With the continuous development of society, a large number of new chemicals are continuously emerging, which presents a challenge to current risk assessment and safety management of chemicals. Traditional toxicology research methods have certain limitations in quickly, efficiently, and accurately assessing the toxicity of many chemicals, and cannot meet the actual needs. In response to this challenge, computational toxicology that use mathematical and computer models to achieve the prediction of chemical toxicity has emerged. In the meantime, as researchers increasingly pay attention to understanding the interaction mechanisms between exogenous chemical substances and the body from the system level, and multiomics technologies develop rapidly such as genomics, transcriptomics, proteomics, and metabolomics, huge amounts of data have been generated, providing rich information resources for studying the interactions between chemical substances and biological molecules. System toxicology and network toxicology have also developed accordingly. Of these, network toxicology can integrate these multiomics data to construct biomolecular networks, and then quickly predict the key toxicological targets and pathways of chemicals at the molecular level. This paper outlined the concept and development of network toxicology, summarized the main methods and supporting tools of network toxicology research, expounded the application status of network toxicology in studying potential toxicity of exogenous chemicals such as agricultural chemicals, environmental pollutants, industrial chemicals, and foodborne chemicals, and analyzed the development prospects and limitations of network toxicology research. This paper aimed to provide a reference for the application of network toxicology in other fields.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

Background With the progression of industrialization, an increasing number of emerging contaminants are entering aquatic environments, posing significant threats to the safety of drinking water. Therefore, establishing a system for identifying unknown hazardous factors and implementing safety warning mechanisms for drinking water is of paramount importance. Among these efforts, non-target screening plays a critical role, but its effectiveness is largely constrained by the scope of coverage of sample pre-treatment methods. Objective To integrate modern chromatography/mass spectrometry techniques with advanced data mining methods to develop a non-discriminatory sample pre-treatment method for comprehensive enrichment of unknown contaminants in drinking water, laying a technical foundation for the discovery and identification of unknown organic hazardous factors in drinking water. Methods A non-discriminatory pre-treatment method based on supramolecular and solid-phase extraction was developed. The final target compounds including 333 pesticides, 194 pharmaceuticals and personal care products (PPCPs), and 59 per- and polyfluoroalkyl substances (PFASs) were used for optimizing the pre-treatment method, confirming its coverage. The impacts of different eluents on the absolute recovery rates of target compounds were compared to select the conditions with the highest recovery for sample pre-treatment. The effects of different supramolecular solvents and salt concentrations on target compound recovery were also evaluated to determine the most suitable solvent and salt concentration. Results The solid-phase extraction elution solvents, supramolecular extraction solvents, and salt concentrations were optimized based on the target compound recovery rates. The optimal recovery conditions were achieved using 2 mL methanol, 2 mL methanol (containing 1% formic acid), 2 mL ethyl acetate, 2 mL dichloromethane, hexanediol supramolecular solvent, and 426 mg salt. The detection method developed based on these conditions showed a good linear relationship for all target compounds in the range of 0.1-100.0 ng·mL⁻¹, with R² > 0.99. The method’s limit of detection ranged from 0.01 ng⁻¹ to 0.95 ng⁻¹, and 95% of target compounds were recovered in the range of 20%-120%, with relative standard deviation (RSD) less than 30%, indicating good precision. Conclusion The combined pre-treatment method of solid-phase extraction and supramolecular solvent extraction can effectively enrich contaminants in drinking water across low, medium, and high polarities, enabling broad-spectrum enrichment of diverse trace contaminants in drinking water. It provides technical support for broad-spectrum, high-throughput screening and identification of organic pollutants in drinking water, and also serves as a reference for establishing urban drinking water public safety warning systems.

In recent years, the deep integration of artificial intelligence (AI) into medical education has created new opportunities for teaching Biochemistry and Molecular Biology, while also offering innovative solutions to the pedagogical challenges associated with protein structure and function. Focusing on the case of anaplastic lymphoma kinase (ALK) gene mutations in non-small-cell lung cancer (NSCLC), this study integrates AI into case-based learning (CBL) to develop an AI-CBL hybrid teaching model. This model features an intelligent case-generation system that dynamically constructs ALK mutation scenarios using real-world clinical data, closely linking molecular biology concepts with clinical applications. It incorporates AI-powered protein structure prediction tools to accurately visualize the three-dimensional structures of both wild-type and mutant ALK proteins, dynamically simulating functional abnormalities resulting from conformational changes. Additionally, a virtual simulation platform replicates the ALK gene detection workflow, bridging theoretical knowledge with practical skills. As a result, a multidimensional teaching system is established—driven by clinical cases and integrating molecular structural analysis with experimental validation. Teaching outcomes indicate that the three-dimensional visualization, dynamic interactivity, and intelligent analytical capabilities provided by AI significantly enhance students’ understanding of molecular mechanisms, classroom engagement, and capacity for innovative research. This model establishes a coherent training pathway linking “fundamental theory-scientific research thinking-clinical practice”, offering an effective approach to addressing teaching challenges and advancing the intelligent transformation of medical education.

