Explore the vast range of titles available.

Objective This systematic review provides an overview of machine learning (ML) methods for predicting diabetic peripheral neuropathy (DPN). Method We searched PubMed, Embase, Cochrane Library, and Web of Science databases with the search period limited from their inception to December 3, 2024 (the last search date). The search terms were restricted to “diabetes,” “neuropathy,” and “machine learning.” All studies that developed or validated prognostic models for DPN using ML were considered. Prediction model Risk of Bias ASsessment Tool (PROBAST) was used to assess the risk of bias and applicability of included studies. Results A total of 888 studies were retrieved and 15 articles were included. Most were retrospective studies, with sample sizes ranging from 90 to 102,876 patients. All 15 studies utilised internal validation methods, three studies employed both internal and external validation methods. Internal validation methods like cross-validation were widely used, with area under the curve (AUC) ranging from 0.640 to 0.900. A total of six studies reported complete AUC values yielding a pooled AUC of 0.773 (CI: 0.707–0.839, I²= 99.14). A total of 34 different ML algorithms were utilised across the studies, with the top five being logistic regression, random forest, support vector machine, decision tree, and XGBoost. Calibration was reported in 6 studies, showing satisfactory performance. All studies had a high risk of bias, but most models demonstrated good applicability. Conclusion Existing DPN prediction models demonstrate good performance in discrimination. However, the evaluation indicates that the overall risk of bias in the included studies is high, and their applicability is limited. Future efforts should prioritize prospective, large, multicentre datasets, external validation, and adherence to PROBAST guidelines to reduce bias and enhance applicability for clinical application.

Plant innate immunity relies on successful detection of microbe-associated molecular patterns (MAMPs) of invading microbes via pattern recognition receptors (PRRs) at the plant cell surface. Here, we report two homologous rice (Oryza sativa) lysin motif-containing proteins, LYP4 and LYP6, as dual functional PRRs sensing bacterial peptidoglycan (PGN) and fungal chitin. Live cell imaging and microsomal fractionation consistently revealed the plasma membrane localization of these proteins in rice cells. Transcription of these two genes could be induced rapidly upon exposure to bacterial pathogens or diverse MAMPs. Both proteins selectively bound PGN and chitin but not lipopolysaccharide (LPS) in vitro. Accordingly, silencing of either LYP specifically impaired PGN-or chitin-but not LPS-induced defense responses in rice, including reactive oxygen species generation, defense gene activation, and callóse deposition, leading to compromised resistance against bacterial pathogen Xanthomonas oryzae and fungal pathogen Magnaporthe oryzae. Interestingly, pretreatment with excess PGN dramatically attenuated the alkalinization response of rice cells to chitin but not to flagellin; vice versa, pretreatment with chitin attenuated the response to PGN, suggesting that PGN and chitin engage overlapping perception components in rice. Collectively, our data support the notion that LYP4 and LYP6 are promiscuous PRRs for PGN and chitin in rice innate immunity.

The prevention of postprandial hyperglycemia and diabetic complications is crucial for diabetes management. Inhibition of α-glucosidase to slow carbohydrate metabolism is a strategy to alleviate postprandial hyperglycemia. In addition, suppression of non-enzymatic glycation can diminish the advanced glycation end products and reduce the oxidative stress and inflammation, thereby preventing the diabetic complications. In this study, an anti-oxidative proteoglycan (named FYGL) extracted from Ganoderma lucidum was investigated in vitro for its inhibitory effect on α-glucosidase and non-enzymatic glycation using molecular kinetics, intrinsic fluorescence assay, and bovine serum albumin glycation models. The molecular kinetics and fluorescence assay revealed that FYGL decreases α-glucosidase activity by forming a FYGL–α-glucosidase complex. To evaluate the anti-glycation effect, fructose-glycated and methylglyoxal-glycated BSA models were analyzed by spectroscopic and SDS-PAGE methods. Results showed that FYGL inhibited the glycation at every stage and suppressed glycoxidation, possibly due to its anti-oxidative capacity and FYGL–BSA complex formation. Furthermore, we demonstrated in vivo that FYGL could alleviate postprandial hyperglycemia in db/db mice as well as AGE accumulation and vascular injury in diabetic rats. Overall, FYGL possesses anti-postprandial hyperglycemia and anti-glycation functions and would be potentially used in clinic for diabetes and related complication management.

