A Novel Method for Nonparametric Statistical Inference for Niche Overlap in Multiple Species.

The understanding of species interactions and ecosystem dynamics hinges upon the study of ecological niches. Quantifying the overlap of Hutchinsonian-niches has garnered significant attention, with many recent publications addressing the issue. Prior work on estimating niche overlap often did not provide confidence intervals or assumed multivariate normality, seriously limiting applications in ecology, and biodiversity research. This paper extends a nonparametric approach, previously applied to the two-species case, to multiple species. For estimation, a consistent plug-in estimator based on rank sums is proposed and its asymptotic distribution is derived under weak conditions. The novel methodology is then applied to a study comparing the ecological niches of the Eurasian eagle owl, common buzzard, and red kite. These species share a habitat in Central Europe but exhibit distinct population trends. The analysis explores their breeding habitat preferences, considering the intricate competition dynamics and utilizing the nonparametric approach to niche overlap estimation. Our proposed method provides a valuable inferential tool for the quantitative evaluation of differences and overlap between niches.

A nonparametric measure of contrast in x-ray images.

Objective.We propose a nonparametric figure of merit, the contrast equivalent distance CED, to measure contrast directly from clinical images.Approach.A relative brightness distanceδis calculated by making use of the order statistic of the pixel values. By multiplyingδwith the grey value rangeR, the mean brightness distance MBD is obtained. From the MBD, the CED and the distance-to-noise ratio DNR can be derived. The latter is the ratio of the MBD and a previously suggested nonparametric measureτfor the noise. Since the order statistic is independent of the spatial arrangement of the pixel values, the measures can be obtained directly from clinical images. We apply the new measures to mammography images of an anthropomorphic phantom and of a phantom with a step wedge as well as to CT images of a head phantom.Main results.For low-noise images of a step wedge, the MBD is equivalent to the conventional grey value distance. While this measure permits the evaluation of clinical images, it is sensitive to noise. Therefore, noise has to be quantified at the same time. When the ratioσ/τof the noise standard deviationσtoτis available, validity limits for the CED as a measure of contrast can be established. The new figures of merit can be calculated for entire images as well as on regions of interest (ROI) with an edge length not smaller than 32 px.Significance.The new figures of merit are suited to quantify the quality of clinical images without relying on the assumption of a linear, shift-invariant system. They can be used for any kind of greyscale image, provided the ratioσ/τcan be estimated. This will hopefully help to achieve the optimisation of image quality vs dose required by radioprotection laws.

Clinical performance of ultrasonic backscatter parametric and nonparametric statistics in detecting early hepatic steatosis.

Diagnosis of early hepatic steatosis would allow timely intervention. B-mode ultrasound imaging was in question for detecting early steatosis, especially with a variety of concomitant parenchymal disease. This study aimed to use the surgical specimen as a reference standard to elucidate the clinical performance of ultrasonic echogenicity and backscatter parametric and nonparametric statistics in real-world scenarios. Ultrasound radio-frequency (RF) signals of right liver lobe and patient data were collected preoperatively. Surgical specimen was then used to histologically determine staging of steatosis. A backscatter nonparametric statistic (h), a known backscatter parametric statistic, i.e., the Nakagami parameter (m), and a quantitative echo intensity (env) were calculated. Among the 236 patients included in the study, 93 were grade 0 (<5% fat) and 143 were with steatosis. All the env, m and h statistics had shown significant discriminatory power of steatosis grades (AUC = 0.643-0.907 with p-value < 0.001). Mann-Whitney U tests, however, revealed that only the backscatter statistics m and h were significantly different between the groups of grades 0 and 1 steatosis. The two-way ANOVA showed a significant confounding effect of the elevated ALT on env (p-value = 0.028), but no effect on m or h. Additionally, the severe fibrosis was found to be a significant covariate for m and h. Ultrasonic signals acquired from different scanners were found linearly comparable.

A nonparametric relative treatment effect for direct comparisons of censored paired survival outcomes.

