Combining enhanced spectral resolution of EMG and a deep learning approach for knee pathology diagnosis.

Knee osteoarthritis (OA) is a prevalent, debilitating joint condition primarily affecting the elderly. This investigation aims to develop an electromyography (EMG)-based method for diagnosing knee pathologies. EMG signals of the muscles surrounding the knee joint were examined and recorded. The principal components of the proposed method were preprocessing, high-order spectral analysis (HOSA), and diagnosis/recognition through deep learning. EMG signals from individuals with normal and OA knees while walking were extracted from a publicly available database. This examination focused on the quadriceps femoris, the medial gastrocnemius, the rectus femoris, the semitendinosus, and the vastus medialis. Filtration and rectification were utilized beforehand to eradicate noise and smooth EMG signals. Signals' higher-order spectra were analyzed with HOSA to obtain information about nonlinear interactions and phase coupling. Initially, the bicoherence representation of EMG signals was devised. The resulting images were fed into a deep-learning system for identification and analysis. A deep learning algorithm using adapted ResNet101 CNN model examined the images to determine whether the EMG signals were conventional or indicative of knee osteoarthritis. The validated test results demonstrated high accuracy and robust metrics, indicating that the proposed method is effective. The medial gastrocnemius (MG) muscle was able to distinguish Knee osteoarthritis (KOA) patients from normal with 96.3±1.7% accuracy and 0.994±0.008 AUC. MG has the highest prediction accuracy of KOA and can be used as the muscle of interest in future analysis. Despite the proposed method's superiority, some limitations still require special consideration and will be addressed in future research.

Investigating the impact of smoking habits through photoplethysmography analysis.

Smoking is widely recognized as a significant risk factor in the progression of arterial stiffness and cardiovascular diseases. Valuable information related to cardiac arrhythmias and heart function can be obtained by analyzing biosignals such as the electrocardiogram (ECG) and the photoplethysmogram (PPG). The PPG signal is a non-invasive optical technique that can be used to evaluate the changes in blood volume, and thus it can be linked to the health of the vascular system.Objective. In this study, the impact of three smoking habits-cigarettes, shisha, and electronic cigarettes (e-cigarettes)-on the features of the PPG signal were investigated.Approach. The PPG signals are measured for 45 healthy smokers before, during, and after the smoking session and then processed to extract the morphological features. Quantitative statistical techniques were used to analyze the PPG features and provide the most significant features of the three smoking habits. The impact of smoking is observed through significant changes in the features of the PPG signal, indicating blood volume instability.Main results. The results revealed that the three smoking habits influence the characteristics of the PPG signal significantly, which presentseven after 15 min of smoking. Among them, shisha has the greatest impact on PPG features, particularly on heart rate, systolic time, augmentation index, and peak pulse interval change. In contrast, e-cigarettes have the least effect on PPG features. Interestingly, smoking electronic cigarettes, which many participants use as a substitute for traditional cigarettes when attempting to quit smoking, has nearly a comparable effect to regular smoking.Significance. The findings suggest that individuals who smoke shisha are more likely to develop cardiovascular diseases at an earlier age compared to those who have other smoking habits. Understanding the variations in the PPG signal caused by smoking can aid in the early detection of cardiovascular disorders and provide insight into cardiac conditions. This ultimately contributes to the prevention of the development of cardiovascular diseases and the development of a health screening system.

Prevalence of vitamin B12 deficiency in type 2 diabetic patients taking metformin, a cross-sectional study in primary healthcare.

Background: Several studies documented that metformin use contributes to vitamin B12 deficiency in patients with type 2 diabetes mellitus (T2DM). However, there has been a lack of data assessing this issue in Jordan. Aims: Assess the vitamin B12 serum levels, frequency of vitamin B12 deficiency, and related factors among Jordanian patients with T2DM patients treated with metformin. Methods: a total of 447 subjects attending a primary health care center were included in this cross-sectional study consisting of T2DM patients who use metformin and a control group of non-diabetics. Serum B12 levels were evaluated and B12 deficiency was defined as serum B12 levels ≤ 200 pmol/L. Associations of B12 serum levels or B12 deficiency with other factors like gender, age, and duration of T2DM were analyzed. Results: There was no significant difference in serum B12 levels nor the frequency of vitamin B12 deficiency between T2DM metformin-treated patients and control groups. Among metformin-treated patients there was no difference relating to age, type 2 diabetes mellitus duration, proton pump inhibitors use, and metformin use (duration, dose) between patients with or without B12 deficiency. Conclusion: The prevalence of vitamin B12 deficiency among T2DM patients on metformin treatment in this study was high (48.9%). However, the treatment with metformin and the dose of metformin use was not associated with vitamin B12 deficiency.

Automated Prediction of Osteoarthritis Level in Human Osteochondral Tissue Using Histopathological Images.

