Search Results (24,547)

Background: Triple−negative breast cancer (TNBC) remains among the most aggressive and therapeutically unresponsive subtypes due to the absence of ER, PR, and HER2 targets. Casein Kinase II (CK2), a pleiotropic serine/threonine kinase overexpressed in TNBC, represents a compelling target for rational drug design. Methods: Here, we present an AI−integrated benchmarking framework combining virtual drug discovery, molecular dynamics simulations, machine learning−driven QSAR modeling, and quantum−mechanical electronic structure analysis to identify potent CK2 inhibitors from natural product chemical space. Results: A validated XP docking protocol (ROC–AUC = 0.748) screened ~480,000 compounds, yielding seven hits, with superior binding to the reference inhibitor CX−4945. Among these, Anastatin B, 3,4,8,9,10−pentahydroxy−dibenzo−[b,d]pyran−6−one, Rhein, and aloe emodin acetate exhibited highly favorable docking scores (−11.6 to −13.1 kcal mol⁻¹) and stable 200 ns binding dynamics, reflected by RMSD ≤ 2 Å and compact Rg trajectories. MM−PBSA/MM−GBSA analyses confirmed robust thermodynamic stability, while DFT−derived HOMO–LUMO gaps (3.8–4.3 eV) suggested optimal electronic reactivity for kinase inhibition. Machine learning QSAR models demonstrated strong predictive performance, with the best stacking models achieving test R² ≈ 0.69 and consistent cross−validation performance (CV R² ≈ 0.66–0.69), supporting reliable prediction of pIC₅₀ values and prioritization of top−ranked scaffolds. Conclusions: Collectively, this integrative framework bridges AI−based learning and biophysical validation, establishing a reproducible paradigm for de novo CK2 inhibitor discovery in TNBC. Full article

Background/Objectives: A central question in molecular genetics concerns how transcriptional regulatory sequences and de novo genes originate and reach evolutionary fixation. In this study, we utilize the human bicistronic gene SMIM45 as a model to analyze the evolutionary trajectories of gene development. This locus comprises several functional units: three enhancers (one featuring an embedded silencer), an exonic silencer that partially overlaps an ORF, a highly conserved ancestral sequence encoding a 68 aa microprotein, and a human-specific de novo gene encoding a 107 aa protein expressed spatiotemporally in embryonic brain tissues. Methods: The alignment of gene sequences from different species was used to determine the evolutionary development of enhancers and silencers, and the development of the exonic silencer was determined through application of the cultivator model and assessment of nearest-neighbor bases. Results: We identify significant disparities in formation mechanisms; for example, the LOC127896430 NANOG hESC enhancer originated simply via two Alu insertions that constitute the enhancer. In contrast, the exonic silencer (a segment of the LOC130067579 ATAC-STARR-seq lymphoblastoid silent region 13815)—a distinct, novel type of silencer—originated from a combination of diverse mechanisms, including a “cultivator gene” process of base pair fixation, consistent with the cultivator model proposed by Li Zhao and coworkers. Conclusions: SMIM45 exemplifies novel development mechanisms occurring over hundreds of millions of years, culminating in the birth of a human-specific, de novo 107 aa cistron. The associated complex of enhancers and silencers suggests intricate regulation of the 107 aa protein in fetal brain tissues. Full article

Continuous introduction of advanced optimization algorithms promotes the development of electromagnetic (EM) technology in radar and communication systems. Wideband antenna design within a given space and wideband array pattern synthesis, especially in the scenario of strong mutual coupling, are two typical challenging electromagnetic problems. In this paper, a nature-inspired algorithm, i.e., the water cycle algorithm (WCA), is introduced to resolve the above two EM problems. Two typical wideband antennas, i.e., the dual-band E-shaped microstrip antenna and the typical magnetoelectric (ME) dipole antenna, are designed on the basis of the established WCA-based antenna design scheme. Compared with the well-known algorithms that have been introduced in antenna design, including the differential evolution (DE) algorithm and the gray wolf optimizer (GWO), better results can be achieved with WCA. In the sequel, a WCA-based low peak sidelobe level (PSLL) pattern synthesis is implemented based on a uniformly spaced 27-element folded fractal ME dipole array antenna with mutual coupling as high as −10 dB, the results of which further validate the superiority of WCA in array pattern synthesis and demonstrate the value of this application innovation. Full article

