Search Results (49)

The present work set out to examine the antidiabetic capacity of Abelmoschus esculentus (okra) fruit extract through a combined experimental and computational framework. Enzyme inhibition assays were carried out against four metabolic targets, and IC₅₀ values stood at 7.66 ± 0.31 mg/mL for alpha-glucosidase, 5.21 ± 0.18 mg/mL for alpha-amylase, 2.11 ± 0.15 microg/mL for DPP-4, and 9.17 ± 0.54 mg/mL for pancreatic lipase. The extract showed moderate-to-weak activity relative to standard inhibitors acarbose, sitagliptin, and orlistat. Sixteen drug-like phytochemicals obtained from the IMPPAT 2.0 database were docked against the crystal structures of all four tested enzymes (PDB: 8CB1, 5E0F, 2ONC, 1LPB). Alpha-Carotene, Vitamin E, and Spiraeoside emerged as the top-ranked compounds across all targets, with alpha-Carotene recording the strongest binding affinity of −11.1 kcal/mol against pancreatic lipase, which was 4.2 kcal/mol more negative than the positive control orlistat (−6.9 kcal/mol). PLIP-based interaction profiling mapped out hydrogen bonds, hydrophobic contacts, pi-stacking, and salt bridges at the atomic level. Absorption, distribution, metabolism, and excretion (ADME) and toxicity screening of alpha-Carotene returned a favourable pharmacokinetic profile with predicted LD₅₀ of 1510 mg/kg (Class 4) and inactivity across most toxicity endpoints. A 100 ns molecular dynamics simulation of the pancreatic lipase-alpha–Carotene complex, alongside the orlistat control, showed stable root mean square deviation (RMSD) (0.15–0.22 nm), a consistent Rg (~1.97 nm), and sustained hydrogen bonding throughout the trajectory. Free-energy landscape analysis revealed a well-defined single energy basin for alpha-Carotene, suggesting a thermodynamically stable binding conformation. These findings lay the molecular basis for using okra phytochemicals as adjunctive agents in diabetes management, though in vivo validation remains necessary. Full article

Against the backdrop of advancing the “dual carbon” goals (carbon peaking and carbon neutrality), the “fishery–photovoltaic complementary” model—integrating solar power generation with salt pan production—has been widely adopted in Tianjin. However, large-scale photovoltaic (PV) facility construction exerts complex impacts onsalt panns, a wetland ecosystem of unique ecological value, by blocking sunlight, altering local microclimates, and regulating water evaporation. Currently, systematic field studies on the comprehensive effects of PV facilities onsalt pans ecosystems remain scarce, particularly those focusing on impacts on primary producers and key environmental factors. Pond sediments harbor the densest and most diverse aquatic microbial communities. In this study, sediment samples were collected from four typical ponds in Tianjin’salt panan region in April, July, and September 2024. Post sample processing, multiple statistical analyses were conducted, including alpha diversity indexing, species abundance clustering, and beta diversity analysis (non-metric multidimensional scaling, NMDS). The results showed the following: (1) Microbial communities existed in both PV-equipped and non-PV areas, indicating no significant correlation between PV presence and alpha diversity indices. (2) Species and genus compositions aggregated in PV-equipped areas with generally consistent community structures, whereas they displayed high dispersion in non-PV areas. This regulatory effect of PV facilities was relatively stable, with deviations only at a few sampling sites, confirming that PV presence significantly affects community composition patterns at both species and genus levels. (3) Cluster heatmap analysis revealed distinct seasonal variations in clustering relationships between sampling stations and microbial genera. Among dominant genera, only Desulfotignum was unaffected by PV facilities or seasonal changes, while the distribution of other dominant genera was significantly influenced by PV construction. Full article

To address the problem that the detection performance of existing spectrum sensing algorithms degrades or even fails under impulsive noise, this paper proposes a generalized energy detection-based spectrum sensing algorithm. Theoretical analysis verifies that the proposed algorithm can effectively mitigate the adverse effects of impulsive noise, realize high-precision signal detection, and enhance system reliability with fewer samples. Furthermore, through statistical theoretical analysis, the probability density function of the detection statistic is provided for both scenarios where the primary user signal is absent and present. The probabilities of false alarm and missed detection are also derived, and the threshold corresponding to a prescribed false alarm probability is determined. Finally, simulation results demonstrate the effectiveness of the generalized energy detection algorithm. Full article

