Antibacterial activity of lysozyme after association with carboxymethyl konjac glucomannan.

The unique high isoelectric point of lysozyme (LYZ) restricts its application in composite antibacterial coating due to the unfavorable liability to electrostatic interaction with other components. In this work, the antibacterial activity of a dispersible LYZ-carboxymethyl konjac glucomannan (CMKGM) polyelectrolyte complex was evaluated. Kinetic analysis revealed that, compared with free LYZ, the complexed enzyme exhibited decreased affinity (Km) but markedly increased Vmax against Micrococcus lysodeikticus, and QCM and dynamic light scattering analysis confirmed that the complex could bind with the substrate but in a much lower ratio. The complexation with CMKGM did not alter the antibacterial spectrum of LYZ, and the complex exerted antibacterial function by delaying the logarithmic growth phase and impairing the cell integrity of Staphylococcus aureus. Since the LYZ-CMKGM complex is dispersible in water and could be assembled easily, it has great potential as an edible coating in food preservation.

Development and Internal Validation of a Risk Prediction Model for Carotid Atherosclerosis in the Hyperuricemia Population.

Purpose: The aim of this study was to identify independent risk factors for carotid atherosclerosis (CAS) in a population with hyperuricemia (HUA) and develop a CAS risk prediction model. Patients and Methods: This retrospective study included 3579 HUA individuals who underwent health examinations, including carotid ultrasonography, at the Zhenhai Lianhua Hospital in Ningbo, China, in 2020. All participants were randomly assigned to the training and internal validation sets in a 7:3 ratio. Multivariable logistic regression analysis was used to identify independent risk factors associated with CAS. The characteristic variables were screened using the least absolute shrinkage and selection operator combined with 10-fold cross-validation, and the resulting model was visualized by a nomogram. The discriminative ability, calibration, and clinical utility of the risk model were validated using the receiver operating characteristic curve, calibration curve, and decision curve analysis. Results: Sex, age, mean red blood cell volume, and fasting blood glucose were identified as independent risk factors for CAS in the HUA population. Age, gamma-glutamyl transpeptidase, serum creatinine, fasting blood glucose, total triiodothyronine, and direct bilirubin, were screened to construct a CAS risk prediction model. In the training and internal validation sets, the risk prediction model showed an excellent discriminative ability with the area under the curve of 0.891 and 0.901, respectively, and a high level of fit. Decision curve analysis results demonstrated that the risk prediction model could be beneficial when the threshold probabilities were 1-87% and 1-100% in the training and internal validation sets, respectively. Conclusion: We developed and internally validated a risk prediction model for CAS in a population with HUA, thereby contributing to the CAS early identification.

Predicting the risk of subclinical atherosclerosis based on interpretable machine models in a Chinese T2DM population.

Background: Cardiovascular disease (CVD) has emerged as a global public health concern. Identifying and preventing subclinical atherosclerosis (SCAS), an early indicator of CVD, is critical for improving cardiovascular outcomes. This study aimed to construct interpretable machine learning models for predicting SCAS risk in type 2 diabetes mellitus (T2DM) patients. Methods: This study included 3084 T2DM individuals who received health care at Zhenhai Lianhua Hospital, Ningbo, China, from January 2018 to December 2022. The least absolute shrinkage and selection operator combined with random forest-recursive feature elimination were used to screen for characteristic variables. Linear discriminant analysis, logistic regression, Naive Bayes, random forest, support vector machine, and extreme gradient boosting were employed in constructing risk prediction models for SCAS in T2DM patients. The area under the receiver operating characteristic curve (AUC) was employed to assess the predictive capacity of the model through 10-fold cross-validation. Additionally, the SHapley Additive exPlanations were utilized to interpret the best-performing model. Results: The percentage of SCAS was 38.46% (n=1186) in the study population. Fourteen variables, including age, white blood cell count, and basophil count, were identified as independent risk factors for SCAS. Nine predictors, including age, albumin, and total protein, were screened for the construction of risk prediction models. After validation, the random forest model exhibited the best clinical predictive value in the training set with an AUC of 0.729 (95% CI: 0.709-0.749), and it also demonstrated good predictive value in the internal validation set [AUC: 0.715 (95% CI: 0.688-0.742)]. The model interpretation revealed that age, albumin, total protein, total cholesterol, and serum creatinine were the top five variables contributing to the prediction model. Conclusion: The construction of SCAS risk models based on the Chinese T2DM population contributes to its early prevention and intervention, which would reduce the incidence of adverse cardiovascular prognostic events.

