a sixteen-electrode configuration. Nine architectures of neuron networks were investigated by simulation. Eleven body models were created, consisting of muscle, fat, pelvis bone, rectum, and bladder. Seven bladder sizes, eleven conductivities, and eight levels of Signal-to-Noise Ratio (SNRs) were simulated.
Results: The result showed that the neural network method could efficiently estimate with an average of 1.04% volume error and 2.85% conductivity error. The performance remained stable with a signal-to-noise ratio higher than 60 dB, but it may reduce 2-8 times at lower SNRs. The moderate fat content provided high performance. The performance would be worsened if the bladder size was very small and the conductivity was low. The performance was increased when the volume was moderate, i.e. 302 ml, and the conductivity was higher than 1.76 S/m. The 3-layer architecture with 1024, 512, and 2 neurons yielded the highest performance. The 2-layer architecture with hidden neurons higher than 512 provided a comparative performance with only 0.9-1.5% lesser performance.
Conclusion: Neural network technique can be used to estimate urine volume and urine conductivity with excellent performance.

Purpose: Executive functions and attention are often impaired in neurological, medical, and psychiatric disorders. This study aimed to, in addition to collecting Iranian normative data, examine whether the demographic variables are associated with performance in one of the most widely used neuropsychological tools to measure cognitive status.
Materials & Methods: The present study as part of the Iranian Brain Imaging Database project was conducted on 300 healthy individuals in the age range of 20 to 70 years, with an equal number of participants and an equal proportion of genders in each age decade. Independent and dependent variables, respectively, were age (classified by five decades including 20-30-year-olds, 31-40-year-olds, 41-50-year-olds, 51-60-year-olds, and 61-70-year-olds) and performance in the Trail Making Test (TMT; defined in terms of two scores of the completion time of TMT-A and TMT-B).
Results: According to correlation coefficients, age and education had a significant negative and positive correlation with both TMT-A and TMT-B (p=0.01), respectively; however, no significant correlation was observed between gender and scores (p>0.05). According to multivariate analysis of variance, the interaction of age, gender, and education did not lead to a significant difference in the TMT scores (p=0.309). In addition, Tukey's post hoc test showed that participants under 40 took significantly less time to complete TMT-A than those over 50, while in TMT-B, participants under 30 years completed in a shorter time than those over 30 years old (p<0.01).
Conclusion: Our findings indicate that age and education have a significant association with the performance of the Iranian healthy population in the well-known measure of executive function and attention, and it is necessary to interpret TMT scores using regional normative data in clinical and research settings.

Purpose: COVID-19 disease is associated with pericardial effusion through both direct invasions of myocardial tissue and activation of inflammatory processes and oxidative stress. However, its exact mechanism and related implications are unclear. We aimed to evaluate the pericardial effusion in hospitalized patients with a definite diagnosis of COVID-19 and finally to determine underlying factors related to this cardiac event. Finally, the hospital outcome of patients with and without pericardial involvement was compared.
Materials and Methods: The hospital records of 1824 patients suffering from COVID-19 were reviewed with respect to pieces evidence of pericardial effusion. Baseline characteristics, cardiovascular risk profiles, laboratory and echocardiography parameters as well as hospital outcomes were reviewed.
Results: Out of 1824 patients hospitalized with COVID-19 in our medical center in Intensive Care Unit (ICU) sections, a total of 300 cases (16.4%) (P value <0.05) had evidence of pericardial effusion. Patients with pericardial effusion had much higher mean age, higher mean heart rate and also a higher prevalence of hypertension, diabetes mellitus, and a history of ischemic heart disease compared to those without this complication. The changes in some echocardiography parameters, including left ventricular end-diastolic diameter, E/A ratio, E/Ep ratio, and tricuspid annular plane systolic excursion were more prominent in those with pericardial effusion. Those with pericardial effusion experienced longer hospitalization and ICU admission and the death rate was significantly higher in such patients.
Conclusion: The occurrence of pericardial effusion is predictable in about 16.4% of patients with COVID-19, which occurs mainly in older people and people with a history of cardiovascular risk profiles. Pericardial effusion in COVID-19 patients leads to poorer in-hospital outcome.

