Objective:To investigate the predictive value of radiomics features derived from dynamic contrast-enhanced MRI (DCE-MRI) based on habitat imaging technology for pathological complete response after neoadjuvant therapy (NAT) for breast cancer.Methods:All patients were female, aged 25-67 years. Patients were stratified into training ( n=83) and validation ( n=36) sets via stratified random sampling (7∶3 ratio). Pathological complete remission (pCR) and non-pathological complete remission (non-pCR) were defined using the Miller-Payne grading system. All patients underwent DCE-MRI before NAT. ITK-Snap software was used to outline the region of interest (ROI), the imaging histological features of the entire tumor region were extracted and screened, a traditional imaging histological model for predicting post-NAT pCR (ROI overall model) was constructed; the tumor region was divided into three subregions using habitat imaging technology, and the imaging histological features within ROI subregion 1, ROI subregion 2, and ROI subregion 3 were extracted and screened, and the habitat imaging model for predicting post-NAT pCR were constructed (ROI subregion 1 model, ROI subregion 2 model, ROI subregion 3 model). Univariate logistic regression identified clinical predictors of pCR for clinical model construction. Combined models integrating clinical predictors and habitat imaging features were established. The efficacy of each model in predicting pCR after NAT in breast cancer was evaluated using receiver operating characteristic curves and area under the curve (AUC), and the efficacy of clinical application of the models was evaluated using decision curve analysis (DCA). Results:Of the 119 patients, 74 were pCR patients, with 52 in the training set and 22 in the validation set, and 45 were non-pCR patients, with 31 in the training set and 14 in the validation set. Logistic regression analysis showed that human epidermal growth factor receptor 2 status ( OR=0.254, 95% CI 0.093-0.697, P=0.008) was an independent predictor of pCR after NAT, and this was used to construct a clinical prediction model. The predictive efficacy of ROI subregion 1 model and ROI subregion 2 model in the habitat model was higher than that of the traditional imaging histology model (ROI overall model), with AUCs of 0.805, 0.748,0.728 for the training set and 0.776,0.718,0.708 for the validation set, respectively. The combined clinical prediction model for predicting pCR after NAT in breast cancer had AUCs of 0.877 and 0.818 for the training and validation sets, respectively. DCA showed a higher net benefit for the combined model than for the traditional imaging histology model and the habitat imaging histology model. Conclusion:Compared with the traditional method of extracting the entire tumor region, extracting radiomics features from DCE-MRI subregions based on habitat imaging technology can improve the predictive performance of NAT efficacy in breast cancer.

Objective:To investigate the clinical characteristics of sepsis-induced myocardial injury and microbial distribution.Methods:It was a retrospective observational study conducted from Jan 2023 to Dec 2023 in the Department of Emergency Intensive Care Medicine, Changshu Hospital Affiliated to Soochow University. Patients meeting the sepsis 3.0 criteria were included, excluding those with underlying cardiovascular diseases or incomplete data. Patients were categorized into myocardial injury (SIMI) and non-myocardial injury (Non-SIMI) groups based on troponin levels. General patient information, laboratory results, microbial findings, and prognostic indicators were collected. Differences in clinical parameters between the two groups were compared. Factors showing statistical differences in univariate analysis were further analyzed using multivariable logistic regression to identify risk factors for SIMI. Conduct propensity score matching among Pulmonary infection patients who underwent bronchoalveolar lavage high-throughput sequencing to compare microbial distribution between groups. Bracken was used to estimate species-level abundance from Kraken2 results, and α and β diversity analyses were conducted on the metagenomic samples.Results:A total of 179 patients were included in the study, with 98 (54.4%) in the Non-SIMI group and 81 (45.5%) in the SIMI group. There were 69 deaths overall (38.5%), with 23 (23.7%) in the Non-SIMI group and 46 (56.8%) in the SIMI group (χ 2=20.347, P<0.01). The 28-day survival curve indicated survival rates in the SIMI group were significantly lower compared to the Non-SIMI group (Log Rank χ 2=21.270, P<0.01). Univariate analysis revealed that fungal infection rate ( P=0.007), C-reactive protein ( P=0.021), procalcitonin, blood urea nitrogen, creatinine, alanine transaminase, and lactate levels were higher in the SIMI group compared to the Non-SIMI group (all P<0.01), prothrombin time was prolonger ( P<0.01) and APACHEⅡ scores were higher ( P<0.01), while serum albumin, base excess, and platelet levels were lower (all P<0.01). Multivariable logistic regression analysis indicated that fungal infection ( OR=3.441, P=0.015) was a risk factor for SIMI, whereas base excess and platelets were protective factors ( OR=0.845, 0.988, both P<0.01). Comparison of bronchoalveolar lavage high-throughput sequencing results in the pulmonary infection subgroup showed the relative abundance of Haemophilus paraininfluenzae in Non-SIMI group was higher than SIMI group among the top 20 species ( P=0.013). There were no statistically significant differences in microbial αand β-diversity between the two groups. Conclusions:The incidence of SIMI is relatively highamong sepsis patients and it affects their prognosis. Risk factors for SIMI include fungal infection, decreased platelet count, and reduced base excess levels. Among patients with pulmonary infections, there is a lower risk of SIMI associated with Haemophilus influenzae infection.