A frequently addressed issue in clinical trials is the comparison of censored paired survival outcomes, for example, when individuals were matched based on their characteristics prior to the analysis. In this regard, a proper incorporation of the dependence structure of the paired censored outcomes is required and, up to now, appropriate methods are only rarely available in the literature. Moreover, existing methods are not motivated by the strive for insights by means of an easy-to-interpret parameter. Hence, we seek to develop a new estimand-driven method to compare the effectiveness of two treatments in the context of right-censored survival data with matched pairs. With the help of competing risks techniques, the so-called relative treatment effect is estimated. This estimand describes the probability that individuals under Treatment 1 have a longer lifetime than comparable individuals under Treatment 2. We derive hypothesis tests and confidence intervals based on a studentized version of the estimator, where resampling-based inference is established by means of a randomization method. In a simulation study, we demonstrate for numerous sample sizes and different amounts of censoring that the developed test exhibits a good power. Finally, we apply the methodology to a well-known benchmark data set from a trial with patients suffering from diabetic retinopathy.

Flexible variable selection in the presence of missing data.

In many applications, it is of interest to identify a parsimonious set of features, or panel, from multiple candidates that achieves a desired level of performance in predicting a response. This task is often complicated in practice by missing data arising from the sampling design or other random mechanisms. Most recent work on variable selection in missing data contexts relies in some part on a finite-dimensional statistical model, e.g., a generalized or penalized linear model. In cases where this model is misspecified, the selected variables may not all be truly scientifically relevant and can result in panels with suboptimal classification performance. To address this limitation, we propose a nonparametric variable selection algorithm combined with multiple imputation to develop flexible panels in the presence of missing-at-random data. We outline strategies based on the proposed algorithm that achieve control of commonly used error rates. Through simulations, we show that our proposal has good operating characteristics and results in panels with higher classification and variable selection performance compared to several existing penalized regression approaches in cases where a generalized linear model is misspecified. Finally, we use the proposed method to develop biomarker panels for separating pancreatic cysts with differing malignancy potential in a setting where complicated missingness in the biomarkers arose due to limited specimen volumes.

Effect of treatment with original or biosimilar adalimumab on SARS-CoV2 vaccination antibody titers.

The technological process of production of biosimilars determines the degree of biosimilarity to the original biological drug. In particular, the focus is on the similarity of immunogenic responses. The primary endpoint of our retrospective study was to find the differences in SARS-CoV-2 antibody amount between patients treated with original adalimumab and biosimilar adalimumab MSB11022 (Idacio) and the differences in the SARS-CoV-2 antibody amount between patients treated with and without biological treatment. We collected the gender, autoimmune disease type, age, and treatment data of the patients in the outpatient clinic MEDICAL PLUS, s.r.o., Uherske Hradiste. These patients suffer from autoimmune rheumatic diseases. All patients received the mRNA vaccine (Pfizer/BioNTech - BNT162b2), with a 21-day (interquartile range, 21-24) gap between the two vaccinations. Patients receiving adalimumab were able to develop cellular immune responses after the second vaccination dose, as well as the individuals without adalimumab. In the period of 6-23 weeks after the second vaccination dose (D63 - D182), the SARS-CoV-2 antibody levels did not change significantly in the patients receiving the original adalimumab, while in the patients receiving biosimilar adalimumab a significant decrease was revealed. A statistically significant difference in the SARS-CoV-2 antibody amount between the patients without biological treatment (median: 504.3 U/mL) and with biological treatment (Original and Biosimilar - median: 47.2 and 28.2 U/mL, respectively) was confirmed on day 182. According to our observation, the effect of the treatment type on the increase/decrease of antibodies over time is dominant, while the impact of other variables (gender, methotrexate treatment, autoimmune disease type, and age) was confirmed as insignificant or minor.

Nonparametric prediction distribution from resolution-wise regression with heterogeneous data.

