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Charantin targets HMGCR-PCSK9 axis and activates PPAR-α signaling to ameliorate hyperlipidemia: Mechanistic insights from bioinformatics and in-vivo studies

Plant-derived compounds have recently gained attention owing to their better safety profile and multi-targeted actions. Charantin, a plant-based natural compound known for its diverse pharmacological properties, was investigated for its anti-hyperlipdemic activity using both in-silico and in-vivo approaches. A detailed network pharmacology analysis was used to predict charantin-related targets, cross-referenced with hyperlipidemia-associated genes from GeneCards, DisGeNET, and CTD. Shared targets were subjected to protein-protein interaction analysis and functional enrichment using STRING, Cytoscape, and ShinyGO. Molecular docking studies assessed charantin’s binding interactions with key lipid-regulating proteins (HMGCR, PCSK9, LDLR, PPAR-α, PI3K). In-vivo efficacy of charantin (100 and 200 mg/kg) was evaluated in Sprague-Dawley rats fed with high-lipid diet (HLD) for 12 days. Lipid profiles, liver enzymes and transcript levels of lipid-regulating genes were analyzed. A total of 242 overlapping genes were identified between charantin targets and hyperlipidemia-associated genes, with enrichment analyses highlighting key lipid metabolic and inflammatory pathways. Molecular docking revealed that charantin exhibited stronger binding affinities than simvastatin across multiple targets. In HLD animal model, charantin significantly reduced total cholesterol, triglycerides, LDL, and VLDL, while increasing HDL levels in a dose-dependent manner. Liver function remained preserved, accompanied by downregulation of HMGCR, PCSK9, and APOB, and upregulation of LDLR and PPAR-α at both gene and protein levels. Charantin exerts potent lipid-lowering effects through modulation of multiple pathways, including cholesterol biosynthesis, lipoprotein metabolism, and nuclear receptor activation. Its efficacy and hepatoprotective properties reiterate its potential as a safe, effective alternative or adjunct to conventional therapies for hyperlipidemia.

Perirenal fat thickness may be a significant predictor of prognosis and postoperative renal function in renal cancer surgery patients

Willingness to use long-acting injectable pre-exposure prophylaxis (LAI-PrEP) among black cisgender women in the Southern United States

Background Long-acting injectable pre-exposure prophylaxis (LAI-PrEP) for HIV prevention may improve adherence for those with concerns with daily pills. Limited data exist on LAI-PrEP acceptability among Black women in the U.S., a population vulnerable to HIV. We assessed willingness to use LAI-PrEP among Black women eligible for PrEP in the Southern U.S. Methods We conducted a cross-sectional online survey of HIV-negative Black women from March to June 2022 in the U.S. South. Participants provided information on sociodemographic characteristics, HIV knowledge, PrEP awareness, and use, stigma, risk perception, medical mistrust, and healthcare access. Multivariate logistic regression models determined factors associated with willingness to use LAI-PrEP. Results Of 491 women, the mean (SD) age was 40.1 (17.5), 53% of participants had a college degree or lower, 79% were single, and 80% resided in urban/suburban settings. Thirty-nine percent were aware of PrEP before the study and 36.7% of women were willing to use LAI-PrEP. In multivariate analyses, PrEP awareness [adjusted odds ratio (aOR=2.37, 95% CI 1.40, 3.73, p < 0.001), having a personal clinician (aOR=2.01, 95% CI 1.10, 3.68, p = 0.02), HIV worried (aOR=1.78, 95% CI 1.09, 2.89, p = 0.02), and medical trust (aOR=1.41, 95% CI 1.03, 1.93, p = 0.04) were statistically associated with willingness to use LAI-PrEP. However, the healthcare stereotype (beliefs that healthcare is biased) had lower odds of using LAI-PrEP (aOR=0.94, 95% CI 0.89, 0.99, p = 0.04). Conclusion Black women at risk for HIV are more likely to consider injectable PrEP when they understand HIV risk factors, are aware of PrEP, have a clinician, and trust the medical care. Implementing client-centered care interventions could effectively address medical mistrust and enhance engagement in HIV prevention services among Black women.

