An Exploratory Approach of Clinically Useful Biomarkers of Cvid by Logistic Regression.

Despite advancements in genetic and functional studies, the timely diagnosis of common variable immunodeficiency (CVID) remains a significant challenge. This exploratory study was designed to assess the diagnostic performance of a novel panel of biomarkers for CVID, incorporating the sum of κ+λ light chains, soluble B-cell maturation antigen (sBCMA) levels, switched memory B cells (smB) and the VISUAL score. Comparative analyses utilizing logistic regression were performed against established gold-standard tests, specifically antibody responses. Our research encompassed 88 subjects, comprising 27 CVID, 23 selective IgA deficiency (SIgAD), 20 secondary immunodeficiency (SID) patients and 18 healthy controls. We established the diagnostic accuracy of sBCMA and the sum κ+λ, achieving sensitivity (Se) and specificity (Spe) of 89% and 89%, and 90% and 99%, respectively. Importantly, sBCMA showed strong correlations with all evaluated biomarkers (sum κ+λ, smB cell and VISUAL), whereas the sum κ+λ was uniquely independent from smB cells or VISUAL, suggesting its additional diagnostic value. Through a multivariate tree decision model, specific antibody responses and the sum κ+λ emerged as independent, signature biomarkers for CVID, with the model showcasing an area under the curve (AUC) of 0.946, Se 0.85, and Spe 0.95. This tree-decision model promises to enhance diagnostic efficiency for CVID, underscoring the sum κ+λ as a superior CVID classifier and potential diagnostic criterion within the panel.

A novel model for predicting prognosis and response to immunotherapy in nasopharyngeal carcinoma patients.

Blood-based biomarkers of immune checkpoint inhibitors (ICIs) response in patients with nasopharyngeal carcinoma (NPC) are lacking, so it is necessary to identify biomarkers to select NPC patients who will benefit most or least from ICIs. The absolute values of lymphocyte subpopulations, biochemical indexes, and blood routine tests were determined before ICIs-based treatments in the training cohort (n = 130). Then, the least absolute shrinkage and selection operator (Lasso) Cox regression analysis was developed to construct a prediction model. The performances of the prediction model were compared to TNM stage, treatment, and Epstein-Barr virus (EBV) DNA using the concordance index (C-index). Progression-free survival (PFS) was estimated by Kaplan-Meier (K-M) survival curve. Other 63 patients were used for validation cohort. The novel model composed of histologic subtypes, CD19+ B cells, natural killer (NK) cells, regulatory T cells, red blood cells (RBC), AST/ALT ratio (SLR), apolipoprotein B (Apo B), and lactic dehydrogenase (LDH). The C-index of this model was 0.784 in the training cohort and 0.735 in the validation cohort. K-M survival curve showed patients with high-risk scores had shorter PFS compared to the low-risk groups. For predicting immune therapy responses, the receiver operating characteristic (ROC), decision curve analysis (DCA), net reclassifcation improvement index (NRI) and integrated discrimination improvement index (IDI) of this model showed better predictive ability compared to EBV DNA. In this study, we constructed a novel model for prognostic prediction and immunotherapeutic response prediction in NPC patients, which may provide clinical assistance in selecting those patients who are likely to gain long-lasting clinical benefits to anti-PD-1 therapy.

Construction of an early differentiation diagnosis model for patients with severe fever with thrombocytopenia syndrome and hemorrhagic fever with renal syndrome.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with a high mortality rate. Differentiating between SFTS and hemorrhagic fever with renal syndrome (HFRS) is difficult and inefficient. Retrospective analysis of the medical records of individuals with SFTS and HFRS was performed. Clinical and laboratory data were compared, and a diagnostic model was developed based on multivariate logistic regression analyzes. Receiver operating characteristic curve analysis was used to evaluate the diagnostic model. Among the 189 patients, 113 with SFTS and 76 with HFRS were enrolled. Univariate analysis revealed that more than 20 variables were significantly associated with SFTS. Multivariate logistic regression analysis revealed that gender, especially female gender (odds ratio [OR]: 4.299; 95% confidence interval [CI]: 1.163-15.887; p = 0.029), age ≥65 years (OR: 16.386; 95% CI: 3.043-88.245; p = 0.001), neurological symptoms (OR: 12.312; 95% CI: 1.638-92.530; p = 0.015), leukopenia (<4.0 × 109/L) (OR: 17.355; 95% CI: 3.920-76.839; p < 0.001), and normal Cr (OR: 97.678; 95% CI: 15.483-616.226; p < 0.001) were significantly associated with SFTS but not with HFRS. The area under the curve of the differential diagnostic model was 0.960 (95% CI: 0.936-0.984), which was significantly better than that of each single factor. In addition, the model exhibited very excellent sensitivity and specificity (92.9% and 85.5%, respectively). In cases where HFRS and SFTS are endemic, a diagnostic model based on five parameters, such as gender, age ≥65 years, neurological symptoms, leukopenia and normal Cr, will facilitate the differential diagnosis of SFTS and HFRS in medical institutions, especially in primary care settings.

