Poly(ADP-ribose) polymerase 1 (PARP1) is a multifunctional protein involved in diverse cellular functions, notably DNA damage repair. Pharmacological inhibition of PARP1 has therapeutic benefits for various pathologies. Despite the increased use of PARP inhibitors, challenges persist in achieving PARP1 selectivity and effective blood-brain barrier (BBB) penetration. The development of a PARP1-specific positron emission tomography (PET) radioligand is crucial for understanding disease biology and performing target occupancy studies, which may aid in the development of PARP1-specific inhibitors. In this study, we leverage the recently identified PARP1 inhibitor, AZD9574, to introduce the design and development of its ¹⁸F-isotopologue ([¹⁸F]AZD9574). Our comprehensive approach, encompassing pharmacological, cellular, autoradiographic, and in vivo PET imaging evaluations in non-human primates, demonstrates the capacity of [¹⁸F]AZD9574 to specifically bind to PARP1 and to successfully penetrate the BBB. These findings position [¹⁸F]AZD9574 as a viable molecular imaging tool, poised to facilitate the exploration of pathophysiological changes in PARP1 tissue abundance across various diseases.

Prostate specific membrane antigen (PSMA) PET demonstrates superior accuracy compared to alternative imaging modalities in the diagnosis and treatment of prostate cancer. Nonetheless, subjective interpretation processes still result in false positives and false negatives, leading to erroneous or missed diagnoses, thereby compromising diagnostic efficacy, influencing preoperative grading and risk stratification, and interfering with clinical decision-making. With the rapid advancement of medical imaging technology and artificial intelligence, the role of radiomics is gaining prominence. By leveraging extracted image features and employing quantitative analysis, radiomics based on PSMA PET images holds great potential to enhance the capabilities of diagnosing, grading, quantifying risk, and predicting treatment efficacy in prostate cancer. This article aims to comprehensively review the research progress of PSMA PET radiomics in the clinical diagnosis and treatment of prostate cancer.

Objective To study physicians'use of Artificial Intelligence-based Clinical Decision Support System(AI-CDSS)and its influencing factors in primary and secondary hospitals in China.Methods 443 physicians in prima-ry and secondary hospitals were surveyed by questionnaire.Univariate Chi-square test and logistic regression analy-sis were used to explore the influencing factors of doctors'use of Al-CDSS.Results Through univariate analysis,the effects of job title,working years,major,performance expectation,social influence,technical anxiety and indi-vidual innovation on use behavior were statistically significant(P＜0.05).logistic regression analysis showed that in terms of working years,1 year or less,2 to 5 years,6 to 10 years,11 to 15 years,16 to 20 years and conve-nience were generally protective factors for physicians to use AI-CDSS,while attending physician,surgical direc-tion,non-existence of social influence,average social influence and personal innovation were generally risk factors.The differences were statistically significant(P＜0.05).Conclusion Physicians'use of AI-CDSS is influenced by their working years and social influences.It is necessary to improve the cognition of physicians with long working years on AI-CDSS,give play to the radiation driving role of the surrounding environment,and pay attention to the cultivation of personal innovation.

Prostate specific membrane antigen (PSMA) PET demonstrates superior accuracy compared to alternative imaging modalities in the diagnosis and treatment of prostate cancer. Nonetheless, subjective interpretation processes still result in false positives and false negatives, leading to erroneous or missed diagnoses, thereby compromising diagnostic efficacy, influencing preoperative grading and risk stratification, and interfering with clinical decision-making. With the rapid advancement of medical imaging technology and artificial intelligence, the role of radiomics is gaining prominence. By leveraging extracted image features and employing quantitative analysis, radiomics based on PSMA PET images holds great potential to enhance the capabilities of diagnosing, grading, quantifying risk, and predicting treatment efficacy in prostate cancer. This article aims to comprehensively review the research progress of PSMA PET radiomics in the clinical diagnosis and treatment of prostate cancer.