Hashimoto thyroiditis (HT) is a chronic inflammation of the thyroid gland mediated by autoimmune disorders, often leading to hypothyroidism and a significant reduction in a patient's quality of life. At the time of this writing, there is a lack of effective clinical treatments for early-stage HT. Kangjia decoction granules (KDGs) were developed based on clinical experience and results analysis, showing promising outcomes in improving antibody levels and quality of life in patients with HT. However, there is a lack of further evaluation of the efficacy and safety of KDGs. This pilot study aims to further understand and validate the efficacy and safety of KDGs for treating HT through clinical research and comprehensively assess the benefits of this intervention for patients. This study is a multicenter, randomized, double-blind, placebo-controlled clinical trial. Participants meeting the HT diagnostic criteria will be randomly allocated to the intervention and control groups (n1=n2=70). The intervention group will receive KDG treatment, whereas the control group will receive a placebo treatment. All participants will undergo treatment for 3 months. Changes in antithyroid peroxidase antibody (TPOAb) levels will be the primary outcome. Secondary outcomes include antithyroglobulin antibodies (TGAb), thyrotropin, also known as thyroid stimulating hormone (TSH), triiodothyronine (T3), thyroid hormone (T4), serum free triiodothyronine (FT3), serum free thyroxine (FT4), thyroid ultrasonography, IL17 mRNA and FOXP3 mRNA, traditional Chinese medicine (TCM) syndrome efficacy scores, and quality of life scale scores. Throughout the treatment and follow-up periods, safety indicators, such as routine blood and urine tests, hepatic and renal function, electrocardiography, and major adverse reactions, will be monitored. The research protocol and informed consent form received approval from the Clinical Research Ethics Committee of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, affiliated with Shanghai University of Traditional Chinese Medicine, on December 14, 2022 (Approval No. 2022-123). Participant recruitment commenced in June 2023. All intervention and concurrent data collection activities were scheduled for completion by October 2025. Data management is still ongoing; therefore, data analysis has not yet been performed. This study's findings will offer initial clinical evidence regarding the efficacy of the TCM compound KDGs in modulating peripheral immunity in patients with HT, decreasing autoimmune antibody levels, ameliorating TCM syndromes, and enhancing quality of life. These results will serve as a basis for future large-scale trial designs. China Clinical Trials Registry ChiCTR2300070184; https://www.chictr.org.cn/showprojEN.html?proj=189169. DERR1-10.2196/80993.

Obesity is often accompanied by metabolic disorder and insulin resistance, resulting in type 2 diabetes. Based on previous findings, FYGL , a natural hyperbranched proteoglycan extracted from the G. lucidum fruiting body, can decrease blood glucose and reduce body weight in diabetic mice. In this article, the underlying mechanism of FYGL in ameliorating obesity-induced diabetes was further investigated both in vivo and in vitro. FYGL upregulated expression of metabolic genes related to fatty acid biosynthesis, fatty acid β-oxidation and thermogenesis; downregulated the expression of insulin resistance-related genes; and significantly increased the number of beige adipocytes in db/db mice. In addition, FYGL inhibited preadipocyte differentiation of 3T3-L1 cells by increasing the expression of FABP-4. FYGL not only promoted fatty acid synthesis but also more significantly promoted triglyceride degradation and metabolism by activating the AMPK signalling pathway, therefore preventing fat accumulation, balancing adipocyte production and lipid metabolism, and regulating metabolic disorders and unhealthy obesity. FYGL could be used as a promising pharmacological agent for the treatment of metabolic disorder-related obesity.

