Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Fermented traditional Chinese medicine(TCM) has a long history of medicinal use, such as Sojae Semen Praeparatum, Arisaema Cum Bile, Pinelliae Rhizoma Fermentata, red yeast rice, and Jianqu. Fermentation technology was recorded in the earliest TCM work, Shen Nong's Classic of the Materia Medica. Microorganisms are essential components of the fermentation process. However, the contamination of fermented TCM by toxigenic fungi and mycotoxins due to unstandardized fermentation processes seriously affects the quality of TCM and poses a threat to the life and health of consumers. In this paper, the characteristics, microbial composition, and mycotoxin profile of fermented TCM are systematically summarized to provide a theoretical basis for its quality and safety control.
Fermentation ; Mycotoxins/analysis* ; Drugs, Chinese Herbal/analysis* ; Fungi/classification* ; Bacteria/genetics* ; Drug Contamination ; Medicine, Chinese Traditional

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective To explore the feasibility and corresponding implementation methods of constructing a sample resource bank based on individual-level data of completed clinical trials and using it to construct external controls for drug/device clinical trials.Methods Taking the pre-marketing clinical trial of transcatheter active valve replacement(TAVR)for the treatment of aortic valve stenosis as an example,the individual-level databases of multiple trials were standardized to form a sample bank.The original data of any trial in the sample bank were selected as the experimental group,and the remaining samples were selected as the control group.The potential confounding was handled by using the propensity score matching and stratification methods to clarify the process of constructing external controls based on the sample bank of individual-level data of clinical trials.Results This study included individual-level data of single-group trials of 4 TAVR devices,with a total of 569 subjects(59.2%male).The number of subjects in Trials 1 to 4 was 120,120,163,and 166,respectively.Propensity score matching enabled the matching of 113,117,125,and 147 subjects with comparable or similar characteristics from individual-level data from other trials,respectively,demonstrating a high matching success rate.The PS score distribution plot after stratification showed that the proportions of subjects in the experimental and control groups in strata 1 to 5 in scheme 1 were 4/103,11/103,22/92,32/87,and 51/64,respectively.For all constructed external controlled trials,a certain number of control samples with similar baseline characteristics to the experimental groups were distributed within each propensity score stratum.The results of the simulation test also reflected the potential differences between different devices in the 12-month all-cause mortality rate.Conclusions The sample bank constructed with individual-level data from clinical trials,as a high-quality data source,can serve as a source of external control for single-arm trials in the same field,and as a useful supplement to the external control scenario of real-world evidence to support drug and device development.At the same time,targeted research on research methods and bias control measures in related fields is also needed.

Objective To explore the feasibility and corresponding implementation methods of constructing a sample resource bank based on individual-level data of completed clinical trials and using it to construct external controls for drug/device clinical trials.Methods Taking the pre-marketing clinical trial of transcatheter active valve replacement(TAVR)for the treatment of aortic valve stenosis as an example,the individual-level databases of multiple trials were standardized to form a sample bank.The original data of any trial in the sample bank were selected as the experimental group,and the remaining samples were selected as the control group.The potential confounding was handled by using the propensity score matching and stratification methods to clarify the process of constructing external controls based on the sample bank of individual-level data of clinical trials.Results This study included individual-level data of single-group trials of 4 TAVR devices,with a total of 569 subjects(59.2%male).The number of subjects in Trials 1 to 4 was 120,120,163,and 166,respectively.Propensity score matching enabled the matching of 113,117,125,and 147 subjects with comparable or similar characteristics from individual-level data from other trials,respectively,demonstrating a high matching success rate.The PS score distribution plot after stratification showed that the proportions of subjects in the experimental and control groups in strata 1 to 5 in scheme 1 were 4/103,11/103,22/92,32/87,and 51/64,respectively.For all constructed external controlled trials,a certain number of control samples with similar baseline characteristics to the experimental groups were distributed within each propensity score stratum.The results of the simulation test also reflected the potential differences between different devices in the 12-month all-cause mortality rate.Conclusions The sample bank constructed with individual-level data from clinical trials,as a high-quality data source,can serve as a source of external control for single-arm trials in the same field,and as a useful supplement to the external control scenario of real-world evidence to support drug and device development.At the same time,targeted research on research methods and bias control measures in related fields is also needed.

