Browse Articles

Glucose-to-potassium ratio has a non-linear J-shaped association with acute kidney injury risk in traumatic brain injury

Abstract Acute kidney injury (AKI) is a severe complication of traumatic brain injury (TBI) associated with poor prognosis. The glucose-to-potassium ratio (GPR), an emerging marker of metabolic stress, may play a role in post-TBI AKI, but its precise relationship is unclear. This retrospective cohort study investigated the association between early-admission GPR and AKI in 2,388 TBI patients from the MIMIC-IV database. Using multivariable logistic regression and restricted cubic spline (RCS) models, we explored both linear and non-linear associations. Of the patients, 56.7% (1,355/2,388) developed AKI. While a simple linear association between GPR and AKI was not significant after adjusting for confounders ( P = 0.20), we discovered a significant non-linear "J-shaped" relationship (P for non-linearity = 0.012). The risk of AKI increased steeply once GPR surpassed an inflection point of approximately 30. A random forest model incorporating multiple clinical variables demonstrated significantly superior predictive performance (AUC = 0.808) compared to traditional logistic regression (AUC = 0.785; DeLong test P = 0.025). These findings reveal that a high GPR (> 30) acts as an independent non-linear risk indicator for post-TBI AKI, Rather than a direct driver of injury, an elevated GPR represents a valuable early warning signal for metabolic decompensation, suggesting clinicians should closely monitor high-risk patients to facilitate early recognition of AKI.

Adaptive quantum kernel selection via leakage-free stacking for clinical diagnostics on NISQ hardware

Experimental and numerical investigation on basaltic soil under RC square footings

Correction: A comparative evaluation of sustainable asphalt binder modifiers for enhanced performance

Spatially context aware multilevel color image segmentation using a Hybrid Artificial Hummingbird and Great Deluge mechanism

Abstract Multilevel image thresholding is an important segmentation technique that partitions an image into meaningful regions in applications such as object recognition, medical imaging, and satellite image analysis. However, conventional techniques are limited by their poor sensitivity to initial conditions, lack of sufficient spatial contextual information, and slow convergence. The proposed method uses a novel hybrid model based on the Artificial Hummingbird Algorithm (AHA) to address the limitations of existing approaches. Here, Latin Hypercube, Sobol, Halton, and Sierpinski strategies are used during the population initialization phase to improve population diversity and search space coverage. This improves the exploration capability of the algorithm and supports better search space. The proposed methodology also uses spatial contextual information to improve the quality of segmentation. It also incorporates a relationship between neighboring pixels in order to retain more structure and enhance the visual performance of the output. For the exploitation phase, the Great Deluge Algorithm (GDA) is utilized as the optimization algorithm. Furthermore, the use of GDA serves as an adaptive acceptance function which reduces the chance of getting stuck during the search process. Minimum Cross Entropy Measure (MCEM) is used as the objective function to obtain optimal threshold values. Different evaluation metrics have been used to compare the results of the proposed method with other existing metaheuristic algorithms. The code is available at https://github.com/suprajatirumalasetti/AHA_GDA_Image_Segmentation_Code .

Pv-aCO2 and Pv-aCO2/Ca-vO2 as surrogates for cardiac output and oxygen metabolism in septic shock patients

Method for determining the flowback rate of fracturing fluid considering gel degradation during hydraulic fracturing

Deep feature extraction and explanation consistency analysis for fine-grained aircraft type classification in remote sensing imagery

Anbalcabtagene autoleucel (PD-1 and TIGIT knockdown CD19 CAR-T) for relapsed/refractory large B-cell lymphoma (CRC01-01)