Modeling and inference for heterogeneous data have gained great interest recently due to rapid developments in personalized marketing. Most existing regression approaches are based on the conditional mean and may require additional cluster information to accommodate data heterogeneity. In this paper, we propose a novel nonparametric resolution-wise regression procedure to provide an estimated distribution of the response instead of one single value. We achieve this by decomposing the information of the response and the predictors into resolutions and patterns respectively based on marginal binary expansions. The relationships between resolutions and patterns are modeled by penalized logistic regressions. Combining the resolution-wise prediction, we deliver a histogram of the conditional response to approximate the distribution. Moreover, we show a sure independence screening property and the consistency of the proposed method for growing dimensions. Simulations and a real estate valuation dataset further illustrate the effectiveness of the proposed method.

Assessing the cardiac autonomic response to bicycle exercise in Olympic athletes with different loads of endurance training: new insights from statistical indicators based on multilevel exploratory factor analysis.

Aim: The mechanisms governing the organism's response to exercise are complex and difficult to study. Spectral analysis of heart rate variability (HRV) could represent a convenient methodology for studying humans' autonomic nervous system (ANS). However, difficulties in interpreting the multitude of correlated HRV-derived indices, mainly when computed over different time segments, may represent a barrier to its usage. This preliminary investigation addressed to elite athletes proposes a novel method describing the cardiac autonomic response to exercise based on multilevel exploratory factor analysis (MEFA), which reduces the multitude of HRV-derived indices to fewer uncorrelated ANS indicators capable of accounting for their interrelationships and overcoming the above difficulties. Methods: The study involved 30 Italian Olympic athletes, divided into 15 cyclists (prevalent high-intensity endurance training) and 15 shooters (prevalent technical training with low-intensity endurance component). All athletes underwent a complete test of a dynamic protocol, constituted by a rest-stand test followed by a stepwise bicycle stress test subdivided into a single bout of progressive endurance (from aerobic to anaerobic) exercise and recovery. Then, by spectral analysis, values of 12 ANS proxies were computed at each time segment (9 epochs in all) of the complete test. Results: We obtained two global ANS indicators (amplitude and frequency), expressing the athletes' overall autonomic response to the complete test, and three dynamic ANS indicators (amplitude, signal self-similarity, and oscillatory), describing the principal dynamics over time of the variability of RR interval (RRV). Globally, cyclists have significantly higher amplitude levels (median ± MAD: cyclists 69.9 ± 20.5; shooters 37.2 ± 19.4) and lower frequency levels (median ± MAD: cyclists 37.4 ± 14.8; shooters 78.2 ± 10.2) than shooters, i.e., a parasympathetic predominance compared to shooters. Regarding the RRV dynamics, the signal self-similarity and oscillatory indicators have the strongest sensitivity in detecting the rest-stand change; the amplitude indicator is highly effective in detecting the athletes' autonomic changes in the exercise fraction; the amplitude and oscillatory indicators present significant differences between cyclists and shooters in specific test epochs. Conclusion: This MEFA application permits a more straightforward representation of the complexity characterizing ANS modulation during exercise, simplifying the interpretation of the HRV-derived indices and facilitating the possible real-life use of this non-invasive methodology.

Correlated depressive symptoms within seroconcordant, expectant partners living with HIV in Zambézia Province, Mozambique: a cross-sectional study

Approximately 15% of people with HIV in sub-Saharan Africa have comorbid depression, which impacts treatment outcomes. We describe predictors of baseline depressive symptoms in 1079 female and 1079 male participants in a cluster-randomized trial in Zambézia Province, Mozambique from November 2017 to December 2020. We modeled each partners' depressive symptoms (Patient Health Questionnaire-9 [PHQ-9]) using proportional odds models adjusted for enrollment date, age, body mass index [BMI], partner's PHQ-9 score, district, relationship status, education, occupation, WHO HIV clinical stage, and antiretroviral therapy use history. A post hoc analysis assessed covariate-adjusted rank correlation between partner depressive symptoms. Females were younger than males (median 23 vs. 28 years) and more likely to report no education (20.7% vs. 7.9%). Approximately 10% screened positive for depression (PHQ-9 score ≥ 10). Partner depressive symptoms were predictive of higher participant PHQ-9 scores. A male partner PHQ-9 score of 10 (versus 5) increased the odds that the female partner would have a higher PHQ-9 score (adjusted odds ratio: 7.25, 95% Confidence Interval [CI]: 5.43-9.67). Partner PHQ-9 scores were highly correlated after covariate adjustment (Spearman's rho 0.65, 95% CI 0.57-0.72). Interventions aimed to reduce depressive symptoms and improve HIV-related outcomes during pregnancy should address both partners' depressive symptoms.