Background/Objectives: Chronic Obstructive Pulmonary Disease (COPD) is frequently associated with multiple comorbidities that influence prognosis. The comorbidome is a graphical representation of both the prevalence and strength of association of each comorbidity with COPD, allowing rapid identification of the most relevant risk factors. The aim of this study was to evaluate the association between comorbidities and mortality in patients with COPD using a comorbidome approach. Methods: We conducted a retrospective cohort study of 500 patients aged ≥40 years with COPD treated between 2005 and 2020 at Clínica Universidad de La Sabana (Chía, Colombia). Demographic variables, comorbidities, and mortality were recorded. The prevalence of each comorbidity was expressed as a percentage, and their association with mortality was assessed using odds ratios (OR) derived from univariate contingency tables with 95% confidence intervals (95% CI). The comorbidome was constructed by plotting the inverse odds ratio (1/OR) against the prevalence of each condition. Results: The mean age was 76.6 years (SD 11.3). Overall mortality was 28.4%. The most prevalent comorbidities were hypertension (45.2%) and smoking (38%). Comorbidities significantly associated with mortality in unadjusted analyses included congestive heart failure (OR: 4.28; 95% CI: 2.55–7.18), arrhythmias (OR: 2.86; 95% CI: 1.60–5.13), acute myocardial infarction (OR: 2.58; 95% CI: 1.52–4.38), moderate or severe renal disease (OR: 2.08; 95% CI: 1.07–4.04), peripheral vascular disease (OR: 1.94; 95% CI: 1.10–3.40), and hypertension (OR: 1.66; 95% CI: 1.12–2.46). Conclusions: The most prevalent comorbidities were hypertension and smoking. However, the conditions significantly associated with mortality in unadjusted analyses were congestive heart failure, arrhythmias, acute myocardial infarction, moderate or severe renal disease, peripheral vascular disease, and hypertension. Full article

Background/Objectives: Relapse is the greatest obstacle in treating acute myeloid leukemia (AML), occurring in 40–50% of younger patients and most elderly patients. Current immunophenotyping panels for diagnosis and detection of measurable residual disease (MRD) primarily focus on detecting blasts, thereby overlooking AML heterogeneity, which could provide valuable prognostic insights. In this context, we propose repositioning EuroFlow panels within a new set of analyses to assess leukemia stem-like phenotypes for AML prognostic monitoring. Methods: We performed a retrospective study with AML patients at the Hospital de Clínicas de Porto Alegre between 2015 and 2023. Results: Our findings highlight the relevance of leukemia stem cell-like phenotypes, particularly the CD123+, CD34+CD123+, and CD34+CD38− subpopulations, for complementary prognostication of adult and elderly patients. A higher percentage of CD123+ cells was a risk factor for both RFS (p = 0.04) and OS (p = 0.02), whereas an increase in CD34+CD123+ cells was a risk factor for RFS (p = 0.04) only. A higher percentage of CD34+CD38− cells was a risk factor for OS (p = 0.002). From diagnosis to the second monitoring, the CD34+ subpopulation was a more relevant prognostic factor in elderly individuals than in adults. CD34&CD38 most immature subpopulations may increase with poor-prognosis markers in both adults and the elderly. Conclusions: Repositioning immunophenotypic analyses may offer a cost-effective alternative for refined prognostication, particularly in healthcare centers that already have flow cytometry-based AML diagnostics. Full article

