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Background: Scoliosis is the most common type of spinal deformity. A universal and standard method for evaluating scoliosis is Cobb angle measurement, but several studies have shown that there is intra- and inter- observer variation in measuring cobb angle manually. Objective: Develop a computer- assisted system to decrease operator-dependent errors in Cobb angle measurement. Methods: The spinal cord in the given x-ray image of the spine is highlighted using contract-stretching technique. The overall structural curvature of the spine is determined by a semi-automatic algorithm aided by the operator. Once the morphologic curve of the spine is determined, in the last step the cobb-angle is estimated by calculating the angle between two normal lines to the spinal curve at the inflection points of the curve. Results: Evaluation results of the developed algorithms using 14 radiographs of patients (4 - 40 years old) with cobb angle ranges from 34 - 82 degrees, revealed that the developed algorithm accurately estimated cobb angle. Statistical analysis showed that average angle values estimated using the developed method and that provided by experts are statistically equal. The correlation coefficient between the angle values estimated using the developed algorithm and those provided by the expert is 0.81. Conclusion: Compared with previous algorithms, the developed system is easy to use, less operator-dependent, accurate, and reliable. The obtained results are promising and show that the developed computer-based system could be used to quantify scoliosis by measuring Cobb angle. Citation: Safari A, Parsaei H, Zamani A, Pourabbas B. A Semi-Automatic Algorithm for Estimating Cobb Angle. J Biomed Phys Eng. 2019;9(3):317-326. https://doi.org/10.31661/jbpe.v9i3Jun.730.

Background: Electromyography (EMG) signal processing and Muscle Onset Latency (MOL) are widely used in rehabilitation sciences and nerve conduction studies. The majority of existing software packages provided for estimating MOL via analyzing EMG signal are computerized, desktop based and not portable; therefore, experiments and signal analyzes using them should be completed locally. Moreover, a desktop or laptop is required to complete experiments using these packages, which costs. Objective: Develop a non-expensive and portable Android application (app) for estimating MOL via analyzing surface EMG. Material and Methods: A multi-layer architecture model was designed for implementing the MOL estimation app. Several Android-based algorithms for analyzing a recorded EMG signal and estimating MOL was implemented. A graphical user interface (GUI) that simplifies analyzing a given EMG signal using the presented app was developed too. Results: Evaluation results of the developed app using 10 EMG signals showed promising performance; the MOL values estimated using the presented app are statistically equal to those estimated using a commercial Windows-based surface EMG analysis software (MegaWin 3.0). For the majority of cases relative error

Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impact on the performance of a decomposition system. EMG decomposition has been studied well and several systems were proposed, but feature extraction step has not been investigated in detail. Objective: Several EMG signals were generated using a physiologically-based EMG signal simulation algorithm. For each signal, the firing patterns of motor units (MUs) provided by the simulator were used to extract MUPs of each MU. For feature extraction, different wavelet families including Daubechies (db), Symlets, Coiflets, bi-orthogonal, reverse bi-orthogonal and discrete Meyer were investigated. Moreover, the possibility of reducing the dimensionality of MUP feature vector is explored in this work. The MUPs represented using wavelet-domain features are transformed into a new coordinate system using Principal Component Analysis (PCA). The features were evaluated regarding their capability in discriminating MUPs of individual MUs. Results: Extensive studies on different mother wavelet functions revealed that db2, coif1, sym5, bior2.2, bior4.4, and rbior2.2 are the best ones in differentiating MUPs of different MUs. The best results were achieved at the 4th detail coefficient. Overall, rbior2.2 outperformed all wavelet functions studied; nevertheless for EMG signals composed of more than 12 MUPTs, syms5 wavelet function is the best function. Applying PCA slightly enhanced the results.

Background: Cardiac output (CO) is the total volume of blood pumped by the heart per minute and is a function of heart rate and stroke volume. CO is one of the most important parameters for monitoring cardiac function, estimating global oxygen delivery and understanding the causes of high blood pressure. Hence, measuring CO has always been a matter of interest to researchers and clinicians. Several methods have been developed for this purpose, but a majority of them are either invasive, too expensive or need special expertise and experience. Besides, they are not usually risk free and have consequences. Objective: Here, a semi-invasive system was designed and developed for continuous CO measurement via analyzing and processing arterial pulse waves. Results: Quantitative evaluation of developed CO estimation system was performed using 7 signals. It showed that it has an acceptable average error of (6.5%) in estimating CO. In addition, this system has the ability to consistently estimate this parameter and to provide a CO versus time curve that assists in tracking changes of CO. Moreover, the system provides such curve for systolic blood pressure, diastolic blood pressure, average blood pressure, heart rate and stroke volume. Conclusion: Evaluation of the results showed that the developed system is capable of accurately estimating CO. The curves which the system provides for important parameters may be valuable in monitoring hemodynamic status of high-risk surgical patients and critically ill patients in Intensive Care Units (ICU). Therefore, it could be a suitable system for monitoring hemodynamic status of critically ill patients.

