Explore the vast range of titles available.

Neuropathic pain is a prevalent issue that often arises following injuries to the peripheral or central nervous system. Unfortunately, there is currently no definitive and flawless treatment available to alleviate this type of pain. However, exercise has emerged as a promising nonpharmacological and adjunctive approach, demonstrating a significant impact in reducing pain intensity. This is why physical therapy is considered a beneficial approach for diminishing pain and promoting functional recovery following nerve injuries. Regular physical activity exerts its hypoalgesic effects through a diverse array of mechanisms. These include inhibiting oxidative stress, suppressing inflammation, and modulating neurotransmitter levels, among others. It is possible that multiple activated mechanisms may coexist within an individual. However, the priming mechanism does not need to be the same across all subjects. Each person’s response to physical activity and pain modulation may vary depending on their unique physiological and genetic factors. In this review, we aimed to provide a concise overview of the mechanisms underlying the beneficial effects of regular exercise on neuropathic pain. We have discussed several key mechanisms that contribute to the improvement of neuropathic pain through exercise. However, it is important to note that this is not an exhaustive analysis, and there may be other mechanisms at play. Our goal was to provide a brief yet informative exploration of the topic.

Drug discovery relies on predicting drug-target interaction (DTI), which is an important challenging task. The purpose of DTI is to identify the interaction between drug chemical compounds and protein targets. Traditional wet lab experiments are time-consuming and expensive, that’s why in recent years, the use of computational methods based on machine learning has attracted the attention of many researchers. Actually, a dry lab environment focusing more on computational methods of interaction prediction can be helpful in limiting search space for wet lab experiments. In this paper, a novel multi-stage approach for DTI is proposed that called SRX-DTI. In the first stage, combination of various descriptors from protein sequences, and a FP2 fingerprint that is encoded from drug are extracted as feature vectors. A major challenge in this application is the imbalanced data due to the lack of known interactions, in this regard, in the second stage, the One-SVM-US technique is proposed to deal with this problem. Next, the FFS-RF algorithm, a forward feature selection algorithm, coupled with a random forest (RF) classifier is developed to maximize the predictive performance. This feature selection algorithm removes irrelevant features to obtain optimal features. Finally, balanced dataset with optimal features is given to the XGBoost classifier to identify DTIs. The experimental results demonstrate that our proposed approach SRX-DTI achieves higher performance than other existing methods in predicting DTIs. The datasets and source code are available at: https://github.com/Khojasteh-hb/SRX-DTI .

Homozygosity mapping is a powerful method for identifying mutations in patients with recessive conditions, especially in consanguineous families or isolated populations. Historically, it has been used in conjunction with genotypes from highly polymorphic markers, such as DNA microsatellites or common SNPs. Traditional software performs rather poorly with data from Whole Exome Sequencing (WES) and Whole Genome Sequencing (WGS), which are now extensively used in medical genetics. We develop AutoMap, a tool that is both web-based or downloadable, to allow performing homozygosity mapping directly on VCF (Variant Call Format) calls from WES or WGS projects. Following a training step on WES data from 26 consanguineous families and a validation procedure on a matched cohort, our method shows higher overall performances when compared with eight existing tools. Most importantly, when tested on real cases with negative molecular diagnosis from an internal set, AutoMap detects three gene-disease and multiple variant-disease associations that were previously unrecognized, projecting clear benefits for both molecular diagnosis and research activities in medical genetics. Homozygosity mapping is a useful tool for identifying candidate mutations in recessive conditions, however application to next generation sequencing data has been sub-optimal. Here, the authors present AutoMap, which efficiently identifies runs of homozygosity in whole exome/genome sequencing data.

Accurate prediction of drug response in cancer cells is a fundamental step toward achieving precision medicine and designing personalized therapies. In this study, a multi-branch deep learning framework is proposed that integrates multi-omics cellular data including gene expression, mutation, methylation, and biological pathways with structural features of drugs (molecular graphs and various chemical fingerprints) to enable drug response prediction. The graph structure of the drug is modeled using a three-layer Graph Convolutional Network (GCN), and chemical fingerprints are compressed using MLP networks. These multiple representations of drugs are integrated and then combined with cellular features in a Multi-head Bilinear Attention module to model the complex interactions between cells and drugs. In the final stage, an ensemble model based on XGBoost is used to refine the outputs. The MoGraphDRP model demonstrates significantly higher accuracy in drug response prediction compared to existing state-of-the-art methods. Experimental results show that the MoGraphDRP model outperforms advanced methods such as BANDRP, DeepCDR, and DeepTTA, achieving PCC = 0.9689, RMSE = 0.6622, and R² = 0.9388. This model not only accurately reconstructs missing IC50 values but also effectively distinguishes between sensitive and resistant drugs in unknown combinations. The MoGraphDRP framework can serve as a powerful, interpretable, and reliable tool for analyzing drug response and designing preclinical treatments.