Ganoderic acid A attenuated hepatic impairment by down-regulating the intracellular JAK2-STAT3 signaling pathway in induced mushroom poisoning.

BACKGROUND: Mushroom poisoning is one of the most prominent public health problems. However, there is no special antidote so far. In the present study, we verified that Ganoderma lucidum may be an effective approach for treatment of acute mushroom poisoning. METHODS: A retrospective study was performed within the past 20 years, we compiled information on the treatment of α-Amatoxin mushroom poisoning with Ganoderma lucidum by evaluating the mortality rate and liver function before and after treatment. Moreover, we explore the potential underlying mechanism of Ganoderma lucidum in the treatment of α-amanita poisoning in both in vivo animal experiments and in vitro cell experiments. RESULTS: In our study, a total of 556 cases of mushroom poisoning were integrated over the past 20 years, the primary outcome was in-hospital mortality. Specificity, descriptive data of ALT, AST, BA and STB were evaluated for the effectiveness of protection to acute liver damage. From 1994 to 2002, there were 55 cases of mushroom poisoning in which 372 individuals were poisoned, 129 individuals died, with a mortality of 35%. Since 2002, after being treated with Ganoderma lucidum, surprisingly, the mortality decreased to 0%, and all the 184 patients were cured, the hepatic impairment improved significantly within 10 days. Based on a multivariate logistic regression analyses, after adjusting for age, gender and baseline clinical indicators, it was found that Ganoderma lucidum treatment was effective in reducing the morbidity (OR = 0.58), and Ganoderma lucidum treatment also showed an improvement in liver enzymes and in shortening the length of hospitalization significantly. Meanwhile, the main components of Ganoderma lucidum, Ganoderic acid A could significantly improve the survival rate and liver function in α-Amatoxin poisoned mice and may effectively inhibit the JAK2-STAT3 pathway, which could contribute to the detoxification in poisoned patients. CONCLUSION: Ganoderma lucidum is very effective in treating mushroom poisoning by α-amanita and is worth promoting.

Interleukin-41 as a biomarker of the anti-inflammatory response associated with hyperuricemia.

BACKGROUND: Interleukin (IL)-41 is a recently discovered secreted protein that is expressed in a variety of tissues, and it is associated with several immune and metabolic diseases. However, IL-41 has not been studied in hyperuricemia (HUA). METHODS: Forty-four HUA patients and 44 healthy controls (HCs) were included in this study, and we collected theirgeneral and biochemical parameters, including white blood cell, neutrophil, lymphocyte, and platelet counts, mean platelet volume, platelet distribution width, serum creatinine, blood urea nitrogen, fasting blood glucose, total triglyceride, total cholesterol, high-density lipoprotein, low-density lipoprotein, total protein, albumin, alkaline phosphatase, gamma-glutamyltransferase, and hemoglobin concentration. The level of serum IL-41 was determined using an enzyme-linked immunosorbent assay. Multivariate logistic regression analysis was exploited to identify the independent risk factors associated with HUA, and the clinical diagnostic value of IL-41 was analyzed by applying the receiver operating characteristic (ROC) curve. We assessed the association between IL-41 and clinical parameters with Spearman's rank correlation. RESULTS: Circulating IL-41 levels were significantly higher in HUA patients than in the HCs group (460.3 pg/mL vs. 261.3 pg/mL, respectively; P < 0.001). The area under the ROC curve (AUC) for IL-41 in HUA patients was 0.7443 (with a cut-off value of 311.055 pg/mL, a sensitivity of 68.18 %, and a specificity of 72.73 %), while the AUC for IL-41 combined with the platelet count was 0.8109. Correlation analysis revealed that the circulating IL-41 level was positively correlated with age in HCs and HUA patients. CONCLUSIONS: We herein demonstrated that serum IL-41 was elevated in HUA patients and that it may constitute a novel biomarker of anti-inflammatory response related to HUA.

