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Uncovering the antiproliferative potential of Lobophytum pauciflorum metabolites through chemoinformatics and in vitro approaches
Abstract Marine organisms such as soft corals represent a promising source of bioactive compounds for pharmaceutical development. However, studies on the antiproliferative activity of the Red Sea soft coral Lobophytum pauciflorum remain limited. This study aimed to investigate the chemical diversity of L. pauciflorum through a chemoinformatics approach combined with in vitro analyses. Chemical composition was characterized using ultra-high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS/MS) for metabolite annotation. Anticancer activity was evaluated against eight human cancer cell lines, with the extract showing significant cytotoxicity against MDA-MB-231 and MCF-7 cells, exhibiting IC₅₀ values of 9.46 and 11.59 µg/mL, respectively. These findings indicate that L. pauciflorum possesses anticancer potential, and isolated compounds from its methanolic extract warrant further evaluation.
Transient remodeling of gut metabolism supports juvenile growth and adult fitness in Drosophila
Abstract Animals develop through discrete life stages with specific goals, such as growth or reproduction. Achieving these goals may require temporary changes in the function of certain organs, but these adaptations remain poorly understood. In Drosophila , the juvenile phase is characterized by a several-hundred-fold increase in body mass, which culminates in a rapid growth spurt before puberty. Here, we show that this growth spurt is supported by acute remodeling of gut metabolism. Midway through the juvenile phase, steroid hormone and hepatocyte nuclear factor 4 induce digestive function, lipid metabolism and nutrient export in the intestine, thereby accelerating growth and maturation. Gut metabolic remodeling ceases at puberty but has a lasting impact on physiology, enhancing adult reproductive fitness and resilience to environmental stress. Our work identifies an endocrine-metabolic axis that synchronizes gut function with developmental demands, and provides insights into how systemic signals dynamically remodel organ metabolism to optimize life history strategies.
Research on instrument mix and regional suitability of digital publishing industrial policies: An empirical exploration based on qualitative comparative analysis of fuzzy set
Taking the digital publishing industry as its research object and employs the fuzzy-set qualitative comparative analysis (fsQCA) method to systematically examine the causal relationships among the Instrument Mix, regional conditions, and industrial performance, drawing on policy texts and development data from 16 major Chinese provinces. The findings indicate that neither a single Policy instrument nor an isolated regional condition is sufficient to sustain high-quality development; rather, industry success depends on specific configurations of multiple interacting conditions. Content application and scenario promotion, together with industry chain shaping and cluster development, form the core elements of multiple high-performance pathways. In contrast, fiscal and financial instruments play a relatively peripheral role, confirming both synergistic and substitutive relationships among Policy instruments. Further path analysis reveals three distinct realization logic in the digital publishing industry: (1) a regional environment–driven model relying on economic and cultural endowments; (2) a policy–economy dual-drive model characterized by joint government–market action; and (3) a systemic synergy model where multiple factors interact comprehensively. In contrast, non-high-performing configurations demonstrate causal asymmetry, indicating that industrial underperformance is not a simple inversion of success but rather the result of compensatory interactions among regional conditions.
The impact of an educational program on the electronic waste management knowledge and practices of dental interns: an interventional study
Abstract The increasing volume of electronic waste (e-waste) poses considerable risks to the environment and humans. This study evaluated an educational program impact on e-waste management among dental interns at Faculty of Dentistry, Alexandria University, Egypt. This interventional quasi-experimental study without control group used a validated self-administered questionnaire to assess participants’ awareness, knowledge, and reported practices before, immediately after, and three months following the educational intervention on 76 participants. Data was analyzed using the Friedman test with Dunn’s post-hoc comparisons and Spearman’s correlation. At baseline, awareness, knowledge, and practice mean score percentages (15.45%±5.26%, 16.07%±13.81%, 17.94%±14.84%, respectively) were low. Following the intervention, significant increases were observed across all domains at both the immediate and three-month intervals ( p < 0.001). While awareness and knowledge scores peaked immediately and showed slight decline at three months, self-reported practice mean score percentages demonstrated a progressive upward trend, reaching 72.13%±17.08% at the final follow-up. Significant positive correlations between knowledge and practice before and after the program were observed ( p < 0.05). The present program was associated with improvements in participants’ awareness, knowledge, and practices regarding e-waste management; however, these findings should be interpreted cautiously due to the pre–post design, reliance on self-reporting and absence of a control group.
