Predictors of Local Invasion in Infiltrative Basal Cell Carcinoma: Tumour Budding Outperforms the WHO Subtyping.

Tumour budding (TB) correlates with increased local invasion in various neoplasms. Certain basal cell carcinomas (BCCs) exhibit local aggressiveness. Detecting adverse prognostic factors in partial biopsies could aid in identifying cases with heightened local risk. The absolute number of TB (≤ 3 tumour cells) in excision specimens of 271 infiltrative BCCs (0: absent; 1: 1-2 foci; 2: ≥ 3 foci; 3: ≥ 10 foci), the histopathological subtype and depth of infiltration, perineural invasion, and other histological features were evaluated. A significant correlation was found between TB and both depth of infiltration (rho 0.445, p < 0.001) and perineural invasion (p = 0.009). In the multivariate analysis of depth and perineural invasion (multiple regression, stepwise), TB was identified as a significant covariate together with diameter, inflammation, and perineural invasion for the former, and depth for the latter. Conversely, no correlation existed between the WHO histological subtypes (infiltrating, sclerosing, and micronodular), and depth of infiltration or perineural invasion. This study demonstrates the value of TB as a biomarker for local invasiveness in BCC. In routine practice, a count of ≥ 3 TB foci in lesions incompletely excised or with narrow tumour-free surgical margins would be a straightforward and reproducible method to guide BCC treatment.

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.

High prevalence of orofacial pain in juvenile fibromyalgia as detected by a novel tool specifically devised for children and adolescents.

OBJECTIVES: To examine the prevalence of temporomandibular disorders (TMD) in patients with juvenile fibromyalgia syndrome (JFS) and identify TMD characteristics specifically associated to JFS. METHODS: Signs and symptoms of TMD were assessed using a novel clinical tool specifically devised for children that consists of: 1. a self-report multiple-choice questionnaire; 2. a protocol for the clinical examination of the orofacial region. Multivariate logistic regression model was used to identify TMD features associated with JFS. RESULTS: Thirty JFS patients (median age 15.5 years) and 45 healthy controls (median age 15.0 years) were included in this cross-sectional study. Orofacial pain was reported by 26 of 30 JFS patients (86.7%) and by 3 of 45 controls (6.7%; p<0.001). Pain on TMJ palpation was present in 18 of 30 JFS patients (60%) and in 5 of 45 controls (11.1%; p<0.001). Median values of maximum spontaneous mouth opening, voluntary active opening and assisted passive opening were significantly higher in JFS patients than in controls. On multiple regression analysis spontaneous orofacial pain (OR: 21.0; p=0.005), diffuse tenderness on palpation of the masticatory muscles (OR: 14.9; p=0.026) and TMJ hypermobility (OR 1.42; p=0.008) were independently associated with JFS. CONCLUSIONS: The high prevalence of TMD in JFS highlights the need for a broader interdisciplinary evaluation of JFS patients. TMJ hypermobility, in addition to orofacial and masticatory muscle pain, is an important clue for the diagnosis of TMD in adolescents with JFS. Elucidating the link between these disorders will advance individualised management and improve treatment efficacy.

A multivariable analysis to predict variations in hospital mortality using systems-based factors of healthcare delivery to inform improvements to healthcare design within the English NHS.

Over the last decade, the strain on the English National Health Service (NHS) has increased. This has been especially felt by acute hospital trusts where the volume of admissions has steadily increased. Patient outcomes, including inpatient mortality, vary between trusts. The extent to which these differences are explained by systems-based factors, and whether they are avoidable, is unclear. Few studies have investigated these relationships. A systems-based methodology recognises the complexity of influences on healthcare outcomes. Rather than clinical interventions alone, the resources supporting a patient's treatment journey have near-equal importance. This paper first identifies suitable metrics of resource and demand within healthcare delivery from routinely collected, publicly available, hospital-level data. Then it proceeds to use univariate and multivariable linear regression to associate such systems-based factors with standardised mortality. Three sequential cross-sectional analyses were performed, spanning the last decade. The results of the univariate regression analyses show clear relationships between five out of the six selected predictor variables and standardised mortality. When these five predicators are included within a multivariable regression analysis, they reliably explain approximately 36% of the variation in standardised mortality between hospital trusts. Three factors are consistently statistically significant: the number of doctors per hospital bed, bed occupancy, and the percentage of patients who are placed in a bed within four hours after a decision to admit them. Of these, the number of doctors per bed had the strongest effect. Linear regression assumption testing and a robustness analysis indicate the observations have internal validity. However, our empirical strategy cannot determine causality and our findings should not be interpreted as established causal relationships. This study provides hypothesis-generating evidence of significant relationships between systems-based factors of healthcare delivery and standardised mortality. These have relevance to clinicians and policymakers alike. While identifying causal relationships between the predictors is left to the future, it establishes an important paradigm for further research.

