Hepatocellular carcinoma(HCC)expresses abundant glycolytic enzymes and displays comprehensive glucose metabolism reprogramming.Aldolase A(ALDOA)plays a prominent role in glycolysis;however,little is known about its role in HCC development.In the present study,we aim to explore how ALDOA is involved in HCC proliferation.HCC proliferation was markedly suppressed both in vitro and in vivo following ALDOA knockout,which is consistent with ALDOA overexpression encouraging HCC prolifera-tion.Mechanistically,ALDOA knockout partially limits the glycolytic flux in HCC cells.Meanwhile,ALDOA translocated to nuclei and directly interacted with c-Jun to facilitate its Thr93 phosphorylation by P21-activated protein kinase;ALDOA knockout markedly diminished c-Jun Thr93 phosphorylation and then dampened c-Jun transcription function.A crucial site Y364 mutation in ALDOA disrupted its interaction with c-Jun,and Y364S ALDOA expression failed to rescue cell proliferation in ALDOA deletion cells.In HCC patients,the expression level of ALDOA was correlated with the phosphorylation level of c-Jun(Thr93)and poor prognosis.Remarkably,hepatic ALDOA was significantly upregulated in the promotion and progression stages of diethylnitrosamine-induced HCC models,and the knockdown of Aldoa strikingly decreased HCC development in vivo.Our study demonstrated that ALDOA is a vital driver for HCC development by activating c-Jun-mediated oncogene transcription,opening additional avenues for anti-cancer therapies.

Colorectal inflammatory cancer transformation poses a major risk to patients with colitis.Patients with chronic intestinal inflammation have an approximately 2-3 fold increased risk of developing colorectal cancer(CRC).Unfortunately,there is currently no effective intervention available.Huangqin decoction(HQD),a well-known traditional Chinese medicine(TCM)formula,is frequently clinically prescribed for treating patients with colitis,and its active ingredients have effective antitumour efficacy.Nonetheless,the mechanism of HQD-mediated prevention of colorectal inflammatory cancer transformation remains unclear.A strategy integrating metagenomic,lipidomic,and messenger RNA(mRNA)sequencing analysis was used to investigate the regulatory effects of HQD on the gut microbiome,metabolism and potential mechanisms involved in colorectal inflammatory cancer transformation.Our study revealed that HQD suppressed colorectal inflammatory cancer transformation,which was associated with enhanced in-testinal barrier function,decreased the inflammatory response,and regulation of the gut microbiome.Notably,cohousing experiments revealed that the transfer of the gut microbiome from HQD-treated mice largely inhibited the pathological transformation of colitis.Moreover,gut microbiome transfer from HQD-treated mice primarily resulted in the altered regulation of fatty acid metabolism,especially the remodeling of arachidonic acid metabolism,which was associated with the amelioration of pathological transformation.Arachidonic acid metabolism and the key metabolic enzyme arachidonic acid 12-lipoxygenase(ALOX12)were affected by HQD treatment,and no obvious protective effect of HQD was observed in Alox12-/-mice,which revealed that ALOX12 was a critical mediator of HQD protection against colorectal inflammatory cancer transformation.In summary,multiple omics analyses were applied to produce valuable data and theoretical support for the application of HQD as a promising intervention for the transformation of inflammatory CRC.

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 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]

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.