Objective To establish a rapid on-site detection method for methamphetamine(MA)based on surface-enhanced Raman spectroscopy(SERS).Methods Laboratory-synthesized colloidal silver nanoparticles(AgNPs)were employed as SERS substrates to rapidly detect methamphetamine in typical crime scene matrices,including beer,cigarette ash,and plastic containers.Results The method requires no sample pretreatment,is simple to operate,and offers high sensitivity with fast detection speed.The limit of detection(LOD)reached 10 ppb(signal-to-noise ratio,S/N＞3).The qualitative results of methamphetamine analysis from forensic case samples were highly consistent with gas chromatography-mass spectrometry(GC-MS)findings.Conclusion Methamphetamine is frequently encountered in illicit settings and criminal activities.This study achieved rapid detection of methamphetamine in complex matrices(beer,cigarette ash,and plastic containers),demonstrating good application prospects in practice and providing important value for law enforcement,public safety,and criminal investigation.

Objective:To explore the relationship between irrational procrastination and compulsory citizenship behavior among clinical nurses,and to investigate the role of psychological entitlement in this relationship.Meth-ods:A sample of 413 clinical nurses from a tertiary hospital was selected for this study.The Irrational Procrastina-tion Scale(IPS),Compulsory Citizenship Behavior Scale(CCBS),and Psychological Entitlement Scale(PES)were used for evaluation.Results:The scores of IPS,CCBS and PES were positively correlated with each other(r=0.53,0.56,0.39,Ps＜0.01).Psychological entitlement partially mediated the relationship between compulsory citi-zenship behavior and irrational procrastination among clinical nurses,with an indirect effect of 0.17,accounting for 13.85％of the total effect.Conclusion:Psychological entitlement plays a partial mediating role in the relationship between compulsory citizenship behavior and irrational procrastination among clinical nurses.

Objective:To explore the relationship between irrational procrastination and compulsory citizenship behavior among clinical nurses,and to investigate the role of psychological entitlement in this relationship.Meth-ods:A sample of 413 clinical nurses from a tertiary hospital was selected for this study.The Irrational Procrastina-tion Scale(IPS),Compulsory Citizenship Behavior Scale(CCBS),and Psychological Entitlement Scale(PES)were used for evaluation.Results:The scores of IPS,CCBS and PES were positively correlated with each other(r=0.53,0.56,0.39,Ps＜0.01).Psychological entitlement partially mediated the relationship between compulsory citi-zenship behavior and irrational procrastination among clinical nurses,with an indirect effect of 0.17,accounting for 13.85％of the total effect.Conclusion:Psychological entitlement plays a partial mediating role in the relationship between compulsory citizenship behavior and irrational procrastination among clinical nurses.