Development and validation of a prediction model for falls among older people using community-based data.

This is the first study to employ multilevel modeling analysis to develop a predictive tool for falls in individuals who have participated in community group exercise over a year. The tool may benefit healthcare workers in screening community-dwelling older adults with various levels of risks for falls. PURPOSE: The aim of this study was to develop a calculation tool to predict the risk of falls 1 year in the future and to find the cutoff value for detecting a high risk based on a database of individuals who participated in a community-based group exercise. METHODS: We retrospectively reviewed a total of 7726 physical test and Kihon Checklist data from 2381 participants who participated in community-based physical exercise groups. We performed multilevel logistic regression analysis to estimate the odds ratio of falls for each risk factor and used the variance inflation factor to assess collinearity. We determined a cutoff value that effectively distinguishes individuals who are likely to fall within a year based on both sensitivity and specificity. RESULTS: The final model included variables such as age, sex, weight, balance, standing up from a chair without any aid, history of a fall in the previous year, choking, cognitive status, subjective health, and long-term participation. The sensitivity, specificity, and best cutoff value of our tool were 68.4%, 53.8%, and 22%, respectively. CONCLUSION: Using our tool, an individual's risk of falls over the course of a year could be predicted with acceptable sensitivity and specificity. We recommend a cutoff value of 22% for use in identifying high-risk populations. The tool may benefit healthcare workers in screening community-dwelling older adults with various levels of risk for falls and support physicians in planning preventative and follow-up care.

Predictive models of recurrent implantation failure in patients receiving ART treatment based on clinical features and routine laboratory data.

STUDY QUESTION: The objective was to construct a model for predicting the probability of recurrent implantation failure (RIF) after assisted reproductive technology (ART) treatment based on the clinical characteristics and routine laboratory test data of infertile patients. A model was developed to predict RIF. The model showed high calibration in external validation, helped to identify risk factors for RIF, and improved the efficacy of ART therapy. WHAT IS KNOWN ALREADY: Research on the influencing factors of RIF has focused mainly on embryonic factors, endometrial receptivity, and immune factors. However, there are many kinds of examinations regarding these aspects, and comprehensive screening is difficult because of the limited time and economic conditions. Therefore, we should try our best to analyse the results of routine infertility screenings to make general predictions regarding the occurrence of RIF. STUDY DESIGN, SIZE, DURATION: A retrospective study was conducted with 5212 patients at the Reproductive Center of the First Affiliated Hospital of USTC from January 2018 to June 2022. PARTICIPANTS/MATERIALS, SETTING, METHODS: This study included 462 patients in the RIF group and 4750 patients in the control group. The patients' basic characteristics, clinical treatment data, and laboratory test indices were compared. Logistic regression was used to analyse RIF-related risk factors, and the prediction model was evaluated by receiver operating characteristic (ROC) curves and the corresponding areas under the curve (AUCs). Further analysis of the influencing factors of live births in the first cycle of subsequent assisted reproduction treatment in RIF patients was performed, including the live birth subgroup (n = 116) and the no live birth subgroup (n = 200). MAIN RESULTS AND THE ROLE OF CHANCE: (1) An increased duration of infertility (1.978; 95% CI, 1.264-3.097), uterine cavity abnormalities (2.267; 95% CI, 1.185-4.336), low AMH levels (0.504; 95% CI, 0.275-0.922), insulin resistance (3.548; 95% CI, 1.931-6.519), antinuclear antibody (ANA)-positive status (3.249; 95% CI, 1.20-8.797) and anti-ß2-glycoprotein I antibody (A-ß2-GPI Ab)-positive status (5.515; 95% CI, 1.481-20.536) were associated with an increased risk of RIF. The area under the curve of the logistic regression model was 0.900 (95% CI, 0.870-0.929) for the training cohort and 0.895 (95% CI, 0.865-0.925) for the testing cohort. (2) Advanced age (1.069; 95% CI, 1.015-1.126) was a risk factor associated with no live births after the first cycle of subsequent assisted reproduction treatment in patients with RIF. Blastocyst transfer (0.365; 95% CI = 0.181-0.736) increased the probability of live birth in subsequent cycles in patients with RIF. The area under the curve of the logistic regression model was 0.673 (95% CI, 0.597-0.748). LIMITATIONS, REASONS FOR CAUTION: This was a single-centre regression study, for which the results need to be evaluated and verified by prospective large-scale randomized controlled studies. The small sample size for the analysis of factors influencing pregnancy outcomes in subsequent assisted reproduction cycles for RIF patients resulted in the inclusion of fewer covariates, and future studies with larger samples and the inclusion of more factors are needed for assessment and validation. WIDER IMPLICATIONS OF THE FINDINGS: Prediction of embryo implantation prior to transfer will facilitate the clinical management of patients and disease prediction and further improve ART treatment outcomes. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the General Project of the National Natural Science Foundation of China (Nos. 82,201,792, 82,301,871, 81,971,446, and 82,374,212) and the Natural Science Foundation of Anhui Province (No. 2208085MH206). There are no conflicts of interest to declare. TRIAL REGISTRATION NUMBER: This study was registered with the Chinese Clinical Trial Register (Clinical Trial Number: ChiCTR1800018298 ).

