Category: Nevin Manimala

Biometrics. 2023 Mar 10. doi: 10.1111/biom.13853. Online ahead of print.

ABSTRACT

The Dirichlet-multinomial (DM) distribution plays a fundamental role in modern statistical methodology development and application. Recently, the DM distribution and its variants have been used extensively to model multivariate count data generated by high-throughput sequencing technology in omics research due to its ability to accommodate the compositional structure of the data as well as overdispersion. A major limitation of the DM distribution is that it is unable to handle excess zeros typically found in practice which may bias inference. To fill this gap, we propose a novel Bayesian zero-inflated DM model for multivariate compositional count data with excess zeros. We then extend our approach to regression settings and embed sparsity-inducing priors to perform variable selection for high-dimensional covariate spaces. Throughout, modeling decisions are made to boost scalability without sacrificing interpretability or imposing limiting assumptions. Extensive simulations and an application to a human gut microbiome data set are presented to compare the performance of the proposed method to existing approaches. We provide an accompanying R package with a user-friendly vignette to apply our method to other data sets. This article is protected by copyright. All rights reserved.

PMID:36896642 | DOI:10.1111/biom.13853

J Clin Endocrinol Metab. 2023 Mar 10:dgad108. doi: 10.1210/clinem/dgad108. Online ahead of print.

ABSTRACT

CONTEXT: Liver function abnormalities (LFA) have been described in patients with Turner Syndrome (TS). Although a high risk of cirrhosis has been reported, there is a need to assess the severity of liver damage in a large cohort of adult patients with TS.

OBJECTIVE: Evaluate the types of LFA and their respective prevalence, search for their risk factors and evaluate the severity of liver impairment by using a non-invasive fibrosis marker.

DESIGN AND SETTINGS: A monocentric retrospective cross-sectional study.

PATIENTS AND INTERVENTION: Data were collected during a day hospital.

MAIN OUTCOME MEASURES: Liver enzymes (ALT, AST, GGT, ALP), FIB-4 score, liver ultrasound imaging, elastography and liver biopsies, when available.

RESULTS: 264 patients with TS were evaluated at a mean age of 31.15 ± 11.48 years. The overall prevalence of LFA was 42.8%. Its risk factors were age, BMI, insulin resistance and an X isochromosome (Xq). The mean FIB-4 sore of the entire cohort was 0.67 ± 0.41. Less than 10% of patients were at risk of developing fibrosis. Cirrhosis was observed in 2/19 liver biopsies. There was no significant difference in the prevalence of LFA between premenopausal patients with natural cycles and those receiving hormone replacement therapy (HRT) (p = 0.063). A multivariate analysis adjusted for age showed no statistically significant correlation between HRT and abnormal GGT levels (p = 0.12).

CONCLUSION: Patients with TS have a high prevalence of LFA. However, 10% are at high risk of developing fibrosis. The FIB-4 score is useful and should be part of the routine screening strategy. Longitudinal studies and better interactions with hepatologists should improve our knowledge of liver disease in patients with TS.

PMID:36896592 | DOI:10.1210/clinem/dgad108

Ann Med. 2023 Dec;55(1):898-907. doi: 10.1080/07853890.2023.2184859.

ABSTRACT

BACKGROUND: The purpose of this study was to explore the changes in blood cellular and biochemical parameters of rats in a natural environment of low pressure and low oxygen on the plateau.

METHODS: Male Sprague-Dawley rats in two groups were raised in different environments from 4 weeks of age for a period of 24 weeks. They were raised to 28 weeks of age and then transported to the plateau medical laboratory of Qinghai University. Blood cellular and biochemical parameters were measured and the data of the two groups were statistically analyzed.

