Browse Articles

Drivers of physical connectivity between coral reefs along the Southeast African coastline

Abstract The resilience and persistence of coral reef metapopulations strongly depend on their dispersal potential. Larval dispersal influences the diversity and genetic structure of coral populations and contributes to population recovery following disturbances. We assessed the connectivity of coral reefs in the Western Indian Ocean (WIO) during peak spawning periods between 1994 and 2014. The study focused on a broadcast coral, Acropora austera, which has a short pelagic larval duration (PLD). High-velocity streams offshore of the Delagoa Bight connect distant reef complexes on the Southeast African coastline. Complex interactions between regional ocean currents and the African continent drive their formation. These regional flow patterns are part of the larger Agulhas Current system, facilitating inter-reef connectivity within the virtual larvae’s PLD due to high current speeds. The evolutionary connectivity of short-lifespan corals identified between Mozambican and South African reefs is also regulated by intermittent regional flow patterns.

Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms

Brief memory reactivations enable generalization of offline visual perceptual learning mechanisms

Abstract Perceptual learning can significantly improve visual sensitivity even in fully matured adults. However, the ability to generalize learning to untrained conditions is often limited. While traditionally, perceptual learning is attributed to practice-dependent plasticity mechanisms, recent studies suggest that brief memory reactivations can efficiently improve visual perception, recruiting higher-level brain regions. Here we provide evidence that similar memory reactivation mechanisms promote generalization of offline learning mechanisms. Human participants encoded a visual discrimination task with the target stimulus at retinotopic location A. Then, brief memory reactivations of only five trials each were performed on separate days at location A. Generalization was tested at retinotopic location B. Results indicate remarkable enhancement of location B performance following memory reactivations, pointing to efficient offline generalization mechanisms. A control experiment with no reactivations showed minimal generalization. These findings suggest that reactivation-induced learning further enhances learning efficiency by promoting offline generalization mechanisms to untrained conditions, and can be further tested in additional learning domains, with potential future clinical implications.

Configuration effects of enterprise digitization and innovation capability of strategic emerging industries

Cytochrome P450BM-3 and P450 11A1 retain Compound I (FeO3+) chemistry with electrophilic substrates poised for Compound 0 (Fe3+O2−) reactions

Identification of digital clinical decision support systems for supporting diagnosis and triage of patients with shoulder disorders: A scoping review protocol

Background Clinical decision support systems (CDSSs) are computerized tools that support clinical decision-making processes. Primary care decision-making is complex and has the potential to influence quality of care provided and patient outcomes. CDSS not only assist providers with clinical decision-making to ensure quality standards are met, reflect evidence-informed practice, and reduce variation in care, but also help patients navigate and receive an appropriate care pathway amidst numerous, often complex, options. Therefore, this scoping review will aim to identify existing CDSSs for supporting primary point-of-care providers, directing patients to appropriate management pathways, and supporting the clinical examination (i.e., medical history-taking and physical examination) process for patients with shoulder disorders. At the primary point-of-care system level, a CDSS for shoulder disorders will improve clinical efficiency and support decision-making. Methods Scoping review methodology and reporting will be conducted according to Arksey and O’Malley’s 6-step framework, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P), and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) extension for Scoping Reviews (PRISMA-ScR) reporting guide. A robust search strategy will be applied across four databases: MEDLINE (Ovid), EMBASE (Ovid), CINAHL (Ebsco), and Scopus (Elsevier). Two blinded reviewers will independently evaluate all titles and corresponding abstracts based on pre-specified inclusion and exclusion criteria. Inter-rater reliability (IRR) agreement will be established during an initial pilot-screening phase against a random selection of 20 records (minimum) until reaching Cohen’s Kappa ≥ 0.81. Data extraction will be completed by one reviewer and validated by a second. Discussion An effective and high-quality CDSS that is affordable, easy to use, easily accessible, compatible with existing clinical processes, and generalizable across diverse settings will help to support primary point-of-care providers in diagnosing and managing patients presenting with shoulder disorders, thus improving quality of care for patients.

Association between long-term exposure to ambient air pollution and an increased risk of steatotic liver disease

Differential effects of S-allyl cysteine and cannabidiol on enterocytic and plasma amyloid-β in db/db diabetic mice

Bayesian changepoint detection for epidemic models

Abstract This paper demonstrates how Bayesian stochastic filtering techniques can be used to detect changepoints in the transmission rate, as well as identify the rate itself, in the spread of disease using the susceptible-infectious-recovered (SIR) model. To better model real-world scenarios, a stochastic SIR model is considered where the transmission rate is unknown a priori, the number of people moving between compartments is perturbed by additional randomness, and the rate changes at unknown points in time. Changepoints can be used to model disruptions in disease spread, such as those caused by public health measures or new variants. We consider this problem in a Bayesian setting, where the unknown rate and changepoints are modelled as random variables with known prior distributions. This rate can be observed indirectly via the drift of a Brownian motion, before optimally filtering the transmission rate along with any changepoints using Bayesian stochastic filtering techniques. The methods are illustrated with an example using a real dataset from the COVID-19 pandemic, effectively detecting changepoints related to public health measures and the spread of the Omicron variant in the United Kingdom.

