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BackgroundAngiogenesis 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.MethodsCD-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.ResultsBased 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.ConclusionComprehensive 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.

Abstract 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.

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.

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.

Background: Angiogenesis response plays a crucial role in the occurrence and development of Crohn's disease (CD) and may involve the mechanismof 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: Basedonangiogenesis-relatedgenes, we identified two distinct CDclusters (C1 and C2). Compared toC1, themetabolic pathways inC2were 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. [ABSTRACT FROM AUTHOR]

Background IgA nephropathy (IgAN) is the most common primary glomerular disease, and one major cause of malignant hypertension (MHT) secondary to renal parenchymal disease. The clinical conditions and severity of renal lesion have been reported to be more serious when IgAN is accompanied by MHT, but risk factors of MHT in IgAN analyzed from Traditional Chinese Medicine (TCM) perspective are still unclear. Objective To explore the TCM-related factors and pathogenesis associated with MHT in IgAN patients. Methods From 518 cases of primary IgAN who were diagnosed by renal biopsy in First Medical Center of Chinese PLA General Hospital during December 2013 to September 2021, a sample of 12 cases accompanied by MHT (IgAN-MHT group) computed by PASS 15.0 were selected for correlation power analysis, and they were matched at a ratio of 1∶5 with other randomly selected 85 cases without MHT (IgAN group) . Clinical, pathological and TCM syndromes between the two groups were compared. Lasso regression was used to screen 93 TCM syndromes, among which those with significant associations with IgAN-MHT were identified by multivariate Logistic regression. Results Compared with IgAN group, IgAN-MHT group had higher prevalence of headache and dizziness or nausea and vomiting as the first clinical manifestations, and clinically diagnosed nephrotic syndrome, higher levels of baseline mean arterial pressure, highest systolic and diastolic blood pressure, blood urea nitrogen, serum creatinine, serum inorganic phosphorus, serum magnesium, and quantitatively estimated 24-hour urinary protein excretion, higher prevalence of CKD stages 3-5, blood stasis syndrome, subtypes of qi-deficiency syndromes (including mental fatigue and lack of strength, limb fatigue, tibia and leg weakness, dizziness, headache with nausea, and vomiting) , subtypes of yin-deficiency syndromes (including blurred vision and eye floaters) , subtypes of blood stasis syndromes (including nail cyanosis, and dark purple tongue) , and intrarenal arteriosclerosis (P