Multivariate Analysis and Correlation Study Shows the Impact of Anthropometric and Demographic Variables on Gut Microbiota in Obese Egyptian Children.

Deciphering the gut microbiome's link to obesity is crucial. Our study characterized the gut microbial community in Egyptian children and investigated the effect of covariates on the gut microbiome, body mass index (BMI), geographical location, gender, and age. We used 16S rRNA sequencing to characterize the gut microbial communities of 49 children. We then evaluated these communities for diversity, potential biomarkers, and functional capacity. Alpha diversity of the non-obese group was higher than that of the obese group (Chao1, P = 0.006 and observed species, P = 0.003). Beta diversity analysis revealed significant variations in the gut microbiome between the two geographical locations, Cairo and Ismailia (unweighted UniFrac, P = 0.03) and between obesity statuses, obese and non-obese (weighted UniFrac, P = 0.034; unweighted UniFrac, P = 0.015). We observed a significantly higher Firmicutes/Bacteroidetes ratio in obese males than in non-obese males (P = 0.004). Interestingly, this difference was not seen in females (P = 0.77). Multivariable association with linear models (MaAsLin2) identified 8 microbial features associated with obesity, 12 associated with non-obesity, and found 29 and 13 features specific to Cairo and Ismailia patients, respectively. It has also shown one microbial feature associated with patients under five years old. MaAsLin2, however, failed to recognize any association between gender and the gut microbiome. Moreover, it could find the most predominant features in groups 2-9 but not in group 1. Another method used in the analysis is the Linear discriminant analysis Effect Size (LEfSe) approach, which effectively identified 19 biomarkers linked to obesity, 9 linked non-obesity, 20 linked to patients residing in Cairo, 14 linked to patients in Ismailia, one linked to males, and 12 linked to females. LEfSe could not, however, detect any prevalent bacteria among children younger or older than five. Future studies should take advantage of such correlations, specifically BMI, to determine the interventions needed for obesity management.

Association Between Urinary Phthalate Metabolites and Chronic Obstructive Pulmonary Disease: A Cross-Sectional Study.

Objective: To determine the association of urinary phthalate metabolites with chronic obstructive pulmonary disease (COPD), airflow obstruction, lung function and respiratory symptoms. Methods: Our study included a total of 2023 individuals aged ≥ 40 years old in the National Health and Nutrition Examination Survey (NHANES). Multivariate logistic regression was conducted to explore the correlation of eleven urinary phthalate metabolites (MCNP, MCOP, MECPP, MnBP, MCPP, MEP, MEHHP, MEHP, MiBP, MEOHP, and MBzP) with COPD, airflow obstruction and respiratory symptoms. Linear regression analyses were used to evaluate the relationship between urinary phthalate metabolites and lung function. Results: When compared to the first tertile, the third tertile of MEHHP was associated with the risk of COPD [OR: 2.779; 95% confidence interval (CI): 1.129-6.840; P = 0.026]. Stratified analysis showed that MEHHP increased the risk of COPD by 7.080 times in male participants. Both MCPP and MBzP were positively correlated with the risk of airflow obstruction. The third tertile of MBzP increased the risk of cough by 1.545 (95% CI: 1.030-2.317; P = 0.035) times. Both FEV1 and FVC were negatively associated with MEHHP, MECPP, MnBP, MEP, MiBP and MEOHP. Conclusion: Higher levels of MEHHP are associated with increased risk of COPD, and lower measures of FEV1 and FVC. MBzP is positively related to airflow obstruction and cough.

