With the rapid development of deep learning(DL)technology,its potential applications in the medical field have become increasingly prominent.As a refractory disease,osteonecrosis of the femoral head(ONFH)has certain limitations in traditional diagnostic and therapeutic approaches.The application of DL technology is expected to overcome these limitations and improve diagnosis and treatment outcomes.At present,the applications of DL models-including enhancing image clarity,improving diagnostic accuracy and efficiency,conducting prognostic evaluations,optimizing preoperative planning,assisting intraoperative imaging,and customizing personalized treatment plans-have fully demonstrated their tremendous potential in the diagnosis and treatment of ONFH.This review summarizes the current application status of DL in ONFH diagnosis and treatment,aiming to provide references and insights for future related research.

Objective:To investigate the predictive efficacy of Delta radiomics for the patholo-gical complete remission (pCR) of pancreatic cancer after total neoadjuvant therapy (TNT).Methods:The retrospective cohort study was conducted. The clinicopathological data of 263 patients with pancreatic cancer who were admitted to Henan Provincial People′s Hospital (Zhengzhou University People's Hspital) from January 2019 to September 2024 were collected. There were 166 males and 97 females, aged (56±12)years. All patients underwent TNT. The 263 patients were randomly divided into a training set of 184 cases and a test set of 79 cases using a 7∶3 random seed count. The training set was used to construct the prediction model, and the test set was used to validate the performance of the prediction model. Observation indicators: (1) postoperative and follow-up condi-tions; (2) imaging feature selection and model construction; (3) evaluation of predictive efficacy of different radiomic models. Comparison of measurement data with normal distribution between groups was conducted using the t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. The Kaplan-Meier method was used to calculate the survival rate and draw survival curve. The Log-rank test was used for survival analysis. The perfor-mance of the prediction model for pCR after TNT was evaluated using the receiver operator charac-teristic (ROC) curve, precision-recall (P-R) curve and Bootstrap method, along with the calculation of area under the curve (AUC), precision rate, recall rate, F1-score. Results:(1) Postoperative and follow-up conditions. All 263 patients underwent surgery after TNT, with pathological examination revealing 124 cases of pCR (86 cases in the training set, 38 cases in the test set) and 139 cases of non-pCR (98 cases in the training set, 41 cases in the test set), respectively. All 263 patients were followed up for 6(range, 3-12) months after surgery, of which 15 cases (4 cases of pCR and 11 cases of non-pCR) were lost to follow-up or died due to non-tumor reasons within 6 months after surgery. The postoperative 6-month recurrence-free survival rate of 124 pCR patients and 139 non-pCR patients were 80% and 50%, respectively, showing a significant difference between the two groups of patients ( χ2=22.84, P<0.05). (2) Imaging feature selection and model construction. Construction of the traditional radiology model: based on the response evaluation criteria in solid tumors 1.1, the Logistic regression model was constructed using the relative shrinkage (D%) as a predictive variable. The AUC of traditional radiology model was 0.72 [95% confidence interval ( CI) as 0.63?0.81] in the training set and 0.75 (95% CI as 0.66?0.84) in the test set, respectively. Construction of the Delta radiomics model: 10 non-zero coefficient features were selected. The Delta radiomics models were constructed by using the regularized Logistic regression, random forest, gradient boosting machine, and support vector machine algorithms through using selected features as input variables. (3) Evaluation of predictive efficacy of different radiomic models. The AUC of Delta radiomics model constructed by regularized Logistic regression algorithm in the test set for predicting pCR in pancreatic cancer after TNT was 0.90, higher than that of the random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm (AUC as 0.81, 0.81, 0.83), and higher than that of the traditional radiology model (AUC as 0.72). Results of Bootstrap method revealed significant differences in the predictive efficacy of Delta radiomics model constructed by regularized Logistic regression algorithm compared to the Delta radiomics model constructed by random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm and the tradi-tional radiology model (95% CI as 0.03?0.16, 0.03?0.16, 0.03?0.13, 0.08?0.29, P<0.05). The regularized Logistic regression algorithm within the Delta radiomics model demonstrated the best overall performance among the above models evaluated. Conclusion:Compared to the traditional radiology model, the Delta radiomics model offers superior efficacy in predicting pCR of pancreatic cancer after TNT, in which the regularized Logistic regression algorithm demonstrates the best overall performance metrics.