Background: The basic requirements for monitoring anesthetized patients during surgery are assessing cardiac and respiratory function. Esophageal stethoscopes have been developed for this purpose, but these devices may not provide clear heart and lung sound due to existence of various noises in operating rooms. In addition, the stethoscope is not applicable for continues monitoring, and it is unsuitable for observing inaccessible patients in some conditions such as during CT scan. Objective: A wireless electronic esophageal stethoscope is designed for continues auscultation of heart and lung sounds in anesthetized patients. The system consists of a transmitter and a receiver. The former acquires, amplifies and transmits the acquired sound signals to the latter via a frequency modulation transmitter. The receiver demodulates, amplifies, and delivers the received signal to a headphone to be heard by anesthesiologist. Results: The usability and effectiveness of the designed system was qualitatively evaluated by 5 anesthesiologists in Namazi Hospital and Shahid Chamran Hospital, Shiraz, Iran on 30 patients in several operating rooms in different conditions; e.g., when electro surgery instruments are working. Fortunately, the experts on average ranked good quality for the heard heart and lung sounds and very good on the user friendly being of the instrument. Conclusion: Evaluation results demonstrate that the developed system is capable of capturing and transmitting heart and lung sounds successfully. Therefore, it can be used to continuously monitor anesthetized patients’ cardiac and respiratory function. Since via the instrument wireless auscultation is possible, it could be suitable for observing inaccessible patients in several conditions such as during CT scan.

Background: Effects of electromagnetic fields on healing have been investigated for centuries. Substantial data indicate that exposure to electromagnetic field can lead to enhanced healing in both soft and hard tissues. Helmholtz coils are devices that generate pulsed electromagnetic fields (PEMF). Objective: In this work, a pair of Helmholtz coils for enhancing the healing process in periodontitis was designed and fabricated. Method: An identical pair of square Helmholtz coils generated the 50 Hz magnetic field. This device was made up of two parallel coaxial circular coils (100 turns in each loop, wound in series) which were separated from each other by a distance equal to the radius of one coil (12.5 cm). The windings of our Helmholtz coil was made of standard 0.95mm wire to provide the maximum possible current. The coil was powered by a function generator. Results: The Helmholtz Coils generated a uniform magnetic field between its coils. The magnetic field strength at the center of the space between two coils was 97.6 μT. Preliminary biological studies performed on rats show that exposure of laboratory animals to pulsed electromagnetic fields enhanced the healing of periodontitis. Conclusion: Exposure to PEMFs can lead to stimulatory physiological effects on cells and tissues such as enhanced healing of periodontitis.

Background: The time and frequency features of motor unit action potentials (MUAPs) extracted from electromyographic (EMG) signal provide discriminative information for diagnosis and treatment of neuromuscular disorders. However, the results of conventional automatic diagnosis methods using MUAP features is not convincing yet. Objective: The main goal in designing a MUAP characterization system is obtaining high classifcation accuracy to be used in clinical decision system. For this aim, in this study, a robust classifer is proposed to improve MUAP classifcation performance in estimating the class label (myopathic, neuropathic and normal) of a given MUAP. Method: The proposed scheme employs both time and time–frequency features of a MUAP along with an ensemble of support vector machines (SVMs) classifers in hybrid serial/parallel architecture. Time domain features includes phase, turn, peak to peak amplitude, area, and duration of the MUAP. Time–frequency features are discrete wavelet transform coeffcients of the MUAP. Results: Evaluation results of the developed system using EMG signals of 23 subjects (7 with myopathic, 8 with neuropathic and 8 with no diseases) showed that the system estimated the class label of MUAPs extracted from these signals with average of accuracy of 91% which is at least 5% higher than the accuracy of two previously presented methods. Conclusion: Using different optimized subsets of features along with the presented hybrid classifer results in a classifcation accuracy that is encouraging to be used in clinical applications for MUAP characterization.

