Browse Articles

Prevalence and associated factors of academic burnout among undergraduate health science students in Cameroon: a cross-sectional study

Diagnostic and prognostic value of serum miR-155 in chronic obstructive pulmonary disease

ROS-NLRP3 participates in the pyroptosis response of excretory-secretory products from protoscoleces of Echinococcus granulosus in hepatocytes

Optimization of cross-institutional medical federated learning framework driven by confidential computing

Experimental and numerical study on movement and accumulation behaviour of rock avalanche by simulating actual 3D terrain conditions

Lipid alterations and endothelial dysfunction are associated with multiple sclerosis pathophysiology

Information theory and thermal properties of an extended cosine hyperbolic potential model

Preliminary efficacy of cognitive multisensory rehabilitation for neuropathic pain in chronic spinal cord injury: a phase I randomized controlled trial

Finite element analysis of sacral fixation strategies for fragility fractures of the pelvis

Abstract This study aimed to evaluate the biomechanical performance of different sacroiliac screw fixation strategies for posterior pelvic ring injuries in older adults with fragility fractures of the pelvis. A finite element model was created using the pelvis of an older woman with combined anterior and posterior ring injuries, simulating a unilateral pubic rami fracture and a Denis zone I sacral fracture. A subcutaneous internal fixator (INFIX) system was used to support the anterior pelvic ring. Percutaneous sacroiliac screws of different lengths and fixation levels were used to create six posterior fixation configurations. The peak von Mises stress within the INFIX system remained below 4 MPa across all configurations, whereas the maximum displacement at the pubic fracture site was < 0.04 mm. Among posterior constructs, the dual-segment long screw configuration showed the lowest sacral fracture displacement (0.02 mm) and the highest screw stress (28.66 MPa). Compared with single-level fixation, constructs with both S1 and S2 fixation demonstrated less fracture displacement and superior load distribution patterns. Furthermore, compared with short screws, long screws exhibited distinct load-sharing features, suggesting improved stress transfer through the posterior pelvic ring. In conclusion, dual-segment sacroiliac screw fixation—particularly using long trans-iliac–trans-sacral screws spanning both S1 and S2 levels—provided improved fracture stability and more advantageous load-sharing behavior in this simulation setting, both in the osteoporotic finite element model and under static, symmetric loading conditions.

A case-control study identifying critical exposure windows in the association between ambient air pollution and spontaneous abortion

Adaptive multi-objective optimization of microgrid energy management using deep reinforcement learning considering battery degradation and renewable uncertainty

Mirrorless open cavities enabled by boundary incompatibility between perfect electric conductor and perfect magnetic conductor parallel-plate waveguides

Integrated approach for edge coverage enhancement based on IRS phase shift control and AP selection in dense user communication system

Photothermal solar assisted Madhuca diethyl ether fuel processing for LHR engines with AI-based performance and yield prediction

Lightweight model LMW-YOLO for small object detection in remote sensing images

Disease burden and associated factors among caregivers of children with congenital heart disease at tertiary hospitals in Addis Ababa, Ethiopia

Adaptive feature selection with gradient-based relevance for intrusion detection systems

Collagen-specific molecular chaperone Hsp47 in inguinal white adipose tissue promotes high-fat diet-induced inflammatory gene expression in male mice

Cysteine-reactive mitigators of small vessel disease-related NOTCH3 mutants

Abstract Pathogenic alterations in NOTCH3 cause CADASIL, an accelerated and currently untreatable form of cerebrovascular disease. CADASIL mutant NOTCH3, which frequently harbors abnormalities in EGF repeat cysteine number, adopt disulfide dependent abnormal conformations. To seek potential strategies to mitigate the impact of CADASIL mutations on NOTCH3, we investigated whether cysteine-targeting compounds may affect pathological NOTCH3. LSL-NOTCH3, a split luciferase assay that discriminates between benign and pathogenic NOTCH3 conformations, was used to quantify the capacity of 21 small molecule compounds to restore NOTCH3 reporter activity. We assayed the activity of each of the small molecules on 16 different pathogenic mutants distributed over three different regions of NOTCH3. Ten of 21 compounds had statistically significant effects on at least one mutant NOTCH3. Five of 21 compounds had mitigating effects on a majority of mutant NOTCH3 in three regions of the protein, with disulfiram and auranofin targeting the most favorable range of mutants. These findings support the concept that cysteine-targeting can potentially mitigate the effects of a broad range of NOTCH3 pathogenic mutants and provide an impetus to investigate whether cysteine-targeting strategies affect disease-relevant phenotypes in EGF repeat-related human disorders.

Proactive fault prediction in marine diesel engines using multivariate machine learning

Abstract Ocean shipping is the backbone of international trade contributing to global economic growth. Consequently, ensuring that ships operate in an energy-efficient manner is crucial to a more sustainable global transportation. Engine failures in these contexts can lead to severe consequences including compromised safety, operational disruptions, and substantial economic losses ranging between 10% and 30% of total operating costs due to unscheduled maintenance. The proposed research integrates marine diesel engines diagnostics with machine learning (ML) algorithms to develop an advanced proactive maintenance strategy to anticipate engine performance trends and proactively identify potential faults before they escalate. Employing an experimental approach on a 4-stroke diesel engine, the controlled simulations were conducted to replicate various failure scenarios to collect data and capture crucial metrics such as temperatures across cylinders, vibrations along axes, and fluctuations in cooling water temperatures. The data were analysed using advanced ML algorithms aimed at enhancing the accuracy and reliability of future fault prediction, by employing a multivariate convolutional long short-term memory (ConvLSTM) model tailored for time series analysis, and a classification model using a random forest (RF) classifier. As a result, the ConvLSTM model decreased the RMSE by 15.4453% compared to decision tree regression models, while the RF classifier achieved an accuracy of 82.168%.