Assessing the spatial risk of wild birds in avian influenza transmission using global risk score

Ethical implications of neurotechnology in industry-academia partnerships: Insights from patient and research participant interviews

Background Neurotechnologies often advance through industry-academia (IA) partnerships and offer insight into brain and nervous system functions, bringing improved diagnosis and treatment options to patients. Both neurotechnology and IA partnerships pose ethical challenges that can impact research participation experiences, patient treatment, and health outcomes. Methods Investigators conducted interviews with 16 patients who used neurotechnology devices in therapeutic or research settings. Interviews explored participants’ experiences using neurotechnology, perspectives on IA partnerships, preferences for neural data use and long-term care, and advice for future neurotechnology device users. Data were analyzed using inductive thematic analysis. Results Participants were generally supportive of IA partnerships. However, they also recognized that these relationships could unduly influence research and clinical decisions. While participants appreciated the information shared with them prior to using the neurotechnology, informational gaps were still identified regarding the impact of devices on daily living, disclosure of relationships with industry, plans for data use and sharing, and plans for long-term care and upkeep of the device. Participants generally supported neural data sharing to advance research or improve patient care, although for some this depended on data sensitivity and how privacy would be protected. Participants advocated for post-trial access to experimental neurotechnologies and felt that responsibility for long-term care and device maintenance is best shared among companies, doctors, academic researchers, insurance companies, and patients themselves. Future device users were advised to self-advocate, maintain realistic expectations, and learn about a device before engaging with it. Conclusion Given current and future capabilities of neurotechnologies and the data they generate, IA partnerships that develop and commercialize neurotechnologies require careful consideration and implementation of practices that meaningfully consider patient perspectives, needs, and safety. Such practices include bias management in the design, conduct, and reporting of neurotechnology research, neural data sharing and use, post-trial device access, and informed consent processes.

Assessment of health-related quality of life in transfusion dependent beta thalassemia

Psychometric evaluation of the Persian nursing students’ learning self-efficacy instrument

The preparedness of the nursing workforce is a critical determinant of patient safety and healthcare quality, with academic performance serving as a key contributor to this preparedness. Self-efficacy plays a significant role in shaping the motivation and success of nursing students, necessitating the availability of valid and reliable instruments for its assessment. This study aimed to translate a multidimensional learning self-efficacy instrument for nursing students into Persian and evaluate its psychometric properties within the Iranian context. Employing a methodological design, the study recruited 450 nursing students through convenience sampling between September and December 2024. The instrument underwent translation/back-translation, with face validity (Impact Score > 1.5), content validity ratio (CVR = 0.8–1), and content validity index (CVI = 80–100%) established. Psychometric evaluation included construct, convergent, and discriminant validity, internal consistency, and test-retest reliability. Students from all academic years participated in construct validity testing through Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) using SPSS 24.0 and AMOS 24.0. EFA identified a five-factor structure explaining 73.43% of variance, supported by strong sampling adequacy (Kaiser-Meyer-Olkin = 0.87) and significant Bartlett’s test (p < 0.001). CFA confirmed model fit for the 21-item Nursing Learning Self-Efficacy (NLSE) instrument, with robust indices: Parsimonious Comparative Fit Index (PCFI = 0.75), Parsimonious Normed Fit Index (PNFI = 0.72), Incremental Fit Index (IFI = 0.93), and Comparative Fit Index (CFI = 0.93). Internal consistency (Cronbach’s α = 0.93) and test-retest reliability (Intraclass Correlation Coefficient = 0.89) were excellent. These findings establish the Persian version of the NLSE as a valid and reliable self-report instrument. By providing a culturally adapted instrument, this study equips nursing educators and researchers with a practical means to assess and enhance self-efficacy among nursing students, ultimately contributing to improved educational outcomes and a more competent nursing workforce capable of addressing Iran’s evolving healthcare demands.

Efficacy of combined undenatured type II collagen and hydrolysed collagen supplementation in knee osteoarthritis: a randomised controlled trial

Analysis of global, regional, and national burden and attributable risk factors of acute lymphoblastic leukemia and acute myeloid leukemia from 1990 to 2021

Background Acute Leukemia (AL) is a prevalent subtype of leukemia, mainly including acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). The disease burden of acute leukemia has significantly shifted in recent years. The aim of this study was to evaluate global trends in the burden of disease for ALL and AML from 1990 to 2021. Methods Data on ALL and AML, encompassing incidence, mortality, disability-adjusted life-years (DALYs), and associated risk factors from 1990 to 2021, were extracted from the Global Burden of Disease (GBD) 2021 database. Estimated annual percentage changes (EAPC) were calculated to assess the changes in age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR), and age-standardized DALYs rate (ASDR). The associations between cancers burden and socio-demographic index (SDI) were also analyzed. Results Compared with 1990, the global incidence of ALL and AML in 2021 is 5.69% and 82.25% higher, respectively. During the period from 1990 to 2021, ASMR in ALL showed a large decline, while AML remained stable. The ASDR of both showed a downward trend (EAPC = −2.11% and −0.84%). Regions and countries with higher SDI also have higher rates of acute leukemia. The burden of AML is mainly distributed in the elderly population, while the burden of ALL is heavier in children. Smoking, high BMI, and occupational exposure to benzene and formaldehyde are major risk factors for AML and ALL-related deaths. Conclusion Acute leukemia remains one of the major global public health challenges, but there are different trends in different regions and countries. Acute myeloid leukemia has had a higher disease burden in recent years than acute lymphoblastic leukemia. Policy makers should develop targeted public health policies to further reduce the global burden of acute leukemia.

