Search Results (7,330)

High levels of low-density lipoprotein cholesterol (LDL-c) have been recognized as the main causal factor of atherosclerotic cardiovascular disease (ASCVD) and are influenced by both genetic and environmental factors. Among genetic determinants, Familial Hypercholesterolemia (FH) is the most common monogenic disorder, caused by rare high-impact variants in genes involved in LDL uptake. Other monogenic causes of hypercholesterolemia include sitosterolemia, cerebrotendinous xanthomatosis and lysosomal acid lipase deficiency (LALD). However, monogenic disorders only account for a small proportion of inherited hypercholesterolemia. In many individuals, increased LDL-c levels are caused by the contemporary presence of different single-nucleotide polymorphisms (SNPs) with a moderate/low impact. These SNPs could be summarized through polygenic risk scores (PRS) that attribute relative weight to each of these. Another genetic determinant of hypercholesterolemic phenotypes is high levels of lipoprotein(a)—Lp(a). Lp(a) is an LDL particle modified by the binding of apolipoprotein(a)—apo(a)—which represents an independent risk factor for ASCVD. Lp(a) levels are mainly genetically determined by variation in the number of kringle IV type 2 (K-IV₂) repeats, as well as by several SNPs, and remain stable throughout life. The aim of this narrative review is to report an updated overview of the genetic mechanisms underlying hypercholesterolemia, including monogenic disorders, PRS and Lp(a), focusing on their potential repercussion in clinical practice by the integration into cardiovascular risk stratification beyond traditional clinical assessment. This integration could lead to a more comprehensive and individualized approach to cardiovascular prevention, with emerging perspectives including the possible use of artificial intelligence (AI). Full article

Background: Metastases to the uterine cervix from extragenital malignancies represent uncommon clinical events, with breast invasive lobular carcinoma (ILC) documented as the predominant primary source in reported literature. Objectives/Aim: To characterize the clinicopathologic features of ILCs metastatic to the uterine cervix. Methods: We performed a PubMed search using several keywords. Results: A total of 29 studies were included in the final analysis. The mean age at presentation of cervical metastasis was 56.8 ± 2.0 years. The mean interval between the initial diagnosis of ILC and the detection of cervical metastasis was 55.6 ± 8.2 months. Clinical presentations included vaginal bleeding, pelvic pain, and unhealthy enlarged, indurated uterine cervix on local examination. The diagnosis was established via tissue biopsy and immunohistochemical stains (positive reactivity for CK7, ER, PR, E-Cadherin, GATA3, GCDP-15 and mammaglobin). There are no consensus treatment protocols, and therapy should be tailored individually based on the extent of disease. Combined surgical and systemic therapy was the most commonly used modality. Conclusions: Metastasis of breast ILCs to the uterine cervix poses a significant diagnostic challenge. A high index of clinical suspicion and detailed clinical history are essential for accurate diagnosis. Full article

Pathogenesis-related (PR) proteins are crucial for plant defense against pathogen infection. However, the specific role of thaumatin-like proteins (TLPs), which comprise the PR-5 family, in plant immune responses has not been thoroughly investigated. Filipendula ulmaria is a medicinal plant with valuable pharmacological properties, including antimicrobial, anti-inflammatory, gastroprotective, immunomodulatory, and anticancer activities. The structure of the TLP family and its role in the immune system of meadowsweet have not been studied so far. The goal of this study was to analyze in detail the TLP gene family in meadowsweet and explore its response to fungal infection. In the meadowsweet genome, we identified 27 putative TLP genes, examined their structure and location on chromosomes, analyzed cis-regulatory elements in the promoter regions, predicted the structure and physicochemical characteristics of the encoded proteins, and performed a phylogenetic analysis. We also studied the differential expression of TLP genes under Bipolaris sorokiniana infection. Of six differentially expressed genes, three genes were up-regulated 48 h post-infection, suggesting their involvement in defense response to the fungus. The results obtained shed light on the role of the TLP gene family in the immune system of F. ulmaria and form the foundation for the creation of disease-resistant crops in agriculture and the development of bio-based antimicrobials in medicine. Full article

