Asthma is the second leading cause of mortality among chronic respiratory illnesses. This study provided a comprehensive analysis of the burden of asthma. Data on asthma were extracted from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. We focused on the effects of age, sex, risk factors, and the socio-demographic index (SDI) on the burden of asthma and calculated the average annual percent change (AAPC) via joinpoint regression. Two-sample Mendelian randomization (MR) was adopted to estimate the causal relationships between risk factors and asthma. The Bayesian age-period-cohort (BAPC) model was used to predict incidence patterns of asthma from 2022 to 2050. In 2021, there was an observed prevalence of asthma, with 3,340 cases per 100,000 people. Males who were below 20 years old had a greater prevalence of asthma. The incidence and prevalence correlated positively with the SDI, whereas mortality and disability-adjusted life years (DALYs) correlated negatively. The contribution of high body mass index (BMI) to asthma DALYs increased by 4.3% worldwide between 1990 and 2021. MR studies have confirmed that high BMI and smoking can increase the risk of asthma. The prediction results indicated that the global age-standardised incidence rate will remain high from 2022 to 2050. The global mortality of patients with asthma is a significant concern. The analysis of the burden of asthma can help formulate public health policies, allocate resources, and prevent asthma. This study was supported by the 10.13039/501100001809 National Natural Science Foundation of China ; Program for Young Talents of Basic Research in Universities of Heilongjiang Province; Marshal Initiative Funding; Mathematical Tianyuan Fund of the 10.13039/501100001809 National Natural Science Foundation of China ; XingLian Outstanding Talent Support Program 2024.

Background: To provide insights into rheumatoid arthritis (RA) epidemiological trends, including prevalence, incidence, disability-adjusted life years (DALYs), corresponding average annual percentage change (AAPC), gender disparities, regional variations, age-specific rates, socio-economic correlations, risk factors, and future projections. Methods: Data were extracted from the Global Burden of Disease Study (GBD) 2021. AAPC was calculated by joinpoint regression and two-sample Mendelian randomization (MR) analysis was performed to verify the causal relationship between the smoking factor and RA. The future incidence trend was predicted by the Bayesian age-period-cohort (BAPC) model. Results: Global age-standardized prevalence rate (ASPR) and age-standardized incidence rate (ASIR) increased significantly while age-standardized DALYs rate (ASDR) decreased from 1990 to 2021. Regional variations were pronounced, with Andean Latin America reporting the highest burden. Females consistently exhibited higher age-standardized rate (ASR) across all metrics. Age-specific prevalence, incidence, and DALYs rates peaked at different age groups, highlighting complex demographic dynamics. Socio-demographic index (SDI) analysis demonstrated a positive correlation between RA burden and socio-economic development. The two-sample MR analysis confirmed a causal effect between smoking and RA. From 2022 to 2050, the ASIR will increase moderately. Conclusions: The study underscores the escalating burden of RA globally, emphasizing the need for healthcare providers to be aware of the effects of aging populations and other societal factors on the risk of developing RA, and to develop targeted interventions, including smoking cessation programs, age- and gender-appropriate healthcare, and early diagnosis strategies.

As an emerging fringe science, bionics integrates the understanding of nature, imitation of nature, and surpassing nature in one aspect, and it organically combines the synergistic complementarity of function and structure–function integrated materials which is of great scientific interest. By imitating the microstructure of a natural biological surface, the bionic superhydrophobic surface prepared by human beings has the properties of self-cleaning, anti-icing, water collection, anti-corrosion and oil–water separation, and the preparation research methods are increasing. The preparation methods of superhydrophobic surface include vapor deposition, etching modification, sol–gel, template, electrostatic spinning, and electrostatic spraying, which can be applied to fields such as medical care, military industry, ship industry, and textile. The etching modification method can directly modify the substrate, so there is no need to worry about the adhesion between the coating and the substrate. The most obvious advantage of this method is that the obtained superhydrophobic surface is integrated with the substrate and has good stability and corrosion resistance. In this article, the different preparation methods of bionic superhydrophobic materials were summarized, especially the etching modification methods, we discussed the detailed classification, advantages, and disadvantages of these methods, and the future development direction of the field was prospected.

