[Clinical Observation on Haploid Hematopoietic Stem Cells Combined with Umbilical Cord Blood Double Transplantation for Aplastic Anaemia in Children].

OBJECTIVE: To explore the efficacy and safety of haploidentical hematopoietic stem cell transplantation combined with umbilical cord blood infusion for the treatment of aplastic anaemia in children. METHODS: Nine cases of children with aplastic anaemia treated with umbilical cord blood combined with haploidentical hematopoietic stem cell transplantation at the People's Hospital of Henan University of Chinese Medicine from January 1, 2021 to September 15, 2023 with a median age of 11(2-13) years and a median follow up of 18(7.5-21) months were included, and the clinical data were retrospectively analyzed. Hematopoiesis reconstitution, the incidence of graft-versus-host disease(GVHD), infections and survival of the patients were analyzed. RESULTS: All 9 children were successfully implanted. The median time to neutrophil and platelet implantation was 11.11±1.27 d and 12.44±3.36 d, respectively. One case developed acute gastrointestinal GVHD of degree I, which was improved after treatment, and the patient developed superficial gastritis and chronic gastrointestinal GVHD at a later stage, which is currently under clinical follow-up. Acute GVHD of II-IV degree was 0%. Hemorrhagic cystitis in 3 cases, CMV infection in 5 cases and bacterial and fungal infections in 5 cases improved with symptomatic treatment.All 9 children demonstrated complete donor chimerism within 1 month after transplantation, at two years of follow-up, all nine children survived without recurrence or development of grade II-IV GVHD, and there were no children with transplant-related deaths. CONCLUSION: Haploidentical hematopoietic stem cell transplantation combined with umbilical cord blood transfusion for aplastic anaemia in children has a low incidence and mild degree of GVHD, with significant efficacy, and can be used as a therapeutic option for children without an HLA full donor chimeric match.

Treatment of myelofibrosis with refractory anemia with luspatercept: a multicenter Chinese study.

Myelofibrosis is a rare and often fatal hematological neoplasm, and the treatment of myelofibrosis-associated anemia remains suboptimal, with no improved therapies. Luspatercept was shown to display some efficacy in a phase 2 clinical trial for Myelofibrosis with anemia, yet relevant research are limited. Threrfore, data from patients diagnosed with refractory anemic primary or post-essential thrombocythemia/polycythemia vera myelofibrosis, who were treated with luspatercept for at least 9 weeks, were retrospectively collected. Eighteen patients with myelofibrosis treated with luspatercept were enrolled. Median age was 68 years (range, 44-80 years), and 27.8% were males. Ten (55.6%) were transfusion-dependent. Ten (55.6%) were Dynamic International Prognostic Scoring System intermediate-1, and eight (44.4%) were intermediate-2. The median follow-up was 7 (4-16) months. Erythroid response occurred in eight patients (44.4%) at week 12, four patients (30.8%) at week 24, and nine (50%) at the end of follow-up. Patients who were transfusion-dependent and not transfusion-dependent had similar HI-E responses, at different time points (P > 0.05). Patients had a significantly higher hemoglobin level at 12 weeks, 24 weeks, and at the end of follow-up, than at baseline (P = 0.001, P = 0.021, and P = 0.005, respectively). Treatment-related adverse events occurred in five (16.7%) patients, with no serious adverse events. Two (11.1%) patients relapsed at weeks 15 and 31. One patient progressed to acute myeloid leukemia. No patients had died by the end of follow-up. Luspatercept induced a good response in patients with anemic myelofibrosis, with a low relapse rate and good tolerance.

Programmed Cell Death-Related Gene Signature Associated with Prognosis and Immune Infiltration and the Roles of HMOX1 in the Proliferation and Apoptosis were Investigated in Uveal Melanoma.