Predictive Value of the Diffusion Magnetic Resonance Imaging Technique for the Postoperative Outcome of Cervical Spondylotic Myelopathy.

BACKGROUND: Diffusion magnetic resonsance imaging (dMRI) can potentially predict the postoperative outcome of cervical spondylotic myelopathy (CSM). PURPOSE: To explore preoperative dMRI parameters to predict the postoperative outcome of CSM through multifactor correlation analysis. STUDY TYPE: Prospective. POPULATION: Post-surgery CSM patients; 102 total, 73 male (52.42 ± 10.60 years old) and 29 female (52.0 ± 11.45 years old). FIELD STRENGTH/SEQUENCE: 3.0 T/Turbo spin echo T1/T2-weighted, T2*-weighted multiecho gradient echo and dMRI. ASSESSMENT: Spinal cord function was evaluated using modified Japanese Orthopedic Association (mJOA) scoring at different time points: preoperative and 3, 6, and 12 months postoperative. Single-factor correlation and t test analyses were conducted based on fractional anisotropy (FA), mean diffusivity, intracellular volume fraction, isotropic volume fraction, orientation division index, increased signal intensity, compression ratio, age, sex, symptom duration and operation method, and multicollinearity was calculated. The linear quantile mixed model (LQMM) and the linear mixed-effects regression model (LMER) were used for multifactor correlation analysis using the combinations of the above variables. STATISTICAL TESTS: Distance correlation, Pearson's correlation, multiscale graph correlation and t tests were used for the single-factor correlation analyses. The variance inflation factor (VIF) was used to calculate multicollinearity. LQMM and LMER were used for multifactor correlation analyses. P < 0.05 was considered statistically significant. RESULTS: The single-factor correlation between all variables and the postoperative mJOA score was weak (all r < 0.3). The linear relationship was stronger than the nonlinear relationship, and there was no significant multicollinearity (VIF = 1.10-1.94). FA values in the LQMM and LMER models had a significant positive correlation with the mJOA score (r = 5.27-6.04), which was stronger than the other variables. DATA CONCLUSION: The FA value based on dMRI significantly positively correlated with CSM patient postoperative outcomes, helping to predict the surgical outcome and formulate a treatment plan before surgery. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Assessing the potential for precision medicine in body weight reduction with regard to type 2 diabetes mellitus therapies: A meta-regression analysis of 120 randomized controlled trials.

AIMS: To assess the potential for precision medicine in type 2 diabetes by quantifying the variability of body weight as response to pharmacological treatment and to identify predictors which could explain this variability. METHODS: We used randomized clinical trials (RCTs) comparing glucose-lowering drugs (including but not limited to sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists and thiazolidinediones) to placebo from four recent systematic reviews. RCTs reporting on body weight after treatment to allow for calculation of its logarithmic standard deviation (log[SD], i.e., treatment response heterogeneity) in verum (i.e., treatment) and placebo groups were included. Meta-regression analyses were performed with respect to variability of body weight after treatment and potential predictors. RESULTS: A total of 120 RCTs with a total of 43 663 participants were analysed. A slightly larger treatment response heterogeneity was shown in the verum groups, with a median log(SD) of 2.83 compared to 2.79 from placebo. After full adjustment in the meta-regression model, the difference in body weight log(SD) was -0.026 (95% confidence interval -0.044; 0.008), with greater variability in the placebo groups. Scatterplots did not show any slope divergence (i.e., interaction) between clinical predictors and the respective treatment (verum or placebo). CONCLUSIONS: We found no major treatment response heterogeneity in RCTs of glucose-lowering drugs for body weight reduction in type 2 diabetes. The precision medicine approach may thus be of limited value in this setting.

