ObjectiveThis paper aims to investigate the traditional Chinese medicine syndrome types in patients with chronic hepatitis B （CHB） complicated by metabolic dysfunction-associated fatty liver disease （MAFLD） and explore the correlations between these syndrome types and clinical indicators， as well as ocular manifestation characteristics， thereby providing a reference for syndrome differentiation and treatment strategies in traditional Chinese medicine. MethodsGeneral data， information from the four diagnostic methods of traditional Chinese medicine， clinical indicators， and ocular manifestation data were collected from 506 patients with CHB complicated by MAFLD enrolled at the Public Health Clinical Center of Chengdu between June 2024 and December 2024. Cluster analysis， principal component analysis， and complex network models were employed to identify the distribution patterns of traditional Chinese medicine syndromes. Correlations between different syndrome types and clinical indicators， as well as ocular manifestation characteristics， were further analyzed. ResultsThe predominant syndromes identified in patients with CHB complicated by MAFLD were dampness and heat accumulation （51.58%）， liver depression with spleen deficiency （31.62%）， blood stasis obstructing collaterals （8.89%）， and Qi-Yin deficiency （7.91%）. No statistically significant differences were found among the four syndrome types in routine blood tests and liver function indicators. However， patients with the dampness and heat accumulation type exhibited significantly higher levels of total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein cholesterol （LDL-C）， liver stiffness measurement （LSM）， controlled attenuation parameter （CAP）， and alpha-fetoprotein （AFP）， along with lower levels of high-density lipoprotein cholesterol （HDL-C）， compared with those with other syndrome types. Regarding ocular manifestations， the incidence of moon halo signs was significantly higher in patients with the blood stasis obstructing collaterals type than in those with other syndrome types. Additionally， the incidence in scleral zone 3 （corresponding to the large intestine） was higher in patients with the damp and heat accumulation type. ConclusionDampness and heat accumulation is the core syndrome type in patients with CHB complicated by MAFLD， commonly accompanied by spleen deficiency， liver depression， blood stasis， and Yin deficiency. A complex syndrome pattern characterized by a predominance of dampness and heat， along with a mixture of deficiency and excess， is formed. Different traditional Chinese medicine syndrome types are associated with distinct clinical indicators and ocular manifestation characteristics. Among them， patients with the dampness and heat accumulation type exhibit more pronounced metabolic disturbances and liver injury， whereas those with the blood stasis type show a higher incidence of moon halo signs. Abnormalities in scleral zone 3 are also more prevalent in patients with dampness and heat type.

ObjectiveThis paper aims to investigate the distribution patterns of traditional Chinese medicine syndromes in patients with chronic hepatitis B （CHB） comorbid with metabolically associated fatty liver disease （MAFLD） and analyze their correlation with clinical characteristics and the progression of liver fibrosis. MethodsA cross-sectional study method was employed， and 506 patients with CHB comorbid with MAFLD who attended the Hepatology Outpatient Department of Public Health Clinical Center of Chengdu from June 2024 to December 2024 were enrolled. General information， traditional Chinese medicine syndromes information， laboratory indicators， and imaging examination results were collected using case report forms （CRF）. Tongue images of patients were acquired using a tongue diagnosis instrument， and tongue feature parameters were extracted using computer image processing technology. Frequency analysis， factor analysis， and cluster analysis， and other methods were used to explore syndrome categories and distribution patterns. Non-parametric tests were used to compare the differences in clinical characteristics among different syndromes. Univariate and multivariate logistic regression analyses were performed to investigate the correlation between traditional Chinese medicine syndromes and the progression of liver fibrosis. ResultsThe main traditional Chinese medicine syndromes in patients with CHB comorbid with MAFLD were mainly dominated by damp-heat accumulation syndrome， liver stagnation and spleen deficiency syndrome， and phlegm-blood stasis syndrome， with damp-heat accumulation syndrome accounting for the highest proportion （41.89%）. Compared with those without damp-heat accumulation syndrome， patients with damp-heat accumulation syndrome had significantly lower tongue proper H value， tongue coating H value， and tongue coating a^* value （P<0.05）， significantly higher tongue coating b^* value （P<0.05）， significantly increased levels of white blood cell （WBC）， red blood cell （RBC）， hemoglobin （HGB）， and glucose （GLU）， increased CAP values （P<0.05）， a higher proportion of males （P<0.05）， and a younger age （P<0.05）. Univariate and multivariate logistic regression analyses show that age， hepatitis B surface antigen （HBsAg）， diabetes， and damp-heat accumulation syndrome are independent risk factors for liver fibrosis （P<0.05）， and that damp-heat accumulation syndrome is predominantly distributed in liver fibrosis stage F0-F1. ConclusionDamp-heat accumulation syndrome is a typical syndrome in patients with CHB comorbid with MAFLD， which is significantly associated with enhanced inflammatory response， metabolic disorders， and early liver fibrosis， and is a key link in disease progression. Clinical attention and early intervention are needed.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

