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.

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.

Sodium-glucose co-transporter protein 2 inhibitor(SGLT2i)has steadily demonstrated benefits in the treatment of type 2 diabetes complicated with cardiovascular diseases based on evidence-based medicine,but its precise mechanism is yet unknown.We identified type 2 diabetes patients with HFpEF by searching PubMed,Web of Science,China knowledge network(CNKI),and other databases.We then summarized the pathological mechanism of HFpEF caused by type 2 diabetes.At the same time,to link to evidence-based medical,we explored the future of SGLT2i in clinical application.

Objective To analyze the relationship between serum hyaluronic acid levels and Child classification of liver function in Steatosis patients. Methods A total of 110 Steatosis patients admitted to Shaanxi People's Hospital from January 2017 to September 2018 were selected There were 23 cases in Child a group, 52 cases in Child B group and 35 cases in Child C Group. Sex, age, body mass index (BMI) and serum hyaluronic acid (ha) were compared among the three groups. Results There were no significant differences in sex, age and BMI among Child A, Child B and Child C groups (P>0.05) . The level of serum hyaluronic acid in Child class C (91.39±24.67) was significantly higher than that in Child class B (38.26.9.36) and Child class A (29.55±6.97)（P<0.05）. There was no significant difference in serum hyaluronic acid levels between Child B (38.26.9.36) and Child A (29.55 ± 6.97) groups (P > 0.05). Conclusion Serum hyaluronic acid levels are high in Steatosis patients with poor liver function.

To address the throughput limitations of digital nucleic acid analysis,a tricolor combination-based droplet coding technique was developed to achieve multiplex digital nucleic acid analysis with flexible throughput expansibility.To improve the analysis efficiency,a machine learning-based method was further developed for automatic decoding of color-coded droplet array.The machine learning algorithm empowered the computer program to automatically extract the color-position-quantity information of the droplets.By correlating this color-position-quantity of droplets before and after nucleic acid amplification,the proportion of positive droplets for each target was rapidly determined.This droplet decoding strategy was applied to multiplex digital nucleic acid analysis.The experimental results demonstrated that this droplet decoding method was fast and accurate,with a decoding process completed within 2 min.Furthermore,the droplet identification accuracy exceeded 99%.Additionally,the obtained nucleic acid quantification results exhibited a good correlation(R2＞0.99)with those reported by a commercial digital PCR instrument.

Objective To explore the context and hotspot changes of forensic mixed stain research through bibliometric approach.Methods The literature of forensic mixed stain included in the core col-lection of Web of Science database from 2011 to 2022 were collected as the study object,and the an-nual publication number,countrie(region),institution,journal,keywords,etc.were bibliometrically and visually analyzed using the R-based Bibliometrix 1.1.6 package and VOSviewer 1.6.18 software.Re-sults A total of 732 articles on forensic mixed stain were included from 2011 to 2022,with the an-nual number of articles published and the annual citation frequency showing a steady increase year by year.Among the 59 countries(regions)with the most published articles,the United States ranked first with 246 articles,followed by China with 153 articles.The literature came from 104 journals,and the total number of articles published in the top 10 journals was 633.FORENSIC SCI INT GENET ranked first with 307 articles.Visual analysis using VOSviewer software showed that keywords could be divided into four research clusters,namely the genetic marker development group(blue),the mixed stain typing analysis theory group(red),the sequencing analysis group(yellow),and the case sample research group(green).It can be divided into four development stages in terms of different time peri-ods:early development(2011-2013),middle development(2014-2016),rapid development(2017-2020)and latest development(2021-2022).Conclusion The number of publications by domestic and foreign scholars in the study of mixed stain in forensic science is showing a relatively stable trend.Machine learning,next generation sequencing and other research have been the hottest topics that have attracted the most attention in recent years,which is expected to further develop the theory of mixed stain typing and sequencing analysis in forensic mixed stain research.

AIM To explore the effects of Rosa roxburghii Radix on ulcerative colitis(UC)in rats based on pyroptosis and neutrophil extracellular traps(NETs).METHODS Rats were randomly divided into the normal group and the model group.The successfully established UC rat models by trinitrobenzene sulfonic acid(TNBS)/ethanol enema were then randomly divided into the model group,the sulfasalazine group(0.3 g/kg)and the low,medium and high dose R.roxburghii Radix groups(2,4,8 g/kg),followed by dosing of corresponding drugs by gavage.21 days later,the rats had their disease activity index(DAI)score calculated;their pathological changes of colon tissue observed by HE staining;their levels of serum interleukin(IL)-18,IL-1β and myeloperoxidase(MPO)detected by ELISA;and their protein expressions of NE,MPO,NLRP3,caspase-1 and GSDMD in colon tissue detected by Western blot and immunohistochemistry.RESULTS Compared with the normal group,the model group displayed increased DAI score(P＜0.01),increased serum levels of IL-1β,IL-18 and MPO(P＜0.01),and increased protein expressions of NE,MPO,caspase-1,NLRP3 and GSDMD in colon tissue(P＜0.01).Compared with the model group,the groups intervened with sulfasalazine,or medium,or high dose R.roxburghii Radix demonstrated with decreased DAI scores(P＜0.05,P＜0.01),decreased serum levels of IL-1β,IL-18 and MPO(P＜0.01),and decreased protein expressions of NE,MPO,caspase-1,NLRP3 and GSDMD in colon tissue(P＜0.05,P＜0.01).CONCLUSION R.roxburghii Radix may alleviate the inflammatory reaction in a rat model of UC and improve its pathological injury of colon via regulating pyroptosis and NETs.