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.

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that severely affects the health of the elderly, marked by its incurability, high prevalence, and extended latency period. The current approach to AD prevention and treatment emphasizes early detection and intervention, particularly during the pre-AD stage of mild cognitive impairment (MCI), which provides an optimal “window of opportunity” for intervention. Clinical detection methods for MCI, such as cerebrospinal fluid monitoring, genetic testing, and imaging diagnostics, are invasive and costly, limiting their broad clinical application. Speech, as a vital cognitive output, offers a new perspective and tool for computer-assisted analysis and screening of cognitive decline. This is because elderly individuals with cognitive decline exhibit distinct characteristics in semantic and audio information, such as reduced lexical richness, decreased speech coherence and conciseness, and declines in speech rate, voice rhythm, and hesitation rates. The objective presence of these semantic and audio characteristics lays the groundwork for computer-based screening of cognitive decline. Speech information is primarily sourced from databases or collected through tasks involving spontaneous speech, semantic fluency, and reading, followed by analysis using computer models. Spontaneous language tasks include dialogues/interviews, event descriptions, narrative recall, and picture descriptions. Semantic fluency tasks assess controlled retrieval of vocabulary items, requiring participants to extract information at the word level during lexical search. Reading tasks involve participants reading a passage aloud. Summarizing past research, the speech characteristics of the elderly can be divided into two major categories: semantic information and audio information. Semantic information focuses on the meaning of speech across different tasks, highlighting differences in vocabulary and text content in cognitive impairment. Overall, discourse pragmatic disorders in AD can be studied along three dimensions: cohesion, coherence, and conciseness. Cohesion mainly examines the use of vocabulary by participants, with a reduction in the use of nouns, pronouns, verbs, and adjectives in AD patients. Coherence assesses the ability of participants to maintain topics, with a decrease in the number of subordinate clauses in AD patients. Conciseness evaluates the information density of participants, with AD patients producing shorter texts with less information compared to normal elderly individuals. Audio information focuses on acoustic features that are difficult for the human ear to detect. There is a significant degradation in temporal parameters in the later stages of cognitive impairment; AD patients require more time to read the same paragraph, have longer vocalization times, and produce more pauses or silent parts in their spontaneous speech signals compared to normal individuals. Researchers have extracted audio and speech features, developing independent systems for each set of features, achieving an accuracy rate of 82% for both, which increases to 86% when both types of features are combined, demonstrating the advantage of integrating audio and speech information. Currently, deep learning and machine learning are the main methods used for information analysis. The overall diagnostic accuracy rate for AD exceeds 80%, and the diagnostic accuracy rate for MCI also exceeds 80%, indicating significant potential. Deep learning techniques require substantial data support, necessitating future expansion of database scale and continuous algorithm upgrades to transition from laboratory research to practical product implementation.

In recent years, with the improvement of people's awareness of physical examination and the more accurate detection equipment, the detection rate of pulmonary nodules is getting higher and higher. Surgical resection is the first choice for the treatment of malignant pulmonary nodules, but multiple pulmonary nodules, nodules in complex areas and those with surgical contraindications are not suitable for surgery. As an effective, less invasive and low-cost treatment, ablation has developed rapidly in the treatment of multiple pulmonary nodules. This article introduces the progress of several common ablation techniques (radiofrequency ablation, microwave ablation, cryoablation) in the treatment of multiple pulmonary nodules, the indications and contraindications of ablation techniques, the efficacy evaluation and complications after ablation therapy, and the prospects of ablation techniques in the treatment of multiple pulmonary nodules.

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 study the factors affecting hospital death in elderly patients with novel coronavirus infec-tion/disease 2019(COVID-19),and to build a risk prediction model.Methods According to the diagnostic criteria of Diagnosis and Treatment Protocol for COVID-19 Infection(Trial 10th Edition).Totally 775 elderly patients(≥60 years old)diagnosed as COVID-19 infection in the emergency department and fever clinic of the First Hospital of Changsha were selected as the research objects.General data and serum biomarkers of patients were collected.After treatment,the patients'data were divided into survival group and hospital death group.Binary Logistic regres-sion was used to screen the independent influencing factors of death,and ROC curve was used to analyze the pre-dictive value of related indicators on hospital death.Results After treatment,712 patients(91.9%)survived and 63 patients(8.3%)died in hospital.Binary Logistic regression analysis showed that:≥90 years old[OR=5.065,95%CI(1.427,17.974)],type 2 diabetes mellitus[OR= 3.757,95%CI(1.649,8.559)],COPD[OR= 5.625,95%CI(2.357,13.421)],monocyte ratio[OR=0.908,95%CI(0.857,0.963)],plasma fibringen[OR=1.376,95%CI(1.053,1.800)]and lactate dehydrogenase[OR=1.005,95%CI(1.001,o1.008)]were independent factors of in-hospital death(P＜0.05).The predictive value of diabetes mellitus+COPD+age+monocyte ratio+plasma fibrinogen+lactate dehydrogenase was proved in hospital death from COVID-19 infected patients:the area under the curve(AUC)was 0.883(95%CI:0.827,0.940,P＜0.001),the critical value≥0.710 suggested the risk of death in hospital,the specificity was 0.851,the sensitivity was 0.857.Conclusions The hospital mortality of the elderly after COVID-19 infection is higher and closely related to type 2 diabetes,COPD,monocyte ratio,plasma fibrinogen and lactate dehydrogenase.