Objective To explore the effect and mechanism of hydroxysafflor yellow A(HSYA)on autophagy in bEnd.3 cells after oxygen-glucose deprivation(OGD).Methods The bEnd.3 cells were divided into normal group(conventional culture),model group(OGD model),HSYA group(OGD model+75 μmol·L-1 HSYA),3-methyladenine(3MA)group(5 mmol·L-1 3MA+OGD model)and 3 MA+HSYA group(5 mmol·L-1 3 MA+OGD model+75 μmol·L-1 HSYA).The level of apoptosis was determined by TUNEL fluorescence staining;Western blot was used to detect the expression of autophagy,blood brain barrier(BBB)related proteins;real time fluorescence quantitative polymerase chain reaction method for determining the expression of sirtuin-1(SIRT1)and forkhead box protein O3a(FOXO3A)mRNA.Results In the normal group,model group,HSYA group,3MA group and 3MA+HSYA group,the positive cells selected for TUNEL staining were 5.00±1.00,28.00±2.00,21.00±3.00,35.33±2.51 and 29.67±2.52;the expression levels of microtubule-associated protein 1 light chain 3-Ⅱ/-Ⅰ(LC3-Ⅱ/-Ⅰ)were 0.90±0.20,1.34±0.10,1.95±0.14,0.76±0.15 and 1.14±0.09;sequestosome 1(P62)were 0.99±0.02,0.60±0.02,0.38±0.01,0.67±0.04 and 0.54±0.01;occludin were 1.39±0.17,0.62±0.15,1.00±0.09,0.40±0.13 and 0.80±0.15;zonula occludens-1(ZO-1)were 1.63±0.20,0.64±0.06,0.98±0.14,0.37±0.14 and 0.87±0.04;SIRT1 mRNA were 1.00±0.00,0.75±0.07,1.69±0.09,0.31±0.02 and 0.56±0.01;FOXO3A mRNA were 1.00±0.00,0.80±0.05,1.47±0.09,0.40±0.01 and 0.62±0.09,respectively.Significant differences were found between model group and normal group,HSYA group and model group,3MA+HSYA group and 3MA group(P＜0.05,P＜0.01,P＜0.001).Conclusion HSYA may enhance autophagy levels in bEnd.3 cells after OGD through the SIRT1/FOXO3A pathway,inhibit cell apoptosis and alleviate BBB damage.

Coronary heart disease (CHD) and stroke are the most well-known cardiovascular diseases, which share many common pathological basis. Yindan Xinnaotong soft capsule (YDXNT) is a commonly used Chinese patent medicine in the treatment of stroke and CHD. However, its action of mechanism of co-treatment for stroke and CHD is still unclear. The aim of this study was to explore the common mechanism of YDXNT in co-treatment of CHD and stroke using network pharmacology, experimental verification and molecular docking. An integrated literature mining and databases of IPA, ETCM, HERB, Swiss Target Prediction, OMIM and GeneCards were used to screen and predict active ingredients and potential targets of YDXNT in co-treatment of CHD and stroke. The protein-protein interaction network, GO analysis and pathway analysis were analyzed by IPA software. The effect of YDXNT on core targets was verified by immunofluorescence. UPLC-QTOF/MS and molecular docking were used to screen and predict the main active constituents of YDXNT and their interactions with core targets. A total of 151 potential targets are predicted for YDXNT in co-treatment of CHD and stroke. Hypoxia-inducible factor-1α (HIF1α)-matrix metalloproteinase-9 (MMP9)-mediated HIF1α signaling pathway serves as one of the common mechanisms. YDXNT could reduce the increase of mitochondrial fluorescence intensity and the protein expression of HIF1α and MMP9 in HL-1 and HA induced by oxygen and glucose deprivation/reperfusion (OGD/R) in a dose-dependent manner. Baicalin may be the material basis for treating stroke and CHD with YDXNT. In conclusion, the HIF1α signaling pathway is one of the common key mechanisms of YDXNT in the co-treatment of stroke and CHD. The study provides support and basis for the in-depth scientific connotation of the traditional Chinese medicine theory of "same treatment to different diseases".