Hospital wastewater represents a critical hotspot for the dissemination of antibiotic resistance genes (ARGs), serving both as an environmental reservoir and a transmission pathway for multidrug-resistant bacteria into receiving ecosystems. The intense antibiotic selective pressure within healthcare facilities promotes the emergence, persistence and amplification of resistant strains, posing substantial risks to public health and environmental integrity. This study aimed to characterize Escherichia coli (E. coli) isolates recovered from hospital wastewater effluents in multiple cities across Gabon, with emphasis on bacterial loads, antimicrobial resistance patterns and associated genetic determinants. Wastewater samples were aseptically collected from sewer outlets of eleven healthcare facilities distributed across five provinces over a 12-week period, structured into two six-week sampling campaigns to capture temporal variability. A total of 158 bacterial isolates were obtained, among which 49 were confirmed as E. coli. Mean concentrations of presumptive E. coli ranged from 7.1 × 10³ to 1.49 × 10⁹ CFU/mL, indicating substantial microbial contamination of hospital effluents. Antimicrobial susceptibility testing using the Kirby–Bauer disk diffusion method against 19 antibiotics revealed that all isolates exhibited multidrug-resistant phenotypes. Resistance rates were particularly high to β-lactams and third-generation cephalosporins, reaching 90–100% in most facilities, reflecting strong selective pressure and widespread circulation of resistance mechanisms in urban aquatic environments. In contrast, carbapenems and amikacin remained comparatively effective, with resistance levels below 40%, suggesting partial preservation of last-resort therapeutic options. The values of the Multiple Antibiotic Resistance Index (MARI) ranged from 0.21 to 0.84, indicating selection pressure on different classes of antibiotics. Phylogenetic analysis showed a predominance of phylogroup A, traditionally considered commensal but increasingly associated with the spread of resistance. Groups B2, D/E and F proved to be the most resistant. These groups showed marked resistance to first-line antibiotics. The blaCTX-M-1 was the most prevalent resistance determinant (66.6%), occurring twice as frequently as blaSHV (33.3%), a finding that confirms the significant circulation of extended-spectrum β-lactamase-producing E. coli. Overall, these findings highlight hospital wastewater as a major reservoir and dissemination source of multidrug-resistant E. coli, underscoring the urgent need for improved wastewater treatment, strengthened antimicrobial stewardship and integrated One Health-based surveillance strategies. Full article

In this study, an electrochemical sensor based on magnesium zirconate (MgZrO₃) synthesized using a deep eutectic solvent (DES)-assisted approach was developed for the detection of dopamine. The structural and morphological properties of MgZrO₃ were characterized using X-ray diffraction, Fourier-transform infrared spectroscopy, field-emission scanning electron microscopy, energy-dispersive spectroscopy, and elemental mapping. The electrochemical performance of the MgZrO₃-modified glassy carbon electrode (GCE) was evaluated using cyclic voltammetry and differential pulse voltammetry. The MgZrO₃/GCE exhibited an enhanced redox response and a reduced oxidation potential for dopamine in phosphate-buffered solution (PBS, pH 7.0), indicating improved electrocatalytic activity compared to the bare electrode. This improvement is attributed to the material’s increased active surface area and facilitated charge transfer kinetics. Under optimized conditions, the sensor showed a linear response over a concentration range of 0.3–80 µM, with a detection limit of 127 nM and quantification limit of 423 nM. The MgZrO₃/GCE also demonstrated good selectivity in the presence of common interfering species and was successfully applied for dopamine detection in biological samples, with satisfactory recovery results. The findings presented here contribute to the growing body of knowledge in the field and open up new possibilities for the development of advanced electrochemical sensors for neurotransmitter detection in clinical and research settings related to Breast Cancer Treatment. Full article

Peptide therapeutics occupy a unique chemical space between small molecules and biologics, combining high target specificity with structural programmability and favorable safety profiles. Recent regulatory approvals and expanding clinical pipelines underscore the growing therapeutic and commercial relevance of peptide-based drugs. This review outlines chemical modification approaches and contemporary design strategies, and evaluates their impact on proteolytic stability, pharmacokinetics, membrane permeability, and target engagement. We then highlight recent advances in artificial intelligence (AI)-guided peptide drug design, including machine learning models, protein language models, and generative architectures that enable high-throughput activity prediction, property optimization, and de novo sequence generation. These approaches collectively accelerate the traditional discovery–design–validation cycle while reducing experimental attrition through data-driven, structure-informed modeling frameworks. Among these applications, AI also enables the rational design of cell-penetrating peptides (CPPs) to enhance intracellular delivery and biological activity. Building on these methodological advances, we further examine their application to peptide therapeutics, with particular emphasis on AI-based predictive models for CPPs as well as on therapeutic applications within the central nervous and pulmonary systems. We conclude by outlining future perspectives and emphasize that the systematic integration of AI-enabled sequence design with rational chemical engineering and advanced delivery technologies, supported by rigorous experimental validation, will be critical for developing robust and clinically durable peptide-based medicines. Full article