Background: Brain tissue segmentation for delineation of 3D anatomical structures from magnetic resonance (MR) images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), but only if the obtained segmentation results are correct. Due to image artifacts such as noise, low contrast and intensity non-uniformity, there are some classifcation errors in the results of image segmentation. Objective: An automated algorithm based on multi-layer perceptron neural networks (MLPNN) is presented for segmenting MR images. The system is to identify two tissues of WM and GM in human brain 2D structural MR images. A given 2D image is processed to enhance image intensity and to remove extra cerebral tissue. Thereafter, each pixel of the image under study is represented using 13 features (8 statistical and 5 non- statistical features) and is classifed using a MLPNN into one of the three classes WM and GM or unknown. Results: The developed MR image segmentation algorithm was evaluated using 20 real images. Training using only one image, the system showed robust performance when tested using the remaining 19 images. The average Jaccard similarity index and Dice similarity metric for the GM and WM tissues were estimated to be 75.7 %, 86.0% for GM, and 67.8% and 80.7%for WM, respectively. Conclusion: The obtained performances are encouraging and show that the presented method may assist with segmentation of 2D MR images especially where categorizing WM and GM is of interest.

The COVID-19 pandemic has affected people globally; nowadays several countries are facing a major change in daily life due to universal quarantining, closed schools, social isolation, and shelter-in-place orders. In addition, this pandemic caused an economic crisis. International Labour Organization (ILO) reported that not only COVID-19 is a serious threat to public health but also the economic and social disruption of this crisis threatens the long-term livelihoods and wellbeing of millions of people. In this short paper, we introduce our hypothesis on using artificial intelligence (AI) technology to predict which employees are most vulnerable to the infections caused by the novel coronavirus (SARS-CoV-2). Such a system can be used in a wide variety of work places such as libraries, banks, drugstores, and hotel receptions to reduce the risk of severe infections in employees as well as to ensure the safety of labor force and the sustainability of businesses and jobs. [ABSTRACT FROM AUTHOR]

Over the last three decades, designing cellular manufacturing systems (CMS) still centres on assigning machines to machine cells and parts to part families. This task ends after assigning these part families to the appropriate machine cells. In the past, testing CMS was evaluated according to the efficiency of clustering, but actual testing of CMS after installation is still unexplored. Introducing one or more new parts (products) into CMS without any changes in the installation of the cells during processing of the current parts is a new concept to be considered and evaluated. Transferring these systems from traditional ideologues to advanced ideologues (agile systems) is highly desired. This concept can be considered as part (product) flexibility in CMS. To address this concept, a new similarity coefficient between the new part and the existing manufacturing cell will be created. New productivity and flexibility measurements in CMS will also be suggested. A new strategy for accepting a new part into CMS will be proposed based on machine utilization and flexibility in the cells, cell utilization and flexibility in the system, product flexibility (system flexibility), and similarity of this part with existing manufacturing cells. A complete analytical example will be presented. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is poised to play a pivotal role in future medical support systems, enabling pervasive health monitoring in smart cities. Alzheimer's disease (AD) afflicts millions globally, and this paper explores the potential of electroencephalogram (EEG) data in addressing this challenge. We propose the Convolutional Learning Attention-Bidirectional Time-Aware Long-Short-Term Memory (CL-ATBiLSTM) model, a deep learning approach designed to classify different AD phases through EEG data analysis. The model utilizes Discrete Wavelet Transform (DWT) to decompose EEG data into distinct frequency bands, allowing for targeted analysis of AD-related brain activity patterns. Additionally, the data is segmented into smaller windows to handle the dynamic nature of EEG signals, and these segments are transformed into spectrogram images, visually depicting brain activity distribution over time and frequency. The CL-ATBiLSTM model incorporates convolutional layers to capture spatial features, attention mechanisms to emphasize crucial data, and BiLSTM networks to explore temporal relationships within the sequences. To optimize the model's performance, Bayesian optimization is employed to fine-tune the hyperparameters of the ATBiLSTM network, enhancing its ability to generalize and accurately classify AD stages. Incorporating Bayesian learning ensures the most effective model configuration, improving sensitivity and specificity for identifying AD-related patterns. Our model extracts discriminative features from EEG data to differentiate between AD, Mild Cognitive Impairment (MCI), and healthy controls (CO), offering a more comprehensive approach than existing two-class detection algorithms. By including the MCI category, our method facilitates earlier identification and potentially more impactful therapy interventions. Achieving a 96.52% accuracy on Figshare datasets containing AD, MCI, and CO groups, our approach demonstrates strong potential for practical use, accelerating AD identification, enhancing patient care, and contributing to the development of targeted treatments for this debilitating condition., (© 2024. The Author(s).)