Background: Primary Biliary Cholangitis (PBC) requires early diagnosis and specialized management to prevent progression to cirrhosis. This study evaluates the awareness levels of Turkish physicians from various specialties regarding the clinical features, diagnostic criteria, and current treatment protocols of PBC. Methods: A multi-regional cross-sectional survey was conducted with 269 physicians across Türkiye. Knowledge levels were assessed through a 28-item instrument covering epidemiology, diagnosis and therapy. Data distribution was non-normal (Skewness: −1.296, Kurtosis: 2.857), necessitating the use of the Kruskal–Wallis H test and Dunn–Bonferroni post hoc procedure for inter-group comparisons. Internal consistency was confirmed with a Cronbach’s alpha of 0.80. Results: The overall mean awareness score was 62.6%. Item-level analysis revealed a near-universal understanding of the non-mandatory role of liver biopsy in diagnosis (99.1%) yet identified a critical knowledge gap regarding second-line therapies, particularly the use of steroids (6.8%). Significant disparities were observed among specialties (p < 0.001). Gastroenterologists (Median: 91.07%) and gastroenterology fellows (Median: 85.71%) exhibited significantly higher proficiency compared to general practitioners (64.29%) and family medicine residents (67.86%). Internal medicine specialists outperformed primary care providers, while no significant differences were found among other subgroups after Bonferroni adjustment. Conclusions: Professional specialization is the primary determinant of PBC awareness. While core diagnostic knowledge is stable, significant gaps exist in pharmacological management among non-specialists. Targeted medical education for primary care physicians is essential to ensure timely referral and optimize patient outcomes. Full article

Introduction: Acinetobacter baumannii is a multidrug-resistant pathogen from the ESKAPE group, associated with nosocomial infections. Its resistance to multiple antibiotics poses a global threat. The QACE protein, an efflux pump, has been identified as a key resistance mechanism, making it a promising target for the development of new antibacterial agents. Objective: Our aim is to identify low-molecular-weight compounds derived from natural products with potential inhibitory activity against the QACE protein, using virtual screening and molecular docking studies. Materials and Methods: The three-dimensional structure of QACE was retrieved from AlphaFold, followed by energy minimization and assignment of Kollman-type charges. Ligand screening was performed using the BioMX database through structural similarity analysis (Tanimoto coefficient ≥ 0.85), using ciprofloxacin as the reference compound. Selected molecules were evaluated using SwissADME to predict their pharmacokinetic properties, and three candidates with favorable profiles were chosen. Molecular docking studies were then performed using AutoDock 4 to estimate binding affinities. Results: Voacangine was the compound with the highest structural similarity, strongest binding affinity to QACE (ΔG = −6.2 kcal/mol; ki = 28.51 μM), and stable molecular interactions including hydrogen bonds and π-stacking. It showed favorable tissue distribution, low potential for CYP3A4 inhibition, and minimal predicted cardiotoxicity (hERG channel blockade). Conclusion: Voacangine emerges as a promising candidate for inhibiting the QACE efflux pump in Acinetobacter baumannii. This study highlights the value of computer-aided drug design as an effective strategy in the search for new treatments against multidrug-resistant bacteria. Full article

Background/Objectives: Glioblastoma multiforme (GBM) is an aggressive primary brain tumor with limited therapeutic options. Neoantigen-based immunotherapy offers a promising avenue, but its efficacy primarily depends on the ability of somatic mutations to generate immunogenic peptides effectively presented by HLA class I molecules and recognized by cytotoxic T cells, in concert with innate immune mechanisms such as NK-cell activation and DAMP/PAMP signaling. This study aimed to characterize the MHC-I binding diversity of peptides derived from GBM-associated somatic variants, with a particular focus on interactions involving HLA-A68:01 and HLA-B15:01 alleles. These alleles were selected based on their ethnic prevalence and potential structural compatibility with neoepitopes. Methods: Somatic missense variants from TCGA-GBM were filtered using high-confidence genomic databases, including dbSNP, COSMIC, and MANE. Neoepitope prediction was performed across multiple HLA class I alleles using binding affinity algorithms (MHCflurry2). Peptide–HLA interactions were characterized through motif analysis and anchor residue enrichment. Structural modeling of peptide–HLA complexes was conducted using ColabFold (AlphaFold2-multimer v3) to evaluate conformational stability. The population frequency of selected HLA alleles was examined through epidemiological comparisons. Results: Canonical GBM driver mutations (e.g., EGFR, TP53, PIK3R1) are recurrent and biologically relevant, although pharmacological inhibition of EGFR alone has not consistently improved patient outcomes, underscoring the complex signaling redundancy in glioblastoma. HLA-A68:01 exhibited high binding affinity and favorable motif compatibility, supporting its potential for effective neoantigen presentation. HLA-B15:01 was identified as a viable presenter for the EGFR p.Arg108Lys variant. Structural modeling confirmed stable peptide insertion into the MHC-I binding groove, with high-confidence folding and preserved interface integrity. Ethnic distribution analysis revealed varying GBM incidence across populations expressing these alleles. Conclusions: This integrative analysis identified structurally validated, immunogenically promising neoantigens derived from GBM mutations, particularly for HLA-A68:01 and HLA-B15:01. These findings support allele-informed neoepitope prioritization in personalized immunotherapy, especially for patient populations with corresponding HLA genotypes and MHC-I presentation capacity. Full article