Tukey's Depth for Object Data.

We develop a novel exploratory tool for non-Euclidean object data based on data depth, extending celebrated Tukey's depth for Euclidean data. The proposed metric halfspace depth, applicable to data objects in a general metric space, assigns to data points depth values that characterize the centrality of these points with respect to the distribution and provides an interpretable center-outward ranking. Desirable theoretical properties that generalize standard depth properties postulated for Euclidean data are established for the metric halfspace depth. The depth median, defined as the deepest point, is shown to have high robustness as a location descriptor both in theory and in simulation. We propose an efficient algorithm to approximate the metric halfspace depth and illustrate its ability to adapt to the intrinsic data geometry. The metric halfspace depth was applied to an Alzheimer's disease study, revealing group differences in the brain connectivity, modeled as covariance matrices, for subjects in different stages of dementia. Based on phylogenetic trees of 7 pathogenic parasites, our proposed metric halfspace depth was also used to construct a meaningful consensus estimate of the evolutionary history and to identify potential outlier trees.

Correlación entre glucemia basal y alanina aminotransferasas en pacientes con y sin diabetes de un policlínico de Lima, Perú

Introducción: la alanina aminotransferasa es un nexo importante en el metabolismo de aminoácidos y carbohidratos, asimismo es un marcador de inflamación hepática. Estudios previos mostraron la relación entre la diabetes mellitus y esta enzima bajo diferentes contextos clínicos. Objetivo: evaluar la correlación entre glucosa basal y alanina aminotransferasa tanto en pacientes con diabetes mellitus tipo 2 como sin ella. Metodología: estudio observacional, analítico y transversal realizado desde enero de 2021 a junio de 2022 con una población de 566 pacientes dividida en grupos con diabetes mellitus tipo 2 (n 224) y sin diabetes mellitus tipo 2 (n 342). Fueron incluidos los pacientes de edad igual o mayor a 18 años con y sin diabetes mellitus tipo 2. Se excluyó a pacientes con patologías múltiples y/o con diagnóstico de diabetes inferior a 6 meses. Se realizó el análisis inferencial con la prueba de correlación de Spearman y la prueba de normalidad de Kolmogorov-Smirnov. Los datos fueron procesados con el software SPSS statistics 25™. Resultados: la correlación entre glucosa y alanina aminotransferasa en sujetos sin diabetes fue 0,212 (p=0,003) y la correlación entre glucosa y alanina aminotransferasa en aquellos con diabetes fue -0,434 (p=0,015). Conclusiones: la alanina aminotransferasa se relaciona con mayor intensidad en pacientes con diabetes mellitus tipo 2 que en aquellos sin diabetes. La correlación moderada y negativa en sujetos con diabetes mellitus tipo 2 indicaría alteraciones en la interacción entre la alanina aminotransferasa y la glucosa en los que la hiperglucemia sostenida tendría un papel relevante, probablemente por un incremento en la actividad de transaminación.

Introduction: Alanine aminotransferase is an important nexus in the metabolism of amino acids and carbohydrates, and is also a marker of liver inflammation. Previous studies showed the relationship between diabetes mellitus and this enzyme under different clinical contexts. Objective: To evaluate the correlation between basal glucose and alanine aminotransferase both in patients with and without type 2 diabetes mellitus. Methodology: Observational, analytical, and cross-sectional study conducted from January 2021 to June 2022 with a population of 566 patients divided into groups with type 2 diabetes mellitus (n 224) and without it (n 342). Patients aged 18 years or older with and without type 2 diabetes mellitus were included. Patients with multiple pathologies and/or diagnosed with diabetes less than 6 months were excluded. Inferential analysis was performed with Spearman's correlation test and the Kolmogorov-Smirnov normality test. The data was processed with the SPSS statistics 25™ software. Results: The correlation between glucose and alanine aminotransferase in subjects without diabetes was 0.212 (p=0.003) and the correlation between glucose and alanine aminotransferase in those with diabetes was -0.434 (p=0.015). Conclusions: Alanine aminotransferase is associated with greater intensity in patients with type 2 diabetes mellitus than in those without diabetes. The moderate and negative correlation in subjects with type 2 diabetes mellitus would indicate alterations in the interaction between alanine aminotransferase and glucose in which sustained hyperglycemia would play a relevant role, probably due to an increase in transamination activity.