Background/Objectives: Laparoscopic sleeve gastrectomy (LSG) is the most frequently performed bariatric procedure worldwide; one-anastomosis gastric bypass (OAGB) ranks third. Gastroesophageal reflux disease (GERD) may develop de novo or worsen following either procedure. This systematic review and meta-analysis aimed to compare postoperative GERD outcomes—overall, de novo, and persistent—between LSG and OAGB. Methods: A PRISMA-compliant systematic review and meta-analysis were conducted. PubMed, Embase, the Cochrane Library, and Web of Science were searched for studies published between January 2000 and December 2025. A random-effects model was applied for the meta-analysis. Results: A total of 847 articles were identified. Among them, 15 primary studies met the inclusion criteria (four randomized controlled trials and 11 observational studies involving approximately 1800 LSG and 2450 OAGB patients). LSG was associated with a significantly higher risk of overall GERD (OR, 3.67; 95% CI, 2.54–5.30; p < 0.001; I² = 55%), de novo GERD (OR, 4.12; 95% CI, 2.54–6.69; p < 0.001; I² = 44%), and persistent or worsening GERD (OR, 2.67; 95% CI, 1.34–5.32; p = 0.005; I² = 38%). Hiatal hernia was reported in only four studies; bile reflux was paradoxically higher after LSG than OAGB (74.7% vs. 12.5%). Conclusions: LSG carries significantly higher postoperative GERD risk than OAGB across all evaluated outcomes. Although Roux-en-Y gastric bypass remains the gold standard for bariatric patients requiring GERD control, OAGB represents a well-supported alternative that outperforms LSG in reflux-related outcomes and should be favored when GERD is a clinical concern. Full article

Background: Reliable biomarkers that capture tumor–host interactions and predict treatment resistance in extensive-stage small cell lung cancer (SCLC) remain limited. We evaluated the prognostic and predictive value of the pretreatment lactate dehydrogenase-to-albumin ratio (LAR), an integrative biomarker reflecting metabolic activity, systemic inflammation, and host nutritional status. Methods: This multicenter, retrospective cohort study included patients with extensive-stage SCLC treated at five tertiary centers between 2016 and 2024. Pretreatment LAR was calculated using baseline serum lactate dehydrogenase and albumin levels and dichotomized using a Youden index-derived cut-off at the 12-month overall survival (OS) horizon. Time-dependent receiver operating characteristic (ROC) analyses using inverse probability weighting were performed to assess discriminative performance. Survival outcomes were evaluated using Kaplan–Meier estimates and Cox proportional hazards models. Associations with platinum resistance and lack of objective treatment benefit (defined as progressive disease as best response) were examined using logistic regression models. Results: A total of 223 patients were included. Elevated LAR was associated with inferior OS (median, 15.8 vs. 25.2 months; log-rank p < 0.001) and progression-free survival (7.9 vs. 11.5 months; p < 0.001). In multivariable analysis, LAR remained independently associated with OS (HR, 1.43; 95% CI, 1.04–1.95; p = 0.028). LAR demonstrated modest but consistently superior discriminative performance compared with other inflammatory indices for both 12-month OS (area under the curve [AUC], 0.692) and 6-month progression-free survival (PFS) (AUC, 0.646), with statistically significant differences in DeLong comparisons. Higher LAR was independently associated with increased odds of platinum resistance (adjusted odds ratio [aOR], 2.31; 95% CI, 1.41–3.81; p = 0.001) and lack of objective treatment benefit (adjusted OR, 2.04; 95% CI, 1.33–3.14; p = 0.001). Conclusions: Pretreatment LAR is a clinically accessible and biologically integrative biomarker associated with survival and treatment resistance in extensive-stage SCLC. By capturing tumor–host interactions, LAR may support risk stratification and identify patients at increased risk of early treatment failure. Prospective validation is warranted to define its role in biomarker-driven clinical decision-making. Full article

