Constructing models for Crohn's disease diagnosis and prediction of infliximab non-response based on angiogenesis-related genes.

Background: Angiogenesis response plays a crucial role in the occurrence and development of Crohn's disease (CD) and may involve the mechanism of infliximab non-response. However, the role of angiogenesis-related genes in Crohn's disease has not been comprehensively studied. This study aimed to explore the expression profiles of angiogenesis-related genes in CD patients and construct models for disease diagnosis and prediction of infliximab non-response. Methods: CD-related microarray datasets were collected from the GEO database. Unsupervised consensus clustering analysis was performed based on differentially expressed angiogenesis-related genes to divide CD samples into two distinct clusters. Weighted gene co-expression network analysis (WGCNA) was conducted on the clusters to identify angiogenesis-related module. Based on the differentially expressed genes in the module, machine learning algorithms were employed to further identify hub genes and construct a disease diagnostic model. Subsequently, treatment outcome-related genes were extracted from these hub genes, and a predictive model for infliximab non-response in CD patients was ultimately built. Results: Based on angiogenesis-related genes, we identified two distinct CD clusters (C1 and C2). Compared to C1, the metabolic pathways in C2 were significantly upregulated, and there was a higher abundance of cell clusters such as M1 macrophages and plasma cells. Additionally, C2 showed a poorer response to infliximab. Furthermore, a predictive model for infliximab non-response in CD patients was constructed based on the hub genes, and it was successfully validated using an external dataset. Conclusion: Comprehensive analysis of angiogenesis-related genes revealed different clusters of CD, which exhibited differential response rates to infliximab. The construction of models provides a reference for disease diagnosis and drug selection, aiding in clinical decision-making.

Oral treatment with 10% potassium iodide solution for refractory cutaneous-disseminated sporotrichosis in an immunocompetent adult: Case report.

Sporotrichosis has multiple clinical manifestations, and its cutaneous-disseminated form is uncommon and, in most cases, related to immunosuppressive conditions. We report the case of a 47-year-old male patient who presented with multiple cutaneous nodules and ulcers on the left upper limb and the right thigh, with no other comorbidities. Until the diagnosis was confirmed, the patient was initially given empiric antifungal treatment with itraconazole, which showed unsatisfactory results at a local hospital. Then, he was treated with voriconazole, which led to the slow improvement of his skin lesions. At one point during the voriconazole treatment course, the patient briefly self-discontinued voriconazole for economic reasons, and the lesions recurred and worsened. The patient was finally diagnosed with cutaneous-disseminated sporotrichosis based on the isolation and identification of Sporothrix globosa. Susceptibility testing revealed that the isolate was resistant to itraconazole, fluconazole, voriconazole, terbinafine, and amphotericin. Considering the patient's poor financial condition, potassium iodide was administered. After 1-month of therapy with potassium iodide, he reported rapid improvement of his skin lesions. The patient continued potassium iodide treatment for another 5 months until the full resolution of lesions was achieved.

Relationship between Traditional Chinese Medicine Syndrome Elements and Prognosis of Patients with IgA Nephropathy.