Pharmacogenomics AI offers significant potential for individualized drug therapy; however, its clinical benefits remain unevenly distributed. Models trained predominantly on European-ancestry data consistently underperform in non-European populations, with polygenic risk scores (PRS) showing an estimated 39–73% reduction in predictive accuracy in African-ancestry cohorts across complex traits. These disparities have driven increased interest in moving beyond single-layer genomic approaches. Multi-omics frameworks integrating genomic, transcriptomic, proteomic, and metabolomic data have emerged as a promising strategy to improve prediction across heterogeneous clinical populations, as each molecular layer provides distinct and complementary biological information. Among these layers, metabolomics may represent a particularly transferable component across populations. Metabolite profiles capture the downstream functional output of biological systems influenced by genetic, environmental, dietary, and microbiome-related factors, and may therefore be less reliant on ancestry-stratified allele frequency structures that underlie performance disparities in genomic models. This review synthesizes evidence regarding the mechanistic basis of genomic bias in pharmacogenomics AI, the emerging role of multi-omics integration, especially metabolomics, in improving predictive performance, and the current landscape of computational strategies for bias mitigation, including federated learning, transfer learning, domain adaptation, and synthetic data generation. Collectively, current evidence supports metabolomics-inclusive multi-omics frameworks as a biologically plausible, hypothesis-generating strategy to reduce reliance on ancestry-linked genomic features. However, direct evidence that such frameworks reduce ancestry-related bias in clinical AI outputs remains limited, underscoring the need for globally diverse datasets and prospective multi-population validation. Full article

We described tumor response and survival in dogs with modified Adams stage 4 intranasal tumors treated with once-weekly hypofractionated radiation therapy (RT) combined with the radiosensitizer α-sulfoquinovosyl-acylpropanediol (SQAP), and compared linear and volumetric response assessments. Twenty dogs treated with intensity-modulated RT (8 Gy per fraction, once weekly) and concurrent SQAP were included in this retrospective case series. Tumor response was assessed using RECIST-like linear measurements and volumetric analysis on contrast-enhanced computed tomography. Overall survival (OS) was estimated using Kaplan–Meier analysis. Of the 20 dogs, 4 were classified as stage 4a and 16 as stage 4b. The best RECIST-like responses were complete response (CR) in 5 dogs, partial response (PR) in 12, and stable disease (SD) in 4. Volumetric responses were CR in 5 dogs, PR in 11, and SD in 5. No cases demonstrated progressive disease as the best response. The median OS for all dogs was 342 days (95% confidence interval [CI], 206–419 days). Censoring one non-tumor-related death yielded a median OS of 356 days (95% CI, 231–419 days). Exploratory analysis revealed median OS of 393 and 297 days for stage 4a and 4b dogs, respectively. Volumetric assessment appeared more sensitive for detecting tumor regrowth in selected cases. Dermatologic adverse events were limited to alopecia within the radiation field, and no complete vision loss was observed. Seizure activity was documented in eight dogs. In conclusion, once-weekly hypofractionated intensity-modulated RT combined with concurrent SQAP was associated with clinically meaningful survival outcomes in dogs with advanced intranasal tumors. However, because no radiotherapy-alone control group was available, the independent contribution of SQAP to these outcomes could not be determined. Full article

Prostate cancer (PrC) is one of the most common malignancies among men worldwide. However, the contribution of genetic variation in microRNA (miRNA) biogenesis pathway genes to PrC susceptibility remains poorly characterized in many ethnically diverse populations. We conducted a case–control study involving 532 PrC patients and 550 controls from the Volga-Ural region of Eurasia to evaluate the association of twenty-one single nucleotide polymorphisms (SNPs) with PrC risk using single-variant and polygenic approaches. Association analyses identified rs595055 in the AGO1 gene as significantly associated with PrC risk after correction for multiple testing. To evaluate the cumulative effect of genetic variation, weighted and unweighted polygenic risk scores (PRSs) were constructed. The weighted PRS was significantly associated with PrC risk (odds ratio per standard deviation increase = 1.63, 95% CI [1.43–1.85], P = 1.37 × 10⁻¹³), and demonstrated moderate discriminatory performance (AUC = 63.1%), outperforming the unweighted model. Individuals in the highest PRS quartile had approximately threefold higher odds of PrC than those in the lowest quartile. Combining the weighted PRS with prostate-specific antigen improved discrimination (AUC = 68.1%). These findings support the contribution of miRNA biogenesis pathway genes to PrC susceptibility and highlight the potential value of pathway-based polygenic risk stratification in understudied populations. Full article