Objective:To report the results of national surveillance on the distribution and antimicrobial resistance profile of clinical Gram-negative bacteria isolates from bloodstream infections in China in 2022.Methods:The clinical isolates of Gram-negative bacteria from blood cultures in member hospitals of national bloodstream infection Bacterial Resistant Investigation Collaborative System（BRICS）were collected during January 2022 to December 2022. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute（CLSI）. WHONET 5.6 and SPSS 25.0 software were used to analyze the data.Results:During the study period，9 035 strains of Gram-negative bacteria were collected from 51 hospitals，of which 7 895（87.4%）were Enterobacteriaceae and 1 140（12.6%）were non-fermenting bacteria. The top 5 bacterial species were Escherichia coli（ n=4 510，49.9%）， Klebsiella pneumoniae（ n=2 340，25.9%）， Pseudomonas aeruginosa（ n=534，5.9%）， Acinetobacter baumannii complex（ n=405，4.5%）and Enterobacter cloacae（ n=327，3.6%）. The ESBLs-producing rates in Escherichia coli， Klebsiella pneumoniae and Proteus spp. were 47.1%（2 095/4 452），21.0%（427/2 033）and 41.1%（58/141），respectively. The prevalence of carbapenem-resistant Escherichia coli（CREC）and carbapenem-resistant Klebsiella pneumoniae（CRKP）were 1.3%（58/4 510）and 13.1%（307/2 340）；62.1%（36/58）and 9.8%（30/307）of CREC and CRKP were resistant to ceftazidime/avibactam combination，respectively. The prevalence of carbapenem-resistant Acinetobacter baumannii（CRAB）complex was 59.5%（241/405），while less than 5% of Acinetobacter baumannii complex was resistant to tigecycline and polymyxin B. The prevalence of carbapenem-resistant Pseudomonas aeruginosa（CRPA）was 18.4%（98/534）. There were differences in the composition ratio of Gram-negative bacteria in bloodstream infections and the prevalence of main Gram-negative bacteria resistance among different regions，with statistically significant differences in the prevalence of CRKP and CRPA（ χ2=20.489 and 20.252， P<0.001）. The prevalence of CREC，CRKP，CRPA，CRAB，ESBLs-producing Escherichia coli and Klebsiella pneumoniae were higher in provinicial hospitals than those in municipal hospitals（ χ2=11.953，81.183，10.404，5.915，12.415 and 6.459， P<0.01 or <0.05），while the prevalence of CRPA was higher in economically developed regions（per capita GDP ≥ 92 059 Yuan）than that in economically less-developed regions（per capita GDP <92 059 Yuan）（ χ2=6.240， P=0.012）. Conclusions:The proportion of Gram-negative bacteria in bloodstream infections shows an increasing trend，and Escherichia coli is ranked in the top，while the trend of CRKP decreases continuously with time. Decreasing trends are noted in ESBLs-producing Escherichia coli and Klebsiella pneumoniae. Low prevalence of carbapenem resistance in Escherichia coli and high prevalence in CRAB complex have been observed. The composition ratio and antibacterial spectrum of bloodstream infections in different regions of China are slightly different，and the proportion of main drug resistant bacteria in provincial hospitals is higher than those in municipal hospitals.

Digital polymerase chain reaction(dPCR)is a PCR technology that realizes accurate quantification of single-copy nucleic acid molecules by dividing the reaction system into tens of thousands of independent PCR reaction units for single-molecule-level amplification and integrating Poisson distribution.Due to its single-copy sensitivity and accurate quantification without the need of standard curves,dPCR has been widely used in disease diagnosis.By introducing technologies such as stepped emulsification and three-dimensional imaging,dPCR has been greatly improved in terms of accuracy,multiplexability and turnaround time,significantly enhancing its performance in clinical disease diagnosis.Based on this,this paper traced the technological development history of dPCR,gave an overview of its application in detection of tumors,infections and other diseases,and further discussed the challenges and opportunities of the development of dPCR,with the aim of providing a reference for the development and utilization of dPCR in the future,and promoting the high-quality development of molecular technology in clinical testing.