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.

To achieve rapid and visual detection of Plasmodium vivax,a detection method based on recombinase polymerase amplification(RPA)technology and lateral flow dipstick(LFD)was established and evaluated.Targeting the conserved sequence of the P.vivax 18S rRNA gene(GenBank:DQ660817.1)as the target sequence,primers and probes were designed with Primer Premier 5,and the P.vivax recombinant plasmid(pUCPv)was constructed as the standard.A sensitive and specific RPA-LFD-based rapid visual detection method for P.vivax nucleic acids was established.The plasmid standard was serially diluted 10-fold to concentrations of 1×103,1×102,1×101,1×10?,and 1×10?1 copies/μL for sensitivity testing.To evaluate specificity,whole blood DNA samples from patients infected with Plasmodium falciparum,Plasmodium malariae,Plasmodium ovale,or Leishmania donovani,as well as healthy participants,were tested by RPA-LFD.Additionally,The assay′s accuracy was evaluated by testing whole blood DNA samples from 24 confirmed P.vivax-infected patients.This study successfully established a sensitive,specific,and rapid visual RPA-LFD method for detecting P.vivax nucleic acids.The assay can complete P.vivax detection within 20 minutes under isothermal conditions at 39 ℃,achieving a sensitivity of 1 copy/μL.There is no significant cross reaction with parasites such as other Plasmodium species and L.donovani,and the specificity is 100%.All 24 DNA samples from confirmed P.vivax patients were detected,showing a 100%detection rate.The developed RPA-LFD assay exhibits excellent sensitivity and specificity,requires only simple heating equipment,and is user-friendly.This rapid visual detection method is particularly suitable for P.vivax screening in low-resource settings.

To achieve rapid and visual detection of Plasmodium vivax,a detection method based on recombinase polymerase amplification(RPA)technology and lateral flow dipstick(LFD)was established and evaluated.Targeting the conserved sequence of the P.vivax 18S rRNA gene(GenBank:DQ660817.1)as the target sequence,primers and probes were designed with Primer Premier 5,and the P.vivax recombinant plasmid(pUCPv)was constructed as the standard.A sensitive and specific RPA-LFD-based rapid visual detection method for P.vivax nucleic acids was established.The plasmid standard was serially diluted 10-fold to concentrations of 1×103,1×102,1×101,1×10?,and 1×10?1 copies/μL for sensitivity testing.To evaluate specificity,whole blood DNA samples from patients infected with Plasmodium falciparum,Plasmodium malariae,Plasmodium ovale,or Leishmania donovani,as well as healthy participants,were tested by RPA-LFD.Additionally,The assay′s accuracy was evaluated by testing whole blood DNA samples from 24 confirmed P.vivax-infected patients.This study successfully established a sensitive,specific,and rapid visual RPA-LFD method for detecting P.vivax nucleic acids.The assay can complete P.vivax detection within 20 minutes under isothermal conditions at 39 ℃,achieving a sensitivity of 1 copy/μL.There is no significant cross reaction with parasites such as other Plasmodium species and L.donovani,and the specificity is 100%.All 24 DNA samples from confirmed P.vivax patients were detected,showing a 100%detection rate.The developed RPA-LFD assay exhibits excellent sensitivity and specificity,requires only simple heating equipment,and is user-friendly.This rapid visual detection method is particularly suitable for P.vivax screening in low-resource settings.

