Background and Aims:Mucinous adenocarcinoma of the colorectum(MAC)is a distinct histologic subtype of colorectal cancer characterized by high malignancy and low diagnostic accuracy of preoperative biopsy,posing challenges for clinical decision-making.Given the critical role of the inflammatory microenvironment in tumor progression,this study aimed to develop and validate a nomogram model integrating preoperative systemic inflammatory indicators and clinical features to improve the preoperative diagnosis of MAC.Methods:Clinical data of 293 patients with colorectal cancer who underwent radical resection between June 2017 and June 2022 at the First Affiliated Hospital of the University of South China were retrospectively analyzed.Based on postoperative pathology,patients were classified into the mucinous adenocarcinoma(MAC)group and the non-specific adenocarcinoma(AC)group.Propensity score matching(PSM,1∶1)was used to balance age,T stage,and N stage.Differences in preoperative inflammatory indices were compared between groups.Univariate and multivariate logistic regression analyses were performed to identify independent predictors of MAC,which were incorporated into a diagnostic nomogram.The model's discrimination,calibration,and clinical utility were evaluated using the area under the receiver operating characteristic curve(AUC),calibration plots,and decision curve analysis(DCA).Results:Among the 293 patients,46 had MAC and 247 had AC,with a preoperative colonoscopic diagnostic rate of 54%for MAC.After PSM(43 pairs),platelet count,platelet lymphocyte ratio(PLR),systemic immune inflammation index(SII),inflammation related prognostic index(IPI),and systemic inflammation score(SIS)were significantly higher in the MAC group,while lymphocyte monocyte ratio(LMR)was lower(all P<0.05).Multivariate analysis identified tumor location,maximum tumor diameter,and preoperative IPI as independent predictors.The AUCs of the nomogram in the training(n=206)and validation(n=87)cohorts were 0.759(95%CI=0.662-0.856)and 0.776(95%CI=0.649-0.903),respectively.Calibration plots showed good agreement between predicted and observed probabilities,and DCA demonstrated satisfactory clinical applicability.Conclusion:A nomogram model integrating tumor location,tumor size,and preoperative IPI was successfully developed and validated for preoperative diagnosis of colorectal MAC.This model provides a practical,quantitative tool with good predictive performance to assist clinicians in individualized treatment planning,particularly for patients ineligible for surgical biopsy.

Background and Aims:Mucinous adenocarcinoma of the colorectum(MAC)is a distinct histologic subtype of colorectal cancer characterized by high malignancy and low diagnostic accuracy of preoperative biopsy,posing challenges for clinical decision-making.Given the critical role of the inflammatory microenvironment in tumor progression,this study aimed to develop and validate a nomogram model integrating preoperative systemic inflammatory indicators and clinical features to improve the preoperative diagnosis of MAC.Methods:Clinical data of 293 patients with colorectal cancer who underwent radical resection between June 2017 and June 2022 at the First Affiliated Hospital of the University of South China were retrospectively analyzed.Based on postoperative pathology,patients were classified into the mucinous adenocarcinoma(MAC)group and the non-specific adenocarcinoma(AC)group.Propensity score matching(PSM,1∶1)was used to balance age,T stage,and N stage.Differences in preoperative inflammatory indices were compared between groups.Univariate and multivariate logistic regression analyses were performed to identify independent predictors of MAC,which were incorporated into a diagnostic nomogram.The model's discrimination,calibration,and clinical utility were evaluated using the area under the receiver operating characteristic curve(AUC),calibration plots,and decision curve analysis(DCA).Results:Among the 293 patients,46 had MAC and 247 had AC,with a preoperative colonoscopic diagnostic rate of 54%for MAC.After PSM(43 pairs),platelet count,platelet lymphocyte ratio(PLR),systemic immune inflammation index(SII),inflammation related prognostic index(IPI),and systemic inflammation score(SIS)were significantly higher in the MAC group,while lymphocyte monocyte ratio(LMR)was lower(all P<0.05).Multivariate analysis identified tumor location,maximum tumor diameter,and preoperative IPI as independent predictors.The AUCs of the nomogram in the training(n=206)and validation(n=87)cohorts were 0.759(95%CI=0.662-0.856)and 0.776(95%CI=0.649-0.903),respectively.Calibration plots showed good agreement between predicted and observed probabilities,and DCA demonstrated satisfactory clinical applicability.Conclusion:A nomogram model integrating tumor location,tumor size,and preoperative IPI was successfully developed and validated for preoperative diagnosis of colorectal MAC.This model provides a practical,quantitative tool with good predictive performance to assist clinicians in individualized treatment planning,particularly for patients ineligible for surgical biopsy.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

