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.

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.

Objective To explore the effects of dydrogesterone combined with granulocyte colony-stimulating factor(G-CSF)on blood flow parameters and sex hormone levels in patients with thin endometrial(TE)infertility.Methods TE infertility patients were randomly divided into control group and treatment group.All patients were treated with freezing-thawing embryo transfer.The control group was treated with desdrogesterone after ovulation monitoring by vaginal ultrasound,10 mg each time,bid,and stopped until the 14th day after ovulation.In treatment group,G-CSF was given on the basis of control group,and the endometrial thickness and hemodynamic parameters were monitored by vaginal ultrasound 3 days later,and then embryo transfer was performed.Blood flow parameters,ultrasound parameters,endometrial morphology,sex hormone levels[follicle stimulating hormone(FSH),luteinizing hormone(LH),estradiol(E2),progesterone(P)]and pregnancy status were compared between the two groups.Results There were 49 cases in treatment group and 49 cases in control group.After treatment,endometrial thickness in treatment group and control group were(9.59±1.35)and(7.89±1.14)mm;follicle diameter were(19.35±1.58)and(17.75±1.42)mm;uterine volume were(92.68±7.85)and(85.69±6.74)cm3;PI were 1.29±0.27 and 1.74±0.32;RI were 0.32±0.09 and 0.50±0.11;peak systolic/end-diastolic velocity(S/D)were 2.03±0.28 and 2.21±0.25;P were(45.98±5.75)and(39.15±5.78)nmol·L-1;E2 were(462.58±58.42)and(339.45±45.97)pg·mL-1;FSH were(12.18±2.58)and(9.42±1.42)U·L-1;LH were(29.42±4.18)and(22.25±3.40)U·L-1;there were statistically significant differences between the two groups(all P＜0.05).The clinical pregnancy rate(51.02％)and implantation rate(57.14％)in treatment group were higher than those in control group(30.61％and 36.73％),and the differences were statistically significant(all P＜0.05).Conclusion Dydrogesterone combined with G-CSF can improve blood flow parameters,endometrial thickness and sex hormone levels in TE infertility patients,and help to increase the embryo implantation rate.

Objective This study utilized bioinformatics to investigate the role of secreted phosphoprotein 1(SPP1)in tumors and assess its differential expression and prognostic significance across human cancers.Method Cancer sample data was sourced from the Cancer Genome Atlas(TCGA)and Genotype-tissue Expression(GTEx)database.Analysis of gene expression differences in pan-cancer involving SPP1 utilized the CPTAC database,correlations between SPP1 and patient survival outcomes were assessed using GEPIA2,and alterations in gene mutations for SPP1 across 32 cancers were appraised via the CbioPortal tool.Results Across 32 tumor types,SPP1 mRNA disparity is observed in eight cancer types,decreasing only in clear cell renal carcinoma while increasing in other malignancies.Expression in tumor stage is specific;SPP1 mRNA negatively correlates with patient overall survival(OS)and disease-free survival(DFS);low methylation associated with SPP1 gene activity promotes oncogene activation,impacting tumor cell generation,apoptosis,and proliferation,thereby influencing cancer progression;SPP1 primarily exhibits missense mutations that correlate with poor prognoses in prostate and stomach cancer patients.Conclusion Through pan-cancer studies,SPP1's selective expression in human malignancies was validated and found to be strongly correlated with clinical prognosis.This suggests that SPP1 is a viable target for cancer prognostic precision.

Objective To utilize bioinformatic approaches to elucidate the correlation between secreted phosphoprotein 1(SPP1)and immune infiltration in various malignancies,elucidating the mechanism of gene function in cancer-immune cell interplay.Methods Cancer samples were derived from the Cancer Genome Atlas(TCGA)and Genotype-tissue Expression(GTEx)database,examining SPP1 expression difference between tumors and normal tissues using Gene Expression Profiling Interactive Analysis(GEPIA2),and its correlation with different immune cells in extracellular matrix via TIMER2 Database.SPP1's association with interacting molecules was scrutinized on the STRING website,followed by assessment of its distinct functional state across various cancers from Cancer Single-cell State Atlas Database(Cancer SEA).Results In 32 tumor types,SPP1 mRNA is significantly elevated in 23 types of cancers and correlates heavily with fibroblast,integrin family,and CD44.Furthermore,the SPP1 gene exhibits profound specificity in tumor functions such as vascular invasion,metastasis,DNA damage,and repair.Conclusion Specific expression of SPP1 in tumors signifies a significant correlation with tumor immune cells in the extracellular matrix,promoting tumor progression and invasion.This suggests that targeted monitoring of SPP1 could serve as a prospective cancer diagnostics/therapeutics biomarker.

