Explore the vast range of titles available.

COVID-19 complications still present a huge burden on healthcare systems and warrant predictive risk models to triage patients and inform early intervention. Here, we profile 893 plasma proteins from 50 severe and 50 mild-moderate COVID-19 patients, and 50 healthy controls, and show that 375 proteins are differentially expressed in the plasma of severe COVID-19 patients. These differentially expressed plasma proteins are implicated in the pathogenesis of COVID-19 and present targets for candidate drugs to prevent or treat severe complications. Based on the plasma proteomics and clinical lab tests, we also report a 12-plasma protein signature and a model of seven routine clinical tests that validate in an independent cohort as early risk predictors of COVID-19 severity and patient survival. The risk predictors and candidate drugs described in our study can be used and developed for personalized management of SARS-CoV-2 infected patients. Prognostic markers for patients with COVID-19 are of critical importance in determining the course of SARS-CoV-2 infection and patient handling. Here the authors determine and apply a prognostic proteomic panel for risk and drug prediction in the management of SARS-CoV-2 infected patients.

Purpose: ABO blood group glycol-conjugate expression may influence human susceptibility to infection caused by Toxoplasma gondii. This study aimed to assess the relationship between blood group phenotypes as risk factors for toxoplasmosis and to correlate the prevalence of the disease with other risk factors. Materials and Methods: A total of two-hundred serum samples were collected from pregnant women referred for routine rotary examination in Rabak Teaching Hospital, White Nile State, Sudan, and examined for the parasite Toxoplasma gondii using the latex agglutination test. Results: The overall prevalence of toxoplasmosis in pregnant women (IgG positivity for T. gondii in the absence of IgM) was 41% (82/200). A higher prevalence of the infection was detected in women with blood group type AB 5 (55.6%) among the females in the AB blood group and the lowest in those with blood group type B 11 (35.5%). Those with a history of direct contact with cats reported the possibility of eating undercooked meat and soil-related potential risk factors (working in a garden with bare hands, eating unwashed vegetables and fresh fruits, poor handling of food) recorded 70 (82.4%), 59 (65.6%), 58 (77.3%), 73 (55.7%) and 70 (73.7%) of positive cases, respectively. Statistical analysis revealed a significant difference between Toxoplasma gondii infection and these risk factors. Conclusion: The study concluded that the ABO blood group system was not related to the absence or presence of anti-T. gondii antibodies in pregnant women in the study area. Contact with cat feces, raw meat consumption, and farming were identified as possible important risk factors for T. gondii infection within the study area. Keywords: pregnant women, toxoplasmosis, blood group phenotypes, risk factors, latex agglutination test, prevalence

Concrete is a cost-effective construction material widely used in various building infrastructure projects. High-performance concrete, characterized by strength and durability, is crucial for structures that must withstand heavy loads and extreme weather conditions. Accurate prediction of concrete strength under different mixtures and loading conditions is essential for optimizing performance, reducing costs, and enhancing safety. Recent advancements in machine learning offer solutions to challenges in structural engineering, including concrete strength prediction. This paper evaluated the performance of eight popular machine learning models, encompassing regression methods such as Linear, Ridge, and LASSO, as well as tree-based models like Decision Trees, Random Forests, XGBoost, SVM, and ANN. The assessment was conducted using a standard dataset comprising 1030 concrete samples. Our experimental results demonstrated that ensemble learning techniques, notably XGBoost, outperformed other algorithms with an R-Square (R 2 ) of 0.91 and a Root Mean Squared Error (RMSE) of 4.37. Additionally, we employed the SHAP (SHapley Additive exPlanations) technique to analyze the XGBoost model, providing civil engineers with insights to make informed decisions regarding concrete mix design and construction practices.

