Objective: To quantitatively assess the health risk of preservatives from beverages around primary schools in Anshun City, and to provide scientific basis for precise food safety supervision. Methods: From December 2023 to July 2024, 602 beverage samples were randomly collected from within 100 meters of 19 primary schools in Anshun City. The content of benzoic acid, sorbic acid, and dehydroacetic acid was detected according to GB 5009 series standards. Combined with children s physiological parameters (body weight 30 kg, daily intake 0.15 L), the Hazard Quotient (HQ) and Hazard Index (HI) models were used to evaluate health risks. Results: The total detection rate of preservatives from beverages around primary schools was 63.0%, and the total over limit rate was 9.0%. The detection rate of preservatives in flavored beverages was the highest (72.6%), and the highest over limit rate of preservatives in special purpose beverages was the highest (17.2%). The single preservative HQ (benzoic acid up to 0.47 ) and mixed HI (up to 0.55) of all samples were below 1(safety threshold). However, the HQ value of benzoic acid in flavored beverages (0.47) was 2.9 times that of sorbic acid (0.16), contributing significantly to health risk. Sensitivity analysis showed that if the daily consumption increased to 0.3 L, the HI value of flavored beverages would rise to 1.11, exceeding the safety threshold. Enterprise scale analysis showed that the exceedance rate of special purpose beverages in large enterprises reached 30.0%, while micro enterprises, accounting for a dominant market share (52.2%), constituted the main source of children s daily exposure to their products. Conclusions The overall health risk of perservatives in beverages sold near primary schools in Anshun City is controllable, but there is a noticeable risk of gradient. The risk of children’s exposure to preservatives through beverage consumption should not be ignored.

Purpose: The role of the Clinical Treatment Score post-5 years (CTS5) in male breast cancer (MBC) remains unclear. This study aimed to investigate the characteristics and prognosis of CTS5 between MBC and female breast cancer (FBC). Materials and Methods: Patients diagnosed with human epidermal growth factor receptor 2–negative/estrogen receptor–positive breast cancer between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The chi-square test, Kaplan-Meier analysis, and multivariate Cox proportional hazard model were used for statistical analysis. Results: This study included 169,869 patients, comprising 168,422 (99.1%) FBC and 1,447 (0.9%) MBC patients. More MBC patients had intermediate risk (IR)/high risk (HR) disease compared to FBC patients (17.7% vs. 8.7%, p < 0.001). MBC patients had an inferior overall survival (OS) but similar breast cancer-specific survival compared to those with FBC. Sensitivity analyses showed that sex was an independent prognostic factor associated with OS but not breast cancer-specific survival (BCSS) in both the low-risk (LR) and IR/HR cohorts. Those with MBC exhibited significantly worse OS (p < 0.001) than FBC patients in both cohorts. In MBC patients, those with IR/HR disease had significantly worse OS (p < 0.001) and BCSS (p < 0.001) compared to those with LR disease. For FBC patients, the IR/HR group had also significantly worse OS (p < 0.001) and BCSS (p < 0.001) compared to the LR group. Conclusion Our findings highlight critical differences in clinical characteristics, treatment patterns, and outcomes between MBC and FBC, suggesting the need for sex-tailored approaches in breast cancer management.