Healing diabetic ulcers with chronic inflammation is a major challenge for researchers and professionals, necessitating new strategies. To rapidly treat diabetic wounds in rat models, we have fabricated a composite scaffold composed of alginate (Alg) and silk fibroin (SF) as a wound dressing that is laden with molecules of lithium chloride (LC). The physicochemical, bioactivity, and biocompatibility properties of Alg-SF-LC scaffolds were investigated in contrast to those of Alg, SF, and Alg-SF ones. Afterward, full-thickness wounds were ulcerated in diabetic rats in order to evaluate the capacity of LC-laden scaffolds to regenerate skin. The characterization findings demonstrated that the composite scaffolds possessed favorable antibacterial properties, cell compatibility, high swelling, controlled degradability, and good uniformity in the interconnected pore microstructure. Additionally, in terms of wound contraction, re-epithelialization, and angiogenesis improvement, LC-laden scaffolds revealed better performance in diabetic wound healing than the other groups. This research indicates that utilizing lithium chloride molecules loaded in biological materials supports the best diabetic ulcer regeneration in vivo, and produces a skin replacement with a cellular structure comparable to native skin., Competing Interests: Declaration of competing interest The authors declare that they have no conflicts of interest., (Copyright © 2024. Published by Elsevier B.V.)

Interpreting intramuscular electromyography (iEMG) signals for diagnosing and quantifying the severity of lumbosacral radiculopathy is challenging due to the subjective evaluation of signals. To address this limitation, a clinical decision support system (CDSS) was developed for the diagnosis and quantification of the severity of lumbosacral radiculopathy based on intramuscular electromyography (iEMG) signals. The CDSS uses the EMG interference pattern method (QEMG IP) to directly extract features from the iEMG signal and provide a quantitative expression of injury severity for each muscle and overall radiculopathy severity. From 126 time and frequency domain features, a set of five features, including the crest factor, mean absolute value, peak frequency, zero crossing count, and intensity, were selected. These features were derived from raw iEMG signals, empirical mode decomposition, and discrete wavelet transform, and the wrapper method was utilized to determine the most significant features. The CDSS was trained and tested on a dataset of 75 patients, achieving an accuracy of 93.3%, sensitivity of 93.3%, and specificity of 96.6%. The system shows promise in assisting physicians in diagnosing lumbosacral radiculopathy with high accuracy and consistency using iEMG data. The CDSS's objective and standardized diagnostic process, along with its potential to reduce the time and effort required by physicians to interpret EMG signals, makes it a potentially valuable tool for clinicians in the diagnosis and management of lumbosacral radiculopathy. Future work should focus on validating the system's performance in diverse clinical settings and patient populations., (© 2024. International Federation for Medical and Biological Engineering.)

The regeneration of bone defects that exceed 2 cm is a challenge for the human body, necessitating interventional therapies. Demineralized bone matrices (DBM) derived from biological tissues have been employed for bone regeneration and possess notable osteoinductive and osteoconductive characteristics. Nevertheless, their efficiency in regenerating critically sized injuries is limited, and therefore additional signaling cues are required. Thanks to the piezoelectric properties of the bone, external physical stimulation is shown to accelerate tissue healing. We have implanted human DBM in critically sized cranial bone defects in rat animal models and exposed them to an external magnetic field (1 T) to enhance endogenous bone formation. Our in vitro experiments showed the superior cytocompatibility of DBM compared to cell culture plates. Furthermore, alkaline phosphatase activity after 14 days and Alizarin red staining at 28 days demonstrated differentiation of rat bone marrow mesenchymal stem cells into bone lineage on DBM. Computer tomography images together with histological analyses showed that implanting DBM in the injured rats significantly enhanced bone regeneration. Notably, combining DBM transplantation with a 2 h daily exposure to a 1 T magnetic field for 2 weeks (day 7 to 21 post-surgery) significantly improved bone regeneration compared to DBM transplantation alone. This research indicates that utilizing external magnetic stimulation significantly enhances the potential of bone allografts to regenerate critically sized bone defects.