Preserving spectral selectivity of radiative cooling textile under sweating conditions in hot urban environments
Enhancing Recommendation Systems through SVD-based collaborative filtering and community detection
The recommendation systems often face challenges like low data density, scalability issues and absence of interpretability, whereas classical Collaborative Filtering (CF) which is based on Singular Value Decomposition (SVD) shows support by being scalable, weakened frequently in circumstances of extreme sparsity. Conversely, Graph Neural Networks (GNNs) are very accurate yet do not tend to have explanatory power. In a novel way, this research presents a hybrid framework that is a sequential combination of Louvain community identification using SVD-based collaborative filtering to overcome the sparsity-interpretable trade-off. It is unlike the existing models that utilize communities only, to pre-partition the user space, modularity-based clustering is employed to regularize it, enabling SVD to act on more dense homogeneous sub-matrices. This methodological contribution is a very useful way to cut down on computational noise and overhead and to make the community-level justifications of recommendations. In the experimental analysis, the Netflix Prize data set produced a Root-Mean-Square Error (RMSE) of 0.9966, a Mean value of 0.9966 and an Absolute Error (MAE) of 0.7968. This hybrid model achieves competitive predictive performance with significantly higher interpretability and lower computational cost than complex deep learning baselines, despite a modestly higher RMSE due to the deliberate trade-off for transparency and efficiency on extremely sparse data. The framework enables scalable and transparent recommendation engines suitable for large-scale sparse datasets.
Improved random hierarchical capillary bundle model for simulating the gas permeability evolution of alkali-activated concretes under thermal damage
Engineering of genetically encoded programmable calcium channel inhibitory binders
Time to sputum culture conversion, treatment outcomes, and associated factors among rifampicin-resistant or multidrug-resistant tuberculosis patients in the Sidama region, Ethiopia: A retrospective follow-up study
Background Time to sputum culture conversion is critical for follow-up treatment effectiveness in multidrug-resistant tuberculosis patients. However, the evidence regarding time to culture conversion, treatment outcomes, and the associated factors was sparse and inconsistent. Objective This study aimed to determine the time to culture conversion, treatment outcomes, and associated factors among rifampicin-resistant and multidrug-resistant tuberculosis cases in the Sidama region from April 1 to December 31, 2024. Methods We conducted a retrospective follow-up study of 346 patients who enrolled between January 2013 and June 2024. We collected data from patients’ medical records using a standardized form, entered the data, and analyzed it using Stata 16.1 software. We performed the analysis using the Kaplan-Meier model for time to culture conversion, Weibull distribution gamma frailty, and logistic regression models to assess factors associated with time to culture conversion and treatment outcomes, respectively. We considered an adjusted hazard or odds ratio with a 95% CI and a p-value < 0.05 to determine significance. Results Among the participants, 302 (87.3%) achieved culture conversion in a median time of 76 days (95% CI: 71–79 days). Patients with a history of previous loss to follow-up experienced an approximately fivefold delay in culture conversion (AHR = 0.2; 95% CI: 0.1–0.6; p = 0.001), while relapse cases had a twofold delay (AHR = 0.5; 95% CI: 0.2–0.9; p = 0.02) compared with new patients. The treatment success rate was 234/346 (67.6%). Female patients had higher odds of achieving a favorable treatment outcome (AOR = 1.8; 95% CI: 1.0–3.3; p = 0.04), whereas patients who experienced culture reversion had significantly lower odds of a favorable outcome (AOR = 0.05; 95% CI: 0–0.4; p = 0.001). Conclusions The majority of patients experienced culture conversion within three months. However, patients with a history of loss to follow-up and relapse experienced delayed culture conversion. These findings highlight the urgent need to improve patient adherence.