BACKGROUND: Uveal melanoma (UVM) is the most common primary ocular malignancy, with a wide range of symptoms and outcomes. The programmed cell death (PCD) plays an important role in tumor development, diagnosis, and prognosis. There is still no research on the relationship between PCD-related genes and UVM. A novel PCD-associated prognostic model is urgently needed to improve treatment strategies. OBJECTIVE: We aim to screen PCD-related prognostic signature and investigate its proliferation ability and apoptosis in UVM cells. METHODS: The clinical information and RNA-seq data of the UVM patients were collected from the TCGA cohort. All the patients were classified using consensus clustering by the selected PCD-related genes. After univariate Cox regression and PPI network analysis, the prognostic PCD-related genes were then submitted to the LASSO regression analysis to build a prognostic model. The level of immune infiltration of 8-PCD signature in high- and low-risk patients was analyzed using xCell. The prediction on chemotherapy and immunotherapy response in UVM patients was assessed by GDSC and TIDE algorithm. CCK-8, western blot and Annexin V-FITC/PI staining were used to explore the roles of HMOX1 in UVM cells. RESULTS: A total of 8-PCD signature was constructed and the risk score of the PCD signature was negatively correlated with the overall survival, indicating strong predictive ability and independent prognostic value. The risk score was positively correlated with CD8 Tcm, CD8 Tem and Th2 cells. Immune cells in high-risk group had poorer overall survival. The drug sensitivity demonstrated that cisplatin might impact the progression of UVM and better immunotherapy responsiveness in the high-risk group. Finally, Overespression HMOX1 (OE-HMOX1) decreased the cell viability and induced apoptosis in UVM cells. Recuse experiment results showed that ferrostatin-1 (fer-1) protected MP65 cells from apoptosis and necrosis caused by OE-HMOX1. CONCLUSION: The PCD signature may have a significant role in the tumor microenvironment, clinicopathological characteristics, prognosis and drug sensitivity. More importantly, HMOX1 depletion greatly induced tumor cell growth and inhibited cell apoptosis and fer-1 protected UVM cells from apoptosis and necrosis induced by OE-HMOX1. This work provides a foundation for effective therapeutic strategy in tumour treatment.

Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets.

BACKGROUND: Missing data is frequently an inevitable issue in cohort studies and it can adversely affect the study's findings. We assess the effectiveness of eight frequently utilized statistical and machine learning (ML) imputation methods for dealing with missing data in predictive modelling of cohort study datasets. This evaluation is based on real data and predictive models for cardiovascular disease (CVD) risk. METHODS: The data is from a real-world cohort study in Xinjiang, China. It includes personal information, physical examination data, questionnaires, and laboratory biochemical results from 10,164 subjects with a total of 37 variables. Simple imputation (Simple), regression imputation (Regression), expectation-maximization(EM), multiple imputation (MICE) , K nearest neighbor classification (KNN), clustering imputation (Cluster), random forest (RF), and decision tree (Cart) were the chosen imputation methods. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are utilised to assess the performance of different methods for missing data imputation at a missing rate of 20%. The datasets processed with different missing data imputation methods were employed to construct a CVD risk prediction model utilizing the support vector machine (SVM). The predictive performance was then compared using the area under the curve (AUC). RESULTS: The most effective imputation results were attained by KNN (MAE: 0.2032, RMSE: 0.7438, AUC: 0.730, CI: 0.719-0.741) and RF (MAE: 0.3944, RMSE: 1.4866, AUC: 0.777, CI: 0.769-0.785). The subsequent best performances were achieved by EM, Cart, and MICE, while Simple, Regression, and Cluster attained the worst performances. The CVD risk prediction model was constructed using the complete data (AUC:0.804, CI:0.796-0.812) in comparison with all other models with p<0.05. CONCLUSION: KNN and RF exhibit superior performance and are more adept at imputing missing data in predictive modelling of cohort study datasets.

Validation of non-invasive indicators in the screening of metabolic dysfunction-associated fatty liver disease: a cross-sectional study among Uighurs in rural Xinjiang.

BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD) is the most common chronic liver disease in China. Our study aimed to evaluate the screening value of the fatty liver index (FLI), hepatic steatosis index (HSI), lipid accumulation product (LAP), visceral adiposity index (VAI), and Zhejiang University index (ZJU), as well as other single indicators for MAFLD. We aimed to find the optimal screening tool and its appropriate cut-off values for rural Uyghur adults. METHODS: We completed a survey of 14,321 Uyghur adults in 51 groups in Kashgar, Xinjiang, in 2016 using a typical sampling method, with 12,794 patients ultimately included in statistical analyses. Fatty liver was diagnosed using ultrasonography. RESULTS: The prevalence of fatty liver disease (FLD) and MAFLD was 16.73% and 16.55%, respectively, and the FLI, HSI, LAP, VAI, and ZJU were all independently associated with an increased risk of MAFLD. The areas under the receiver operating characteristic curves (AUCs) of the FLI for diagnosing MAFLD in men and women were 0.853 and 0.847, respectively. The AUCs of the body mass index (BMI) for diagnosing MAFLD in men and women were 0.850 and 0.852, respectively. Compared with other metabolic-related markers, FLI had the largest AUC. In men, the optimal cut-off values of FLI and BMI for diagnosing MAFLD were 45 (sensitivity 84.83%, specificity 69.57%) and 27.4 (sensitivity 78.47%, specificity 76.30%), respectively. In women, the optimal cut-off values of FLI and BMI for diagnosing MAFLD were 45 (sensitivity 80.11%, specificity 74.23%) and 28.0 (sensitivity 79.56%, specificity 75.41%), respectively. In men and women, an FLI score of < 30 ruled out MAFLD, while a score of ≥ 50 was a basis for diagnosis. CONCLUSION: FLI and BMI had good screening ability for MAFLD and were superior to HSI, LAP, VAI, and ZJU in both sexes.