RESULTS: 1. RBC in the HA group was higher than that in the Control group, but there was no significant difference between the two groups (p > 0.05), Compared with the Control group, HGB, MCV, MCH, MCHC and RDW in the HA group were significantly higher (p < 0.05). 2. Compared with the Control group, WBC, LYMP, EO, LYMP% and EO% in the HA group decreased significantly (p < 0.05), and ANC% increased significantly (p < 0.05). 3. In the platelet index, compared with the Control group, PLT in the HA group was significantly reduced (p < 0.05), PDW, MRV, P-LCR were significantly increased (p < 0.05). 4. In blood biochemical indicators, compared with the Control group, AST, TBIL, IBIL, LDH in the HA group decreased significantly (p < 0.05), CK in the HA group increased significantly (p < 0.05).

CONCLUSIONS: 1. The indexes related to red blood cells, white blood cells, platelets and some biochemical indexes in the blood of rats at high altitude have changed. 2. Under the high altitude environment, the oxygen carrying capacity of SD rats is improved, the resistance to disease may be reduced, the coagulation and hemostasis functions may be affected, and there is a risk of bleeding. The liver function, renal function, heart function and skeletal muscle energy metabolism may be affected. 3. This study can provide an experimental basis for the research on the pathogenesis of high-altitude diseases from the perspective of blood.KEY MESSAGESIn this study, red blood cells, white blood cells, platelets and blood biochemical indicators were included in the real plateau environment to comprehensively analyze the changes of blood cellular and biochemical parameters in rats under the chronic plateau hypobaric hypoxia environment.From the perspective of blood, this study can provide an experimental basis for research on the pathogenesis of high-altitude diseases.Explore the data support of oxygen-carrying capacity, disease resistance and energy metabolism of the body in the natural environment at high altitude.

PMID:36896573 | DOI:10.1080/07853890.2023.2184859

Pediatrics. 2023 Mar 10:e2022056617. doi: 10.1542/peds.2022-056617. Online ahead of print.

ABSTRACT

OBJECTIVE: Home caregivers (eg parents) of pediatric patients with cancer with external central lines (CL) must carefully maintain this device to prevent complications. No guidelines exist to support caregiver skill development, assess CL competency, follow-up after initial CL teaching, and support progress over time. We aimed to achieve >90% caregiver independence with CL care within 1 year through a family-centered quality improvement intervention.

METHODS: Drivers to achieve CL care independence were identified using surveys and interviews of patient or caregivers, a multidisciplinary team with patient or family representatives, and piloting clinic return demonstrations (teach-backs). A family-centered CL care skill-learning curriculum, with a postdischarge teach-back program, was implemented using plan-do-study-act cycles. Patients or caregivers participated until independent with CL flushing. Changes included: language iterations to maximize patient or caregiver engagement, developing standardized tools for home use and for teaching and evaluating caregiver proficiency on the basis of number of nurse prompts required during the teach-back, earlier inpatient training, and clinic redesign to incorporate teach-backs into routine visits. The proportion of eligible patients whose caregiver had achieved independence in CL flushing was the outcome measure. Teach-back program participation was a process measure. Statistical process control charts tracked change over time.

RESULTS: After 6 months of quality improvement intervention, >90% of eligible patients had a caregiver achieve independence with CL care. This was sustained for 30 months postintervention. Eighty-eight percent of patients (n = 181) had a caregiver participate in the teach-back program.

CONCLUSION: A family-centered hands-on teach-back program can lead to caregiver independence in CL care.

PMID:36896569 | DOI:10.1542/peds.2022-056617

Math Biosci Eng. 2023 Jan 13;20(3):5949-5965. doi: 10.3934/mbe.2023257.

