BackgroundDiabetes is the most common comorbidity of necrotizing fasciitis (NF), but the effect of stress-induced hyperglycemia (SIH) on diabetic patients with NF has never been investigated. The aim of this study was to assess whether SIH, as determined by the glycemic gap between admission glucose levels and A1C-derived average glucose levels, predicts adverse outcomes in diabetic patients hospitalized with NF.MethodsWe retrospectively reviewed the glycemic gap and clinical outcomes in 252 diabetic patients hospitalized due to NF from 2011 to 2018 in a single medical center in Taiwan. A receiver operating characteristic (ROC) curve was used to analyze the optimal cutoff values for predicting adverse outcomes. Univariate and multivariate logistic regression analyses were employed to identify significant predictors of adverse outcomes.ResultsIn total, 194 diabetic NF patients were enrolled. Compared with patients without adverse outcomes, patients with adverse outcomes had significantly higher glycemic gaps, Acute Physiology and Chronic Health Evaluation (APACHE) II scores and C-reactive protein (CRP) levels; lower albumin and hemoglobin levels; greater incidence of limb loss; and longer hospital and intensive care unit stays. The glycemic gap positively correlates with the laboratory risk indicator for NF scores, APACHE II scores and CRP levels. A glycemic gap of 146 mg/dL was the optimal cutoff value for predicting adverse outcomes using the ROC curve. Compared with patients with glycemic gaps ≤146 mg/dL, those with glycemic gaps >146 mg/dL had higher APACHE II scores and incidence rates of adverse outcomes, especially bacteremia and acute kidney injury. Multivariate analysis revealed that a glycemic gap >146 mg/dL and APACHE II score >15 were independent predictors of adverse outcomes, while the presence of hyperglycemia at admission was not.ConclusionsAn elevated glycemic gap was significantly independently associated with adverse outcomes in diabetic NF patients. Further prospective studies are warranted to validate the role of the glycemic gap in NF patients with diabetes.
Heatstroke (HS) can cause acute lung injury (ALI). Heat stress induces inflammation and apoptosis via reactive oxygen species (ROS) and endogenous reactive aldehydes. Endothelial dysfunction also plays a crucial role in HS-induced ALI. Aldehyde dehydrogenase 2 (ALDH2) is a mitochondrial enzyme that detoxifies aldehydes such as 4-hydroxy-2-nonenal (4-HNE) protein adducts. A single point mutation in ALDH2 at E487K (ALDH2*2) intrinsically lowers the activity of ALDH2. Alda-1, an ALDH2 activator, attenuates the formation of 4-HNE protein adducts and ROS in several disease models. We hypothesized that ALDH2 can protect against heat stress-induced vascular inflammation and the accumulation of ROS and toxic aldehydes. Homozygous ALDH2*2 knock-in (KI) mice on a C57BL/6J background and C57BL/6J mice were used for the animal experiments. Human umbilical vein endothelial cells (HUVECs) were used for the in vitro experiment. The mice were directly subjected to whole-body heating (WBH, 42°C) for 1 h at 80% relative humidity. Alda-1 (16 mg/kg) was administered intraperitoneally prior to WBH. The severity of ALI was assessed by analyzing the protein levels and cell counts in the bronchoalveolar lavage fluid, the wet/dry ratio and histology. ALDH2*2 KI mice were susceptible to HS-induced ALI in vivo. Silencing ALDH2 induced 4-HNE and ROS accumulation in HUVECs subjected to heat stress. Alda-1 attenuated the heat stress-induced activation of inflammatory pathways, senescence and apoptosis in HUVECs. The lung homogenates of mice pretreated with Alda-1 exhibited significantly elevated ALDH2 activity and decreased ROS accumulation after WBH. Alda-1 significantly decreased the WBH-induced accumulation of 4-HNE and p65 and p38 activation. Here, we demonstrated the crucial roles of ALDH2 in protecting against heat stress-induced ROS production and vascular inflammation and preserving the viability of ECs. The activation of ALDH2 by Alda-1 attenuates WBH-induced ALI in vivo.