Clinical characteristics and logistic regression analysis of macrolide-resistant Mycoplasma pneumoniae pneumonia in children.

PURPOSE: To investigate macrolide-resistant Mycobacterium pneumoniae (MRMP) pneumonia in children and construct a logistic regression model for mutations in the Mycoplasma pneumoniae drug-resistant gene. METHODS: Clinical data of 281 children were analyzed. Sequencing confirmed a mutation at the A2063G locus of the 23 S rRNA gene in 227 children (A2063G group); 54 children showed no mutations (non-MRMP [NMRMP] group). We compared clinical features, laboratory tests, imaging, and bronchoscopy results and constructed a multifactorial logistic regression model to analyze risk and protective factors. RESULTS: The A2063G group had longer durations of fever and hospitalization before admission, a higher proportion of treatment with sodium methylprednisolone succinate (MPS)/dexamethasone, longer time to discontinue hormones, and higher probability of combined infections. Monocyte percentage was significantly higher in the A2063G group. Imaging suggested a higher incidence of infections in the right lung compared to both lungs. Univariate analysis revealed fever duration before admission, hormone dose and duration, monocyte percentage, and mixed infections as risk factors for Mycoplasma pneumoniae infection with the A2063G mutation. The logistic regression model showed that mixed infections were an independent risk factor for the A2063G locus mutation, whereas hormone dose was a protective factor. CONCLUSION: A prevalence of macrolide resistance of 80.8% among children was observed in the region. Logistic regression analysis revealed that co-infection with other respiratory pathogens is an independent risk factor for the development of resistance genes, while the use of hormone dosage acts as a protective factor.

Primary Limb-Based Patency for Chronic Limb Threatening Ischemia Treated with Endovascular Therapy Based on the Global Limb Anatomic Staging System.

PURPOSE: This study aimed to validate the correlation between the Global Limb Anatomic Staging System (GLASS) and primary limb-based patency (LBP) and to identify the risk factors associated with LBP loss. MATERIALS AND METHODS: A single-center retrospective analysis was performed on patients with chronic limb-threatening ischemia (CLTI) who underwent endovascular therapy (EVT) between January 2018 and May 2022. All lesions were categorized into three groups (GLASS I, II, and III). The primary LBP rates were analyzed and compared across the GLASS stages. The risk factors for the loss of primary LBP were identified using Cox regression analysis. RESULTS: In total, 236 limbs from 231 patients were included, with 52 (22%) limbs stratified as GLASS I, 59 (25%) limbs as GLASS II, and 125 (53%) limbs as GLASS III. The one-year LBP rates for limbs classified as GLASS I, II, and III were 78.8%, 69.5%, and 41.6%, respectively (P <0.001). The long-term LBP rate was 54.2% in GLASS I, 38.6% in GLASS II, and 10.5% in, GLASS III (P < 0.001). Multivariate analysis revealed that GLASS stages (GLASS Ⅰ vs GLASS Ⅲ: Hazard Ratio [HR]: 0.36; 95% Confidence Interval [CI]: 0.18-0.72; P = 0.004, GLASS Ⅱ vs GLASS Ⅲ: HR: 0.47; 95%CI: 0.25-0.86; P = 0.02), diabetes, smoking, and sex were independently associated with LBP. CONCLUSIONS: GLASS stage III was associated with lower LBP rates in patients with CLTI who underwent EVT. The GLASS stages may serve as prognostic indicators for patients with CLTI after intervention.

Re-expressing coefficients from regression models for inclusion in a meta-analysis.

Meta-analysis poses a challenge when original study results have been expressed in a non-uniform manner, such as when regression results from some original studies were based on a log-transformed key independent variable while in others no transformation was used. Methods of re-expressing regression coefficients to generate comparable results across studies regardless of data transformation have recently been developed. We examined the relative bias of three re-expression methods using simulations and 15 real data examples where the independent variable had a skewed distribution. Regression coefficients from models with log-transformed independent variables were re-expressed as though they were based on an untransformed variable. We compared the re-expressed coefficients to those from a model fit to the untransformed variable. In the simulated and real data, all three re-expression methods usually gave biased results, and the skewness of the independent variable predicted the amount of bias. How best to synthesize the results of the log-transformed and absolute exposure evidence streams remains an open question and may depend on the scientific discipline, scale of the outcome, and other considerations.

