Bronchiolar adenoma (BA) is a rare type of lung tumor characterized by bilayered epithelial cells having a continuous basal layer and a luminal layer. It resembles mucinous adenocarcinoma (MA) on frozen section, with difficulty in distinguishing the basal layer. Immunohistochemistry is the best choice for verifying the diagnosis. This study aimed to comprehensively characterize three cases of BA-combined carcinoma using clinical, histopathological, and genetic features. BA and carcinoma sections were subjected to next-generation sequencing, respectively. It was hypothesized that while different mutation forms matched different regions, BA and lung adenocarcinoma shared the same gene mutation when they co-occurred in the same location. BA with extensive carcinoma is extremely rare and presents diagnostic challenges due to its overlap with conditions such as MA. Because of its distinctive morphological characteristics, BA may be regarded as a low-grade malignancy, particularly during a confusing evaluation. A multifaceted examination of clinical, radiological, immunohistochemical, and genetic data is necessary for an accurate diagnosis.

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.

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.

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.

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.

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.

Type 2 diabetes mellitus (T2DM) is a complex metabolic disorder characterized by chronic hyperglycemia, hyperlipidemia, and peripheral insulin resistance. Endoplasmic reticulum stress (ERS), a response to cellular stress, is activated across various tissues during the progression of T2DM, leading to disruptions in protein synthesis. Notably, epithelial and endocrine cells with hormone-secreting functions are particularly vulnerable to functional impairments induced by ERS. The gut-pancreas axis is essential for regulating metabolism and the progression of T2DM. Intestinal epithelial L cells, integral to the intestinal barrier, can secrete the glucagon-like peptide-1 (GLP-1). This hormone promotes insulin secretion from pancreatic β-cells and plays a critical role in glucose metabolism. Importantly, ERS plays a critical role in regulating glucolipid-induced dysfunction of gut-pancreas axis. For instance, ERS is involved in regulating the intestinal barrier and the secretion of GLP-1 as well as insulin. Therefore, ERS can be a potential target for T2DM treatment. In this paper, we review the regulatory roles of ERS in the gut-pancreas axis during the development of T2DM, and summarize the therapeutic drugs and strategies targeting ERS for T2DM treatment.

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.