Growth performance, meat quality, cecal microbiota and metabolomics profile of turkeys fed diets containing black soldier fly (Hermetia illucens) meal

Integrated analysis of shared gene expression signatures and immune microenvironment heterogeneity in type 2 diabetes mellitus and colorectal cancer

Abstract Emerging evidence suggests a bidirectional relationship between colorectal cancer (CRC) and type 2 diabetes mellitus (T2DM), yet the shared molecular mechanisms and prognostic biomarkers remain poorly characterized. This study aimed to identify novel biomarkers linking CRC and T2DM pathogenesis and evaluate their clinical utility in predicting therapeutic responses and survival outcomes. By integrating multi-omics data from public repositories and applying machine learning-driven feature selection, we identified three core biomarkers—FABP4,CDR2L,and FSTL3 that independently predicted overall survival in CRC patients with diabetes. A prognostic nomogram combining these biomarkers with clinicopathological variables (tumor stage, grade, and age) achieved high accuracy for 1-, 3-, and 5-year survival prediction. Functional characterization revealed strong associations between biomarker overexpression and tumor microenvironment remodeling, particularly through fibroblast-mediated immune cell recruitment and cross-talk with lymphocytes. Critically, low expression of these genes correlated with improved anti-PD-1 immunotherapy responses in an independent clinical cohort. Our findings establish FABP4, CDR2L, and FSTL3 as pivotal regulators at the CRC-diabetes interface, with dual utility as prognostic indicators and predictors of immunotherapy efficacy.

Persistent versus resolved donor-specific antibodies predict 10-year antibody-mediated rejection and kidney transplant outcomes in Thailand

Impact of rehabilitation on quality of life in patients with degenerative cerebellar ataxias using structural equation modeling

Elevated serum CA72-4 as a novel diagnostic biomarker for acute gout flares

Biochemical and structural characterization of a GNAT superfamily protein acetyltransferase from Helicobacter pylori

Association of prior opium addiction with incident non-alcoholic fatty liver disease: A case-control study

Background Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver condition with no approved pharmacological treatments. Given opium’s potential metabolic effects on lipid profiles, blood pressure, and glucose levels, factors known to influence NAFLD, we hypothesized that opium addiction might be inversely associated with NAFLD risk. Objective To investigate the association between opium addiction during a six-year period (2016–2022) and the subsequent incidence and severity of NAFLD in 2022 among participants of the Fasa Adult Cohort Study (FACS). Methods Adults aged 35–70 were selected from the FACS baseline dataset (2016) after excluding individuals with NAFLD (based on the Fatty Liver Index and regional cutoffs), obesity, cancer, chronic liver diseases, or regular alcohol use. Of 550 randomly selected participants invited for sonography in 2022, 396 attended; 170 were newly diagnosed with NAFLD. Cases and controls were matched 1:1 using SPSS based on age, sex, diabetes, and hyperlipidemia. Opium addiction was defined using DSM-5 criteria via structured interviews, while NAFLD diagnosis and grading were performed using blinded ultrasound assessment. Fisher’s exact and Fisher-Freeman-Halton tests were used for analysis. A post hoc power analysis was also conducted. Results The final analysis included 206 participants (103 cases, 103 controls). Opium addiction was observed in 31 NAFLD cases and 72 controls, a non-significant difference. However, the prevalence of opium addiction differed significantly across NAFLD severity grades. The post hoc statistical power was estimated at 60%. Conclusion Although not statistically significant, fewer opium addicts developed NAFLD than non-addicts. This inverse trend, along with significant variation across NAFLD grades, suggests a possible association that warrants further investigation. Larger studies are needed to explore this potential relationship. If confirmed, opioid-based therapies may offer dual benefits for managing chronic pain and metabolic risk in selected NAFLD populations.