Association of Severe Vitamin D Deficiency with Hospitalization in the Previous Year in Hospitalized Exacerbated COPD Patients.

Purpose: Vitamin D deficiency (VDD, 25-hydroxyvitamin D < 20 ng/mL) has been reported associated with exacerbation of chronic obstructive pulmonary disease (COPD) but sometimes controversial. Research on severe vitamin D deficiency (SVDD, 25-hydroxyvitamin D < 10 ng/mL) in exacerbation of COPD is limited. Patients and Methods: We performed a retrospective observational study in 134 hospitalized exacerbated COPD patients. 25-hydroxyvitamin D was modeled as a continuous or dichotomized (cutoff value: 10 or 20 ng/mL) variable to evaluate the association of SVDD with hospitalization in the previous year. Receiver operator characteristic (ROC) analysis was performed to find the optimal cut-off value of 25-hydroxyvitamin D. Results: In total 23% of the patients had SVDD. SVDD was more prevalent in women, and SVDD group tended to have lower blood eosinophils counts. 25-hydroxyvitamin D level was significantly lower in patients who were hospitalized in the previous year (13.6 vs 16.7 ng/mL, P = 0.044), and the prevalence of SVDD was higher (38.0% vs 14.3%, P = 0.002). SVDD was independently associated with hospitalization in the previous year [odds ratio (OR) 4.34, 95% CI 1.61-11.72, P = 0.004] in hospitalized exacerbated COPD patients, whereas continuous 25-hydroxyvitamin D and VDD were not (P = 0.1, P = 0.9, separately). The ROC curve yielded an area under the curve of 0.60 (95% CI 0.50-0.71) with an optimal 25-hydroxyvitamin D cutoff of 10.4 ng/mL. Conclusion: SVDD probably showed a more stable association with hospitalization in the previous year in hospitalized exacerbated COPD patients. Reasons for lower eosinophil counts in SVDD group needed further exploration.

Bayesian inference for multivariate probit model with latent envelope.

The response envelope model proposed by Cook et al. (2010) is an efficient method to estimate the regression coefficient under the context of the multivariate linear regression model. It improves estimation efficiency by identifying material and immaterial parts of responses and removing the immaterial variation. The response envelope model has been investigated only for continuous response variables. In this paper, we propose the multivariate probit model with latent envelope, in short, the probit envelope model, as a response envelope model for multivariate binary response variables. The probit envelope model takes into account relations between Gaussian latent variables of the multivariate probit model by using the idea of the response envelope model. We address the identifiability of the probit envelope model by employing the essential identifiability concept and suggest a Bayesian method for the parameter estimation. We illustrate the probit envelope model via simulation studies and real-data analysis. The simulation studies show that the probit envelope model has the potential to gain efficiency in estimation compared to the multivariate probit model. The real data analysis shows that the probit envelope model is useful for multi-label classification.

Causal relationship between bone mineral density and intervertebral disc degeneration: a univariate and multivariable mendelian randomization study.