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 predictive efficacy of Delta radiomics for the patholo-gical complete remission (pCR) of pancreatic cancer after total neoadjuvant therapy (TNT).Methods:The retrospective cohort study was conducted. The clinicopathological data of 263 patients with pancreatic cancer who were admitted to Henan Provincial People′s Hospital (Zhengzhou University People's Hspital) from January 2019 to September 2024 were collected. There were 166 males and 97 females, aged (56±12)years. All patients underwent TNT. The 263 patients were randomly divided into a training set of 184 cases and a test set of 79 cases using a 7∶3 random seed count. The training set was used to construct the prediction model, and the test set was used to validate the performance of the prediction model. Observation indicators: (1) postoperative and follow-up condi-tions; (2) imaging feature selection and model construction; (3) evaluation of predictive efficacy of different radiomic models. Comparison of measurement data with normal distribution between groups was conducted using the t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. The Kaplan-Meier method was used to calculate the survival rate and draw survival curve. The Log-rank test was used for survival analysis. The perfor-mance of the prediction model for pCR after TNT was evaluated using the receiver operator charac-teristic (ROC) curve, precision-recall (P-R) curve and Bootstrap method, along with the calculation of area under the curve (AUC), precision rate, recall rate, F1-score. Results:(1) Postoperative and follow-up conditions. All 263 patients underwent surgery after TNT, with pathological examination revealing 124 cases of pCR (86 cases in the training set, 38 cases in the test set) and 139 cases of non-pCR (98 cases in the training set, 41 cases in the test set), respectively. All 263 patients were followed up for 6(range, 3-12) months after surgery, of which 15 cases (4 cases of pCR and 11 cases of non-pCR) were lost to follow-up or died due to non-tumor reasons within 6 months after surgery. The postoperative 6-month recurrence-free survival rate of 124 pCR patients and 139 non-pCR patients were 80% and 50%, respectively, showing a significant difference between the two groups of patients ( χ2=22.84, P<0.05). (2) Imaging feature selection and model construction. Construction of the traditional radiology model: based on the response evaluation criteria in solid tumors 1.1, the Logistic regression model was constructed using the relative shrinkage (D%) as a predictive variable. The AUC of traditional radiology model was 0.72 [95% confidence interval ( CI) as 0.63?0.81] in the training set and 0.75 (95% CI as 0.66?0.84) in the test set, respectively. Construction of the Delta radiomics model: 10 non-zero coefficient features were selected. The Delta radiomics models were constructed by using the regularized Logistic regression, random forest, gradient boosting machine, and support vector machine algorithms through using selected features as input variables. (3) Evaluation of predictive efficacy of different radiomic models. The AUC of Delta radiomics model constructed by regularized Logistic regression algorithm in the test set for predicting pCR in pancreatic cancer after TNT was 0.90, higher than that of the random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm (AUC as 0.81, 0.81, 0.83), and higher than that of the traditional radiology model (AUC as 0.72). Results of Bootstrap method revealed significant differences in the predictive efficacy of Delta radiomics model constructed by regularized Logistic regression algorithm compared to the Delta radiomics model constructed by random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm and the tradi-tional radiology model (95% CI as 0.03?0.16, 0.03?0.16, 0.03?0.13, 0.08?0.29, P<0.05). The regularized Logistic regression algorithm within the Delta radiomics model demonstrated the best overall performance among the above models evaluated. Conclusion:Compared to the traditional radiology model, the Delta radiomics model offers superior efficacy in predicting pCR of pancreatic cancer after TNT, in which the regularized Logistic regression algorithm demonstrates the best overall performance metrics.