In the digital currency market, including Bitcoin, price prediction using artificial intelligence (AI) and machine learning (ML) is critical but challenging. Conventional methods such as technical analysis (based on historical market data) and fundamental analysis (based on economic variables) suffer from data noise, processing delays, and insufficient data. To make predictions more accurate, faster, and able to handle more data, the suggested method combines several steps: extracting important information, labeling it, choosing the best features, merging different models, and fine‐tuning the model settings. Based on the price data, this approach initially generates 5 labels with a new labeling method based on the percentage of average price changes in several days and generates signals (hold, buy, sell, strong sell, and strong buy). Thereafter, it extracts 768 features from technical studies using the TA‐Lib library and from an authoritative site. The TLBOA algorithm, which does not get stuck in the local optimum with two updates, was used to select and reduce features to 15 to avoid overfitting. A variety of ML models, including support vector machine and Naive Bayes, use these selected features for training. By using the evolutionary DE algorithm to optimize the XGBoost meta‐parameters, we increased the accuracy by 1%–4%. The proposed strategy has performed better than other models, such as XGBoost with 85.66% and gradient boosting with 84.15%, and has achieved an accuracy of 91%–92%.

Lysine malonylation is one of the most important post-translational modifications (PTMs). It affects the functionality of cells. Malonylation site prediction in proteins can unfold the mechanisms of cellular functionalities. Experimental methods are one of the due prediction approaches. But they are typically costly and time-consuming to implement. Recently, methods based on machine-learning solutions have been proposed to tackle this problem. Such practices have been shown to reduce costs and time complexities and increase accuracy. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features, and inefficient underlying classifiers. A machine learning-based method is proposed in this paper to cope with these problems. In the proposed approach, seven different features are extracted. Then, the extracted features are combined, ranked based on the Fisher’s score (F-score), and the most efficient ones are selected. Afterward, malonylation sites are predicted using various classifiers. Simulation results show that the proposed method has acceptable performance compared with some state-of-the-art approaches. In addition, the XGBOOST classifier, founded on extracted features such as TFCRF, has a higher prediction rate than the other methods. The codes are publicly available at: https://github.com/jimy2020/Malonylation-site-prediction

Objective: Cancer related fatigue is a distressing condition and correlated with decrease in quality of life of patients with malignant conditions. In continuation of our previous research, we assessed long term anti-fatigue effects of melatonin in patients with the breast cancer. Material and methods: In this clinical trial, 92 breast cancer patients were randomly assigned to receive either melatonin (18 mg/day) or placebo from 1 week before the adjuvant treatments until 2 years after their completion. The levels of fatigue were assessed before and after intervention using Brief Fatigue Inventory (BFI) and were compared at a significance level of P ≤ .05. Results: The BFI scores were similar between the 2 groups at the baseline (placebo group: 5.56 ± 1.59 and melatonin group: 5.72 ± 1.68, P = .67). After the intervention, not only the mean fatigue score was significantly lower in melatonin group (2.93 ± 1.04 vs 1.99 ± 1.02, P < .001, P ≤ .05), but also a greater reduction in fatigue score in intervention group was evident over time (P ≤ .001). Conclusion: Long-term usage of melatonin even after completion of adjuvant therapies in women with breast cancer decreased the levels of fatigue associated with the malignant condition and its treatments. The trial registry name and URL, and registration number: Iranian Registry of Clinical Trials, https://en.irct.ir/trial/62267, IRCT20180426039421N3

