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.

Alcoholic liver disease(ALD), a major cause of chronic liver disease worldwide, poses a serious threat to human health. Despite the availability of various drugs for treating ALD, their efficacy is often uncertain, necessitating the search for new therapeutic approaches. Traditional Chinese medicine polysaccharides have garnered increasing attention in recent years due to their versatility, high efficiency, and low side effects, and they have demonstrated significant potential in preventing and treating ALD. Emerging studies have suggested that these polysaccharides exert their therapeutic effects through multiple mechanisms, including the inhibition of oxidative stress and the regulation of lipid metabolism, gut microbiota, and programmed cell death. This review summarizes the recent research progress in the pharmacological effects and regulatory mechanisms of traditional Chinese medicine polysaccharides in treating ALD, aiming to provide a scientific basis and theoretical support for their application in the prevention and treatment of ALD.
Humans ; Liver Diseases, Alcoholic/metabolism* ; Polysaccharides/administration & dosage* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Oxidative Stress/drug effects* ; Medicine, Chinese Traditional ; Gastrointestinal Microbiome/drug effects* ; Lipid Metabolism/drug effects*

Sophorae Flavescentis Radix is one of the commonly used traditional Chinese medicine in China, and a large amount of pharmaceutical residue generated during its processing and production is discarded as waste, which not only wastes resources but also pollutes the environment. Therefore, elucidating the chemical composition of the residue of Sophorae Flavescentis Radix and the differences between the residue and Sophorae Flavescentis Radix itself is of great significance for the comprehensive utilization of the residue. This study, based on ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF-MS) technology combined with multivariate statistical methods, provides a thorough characterization, identification, and differential analysis of the overall components of Sophorae Flavescentis Radix and its residue. Firstly, 61 compounds in Sophorae Flavescentis Radix were rapidly identified based on their precise molecular weight, fragment ions, and compound abundance, using a self-constructed compound database. Among them, 41 compounds were found in the residue, mainly alkaloids and flavonoids. Secondly, through principal component analysis(PCA) and orthogonal partial least squares discriminant analysis(OPLS-DA), 15 key compounds differentiating Sophorae Flavescentis Radix from its residue were identified. These included highly polar alkaloids, such as oxymatrine and oxysophocarpine, which showed significantly reduced content in the residue, and less polar flavonoids, such as kurarinone and kuraridin, which were more abundant in the residue. In summary, this paper clarifies the overall composition, structure, and content differences between Sophorae Flavescentis Radix and its residue, suggesting that the residue of Sophorae Flavescentis Radix can be used as a raw material for the extraction of its high-activity components, with promising potential for development and application in cosmetics and daily care. This research provides a scientific basis for the future comprehensive utilization of Sophorae Flavescentis Radix and its residue.
Drugs, Chinese Herbal/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Mass Spectrometry/methods* ; Sophora/chemistry* ; Flavonoids/chemistry* ; Alkaloids/chemistry*

This study aims to systematically characterize the targeted effects of Quanduzhong Capsules on cartilage lesions in knee osteoarthritis by integrating spatial transcriptomics data mining and animal experiments validation, thereby elucidating the related molecular mechanisms. A knee osteoarthritis model was established using Sprague-Dawley(SD) rats, via a modified Hulth method. Hematoxylin and eosin(HE) staining was employed to detect knee osteoarthritis-associated pathological changes in knee cartilage. Candidate targets of Quanduzhong Capsules were collected from the HIT 2.0 database, followed by bioinformatics analysis of spatial transcriptomics datasets(GSE254844) from cartilage tissues in clinical knee osteoarthritis patients to identify spatially specific disease genes. Furthermore, a "formula candidate targets-spatially specific genes in cartilage lesions" interaction network was constructed to explore the effects and major mechanisms of Quanduzhong Capsules in distinct cartilage regions. Experimental validation was conducted through immunohistochemistry using animal-derived biospecimens. The results indicated that Quanduzhong Capsules effectively inhibited the degenerative changes in the cartilage of affected joints in rats, which was associated with the regulation of Quanduzhong Capsules on the thioredoxin-interacting protein(TXNIP)-NOD-like receptor family pyrin domain containing 3(NLRP3)-bone morphogenetic protein receptor type 2(BMPR2)-fibronectin 1(FN1)-matrix metallopeptidase 2(MMP2) signal axis in the articular cartilage surface and superficial zones, subsequently inhibiting cartilage matrix degradation leading to oxidative stress and inflammatory diffusion. In summary, this study clarifies the spatially specific targeted effects and protective mechanisms of Quanduzhong Capsules within pathological cartilage regions in knee osteoarthritis, providing theoretical and experimental support for the clinical application of this drug in the targeted therapy on the inflamed cartilage.
Animals ; Osteoarthritis, Knee/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Rats ; Male ; Humans ; Capsules ; Female ; Disease Models, Animal