ABSTRACT

Utilizing statistical information from the Seventh National Population Census, statistical yearbook and sampling dynamic survey data, this study examines the distribution characteristics of the floating population in Beijing, Tianjin and Hebei Region as well as the growth trend of the floating population in each region. It also makes assessments using floating population concentration and The Moran Index Computing Methods. According to the study, the spatial distribution of the floating population has a clear clustering pattern in Beijing, Tianjin and Hebei region. Beijing, Tianjin and Hebei region’s mobile population growth patterns differ substantially, and the region’s inflow population is mostly made up of migrant inhabitants of domestic provinces and inflow of people from nearby regions. Most of the mobile population resides in Beijing and Tianjin, whereas the outflow of people originates in Hebei province. The diffusion impact and the spatial features of the floating population in the Beijing, Tianjin and Hebei area have a constant, positive association, according to the timeline between 2014 and 2020.

PMID:36896558 | DOI:10.3934/mbe.2023257

Math Biosci Eng. 2023 Jan 12;20(3):5379-5412. doi: 10.3934/mbe.2023249.

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide for over two years, with millions of reported cases and deaths. The deployment of mathematical modeling in the fight against COVID-19 has recorded tremendous success. However, most of these models target the epidemic phase of the disease. The development of safe and effective vaccines against SARS-CoV-2 brought hope of safe reopening of schools and businesses and return to pre-COVID normalcy, until mutant strains like the Delta and Omicron variants, which are more infectious, emerged. A few months into the pandemic, reports of the possibility of both vaccine- and infection-induced immunity waning emerged, thereby indicating that COVID-19 may be with us for longer than earlier thought. As a result, to better understand the dynamics of COVID-19, it is essential to study the disease with an endemic model. In this regard, we developed and analyzed an endemic model of COVID-19 that incorporates the waning of both vaccine- and infection-induced immunities using distributed delay equations. Our modeling framework assumes that the waning of both immunities occurs gradually over time at the population level. We derived a nonlinear ODE system from the distributed delay model and showed that the model could exhibit either a forward or backward bifurcation depending on the immunity waning rates. Having a backward bifurcation implies that $ R_c < 1 $ is not sufficient to guarantee disease eradication, and that the immunity waning rates are critical factors in eradicating COVID-19. Our numerical simulations show that vaccinating a high percentage of the population with a safe and moderately effective vaccine could help in eradicating COVID-19.

PMID:36896550 | DOI:10.3934/mbe.2023249

Math Biosci Eng. 2023 Jan 11;20(3):5352-5378. doi: 10.3934/mbe.2023248.

ABSTRACT

Penalized Cox regression can efficiently be used for the determination of biomarkers in high-dimensional genomic data related to disease prognosis. However, results of Penalized Cox regression is influenced by the heterogeneity of the samples who have different dependent structure between survival time and covariates from most individuals. These observations are called influential observations or outliers. A robust penalized Cox model (Reweighted Elastic Net-type maximum trimmed partial likelihood estimator, Rwt MTPL-EN) is proposed to improve the prediction accuracy and identify influential observations. A new algorithm AR-Cstep to solve Rwt MTPL-EN model is also proposed. This method has been validated by simulation study and application to glioma microarray expression data. When there were no outliers, the results of Rwt MTPL-EN were close to the Elastic Net (EN). When outliers existed, the results of EN were impacted by outliers. And whenever the censored rate was large or low, the robust Rwt MTPL-EN performed better than EN. and could resist the outliers in both predictors and response. In terms of outliers detection accuracy, Rwt MTPL-EN was much higher than EN. The outliers who “lived too long” made EN perform worse, but were accurately detected by Rwt MTPL-EN. Through the analysis of glioma gene expression data, most of the outliers identified by EN were those “failed too early”, but most of them were not obvious outliers according to risk estimated from omics data or clinical variables. Most of the outliers identified by Rwt MTPL-EN were those who “lived too long”, and most of them were obvious outliers according to risk estimated from omics data or clinical variables. Rwt MTPL-EN can be adopted to detect influential observations in high-dimensional survival data.

PMID:36896549 | DOI:10.3934/mbe.2023248

Math Biosci Eng. 2023 Jan 11;20(3):5333-5351. doi: 10.3934/mbe.2023247.

