Search

: Introduction: The Joint United Nations Programme on HIV/AIDS (UNAIDS) updated the 95‐95‐95 targets for the HIV endgame in 2030. To achieve the first target in a timely manner, we investigate the optimized strategy of resource allocation to maximize timely HIV diagnosis in 14 populations in China. Methods: We developed a mathematical model by integrating epidemiological, demographical and behavioural data from 12 high‐risk and two general populations to evaluate the impact of various resource allocation strategies of HIV testing on HIV incidence in China. We identified the optimized allocation strategy that maximizes the number of HIV diagnoses at an estimated total spending on HIV tests in China and calculated the per‐capita cost of new HIV case detection. Results: We estimated that 144,795 new HIV cases may occur annually in 14 populations in China, with a total annual spending of US$2.8 billion on HIV testing. The largest proportion of spending was allocated to general males (44.0%), followed by general females (42.6%) and pregnant women (5.1%). Despite this allocation strategy, only 45.5% (65,867/144,795, timely diagnosis rate) of annual new infections were diagnosed within a year of acquisition, with a cost of $42,852 required for each new HIV case detection. By optimizing the allocation of HIV testing resources within the same spending amount, we found that general females received the highest proportion of spending allocation (45.1%), followed by low‐risk men who have sex with men (13.9%) and pregnant women (8.4%). In contrast, the proportion of spending allocation for the general males decreased to 0.2%. With this optimized strategy, we estimated that 120,755 (83.4%) of annual new infections would be diagnosed within a year of acquisition, with the cost required for one HIV case detection reduced to $23,364/case. Further spending increases could allow for significant increases in HIV testing among lower‐risk populations. Conclusions: Optimizing resource allocation for HIV testing in high‐risk populations would improve HIV timely diagnosis rate of new infections and reduce cost per HIV case detection., INTRODUCTION The Joint United Nations Programme on HIV/AIDS (UNAIDS) estimated that 39 million people were living with HIV globally in 2022 [1]. In China, the estimated number of people living [...]

The crisp grass carp (CGC; Ctenopharyngodon idellus C. et V.), known for its unique texture and flavour, is a culinary delicacy whose quality is significantly influenced by thermal processing. This study employed 4D label-free proteomics and data mining techniques to investigate the proteomic changes in CGC muscle tissue induced by various heating temperatures. CGC samples were subjected to a series of heat treatments at increasing temperatures from 20 °C to 90 °C. Proteins were extracted, digested, and analysed using high-resolution mass spectrometry. The proteomic data were then subjected to extensive bioinformatics analysis, including GO and KEGG pathway enrichment. We identified a total of 1085 proteins, 516 of which were shared across all the temperature treatments, indicating a core proteome responsible for CGC textural properties. Differential expression analysis revealed temperature-dependent changes, with significant alterations observed at 90 °C, suggesting denaturation or aggregation of proteins at higher temperatures. Functional enrichment analysis indicated that proteins involved in amino acid metabolism, glutathione metabolism, and nucleotide metabolism were particularly affected by heat. Textural analysis correlated these proteomic changes with alterations in CGC quality attributes, pinpointing 70 °C as the optimum temperature for maintaining the desired texture. A strong positive correlation between specific upregulated proteins was identified, such as the tubulin alpha chain and collagen alpha-1(IV) chain, and the improved textural properties of CGC during thermal processing, suggesting their potential as the potential biomarkers. This study offers a comprehensive proteomic view of the thermal stability and functionality of CGC proteins, delivering invaluable insights for both the culinary processing and scientific management of CGC. Our findings not only deepen the understanding of the molecular mechanisms underpinning the textural alterations in CGC during thermal processing but also furnish practical insights for the aquaculture industry. These insights could be leveraged to optimize cooking techniques, thereby enhancing the quality and consumer appeal of CGC products.

Background: Cognitive impairment is a clinical manifestation that occurs in the course of dementia like Alzheimer's disease. The association between cognitive impairment and gut microbiota is unclear. Objective: We aimed to identify gut microbiota characteristics and key gut microbiota biomarkers associated with cognitive impairment in a relatively large cohort of older adults in China. Methods: A total of 229 adults aged ≥60 years from Shenzhen, China were recruited into this cross-sectional study. Participants were divided into cognitive impairment (CI) and no cognitive impairment (NCI) groups according to the results of the Mini-Mental State Examination. Diversity analysis and network analysis were used to characterize the gut microbiota between the two groups. The linear discriminant analysis effect size method and machine learning approaches were sequentially performed to identify gut microbiota biomarkers. The relationship between biomarkers and lifestyle factors was explored using Transformation-based redundancy analysis (tb-RDA). Results: A total of 74 CI participants and 131 NCI participants were included in the analysis. The CI group demonstrated lower α-diversity compared to the NCI group (Shannon: 2.798 versus 3.152, p < 0.001). The density of the gut microbiota interaction network was lower in the CI group (0.074) compared to the NCI group (0.081). Megamonas, Blautia, Pseudomonas, Stenotrophomonas, and Veillonella were key biomarkers for CI. The tb-RDA revealed that increased fruit intake and exercise contribute to a higher abundance of Megamonas, Blautia, and Veillonella. Conclusions: We identified a significantly reduced abundance of certain beneficial gut microbiota in older Chinese adults with cognitive impairment. [ABSTRACT FROM AUTHOR]