Purpose: Muscle synergy is a motor feature composed of synergy patterns and activation coefficients. This study aimed to combine the two-link arm model with synergy patterns and muscle activation coefficients, which in turn leads to selecting the optimum number of synergies by changing the best Variability Account For (VAF) criterion.
Materials and Methods: In this paper, signals were recorded from six arm muscles involved in arm-reaching movement while carrying a certain weight (w=700 g) by 20 subjects. The synergy pattern and activation coefficient matrices were calculated by using the Non-negative Matrix Factorization method (NNMF) and VAF criterion. Subsequently, to find the best VAF threshold, the output of signal preprocessing and NNMF’s output were done on Hill’s model.
Results: Average VAF% for 20 subjects in the mentioned movement was 97.34±2.0%, and four numbers of synergies were determined.
Conclusion: The results of the study suggest that the output of the W*H matrix (W and H are equal to the synergy pattern matrix and the activation coefficient matrix, in turn) had harmony with the output of the signal matrix recorded from all 20 subjects (output means the endpoint position and theta 1 and theta 2 angles) when they were performed as input on the two-link arm model. This harmony can be seen when choosing the best VAF critical threshold (value≥96%) via the aforementioned procedure. This harmony in turn contributes to exerting a positive influence on optimal extracting synergy patterns and describing the arm-reaching space more clearly.

Purpose: This study aimed to test the ability of high-frequency muscle ultrasound in detecting changes in muscle Echo Intensity (EI) in patients with type 2 Diabetes Mellitus (T2DM) and to correlate muscle ultrasonography findings with Nerve Conduction Study (NCS) parameters in those patients. Additionally, we aimed to assess the usefulness of muscle ultrasound in diagnosing diabetic peripheral neuropathy.
Materials and Methods: In this case-control study, 26 diabetic patients with Diabetic Peripheral Neuropathy (DPN) and 25 controls were enrolled. Among the controls, 15 were healthy individuals, and the remaining 10 were diabetic patients without DPN. All participants underwent Nerve Conduction Studies (NCS) of the peroneal and tibial motor nerves, as well as quantitative muscle ultrasound. Ultrasound (US) images of the Abductor Hallucis (AH) muscle, Tibialis Anterior (TA) muscle, and Rectus Femoris (RF) muscle were taken and analyzed using grayscale analysis to measure quantitative Echo Intensity (EI). A comparison between the groups regarding EI was made, and correlations between NCS and quantitative US results were assessed.
Results: Our Study unveiled a statistically significant augmentation in muscular EI within two of the scrutinized muscle groups among individuals afflicted by DPN, relative to the control cohorts. Moreover, a noteworthy correlation was discerned between the parameters of NCS and muscular EI.
Conclusion: Quantitating muscle EI using grayscale analysis of US images is a useful supplementary test for the detection of DPN.

Purpose: This study aimed to evaluate the effect of total antioxidant capacity on periodontal diseases among ionizing radiation workers. The relationship between oxidative stress and periodontal health in this specific occupational group was assessed to gain insights into potential antioxidant supplementation needs and strategies to promote periodontal well-being.
Materials and Methods: This case-control study was conducted among ionizing radiation workers (CT scan section) and control group participants. Salivary samples were collected from both groups, and total antioxidant capacity was measured using an Enzyme-Linked Immunosorbent Assay (ELISA) kit. Clinical periodontal parameters were also assessed. Statistical analysis was performed using T-tests to compare the results between the groups.
Results: The study group showed a significantly lower total antioxidant capacity (2.534) compared to the control group (3.806) (p = 0.022). Significant differences were observed in plaque index, probing pocket depth, and clinical attachment loss between the groups. These findings suggest a potential association between radiation exposure, decreased antioxidant capacity, and periodontal tissue damage.
Conclusion: Radiographic workers exposed to ionizing radiation had lower antioxidant capacity and higher rates of periodontal diseases. Maintaining adequate antioxidants is crucial for protecting periodontal tissues. Further research should investigate mechanisms and exposure variations.