Objective To establish a rapid on-site detection method for methamphetamine(MA)based on surface-enhanced Raman spectroscopy(SERS).Methods Laboratory-synthesized colloidal silver nanoparticles(AgNPs)were employed as SERS substrates to rapidly detect methamphetamine in typical crime scene matrices,including beer,cigarette ash,and plastic containers.Results The method requires no sample pretreatment,is simple to operate,and offers high sensitivity with fast detection speed.The limit of detection(LOD)reached 10 ppb(signal-to-noise ratio,S/N＞3).The qualitative results of methamphetamine analysis from forensic case samples were highly consistent with gas chromatography-mass spectrometry(GC-MS)findings.Conclusion Methamphetamine is frequently encountered in illicit settings and criminal activities.This study achieved rapid detection of methamphetamine in complex matrices(beer,cigarette ash,and plastic containers),demonstrating good application prospects in practice and providing important value for law enforcement,public safety,and criminal investigation.

Objective:To investigate the predictive value of radiomics features derived from dynamic contrast-enhanced MRI (DCE-MRI) based on habitat imaging technology for pathological complete response after neoadjuvant therapy (NAT) for breast cancer.Methods:All patients were female, aged 25-67 years. Patients were stratified into training ( n=83) and validation ( n=36) sets via stratified random sampling (7∶3 ratio). Pathological complete remission (pCR) and non-pathological complete remission (non-pCR) were defined using the Miller-Payne grading system. All patients underwent DCE-MRI before NAT. ITK-Snap software was used to outline the region of interest (ROI), the imaging histological features of the entire tumor region were extracted and screened, a traditional imaging histological model for predicting post-NAT pCR (ROI overall model) was constructed; the tumor region was divided into three subregions using habitat imaging technology, and the imaging histological features within ROI subregion 1, ROI subregion 2, and ROI subregion 3 were extracted and screened, and the habitat imaging model for predicting post-NAT pCR were constructed (ROI subregion 1 model, ROI subregion 2 model, ROI subregion 3 model). Univariate logistic regression identified clinical predictors of pCR for clinical model construction. Combined models integrating clinical predictors and habitat imaging features were established. The efficacy of each model in predicting pCR after NAT in breast cancer was evaluated using receiver operating characteristic curves and area under the curve (AUC), and the efficacy of clinical application of the models was evaluated using decision curve analysis (DCA). Results:Of the 119 patients, 74 were pCR patients, with 52 in the training set and 22 in the validation set, and 45 were non-pCR patients, with 31 in the training set and 14 in the validation set. Logistic regression analysis showed that human epidermal growth factor receptor 2 status ( OR=0.254, 95% CI 0.093-0.697, P=0.008) was an independent predictor of pCR after NAT, and this was used to construct a clinical prediction model. The predictive efficacy of ROI subregion 1 model and ROI subregion 2 model in the habitat model was higher than that of the traditional imaging histology model (ROI overall model), with AUCs of 0.805, 0.748,0.728 for the training set and 0.776,0.718,0.708 for the validation set, respectively. The combined clinical prediction model for predicting pCR after NAT in breast cancer had AUCs of 0.877 and 0.818 for the training and validation sets, respectively. DCA showed a higher net benefit for the combined model than for the traditional imaging histology model and the habitat imaging histology model. Conclusion:Compared with the traditional method of extracting the entire tumor region, extracting radiomics features from DCE-MRI subregions based on habitat imaging technology can improve the predictive performance of NAT efficacy in breast cancer.

Objective:To discuss the research status and hotspots of Kaixin Powder.Methods:Literature about Kaixin Powder was retrieved from CNKI, Wanfang Data, VIP, CBM, PubMed and Web of Science databases from the establishment of the databases to January 10, 2023. CiteSpace 6.2.R2 and VOSviewer 1.6.16 software were used to visualize and analyze data on the types of literature included, source journals, publication volume, authors, institutions, keywords, etc.Results:Totally 235 articles were included, mainly Chinese journal article. There were 87 source journals involved, among the Chinese and English journals, China Journal of Chinese Materia Medica and J Ethnopharmacol published the most articles. The overall annual number of articles published in the Kaixin Powder showed an upward trend. It involved 505 authors, forming research teams with Liu Ping, Jiang Yanyan and others as the core; The authors of the included literature came from 99 research institutions, and the cooperation between institutions was mainly based on units with the same or similar geographical area, TCM universities and their affiliated hospitals. The data results of keyword co-occurrence clustering network, keyword co-occurrence time network and keyword emergence analysis showed that the composition of the main active components (ginsenosides, poria acid, fine octyl ethers, ketones and oligosaccharide esters), detection methods (high performance liquid chromatography and liquid chromatography-mass chromatography), pharmacological effects (anti-Alzheimer's disease, antidepressant), mechanism of action and clinical application of the combination were the current research hotspots and trends in development. Conclusion:The research of Kaixin Powder mostly focuses on the mechanism of action and clinical research of Alzheimer's disease, depression and other diseases, among which the research on the main active components in Kaixin Powder is a hot topic in recent years, while the development trend of pharmacological mechanism of action and clinical application is better, and the correlation between active components and efficacy may become a new hot direction in the research of Kaixin Powder.

