Advances in small-molecule insulin secretagogues for diabetes treatment.

Diabetes, a metabolic disease caused by abnormally high levels of blood glucose, has a high prevalence rate worldwide and causes a series of complications, including coronary heart disease, stroke, peripheral vascular disease, end-stage renal disease, and retinopathy. Small-molecule compounds have been developed as drugs for the treatment of diabetes because of their oral advantages. Insulin secretagogues are a class of small-molecule drugs used to treat diabetes, and include sulfonylureas, non-sulfonylureas, glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase 4 inhibitors, and other novel small-molecule insulin secretagogues. However, many small-molecule compounds cause different side effects, posing huge challenges to drug monotherapy and drug selection. Therefore, the use of different small-molecule drugs must be improved. This article reviews the mechanism, advantages, limitations, and potential risks of small-molecule insulin secretagogues to provide future research directions on small-molecule drugs for the treatment of diabetes.

A case study of lean digital transformation through robotic process automation in healthcare.

Under Taiwan's National Health Insurance (NHI) system, it's crucial for all healthcare providers to accurately submit medical expense claims to the National Health Insurance Administration (NHIA) to avoid incorrect deductions. With changes in healthcare policies and adjustments in hospital management strategies, the complexity of claiming rules has resulted in hospitals expending significant manpower and time on the medical expense claims process. Therefore, this study utilizes the Lean Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) management approach to identify wasteful and non-value-added steps in the process. Simultaneously, it introduces Robotic Process Automation (RPA) tools to replace manual operations. After implementation, the study effectively reduces the process time by 380 min and enhances Process Cycle Efficiency (PCE) from 69.07 to 95.54%. This research validates a real-world case of Lean digital transformation in healthcare institutions. It enables human resources to be allocated to more valuable and creative tasks while assisting hospitals in providing more comprehensive and patient-centric services.

Research progress on drugs for diabetes based on insulin receptor/insulin receptor substrate.

The number of people with diabetes worldwide is increasing annually, resulting in a serious economic burden. Insulin resistance is a major pathology in the early onset of diabetes mellitus, and therefore, related drug studies have attracted research attention. The insulin receptor/insulin receptor substrate (INSR/IRS) serves as the primary conduit in the insulin signal transduction cascade, and dysregulation of this pathway can lead to insulin resistance. Currently, there exist a plethora of hypoglycemic drugs in the market; however, drugs that specifically target INSR/IRS are comparatively limited. The literature was collected by direct access to the PubMed database, and was searched using the terms "diabetes mellitus; insulin resistance; insulin receptor; insulin receptor substrate; diabetes drug" as the main keywords for literature over the last decade. This article provides a comprehensive analysis of the structure and function of INSR and IRS proteins, as well as the drugs used for the treatment of diabetes. Additionally, it serves as a valuable reference for the advancement of novel therapeutic agents for diabetes management.

CFSA-AGD: An accurate crosstalk fluorescence spectroscopic decomposition method for identifying and quantifying FDOMs in aquatic environments.

Fluorescent substances exist in various aquatic environments and other environmental media. It is a critical task to identify the components accurately and quantify their contents precisely. Based on the Crosstalk Fluorescence Spectroscopy Analysis (CFSA) model, a fluorescence spectroscopic decomposition using the Alternating Gradient Descent (AGD) algorithm is developed. By reducing the residual error of the model through alternating iterations, the CFSA-AGD method achieves unsupervised model training and automatic spectroscopic decomposition without extra experimental operations such as dilution or absorbance measurement, exempting from tedious modeling process. The objectives of this work are to validate that the CFSA-AGD method can comprehensively address the decomposition of fluorescence spectral crosstalk. Furthermore, the novel method is applied to the spectroscopic decomposition of natural FDOMs in aquatic environments as a standard tool. The spectral data analyzing the performance of this method is verified and compared with the conventional methods through the experiment on standard samples. The results indicate that CFSA-AGD has higher spectroscopic decomposition accuracy and gives more abundant information on the characteristic spectra with less residual error than parallel factor analysis. This means that the fluorescence spectra of natural FDOMs can be decomposed into the characteristic fluorescence emission spectra of single components with higher accuracy and the characteristic fluorescence absorption spectra that cannot be obtained by the conventional methods. Meanwhile, it improves the analytical precision of the contents (from R2 ≥ 0.9778 to R2 ≥ 0.9920) and reduces the ultimate residual error by two orders of magnitude (from 1.42 × 10-1 to 4.68 × 10-3) when the method is used to estimate the measured fluorescence spectra.

Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods.