[Correlation between Peripheral Blood PLR with Treg, Th17 in Newly Diagnosed Multiple Myeloma and Its Influence on Prognosis].

OBJECTIVE: To investigate the correlation of peripheral blood platelet/lymphocyte ratio (PLR) with Treg and Th17 and its influence on prognosis in newly diagnosed multiple myeloma (MM). METHODS: One hundred thirty-five newly diagnosed multiple myeloma patients admitted to the Department of Hematology of Zhengzhou People's Hospital from June 2015 to October 2022 were selected as MM group. Clinical data included sex, age, immune typing, ISS stage, blood calcium (Ca), albumin (ALB), hemoglobin (Hb), PLR, LDH, ß2 microglobulin (ß2-MG), Treg and Th17 levels. Sixty healthy volunteers who underwent physical examination in our hospital during the same period were selected as the control group. PLR, Treg and Th17 levels in MM group and control group were compared. Pearson was used to analyze the correlation between PLR and Treg, Th17. The relationship between MM patients with different PLR and clinical features and prognosis was analyzed. RESULTS: The PLR and Th17 of MM patients were significantly higher than that of control group, and Treg was significantly lower than that of control group (P<0.05). In MM patients, PLR was negatively correlated with Treg (r=-0.616), and PLR was positively correlated with Th17 (r=0.555). Using mean PLR=132.72 as the boundary, 135 MM patients were divided into high PLR group (n=54) and low PLR group (n=81). In MM patients with high PLR, ISS stage, ALB and Treg were significantly higher than those in low PLR group, while Th17 was significantly lower than those in low PLR group (P<0.05). By univariate and COX regression analysis, PLR was an independent prognostic risk factor for newly diagnosed MM patients (P<0.05). MM patients with high PLR had better PFS and OS, and the difference was statistically significant compared with MM patients with low PLR (P<0.05). 65 patients admitted from June 2015 to December 2018 were used as the training set, and 70 patients admitted from January 2019 to October 2022 were used as the validation set. The OS of MM patients with different PLR were compared respectively. The results showed that the conclusions of the training set and the validation set were consistent. PLR with high expression had higher OS (P<0.01). CONCLUSION: PLR is correlated with Treg and Th17 in newly diagnosed MM patients, and high PLR has better prognosis. PLR can be used to evaluate the prognosis of MM patients.

Whole human-brain mapping of single cortical neurons for profiling morphological diversity and stereotypy.

Quantifying neuron morphology and distribution at the whole-brain scale is essential to understand the structure and diversity of cell types. It is exceedingly challenging to reuse recent technologies of single-cell labeling and whole-brain imaging to study human brains. We propose adaptive cell tomography (ACTomography), a low-cost, high-throughput, and high-efficacy tomography approach, based on adaptive targeting of individual cells. We established a platform to inject dyes into cortical neurons in surgical tissues of 18 patients with brain tumors or other conditions and one donated fresh postmortem brain. We collected three-dimensional images of 1746 cortical neurons, of which 852 neurons were reconstructed to quantify local dendritic morphology, and mapped to standard atlases. In our data, human neurons are more diverse across brain regions than by subject age or gender. The strong stereotypy within cohorts of brain regions allows generating a statistical tensor field of neuron morphology to characterize anatomical modularity of a human brain.

Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning.

Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample. This characteristic makes IR spectroscopy an invaluable and versatile technology for detecting a wide variety of chemicals and is widely used in biological, chemical, and medical scenarios. These include, but are not limited to, micro-organism identification, clinical diagnosis, and explosive detection. However, IR spectroscopy is susceptible to various interfering factors such as scattering, reflection, and interference, which manifest themselves as baseline, band distortion, and intensity changes in the measured IR spectra. Combined with the absorption information of the molecules of interest, these interferences prevent direct data interpretation based on the Beer-Lambert law. Instead, more advanced data analysis approaches, particularly artificial intelligence (AI)-based algorithms, are required to remove the interfering contributions and, more importantly, to translate the spectral signals into high-level biological/chemical information. This leads to the tasks of spectral pre-processing and data modeling, the main topics of this review. In particular, we will discuss recent developments in both tasks from the perspectives of classical machine learning and deep learning.

SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline.

Background: Neuron morphology analysis is an essential component of neuron cell-type definition. Morphology reconstruction represents a bottleneck in high-throughput morphology analysis workflow, and erroneous extra reconstruction owing to noise and entanglements in dense neuron regions restricts the usability of automated reconstruction results. We propose SNAP, a structure-based neuron morphology reconstruction pruning pipeline, to improve the usability of results by reducing erroneous extra reconstruction and splitting entangled neurons. Methods: For the four different types of erroneous extra segments in reconstruction (caused by noise in the background, entanglement with dendrites of close-by neurons, entanglement with axons of other neurons, and entanglement within the same neuron), SNAP incorporates specific statistical structure information into rules for erroneous extra segment detection and achieves pruning and multiple dendrite splitting. Results: Experimental results show that this pipeline accomplishes pruning with satisfactory precision and recall. It also demonstrates good multiple neuron-splitting performance. As an effective tool for post-processing reconstruction, SNAP can facilitate neuron morphology analysis.

Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis.

PURPOSE: With the increase in aging and cardiovascular risk factors, the morbidity and mortality of atherosclerotic cardiovascular disease (ASCVD), represented by ischemic heart disease and stroke, continue to rise in China. For better prevention and intervention, relevant guidelines recommend using predictive models for early detection of ASCVD high-risk groups. Therefore, this study aims to establish a population ASCVD prediction model in rural areas of Xinjiang using survival analysis. METHODS: Baseline cohort data were collected from September to December 2016 and followed up till June 2022. A total of 7975 residents (4054 males and 3920 females) aged 30-74 years were included in the analysis. The data set was divided according to different genders, and the training and test sets ratio was 7:3 for different genders. A Cox regression, Lasso-Cox regression, and random survival forest (RSF) model were established in the training set. The model parameters were determined by cross-validation and parameter tuning and then verified in the training set. Traditional ASCVD prediction models (Framingham and China-PAR models) were constructed in the test set. Different models' discrimination and calibration degrees were compared to find the optimal prediction model for this population according to different genders and further analyze the risk factors of ASCVD. RESULTS: After 5.79 years of follow-up, 873 ASCVD events with a cumulative incidence of 10.19% were found (7.57% in men and 14.44% in women). By comparing the discrimination and calibration degrees of each model, the RSF showed the best prediction performance in males and females (male: Area Under Curve (AUC) 0.791 (95%CI 0.767,0.813), C statistic 0.780 (95%CI 0.730,0.829), Brier Score (BS):0.060, female: AUC 0.759 (95%CI 0.734,0.783) C statistic was 0.737 (95%CI 0.702,0.771), BS:0.110). Age, systolic blood pressure (SBP), apolipoprotein B (APOB), Visceral Adiposity Index (VAI), hip circumference (HC), and plasma arteriosclerosis index (AIP) are important predictors of ASCVD in the rural population of Xinjiang. CONCLUSION: The performance of the ASCVD prediction model based on the RSF algorithm is better than that based on Cox regression, Lasso-Cox, and the traditional ASCVD prediction model in the rural population of Xinjiang.

Gallbladder disease is associated with the risk of cardiovascular disease among Uyghurs in Xinjiang: a prospective cohort study.