Background: One of the most common microvascular complications of diabetes is diabetic kidney disease (DKD). The Huajuxiaoji formula (HJXJ) has shown clinical efficacy for DKD; however, its regulatory mechanisms against DKD remain elusive. We investigated NLRP3 inflammasome and the mechanisms of HJXJ by which HJXJ alleviates DKD. Methods: Phenyl sulfate (PS) was used to establish DKD models. HJXJ was administered to mice through intragastric or made into a pharmaceutical serum for the cell cultures. Biological indicator levels in mouse blood and urine were analyzed, and kidney tissues were used for HE, Masson, and PAS staining. ELISA and western blotting were used to detect inflammatory cytokines and protein levels, respectively. Reactive oxygen species (ROS) production and pyroptosis were evaluated using flow cytometry. Lentiviral vector‐mediated overexpression of NLRP3 was performed to determine whether NLRP3 participates in the antipyroptotic effect of HJXJ. Results: HJXJ significantly reduced the severity of the injury and, in a dose‐dependent manner, decreased the levels of biological markers including creatinine, blood urea nitrogen, urine protein, and endotoxin, as well as inflammatory cytokines such as interleukin (IL)‐1 β , IL‐18, tumor necrosis factor‐ α , and IL‐6 in DKD mice. Treatment with HJXJ reversed the downregulation of podocin, nephrin, ZO‐1, and occludin and upregulated ROS, NLRP3, Caspase‐1 P20, and GSDMD‐N induced by PS. Moreover, the upregulation of NLRP3 expression increased the number of cells positive for pyroptosis. HJXJ suppressed pyroptosis and inflammasome activation by inhibiting NLRP3 expression. Conclusions: Generally, HJXJ has the potential to reduce DKD injury and exerts anti‐DKD effects by inhibiting the NLRP3‐mediated NLRP3 inflammasome activation and pyroptosis in vitro and in vivo.

Ti2AlC is a typical MAX (M: early transition metal, A: main group element, and X: carbon and/or nitrogen) phase with ceramic and metallic properties due to its unique nano-layered structure. In order to investigate the interaction behavior between Ag and Ti2AlC, a sessile drop experiment was conducted at 1080 °C for 5 min. The atomic rearrangement occurred at the Ag–Ti2AlC interface was revealed using high-angle annular dark-field scanning transmission electron microscopy coupled with high-resolution transmission electron microscopy analysis. The results show that Ag nanoclusters generally appeared in most of the Ag–Ti2AlC interaction regions thermally processed at 1080 °C. In addition, Ag can also substitute for Al and Ti atoms in the Ti2AlC, promoting local structural decomposition of the Ti2AlC and producing 4H–Ag with a hexagonal close-packed (hcp) structure. Additionally, Al atoms released from the Ti2AlC lattices can dissolve locally into the liquid Ag, particularly at the grain boundaries. When the loss concentration of Al exceeded the critical level, the Ti2AlC started to decompose and the residual Ti6C octahedrons and Al atoms recombined, giving rise to the production of anti-perovskite Ti3AlC with a cubic structure. Lastly, the discrepancy in substitution behavior of Ag in the Ti2AlC was compared when thermally processed at different temperatures (1030 °C and 1080 °C). This work contributes to the understanding of the intrinsic stability of Ti2AlC MAX ceramics under high-temperature treatment.

Background. Better therapeutic drugs are required for treating hypertensive diabetic nephropathy. In our previous study, the Huaju Xiaoji (HJXJ) formula promoted the renal function of patients with diabetes and hypertensive nephropathy. In this study, we investigated the therapeutic effect and regulation mechanism of HJXJ in hypertensive diabetic mice with nephropathy. Methods. We constructed a mouse hypertensive diabetic nephropathy (HDN) model by treating mice with streptozotocin (STZ) and nomega-nitro-L-arginine methyl ester (LNAME). We also constructed a human glomerular mesangial cell (HGMC) model that was induced by high doses of sugar (30 mmol/mL) and TGFβ1 (5 ng/mL). Pathological changes were evaluated by hematoxylin and eosin (H&E) staining, periodic acid Schiff (PAS) staining, and Masson staining. The fibrosis-related molecules (TGFβ1, fibronectin, laminin, COL I, COL IV, α-SMA, and p-smad2/3) were detected by enzyme-linked immunosorbent assay (ELISA). The mRNA levels and protein expression of endoplasmic reticulum stress, fibrosis molecules, and their downstream molecules were assessed using qPCR and Western blotting assays. Results. Administering HJXJ promoted the renal function of HDN mice. HJXJ reduced the expression of ER stress makers (CHOP and GRP78) and lncMGC, miR379, miR494, miR495, miR377, CUGBP2, CPEB4, EDEM3, and ATF3 in HDN mice and model HGMCs. The positive control drugs (dapagliflozin and valsartan) also showed similar effects after treatment with HJXJ. Additionally, in model HGMCs, the overexpression of CHOP or lncMGC decreased the effects of HJXJ-M on the level of fibrosis molecules and downstream target molecules. Conclusion. In this study, we showed that the HJXJ formula may regulate ERS-lncMGC/miRNA to enhance renal function in hypertensive diabetic mice with nephropathy. This study may act as a reference for further investigating whether combining HJXJ with other drugs can enhance its therapeutic effect. The findings of this study might provide new insights into the clinical treatment of hypertensive diabetic nephropathy with HJXJ.