Effect of dietary heat-killed Lactiplantibacillus plantarum VSG3 on growth, immunity, antioxidant status, and immune-related gene expression in pathogen-aggravated Cyprinus carpio.

Heat-killed probiotics offer an alternative approach to enhance growth and disease resistance in farmed fish. In this study, we isolated Lactiplantibacillus plantarum VSG3 from the gut of Labeo rohita to investigate the effects of heat-killed L. plantarum (HK-LP) on the health and growth performance of Cyprinus carpio fingerlings. Different concentrations of HK-LP (0, 50, 100, 200, 300, and 400 mg/kg) were administered to the fish, followed by a challenge with Aeromonas hydrophila after 8 weeks of feeding. Notably, the LP200 group exhibited significantly improved percentage weight gain and specific growth rate, accompanied by the lowest feed conversion ratio. Post-challenge survival rates were considerably enhanced in the LP200 group, reaching 60.65%. Moreover, serum analysis indicated significantly higher levels of total protein and albumin in the LP200 group than in the control group. Although HK-LP had no substantial impact on certain serum parameters (glucose, total cholesterol, cortisol, and alanine aminotransferase), aspartate aminotransferase levels were considerably low in the LP200 group. Intestinal protease and trypsin activities significantly increased in the LP200 group, while no significant changes were observed in lipase and amylase activities post-pathogen challenge. Serum immunological indices, including lysozyme, alternative complement pathway, and phagocytic activity, improved considerably in the LP200 group. Additionally, serum antioxidant enzyme activities (superoxide dismutase [SOD], glutathione peroxidase [GPx], catalase [CAT], and myeloperoxidase) were significantly elevated in the LP200 group, while malondialdehyde level was reduced. Gene expression analysis in liver tissue indicated strong upregulation of antioxidant-related genes (SOD, CAT, nuclear factor erythroid 2 [NFE2]-related factor 2 [Nrf2], Kelch-like ECH-associated protein 1[Keap1]) in the LP100 and LP200 groups. Pro-inflammatory cytokines (IL-1ß and TNF-α) were considerably downregulated in the kidneys of the LP200 post-challenged fish, although the anti-inflammatory cytokine IL-10 showed an increased expression. Quadratic regression analysis identified the optimal dietary HK-LP level for maximizing growth and immune performance (200.381-270.003 mg/kg). In summary, our findings underscore the potential of HK-LP as a valuable dietary supplement for enhancing carp aquaculture, particularly at the appropriate concentration.

Washed microbiota transplantation improves symptoms and intestinal barrier function in patients with functional bowel disorders: a propensity-score matching analysis.

BACKGROUND: Alterations in the intestinal microbiota may play a role in the pathogenesis of functional bowel disorders (FBDs). Probiotics are widely used to improve intestinal dysbacteriosis in FBDs. In the context of FBDs, washed microbiota transplantation (WMT) appear to be a promising therapeutic option. We aimed to compare probiotics with WMT by using a propensity-score matching analysis (PSMA). METHODS: We conducted a retrospective investigation of 103 patients with FBDs, including irritable bowel syndrome (IBS), functional constipation (FC), functional diarrhea (FDr), functional abdominal bloating (FAB). Patients were divided into the WMT group or probiotics group (taking probiotics capsules). Data on the following parameters were matched for PSMA: age; sex; disease course; body mass index; anxiety; insomnia; tobacco smoking; alcohol consumption; and levels of D-lactate, diamine oxidase, and lipopolysaccharide. Intestinal barrier function (IBF) and symptoms were evaluated both before and after treatment initiation. Prognostic factors were assessed by Cox proportional hazards regression analysis. RESULTS: PSMA identified in 34 matched pairs (11 IBS, 12 FC, 7 FDr, and 4 FAB in the probiotics group and 14 IBS, 13 FC, 5 FDr, and 2 FAB in the WMT group. Improvement of FBD symptoms was greater with WMT than probiotics (P = 0.002). The WMT group had significantly fewer patients with intestinal barrier damage than the probiotics group (38.2% vs. 67.6%, P = 0.041). This improvement of FBD with WMT was further reflected as a reduction in D-lactate levels (P = 0.031). Increased D-lactate levels which were identified as a prognostic factor for FBDs (HR = 0.248, 95%CI 0.093-0.666, P = 0.006) in multivariate Cox regression analysis. CONCLUSION: WMT could improve symptoms and IBF in patients with FBDs. Increased D-lactate levels in patients with FBDs may predict a favorable response to WMT treatment.