RNA-binding proteins (RBPs) are ubiquitous components within cells, fulfilling essential functions in a myriad of biological processes. These proteins interact with RNA molecules to regulate gene expression at various levels, including transcription, splicing, transport, localization, translation, and degradation. Understanding the intricate network of RBP-RNA interactions is crucial for deciphering the complex regulatory mechanisms that govern cellular function and organismal development. Ultravidet (UV) cross-linking and immunoprecipitation (CLIP) stands out as a powerful approach designed to map the precise locations where RBPs bind to RNA. By using UV light to create covalent bonds between proteins and RNA, followed by immunoprecipitation to isolate the protein-RNA complexes, researchers can identify the direct targets of specific RBPs. The advent of high-throughput sequencing technologies has revolutionized CLIP, enabling the identification of not only the types but also the exact sequences of RNA bound by RBPs on a genome-wide scale. The evolution of CLIP has led to the development of specialized variants, each with unique features that address specific challenges and expand the scope of what can be studied. High-throughput sequencing CLIP (HITS-CLIP) was one of the first advancements, significantly increasing the throughput and resolution of RNA-protein interaction mapping. Photoactivatable-ribonucleoside-enhanced CLIP (PAR-CLIP) introduced the use of photoactivatable ribonucleosides to enhance cross-linking efficiency and specificity, reducing background noise and improving the detection of low-abundance RNA-protein interactions. Individual-nucleotide resolution CLIP (iCLIP) further refined the technique, achieving unprecedented precision by resolving individual nucleotides involved in RBP binding, which is particularly valuable for studying the fine details of RNA structure and function. Despite the remarkable progress, there remains room for improvement in CLIP technology. Researchers continue to seek methods to increase sensitivity, reduce technical variability, and improve the reproducibility of results. Advances in sample preparation, data analysis algorithms, and computational tools are critical for addressing these challenges. Moreover, the application of CLIP to more diverse biological systems, including non-model organisms and clinical samples, requires the development of tailored protocols and the optimization of existing ones. Looking forward, the field of RNA biology is poised to benefit greatly from ongoing innovations in CLIP technology. The exploration of non-canonical RNA-protein interactions, such as those involving long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), promises to reveal new layers of cellular regulation and may lead to the discovery of novel therapeutic targets. Furthermore, integrating CLIP data with other omics approaches, such as proteomics and metabolomics, will provide a more comprehensive understanding of the dynamic interplay between RNA and its binding partners within the cell. In conclusion, the continuous refinement and expansion of CLIP techniques have not only deepened our knowledge of RNA biology but have also opened up new avenues for investigating the molecular underpinnings of health and disease. As the technology matures, it is expected to play an increasingly pivotal role in both basic and applied research, contributing to the advancement of medical science and biotechnology.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Objective: Data on syncopal donation-related vasovagal reaction (DRVR) collected from 74 blood centers between 2020 and 2023 was statistically analyzed to provide a reference for developing preventive strategies against syncopal DRVR. Methods: Data on blood donation adverse reactions and basic information of donors from 2020 to 2023 were collected through the information management system at monitoring sentinel sites. Statistical analysis was performed on the following aspects of syncopal DRVR: characteristics of donors who experienced syncope, reported incidence, triggers, duration, presence and occurrence time of syncope-related trauma, clinical management including outpatient and inpatient treatment, and severity grading. Results: From 2020 to 2023, 45 966 donation-related adverse reactions were recorded. Of these, 1 665 (3.72%) cases were syncopal DRVR. The incidence of syncopal DRVR decreased with age, being the highest in the 18-22 age group. Incidence was significantly higher in female donors than male donors, in first-time donors than repeat donors, and in university and individual donors than group donors (all P<0.05). There was no statistically significant difference among different blood donation locations (P>0.05). The top three triggers were tension, fatigue, and needle phobia or fear of blood. Among syncopal DRVR cases, 60.36% occurred during blood collection, 87.63% lasted for less than 60 seconds, and 5.05% were accompanied by trauma. Notably, 57.14% of these traumas occurred after donor had left the blood collection site. Syncope severity was graded based on required treatment: grade 1 (fully recovered without treatment, 95.50%); grade 2 (recovered after outpatient treatment, 4.02%); and grade 3 (recovered after inpatient treatment, 0.48%). Conclusion: By analyzing the data of syncopal DRVR cases, it is possible to provide a reference for formulating blood donor safety policies.

Objective:To investigate lipid metabolism gene mutations and pathogenicity of hypertriglyceridemia acute pancreatitis (HTG-AP) patients.Methods:Clinical data of 495 HTG-AP patients admitted from June 2018 to June 2020 in the center for severe acute pancreatitis of Eastern Theater General Hospital were retrospectively analyzed. Whole-exome sequencing and mutation verification were performed by next-generation sequencing technology and Sanger sequencing. The pathogenicity of gene mutation was analyzed by population mutation ratio, pathogenicity prediction software, conservation scoring software, protein structure prediction, and in vitro experiments. Results:The mutation ratio of lipid metabolism-related genes, namely LPL, APOA5, LMF1, GPIHBP1, and APOC2, were 14.81%, 55.78%, 43.61%, 1.62%, and 0.61%, respectively. Among them, 44 heterozygous mutations in LPL gene were detected including 36 missense mutations, 5 nonsense mutations and 3 frameshift mutations, which were all rarely carried in single patient. Six HTG-AP patients carried the LPL gene heterozygous mutation c.835C>G (p.Leu279Val). The mean level of serum triglyceride at the onset of HTG-AP was 27.4 mmol/L. All of them had a history of recurrent HTG-AP, and most of them had severe acute pancreatitis. The serum LPL concentration and activity were lower than the normal level. The pathogenicity analysis results suggested that the LPL p.Leu279Val was a rare, highly possible pathogenic and highly conserved gene mutation. The in vitro results showed that the LPL p.Leu279Val could significantly reduce the synthesis and secretion ability of LPL as well as its enzymatic activity. Conclusions:The mutation ratio of lipid metabolism-related genes, including LPL, APOA5, LMF1, GPIHBP1, and APOC2, are relatively high in the HTG-AP patients. The LPL p.Leu279Val is a rare and highly possible pathogenic gene mutation, which may lead to recurrent episodes of HTG-AP.