Echinacea purpurea ameliorates Bleomycin-induced pulmonary fibrosis in rats through modulating NADPH oxidase-4 and endothelin-1/connective tissue growth factor/matrix metalloproteinases signalling axis

Abstract Idiopathic pulmonary fibrosis (IPF) is one of the rapidly progressing interstitial lung illnesses. Bleomycin (Bleo) is used as a chemotherapeutic agent for the treatment of lymphoma patients. The major side effects of Bleo include lung fibrosis, characterized by the accumulation of inflammatory cells. Echinacea purpurea (ECH) possesses immune-modulating, antiviral, antimicrobial and anti-inflammatory activities. The current study aims to evaluate the possible protective effects of ECH against Bleomycin-induced pulmonary fibrosis. Forty rats were divided into four groups (n = 10). Group I represented the normal-control group. Group II represented the Bleo-control group. Groups III and IV received intra-tracheal Bleo followed by oral ECH (25 and 50 mg/kg); respectively, for 1 month. Lung tissue contents of reduced glutathione (GSH), malondialdehyde (MDA), transforming growth factor-beta (TGF-β), matrix metalloproteinases (MMP-2 & MMP-9), tissue inhibitor of metalloproteinase 1 (TIMP-1), MMP-9/TIMP-1 ratio, collagen-1 and alpha-Smooth muscle actin (α-SMA) were measured. NADPH oxidase 4 (NOX4), connective tissue growth factor (CTGF) and endothelin-1 (ET-1) genes were quantified using PCR. Moreover, lung tissue histopathological changes were screened. Intra-tracheal Bleo instillation resulted in significant increments in the lung tissue contents of MDA, TGF-β, MMP-2 & MMP-9, TIMP-1, MMP-9/TIMP-1 ratio, collagen-1 and α-SMA. Moreover, Bleo significantly elevated the PCR expression of NOX4, CTGF and ET-1 genes in lung tissues and caused apparent lung tissue histopathological fibrotic changes. ECH treatment ameliorated all the aforementioned parameters and mitigated the lung tissue histopathological fibrotic changes induced by Bleo. The study highlighted for the first time the anti-oxidant, anti-inflammatory and anti-fibrotic effects of ECH against Bleo-induced pulmonary fibrosis in rats. The study suggests that these effects are mainly mediated via the modulation of Gelatinases, NOX4, ET-1 and CTGF. Accordingly, ECH is anticipated as a potential therapy to be added to the treatment regimen of pulmonary fibrosis.

A machine learning approach for detecting WPA3 downgrade attacks in next-generation Wi-Fi systems

This paper presents a hybrid adaptive approach based on machine learning (ML) for classifying incoming traffic, feature selection and thresholding, aimed at enhancing downgrade attack detection in Wi-Fi Protected Access 3 (WPA3) networks. The fast proliferation of WPA3 is regarded critical for securing modern Wi-Fi systems, which have become integral to 5G and Beyond (5G&B) Radio Access Networks (RAN) architecture. However, the wireless communication channel remains inherently susceptible to downgrade attacks, where adversaries intentionally cause networks to revert from WPA3 to WPA2, with the malicious intent of exploiting known security flaws. Traditional Intrusion Detection Systems (IDS), which rely on fixed-threshold statistical methods, often fail to adapt to changing network environments and new, sophisticated attack strategies. To address this limitation, we introduce a novel ML-based Feature Selection and Thresholding for Downgrade Attacks Detection (MFST-DAD) approach, which comprises three stages: traffic data preprocessing, baseline adaptive feature selection, and real-time detection and prevention using ML algorithms. Experimental results on a specially generated dataset demonstrate that the proposed approach detects downgrade attacks in WPA3 networks, achieving 99.8% accuracy with a Naive Bayes classifier in both WPA3 personal and enterprise transition modes. These findings confirm the effectiveness of our proposed approach in securing next-generation Wi-Fi systems.