Spoilage date palm fruits are produced in large quantities and represent an underutilized agrowaste resource. Their high sugar content and balanced nutrient composition make them promising candidates for microbial bioprocessing. This study explored their potential as a low-cost substrate for Talaromyces atroroseus QA2602 (PZ091940) to simultaneously produce biodiesel grade lipids, natural pigments, and fungal chitosan within an integrated biorefinery approach. Spoiled date fruits were chemically characterized and applied at varying concentrations to cultivate T. atroroseus QA2602 (PZ091940). Thermal and thermo-chemical pretreatments were tested to enhance sugar availability. Lipid accumulation, fatty acid methyl esters (FAMEs) profiles, pigment production, and pigment stability were assessed. Biodiesel quality was estimated from FAME composition. De-oiled fungal biomass wastes were further processed to extract and characterize chitosan, and pigment–chitosan composites were evaluated for antioxidant activity. Optimal lipid and pigment production by T. atroroseus occurred at moderate concentration of spoiled date fruit substrate used in the culture medium, while dilute acid pretreatment of spoiled date fruits at high temperature resulted in the highest reducing sugar release from the substrate, which subsequently enhanced fungal biomass formation. The resulting C16–C18 rich oil displayed fuel properties consistent with high quality biodiesel. Pigments exhibited strong pH and thermal stability, along with potent antioxidant activity. De-oiled biomass produced chitosan with a high degree of deacetylation, and the pigment–chitosan composite showed enhanced antioxidant capacity. Rotted date fruits provide an effective, sustainable feedstock enabling the co-production of biodiesel, pigments, and chitosan by Talaromyces atroroseus QA2602 (PZ091940), supporting their integration into circular bioeconomy frameworks. Full article

Ambrosia beetles (Coleoptera: Curculionidae: Scolytinae) are diverse components of tropical forest ecosystems, but their community structure is often influenced by the sampling method used. We assessed species richness, alpha diversity (Hill numbers), and beta diversity of ambrosia beetles attracted to NTP-80 necrotraps in five forest fragments along an altitudinal gradient in the Sierra Norte de Puebla, Mexico. A total of 2074 individuals representing 18 species were recorded, effectively doubling the number of species previously reported for the Sierra Norte de Puebla. Among these, two species—Amphicranus torneutes and Cocotrypes carpophagus—represent new records for the state of Puebla. The assemblage was strongly dominated by the generalist Xyleborus affinis (86% of individuals). Richness estimators, including Chao 1 and ACE, indicated that sampling completeness was highest at the CF Relict site, reaching 88% of the estimated species. Conversely, the TER Nursery site showed the lowest completeness at 40%. These results, coupled with a low observed species richness compared to regional inventories, suggest that a significant portion of the local diversity may remain undetected. The altered evergreen rainforest sites tended to show higher evenness and dominance values than those in the relict cloud forest; however, these patterns should be interpreted cautiously, as the low species richness and strong dominance structure of the assemblage may influence these metrics. Beta diversity was moderate (βsor = 58%) and was primarily due to species turnover rather than nestedness, indicating species replacement in habitats. Our results suggest that necrotraps selectively sample ethanol-sensitive generalist ambrosia beetles, providing information on the dispersal- and disturbance-adapted component of Scolytinae assemblages while underestimating overall richness. Therefore, integrated sampling approaches are needed to accurately characterize ambrosia beetle diversity in tropical mountain systems. Full article

Despite strong policy support for prefabricated interior decoration systems (PIDSs) in China, residential consumer uptake remains limited. Existing research has focused primarily on adoption drivers or industry-side promotion; in contrast, in this study, Innovation Resistance Theory (IRT) is employed to investigate the functional and psychological barriers to consumer acceptance in the Chinese residential market. Utilizing data from 476 Chinese consumers, partial least squares structural equation modeling (PLS-SEM) is applied to test a hierarchical mediation framework. The results demonstrate that functional obstacles, specifically risk and usage barriers, do not exhibit a direct association with resistance intention; rather, a significant indirect effect via perceived value and image is observed. Notably, the tradition barrier emerged as the most dominant predictor of resistance, reflecting a deep-seated cultural path dependency on traditional masonry methods and a perceived loss of construction rituals that disrupts system adoption. Furthermore, multi-group analysis (MGA) reveals a paradox of experience: while uninitiated users are resistant based on abstract stereotypes, those with traditional renovation experience are driven by status quo bias, and early adopters of PIDSs are resistant due to negative disconfirmation regarding usage friction and functional inflexibility. These findings suggest that, to achieve system equilibrium, the industry must transition from an industry-centric narrative to one focused on premium quality and user-centric design. Practical implications include the need to de-stigmatize prefabrication as precision manufacturing and to align policy and market interventions more closely with the concerns of individual end-consumers in order to improve residential market acceptance. Full article