Background: Qualitative and quantitative assessment of retinal perfusion using optical coherence tomography angiography (OCTA) has shown to be effective in the treatment and management of various retinal and optic nerve diseases. However, manual analyses of OCTA images to calculate metrics related to Foveal Avascular Zone (FAZ) morphology, and retinal vascular density and morphology are costly, time-consuming, subject to human error, and are exposed to both inter and intra operator variability., Objective: This study aimed to develop an open-source software framework for quantitative OCTA (QOCTA). Particularly, for analyzing OCTA images and measuring several indices describing microvascular morphology, vessel morphology, and FAZ morphology., Material and Methods: In this analytical study, we developed a toolbox or QOCTA using image processing algorithms provided in MATLAB. The software automatically determines FAZ and measures several parameters related to both size and shape of FAZ including area, perimeter, Feret's diameter circularity, axial ratio, roundness, and solidity. The microvascular structure is derived from the processed image to estimate the vessel density (VD). To assess the reliability of the software, three independent operators measured the mentioned parameters for the eyes of 21 subjects. The consistency of the values was assessed using the intraclass correlation coefficient (ICC) index., Results: Excellent consistency was observed between the measurements completed for the superficial layer, ICC >0.9. For the deep layer, good reliability in the measurements was achieved, ICC >0.7., Conclusion: The developed software is reliable; hence, it can facilitate quantitative OCTA, further statistical comparison in cohort OCTA studies, and can assist with obtaining deeper insights into retinal variations in various populations., Competing Interests: None, (Copyright: © Journal of Biomedical Physics and Engineering.)

Background: Crack heroin is a novel opiate derivative with highly addictive properties and unfamiliar health consequences. It causes a variety of brain dysfunctions that are mediated by neurochemical alterations and abnormal neuroplasticity. Brain-derived neurotrophic factor (BDNF) is a widely recognized biological marker implicated in the neuropathology of substance use during substance use disorder and withdrawal. Its involvement can significantly contribute to the severity of withdrawal symptoms. Hence, this study aimed to evaluate BDNF levels in crack heroin users before and after withdrawal., Methods: In this cross-sectional study, 148 male participants were recruited and divided into two groups: persons with crack heroin use disorder (n = 74) and the controls (n = 74). The BDNF serum levels were measured in both crack heroin users and control groups upon hospitalization and again after twenty-one days of withdrawal using the enzyme-linked immunosorbent assay., Results: The results demonstrated that BDNF levels in persons with crack heroin use disorder upon admission were significantly lower than the levels observed upon discharge and in the control group (p < 0.05). Additionally, a significant difference in BDNF levels was found between persons with crack heroin use disorder at admission and discharge (p = 0.038). Furthermore, BDNF levels showed an inverse correlation with the daily dose of substance use (r= -0.420, p = 0.03) and the duration of crack heroin use (r= -0.235, p = 0.001)., Conclusions: A progressive increment in BDNF levels during early detoxification is associated with the daily amount of substance use and the duration of substance use. Our findings suggest that changes in BDNF serum levels during crack heroin use disorder and withdrawal could serve as potential biomarkers for assessing the intensity of withdrawal symptoms and substance use-related behaviors., (© 2024. The Author(s).)