Mechanistic insights and process optimization of pristine corn husk biosorbent for sustainable and cost effective removal of cationic dyes from waste water
Abstract Ensuring safe and accessible drinking water requires effective wastewater treatment. In this work, pristine corn husk biosorbent (CH) was employed as a low-cost material for the removal of Basic Fuchsin (BF) and Crystal Violet (CV) dyes. The CH biosorbent was comprehensively characterized through elemental analysis, scanning electron microscopy (SEM), N₂ adsorption–desorption isotherms, Brunauer–Emmett–Teller (BET) surface area measurements, Fourier transform infrared spectroscopy (FTIR), point of zero charge (pH PZC ), and thermogravimetric analysis (TGA). Batch adsorption studies were carried out to evaluate the influence of various operating parameters, such as CH biosorbent dosage, contact time, temperature, and initial dye concentration. The adsorption kinetics were found to fit the pseudo-second-order model, while the equilibrium isotherm data aligned best with the Langmuir model, showing high correlation coefficients (R² ≥ 0.999) and low error values compared to other tested models. The maximum adsorption capacities were determined to be 77.3 mg/g for BF and 88.8 mg/g for CV. Thermodynamic analysis indicated that the uptake of both dyes onto the CH biosorbent is spontaneous and endothermic. Response surface methodology (RSM) with a central composite design (CCD) was employed to fine-tune the reaction parameters and optimize the adsorption process. The adsorption mechanism is attributed to a combination of electrostatic attraction, π–π stacking, n–π interactions, hydrogen bonding, and pore diffusion. The CH biosorbent demonstrated good reusability, maintaining its performance through five regeneration cycles. Additionally, antibacterial activity tests of the CH biosorbent were conducted before and after dye uptake to assess potential toxicity. The CH biosorbent successfully removed more than 90% of CV and BF from real water samples, highlighting its promise as a sustainable and environmentally friendly biosorbent for water purification.
TOX3 in hypothalamic POMC-lineage cells regulates energy balance via the PTEN-AKT signaling axis
Novel benzofuran/pterostilbene hybrids trigger programmed cell death and impair migration in CRC cells
Colorectal cancer (CRC) remains one of the most prevalent and lethal malignancies worldwide, highlighting the urgent need for developing effective treatments. Molecular hybridization is a promising strategy for identifying new bioactive compounds. This study focused on designing and synthesizing a novel series of benzofuran-pterostilbene hybrid molecules. These compounds were successfully obtained, and their structures were elucidated by spectroscopic analysis. In addition, the activity of the hybrids was evaluated against colorectal adenocarcinoma cells. After the treatments, hybrids 6d and 6e exhibited the highest activity, with GI 50 values of 11.93 ± 2.38 µM and 4.74 ± 0.38 µM, respectively, suggesting antiproliferative effects and measurable cytotoxicity under the tested conditions. Additionally, Hoechst 33342 fluorescence imaging revealed chromatin condensation and nuclear fragmentation, along with a diffuse DiOC₆ fluorescence pattern relative to the control, suggesting a form of programmed cell death, which was further supported by flow cytometric analysis showing an increased proportion of hypodiploid cells following propidium iodide staining. In parallel, wound-healing assays demonstrated impaired migration and cytotoxic effects that affected cell viability and structural integrity. Molecular docking simulations showed that compounds 6d and 6e bind strongly to mutant p53, CDK4, and PARP-1 proteins, which, in turn, may explain at the molecular level the in vitro cytotoxic effect of these compounds in SW480 colon cancer cells. Lastly, pharmacokinetic and toxicological modelling suggests that hybrids 6d and 6e possess optimal biopharmaceutical profiles with no major safety concerns. All these findings highlight the potential of the benzofuran-pterostilbene scaffold, with compounds 6d and 6e emerging as strong candidates for further evaluation against colorectal cancer.
An accuracy-aware extension to lrp-based pruning for CNNs to prevent cascading accuracy degradation in data-scarce transfer learning
Abstract Convolutional Neural Networks (CNNs) pre-trained on large-scale datasets such as ImageNet are widely used as feature extractors to construct high-accuracy classification models from scarce data for specific tasks. In such scenarios, fine-tuning the pre-trained CNN is difficult due to data scarcity, necessitating the use of fixed weights. However, when the weights are kept fixed, many filters that do not contribute to the target task remain in the model, leading to unnecessary redundancy and reduced efficiency. Therefore, effective methods are needed to reduce model size by pruning filters that are unnecessary for inference. To address this, approaches utilizing Layer-wise Relevance Propagation (LRP) have been proposed. LRP quantifies the contribution of each filter to the inference result, enabling the pruning of filters with low relevance. However, existing LRP-based pruning methods have been observed to cause cascading accuracy degradation. In this study, we introduce an accuracy-aware pruning control mechanism for existing LRP-based filter pruning methods, which suppresses cascading accuracy degradation by dynamically adjusting the pruning rate and the pruning order using the harmonic mean of class accuracy, and compresses the pre-trained model while preserving task-specific performance in a small-data environment. We demonstrate that this control mechanism effectively mitigates cascading accuracy degradation and achieves higher classification accuracy compared to existing LRP-based pruning methods, improving the class-averaged area under the accuracy-pruning-rate curve (AUC) of VGG16 by approximately 15% over conventional LRP-based approaches.
