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Exome sequencing reveals low-frequency and rare variant contributions to multiple sclerosis susceptibility in Turkish families
Effectiveness of exercise therapy on chronic ankle instability: a meta-analysis
Abstract Lateral ankle sprains are one of the most common musculoskeletal injuries. Up to 70% of individuals who sustain lateral ankle sprains develop chronic ankle instability (CAI). Exercise therapy is considered an effective treatment for patients with CAI. This meta-analysis investigated the efficacy of exercise therapy in CAI patients by reviewing 15 randomized controlled trials (RCTs) involving 586 participants. Databases including PubMed, EMBASE, Cochrane Library, and Web of Science were searched from inception to September 13, 2024. The Cochrane Risk of Bias Tool was used to assess study quality. Meta-analysis, sensitivity analysis, and publication bias analysis were conducted using RevMan 5.3.0 and Stata 18.0 software. The Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach was applied to evaluate the quality of evidence. Main outcomes were assessed using the Foot and Ankle Ability Measure (FAAM) and the Star Excursion Balance Test (SEBT). The results demonstrated that exercise therapy significantly improved FAAM-S (MD = 7.98, CI: 4.11 to 11.86, p < 0.0001, I² = 30%). Long-term exercise therapy (over 4 weeks) significantly enhanced FAAM-A (MD = 10.95, CI: 6.60 to 15.29, p < 0.00001, I² = 0%) and dynamic balance ability of ankle joint (SBET-A: MD = 4.83, CI: 1.04 to 8.63, p = 0.01, I² = 62%; SEBT-PM: MD = 6.93, CI: 2.37 to 11.48, p = 0.003, I² = 69%; and SEBT-PL: MD = 8.98, CI: 2.66 to 15.29, p = 0.005, I² = 86%). After categorizing by exercise type, the results indicated that strength training was more effective in improving SEBT-PL (MD = 8.15, CI: 6.09 to 10.21, p < 0.00001, I² = 0%), joint mobilization was more effective in improving SEBT-A (MD = 7.65, CI: 4.93 to 10.37, p < 0.00001, I² = 0%), and proprioceptive training was more effective in improving SEBT-PM (MD = 10.46, CI: 5.27 to 15.65, p < 0.0001, I² = 33%). In conclusion, long-term, multifaceted exercise therapy demonstrates superior rehabilitation efficacy for patients with CAI. Personalized treatment plans, informed by SEBT assessment results, should prioritize targeted interventions such as joint mobilization, strength training, or proprioceptive training. This approach holds significant theoretical and practical value for optimizing CAI treatment strategies and enhancing patient outcomes.
Transcriptomic analysis of the anti-tumor effects of leflunomide in prolactinoma
Study on the phenomenon of the ultra-large drilling cuttings quantity in rockburst coal seam
Later midline shift is associated with better post-hospitalization discharge status after large middle cerebral artery stroke
Sodium propionate protects against bronchopulmonary dysplasia by inhibiting IL-17-mediated apoptosis of alveolar epithelial cells
A machine learning approach for corrosion rate modeling in Patna water distribution network of Bihar
Metabolomic profiling of biphenyl-induced stress response of Brucella anthropi MAPB-9
Abstract The exposure of bacteria to toxic compounds such as polychlorinated biphenyl (PCB) and biphenyl induces an adaptive response at different levels of cell morphology, biochemistry, and physiology. PCB and biphenyl are highly toxic compounds commercially used in the industry. In our previous study, Brucella anthropi MAPB-9 efficiently degraded PCB-77 and biphenyl at a high concentration. In this study, we used metabolomic analyses to understand the metabolic processes occurring in MAPB-9 during exposure to biphenyl. A combination of analytical techniques such as GC-MS/MS and HR-MS study confirmed the complete biphenyl degradation pathway. The intermediate metabolic products identified were cis-2, 3-dihydro-2, 3-dihydroxy biphenyl, 2,3-dihydroxy biphenyl, and 4-dihydroxy-2-oxo-valerate. Further, benzoic acid and 2,3-dihydroxy benzoic acid metabolites identified in the extract revealed the interconnection of biphenyl and benzoic degradation pathways. In addition, the variations in the functioning of the major biochemical pathways in the cells were revealed through changes in the profile of metabolites belonging to glyoxylate, tricarboxylic acid (TCA) cycle, and fatty acid pathways. The exposure to biphenyl inhibited metabolic activity leading to changes in the morphology and metabolism. Despite many adverse changes, the MAPB-9 was able to adapt and grow in the toxic environment undergoing upper and lower biphenyl degradation pathways.