BACKGROUND: Although previous studies have suggested a possible association between bone mineral density (BMD) and intervertebral disc degeneration (IDD), the causal relationship between them remains unclear. Evidence from accumulating studies indicates that they might mutually influence one another. However, observational studies may be affected by potential confounders. Meanwhile, Mendelian randomization (MR) study can overcome these confounders to assess causality. OBJECTIVES: This Mendelian randomization (MR) study aimed to explore the causal effect of bone mineral density (BMD) on intervertebral disc degeneration (IDD). METHODS: Summary data from genome-wide association studies of bone mineral density (BMD) and IDD (the FinnGen biobank) have been acquired. The inverse variance weighted (IVW) method was utilized as the primary MR analysis approach. Weighted median, MR-Egger regression, weighted mode, and simple mode were used as supplements. The Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) and MR-Egger regression were performed to assess horizontal pleiotropy. Cochran's Q test evaluated heterogeneity. Leave-one-out sensitivity analysis was further conducted to determine the reliability of the causal relationship. Multivariate MR (MVMR) analyses used multivariable inverse variance-weighted methods to individually and jointly adjust for four potential confounders, body mass index (BMI), Type2 diabetes, hyperthyroidism and smoking. A reverse MR analysis was conducted to assess potential reverse causation. RESULTS: In the univariate MR analysis, femoral neck bone mineral density (FNBMD), heel bone mineral density (eBMD), lumbar spine bone mineral density (LSBMD), and total body bone mineral density (TB BMD) had a direct causal effect on intervertebral disc degeneration (IDD) [FNBMD-related analysis: OR(95%CI) = 1.17 (1.04 to 1.31), p = 0.008, eBMD-related analysis: OR(95%CI) = 1.06 (1.01 to 1.12), p = 0.028, LSBMD-related analysis: OR(95%CI) = 1.20 (1.10 to 1.31), p = 3.38E-7,TB BMD-related analysis: OR(95%CI) = 1.20 (1.12 to 1.29), p = 1.0E-8]. In the MVMR analysis, it was revealed that, even after controlling for confounding factors, heel bone mineral density (eBMD), lumbar spine bone mineral density (LSBMD), and total body bone mineral density (TB BMD) still maintained an independent and significant causal association with IDD(Adjusting for heel bone mineral density: beta = 0.073, OR95% CI = 1.08(1.02 to 1.14), P = 0.013; Adjusting for lumbar spine bone mineral density: beta = 0.11, OR(95%CI) = 1.12(1.02 to 1.23), P = 0.03; Adjusting for total body bone mineral density: beta = 0.139, OR95% CI = 1.15(1.06 to 1.24), P = 5.53E - 5). In the reverse analysis, no evidence was found to suggest that IDD has an impact on BMD. CONCLUSIONS: The findings from our univariate and multivariable Mendelian randomization analysis establish a substantial positive causal association between BMD and IDD, indicating that higher bone mineral density may be a significant risk factor for intervertebral disc degeneration. Notably, no causal effect of IDD on these four measures of bone mineral density was observed. Further research is required to elucidate the underlying mechanisms governing this causal relationship.

Prevalence and risk factors for obstructive pulmonary dysfunction caused by silica dust exposure: a multicenter cross-sectional study.

OBJECTIVE: To understand the prevalence rate of obstructive pulmonary dysfunction in workers exposed to silica dust and analyze its risk factors, so as to provide reference for the formulation of diagnostic criteria for chronic obstructive pulmonary disease caused by occupational dust. METHODS: Data collection and structured questionnaire were used to collect the data of 2064 workers exposed to silica dust who underwent health examination in Hunan Occupational Disease Prevention and Control Hospital and Yuanling Second People's Hospital from January 1, 2021 to June 30, 2022. The prevalence rate of obstructive pulmonary ventilation dysfunction was analyzed and the risk factors were analyzed. RESULTS: The prevalence rate of obstructive pulmonary ventilation dysfunction (FEV1/FVC < 70%) was 2.3% in 2064 silica dust exposed workers. The prevalence of restrictive pulmonary ventilation dysfunction (FVC/Pre < 80%) was 8.1%. The prevalence of obstructive pulmonary ventilation dysfunction in the high level exposure group was higher than that in the low level exposure group, 8.2 vs0.9% (P < 0.05). The rate of obstructive pulmonary ventilation dysfunction in female group was higher than that in male group (5.3% vs. 1.7%, p = 0.00). Workers with obstructive pulmonary dysfunction were older and worked longer than workers without obstructive pulmonary dysfunction, but there was no statistical difference. Multivariate regression analysis showed that high exposure level was a risk factor for obstructive pulmonary ventilation dysfunction in silica dust exposed workers (P < 0.05). Females were the risk factors for obstructive pulmonary ventilation dysfunction (P < 0.05). CONCLUSION: Silica dust exposure can cause obstructive pulmonary ventilation dysfunction and lead to chronic obstructive pulmonary disease. High level of exposure is a risk factor for obstructive pulmonary ventilation dysfunction. Women exposed to dust are more prone to obstructive pulmonary ventilation dysfunction than men. Early diagnosis of chronic obstructive pulmonary disease caused by silica dust and timely intervention measures are very important to delay the decline of lung function and protect the health of workers.