OBJECTIVE To establish the HPLC fingerprint of Xintong granules and the quantitative analysis of multi-components by single-marker method(QAMS)to determine the contents of 7 components,so as to provide a scientific basis for their quality control.METHODS HPLC method was used to establish the fingerprints for 10 batches of Xintong granules(No.S1-S10),and similarity evaluation,cluster analysis(CA)and partial least squares-discriminant analysis(PLS-DA)were performed.At the same time,the contents of seven components,including puerarin,daidzin,calycosin-7-O-β-D-glucoside,stilbene glycoside,naringin,icariin and tanshinone ⅡA,were determined by QAMS method,and were compared with the results of external standard method.RESULTS A total of 18 common peaks were marked and 7 peaks were identified in the HPLC fingerprints for 10 batches of Xintong granules,namely puerarin(peak 4),daidzin(peak 7),calycosin-7-O-β-D-glucoside(peak 9),stilbene glycoside(peak 10),naringin(peak 12),icariin(peak 17),and tanshinone ⅡA(peak 18);the similarities among them were more than 0.990,and CA and PLS-DA results showed that S4-S5,S8-S10,S1-S3 and S6-S7 were clustered into three categories,respectively.Using naringin as the internal standard,the contents of puerarin,daidzin,calycosin-7-O-β-D-glucoside,stilbene glycoside,icariin and tanshinone ⅡA were determined to be 7.868 1-10.181 2,1.709 2-2.374 1,0.285 2-0.326 3,1.024 1-1.523 9,0.140 2-0.290 4,and 0.077 1-0.219 4 mg/g,respectively,by the QAMS.These results showed no significant differences compared to those obtained by the external standard method.CONCLUSIONS Established HPLC fingerprint and QAMS method are convenient,stable and accurate,which can provide a basis for the quality evaluation of Xintong granules.

Objective To study physicians'use of Artificial Intelligence-based Clinical Decision Support System(AI-CDSS)and its influencing factors in primary and secondary hospitals in China.Methods 443 physicians in prima-ry and secondary hospitals were surveyed by questionnaire.Univariate Chi-square test and logistic regression analy-sis were used to explore the influencing factors of doctors'use of Al-CDSS.Results Through univariate analysis,the effects of job title,working years,major,performance expectation,social influence,technical anxiety and indi-vidual innovation on use behavior were statistically significant(P＜0.05).logistic regression analysis showed that in terms of working years,1 year or less,2 to 5 years,6 to 10 years,11 to 15 years,16 to 20 years and conve-nience were generally protective factors for physicians to use AI-CDSS,while attending physician,surgical direc-tion,non-existence of social influence,average social influence and personal innovation were generally risk factors.The differences were statistically significant(P＜0.05).Conclusion Physicians'use of AI-CDSS is influenced by their working years and social influences.It is necessary to improve the cognition of physicians with long working years on AI-CDSS,give play to the radiation driving role of the surrounding environment,and pay attention to the cultivation of personal innovation.

BACKGROUND:Ferroptosis is strongly associated with the occurrence and progression of osteoarthritis,but the specific characteristic genes and regulatory mechanisms are not known. OBJECTIVE:To identify osteoarthritis ferroptosis signature genes and immune infiltration analysis using the WGCNA and various machine learning methods. METHODS:The osteoarthritis dataset was downloaded from the GEO database and ferroptosis-related genes were obtained from the FerrDb website.R language was used to batch correct the osteoarthritis dataset,extract osteoarthritis ferroptosis genes and perform differential analysis,analyze differentially expressed genes for GO function and KEGG signaling pathway.WGCNA analysis and machine learning(random forest,LASSO regression,and SVM-RFE analysis)were also used to screen osteoarthritis ferroptosis signature genes.The in vitro cell experiments were performed to divide chondrocytes into normal and osteoarthritis model groups.The dataset and qPCR were used to verify expression and correlate immune infiltration analysis. RESULTS AND CONCLUSION:(1)12 548 osteoarthritis genes were obtained by batch correction and PCA analysis,while 484 ferroptosis genes were obtained,resulting in 24 differentially expressed genes of osteoarthritis ferroptosis.(2)GO analysis mainly involved biological processes such as response to oxidative stress and response to organophosphorus,cellular components such as apical and apical plasma membranes,and molecular functions such as heme binding and tetrapyrrole binding.(3)KEGG analysis exhibited that differentially expressed genes of osteoarthritis ferroptosis were related to signaling pathways such as the interleukin 17 signaling pathway and tumor necrosis factor signaling pathway.(4)After using WGCNA analysis and machine learning screening,we obtained the characteristic gene KLF2.After validation by gene microarray,we found that the gene expression of KLF2 was higher in the test group than in the control group in the meniscus(P=0.000 14).(5)In vitro chondrocyte assay showed that type Ⅱ collagen and KLF2 expression was lower in the osteoarthritis group than in the control group in chondrocytes(P<0.05),while in osteoarthritis ferroptosis,mast cells activated was closely correlated with dendritic cells(r=0.99);KLF2 was closely correlated with natural killer cells(r=-1,P=0.017)and T cells follicular helper(r=-1,P=0.017).(6)The findings indicate that using WGCNA analysis and machine learning methods confirmed that KLF2 can be a characteristic gene for osteoarthritis ferroptosis and may improve osteoarthritis ferroptosis by interfering with KLF2.