Alpha-synuclein (αsyn) misfolding and aggregation underlie several neurodegenerative disorders, including Parkinson’s disease. Early oligomeric intermediates are particularly toxic yet remain challenging to detect and characterize within cellular systems. Here, we employed the luminescent conjugated oligothiophene h-FTAA to investigate early aggregation events of human wildtype (huWT) and A53T-mutated αsyn (huA53T) both in vitro and in HEK293 cells stably expressing native human-αsyn. Comparative fibrillation assays revealed that h-FTAA detected αsyn aggregation with higher sensitivity and earlier onset than Thioflavin T, with the A53T variant displaying accelerated fibrillation. HEK293 cells stably expressing huWT- or huA53T-αsyn were exposed to respective pre-formed fibrils (PFFs), assessed via immunocytochemistry, h-FTAA staining, spectral emission profiling, and fluorescence lifetime imaging microscopy (FLIM). Notably, huA53T PFFs promoted earlier aggregation patterns and yielded narrower fluorescence lifetime distributions compared with huWT PFFs. Spectral imaging showed h-FTAA emission maxima (~550–580 nm) red-shifted and broadened in cells along with variable lifetimes (0.68–0.87 ns), indicating heterogeneous aggregate conformations influenced by cellular milieu. These findings demonstrate that h-FTAA is useful for distinguishing early αsyn conformers in living systems and, together with stable αsyn-expressing HEK293 cells, offers a platform for probing early αsyn morphotypes. Taken together, this opens for further discovery of biomarkers and drugs that can interfere with αsyn aggregation. Full article

Endophytic fungi represent key microbial symbionts that colonize internal plant tissues without causing apparent disease, playing vital roles in host growth, stress resistance, and biosynthesis of bioactive compounds. Carpesium lipskyi C. Winkl., a medicinal plant endemic to the Gaoligong Mountains in Yunnan, remains largely unexplored regarding its endophytic fungal composition. In this study, a total of 737 amplicon sequence variants (ASVs) were identified through high-throughput sequencing, spanning 9 phyla, 36 classes, 67 orders, 137 families, 206 genera, and 277 species. The dominant phyla were Ascomycota, Basidiomycota, and Glomeromycota. Alpha diversity in stems and leaves followed a unimodal distribution along the elevational gradient, in contrast to root endophytic communities, which showed no significant correlation with altitude. Peak diversity occurred at 2734 m, indicating a non-linear altitude-diversity relationship. Altitude, along with stable precipitation and temperature (2600–3210 m), significantly influenced fungal diversity. Medicinal fungi such as Cladosporium sp., Meyerozyma guilliermondii, Phialocephala fortinii, and Rhodotorula mucilaginosa were found in either roots or stems. This is the first comprehensive assessment of endophytic fungi in C. lipskyi from this region, providing a foundation for future ecological and pharmacological studies. Full article

This study explores advanced methods for analyzing the two-parameter alpha-power exponential (APE) distribution using data from a novel generalized progressive hybrid censoring scheme. The APE model is inherently asymmetric, exhibiting positive skewness across all valid parameter values due to its right-skewed exponential base, with the alpha-power transformation amplifying or dampening this skewness depending on the power parameter. The proposed censoring design offers new insights into modeling lifetime data that exhibit non-monotonic hazard behaviors. It enhances testing efficiency by simultaneously imposing fixed-time constraints and ensuring a minimum number of failures, thereby improving inference quality over traditional censoring methods. We derive maximum likelihood and Bayesian estimates for the APE distribution parameters and key reliability measures, such as the reliability and hazard rate functions. Bayesian analysis is performed using independent gamma priors under a symmetric squared error loss, implemented via the Metropolis–Hastings algorithm. Interval estimation is addressed using two normality-based asymptotic confidence intervals and two credible intervals obtained through a simulated Markov Chain Monte Carlo procedure. Monte Carlo simulations across various censoring scenarios demonstrate the stable and superior precision of the proposed methods. Optimal censoring patterns are identified based on the observed Fisher information and its inverse. Two real-world case studies—breast cancer remission times and global oil reserve data—illustrate the practical utility of the APE model within the proposed censoring framework. These applications underscore the model’s capability to effectively analyze diverse reliability phenomena, bridging theoretical innovation with empirical relevance in lifetime data analysis. Full article