Hyperbaric oxygen therapy characterized by fewer side effects and simple operation has been explored as a potential therapy for depression. This article provides a review of researches relevant to current clinical application and mechanism of hyperbaric oxygen therapy for depression， aiming to provide valuable references for the formulation of new strategies for the treatment of depression. Hyperbaric oxygen therapy has been demonstrated to be useful as an adjunctive therapy for depression， which can effectively alleviate depression by regulating the homeostasis of hypothalamus-pituitary-adrenal axis， inhibiting inflammation and enhancing synaptic plasticity. And hyperbaric oxygen therapy as adjuvant to antidepressants for depression can contribute to increasing the treatment effectiveness to some extent.

Based on the interaction between supramolecule of traditional Chinese medicine and enterobacteria, the material basis of Rhei Radix et Rhizoma and Coptidis Rhizoma was explored. Scanning electron microscopy (SEM) and dynamic light scattering (DLS) were used to characterize the morphological differences of Rhubarb single decoction, Coptis single decoction and Rhubarb and Coptis co-decoction. An in vitro antibacterial model (E. coli, E. faecium and B. subtilis) was established to evaluate the damage effect of the combination of Rhei Radix et Rhizoma and Coptidis Rhizoma on enterobacteria. Ultra high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to analyze the changes of chemical components of single decoctions and co-decoctions. The co-decoction of Rhei Radix et Rhizoma and Coptidis Rhizoma was turbid after decocting. The spherical particles of 300-400 nm were observed under SEM, and the co-decoction was more uniform and stable than that of single decoction. The interaction between supramolecules formed after the combination of Rhei Radix et Rhizoma and Coptidis Rhizoma and enterobacteria was significantly different from that of single decoction. In the process of interaction between supramolecules and enterobacteria, the spherical state was maintained, and the medicinal ingredients in Coptidis Rhizoma or Rhei Radix et Rhizoma were blocked, which could effectively alleviate the damage to enterobacteria. This study provided a reference for subsequent studies on the regulation of intestinal flora homeostasis by the combination of Rhei Radix et Rhizoma and Coptidis Rhizoma.

Objective To investigate the mutation types of colorectal neuroendocrine tumors(NETs)and better un-derstand the pathogenesis of colorectal nets.Methods Patients undergoing colorectal NETs surgery were recruited,colorectal NETs and corresponding adjacent cancerous tissues were collected,and whole genome sequencing(WGS)was performed and further deeply analyzed.Results WGS sequencing showed that the mutation types of colorectal NETs included single nucleotide mutations,insertion and deletion mutations(InDel,less than 50 bp in length),copy number variations(CNV),and large structural variations(SV,more than 50 bp in length),such as insertion(INS),deletion(DEL),intra chromosomal translocation(ITX),inter chromosomal translocation(CTX)and inversion(INV).Conclusions A large number of somatic mutations occur in colorectal NETs,especially chro-mosome translocation

Objective:To investigate the incidence of venous thromboembolism (VTE) in patients with esophageal cancer (EC).Methods:The retrospective cohort study was conducted. The clinicopathological data of 8 458 EC patients who were admitted to Sichuan Cancer Hospital from January 2017 to December 2021 were collected. There were 6 923 males and 1 535 females, aged (64±9)years. There were 3 187 patients undergoing surgical treatment, and 5 271 cases undergoing non-surgical treatment. Observation indicators: (1) incidence of VTE in EC patients; (2) treatment and outcomes of patients with VTE. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the t test. Measurement data with skewed distribution were represented as M(range), and comparison between groups was analyzed using the nonparameter rank sum test. Count data were expressed as absolute numbers or percentages, and comparison between groups was analyzed using the chi-square test or Fisher exact probability. Comparison of ordinal data was analyzed using the nonparameter rank sum test. Results:(1) Incidence of VTE in EC patients. Of 8 458 EC patients, 175 cases developed VTE, with an incidence rate of 2.069%(175/8 458). Among 175 VTE patients, there were 164 cases of deep venous thrombosis (DVT), 4 cases of pulmonary embolism (PE), 7 cases of DVT and PE. There were 59 surgical patients and 116 non-surgical patients. There was no significant difference in thrombus type between surgical and non-surgical EC patients with VTE ( χ2=1.95, P>0.05). Of 3 187 surgical patients, the incidence of VTE was 1.851%(59/3 187), including an incidence of 0.157%(5/3 187) of PE. PE accounted for 8.475%(5/59) of surgical patients with VTE. Of 5 271 non-surgical patients, the incidence of VTE was 2.201%(116/5 271), including an incidence of 0.114%(6/5 271) of PE. PE accounted for 5.172%(6/116) of non-surgical patients with VTE. There was no significant difference in the incidence of VTE or PE between surgical patients and non-surgical patients ( χ2=1.20, 0.05, P>0.05). (2) Treatment and outcomes of patients with VTE. Among 175 EC patients with VTE, 163 cases underwent drug treatment, and 12 cases did not receive treatment. Among 163 cases with drug therapy, 158 cases underwent anticoagulant therapy, 5 cases were treated with thrombolysis. All the 163 patients were improved and discharged from hospital. Conclusions:The incidence of VTE in patients with EC is relatively low, as 2.069%. There is no significant difference in the incidence of VTE or thrombus type between surgical EC patients and non-surgical EC patients.

