Background Exposure to polycyclic aromatic hydrocarbons (PAHs) is associated with the development of metabolic syndrome (MS). However, the role of amino acids in PAH-induced MS remains unclear. Objective To explore the impact of PAHs exposure on the incidence of MS among coking workers, and to determine potential modifying effect of amino acid on this relationship. Methods Unmatched nested case-control design was adopted and the baseline surveys of coking workers were conducted in two plants in Taiyuan in 2017 and 2019, followed by a 4-year follow-up. The cohort comprised 667 coking workers. A total of 362 participants were included in the study, with 84 newly diagnosed cases of MS identified as the case group and 278 as the control group. Urinary levels of 11 PAH metabolites and plasma levels of 17 amino acids were measured by ultrasensitive performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Logistic regression was used to estimate the association between individual PAH metabolites and MS. Stratified by the median concentration of amino acids, Bayesian kernel machine regression (BKMR) model was employed to assess the mixed effects of PAHs on MS. Due to the skewed data distribution, all PAH metabolites and amino acids in the analysis were converted by natural logarithm ln (expressed as lnv). Results The median age of the 362 participants was 37 years, and 83.2% were male. Compared to the control group, the case group exhibited higher concentrations of urinary 2-hydroxyphenanthrene (2-OHPhe), 9-hydroxyphenanthrene (9-OHPhe), and hydroxyphenanthrene (OHPhe) (P=0.005, P=0.049, and P=0.004, respectively), as well as elevated levels of plasma branched chain amino acid (BCAA) and aromatic amino acid (AAA) (P<0.05). After being adjusted for confounding factors, for every unit increase in lnv_2-OHPhe in urine, the OR (95%CI) of MS was 1.57 (1.11, 2.26), and for every unit increase in lnv_OHPhe, the OR (95%CI) of MS was 1.82 (1.16, 2.90). Tyrosine, leucine, and AAA all presented a significant nonlinear correlation with MS. At low levels, tyrosine, leucine, and AAA did not significantly increase the risk of MS, but at high levels, they increased the risk of MS. In the low amino acid concentration group, as well as in the low BCAA and low AAA concentration groups, it was found that compared to the PAH metabolite levels at the 50th percentile (P₅₀), the log-odds of MS when the PAH metabolite levels was at the 75th percentile (P₇₅) were 0.158 (95%CI: 0.150, 0.166), 0.218 (95%CI: 0.209, 0.227), and 0.262 (95% CI: 0.241, 0.282), respectively, However, no correlation between PAHs and MS was found in the high amino acid concentration group. Conclusion Amino acids modify the effect of PAHs exposure on the incidence of MS. In individuals with low plasma amino acid levels, the risk of developing MS increases with higher concentrations of mixed PAH exposure. This effect is partly due to the low concentrations of BCAA and AAA.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

OBJECTIVE: To analyze the distribution of thalassemia (referred to as "thalassemia") gene variant types in the population of the Wuhan area, aiming to provide a genetic basis for the precise prevention and control as well as clinical diagnosis of thalassemia in the Wuhan region. METHODS: In this study, 2 133 suspected thalassemia patients and individuals undergoing prenatal screening who visited the Department of Hematology, Obstetrics and Gynecology, Reproductive Medicine, Pediatrics, and Neurology at Wuhan First Hospital from October 2022 to October 2024 were selected as the research subjects. Peripheral blood samples were collected from the patients. The common 27 thalassemia genotypes of α- and β-thalassemia were initially screened using fluorescence PCR melting curve analysis technology. For samples where the fluorescence PCR melting curve results indicated unknown variants or where the clinical phenotype was inconsistent with the common genotypes, Sanger sequencing technology was used for review and verification. RESULTS: Among the 2 133 specimens analyzed, common thalassemia gene variants were detected in 210 cases (9.85%, 210/2 133). A total of 156 cases (8.05%, 156/1 938) of thalassemia gene variants were detected in females and 54 cases (27.69%, 54/195) in males. A total of 94 cases (4.41%, 94/2 133) of α-thalassemia were detected, including 46 cases (2.16%, 46/2 133) of silent α-thalassemia, 47 cases (2.20%, 47/2 133) of mild α-thalassemia, and 1 case (0.05%, 1/2 133) of intermediate α-thalassemia. Additionally, 111 cases of β-thalassemia were identified (5.20%, 111/2 133), including 51 cases of β/β⁺ thalassemia (2.39%, 51/2 133), 59 cases of β/β⁰ thalassemia (2.77%, 59/2 133), and 1 case of β⁺/HbE thalassemia (0.05%, 1/2 133). αβ-composite thalassemia gene variants were detected in 5 cases (0.23%, 5/2 133), including 1 complex variant with a genotype of --^SEA/αα combined with CD41-42 (-TTCT) and 29(A>G), representing a heterozygous variant of three genotypes. Rare globin gene variants were detected in 3 cases, including HBB:c.60C>T, HBB:c.-146G>T, and HBA2:c.*12G>A. CONCLUSION The Wuhan region exhibits a relatively high prevalence of thalassemia genes with notable gender disparities. While maintaining focus on thalassemia screening for females, enhanced males screening efforts and genetic counseling should be implemented in future prevention programs.
Humans ; Female ; Male ; Genotype ; beta-Thalassemia/genetics* ; China ; Thalassemia/genetics* ; alpha-Thalassemia/genetics* ; Genetic Variation

