Disulfidptosis is a novel pattern of cell death caused by disulfide stress and inadequate NADPH. Nonalcoholic fatty liver disease （NAFLD） is a group of metabolic diseases with the main pathological feature of fatty infiltration， and it is closely associated with insulin resistance and genetic susceptibility. Currently， the latest studies have shown that disulfide stress caused by disulfidptosis can result in hepatocyte death， thereby accelerating the progression of NAFLD. This article summarizes and analyzes the latest studies on disulfidptosis in NAFLD， in order to explore the application of disulfidptosis in NAFLD and provide new ideas for the prevention and treatment of NAFLD.

Effectively assessing oxygen delivery and demand is one of the key targets for fluid resuscitation in sepsis. Clinical signs and symptoms, blood lactic acid levels, and mixed venous oxygen saturation (S O 2) or central venous oxygen saturation (ScvO 2) all have their limitations. In recent years, these limitations have been overcome through the use of derived indicators from carbon dioxide (CO 2) such as mixed veno-arterial carbon dioxide partial pressure difference (P -aCO 2, PCO 2 gap, or ΔPCO 2), the ratio of mixed veno-arterial carbon dioxide partial pressure difference to arterial-mixed venous oxygen content difference (P -aCO 2/Ca- O 2). P -aCO 2, PCO 2 gap or ΔPCO 2 is not a purely anaerobic metabolism indicator as it is influenced by oxygen consumption. However, it reliably indicates whether blood flow is sufficient to carry CO 2 from peripheral tissues to the lungs for clearance, thus reflecting the adequacy of cardiac output and metabolism. The P -aCO 2/Ca- O 2 may serve as a marker of hypoxia. S O 2 and ScvO 2 represent venous oxygen saturation, reflecting tissue oxygen utilization. When oxygen delivery decreases but tissues still require more oxygen, oxygen extraction rate usually increases to meet tissue demands, resulting in decreased S O 2 and ScvO 2. But in some cases, even if the oxygen delivery rate and tissue utilization rate of oxygen are reduced, it may still lead to a decrease in S O 2 and ScvO 2. Sepsis is a classic example where tissue oxygen utilization decreases due to factors such as microcirculatory dysfunction, even when oxygen delivery is sufficient, leading to decrease in S O 2 and ScvO 2. Additionally, the solubility of CO 2 in plasma is approximately 20 times that of oxygen. Therefore, during sepsis or septic shock, derived variables of CO 2 may serve as sensitive markers for monitoring tissue perfusion and microcirculatory hemodynamics. Its main advantage over blood lactic acid is its ability to rapidly change and provide real-time monitoring of tissue hypoxia. This review aims to demonstrate the principles of CO 2-derived variables in sepsis, assess the available techniques for evaluating CO 2-derived variables during the sepsis process, and discuss their clinical relevance.

In light of the lack of reliable molecular markers for odontogenic myxoma (OM), the detection of copy number variation (CNV) may present a more objective method for assessing ambiguous cases. In this study, we employed multiregional microdissection sequencing to integrate morphological features with genomic profiling. This allowed us to reveal the CNV profiles of OM and compare them with dental papilla (DP), dental follicle (DF), and odontogenic fibroma (OF) tissues. We identified a distinct and robustly consistent CNV pattern in 93.75% (30/32) of OM cases, characterized by CNV gain events in chromosomes 4, 5, 8, 10, 12, 16, 17, 20, and 21. This pattern significantly differed from the CNV patterns observed in DP, DF, and OF. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated potential links between this CNV patterns and the calcium signaling pathway and salivary secretion, while Gene Ontology (GO) term analysis implicated CNV patterns in tumor adhesion, tooth development, and cell proliferation. Comprehensive CNV analysis accurately identified a case that was initially disputable between OF and OM as OM. Our findings provide a reliable diagnostic clue and fresh insights into the molecular biological mechanism underlying OM.
Humans ; DNA Copy Number Variations ; Odontogenic Tumors/diagnosis* ; Myxoma/genetics* ; Female ; Male ; Adult ; Adolescent ; Middle Aged ; Dental Papilla ; Young Adult ; Fibroma/genetics* ; Dental Sac ; Child

