Impaired glucose regulation （IGR） is an intermediate state between normal blood glucose and diabetes， falling under the category of splenic pure heat in traditional Chinese medicine （TCM）. The core pathogenesis is believed to be the dysfunction of the zang-fu （脏腑） organs， leading to metabolic abnormalities of subtle essence and the generation of "turbid pathogen". Its accumulation can block qi movement， damage the vessel collaterals， and promote the disease progression toward diabetes. The concept of "turbid pulse" is proposed as an early sign of IGR， characterized by full and heavy sensation with stagnated and astringent circulation， which can serve as an effective supplement to clinical screening. The differentiation and treatment approach in clinical practice is summarized as follows. If the ascending and descending mechanisms of qi movement in the middle jiao （焦） are disrupted， with mutual interference between the clear and the turbid， treatment should focus on harmonizing the ascending and the descending， and transforming dampness with aromatic medicinals. For spleen-stomach qi deficiency due to long-term restriction of the spleen and stomach's function of transportation and transformation， it is suggested to fortify spleen and harmonize stomach， and transport turbid with sweet-warm medicinals. For spleen deficiency and liver constraint induced by internal generation of damp-heat in the middle jiao， the treatment should focus on soothing the liver and opening constraint， and directing the turbid downward with sour-sweat medicinals. For solid accumulation and stagnation caused by liver qi constraint， it is recommended to unblock intestine and remove stagnation， and discharge turbid with bitter-cold medicinals. When the disease is transmitted to the lower jiao， and the kidneys are affected， the treatment should be regulating spleen and kidney， and rectifying turbid with salty-sour medicinals. In conclusion， it is emphasized to promote the transformation of turbid pathogen to restore metabolic homeostasis， providing methods for the clinical diagnosis and treatment of IGR.

ObjectiveThis study aimed to evaluate the effectiveness of various statistical methods based on the complementary sensitivity analysis framework recommended by the European Medicines Agency (EMA) in Alzheimer’s disease clinical trials, providing a reference for statistical practice that meets international regulatory standards for similar studies. MethodsBased on longitudinal data from a phase Ⅲ clinical trial for the treatment of Alzheimer’s disease, the change from baseline in Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) score from baseline at week 26 (with a decrease indicating cognitive improvement) was used as the primary outcome measure. This study employed a mixed-effects model with repeated measures (MMRM) under the assumption of missing at random (MAR) as the primary analysis, while using critical point analysis, rank-based analysis, and slope-based analysis for sensitivity verification. A systematic comparison of the efficacy estimation results from the four analytical methods was conducted to compare the consistency and robustness differences in research conclusions across different analytical strategies. ResultsThe primary analysis results from the MMRM showed that the improvement in ADAS-Cog score from baseline at week 26 in the treatment group was superior to that in the placebo group (LSMD=-3.22, 95%CI: -4.55 to -1.88, P<0.001). Both rank-based analyses and slope-based analyses indicated significant differences between groups (both P<0.001). Critical point analyses revealed that the treatment effect was negative under most reasonable missing hypothesis scenarios, with the effect value only potentially turning positive in extreme non-random missing scenarios. There was no overall reversal of the efficacy conclusion. ConclusionThe combination of MMRM, critical point analysis, rank-based analysis, and slope-based analysis can effectively address the challenge of missing data in clinical trials for progressive diseases. This strategy fully complies with international regulatory standards, verifies efficacy conclusions from multiple perspectives, and enhances the credibility and scientific validity of clinical trial results. It can provide a practical reference for statistical analysis of missing data in similar clinical trials for progressive diseases.

Glioma is the most common primary malignant tumor of the central nervous system in adults. Despite the standard treatment of surgery combined with radiotherapy and chemotherapy, the prognosis for high-grade glioma patients remains poor, highlighting the urgent need to further explore its pathogenesis and develop new therapeutic strategies. This article reviews the research progress in the field of glioma in China in 2024, covering tumorigenesis mechanisms, tumor immune microenvironment composition, advances in imaging techniques and novel imaging agents, improvements in surgical approaches, mechanisms of radio- and chemoresistance, and explorations of new therapeutic modalities. These studies provide a solid theoretical foundation for advancing clinical diagnosis and treatment of gliomas and may offer new opportunities to improve patient outcomes.

In recent years,artificial intelligence(AI)has been widely used in the medical field.Research shows that medical AI can not only improve diagnosis and treatment efficiency,but also enable personalized health management and optimize the allocation of medical resources,demonstrating enormous potential.However,while"technology empowers",medical AI is also accompanied by a series of ethical paradoxes,mainly manifested as the conflict between technological innovation and the protection of humanistic value.Based on the AI ethical framework proposed by Italian philosophy and ethicist Floridi,this paper systematically analyzes paradoxical manifestations of medical AI and the possible causes in 5 core ethical principles:respecting autonomy,not harming,doing good,fairness,and interpretability.It also explains the causes of these paradoxes from technical,social,and philosophical levels,and puts forward the following coping strategies:first,to improve explainability,algorithm audit technology,differentiated data collection technology,privacy protection technology,and human supervision and control technology,strengthening technical governance;second,to improve ethical regulations and establish a dynamic ethical governance mechanism that adapts to the pace of technological development,a hierarchical management model,and a responsible innovation mechanism,further improving the legal and regulatory frameworks in terms of data security,algorithm ethical responsibility recognition,etc;and third,to establish a global collaborative system,build a global governance framework for medical AI,establish unified development and application standards,and promote cross-border policy coordination and technical standard docking.