Anbalcabtagene autoleucel (Anbal-cel) is a CD19-directed CAR T-cell therapy incorporating dual PD-1 and TIGIT knockdown to enhance antitumor function and durability. We report the results of a Phase 1/2 study in patients with relapsed or refractory large B-cell lymphoma (LBCL). In Phase 2, 79 patients received Anbal-cel, and efficacy was evaluated in 73 patients. The complete response (CR) and partial response (PR) rates were 67.1% and 8.2%, respectively. Median progression-free survival (PFS) was 6.04 months (95% CI, 4.34-16.46), and the 6-, 12-, and 18-month PFS rates were 50.9%, 41.1%, and 35.2%, respectively. Median overall survival (OS) was not reached, with 12- and 18-month OS rates of 66.6% and 57.3%. CAR T-cell expansion was significantly greater in responders than in non-responders (median Cmax: 20,403 vs. 8,580 copies/μg). Patients were categorized into long-term response (LR) group or non-LR group based on sustained CR at 6 months. Reduced PD-1 and TIGIT expression on CAR-positive T cells was observed in LR group. Most patients (97.5%) experienced grade ≥3 adverse events, most commonly neutropenia (93.7%), followed by thrombocytopenia (41.8%) and anemia (30.4%). Cytokine release syndrome and neurologic events occurred in 57.0% and 13.9% of patients, respectively. Grade 3 CRS occurred in 8.9% of patients, with no grade 4 events reported, and grade ≥3 neurologic events occurred in 3.8%. Serious infections occurred in 25.3% of patients, and grade 5 infection was reported in 3 patients. This trial was registered at Clinicaltrials.gov (NCT04836507).

Real-world and computational identification of herbal candidates associated with adverse event patterns in glucagon-like peptide-1 therapy for obesity

Retrospective case-control study of pre-diagnosis observational and prescription data in Parkinson’s disease

Correction: Protective effects of liraglutide on hypercholesterolemia-associated atherosclerosis involve attenuation of endothelial-monocyte adhesion through down-regulating the LOX-1/NF-κB signaling pathway

Integrated analysis of nutritional composition, phytochemical, and antioxidant activity in Cotoneaster microphyllus (Wall. ex Lindl.)

Ungulate geophagy maintains termite mound habitat heterogeneity and increases internal mound temperature

Gender-specific spKt/V thresholds associated with mortality in maintenance hemodialysis patients: a nationwide Korean cohort study

Hybrid electricity management system for residential power block applications

Development, psychometric evaluation, and application of an eco-driving questionnaire based on the Health Belief Model

Victim framing shapes attitudes across diverse contexts

A person accused of victimizing others may be described as the “real” victim by their defenders to garner empathy and mitigate blame. Recent research shows that this rhetorical strategy, known as “victim framing,” can increase support for a man accused of sexually assaulting a woman. Little is known, however, about its effects in other contexts. Across five experiments ( N  = 2,941), we investigated whether victim framing generalizes beyond prototypical sexual assault cases. Participants read fictionalized news reports where one party was labeled the victim (or neither was) and expressed support for the individuals involved. We found significant framing effects across diverse scenarios: (a) a man accused of sexual assault who self-described as the victim; (b) a woman accused of sexually assaulting a man; (c) same-sex assault allegations involving men or women; (d) a celebrity or stranger accused of physically assaulting his girlfriend; and (e) a police officer who shot an unarmed civilian. As in prior work, only participants who explicitly cited the victim-related language as influencing their evaluations showed robust and reliable framing effects. Multiple observer characteristics (e.g., gender, political ideology) predicted attitudes in expected ways, yet victim framing effects persisted when controlling for these individual differences. Taken together, these findings are consistent with a social-pragmatic account of victim framing: many people treat a victim label as communicating relevant information and adjust their evaluations accordingly, while others either do not draw this inference or weigh other information more strongly. Our findings highlight the power and limits of explicit forms of linguistic framing.

Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques

Abstract Accurate timing prediction of surgery is essential for efficient operating room scheduling and ensuring patient care. This study proposes a two-layered Accurate Surgical Time Prediction (ASTP) framework. The first layer combines feature importance and advanced machine learning models to estimate surgical time. After preprocessing, two complementary interpretable AI methods were used: Long Short Term Memory with SHapley Additive exPlanations (SHAP) values and Random Forest permutation importance, to determine the importance of features. In the second layer, subsets of features (TOP-K) were progressively evaluated using HGBR and compared with multiple models: artificial neural networks (ANNs) and recurrent models (Long Short Term Memory, Gated Recurrent Unit, and hybrid Long Short-Term Memory + Gated Recurrent Unit). The proposed framework was evaluated on two datasets: a real-world dataset from an operating room at Nile Hospital and a public dataset from the Medical Informatics Operating Room Vitals and Events Repository (MOVER). In the Nile Hospital dataset, the results show that the Histogram Gradient Boosting Regression (HGBR) approach achieves the best balance, with a Mean Absolute Error of 8.89 min, Root Mean Square Error of 19.5 min, and R-squared of 0.26 using only four features, outperforming other methods. On the MOVER dataset, HGBR also demonstrated the strongest overall predictive behavior, achieving its best numerical result at TOP-12 and best subset at TOP-7, which preserved near-optimal performance with reduced input complexity. The proposed ASTP framework provides an interpretable and resource-efficient solution for surgical time prediction, supporting more intelligent operating room scheduling and facilitating future integration into hospital decision-making.

Implementation bottlenecks of near point of care HIV viral load monitoring for children and young people in Tanzania: A Qualitative Study

Background Near point-of-care (n)POC Human Immunodeficiency Virus (HIV) viral load (VL) monitoring, consisting of VL testing in laboratories close to HIV treatment facilities, improves turnaround time from sampling to result. Other benefits of using nPOC monitoring are reduced laboratory workload, and limited loss of results, all leading to improved clinic retention and treatment adherence. However, the specific implementation bottlenecks are still unclear. This study aims to investigate the bottlenecks in the implementation of nPOC HIV VL monitoring among children and young people (ages 0–24 years) living with HIV, as experienced by healthcare workers (HCWs) in Tanzania. Methods We conducted observations in clinics and in-depth interviews with HCWs from January 2023 to January 2024 at Tanzanian intervention sites within the East Africa Point-of-Care Viral Load Monitoring (EAPOC-VL) study. The EAPOC-VL study was conducted in four countries in East Africa (Tanzania, Kenya, Rwanda, Uganda). It was a cluster-randomised controlled trial, in which participants were tested at three time points (months 0, 6, and 12). We purposively selected 25 HCWs involved in implementing nPOC at the intervention sites for in-depth interviews. We interviewed HCWs at baseline (month 0) and after the initiation of nPOC HIV VL monitoring (months 1 and 6). We conducted deductive thematic framework analysis using the Measurement Instrument for Determinants of Innovations (MIDI), to which an inductive approach was added to identify facilitators and barriers across intervention, provider, organisational, social and political contexts. We used NVivo 12 to organise the data. Results A total of 75 interviews were conducted among 43 HCWs across 3 time points: 33 at baseline (T0), 25 at month 1 (T1; 19 participants from T0 and 6 new participants) and 17 at month 6 follow up (T2;6 participants from T1, 7 returning participants from T0 and 4 new participants). Observations and interviews showed that nPOC HIV VL testing improved result turnaround time and enabled same-day counselling, which motivated both HCWs and clients. This data showed that knowledge, confidence, and adherence to procedures after training. Near POC, HIV VL was supported by compatibility with existing practices, strong teamwork, and management commitment. However, challenges included clients waiting at the clinic for over two hours to receive their results, the scarcity of resources, such as rooms and electricity, and staff shortages. Finally, delays were observed when samples had to be transported to nearby laboratories. Conclusion Near POC HIV VL monitoring shortens turnaround times and enables immediate counselling. To maximise these benefits, there is a need to prioritise investment in staff training, infrastructure, improving sample handling/turnaround time and guideline alignment. Developing these areas will enhance service delivery and allow for improved outcomes among children and young people living with HIV.