Research on strategies for enhancing system security resilience of prefabricated building engineering

Cyclic assisted cloning of arbitrary unknown single-particle states in amplitude damping channel

In this paper, two conclusive three-party cyclic assisted cloning protocols in amplitude damping (AD) channel are put forward that, respectively clone three arbitrary unknown single-qubit states and single-qutrit states with the help of a state preparer. Each of our protocols includes three consecutive stages: quantum channel preparation, cyclic quantum teleportation (CQT), and multi-party assisted cloning. The first stage of each protocol proposes the detailed processes of sharing a pure entangled quantum state as a component of a quantum channel in AD channel via entanglement compensation. In second stage, a three-party CQT is presented where three unknown single-qubit states (or single-qutrit states) are reconstructed simultaneously in three different places, respectively, by introducing auxiliary qubits and performing appropriate operations. In the third stage, the state preparer Victor performs one multi-qubit measurement (or one unitary transformation and one multi-qutrit measurement) and informs the three communicators of his outcome, three distinct unknown single-qubit states or their orthogonal complement states (or single-qutrit states) are cloned simultaneously and with probability at three separate locations,respectively. Furthermore, we extend the above protocols from two aspects: (i) the extension to the case of (N+1) participants; (ii) extension to the case of d-dimensional unknown single-qudit state cycle-assisted cloning.

Circular RNA RORβ regulates TGFβR1 in alcohol-induced fibroblast-to-myofibroblast differentiation

Defining metabolic abnormalities in acute human traumatic brain injury with cerebral microdialysis and multimodality monitoring

Objective We aimed to compare the prevalence and multimodal associations of mitochondrial dysfunction as defined by published cerebral-microdialysis-based criteria versus our novel multimodality-monitoring-based criteria in acute traumatic brain injury patients. Methods We retrospectively analyzed neurocritical care monitoring data from 619 acute traumatic brain injury patients. Monitoring modalities included cerebral microdialysis, intracranial pressure, brain tissue oxygenation, cerebral perfusion pressure, and the pressure reactivity index. The cerebral-microdialysis-based criteria we compared combine an elevated lactate/pyruvate ratio (25 or 30) with raised concentrations of lactate (2.5 mM) or pyruvate (70 μM or 120 μM). Our multimodality-monitoring-based criteria comprise a consistent lactate/pyruvate ratio > 25 with intracranial pressure ≤ 20 mmHg, brain tissue oxygenation ≥ 15 mmHg, a pressure reactivity index ≤ 0.3, and cerebral glucose ≥ 1.0 mM. Results Across 592 analyzable patients, a lactate/pyruvate ratio > 25 was common, with a median prevalence of 48.9% (41.5% with consistency) and a U-shaped, bimodal distribution. A lactate/pyruvate ratio > 25 was associated with lower glucose and higher glycerol, and when accompanied by high pyruvate (> 120 μM), this derangement was further distinguished by higher glutamate and cerebral perfusion pressure. Using multimodal criteria on a cohort of 268 patients, consistent mitochondrial dysfunction was identified in 25.7% to 41.0% of patients, often in the absence of other physiological derangements. Conclusions Many acute traumatic brain injury patients constantly demonstrate neurometabolic derangements, among which clinical mitochondrial dysfunction is highly prevalent despite normal cerebral pressure, oxygenation, and perfusion. There is necessity for targeted, neurometabolic therapies in neurocritical care that address this abnormality.

Research on the inversion model of soil moisture content based on a novel ReMPDI index in mining areas

Improving adherence to physical activity in treatment-resistant depression: Protocol for a pilot randomized controlled trial of a remotely delivered program

Background At least 30% of individuals with major depressive disorder do not respond to conventional treatments (i.e., they meet the criteria for treatment-resistant depression [TRD]). Alternative therapeutic modalities are needed. Some studies have reported that physical activity (PA) programs can improve depressed mood and reduce depressive symptoms. However, few studies to date have examined the effects of PA as an adjunct to standard treatment for TRD. The MoveU.HappyU PA program has been shown to improve depressive symptoms in university students. Before a definitive trial testing MoveU.HappyU in TRD can be designed, pilot data is needed. Methods The current study is a single-site, pilot, two-arm randomized controlled trial. It will investigate the feasibility of randomizing 30 adult participants with TRD to: (1) a remotely delivered four-week MoveU.HappyU adjunct to treatment as usual (TAU), or (2) TAU. Acceptability of the PA program will also be assessed. Participants randomized to the PA program will meet weekly with a program trainer to engage in PA counselling and structured PA. They will also be instructed to independently complete 120 minutes of PA per week. The four-week intervention period will be followed by six weeks of observation. Throughout the study, both groups will receive the same digital monitoring via self-report questionnaires and a wearable device, as well as traditional monitoring (i.e., clinical assessments administered by a masked rater). Discussion This pilot study will assess the feasibility of a trial implementing MoveU.HappyU for TRD and generate clinical parameter estimates for larger studies. This line of research highlights the importance of PA programs that integrate personalized PA with PA counselling. It will also influence the development of interventions that are more tailored and effective. Trial registration ClinicalTrials.gov NCT06404320

