Risk factors and a predictive model of diabetic foot in hospitalized patients with type 2 diabetes

BACKGROUND The risk factors and prediction models for diabetic foot(DF)remain incompletely understood,with several potential factors still requiring in-depth investigations.AIM To identify risk factors for new-onset DF and develop a robust prediction model for hospitalized patients with type 2 diabetes.METHODS We included 6301 hospitalized patients with type 2 diabetes from January 2016 to December 2021.A univariate Cox model and least absolute shrinkage and selection operator analyses were applied to select the appropriate predictors.Nonlinear associations between continuous variables and the risk of DF were explored using restricted cubic spline functions.The Cox model was further employed to evaluate the impact of risk factors on DF.The area under the curve(AUC)was measured to evaluate the accuracy of the prediction model.RESULTS Seventy-five diabetic inpatients experienced DF.The incidence density of DF was 4.5/1000 person-years.A long duration of diabetes,lower extremity arterial disease,lower serum albumin,fasting plasma glucose(FPG),and diabetic nephropathy were independently associated with DF.Among these risk factors,the serum albumin concentration was inversely associated with DF,with a hazard ratio(HR)and 95%confidence interval(CI)of 0.91(0.88-0.95)(P<0.001).Additionally,a U-shaped nonlinear relationship was observed between the FPG level and DF.After adjusting for other variables,the HRs and 95%CI for FPG<4.4 mmol/L and≥7.0 mmol/L were 3.99(1.55-10.25)(P=0.004)and 3.12(1.66-5.87)(P<0.001),respectively,which was greater than the mid-range level(4.4-6.9 mmol/L).The AUC for predicting DF over 3 years was 0.797.CONCLUSION FPG demonstrated a U-shaped relationship with DF.Serum albumin levels were negatively associated with DF.The prediction nomogram model of DF showed good discrimination ability using diabetes duration,lower extremity arterial disease,serum albumin,FPG,and diabetic nephropathy(Clinicaltrial.gov NCT05519163).

Effects of short-range attraction on Jamming transition

Enormous progresses to understand the jamming transition have been driven via simulating purely repulsive particles which were somehow idealized in the past two decades. While the attractive systems are both theoretical and practical compared with repulsive systems. By studying the statistics of rigid clusters, we find that the critical packing fraction φ_(c) varies linearly with attraction μ for different system sizes when the range of attraction is short. While for systems with long-range attractions, however, the slope of φ_(c) appears significantly different, which means that there are two distinct jamming scenarios. In this paper, we focus our main attention on short-range attractions scenario and define a new quantity named "short-range attraction susceptibility" χ_(p), which describes the degree of response of the probability of finding jammed states pjto short-range attraction strength μ. Our central results are that χ_(p) diverges in the thermodynamic limit as χ_(p) ∝|φ-φ_(c)^(∞)|^(-γ_(p)), where φ_(c)^(∞) is the packing fraction at the jamming transition for the infinite system in the absence of attraction. χ_(p) obeys scaling collapse with a scaling function in both two and three dimensions, illuminating that the jamming transition can be considered as a phase transition as proposed in previous work.

Exponential convergence of the deep neural network approximation for analytic functions Dedicated to Professor Ta Tsien Li on the Occasion of His 80th Birthday

We prove that for analytic functions in low dimension, the convergence rate of the deep neural network approximation is exponential.

Exploring noncoding variants in genetic diseases:from detection to functional insights

Previous studies on genetic diseases predominantly focused on protein-coding variations, overlooking the vast noncoding regions in the human genome. The development of high-throughput sequencing technologies and functional genomics tools has enabled the systematic identification of functional noncoding variants. These variants can impact gene expression, regulation, and chromatin conformation, thereby contributing to disease pathogenesis. Understanding the mechanisms that underlie the impact of noncoding variants on genetic diseases is indispensable for the development of precisely targeted therapies and the implementation of personalized medicine strategies. The intricacies of noncoding regions introduce a multitude of challenges and research opportunities. In this review, we introduce a spectrum of noncoding variants involved in genetic diseases, along with research strategies and advanced technologies for their precise identification and in-depth understanding of the complexity of the noncoding genome. We will delve into the research challenges and propose potential solutions for unraveling the genetic basis of rare and complex diseases.

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2(AF2)is an artificial intelligence(AI)system developed by DeepMind that can predict three-dimensional(3D)structures of proteins from amino acid sequences with atomic-level accuracy.Protein structure prediction is one of the most challenging problems in computational biology and chemistry,and has puzzled scientists for 50 years.The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention.Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community,especially in the fields of biology and medicine.AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information,such as drug discovery,protein design,prediction of protein function,et al.Though the time is not long since AF2 was developed,there are already quite a few application studies of AF2 in the fields of biology and medicine,with many of them having preliminarily proved the potential of AF2.To better understand AF2 and promote its applications,we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success,and particularly focus on reviewing its applications in the fields of biology and medicine.Limitations of current AF2 prediction will also be discussed.

SmProt: A Reliable Repository with Comprehensive Annotation of Small Proteins Identified from Ribosome Profiling

Small proteins specifically refer to proteins consisting of less than 100 amino acids translated from small open reading frames(s ORFs),which were usually missed in previous genome annotation.The significance of small proteins has been revealed in current years,along with the discovery of their diverse functions.However,systematic annotation of small proteins is still insufficient.Sm Prot was specially developed to provide valuable information on small proteins for scientific community.Here we present the update of Sm Prot,which emphasizes reliability of translated s ORFs,genetic variants in translated s ORFs,disease-specific s ORF translation events or sequences,and remarkably increased data volume.More components such as non-ATG translation initiation,function,and new sources are also included.Sm Prot incorporated638,958 unique small proteins curated from 3,165,229 primary records,which were computationally predicted from 419 ribosome profiling(Ribo-seq)datasets or collected from literature and other sources from 370 cell lines or tissues in 8 species(Homo sapiens,Mus musculus,Rattus norvegicus,Drosophila melanogaster,Danio rerio,Saccharomyces cerevisiae,Caenorhabditis elegans,and Escherichia coli).In addition,small protein families identified from human microbiomes were also collected.All datasets in Sm Prot are free to access,and available for browse,search,and bulk downloads at http://bigdata.ibp.ac.cn/SmProt/.

Association between antipsychotic agents and risk of lung cancer: a nested case-control study

Dear Editor,Antipsychotics are a class of psychotropic medication pri-marily used for the treatment of schizophrenia and a range of other psychotic disorders.They are antagonists of multiple receptors,such as dopamine D 1,dopamine D 2,serotonin 5HT 2A,and serotonin 5HT 1A receptors.Serotonin antagonists have been identified as growth-inhibiting agents in cancer cells,and they not only inhibit the growth of cancer cells but may also induce apoptosis in these cells[1].Several studies have examined the asso-ciation between antipsychotics and certain cancers,but the relationship between antipsychotics and lung cancer remains largely unknown.