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Background: Heart failure (HF) remains a significant public health challenge globally. This study aims to systematically analyze the global HF disease burden from 1990 to 2021 across temporal, spatial, and demographic dimensions to provide evidence for targeted prevention and control strategies. Methods: Using data from the Global Burden of Disease (GBD) 2021 study, we analyzed the global HF burden through prevalent cases, years lived with disability (YLDs), and age-standardized rates per 100,000 population. Temporal trends were evaluated using estimated annual percentage change (EAPC) and joinpoint regression analysis. The relationship between the Socio-demographic Index (SDI) and disease burden was explored through Pearson correlation analysis, while attribution analysis identified the main causes of HF. When appropriate, analyses were stratified by 5 SDI regions, 21 GBD regions, 204 countries and territories, 20 age groups, and both sexes. Results: Global HF prevalence and YLDs burden showed substantial increases from 1990 to 2021, with age-standardized prevalence increasing from 641.14 to 676.68 per 100,000 population. Notably, high-SDI regions exhibited a declining burden since 2019, indicating a potential global turning point. High-income North America bears the heaviest burden while South Asia shows the fastest growth rate. The correlation between disease burden and SDI level was negligible. The disease burden in males consistently exceeded that in females, with prevalence and YLDs rates rising sharply after age 60. The main causes and their attributable proportions were: ischemic heart disease (34.53%), hypertensive heart disease (22.53%), other cardiomyopathies (7.61%), chronic obstructive pulmonary disease (6.51%), and congenital heart anomalies (5.69%), with their distribution patterns differing across age groups and regions. Conclusion: Global burden of HF increased significantly over recent decades, with a potential turning point in 2019 and marked regional disparities. It is essential to prioritize regions with heavy burdens or rapid growth rates, strengthen the management of major causes, and monitor HF burden trends in the post-COVID era. [ABSTRACT FROM AUTHOR]

Background: Zika virus (ZIKV) is an emerging human pathogen. Since its arrival in the Western hemisphere, from Africa via Asia, it has become a serious threat to pregnant women, causing microcephaly and other neuropathies in developing fetuses. The mechanisms behind these teratogenic effects are unknown, although epidemiological evidence suggests that microcephaly is not associated with the original, African lineage of ZIKV. The sequences of 196 published ZIKV genomes were used to assess whether recently proposed mechanistic explanations for microcephaly are supported by molecular level changes that may have increased its virulence since the virus left Africa. For this we performed phylogenetic, recombination, adaptive evolution and tetramer frequency analyses, and compared protein sequences for the presence of protease cleavage sites, Pfam domains, glycosylation sites, signal peptides, trans-membrane protein domains, and phosphorylation sites. Results: Recombination events within or between Asian and Brazilian lineages were not observed, and likewise there were no differences in protease cleavage, glycosylation sites, signal peptides or trans-membrane domains between African and Brazilian strains. The frequency of Retinoic Acid Response Element (RARE) sequences was increased in Brazilian strains. Genetic adaptation was also apparent by tetramer signatures that had undergone major changes in the past but has stabilized in the Brazilian lineage despite subsequent geographic spread, suggesting the viral population presently propagates in the same host species in various regions. Evidence for selection pressure was recognized for several amino acid sites in the Brazilian lineage compared to the African lineage, mainly in nonstructural proteins, especially protein NS4B. A number of these positively selected mutations resulted in an increased potential to be phosphorylated in the Brazilian lineage compared to the African linage, which may have increased their potential to interfere with neural fetal development. Conclusions: ZIKV seems to have adapted to a limited number of hosts, including humans, during which its virulence increased. Its protein NS4B, together with NS4A, has recently been shown to inhibit Akt-mTOR signaling in human fetal neural stem cells, a key pathway for brain development. We hypothesize that positive selection of novel phosphorylation sites in the protein NS4B of the Brazilian lineage could interfere with phosphorylation of Akt and mTOR, impairing Akt-mTOR signaling and this may result in an increased risk for developmental neuropathies. [ABSTRACT FROM AUTHOR]