A supervised machine-learning approach for the efficient development of a multi method (LC-MS) for a large number of drugs and subsets thereof: focus on oral antitumor agents.

OBJECTIVES: Accumulating evidence argues for a more widespread use of therapeutic drug monitoring (TDM) to support individualized medicine, especially for therapies where toxicity and efficacy are critical issues, such as in oncology. However, development of TDM assays struggles to keep pace with the rapid introduction of new drugs. Therefore, novel approaches for faster assay development are needed that also allow effortless inclusion of newly approved drugs as well as customization to smaller subsets if scientific or clinical situations require. METHODS: We applied and evaluated two machine-learning approaches i.e., a regression-based approach and an artificial neural network (ANN) to retention time (RT) prediction for efficient development of a liquid chromatography mass spectrometry (LC-MS) method quantifying 73 oral antitumor drugs (OADs) and five active metabolites. Individual steps included training, evaluation, comparison, and application of the superior approach to RT prediction, followed by stipulation of the optimal gradient. RESULTS: Both approaches showed excellent results for RT prediction (mean difference ± standard deviation: 2.08 % ± 9.44 % ANN; 1.78 % ± 1.93 % regression-based approach). Using the regression-based approach, the optimum gradient (4.91 % MeOH/min) was predicted with a total run time of 17.92 min. The associated method was fully validated following FDA and EMA guidelines. Exemplary modification and application of the regression-based approach to a subset of 14 uro-oncological agents resulted in a considerably shortened run time of 9.29 min. CONCLUSIONS: Using a regression-based approach, a multi drug LC-MS assay for RT prediction was efficiently developed, which can be easily expanded to newly approved OADs and customized to smaller subsets if required.

Solving the puzzle of quality of life in cancer: integrating causal inference and machine learning for data-driven insights.

BACKGROUND: Understanding the determinants of global quality of life in cancer patients is crucial for improving their overall well-being. While correlations between various factors and quality of life have been established, the causal relationships remain largely unexplored. This study aimed to identify the causal factors influencing global quality of life in cancer patients and compare them with known correlative factors. METHODS: We conducted a retrospective analysis of European Organization for Research and Treatment of Cancer Quality of Life Questionnaire data, alongside demographic and disease-related features, collected from new cancer patients during their initial visit to an oncology outpatient clinic. Correlations with global quality of life were identified using univariate and multivariate regression analyses. Causal inference analysis was performed using two approaches. First, we employed the Dowhy Python library for causal analysis, incorporating prior information and manual characterization of an acyclic graph. Second, we utilized the Linear Non-Gaussian Acyclic Model (LiNGAM) machine learning algorithm from the Lingam Python library, which automatically generated an acyclic graph without prior information. The significance level was set at p < 0.05. RESULTS: Multivariate analysis of 469 new admissions revealed that disease stage, role functioning, emotional functioning, social functioning, fatigue, pain and diarrhea were linked with global quality of life. The most influential direct causal factors were emotional functioning, social functioning, and physical functioning, while the most influential indirect factors were physical functioning, emotional functioning, and fatigue. Additionally, the most prominent total causal factors were identified as type of cancer (diagnosis), cancer stage, and sex, with total causal effect ratios of -9.47, -4.67, and - 1.48, respectively. The LiNGAM algorithm identified type of cancer (diagnosis), nausea and vomiting and social functioning as significant, with total causal effect ratios of -9.47, -0.42, and 0.42, respectively. CONCLUSIONS: This study identified that causal factors for global quality of life in new cancer patients are distinct from correlative factors. Understanding these causal relationships could provide valuable insights into the complex dynamics of quality of life in cancer patients and guide targeted interventions to improve their well-being.

Understanding the effects of sub-inhibitory antibiotic concentrations on the development of ß-lactamase resistance based on quantile regression analysis.

