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CD44 and phosphorylated ERK1/2 coexpression predicts distant metastasis in colorectal cancer based on a study of 1137 Saudi patients
The chromatin guardian ATRX is a strong prognostic biomarker in melanoma
High-efficiency tandem DSSCs based on tailored naphthalene sensitizers for indoor DSSC efficiency above 25%
Abstract Dye-sensitized solar cells (DSSCs) are among the most promising photovoltaic technologies for both outdoor and indoor energy harvesting due to their low cost and spectral versatility. In this work, a new series of organic donor–π–acceptor (D–π–A) sensitizers ( BAM-1–BAM-4 ) featuring a naphthalene donor and a phenyl-pyrazole π-bridge were designed to explore how structural variations in electron-withdrawing acceptors influence light absorption, charge transfer, and device efficiency. The dyes were systematically investigated using UV–Vis spectroscopy, electrochemical analysis, and density functional theory calculations, and were applied individually and jointly with the benchmark Black dye in single and parallel tandem DSSCs (PT-DSSCs). BAM-3 and BAM-4 exhibited strong intramolecular charge transfer, red-shifted absorption, and efficient electron injection, achieving power conversion efficiencies of 9.85% and 8.89%, respectively, when co-sensitized with Black dye. The optimized PT-DSSC employing BAM-3 + BAM-4 as the bottom photoanode and Black dye as the top achieved a remarkable 12.13% efficiency under AM 1.5G and 25.85% under 1000 lx indoor illumination, maintaining 95% stability after 300 h of continuous operation. These results demonstrate that molecular engineering of D–π–A dyes combined with tandem co-sensitization provides an effective pathway to achieve high-efficiency, stable DSSCs suitable for both solar and ambient-light applications.
In vitro and in silico evaluation of flavonoids from Erythrina crista-galli with cytotoxic potential against MCF-7 breast cancer cell
Abstract Naturally occurring flavonoids have garnered significant interest as potential anticancer agents owing to their structural diversity and ability to modulate multiple molecular targets. This study aimed to investigate the cytotoxic potential of flavonoids isolated from Erythrina crista-galli in MCF-7 breast cancer cells using in vitro and in silico analyses. Three flavonoids were isolated from the twigs of E. crista-galli using multiple column chromatography. Cytotoxic activity was evaluated against the MCF-7 breast cancer cell line using the MTT assay, whereas the in silico study was evaluated using molecular docking and molecular dynamics against the epidermal growth factor receptor (EGFR). Three flavonoids, naringenin (1), daidzein (2), and isoliquiritigenin (3), were isolated from the ethanol extract of E. crista-galli twigs. Their structures were confirmed using multiple spectroscopic methods. In vitro cytotoxicity testing against MCF-7 breast cancer cells showed that naringenin (1) and isoliquiritigenin (3) exhibited moderate activity, with IC 50 values of 105.60 and 84.21 µg/mL, respectively, whereas daidzein (2) displayed weak activity, with IC 50 of 239.77 µg/mL. Network pharmacology analysis identified multiple targets of isoliquiritigenin related to breast cancer, with epidermal growth factor receptor (EGFR) as one of the key hub. Molecular docking indicated a stronger binding affinity of isoliquiritigenin compared to ATP, while molecular dynamics simulations confirmed the stability of the EGFR–isoliquiritigenin complex with binding free energy estimated at − 30.002 ± 3.879 kcal/mol. Collectively, these results highlight isoliquiritigenin as a promising EGFR-targeted cytotoxic compound and E. crista-galli as a valuable source of bioactive flavonoids for anticancer drug development, while emphasizing the need for further in vivo validation and toxicity assessment.
Newborn screening for inherited metabolic disorders in central China: a retrospective study of 153,956 infants using non-derivatized tandem mass spectrometry
Properties of sustainable concrete containing demolished concrete and tile waste powders
Drivers of public participation in urban regeneration: an integrated choice and latent variable analysis
Distributed robust optimization strategy for multi-energy virtual power plant clusters
Advanced ANN-LMB modeling of hepatitis B transmission across sexual networks and its disability burden
Abstract Hepatitis B virus (HBV) remains a major global health concern, with sexual transmission being a key driver among adults. This study develops a gender-stratified compartmental model of HBV spread that integrates long-term disability through gender-specific parameters. A key contribution is the integration of mechanistic modeling with artificial neural networks (ANNs), enabling efficient emulation of the model’s nonlinear dynamics. Numerical solutions from the classical Runge-Kutta 4th-order (RK4) method were used as training data for an ANN optimized with the Levenberg–Marquardt algorithm (ANN–LMB). The trained ANN accurately reproduces compartmental dynamics with minimal error and provides a fast surrogate for sensitivity exploration. Analysis of the basic reproduction number ( $$R_0$$ ) reveals the strong influence of same-sex transmission rates, contact patterns, and disability onset parameters. These findings highlight the importance of behavioral interventions, vaccination coverage, and early detection of chronic carriers. Overall, the proposed ANN–LMB framework enhances computational efficiency and offers a biologically informed approach for exploring complex HBV transmission dynamics.