A method was developed for determination of dilauryl thiodipropionate(DLTDP)in fried foods by coupling solid-phase extraction(SPE)pretreatment with reverse-phase liquid chromatography-tandem mass spectrometry(RPLC-MS/MS)detection.Samples were extracted with n-hexane as the solvent,purified using a neutral alumina SPE cartridge,and finally analyzed by RPLC-MS/MS.Quantitative analysis was performed using matrix-matched calibration curves combined with an external standard method under optimal experimental conditions.The results showed that DLTDP exhibited good linearity in the range of 2.0-50.0 μg/L,with a correlation coefficient(R2)≥0.999.The limit of detection(LOD)and the limit of quantification(LOQ)of the method were 0.15 mg/kg and 0.5 mg/kg,respectively.The mean recoveries at three fortification levels(0.5,1.0,and 200 mg/kg)in different samples ranged from 84.8%to 96.8%,with the relative standard deviations(RSDs)all less than 8.0%.The developed method was highly sensitive,accurate and reliable,and easy to operate,making it well suited for the routine quantitative analysis of DLTDP in fried foods.

Prostate cancer is the most prevalent malignant tumor among men, ranking first in incidence and second in mortality globally. Novel hormone therapies (NHT) targeting the androgen receptor (AR) pathway have become the standard of care for metastatic prostate cancer. This review offers a comprehensive overview of NHT, including abiraterone, enzalutamide, apalutamide, darolutamide, and rezvilutamide, which have demonstrated efficacy in delaying disease progression and improving patient survival and quality of life. Nevertheless, resistance to NHT remains a critical challenge. The mechanisms underlying resistance are complex, involving AR gene amplification, mutations, splice variants, increased intratumoral androgens, and AR-independent pathways such as the glucocorticoid receptor, neuroendocrine differentiation, DNA repair defects, autophagy, immune evasion, and activation of alternative signaling pathways. This review discusses these resistance mechanisms and examines strategies to counteract them, including sequential treatment with novel AR-targeted drugs, chemotherapy, poly ADP-ribose polymerase inhibitors, radionuclide therapy, bipolar androgen therapy, and approaches targeting specific resistance pathways. Future research should prioritize elucidating the molecular basis of NHT resistance, optimizing existing therapeutic strategies, and developing more effective combination regimens. Additionally, advanced sequencing technologies and resistance research models should be leveraged to identify novel therapeutic targets and improve drug delivery efficiencies. These advancements hold the potential to overcome NHT resistance and significantly enhance the management and prognosis of patients with advanced prostate cancer.

Objective To investigate the status of population dynamics and distribution changes of Aedes aegypti in Guangdong Province.Methods Continuous monitoring was conducted from May 2018 to July 2024 in Wushi Town and Qishui Town,Leizhou City,Zhanjiang City,Guangdong Province.Additionally,a survey of the distribution of Ae.aegypti along the Leizhou Peninsula coast was carried out.Results The density of Ae.aegypti in Zhanjiang showed a gradual decline from 2018 to 2024.The last detection of adult Ae.aegypti in Wushi Town was in September 2021,and the last larva was found in October 2023.No Ae.aegypti was detected in Qishui Town during surveys from 2021 to 2024.A survey of 18 coastal villages in the Leizhou Peninsula revealed no detections of Ae.aegypti.Conclusions This study provides a basis for understanding the distribution and population density fluctuations of Ae.aegypti,assessing its invasion risk,and scientifically conducting relevant prevention and control efforts.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.