Multivariate optimization and validation of 200 pesticide residues in the banana matrix by GC-MS/MS.

GC-MS/MS has been observed from past studies to be an appropriate choice for designing a simple, efficient and sensitive analytical technique. Accordingly, the linearity and working range, Method Limit of Detection (MLOD), Method Limit of Quantification (MLOQ), accuracy, precision (intra-day and inter-day), Matrix Effect (ME) and selectivity were analyzed for the assessment of 200 pesticide residues [organophosphorus pesticides (OPP), organochlorine pesticides (OCP), organonitrogen pesticides (ONP), synthetic pyrethroid pesticides (SPP), and herbicide methyl esters (HME)] in the banana matrix. The procedure involved QuEChERS (quick, easy, cheap, effective, rugged, and safe) extraction and clean-up with Multi-Walled Carbon Nanotubes (MWCNTs) and Primary Secondary Amine (PSA) wherein the factors were optimized using the Plackett-Burman and central composite designs. The performance of the method in order to quantitate 200 pesticides at trace levels was evaluated by matrix-matched calibration. The linearity was observed to range from 1 to 100 µg L-1 with determination coefficient (r2) > 0.99. Recovery studies were conducted at 2 levels, 10 µg kg-1 and 25 µg kg-1, and the values obtained were in the range of 71-116% and 72-119%, respectively. The Relative Standard Deviation (RSD) was observed to be less than 20% in line with the recommended guidelines (SANTE/11312/2021). The MLOD and MLOQ were found to be in the range of 0.45-6.33 µg kg-1 and 1.44-9.59 µg kg-1 respectively. The developed method was applied satisfactorily to analyse banana samples cultivated in different regions of Gujarat, India.

Multivariate signal-to-noise ratio as a metric for characterizing spectral computed tomography.

Objective.With the introduction of spectral CT techniques into the clinic, the imaging capacities of CT were expanded to multiple energy levels. Due to a variety of factors, the acquired signal in spectral CT datasets is shared between these images. Conventional image quality metrics assume independence between images which is not preserved within spectral CT datasets, limiting their utility for characterizing energy selective images. The purpose of this work was to develop a metrology to characterize energy selective images by incorporating the shared information between images within a spectral CT dataset.Approach.The signal-to-noise ratio (SNR) was extended into a multivariate space where each image within a spectral CT dataset was treated as a separate information channel. The general definition was applied to the specific case of contrast to define a multivariate contrast-to-noise ratio (CNR). The matrix contained two types of terms: a conventional CNR term which characterized image quality within each image in the spectral CT dataset and covariance weighted CNR (Covar-CNR) which characterized the contrast in each image relative to the covariance between images. Experimental data from an investigational photon-counting CT scanner was used to demonstrate the insight of this metrology. A cylindrical water phantom containing vials of iodine and gadolinium (2, 4, and 8 mg ml-1) was imaged under conditions of variable tube current, tube voltage, and energy threshold. Two image series (threshold and bin images) containing two images each were defined based upon the contribution of photons to reconstructed images. Analysis of variance (ANOVA) was calculated between CNR terms and image acquisition variables. A multivariate regression was then fitted to experimental data.Main Results.Image type had a major difference on how Covar-CNR values were distributed. Bin images had a slightly higher mean and wider standard deviation (Covar-CNRlo: 3.38 ±17.25, Covar-CNRhi: 5.77 ± 30.64) compared to threshold images (Covar-CNRlo: 2.08 ±1.89, Covar-CNRhi: 3.45 ± 2.49) across all conditions. ANOVA found that each acquisition variable had a significant relationship with both Covar-CNR terms. The multivariate regression model suggested that material concentration had the largest impact on all CNR terms.Signficance.In this work, we described a theoretical framework to extend the SNR to a multivariate form that is able to characterize images independently and also provide insight regarding the relationship between images. Experimental data was used to demonstrate the insight that this metrology provides about image formation factors in spectral CT.

The impact of MELD exception points for hydrothorax on posttransplant mortality: a propensity score-matched analysis.