Background: Functional annotation of bacterial genomes is an obligatory and crucially important step of information processing from the genome sequences into cellular mechanisms. However, there is a lack of computational methods to evaluate the quality of functional assignments. Results: We developed a genome-scale model that assigns Bayesian probability to each gene utilizing a known property of functional similarity between neighboring genes in bacteria. Conclusions: Our model clearly distinguished true annotation from random annotation with Bayesian annotation probability >0.95. Our model will provide a useful guide to quantitatively evaluate functional annotation methods and to detect gene sets with reliable annotations. [ABSTRACT FROM AUTHOR]

Background: For decades there has been increasing interest in understanding the relationships between microbial communities and ecosystem functions. Current DNA sequencing technologies allows for the exploration of microbial communities in two principle ways: targeted rRNA gene surveys and shotgun metagenomics. For large study designs, it is often still prohibitively expensive to sequence metagenomes at both the breadth and depth necessary to statistically capture the true functional diversity of a community. Although rRNA gene surveys provide no direct evidence of function, they do provide a reasonable estimation of microbial diversity, while being a very cost-effective way to screen samples of interest for later shotgun metagenomic analyses. However, there is a great deal of 16S rRNA gene survey data currently available from diverse environments, and thus a need for tools to infer functional composition of environmental samples based on 16S rRNA gene survey data. Results: We present a computational method called pangenome-based functional profiles (PanFP), which infers functional profiles of microbial communities from 16S rRNA gene survey data for Bacteria and Archaea. PanFP is based on pangenome reconstruction of a 16S rRNA gene operational taxonomic unit (OTU) from known genes and genomes pooled from the OTU's taxonomic lineage. From this lineage, we derive an OTU functional profile by weighting a pangenome's functional profile with the OTUs abundance observed in a given sample. We validated our method by comparing PanFP to the functional profiles obtained from the direct shotgun metagenomic measurement of 65 diverse communities via Spearman correlation coefficients. These correlations improved with increasing sequencing depth, within the range of 0.8–0.9 for the most deeply sequenced Human Microbiome Project mock community samples. PanFP is very similar in performance to another recently released tool, PICRUSt, for almost all of survey data analysed here. But, our method is unique in that any OTU building method can be used, as opposed to being limited to closed-reference OTU picking strategies against specific reference sequence databases. Conclusions: We developed an automated computational method, which derives an inferred functional profile based on the 16S rRNA gene surveys of microbial communities. The inferred functional profile provides a cost effective way to study complex ecosystems through predicted comparative functional metagenomes and metadata analysis. All PanFP source code and additional documentation are freely available online at GitHub (https://github. com/srjun/PanFP). [ABSTRACT FROM AUTHOR]

Background: The total effects of adequate real acupuncture treatment consist of pathologic-specific and non-specific physiological effects. The latter may be the fundamental component of the therapeutic effects of acupuncture. This study investigated the physiological background effects of acupuncture in normal rats treated with acupuncture., Methods: Manual acupuncture was performed on normal rats at experienced acupoints, GV14 (Dazhui), BL12 (Fengmen) and BL13 (Feishu), once every other day for two weeks. The proteomic profile of rat lung tissue was examined using 2-DE/MS-based proteomic techniques. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were analyzed for differentially expressed proteins using the WebGestalt toolkit., Results: In total, 25 differentially expressed protein spots were detected in the 2-DE gels. Among these spots, 24 corresponded to 20 unique proteins that were successfully identified using mass spectrometry. Subsequent GO and KEGG pathway analyses demonstrated that these altered proteins were mainly involved in biological processes, such as 'protein stabilization', 'glycolysis/gluconeogenesis' and 'response to stimulus'., Conclusions: Our study indicated the non-specific background effects of acupuncture at acupoints GV14, BL12 and BL13 likely maintained internal homeostasis via regulation of the local stimulus response, energy metabolism, and biomolecule function balance, which may be important contributors to the therapeutic effects of acupuncture.