This paper summarizes the experience of professor DING Yuanqing in treating post-stroke depression （PSD） of young and middle-aged patients with the method of regulating qi and promoting blood circulation. PSD is a syndrome resulting by vascular injury and impairment of brain marrow and vital activity after the stroke. Factors such as poor lifestyle， improper control of chronic diseases and sleep disorders，etc.，which can be harmful individually， or they can interact. Over time，these factors can block yang of defensive qi，obstract blood circulationg and disturb qi movement. Reverse ascending of defensive qi can generate wind and fire，generate phlegm and stasis from the fluid the blood. Qi stagnation， phlegm and stasis can combined with stagnation heat， phlegm heat， blood stasis heat which caused by stroke ， which can further aggravate pulse accumulation， damage the blood vessels and block the collaterals. Consequently， defensive qi is floating over and nutrient qi is not smooth， resulting in inadequate nourishment of the brain marrow，and disfunction of vital activity， causing depressive symptoms. Professor DING innovatively applied the method of regulating qi and promoting blood circulation. He selected the classic prescriptions such as Guizhi Decoction（桂枝汤）， Baoyuan Decoction（保元汤）， as well as self-fitting prescriptions like Erdan Decoction（二丹汤）， Erzhu Decoction（二竹汤）， to relieve qi and tonify qi，promote harmonious blood circulation， facilitate vasodilation， ease symptoms of depression， invigorate the mind， and provide an effective treatment for PSD.

Objective:To construct machine learning-based delirium prediction models for ICU patients with multiple trauma and evaluate their prediction efficiency.Methods:A retrospective case-control study was conducted to analyze the clinical data of 417 ICU multiple trauma patients admitted to the First Affiliated Hospital of Zhengzhou University from July 2019 to June 2022, including 305 males and 112 females, aged 18-88 years [(47.8±15.7)years]. The score of acute physiology and chronic health status assessment II (APACHE II) was 0-50 points [(9.80±0.29)points]. The patients were randomly divided into training set ( n=291) and test set ( n=126) with a ratio of 7∶3. The demographic data, past history, treatment and laboratory results of the patients were collected. Lasso regression analysis was applied to screen variables that were significantly correlated to the incidence of delirium in the training set and the variables were then included into the machine learning models. Six machine learning methods including the random forest, gradient boosting tree, extreme gradient boosting, logistic regression, support vector machine and K nearest neighbor were used to construct the delirium prediction models for ICU multiple trauma patients. The accuracy, sensitivity, precision, F1 fraction and area under the curve (AUC) of the receiver′s operating characteristics (ROC) curve were calculated by using the data in the test set to evaluate the prediction efficiency of the models. Results:With regards to the six prediction models, namely random forests, gradient boosting tree, extreme gradient boosting, logistic regression, support vector machine and K nearest neighbor prediction models, the accuracy in the test set was 0.70, 0.68, 0.69, 0.73, 0.70 and 0.60 respectively; the sensitivity was 0.74, 0.80, 0.81, 0.86, 0.85 and 0.69 respectively; the precision was 0.72, 0.69, 0.70, 0.73, 0.71 and 0.65 respectively; the F1 fraction was 0.73, 0.74, 0.75, 0.79, 0.78 and 0.67 respectively; the AUC was 0.72, 0.73, 0.72, 0.80, 0.74 and 0.64 respectively. Among them, the logistic regression model had the best discriminability.Conclusion:Delirium prediction models for ICU patients with multiple trauma have been successfully constructed, among which the logistic regression model has the best prediction efficiency and can serve as an effective tool for early prediction and prevention of delirium in the clinical care of patients with multiple trauma.

Objective This article systematically reviews the interaction between autophagy and herb-induced liver injury,and it elucidates the mechanism of liver injury.Also,it summarizes the control strategies of herb-induced liver injury.Methods Based on the retrieval of CNKI,Wanfang,PubMed and Web-of-Science databases,the literature on autophagy and herb-induced liver injury in the past decade was reviewed to clarify the relationship between autophagy and disease occurrence.Then we summarize common drugs and the mechanism of herb-induced liver injury caused by autophagy.The control strategies of herb-induced liver injury were summarized,too.Results Autophagy plays a dual role in the herb-induced liver injury.Autophagy pathway and autophagy-related molecules can be changed in the case of excessive activation or excessive inhibition to cause herb-induced liver injury.Conclusion The regulation of autophagy pathway is expected to be a new method for the prevention and treatment of herb-induced liver injury.Targeted control strategies are important measures to realize the safety risk monitoring of Chinese herbal medicine.