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This study investigates the relationship between cardiovascular health (CVH), as quantified by the American Heart Association's Life's Essential 8 (LE8) metric, and female infertility, utilizing data from the National Health and Nutrition Examination Survey (NHANES) spanning 2013-2018. We encompassed females aged 20-49 years and above from the NHANES in this cross-sectional analysis. We assessed CVH using the LE8 score, encompassing eight domains: dietary pattern, physical activity, nicotine exposure, sleep duration, body mass index (BMI), lipid profile, fasting blood glucose, and blood pressure levels. Logistic regression models were applied to explore the association between CVH scores and reported infertility, adjusting for potential confounders including age, race/ethnicity, and socioeconomic status. Findings revealed a notable inverse association between CVH scores (per 10 scores) and female infertility [OR = 0.93, 95%CI: 0.90-0.96], Participants with higher CVH levels were 41% less likely to had female infertility compared to those with lower levels [OR = 0.59, 95%CI: 0.41-0.84]. Higher overall CVH scores, particularly in physical activity, BMI, and blood glucose, were associated with lower odds of infertility. This trend remained consistent across various demographic subgroups. Our findings underscore the significance of maintaining optimal cardiovascular health, as evidenced by higher LE8 scores, in mitigating the risk of female infertility. These insights advocate for the integration of CVH improvement strategies within the broader framework of reproductive health care, emphasizing the dual benefits of cardiovascular and reproductive health optimization.

Gestational diabetes mellitus (GDM) is a common complication during pregnancy, with its incidence increasing year by year. It poses numerous adverse health effects on both mothers and newborns. Accurate prediction of GDM can significantly improve patient prognosis. In recent years, artificial intelligence (AI) algorithms have been increasingly used in the construction of GDM prediction models. However, there is still no consensus on the most effective algorithm or model. This study aimed to evaluate and compare the performance of existing GDM prediction models constructed using AI algorithms and propose strategies for enhancing model generalizability and predictive accuracy, thereby providing evidence-based insights for the development of more accurate and effective GDM prediction models. A comprehensive search was conducted across PubMed, Web of Science, Cochrane Library, EMBASE, Scopus, and OVID, covering publications from the inception of databases to June 1, 2025, to include studies that developed or validated GDM prediction models based on AI algorithms. Study selection, data extraction, and risk of bias assessment using the Prediction Model Risk of Bias Assessment Tool were performed independently by 2 reviewers. A bivariate mixed-effects model was used to summarize sensitivity and specificity and to generate a summary receiver operating characteristic (SROC) curve, calculating area under the curve (AUC). The Hartung-Knapp-Sidik-Jonkman method was further used to adjust for the pooled sensitivity and specificity. Between-study standard deviation (τ) and variance (τ²) were extracted from the bivariate model to quantify absolute heterogeneity. The Deek test was used to evaluate small-study effects among included studies. Additionally, subgroup analysis and meta-regression were conducted to compare the performance differences among algorithms and to explore sources of heterogeneity. Fourteen studies reported on the predictive value for AI algorithms for GDM. After adjustment with the Hartung-Knapp-Sidik-Jonkman method, the pooled sensitivity and specificity were 0.78 (95% CI 0.69-0.86; τ=0.15, τ2=0.02; PI 0.47-1.09) and 0.85 (95% CI 0.78-0.92; τ=0.11, τ2=0.01; PI 0.59-1.11), respectively. The SROC curve showed that the AUC for predicting GDM using AI algorithms was 0.94 (95% CI 0.92-0.96), indicating a strong predictive capability. Deek test (P=.03) and the funnel plot both showed clear asymmetry, suggesting the presence of small-study effects. Subgroup analysis showed that the random forest algorithm exhibited the highest sensitivity (0.83, 95% CI 0.74-0.93), while the extreme gradient boosting algorithm exhibited the highest specificity (0.82, 95% CI 0.77-0.87). Meta-regression further revealed an evaluation in predictive accuracy in prospective study designs (regression coefficient=2.289, P=.001). Unlike previous narrative reviews, this systematic review innovatively provided a comparative and quantitative synthesis of AI algorithms for GDM prediction. This established an evidence-based framework to guide model selection and identified a critical evidence gap. The key implication for real-world application was the demonstrated necessity of local validation before clinical adoption. Therefore, future work should focus on large-scale, prospective validation studies to develop clinically applicable tools.