Acute liver injury (ALI) serves as a critical precursor and major etiological factor in the progression and ultimate manifestation of various hepatic disorders. The prevention and treatment of ALI is still a serious global challenge. Given the limited therapeutic options for ALI, exploring novel targeted therapeutic agents becomes imperative. The potential therapeutic efficacy of inhibiting RIPK2 is highlighted, as it may provide significant benefits by attenuating the MAPK pathway and NF-κB signaling. Herein, we propose a CMD-OPT model, a two-stage molecular optimization tool for the rapid discovery of RIPK2 inhibitors with optimal properties. Compound RP20, which targets the ATP binding site, demonstrated excellent kinase specificity, ideal oral pharmacokinetics, and superior therapeutic effects in a model of APAP-induced ALI, positioning RP20 as a promising preclinical candidate. This marks the first application of RIPK2 inhibitors in ALI treatment, opening a novel therapeutic pathway for clinical applications. These results highlight the efficacy of the CMD-OPT model in producing lead compounds from known active molecules, showcasing its significant potential in drug discovery.

Traditional development of small protein scaffolds has relied on display technologies and mutation-based engineering, which limit sequence and functional diversity, thereby constraining their therapeutic and application potential. Protein design tools have significantly advanced the creation of novel protein sequences, structures, and functions. However, further improvements in design strategies are still needed to more efficiently optimize the functional performance of protein-based drugs and enhance their druggability. Here, we extended an evolution-based design protocol to create a novel minibinder, BindHer, against the human epidermal growth factor receptor 2 (HER2). It not only exhibits super stability and binding selectivity but also demonstrates remarkable properties in tissue specificity. Radiolabeling experiments with ^99mTc, ⁶⁸Ga, and ¹⁸F revealed that BindHer efficiently targets tumors in HER2-positive breast cancer mouse models, with minimal nonspecific liver absorption, outperforming scaffolds designed through traditional engineering. These findings highlight a new rational approach to automated protein design, offering significant potential for large-scale applications in therapeutic mini-protein development.