This study presents a detailed analysis of a Co-generation system specifically designed to fulfill energy (Electricity – Cooling – Heating) requirements of a Zero energy building (ZEB) school in Dubai. The proposed system integrates an electric compression chiller, which plays a crucial role in efficiently managing both heating and cooling demands within the educational facility. To generate clean electricity, the system utilizes a combination of advanced technologies, including a steam Rankine cycle turbine, an organic Rankine cycle turbine, and a gas turbine. The main goal of this research is to supply the definition of a ZEB by providing energy consumed by Zero Energy school building (ZESB) with a biomass system. To optimize energy consumption within the building, the innovative Building Energy Optimization Tool (BEopt) is employed, providing insights into energy efficiency improvements. Optimization of the biomass-based energy production system are executed applied EES software (Engineering Equation Solver) alongside the response surface methodology, ensuring a robust analytical framework for performance evaluation. The suggested co-generation consisted of modified Brayton cycle units (biogas fuel), steam Rankine cycle, organic Rankine cycle, and compression chiller for electricity generation, cooling, and heating. Annual energy consumption metrics for the school indicate a total electricity usage of 43,539.48 kWh, with a heating load of 0.94 kWh and a cooling load of 1,115.68 kWh. Through strategic optimization of energy consumption patterns, the system achieves a notable reduction in carbon dioxide emissions, amounting to 24,548.97 kg per year. The optimized energy system operates with an overall efficiency of 31.79% and incurs operational costs estimated at $88.02 per hour. In terms of output generation, the biomass energy system is projected to yield approximately 151,746,087 kWh of electricity, 194,610,878 kWh of heating bar, and 158,962,204 kWh of cooling bar annually. Comparative analysis demonstrates that this innovative biomass-based energy system can effectively meet the school’s energy demands throughout the year while contributing to sustainability goals and reducing environmental impact. A comparison of the school’s consumption and the system’s production showed that 151,702,547.5 kWh of electricity, 194,609,762.3 kWh of heating, and 158,774,864.1 kWh of cooling could be saved in one year to offset costs of co-generation systems. This research underscores the potential for integrating diversified renewable energy technologies in educational settings, thereby promoting sustainable practices within the context of Dubai’s commitment to supplying ZEB consumption in its ZEBs by 2050.

Background Three-dimensional (3D) printing is a capable approach for the fabrication of bone tissue scaffolds. Nevertheless, a purely made scaffold such as polylactic acid (PLA) may suffer from shortcomings and be restricted due to its biological behavior. Gelatin, hydroxyapatite and platelet-rich plasma (PRP) have been revealed to be of potential to enhance the osteogenic effect. In this study, it was tried to improve the properties of 3D-printed PLA scaffolds by infilling them with gelatin-nano-hydroxyapatite (PLA/G-nHA) and subsequent coating with PRP. For comparison, bare PLA and PLA/G-nHA scaffolds were also fabricated. The printing accuracy, the scaffold structural characterizations, mechanical properties, degradability behavior, cell adhesion, mineralization, systemic effect of the scaffolds on the liver enzymes, osteocalcin level in blood serum and in vivo bone regeneration capability in rat critical-sized calvaria defect were evaluated. Results High printing accuracy (printing error of < 11%) was obtained for all measured parameters including strut thickness, pore width, scaffold density and porosity%. The highest mean ultimate compression strength (UCS) was associated with PLA/G-nHA/PRP scaffolds, which was 10.95 MPa. A slow degradation rate was observed for all scaffolds. The PLA/G-nHA/PRP had slightly higher degradation rate, possibly due to PRP release, with burst release occurred at week 4. The MTT results showed that PLA/G-nHA/PRP provided the highest cell proliferation at all time points, and the serum biochemistry (ALT and AST level) results indicated no abnormal/toxic influence caused by scaffold biomaterials. Superior cell adhesion and mineralization were obtained for PLA/G-nHA/PRP. Furthermore, all the developed scaffolds showed bone repair capability. The PLA/G-nHA/PRP scaffolds could better support bone regeneration than bare PLA and PLA/G-nHA scaffolds. Conclusion The PLA/G-nHA/PRP scaffolds can be considered as potential for hard tissue repair.

This study aims to assess the effects of central and general adiposity on development of cardiovascular diseases (CVDs) mediated by cardiometabolic risk factors and to analyze their degree of dependency for mediating their effects. To this end, data from the the Tehran Lipid and Glucose Study cohort with 6280 participants were included in this study. The hazard ratios were calculated using a 2-stage regression model in the context of a survival model. Systolic blood pressure (BP), total serum cholesterol, and fasting plasma glucose were designated as mediators. Assessing the interactions revealed that BP was the most important mediator for general ( (HR NIE : 1.11, 95% CI 1.17–1.24) and central obesity (CO) (HR NIE : 1.11, 95% CI 1.07–1.15) with 60% and 36% proportion of the effects mediated in the total population, respectively. The proportion of mediated risk for all three metabolic risk factors was 46% (95% CI 31–75%) for overweight, 66% (45–100%) for general obesity and 52% (39–87%) for central obesity. BP was the most important mediator for overweight and central obesity in men, comprising 29% and 36% of the risk, respectively. The proportion of the risk mediated through all three metabolic risk factors in women was 23% (95% CI 13–50%) for overweight, 36% (21–64%) for general obesity and 52% (39–87%) for central obesity. Based on the results of this study, cardiometabolic mediators have conciliated more than 60% of the adverse effects of high BMI on CVDs in men. Controlling the metabolic risk factors in women does not efficiently contribute to decreasing CVDs as effectively.