Objective To construct an enhancer-based prognostic risk prediction model for non-small cell lung cancer (NSCLC) by integrating DNA methylome data and transcriptome data. Methods The weighted gene co-expression network analysis (WGCNA) was used to identify NSCLC related genes from the differentially methylated positions (DMPs) of enhancers. Based on the transcriptome data,the prognostic risk prediction model was constructed using LASSO-Cox regression algorithm. Results Through the analysis on DNA methylome data of NSCLC,19784 DMPs were obtained and their distribution patterns were characterized,including 6089 DMPs of enhancers. WGCNA analysis screened 79 highly correlated DMPs of enhancer with NSCLC from the 6089 DMPs. After analyzing the target genes of 79 DMPs with LASSO-Cox regression based on the transcriptome data,10 genes were used to construct a prognostic risk prediction model. The prognostic risk prediction model was evaluated by calculating the areas under the curve (AUC) of 3-,5-,and 10-year time-dependent receiver operating characteristic (ROC) curves in training set and validation set;and the results showed that the 3-,5-,and 10-year AUC in training set and validation set were all higher than 0.7. Finally,a nomogram was constructed to predict the 3-,5-,and 10-year survival probabilities of NSCLC. Conclusion This study provides new insights into the role of enhancers in NSCLC and has the potential to improve the prognosis by guiding personalized treatment decisions.

AIM To explore the clinical effects of Heiguteng Zhuifeng Huoluo Capsules combined with warm acupuncture at musculotendinous pathological nodes on patients with knee osteoarthritis due to Cold-Dampness Obstruction and Fixed Impediment.METHODS One hundred and sixty patients were randomly assigned into control group(80 cases)for 4-week intervention of both warm acupuncture at musculotendinous pathological nodes and Celecoxib Capsules,and observation group(80 cases)for 4-week intervention of Heiguteng Zhuifeng Huoluo Capsules,warm acupuncture at musculotendinous pathological nodes and Celecoxib Capsules.The changes in clinical effects,TCM syndrome scores,WOMAC scores,Lequesne indices,pain mediators(PGE2,β-EP),growth factors(TGF-β1,IGF-1),bone metabolism indices(OPG,RANKL),TLR4/NF-κB signaling pathway and safety indices were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P＜0.05).After the treatment,the two groups displayed decreased TCM syndrome scores,WOMAC scores,Lequesne indices,PGE2,RANKLE,TLR4,NF-κB(P＜0.05),and increased β-EP,growth factors,OPG(P＜0.05),especially for the observation group(P＜0.05).No serious adverse events or reactions were observable in the two groups.CONCLUSION For the patients with knee osteoarthritis due to Cold-Dampness Obstruction and Fixed Impediment,Heiguteng Zhuifeng Huoluo Capsules combined with warm acupuncture at musculotendinous pathological nodes can safely and effectively alleviate clinical symptoms,improve knee joint pain,and enhance joint functions,whose action mechanisms may contribute to the inhibition of TLR4/NF-κB signaling pathway expression and regulations of serum TGF-β1,IGF-1,OPG,RANKL levels.