Objective:To investigate the value of a preoperatively MRI-based deep learning (DL) radiomics machine learning model to distinguish low-grade and high-grade soft tissue sarcomas (STS).Methods:From November 2007 to May 2019, 151 patients with STS confirmed by pathology in the Affiliated Hospital of Qingdao University were enrolled as training sets, and 131 patients in the Affiliated Hospital of Shandong First Medical University and the Third Hospital of Hebei Medical University were enrolled as external validation sets. According to the French Federation Nationale des Centres de Lutte Contre le Cancer classification (FNCLCC) system, 161 patients with FNCLCC grades Ⅰ and Ⅱ were defined as low-grade and 121 patients with grade Ⅲ were defined as high-grade. The hand-crafted radiomic (HCR) and DL radiomic features of the lesions were extracted respectively. Based on HCR features, DL features, and HCR-DL combined features, respectively, three machine-learning models were established by decision tree, logistic regression, and support vector machine (SVM) classifiers. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each machine learning model and choose the best one. The univariate and multivariate logistic regression were used to establish a clinical-imaging factors model based on demographics and MRI findings. The nomogram was established by combining the optimal radiomics model and the clinical-imaging model. The AUC was used to evaluate the performance of each model and the DeLong test was used for comparison of AUC between every two models. The Kaplan-Meier survival curve and log-rank test were used to evaluate the performance of the optimal machine learning model in the risk stratification of progression free survival (PFS) in STS patients.Results:The SVM radiomics model based on HCR-DL combined features had the optimal predicting power with AUC values of 0.931(95%CI 0.889-0.973) in the training set and 0.951 (95%CI 0.904-0.997) in the validation set. The AUC values of the clinical-imaging model were 0.795 (95%CI 0.724-0.867) and 0.615 (95%CI 0.510-0.720), and of the nomogram was 0.875 (95%CI 0.818-0.932) and 0.786 (95%CI 0.701-0.872) in the training and validation sets, respectively. In validation set, the performance of SVM radiomics model was better than those of the nomogram and clinical-imaging models ( Z=3.16, 6.07; P=0.002,<0.001). Using the optimal radiomics model, there was statistically significant in PFS between the high and low risk groups of STS patients (training sets: χ2=43.50, P<0.001; validation sets: χ2=70.50, P<0.001). Conclusion:Preoperative MRI-based DL radiomics machine learning model has accurate prediction performance in differentiating the histopathological grading of STS. The SVM radiomics model based on HCR-DL combined features has the optimal predicting power and was expected to undergo risk stratification of prognosis in STS patients.

The real-time reported data of treated patients from July 2017 to June 2019 Nanxiang Hospital of Jiading District were collected from chest pain center platform. The results showed that the average time of completing ECG examination from the first medical contact was 1.3 to 6.9 min with a median of 1.9 min (1.7, 2.2), meeting the quality control requirements (10 min); the time required to obtain troponin test results was 13.0 to 48.4 min with a median of 14.1 min (13.4, 18.1), meeting the requirements for quality control of 20 min; time from entry to transfer out of PCI patients was 19.0-100.0 min, with median 37.2 (29.3, 66.6) min, basically reaching quality control (30 min); the entering catheter chamber rate of STEMI patients was 50.0% to 100.0% with a median of 100.0% (73.3%, 100.0%), meeting the requirements of quality control (≥50%). Through the active construction, the main quality control indicators were well reached, the reported cases were basically stable, and the disease distribution was basically reasonable in the primary-level chest pain centers. Informed notification of transshipment and subsequent management of low-risk chest pain patients need to be further strengthened. It is suggested that the construction of chest pain centers should establish long-term normal working mechanism, strengthening the control of key quality control indicators, to play the important role of the regional treatment system.

OBJECTIVETo compare the CT image of gastrointestinal schwannomas (GIS) and gastrointestinal stromal tumors(GIST), and to find the CT features to be helpful for differentiation of GIS from GIST.

METHODSClinical and iconography data of 15 GIS patients and 50 GIST patients who underwent stomach CT scan with postoperatively confirmed histopathology between January 2000 and July 2014 in our department were collected retrospectively. CT findings of these two tumors were compared. Then the ROC curve was drawn based on the significant CT findings and area under the curve (AUC) was calculated to assess the sensibility and specificity for the differential diagnosis of GIS and GIST.

RESULTCT findings, such as the tumor size, shape, cystic change, perilesional lymph nodes, vessel seeding, enhancement pattern and degree, absolute and standardized CT value were significantly different between GIS and GIST(all P<0.05). Among the CT findings, tumor size, cystic change, perilesional lymph nodes, enhancement pattern and arterial phase standardized CT value (Sa) were better differential than others (all AUC>0.7). Tumor size showed the highest sensibility(90%), and cystic change and Sa showed the highest specificity(87%).