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 explore how to organically integrate the human anatomy curriculum with medical imaging,thereby enhancing medical students' spatial understanding and 3D reconstruction skills,and strengthening their anatomical foundation and clinical competence.This approach aims to bridge the gap between basic science and clinical practice while cultivating clinical thinking abilities.Methods In this study,the medical imaging knowledge was introduced into the anatomy curriculum in Peking University,enabling students to better understand the human body structure and its relationship to the clinical practice with aid of the ultrasound and MRI method.After the course concluded,we evaluated the examination result and learning satisfaction data from the anatomy course.Results The result showed that students provided positive feedback,showing increased interest in learning,enhanced initiative,significant improvement in their anatomy grades(P＜0.01),and a notable enhancement in their ability to apply basic knowledge to solve clinical problems(P＜0.05).Conclusion The integrated teaching approach of medical imaging and human anatomy courses provides innovative ideas and practical method for medical students to learn the basic medical course and enhance their clinical skills in the future.

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.

This study aims to explore the effects and mechanisms of 4'-O-methylbavachalcone(MeBavaC), an active compound from Psoraleae Fructus, in regulating white matter neuroinflammation to improve vascular cognitive impairment. Male Sprague-Dawley(SD) rats were randomly divided into four groups: sham group, model group, high-dose MeBavaC group(14 mg·kg~(-1)), and low-dose MeBavaC group(7 mg·kg~(-1)). The rat model of chronic cerebral hypoperfusion(CCH) was established using bilateral common carotid artery occlusion. The Morris water maze test was performed to evaluate the learning and memory abilities of the rats. Luxol fast blue staining, Nissl staining, immunofluorescence, immunohistochemistry, and transmission electron microscopy were utilized to observe the morphology and ultrastructure of the white matter myelin sheaths, axon integrity, the morphology and number of hippocampal neurons, and the loss and activation of glial cells in the white matter. Transcriptome analysis was performed to explore the potential mechanisms of white matter injury induced by CCH. Western blot and quantitative real-time polymerase chain reaction(qRT-PCR) assays were conducted to measure the expression levels of NOD-like receptor protein 3(NLRP3), absent in melanoma 2(AIM2), gasdermin D(GSDMD), cysteinyl aspartate-specific proteinase-1(caspase-1), interleukin-18(IL-18), interleukin-1β(IL-1β), erythropoietin(EPO), nuclear factor erythroid 2-related factor 2(Nrf2), and heme oxygenase-1(HO-1) in the white matter of rats. The results showed that compared with the model group, MeBavaC significantly improved the learning and memory abilities of rats with CCH, improved the damage of white matter myelin sheath, maintained axonal integrity, reduced the loss of hippocampal neurons and oligodendrocytes in the white matter, inhibited the activation of microglia and the proliferation of astrocytes in the white matter, and suppressed the NLRP3/AIM2/caspase-1/GSDMD pathway. The expression levels of inflammatory cytokines IL-1β and IL-18 were significantly reduced, while EPO expression and the expression of Nrf2/HO-1 antioxidant pathway were notably elevated. In conclusion, MeBavaC can alleviate cognitive impairment in rats with CCH and suppress neuroinflammation in cerebral white matter. The mechanism of action may involve activation of EPO activity, promotion of endogenous antioxidant pathways, and inhibition of neuroinflammation in the white matter. This study suggests that MeBavaC exhibits antioxidant and anti-neuroinflammatory effects, showing potential application in improving cognitive dysfunction.
Animals ; Male ; Rats, Sprague-Dawley ; NF-E2-Related Factor 2/immunology* ; Rats ; Chalcones/administration & dosage* ; Cognitive Dysfunction/metabolism* ; Signal Transduction/drug effects* ; Neuroinflammatory Diseases/drug therapy* ; Heme Oxygenase-1/metabolism* ; Humans ; Heme Oxygenase (Decyclizing)/genetics*