Testing for an ignorable sampling bias under random double truncation.

In clinical and epidemiological research doubly truncated data often appear. This is the case, for instance, when the data registry is formed by interval sampling. Double truncation generally induces a sampling bias on the target variable, so proper corrections of ordinary estimation and inference procedures must be used. Unfortunately, the nonparametric maximum likelihood estimator of a doubly truncated distribution has several drawbacks, like potential nonexistence and nonuniqueness issues, or large estimation variance. Interestingly, no correction for double truncation is needed when the sampling bias is ignorable, which may occur with interval sampling and other sampling designs. In such a case the ordinary empirical distribution function is a consistent and fully efficient estimator that generally brings remarkable variance improvements compared to the nonparametric maximum likelihood estimator. Thus, identification of such situations is critical for the simple and efficient estimation of the target distribution. In this article, we introduce for the first time formal testing procedures for the null hypothesis of ignorable sampling bias with doubly truncated data. The asymptotic properties of the proposed test statistic are investigated. A bootstrap algorithm to approximate the null distribution of the test in practice is introduced. The finite sample performance of the method is studied in simulated scenarios. Finally, applications to data on onset for childhood cancer and Parkinson's disease are given. Variance improvements in estimation are discussed and illustrated.

Flexible joint modeling of mean and dispersion for the directional tuning of neuronal spike counts.

The study of how the number of spikes in a middle temporal visual area (MT/V5) neuron is tuned to the direction of a visual stimulus has attracted considerable attention over the years, but recent studies suggest that the variability of the number of spikes might also be influenced by the directional stimulus. This entails that Poisson regression models are not adequate for this type of data, as the observations usually present over/underdispersion (or both) with respect to the Poisson distribution. This paper makes use of the double exponential family and presents a flexible model to estimate, jointly, the mean and dispersion functions, accounting for the effect of a circular covariate. The empirical performance of the proposal is explored via simulations and an application to a neurological data set is shown.

The Sign Test, Paired Data, and Asymmetric Dependence: A Cautionary Tale.

In the paired data setting, the sign test is often described in statistical textbooks as a test for comparing differences between the medians of two marginal distributions. There is an implicit assumption that the median of the differences is equivalent to the difference of the medians when employing the sign test in this fashion. We demonstrate however that given asymmetry in the bivariate distribution of the paired data, there are often scenarios where the median of the differences is not equal to the difference of the medians. Further, we show that these scenarios will lead to a false interpretation of the sign test for its intended use in the paired data setting. We illustrate the false-interpretation concept via theory, a simulation study, and through a real-world example based on breast cancer RNA sequencing data obtained from the Cancer Genome Atlas (TCGA).

Inverse Probability Weights for Quasicontinuous Ordinal Exposures With a Binary Outcome: Method Comparison and Case Study.

Inverse probability weighting (IPW), a well-established method of controlling for confounding in observational studies with binary exposures, has been extended to analyses with continuous exposures. Methods developed for continuous exposures may not apply when the exposure is quasicontinuous because of irregular exposure distributions that violate key assumptions. We used simulations and cluster-randomized clinical trial data to assess 4 approaches developed for continuous exposures-ordinary least squares (OLS), covariate balancing generalized propensity scores (CBGPS), nonparametric covariate balancing generalized propensity scores (npCBGPS), and quantile binning (QB)-and a novel method, a cumulative probability model (CPM), in quasicontinuous exposure settings. We compared IPW stability, covariate balance, bias, mean squared error, and standard error estimation across 3,000 simulations with 6 different quasicontinuous exposures, varying in skewness and granularity. In general, CBGPS and npCBGPS resulted in excellent covariate balance, and npCBGPS was the least biased but the most variable. The QB and CPM approaches had the lowest mean squared error, particularly with marginally skewed exposures. We then successfully applied the IPW approaches, together with missing-data techniques, to assess how session attendance (out of a possible 15) in a partners-based clustered intervention among pregnant couples living with human immunodeficiency virus in Mozambique (2017-2022) influenced postpartum contraceptive uptake.

