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In addressing the complexity, limited information, and dynamic spatiotemporal characteristics encountered in predicting pellet strength with traditional methods, this study proposes a novel prediction model for the strength of fusible pellets, developed on a Particle Swarm Optimization Deep Fuzzy Neural Network (PSO-DFNN). Initially, the model is constructed by observing and extracting fractal features of the microstructure of pellet ore. Subsequently, the fuzzy system is utilized to partition the spatiotemporal data and generate multi-layer fuzzy rules, thus constructing a deep fuzzy neural network. Lastly, the Particle Swarm Optimization algorithm is employed to optimize the fuzzy membership rule weights, achieving precise prediction of pellet strength. The results indicate a Mean Absolute Error (MAE) of 3.7218 and a Symmetric Mean Absolute Percentage Error (SMAPE) of 3.72 % when predicting pellet strength during the pellet roasting drying stage. The PSO-DFNN model exhibits high prediction accuracy, meeting the needs for pellet strength prediction and providing a more reliable basis for decision-making in the production process.

Sintering performance index can reflect the quality of sintered ore, and the level of sintered ore performance directly affects the stability of blast furnace production. Accurate prediction of sinter performance metrics is essential to optimise sinter quality, improve production efficiency and reduce energy consumption. Based on the sintering big data, this paper proposes the Optuna-DFNN model by combining the fuzzy neural network algorithm, the deep neural network algorithm and the Optuna framework to address the problems of difficulty in tuning the parameter, lack of self-learning ability, and poor generalisation of the model of the traditional method when faced with the input of multiple parameters. The paper predicts the four prediction indexes of yield rate, return fines ratio, drum index and RDI+3.15 respectively. Among them, the relative error of the prediction of the yield rate is within 1.3 %, the relative error of the prediction of the return fines ratio is within 2.8 %, the relative error of the prediction of the drum index is within 0.32 %, and the relative error of the prediction of the RDI+3.15 is within 1.4 %. The results indicate that the Optuna-DFNN model has better performance in predicting sintering performance metrics. Based on the Optuna-DFNN sintered ore performance prediction model, the quality of sintered ore can be accurately predicted, the sintering process parameters can be adjusted in a timely manner, the sintering process can be optimized, and the utilization coefficient of sintering can be improved. Provide a reference for the optimisation of production processes, with a positive impact on cost reduction, environmental protection and increased sustainability of production.

Abstract Metals have been proved to be one of risk factors for chronic kidney disease (CKD) and diabetes, but the effect of mixed metal co-exposure and potential interaction between metals are still unclear. We assessed the urine and whole blood levels of cadmium (Cd), manganese (Mn), lead (Pb), mercury (Hg), and renal function in 3080 adults from National Health and Nutrition Survey (NHANES) (2011–2018) to explore the effect of mixed metal exposure on CKD especially in people with type 2 diabetes mellitus (T2DM). Weighted quantile sum regression model and Bayesian Kernel Machine Regression model were used to evaluate the overall exposure impact of metal mixture and potential interaction between metals. The results showed that the exposure to mixed metals was significantly associated with an increased risk of CKD in blood glucose stratification, with the risk of CKD being 1.58 (1.26,1.99) times in urine and 1.67 (1.19,2.34) times in whole blood higher in individuals exposed to high concentrations of the metal mixture compared to those exposed to low concentrations. The effect of urine metal mixture was elevated magnitude in stratified analysis. There were interactions between urine Pb and Cd, Pb and Mn, Pb and Hg, Cd and Mn, Cd and Hg, and blood Pb and Hg, Mn and Cd, Mn and Pb, Mn and Hg on the risk of CKD in patients with T2DM and no significant interaction between metals was observed in non-diabetics. In summary, mixed metal exposure increased the risk of CKD in patients with T2DM, and there were complex interactions between metals. More in-depth studies are needed to explore the mechanism and demonstrate the causal relationship.

ABSTRACT Aims/Introduction To determine the population health burden attributable to the development of diabetes among women with a history of gestational diabetes mellitus (GDM). Materials and Methods We conducted a retrospective analysis of women with a history of GDM attending the Hong Kong Hospital Authority between 2000 and 2019. The time‐varying population attributable fraction was calculated. Results A total of 76,181 women with a history of gestational diabetes mellitus were included, 6,606 of them developed diabetes during a median follow‐up of 8.6 years. The respective hazard ratios (95% confidence interval) among women with GDM who developed diabetes vs those with GDM only were 2.8 (2.2, 3.7) for cardiovascular disease (CVD), 4.8 (3.0, 7.7) for end‐stage kidney disease (ESKD), 2.2 (1.9, 2.6) for infection‐related hospitalization, and 1.8 (1.3, 2.4) for all‐cause mortality. The development of diabetes was associated with 1.3 (0.8, 1.7), 0.6 (0.3, 0.8), 3.2 (2.4, 4.0), and 0.5 (0.2, 0.9) additional incident cases per 1,000 person‐years, accounting for 24.0% (13.2%, 35.9%), 42.0% (22.5%, 58.8%), 10.8% (7.1%, 14.9%), and 6.0% (−3.1%, 16.1%) of absolute number of CVD, ESKD, infection‐related hospitalization, and all‐cause mortality over 20 years after GDM, respectively. Conclusions Diabetes is a significant contributor to the population health burden of some clinical outcomes in women with a history of gestational diabetes mellitus, but other risk factors need to be considered.

Summary: S-acylation of proteins allows their association with membranes. Here, we present a protocol for establishing a platform for membrane affinity evaluation of S-acylated proteins in vitro. We describe steps for preparing lipid-maleimide compounds, mCherry-p62 recombinant proteins, and total cellular membranes. We then detail procedures for synthesizing protein-lipid conjugates using lipid-maleimide compounds and recombinant proteins and evaluating the membrane affinity of protein-lipid conjugates.For complete details on the use and execution of this protocol, please refer to Huang Xue et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Abstract Background Patients with type 2 diabetes (T2D) are at high risk of developing multiple complications, affecting their health‐related quality of life (HRQoL). Existing studies only considered impact of complication on HRQoL in the year of occurrence but not its residual impacts in subsequent years. We investigated temporal impacts of diabetes‐related complications on HRQoL in a 12‐year prospective cohort of ambulatory Chinese patients with T2D enrolled in the clinic‐based Joint Asia Diabetes Evaluation (JADE) Register. Methods HRQoL utility measures were derived from EuroQol five‐dimensional three‐level questionnaire (EQ‐5D‐3L) questionnaires completed by 19 322 patients with T2D in Hong Kong (2007–2018). Temporal EQ‐5D utility decrements associated with subtypes of cardiovascular‐renal events were estimated using generalized linear regression model after stepwise selection of covariates with p

Summary: Background: Current labelling advises discontinuation of metformin when estimated glomerular filtration rate (eGFR) 5.0 mmol/L with a concomitant blood pH

Abstract The purpose of this article is to explore the relationship between the total dose of follicle-stimulating hormone (FSH) applied during controlled ovulation stimulation and the live birth rates (LBRs) in non-PCOS population. Many studies have found no difference between the dose of FSH application and pregnancy outcomes such as clinical pregnancy rates after fresh embryo transfer. However, a recent large retrospective analysis found a negative correlation between live birth rates and increasing dose of FSH. It is still controversial about the association between FSH dose and LBRs. In addition, no studies have yet explored the nonlinear relationship between FSH and LBRs. This cohort study included a total of 11,645 patients who had accepted IVF/intracytoplasmic sperm injection (ICSI) at the second hospital of Hebei medical university between December 2014 to December 2019. PCOS was identified by Rotterdam PCOS criteria. We researched the association between FSH total dose and live birth rates (LBRs) using multivariate regression analysis. In addition, a model for nonlinear relationships based on a two-part linear regression was applied. The analysis of threshold effects indicated that LBR increased with every 1000 IU FSH when the concentration of FSH was lower than 1410 IU (OR 1.55, 95% CI [1.05, 2.28]); however, a negative association between FSH dose and LBR (OR 0.94, 95% CI [0.89, 0.99]) was found when the FSH total dose was higher than 1410 IU. It is worth noting that the relationship between LBR and FSH dose varied among patients of different ages (OR 0.92 vs 1.06, P for interaction