Background Stable intertrochanteric fractures of the hip are common injuries, particularly among the elderly population. Effective surgical intervention is crucial for improving patient outcomes and recovery. Two widely used fixation techniques are the Dynamic Hip Screw (DHS) and Proximal Femoral Nailing (PFN). Both methods aim to provide stability and help early mobilization, yet they differ in their biomechanical properties and clinical implications. Objective This review article aims to compare the efficacy and outcomes of DHS and PFN in the treatment of stable intertrochanteric fractures, focusing on key metrics such as the Harris Hip Score (HHS), pain management, functional recovery, and complication rates. By synthesizing findings from recent studies, the review seeks to provide a comprehensive understanding of the advantages and limitations of each technique. Results Comparative analysis demonstrated that proximal femoral nailing (PFN) was associated with shorter hospital stays (average: 7.8 days compared to 12.4 days), earlier mobilization (7.93 weeks compared to 11.80 weeks to full weight-bearing), and better early postoperative functional results (Harris Hip Scores: 90.33 compared to 89.08 at the 12-month follow-up) compared to dynamic hip screw (DHS). However, PFN was associated with a longer fluoroscopy exposure and higher rates of implant cut-out complications, whereas DHS was associated with higher intraoperative risks of lateral wall fractures (32% rate), higher blood loss, and reoperation rates. Economic evaluation revealed comparable overall costs for both modalities, although the initial implantation costs were greater for PFN. Fracture union timelines were statistically comparable (mean: ~130 days). Conclusion The diagnosis and management of intertrochanteric fractures remain a subject of considerable debate, both techniques have their distinct sets of benefits and drawbacks, highlighting the necessity for a tailored approach depending on patient-specific factors and surgical settings. Ultimately, these multifaceted findings underscore the need for further comparative studies to better understand these differences and aid in improving surgical approaches for intertrochanteric fractures. This will enable more informed decision-making, potentially improving patient outcomes and optimizing resource use in healthcare settings.

Background Mentorship is essential in nursing education to foster clinical skills, critical thinking, and professional identity. Despite extensive research on mentorship, few studies have addressed its role in the Arab cultural context. This study explored nursing mentors' clinical learning experiences through mentorship within the cultural context of the United Arab Emirates (UAE). Methods A qualitative approach was employed, involving 20 mentors supervising fourth-year nursing students during their final clinical placement at a semi-public university in the UAE. The placement occurred from January to May 2024, as part of the Consolidation of Practice course, comprising 240 hours of clinical training. Structured and semi-structured in-depth interviews were conducted with the participants, and the data were transcribed verbatim. To analyse the data, an inductive thematic approach was adopted, and some data were quantified for additional insights. Results Four main themes emerged regarding the benefits of structured mentoring within the cultural context: critical for practical training, confidence building, bridging theory and practice and mutual learning. The essential mentoring skills identified were effective communication, patience, and understanding. Structured mentoring frequency positively influenced students' clinical learning. The strengths of the structured mentorship included exposure to real-life scenarios, improved communication, and the development of practical skills. Opportunities for improvement included increasing mentor-student interactions, enhancing the programme's structured nature, and integrating technological tools. The mentors recommended reassessing mentorship duration, increasing hands-on clinical exposure, strengthening mentor collaboration, and promoting student accountability. Conclusion Effective mentorship in nursing education in the UAE requires integrating theory and practice, clear communication, and leveraging technology to overcome barriers. Strengthening structured mentor-student interactions through focused workshops and refined programme structures can bridge educational gaps. Such enhancements can enable nursing students to develop into competent and confident healthcare professionals, who are familiar with culturally informed mentorship practices.