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

The purpose of this study was to identify the tick species native to Xindi Township,Yumin County,Xinjiang,China.Preliminary morphological identification of parasitic ticks collected from animals in the area was conducted with an ultra-depth of field three-dimensional VHX 600 digital stereo microscope.Total DNA of the ticks was extracted,amplified by PCR based on the COI and ITS2 gene loci,and the posi-tive PCR products were sequenced.The sequence were a-ligned with reference sequences from the NCBI database were aligned with the Basic Local Alignment Search Tool.A genet-ic phylogenetic tree was generated with the neighbor-joining method of MEGA 7.0 software to determine the evolutionary biological characteristics of ticks.Morphological identification showed that the ticks collected from Xindi Township of Yu-min County were consistent with the characteristics of Hya-lomma asiaticum.An evolutionary tree based on the COI and ITS2 gene sequences showed that the ticks collected in this study were clustered with known H.asiaticum sequences.The PCR products of COI and ITS2 were sequenced and compared,which confirmed that the collected tick species were H.asiaticum,in agreement with the morphological and molecular biological results.These findings help to clarify the distribution of ticks in Xindi Township of Xinjiang,and provide basic data for the analysis of tick genetic and evolutionary characteristics,as reference for surveillance and control of ticks in the Xinjiang Uygur Autonomous Region.

Objective:To analyze the immune reconstitution after BTKi treatment in patients with chronic lymphocytic leukemia(CLL).Methods:The clinical and laboratorial data of 59 CLL patients admitted from January 2017 to March 2022 in Fujian Medical University Union Hospital were collected and analyzed retrospectively.Results:The median age of 59 CLL patients was 60.5(36-78).After one year of BTKi treatment,the CLL clones(CD5+/CD19+)of 51 cases(86.4％)were significantly reduced,in which the number of cloned-B cells decreased significantly from(46±6.1)× 109/L to(2.3±0.4)× 109/L(P=0.0013).But there was no significant change in the number of non-cloned B cells(CD19+minus CD5+/CD19+).After BTKi treatment,IgA increased significantly from(0.75±0.09)g/L to(1.31±0.1)g/L(P＜0.001),while IgG and IgM decreased from(8.1±0.2)g/L and(0.52±0.6)g/L to(7.1±0.1)g/L and(0.47±0.1)g/L,respectively(P＜0.001,P=0.002).BTKi treatment resulted in a significant change in T cell subpopulation of CLL patients,which manifested as both a decrease in total number of T cells from(2.1±0.1)× 109/L to(1.6±0.4)× 109/L and NK/T cells from(0.11±0.1)× 109/L to(0.07±0.01)× 109/L(P=0.042,P=0.038),both an increase in number of CD4+cells from(0.15±6.1)× 109/L to(0.19±0.4)× 109/L and CD8+cells from(0.27±0.01)× 109/L to(0.41±0.08)× 109/L(both P＜0.001).BTKi treatment also up-regulated the expression of interleukin(IL)-2 while down-regulated IL-4 and interferon(IFN)-γ.However,the expression of IL-6,IL-10,and tumor necrosis factor(TNF)-α did not change significantly.BTKi treatment could also restored the diversity of TCR and BCR in CLL patients,especially obviously in those patients with complete remission(CR)than those with partial remission(PR).Before and after BTKi treatment,Shannon index of TCR in patients with CR was 0.02±0.008 and 0.14±0.001(P＜0.001),while in patients with PR was 0.01±0.03 and 0.05±0.02(P＞0.05),respectively.Shannon index of BCR in patients with CR was 0.19±0.003 and 0.33±0.15(P＜0.001),while in patients with PR was 0.15±0.009 and 0.23±0.18(P＜0.05),respectively.Conclusions:BTKi treatment can shrink the clone size in CLL patients,promote the expression of IgA,increase the number of functional T cells,and regulate the secretion of cytokines such as IL-2,IL-4,and IFN-γ.BTKi also promote the recovery of diversity of TCR and BCR.BTKi treatment contributes to the reconstitution of immune function in CLL patients.