AIMS: Quantile regression is an alternate type of regression analysis that has been shown to have numerous advantages over standard linear regression. Unlike linear regression, which uses the mean to fit a linear model, quantile regression uses a data set's quantiles (or percentiles), which leads to a more comprehensive analysis of the data. However, while relatively common in other scientific fields such as economic and environmental modeling, it is infrequently used to understand biological and microbiological systems. METHODS AND RESULTS: We analyzed a set of bacterial growth rates using quantile regression analysis to better understand the effects of antibiotics on bacterial fitness. Using a bacterial model system containing 16 variant genotypes of the TEM ß-lactamase enzyme, we compared our quantile regression analysis to a previously published study that uses the Tukey's range test, or Tukey honestly significantly difference (HSD) test. We find that trends in the distribution of bacterial growth rate data, as viewed through the lens of quantile regression, can distinguish between novel genotypes and ones that have been clinically isolated from patients. Quantile regression also identified certain combinations of genotypes and antibiotics that resulted in bacterial populations growing faster as the antibiotic concentration increased-the opposite of what was expected. These analyses can provide new insights into the relationships between enzymatic efficacy and antibiotic concentration. CONCLUSIONS: Quantile regression analysis enhances our understanding of the impacts of sublethal antibiotic concentrations on enzymatic (TEM ß-lactamase) efficacy and bacterial fitness. We illustrate that quantile regression analysis can link patterns in growth rates with clinically relevant mutations and provides an understanding of how increasing sub-lethal antibiotic concentrations, like those found in our modern environment, can affect bacterial growth rates, and provide insight into the genetic basis for varied resistance.

Hypertension in non-obese children and BMI in adulthood: the Bogalusa heart study.

OBJECTIVE: This study explored the association between hypertension(HTN) in non-obese children body mass index (BMI) in adulthood. METHODS: A retrospective analysis of 1111 participants from the Bogalusa Heart Study was conducted, in which data on hypertension history during childhood in non-obese children, anthropometric and cardiovascular risk factors and other indicators from cross-sectional examinations in adulthood were collected. BMI was used as both a continuous and a categorical variable, and multivariate linear regression modelling and logistic regression modelling were used. RESULTS: Of the 1111 participants finally enrolled, 40 (3.60%) had HTN during childhood. After adjusting for demographic characteristics, lipid, glucose and insulin levels in childhood, and smoking status, alcohol intake, and disease history as adults, HTN among non-obese children was positively associated with BMI in adulthood (ß = 2.64 kg/m2, 95% CI: 0.88-4.40, P = 0.0033), and the odds of being overweight or obese was 3.71 times higher in the group with a history of hypertension in childhood than those without a history of HTN(95% CI: 1.11-12.46, P = 0.0337). CONCLUSION: Among non-obese children, hypertension is at risk for higher levels of BMI in adulthood. Identifying and controlling blood pressure and childhood may aid in the prevention of adult obesity.

Trends in polypharmacy among U.S. adults with asthma, 2001-2020.

OBJECTIVE: This study aimed to evaluate trends in polypharmacy prevalence among adults with asthma in the United States. METHODS: Data from the 2001-2020 National Health and Nutrition Examination Survey were used to estimate the weighted prevalence of polypharmacy. Joinpoint regression analysis was conducted to evaluate trends in polypharmacy. Trends were first evaluated overall and then stratified by asthma severity and asthma control. A multivariable logistic regression model was used to identify factors associated with polypharmacy. RESULTS: From 2001 to 2020, a stable trend in polypharmacy among U.S. adults with asthma was observed (average annual percent change [AAPC] = 1.02, P = 0.71). Trends across different asthma severity were stable (mild asthma: AAPC = 2.93, P = 0.20; moderate asthma: AAPC= -2.22, P = 0.35; severe asthma: AAPC = 0.45, P = 0.82). Trends in adults with good asthma control and those with poor control stayed constant (good control: AAPC = 0.82, P = 0.68; poor control: AAPC= -1.22, P = 0.82). Several factors, including older age, females, Non-Hispanic Black, health insurance coverage, family income, number of healthcare visits, former smokers, multi-morbidities, asthma severity, and asthma control, were associated with polypharmacy. CONCLUSIONS: Polypharmacy prevalence has remained constant among U.S. adults with asthma over the past two decades. Despite a stable overall trend, disparities in polypharmacy prevalence persist across different asthma severities and control statuses, underscoring the need for tailored medication management to improve asthma care.

Impact of the pulmonary ventilation function on the prognosis of suspected asthma patients: a retrospective observational study.

OBJECTIVE: Asthma is a common chronic respiratory diseases, and the relationship between pulmonary ventilation function and the prognosis of patients with suspected asthma is not well understood. This study aims to explore the impact of pulmonary ventilation functions on the prognosis of patients with suspected asthma. METHODS: This retrospective observational study included patients with suspected asthma who were diagnosed and treated at the Guangdong Provincial Hospital of Traditional Chinese Medicine between August 2015 and January 2020. The primary outcome of interest was improvement in asthma symptoms, as measured by bronchial provocation test (BPT) results within one year after diagnosis. The impact of pulmonary ventilation functions on prognosis was explored by multivariable logistic regression analysis. RESULTS: Seventy-two patients were included in the study. Patients with normal (OR = 0.123, p = .004) or generally normal (OR = 0.075, p = .039) pulmonary ventilation function were more likely to achieve improvement in asthma symptoms compared with patients with mild obstruction. There were no significant differences between the improvement and non-improvement groups in baseline characteristics. CONCLUSION: These results suggest that suspected asthma patients with normal or generally normal pulmonary ventilation function are more likely to achieve improvement in asthma symptoms within one year compared to patients with mild obstruction.