NaRnEA: An Information Theoretic Framework for Gene Set Analysis.

Gene sets are being increasingly leveraged to make high-level biological inferences from transcriptomic data; however, existing gene set analysis methods rely on overly conservative, heuristic approaches for quantifying the statistical significance of gene set enrichment. We created Nonparametric analytical-Rank-based Enrichment Analysis (NaRnEA) to facilitate accurate and robust gene set analysis with an optimal null model derived using the information theoretic Principle of Maximum Entropy. By measuring the differential activity of ~2500 transcriptional regulatory proteins based on the differential expression of each protein's transcriptional targets between primary tumors and normal tissue samples in three cohorts from The Cancer Genome Atlas (TCGA), we demonstrate that NaRnEA critically improves in two widely used gene set analysis methods: Gene Set Enrichment Analysis (GSEA) and analytical-Rank-based Enrichment Analysis (aREA). We show that the NaRnEA-inferred differential protein activity is significantly correlated with differential protein abundance inferred from independent, phenotype-matched mass spectrometry data in the Clinical Proteomic Tumor Analysis Consortium (CPTAC), confirming the statistical and biological accuracy of our approach. Additionally, our analysis crucially demonstrates that the sample-shuffling empirical null models leveraged by GSEA and aREA for gene set analysis are overly conservative, a shortcoming that is avoided by the newly developed Maximum Entropy analytical null model employed by NaRnEA.

Correlated depressive symptoms within seroconcordant, expectant partners living with HIV in Zambézia Province, Mozambique: a cross-sectional study.

Approximately 15% of people with HIV in sub-Saharan Africa have comorbid depression, which impacts treatment outcomes. We describe predictors of baseline depressive symptoms in 1079 female and 1079 male participants in a cluster-randomized trial in Zambézia Province, Mozambique from November 2017 to December 2020. We modeled each partners' depressive symptoms (Patient Health Questionnaire-9 [PHQ-9]) using proportional odds models adjusted for enrollment date, age, body mass index [BMI], partner's PHQ-9 score, district, relationship status, education, occupation, WHO HIV clinical stage, and antiretroviral therapy use history. A post hoc analysis assessed covariate-adjusted rank correlation between partner depressive symptoms. Females were younger than males (median 23 vs. 28 years) and more likely to report no education (20.7% vs. 7.9%). Approximately 10% screened positive for depression (PHQ-9 score ≥ 10). Partner depressive symptoms were predictive of higher participant PHQ-9 scores. A male partner PHQ-9 score of 10 (versus 5) increased the odds that the female partner would have a higher PHQ-9 score (adjusted odds ratio: 7.25, 95% Confidence Interval [CI]: 5.43-9.67). Partner PHQ-9 scores were highly correlated after covariate adjustment (Spearman's rho 0.65, 95% CI 0.57-0.72). Interventions aimed to reduce depressive symptoms and improve HIV-related outcomes during pregnancy should address both partners' depressive symptoms.

A flexible approach for predictive biomarker discovery.