Objective:To systematically evaluate the effectiveness of Kunxian capsule related regimens for patients with lupus nephritis(LN)in order to provide a reference basis for treatment strategies for LN patients.Methods:The computer searched the rele-vant studies of Kunxian capsule in PubMed,Web of Science,Cochrane Library,CBM,CNKI,Wanfang and VIP databases on the treatment of LN,the limited time for the establishment of the database is April 6,2022,and used R 4.0.2 software and Revman 5.3 software for Meta-analysis.Results:Four RCTs with 1 cohort study including 310 patients were finally included.The results of the Me-ta-analysis showed that:In terms of 24 h urinary protein and SLEDAI score,Glucocorticoid+Cyclophosphamide+Kunxian capsule achieved the best result after treatment;in terms of Scr,IgE,and IgG,the levels of each index were significantly lower in Glucocorti-coid+Cyclophosphamide+Kunxian capsules than in Glucocorticoid+Cyclophosphamide(P<0.05).Conclusion:The 5 regimens may work best as Glucocorticoids+Cyclophosphamide+Kunxian capsules in terms of clinical efficacy in treating LN patients.Because of the quality and quantity limitations of the included studies,more high-quality studies are needed for validation.

Objective:To identify early predictors of factors influencing the efficacy of infliximab (IFX) treatment in patients with Crohn's disease (CD) .Methods:This study is a nested case-control study, including CD patients treated with IFX at the Second Affiliated Hospital of Soochow University from November 2015 to April 2021 and at the First Affiliated Hospital of Nanjing Medical University from November 2015 to December 2022. All the patients were followed up until June 2023 and categorized into IFX non-response and treatment-effective groups based on changes in clinical symptoms and endoscopic image during the follow-up. Laboratory data of inflammatory markers, post-induction trough IFX concentration and antibody levels in both groups were retrospectively collected and compared. Logistic regression models were employed to identify potential factors associated with the risk of IFX non-responsiveness. Machine learning using random forest analysis was utilized to quantitatively assess the predictive features for IFX treatment efficacy and ROC curves was used to evaluate the model's accuracy.Results:This study included 147 CD patients undergoing IFX treatment, with 58 from the Second Affiliated Hospital of Soochow University and 89 from the First Affiliated Hospital of Nanjing Medical University. Among them, 38 were classified as the IFX non-response group, and 109 as the effective group. Patients in the IFX non-response group had lower trough concentration ( P < 0.001), higher antibody levels ( P < 0.001), and a less pronounced reduction in ESR during the induction therapy ( P < 0.001). Univariate and multi-variate Logistic regression models demonstrated that IFX trough concentration and the ratio of ESR before and after induction therapy was associated with the risk of non-responsiveness. After the induction period, for each unit increase in IFX trough concentration (1 μg/ml), the risk of IFX non-response decreased by 23% ( RR = 0.77, 95% CI = 0.68-0.89), while each doubling of the ESR ratio after induction was associated with a 1.43-fold increase in the risk of non-response ( RR = 2.43, 95% CI = 1.48-4.00). Random forest machine learning analysis revealed that IFX trough concentration below 1.5 μg/ml could predict IFX non-response, with area under the ROC curve was 0.722. Conclusion:Lower post-induction IFX trough concentrations is predictive of IFX non-response, while a lack of significant decrease in ESR during the induction phase is also significantly associated with IFX non-response.