Trends in the prevalence and risk factors for peripherally inserted central catheter-related complications in cancer patients from 2016 to 2022: a multicenter study.

PURPOSE: To identify the trends in the prevalence of peripherally inserted central catheter (PICC) related complications in cancer patients and explore the risk factors for complications and occurrence speed. METHODS: A total of 3573 cancer patients with PICC were recruited at 17 hospitals from 2016 to 2022. Logistic and COX regression were performed to identify influencing factors of PICC-related complications and incidence speed, respectively. RESULTS: The proportion of symptomatic PICC-related thrombosis, phlebitis, and infections reported had decreased from 59.1% (in 2017), 11.9% (in 2016), and 11.1% (in 2016) to 15.3% (in 2022), 2.9% (in 2022), and 7.4% (in 2022), and adhesive-related skin injuries and bleeding/oozing reported had increased from 4.8% (in 2016) and 0.0% (in 2016) to 45.5% (in 2022) and 3.4% (in 2022), respectively. Catheter occlusion showed a trend of first increasing and then decreasing from 2.4 (in 2016) to 12.0 (in 2020) to 5.8% (in 2022). Logistic regression showed that hospital level, nature, the patient's gender, age, diagnosis, history of deep vein catheterization, chemotherapy drug administration, and type of PICC were influencing factors of complications. COX regression showed that the patient's gender, age, diagnosis, history of deep vein thrombosis and thrombophlebitis, history of deep vein catheterization, chemotherapy drug administration, type of PICC, type of connector, and StatLock used for fixation were influencing factors of incidence speed. CONCLUSION: The composition ratios of PICC-related complications in cancer patients in China have changed in recent years. Chemotherapy drug administration was a significant risk factor accelerating the occurrence of complications. Maintenance factors had the maximum weight on the COX model, followed by patient factors. It is suggested that patients with high-risk factors be closely monitored and proper maintenance be performed to prevent and delay the occurrence of PICC-related complications.

Occurrence, source apportionment, and ecological risk of legacy and emerging per- and poly-fluoroalkyl substances (PFASs) in the Dahei river basin of a typical arid region in China.

Per- and poly-fluoroalkyl substances (PFASs) are artificial chemicals with broad commercial and industrial applications. Many studies about PFASs have been conducted in densely industrial and populated regions. However, fewer studies have focused on the PFASs' status in a typical arid region. Here, we investigated 30 legacy and emerging PFASs in surface water from the mainstream and tributaries of the Dahei River. Our results revealed that total PFASs concentrations (∑30PFASs) in water ranged from 3.13 to 289.1 ng/L (mean: 25.40 ng/L). Perfluorooctanoic acid (PFOA) had the highest mean concentration of 2.44 ng/L with a 100% detection frequency (DF), followed by perfluorohexanoic acid (PFHxA) (mean concentration: 1.34 ng/L, DF: 59.26%). Also, perfluorohexane sulfonate (DF: 44.44%), perfluorobutane sulfonate (DF: 88.89%), and perfluorooctane sulfonate (PFOS) (DF: 92.59%) had mean concentrations of 12.94, 2.00, and 1.05 ng/L, respectively. Source apportionment through ratio analysis and principal component analysis-multiple linear regression analysis showed that treated or untreated sewage, aqueous film-forming foam, degradation of precursors, and fluoropolymer production were the primary sources. The PFOS alternatives were more prevalent than those of PFOA. Conductivity, total phosphorus, and chlorophyll a positively correlated with Σ30PFASs and total perfluoroalkane sulfonates concentrations. Furthermore, ecological risk assessment showed that more attention should be paid to perfluorooctadecanoic acid, perfluorohexadecanoic acid, perfluorooctane sulfonate, perfluorohexane sulfonate, and (6:2 and 6:2/8:2) polyfluoroalkyl phosphate mono- and di-esters. The mass load of PFASs to the Yellow River was 1.28 kg/year due to the low annual runoff in the Dahei River in the arid region. This study provides baseline data for PFASs in the Dahei River that can aid in the development of effective management strategies for controlling PFASs pollution in typical arid regions in China.