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) has emerged as a worldwide public health issue. Identifying and targeting populations at a heightened risk of developing NAFLD over a 5-year period can help reduce and delay adverse hepatic prognostic events. OBJECTIVE: This study aimed to investigate the 5-year incidence of NAFLD in the Chinese population. It also aimed to establish and validate a machine learning model for predicting the 5-year NAFLD risk. METHODS: The study population was derived from a 5-year prospective cohort study. A total of 6196 individuals without NAFLD who underwent health checkups in 2010 at Zhenhai Lianhua Hospital in Ningbo, China, were enrolled in this study. Extreme gradient boosting (XGBoost)-recursive feature elimination, combined with the least absolute shrinkage and selection operator (LASSO), was used to screen for characteristic predictors. A total of 6 machine learning models, namely logistic regression, decision tree, support vector machine, random forest, categorical boosting, and XGBoost, were utilized in the construction of a 5-year risk model for NAFLD. Hyperparameter optimization of the predictive model was performed in the training set, and a further evaluation of the model performance was carried out in the internal and external validation sets. RESULTS: The 5-year incidence of NAFLD was 18.64% (n=1155) in the study population. We screened 11 predictors for risk prediction model construction. After the hyperparameter optimization, CatBoost demonstrated the best prediction performance in the training set, with an area under the receiver operating characteristic (AUROC) curve of 0.810 (95% CI 0.768-0.852). Logistic regression showed the best prediction performance in the internal and external validation sets, with AUROC curves of 0.778 (95% CI 0.759-0.794) and 0.806 (95% CI 0.788-0.821), respectively. The development of web-based calculators has enhanced the clinical feasibility of the risk prediction model. CONCLUSIONS: Developing and validating machine learning models can aid in predicting which populations are at the highest risk of developing NAFLD over a 5-year period, thereby helping delay and reduce the occurrence of adverse liver prognostic events.

Roles of Amides on the Formation of Atmospheric HONO and the Nucleation of Nitric Acid Hydrates.

Nitrous acid (HONO) is hazardous to the human respiratory system, and the hydrolysis of NO2 is the source of HONO. Hence, the investigation on the removal and transformation of HONO is urgently established. The effects of amide on the mechanism and kinetics of the formation of HONO with acetamide, formamide, methylformamide, urea, and its clusters of the catalyst were studied theoretically. The results show that amide and its small clusters reduce the energy barrier, the substituent improves the catalytic efficiency, and the catalytic effect order is dimer > monohydrate > monomer. Meanwhile, the clusters composed of nitric acid (HNO3), amides, and 1-6 water molecules were investigated in the amide-assisted nitrogen dioxide (NO2) hydrolysis reaction after HONO decomposes by combining the system sampling technique and density functional theory. The study on thermodynamics, intermolecular forces, optics properties of the clusters, as well as the influence of humidity, temperature, atmospheric pressure, and altitude shows that amide molecules promote the clustering and enhance the optical properties. The substituent facilitates the clustering of amide and nitric acid hydrate and lowers the humidity sensitivity of the clusters. The findings will help to control the atmospheric aerosol particle and then reduce the harm of poisonous organic chemicals on human health.

Clinical diagnostic value of serum soluble IL-2 receptor for stage I sarcoidosis in benign isolated mediastinal and hilar lymphadenopathy.

BACKGROUND: Serum soluble interleukin-2 receptor (sIL-2R) is recognized as a marker of T-cell activation and is abnormally elevated in sarcoidosis. However, its value for stage I sarcoidosis in benign granulomatous diseases is unclear. METHODS: We enrolled 33 stage I sarcoidosis patients, 17 lymph node tuberculosis patients, 15 reactive lymphadenopathy patients, and 11 healthy controls. Serum biomarkers concentrations were collected and collated. RESULTS: Serum sIL-2R concentrations were the highest in stage I sarcoidosis. The AUC of serum sIL-2R for stage I sarcoidosis was 0.7452 in all subjects and 0.6861 in granulomatous diseases. The AUCs of two combined diagnostic forms, sIL-2R with angiotensin-converting enzyme (ACE) and sIL-2R with ACE, erythrocyte sedimentation rate (ESR), and lactate dehydrogenase (LDH) were 0.7994 and 0.891 in all subjects, respectively. In granulomatous disease groups for ROC analysis, the best cut-off value of sIL-2R was 745.00 U/ml with 48.50% sensitivity and 84.40% specificity. The combination of four parameters increased the diagnostic accuracy for stage I sarcoidosis in granulomatous diseases (74.10% sensitivity and 100% specificity). Serum sIL-2R concentrations were positively correlated with serum ACE (r = 0.4652, P = 0.0126). CONCLUSION: Serum sIL-2R appeared to be valuable in identifying stage I sarcoidosis in a group of benign granulomatous disorders.