Objective:To analyze the clinicopathological features of gastric oxyntic gland neo-plasms.Methods:Forty-nine cases of stomach oxyntic gland neoplasms including oxyntic gland adenoma(OGA)and gastric adenocarcinoma of the fundic gland type(GA-FG)diagnosed in the Sec-ond Hospital of Shandong University from January 2016 to December 2020 were selected.The clini cal information,endoscopic appearance,histological features and immunophenotype were analyzed retrospectively,and followed up.Results:Age of the gastric oxyntic gland neoplasm patients ranged from 19 to 83 years old,with an average age of(57.3±2.4)years old.The male-to-female ratio was 24:25.Most of the lesions were located in the gastric body(27/49)and fundus(15/49).There were four endoscopic phenotypes:flat bulging,polypoid,flat and depression.In some lesions,there were dilated dendritic vessels.48 cases were single onset.The mean maximum diameter of lesions was(3.9±0.5)mm(1.0～7.0 mm).Seven cases showed submucosal invasion,and the inva-sion depth was less than 500 μm.The tumor consists of the dense glandular and the glandular con-nects to form a strip shape,which is irregularly branched and labyrinthlike under the microscope.These tumor cells were well differentiated and the morphology was similar to oxyntic gland cells.The chief cells were the predominant cells.The nucleus was mildly enlarged with slight pleomorphism and the mitosis was uncommon.The oxyntic gland neoplasms of the stomach were diffusely posi-tive for Mucin-6(MUC6)(100％)and Pepsinogen Ⅰ(83％),focally positive for H+/K+-ATPase(58％).Conclusions:The stomach oxyntic gland neoplasm is a new histology type with unique clinico-pathological features.The incidence of this neoplasm is low and the prognosis is good but it still needs long-term follow-up.

This study aimed to investigate the role and mechanism of sappanone A (SA) in regulating renal ischemia-reperfusion injury (IRI) in rats. The animal experiment has been approved by the Ethics Committee of Suzhou Wujiang District Children's Hospital (approval number: 2022010). First, hematoxylin-eosin (H&E) staining was used to evaluate the effects of SA on IRI, and renal damage was scored. Serum creatinine (SCr), blood urea nitrogen (BUN) and cystatin C (Cystatin C) were analyzed. The effect of sappanone A on the apoptosis of renal tubular epithelial cells induced by IRI was analyzed by TUNEL staining. Protein expression levels of p-JNK/JNK, p-ERK/ERK, Bcl2, Bax and cleaved-caspase 3 in renal tissues were detected by Western blot. Finally, H&E staining, serological analysis, TUNEL staining and Western blot were used to determine whether JNK activator anisomycin could reverse the effect of SA on IRI in rats. The results showed SA significantly reduced the renal tubule injury caused by ischemia-reperfusion, and decreased the level of SCr, BUN and Cys C in serum. TUNEL staining showed that SA significantly reduced the apoptosis of renal tubular epithelial cells induced by IRI. Western blot analysis of kidney tissue showed that SA significantly promoted the expression of apoptosis inhibiting protein Bcl2 and inhibited the expression of apoptosis-promoting proteins Bax and cleaved-caspase 3. Further analysis elucidated that SA did not affect the phosphorylation of ERK but decreased the phosphorylation of JNK. Finally, H&E staining, serological analysis, TUNEL staining and Western blot confirmed that JNK activator anisomycin could reverse the alleviating effect of SA on IRI in rats. The above findings suggest that SA could alleviate IRI in rats by inhibiting JNK phosphorylation.