The ongoing rise in global electricity demand highlights the need for advanced, efficient, and environmentally responsible energy conversion technologies. This research presents a comprehensive design, modeling, and experimental validation of a tubular permanent magnet linear alternator (PMLA) integrated with a free piston engine system. Linear alternators offer a direct conversion of linear motion to electricity, eliminating the complexity and losses associated with rotary generators and enabling higher efficiency and simplified system architecture. The study combines analytical modeling, finite element simulations, and a sensitivity-based design optimization to guide alternator and engine integration. Two prototype systems, designated as alpha and beta, were developed, modeled, and tested. The beta prototype achieved a maximum electrical output of 550 W at 57% efficiency using natural gas fuel, demonstrating reliable performance at elevated reciprocating frequencies. The design and optimization of specialized flexure springs were essential in achieving stable, high-frequency operation and improved power density. These results validate the effectiveness of the proposed design approach and highlight the scalability and adaptability of PMLA technology for sustainable power generation. Ultimately, this study demonstrates the potential of free piston linear generator systems as efficient, robust, and environmentally friendly alternatives to traditional rotary generators, with applications spanning hybrid electric vehicles, distributed energy systems, and combined heat and power. Full article

Federated learning (FL) has emerged as a prominent distributed machine learning paradigm that facilitates collaborative model training across multiple clients while ensuring data privacy. Despite its growing adoption in practical applications, performance degradation caused by data heterogeneity—commonly referred to as the non-independent and identically distributed (non-IID) nature of client data—remains a fundamental challenge. To mitigate this issue, a heterogeneity-aware and robust FL framework is proposed to enhance model generalization and stability under non-IID conditions. The proposed approach introduces two key innovations. First, a heterogeneity quantification mechanism is designed based on statistical feature distributions, enabling the effective measurement of inter-client data discrepancies. This metric is further employed to guide the model aggregation process through a heterogeneity-aware weighted strategy. Second, a multi-loss optimization scheme is formulated, integrating classification loss, heterogeneity loss, feature center alignment, and L2 regularization for improved robustness against distributional shifts during local training. Comprehensive experiments are conducted on four benchmark datasets, including CIFAR-10, SVHN, MNIST, and NotMNIST under Dirichlet-based heterogeneity settings (alpha = 0.1 and alpha = 0.5). The results demonstrate that the proposed method consistently outperforms baseline approaches such as FedAvg, FedProx, FedSAM, and FedMOON. Notably, an accuracy improvement of approximately 4.19% over FedSAM is observed on CIFAR-10 (alpha = 0.5), and a 1.82% gain over FedMOON on SVHN (alpha = 0.1), along with stable enhancements on MNIST and NotMNIST. Furthermore, ablation studies confirm the contribution and necessity of each component in addressing data heterogeneity. Full article

Deciphering the spatiotemporal distribution of bacteria during breast cancer progression may provide critical insights for developing bacterial-based therapeutic strategies. Using a murine breast cancer model, we longitudinally profiled the microbiota in breast tumor tissue, mammary gland, spleen, and cecal contents at 3-, 5-, and 7- weeks post-tumor implantation through 16S rRNA gene sequencing. Breast tumor progression was associated with lung metastasis and splenomegaly, accompanied by distinct tissue-specific microbial dynamics. While alpha diversity remained stable in tumors, mammary tissue, and cecal contents, it significantly increased in the spleen (p < 0.05). Longitudinal analysis revealed a progressive rise in Firmicutes and a decline in Proteobacteria abundance within tumors, mammary tissue, and cecum, whereas the spleen microbiota displayed unique phylum-level compositional shifts. Tissue- and time-dependent microbial signatures were identified at phylum, genus, and species levels during breast tumor progression. Strikingly, the spleen microbiota integrated nearly all genera enriched in other sites, suggesting its potential role as a microbial reservoir. Gut-associated genera (Lactobacillus, Desulfovibrio, Helicobacter) colonized both cecal contents and the spleen, with Lactobacillus consistently detected across all tissues, suggesting microbial translocation. The spleen exhibited uniquely elevated diversity and compositional shifts, potentially driving splenomegaly. These results delineated the trajectory of microbiota translocation and colonization, and demonstrated tissue-specific microbial redistribution during breast tumorigenesis, offering valuable implications for advancing microbiome-targeted cancer therapies. Full article