Characterization and stability evaluation of Ca2+ cross-linked soybean protein isolate/chitosan/sodium alginate ternary complex coacervate phase.

To improve the stability of the soybean protein isolate/chitosan/sodium alginate ternary complex coacervate phase against environmental pH and ionic strength, the complex ternary phase cross-linked by Ca2+ was characterized and evaluated. The viscoelastic properties, thermal properties, microstructure, and texture profile were characterized using rheology, differentia scanning calorimetry as well as thermmogravimetric analysis, scanning electron microscopy as well as transmission electron microscopy, and texture profile analysis, respectively. Compared with the uncross-linked ternary complex coacervate, the complex in situ cross-linked with 1.0 % Ca2+ for 1 h still retains its typical solid characteristics, and has a more compact network structure and better stability. Our research results also showed that prolonging the cross-linking time (from 3 h to 5 h) and increasing the concentration of the cross-linking agent (from 1.5 % to 2.0 %) did not further improve the rheological, thermodynamic and textural properties of the complex coacervate. The ternary complex coacervate phase cross-linked in situ under 1.5 % concentration of Ca2+ for 3 h showed significantly improved stability at low pH 1.5-3.0, which indicats that the ternary complex coacervate phase cross-linked in situ by Ca2+ can be used as a potential delivery platform for the effective delivery of biomolecules under physiological conditions.

Phenolic compound SG-1 from Balanophora harlandii and its derivatives exert anti-influenza A virus activity via activation of the Nrf2/HO-1 pathway.

Influenza A virus (IAV) is one of the leading causes of respiratory illness and continues to cause pandemics around the world. Against this backdrop, drug resistance poses a challenge to existing antiviral drugs, and hence, there is an urgent need for developing new antiviral drugs. In this study, we obtained a phenolic compound SG-7, a derivative of natural compound 2-hydroxymethyl-1,4-hydroquinone, which exhibits inhibitory activity toward a panel of influenza viruses and has low cellular toxicity. Mechanistic studies have shown that SG-7 exerts its anti-IAV properties by acting on the virus itself and modulating host signaling pathways. Namely, SG-7 targets the HA2 subunit of hemagglutinin (HA) to block the fusion of viral-cellular membranes and inhibits IAV-induced oxidative stress and overexpression of pro-inflammatory factors by activating the Nrf2/HO-1 pathway and reducing NF-κB activation. In addition, SG-7 can enhance type I IFN antiviral response by inducing Nrf2 expression. Importantly, SG-7 showed the ability to inhibit viral replication in the lungs of IAV-infected mice and reduce their mortality. Therefore, SG-7 may be a promising lead compound for anti-influenza drug development.

High free triiodothyronine, and free-triiodothyronine-to-free-thyroxine ratio are associated with metabolic syndrome in a euthyroid employee population: the Zhejiang Zhenhai study.

Objective: The aim of this study was to elaborate the link of thyroid hormones (THs) and metabolic syndrome (MetS) in a Chinese euthyroid employee population with MetS component(s). Methods: An annual health checkup was performed on employees in 2019. Anthropometric parameters, metabolic parameters, and thyroid function were measured. A questionnaire was used in conjunction with Zhenhai Lianhua Hospital database to receive employees' medication records and thyroid surgical history records. Results: A total of 5486 eligible employees were included; the prevalence of MetS was generally higher in males than in females (38.9 vs. 30.4%, P < 0.001). Among employees with central obesity, hypertriglyceridemia, hyperglycemia, hypertension, and low high-density lipoprotein cholesterol (HDL-C), the prevalence of MetS was 68.8, 63.6, 68.2, 48.8, and 60.0% in males and 72.6, 63.3, 61.3, 42.3, and 42.3% in females, respectively. Logistic regression analysis showed that thyroid-stimulating hormone and free thyroxine (FT4) quartiles had no significant impact on MetS. Free triiodothyronine/free thyroxine (FT3/FT4) and free triiodothyronine (FT3)) quartiles were positively associated with the increased odds ratio (OR) for MetS and dyslipidemia (hypertriglyceridemia and low HDL-C), regardless of gender. In males, FT3 and FT3/FT4 quartiles were positively associated with the OR for central obesity, whereas FT4 quartiles were negatively associated; both FT3 and FT4 quartiles were positively associated with increased OR of hyperglycemia, while similar results were not observed in females. Interaction analysis indicated no significant effect of gender and TH interactions on risk of MetS. Conclusion: High FT3 and FT3/FT4 were strongly linked with MetS and dyslipidemia in our study, even in the euthyroid individuals. Tighter control of thyroid function was necessary for those with preexisting MetS component(s).