Abstract Background To compare the expression levels of long non-coding RNA (lncRNA) and messenger RNA (mRNA) in pre-receptive endometrium between patients with Polycystic Ovary Syndrome (PCOS)and normal ovulation undergoing in vitro fertilization-embryo transfer (IVF-ET). Methods Endometrial tissues were collected with endometrial vacuum curette in pre-receptive phase (3 days after oocytes retrieval) from PCOS and control groups. LncRNAs and mRNAs of endometrium were identified via RNA sequencing and alignments. A subset of 9 differentially expressed lncRNAs and 11 mRNAs were validated by quantitative reverse transcription polymerase chain reaction(qRT-PCR)in 22 PCOS patients and 18 ovulation patients. The function of mRNAs with differential expression patterns were explored using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Results We found out 687 up-regulated and 680 down-regulated mRNAs, as well as 345 up-regulated and 63 down-regulated lncRNAs in the PCOS patients in contrast to normal ovulation patients. qRT-PCR was used to detect the expression of 11 mRNAs, and validated that the expression of these 6 mRNAs CXCR4, RABL6, OPN3, SYBU, IDH1, NOP10 were significantly elevated among PCOS patients, and the expression of ZEB1 was significantly decreased. qRT-PCR was performed to detect the expression of 9 lncRNAs, and validated that the expression of these 7 lncRNAs IDH1-AS1, PCAT14, FTX, DANCR, PRKCQ-AS1, SNHG8, TPT1-AS1 were significantly enhanced among PCOS patients. Bioinformatics analysis showed that differentially expressed genes (DEGs) involved KEGG pathway were tyrosine metabolism, PI3K-Akt pathway, metabolic pathway, Jak-STAT pathway, pyruvate metabolism, protein processing in endoplasmic reticulum, oxidative phosphorylation and proteasome. The up-regulation of GO classification was involved in ATP metabolic process, oxidative phosphorylation, RNA catabolic process, and down-regulation of GO classification was response to corticosteroid, steroid hormone, and T cell activation. Conclusion Our results determined the characteristics and expression profile of endometrial lncRNAs and mRNAs in PCOS patients in pre-receptive phase, which is the day 3 after oocytes retrival. The possible pathways and related genes of endometrial receptivity disorders were found, and those lncRNAs may be developed as a predictive biomarker of endometrium in pre-receptive phase.

Sintered ore is the most important incoming iron material for blast furnaces in China. The foundation characteristics of sintering are an important basis for the sintering process of iron ore powder and a key indicator for evaluating the quality of iron ore powder. The chemical composition of iron ore powder directly affects the foundation characteristics of sintering. Based on the chemical composition and sintering characteristics of iron ore powder, the article adopts the FOA-Catboost algorithm to establish a prediction model for the foundation characteristics of single iron ore powder and mixed iron ore powder. Among the foundation characteristics prediction indicators of a single type of iron ore powder, the accuracy of assimilation temperature prediction is 95.65 %, the accuracy of fluidity index prediction is 95.23 %, and the accuracy of bonding phase strength prediction is 97.46. Among the foundation characteristics prediction indicators of mixed iron ore powder, the accuracy of assimilation prediction is 95.82 %, the accuracy of fluidity index prediction is 91.66 %, and the accuracy of bonding phase strength prediction is 93.7 %. On the basis of predicting the foundation characteristics of a single type of iron ore powder, the model further predicts the foundation characteristics of mixed iron ore powder, which can reduce the workload of laboratory experiments on the foundation characteristics of iron ore powder and reduce the amount of experiments in the process of finding the optimal proportion of mixed ore. Based on the foundation characteristics model of mixed ore, determine the optimal ratio of sintered ore powder to improve the quality of sintered ore. The establishment of the model has a significant contribution to improving experimental and production efficiency, reducing costs, improving sinter quality, and reducing environmental impact.

Abstract Antimicrobial peptides are promising alternatives to conventional antibiotics. Herein, we report a class of “tadpole-like” peptides consisting of an amphipathic α-helical head and an aromatic tail. A structure-activity relationship (SAR) study of “tadpole-like” temporin-SHf and its analogs revealed that increasing the number of aromatic residues in the tail, introducing Arg to the α-helical head and rearranging the peptide topology dramatically increased antimicrobial activity. Through progressive structural optimization, we obtained two peptides, HT2 and RI-HT2, which exhibited potent antimicrobial activity, no hemolytic activity and cytotoxicity, and no propensity to induce resistance. NMR and molecular dynamics simulations revealed that both peptides indeed adopted “tadpole-like” conformations. Fluorescence experiments and electron microscopy confirmed the membrane targeting mechanisms of the peptides. Our studies not only lead to the discovery of a series of ultrashort peptides with potent broad-spectrum antimicrobial activities, but also provide a new strategy for rational design of novel “tadpole-like” antimicrobial peptides.

BackgroundOlder adults with diabetes are at high risk of severe hypoglycemia (SH). Many machine-learning (ML) models predict short-term hypoglycemia are not specific for older adults and show poor precision-recall. We aimed to develop a multidimensional, electronic health record (EHR)-based ML model to predict one-year risk of SH requiring hospitalization in older adults with diabetes.Methods and findingsWe adopted a case-control design for a retrospective territory-wide cohort of 1,456,618 records from 364,863 unique older adults (age ≥65 years) with diabetes and at least 1 Hong Kong Hospital Authority attendance from 2013 to 2018. We used 258 predictors including demographics, admissions, diagnoses, medications, and routine laboratory tests in a one-year period to predict SH events requiring hospitalization in the following 12 months. The cohort was randomly split into training, testing, and internal validation sets in a 7:2:1 ratio. Six ML algorithms were evaluated including logistic-regression, random forest, gradient boost machine, deep neural network (DNN), XGBoost, and Rulefit. We tested our model in a temporal validation cohort in the Hong Kong Diabetes Register with predictors defined in 2018 and outcome events defined in 2019. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) statistics, and positive predictive value (PPV). We identified 11,128 SH events requiring hospitalization during the observation periods. The XGBoost model yielded the best performance (AUROC = 0.978 [95% CI 0.972 to 0.984]; AUPRC = 0.670 [95% CI 0.652 to 0.688]; PPV = 0.721 [95% CI 0.703 to 0.739]). This was superior to an 11-variable conventional logistic-regression model comprised of age, sex, history of SH, hypertension, blood glucose, kidney function measurements, and use of oral glucose-lowering drugs (GLDs) (AUROC = 0.906; AUPRC = 0.085; PPV = 0.468). Top impactful predictors included non-use of lipid-regulating drugs, in-patient admission, urgent emergency triage, insulin use, and history of SH. External validation in the HKDR cohort yielded AUROC of 0.856 [95% CI 0.838 to 0.873]. Main limitations of this study included limited transportability of the model and lack of geographically independent validation.ConclusionsOur novel-ML model demonstrated good discrimination and high precision in predicting one-year risk of SH requiring hospitalization. This may be integrated into EHR decision support systems for preemptive intervention in older adults at highest risk.