Objective:This study aimed to investigate the association between genetic factors and the risk of developing treatment-resistant depression (TRD), as well as the efficacy of modified electroconvulsive therapy (MECT), with a specific focus on identifying gene polymorphisms that can differentiate TRD from non-TRD.Methods:This case-control study included inpatients with depression in Adult Psychiatry Department, Affective Disorders Department and Geriatrics Department of Guangzhou Medical University Affiliated Brain Hospital from January 2023 to June 2024, as well as healthy individuals undergoing physical examinations in the outpatient department. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was utilized to genotype 16 loci across 10 candidate genes in 107 non-TRD patients, 101 TRD patients and 281 healthy controls. Hardy-Weinberg equilibrium testing, genotype frequency distribution analysis, and genetic association studies were conducted using PLINK software. Univariate binary logistic regression under a dominant model was performed using R software to analyze gene loci associated with non-TRD and TRD.Results:All 16 gene loci in the control group, the TRD group, and the non-TRD group were found to be in Hardy-Weinberg equilibrium ( P>0.05). No significant differences were observed in the genotype distribution of these gene loci across the groups ( P>0.05). Univariate binary logistic regression analysis revealed that individuals with depression carrying the HTR2A-rs7997012 G allele had a significantly lower risk of developing TRD ( OR=0.26, P=0.047). Among the patients receiving MECT, the proportion of G allele carriers who showed improvement at 2, 4, and 6 weeks of treatment was significantly higher compared to those who did not show improvement (96.61% vs. 80.95%, 96.55% vs. 50.00%, 96.59% vs. 46.15%, respectively), with χ2 values of 6.743, 29.295, and 32.300, respectively, and all P values <0.05. Conclusion:The HTR2A-rs7997012 polymorphism may represent a genetic distinction between TRD and non-TRD. Depressed patients with the rs7997012 G allele have a reduced likelihood of developing TRD, moreover, MECT demonstrates superior efficacy in this patient population.

Objective:To investigate the prevalence and severity of symptom burden among long-term survivors of oropharyngeal cancer after radiotherapy, to identify core symptom clusters, and to explore their correlation with quality of life.Methods:A previous retrospective study was conducted by the Cancer Hospital, Chinese Academy of Medical Sciences on patients with oropharyngeal cancer who underwent radiotherapy between January 2010 and December 2020. Patients who were still alive as of December 2023 were further followed and analyzed. From December 2023 to August 2024, symptom burden and quality of life were assessed using the Chinese version of the MD Anderson Symptom Inventory–Head and Neck Module (MDASI-HN) and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ). Exploratory factor analysis (principal component analysis with Promax rotation) were used to identify symptom clusters. Spearman correlation analysis was performed to explore the relationship between total symptom cluster scores and standardized domain scores of quality of life. Multivariate linear regression analysis was further employed to determine the relationship between identified symptom clusters and overall quality of life.Results:A total of 273 patients were included, with a median follow-up duration of 6.2 years (range: 3.5-14.5 years) and a median age of 61 years (range: 27-88 years) at follow-up. The top 5 incidence rates of symptom reported by patients were mucus problems in the mouth or throat (147 cases, 53.8%), dental or gum issues (143 cases, 52.4%), xerostomia (140 cases, 51.3%), difficulty swallowing or chewing (95 cases, 34.8%), and taste disturbance (79 cases, 28.9%). Among them, xerostomia was the most serious symptom. The most frequently reported interference was impact on work (including household chores) (55 cases, 20.1%). Exploratory factor analysis identified 3 symptom clusters: fatigue-nausea cluster, eating-voice cluster, and xerostomia-sleep cluster, all of which were significantly correlated with lower overall quality of life of patients (all P<0.001). Conclusion:Long-term survivors of oropharyngeal cancer after radiotherapy experience substantial symptom burden. The fatigue-nausea, eating-voice, and xerostomia-sleep clusters are the core symptom clusters impacting quality of life.

Objective To investigate the influence of the and mechanism of liver failing to convey and disperse on age-related changes in attentional search based on ERPs.Methods oddball attention search task was administrated to record and analyze behavioral and EEG data(N2pc、SPCN、N2pc-Ptc components)of 120 subjects.Results Compared with liver controlling conveyance and dispersion group,the accuracy in subjects with liver failing to convey and disperse decreased significantly(P<0.05).The elderly group had a lower accuracy(P<0.001)and a longer reaction time(P<0.001)compared to the young group.The N2pc amplitude in subjects with liver failing to convey and disperse was significantly greater than that in subjects with liver controlling conveyance and dispersion(P<0.05).The interaction effect of SPCN amplitude between age and liver failing to convey and disperse status was significant(P=0.024).And in the elderly group,SPCN amplitude in subjects with liver dysregulation was significantly smaller than that of liver controlling conveyance and dispersion(P=0.042).The N2pc-Ptc peak to peak amplitude interaction effect between age and liver regulation status was marginal significant(P=0.087),and in liver failing to convey and disperse group,N2pc-Ptc peak to peak amplitude of the elderly was significantly smaller than that of the young(P=0.008).Conclusion Attention search ability is impaired in the elderly with liver failing to convey and disperse,and the electrophysiological abnormalities,such as directed attention allocation,spatiotemporal dynamic cohesion and short-term memory maintenance,may be part of the mechanism.