Interfacial adsorption behavior of the Aspergillus oryzae lipase-chitosan complex and stability evaluation of the resultant Pickering emulsion.

To prompt the application of the chitosan (CS)-Aspergillus oryzae lipase (AOL) complex in the construction of novel biphasic catalysis medium, its Pickering emulsion stabilization ability as well as adsorption behavior in the oil-water interface were investigated and the stability of resultant emulsion was evaluated. The results indicated that the CS-AOL complex assembled in mass ratio 1:5 was an effective Pickering stabilizer and up to 90 % AOL could be retained in the emulsion interface. Quartz crystal microbalance with dissipation monitoring suggested that the CS-AOL complex spontaneously absorbed to oil-water interface; absorption dynamics analysis revealed that the adsorption was driven by diffusion accompanied by rapid structural rearrangement; while interfacial dilatational rheology demonstrated the formation of an elastic film in the oil-water interface. The Pickering emulsions were pseudoplastic and that in oil fraction 0.6 exhibited the elastic behavior in contrast to the viscous behavior in oil fractions 0.2 and 0.4. The Pickering emulsion exhibited excellent stability against storage for up to 28 d, pHs 2.0-12.0, heating at 25-90 °C, and up to 500 mmol/L NaCl, and the corresponding interfacial AOL retentions exceeded 80 % during exposure to these conditions. Hence, the CS-AOL complex could be used as a stabilizer to construct Pickering emulsion-based biphasic catalysis systems.

Novel Uracil-Functionalized Poly(ionic liquid) Hydrogel: Highly Stretchable and Sensitive as a Direct Wearable Ionic Skin for Human Motion Detection.

Conductive hydrogel-based ionic skins have attracted immense attention due to their great application prospects in wearable electronic devices. However, simultaneously achieving a combination of a single hydrogel system and excellent comprehensive performance (i.e., mechanical durability, electrical sensitivity, broad-spectrum antibacterial activity, and biocompatibility) remains a challenge. Thus, a novel poly(ionic liquid) hydrogel consisting of poly(acrylamide-co-lauryl methacrylate-co-methyl-uracil-imidazolium chloride-co-2-acryloylamino-2-methyl-1-propane sulfonic acid) (AAm-LMA-MUI-AMPS) was prepared by a micellar copolymerization method. Herein, MUI serves as a supramolecular crosslinker and conductive and bacteriostatic components. Owing to the multiple supramolecular crosslinks and hydrophobic association in the network, the hydrogel exhibits excellent mechanical properties (624 kPa of breaking stress and 1243 kPa of compression stress), skin-like modulus (46.2 kPa), stretchability (1803%), and mechanical durability (200 cycles under 500% strain can be completely recovered). Moreover, with the coordinated combination of each monomer, the hydrogel exhibits the unique advantage of high conductivity (up to 59.34 mS/cm). Hence, the hydrogel was further assembled as an ionic skin sensor, which exhibited a gauge factor (GF) of 10.74 and 7.27 with and without LiCl over a broad strain range (1-1000%), respectively. Furthermore, the hydrogel sensor could monitor human movement in different strain ranges, including body movement and vocal cord vibration. In addition, the antibacterial activity and biocompatibility of the hydrogel sensor were investigated. These findings present a new strategy for the design of new-generation wearable devices with multiple functions.

The Metabolic Score for Insulin Resistance Index is Superior to the Triglyceride and Glucose Index in Identifying Nonalcoholic Fatty Liver Disease in Hyperuricemia Subjects.