BackgroundPsychosocial status and patient reported outcomes (PRO) [depression and health-related quality-of-life (HRQoL)] are major health determinants. We investigated the association between depression and clinical outcomes in Chinese patients with type 2 diabetes (T2D), adjusted for PRO.MethodsUsing prospective data from Hong Kong Diabetes Register (2013-2019), we estimated the hazard-ratio (HR, 95%CI) of depression (validated Patient Health Questionnaire 9 (PHQ-9) score≥7) with incident cardiovascular disease (CVD), ischemic heart disease (IHD), chronic kidney disease (CKD: eGFR

Abstract Background Despite the popularity of dietary supplements, their effectiveness and safety in patients with diabetes remain controversial. Furthermore, evidence from clinical trials may not be generalizable to real-world settings. This study examined the association between dietary supplement use and mortality outcomes among patients with diabetes based on a nationally representative sample of US adults. Methods This study analyzed data from National Health and Nutrition Examination Survey (NHANES) 1999–2018. Supplement users referred to adults with diabetes who reported the use of any dietary supplements in the last 30 days, and with a cumulative duration of ≥ 90 days. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between supplement use and all-cause mortality, and mortality from cardiovascular diseases (CVD), diabetes, and cancer. Subgroup analysis of different supplement classes (vitamins, minerals, botanicals, amino acids, fatty acids, probiotics and glucosamine) were also conducted. Results We included 8,122 adults with diabetes (mean age: 59.4 years; 48.7% female), of whom 3,997 (54.0%) reported using supplements regularly. Vitamins (87.3%), minerals (75.3%) and botanicals (51.8%) were the most popular supplements. At a median follow-up of 6.9 years, 2447 all-cause deaths had occurred. Overall supplement use was not associated with risk of all-cause mortality among patients with diabetes (HR = 0.97, 95% CI: 0.87 to 1.08, P = 0.56). Subgroup analyses suggested that amino acid use was associated with a lower all-cause mortality (HR = 0.66, 95% CI: 0.46 to 0.96, P = 0.028), while the use of fatty acids (HR = 0.62, 95% CI: 0.42 to 0.92, P = 0.018) and glucosamine (HR = 0.69, 95% CI: 0.51 to 0.95, P = 0.022) supplements were significantly associated with lower CVD mortality. Conclusions Our results derived from real-world data suggested that overall supplement use was not associated with any mortality benefit in patients with diabetes. However, there is preliminary evidence that suggests a protective effect of amino acid use on all-cause mortality, and a benefit of fatty acids and glucosamine supplement use on CVD mortality. Future large-scale longitudinal studies are needed to investigate the association between dietary supplement use and other intermediate diabetes-related outcomes, such as glucose control and reducing diabetes-related complications.

The problems of asymmetry in information features and redundant features in datasets, and the asymmetry of network traffic distribution in the field of network intrusion detection, have been identified as a cause of low accuracy and poor generalization of traditional machine learning detection methods in intrusion detection systems (IDSs). In response, a network intrusion detection method based on the integration of bootstrap aggregating (bagging) is proposed. The extreme random tree (ERT) algorithm was employed to calculate the weights of each feature, determine the feature subsets of different machine learning models, then randomly sample the training samples based on the bootstrap sampling method, and integrated classification and regression trees (CART), support vector machine (SVM), and k-nearest neighbor (KNN) as the base estimators of bagging. A comparison of integration methods revealed that the KNN-Bagging integration model exhibited optimal performance. Subsequently, the Bayesian optimization (BO) algorithm was employed for hyper-parameter tuning of the base estimators’ KNN. Finally, the base estimators were integrated through a hard voting approach. The proposed BO-KNN-Bagging model was evaluated on the NSL-KDD dataset, achieving an accuracy of 82.48%. This result was superior to those obtained by traditional machine learning algorithms and demonstrated enhanced performance compared with other methods.

BackgroundClinical trials have demonstrated that remission of type 2 diabetes can be achieved following sustained weight loss. However, the feasibility of achieving diabetes remission through weight management in real-world settings remains unclear. In this study, we aimed to examine the association of weight change at 1 year after diabetes diagnosis with long-term incidence and sustainability of type 2 diabetes remission in real-world settings in Hong Kong.Methods and findingsThis was a population-based observational cohort study. The territory-wide Risk Assessment and Management Programme for Diabetes Mellitus (RAMP-DM) provides regular comprehensive assessments of metabolic control and complication screening for people with diabetes in Hong Kong. We included 37,326 people with newly diagnosed type 2 diabetes who were enrolled in the RAMP-DM between 2000 and 2017, followed until 2019. Diabetes remission was defined as 2 consecutive HbA1c ConclusionsIn this study, greater weight loss within the first year of diabetes diagnosis was associated with an increased likelihood of achieving diabetes remission and a decreased risk of returning to hyperglycaemia among those who had achieved diabetes remission. However, both the incidence of diabetes remission and the probability of its long-term sustainability were low with conventional management in real-world settings, in an era when the importance of weight loss was not fully appreciated. Our study provides evidence for policymakers to design and implement early weight management interventions and diabetes remission initiatives.

Abstract Background Diabetes is a chronic non-communicable disease that causes a substantial economic burden on diabetic suffers and their households. The aim of this study was to explore the prevalence, equity, and determinants of catastrophic health expenditure (CHE) among households with people with diabetes in Northwest China. Methods A total of 3,000 households were included in the 6th Health services survey in Gansu Province, China of which 270 households with people with diabetes. The equity of CHE was evaluated by concentration curve and concentration index (CI). We adopted the Pareto chart to analyze the main economic intervals of the occurrence of CHE. Finally, we combined the decision tree and logistic model and analyzed the determinants of the occurrence of CHE. Results The incidence of CHE at 15%, 25% and 40% were 75.19%, 58.89% and 35.19%, respectively. CHE tended to occur in households with a lower economic level, with the phenomenon being more pronounced at Z = 40%. The Pareto chart showed that households in the group with an annual per capita income of 0–740 USD (0–5,000 Chinese Yuan) were most likely to experience CHE. Both decision tree and logistic models suggested that economic level, comorbidities, and small household size were potential risk factors. In addition, the decision tree model also suggested the interaction between the influencing factor of health checks in the past 12 months and the number of chronic diseases. Conclusions In summary, Households with people with diabetes were more likely to incur CHE. It is essential to focus on low- and middle-income households with people with diabetes, strengthen the management of patients with diabetes, and provide timely health interventions to reduce the occurrence of chronic comorbidity and the risk of CHE in households.

In recent years, machine learning has made great progress in intrusion detection, network protection, anomaly detection, and other issues in cyberspace. However, these traditional machine learning algorithms usually require a lot of data to learn and have a low recognition rate for unknown attacks. Among them, “one-shot learning”, “few-shot learning”, and “zero-shot learning” are challenges that cannot be ignored for traditional machine learning. The more intractable problem in cyberspace security is the changeable attack mode. When a new attack mode appears, there are few or even zero samples that can be learned. Meta-learning comes from imitating human problem-solving methods as humans can quickly learn unknown things based on their existing knowledge when learning. Its purpose is to quickly obtain a model with high accuracy and strong generalization through less data training. This article first divides the meta-learning model into five research directions based on different principles of use. They are model-based, metric-based, optimization-based, online-learning-based, or stacked ensemble-based. Then, the current problems in the field of cyberspace security are categorized into three branches: cyber security, information security, and artificial intelligence security according to different perspectives. Then, the application research results of various meta-learning models on these three branches are reviewed. At the same time, based on the characteristics of strong generalization, evolution, and scalability of meta-learning, we contrast and summarize its advantages in solving problems. Finally, the prospect of future deep application of meta-learning in the field of cyberspace security is summarized.