Human immunodeficiency virus type 1 exhibits extensive genetic diversity, which has important implications for molecular epidemiology, recombinant-pattern assessment, and antiretroviral resistance surveillance. In Mexico, HIV-1 molecular surveillance has historically relied mainly on partial pol gene sequencing, limiting the ability to compare lineage assignments across gag, pol, and env regions. We analyzed plasma samples from 40 treatment-naïve adults receiving care at a tertiary-care hospital in Mexico using a commercial amplicon-based multiregion HIV-1 genomic sequencing workflow. DeepChek^® was used as the primary workflow for read processing, mutation calling, region-level subtype assignment, and antiretroviral resistance interpretation. Resistance interpretation was restricted to antiretroviral target regions with sufficient coverage, mainly reverse transcriptase, protease, integrase, and capsid, when available. Drug resistance mutations were identified in 6/40 participants (15.0%) when mutation-level resistance findings in RT, PR, and IN were considered; one additional sample showed a capsid inhibitor-nonsusceptible NGS call. NNRTI-associated findings were identified in 2/40 patients (5.0%), whereas NRTI- and PI-associated findings were identified in 1/40 patients (2.5%). Accessory or secondary INSTI-associated substitutions were detected in 2/40 patients (5.0%). Region-level subtype analysis revealed frequent discordant assignments across amplified segments, which is consistent with complex mosaic profiles; however, these findings are interpreted as region-level subtypes and recombinant-pattern assignments rather than continuous whole-genome recombination maps. One sample had insufficient RT/PROT/INT coverage for drug resistance interpretation in the complete DeepChek report and was retained only for regions meeting quality thresholds. These findings support the value of multiregion HIV-1 sequencing for local molecular surveillance while emphasizing the need for transparent region-level coverage reporting, cautious interpretation of recombinant-pattern calls, and transparent repository reporting. Full article

With the widespread adoption of intelligent terminals, edge devices, and distributed information systems in the financial domain, financial security sensing data exhibit multisource heterogeneity, dynamic temporal patterns, and high privacy sensitivity. Traditional centralized anomaly detection methods are no longer able to simultaneously satisfy the requirements of cross-institutional or cross-node collaborative modeling, client data privacy protection, and robust monitoring of transaction and system anomalies. To address this challenge, a data-local federated deep anomaly detection framework has been proposed for distributed financial security sensing systems. Initially, a local deep financial security sensing representation module is constructed to perform temporal encoding and attention-based modeling on multisource financial signals, including terminal operation status, network transaction communication, backend server operation, identity authentication, and anomaly alerts, thereby extracting representations relevant to anomalous behaviors. Subsequently, a data-local federated optimization and personalized aggregation mechanism is developed to enable cross-node knowledge sharing without transmitting raw transaction or client data, while local personalized detection heads are employed to adapt to non-independent and identically distributed (non-IID) financial institution data. Furthermore, an adversarially robust security detection and trust-aware aggregation strategy is introduced to enhance model stability under input noise, feature masking, anomaly camouflage, and potential malicious client updates. Experimental results demonstrate that the proposed method achieves an Accuracy of $92.37\%$, a Precision of $89.41\%$, a Recall of $88.26\%$, an F1-score of $88.83\%$, an AUC of $93.06\%$, and a PR-AUC of $89.15\%$ in the primary financial anomaly detection task, significantly outperforming baseline methods such as Isolation Forest, Autoencoder, LSTM, Transformer, FedAvg, FedProx, SCAFFOLD, and MOON. In robustness experiments, the method attains F1-scores of $87.95\%$, $86.42\%$, $86.88\%$, $84.57\%$, $86.73\%$, and $83.91\%$ under Gaussian noise, feature masking, temporal shift, adversarial perturbation, and $20\%$ and $30\%$ malicious client scenarios, respectively. Ablation studies further confirm the effectiveness of local representation learning, personalized federated optimization, adversarial training, and trust-aware aggregation mechanisms. Overall, the proposed approach provides an efficient intelligent anomaly detection solution for financial AI security monitoring scenarios characterized by data localization requirements, node heterogeneity, and attack perturbations. Full article