Background: The development of robust artificial intelligence (AI) models for diagnosing Temporomandibular Disorders (TMDs) is severely constrained by data scarcity and patient privacy regulations. Cone-beam computed tomography (CBCT), the gold standard for assessing osseous changes in the temporomandibular joint (TMJ), inherently contains sensitive biometric facial features, making de-identification difficult without losing critical anatomical information. This study aims to develop and evaluate TMJCTGenerator, a specialized latent diffusion model (LDM) framework designed to synthesize high-fidelity, diverse, and anonymous TMJ CBCT images. We hypothesize that this LDM approach can achieve superior anatomical fidelity and diversity compared to traditional generative adversarial network (GAN)- and variational autoencoder (VAE)-based methods, specifically in capturing fine osseous details within sagittal and coronal views of the mandibular condyle. Methods: A training dataset comprising 348 anonymized CBCT volumes was obtained in this retrospective comparative study to extract high-resolution sagittal and coronal regions of interest of the mandibular condyle. An independent test set of 39 anonymized CBCT volumes was further included. We developed a class-conditional LDM that integrates a pre-trained VAE for perceptual compression with a conditional U-Net for iterative denoising in the latent space. Performance was evaluated via qualitative anatomical fidelity assessment, Fréchet Inception Distance (FID), and a blinded Visual Turing test conducted by experienced clinicians to determine the distinguishability of synthetic images from real data. Results: Qualitative analysis revealed that TMJCTGenerator produced images with superior sharpness and anatomical consistency compared to baseline models, successfully reconstructing fine bone structures essential for diagnosing degenerative joint disease. TMJCTGenerator achieved lower FID scores than both VAE and GAN baselines. In the visual Turing test, clinicians were unable to reliably distinguish the generated images from real scans, and non-inferiority analysis confirmed that the synthetic data were statistically non-inferior to real data. Furthermore, TMJCTGenerator demonstrated the capability to generate diverse pathological conditions, ranging from normal anatomy to severe osteoarthritic changes. Conclusions: The proposed LDM framework effectively addresses the data scarcity and privacy bottlenecks in TMJ AI research by generating realistic, fully anonymous medical imaging data. TMJCTGenerator outperforms traditional generative methods in both visual fidelity and diversity, offering a viable solution for training downstream diagnostic algorithms. The source code and pre-trained models of TMJCTGenerator have been made open-source. Full article

Staphylococcus aureus remains a leading cause of morbidity and mortality worldwide, with persistent and relapsing infections posing a major global health threat. Here, we report that baloxavir, an FDA-approved influenza antiviral, exhibits antibacterial activity against S. aureus. Baloxavir demonstrated potent activity against both MSSA and MRSA clinical isolates with MICs of 2–4 μg/mL and exhibited concentration-dependent antibacterial activity in time-kill assays. Notably, baloxavir effectively eliminated intracellular S. aureus in both A549 alveolar epithelial cells and RAW264.7 macrophages at 10 μg/mL and achieved complete eradication in A549 cells at 50 μg/mL. In vivo, baloxavir (20–40 mg/kg) significantly improved survival in MRSA-infected mice from 12.5% to 75–87.5%. Transcriptomic analysis revealed significant downregulation of purine de novo biosynthesis genes, including purF and purK, which was validated by RT-qPCR (r = 0.862, p = 0.027). This study demonstrates for the first time that baloxavir possesses significant antibacterial activity against S. aureus including MRSA, positioning it as a promising repurposed candidate for treating persistent intracellular infections and post-viral superinfections. Full article