Objective: It is not clear which Traditional Chinese Medicine- (TCM-) related elements affect primary IgA nephropathy (IgAN) progression. Here, we explored the risk factors, based on TCM syndrome elements, related to the prognosis of primary IgAN patients. Methods: We analyzed patients with newly diagnosed, biopsy-proven IgAN at a single institution from December 2013 to September 2021. Basic clinical and pathological characteristics were assessed at the time of renal biopsy. The study endpoint was end-stage renal disease (ESRD: eGFR <15 ml/min per 1.73 m2, dialysis, or kidney transplantation) and/or eGFR decreased by >30% from baseline. Kaplan‒Meier survival analysis was used to explore the role of TCM syndrome elements in IgAN progression. Multivariate Cox regression analysis with adjustment for traditional risk factors was performed to explore TCM syndrome elements that may influence patient prognosis. The factors correlated with TCM syndrome elements in IgAN patients were further evaluated by logistic regression analysis. Results: During a median follow-up of 22.0 months, 53 (12.5%) of the 423 included IgAN patients reached the study endpoint. The main IgAN disease location elements were the kidney, liver, and spleen. The main IgAN disease nature elements were Yin-deficiency and Qi-deficiency, dampness, Yang-deficiency, phlegm, and Blood-deficiency. Kaplan‒Meier analysis identified three disease locations (liver, spleen, and kidney) and four disease natures (Qi-deficiency, Yang-deficiency, phlegm, and dampness) as elements associated with poor renal survival in IgAN patients. In multivariate Cox regression analysis, baseline Yang-deficiency was an independent risk predictor of poor prognosis in primary IgAN patients (hazard ratio 2.338; 95% confidence interval [CI]: 1.208-4.525; P=0.012) after adjustment for traditional risk factors. Furthermore, logistic regression analysis identified being female (odds ratio [OR] 2.518; 95% CI: 1.538-4.122; P < 0.001), older age (OR 1.043; 95% CI: 1.022-1.065; P < 0.001), low hemoglobin levels (OR 0.984; 95% CI: 0.971-0.996; P=0.013), and cellular/fibrocellular crescents (OR 1.706; 95% CI: 1.068-2.728; P=0.026) as factors affecting Yang-deficiency in IgAN patients. Conclusions: Yang-deficiency independently predicts the risk of poor prognosis in primary IgAN patients. Being female, older age, low hemoglobin levels, and cellular/fibrocellular crescents were independently associated with Yang-deficiency in IgAN patients.

Formation and Identification of a 5-(Hydroxymethyl)-2-Furfural-Zingerone Condensate and Its Cytotoxicity in Caco-2 Cells.

5-(Hydroxymethyl)-2-furfural (HMF), an active furfural, widely exists in various food products and has potential safety risks. It can be eliminated by occurring aldol condensation with α-unsubstituted ketones in the presence of catalysts. However, the elimination process between HMF and ketones from food is rarely studied. In this study, the adduct formation between HMF and zingerone (ZGR) catalyzed by proline was investigated. It revealed that the adduct formation led to 99.75% of HMF being trapped under the optimized reaction condition. Moreover, the in vitro digestion stability of HMF-ZGR adduct (HMZ) and its cytotoxicity against Caco-2 cells were evaluated. The results indicated that more than 75% of HMZ was remained after a three-stage simulated digestion. Following 24 and 48 h of incubation, HMZ exhibited cytotoxicity against Caco-2 cells with IC50 values of 41.47 ± 5.33 and 25.39 ± 3.12 mM, respectively, versus 35.39 ± 4.03 and 19.17 ± 2.10 mM by HMF.

Prediction of 3-year risk of diabetic kidney disease using machine learning based on electronic medical records.

BACKGROUND: Established prediction models of Diabetic kidney disease (DKD) are limited to the analysis of clinical research data or general population data and do not consider hospital visits. Construct a 3-year diabetic kidney disease risk prediction model in patients with type 2 diabetes mellitus (T2DM) using machine learning, based on electronic medical records (EMR). METHODS: Data from 816 patients (585 males) with T2DM and 3 years of follow-up at the PLA General Hospital. 46 medical characteristics that are readily available from EMR were used to develop prediction models based on seven machine learning algorithms (light gradient boosting machine [LightGBM], eXtreme gradient boosting, adaptive boosting, artificial neural network, decision tree, support vector machine, logistic regression). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was used to interpret the results of the best performing model. RESULTS: The LightGBM model had the highest AUC (0.815, 95% CI 0.747-0.882). Recursive feature elimination with random forest and SHAP plot based on LightGBM showed that older patients with T2DM with high homocysteine (Hcy), poor glycemic control, low serum albumin (ALB), low estimated glomerular filtration rate (eGFR), and high bicarbonate had an increased risk of developing DKD over the next 3 years. CONCLUSIONS: This study constructed a 3-year DKD risk prediction model in patients with T2DM and normo-albuminuria using machine learning and EMR. The LightGBM model is a tool with potential to facilitate population management strategies for T2DM care in the EMR era.