Temperature-dependent luminescence of Pr³⁺-doped materials was investigated using both conventional luminescence intensity ratio (LIR) and principal component analysis (PCA)-based thermometry. Three host matrices with distinct structural properties, LiLaP₄O₁₂, YNbO₄, and Y₂O₃, were selected to evaluate the influence of crystal structure on thermometric performance. Temperature-resolved emission spectra recorded over the 103–523 K (−170 to 250 °C) range were analyzed using both approaches, with the first principal component (PC₁) serving as a thermometric parameter in the PCA. The results show that crystal symmetry and site multiplicity strongly influence the temperature-dependent spectral evolution and, consequently, the thermometric response. LiLaP₄O₁₂ exhibits stable and well-defined spectral evolution, resulting in balanced thermometric accuracy and resolution. YNbO₄ shows enhanced sensitivity to temperature variations due to increased spectral complexity and stronger crystal-field effects, leading to improved resolution but increased calibration uncertainty. In contrast, Y₂O₃ exhibits reduced thermometric performance due to overlapping emissions from multiple crystallographically inequivalent sites with distinct thermal responses. Compared to LIR, PCA provides improved thermometric figures of merit, particularly in systems with complex and strongly overlapping emission bands, demonstrating the potential of full-spectrum analysis in luminescence thermometry. Full article

The discharge of pesticide residues (PRs) from agricultural activities into water bodies has raised concerns about their toxicity to humans and the ecosystem. Traditional methods such as adsorption, membrane filtration, biological treatment, and conventional filtration usually result in incomplete removal of PRs. Currently, removal of PRs using advanced oxidation processes, particularly metal oxide-based photocatalysts, is considered a promising way. This review provides a comprehensive overview of recent advances in the photocatalytic degradation of PRs using TiO₂-based photocatalysts (T-BPs), the most widely investigated metal-oxide photocatalyst systems. First, we discuss the distribution, types, and negative impacts of major PRs on humans and the ecosystem. Next, we explore modification methods to enhance the properties of T-BPs, including light absorption behavior, charge separation rate, and photocatalytic degradation performance toward PRs. Afterward, this review carefully examines current challenges, such as complex water matrices, T-BP stability, energy supply for photocatalysis, and toxicity reduction. Finally, we highlight key future research directions, like the development of visible light-driven photocatalysts, enhanced mineralization efficiency, reduced secondary environmental risks, and the design of highly reliable catalyst and reactor systems for sustainable large-scale applications. Full article

Background/Objectives: Occupation shapes time use, physical demands, stress, and access to health resources, yet it remains an understudied axis of inequality among working women in low- and middle-income countries. This study assessed occupational-group disparities in lifestyle behaviors and adiposity levels among Peruvian working women. Methods: We conducted a pooled repeated cross-sectional analysis of ten Peruvian DHS/ENDES rounds from 2014–2019 and 2021–2024 among working women aged 18–49 years. The exposure was standardized occupational group, using professional/technical/managerial workers as the reference. Outcomes included five lifestyle behaviors and four adiposity indicators. Crude models estimated descriptive prevalence ratios (PRs) or beta coefficients; secondary adjusted models included age group, survey year, education, wealth, residence, natural region, and marital status. Results: A total of 40,726 women were included. Agricultural workers showed lower crude prevalences of almost-daily television viewing (PR 0.49; 95% CI 0.47–0.52), current smoking (PR 0.14; 95% CI 0.10–0.19), current alcohol use (PR 0.39; 95% CI 0.36–0.42), and heavy alcohol use (PR 0.17; 95% CI 0.12–0.27); these contrasts attenuated but generally persisted after adjustment. Insufficient fruit and vegetable intake exceeded 87% in all groups. Sales, domestic/household, services, and skilled manual workers had higher adjusted obesity than the reference group, with adjusted PRs ranging from 1.22 to 1.35. Conclusions: Occupation identifies relevant heterogeneity in lifestyle behaviors and adiposity levels among Peruvian working women. Lifestyle and adiposity profiles did not follow a simple social gradient, supporting occupation-specific strategies for noncommunicable disease prevention. Full article

Polishing enhances the appearance and market competitiveness of rice. To better understand the effect of polishing on rice flavor, volatile flavor compounds in polished rice (PR), unpolished rice (UR), cooked polished rice (CPR), and cooked unpolished rice (CUR) were examined using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS). The results revealed fourteen volatile flavor compounds displayed significant differences in abundance, with eight of these compounds potentially contributing to the overall flavor profile based on their volatility and reported odor characteristics. Among these compounds, only eicosane and hexanal were detected in uncooked rice, whereas acetophenone, hexadecanol, dodecane, and octadecane were unique to CUR. Four compounds were associated with aroma notes reminiscent of flowers, wax, and almond, among others. However, nonanal and nerol were common in both cooked rice samples, and they may contribute to a sweet-like aroma in cooked rice. These findings illuminate the changes in volatile composition, offer insights to prevent over-polishing, and inspire further research toward producing rice with potentially improved aroma profiles. Full article