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy. As data privacy becomes more important, it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data security. Data is forced to be stored independently between companies, creating “data silos”. With the goal of safeguarding data privacy and security, the federated learning framework greatly expands the amount of training data, effectively improving the shortcomings of traditional machine learning and deep learning, and bringing AI algorithms closer to our reality. In the context of the current international data security issues, federated learning is developing rapidly and has gradually moved from the theoretical to the applied level. The paper first introduces the federated learning framework, analyzes its advantages, reviews the results of federated learning applications in industries such as communication and healthcare, then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications, and finally looks into the future of federated learning and the application layer.

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are associated with adverse health effects. This study examined the trend of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) levels in individuals with and without pre-existing comorbidities. We analyzed the characteristics of 13,887 participants across nine U.S. NHANES cycles (1999–2000 to 2017–2018) and calculated the geometric mean (GM) of PFOA and PFOS levels, standardized by sex and age. A joinpoint regression model was used to analyze the temporal trends of serum PFOA and PFOS levels. We observed declining PFOA and PFOS serum levels among adults in NHANES from 1999–2000 to 2017–2018. Serum PFOA and PFOS concentrations were higher in men, smokers, and individuals with pre-existing CKD, hyperlipidemia, CVD, and cancer. We observed faster decline rates in PFOA levels among individuals with diabetes and CKD and faster decline rates in PFOS levels among individuals with diabetes and those without CKD. This study provided evidence of varying levels and changing trends of PFOA and PFOS between groups with and without established chronic disease, highlighting the role of environmental chemicals in the onset and development of chronic diseases.

BackgroundDiabetes disproportionately affects minorities and those with low socioeconomic status (SES) in the United States, and differences in behavioral lifestyles are largely responsible for the unequal distribution of diabetes among different groups.MethodsWith data of 9,969 participants collected in the 2007–2008 and 2009–2010 cycles of the US National Health and Nutrition Examination Survey (NHANES), this study examined several mediators and their mediating effects in the connection between SES and the risk of diabetes. The SES is assessed by the income-to-poverty ratio (IPR), education level, and employment status. For the mediation analysis, we used health-related behaviors as mediators (smoking, alcohol use, consumption of green vegetables and fruits, physical activity and sedentary time, health insurance, and healthcare). In this study, the structural equation model was utilized to evaluate the mediating effects of behavioral lifestyle as a mediator in the relationship between SES and diabetes.ResultsA total of 9,969 participants were included in this study. We found a negative nonlinear association between IPR and diabetes risk (Poverall

Abstract Background Type 2 diabetes mellitus (T2DM) is a chronic metabolic condition that is associated with multiple comorbidities. Apart from pharmacological approaches, patient self‐management remains the gold standard of care for diabetes. Improving patients' self‐management among the elderly with mobile health (mHealth) interventions is critical, especially in times of the COVID‐19 pandemic. However, the extent of mHealth efficacy in managing T2DM in the older population remains unknown. Hence, the present review examined the effectiveness of mHealth interventions on cardiometabolic outcomes in older adults with T2DM. Methods A systematic search from the inception till May 31, 2021, in the MEDLINE, Embase, and PubMed databases was conducted, and 16 randomized controlled trials were included in the analysis. Results The results showed significant benefits on glycosylated hemoglobin (HbA1c) (mean difference −0.24%; 95% confidence interval [CI]: −0.44, −0.05; p = 0.01), postprandial blood glucose (−2.91 mmol/L; 95% CI: −4.78, −1.03; p = 0.002), and triglycerides (−0.09 mmol/L; 95% CI: −0.17, −0.02; p = 0.010), but not on low‐density lipoprotein cholesterol (−0.06 mmol/L; 95% CI: −0.14, 0.02; p = 0.170), high‐density lipoprotein cholesterol (0.05 mmol/L; 95% CI: −0.03, 0.13; p = 0.220), and blood pressure (systolic blood pressure −0.82 mm Hg; 95% CI: −4.65, 3.00; p = 0.670; diastolic blood pressure −1.71 mmHg; 95% CI: −3.71, 0.29; p = 0.090). Conclusions Among older adults with T2DM, mHealth interventions were associated with improved cardiometabolic outcomes versus usual care. Its efficacy can be improved in the future as the current stage of mHealth development is at its infancy. Addressing barriers such as technological frustrations may help strategize approaches to further increase the uptake and efficacy of mHealth interventions among older adults with T2DM.

Abstract Background High-risk differentiated thyroid cancer (DTC) needs effective early prediction tools to improving clinical management and prognosis. This cohort study aimed to investigate the prognostic impact of 99mTc-PEG4-E[PEG4-c(RGDfK)]2 (99mTc-3PRGD2) SPECT/CT in high-risk DTC patients after initial radioactive iodine (RAI) therapy. Methods Thirty-three patients with high-risk DTC were prospectively recruited; all patients underwent total thyroidectomy and received 99mTc-3PRGD2 SPECT/CT before RAI ablation. Follow-up was done with serological and imaging studies. The correlation between 99mTc-3PRGD2 avidity and remission rate for initial RAI therapy was evaluated using logistic regression analysis. The prognostic value of 99mTc-3PRGD2 SPECT/CT was evaluated by Kaplan-Meier curve and Cox regression analysis. Results 99mTc-3PRGD2 avidity was significantly correlated with the efficacy of initial RAI ablation and an effective predictor for non-remission in high-risk DTC (OR = 9.36; 95% CI = 1.10–79.83; P = 0.041). 99mTc-3PRGD2 avidity was associated with poor prognosis in patients with high-risk DTC and an independent prognostic factor for shorter progression-free survival (PFS) (HR = 9.47; 95% CI = 1.08–83.20; P = 0.043). Survival analysis, which was performed between DTC patients with concordant (131I positive/99mTc-3PRGD2 positive) and discordant (131I negative/99mTc-3PRGD2 positive) lesions, indicated that patients with concordant lesions had significantly better PFS than those with discordant lesions (P = 0.022). Moreover, compared with repeated RAI, additional surgery or targeted therapy with multikinase inhibitors could lead to a higher rate of remission in 99mTc-3PRGD2-positive patients with progressive disease. Conclusions 99mTc-3PRGD2 SPECT/CT is a useful modality in predicting progression of the disease after initial RAI and guiding further treatment in high-risk DTC patients.

Abstract Background Although Th17 cells and regulatory T cells play critical roles in autoimmune hepatitis, the balance between them in acute and chronic immune hepatitis is not well‐studied. The objective of this study was to explore the role of Th17/Treg balance in autoimmune hepatitis. Methods Thirty female Wistar rats were randomly divided into the following three groups: acute model (AC), chronic model (CC), and healthy control (HC). Aspartate aminotransferase, alanine aminotransferase, and bilirubin levels were measured. Peripheral blood interleukin (IL)‐17, IL‐6, IL‐10, and transforming growth factor‐β cytokine levels were also assessed. Flow cytometry was used to detect changes in the Th17 cell and Treg cell frequency, and the Th17/Treg ratio was calculated. Results The frequency of Th17 cells and Treg cells were both increased (p

Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states in texts. Various Deep Learning (DL) methods have developed rapidly, and they have proven to be successful in many fields such as audio, image, and natural language processing. This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research. In this paper, we provide an overview of TEA based on DL methods. After introducing a background for emotion analysis that includes defining emotion, emotion classification methods, and application domains of emotion analysis, we summarize DL technology, and the word/sentence representation learning method. We then categorize existing TEA methods based on text structures and linguistic types: text-oriented monolingual methods, text conversations-oriented monolingual methods, text-oriented cross-linguistic methods, and emoji-oriented cross-linguistic methods. We close by discussing emotion analysis challenges and future research trends. We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.