Objective: Based on the respiratory disease big data platform in southern Xinjiang, we established a model that predicted and diagnosed chronic obstructive pulmonary disease, bronchiectasis, pulmonary embolism and pulmonary tuberculosis, and provided assistance for primary physicians. Methods: The method combined convolutional neural network (CNN) and long-short-term memory network (LSTM) for prediction and diagnosis of respiratory diseases. We collected the medical records of inpatients in the respiratory department, including: chief complaint, history of present illness, and chest computed tomography. Pre-processing of clinical records with "jieba" word segmentation module, and the Bidirectional Encoder Representation from Transformers (BERT) model was used to perform word vectorization on the text. The partial and total information of the fused feature set was encoded by convolutional layers, while LSTM layers decoded the encoded information. Results: The precisions of traditional machine-learning, deep-learning methods and our proposed method were 0.6, 0.81, 0.89, and F1 scores were 0.6, 0.81, 0.88, respectively. Conclusion: Compared with traditional machine learning and deep-learning methods that our proposed method had a significantly higher performance, and provided precise identification of respiratory disease.

Distribution and identification of Mycobacterium tuberculosis lineage in Kashgar prefecture.

OBJECTIVES: Kashgar prefecture is an important transportation and trade hub with a high incidence of tuberculosis. The following study analyzed the composition and differences in Mycobacterium tuberculosis (M.tb) lineage and specific tags to distinguish the lineage of the M.tb in Kashgar prefecture, thus providing a basis for the classification and diagnosis of tuberculosis in this area. METHODS: Whole-genome sequencing (WGS) of 161 M.tb clinical strains was performed. The phylogenetic tree was constructed using Maximum Likelihood (ML) based on single nucleotide polymorphisms (SNPs) and verified through principal component analysis (PCA). The composition structure of M.tb in different regions was analyzed by combining geographic information. RESULTS: M.tb clinical strains were composed of lineage 2 (73/161, 45.34%), lineage 3 (52/161, 32.30%) and lineage 4 (36/161, 22.36%). Moreover, the 3 lineages were subdivided into 11 sublineages, among which lineage 2 included lineage 2.2.2/Asia Ancestral 1 (9/73, 12.33%), lineage 2.2.1-Asia Ancestral 2 (9/73, 12.33%), lineage 2.2.1-Asia Ancestral 3 (18/73, 24.66%), and lineage 2.2.1-Modern Beijing (39/73, 53.42%). Lineage 3 included lineage 3.2 (14/52, 26.92%) and lineage 3.3 (38/52, 73.08%), while lineage 4 included lineage 4.1 (3/36, 8.33%), lineage 4.2 (2/36, 5.66%), lineage 4.4.2 (1/36, 2.78%), lineage 4.5 (28/36, 77.78%) and lineage 4.8 (2/36, 5.66%), all of which were consistent with the PCA results. One hundred thirty-six markers were proposed for discriminating known circulating strains. Reconstruction of a phylogenetic tree using the 136 SNPs resulted in a tree with the same number of delineated clades. Based on geographical location analysis, the composition of Lineage 2 in Kashgar prefecture (45.34%) was lower compared to other regions in China (54.35%-90.27%), while the composition of Lineage 3 (32.30%) was much higher than in other regions of China (0.92%-2.01%), but lower compared to the bordering Pakistan (70.40%). CONCLUSION: Three lineages were identified in M.tb clinical strains from Kashgar prefecture, with 136 branch-specific SNP. Kashgar borders with countries that have a high incidence of tuberculosis, such as Pakistan and India, which results in a large difference between the M.tb lineage and sublineage distribution in this region and other provinces of China.

Effects of Mycobacterium tuberculosis lineages and regions of difference (RD) virulence gene variation on tuberculosis recurrence.

Background: China ranks second in the incidence of tuberculosis (TB), and the virulence and infectivity of Mycobacterium tuberculosis (M.tb) in different lineages are different. The variation of virulence genes in the M.tb regions of difference (RD) may be the reason for differences in pathogenicity. Studying the relationship between virulence gene mutations in the RD region of clinical strains of M.tb and TB relapse can provide basic data for the study of TB prevention and control. Methods: A total of 155 M.tb clinical strains were collected in Kashgar Prefecture. Whole-genome sequencing (WGS) was conducted, and mutations in virulence genes in the M.tb RD region were analyzed. The maximum likelihood method was implemented using IQ-TREE software. Logistic regression was used to analyze the relationship between lineage, RD region virulence gene variation, and patient relapse. Results: The 155 strains of M.tb in Kashgar Prefecture belong to 3 M.tb lineages: L2 (45.80%), L3 (32.90%), and L4 (21.30%). In relapsed patients, L2 (70.83%, 17/24) was significantly higher than the other lineages (29.17%, 7/24; P<0.05). Relapse was significantly correlated with L2 [odds ratio (OR) =3.505; 95% confidence interval (CI): 1.341-9.158; P=0.011]. In the virulence genes of the RD region, g.4357804 (T→G, OR =4.278; 95% CI: 1.594-11.481; P=0.004), g.4359653 (C→T, OR =3.356; 95% CI: 1.303-8.644; P=0.012), and g.2627618 (C→A, OR =2.676; 95% CI: 1.101-6.502; P=0.030) mutations were significantly associated with patient relapse. The mutation frequencies of g.4357804, g.4359653, and g.2627618 in L2 were significantly higher than those in the non-L2 group (P<0.05). Conclusions: Patients infected with L2 are more prone to relapse, and RD region virulence gene variation is an important factor for the strong pathogenicity and easy relapse after infection associated with L2.