Honokiol (HNK), a bioactive compound found in Magnolia species, is a promising, multifunctional agent with therapeutic effects on breast cancer (BrCa). Preclinical evidence, including in vitro and in vivo studies, suggests that HNK inhibits essential oncogenic pathways and reduces oxidative stress, inflammation, metabolic reprogramming, and cancer stemness. HNK demonstrates synergistic activity with chemotherapy, endocrine therapy, targeted therapy, and immune checkpoint inhibitors, increasing sensitivity to treatment across models of ER+, PR+, and HER2+ BrCas, as well as triple-negative breast cancers (TNBC). Nanotechnological delivery systems enhance the solubility, bioavailability, and intratumoral accumulation of HNK, increasing its translational capacity. Although clinical data remain very limited, current evidence in humans is insufficient to draw definitive conclusions regarding the safety and efficacy of HNK. This review summarizes mechanistic, preclinical, and emerging clinical data, highlights challenges in formulation and pharmacokinetics, and anticipates future trends in incorporating HNK into multimodal therapy for BrCa. Full article

Environmental contamination by rare earth elements (REEs) is increasing globally due to their extensive use in modern technologies, medicine, agriculture, and aquaculture. Their release into aquatic systems via wastewater discharge, industrial emissions, surface runoff, and atmospheric deposition has raised concerns regarding their environmental fate and potential ecotoxicological effects. Despite this, information on REE accumulation in marine predators remains limited. This study provides a multi-species assessment of REE bioaccumulation in elasmobranchs. Concentrations of 14 REEs (Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Tb, Tm, and Yb) were quantified in liver and muscle tissues of six elasmobranch species collected from demersal and deep-sea habitats along the Portuguese continental shelf. Generalized linear models (GLMs) were used to evaluate differences in REE concentrations among species and tissues, and to explore potential patterns associated with ecological traits. Results indicated that REE concentrations varied significantly across tissues and species, with muscle generally exhibiting higher accumulation than liver. Overall, this study provides the first comprehensive baseline of REE bioaccumulation in elasmobranchs from the Portuguese coast, contributing to a better understanding of emerging contaminants in marine food webs. These findings have important implications for environmental biomonitoring and highlight potential risks associated with seafood consumption. Full article

The development of rapid and reproducible image analysis approaches that support genus-level pre-classification of macrofungi is important for taxonomic pre-evaluation and controlled microscopic data analysis. In this study, an advanced deep learning-based approach, namely the Attention-Enhanced Hybrid CNN–ViT Framework, was rigorously evaluated for genus-level classification, using basidiospore micrographs of five carefully selected macrofungal genera. The proposed approach integrates the ability of convolutional neural networks to identify local texture and contour patterns with the global context-modelling capability of Vision Transformer structures. The objective is to enhance the extraction of distinctive representations from microscopic spore images through feature fusion and attention mechanisms. A series of experiments was conducted on a curated dataset consisting of light microscopy images of the genera Agaricus, Hebeloma, Inocybe, Amanita, and Russula. The models were compared using a range of evaluation metrics, including accuracy, F1-score, MCC, ROC-AUC, and PR-AUC. The results showed that the InceptionV3 + ViT-B16 + Fusion configuration was the most successful hybrid model, achieving an accuracy of 0.9213 ± 0.0182, an F1-score of 0.9212 ± 0.0179, a Matthews correlation coefficient (MCC) of 0.9040 ± 0.0222, a receiver operating characteristic (ROC)-area under the curve (AUC) of 0.9896 ± 0.0069, and a precision-recall (PR)-AUC of 0.9684 ± 0.0192, respectively. The present findings demonstrate that basidiospore images can carry distinctive visual information for genus-level automated classification under controlled conditions. However, it is important to note that these results should not be interpreted as claims of species-level identification or field generalisability. This is due to the use of a single microscope-camera system, a single preparation protocol, and the absence of an independent external test set. The present study demonstrates that deep learning-based microscopic image analysis can be evaluated as a preliminary classification tool in macrofungal taxonomy. It also shows that such tools can provide a foundation for future work supported by specimen-level validation, external test sets, and different imaging protocols. Full article