Objective To evaluate the relationship between diabetic nephropathy(DN)and diabetic retinopathy(DR)in patients with type 2 diabetes mellitus(T2DM)based on imaging and clinical testing data.Methods Totally 600 T2DM patients who visited the First People's Hospital of Ziyang from March 2021 to December 2022 were included.The fundus photography and fundus fluorescein angiography were performed on all these patients and their age,gender,T2DM duration,cardiovascular diseases,cerebrovascular disease,hypertension,smoking history,drinking history,body mass in-dex,systolic blood pressure,diastolic blood pressure and other clinical data were collected.The levels of fasting blood glu-cose(FPG),triglyceride(TG),total cholesterol(TC),high-density lipoprotein cholesterol(HDL-C),low-density lipo-protein cholesterol(LDL-C),glycosylated hemoglobin(HbA1c),24 h urinary albumin(UAlb),urinary albumin to creati-nine ratio(ACR),serum creatinine(Scr)and blood urea nitrogen(BUN)were measured.Logistic regression was used to analyze the risk factors associated with DR.DR staging was performed according to fundus images,and the convolutional neural network(CNN)algorithm was used as an image analysis method to explore the correlation between DR and DN based on emission computed tomography(ECT)and clinical testing data.Results The average lesion area rates of DR and DN detected by the CNN in the non-DR,mild-non-proliferative DR(NPDR),moderate-NPDR,severe-NPDR and pro-liferative DR(PDR)groups were higher than those obtained by the traditional algorithm(TCM).As DR worsened,the Scr,BUN,24 h UAlb and ACR gradually increased.Besides,the incidence of DN in the non-DR,mild-NPDR,moderate-NPDR,severe-NPDR and PDR groups was 1.67％,8.83％,16.16％,22.16％and 30.83％,respectively.Logistic regression analysis showed that the duration of T2DM,smoking history,HbA1c,TC,TG,HDL-C,LDL-C,24 h UAlb,Scr,BUN,ACR and glomerular filtration rate(GFR)were independent risk factors for DR.Renal dynamic ECT analysis demonstrated that with the aggravation of DR,renal blood flow perfusion gradually decreased,resulting in diminished renal filtration.Conclusion The application of CCN in the early stage DR and DN image analysis of T2DM patients will improve the diag-nosis accuracy of DR and DN lesion area.The DN is worsening as the aggravation of DR.

Objective To investigate the distribution characteristics of pathogens in adult community-acquired pneumonia(CAP)patients who visited the fever clinic before and after the outbreak of the novel coronavirus infection,and to provide theoretical basis for the diagnosis,treatment,and prevention of CAP.Methods CAP patients who visited the fever clinic of Peking University Third Hospital from June 2017 to July 2022 were included in the study and divided into pre-outbreak and post-outbreak groups based on the time point(January 24,2020,when Beijing entered the first-level COVID-19 prevention and control).Respiratory samples were collected and pathogen nucleic acid detection was performed using real-time fluorescence quantitative polymerase chain reaction.The detection and distribution of pathogens were analyzed.Results A total of 415 CAP patients were included,divided into pre-outbreak group(312 cases)and post-outbreak group(103 cases).Mycoplasma pneumoniae,Streptococcus pneumoniae,and Influenza A virus were the three most common pathogens in the pre-outbreak CAP group.In the post-outbreak community-acquired pneumonia,Influenza B virus,Klebsiella pneumoniae and Streptococcus pneumoniae were the three most common pathogens.The incidence of Influenza A virus,Parainflluenza virus,and Mycoplasma pneumoniae was significantly higher in the pre-outbreak period than in the post-outbreak period,and the differences between the two groups were statistically significant(all P＜0.05).Conclusion Before and after the epidemic,viral infection are the main pathogens of CAP patients,which is of great significance for future empirical treatment,protection of susceptible population and control of infectious diseases.