BACKGROUND: Hepatic hydrothorax is a challenging complication of end-stage liver disease, and.patients with this complication can receive model for end-stage liver disease (MELD) exception points if they meet specific criteria as defined by United Network for Organ Sharing (UNOS). This research aimed to analyze the effect of receiving MELD exception points for hepatic hydrothorax on posttransplant mortality, using a national transplant database. METHODS: Patients >18 years in the UNOS database awaiting liver transplant between 2012 and 2023 were identified based on their petition for MELD exception points. Using a 1: 1 propensity score-matched analysis, 302 patients who received MELD exception points for hepatic hydrothorax were compared with 302 patients who did not receive MELD exception points.Demographic, clinical and laboratory values were compared. The primary outcome was posttransplant mortality. Multivariate logistic regression controlled for potential confounders. RESULTS: No significant difference was observed in mean age (58.20 vs 57.62 years), mean initial MELD score (16.93 vs 16.54), or mean Child-Pugh score (9.77 vs 9.74) in patients with hepatic hydrothorax receiving MELD exception points versus their matched cohort who did not recieve exception points. The proportion of males was slightly higher among patients who received MELD exception points (57.6% males vs 53.6% males). A majority of patients in both groups had Child-Pugh grade C (>56%). Patients receiving MELD exception points for hepatic hydrothorax had a statistically significant 44% decrease in the odds of posttransplant death compared to those who did not (OR 0.56; 95% CI 0.37-0.88; P  = 0.01). Among the combined cohort, each year increase in age resulted in a 3.9% increase in mortality (OR 1.04; 95% CI 1.01-1.07; P  = 0.005), and every one-unit increase in serum creatinine resulted in a 40% increase in mortality (OR 1.40; 95% CI 1.03-1.92; P  = 0.03). CONCLUSION: Receiving MELD exception points for hepatic hydrothorax is associated with a significant reduction in the odds of posttransplant mortality. These findings underscore the importance of MELD exception points for hepatic hydrothorax among patients with decompensated cirrhosis, potentially improving patient prioritization for liver transplantation and influencing clinical decision-making.

Multivariate Analysis of Solubility Parameters for Drug-Polymer Miscibility Assessment in Preparing Raloxifene Hydrochloride Amorphous Solid Dispersions.

The success of obtaining solid dispersions for solubility improvement invariably depends on the miscibility of the drug and polymeric carriers. This study aimed to categorize and select polymeric carriers via the classical group contribution method using the multivariate analysis of the calculated solubility parameter of RX-HCl. The total, partial, and derivate parameters for RX-HCl were calculated. The data were compared with the results of excipients (N = 36), and a hierarchical clustering analysis was further performed. Solid dispersions of selected polymers in different drug loads were produced using solvent casting and characterized via X-ray diffraction, infrared spectroscopy and scanning electron microscopy. RX-HCl presented a Hansen solubility parameter (HSP) of 23.52 MPa1/2. The exploratory analysis of HSP and relative energy difference (RED) elicited a classification for miscible (n = 11), partially miscible (n = 15), and immiscible (n = 10) combinations. The experimental validation followed by a principal component regression exhibited a significant correlation between the crystallinity reduction and calculated parameters, whereas the spectroscopic evaluation highlighted the hydrogen-bonding contribution towards amorphization. The systematic approach presented a high discrimination ability, contributing to optimal excipient selection for the obtention of solid solutions of RX-HCl.

dCCA: detecting differential covariation patterns between two types of high-throughput omics data.

MOTIVATION: The advent of multimodal omics data has provided an unprecedented opportunity to systematically investigate underlying biological mechanisms from distinct yet complementary angles. However, the joint analysis of multi-omics data remains challenging because it requires modeling interactions between multiple sets of high-throughput variables. Furthermore, these interaction patterns may vary across different clinical groups, reflecting disease-related biological processes. RESULTS: We propose a novel approach called Differential Canonical Correlation Analysis (dCCA) to capture differential covariation patterns between two multivariate vectors across clinical groups. Unlike classical Canonical Correlation Analysis, which maximizes the correlation between two multivariate vectors, dCCA aims to maximally recover differentially expressed multivariate-to-multivariate covariation patterns between groups. We have developed computational algorithms and a toolkit to sparsely select paired subsets of variables from two sets of multivariate variables while maximizing the differential covariation. Extensive simulation analyses demonstrate the superior performance of dCCA in selecting variables of interest and recovering differential correlations. We applied dCCA to the Pan-Kidney cohort from the Cancer Genome Atlas Program database and identified differentially expressed covariations between noncoding RNAs and gene expressions. AVAILABILITY AND IMPLEMENTATION: The R package that implements dCCA is available at https://github.com/hwiyoungstat/dCCA.

