With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT have also started to achieve distinction in this domain. Increasing research shows the undeniable role of AI in reshaping various aspects of medical image analysis, including processes such as image enhancement, segmentation, detection in image preprocessing, and postprocessing related to medical diagnosis and prognosis in clinical settings. However, despite the significant progress in AI research, studies investigating the recent advances in AI technology in the aforementioned aspects, the changes in research hotspot trajectories, and the performance of studies in addressing key clinical challenges in this field are limited. This article provides an overview of recent advances in AI for medical image analysis and discusses the methodological profiles, advantages, disadvantages, and future trends of AI technologies.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Deep Learning ; Diagnostic Imaging/methods*

OBJECTIVE: To explore the key target molecules and potential mechanisms of oridonin against non-small cell lung cancer (NSCLC). METHODS: The target molecules of oridonin were retrieved from SEA, STITCH, SuperPred and TargetPred databases; target genes associated with the treatment of NSCLC were retrieved from GeneCards, DisGeNET and TTD databases. Then, the overlapping target molecules between the drug and the disease were identified. The protein-protein interaction (PPI) was constructed using the STRING database according to overlapping targets, and Cytoscape was used to screen for key targets. Molecular docking verification were performed using AutoDockTools and PyMOL software. Using the DAVID database, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted. The impact of oridonin on the proliferation and apoptosis of NSCLC cells was assessed using cell counting kit-8, cell proliferation EdU image kit, and Annexin V-FITC/PI apoptosis kit respectively. Moreover, real-time quantitative PCR and Western blot were used to verify the potential mechanisms. RESULTS: Fifty-six target molecules and 12 key target molecules of oridonin involved in NSCLC treatment were identified, including tumor protein 53 (TP53), Caspase-3, signal transducer and activator of transcription 3 (STAT3), mitogen-activated protein kinase kinase 8 (MAPK8), and mammalian target of rapamycin (mTOR). Molecular docking showed that oridonin and its key target molecules bind spontaneously. GO and KEGG enrichment analyses revealed cancer, apoptosis, phosphoinositide-3 kinase/protein kinase B (PI3K/Akt), and other signaling pathways. In vitro experiments showed that oridonin inhibited the proliferation, induced apoptosis, downregulated the expression of Bcl-2 and Akt, and upregulated the expression of Caspase-3. CONCLUSION Oridonin can act on multiple targets and pathways to exert its inhibitory effects on NSCLC, and its mechanism may be related to upregulating the expression of Caspase-3 and downregulating the expressions of Akt and Bcl-2.
Diterpenes, Kaurane/chemistry* ; Carcinoma, Non-Small-Cell Lung/pathology* ; Humans ; Network Pharmacology ; Lung Neoplasms/pathology* ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Molecular Docking Simulation ; Protein Interaction Maps/drug effects* ; Cell Line, Tumor ; Signal Transduction/drug effects* ; Gene Expression Regulation, Neoplastic/drug effects* ; Reproducibility of Results ; Gene Ontology

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To assess the effectiveness of the evaluation of military physical function(EMPF)system in predicting the occurrence of military training injuries among new recruits to provide scientific guidance and methodological choice for military training.Methods A total of 527 new recruits from 5 grassroots units from July 2016 to February 2018 were selected for the study.The recruits underwent EMPF testing,and their military training injuries were monitored over a 2-year follow-up period.Those who sustained injuries during training were divided into injury group(n=163),while the remaining recruits were placed in healthy group(n=364).The predictive ability of the total EMPF score for training injuries was assessed using the receiver operating characteristic curve(ROC),and the correlation between the total EMPF score,individual test scores,and military training injuries were analyzed using binary logistic regression.Results The total EMPF score of new recruits in injury group(19.52±1.97)was significantly lower than that of healthy group(24.31±1.54)(P<0.001),which also demonstrated a high diagnostic value in predicting the risk of military training injuries,with an area under the curve(AUC)of ROC of 0.971(P<0.001).A cut-off value of 22 scores was found to have the highest accuracy in predicting future training injuries,with an odds ratio(OR)of 25.63,sensitivity of 0.939,specificity of 0.879,positive likelihood ratio of 7.76,and a post-test probability of 0.67.Binary logistic regression analysis revealed that 6 EMPF tests,including holding the ball over and leaning back,bending forward and touching the ground with the ball,lunge squat and twist,swallow balance with holding the ball afterward,vertical jump,and respiratory pattern assessment,were negatively associated with the risk of military training injuries(P<0.0001).Conclusion The EMPF system can effectively predict the risk of military training injuries,with military personnel whose total EMPF score is less than 22 being at higher risk of sustaining such injuries.