Colorectal cancer is one of the most common malignant tumors of the digestive system in clinical practice.The early detection of colorectal cancer based on artificial intelligence and its further assistance in clinical diagnosis and treatment hold significant clinical importance for achieving long-term benefits for patients.The development and validation of artificial intelligence software rely on high-quality,large-volume,and annotated colorectal cancer imaging datasets.This paper aims to provide a reference for constructing a high-quality colorectal cancer CT database,taking the construction of the database as an example.It discusses the complete process of database establishment,including database description,lesion annotation and storage,database quality evaluation and maintenance.The purpose is to ensure the high quality and exploitability of the source materials in the database,promote the sustainable and healthy development of the medical imaging artificial intelligence industry ecosystem,and accelerate the research,development,and application of industries related to artificial intelligence in colorectal cancer CT imaging.

Purpose To evaluate the value of preoperative MRI-based radiomic models for assessing tumor-infiltrating CD8+T-cell expression in glioblastoma patients,and to identify the most stable and efficient radiomic feature region for predicting prognosis following immunotherapy.Materials and Methods This retrospective study included 150 patients with histopathologically confirmed glioblastoma from Lanzhou University Second Hospital(January 2018 to April 2022).Tumor-infiltrating CD8+T-cell expression was quantitatively assessed using immunohistochemical staining,with patients stratified into CD8-high and CD8-low expression groups based on overall survival.A total of 1 185 radiomic features were extracted from each patient's contrast-enhanced T1C and T2WI images,covering the original tumor region and sequentially expanded peritumoral regions(2.5 mm,5.0 mm,7.5 mm,10.0 mm,12.5 mm,15.0 mm morphological dilation of tumor core+peritumoral area).Feature selection was performed using variance threshold,minimum redundancy maximum relevance,and least absolute shrinkage and selection operator methods.XGBoost classifier was employed to construct clinical,radiomic,and clinical-radiomic multimodal combined prediction models.Diagnostic performance was evaluated using receiver operating characteristic curve analysis.Results The radiomic model based on tumor expansion of 7.5 mm(tumor+peritumoral region)demonstrated optimal predictive performance.The clinical-radiomic multimodal combined model showed superior predictive capability compared to clinical and radiomic models alone,achieving an area under the curve of 0.991 and accuracy of 99.0%in the training set,and area under the curve of 0.840 with accuracy of 80.0%in the validation set.Conclusion MRI radiomics provides a feasible approach for evaluating tumor-infiltrating CD8+T-cell expression in glioblastoma patients,offering potential for preoperative prognosis prediction.

Objective To investigate the preoperative prediction of the expression level of programmed cell death ligand 1(PD-L1)in non-small cell lung cancer(NSCLC)by a nomogram model constructed with clinical data,conventional CT signs and spectral CT parameters.Methods A retrospective analysis was conducted on 52 patients with pathologically confirmed NSCLC and undergoing preoperative spectral CT examination.The patients were categorized into positive and negative groups according to PD-L1 expression level,and their clinical data,conventional CT signs and spectral CT parameters were collected.Specifically,clinical data included gender,age,Ki-67 and tumor markers;conventional CT signs included tumor density,margins,calcification,spiculation,lobulation,pleural indentation and cavitation;and spectral CT parameters measured in the arterial and venous phases included effective atomic number(Eff-Z),iodine concentration(IC),water concentration(WC)and normalized iodine concentration(NIC).Intergroup differences were analyzed,and multivariate Logistic regression was used to identify independent predictors and establish the prediction model which was evaluated for prediction performance and accuracy using receiver operating characteristic(ROC)curves,calibration curve and decision curve analyses.Results For clinical data,only the difference in gender between two groups had statistical significance(P<0.05).The spectral CT parameters(IC,NIC and Eff-Z)in the arterial and venous phases of PD-L1 positive group were all greater than those of PD-L1 negative group,with statistically significant differences(P<0.05).Multivariate Logistic regression analysis identified gender(P=0.024),venous-phase Eff-Z(P=0.002),and venous-phase IC(P=0.003)as independent predictive factors for PD-L1 expression.The nomogram prediction model constructed with these independent predictors had an area under curve of 0.80,a sensitivity of 88.00%,and a specificity of 59.00%.The calibration curve showed that the predicted values had a high consistency with the actual values.The decision curve revealed that when the high-risk threshold was between 0.10 and 0.83,the model could achieve the maximum net benefit.Conclusion The nomogram model constructed with spectral CT parameters and clinical data has certain value in predicting the expression level of PD-L1 in NSCLC.