CXCL8 is essential for cervical cancer cell acquired radioresistance and acts as a promising therapeutic target in cervical cancer

Abstract Acquired radioresistance critically challenges cervical cancer radiotherapy management. Clinically relevant radioresistant cell models remain scarce, and CXCL8’s role in cervical cancer—despite its tumorigenic/therapy-resistant associations in other cancers—is poorly characterized. Two radioresistant cervical cancer cell strains were established. mRNA-seq and bioinformatics analysis of radiosensitivity regulators identified CXCL8 as a key mediator. In vitro, assays of cell viability, clone formation, apoptosis and cell cycle were conducted following transient transfection of cervical cancer radiotherapy-resistant cell strains with knockdown of CXCL8, as well as subsequent addition of exogenous CXCL8 to cervical cancer parental cell strains. Radioresistant cervical cancer cell lines (Hela-RR/Siha-RR) were established through clinical protocol-mimicking irradiation, validated via proliferation/clonogenic/cell cycle assays. mRNA-seq identified 50 co-upregulated and 54 co-downregulated genes in resistant strains, with CXCL8 among top differentially expressed genes (IL11, CXCL8, MMP1, HSPA8, CA9, PPFIA4, EDN2, GUCY1A2, EFNA3, TNFAIP6). qRT-PCR confirmed CXCL8, TNFAIP6, SRNA8 and PPFIA4 dysregulation. Cox regression analysis of 96 candidate radiosensitivity regulators prioritized CXCL8 among eight key genes in cervical cancer. GEPIA2 and immunohistochemistry revealed CXCL8 overexpression in tumors. Functional studies demonstrated CXCL8 knockdown sensitized resistant cells to radiation, while exogenous CXCL8 induced resistance in parental lines.

Pesticide effects of highly stable green synthesized silver nanocomposites to be used in organic tomato crops

Abstract Greenhouse whitefly, Trialeurodes vaporariorum Westwood (Hemiptera: Aleyrodidae) together with the negative incidence of fungi such as Oidium neolycopersici and phytopathogenic bacteria, are responsible for causing serious economic losses in organic tomato crops. Silver nanoparticles (AgNPs) are a promising solution to problems caused by these pests due to their insecticidal and bactericidal properties. However, these compounds are unstable and tend to form agglomerates. This fact causes them to lose their properties so, preventing its use as an alternative to chemical pesticides in organic cultures. In this research, a novel one-step green synthesis method to obtain silver stable nanocomposites using rosemary extract (Rosmarinus officinalis L.) as green reducing agent was stablished. The polymer polyvinylpyrrolidone (PVP) was used additionally in the same synthesis reaction as AgNPs stabilizing agent. With this scalable one step synthesis, the obtained PVP-AgNPs nanocomposite showed particle sizes of 10.8 nm being highly stable during 326 days. At different assayed doses, this highly stable PVP-AgNPs nanocomposite, was able to control whitefly specimens efficiently with an average mortality rate of 98% after 10 days of the nanocomposite application to naturally infested tomato leaves grown under greenhouse conditions. Additionally, in a diffusion inhibition assay on agar plates, inhibition of Bacillus amyloliquefaciens, Pseudomonas syringae, and Xanthomonas sp growth was found. PVP-AgNPs nanocomposite was also effective to control Oidium neolycopersici in greenhouse grown tomato plants. To our knowledge, this is the first well-founded report related to a PVP-AgNPs nanocomposite obtained by green synthesis using rosemary extracts as reducing agent able to control whitefly and tomato powdery mildew, being a potential alternative to chemical pesticides in organic tomato crops.

Chemotherapy treatment alters DNA methylation patterns in the prefrontal cortex of female rat brain

RETRACTED ARTICLE: Enhancing blockchain transaction classification with ensemble learning approaches

Abstract Since the emergence of Blockchain as Bitcoin, its development has progressed rapidly and attracted the attention of various researchers in academia and industry. Blockchain technology is becoming an increasingly secure and effective way to share information in various industries, including finance, supply chain management (SCM), and the Internet of Things (IoT). The increase in the number of Blockchain users demands malicious and non-malicious transactions to maintain the trust in Blockchain. This research aims to develop a machine learning (ML) based model for classifying blockchain transactions into risky or non-risky ones. The model comprises four feature selection approaches, including Correlation-based Feature Selection (CFS), Recursive Feature Elimination (RFE), Random Forest (RF), and Information Gain (IG). Then, two ensemble feature selection methods, known as rank averaging and rank aggregation, are applied to combine the features selected from the initial feature selection methods. Various ML classification algorithms are applied to the selected features from two ensemble feature selection algorithms as the base learners to make initial predictions. Finally, three different ensemble base classifiers, including hard voting, soft voting, and weighted averaging, are applied to these initial predictions to make the final prediction. Three blockchain transactional datasets are considered for evaluating the proposed ensemble-based model. The empirical analysis of the reported work shows that the maximum accuracy obtained using the Rank Averaging ensemble feature selection technique is 99.24%, whereas the maximum accuracy using the Rank Aggregation ensemble feature technique is 98.73%.