SMAD3 rs36221701 T>C polymorphism impacts COPD susceptibility in the Kashi population.

Small mother against decapentaplegic (SMAD) family member 3 (SMAD3) is well correlated with the inflammatory response of chronic obstructive pulmonary disease (COPD). A previous study indicated that the single nucleotide polymorphism (SNP) rs36221701 of SMAD3 was related to the risk of inflammatory disease. Hence, given the pathogenesis of COPD is intently associated with smoking and gene polymorphism, this study aims to analyze the relationship between SMAD3 rs36221701 and COPD susceptibility, and to explore whether the interaction is related to smoking status. We studied the association between the rs36221701 and rs34307601 of SMAD3 and COPD susceptibility, a total of 541 COPD patients and 534 controls of the Uyghur population were recruited at the First People's Hospital and the village of Kashi. The interrelation of the two SNPs with the risk of COPD was determined by calculating odds ratio (OR) and 95% confidence interval (95% CI). We found a significant association between the rs36221701 and COPD risk in the non-smoking population. TC genotype showed a significant decreased association with COPD risk (OR = 0.59, 95% CI = 0.41-0.83, P < 0.05), but CC genotype can increased the COPD risk (OR > 1, P < 0.05). In addition, COPD susceptibility was not related to the genetic variations in the rs34307601 (P > 0.05). In conclusion, we confirmed that the SMAD3 rs36221701 may be associated with COPD susceptibility in the Chinese Uyghur population, especially among non-smokers. Our data provide new light for the relationship between SMAD3 gene polymorphisms and COPD susceptibility in the Chinese Uyghur population.

The rs74794265 SNP of the SREK1 Gene is Associated with COPD in Kashi, China.

BACKGROUND: Kashi city is situated near the Taklamakan desert and has a high incidence rate of chronic obstructive pulmonary disease (COPD). In this study, we aimed to explore the relationship between the SNP of the SREK1 gene locus rs74794265 and the susceptibility to COPD among the Uyghur population in Kashi, XinJiang, China. METHODS: A total of 541 patients with COPD and 534 control subjects were included in this study. Sanger sequencing was used to analyze the SNP of the SREK1 gene locus rs74794265 site. The distribution of genotypes in different genetic models between the case and control group were analyzed by logistic regression analysis after adjusting for age, sex, and smoking history. RESULTS: The SREK1 gene SNP locus rs74794265 included two genotypes, namely, C/C and C/T, of which C/C was the wildtype; The risk of COPD was significantly lower in patients with heterozygous C/T in rs74794265 [p=0.0236, OR=0.3677 (0.1547-0.8742)], and the allele frequency of T was also significantly lower in the patient group [p=0.0245, OR=0.3728 (0.1577-0.8811)]. The heterozygous C/T of rs74794265 among non-smoking COPD patients was significantly lower than other COPD patients [p=0.0298, OR=0.3217 (0.1156-0.8949)], and there was no significant correlation of the heterozygous C/T genotype in smokers. CONCLUSION: We found that the rs74794265 heterozygous C/T genotype significantly reduces the risk of COPD. The C/T genotype is likely a protective factor for COPD in the Kashi region. We speculate that the occurrence of COPD in this area is probably more related to desert climate condition and genetic factors than smoking status.

Whole-Exome Sequencing Implicates the USP34 rs777591A > G Intron Variant in Chronic Obstructive Pulmonary Disease in a Kashi Cohort.

Genetic factors are important factors in chronic obstructive pulmonary disease (COPD) onset. Plenty of risk and new causative genes for COPD have been identified in patients of the Chinese Han population. In contrast, we know considerably little concerning the genetics in the Kashi COPD population (Uyghur). This study aims at clarifying the genetic maps regarding COPD susceptibility in Kashi (China). Whole-exome sequencing (WES) was used to analyze three Uyghur families with COPD in Kashi (eight patients and one healthy control). Sanger sequencing was also used to verify the WES results in 541 unrelated Uyghur COPD patients and 534 Uyghur healthy controls. WES showed 72 single nucleotide variants (SNVs), two deletions, and small insertions (InDels), 26 copy number variants (CNVs), and 34 structural variants (SVs), including g.71230620T > A (rs12449210T > A, NC_000,016.10) in the HYDIN axonemal central pair apparatus protein (HYDIN) gene and g.61190482A > G (rs777591A > G, NC_000002.12) in the ubiquitin-specific protease 34 (USP34) gene. After Sanger sequencing, we found that rs777591"AA" under different genetic models except for the dominant model (adjusted OR = 0.8559, 95%CI 0.6568-1.115, p > .05), could significantly reduce COPD risk, but rs12449210T > A was not related to COPD. In stratified analysis of smoking status, rs777591"AA" reduced COPD risk significantly among the nonsmoker group. Protein and mRNA expression of USP34 in cigarette smoke extract-treated BEAS-2b cells increased significantly compared with those in the control group. Our findings associate the USP34 rs777591"AA" genotype as a protector factor in COPD.