ObjectiveTo analyze the epidemiological characteristics and spatial clustering patterns of brucellosis in humans in Zhangjiakou City, Heibei Province from 2018 to 2023, so as to provide a basis for the prevention and control of brucellosis. MethodsIncidence data of brucellosis in Zhangjiakou City from 2018 to 2023 were collected. Descriptive epidemiological analysis, Joinpoint regression modelling, and spatial autocorrelation analysis were used to analyze the temporal trends and spatial clustering patterns of the epidemic. ResultsA total of 3 812 cases of brucellosis were reported in Zhangjiakou City from 2018 to 2023, with no death case, yielding an average annual incidence rate of 15.43/100 000 (incidence range: 12.82/100 000‒17.76/100 000). Cases of brucellosis occurred year-round, with a distinct seasonal pattern, predominantly concentrated between March and September, peaking in May and June. The male-to-female ratio was 2.58∶1, with a higher incidence in males than that in females. The highest incidence rates were observed in the 40‒<50 years (74.98/100 000) and 50‒<60 years age group (87.14/100 000). The majority of cases were farmers and herdsmen (3 557 cases, 93.31%). Joinpoint regression analyses indicated that from 2018 to 2023, the incidence rate of human brucellosis in pastoral areas of Zhangjiakou City showed a declining trend (APC=-9.70%, 95%CI: -15.31%‒ -4.63%), while the incidence rate in mixed-use areas exhibited an increasing trend (APC=6.90%, 95%CI: 0.17%‒14.30%). Spatial clustering analyses showed that the incidence of brucellosis in Zhangjiakou from 2018 to 2023 was non-randomly distributed across the whole city, with a positive spatial correlation and significant clustering (Moran’s I>0, all P<0.001). Local spatial autocorrelation analyses showed that the high-high clusters were concentrated in the pastoral areas during 2018‒2020. From 2021 onward, the number of high-high clusters in mixed and non-pastoral regions exceeded those in traditional pastoral areas. ConclusionFrom 2018 to 2023, the incidence of brucellosis in Zhangjiakou City showed a declining trend, with significant spatial clustering observed across the city. It is recommended to intensify health education among males aged 40‒<60 years. Scientific livestock management practices should be promoted in non-pastoral and mixed areas, and cross-sectoral quarantine and joint prevention and control efforts should be strengthened as well.

In recent years, global outbreaks of infectious diseases, such as COVID-19, have triggered great concern about emerging infectious diseases. With the rapid development of next-generation sequencing technology and bioinformatic tools for viral metagenomics, there is now a widespread capability to detect and identify various known and unknown pathogenic microorganisms within both environmental and biological contexts. Furthermore, the continuous evolution of machine learning methods has led to the development and application of multiple rapid and highly accurate approaches for virus identification. Concurrently, owing to the continual progress in machine learning methods, several rapid and accurate virus identification techniques have been widely developed and applied. Therefore, this review aims to systematically summarize the key methodologies, frameworks, and the scope of applicability within the field of viral metagenomics, with a specific focus on virus identification and prediction. It could facilitate a deeper understanding of viral characteristics, identify potential novel pathogens, and provide technical support for the early prevention and control of infectious diseases.

Public health and social measures (PHSMs) are one of the most important measures in the prevention and control of COVID-19 and have also been effective in suppressing the spread of influenza viruses, but their effectiveness has not been fully investigated. This study aimed to review the progress of research on the impact of PHSMs on influenza during the COVID-19 pandemic based on the latest evidence of the effectiveness of various PHSMs in controlling transmission of influenza viruses, to provide scientific evidence for optimizing influenza prevention and control strategies.

Objective To investigate the regulatory effect of small interfering RNA(siRNA)silencing zinc finger protein A20 on pyroptosis of rheumatoid arthritis(RA)fibroblast-like synoviocytes(HFLS-RA).Methods Hu-man FLS-RA cell line MH7A cells were cultivated,A20 siRNA silencing group was synthesized for knocking down the human A20 gene,and then specific A20 gene siRNA and siRNA-NC(negative control)were transfected into MH7A cells using liposome method.RT-qPCR was applied to detect the expression of NLRP3 and Caspase-1 mRNA in cells.The protein expression of NLRP3 and Caspase-1 was detected by Western blot,and IL-1β and IL-18 in cell culture medium were detected by ELISA method.Transmission electron microscopy(TEM)was used to detect pyroptosis.Results After A20 knockdown,the mRNA and expression of NLRP3 and Caspase-1 in MH7A cells in the siRNA-A20 group were significantly increased as compared with the siRNA-NC group(P＜0.01).The concentration of IL-1β and IL-18 in the cell culture supernatant of the siRNA-A20 group was sig-nificantly increased compared with the siRNA-NC group(P＜0.01).Compared with the siRNA-NC group,some cells in the siRNA-A20 group showed swollen and ruptured.The integrity of the cell membrane was also lost,and a large area of edema was present in the cell.In addition,a blurred depression of the local nuclear membrane was noted,while an increase in heterochromatin pyknosis was accompanied by their uneven distribution as well as their aggregation around the nuclear membrane.Conclusions Silencing of A20 gene with siRNA might promote NLRP3/Caspase-1 mediated pyroptosis in HFLS-RA,which lays an experimental foundation for new clinical treatment meth-ods of RA.