Big biological data contains a large amount of life science information, yet extracting meaningful insights from this data remains a complex challenge. The hidden Markov model (HMM), a statistical model widely utilized in machine learning, has proven effective in addressing various problems in bioinformatics. Despite its broad applicability, a more detailed and comprehensive discussion is needed regarding the specific ways in which HMMs are employed in this field. This review provides an overview of the HMM, including its fundamental concepts, the three canonical problems associated with it, and the relevant algorithms used for their resolution. The discussion emphasizes the model's significant applications in bioinformatics, particularly in areas such as transmembrane protein prediction, gene discovery, sequence alignment, CpG island detection, and copy number variation analysis. Finally, the strengths and limitations of the HMM are discussed, and its prospects in bioinformatics are predicted. HMMs can play a pivotal role in addressing complex biological problems and advancing our understanding of biological sequences and systems. This review can provide bioinformatics researchers with comprehensive information on HMM and guide their work.

The dilemma of insufficient wood resources and poor properties of fast-growing wood necessitates a breakthrough in the expansion and efficient utilization of wood resources to meet the real needs. With the aim of enhancing the properties of fast-growing poplar wood, a combination of PVA and nano-silica sol impregnation treatment was carried out to develop a novel functional wood material with good dimensional stability, compressive strength and thermal stability, while fully exploiting the unique structural properties of the wood itself. In this work, PVA-nano-silica sol composite wood was prepared by in-situ impregnation of poplar fast-growing wood via ultrasound-assisted and vacuum impregnation methods. Firstly, the effects of different treatments on the impregnation effect and strengthening effect of modified wood were explored. The results showed that the PVA-nano-silica sol dispersion system had no negative effect on the impregnation modification, and the compressive strength of modified wood was obviously improved compared with natural wood. Among them, the most effective modification was achieved under the condition of vacuum-assisted impregnation. The effect of compound impregnation modification on the structure of poplar fast-growing wood was analyzed by FTIR, XRD, SEM, and EDS, in which PVA and nano-silica sol can form a certain cross-linking system and form chemical bonds such as Si-O-C and Si-O-Si, enabling them to enter the structure of wood and react with wood cell walls effectively. The compressive strength of modified poplar wood was increased by 72.29% compared with natural poplar wood. According to the thermogravimetric analysis, the thermal stability of the modified poplar wood was improved to some extent. In view of all the results, the method proposed in this study provides an opportunity for the high value utilization of low quality wood, and this modified wood can be considered as a candidate for new lightweight and high performance materials in the construction field.

Advances in sequencing technologies have led to the rapid growth of multi-omics data on rheumatoid arthritis (RA). However, a comprehensive database that systematically collects and classifies the scattered data is still lacking. Here, we developed the Rheumatoid Arthritis Bioinformatics Center (RABC, http://www.onethird-lab.com/RABC/), the first multi-omics data resource platform (data hub) for RA. There are four categories of data in RABC: (i) 175 multi-omics sample sets covering transcriptome, epigenome, genome, and proteome; (ii) 175 209 differentially expressed genes (DEGs), 105 differentially expressed microRNAs (DEMs), 18 464 differentially DNA methylated (DNAm) genes, 1 764 KEGG pathways, 30 488 GO terms, 74 334 SNPs, 242 779 eQTLs, 105 m6A-SNPs and 18 491 669 meta-mQTLs; (iii) prior knowledge on seven types of RA molecular markers from nine public and credible databases; (iv) 127 073 literature information from PubMed (from 1972 to March 2022). RABC provides a user-friendly interface for browsing, searching and downloading these data. In addition, a visualization module also supports users to generate graphs of analysis results by inputting personalized parameters. We believe that RABC will become a valuable resource and make a significant contribution to the study of RA.