Objective:To investigate the use of apolipoprotein B (ApoB) diagnostic algorithm for hyperlipidemia typing combined with whole exome sequencing in the precise diagnosis of familial hyperlipidemia.Methods:A retrospective observational study was conducted by collecting clinical information on all patients who attended our hospital, and had their lipid levels tested from January 2023 to May 2024, including 440 patients with low-density lipoprotein cholesterol (LDL-C)>4.10 mmol/L. Family history, current lipid levels, medication use, and comorbidities were collected by telephone follow-ups. Among them, 10 patients had a family history of hyperlipidemia. Peripheral venous blood samples were collected from patients with a family history of hyperlipidemia, and whole exome sequencing was performed.Results:According to the Fredrickson typing of WHO, 10 patients (P1 to P10) could be categorized into two groups, of which only type Ⅱa could be excluded in 6 cases, and the typing could not be determined in 4 cases. The ApoB diagnostic algorithm of hyperlipidemia typing could classify patients P1 and P2 as type Ⅱa, patients P3 to P7, P9 and P10 as type Ⅱb, and patient P8 as type Ⅴ, respectively. Whole exome sequencing detected mutations in LDLR, PCSK9, C5AR2, KIF12, ALMS1, ABCG5, COL4A3, and MTTP genes.Conclusion:The ApoB diagnostic algorithm for hyperlipidemia can be used for accurate typing of hyperlipidemia, and ApoB could be recommended as a routine lipid testing parameter. The ApoB diagnostic algorithm for hyperlipidemia combined with whole exome sequencing could be used for the accurate typing of patients with familial hyperlipidemia and defining the underlying gene mutations.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To investigate the use of apolipoprotein B (ApoB) diagnostic algorithm for hyperlipidemia typing combined with whole exome sequencing in the precise diagnosis of familial hyperlipidemia.Methods:A retrospective observational study was conducted by collecting clinical information on all patients who attended our hospital, and had their lipid levels tested from January 2023 to May 2024, including 440 patients with low-density lipoprotein cholesterol (LDL-C)>4.10 mmol/L. Family history, current lipid levels, medication use, and comorbidities were collected by telephone follow-ups. Among them, 10 patients had a family history of hyperlipidemia. Peripheral venous blood samples were collected from patients with a family history of hyperlipidemia, and whole exome sequencing was performed.Results:According to the Fredrickson typing of WHO, 10 patients (P1 to P10) could be categorized into two groups, of which only type Ⅱa could be excluded in 6 cases, and the typing could not be determined in 4 cases. The ApoB diagnostic algorithm of hyperlipidemia typing could classify patients P1 and P2 as type Ⅱa, patients P3 to P7, P9 and P10 as type Ⅱb, and patient P8 as type Ⅴ, respectively. Whole exome sequencing detected mutations in LDLR, PCSK9, C5AR2, KIF12, ALMS1, ABCG5, COL4A3, and MTTP genes.Conclusion:The ApoB diagnostic algorithm for hyperlipidemia can be used for accurate typing of hyperlipidemia, and ApoB could be recommended as a routine lipid testing parameter. The ApoB diagnostic algorithm for hyperlipidemia combined with whole exome sequencing could be used for the accurate typing of patients with familial hyperlipidemia and defining the underlying gene mutations.

Objective:To investigate associations between cardiac biomarkers with stroke severity and short-term outcome in patients with acute ischemic stroke (AIS).Methods:Patients with AIS admitted to the Affiliated Hospital of Qingdao University from June 2018 to February 2024 whose etiological classification was large artery atherosclerosis (LAA), small vessel occlusion (SVO) or cardioembolism (CE) were included retrospectively. According to the National Institutes of Health Stroke Scale score at admission, patients were divided into mild stroke group (≤8) and moderate to severe stroke group (>8). According to the modified Rankin Scale score at discharge, patients were divided into good outcome group (≤2) and poor outcome group (>2). Multivariate logistic regression analysis was used to determine the independent correlation between cardiac biomarkers and short-term outcome. The predictive value of cardiac biomarkers for poor outcome in patients with AIS and different stroke etiology subtypes were evaluated using receiver operating characteristic (ROC) curves. Results:A total of 2 151 patients with AIS were enrolled, including 1 256 males (58.4%), aged 67.40±11.34 years. 1 079 patents were LAA type (50.2%), 679 were SVO type (31.6%), and 393 were CE type (18.3%); 1 223 were mild stroke (56.86%) and 928 (43.14%) were moderate to severe stroke; 1 357 patients (63.09%) had good short-term outcome, and 794 (36.91%) had poor short-term outcome. Multivariate logistic regression analysis showed that N-terminal pro-B type natriuretic peptide (NT-proBNP), NT-proBNP/creatine kinase (CK) isoenzyme MB (CK-MB) ratio, and CK-MB/CK ratio were independent risk factors for poor short-term outcome. ROC curve analysis shows that the CK-MB/CK ratio had a higher predictive value for short-term poor outcome in patients with AIS (the area under the curve, 0.859, 95% confidence interval 0.839-0.879). Various cardiac biomarkers had a higher predictive value for short-term outcome of CE type and LAA type, but the predictive value for short-term outcome of SVO type was lower. Conclusions:Cardiac biomarkers are associated with the severity and poor outcome of AIS. NT-proBNP/CK-MB and CK-MB/CK ratios have higher predictive value for short-term poor outcome of AIS, especially in patients with CE type.

Chinese materia medica and its compound formulas have the characteristics of multi-component,multi-target,and multi pathway effects,and have unique advantages in preventing and treating complex diseases.Network pharmacology explains disease patterns and drug mechanisms from the perspective of complex biological networks,and explores the pharmacological substance basis and target of Chinese medicine.This article summarized the application of network pharmacology from the aspects of biological basis of TCM syndrome,the substance basis and pharmacological mechanism of TCM,compatibility theory of TCM compound formulas,etc.,with the purpose to provide a reference for the research and application of network pharmacology in TCM.

In recent years, targeted therapy and immunotherapy have been applied more and more widely in tumor treatment, and reports on psoriasis induced or exacerbated by these drugs have emerged continuously. This review summarizes and evaluates targeted therapeutic drugs and immunotherapeutic drugs that allegedly induce or aggravate psoriasis according to their targets and possible mechanisms, including programmed death receptor 1 inhibitors, epidermal growth factor receptor inhibitors, human epidermal growth factor receptor 2 inhibitors, anti-CD20 monoclonal antibodies, phosphoinositide 3-kinase δ inhibitors and multi-targeted drugs. In most cases, the relationships between drugs and the occurrence of psoriasis are possible or probable, and in only a few cases the causality is certain.