Scrub typhus is a mite-borne infectious disease caused by Orientia tsutsugamushi (previously called Rickettsia tsutsugamushi). The severity of this disease varies from only mild symptoms to death, and its manifestations are nonspecific. Therefore, clinicians may not correctly diagnose scrub typhus early enough for successful treatment. Reports of infections in travelers returning from Asia to their home countries are increasingly common. Thus, it is important that even clinicians in nonepidemic regions be alert for this disease. Here we describe the epidemiological aspects and clinical manifestations of scrub typhus encountered at a teaching hospital in Penghu, Taiwan, over the past 5 years. A total of 126 patients were confirmed to be positive for scrub typhus at the hospital from 2006 to 2010. All cases were confirmed by the Centers for Disease Control and Prevention or its contract laboratory through pathogen isolation and an indirect immunofluorescence assay. Medical records of these patients were reviewed, and demographic and clinical characteristics, laboratory data, seasonal data, geographic distribution, complications, and outcome were analyzed. The incidence of scrub typhus peaked in individuals aged 0-10 and 51-60 years, with the highest incidence among those ≤10 years of age. No significant difference was noted between sexes. Fever was the most common symptom (93.6%), followed by chills (23.8%), cough (18.3%), and headache (14.3%). Eschars were observed in 78 (61.9%) patients, with the axilla being the most frequent site (n=17; 21.8%). Most patients were retirees (n=63; 50%), followed by students (n=16; 12.7%). Patients were more likely to live in rural areas than urban areas. Scrub typhus was epidemic in the spring (April to June) and fall (October to December) in a bimodal distribution similar to that observed in Japan. Leukocytosis was not common, but most patients had abnormal C-reactive protein levels, thrombocytopenia, and elevated liver function test results. Residents of Penghu, particularly Makung City and Husi Township, as well as travelers to the region during the spring and fall seasons should be educated about the signs and symptoms of scrub typhus. All physicians who come into contact with individuals residing in or traveling to or from epidemic regions should remain alert about the manifestations of this disease.
Background: Colorectal cancer (CRC) is one of the most prevalent malignant diseases worldwide. Risk prediction for tumor recurrence is important for making effective treatment decisions and for the survival outcomes of patients with CRC after surgery. Herein, we aimed to explore a prediction algorithm and the risk factors for postoperative tumor recurrence using a machine learning (ML) approach with standardized pathology reports for patients with stage II and III CRC. Methods: Pertinent clinicopathological features were compiled from medical records and standardized pathology reports of patients with stage II and III CRC. Four ML models based on logistic regression (LR), random forest (RF), classification and regression decision trees (CARTs), and support vector machine (SVM) were applied for the development of the prediction algorithm. The area under the curve (AUC) of the ML models was determined in order to compare the prediction accuracy. Genomic studies were performed using a panel-targeted next-generation sequencing approach. Results: A total of 1073 patients who received curative intent surgery at the National Cheng Kung University Hospital between January 2004 and January 2019 were included. Based on conventional statistical methods, chemotherapy (p = 0.003), endophytic tumor configuration (p = 0.008), TNM stage III disease (p < 0.001), pT4 (p < 0.001), pN2 (p < 0.001), increased numbers of lymph node metastases (p < 0.001), higher lymph node ratios (LNR) (p < 0.001), lymphovascular invasion (p < 0.001), perineural invasion (p < 0.001), tumor budding (p = 0.004), and neoadjuvant chemoradiotherapy (p = 0.025) were found to be correlated with the tumor recurrence of patients with stage II–III CRC. While comparing the performance of different ML models for predicting cancer recurrence, the AUCs for LR, RF, CART, and SVM were found to be 0.678, 0.639, 0.593, and 0.581, respectively. The LR model had a better accuracy value of 0.87 and a specificity value of 1 in the testing set. Two prognostic factors, age and LNR, were selected by multivariable analysis and the four ML models. In terms of age, older patients received fewer cycles of chemotherapy and radiotherapy (p < 0.001). Right-sided colon tumors (p = 0.002), larger tumor sizes (p = 0.008) and tumor volumes (p = 0.049), TNM stage II disease (p < 0.001), and advanced pT3–4 stage diseases (p = 0.04) were found to be correlated with the older age of patients. However, pN2 diseases (p = 0.005), lymph node metastasis number (p = 0.001), LNR (p = 0.004), perineural invasion (p = 0.018), and overall survival rate (p < 0.001) were found to be decreased in older patients. Furthermore, PIK3CA and DNMT3A mutations (p = 0.032 and 0.039, respectively) were more frequently found in older patients with stage II–III CRC compared to their younger counterparts. Conclusions: This study demonstrated that ML models have a comparable predictive power for determining cancer recurrence in patients with stage II–III CRC after surgery. Advanced age and high LNR were significant risk factors for cancer recurrence, as determined by ML algorithms and multivariable analyses. Distinctive genomic profiles may contribute to discrete clinical behaviors and survival outcomes between patients of different age groups. Studies incorporating complete molecular and genomic profiles in cancer prediction models are beneficial for patients with stage II–III CRC.
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