The present study aimed to explore whether hydrogen sulfide (H₂S) improved hypoxic pulmonary hypertension (HPH) in rats by inhibiting aerobic glycolysis-pyroptosis. Male Sprague-Dawley (SD) rats were randomly divided into normal group, normal+NaHS group, hypoxia group, and hypoxia+NaHS group, with 6 rats in each group. The control group rats were placed in a normoxic (21% O₂) environment and received daily intraperitoneal injections of an equal volume of normal saline. The normal+NaHS group rats were placed in a normoxic environment and intraperitoneally injected with 14 μmol/kg NaHS daily. The hypoxia group rats were placed in a hypoxia chamber, and the oxygen controller inside the chamber maintained the oxygen concentration at 9% to 10% by controlling the N₂ flow rate. An equal volume of normal saline was injected intraperitoneally every day. The hypoxia+NaHS group rats were also placed in an hypoxia chamber and intraperitoneally injected with 14 μmol/kg NaHS daily. After the completion of the four-week modeling, the mean pulmonary artery pressure (mPAP) of each group was measured using right heart catheterization technique, and the right ventricular hypertrophy index (RVHI) was weighed and calculated. HE staining was used to observe pathological changes in lung tissue, Masson staining was used to observe fibrosis of lung tissue, and Western blot was used to detect protein expression levels of hexokinase 2 (HK2), pyruvate dehydrogenase (PDH), pyruvate kinase isozyme type M2 (PKM2), nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3), GSDMD-N-terminal domain (GSDMD-N), Caspase-1, interleukin-1β (IL-1β) and IL-18 in lung tissue. ELISA was used to detect contents of IL-1β and IL-18 in lung tissue. The results showed that, compared with the normal control group, there were no significant changes in all indexes in the normal+NaHS group, while the hypoxia group exhibited significantly increased mPAP and RVHI, thickened pulmonary vascular wall, narrowed lumen, increased collagen fibers, up-regulated expression levels of aerobic glycolysis-related proteins (HK2 and PKM2), up-regulated expression levels of pyroptosis-related proteins (NLRP3, GSDMD-N, Caspase-1, IL-1β, and IL-18), and increased contents of IL-1β and IL-18. These changes of the above indexes in the hypoxia group were significantly reversed by NaHS. These results suggest that H₂S can improve rat HPH by inhibiting aerobic glycolysis-pyroptosis.
Animals ; Rats, Sprague-Dawley ; Male ; Hypertension, Pulmonary/metabolism* ; Glycolysis/drug effects* ; Hydrogen Sulfide/therapeutic use* ; Hypoxia/complications* ; Rats ; Pyroptosis/drug effects*

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]

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Colorectal cancer(CRC) is a common malignant tumor worldwide. Due to the treatment intolerance and side effects, CRC rank the top among various cancers regarding the incidence and mortality rates. Therefore, exploring new therapies is of great significance for the treatment of CRC. Aerobic glycolysis(AEG) plays an important role in the microenvironment formation, proliferation, metastasis, and recurrence of CRC and other tumor cells. It has been confirmed that intervening in the AEG pathway can effectively curb CRC. The active ingredients and compound prescriptions of traditional Chinese medicine(TCM) can effectively inhibit the proliferation, metastasis, and drug resistance and regulate the apoptosis of tumor cells by modulating AEG-associated transport proteins [eg, glucose transporters(GLUT)], key enzymes [hexokinase(HK) and phosphofructokinase(PFK)], key genes [hypoxia-inducible factor 1(HIF-1) and oncogene(c-Myc)], and signaling pathways(MET/PI3K/Akt/mTOR). Accordingly, they can treat CRC, reduce the recurrence, and improve the prognosis of CRC. Although AEG plays a key role in the development and progression of CRC, the specific mechanisms are not yet fully understood. Therefore, this article delves into the intrinsic connection of the targets and mechanisms of the AEG pathway with CRC from the perspective of tumor cell glycolysis and explores how active ingredients(oxymatrine, kaempferol, and dioscin) and compound prescriptions(Quxie Capsules, Jiedu Sangen Decoction, and Xianlian Jiedu Prescription) of TCM treat CRC by intervening in the AEG pathway. Additionally, this article explores the shortcomings in the current research, aiming to provide reliable targets and a theoretical basis for treating CRC with TCM.
Humans ; Colorectal Neoplasms/genetics* ; Drugs, Chinese Herbal/therapeutic use* ; Glycolysis/drug effects* ; Animals ; Medicine, Chinese Traditional ; Signal Transduction/drug effects*