BACKGROUND: Gallbladder disease (GBD) can increase the risk of cardiovascular disease (CVD). However, GBD has rarely been reported in the less developed, rural areas of Xinjiang. This study aimed to determine the prevalence of GBD and incidence of CVD in a prospective cohort study in rural Xinjiang. Moreover, the study aimed to explore the association between GBD and CVD within this cohort. METHODS: The study cohort included 11,444 Uyghur adults in Xinjiang, 3rd division, from the 51st Mission. Study groups were classified according to whether GBD was present or absent at baseline. The occurrence of CVD was the end event. Demographic, anthropometric, and biochemical data were recorded, and the incidence of CVD in the GBD and non-GBD groups analysed. Cox proportional hazards regression models were used to assess the association between GBD and CVD and factors associated with their incidence. Several subgroup analyses were performed to assess CVD incidence in different subgroups. The interaction between GBD and cardiometabolic risk factors, and subsequent risk of developing CVD, was evaluated. RESULTS: Prevalence of GBD in the study cohort was 10.29%. After a median follow-up of 4.92 years, the cumulative incidence of CVD in the study cohort was 10.49%, 8.43% in males and 12.65% in females. CVD incidence was higher in the GBD group (34.04% vs. 7.78%, HR = 4.96, 95% CI: 4.40-5.59). After multivariate adjustment, the risk of CVD remained higher in the GBD group (HR = 2.89, 95% CI: 2.54-3.29). Subgroup analyses showed male sex, smoking, alcohol consumption, lack of exercise, and abnormal renal function were all associated with increased risk of CVD. Moreover, the risk of CVD was markedly higher in GBD combined with cardiometabolic risk factors (hypertension, T2DM, dyslipidaemia, overweight, and abdominal obesity), than in cardiometabolic risk factors alone and this was higher in the GBD group than in the non-GBD group regardless of whether cardiometabolic risk factors were combined. CONCLUSION: GBD is an important independent risk factor for CVD development. Awareness of these associations will raise concerns among clinicians about the risk of cardiovascular disease in patients with GBD.

Impact of the baseline insulin resistance surrogates and their longitudinal trajectories on cardiovascular disease (coronary heart disease and stroke): a prospective cohort study in rural China.

Background: This study aimed to assess the association of baseline insulin resistance (IR) surrogates and their longitudinal trajectories with cardiovascular diseases (CVD) to provide a useful reference for preventing CVD. Methods: This study was a prospective cohort study conducted in the 51st Regiment of the Third Division of Xinjiang Corps. A total of 6362 participants were recruited in 2016 to conduct the baseline survey, and the follow-up surveys in 2019, 2020, 2021, and 2022. The Kaplan-Meier method was used to estimate the cumulative incidence of CVD according to the baseline IR surrogates of metabolic insulin resistance score (METS-IR) and triglyceride-glucose (TyG) index. Cox regression models were used to assess the association between the baseline IR surrogates and CVD. The impact of the longitudinal trajectories of the IR surrogates on CVD was analyzed after excluding those with IR surrogate data measured ≤2 times. Based on the group-based trajectory model (GBTM), the trajectory patterns of IR surrogates were determined. The Kaplan-Meier method was used to estimate the cumulative incidence of CVD in each trajectory group of METS-IR and TyG index. Cox regression models were used to analyze the association between different trajectory groups of each index and CVD. In addition, the Framingham model was utilized to evaluate whether the addition of the baseline IR surrogates increased the predictive potential of the model. Results: Baseline data analysis included 4712 participants. During a median follow-up of 5.66 years, 572 CVD events were recorded (mean age, 39.42 ± 13.67 years; males, 42.9%). The cumulative CVD incidence increased with the ascending baseline METS-IR and TyG index quartiles (Q1-Q4). The hazard ratio and 95% confidence interval for CVD risk in Q4 of the METS-IR and TyG index were 1.79 (1.25, 2.58) and 1.66 (1.28, 2.17), respectively, when compared with Q1. 4343 participants were included in the trajectory analysis, based on the longitudinal change patterns of the METS-IR and TyG index, the following three trajectory groups were identified: low-increasing, moderate-stable, and elevated-increasing groups. Multivariate Cox regression revealed that the hazard ratio (95% confidence interval) for CVD risk in the elevated-increasing trajectory group of the METS-IR and TyG index was 2.13 (1.48, 3.06) and 2.63 (1.68, 4.13), respectively, when compared with the low-rising group. The C-index, integrated discrimination improvement value, and net reclassification improvement value were enhanced after adding the baseline METS-IR and TyG index values to the Framingham model (P<0.05). Conclusions: Elevated baseline IR surrogates and their higher long-term trajectories were strongly associated with a high risk of CVD incidence in Xinjiang's rural areas. Regular METS-IR and TyG index monitoring can aid in the early detection of CVD-risk groups.

Association of H-Type Hypertension with miR-21, miR-29, and miR-199 in Kazahks of Xinjiang, China.