Opioid action in the brain involves the dopamine-reward system as well as non-dopamine pathways. Since vitamin D also modulates the brain's dopamine system, the question of this study was how vitamin D might affect the opioid influences on the reward system. Therefore, the objective of this study was to investigate the possible effect of vitamin D on the conditioned place preference (CPP) induced by morphine, as a valuable model of assessment of the reinforcing properties of opioids by associating the context to the rewarding properties of the addictive drugs. Male Wistar rats were randomly divided into two main groups that either received saline (morphine vehicle) or morphine (5 mg/kg, intraperitoneally) for CPP. Each of the main groups was divided into three vitamin D treatment subgroups: vitamin D vehicle and vitamin D (5 and 10 μg/kg, intraperitoneally). Vitamin D injections were started 1 week ahead of the experiment (two injections) or immediately after post-conditioning and in both cases, it was continued twice weekly throughout the CPP. Administration of vitamin D (10 μg/kg) before conditioning in CPP markedly attenuated morphine expression in the post-conditioning test. Receiving vitamin D (5 or 10 μg/kg) before or after conditioning significantly attenuated morphine reinstatement. Administration of vitamin D after opioid conditioning facilitated morphine memory extinction and attenuated morphine reinstatement. Vitamin D is probably a valuable addition to be considered as a part of the treatment for prevention or minimizing the dependency or relapse to opioids., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives. A posture maintained for a long period can be harmful to the health of office workers. This study aimed to estimate the recommended ergonomic duration for maintaining different sitting postures. Methods. Forty healthy male and female students participated in this experiment designed to measure perceived discomfort caused by maintaining common static sitting postures of office workers in a simple ergonomic set-up for 4 min. The Borg CR10 scale was given to the participants to assess the discomfort in different body parts, before and after each experiment. Based on the mean group discomfort level of 2, the recommended holding time of each posture was estimated. Results . The recommended holding time and its discomfort score for each studied posture were tabulated. The shortest holding time of a posture was obtained for the moderate neck flexion (1.61 min), and the longest holding time was obtained for a leg posture with 90° knee flexion (6.45 min). Conclusions . The recommended holding time in this study may help to assess the risk of musculoskeletal disorders (MSDs) in office workers and train the individuals involved in office tasks in proper sitting behavior.

Background: Phonocardiogram (PCG) signal provides valuable information for diagnosing heart diseases. However, its applications in quantitative analyses of heart function are limited because the interpretation of this signal is difficult. A key step in quantitative PCG is the identification of the first and second sounds (S1 and S2) in this signal., Objective: This study aims to develop a hardware-software system for synchronized acquisition of two signals electrocardiogram (ECG) and PCG and to segment the recorded PCG signal via the information provided in the acquired ECG signal., Material and Methods: In this analytical study, we developed a hardware-software system for real-time identification of the first and second heart sounds in the PCG signal. A portable device to capture synchronized ECG and PCG signals was developed. Wavelet de-noising technique was used to remove noise from the signal. Finally, by fusing the information provided by the ECG signal (R-peaks and T-end) into a hidden Markov model (HMM), the first and second heart sounds were identified in the PCG signal., Results: ECG and PCG signals from 15 healthy adults were acquired and analyzed using the developed system. The average accuracy of the system in correctly detecting the heart sounds was 95.6% for S1 and 93.4% for S2., Conclusion: The presented system is cost-effective, user-friendly, and accurate in identifying S1 and S2 in PCG signals. Therefore, it might be effective in quantitative PCG and diagnosing heart diseases., Competing Interests: None, (Copyright: © Journal of Biomedical Physics and Engineering.)

The future of molecular-level therapy, efficient medical diagnosis, and drug delivery relies on the effective theragnostic function which can be achieved by the synergistic effect of fluorescent carbon dots (FCDs) liposomes (L) and nanoliposomes. FCDs act as the excipient navigation agent while liposomes play the role of the problem-solving agent, thus the term "theragnostic" would describe the effect of LFCDs properly. Liposomes and FCDs share some excellent at-tributes such as being nontoxic and biodegradable and they can represent a potent delivery system for pharmaceutical compounds. They enhance the therapeutic efficacy of drugs via stabilizing the encapsulated material by circumventing barriers to cellular and tissue uptake. These agents facilitate long-term drug biodistribution to the intended locations of action while eliminating systemic side effects. This manuscript reviews recent progress with liposomes, nanoliposomes (collectively known as lipid vesicles) and fluorescent carbon dots, by exploring their key characteristics, applications, characterization, performance, and challenges. An extensive and intensive understanding of the synergistic interaction between liposomes and FCDs sets out a new research pathway to an efficient and theragnostic / theranostic drug delivery and targeting diseases such as cancer., Competing Interests: Declaration of Competing Interest Authors declare no conflict of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Nowadays, there is a growing global concern over rapidly increasing screen time (smartphones, tablets, and computers). An accumulating body of evidence indicates that prolonged exposure to short-wavelength visible light (blue component) emitted from digital screens may cause cancer. The application of machine learning (ML) methods has significantly improved the accuracy of predictions in fields such as cancer susceptibility, recurrence, and survival., Objective: To develop an ML model for predicting the risk of breast cancer in women via several parameters related to exposure to ionizing and non-ionizing radiation., Material and Methods: In this analytical study, three ML models Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron Neural Network (MLPNN) were used to analyze data collected from 603 cases, including 309 breast cancer cases and 294 gender and age-matched controls. Standard face-to-face interviews were performed using a standard questionnaire for data collection., Results: The examined models RF, SVM, and MLPNN performed well for correctly classifying cases with breast cancer and the healthy ones (mean sensitivity> 97.2%, mean specificity >96.4%, and average accuracy >97.1%)., Conclusion: Machine learning models can be used to effectively predict the risk of breast cancer via the history of exposure to ionizing and non-ionizing radiation (including blue light and screen time issues) parameters. The performance of the developed methods is encouraging; nevertheless, further investigation is required to confirm that machine learning techniques can diagnose breast cancer with relatively high accuracies automatically., Competing Interests: None, (Copyright: © Journal of Biomedical Physics and Engineering.)