CONCLUSIONSGIS seems to show a homogeneous tumor more frequently, presenting light and moderate enhancement pattern and obvious enhancement of perilesional lymph nodes, while GIST seems to reveal malignant features, such as abundant blood supply, larger volume, frequent necrosis and cystic change. Cystic change and Sa value possess the better differential ability in diagnosis of these two tumors.

Objective To compare the CT image of gastrointestinal schwannomas ﹙GIS) and gastrointestinal stromal tumors ﹙GIST), and to find the CT features to be helpful for differentiation of GIS from GIST. Methods Clinical and iconography data of 15 GIS patients and 50 GIST patients who underwent stomach CT scan with postoperatively confirmed histopathology between January 2000 and July 2014 in our department were collected retrospectively. CT findings of these two tumors were compared. Then the ROC curve was drawn based on the significant CT findings and area under the curve ﹙AUC) was calculated to assess the sensibility and specificity for the differential diagnosis of GIS and GIST. Result CT findings, such as the tumor size, shape, cystic change, perilesional lymph nodes, vessel seeding, enhancement pattern and degree, absolute and standardized CT value were significantly different between GIS and GIST ﹙all P<0.05). Among the CT findings, tumor size, cystic change, perilesional lymph nodes, enhancement pattern and arterial phase standardized CT value ﹙Sa) were better differential than others ﹙all AUC>0.7). Tumor size showed the highest sensibility ﹙90%), and cystic change and Sa showed the highest specificity ﹙87%). Conclusions GIS seems to show a homogeneous tumor more frequently, presenting light and moderate enhancement pattern and obvious enhancement of perilesional lymph nodes, while GIST seems to reveal malignant features, such as abundant blood supply, larger volume, frequent necrosis and cystic change. Cystic change and Sa value possess the better differential ability in diagnosis of these two tumors.

Objective To compare the CT image of gastrointestinal schwannomas ﹙GIS) and gastrointestinal stromal tumors ﹙GIST), and to find the CT features to be helpful for differentiation of GIS from GIST. Methods Clinical and iconography data of 15 GIS patients and 50 GIST patients who underwent stomach CT scan with postoperatively confirmed histopathology between January 2000 and July 2014 in our department were collected retrospectively. CT findings of these two tumors were compared. Then the ROC curve was drawn based on the significant CT findings and area under the curve ﹙AUC) was calculated to assess the sensibility and specificity for the differential diagnosis of GIS and GIST. Result CT findings, such as the tumor size, shape, cystic change, perilesional lymph nodes, vessel seeding, enhancement pattern and degree, absolute and standardized CT value were significantly different between GIS and GIST ﹙all P<0.05). Among the CT findings, tumor size, cystic change, perilesional lymph nodes, enhancement pattern and arterial phase standardized CT value ﹙Sa) were better differential than others ﹙all AUC>0.7). Tumor size showed the highest sensibility ﹙90%), and cystic change and Sa showed the highest specificity ﹙87%). Conclusions GIS seems to show a homogeneous tumor more frequently, presenting light and moderate enhancement pattern and obvious enhancement of perilesional lymph nodes, while GIST seems to reveal malignant features, such as abundant blood supply, larger volume, frequent necrosis and cystic change. Cystic change and Sa value possess the better differential ability in diagnosis of these two tumors.

Purpose To summarize the characteristics of computed tomography enteroclysis (CTE) in active ulcerative colitis (UC), and to explore the value of multi-slice CTE in the evaluation of UC. Materials and Methods Thirty-five patients with active UC confirmed by clinical manifestation, colonoscopy and pathology underwent CTE examination in the study. According to the modiifed Mayo-score, the patients were divided into mild group, moderate group and severe group, and the CTE manifestations were compared among the three groups. Results Among 35 patients, 6 patients were in the mild group, 13 in the moderate group, and 16 in the severe group. Submucosal bubbles had signiifcant differences between mild and moderate groups (P<0.05), bowel wall stratiifcation, disappearance of haustra and enlarged mesenteric lymph nodes were signiifcantly different between moderate and severe groups (P<0.05), and engorged vasa recta was significantly different between the 3 groups (P<0.05). However, bowel wall thickening, mural hyperenhencement, narrow lumen and fatty deposits around the rectum showed no difference between the three groups (P>0.05). Conclusion Multi-slice CTE can provide image features of bowel wall, intestinal tube and structures outside intestine in the evaluation of UC, thus it is useful in the diagnosis of active UC as well as in its clinical grading.