Large-scale single-neuron recording in the human cortex using an ultra-flexible electrode array
µCT scanning effects on aDNA and a multi-step workflow for archaeological petrous portions
The petrous portion of the temporal bone (often informally referred to as the “petrous bone”) is a key element in human evolutionary studies due to its exceptional preservation of biomolecules and morphological information. Intensive and often redundant sampling raises concerns about sustainability and long-term conservation, however. Digital recording of morphology (micro-computed and medical tomography) can preserve what destructive sampling destroys, enabling future analyses, yet there have been concerns regarding its effect on biomolecular preservation. Here, we present a systematic observational assessment of whether micro-computed tomography (µCT)—a widely used tool for digital preservation—affects ancient DNA (aDNA) integrity in archaeological human petrous portions. We analyzed 93 archaeological samples from Argentina, of which 50 were µCT-scanned prior to molecular analysis and 43 were not. We compared six commonly used molecular parameters, including endogenous DNA content, read length, cytosine deamination patterns, and mitochondrial and nuclear contamination estimates. No statistically significant differences were observed between scanned and unscanned samples across these parameters (Mann-Whitney/Wilcoxon tests, p > 0.05). Although mitochondrial contamination estimates were marginally higher in scanned samples ( p = 0.051), they largely remain below the widely accepted 5% threshold for genomic analysis. Moreover, this pattern was not observed when considering nuclear contamination. Within the limits of this non-paired design, these results suggest that µCT imaging, under the scanning parameters applied here, does not introduce large or systematic bias derived from disruptions in standard aDNA preservation metrics. Building on these observations and on our collaborative experience with the shared use of archaeological samples across complementary research lines, and given that our results show that µCT imaging under appropriate scanning conditions does not significantly compromise DNA preservation, we propose a sustainable, multi-step workflow that integrates biological profiling, osteobiography, imaging, and compositional pre-screening prior to molecular sampling. This approach aims to maximize the scientific information obtained from skeletal collections while minimizing destructive practices, thereby promoting ethical and sustainable research on irreplaceable anthropological remains, and fostering interdisciplinary collaboration.
Radiographic assessment of post-endodontic filling features on PAN and CBCT: diagnostic agreement of an AI platform against CBCT consensus
Enzyme-responsive peptide dendron nanoassemblies for targeting and eliminating intracellular drug-resistant bacteria
Genome-wide bioinformatics analysis of the MATE gene family for abiotic stress tolerance in sunflower (Helianthus annuus L.)
Abiotic stressors, such as drought, salinity, and heavy metals, induce physiological changes, nutritional imbalances, molecular alterations, and oxidative stress in plants, which significantly reduce productivity. However, the secondary transporters, multidrug and toxic compound extrusion ( MATE ) proteins, transport substrates and metabolites. Accordingly, in response to abiotic stressors, these proteins strengthen plants’ immune systems, detoxify toxins, and enhance growth and development. Although the roles of MATE proteins responding to abiotic stresses have been investigated in several plants, their functions in sunflower have not yet been discovered. Therefore, this study identified 74 MATE proteins in sunflower ( HanMATE ) based on phylogenetic analysis, which were distributed into four subgroups. Their MATE -like properties were then validated using the domain, motif, gene structure, gene duplication, and physicochemical analysis. The HanMATE proteins in various cell organelles play a crucial role in abiotic stress tolerance, scavenging reactive oxygen species (ROS), and regulating transcription. Subsequently, Most HanMATE genes are enriched with biological processes and molecular functions that transport micro- and macro-molecules, drugs, negatively charged ions, organic anions, and citrate. The important Cis -regulatory elements (CREs), abscisic acid-, light-, and MeJA-responsive elements in HanMATE genes regulate plants’ growth and development in stress conditions. The synteny analysis indicated that 41 HanMATE proteins exhibit over 75% sequence similarity with 40 established stress-responsive (SR) MATE proteins from various plant species, suggesting their potential SR characteristics. Furthermore, this study identified 136 microRNAs linked to 58 HanMATE proteins, including 19 major hub microRNAs and 31 hub HanMATE proteins, which may enhance sunflower agronomic traits and abiotic stress resistance. The HanMATE proteins are conserved in other species that contribute to detoxification and have stable binding affinity with flavonoids and citric acid, validated from 3D structural modeling, molecular docking (MD), dynamic simulation, and functional prediction. These findings demonstrate that HanMATE genes are essential for sunflower abiotic stress tolerance (AST), and genetic engineering can be applied to develop more robust sunflower.