Browse Articles

Professional role and hierarchy shape adherence to electronic alerts for laboratory test ordering

Rapid clearance of bacteria from maternal bloodstream after delivery in pregnancies complicated by preterm pre-labor rupture of the membranes

A systematic review and meta-analysis of the mechanism of action of Tai Chi on cardiovascular disease: evidence map of aerobic and mind-body exercise pathways

HDACi induces apoptosis of osteosarcoma cells by inhibiting the MAPK pathway

Knowledge, attitudes, and practices (KAP) regarding physical activity among patients aged 20–60 with coronary heart disease

Machine learning models for predicting treatment outcomes in chronic non-specific back pain patients undergoing lumbar extension traction

Recombinase polymerase amplification-based rapid detection of Oryctes rhinoceros nudivirus (OrNV) infection in coconut rhinoceros beetles

Mechanistic and clinical insights into a PmrB mutation driving colistin resistance and virulence in Acinetobacter baumannii

Standardizing postpartum family planning counseling guidance in Ghana: A stepped-wedge cluster randomized implementation effectiveness trial

Background Postpartum family planning can reduce morbidity and mortality for parents and children, however, up to 62% of birthing people have an unmet need for contraception due to implementation challenges. In this study, we aimed to evaluate implementation and effectiveness of the Postpartum Family Planning Package, a multifaceted implementation strategy combining provider use of the World Health Organization Medical Eligibility Criteria Mobile App (WHO MEC app), provider education, and counseling restructuring on the postnatal ward, to promote individualized family planning counseling prior to hospital discharge after childbirth. Methods We conducted a stepped-wedge trial in the Greater Accra and Eastern regions of Ghana. The Postpartum Family Planning Package implementation strategy was introduced sequentially at three public hospitals. We used a generalized linear mixed effects model to adjust for the time variable via the random effects part of the model, controlling for all other independent variables. Additionally, we assessed for time by intervention interaction. Results From 5 th October 2020–1 st October 2021, we enrolled 2096 patients and 191 providers. Post-intervention encounters were more likely to include discussion of all appropriate postpartum family planning methods compared to pre-intervention encounters (63% vs 39%). Patients counseled individually post-intervention were four times more likely to have all appropriate family planning methods discussed (aOR 4.28; 95% CI 2.35, 7.78). A family planning method decision was made before discharge in 49.5% of post-intervention encounters, compared to 18.3% pre-intervention (aOR 4.45; 95% CI 2.85, 6.93). Individual counseling was associated with higher uptake of family planning methods prior to discharge (aOR 1.74; 95% CI 1.04, 2.91). Conclusion Implementation of the Postpartum Family Planning Package resulted in high fidelity to the intervention and was effective in promoting patient decision to select contraceptive methods postpartum. Future research should examine the effect of our strategy when used during antenatal and all other postpartum encounters, as well as mechanisms to improve method uptake.

Investigating the causal effects of religiosity on childbearing among U.S. adolescents using a three-wave longitudinal design

Antiapoptotic BCL2 family proteins BCL-XL and MCL1 as factors predicting resistance against venetoclax plus azacitidine for patients with newly diagnosed acute myelogenous leukemia

The combination of Venetoclax (VEN), a BCL2 inhibitor, and Azacitidine (AZA), a hypomethylating agent, is the standard treatment for acute myelogenous leukemia (AML) in patients older than 65 years who are not eligible for intensive chemotherapy. While high response rates for this treatment have been noted, it has been also reported that the anti-apoptotic BCL2 family proteins BCL-XL and MCL1 may be involved in VEN resistance. However, no study has heretofore been conducted to investigate the effectiveness of treatment and the expression of BCL-XL or MCL1 in patients treated with VEN + AZA therapy. In this study, we analyzed blasts from patients with newly diagnosed AML treated with VEN + AZA therapy by qPCR, confirmed by siRNA in cultured cell lines, and evaluated the validity of the immunostaining method. We demonstrated that BCL-XL or MCL1 was highly expressed in leukemia cells of patients who did not respond to this treatment. In addition, leukemia cells from patients who had responded to VEN + AZA but relapsed during the course of treatment showed increased expression of BCL-XL or MCL1 compared to pre-treatment levels. Furthermore, downregulation of BCL-XL expression in a VEN-resistant AML cell line with siRNA increased sensitivity to VEN. On the other hand, the expression of BCL-XL and MCL1 in leukemia cells could be easily semi-quantified by immunostaining, with these results correlating with those obtained by qPCR. These results indicate that immunostaining for BCL-XL and MCL1 upon bone marrow examination at diagnosis not only can predict susceptibility to VEN + AZA therapy, but may also be useful for patient stratification for VEN + AZA treatment in the future.