Glioblastoma multiforme (GBM) is a common, aggressive, fast-growing tumor of the central nervous system that currently has no effective treatment. Although stem cell therapy has shown promising in vitro achievements, the blood-brain barrier (BBB) has always been a major hurdle to clinical success. To overcome this challenge, exosomes have been targeted as attractive drug delivery agents in numerous studies since they are small enough to enter the BBB. Furthermore, exosomes' characteristics and compositions are directly determined by the parent cell and these heritable traits affect their cell interactions. This article focuses on exosomes as an alternative to stem cell therapy to regulate glioma cell activity. Exosomes were isolated from rat bone marrow mesenchymal stem cells (rBMMSCs) by ultracentrifugation method and then characterized via western blot, dynamic light scattering, scanning, and transmission electron microscopy. Next, various concentrations of the exosomes were incubated with C6 cells and their effects at different time points were evaluated in vitro. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and Annexin/Pi assay results confirmed that the isolated exosomes cause cell death mostly through apoptosis, and a linear correlation was observed between exosomes' concentration and their cytotoxicity. Following that, the scratch test, colony formation test, and Transwell assay confirmed exosomes' significant impact on the migration and invasion behavior of C6 cells. For the first time, rBMMSC-derived exosomes have been used as a single treatment for GBM rather than in combination with other treatments or as a pharmaceutical carrier., (© 2022 International Federation for Cell Biology.)

Objectives. Common ergonomic office workstations are designed for a few optimum postures. Nonetheless, sitting is a dynamic activity and the ideal sitting posture is rarely maintained in practice. Therefore, the present study aimed to investigate the sitting behavior of office workers in an actual working environment using ergonomically adjusted workstations to examine whether they promote maintaining appropriate sitting postures. Methods . Sitting behaviors (frequency of postures and position changes in different body parts) were explored among 26 office workers during a 60-min sitting duration, using the posture recording and classification method developed by Graf et al. The rapid upper limb assessment (RULA) method was also used to assess postural load. Then, the results of the RULA method were compared with the results from investigating the sitting behavior of office workers. Results . Common ergonomic workstations were effective in eliminating some awkward postures. However, some important risk factors such as holding postures with an inappropriate lumbar spine curve (86% of the observations) and maintaining a posture for a long time (for 7-12 min) were observed in the participants' sitting behaviors, while they were neglected in the RULA method. Conclusions . The common ergonomic workstations could not guarantee the users' appropriate sitting behaviors.

Background: A significant error that may occur during ergonomic risk assessment and invalidate assessment reliability corresponds to technique selection. This study aimed to develop a new tool called the Decision Aid Tool (DAT) to reduce pen-paper observational technique selection errors., Methods: This quasi-experiment before-after study was performed in three phases. In the first phase, the participants' skills in technique selection were examined by showing them 20 videos of different single-task jobs. In the second phase, the DAT was designed using pen-paper observational techniques. Finally, in the third phase, 115 occupational health specialists included in the study through purposive sampling of experts evaluated the tool's efficacy., Results: The results of the first phase showed that 62% of participants made an error in selecting the proper technique. The mean and standard deviation scores from the first and third phases were 11.4 ± 6.59 and 39.01 ±1.89, respectively. The mean scores increased significantly after using DAT, and 97.5% of participants could correctly select task techniques., Conclusions: The efficacy of DAT was confirmed in a quasi-experimental before-and-after study. Using DAT increases the participants' ability to choose the correct technique. The DAT can be functional for practitioners to select the pen-paper observational techniques correctly under the purpose of assessment, the body areas, and the characteristics of the task to be assessed.