Poincaré plots to analyze photoplethysmography signal between non-smokers and smokers

<span>An analysis of blood circulation was used to identify variations of heart rate and to create an early warning system of autonomic dysfunction. The Poincaré plot analyzed blood circulation using photoplethysmography (PPG) signals between non-smokers and smokers in three different indices: SD1, SD2, and SD1 SD2 ratio (SSR). There were twenty subjects separated into non-smoker and smoker groups with sample sizes of 10, respectively. An independent sample t-test to compare the continuous variables. Whereas, the comparison between two groups employed Fisher’s exact test for categorical variables. The result showed that SD1 was found to be considerably lower in the group of smokers (0.03±0.01) than that of the non-smokers (0.06±0.03). Similarly, SSR was recorded at 0.0012±0.0005 and 0.0023±0.0012 for smoking and non-smoking subjects, respectively. As a comparison, SD2 for non-smokers (25.7±0.5) was lower than smokers (27.3±0.4). In conclusion, we revealed that the parameters of Poincaré plots (SD1, SD2, and SSR) exert good performances to significantly differentiate the PPG signals of the group of non-smokers from those of smokers. We also supposed that the method promises to be a suitable method to distinguish the cardiovascular disease group. Therefore, this method can be applied as a part of early detection system of cardiovascular diseases.</span>

Examining proximity to death and health care expenditure by disease: a Bayesian-based descriptive statistical analysis from the National Health Insurance database in Japan

Background Japan is one of the Organization for Economic Co-operation and Development (OECD) countries where population aging and increasing health care expenditures (HCE) are urgent issues. Recent studies have identified factors other than age, such as proximity to death and morbidity, as contributing factors to the increase in medical costs. It is important to assess HCE by disease and analyze their factors to estimate and improve future HCE. Methods We extracted individual records spanning approximately 2 years prior to the death of persons aged 65 to 95 years from the National Health Insurance data in Japan, and used a Bayesian approach to decompose monthly HCE into five disease groups (circulatory, chronic kidney disease, neoplasms, respiratory, and others). The relationship between the proximity to death and the average HCE in each disease group was stratified by sex and age and analyzed using a descriptive statistical method similar to the two-part model. Results The average HCE increased rapidly as death approached in most disease groups, but the increase-pattern differed greatly among disease groups, sex, and age groups. The effect of proximity to death on average HCE was small for chronic diseases, but large for lethal diseases. When stratified by age and sex, younger and male decedents tended to have higher average HCE, but the extent of this varied by disease group. The two-year cumulative average HCE for neoplasms in the 65–75 years age group was about six times larger than those in the 85–95 years age group. Conclusions In Japan, it was suggested that disease, proximity to death, age, and sex may contribute to HCE. However, these factors interact in a complex manner, and it is important to analyze HCE by disease. In addition, preventing or delaying the severity of diseases with high medical burdens in younger people may be effective in reducing future terminal care costs. These findings have important implications for future projections and improvements of HCE.

Comparison of hematological parameters in mild and severe COVID-19 infected patients: A retrospective observational study

Introduction and Aim: With the coronavirus disease 2019 (COVID-19) pandemic raging on, there is a need to identify clinical and laboratory predictors which predict progression towards severe and fatal forms of this illness. Our study aims to evaluate the ability of hematologic and biochemical biomarkers to discriminate between patients with and without severe or fatal forms of COVID-19. Materials and Methods: A retrospective study was conducted on 200 Covid positive patients;100 with mild disease and 100 with severe disease. Medical records were reviewed to collect demographic data and results of the following blood investigations were noted at admission: Hb, Platelet count, Total and Differential leukocyte count, CRP, AST, ALT, LDH, Ferritin and D-Dimer. Comparative analysis was performed between the 2 groups. Results: A significant difference in the basophil count (mean 2.35 and 5.92) among those with mild and severe disease respectively was noted as also with the eosinophil count (mean 6.88 and 1.79). The levels of CRP were higher in those with severe disease as compared to the mild disease group (mean 276.29 and 65.23). Ferritin levels were markedly increased severe disease patients (mean 1275.66 and 533.94). D-dimer was markedly increased in COVID-19 patients with severe disease (mean 3813.91 ng/ml) compared to those with mild disease group (mean 521.78 ng/ml). Conclusion: Hematological and biochemical markers positively correlate to the severity of covid infection, thus highlighting their role in the early diagnosis of the disease and can act as independent markers in predicting severity and prognosis of disease.

Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin

Background: The etiology of fever of unknown origin (FUO) is complex and remains a major challenge for clinicians. This study aims to investigate the distribution of the etiology of classic FUO and the differences in clinical indicators in patients with different etiologies of classic FUO and to establish a machine learning (ML) model based on clinical data.Methods: The clinical data and final diagnosis results of 527 patients with classic FUO admitted to 7 medical institutions in Chongqing from January 2012 to August 2021 and who met the classic FUO diagnostic criteria were collected. Three hundred seventy-three patients with final diagnosis were divided into 4 groups according to 4 different etiological types of classical FUO, and statistical analysis was carried out to screen out the indicators with statistical differences under different etiological types. On the basis of these indicators, five kinds of ML models, i.e., random forest (RF), support vector machine (SVM), Light Gradient Boosting Machine (LightGBM), artificial neural network (ANN), and naive Bayes (NB) models, were used to evaluate all datasets using 5-fold cross-validation, and the performance of the models were evaluated using micro-F1 scores.Results: The 373 patients were divided into the infectious disease group (n = 277), non-infectious inflammatory disease group (n = 51), neoplastic disease group (n = 31), and other diseases group (n = 14) according to 4 different etiological types. Another 154 patients were classified as undetermined group because the cause of fever was still unclear at discharge. There were significant differences in gender, age, and 18 other indicators among the four groups of patients with classic FUO with different etiological types (P &lt; 0.05). The micro-F1 score for LightGBM was 75.8%, which was higher than that for the other four ML models, and the LightGBM prediction model had the best performance.Conclusions: Infectious diseases are still the main etiological type of classic FUO. Based on 18 statistically significant clinical indicators such as gender and age, we constructed and evaluated five ML models. LightGBM model has a good effect on predicting the etiological type of classic FUO, which will play a good auxiliary decision-making function.

Nephroprotective Activity of Ethanolic Extract of Carissa carandas Leaves against Gentamicin-Induced Acute Kidney Injury in Wistar Albino Rats

The present study was aimed to evaluate the nephroprotective activity of ethanolic extract of Carissa carandas Linn. Leaves (EECC) in Gentamicin-induced nephrotoxicity in Wistar albino rats. The renal damage was induced by Gentamicin (80mg/kg body weight, i.p.). Nephroprotective activity was investigated by the administration of EECC at two different doses (100 and 200mg/kg body weight, p.o) for 28 days and by assessing serum parameters, renal oxidative stress markers and histopathological studies. Gentamicin-treated animals showed an increase in serum creatinine, uric acid, urea, and malondialdehyde (MDA) levels and decrease in total protein, reduced glutathione (GSH), and catalase(CAT) compared to normal control animals, which indicates severe nephrotoxicity. Histopathological studies of kidney Gentamicin-treated animals showed extensive acute tubular necrosis and peri-tubular inflammation. Administration of EECC showed a significant improvement (p<0.05) in biochemical and oxidative stress markers compared to the disease group. EECC treated groups showed better histological appearance when compared to the disease group. Ethanolic extract of Carissa carandas Linn. Leaves showed significant nephroprotective activity against gentamicin-induced acute kidney injury.

The relationship between arterial stiffness index and coronary heart disease and its severity

Background Arterial stiffness index (ASI) is closely related to coronary atherosclerosis. This study aims to explore whether ASI can predict coronary heart disease (CHD) and its severity. Methods In this study, a total of 726 patients with suspected CHD were recruited. Based on coronary angiography results, the subjects were assigned into three groups: the control group (without obvious coronary artery disease), single-vessel disease group, and multi-vessel disease group (the number of vessels diseased ≥ 2). At the same time, according to the results of angiography, myocardial enzyme spectrum, electrocardiogram, color Doppler echocardiography and clinical manifestations, these patients were divided into four groups: the control group, stable angina (SA) Group, unstable angina (UA) group, and acute myocardial infarction (AMI) group. We have compared whether there were differences in ASI and related baseline data between groups. Receiver operating curve (ROC) analysis was conducted to determine whether ASI could predict CHD and evaluate the severity. Results ASI was positively correlated with the number of diseased branches of coronary artery. The value of ASI was increased as the number of the diseased branches increased. The ASI value in the SA group was significantly higher compared with the control group. Furthermore, the ASI value in the UA and AMI groups was remarkably increased compared with the control and SA groups. The results of ROC analysis indicated that the sensitivity and specificity of ASI was 71.0% and 85.4% in diagnosing CHD, respectively. While ASI was used in predicting the severity of CHD, the sensitivity was 72.1% and specificity 57.9%. Conclusion ASI is of great value in the diagnosis of coronary heart disease and the prediction of its severity.

A pilot study evaluating the Calibrated Automated Thrombogram assay and application of plasma-thromboelastography for detection of hemostatic aberrations in horses with gastrointestinal disease