OBJECTIVE: Tumor-derived exosomes (TDEs) play crucial roles in intercellular communication. Hypoxia in the tumor microenvironment enhances secretion of TDEs and accelerates tumor metastasis. Jiedu recipe (JR), a traditional Chinese medicinal formula, has demonstrated efficacy in preventing the metastasis of hepatocellular carcinoma (HCC). However, the underlying mechanism remains largely unknown. METHODS: Animal experiments were performed to investigate the metastasis-preventing effects of JR. Bioinformatics analysis and in vitro assays were conducted to explore the potential targets and active components of JR. TDEs were assessed using nanoparticle tracking analysis (NTA) and Western blotting (WB). Exosomes derived from normoxic or hypoxic HCC cells (H-TDEs) were collected to establish premetastatic mouse models. JR was intragastrically administered to evaluate its metastasis-preventive effects. WB and lysosomal staining were performed to investigate the effects of JR on lysosomal function and autophagy. Bioinformatics analysis, WB, NTA, and immunofluorescence staining were used to identify the active components and potential targets of JR. RESULTS: JR effectively inhibited subcutaneous-tumor-promoted lung premetastatic niche development and tumor metastasis. It inhibited the release of exosomes from tumor cells under hypoxic condition. JR treatment promoted both lysosomal acidification and suppressed secretory autophagy, which were dysregulated in hypoxic tumor cells. Quercetin was identified as the active component in JR, and the epidermal growth factor receptor (EGFR) was identified as a potential target. Quercetin inhibited EGFR phosphorylation and promoted the nuclear translocation of transcription factor EB (TFEB). Hypoxia-impaired lysosomal function was restored, and secretory autophagy was alleviated by quercetin treatment. CONCLUSION JR suppressed HCC metastasis by inhibiting hypoxia-stimulated exosome release, restoring lysosomal function, and suppressing secretory autophagy. Quercetin acted as a key component of JR and regulated TDE release through EGFR-TFEB signaling. Our study provides a potential strategy for retarding tumor metastasis by targeting H-TDE secretion. Please cite this article as: Jia WT, Xiang S, Zhang JB, Yuan JY, Wang YQ, Liang SF, Lin WF, Zhai XF, Shang Y, Ling CQ, Cheng BB. Jiedu recipe, a compound Chinese herbal medicine, suppresses hepatocellular carcinoma metastasis by inhibiting the release of tumor-derived exosomes in a hypoxic microenvironment through the EGFR-TFEB signaling pathway. J Integr Med. 2024; 22(6): 697-709.
Exosomes/drug effects* ; Animals ; Carcinoma, Hepatocellular/genetics* ; Drugs, Chinese Herbal/pharmacology* ; Liver Neoplasms/pathology* ; Tumor Microenvironment/drug effects* ; Mice ; Humans ; Cell Line, Tumor ; Mice, Inbred BALB C ; Neoplasm Metastasis ; Male ; Mice, Nude

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

This study investigated the characteristics of gut microbiota imbalance in patients with infectious diarrhea caused by various pathogenic infections,and the role of Bacteroides in maintaining homeostasis in the intestinal environment.The gut microbiota in patients with diarrhea caused by pathogenic infections,such as viral and bacterial infections,was determined through full-length 16S rRNA amplicon sequencing.Patients with diarrhea were grouped and analyzed according to the presence of single bacterial infection,single viral infection,mixed infection,or Clostridioides difficile infection.Bacteroides had the highest absolute number and relative abundance in the gut microbiota in healthy people,whereas patients with infectious diar-rhea showed lower relative abundance of Bacteroides at each phylum/order/family/genus taxonomic level.Alpha diversity anal-ysis indicated no significant differences among groups.NMDS and PCoA indicated formation of distinct clusters in the control group compared with the different infectious diarrhea groups.The diversity of the gut microbiota was higher in the control group than the infectious diarrhea groups.Patients with infec-tious diarrhea caused by different pathogens showed differing predominant gut microbiota.Bifidobacterium predominated in the single viral infection group,Streptococcus predominated in the single bacterial infection group,and Lachnoclostridium predominated in the mixed infection group.Escherichia and Klebsiella were the major gut microbiota in the C.difficile infection group.Meanwhile,the dominant gut microbiota in the healthy population was Bacteroides.COG function prediction revealed that the healthy control group formed a distinct cluster from the different infection groups.The functions of defense mechanisms,cell wall synthesis,protein modification,cellular differentiation,and replication and recombination were signifi-cantly diminished in all infectious diarrhea groups.In general,patients with infectious diarrhea caused by different pathogens showed dysbiosis,with diminished gut microbiota diversity and the emergence of related biomarkers.Our findings indicated that Bacteroides has a key role in maintaining the homeostasis of the human intestinal environment,thus providing new ideas for the subsequent treatment of infectious diarrhea and research in other fields.