A novel reinforcement learning-based approach for short-term load and price forecasting in energy markets

Uralenol, Glycyrol, and Abyssinone II as potent inhibitors of fibroblast growth factor receptor 2 from anti-cancer plants: A deep learning and molecular dynamics approach

Fibroblast Growth Factor Receptor 2 (FGFR2) plays a critical role in cellular proliferation and differentiation, and its dysregulation is associated with multiple cancers. This study integrates molecular docking, deep learning, pharmacokinetic profiling, and molecular dynamics (MD) simulations to identify potential FGFR2 inhibitors from a library of 1,350 phytochemicals derived from 51 anti-cancer medicinal plants that were traditionally used for anticancer purposes. Initial screening through AutoDock Vina revealed several top candidates with high binding affinities to FGFR2. The top three compounds, uralenol, glycyrol, and abyssinone II, underwent further evaluation via deep learning models, which predicted the potential efficacy of the pIC₅₀ (negative logarithm of the half-maximal inhibitory concentration) values. The ADME/T (absorption, distribution, metabolism, excretion, and toxicity) analysis confirmed favorable pharmacokinetic profiles and low toxicity risks. MD simulations validated the stability and compactness of protein–ligand complexes, with principal component analysis (PCA) and free energy landscape analyses confirming these interactions’ conformational stability and thermodynamic favorability. These findings suggest that uralenol, glycyrol, and abyssinone II are potential FGFR2 inhibitors and need further experimental validation for potential therapeutic use in cancer treatment.

¹H-NMR serum metabolomic profiling from clinical routine identifies signatures of progressive melanoma metastasis

Abstract Early detection of active melanoma metastasis is crucial. Serum metabolomics may offer non-invasive biomarkers, but real-world applicability needs validation. This study aimed to identify ¹H-NMR-based serum metabolic signatures for active metastasis in a large clinical cohort. Serum from 963 melanoma patients (1698 samples) underwent ¹H-NMR spectroscopy. Patients were classified by active metastasis status. OPLS-DA and RFE followed by logistic regression models were developed on a patient-level training/test split. Subgroup analyses assessed signatures related to Immune Checkpoint Inhibitor (ICI) therapy, brain metastases, and BRAF status. Models for active metastasis showed moderate test set discrimination (Area Under the Curve [AUCs]: OPLS-DA 0.609, RFE 0.630). The RFE-model highlighted seven significant metabolites: increased pyruvate, phenylalanine, acetoacetate, glutamate, glucose, and decreased histidine and citrate were associated with active metastasis. OPLS-DA yielded concordant metabolites. Subgroup analyses revealed distinct metabolic associations, e.g., for ICI therapy (citrate, RFE AUC 0.721) and BRAF status (acetate, RFE AUC 0.655), but limited performance for brain metastases (RFE AUC 0.553). ¹H-NMR serum metabolomics detects systemic metabolic alterations of active melanoma metastasis with moderate accuracy in a real-world setting. Identified disruptions in energy and amino acid metabolism offer pathobiological insights and warrant investigation for multimodal biomarker panels.

A HLBDA, GA, and COA for optimal operation of distributed energy resources

Although renewable energy sources offer enormous potential to improve environmental sustainability, maximizing economic benefits inside microgrids requires resolving their intermittency and irregularity. A viable alternative is to combine energy storage with renewable energy technologies. This article introduced a energy management system for hybrid renewable power plants that includes fuel cells, wind turbines, solar cells, battery energy storage devices, and micro-turbines. Optimization problem is formulated as Hyper Learning Binary Dragonfly Algorithm (HLBDA) for optimizing economic benefits and with objectives of minimizing operating costs and pollutant gas emissions. Suggested model is compared with existing methods like Genetic Algorithms (GA), and Crayfish Optimization Algorithm (COA). Also, stochastic framework is considered suitable solution for achieving optimal operation point in microgrids to cope with uncertain parameters. According to the simulation results, suggested method proves reductions in overall system costs and pollutant gas emissions. The proposed system achieved significant superiority across all indicators. In the area of cost reduction, the algorithms demonstrated remarkable progress. The algorithms achieved significant improvements in cost reduction compared to genetic algorithm (GA). HLBDA algorithm achieved a 12.4% cost saving compared to GA, and the COA algorithm showed a 3.24% improvement in cost reduction. In the area of carbon emission reduction, the algorithms also showed significant progress: the HLBDA algorithm recorded the highest emission reduction rate at 9.54%, and the COA algorithm showed a 2.40% improvement in emission reduction.

The relationship between health promotion and renewable energy sources in the attitudes of Polish medical students

LDA-DETR: A lightweight dynamic attention-enhanced DETR for small object detection

