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.

Modulating Tankyrases (TNKS), interactions with USP25 to promote TNKS degradation, rather than inhibiting their enzymatic activities, is emerging as an alternative/specific approach to inhibit the Wnt/β-catenin pathway. Here, we identified UAT-B, a novel neoantimycin analog isolated from Streptomyces conglobatus, as a small-molecule inhibitor of TNKS-USP25 protein-protein interaction (PPI) to overcome multi-drug resistance in colorectal cancer (CRC). The disruption of TNKS-USP25 complex formation by UAT-B led to a significant decrease in TNKS levels, triggering cell apoptosis through modulation of the Wnt/β-catenin pathway. Importantly, UAT-B successfully inhibited the CRC cells growth that harbored high TNKS levels, as demonstrated in various in vitro and in vivo studies utilizing cell line-based and patient-derived xenografts, as well as APC^min/+ spontaneous CRC models. Collectively, these findings suggest that targeting the TNKS-USP25 PPI using a small-molecule inhibitor represents a compelling therapeutic strategy for CRC treatment, and UAT-B emerges as a promising candidate for further preclinical and clinical investigations.

Objective To investigate the detection rate,epidemiology and resistance mechanism of methicil-lin-resistant Staphylococcus aureus(MRSA)in a hospital in recent 5 years.Methods A total of 762 strains of non repetitive Staphylococcus aureus detected from 2016 to 2020 in a hospital were collected retrospectively.Methicillin-sensitive Staphylococcus aureus(MSSA)was 392 strains(MSSA group)and 370 strains caused by MRSA(MRSA group),and 95 strains of MRSA isolated in 2020 were further used for resistance mechanism.Staphylococcus aureus was identified and tested for drug sensitivity by Vitek 2 automatic microbial system.Molecular epidemiological typing was determined by multilocus sequence typing.The biofilm formation was performed by crystal violet staining.PCR amplification was used to detect drug resistance genes,virulence genes and biofilm related genes,and logistic regression analysis was used to investigate the independent risk factors of its occurrence.Results The detection rate of MRSA in past five years was 48.56％,mainly was from pus samples and secretion samples(38.38％,33.51％respectively).MRSA was found in the general sur-gery(18.65％)and otorhinolaryngology(12.70％).ST88 was the most common multilocus sequence typing(37.89％),and followed by ST951(24.21％).Moderate biofilm formation was the most common,accounting for 74.73％.Multivariate regression analysis showed that compared with MSSA group,hypoproteinemia,en-docrine system diseases,wound infection and history of antibiotic use within six months were the independent risk factors for infection in MRSA group.Compared with the control group,hospital transfer,wound infection and tumor were independent risk factors for infection in MRSA group(P＜0.05).Conclusion The detection rate of MRSA in a hospital is high,and the carrying rate of various drug-resistant genes is high.The hospital should pay attention to the prevalence of MRSA and related risk factors,so as to prevent it early.

Background: Genetic defects in the human thyroid-stimulating hormone (TSH) receptor (TSHR) gene can cause congenital hypothyroidism (CH). However, the biological functions and comprehensive genotype–phenotype relationships for most TSHR variants associated with CH remain unexplored. We aimed to identify TSHR variants in Chinese patients with CH, analyze the functions of the variants, and explore the relationships between TSHR genotypes and clinical phenotypes. Methods: In total, 367 patients with CH were recruited for TSHR variant screening using whole-exome sequencing. The effects of the variants were evaluated by in-silico programs such as SIFT and polyphen2. Furthermore, these variants were transfected into 293T cells to detect their Gs/cyclic AMP and Gq/11 signaling activity. Results: Among the 367 patients with CH, 17 TSHR variants, including three novel variants, were identified in 45 patients, and 18 patients carried biallelic TSHR variants. In vitro experiments showed that 10 variants were associated with Gs/cyclic AMP and Gq/11 signaling pathway impairment to varying degrees. Patients with TSHR biallelic variants had lower serum TSH levels and higher free triiodothyronine and thyroxine levels at diagnosis than those with DUOX2 biallelic variants. Conclusions We found a high frequency of TSHR variants in Chinese patients with CH (12.3%), and 4.9% of cases were caused by TSHR biallelic variants. Ten variants were identified as loss-of-function variants. The data suggest that the clinical phenotype of CH patients caused by TSHR biallelic variants is relatively mild. Our study expands the TSHR variant spectrum and provides further evidence for the elucidation of the genetic etiology of CH.