Abstract Background Family history (FamH) of type 2 diabetes might indicate shared genotypes, environments, and/or behaviors. We hypothesize that FamH interacts with unhealthy behaviors to increase the risk of early onset of diabetes and poor cardiometabolic control. Methods In a cross-sectional analysis of the prospective Joint Asia Diabetes Evaluation Register including patients from 427 clinics in 11 Asian countries/regions in 2007–2021, we defined positive FamH as affected parents/siblings and self-management as (1) healthy lifestyles (balanced diet, non-use of alcohol and tobacco, regular physical activity) and (2) regular self-monitoring of blood glucose (SMBG). Results Among 86,931 patients with type 2 diabetes (mean±SD age: 56.6±11.6 years; age at diagnosis of diabetes: 49.8±10.5 years), the prevalence of FamH ranged from 39.1% to 85.3% in different areas with FamH affecting mother being most common (32.5%). The FamH group (n=51,705; 59.5%) was diagnosed 4.6 years earlier than the non-FamH group [mean (95% CI): 47.9 (47.8–48.0) vs. 52.5 (52.4–52.6), logrank p

Liver is a major site for glucose metabolism. Patients with type 2 diabetes (T2D) and obesity have increased risk of liver cancer. We explored the association of glycemic burden (GB) and obesity with liver cancer in T2D in the prospective Hong Kong Diabetes Register (1995‐2019). We calculated GB using the area under the curve above hemoglobin A1c (HbA1c) of 5.7% and defined obesity as body mass index (BMI) ≥ 25 kg/m2. We used Cox proportional hazards models to evaluate the association between GB and liver cancer. We included 15,280 patients with at least 10 years of disease duration before liver cancer occurred or censor date, ≥3 years of observation, and ≥5 HbA1c measurements (64% male, age: 58.23 ± 12.47 years, HbA1c: 7.60 ± 1.65%, BMI: 25.58 ± 4.10 kg/m2). We excluded 3 years of HbA1c values before liver cancer to avoid reverse causality. Every 1‐SD increase in GB was associated with an adjusted hazard ratio (aHR) of liver cancer of 1.22 (95% confidence interval [CI]: 1.01‐1.47). The top GB quartile group (range: >2.41) had aHR of 1.78 (1.01‐3.13) versus the lowest quartile group (0‐1.19). The aHRs for each SD increase in GB were 1.34 (1.05, 1.70) in the obese group and 1.12 (0.81‐1.53) in the nonobese group, but no interaction (Pinteraction = 0.120). When stratified by GB median (1.69 [1.13, 2.43]) and obesity, obese patients with high GB had the highest aHR of 2.51 (1.44‐4.37) for liver cancer versus the nonobese group with low GB, but no interaction (Pinteraction = 0.071). Subgroup analysis of patients with available hepatitis B surface antigen status (n = 9,248) yielded similar results. Conclusion: Our results emphasized the importance of glycemic and weight control for reducing the risk of liver cancer in T2D.

IntroductionAlthough waist-to-height ratio (WHtR) has established association with cardiometabolic disease, the trend of changes in elevated WHtR among general population have not been examined adequately.MethodsThis study examined the prevalence of elevated WHtR and waist circumference (WC) and their trends over time using Joinpoint regression models among adults who participated in the United States National Health and Nutrition Examination Survey (U.S. NHANES) 1999–2018. We performed weighted logistic regression to identify the association between central obesity subtypes and the prevalence of comorbidities, including diabetes, chronic kidney disease, hypertension, cardiovascular disease, and cancer.ResultsThe prevalence of elevated WHtR has increased from 74.8% in 1999–2000 to 82.7% in 2017–2018 while elevated WC also increased from 46.9% in 1999–2000 to 60.3% in 2017–2018. Men, older adults, former smokers, and people with lower education levels were more likely to have elevated WHtR. A total of 25.5% of American adults had normal WC but elevated WHtR, and they had a significantly higher chance of suffering from diabetes (odds ratio [OR] = 2.06 [1.66, 2.55]), hypertension (OR = 1.75 [1.58, 1.93]) and CVD (OR = 1.32 [1.11, 1.57]).DiscussionIn conclusion, the burden of elevated WHtR and WC have been increasing among U.S. adults throughout the years, and the changes have been more significant across most subgroups. It is also notable that approximately a quarter of the population had normal WC but elevated WHtR, which had increased likelihood of having cardiometabolic diseases, especially diabetes. Future clinical practices should pay more attention to this subgroup of the population with overlooked health risks.

In this paper, an improved Internet of Things (IoT) network security situation assessment model is designed to solve the problems arising from the existing IoT network security situation assessment approach regarding feature extraction, validity, and accuracy. Firstly, raw data are dimensionally reduced using independent component analysis (ICA), and the weights of all features are calculated and fused using the maximum relevance minimum redundancy (mRMR) algorithm, Spearman’s rank correlation coefficient, and extreme gradient boosting (XGBoost) feature importance method to filter out the optimal subset of features. Piecewise chaotic mapping and firefly perturbation strategies are then used to optimize the sparrow search algorithm (SSA) to achieve fast convergence and prevent getting trapped in local optima, and then the optimized algorithm is used to improve the light gradient boosting machine (LightGBM) algorithm. Finally, the improved LightGBM method is used for training to calculate situation values based on a threat impact to assess the IoT network security situation. The research findings reveal that the model attained an evaluation accuracy of 99.34%, sustained a mean square error at the 0.00001 level, and reached its optimum convergence value by the 45th iteration with the fastest convergence speed. This enables the model to more effectively evaluate the IoT network security status.

Since supervised learning intrusion detection models rely on manually labeled data, the process often requires a lot of time and effort. To make full use of unlabeled network traffic data and improve intrusion detection, this paper proposes an intrusion detection method for industrial control systems based on improved comparative learning SimCLR. Firstly, a feature extraction network is trained on SimCLR using unlabeled data; a linear classification layer is added to the trained feature extraction network model; and a small amount of labeled data is used for supervised training and fine-tuning of the model parameters. The trained model is simulated on the Secure Water Treatment (SWaT) dataset and the publicly available industrial control dataset from Mississippi State University, and the results show that the method has better results in all evaluation metrics compared with the deep learning algorithm using supervised learning directly, and the comparative learning has research value in industrial control system intrusion detection.

BackgroundThe prevalence of type 2 diabetes has increased in both young and old people. We examined age-specific associations and population attributable fractions (PAFs) of risk factors for all-cause and cause-specific mortality in people with type 2 diabetes.Methods and findingsWe analysed data from 360,202 Chinese with type 2 diabetes who participated in a territory-wide diabetes complication screening programme in Hong Kong between January 2000 and December 2019. We compared the hazard ratios and PAFs of eight risk factors, including three major comorbidities (cardiovascular disease [CVD], chronic kidney disease [CKD], all-site cancer) and five modifiable risk factors (suboptimal HbA1c, suboptimal blood pressure, suboptimal low-density lipoprotein cholesterol, smoking, and suboptimal weight), for mortality across four age groups (18 to 54, 55 to 64, 65 to 74, and ≥75 years). During a median 6.0 years of follow-up, 44,396 people died, with cancer, CVD, and pneumonia being the leading causes of death. Despite a higher absolute mortality risk in older people (crude all-cause mortality rate: 59.7 versus 596.2 per 10,000 person-years in people aged 18 to 54 years versus those aged ≥75 years), the relative risk of all-cause and cause-specific mortality associated with most risk factors was higher in younger than older people, after mutually adjusting for the eight risk factors and other potential confounders including sex, diabetes duration, lipid profile, and medication use. The eight risk factors explained a larger proportion of mortality events in the youngest (PAF: 51.6%, 95% confidence interval [CI] [39.1%, 64.0%], p < 0.001) than the oldest (PAF: 35.3%, 95% CI [27.2%, 43.4%], p < 0.001) age group. Suboptimal blood pressure (PAF: 16.9%, 95% CI [14.7%, 19.1%], p < 0.001) was the leading attributable risk factor for all-cause mortality in the youngest age group, while CKD (PAF: 15.2%, 95% CI [14.0%, 16.4%], p < 0.001) and CVD (PAF: 9.2%, 95% CI [8.3%, 10.1%], p < 0.001) were the leading attributable risk factors in the oldest age group. The analysis was restricted to Chinese, which might affect the generalisability to the global population with differences in risk profiles. Furthermore, PAFs were estimated under the assumption of a causal relationship between risk factors and mortality. However, reliable causality was difficult to establish in the observational study.ConclusionsMajor comorbidities and modifiable risk factors were associated with a greater relative risk for mortality in younger than older people with type 2 diabetes and their associations with population mortality burden varied substantially by age. These findings highlight the importance of early control of blood pressure, which could reduce premature mortality in young people with type 2 diabetes and prevent the onset of later CKD and related mortality at older ages.