[Multivariate analysis and construction and validation of a nomogram model from data of 1610 patients with non-tumor-related anastomotic stenosis after rectal cancer surgery].

Objective: To assess the risk factors affecting development of non-tumor- related anastomotic stenosis after rectal cancer and to construct a nomogram prediction model. Methods: This was a retrospective study of data of patients who had undergone excision with one-stage intestinal anastomosis for rectal cancer between January 2003 and September 2018 in Nanfang Hospital of Southern Medical University. The exclusion criteria were as follows: (1) pathological examination of the operative specimen revealed residual tumor on the incision margin of the anastomosis; (2) pathological examination of postoperative colonoscopy specimens revealed tumor recurrence at the anastomotic stenosis, or postoperative imaging evaluation and tumor marker monitoring indicated tumor recurrence; (3) follow-up time <3 months; and (4) simultaneous multiple primary cancers. Univariate analysis using the χ2 or Fisher's exact test was performed to assess the study patients' baseline characteristics and variables such as tumor-related factors and surgical approach (P<0.05). Multivariate analysis using binary logistic regression was then performed to identify independent risk factors for development of non-tumor-related anastomotic stenosis after rectal cancer. Finally, a nomogram model for predicting non-tumor-related anastomotic stenosis after rectal cancer surgery was constructed using R software. The reliability and accuracy of this prediction model was evaluated using internal validation and calculation of the area under the curve of the model's receiver characteristic curve (ROC). Results: The study cohort comprised 1,610 patients, including 1,008 men and 602 women of median age 59 (50, 67) years and median body mass index 22.4 (20.2, 24.5) kg/m². Non-tumor-related anastomotic stenosis developed in 121 (7.5%) of these patients. The incidence of non-tumor-related anastomotic stenosis in patients who had undergone neoadjuvant chemotherapy, neoadjuvant radiotherapy, and surgery alone was 11.2% (10/89), 26.4% (47/178), and 4.8% (64/1,343), respectively. Neoadjuvant treatment (neoadjuvant chemotherapy: OR=2.455, 95%CI: 1.148-5.253, P=0.021; neoadjuvant chemoradiotherapy, OR=3.882, 95%CI: 2.425-6.216, P<0.001), anastomotic leakage (OR=7.960, 95%CI: 4.550-13.926, P<0.001), open laparotomy (OR=3.412, 95%CI: 1.772-6.571, P<0.001), and tumor location (distance of tumor from the anal verge 5-10 cm: OR=2.381, 95%CI:1.227-4.691, P<0.001; distance of tumor from the anal verge <5 cm: OR=5.985,95% CI: 3.039-11.787, P<0.001) were identified as independent risk factors for non-tumor-related anastomotic stenosis. Thereafter, a nomogram prediction model incorporating the four identified risk factors for development of anastomotic stenosis after rectal cancer was developed. The area under the curve of the model ROC was 0.815 (0.773-0.857, P<0.001), and the C-index of the predictive model was 0.815, indicating that the model's calibration curve fitted well with the ideal curve. Conclusion: Non-tumor-related anastomotic stenosis after rectal cancer surgery is significantly associated with neoadjuvant treatment, anastomotic leakage, surgical procedure, and tumor location. A nomogram based on these four factors demonstrated good discrimination and calibration, and would therefore be useful for screening individuals at risk of anastomotic stenosis after rectal cancer surgery.

Application of Medical Statistical and Machine Learning Methods in the Age Estimation of Living Individuals.

In the study of age estimation in living individuals, a lot of data needs to be analyzed by mathematical statistics, and reasonable medical statistical methods play an important role in data design and analysis. The selection of accurate and appropriate statistical methods is one of the key factors affecting the quality of research results. This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics, difference analysis, consistency test and multivariate statistical analysis, as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals, and summarizes the relevance and application prospects between medical statistical methods and machine learning methods. This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results.