Objective: This study aims to analyze the expressions of miR-21, miR-29, and miR-199 in the serum of the patients with H-type hypertension among Kazakhs. Then, we analyzed the effect of MTHFR 677C > T polymorphism on the association between the above miRNA and H-type hypertension. Method: In this study, the expression of miR-21, miR-29, and miR-199 was quantitatively measured in 120 serum samples and then stratified according to the C677T polymorphism to analyze the relationship between target miRNAs and HHcy. Results: The expression of miR-21/-29 in the hypertension group was higher than the normal group (P < 0.001). And the expression of miR-199 was higher in the hcy group than in the normal group (P < 0.001). In the CC and CT genotypes of MTHFR 677C > T, the expression of miR-21 was lower in the HHcy patients than in the normal individuals (P = 0.005 and P = 0.001) and miR-199 was significantly higher in the HHcy patients than in the normal ones (P = 0.002 and P = 0.048). No such difference was found in the TT genotype. Logistic regression analysis showed that after adjusting for sex, age, BMI, systolic blood pressure, diastolic blood pressure, and MTHFRC677 T gene polymorphism, miR-21 was negatively correlated with hcy (OR = 0.222, 95% CI (0.101-0.485), P < 0.001) and miR-199 was positively correlated with hcy (OR = 1.823,95%CI (1.272∼2.614), P = 0.001). Conclusion: miR-21, miR-29, and miR-199 are associated with H-type hypertension in the Kazakhs, especially hyperhomocysteinemia. And these three miRNAs may serve as biomarkers to provide clues to the potential pathogenesis of H-type hypertension.

Association of three insulin resistance indices with hypertension and body weight among Uyghur adults in rural areas of Xinjiang, China.

Obesity and insulin resistance are significant contributors to hypertension. There is a high prevalence of obesity among Uyghurs in the rural areas of Xinjiang, China. Therefore, this study aimed to explore the association between insulin resistance indices and hypertension according to different body weights in rural Uyghur residents of Xinjiang, China. A total of 12 813 local Uyghur residents were recruited for the study. Excluding those with incomplete data and those using antihypertensive, lipid-lowering, or glycemic drugs, 9577 permanent residents were eligible for the study. Three insulin resistance indicators were calculated: triglyceride to high-density lipoprotein cholesterol ratio, product of fasting triglyceride and glucose (TYG), and metabolic score for insulin resistance. Multivariate logistic regression analysis was performed to estimate the association between the three non-insulin-based insulin resistance indices and the risk of hypertension for different body weights. TYG was significantly associated with hypertension in the normal-weight group, particularly in women. In the obese group that was obese, all three indicators were associated with hypertension. Since TYG was associated with hypertension in the groups with normal weight and obesity, it may be useful as a reference indicator for insulin resistance. This indicator may provide a basis for the identification and management of hypertension risk among adults in the Uyghur population.

Comparison of obesity-related indicators for identifying metabolic syndrome among normal-weight adults in rural Xinjiang, China.

BACKGROUND: This study aimed to compare the ability of certain obesity-related indicators to identify metabolic syndrome (MetS) among normal-weight adults in rural Xinjiang. METHODS: A total of 4315 subjects were recruited in rural Xinjiang. The questionnaire, biochemical and anthropometric data were collected from them. Binary logistic regression was used to analyze the association between the z-score of each index and MetS. The area under the receiver-operating characteristic (ROC) curves were used to compare the diagnostic ability of each index. According to the cut-off value of each index, nomogram models were established and their diagnostic ability were evaluated. RESULTS: After adjusting for confounding factors, each indicator in different genders was correlated with MetS. Triglyceride-glucose index (TyG index) showed the strongest association with MetS in both males (OR = 3.749, 95%CI: 3.173-4.429) and females (OR = 3.521,95%CI: 2.990-4.148). Lipid accumulation product (LAP) showed the strongest diagnostic ability in both males (AUC = 0.831, 95%CI: 0.806-0.856) and females (AUC = 0.842, 95%CI: 0.820-0.864), and its optimal cut-off values were 39.700 and 35.065, respectively. The identification ability of the TyG index in different genders (males AUC: 0.817, females AUC: 0.817) was slightly weaker than LAP. Waist-to-height ratio (WHtR) had the similar AUC (males: 0.717, females: 0.747) to conicity index (CI) (males: 0.734, females: 0.749), whereas the identification ability of a body shape index (ABSI) (males AUC: 0.700, females AUC: 0.717) was relatively weak. Compared with the diagnostic ability of a single indicator, the AUC of the male nomogram model was 0.876 (95%CI: 0.856-0.895) and the AUC of the female model was 0.877 (95%CI: 0.856-0.896). The identification ability had been significantly improved. CONCLUSION: LAP and TyG index are effective indicators for identifying MetS among normal-weight adults in rural Xinjiang. Nomogram models including age, CI, LAP, and TyG index can significantly improve diagnostic ability.

Updating Framingham CVD risk score using waist circumference and estimated cardiopulmonary function: a cohort study based on a southern Xinjiang population.

PURPOSE: To explore the association between waist circumference (WC), estimated cardiopulmonary function (eCRF), and cardiovascular disease (CVD) risk in southern Xinjiang. Update the Framingham model to make it more suitable for the southern Xinjiang population. METHODS: Data were collected from 7705 subjects aged 30-74 years old in Tumushuke City, the 51st Regiment of Xinjiang Production and Construction Corps. CVD was defined as an individual's first diagnosis of non-fatal acute myocardial infarction, death from coronary heart disease, and fatal or non-fatal stroke. The Cox proportional hazards regression analysis was used to analyze the association between WC, eCRF and CVD risk. Restricted cubic spline plots were drawn to describe the association of the two indicators with CVD risk. We update the model by incorporating the new variables into the Framingham model and re-estimating the coefficients. The discrimination of the model is evaluated using AUC, NRI, and IDI metrics. Model calibration is evaluated using pseudo R2 values. RESULTS: WC was an independent risk factor for CVD (multivariate HR: 1.603 (1.323, 1.942)), eCRF was an independent protective factor for CVD (multivariate HR: 0.499 (0.369, 0.674)). There was a nonlinear relationship between WC and CVD risk (nonlinear χ2 = 12.43, P = 0.002). There was a linear association between eCRF and CVD risk (non-linear χ2 = 0.27, P = 0.6027). In the male, the best risk prediction effect was obtained when WC and eCRF were added to the model (AUC = 0.763((0.734,0.792)); pseudo R2 = 0.069). In the female, the best risk prediction effect was obtained by adding eCRF to the model (AUC = 0.757 (0.734,0.779); pseudo R2 = 0.107). CONCLUSION: In southern Xinjiang, WC is an independent risk factor for CVD. eCRF is an independent protective factor for CVD. We recommended adding WC and eCRF in the male model and only eCRF in the female model for better risk prediction.

A Cardiovascular Disease Prediction Model Based on Routine Physical Examination Indicators Using Machine Learning Methods: A Cohort Study.

Background: Cardiovascular diseases (CVD) are currently the leading cause of premature death worldwide. Model-based early detection of high-risk populations for CVD is the key to CVD prevention. Thus, this research aimed to use machine learning (ML) algorithms to establish a CVD prediction model based on routine physical examination indicators suitable for the Xinjiang rural population. Method: The research cohort data collection was divided into two stages. The first stage involved a baseline survey from 2010 to 2012, with follow-up ending in December 2017. The second-phase baseline survey was conducted from September to December 2016, and follow-up ended in August 2021. A total of 12,692 participants (10,407 Uyghur and 2,285 Kazak) were included in the study. Screening predictors and establishing variable subsets were based on least absolute shrinkage and selection operator (Lasso) regression, logistic regression forward partial likelihood estimation (FLR), random forest (RF) feature importance, and RF variable importance. The selected subset of variables was compared with L1 regularized logistic regression (L1-LR), RF, support vector machine (SVM), and AdaBoost algorithm to establish a CVD prediction model suitable for this population. The incidence of CVD in this population was then analyzed. Result: After 4.94 years of follow-up, a total of 1,176 people were diagnosed with CVD (cumulative incidence: 9.27%). In the comparison of discrimination and calibration, the prediction performance of the subset of variables selected based on FLR was better than that of other models. Combining the results of discrimination, calibration, and clinical validity, the prediction model based on L1-LR had the best prediction performance. Age, systolic blood pressure, low-density lipoprotein-L/high-density lipoproteins-C, triglyceride blood glucose index, body mass index, and body adiposity index were all important predictors of the onset of CVD in the Xinjiang rural population. Conclusion: In the Xinjiang rural population, the prediction model based on L1-LR had the best prediction performance.