With the spread of COVID-19 around the world, the education industry faces enormous challenges. Some colleges and universities have launched online teaching. Comprehensive online teaching and student health checkups help students complete the set teaching content and return to school as soon as possible. With the development of big data, combined with the epidemic risk we are facing, the rational use of big data and the internet for innovative online education has become a mainstream teaching method. Colleges and universities are not yet familiar with the development prospects and future of online education. Through the research of this paper, we can understand the combination of online education and the development of big data and promote its application in colleges and universities. Not only have innovative online education platforms such as MOOC and DingTalk been widely used, but innovative online education methods such as virtual classrooms also have been created. Based on the current epidemic background, this paper analyzes the development of online education, introduces the impact of the combination of online education and big data, and introduces innovative online education technologies and their effects. It helps online education under the influence of the new coronavirus epidemic, operating big data technology to analyze the current prospects and development of online education, showing the combination of big data technology and online education through the analysis of big data technology, and ending with more expectations on other aspects of the use of big data, which affects the online education industry as well as other industries. Finally, we summarize the combination of big data and innovative online education since the emergence of COVID-19 and introduce the concepts and methods of combining online education and big data technology in detail. The online education platform also makes a reasonable introduction. The thesis can be used to understand the problems and challenges faced by innovative online education in the context of the new coronavirus epidemic and look forward to the future on this basis.

Summary: Background: Renin-angiotensin-system inhibitors (RASi), that include angiotensin converting enzyme inhibitors (ACEis) and angiotensin receptor blockers (ARBs) reduce proteinuria, delay chronic kidney disease (CKD) progression, protect against cardiovascular events and heart failure hospitalizations. We examined the associations of discontinuation of ACEi/ARBs with risk of clinical outcomes in Chinese patients with type 2 diabetes (T2D) and advanced-CKD (estimated-glomerular filtration rate [eGFR] 5.5 mmol/L) in discontinued-ACEi/ARBs versus continued-ACEi/ARBs users was assessed in a register-based cohort. Findings: In the population-based cohort of 10,400 ACEi/ARBs users with new-onset eGFR

Summary: Background: Intensive lifestyle modification showed variable success in the prevention of major clinical events and mortality among people with prediabetes. We propose that age may partly explain the heterogeneity and that health hazards related to prediabetes are age-specific. Methods: We conducted a retrospective analysis of a territory-wide diabetes surveillance dataset from the Hong Kong Hospital Authority between 2000 and 2019. Prediabetes was defined according to the American Diabetes Association criteria. Proportional Cox regression was performed, stratified by baseline age categories (20-39, 40-59, 60-79 and ≥80 years). Findings: 1,630,942 individuals were included in the analysis. Compared with normoglycaemia, prediabetes was associated with greater hazards for cardiovascular disease (CVD) and all-cause mortality in most age groups but the effect size attenuated with ascending age (p value for trend

Although previous studies have examined the hepatotoxicity of single metal exposure, the associations between metal mixture and non-alcoholic fatty liver disease (NAFLD) or fibrosis remain unclear. This study investigated the associations of urinary metal mixture with the risks of NAFLD and liver fibrosis in US adults using data from the National Health and Nutrition Examination Survey (NHANES) from 2017.01 to 2020.03. Vibration-controlled transient elastography was used to detect the controlled attenuation parameter (CAP) and liver stiffness measurement (LSM), which are indicators of NAFLD and liver fibrosis respectively. Three novel mixture modeling approaches including the Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS) regression and quantile g-computation (qgcomp) were used to estimate the associations of the urinary fourteen-metal mixture with Ln CAP and Ln LSM. There were 2283 adults aged over 18 years (1209 women and 1074 men) were included. Among women, urinary metal mixture was positively associated with Ln CAP in the BKMR and qgcomp models (both P 0.05). The metal mixture was not associated with Ln LSM in the three models regardless of genders (all P > 0.05). In conclusion, we observed sex-specific associations between urinary metal mixture and the prevalence of NAFLD in US adults. These findings emphasize the role of environmental heavy metal exposure in the development of NAFLD, and confirm the need for more prospective cohort studies on sex-specific manner.

The significance of biodiversity research is to understand the structure and function of the community, and then to protect and monitor the community. The metric of biodiversity is the base of biodiversity conservation. Species richness and evenness are the most common descriptors of biodiversity. Whether it is diversity information measure, probability measure or geometric measure, they all express the combination of species richness and evenness in different ways. This study presents a new biologically meaningful measure of species diversity, which evaluates species richness and evenness independently, designated as DRE. The novelty of our method is to use “absolute discrepancy” to express the dissimilarity between the observed community and the uniform distribution community with the same species composition and same abundance of each species, and then measure the species evenness. The logarithmic transformation of the species number is used to measure species richness with values ranging between 0 and 1. We test the performance of this measure using simulated data and observations of natural and planted forests in different climatic zones. The results showed that the new diversity index (DRE) has superior statistical qualities compared with the traditional indices. Especially, in extremely uneven communities, the new measure describes the causes of diversity changes than the traditional DRE. In addition, DRE is more sensitive to the abundance changes of rare species in the simulated community, and the interpretation of the results is more intuitive and meaningful. It is an improved method to evaluate the species diversity of any ecosystem.

Summary: Background: Improvements in glycemic outcomes have stalled since 2010 in several international surveys. We previously reported improvements in glycemic control in 2007-2014 in Hong Kong coinciding with primary care reforms, use of dipeptidyl-peptidase 4 inhibitors (DPP-4is) and metformin. The aim of this study was to estimate more recent trends in drug use and glycemic outcomes following introduction of newer classes of glucose-lowering drugs (GLDs). Methods: Using population-based data from the Hong Kong Diabetes Surveillance Database, we explored age-specific trends in proportion of patients reaching glycemic targets and incidence rates of severe hypoglycemia (SH) in 963,612 adults with diabetes in 2002-2019. We further assessed patterns of GLDs utilisation by presence of atherosclerotic-cardiovascular disease (ASCVD), heart failure, and estimated-glomerular filtration rate (eGFR). Findings: Following rapid decline in HbA1c from 7·7% to 7·2% in 2005-2014 (annual percentage change [APC]= -0·8, 95% CI:-1·0,-0·6), standardized mean HbA1c plateaued since 2014 (HbA1c 7·2% in 2019, APC=0·0, 95% CI:-0·2, 0·2). The incidence rates of SH declined from 3·4 to 0·7 events per 100-person years, but improvements levelled off since 2014. Use of metformin steadily increased (41·1 to 58·7%), sulfonylureas decreased (52·2 to 31·1%) while insulin remained static in 2002-2019. Adoption of DPP-4is slowed following initial rapid uptake in 2007-2011. DPP-4is remained the most widely prescribed newer GLD in all ages (14·3% in 2019). Use of glucagon-like-peptide 1 receptor agonists (GLP1-RAs) and sodium glucose co-transporter-2 inhibitors (SGLT2is) increased rapidly in 2015-2019 with 0·5% and 6% of users respectively in 2019. Interpretation: Following rapid improvement in 2007-2014, glycemic control and SH rates had plateaued despite changing patterns of newer GLDs use in Hong Kong. Funding: Dr. Aimin Yang was supported by a CUHK Impact Research Fellowship Scheme.