Doppler shift estimation continues to be a critical challenge of utmost significance in both theoretical research and practical engineering applications. Many innovators have crafted solutions specific to this issue, with notable contributions across various signals and scenarios. Given that cyclostationary signals are prevalent in both artificial and natural phenomena, we propose a novel framework based on the phased fractional lower-order moment (PFLOM) for estimating Doppler shift in mixed cyclostationary signals. During the estimation process, a more realistic impulse noise model is examined in contrast to the ideal Gaussian noise typically assumed in conventional methods. This approach is meticulously derived through a series of detailed steps in line with cyclostationary signal processing and PFLOM principles. Furthermore, an extensive simulation has been conducted to validate the efficacy and robustness of our proposed method. It is anticipated that the concept and method presented here could be applied more broadly due to its solid theoretical underpinnings. Full article

Optically pumped magnetometer magnetoencephalography (OPM-MEG) represents a novel method for recording neural signals in the brain, offering the potential to measure critical neuroimaging characteristics such as effective brain networks. Effective brain networks describe the causal relationships and information flow between brain regions. In constructing effective brain networks using Granger causality, the noise in the multivariate autoregressive model (MVAR) is typically assumed to follow a Gaussian distribution. However, in experimental measurements, the statistical characteristics of noise are difficult to ascertain. In this paper, a Granger causality method based on a cost-reference particle filter (CRPF) is proposed for constructing effective brain networks under unknown noise conditions. Simulation results show that the average estimation errors of the MVAR model coefficients using the CRPF method are reduced by 53.4% and 82.4% compared to the Kalman filter (KF) and maximum correntropy filter (MCF) under Gaussian noise, respectively. The CRPF method reduces the average estimation errors by 88.1% and 85.8% compared to the MCF under alpha-stable distribution noise and the KF method under pink noise conditions, respectively. In an experiment, the CRPF method recoversthe latent characteristics of effective connectivity of benchmark somatosensory stimulation data in rats, human finger movement, and auditory oddball paradigms measured using OPM-MEG, which is in excellent agreement with known physiology. The simulation and experimental results demonstrate the effectiveness of the proposed algorithm and OPM-MEG for measuring effective brain networks. Full article

Background/Objectives: Faecal microbiota transplantation (FMT) is considered a safe and effective therapy for recurrent Clostridioides difficile infection. It is the only current clinical indication for this technique, although numerous clinical research studies and trials propose its potential usefulness for treating other pathologies. Donor selection is a very rigorous process, based on a personal lifestyle interview and the absence of known pathogens in faeces and serum, leading to only a few volunteers finally achieving the corresponding certification. However, despite the high amount of data generated from the ongoing research studies relating microbiota and health, there is not yet a consensus defining what is a “healthy” microbiota. To date, knowledge of the composition of the microbiota is not a requirement to be a faecal donor. The aim of this work was to evaluate whether the analysis of the composition of the microbiota by massive sequencing of 16S rDNA could be useful in the selection of the faecal donors. Methods: Samples from 10 certified donors from Mikrobiomik Healthcare Company were collected and sequenced using 16S rDNA in a MiSeq (Illumina) platform. Alpha (Chao1 and Shannon indices) and beta diversity (Bray–Curtis) were performed using the bioinformatic web server Microbiome Analyst. The differences in microbial composition at the genera and phyla levels among the donors were evaluated. Results: The microbial diversity metric by alpha diversity indexes showed that most donors exhibited a similar microbial diversity and richness, whereas beta diversity by 16S rDNA sequencing revealed significant inter-donor differences, with a more stable microbial composition over time in some donors. The phyla Bacillota and Bacteroidota were predominant in all donors, while the density of other phyla, such as Actinomycota and Pseudomonota, varied among individuals. Each donor exhibited a characteristic genera distribution pattern; however, it was possible to define a microbiome core consisting of the genera Agathobacter, Eubacterium, Bacteroides, Clostridia UCG-014 and Akkermansia. Conclusions: The results suggest that donor certification does not need to rely exclusively on their microbiota composition, as it is unique to each donor. While one donor showed greater microbial diversity and richness, clear criteria for microbial normality and health have yet to be established. Therefore, donor certification should focus more on clinical and lifestyle aspects. Full article