An endeavor central to precision medicine is predictive biomarker discovery; they define patient subpopulations which stand to benefit most, or least, from a given treatment. The identification of these biomarkers is often the byproduct of the related but fundamentally different task of treatment rule estimation. Using treatment rule estimation methods to identify predictive biomarkers in clinical trials where the number of covariates exceeds the number of participants often results in high false discovery rates. The higher than expected number of false positives translates to wasted resources when conducting follow-up experiments for drug target identification and diagnostic assay development. Patient outcomes are in turn negatively affected. We propose a variable importance parameter for directly assessing the importance of potentially predictive biomarkers and develop a flexible nonparametric inference procedure for this estimand. We prove that our estimator is double robust and asymptotically linear under loose conditions in the data-generating process, permitting valid inference about the importance metric. The statistical guarantees of the method are verified in a thorough simulation study representative of randomized control trials with moderate and high-dimensional covariate vectors. Our procedure is then used to discover predictive biomarkers from among the tumor gene expression data of metastatic renal cell carcinoma patients enrolled in recently completed clinical trials. We find that our approach more readily discerns predictive from nonpredictive biomarkers than procedures whose primary purpose is treatment rule estimation. An open-source software implementation of the methodology, the uniCATE R package, is briefly introduced.

Relationship between sonorheometry parameters and laboratory values in a critical care setting in Italy: a retrospective cohort study.

Purpose: This preliminary retrospective cohort study analyzed the relationship between the parameters provided by sonorheometry device Quantra and the coagulation values obtained from standard venous blood samples in patients admitted in intensive care unit (ICU). Methods: We reviewed medical charts of 13 ICU adult patients in whom at least one coagulation study with Quantra was performed. The relationship between Quantra and laboratory data was analyzed with the Spearman rank correlation coefficient (rho). The 95% confidence interval (CI) was computed. A P-value <0.05 was considered statistically significant. Results: We collected 28 data pairs. Statistically significant moderate correlations were found for the following parameters: clot time (CT) and activated partial thromboplastin time (rho=0.516; 95% CI, 0.123-0.904; P=0.009; clot stiffness (CS) and the international normalized ratio (INR; rho=0.418; 95% CI, 0.042-0.787; P=0.039); INR and platelet contribution to CS (rho=0.459; 95% CI, 0.077-0.836; P=0.022); platelet count and platelet contribution to CS (PCS; rho=0.498; 95% CI, 0.166-0.825; P=0.008); and fibrinogen and fibrinogen contribution to CS (FCS; rho=0.620; 95% CI, 0.081-0.881; P=0.001). Conclusions: Quantra can provide useful information regarding coagulation status, showing modest correlations with the parameters obtained from laboratory tests. During diffuse bleeding, CT and FCS values can guide the proper administration of clotting factors and fibrinogens. However, the correlation of INR with CS and PCS can cause misinterpretation. Further studies are needed to clarify the relationship between Quantra parameters and laboratory tests in the critical care setting and the role of sonorheometry in guiding targeted therapies and improving outcomes.

The relationship between thermal sensation and cardiovascular patients' admission rates in Tabriz, Iran.

Atmospheric conditions in any place can affect people's health. In recent years, researchers have focused on heat stress and its effect on the exacerbation of some diseases. The main objective of this study is to identify the bioclimatic conditions and its relationship with the admission rate of cardiovascular patients in of Tabriz city. In addition to meteorological variables, daily cardiovascular patient admission rates were obtained from Shahid Madani Heart Hospital in Tabriz during the statistical period of March 27th, 2007 to February 17th, 2017. To do so, the bioclimatic conditions of Tabriz were identified on a daily scale based on bioclimatic indices including Perceived Temperature (PT), Physiological Equivalent Temperature (PET) and Predicted Mean Vote (PMV). Then, the relationship between each bioclimatic condition and the number of cardiovascular patients' referrals in Tabriz was investigated using Kruskal-Wallis test. Findings illustrated that the impact of cold stress in the rate of cardiovascular patients was more than that of the warm stress, which was obtained for all study indicators in a similar way. On the other hand, the results showed that based on PET and PMV indices, there is a significant difference between various bioclimatic classes in the rate of cardiovascular patients' admission. The results of Kruskal-Wallis test include Sig = 0.040 and Sig = 0.049 for PET and PMV, respectively. However, Sig values for and PT indice showed no significant difference between bioclimatic classes in the rate of admission of cardiovascular patients. Generally, it was found that there is a significant difference (Sig = 0.000) between the three classes of bioclimatic cold, warm and comfort with the number of hospital admissions of cardiovascular patients.