Reproductive performance of the first generation Stichopus horrens broodstock
Prospective observational study of cell-free DNA as a prognostic biomarker in COVID-19 and bacterial sepsis: COVSEP-study
Abstract Hyperinflammation and extensive cell damage characterize both COVID-19-sepsis and bacterial sepsis, contributing to poor clinical outcomes. Cell-free DNA (cfDNA), a damage-associated molecular pattern (DAMP), reflects ongoing tissue injury and may predict mortality. We aimed to evaluate cfDNA as a prognostic biomarker for 30-day mortality in ICU patients with COVID-19- vs. bacterial sepsis, and its association with inflammatory markers and disease progression. In a prospective observational study (ethics approval: 2020–15,535; DRKS-ID: DRKS00025222), cfDNA was quantified in 64 ICU patients (COVID-19-sepsis n = 27, bacterial sepsis n = 37) at four time points using quantitative PCR targeting 90 bp and 222 bp fragments of LINE-1 elements. An Integrity Index (222/90 bp) was calculated to infer the predominant mode of cell death. Nineteen healthy individuals served as controls. Associations with mortality and clinical parameters were analyzed using adjusted Cox regression, time-dependent models, and correlation analyses. Higher cfDNA levels (90 bp) within the first 24 h were strongly associated with 30-day ( p = 0.003) and 180-day mortality ( p = 0.003) in COVID-19-sepsis, but not in bacterial sepsis. COVID-19 patients showed significantly higher cfDNA levels ( p < 0.01), which correlated with CRP, PCT, LDH, and lactate. The Integrity Index increased over time in bacterial sepsis and remained stable in COVID-19-sepsis, but was not predictive of survival. Elevated cfDNA levels were associated with ECMO therapy but not with renal replacement therapy. cfDNA is a valuable early prognostic biomarker in COVID-19-sepsis. Its rapid dynamics and strong correlation with clinical outcomes highlight its potential for real-time monitoring and risk stratification in viral sepsis.
Telemetry reveals potential mating aggregation behavior of tiger sharks (Galeocerdo cuvier) in Hawaiʻi
Temporal endocrine and hematological consequences of biological extremes in porcine birth weight
Secure localization of land vehicles under GPS spoofing attack with decomposition Kalman filter
Dietary habits, nutritional supplement use, and adherence to national dietary guidelines in patients with psoriasis
Nature’s 10: Ten people who shaped science in 2025
Notch1 regulates Orai1 and Orai3 expression in breast cancer cells
This scientist is breeding billions of mosquitoes to fight disease in Brazil
Staged identification of CAP in fever patients across epidemic environments: modeling & validation
Abstract Diagnosing community-acquired pneumonia (CAP) relies on costly imaging, posing challenges in resource-limited settings. Traditional tools focus on diagnostic tests for clinicians rather than patient use. Additionally, classification of subtypes in traditional Chinese medicine (TCM) lacks criteria. We developed a multimodal fusion model using machine learning algorithms and clinical variables from basic information, medical records, and lab tests to assess CAP risk in fever patients. The model integrates top-performing models via ensemble learning to predict pneumonia probability. We trained on 2,193 visits at Beijing Traditional Chinese Medicine Hospital’s fever clinic from Dec 2021 to Dec 2022, and validated on 300 visits from Jan to July 2024. Use unsupervised learning to classify subtypes. The training cohort included 1,781 CAP and similar patients, with 210 in the external validation cohort. CAPs were diagnosed via chest CT. The α model, based on pre-visit medical records, performed well (AUC internal =0.80, 95%CI 0.77–0.83; AUC external =0.80, 95%CI 0.71–0.87). The β model added four lab indicators, optimizing performance (AUC internal =0.93, 95%CI 0.92–0.95; AUC external =0.81, 95%CI 0.70–0.90). Two models were developed into online calculators. Latent class analysis distinguished Cold/Heat syndrome as subtypes. Despite the single-center, retrospective design, two final models performed good for identifying CAP across epidemic environments.