ML techniques increasing the power factor of a compression ignition engine that is powered by Annona biodiesel using SATACOM
Effect of different types of exercise on bone mineral density in postmenopausal women: a systematic review and network meta-analysis
Service recommendation method based on text view and interaction view
Abstract With the increasing prosperity of web service-sharing platforms, more and more software developers are integrating and reusing Web services when developing applications. This approach not only meets the needs of developers but also is cost-effective and widely used in the field of software development. Usually, software developers can browse, evaluate, and select corresponding Web services from a web service-sharing platform to create various applications with rich functionality. However, a large number of candidate Web services have placed a heavy burden on the selection decisions of software developers. Existing web service recommendation systems often face two challenges. Firstly, developers discover services by inputting development requirements, but the user’s input is arbitrary and can not fully reflect the user’s intention. Secondly, the application service interaction record is too sparse, reaching 99.9%, making it particularly difficult to extract services that meet the requirements. To address the above challenges, in this paper, we propose a service recommendation method based on text and interaction views (SRTI). Firstly, SRTI employs graph neural network algorithms to deeply mine the historical records, extract the features of applications and services, and calculate their preferences. Secondly, SRTI uses Transformer to analysis develop requirements and uses fully connected neural networks to deeply mine the matching degree between candidate services and development requirements. Finally, we integrate the above two to obtain the final service list. Extensive experiments on real-world datasets have shown that SRTI outperforms several state-of-the-art methods in service recommendation.
Machine learning of clinical phenotypes facilitates autism screening and identifies novel subgroups with distinct transcriptomic profiles
Enhanced variable step sizes perturb and observe MPPT control to reduce energy loss in photovoltaic systems
Psychometric properties of the Arabic version of the Eco guilt and Eco grief scales
Design and experiment of automatic grasping manipulator for side-mounted garbage truck
Traditional Chinese medicine-based therapeutics for Pediatric pneumonia-related acute lung injury and acute respiratory distress syndrome
Interim results of exoskeletal wearable robot for gait recovery in subacute stroke patients
Synthesis of poly (acrylic acid) modified graphene/MoS2 heterostructure-based composite: an effective removal of Pb(II), Cd(II) and Zn(II) from wastewater
Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine learning algorithms
Abstract Early detection of pancreatic cancer (PC) remains challenging largely due to the low population incidence and few known risk factors. However, screening in at-risk populations and detection of early cancer has the potential to significantly alter survival. In this study, we aim to develop a predictive model to identify patients at risk for developing new-onset PC at two and a half to three year time frame . We used the Electronic Health Records (EHR) of a large medical system from 2000 to 2021 (N = 537,410). The EHR data analyzed in this work consists of patients’ demographic information, diagnosis records, and lab values, which are used to identify patients who were diagnosed with pancreatic cancer and the risk factors used in the machine learning algorithm for prediction. We identified 73 risk factors of pancreatic cancer with the Phenome-wide Association Study (PheWAS) on a matched case–control cohort. Based on them, we built a large-scale machine learning algorithm based on EHR. A temporally stratified validation based on patients not included in any stage of the training of the model was performed. This model showed an AUROC at 0.742 [0.727, 0.757] which was similar in both the general population and in a subset of the population who has had prior cross-sectional imaging. The rate of diagnosis of pancreatic cancer in those in the top 1 percentile of the risk score was 6 folds higher than the general population. Our model leverages data extracted from a 6-month window of time in the electronic health record to identify patients at nearly sixfold higher than baseline risk of developing pancreatic cancer 2.5–3 years from evaluation. This approach offers an opportunity to define an enriched population entirely based on static data, where current screening may be recommended.