Purpose: This study characterized and incorporated a novel Boron Nitride Nanoplatelet (BNNP) into conventional cement known as Glass Ionomer Cement (GIC) with changed ratios (range from 1%, 3%, 5%, and 7%wt) Subsequently.
Materials and Methods: The study examined the impact of adding BNNP on the mechanical characteristics of GIC, including its Flexural Strength (FS), Diametral Tensile Strength (DTS), water sorption/solubility, and setting times. The BNNP was characterized using Physio-Chemical Characterization, and Brunauer-Emmett-Teller (BET) testing, and density was also measured. In addition to showing considerably greater DTS (16.34± 1.26MPa) and FS (24.037 ± 0.816 MPa), the results showed that 3% wt. BNNP-modified GIC specimens decreased in water sorption/solubility 19.358±2.40 and 2.979±0.65 µg/mm³, respectively, compared with traditional GIC.
Results: In this work, a novel BNNP containing GIC was created, resulting in a 15% reduction in water sorption. When compared to commercial GIC, the demonstrated GIC can quadruple the DTS and FS.
Conclusion: For water-based cement types, the glass-ionomer formulations including BNNP exhibit equivalent and acceptable working qualities.

Purpose: This study aims to evaluate the dosimetric result of the Field-In-Field (FIF) plans compared with Tangential Wedged Beams (TWB) plans for whole breast radiotherapy of patients.
Materials and Methods: In this survey, we entered fifty patients with breast-conserving surgery and postoperative whole-breast radiotherapy. FIF and a TWB plan were made for each patient to compare dosimetric outcomes.
Results: The Homogeneity Index (HI) and Conformity Index (CI) were specified for the evaluation of Planning Target Volume (PTV). The mean dose of the ipsilateral lung and contra-lateral breast for the evaluation of organs at risk dose were used. The FIF plans had significantly lower HI (p < 0.01) and CI (p < 0.01) than those of the TWB plans. It means in the dosimetric comparisons of the PTV, the FIF plans were better than the TWB plans. The V10lung (31.152vs. 32.72%, p < 0.01), V20lung (25.6064vs. 26.6%, p < 0.01) V30lung (17.4% vs. 18.4%, p < 0.01) were lower with the FIF plans compared with those of the TWB plans, with statistical significance. The FIF plans had a lower mean dose for the lung than those of TWB plans (1225.48 vs. 1670.32 cGy) but no statistical significance (p=0.06). The mean dose in the contra-lateral of the breast in FIF plans was lower than in TWB plans (61.666 vs. 163.45 cGy), with statistical significance (p < 0.01).
Conclusion: The FIF plans increased the dose homogeneity, and conformity of the target volume for the whole-breast irradiation compared with the TWB plans contralateral breast. Moreover, the doses of organs at risk (ipsilateral lung and contralateral breast) were reduced with FIF plans.

Purpose: Artificial Intelligence (AI), which mimics the human brain structure and operation, simulates intelligence. The aim of Machine Learning (ML), which is a branch of artificial intelligence, is to create models by analyzing data. Another type of artificial intelligence, Deep Learning (DL), depicts geometric changes using several layers of model representations. Since DL broke the computational analysis record, AI has advanced in many areas.
Materials and Methods: Contrary to the widespread use of conventional ML methodologies, there is still a need to promote the use and popularity of DL for pharmaceutical research and development. Drug discovery and design have been enhanced by ML and DL in major research projects. To fully realize its potential, drug design must overcome many challenges and issues. Various aspects of medication design must be considered to successfully address these concerns and challenges. This review article explains DL's significance both in technological breakthroughs and in effective medications.
Results: There are numerous barriers and substantial challenges associated with drug design associated with DL architectures and key application domains. The article discusses several elements of medication development that have been influenced by existing research. Two widely used and efficient Neural Network (NN) designs are discussed in this article: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Conclusion: It is described how these tools can be utilized to design and discover small molecules for drug discovery. They are also given an overview of the history of DL approaches, as well as a discussion of some of their drawbacks.

Cancer is one of the most devastating disorders of the 21st century, creating a major concern among clinicians and researchers. Many different treatment strategies are being tried to fight the war against cancer have been tested. Various inorganic nanoparticles have been investigated to induce cytotoxicity in cancer cells and one of the successfully tried nanoparticles is Selenium Nanoparticles (SeNPs). Green synthesized SeNPs are a promising source of new antioxidant and anti-inflammatory agents, given the multiplicity of its mechanism. SeNPs displayed antiproliferative potential against colon, liver, cervical, breast, melanoma, and prostate cancer cells by several mechanisms, including triggering apoptotic signal transduction pathways or slow down the angiogenic signalling in cancer cells. Metal nano-therapies such as SeNPs are granted research consideration for cancer treatment. The biocompatibility achieved through green synthesis suggests its possible use not only in specific cancer conditions but also in other types of cancer without any risk of toxicity of these molecules.