Introduction Papillary thyroid carcinoma (PTC) generally has a favorable prognosis, yet advanced PTC has higher recurrence and mortality risks. This study constructs and validates a competing risk model for cause-specific mortality (CSM) in advanced PTC. Methods Stage III-IV PTC cases (AJCC 7th edition) from the SEER database (2010-2015) were analyzed. Patients were split into training and validation sets (7:3). Univariate and multivariate analyses identified independent CSM predictors, forming the basis of a risk prediction nomogram. Model accuracy was evaluated via the C-index and calibration curve. Results A total of 11 913 advanced PTC cases were analyzed. Competing risk model analysis unraveled that age, race, sex, grade, stage, T stage, M stage, surgery, chemotherapy, and tumor size were risk factors for CSM in advanced PTC. The AUC values of the constructed nomogram in predicting 3-, 5-, and 8-year survival were 0.931 (95%CI 0.909-0.953), 0.915 (95%CI 0.897-0.933), and 0.902 (95%CI 0.883-0.92) in the training set, and 0.948 (95%CI 0.916-0.981), 0.93 (95 % CI 0.903-0.957), and 0.917 (95%CI 0.891-0.943) in the validation cohort, respectively. The C-index of the nomogram for advanced PTC was 0.908 and 0.921 in the training and validation cohorts, respectively. The calibration curve unveiled that the predicted estimates by the model were basically congruent with the observed values, suggesting a high degree of calibration. Conclusion The competing risk model offers a reliable tool for assessing prognosis in advanced PTC, supporting personalized treatment and risk management in clinical practice. Plain Language Summary Papillary thyroid cancer (PTC) is the most common type of thyroid cancer, and it usually has a good outlook. However, for patients with advanced stages of the disease, the risks of complications and death are higher. This study aimed to identify the factors that contribute to death caused specifically by advanced PTC and to develop a tool to predict these risks. We analyzed data from over 11 000 patients with advanced PTC collected between 2010 and 2015 from a large database in the United States. By using advanced statistical methods, we identified key factors that affect the risk of death from advanced PTC, such as age, tumor size, cancer stage, and whether the patient underwent surgery or chemotherapy. We used this information to create a prediction tool called a “nomogram,” which can estimate the chances of survival for three, five, and eight years after diagnosis. The tool was tested and found to be highly accurate in predicting outcomes, making it a valuable resource for doctors. It helps them assess risks and make more personalized treatment decisions for patients with advanced PTC. For example, doctors can use the nomogram to identify patients who may benefit from surgery or other targeted treatments.

Background People live a long time in pre-diabetes/early diabetes without a formal diagnosis or management. Heterogeneity of progression coupled with deficiencies in electronic health records related to incomplete data, discrete events, and irregular event intervals make identification of pre-diabetes and critical points of diabetes progression challenging. Methods We utilized longitudinal electronic health records of 9298 patients with type 2 diabetes or prediabetes from 2005 to 2016 from a large regional healthcare delivery network in China. We optimized a generative Markov-Bayesian-based model to generate 5000 synthetic illness trajectories. The synthetic data were manually reviewed by endocrinologists. Results We build an optimized generative progression model for type 2 diabetes using anchor information to reduce the number of parameters learning in the third layer of the model from O N × W to O ( N - C ) × W , where N is the number of clinical findings, W is the number of complications, C is the number of anchors. Based on this model, we infer the relationships between progression stages, the onset of complication categories, and the associated diagnoses during the whole progression of type 2 diabetes using electronic health records. Discussion Our findings indicate that 55.3% of single complications and 31.8% of complication patterns could be predicted early and managed appropriately to potentially delay (as it is a progressive disease) or prevented (by lifestyle modifications that keep patient from developing/triggering diabetes in the first place). Conclusions The full type 2 diabetes patient trajectories generated by the chronic disease progression model can counter a lack of real-world evidence of desired longitudinal timeframe while facilitating population health management.