Background Critically ill horses, such as horses with gastrointestinal (GI) disease, often suffer from hemostatic aberrations. Global hemostatic tests examining the initiation of coagulation, clot strength and fibrinolysis, such as the Calibrated Automated Thrombogram (CAT) and plasma-thromboelastography (TEG) have not been evaluated in horses. This study aimed to evaluate CAT and apply plasma-TEG in horses. Test performance of CAT was evaluated on equine platelet poor plasma with intra- and inter-assay variability (CV) and a heparin dilution curve. To examine clinical performance of both tests, group comparisons were assessed comparing healthy horses, horses with mild and severe GI disease with both CAT and plasma-TEG. Results For CAT, intra- and inter-assay CVs were established for lag-time (1.7, 4.7%), endogenous thrombin potential (1.6, 4.6%), peak (2.6, 3.9%) and time to peak (ttPeak) (1.9, 3.4%). Increasing heparin concentrations led to the expected decrease in thrombin generation. In the group comparison analysis, CAT showed significant higher peak (p = 0.04) and ttPeak (p = 0.008) in the severe GI disease group compared to horses with mild GI disease and healthy horses, respectively. Plasma-TEG showed an increased angle (p = 0.032), maximum amplitude (p = 0.017) and shear elastic force (G) (p = 0.017) in the severe GI disease group compared to healthy horses. Conclusions CAT performed well in horses. Both CAT and plasma-TEG identified hemostatic aberrations in horses with severe GI disease compared to healthy horses. Further studies including more horses, are needed to fully appreciate the use of CAT and plasma-TEG in this species.

ALK+ histiocytosis: a new clinicopathologic spectrum highlighting neurologic involvement and responses to ALK inhibition

ALK-related histiocytosis (formerly ALK-positive histiocytosis) is a rare subtype of histiocytic neoplasm first described in 2008 in three infants with multisystemic disease involving the liver and hematopoietic system. This entity has subsequently been documented in case reports and series to occupy a wider clinicopathologic spectrum with recurrent KIF5B-ALK fusions. The full clinicopathologic and molecular spectra of ALK-related histiocytosis remain, however, poorly characterized. Here, we describe the largest study of ALK-related histiocytosis to date, with detailed clinicopathologic data of 39 cases, including 37 cases with confirmed ALK rearrangements. The clinical spectrum comprised distinct clinical phenotypic groups: infants with multisystemic disease with liver and hematopoietic involvement, as originally described (Group 1A: 6/39), other patients with multisystemic disease (Group 1B: 10/39), and patients with single-system disease (Group 2: 23/39). Nineteen patients of the entire cohort (49%) had neurologic involvement (seven and twelve from Groups 1B and 2, respectively). Histology included classic xanthogranuloma features in almost one third of cases, whereas the majority displayed a more densely cellular, monomorphic appearance without lipidized histiocytes but sometimes more spindled or epithelioid morphology. Neoplastic histiocytes were positive for macrophage markers and ALK, and often conferred strong expression of phosphorylated-ERK, confirming MAPK pathway activation. KIF5B-ALK fusions were detected in 27 patients, while CLTC-ALK, TPM3-ALK, TFG-ALK, EML4-ALK and DCTN1-ALK fusions were identified in single cases. Robust and durable responses were observed in 11/11 patients treated with ALK inhibition, ten with neurologic involvement. This study presents the existing clinicopathologic and molecular landscape of ALK-related histiocytosis, and provides guidance for the clinical management of this emerging histiocytic entity.

The Effect of the APOE-ε4 Allele on the Cholinergic Circuitry for Subjects With Different Levels of Cognitive Impairment

Background: Cholinergic deficiency has been suggested to associate with the abnormal accumulation of Aβ and tau for patients with Alzheimer's disease (AD). However, no studies have investigated the effect of APOE-ε4 and group differences in modulating the cholinergic basal forebrain–amygdala network for subjects with different levels of cognitive impairment. We evaluated the effect of APOE-ε4 on the cholinergic structural association and the neurocognitive performance for subjects with different levels of cognitive impairment.Methods: We used the structural brain magnetic resonance imaging scans from the Alzheimer's Disease Neuroimaging Initiative dataset. The study included cognitively normal (CN, n = 167) subjects and subjects with significant memory concern (SMC, n = 96), early mild cognitive impairment (EMCI, n = 146), late cognitive impairment (LMCI, n = 138), and AD (n = 121). Subjects were further categorized according to the APOE-ε4 allele carrier status. The main effects of APOE-ε4 and group difference on the brain volumetric measurements were assessed. Regression analyses were conducted to evaluate the associations among cholinergic structural changes, APOE-ε4 status, and cognitive performance.Results: We found that APOE-ε4 carriers in the disease group showed higher brain atrophy than non-carriers in the cholinergic pathway, while there is no difference between carriers and non-carriers in the CN group. APOE-ε4 allele carriers in the disease groups also exhibited a stronger cholinergic structural correlation than non-carriers did, while there is no difference between the carriers and non-carriers in the CN subjects. Disease subjects exhibited a stronger structural correlation in the cholinergic pathway than CN subjects did. Moreover, APOE-ε4 allele carriers in the disease group exhibited a stronger correlation between the volumetric changes and cognitive performance than non-carriers did, while there is no difference between carriers and non-carriers in CN subjects. Disease subjects exhibited a stronger correlation between the volumetric changes and cognitive performance than CN subjects did.Conclusion: Our results confirmed the effect of APOE-ε4 on and group differences in the associations with the cholinergic structural changes that may reflect impaired brain function underlying neurocognitive degeneration in AD.

A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations

Background Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links. Results In this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites “robustly well-predicted”. To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite’s level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis. Conclusions Combined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies.