Current Status and Influencing Factors of Readiness for Hospital Discharge of Lung Cancer Patients Receiving ERAS-Guided Postoperative Management.

OBJECTIVE: This study ascertained current status and influencing factors of readiness for hospital discharge (RHD) of lung cancer (LC) patients with enhanced recovery after surgery (ERAS) concept-guided postoperative management. METHODS: This study prospectively and consecutively included 217 LC patients who underwent whole-course ERAS concept-guided postoperative management at the Department of Thoracic Surgery of Guangzhou Institute of Cancer Research, the Affiliated Cancer Hospital, Guangzhou Medical University from November 2023 to April 2024. RHD, quality of discharge teaching (QDT), and social support (SS) were evaluated using RHDS, QDTS, and SSRS, followed by correlation analyses of RHD with the other 2 factors. The clinical baseline and pathological data were compared between the high and low RHD groups, and the characteristics showing statistical significance were assigned as independent variables for regression analysis with RHD as the dependent variable. RESULTS: RHD, QDT, and SS were above average among LC patients with ERAS concept-guided postoperative management, and RHD was positively correlated with both QDT and SS. Age, education level, self-care ability, number of admissions, and presence of drainage tubes were independent influence factors for RHD of LC patients with ERAS concept-guided postoperative management. CONCLUSION: In LC patients with ERAS concept-guided postoperative management, RHD may be improved by increasing QDT and SS and intervened by factors such as age, education level, self-care ability, number of admissions, and presence of drainage tubes.

Safety signal detection with control of latent factors.

Postmarket drug safety database like vaccine adverse event reporting system (VAERS) collect thousands of spontaneous reports annually, with each report recording occurrences of any adverse events (AEs) and use of vaccines. We hope to identify signal vaccine-AE pairs, for which certain vaccines are statistically associated with certain adverse events (AE), using such data. Thus, the outcomes of interest are multiple AEs, which are binary outcomes and could be correlated because they might share certain latent factors; and the primary covariates are vaccines. Appropriately accounting for the complex correlation among AEs could improve the sensitivity and specificity of identifying signal vaccine-AE pairs. We propose a two-step approach in which we first estimate the shared latent factors among AEs using a working multivariate logistic regression model, and then use univariate logistic regression model to examine the vaccine-AE associations after controlling for the latent factors. Our simulation studies show that this approach outperforms current approaches in terms of sensitivity and specificity. We apply our approach in analyzing VAERS data and report our findings.

Effect of interpregnancy interval on the risk of gestational diabetes mellitus during a second pregnancy.

BACKGROUND: Interpregnancy interval (IPI) is associated with the risk of GDM in a second pregnancy. However, an optimal IPI is still need to be determined based on the characteristics of the population. This study aimed to analyze the effect of interpregnancy interval (IPI) on the risk of gestational diabetes mellitus (GDM) in the Chinese population. METHODS: We conducted a retrospective cohort study on female participants who had consecutive deliveries at Peking University Shenzhen Hospital from 2013 to 2021. The IPI was categorized into 7 groups and included into the multivariate logistic regression model with other confound factors. Analysis was also stratified based on age of first pregnancy, BMI, and history of GDM. Adjusted OR values (aOR) and 95% confidence intervals (CI) calculated. The regression coefficient of IPI months on GDM prediction risk was analyzed using a linear regression model. RESULTS: A total of 2,392 participants were enrolled. The IPI of the GDM group was significantly greater than that of the non-GDM group (P < 0.05). Compared with the 18-24 months IPI category, participants with longer IPIs (24-36 months, 36-48 months, 48-60 months, and ≥ 60 months) had a higher risk of GDM (aOR:1.585, 2.381, 2.488, and 2.565; 95% CI: 1.021-2.462, 1.489-3.809, 1.441-4.298, and 1.294-5.087, respectively). For participants aged < 30 years or ≥ 30 years or without GDM history, all longer IPIs (≥ 36 months) were all significantly associated with the GDM risk in the second pregnancy (P < 0.05), while any shorter IPIs (< 18 months) was not significantly associated with GDM risk (P > 0.05). For participants with GDM history, IPI 12-18 months, 24-36 months, 36-48 months, and ≥ 60 months were all significantly associated with the GDM risk (aOR: 2.619, 3.747, 4.356, and 5.373; 95% CI: 1.074-6.386, 1.652-8.499, 1.724-11.005, and 1.078-26.793, respectively), and the slope value of linear regression (0.5161) was significantly higher compared to participants without a history of GDM (0.1891) (F = 284.168, P < 0.001). CONCLUSIONS: Long IPI increases the risk of GDM in a second pregnancy, but this risk is independent of maternal age. The risk of developing GDM in a second pregnancy for women with GDM history is more significantly affected by IPI.

FT4 is a novel indicator for risk assessment of severe hypocalcemia following parathyroidectomy.

OBJECTIVE: To analyze the risk factors associated with the development of severe hypocalcemia (SH) in patients who have undergone parathyroidectomy (PTX). METHODS: This research involved patients with chronic kidney disease-secondary hyperparathyroidism who underwent PTX between June 1, 2021, and May 31, 2023. SH was characterized by a serum total calcium (tCa) level below 1.8 mmol/L. This study aimed to analyze differences in preoperative laboratory findings and clinical manifestations between patients with and without SH. Logistic regression analysis was used to identify potential risk factors associated with the development of SH. RESULTS: The incidence of SH was 23% (n = 176). Significant differences were observed in free thyroxine (FT4), free triiodothyronine, alanine aminotransferase, osteocalcin, tCa, alkaline phosphatase (ALP), C-terminal cross-linked telopeptide of type I collagen, and parathyroid hormone between the SH and non-SH groups. The three independent risk factors for SH were tCa [odds ratio (OR) 0.063, 95% confidence interval (95% CI) 0.006-0.663], ALP (OR 1.003, 95% CI 1.001-1.005), and FT4 (OR 0.439, 95%CI 0.310-0.621). The area under the curve, sensitivity, specificity, and overall accuracy of this model were 0.904 (95% CI 0.856-0.952), 46.3%(95% CI 32.0%-61.3%), 94.8% (95% CI 89.7%-97.5%), and 83.5% (95% CI 77.3%-88.3%), respectively. CONCLUSION: The preoperative level of FT4 plays a crucial role in predicting the risk of SH after PTX. The combined FT4-ALP-tCa model demonstrates the ability to predict SH risk, providing valuable insights for customizing calcium supplementation strategies and improving clinical decision-making.

Multivariate logistic regression analysis of poor prognosis of dermatomyositis and clinical value of ferritin/Kl-6 in predicting prognosis.

BACKGROUND: Dermatomyositis (DM) is a rare inflammatory disease. Our research focuses on predicting poor prognosis in DM patients and evaluating the prognostic significance of ferritin and Salivary Sugar Chain Antigen-6 (KL-6) through multivariate logistic regression analysis. METHODS: Between February 2018 and April 2020, 80 DM patients at our hospital were categorized into MDA5 positive (n = 20) and negative (n = 60) groups. We conducted multivariate logistic regression to determine DM's poor prognosis risk factors and evaluate ferritin/KL-6's predictive value for prognosis. RESULTS: Analysis showed no gender, age, body mass index (BMI), or lifestyle (smoking, drinking) differences, nor in dyspnea, muscle weakness, skin ulcers, and acetylcysteine treatment effects (p > 0.05). Significant differences emerged in arrhythmias, interstitial pneumonia, C-reactive protein, albumin, and lactate dehydrogenase levels (p < 0.05). Before treatment, differences were negligible (p > 0.05), but post-treatment, serum KL-6 and ferritin levels dropped. MDA5 positive patients had elevated serum KL-6 and ferritin levels than survivors (p < 0.05), with a strong correlation to DM. Combined diagnosis using serum KL-6 and ferritin for DM prognosis showed area under curves of 0.716 and 0.634, significantly outperforming single-index diagnoses with an area under curve (AUC) of 0.926 (p < 0.05). CONCLUSION: Serum KL-6 and ferritin show marked abnormalities in DM, useful as indicators for evaluating polymyositis and DM conditions. However, the study's small sample size is a drawback. Expanding the sample size is essential to monitor serum KL-6 and ferritin changes in DM patients under treatment more closely, aiming to improve clinical assessment and facilitate detailed research.

Determinants of health poverty vulnerability in rural areas of Western China in the post-poverty relief era: an analysis based on the Anderson behavioral model.

BACKGROUND: Although China has eliminated absolute poverty, the effects of sickness still pose a threat to the prospect of returning to poverty in western rural areas. However, poverty governance extends beyond solving absolute poverty, and should enhance the family's ability to resist risks, proactively identify the existence of risks, and facilitate preventive measures to reduce the probability of falling into poverty again. This study aimed to assess the health poverty vulnerability of rural households in western China and decompose its determinants. METHODS: Based on survey data from 2022, the three-stage feasible generalized least squares method was used to calculate the health poverty vulnerability index. Then, Anderson's health behavior theory model was extended to analyse various influencing factors using binary logistic regression, and the contribution of each influencing factor was decomposed using the Shapley index. Finally, Tobit regression and the censored least absolute deviations estimation (clad) method were used to test the model's robustness. RESULTS: A total of 5455 families in the rural Ningxia region of western China were included in the study. The health poverty vulnerability index of the sample population in 2022 was 0.3000 ± 0.2223, and families with vulnerability ≥0.5 accounted for 16.9% of the sample population. From the Anderson behavioral model, the three models including propensity, enabling, and demand factors had the best fit, and the AIC and BIC values were the smallest. The Shapley decomposition showed that the dimensions of the propensity factor, number of residents, age and educational level of the household head, and dependency ratio were the most important factors influencing vulnerability to health poverty. Tobit regression and the clad method proved the reliability of the constructed model through a robustness test. CONCLUSION: Rural areas still face the risk of becoming poor or falling into poverty owing to residents' health problems. Health poverty alleviation should gradually change from a focus on treatment to prevention, and formulate a set of accurate and efficient intervention policies from a forward-looking perspective to consolidate the results of health poverty alleviation and prevent widescale poverty return.

Multivariate analysis of alveolar bone dehiscence and fenestration in anterior teeth after orthodontic treatment: A retrospective study.

OBJECTIVE: To compare the prevalence of fenestration and dehiscence between pre- and post-orthodontic treatment and to explore the factors related to fenestration and dehiscence in the anterior teeth after treatment. METHODS: This study included 1000 cone-beam computed tomography (CBCT) scans of 500 patients before (T1) and after (T2) orthodontic treatment. These images were imported into Dolphin 11.9 software to detect alveolar fenestration and dehiscence in the anterior teeth area. The chi-square test and Fisher's exact test were performed to compare the prevalence of alveolar bone defects between time points T1 and T2. A total of 499 patients were selected for logistic regression analysis to examine the correlation among age, sex, crowding, sagittal facial type, extraction, miniscrew use and fenestration or dehiscence post-treatment. RESULTS: Except for the maxillary lingual fenestration and labial fenestration of mandibular canines, a significant change in the prevalence of fenestration and dehiscence was noted between time points T1 and T2 (P < .025). Multinomial logistic regression showed that age, miniscrew use and extraction highly influenced the prevalence of anterior lingual dehiscence (P < .05). Dehiscence of the mandibular labial side (skeletal Class III vs. I, OR = 2.368, P = .000) and fenestration of the mandibular lingual side (skeletal Class II vs. I, OR = 2.344, P = .044) were strongly correlated with the sagittal facial type. Dehiscence of the maxillary labial side (moderate vs. mild, OR = 1.468, P = .017) was significantly associated with crowding. CONCLUSIONS: Older age, maxillary moderate crowding, skeletal Class III, extraction and miniscrew potentially significantly affect the prevalence of anterior teeth dehiscence. Adult females, skeletal Class III patients on the mandibular labial side and skeletal Class II patients on the mandibular lingual side should be monitored for anterior teeth fenestration.

Subtyping of Performance Trajectory During Medical School, Medical Internship, and the First Year of Residency in Training Physicians: A Longitudinal Cohort Study.