BACKGROUND:Studies have shown that there is a close association between spinal cord injury and ferroptosis,and that tetramethylpyrazine has the function of regulating redox reactions. OBJECTIVE:To investigate the regulatory effect of tetramethylpyrazine on ferroptosis in rats with spinal cord injury and its mechanism. METHODS:Thirty-six female specific pathogen-free Sprague-Dawley rats were randomly divided into sham-operated group,model group and tetramethylpyrazine group,with 12 rats in each group.Animal models of spinal cord injury were established using the modified Allen's method in the latter two groups.No treatment was given in the sham-operated group,while rats in the model and tetramethylpyrazine groups were given intraperitoneal injection of normal saline and tetramethylpyrazine solution,once a day,for 28 days. RESULTS AND CONCLUSION:The Basso,Beattie&Bresnahan Locomotor Rating Scale score in the tetramethylpyrazine group was lower than that in the sham-operated group but higher than that in the model group after 14,21,and 28 days of treatment(P＜0.05).After 28 days of treatment,hematoxylin-eosin staining showed that in the model group,the spinal cord tissue of rats showed cavity formation,necrotic tissue and inflammatory infiltration with fibrous tissue formation;in the tetramethylpyrazine group,the area of spinal cord tissue defects was smaller,and inflammatory infiltration and fibrous tissue formation were less than those in the model group.After 28 days of treatment,Prussian blue staining showed that a large amount of iron deposition was seen in the spinal cord tissue of rats in the model group,and less iron deposition was seen in the spinal cord tissue of rats in the tetramethylpyrazine group than in the model group.After 28 days of treatment,the levels of glutathione and superoxide dismutase in the rat spinal cord tissue were decreased(P＜0.05)and the level of malondialdehyde was increased in the model group compared with the sham-operated group(P＜0.05);the levels of glutathione and superoxide dismutase in the rat spinal cord tissue were increased(P＜0.05)and the level of malondialdehyde was decreased in the tetramethylpyrazine group compared with the model group(P＜0.05).After 28 days of treatment,qRT-PCR and western blot assay showed that the mRNA and protein levels of glutathione peroxidase 4,ferritin heavy chain,and ferroportin in the rat spinal cord tissue in the model group were decreased compared with those in the sham-operated group(P＜0.05),while the mRNA and protein levels of glutathione peroxidase 4,ferritin heavy chain,and ferroportin in the rat spinal cord tissue in the tetramethylpyrazine group were increased compared with those in the model group(P＜0.05).Immunofluorescence staining showed that after 28 days of treatment,the neuronal nuclei positive staining in the spinal cord of rats was the most in the sham-operated group and the least in the model group.To conclude,tetramethylpyrazine can improve motor function and play a neuroprotective role in rats with spinal cord injury by regulating ferroptosis.

Objective To acquire,evaluate and integrate the best evidence of perioperative exercise interventions in patients with liver cancer and provide evidence-based references for clinical medical staff.Methods Following the"6S"Evidence Resource Pyramid model,literatures in perioperative exercise interventions published between January 2010 and June 2022 were retrieved from various databases,including BMJ Best Practice,UpToDate,Guidelines International Network,National Guideline Clearinghouse,National Institute for Health and Clinical Excellence,Scottish Intercollegiate Guidelines Network,Medlive,Cochrane Library,JBI,Web of Science,PubMed,Embase,CINAHL,CNKI,SinoMed,Wanfang Data,American Cancer Society,American College of Sports Medicine and International Liver Cancer Association from January 2010 to June 2022.Two researchers evaluated the quality of the retrieved literatures and extracted evidences that met the inclusion criteria.Results A total of 22 articles were included,yielding 26 pieces of evidence across seven themes:the necessity of exercise,evaluation before exercise,preoperative exercise program,postoperative exercise program,exercise monitoring,health education and effect evaluation.Conclusions This study provides a summary of the best evidence regarding perioperative exercise interventions in the patients with liver cancer.The findings offer valuable references for clinical healthcare providers to deliver evidence-based care for the patients with liver cancer.

Humans ; Ulcer ; Brucellosis/complications* ; Aorta, Abdominal