Background Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication that can pose numerous adverse health effects on both mothers and newborns. Accurate prediction of the risk of GDM serves as a valuable supplement to prenatal education and clinical decision-making. Compared with traditional prediction models, artificial intelligence (AI) algorithms have demonstrated higher predictive accuracy and stronger individualization capabilities. However, the application of AI models in GDM prediction is still in a developmental stage, and their performance and clinical utility have not been thoroughly evaluated. Therefore, this study aims to systematically review and critically appraise the published predictive performance of AI models for GDM prediction and to offer insights for future research and practical application. Methods A systematic literature search will be performed across six databases (PubMed, Web of Science, Cochrane Library, Scopus, EMBASE, and OVID). Screening of titles and abstracts, full-text review, and data extraction will be independently completed by two authors. Qualitative data on the characteristics of the included studies, methodological quality, and the applicability of models will be summarized through narrative descriptions and tabulated formats. For models with predictive performance data from multiple studies, a random-effects meta-analysis or meta-regression will be employed to synthesize the findings, considering potential heterogeneity. Ethics and dissemination Ethical approval is deemed not applicable for this systematic review and meta-analysis. The findings will be based on published literature, disseminated through publication in a peer-reviewed journal, and presented at major conferences focused on clinical healthcare. Systematic review registration PROSPERO registration number CRD42025645913

This study was conducted to investigate the effect of inoculation with Exiguobacterium profundum FELA1 isolated from traditional shrimp paste and koji on the taste, flavor characteristics, and bacterial community of rapidly fermented shrimp paste. E-nose and e-tongue results showed higher levels of alcohols, aldehydes, and ketones, enhanced umami and richness, and reduced bitterness and astringency in samples of shrimp paste inoculated with fermentation (p < 0.05). Eighty-two volatile compounds were determined using headspace solid-phase microextraction and gas chromatography–mass spectrometry (HS-SPEM-GC-MS). The contents of 3-methyl-1-butanol, phenylethanol, isovaleraldehyde, and 2-nonanone in the inoculated samples were significantly increased (p < 0.05), resulting in pleasant odors such as almond, floral, and fruity. High-throughput sequencing results showed that the addition of koji and FELA1 changed the composition and abundance of bacteria and reduced the abundance of harmful bacteria. Spearman’s correlation coefficient indicated that the alcohols, aldehydes, and ketones of the inoculated fermented samples showed a strong correlation (|ρ| > 0.6) with Virgibacillus and Exiguobacterium, which contributed to the formation of good flavor in the fast fermented shrimp paste. This study may offer new insights into the production of rapidly fermented shrimp paste with better taste and flavor.

Purpose: This study aimed to screen differentially expressed proteins (DEPs) in plasma of patients with sepsis through data-independent acquisition (DIA) and enzyme-linked immunosorbent assays (ELISAs), and provide convenient and accurate serum markers for determining the condition of septic patients. Methods: A total of 53 septic patients and 16 normal controls who were admitted to the Affiliated Hospital of Southwest Medical University between January 2019 and December 2020 were enrolled in this study; 6 specimens from the normal group and 15 from the sepsis group were randomly selected for DIA-based quantitative proteomic analysis. The acquired data were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and a protein-protein interaction (PPI) network was constructed to screen potential markers. The selected proteins were further verified through ELISAs. The differences between control and sepsis groups and between survivors and non-survivors were analysed. Receiver operating characteristic (ROC) curves were drawn to explore their diagnostic value and prognostic efficacy. Results: A total of 149 DEPs were identified by bioinformatics methods. The analyses showed that these proteins are mainly involved in biological processes such as cell movement, stress response, cell proliferation, and immune response. Functional pathway analysis showed that they are mainly involved in leukocyte transendothelial migration, protein synthesis and processing, and various bacterial infections. LGALS3BP was selected as a potential plasma biomarker and further verified through an ELISA. Its level in septic patients was significantly higher than that in normal controls, and its level in non-survivors was also higher than that in survivors. The ROC curves suggested its great diagnostic efficacy and prognostic ability in sepsis. Conclusion: LGALS3BP levels were significantly different between the normal and sepsis groups; it has good diagnostic value in sepsis, and is related to patient prognosis; thus, it might be a biomarker for sepsis. Keywords: LGALS3BP, sepsis, plasma biomarker, DIA, ELISA

ABSTRACT The structure of the microbial community during sand crab juice fermentation was analyzed using culture-based methods and high-throughput 16S rRNA gene sequencing. Additionally, the changes in amino acid nitrogen (AAN) and total volatile basic nitrogen (TVB-N) were evaluated. Staphylococcus equorum, Staphylococcus arlettae, Staphylococcus saprophyticus, Salinicoccus amylolyticus and Bacillus cereus were isolated by traditional culture isolation technique. The Good's coverage obtained by high-throughput sequencing was over 99.5%, and the Chao1 and Simpson indices showed small fluctuations, indicating that the species abundance and diversity did not change significantly during the fermentation process, although the abundance decreased. Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria were the dominant bacterial phyla observed during fermentation, whereas Aquabacterium, Roseovarius, Muribaculaceae, and Silicimonas were the dominant bacterial genera. The AAN content increased from 0.15 to 0.43 g/100 mL during the 15-day fermentation, indicating the production of small peptides and amino acids during fermentation. The TVB-N content (25.2 mg/100 mL) on day 15 indicated slight spoilage of sand crab juice, although the freshness conformed to the production standard. These results provide a theoretical basis for improving the quality and optimizing the production process of sand crab juice. Microbiological changes in sand crab juice during fermentation.

Chronic hepatitis B is a significant public health problem and complex pathologic process, and unraveling the underlying mechanisms and pathophysiology is of great significance. Data independent acquisition mass spectrometry (DIA-MS) is a label-free quantitative proteomics method that has been successfully applied to the study of a wide range of diseases. The aim of this study was to apply DIA-MS for proteomic analysis of patients with chronic hepatitis B. We performed comprehensive proteomics analysis of protein expression in serum samples from HBV patients and healthy controls by using DIA-MS. Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein network analysis were performed on differentially expressed proteins and were further combined with literature analysis. We successfully identified a total of 3786 serum proteins with a high quantitative performance from serum samples in this study. We identified 310 differentially expressed proteins (DEPs) (fold change > 1.5 and P value < 0.05 as the criteria for a significant difference) between HBV and healthy samples. A total of 242 upregulated proteins and 68 downregulated proteins were among the DEPs. Some protein expression levels were significantly elevated or decreased in patients with chronic hepatitis B, indicating a relation to chronic liver disease, which should be further investigated.

Background Cassava is highly tolerant to stressful conditions, especially drought stress conditions; however, the mechanisms underlying this tolerance are poorly understood. The GRAS gene family is a large family of transcription factors that are involved in regulating the growth, development, and stress responses of plants. Currently, GRAS transcription factors have not been systematically studied in cassava, which is the sixth most important crop in the world. Results Seventy-seven MeGRAS genes were identified from the cassava genome database. Phylogenetic analysis revealed that the MeGRAS proteins could be divided into 14 subfamilies. The gene structure and motif compositions of the proteins were considerably conserved within the same subfamily. Duplication events, particularly segmental duplication, were identified as the main driving force for GRAS gene expansion in cassava. Global expression analysis revealed that MeGRAS genes exhibited similar or distinct expression profiles within different tissues among different varieties. Moreover, qRT-PCR analysis revealed the expression patterns of MeGRAS genes in response to abiotic stress (drought, salt, cold, and H 2 O 2 ), and the results suggest that these genes may have multiple functions. Conclusion This study is the first to provide comprehensive information on GRAS gene family members in cassava. The data will increase our understanding of both the molecular basis and the effects of GRAS genes. In addition, the results will contribute further to identifying the responses to various environmental conditions and provide insights into the potential functions of GRAS genes.

A DeepSA-UNet model for automatic recognition and segmentation of defects in laser cladding coatings was proposed in the work. This model integrated dual-attention residual and deep guidance modules. First, a dual-attention residual module was introduced at the encoder end’s bottleneck layer. This addressed the issue of ignored detailed information due to the encoder’s continuous pooling and downsampling. Second, a deep guidance module was introduced to prevent the loss of semantic information like defect location and category during transmission in the original network. This module integrated deep semantic information into the shallow feature layer. Third, a feature fusion module was introduced in the decoder to balance deep and shallow feature differences. This module increased the feature maps’ ability to express details and location information. Finally, a joint optimization strategy was adopted using Dice loss and Focal loss functions. This strategy addressed the imbalance between background and defect area proportions. Experimental results showed that the model achieved 94.79% of mIoU, 96.87% of MR, 97.64% of MP, and 86.36% of F1 Score in defect recognition. mIoU, MR, MP, and F1 Score improved by 2.02, 2.01, 2.78, and 6.52%, respectively, compared to the original UNet network. An automatic measurement method for coating defect data was designed based on the DeepSA-UNet model. The results indicated an analysis accuracy above 95%, with significantly increased measurement efficiency. This method provides a fast, accurate, and intelligent solution for automatically measuring and analyzing laser cladding coating defects.

RNA interference (RNAi) can mediate gene-silencing by knocking down the expression of a target gene via cellular machinery with much higher efficiency in contrast to other antisense-based approaches which represents an emerging therapeutic strategy for combating cancer. Distinct characters of nanoparticles, such as distinctive size, are fundamental for the efficient delivery of RNAi therapeutics, allowing for higher targeting and safety. In this review, we present the mechanism of RNAi and briefly describe the hurdles and concerns of RNAi as a cancer treatment approach in systemic delivery. Furthermore, the current nanovectors for effective tumor delivery of RNAi therapeutics are classified, and the characteristics of different nanocarriers are summarized.