Objective:To investigate the differences of the glymphatic system (GS) function between patients with mesial temporal lobe epilepsy with hippocampal sclerosis (mTLE-HS) and healthy controls and between different seizure types by using diffusion tensor imaging along perivascular space (DTI-ALPS), and to analyze the correlation between GS function and the course of disease, as well as the efficacy of predicting the surgical outcome.Methods:This study was a cross-sectional study. A total of 171 patients with mTLE-HS (mTLE-HS group) and 75 healthy volunteers (HC group) were retrospectively enrolled from July 2009 to July 2021 at Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University. The general information of all subjects, such as seizure type (partial seizure, secondary generalized seizure), surgical outcome, etc., was analyzed. The 3D magnetization prepared rapid gradient echo T 1WI and DTI sequence images were collected. The VBM analysis method was used to segment cerebrospinal fluid and calculate the volume. The ALPS index of the bilateral brain was calculated using the Atlas-based DTI-ALPS method. Independent sample t-test or paired t test were used to compare the ALPS index between the mTLE-HS group and HC group, and between patients with different seizure types. Pearson correlation analysis was used to analyze the correlation between bilateral ALPS index and disease duration in mTLE-HS group. The predictive value of the ALPS index for surgical outcomes was evaluated by receiver operating characteristics curve and area under the curve. Results:Among the 171 mTLE-HS patients, 98 patients were mTLE with left-side HS (mTLE-LHS) and 73 patients were mTLE with right-side HS (mTLE-RHS); 37 patients underwent surgical treatment, including 27 with good prognosis and 10 with poor prognosis. Compared with the HC group, the left-side ALPS index of mTLE-LHS and mTLE-RHS were both decreased ( P<0.05). The right-side ALPS index in mTLE-RHS was lower than that in the HC group ( P<0.001). There was no significant difference in the right-side ALPS index between mTLE-LHS and HC group ( P=0.080). The ALPS index on the affected side of patients with secondary generalized seizures was significantly lower than that of patients with only partial seizures (all P<0.05), but the difference in ALPS index on the healthy side was not statistically significant( P>0.05). The left-side and right-side ALPS index in mTLE-LHS were negatively correlated with disease duration ( r=-0.272, P=0.007; r=-0.307, P=0.002), but no significant correlation was found between the left-side or right-side ALPS index in mTLE-RHS (all P>0.05). The DTI-ALPS index on the affected side in mTLE-HS patients exhibited good diagnostic accuracy for surgical outcome classification, with an area under the curve of 0.778. Conclusions:The patients with mTLE-HS exhibit dysfunction of the GS, and the degree of impairment is related to the type of seizure and the course of epilepsy. The ALPS index, which characterizes the function of GS, demonstrates good diagnostic accuracy for classifying surgical outcomes.

Objective:To comprehensively evaluate the changes in pulmonary artery hemodynamics in patients with repaired tetralogy of Fallot (rTOF) using four-dimensional flow (4D Flow) MRI, and to explore the value of viscous energy loss (EL) as an evaluation parameter of the right heart in patients with rTOF.Methods:A total of 30 rTOF patients who were prospectively admitted at Fuwai Hospital between October 2017 and November 2019, and 19 healthy controls who were prospectively recruited on March 2023, were enrolled in this study. All participants underwent a comprehensive 4D Flow MRI evaluation, and indicators of comparison between the two groups including quantitative flow analysis, wall shear stress (WSS) assessment, and EL evaluation in four planes: the right ventricular outflow tract (RVOT), main pulmonary artery (MPA), right pulmonary artery (RPA), and left pulmonary artery (LPA). The correlation between EL and traditional right ventricular (RV) function parameters, as well as WSS, was analyzed.Results:The RV function was generally decreased in rTOF patients, with an RV ejection fraction of 41.42 (35.85, 43.33) %. Compared with healthy controls, the volumes of backward flow, peak velocities, and both axial and circumferential WSS at the RVOT, MPA, RPA, and LPA were significantly increased in rTOF patients (all P<0.05). The EL in the pulmonary artery was extremely low in healthy controls, with the maximum EL values for the MPA-RPA and MPA-LPA segments during cardiac cycle being 0.51 (0.42, 0.73) and 0.68 (0.40, 0.94) mW, respectively. However, in rTOF patients, the EL values were as high as 63.63 (35.35, 82.15) and 56.41 (34.96, 88.76) mW, respectively. There was a significant moderate negative correlation between EL and RV ejection fraction, and a moderate to high positive correlation between EL with RV volume index, regurgitation fraction, as well as peak velocities (all P<0.05). Additionally, there was a significant positive correlation between EL and axial and circumferential WSS, with the correlation coefficients at MPA ranging from 0.67 to 0.88 (all P<0.05). Conclusions:Disordered flow in the pulmonary artery is common in patients with rTOF and should not be overlooked. The extensive viscous EL is closely associated with traditional RV function parameters and WSS. EL is expected to become an crucial parameter for evaluating right heart function in rTOF patients.