To assess surgeon preferences, challenges, and graft-size customization practices in Descemet membrane endothelial keratoplasty (DMEK), and to explore their association with surgical confidence and adoption. A cross-sectional, 22-item online survey was distributed to corneal surgeons to evaluate surgical experience, preferences, graft-size customization practices, and challenges related to DMEK adoption. A total of 55 complete responses were analyzed. Categorical variables were summarized as frequencies and percentages. Multiple-response items were analyzed independently. Selected variables were grouped into clinically meaningful categories. Exploratory subgroup analyses were performed based on DMEK case volume, and associations were assessed using Fisher's exact test. A total of 55 participants completed the survey, of whom 47 (85.45%) had performed DMEK. Most participants were male (44, 80.0%) and aged 30-39 years (24, 43.63%). Customized graft sizing was used by 35 (74.47%) participants, and 36 (76.60%) reported that it influenced surgical decision-making or outcomes. Graft unfolding and preparation were the most challenging steps, with 27 (57.45%) and 24 (51.06%) participants reporting moderate to high difficulty, whereas patient selection was generally less difficult (28, 59.57% reporting no difficulty). Higher competence was reported for Descemetorhexis (29, 61.70%) and tissue selection (27, 57.45%), while lower competence was observed for graft unfolding (15, 31.91%). Hands-on and supervised learning methods were most valued, including wet-lab training (32, 68.09%) and one-on-one operating room training (34, 72.34%). Increasing experience was associated with improved performance but was not statistically significant. DMEK adoption is influenced by both technical complexity and training exposure. While surgeons demonstrate confidence in foundational steps, graft preparation and unfolding remain key challenges. Structured hands-on training and supervised experience appear critical to improving surgical confidence and may support broader adoption of DMEK.

Metformin, a long-established antidiabetic agent, is undergoing a renaissance as a prototype gerotherapeutic and immunometabolic oncology adjuvant. Mechanistic advances reveal that metformin modulates an integrated network of metabolic, immunological, microbiome-mediated, and epigenetic pathways that impact the hallmarks of aging and cancer biology. Clinical data now demonstrate its ability to reduce cancer incidence, enhance immunotherapy outcomes, delay multimorbidity, and reverse biological age markers. Landmark trials such as UKPDS, CAMERA, and the ongoing TAME study illustrate its broad clinical impact on metabolic health, cardiovascular risk, and age-related disease trajectories. In oncology, trials such as MA.32 and METTEN evaluate its influence on progression-free survival and tumor response, highlighting its evolving role in cancer therapy. This review critically synthesizes the molecular underpinnings of metformin’s polypharmacology, examines results from pivotal clinical trials, and compares its effectiveness with emerging gerotherapeutics and senolytics. We explore future directions, including optimized dosing, biomarker-driven personalization, rational combination therapies, and regulatory pathways, to expand indications for aging and oncology. Metformin stands poised to play a pivotal role in precision strategies that target the shared roots of aging and cancer, offering scalable global benefits across health systems.

Background Multiple Myeloma (MM) is a plasma cell malignancy associated with systemic and renal complications. This study evaluates the prognostic and diagnostic significance of poliovirus receptor (PVR) gene expression and protein levels, serum amylase, and urinary biomarkers (IGFBP-7, TIMP-2) in MM patients. Methods In a prospective case-control study, 50 MM patients and 50 healthy controls were assessed. PVR gene expression (qPCR), serum PVR and amylase (ELISA/chemistry analyzer), and urinary IGFBP-7 and TIMP-2 (ELISA) were analyzed. Statistical analyses included correlation tests, Kaplan-Meier survival analysis, Cox regression, stratified quartile analysis, and receiver operating characteristic (ROC) curve evaluation. Multiple testing corrections (Bonferroni and FDR) were applied. Results MM patients showed significantly elevated PVR expression and protein levels, serum amylase, and urinary biomarkers compared to controls ( p <0.001). High PVR expression was associated with advanced disease stage, TP53 mutations, and reduced overall survival (OS: 44.84 vs. 48.0 months; p =0.044). High serum amylase and urinary IGFBP-7 were linked to significantly poorer OS and progression-free survival (PFS). Multivariate Cox regression confirmed PVR expression (HR=12.2), serum amylase (HR=11.5), and IGFBP-7 (HR=11.9) as independent predictors of poor OS, findings that remained robust in bootstrapped and penalized regression models. Stratified analysis revealed that patients in the highest biomarker quartiles had significantly worse outcomes and higher TP53 mutation rates. ROC analysis showed excellent diagnostic performance for the combined panel (PVR + amylase + IGFBP-7; AUC=0.97, sensitivity =90%, specificity = 88%), outperforming individual markers. Significant associations remained after multiple testing correction. Conclusion PVR gene expression, serum amylase, and urinary IGFBP-7 are independent and robust prognostic biomarkers in MM. Their combined use enhances diagnostic accuracy and risk stratification, supporting their integration into clinical decision-making. Validation in larger, multi-center studies is recommended . Limitations include the single-center design, modest sample size, absence of disease comparator groups, and the cross-sectional nature of biomarker evaluation. These findings warrant validation in larger, multi-institutional, and longitudinal studies.