Metabolic Dysfunction-Associated Fatty Liver Disease Is Associated with the Risk of Incident Cardiovascular Disease: A Prospective Cohort Study in Xinjiang.

In 2020, a group of international experts proposed a new term 'metabolic dysfunction-associated fatty liver disease' (MAFLD) to replace 'non-alcoholic fatty liver disease'. This study aimed to describe the epidemic characteristics of MAFLD, incidence of cardiovascular disease (CVD), and relationship between MAFLD and incident CVD. In 2016, 12,794 Uyghur adults from Kashgar, Xinjiang, were grouped according to the presence or absence of MAFLD. The primary outcome was the occurrence of CVD events. Fatty liver was diagnosed using ultrasound. The prevalence of MAFLD was 16.55%. After excluding patients with previous CVD, 11,444 participants were followed up for a median period of 4.7 years. During the follow-up period, the overall CVD incidence was 10.40% (1190/11,444). The incidence of CVD in the patients with MAFLD was significantly higher than that in the non-MAFLD patients (18.38% vs. 9.02%, p < 0.001; multivariable-adjusted hazard ratio = 1.37, 95% CI = 1.20−1.56). The prevalence of MAFLD was relatively low, whereas the incidence of CVD was relatively high among the Uyghur adults in rural Xinjiang. Individuals with MAFLD have a higher risk of developing CVD independent of traditional cardiovascular risk factors, obesity, type 2 diabetes mellitus (T2DM), and dyslipidaemia.

Efficacy and Safety of a Pegasparaginase-Based Chemotherapy Regimen vs an L-asparaginase-Based Chemotherapy Regimen for Newly Diagnosed Advanced Extranodal Natural Killer/T-Cell Lymphoma: A Randomized Clinical Trial.

Importance: The L-asparaginase-based SMILE (dexamethasone, methotrexate, ifosfamide, L-asparaginase, and etoposide) chemotherapy regimen has shown higher response rates and survival benefit over an anthracycline-containing regimen. However, the safety profile was not satisfied. A well-tolerated regimen with promising efficacy is lacking. Objective: To compare the efficacy and safety of the DDGP (dexamethasone, cisplatin, gemcitabine, and pegaspargase) regimen with the SMILE regimen in newly diagnosed advanced-stage (III/IV) extranodal natural killer/T-cell lymphoma (ENKL). Design, Setting, and Participants: This was an open-label, multicenter, randomized clinical trial that took place across 12 participating hospitals in China from January 2011 to February 2019. Patients were eligible if they were 14 to 70 years old with newly diagnosed ENKL in stages III/IV and had an Eastern Cooperative Oncology Group performance status of 0 to 2. Eligible patients were evenly randomized to either the DDGP or SMILE group. Interventions: Patients in each group were treated with the assigned regimen every 21 days for 6 cycles. Main Outcomes and Measures: The primary end point was progression-free survival (PFS), and secondary end points included overall response rate and overall survival (OS). The adverse events between the DDGP and SMILE groups were compared. Results: Among the 87 randomized patients, 80 received treatment (40 in the DDGP group and 40 in the SMILE group); the median (IQR) age was 43 (12) years, and 51 (64%) were male. The baseline characteristics were similar between the groups. At a median follow-up of 41.5 months, the median PFS was not reached in the DDGP group vs 6.8 months in the SMILE group (HR, 0.42; 95% CI, 0.23-0.77; P = .004), and the median OS was not reached in the DDGP group vs 75.2 months in the SMILE group (HR, 0.41; 95% CI, 0.19-0.89, P = .02). The PFS rate at 3 years and OS rate at 5 years were higher in the DDGP group vs the SMILE group (3-year PFS, 56.6% vs 41.8%; 5-year OS, 74.3% vs 51.7%). The overall response rate was higher in the DDGP group than in the SMILE group (90.0% vs 60.0%; P = .002). Grade 3 and 4 hematologic toxic effects were more frequently reported in the SMILE group vs the DDGP group (leukopenia, 85.0% vs 62.5%; neutropenia, 85.0% vs 65.0%). Conclusions and Relevance: In this randomized clinical trial, the DDGP regimen showed promising preliminary results for patients with newly diagnosed local advanced ENKL. A confirmation trial based on larger population is warranted. Trial Registration: ClinicalTrials.gov Identifier: NCT01501149.

Correction: An integrated surgical training program for hepatic cystic echinococcosis in Xinjiang of China.

[This corrects the article DOI: 10.1371/journal.pntd.0008023.].