Purpose: Hyperuricemia (HUA) is a recognized risk factor for non-alcoholic fatty liver disease (NAFLD). Early diagnosis and the identification of predictive factors are important for the HUA population. This study aimed to investigate the potential relationship between the metabolic score for insulin resistance (METS-IR) index and the triglyceride and glucose (TyG) index with NAFLD in individuals with HUA. Patients and Methods: A total of 5537 HUA participants were included in the study. Hepatic ultrasonography was conducted following the latest diagnostic criteria to diagnose NAFLD. The relationship between the TyG and METS-IR index and NAFLD was evaluated by multivariable logistic regression analysis and restricted cubic spline model (RCS). In addition, the area under the curve (AUC), positive likelihood ratio, and negative likelihood ratio were explored to compare the predictive value of IR surrogates for NAFLD with HUA. The AUCs of the two indicators were compared using the DeLong test. Results: Multivariable logistic regression analysis revealed that elevated TyG (OR = 2.285, 95% CI: 1.525, 3.428) and METS-IR (OR = 1.242, 95% CI: 1.219, 1.266) indices significantly increase the risk of NAFLD. Meanwhile, the RCS analysis revealed a -log-shaped nonlinear relationship between NAFLD risk and the METS-IR index (P non-linear < 0.001), contrasting with the linear association observed with the TyG index (P non-linear = 0.763). Notably, the risk of NAFLD demonstrated a significant escalation when the METS-IR index exceeded the threshold of 39.208 (OR=1). Compared to the TyG index (AUC = 0.734, 95% CI: 0.721~0.748), the METS-IR index (AUC = 0.821, 95% CI: 0.810~0.832) demonstrated superior predictive value for NAFLD in individuals with HUA according to the DeLong test. Conclusion: In the HUA population, the METS-IR index has a higher predictive value for NAFLD than the TyG index, contributing to early diagnosis and disease prevention.

Interpretable Machine Learning to Optimize Early In-Hospital Mortality Prediction for Elderly Patients with Sepsis: A Discovery Study.

Sepsis-related mortality rates are high among elderly patients, especially those in intensive care units (ICUs). Early prediction of the prognosis of sepsis is critical, as prompt and effective treatment can improve prognosis. Researchers have predicted mortality and the development of sepsis using machine learning algorithms; however, few studies specifically focus on elderly patients with sepsis. This paper proposes a viable model for early prediction of in-hospital mortality among elderly patients diagnosed with sepsis. We extracted patient information from the Medical Information Mart for Intensive Care IV database. We employed several machine learning algorithms to predict the in-hospital mortality of elderly ICU patients with sepsis. The performance of the model was evaluated by using the AUROC and F1 score. Furthermore, the SHAP algorithm was used to explain the model, analyze how the individual features affect the model output, and visualize the Shapley value for a single individual. Our study included 18522 elderly patients, with a mortality of 15.4%. After screening, 59 clinical variables were extracted to develop models. Feature importance analysis showed that age, PO2, RDW, SPO2, WBC, and urine output were significantly related to the in-hospital mortality. According to the results of AUROC (0.871 (95% CI: 0.854-0.888)) and F1 score (0.547 (95% CI: 0.539-0.661)) analyses, the extreme gradient boosting (XGBoost) model outperformed the other models (i.e., LGBM, LR, RF, DT, and KNN). Furthermore, SHAP force analysis illustrated how the constructed model visualized the individualized prediction of death. XGBoost machine learning framework gives good in-hospital mortality prediction of elderly patients with sepsis and can maximize prediction model accuracy. The XGBoost model could be an effective tool to assist doctors in identifying high-risk cases of in-hospital mortality among elderly patients with sepsis. This could be used to create a clinical decision support system in the future.

Development and internal validation of a risk model for hyperuricemia in diabetic kidney disease patients.