ObjectiveTo understand the process and influencing factors affecting the utilization of influenza vaccination services and vaccination decision-making among the elderly in Shanghai, to explore the delivery of influenza vaccination services and the difficulties faced by the health service system, and to provide guidance for optimizing immunization strategies. MethodsBased on the vaccine hesitancy determinants matrix, semi-structured personal interviews were conducted with stakeholders involved in influenza vaccination services in Shanghai from January to February 2024, using a purposive sampling method. Participants were included until thematic saturation was achieved. Interview data were audio-recorded, transcribed, coded, and organized using NVivo 20 software, and analyzed using the thematic framework method. ResultsA total of 25 interviewees were included, including 9 medical staff, 12 elderly people aged 60 and above, and 4 family members. The study found that Shanghai had a well-managed and standardized influenza vaccination service. However, the promotion of vaccine-related information at the grassroots level was passive and limited. Out-of-pocket payment of the vaccine and cultural beliefs of the elderly negatively impacted vaccination rates. Meanwhile, recommendations from family, friends, and medical staff facilitated vaccination, although the impact varied depending on the type of medical staff. Neighborhood committees in townships and streets played a crucial role in delivering vaccination information to the target population. Additionally, the internet, social media, and the COVID-19 vaccine had both positive and negative impacts on influenza vaccination. Strategic optimization of vaccination should prioritize price concessions, enhance publicity strategies, and improve awareness, professionalism, and willingness among medical and healthcare workers to recommend vaccination. ConclusionThe influenza vaccination service in Shanghai is well-managed and standardized. However, it is essential to consider the influence of family and other support systems on the elderly. It is also necessary to enhance the professionalism, service awareness, and willingness to recommend among the medical staff. Furthermore, systematic interventions and publicity efforts should be effectively integrated with social media and the functions of neighborhood committees.

Objective To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrasonographic images and clinical laboratory indicators. Methods Ultrasound images and clinical laboratory testing data were captured from schistosomiasis patients admitted to the Second People’s Hospital of Duchang County, Jiangxi Province from 2018 to 2022. Patients with grade I schistosomiasis-induced liver fibrosis were enrolled in Group 1, and patients with grade II and III schistosomiasis-induced liver fibrosis were enrolled in Group 2. The machine learning binary classification tasks were created based on patients’radiomics and clinical laboratory data from 2018 to 2021 as the training set, and patients’radiomics and clinical laboratory data in 2022 as the validation set. The features of ultrasonographic images were labeled with the ITK-SNAP software, and the features of ultrasonographic images were extracted using the Python 3.7 package and PyRadiomics toolkit. The difference in the features of ultrasonographic images was compared between groups with t test or Mann-Whitney U test, and the key imaging features were selected with the least absolute shrinkage and selection operator (LASSO) regression algorithm. Four machine learning models were created using the Scikit-learn repository, including the support vector machine (SVM), random forest (RF), linear regression (LR) and extreme gradient boosting (XGBoost). The optimal machine learning model was screened with the receiver operating characteristic curve (ROC), and features with the greatest contributions to the differentiation features of ultrasound images in machine learning models with the SHapley Additive exPlanations (SHAP) method. Results The ultrasonographic imaging data and clinical laboratory testing data from 491 schistosomiasis patients from 2019 to 2022 were included in the study, and a total of 851 radiomics features and 54 clinical laboratory indicators were captured. Following statistical tests (t = −5.98 to 4.80, U = 6 550 to 20 994, all P values < 0.05) and screening of key features with LASSO regression, 44 features or indicators were included for the subsequent modeling. The areas under ROC curve (AUCs) were 0.763 and 0.611 for the training and validation sets of the SVM model based on clinical laboratory indicators, 0.951 and 0.892 for the training and validation sets of the SVM model based on radiomics, and 0.960 and 0.913 for the training and validation sets of the multimodal SVM model. The 10 greatest contributing features or indicators in machine learning models included 2 clinical laboratory indicators and 8 radiomics features. Conclusions The multimodal machine learning models created based on ultrasound-based radiomics and clinical laboratory indicators are feasible for intelligent identification of schistosomiasis-induced liver fibrosis, and are effective to improve the classification effect of one-class data models.