OBJECTIVES: To study clinical manifestations and gene mutation features of neonates with centronuclear myopathy. METHODS: A retrospective analysis was conducted on the medical data of 5 neonates with centronuclear myopathy diagnosed in the Neonatal Intensive Care Unit of Children's Hospital, Zhejiang University School of Medicine from January 2020 to August 2024. The data included gender, gestational age, birth weight, Apgar score, clinical manifestations, creatine kinase level, electromyography, genetic testing results and the outcomes of the infants. RESULTS: All 5 male neonates had a history of postpartum asphyxia and resuscitation. They all presented with hypotonia, myasthenia, and respiratory failure; two neonates also had swallowing dysfunction. Of the five neonates, three had normal creatine kinase levels, while two had slightly elevated levels. Electromyography was performed for three neonates, among whom two had myogenic damage. MTM1 gene mutations were identified by genetic testing in all five neonates, including two nonsense mutations and three missense mutations, among which one variant had not been previously reported. Four mutations were inherited from the mother, and the other one was a de novo mutation. The five neonates showed no clinical improvement following treatment, failed weaning from mechanical ventilation, and ultimately died after withdrawal of life-sustaining therapy. CONCLUSIONS Centronuclear myopathy caused by MTM1 gene mutation often has a severe phenotype and a poor prognosis, and it should be considered for neonates with hypotonia and myasthenia after birth. Genetic testing should be performed as soon as possible.
Humans ; Myopathies, Structural, Congenital/genetics* ; Male ; Infant, Newborn ; Retrospective Studies ; Mutation ; Female ; Protein Tyrosine Phosphatases, Non-Receptor/genetics*

Objective To analyze the influencing factors of death in the elderly with coronavirus disease 2019(COVID-19).Methods The case data of death caused by COVID-19 in West China Fourth Hospital from January 1 to July 8,2023 were collected,and surviving cases from the West China Elderly Health Cohort infected with COVID-19 during the same period were selected as the control.LASSO-Logistic regression was adopted to analyze the data after propensity score matching and the validity of the model was verified by drawing the receiver operating characteristic curve.Results A total of 3 239 COVID-19 survivors and 142 deaths with COVID-19 were included.The results of LASSO-Logistic regression showed that smoking(OR=3.33,95%CI=1.46-7.59,P=0.004),stroke(OR=3.55,95%CI=1.15-10.30,P=0.022),malignant tumors(OR=19.93, 95%CI=8.52-49.23, P<0.001),coronary heart disease(OR=7.68, 95%CI=3.52-17.07, P<0.001),fever(OR=0.51, 95%CI=0.26-0.96, P=0.042),difficulty breathing or asthma symptoms(OR=21.48, 95%CI=9.44-51.95, P<0.001),and vomiting(OR=8.19,95%CI=2.87-23.58, P<0.001)increased the risk of death with COVID-19.The prediction model constructed based on the influencing factors achieved an area under the curve of 0.889 in the test set.Conclusions Smoking,stroke,malignant tumors,coronary heart disease,fever,breathing difficulty or asthma symptoms,and vomiting were identified as key factors influencing the death risk in COVID-19.
Humans ; COVID-19/mortality* ; Aged ; Propensity Score ; China/epidemiology* ; Risk Factors ; Logistic Models ; Smoking ; SARS-CoV-2 ; Male ; Female ; Stroke ; Neoplasms