Summary: Background: Cancer is replacing cardiovascular-disease as a leading cause of death in type 2 diabetes (T2D). The association of RAS-inhibitors (RASi) and cancer, including differences between angiotensin-converting-enzyme-inhibitor (ACEi) and angiotensin-receptor-blocker (ARBs) as well as their associations independent of blood pressure lowering, remains inconclusive in T2D. Methods: We conducted a cohort study with new-user design in 253,491 patients in the Hong-Kong-Diabetes-Surveillance-Database (HKDSD) in 2002-2019. We evaluated the associations of time-varying RASi use (ACEi and ARBs) with all-site cancer, diabetes-related cancers, and cancer-specific mortality including comparison with new-users of calcium-channel-blockers (CCBs) as an active-comparator group. Findings: Of 253,491, 133,730 (52.8%) were new-RASi and 119,761 (47.2%) were non-RASi users with a median follow-up period of 6.3 (interquartile ragne: 3.4-9.2) years (1,678,719 patient-years). After propensity-score weighting and adjustment for time-varying covariables, RASi use was associated with lower risk of all-site cancer (HR=0.76, 95%CI: 0.74-0.79), diabetes-related cancer (HR=0.79, 95%CI: 0.75-0.84), cancer-specific mortality (HR=0.50, 95%CI: 0.47-0.53), and diabetes-related cancer mortality (HR=0.49, 95%CI: 0.45-0.54) versus non-RASi. Amongst RASi users, ARBs use was associated with lower risk of cancer-specific mortality versus ACEi (HR=0.77, 95%CI: 0.66-0.91). Use of RASi was associated with an estimated-prevention of 2.6 (95%CI: 2.3-3.0) all-site cancer per-1000-person-years and 2.2 (95%CI: 2.0-2.5) cancer-related mortality per-1000-person-years. Lower risk of cancer-specific mortality was similarly observed in new-RASi compared with new-CCBs users. Interpretation: RASi use was independently associated with lower cancer risk in T2D with stronger associations in users of ARBs than ACEi. The benefits of RASi in patients with diabetes might go beyond cardiovascular-renal protection if confirmed by other real-world studies and trials. Funding: Dr. Aimin Yang was supported by a CUHK Impact-Research-Fellowship Scheme.

IntroductionThere are numerous findings over the past decade have indicated that Merkel cell carcinoma (MCC) may have two pathways of pathogenesis: one related to ultraviolet irradiation and the other to the Merkel cell polyomavirus (MCPyV). However, the predictive and clinicopathological value of MCPyV positivity in MCC patients is still debatable. This article aims to examine the most recent data regarding this issue.MethodsThe thorough literature searches were conducted in the Medline Ovid, PubMed, Web of Science, the Cochrane CENTRAL Databases, and Embase Databases until December 31, 2021. The associations between overall survival (OS), Merkel cell carcinoma-specific survival (MSS), recurrence-free survival (RFS), progression-free survival (PFS), clinicopathologic features, and MCPyV positivity were examined in our meta-analysis.ResultsThis meta-analysis included a total of 14 studies involving 1595 patients. Our findings demonstrated a significant correlation between MCPyV positivity and improved OS (HR=0.61, 95%CI:0.39-0.94, P=0.026) and improved PFS (HR=0.61, 95% CI: 0.45-0.83, P=0.002). MCPyV positivity did not, however, appear to be associated with either MSS (HR=0.61, 95%CI: 0.28-1.32, P=0.209) or RFS (HR= 0.93, 95%CI: 0.37-2.34, P=0.873). Pooled results revealed a correlation between MCPyV positivity with gender (male vs. female, OR=0.606, 95%CI: 0.449-0.817, P=0.001), histopathological stage (AJCC I-II vs. III-IV, OR=1.636, 95%CI: 1.126-2.378, P=0.010) and primary site (head and neck vs. other sites, OR=0.409, 95%CI: 0.221-0.757, P=0.004).ConclusionThese results imply that MCPyV positivity may present a promising predictive biomarker for human MCC and call for further study.

Mathematical modeling can help the medical community to more fully understand and explore the physiological and pathological processes within the human body and can provide more accurate and reliable medical predictions and diagnoses. Neural network models, machine learning models, and statistical models, among others, have become important tools. The paper details the applications of mathematical modeling in the medical field: by building differential equations to simulate the patient’s cardiovascular system, physicians can gain a deeper understanding of the pathogenesis and treatment of heart disease. With machine learning algorithms, medical images can be better quantified and analyzed, thus improving the precision and accuracy of diagnosis and treatment. In the drug development process, network models can help researchers more quickly screen for potentially active compounds and optimize them for eventual drug launch and application. By mining and analyzing a large number of medical data, more accurate and comprehensive disease risk assessment and prediction results can be obtained, providing the medical community with a more scientific and accurate basis for decision-making. In conclusion, research on medical problems based on mathematical models has become an important part of modern medical research, and great progress has been made in different fields.

Hypoxia induces a series of cellular adaptive responses that enable promotion of inflammation and cancer development. Hypoxia-inducible factor-1α (HIF-1α) is involved in the hypoxia response and cancer promotion, and it accumulates in hypoxia and is degraded under normoxic conditions. Here we identify prostate cancer associated transcript-1 (PCAT-1) as a hypoxia-inducible long non-coding RNA (lncRNA) that regulates HIF-1α stability, crucial for cancer progression. Extensive analyses of clinical data indicate that PCAT-1 is elevated in breast cancer patients and is associated with pathological grade, tumor size, and poor clinical outcomes. Through gain- and loss-of-function experiments, we find that PCAT-1 promotes hypoxia-associated breast cancer progression including growth, migration, invasion, colony formation, and metabolic regulation. Mechanistically, PCAT-1 directly interacts with the receptor of activated protein C kinase-1 (RACK1) protein and prevents RACK1 from binding to HIF-1α, thus protecting HIF-1α from RACK1-induced oxygen-independent degradation. These findings provide new insight into lncRNA-mediated mechanisms for HIF-1α stability and suggest a novel role of PCAT-1 as a potential therapeutic target for breast cancer.