BamClassifier: a machine learning method for assessing iron deficiency

Abstract Iron deficiency (ID) is a well-known cause of anaemia and could lead to adverse clinical and functional impairments. However, ID is under-diagnosed due to non-specific symptoms, difficulties in interpreting ambiguous assessment outcomes and suboptimal sensitivities of methods in some circumstances. In this study, we present BamClassifier, a machine learning method for assessment of ID. This method proceeds by repeated selection of samples of instances from routine complete blood count data in such a way that each observation is included in exactly one sample. Then, a median-supplement machine learning model built from each sample, and the performance of the model on test instances are aggregated into a bag of predictions from which ID statuses are assigned to samples by way of the highest frequency counts. We show the effectiveness of our method by applying to real datasets obtained from different investigations in Ghana and simulated data as well. Our method obtained perfect area under receiver operating characteristic curve in all experiments and significantly outperformed other well-established methods in terms of accuracy, sensitivity, specificity, precision, and diagnostic odds ratio in all our evaluations. A successful application of the method will permit the study of large collections of samples for ID assessments, save time and cost using complete blood count parameters, while standardizing interpretation of outcomes of such investigations.

Deep learning methods to forecasting human embryo development in time-lapse videos

Background In assisted reproductive technology, evaluating the quality of the embryo is crucial when selecting the most viable embryo for transferring to a woman. Assessment also plays an important role in determining the optimal transfer time, either in the cleavage stage or in the blastocyst stage. Several AI-based tools exist to automate the assessment process. However, none of the existing tools predicts upcoming video frames to assist embryologists in the early assessment of embryos. In this paper, we propose an AI system to forecast the dynamics of embryo morphology over a time period in the future. Methods The AI system is designed to analyze embryo development in the past two hours and predict the morphological changes of the embryo for the next two hours. It utilizes a novel predictive model incorporating Convolutional LSTM layers for recursive forecasting, enabling prediction of future embryo morphology by analyzing prior changes in the video sequence and predicting embryo development up to 23 hours ahead. Results The results demonstrated that the AI system could accurately forecast embryo development at the cleavage stage on day 2 and the blastocyst stage on day 4. The system provided valuable information on the cell division processes on day 2 and the start of the blastocyst stage on day 4. The system focused on specific developmental features effective across both the categories of embryos. The embryos that were transferred to the female, and the embryos that were discarded. However, in the ‘transfer’ category, the forecast had a clearer cell membrane and less distortion as compared to the ‘avoid’ category. Conclusion This study assists in the embryo evaluation process by providing early insights into the quality of the embryo for both the transfer and avoid categories of videos. The embryologists recognize the ability of the forecast to depict the morphological changes of the embryo. Additionally, enhancement in image quality has the potential to make this approach relevant in clinical settings.

Spatio-temporal transformer traffic prediction network based on multi-level causal attention

Traffic prediction is a core technology in intelligent transportation systems with broad application prospects. However, traffic flow data exhibits complex characteristics across both temporal and spatial dimensions, posing challenges for accurate prediction. In this paper, we propose a spatiotemporal Transformer network based on multi-level causal attention (MLCAFormer). We design a multi-level temporal causal attention mechanism that captures complex long- and short-term dependencies from local to global through a hierarchical architecture while strictly adhering to temporal causality. We also present a node-identity-aware spatial attention mechanism, which enhances the model’s ability to distinguish nodes and learn spatial correlations by assigning a unique identity embedding to each node. Moreover, our model integrates several input features, including original traffic flow data, cyclical patterns, and collaborative spatio-temporal embedding. Comprehensive tests on four real-world traffic datasets—METR-LA, PEMS-BAY, PEMS04, and PEMS08—show that our proposed MLCAFormer outperforms current benchmark models.