MANOCCA: a robust and computationally efficient test of covariance in high-dimension multivariate omics data.

Multivariate analysis is becoming central in studies investigating high-throughput molecular data, yet, some important features of these data are seldom explored. Here, we present MANOCCA (Multivariate Analysis of Conditional CovAriance), a powerful method to test for the effect of a predictor on the covariance matrix of a multivariate outcome. The proposed test is by construction orthogonal to tests based on the mean and variance and is able to capture effects that are missed by both approaches. We first compare the performances of MANOCCA with existing correlation-based methods and show that MANOCCA is the only test correctly calibrated in simulation mimicking omics data. We then investigate the impact of reducing the dimensionality of the data using principal component analysis when the sample size is smaller than the number of pairwise covariance terms analysed. We show that, in many realistic scenarios, the maximum power can be achieved with a limited number of components. Finally, we apply MANOCCA to 1000 healthy individuals from the Milieu Interieur cohort, to assess the effect of health, lifestyle and genetic factors on the covariance of two sets of phenotypes, blood biomarkers and flow cytometry-based immune phenotypes. Our analyses identify significant associations between multiple factors and the covariance of both omics data.

Multivariate investigation of Moringa oleifera morpho-physiological and biochemical traits under various water regimes.

BACKGROUND: The climatic changes crossing the world menace the green life through limitation of water availability. The goal of this study was to determine whether Moringa oleifera Lam. trees cultivated under Tunisian arid climate, retain their tolerance ability to tolerate accentuated environmental stress factors such as drought and salinity. For this reason, the seeds of M. oleifera tree planted in Bouhedma Park (Tunisian arid area), were collected, germinated, and grown in the research area at the National Institute of Research in Rural Engineering, Waters and Forests (INRGREF) of Tunis (Tunisia). The three years aged trees were exposed to four water-holding capacities (25, 50, 75, and 100%) for 60 days to realise this work. RESULTS: Growth change was traduced by the reduction of several biometric parameters and fluorescence (Fv/Fm) under severe water restriction (25 and 50%). Whereas roots presented miraculous development in length face to the decrease of water availability (25 and 50%) in their rhizospheres. The sensitivity to drought-induced membrane damage (Malondialdehyde (MDA) content) and reactive oxygen species (ROS) liberation (hydrogen peroxide (H2O2) content) was highly correlated with ROS antiradical scavenging (ferric reducing antioxidant power (FRAP) and (2, 2'-diphenyl-1-picrylhydrazyle (DPPH)), phenolic components and osmolytes accumulation. The drought stress tolerance of M. oleifera trees was associated with a dramatic stimulation of superoxide dismutase (SOD), catalase (CAT), glutathione reductase (GR), ascorbate peroxidase (APX), and glutathione peroxidase (GPX) activities. CONCLUSION: Based on the several strategies adopted, integrated M. oleifera can grow under drought stress as accentuated adverse environmental condition imposed by climate change.

Anomaly detection in multivariate time series data using deep ensemble models.

Anomaly detection in time series data is essential for fraud detection and intrusion monitoring applications. However, it poses challenges due to data complexity and high dimensionality. Industrial applications struggle to process high-dimensional, complex data streams in real time despite existing solutions. This study introduces deep ensemble models to improve traditional time series analysis and anomaly detection methods. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks effectively handle variable-length sequences and capture long-term relationships. Convolutional Neural Networks (CNNs) are also investigated, especially for univariate or multivariate time series forecasting. The Transformer, an architecture based on Artificial Neural Networks (ANN), has demonstrated promising results in various applications, including time series prediction and anomaly detection. Graph Neural Networks (GNNs) identify time series anomalies by capturing temporal connections and interdependencies between periods, leveraging the underlying graph structure of time series data. A novel feature selection approach is proposed to address challenges posed by high-dimensional data, improving anomaly detection by selecting different or more critical features from the data. This approach outperforms previous techniques in several aspects. Overall, this research introduces state-of-the-art algorithms for anomaly detection in time series data, offering advancements in real-time processing and decision-making across various industrial sectors.