The issues of complex background interference, dense distribution, and insufficient feature representation for small objects have become significant challenges and research hotspots in computer vision. Particularly when the algorithm needs to be deployed in practical applications, many state-of-the-art detectors struggle to balance accuracy and efficiency, often requiring extensive computational power or suffering from degraded detection performance on small objects. To tackle these problems, this paper proposes a lightweight dynamic attention-enhanced DETR (LDA-DETR). Firstly, a lightweight feature extraction backbone (LFEB) is designed to improve the efficiency of object detection under limited computational resources. The proposed backbone enhances gradient flow and reduces the model’s parameters through residual structures and partial convolution operations. Then, a Dynamic Multi-Scale Fusion Module (DMSFM) is proposed to improve the model’s adaptability and the ability to fuse diverse features. The proposed module enhances feature representation ability and inference performance by performing convolutions at different scales across multiple branches and dynamically selecting operations. Finally, considering shallow features contain more detailed information, the Attention-Enhanced Fusion Network (AEFN) is constructed. The proposed approach refines and enriches features through attention mechanisms and cascading operations, endowing the features with comprehensive semantic and spatial details. Extensive experiments on the RSOD, NWPU VHR-10, URPC2020, and VisDrone-DET datasets demonstrate that LDA-DETR outperforms the state-of-the-art detection methods and further validate that the technique is better suited for small object detection applications.

Leveraging universal and transfer learning models for influenza prediction in Thailand

Abstract Influenza is a major respiratory disease that causes significant morbidity and mortality worldwide. Accurate predictions of influenza incidence enable public health organizations to monitor and prepare for outbreaks, ultimately reducing mortality and optimizing resource allocation. However, many countries, including Thailand, face challenges in generating accurate forecasts due to limited feature data in certain regions. To address this, we developed universal deep learning (DL)-based models to predict influenza incidence across multiple provinces in Thailand from 2010 to 2019. We evaluated various model configurations and implemented a feature selection process to enhance model generalizability and performance by ensuring equal contributions from multiple time series features. Our findings indicate that single hidden layer models with 128 nodes performed the best in the universal framework. To extend predictions to provinces without meteorological and PM10 data, we applied transfer learning (TL) using pre-trained models. The TL-based model, fine-tuned for each province, significantly outperformed baseline models trained solely on previous incidence, achieving the highest accuracy. Our results demonstrate the potential of universal DL and TL frameworks in forecasting influenza trends, even in limited data regions, and highlight the importance of incorporating domain-specific knowledge for robust epidemic management strategies.

Multimodal imaging of tibialis anterior muscle adaptations to neutral-position immobilization

Muscle disuse atrophy is a frequent consequence of therapeutic immobilization following sport injuries, bone fractures, and ligament tears, often resulting in marked reduction of muscle volume, mass, and strength. Despite the widespread use of neutral-position limb immobilization in clinical practice, its physiological effects remain insufficiently characterized. To address this gap, we employed thermographic, tomographic, and ultrasound imaging to assess how neutral-position immobilization (Imm) affects the tibialis anterior, a predominantly fast-twitch ankle dorsiflexor muscle that plays a key role in foot deceleration after heel strike, provides functional stability during gait preventing falls and contributes substantially to load absorption, in twenty-seven young male Wistar rats after 7 and 14 days of treatment. To complement these, force measurements and histology were analyzed. Our results showed a significant limb temperature increase of up to 10% after 14 days compared to controls accompanied by a volume reduction of 38% (p < 0.05) confirmed by tomography and a 2-fold (p < 0.05) increment of CNFs denoted by histology (H&E). At 14 days of Imm ultrasound imaging highlighted changes in subcutaneous tissue thickness, and increased connective tissue; a significant 2-fold reduction in specific force during muscle twitch and 28% (p < 0.05) in tetany. Fiber type conversion mainly to type IIA (intermediate) was evident on histology and supported by the prolonged fatigue time following two fatigue protocols (continuous stimulation and repeated short-tetany) for up to 50% (p < 0.05) after 14 d of Imm. Our results demonstrate that, although immobilization in a neutral position is the best practice in the clinic, it carries important detrimental changes in muscle structure and physiology. These findings underscore the importance of integrating clinical imaging techniques to monitor muscle status during immobilization and rehabilitation, enabling more effective and timely interventions.

Attention driven deep convolutional network with optimized learning for accurate landslide detection and monitoring

Abstract Effective landslide monitoring is essential for mitigating risks to infrastructure and communities, particularly in geologically unstable regions. Traditional monitoring methods, such as ground surveys and visual inspections, are time-intensive and lack early detection capabilities. To address these limitations, this study employs feature fusion and enhanced Deep Convolutional Neural Networks (DCNNs) for landslide detection. The model is built upon a fine-tuned, pre-trained VGG16 architecture, adapted to a new landslide dataset. Key modifications include the integration of a spatial attention mechanism, optimized learning rate schedules, attention-based Global Average Pooling (GAP), and the Lookahead Adam optimizer, all aimed at improving feature extraction, model convergence, and generalization. Experimental results demonstrate that the proposed approach achieves high accuracy, with performance ranging from 90% to 96% across different datasets and training iterations. Using the Kaggle Landslide Dataset, the model attained a training accuracy of 93%, with validation and testing accuracies of 95.2% and 95.8%, respectively. Comparable results were observed with the NASA Landslide Inventory, confirming the robustness of the method. The findings highlight the potential of DCNN-based models, augmented with attention mechanisms, as a reliable and efficient tool for landslide monitoring, significantly outperforming conventional assessment methods.