Purpose: This research aimed to identify independent risk factors for hyperuricemia (HUA) in diabetic kidney disease (DKD) patients and develop an HUA risk model based on a retrospective study in Ningbo, China. Patients and methods: Six hundred and ten DKD patients attending the two hospitals between January 2019 and December 2020 were enrolled in this research and randomized to the training and validation cohorts based on the corresponding ratio (7:3). Independent risk factors associated with HUA were identified by multivariable logistic regression analysis. The characteristic variables of the HUA risk prediction model were screened out by the least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross-validation, and the model was presented by nomogram. The C-index and receiver operating characteristic (ROC) curve, calibration curve and Hosmer-Lemeshow test, and decision curve analysis (DCA) were performed to evaluate the discriminatory power, degree of fitting, and clinical applicability of the risk model. Results: Body mass index (BMI), HbA1c, estimated glomerular filtration rate (eGFR), and hyperlipidemia were identified as independent risk factors for HUA in the DKD population. The characteristic variables (gender, family history of T2DM, drinking history, BMI, and hyperlipidemia) were screened out by LASSO combined with 10-fold cross-validation and included as predictors in the HUA risk prediction model. In the training cohort, the HUA risk model showed good discriminatory power with a C-index of 0.761 (95% CI: 0.712-0.810) and excellent degree of fit (Hosmer-Lemeshow test, P > 0.05), and the results of the DCA showed that the prediction model could be beneficial for patients when the threshold probability was 9-79%. Meanwhile, the risk model was also well validated in the validation cohort, where the C-index was 0.843 (95% CI: 0.780-0.906), the degree of fit was good, and the DCA risk threshold probability was 7-100%. Conclusion: The development of risk models contributes to the early identification and prevention of HUA in the DKD population, which is vital for preventing and reducing adverse prognostic events in DKD.

Addition of Chelators Increased the Stability of Black Rice Anthocyanins against the Metallic Ions in Tap Water and Improved the Coloration of Steamed Cold Noodles.

The anthocyanins in black rice extract (BRA) are sensitive to metallic ions, which restrict its application in the coloration of steamed cold noodles in China that uses tap water as the solvent. Food-grade chelators were added to check if they could increase the stability of BRA. The results indicated that the color decay of BRA in tap water was mainly caused by Fe3+, Cu2+, and Fe2+, and the addition of chelators could effectively antagonize this effect. Coloration with the BRA solution containing the optimized chelator formulation of 0.01% ethylenediaminetetraacetic acid disodium, 0.08% sodium hexametaphosphate, and 0.064% sodium tartrate conferred comparable appearance and chromatic attributes with those of the noodle colored by deionized water-dissolved BRA. The steamed cold noodles colored by the chelators-containing BRA exhibited increased springiness and decreased starch retrogradation, and possessed potential health functions due to its slightly increased resistant starch content and markedly enhanced antioxidant capacity. Hence, the addition of chelators is a feasible way to increase the color stability of BRA in tap water, and the chelators-supplemented BRA could be used to produce steamed cold noodles with attractive color and health benefits.

Development and validation of a carotid atherosclerosis risk prediction model based on a Chinese population.

Purpose: This study aimed to identify independent risk factors for carotid atherosclerosis (CAS) and construct and validate a CAS risk prediction model based on the Chinese population. Methods: This retrospective study included 4,570 Chinese adults who underwent health checkups (including carotid ultrasound) at the Zhenhai Lianhua Hospital, Ningbo, China, in 2020. All the participants were randomly assigned to the training and validation sets at a ratio of 7:3. Independent risk factors associated with CAS were identified using multivariate logistic regression analysis. The least absolute shrinkage and selection operator combined with 10-fold cross-validation were screened for characteristic variables, and nomograms were plotted to demonstrate the risk prediction model. C-index and receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA) were used to evaluate the risk model's discrimination, calibration, and clinical applicability. Results: Age, body mass index, diastolic blood pressure, white blood cell count, mean platelet volume, alanine transaminase, aspartate transaminase, and gamma-glutamyl transferase were identified as independent risk factors for CAS. In the training, internal validation, and external validation sets, the risk model showed good discriminatory power with C-indices of 0.961 (0.953-0.969), 0.953 (0.939-0.967), and 0.930 (0.920-0.940), respectively, and excellent calibration. The results of DCA showed that the prediction model could be beneficial when the risk threshold probabilities were 1-100% in all sets. Finally, a network computer (dynamic nomogram) was developed to facilitate the physicians' clinical operations. The website is https://nbuhgq.shinyapps.io/DynNomapp/. Conclusion: The development of risk models contributes to the early identification and prevention of CAS, which is important for preventing and reducing adverse cardiovascular and cerebrovascular events.