Osteoarthritis (OA) is the most common arthritis and the leading cause of lower extremity disability in older adults. Understanding OA progression is important in the development of patient-specific therapeutic techniques at the early stage of OA rather than at the end stage. Histopathology scoring systems are usually used to evaluate OA progress and the mechanisms involved in the development of OA. This study aims to classify the histopathological images of cartilage specimens automatically, using artificial intelligence algorithms. Hematoxylin and eosin (HE)- and safranin O and fast green (SafO)-stained images of human cartilage specimens were divided into early, mild, moderate, and severe OA. Five pre-trained convolutional networks (DarkNet-19, MobileNet, ResNet-101, NasNet) were utilized to extract the twenty features from the last fully connected layers for both scenarios of SafO and HE. Principal component analysis (PCA) and ant lion optimization (ALO) were utilized to obtain the best-weighted features. The support vector machine classifier was trained and tested based on the selected descriptors to achieve the highest accuracies of 98.04% and 97.03% in HE and SafO, respectively. Using the ALO algorithm, the F1 scores were 0.97, 0.991, 1, and 1 for the HE images and 1, 0.991, 0.97, and 1 for the SafO images for the early, mild, moderate, and severe classes, respectively. This algorithm may be a useful tool for researchers to evaluate the histopathological images of OA without the need for experts in histopathology scoring systems or the need to train new experts. Incorporating automated deep features could help to improve the characterization and understanding of OA progression and development.

The Effect of Gender and Arm Anatomical Position on the Hand Grip Strength and Fatigue Resistance during Sustained Maximal Handgrip Effort.

Background: Handgrip or Grip strength (GS) is a common method used to evaluate muscle strength and affected by different factors, including age, gender, and arm's positions. Objective: This study aims to investigate the effect of both the gender and arm's positions on the handgrip strength and the fatigue resistance (FR), which is the time needed for the handgrip strength to drop to 75% (FR75), 50% (FR50), and 25% (FR25) of its maximum strength during sustained maximal handgrip effort. Material and Methods: In this experimental study, 59 male and 41 female participants were asked to grip forcefully on a dynamometer for the longest period. GS and FR75, FR50, and FR25 values were recorded for 7 different arm positions. Factorial ANOVA was used to find the main effect of gender and position and the interaction between them. Sidak and Tukey's HSD tests were used to find the gender and arm position effects, respectively. Results: The results showed a significant effect for gender and arm position on GS and FR and a significant interaction effect for GS that was significantly higher in males than females for all positions. The gender difference in FR depends on arm's positions and the level at which the FR was measured. GS was higher when arm adduction with 90 ͦ forward at the elbow as compared to arm abduction with 180 ͦ at the shoulder and 90 ͦ at the elbow. Conclusion: The results confirmed the significant effect of the gender and arm's positions on the maximal handgrip strength and fatigue resistance during sustained maximal handgrip effort.

Biodegradable zinc oxide composite scaffolds promote osteochondral differentiation of mesenchymal stem cells.

Osteoarthritis (OA) involves the degeneration of articular cartilage and subchondral bone. The capacity of articular cartilage to repair and regenerate is limited. A biodegradable, fibrous scaffold containing zinc oxide (ZnO) was fabricated and evaluated for osteochondral tissue engineering applications. ZnO has shown promise for a variety of biomedical applications but has had limited use in tissue engineering. Composite scaffolds consisted of ZnO nanoparticles embedded in slow degrading, polycaprolactone to allow for dissolution of zinc ions over time. Zinc has well-known insulin-mimetic properties and can be beneficial for cartilage and bone regeneration. Fibrous ZnO composite scaffolds, having varying concentrations of 1-10 wt.% ZnO, were fabricated using the electrospinning technique and evaluated for human mesenchymal stem cell (MSC) differentiation along chondrocyte and osteoblast lineages. Slow release of the zinc was observed for all ZnO composite scaffolds. MSC chondrogenic differentiation was promoted on low percentage ZnO composite scaffolds as indicated by the highest collagen type II production and expression of cartilage-specific genes, while osteogenic differentiation was promoted on high percentage ZnO composite scaffolds as indicated by the highest alkaline phosphatase activity, collagen production, and expression of bone-specific genes. This study demonstrates the feasibility of ZnO-containing composites as a potential scaffold for osteochondral tissue engineering.

Sodium Tungstate for Promoting Mesenchymal Stem Cell Chondrogenesis.

Articular cartilage has a limited ability to heal. Mesenchymal stem cells (MSCs) derived from the bone marrow have shown promise as a cell type for cartilage regeneration strategies. In this study, sodium tungstate (Na2WO4), which is an insulin mimetic, was evaluated for the first time as an inductive factor to enhance human MSC chondrogenesis. MSCs were seeded onto three-dimensional electrospun scaffolds in growth medium (GM), complete chondrogenic induction medium (CCM) containing insulin, and CCM without insulin. Na2WO4 was added to the media leading to final concentrations of 0, 0.01, 0.1, and 1 mM. Chondrogenic differentiation was assessed by biochemical analyses, immunostaining, and gene expression. Cytotoxicity using human peripheral blood mononuclear cells (PBMCS) was also investigated. The chondrogenic differentiation of MSCs was enhanced in the presence of low concentrations of Na2WO4 compared to control, without Na2WO4. In the induction medium containing insulin, cells in 0.01 mM Na2WO4 produced significantly higher sulfated glycosaminoglycans, collagen type II, and chondrogenic gene expression than all other groups at day 28. Cells in 0.1 mM Na2WO4 had significantly higher collagen II production and significantly higher sox-9 and aggrecan gene expression compared to control at day 28. Cells in GM and induction medium without insulin containing low concentrations of Na2WO4 also expressed chondrogenic markers. Na2WO4 did not stimulate PBMC proliferation or apoptosis. The results demonstrate that Na2WO4 enhances chondrogenic differentiation of MSCs, does not have a toxic effect, and may be useful for MSC-based approaches for cartilage repair.