Intelligent transportation systems (ITS) have experienced an important development in the past decade because of developments in communication, control, and information technology deployed to roads, vehicles, and traffic controller systems. Vehicle form classification plays an essential role in applying ITS owing to its capability for collecting valuable traffic information, providing further development of transport structures, and improving human convenience. Nevertheless, the present service structure implements artificial intelligence (AI) methods with universal patterns for every vehicle. Still, the computational efficiency and needs of deep learning (DL) methods pose difficulties for real-time applications. DL is a useful device for classifying vehicle categories because it can take composite traffic data features and learns from larger data amounts. This manuscript develops a Secure Elliptic Galois Cryptography Framework for Vehicle Image Classification in Intelligent Transportation Systems (SEGCF-VICITS) method. ​The primary aim of the SEGCF-VICITS method is to ensure secure data transmission and intelligent decision-making in ITS environments. Initially, the SEGCF-VICITS model employs the elliptic galois cryptography (EGC) model to provide strong encryption for sensitive vehicular data, utilizing a key that is subsequently used for data decryption. Besides, the SE-DenseNet model is utilized for feature extraction. Additionally, the convolutional sparse autoencoder (CSAE) method is used for vehicle classification. The experimental validation of the SEGCF-VICITS method portrayed a superior accuracy value of 95.48% over existing models under the vehicle image classification dataset.

Background Coronavirus disease 2019 (COVID-19) is a pandemic caused by a newly discovered coronavirus. Although clinical manifestations of COVID-19 are mainly pulmonary, some patients have other systemic manifestations. This study aimed to describe the clinical finding and outcomes in Sudanese patients diagnosed with COVID-19. Methods This retrospective observational study is based on documented files that included patients diagnosed with COVID-19 in seven selected hospitals inside Khartoum. Clinical manifestations, complications and outcomes were extracted from patients’ records using an extraction form designed for this study. Results Data of 243 patients diagnosed with COVID-19 were analyzed. The mean (SD) age in years was 55.8 (18.4). Out of 116 participants, 27 of them (23.3%) had severe disease, 15 (12.9%) were critically ill. 67.5% of patients were admitted to the hospital within 7 days from onset of symptoms; most of them were admitted to the wards ( n  = 140,72.5%). Fever (83.2%), cough (70.7%), and shortness of breath (69.2%) were the most commonly recorded clinical manifestations. Sepsis (9.8%) and acidosis (7.8%) were the most frequently reported complications. Death was the final outcome in 21.4% (56/243). Older age and presence of diabetes were found significantly associated with in-hospital death. The laboratory results showed high CRP in 85.6% (119/139), high ferritin in 88.9% (24/27), lactate dehydrogenase had a median of 409.0 (359–760), D-dimer had a median of 3.3 (1.2–16. 6), and 53/105 (50.5%) had low albumin. Conclusions Fever was the most mentioned sign among the participants, followed by fatigue. Cough and shortness of breath were the most commonly recorded pulmonary symptoms manifested. Our study showed multiple variables were associated with in-hospital death. The mortality rate was high among severe and critically ill patients diagnosed with COVID-19.