BackgroundLittle is known about the lifetime risk of progression to diabetes in the Asian population. We determined remaining lifetime risk of diabetes and life years spent with diabetes in Chinese people with normoglycemia and prediabetes.Methods and findingsUsing territory-wide diabetes surveillance data curated from electronic medical records of Hong Kong Hospital Authority (HA), we conducted a population-based cohort study in 2,608,973 individuals followed from 2001 to 2019. Prediabetes and diabetes were identified based on laboratory measurements, diagnostic codes, and medication records. Remaining lifetime risk and life years spent with diabetes were estimated using Monte Carlo simulations with state transition probabilities based on a Markov chain model. Validations were performed using several sensitivity analyses and modified survival analysis. External replication was performed using the China Health and Retirement Longitudinal Survey (CHARLS) cohort (2010 to 2015). The expected remaining lifetime risk of developing diabetes was 88.0 (95% confidence intervals: 87.2, 88.7)% for people with prediabetes and 65.9 (65.8, 65.9)% for people with normoglycemia at age 20 years. A 20-year-old person with prediabetes would live with diabetes for 32.5 (32.0, 33.1) years or 51.6 (50.8, 52.3)% of remaining life years, whereas a person with normoglycemia at 20 years would live 12.7 (12.7, 12.7) years with diabetes or 18.4 (18.4, 18.5)% of remaining life years. Women had a higher expected remaining lifetime risk and longer life years with diabetes compared to men. Results are subjected to possible selection bias as only people who undertook routine or opportunistic screening were included.ConclusionsThese findings suggest that Hong Kong, an economically developed city in Asia, is confronted with huge challenge of high lifetime risk of diabetes and long life years spent with diabetes, especially in people with prediabetes. Effective public health policies and targeted interventions for preventing progression to diabetes are urgently needed.

ObjectiveTo retrospectively analyze the association of serum estradiol (E2) levels on human chorionic gonadotropin (hCG) trigger day and live birth rates (LBRs) in women undergoing fresh embryo transfer and not exhibiting polycystic ovary syndrome.DesignRetrospective cohort study.MethodsAnalysis of 13,950 patients who had fresh embryo transfer between December 2013 and December 2019. The main outcome measurement was LBRs. Multivariable regression analysis was performed to investigate associations between E2 levels on the hCG trigger day and LBRs. Stratification analysis was performed to test for effect modification in subgroups. Furthermore, a two-piecewise linear regression model was established to find nonlinear relationships.ResultsMultivariable regression analysis showed a significant association between serum E2 levels on the hCG trigger day and LBRs, adjusting for covariates [relative risk (RR) 1.027, 95% confidence interval (CI) 1.007, 1.049]. Stratification analysis showed that the LBRs were positively associated (RR 1.052, 95% CI 1.004, 1.102) with every 1 ng/ml increase of serum E2 on the hCG trigger day for the subgroup with low antral follicle counts on the trigger day. Specifically, a two-piecewise linear regression model showed that there was a positive association (RR 1.188, 95% CI 1.057, 1.334) between serum E2 and LBR for every increase of 1 ng/ml E2 when the concentration of serum E2 was lower than 2.1 ng/ml. However, there was no significant association (RR 1.002, 95% CI 0.971, 1.032) between E2 levels and LBRs when the concentration of E2 was higher than the 2.1ng/ml inflection point.ConclusionsSerum E2 levels on the hCG trigger day were segmentally connected with LBRs.

The adjustment of sintering raw materials has a decisive influence on the composition of blast furnace slag and the properties of sinter. In order to smelt high-quality molten iron in the blast furnace, the composition of the sinter must be properly adjusted so that the composition of the blast furnace slag and the metallurgical properties of the sinter are optimal for the quality of the iron and are conducive to the smooth operation of the blast furnace. In view of the huge difference in the quality and price of sintering raw materials, this paper proposes an automatic sintering ore blending model to quickly configure sintering raw materials according to the requirements of the production line. This method is based on the calculation process of blast furnace charge, combined with the constraints of process composition and cost performance, to establish a multi-decision sintering ore blending model based on the OLS(Ordinary least squares) algorithm to automatically screen from available raw materials and give the sinter that meets the requirements of the furnace. The plan finally makes TFe, CaO, MgO, SiO2, TiO2, Al2O3, P, Mn, Na2O, K2O, Zn, and other components meet the requirements of the production line, and meet the cost performance requirements of the enterprise for sinter. The model can complete the screening and proportioning of 43 kinds of raw materials within 10 s, and its performance can meet the requirements of the production of variable materials. Combined with an example, a comparative analysis experiment is carried out on the accuracy and practicability of the designed sintering and ore blending model. The experimental results show that the accuracy and efficiency of the method proposed in this paper are higher than those of the current ore blending scheme designed by enterprise engineers. This method can provide an effective reference for the stable operation of the sintering production line.

This paper applies a recently proposed numerical algorithm to discuss the deflection of viscoelastic micro-beams in the time domain with direct access. A nonlinear-fractional order model for viscoelastic micro-beams is constructed. Before solving the governing equations, the operator matrices of shifted Chebyshev polynomials are derived first. Shifted Chebyshev polynomials are used to approximate the deflection function, and the nonlinear fractional order governing equation is expressed in the form of operator matrices. Next, the collocation method is used to discretize the equations into the form of algebraic equations for solution. It simplifies the calculation. MATLAB software was used to program this algorithm to simulate the numerical solution of the deflection. The effectiveness and accuracy of the algorithm are verified by the numerical example. Finally, numerical simulations are carried out on the viscoelastic micro-beams. It is found that the viscous damping coefficient is inversely proportional to the deflection, and the length scale parameter of the micro-beam is also inversely proportional to the deflection. In addition, the stress and strain of micro-beam, the change of deflection under different simple harmonic loads, and potential energy of micro-beam are discussed. The results of the study fully demonstrated that the shifted Chebyshev polynomial algorithm is effective for the numerical simulations of viscoelastic micro-beams.

The particle size of pellets is an important parameter in steel big data, and the high density and high overlap rate of pellets bring a great challenge to particle size detection. To address this problem, a particle size intelligent detection algorithm with an improved watershed and a Gaussian mixture model (GMM) is proposed. First, the initial segmentation of the pellets and background is achieved by using adaptive binary segmentation, and then the secondary fine segmentation of the pellets and background is achieved by combining morphological operations such as skeleton extraction and marked watershed segmentation; then, the contour of the connected domain of pellets is calculated, and the non-overlapping pellets in the foreground and the overlapping pellets are filtered according to the roundness of their contours. Finally, the number of overlapping pellets is predicted by Gaussian reconstruction of the grayscale image of the overlapping pellets, and the number and granularity of the overlapping pellets are predicted by the Gaussian reconstruction of the overlapping pellets. The experimental results showed that the algorithm achieved a 91.98% segmentation accuracy in the experimental images. Compared with other algorithms, the algorithm can also effectively suppress the over-segmentation and under-segmentation problems, and it can effectively realize the pellet size detection of dense, overlapping pellets such as those on a pelletizing disk, which provides an effective technical means for the metallurgical performance analysis of pellet ore and intelligent pellet-making driven by big data.

The iron and steel industry has made an important contribution to China’s economic development, and sinter accounts for 70–80% of the blast furnace feed charge. However, the average grade of domestic iron ore is low, and imported iron ore is easily affected by transportation and price. The intelligent ore blending model with an intelligent algorithm as the core is studied. It has a decisive influence on the development of China’s steel industry. This paper first analyzes the current situation of iron ore resources, the theory of sintering ore blending, and the difficulties faced by sintering ore blending. Then, the research status of the neural network algorithms, genetic algorithms, and particle swarm optimization algorithms in the intelligent ore blending model is analyzed. On the basis of the neural network algorithm, genetic algorithm and particle swarm algorithm, linear programming method, stepwise regression analysis method, and partial differential equation are adopted. It can optimize the algorithm and make the model achieve better results, but it is difficult to adapt to the current complex situation of sintering ore blending. From the sintering mechanism, sintering foundation characteristics, liquid phase formation capacity of the sinter, and the influencing factors of sinter quality were studied, it can carry out intelligent ore blending more accurately and efficiently. Finally, the research of intelligent sintering ore blending model has been prospected. On the basis of sintering mechanism research, combined with an improved intelligent algorithm. An intelligent ore blending model with raw material parameters, equipment parameters, and operating parameters as input and physical and metallurgical properties of the sinter as output is proposed.