ABSTRACT

As COVID-19 continues to spread across the world and causes hundreds of millions of infections and millions of deaths, medical institutions around the world keep facing a crisis of medical runs and shortages of medical resources. In order to study how to effectively predict whether there are risks of death in patients, a variety of machine learning models have been used to learn and predict the clinical demographics and physiological indicators of COVID-19 patients in the United States of America. The results show that the random forest model has the best performance in predicting the risk of death in hospitalized patients with COVID-19, as the COVID-19 patients’ mean arterial pressures, ages, C-reactive protein tests’ values, values of blood urea nitrogen and their clinical troponin values are the most important implications for their risk of death. Healthcare organizations can use the random forest model to predict the risks of death based on data from patients admitted to a hospital due to COVID-19, or to stratify patients admitted to a hospital due to COVID-19 based on the five key factors this can optimize the diagnosis and treatment process by appropriately arranging ventilators, the intensive care unit and doctors, thus promoting the efficient use of limited medical resources during the COVID-19 pandemic. Healthcare organizations can also establish databases of patient physiological indicators and use similar strategies to deal with other pandemics that may occur in the future, as well as save more lives threatened by infectious diseases. Governments and people also need to take action to prevent possible future pandemics.

PMID:36896548 | DOI:10.3934/mbe.2023247

Math Biosci Eng. 2023 Jan 9;20(3):5194-5222. doi: 10.3934/mbe.2023241.

ABSTRACT

Healthcare is the method of keeping or enhancing physical and mental well-being with its aid of illness and injury prevention, diagnosis, and treatment. The majority of conventional healthcare practices involve manual management and upkeep of client demographic information, case histories, diagnoses, medications, invoicing, and drug stock upkeep, which can result in human errors that have an impact on clients. By linking all the essential parameter monitoring equipment through a network with a decision-support system, digital health management based on Internet of Things (IoT) eliminates human errors and aids the doctor in making more accurate and timely diagnoses. The term “Internet of Medical Things” (IoMT) refers to medical devices that have the ability to communicate data over a network without requiring human-to-human or human-to-computer interaction. Meanwhile, more effective monitoring gadgets have been made due to the technology advancements, and these devices can typically record a few physiological signals simultaneously, including the electrocardiogram (ECG) signal, the electroglottography (EGG) signal, the electroencephalogram (EEG) signal, and the electrooculogram (EOG) signal. Yet, there has not been much research on the connection between digital health management and multi-modal signal monitoring. To bridge the gap, this article reviews the latest advancements in digital health management using multi-modal signal monitoring. Specifically, three digital health processes, namely, lower-limb data collection, statistical analysis of lower-limb data, and lower-limb rehabilitation via digital health management, are covered in this article, with the aim to fully review the current application of digital health technology in lower-limb symptom recovery.

PMID:36896542 | DOI:10.3934/mbe.2023241

Math Biosci Eng. 2023 Jan 6;20(3):5043-5065. doi: 10.3934/mbe.2023234.

ABSTRACT

The influence of residents’ income on mental health is complex, and there are heterogeneous effects of residents’ income on different types of mental health. Based on the annual panel data of 55 countries from 2007 to 2019, this paper divides residents’ income into three dimensions: absolute income, relative income and income gap. Mental health is divided into three aspects: subjective well-being, prevalence of depression and prevalence of anxiety. Panel Tobit model is used to study the heterogeneous impact of residents’ income on mental health. The results show that, on the one hand, different dimensions of residents’ income have a heterogeneous impact on mental health, specifically, absolute income has a positive impact on mental health, while relative income and income gap have no significant impact on mental health. On the other hand, the impact of different dimensions of residents’ income on different types of mental health is heterogeneous. Specifically, absolute income and income gap have heterogeneous effects on different types of mental health, while relative income has no significant impact on different types of mental health.

PMID:36896535 | DOI:10.3934/mbe.2023234