BACKGROUND: Developmental trajectories of clinical skills in training physicians vary among tasks and show interindividual differences. This study examined the predictors of medical internship performance and residency entrance and found subtypes of performance trajectory in training physicians. METHODS: This retrospective cohort study involved 888 training physicians who completed a medical internship between 2015 and 2019. After the internship, 627 physicians applied for residency training between 2016 and 2020. Finally, 160 of them completed their first-year residency in internal medicine, surgery, pediatrics, and psychiatry departments between 2016 and 2020. Pearson's correlation coefficients of internship performance and first year-residency performance (n = 160) were calculated. Latent profile analysis identified performance trajectory subtypes according to medical school grade point average (GPA), internship performance, English proficiency, and residency selection procedures. Multivariate logistic regression models of residency acceptance (n = 627) and performance in the top 30%/lower 10% in the first year of residency were also constructed. RESULTS: Medical internship performance showed a significant positive correlation with the medical school GPA (r = 0.194) and the written score for the medical licensing examination (r = 0.125). Higher scores in the interview (adjusted odds ratio [aOR], 2.57) and written examination (aOR, 1.45) of residency selection procedures and higher medical internship performance (aOR, 1.19) were associated with a higher chance of residency acceptance. The latent profile analyses identified three training physician subgroups: average performance, consistently high performance (top 30%), and adaptation to changes (lowest 10%). Higher scores in the interview for residency selection (aOR, 1.35) and lower scores for medical internship performance (aOR, 0.79) were associated with a higher chance of performing in the top 30% or lowest 10% in the first year of residency, respectively. CONCLUSION: Performance in the interview and medical internship predicted being among the top 30% and lowest 10% of performers in the first year of residency training, respectively. Individualized educational programs to enhance the prospect of trainees becoming high-functioning physicians are needed.

Bayesian Multivariate Logistic Regression for Superiority and Inferiority Decision-Making under Observable Treatment Heterogeneity.

The effects of treatments may differ between persons with different characteristics. Addressing such treatment heterogeneity is crucial to investigate whether patients with specific characteristics are likely to benefit from a new treatment. The current paper presents a novel Bayesian method for superiority decision-making in the context of randomized controlled trials with multivariate binary responses and heterogeneous treatment effects. The framework is based on three elements: a) Bayesian multivariate logistic regression analysis with a Pólya-Gamma expansion; b) a transformation procedure to transfer obtained regression coefficients to a more intuitive multivariate probability scale (i.e., success probabilities and the differences between them); and c) a compatible decision procedure for treatment comparison with prespecified decision error rates. Procedures for a priori sample size estimation under a non-informative prior distribution are included. A numerical evaluation demonstrated that decisions based on a priori sample size estimation resulted in anticipated error rates among the trial population as well as subpopulations. Further, average and conditional treatment effect parameters could be estimated unbiasedly when the sample was large enough. Illustration with the International Stroke Trial dataset revealed a trend toward heterogeneous effects among stroke patients: Something that would have remained undetected when analyses were limited to average treatment effects.

Functional Multivariable Logistic Regression With an Application to HIV Viral Suppression Prediction.

Motivated by improving the prediction of the human immunodeficiency virus (HIV) suppression status using electronic health records (EHR) data, we propose a functional multivariable logistic regression model, which accounts for the longitudinal binary process and continuous process simultaneously. Specifically, the longitudinal measurements for either binary or continuous variables are modeled by functional principal components analysis, and their corresponding functional principal component scores are used to build a logistic regression model for prediction. The longitudinal binary data are linked to underlying Gaussian processes. The estimation is done using penalized spline for the longitudinal continuous and binary data. Group-lasso is used to select longitudinal processes, and the multivariate functional principal components analysis is proposed to revise functional principal component scores with the correlation. The method is evaluated via comprehensive simulation studies and then applied to predict viral suppression using EHR data for people living with HIV in South Carolina.

Prediction model for severe maternal morbidity in pregnant women with hypertensive disorders of pregnancy.

Objectives: To investigate risk factors for severe maternal morbidity (SMM) in pregnant women with hypertensive disorders of pregnancy (HDP) and to develop a risk prediction model. Methods: A prospective observational cohort study was conducted among pregnant women who were hospitalized for hypertensive disorders of pregnancy (HDP) between January 2016 and December 2020 in Fujian College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Province, China (a training set), and a risk predictive model was constructed. Pregnant women with HDP who were hospitalized between January 2021 and December 2021 were selected as a validation set. Concordance index (C-index) and calibration curves were used to test predictive model discrimination and calibration. Results: We included 970 pregnant women (790 in the training set and 180 in the validation set). Least absolute shrinkage and selection operator regression was used to screen for nine related variables such as intra-uterine growth retardation (IUGR), diastolic blood pressure (DBP) and systolic blood pressure (SBP) at suspected diagnosis, total bilirubin, albumin (ALB), uric acid, total cholesterol, serum magnesium, and suspected gestational age. SBP at suspected diagnosis (OR =1.22, 95%CI:1.08-1.42) and total cholesterol (OR = 1.78, 95%CI:1.17-2.80) were independent risk factors of severe maternal morbidity in pregnant women with HDP. A nomogram was constructed, and internal validation of the nomogram model was done using the bootstrap self-sampling method. C-index in the training and the validation set was 0.798 and 0.909, respectively. Conclusion: Our prediction model can be used to determine gestational hypertension severity in pregnant women.

Bayesian multilevel multivariate logistic regression for superiority decision-making under observable treatment heterogeneity.

BACKGROUND: In medical, social, and behavioral research we often encounter datasets with a multilevel structure and multiple correlated dependent variables. These data are frequently collected from a study population that distinguishes several subpopulations with different (i.e., heterogeneous) effects of an intervention. Despite the frequent occurrence of such data, methods to analyze them are less common and researchers often resort to either ignoring the multilevel and/or heterogeneous structure, analyzing only a single dependent variable, or a combination of these. These analysis strategies are suboptimal: Ignoring multilevel structures inflates Type I error rates, while neglecting the multivariate or heterogeneous structure masks detailed insights. METHODS: To analyze such data comprehensively, the current paper presents a novel Bayesian multilevel multivariate logistic regression model. The clustered structure of multilevel data is taken into account, such that posterior inferences can be made with accurate error rates. Further, the model shares information between different subpopulations in the estimation of average and conditional average multivariate treatment effects. To facilitate interpretation, multivariate logistic regression parameters are transformed to posterior success probabilities and differences between them. RESULTS: A numerical evaluation compared our framework to less comprehensive alternatives and highlighted the need to model the multilevel structure: Treatment comparisons based on the multilevel model had targeted Type I error rates, while single-level alternatives resulted in inflated Type I errors. Further, the multilevel model was more powerful than a single-level model when the number of clusters was higher. A re-analysis of the Third International Stroke Trial data illustrated how incorporating a multilevel structure, assessing treatment heterogeneity, and combining dependent variables contributed to an in-depth understanding of treatment effects. Further, we demonstrated how Bayes factors can aid in the selection of a suitable model. CONCLUSION: The method is useful in prediction of treatment effects and decision-making within subpopulations from multiple clusters, while taking advantage of the size of the entire study sample and while properly incorporating the uncertainty in a principled probabilistic manner using the full posterior distribution.

Can government subsidies and public mechanisms alleviate the physical and mental health vulnerability of China's urban and rural residents?

BACKGROUND: Poverty vulnerability has been defined as the likelihood of a family falling into poverty in the upcoming months. Inequality is a major cause of poverty vulnerability in developing countries. There is evidence that establishing effective government subsidies and public service mechanisms significantly reduces health poverty vulnerability. One of the ways to study poverty vulnerability is by using empirical data such as income elasticity of demand to perform the analysis. Income elasticity refers to the extent to which changes in consumers' income affect changes in demand for commodities or public goods. In this work, we assess health poverty vulnerability in rural and urban China. We provide two levels of evidence on the marginal effects of the design and implementation of government subsidies and public mechanisms in reducing health poverty vulnerability, before and after incorporating the income elasticity of demand for health. METHODS: Multidimensional physical and mental health poverty indexes, according to the Oxford Poverty & Human Development Initiative and the Andersen model, were implemented to measure health poverty vulnerability by using the 2018 China Family Panel Survey database (CFPS) as the data source for empirical analysis. The income elasticity of demand for health care was used as the key mediating variable of impact. Our assessment was conducted by a two-level multidimensional logistic regression using STATA16 software. RESULTS: The first level regression indicates that the marginal utility of public mechanism (PM) in reducing urban and rural vulnerability as expected poverty on physical and mental health (VEP-PH&MH) was insignificant. On the other hand, government subsidies (GS) policies had a positive suppression effect on VEP-PH&MH to a relatively low degree. The second level regression found that given the diversity of health needs across individual households, i.e., the income elasticity of demand (HE) for health care products, PM and GS policies have a significant effect in reducing VEP-PH&MH in rural and urban areas. Our analysis has verified the significant positive impact of enacting accurate GS and PM policies on effectively reducing VEP-PH&MH in rural as well as urban areas. CONCLUSIONS: This study shows that implementing government subsidies and public mechanisms has a positive marginal effect on reducing VEP-PH&MH. Meanwhile, there are individual variations in health demands, urban-rural disparities, and regional disparities in the effects of GS and PM on inhibiting VEP-PH&MH. Therefore, special consideration needs to be given to the differences in the degree of health needs of individual residents among urban and rural areas and regions with varying economic development. Furthermore, considerations of this approach in the current worldwide scenario are analyzed.

Anatomical location, risk factors, and outcomes of lower gastrointestinal bleeding in colorectal cancer patients: a national inpatient sample analysis (2009-2019).

PURPOSE: This study aimed to investigate the incidence, predictors, and impact of lower gastrointestinal bleeding (LGIB) on inpatient mortality among colorectal cancer patients, due to its clinical significance and potential influence on patient outcomes. METHODS: We conducted a retrospective analysis of data from the National Inpatient Sample database between 2009 and 2019, including 2,598,326 colorectal cancer patients with and without LGIB. Univariate and multivariate logistic regression analyses were performed to determine predictors of LGIB and its association with inpatient outcomes. RESULTS: The highest incidence of LGIB was observed in rectal cancer patients (3.8%), followed by distal colon cancer patients (1.4%) and proximal colon cancer patients (1.2%). Several factors were significantly associated with LGIB, including older age; male sex; certain racial such as Black, Hispanic, and Asia/Pacific Islander patients; or lower socioeconomic status. Multivariate analysis identified independent predictors of LGIB, such as severe sepsis, use of anticoagulants, long-term use of aspirin or antiplatelet drugs, palliative care, malnutrition, cachexia, chemotherapy or immunotherapy, metastasis, alcohol abuse, hypertension, obesity, and family history of digestive cancer. No significant difference in inpatient mortality was observed between patients with and without LGIB. CONCLUSION: Our study underscores the importance of considering colorectal cancer location and identified risk factors for LGIB assessment. Clinicians should address modifiable risk factors and healthcare disparities. Future research should explore underlying mechanisms, targeted interventions, and long-term outcomes beyond inpatient mortality.

Consumer acceptance for sheep milk-based yogurt-Evidence from a large sample of Italian consumers.

Testing consumer acceptance for a new product, such as the sheep milk-based yogurt, provides a measure of its market success, thus it informs producers on the effectiveness of their decision to transform sheep milk into yogurt to increase their revenues. This work explores to what extent consumers accept sheep milk-based yogurt and tests the role of personal-related factors and product-related features on shaping its acceptance. The study collects data from a representative sample of Italian yogurt consumers, and data are then analyzed via a logistic regression. Results show that male, highly educated, and high-income consumers are more likely than others to accept sheep milk-based yogurt. Findings suggest that consumers' food neophobia and variety seeking traits play a pivotal role in affecting consumer acceptance. Lastly, interest in nutritional and health-related yogurt features increases the probability of accepting sheep milk-based yogurt. Thus, sheep milk-based yogurt should be targeted at high-end male consumers and those interested in nutritional and health-related aspects of yogurt. Informing consumers about the sheep milk yogurt properties may further increase its acceptance and curb food neophobia, which we found to be one of the main barriers for the product acceptance. Future studies will explore consumer acceptance by using a real product and taste experiments.

Development of a Nomogram Based on Diffusion-Weighted Imaging and Clinical Information to Predict Delayed Encephalopathy after Acute Carbon Monoxide Poisoning.

BACKGROUND: Delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) is a severe complication that can arise from acute carbon monoxide poisoning (ACOP). This study aims to identify the independent risk factors associated with DEACMP and to develop a nomogram to predict the probability of developing DEACMP. METHODS: The data of patients diagnosed with ACOP between September 2015 and June 2021 were analyzed retrospectively. The patients were divided into the two groups: the DEACMP group and the non-DEACMP group. Univariate analysis and multivariate logistic regression analysis were conducted to identify the independent risk factors for DEACMP. Subsequently, a nomogram was constructed to predict the probability of DEACMP. RESULTS: The study included 122 patients, out of whom 30 (24.6%) developed DEACMP. The multivariate logistic regression analysis revealed that acute high-signal lesions on diffusion-weighted imaging (DWI), duration of carbon monoxide (CO) exposure, and Glasgow Coma Scale (GCS) score were independent risk factors for DEACMP (Odds Ratio = 6.230, 1.323, 0.714, p < 0.05). Based on these indicators, a predictive nomogram was constructed. CONCLUSIONS: This study constructed a nomogram for predicting DEACMP using high-signal lesions on DWI and clinical indicators. The nomogram may serve as a dependable tool to differentiate high-risk patients and enable the provision of personalized treatment to lower the incidence of DEACMP.

Construction of an Analysis Model of mRNA Markers in Menstrual Blood Based on Naïve Bayes and Multivariate Logistic Regression Methods.

OBJECTIVES: To establish the menstrual blood identification model based on Naïve Bayes and multivariate logistic regression methods by using specific mRNA markers in menstrual blood detection technology combined with statistical methods, and to quantitatively distinguish menstrual blood from other body fluids. METHODS: Body fluids including 86 menstrual blood, 48 peripheral blood, 48 vaginal secretions, 24 semen and 24 saliva samples were collected. RNA of the samples was extracted and cDNA was obtained by reverse transcription. Five menstrual blood-specific markers including members of the matrix metalloproteinase (MMP) family MMP3, MMP7, MMP11, progestogens associated endometrial protein (PAEP) and stanniocalcin-1 (STC1) were amplified and analyzed by electrophoresis. The results were analyzed by Naïve Bayes and multivariate logistic regression. RESULTS: The accuracy of the classification model constructed was 88.37% by Naïve Bayes and 91.86% by multivariate logistic regression. In non-menstrual blood samples, the distinguishing accuracy of peripheral blood, saliva and semen was generally higher than 90%, while the distinguishing accuracy of vaginal secretions was lower, which were 16.67% and 33.33%, respectively. CONCLUSIONS: The mRNA detection technology combined with statistical methods can be used to establish a classification and discrimination model for menstrual blood, which can distignuish the menstrual blood and other body fluids, and quantitative description of analysis results, which has a certain application value in body fluid stain identification.

Comparative study of TNF-α and vitamin D reveals a significant role of TNF-α in NSCLC in an ethnically conserved vitamin D deficient population.

Growing evidence has implicated tumor necrosis factor-alpha (TNF-α) as an important regulator of the tumor microenvironment. Moreover, various molecular epidemiological studies have proposed vitamin D deficiency to be a mediator of cancer progression. Here we comparatively analyzed the role of TNF-α and vitamin D in non-small cell lung cancer (NSCLC) in an ethnically conserved vitamin D deficient population. Confirmed NSCLC cases (n = 190) matchedfor age, gender, dwelling, and smoking against cancer-free healthy controls ((n = 200) were genotyped for TNF-α promoter polymorphisms (rs361525 and rs1800629) by PCR-RFLP. 48 NSCLC tumor and adjacent normal tissues were quantified for TNF-α mRNA expression by RT-qPCR. 48 NSCLC cases and 60 healthy controls were analyzed for TNF-α and vitamin D serum levels by ELISA and chemiluminescence respectively. Our study indicates thatrs361525 and rs1800629 bear a significant risk towards NSCLC. Both mutant genotype and mutant allele of rs361525 elicit a likelihood of NSCLC reflected by their odds ratio (OR) of 3.16 and 1.81 respectively. In case of rs1800629, the heterogeneous genotype (GA) showed two fold higher risk for NSCLC (OR-2.07, P = 0.006), which could be attributed to the presence of the mutant allele as reflected by overall frequency of mutant A allele vs wild G allele (OR-1.92, P = 0.01). A combined effect of genotypes for rs361525 and rs1800629 revealed a 3.7 fold higher risk towards NSCLC for the presence of heterozygous genotype at both loci. Our preliminary expression results showed significant increase of TNF-α mRNA expression in tumor tissues of NSCLC as compared to adjacent normal tissues [cases- 8.56 ± 3.90vs controls-4.88 ± 2.96, P < 0.0001)] which was further affirmed by extrapolation of TNF-α expression in serum (Cases- 55.75 ± 22.50vs controls- 21.46 ± 27.75, P < 0.0001). Multivariate regression analyses revealed TNF-α mRNA expression to be significantly associated with NSCLC cases less than 50 years of age (P < 0.05). In comparison to the putative role of TNF-α in NSCLC as suggested by the results observed, vitamin D showed no significance towards any of the analyzed parameters or with the risk of NSCLC. This study suggests that TNF-α could be a potential mediator of NSCLC which bears important clinical implications and could be an important therapeutic marker in NSCLC.

Multilevel multivariate analysis on the anthropometric indicators of under-five children in Ethiopia: EMDHS 2019.

BACKGROUND: Undernutrition is the main cause of morbidity and mortality of children aged under five and it is an important indicator of countries' economic and health status. Limited attention is given to research papers conducted in Ethiopia that identified and estimates the determinants of under-five anthropometric indicators by considering their association and clustering effect. Therefore, this study aimed to identify and estimate the effects of important determinants of anthropometric indicators by taking into account their association and cluster effects. METHODS: In this study, a cross-sectional study design was implemented based on the data obtained from the 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) consists a total of 5027 under-five children. A multilevel multivariate logistic regression model was employed to estimate the effect of the determinants given their association of anthropometric indicators and clustering effect. RESULTS: Among 5027 children considered in the study 36.0, 23.3, and 9.1% of them were stunted, underweight, and wasted, respectively. Whereas the total number of undernourished (stunting, underweight and/or wasting) children was 42.9%. More than half of the children (51.2%) were males and 77.0% lived in rural area. The estimated odds of children from households with secondary and above education levels being stunted was 0.496 (OR = 0.496) times the estimated odds of children from households with no education. Whereas children from the richest households were less likely to be stunted as compared to children from the poorest households (OR = 0.485). The estimated odds of children from urban areas being underweight and wasting were lower by 24.9 and 33.7% of estimated odds of children from rural areas respectively. CONCLUSION: The prevalence of anthropometric indicators of stunting, underweight, and wasting in Ethiopia was increased. The children underweight has significant dependency with both stunting and wasting. The sex of the child, wealth index, and education level of a household are the common important determinants of stunting, underweight and wasting. The undernourished status of children was more alike within the region and differences between regions.

Assessing the impact of public transfer payments on the vulnerability of rural households to healthcare poverty in China.

BACKGROUND: China completed the task of eliminating absolute poverty, following the 18th National Congress. However, after 2020, rural poverty in China has entered a new stage that is characterised by transformational secondary poverty and relative poverty; thus, the poverty vulnerable group is the new target group. Public transfer payments play a vital role in reducing the vulnerability of rural households to healthcare poverty. Assessing the effectiveness of public transfer payments in rural households can improve the vulnerability of rural households to healthcare poverty. METHODS: In total, 5754 rural households were included each year, which accounted for a total of 16,722 rural households during the three-year study period. The multidimensional poverty and the vulnerability to healthcare poverty of rural households were assessed and compared. Two series of multivariate logistic regression models were further used to assess the effects of public transfer payments on improving the vulnerability of rural households to healthcare poverty. RESULTS: When the poverty line was set at $1.90 and $3.20, rural households in all the three study years exhibited a higher vulnerability to healthcare poverty than the actual incidence of multidimensional poverty in healthcare, and the Eastern regions exhibited higher vulnerability to poverty than the Western regions of China. The series of multivariate logistic models employed to evaluate the effects of public transfer payments on the rural households' vulnerability to healthcare poverty indicated that considering the differences in rural households' demands for healthcare is vital for the government to fulfill the effects of public transfer payments. When income elasticity indicators for health care needs were included, the effect of public transfer payments on improving the vulnerability of rural households changed from less significant in 2014 and 2016. In 2018, however, the effect of public transfers on improving the vulnerability of rural households has increased compared to the non-inclusion elasticity. CONCLUSIONS: The imbalance of development between urban and rural areas in China is increasing, and rural households with heavy economic burdens are facing the risk of low healthcare services. Our findings highlight the importance of government departments in improving public transfer payments to reduce rural households' vulnerability to healthcare poverty.