Interactions between and within abiotic and biotic processes generate nonadditive density-dependent effects on species performance that can vary in strength or direction across environments. If ignored, nonadditivities can lead to inaccurate predictions of species responses to environmental and compositional changes. While there are increasing empirical efforts to test the constancy of pairwise biotic interactions along environmental and compositional gradients, few assess both simultaneously. Using a nationwide forest inventory that spans broad ambient temperature and moisture gradients throughout New Zealand, we address this gap by analyzing the diameter growth of six focal tree species as a function of neighbor densities and climate, as well as neighbor × climate and neighbor × neighbor statistical interactions. The most complex model featuring all interaction terms had the highest predictive accuracy. Compared with climate variables, biotic interactions typically had stronger effects on diameter growth, especially when subjected to nonadditivities from local climatic conditions and the density of intermediary species. Furthermore, statistically strong (or weak) nonadditivities could be biologically irrelevant (or significant) depending on whether a species pair typically interacted under average or more extreme conditions. Our study highlights the importance of considering both the statistical potential and the biological relevance of nonadditive biotic interactions when assessing species performance under global change. [ABSTRACT FROM AUTHOR]

Aim: Coronary artery disease (CAD) is a heterogeneous disorder with high morbidity, mortality, and healthcare costs, representing a major burden on public health. Here, we aimed to improve our understanding of the genetic drivers of ferroptosis and necroptosis and the clustering of gene expression in CAD in order to develop novel personalized therapies to slow disease progression.Methods: CAD datasets were obtained from the Gene Expression Omnibus. The identification of ferroptosis- and necroptosis-related differentially expressed genes (DEGs) and the consensus clustering method including the classification algorithm used km and distance used spearman were performed to differentiate individuals with CAD into two clusters (cluster A and cluster B) based expression matrix of DEGs. Next, we identified four subgroup-specific genes of significant difference between cluster A and B and again divided individuals with CAD into gene cluster A and gene cluster B with same methods. Additionally, we compared differences in clinical information between the subtypes separately. Finally, principal component analysis algorithms were constructed to calculate the cluster-specific gene score for each sample for quantification of the two clusters.Results: In total, 25 ferroptosis- and necroptosis-related DEGs were screened. The genes in cluster A were mostly related to the neutrophil pathway, whereas those in cluster B were mostly related to the B-cell receptor signaling pathway. Moreover, the subgroup-specific gene scores and CAD indices were higher in cluster A and gene cluster A than in cluster B and gene cluster B. We also identified and validated two genes showing upregulation between clusters A and B in a validation dataset.Conclusion: High expression of CBS and TLR4 was related to more severe disease in patients with CAD, whereas LONP1 and HSPB1 expression was associated with delayed CAD progression. The identification of genetic subgroups of patients with CAD may improve clinician knowledge of disease pathogenesis and facilitate the development of methods for disease diagnosis, classification, and prognosis.

OBJECTIVES To evaluate the impact of previous cardiac surgery (PCS) on clinical outcomes after reoperative extended arch repair for acute type A aortic dissection. METHODS This study included 37 acute type A aortic dissection patients with PCS (PCS group) and 992 without PCS (no-PCS group). Propensity score-matching yielded a subgroup of 36 pairs (1:1). In-hospital outcomes and mid-term survival were compared between the 2 groups. RESULTS The PCS group was older (56.7 ± 14.2 vs 52.2 ± 12.6 years, P  = 0.036) and underwent a longer cardiopulmonary bypass (median, 212 vs 183 min, P  < 0.001) compared with the no-PCS group. Operative death occurred in 88 (8.6%) patients, exhibiting no significant difference between groups (13.5% vs 8.4%, P  = 0.237). Major postoperative morbidity was observed in 431 (41.9%) patients, also showing no difference between groups (45.9% vs 41.7%, P  = 0.615). Moreover, the multivariable logistic regression analysis revealed that PCS was not significantly associated with operative mortality (adjusted odds ratio 2.58, 95% confidence interval 0.91–7.29, P  = 0.075) or major morbidity (adjusted odds ratio 1.92, 95% confidence interval 0.88–4.18, P  = 0.101). The 3-year cumulative survival rates were 71.1% for the PCS group and 83.9% for the no-PCS group (log-rank P  = 0.071). Additionally, Cox regression indicated that PCS was not significantly associated with midterm mortality (adjusted hazard ratio 1.40, 95% confidence interval 0.44–4.41, P  = 0.566). After matching, no significant differences were found between groups in terms of operative mortality (P  > 0.999), major morbidity (P  > 0.999) and midterm survival (P  = 0.564). CONCLUSIONS No significant differences were found between acute type A aortic dissection patients with PCS and those without PCS regarding in-hospital outcomes and midterm survival after extended arch repair. [ABSTRACT FROM AUTHOR]