The study was conducted in order to study breakfast skipping (BKS) frequency, factors associated with, health consequence and undergraduate students academic performance during Covid-19 pandemic as earliest studies focusing on this area. A cross-sectional study was carried out among 2225 of undergraduate students. The study was carried between the period of 15/1/2020 to 3/4/2020 using an online self-report Breakfast Eating Habit Survey (BEHS). The BEHS survey was divided into two sections. The first sections included sociodemographic information (gender, BMI, age, smoking, residency, parental education, family income, studying system and stage (public or private), and studying institution (university or institute) academic performance. The second part included questions regarding breakfast eating habits including frequency of skipping meals, factors related to BKS health consequences and types of snacks. Logistic regression is a common technique used for modeling outcomes that fall into the range of 1 and 0. For this purpose, a logistic regression was performed to find adjusted odds ratio and crude odds ratio. The results showed that the majority of participants were female (1238, 55.7%). Out of 2,224 students, 2059 are aged between 18 to 24 years. Most of the participants were from first level (26.5%), second level (32.8%), third level (17.6%) or the fourth level (21.3%). Over 92% of participants were single and about 68% came from families of medium income families. The statistical analysis showed that the odds of BKS is reduced among students who live in accommodation by 54% (odds ratio = 54%, CI (41-71%), p value = 0.000). It seems that students with low income and normal or higher BMI are more likely to skip breakfast more regularly. The odds of skipping breakfast among students with BMI of 18-24.9 is reduced by 41% (odds ratio = 59%, CI (27%-93%), p value = 0.027) and the odds of BKS is reduced among students with BMI of 25-29.9 by 45% (odds ratio = 55%, CI (31-95%). Additionally, students with medium or high incomes are more likely to skip breakfast as much as twofold in comparison with students with low income (medium income (odds ratio = 1.85, CI (1.08-3.17), p-value = 0.024), high income (odds ratio = 1.98, CI (1.12-3.51), p-value = 0.019). The most common reasons for skipping breakfast included include time constraint, not hungry, breakfast is not ready, afraid to be overweight and lack of appetite. The consequences of skipping breakfast were feeling hungry throughout the day, feeling tired, and not paying attention in class and low academic performance. To concluded, BKS during Covid-19 is more common among students with higher BMI, higher income and living in accommodation. The main reason is time constraint and the most common health problems are being tired and luck of attention.

UNASSIGNED: African swine fever (ASF) is a disease with a high mortality rate and high transmissibility. Identifying high-risk clusters and understanding the transmission characteristics of ASF in advance are essential for preventing its spread in a short period of time. This study investigated the spatial and temporal heterogeneity of ASF in the Republic of Korea by analyzing surveillance data on wild boar carcasses.
UNASSIGNED: We observed a distinct annual propagation pattern, with the occurrence of ASF-infected carcasses trending southward over time. We developed a rank-based statistical model to evaluate risk by estimating the average weekly number of carcasses per district over time, allowing us to analyze and identify risk clusters of ASF. We conducted an analysis to identify risk clusters for two distinct periods, Late 2022 and Early 2023, utilizing data from ASF-infected carcasses. To address the underestimation of risk and observation error due to incomplete surveillance data, we estimated the number of ASF-infected individuals and accounted for observation error via different surveillance intensities.
UNASSIGNED: As a result, in Late 2022, the risk clusters identified by observed and estimated number of ASF-infected carcasses were almost identical, particularly in the northwestern Gyeongbuk region, north Chungbuk region, and southwestern Gangwon region. In Early 2023, we observed a similar pattern with numerous risk clusters identified in the same regions as in Late 2022.
UNASSIGNED: This approach enhances our understanding of ASF spatial dynamics. Additionally, it contributes to the epidemiology and study of animal infectious diseases by highlighting areas requiring urgent and focused intervention. By providing crucial data for the targeted allocation of resources for disease management and preventive measures, our findings lay vital groundwork for improving ASF management strategies, ultimately aiding in the containment and control of this devastating disease.

Radiation therapy (RT) is a frontline approach to treating cancer. While the target of radiation dose delivery is the tumor, there is an inevitable spill of dose to nearby normal organs causing complications. This phenomenon is known as radiotherapy toxicity. To predict the outcome of the toxicity, statistical models can be built based on dosimetric variables received by the normal organ at risk (OAR), known as Normal Tissue Complication Probability (NTCP) models. To tackle the challenge of the high dimensionality of dosimetric variables and limited clinical sample sizes, statistical models with variable selection techniques are viable choices. However, existing variable selection techniques are data-driven and do not integrate medical domain knowledge into the model formulation. We propose a knowledge-constrained generalized linear model (KC-GLM). KC-GLM includes a new mathematical formulation to translate three pieces of domain knowledge into non-negativity, monotonicity, and adjacent similarity constraints on the model coefficients. We further propose an equivalent transformation of the KC-GLM formulation, which makes it possible to solve the model coefficients using existing optimization solvers. Furthermore, we compare KC-GLM and several well-known variable selection techniques via a simulation study and on two real datasets of prostate cancer and lung cancer, respectively. These experiments show that KC-GLM selects variables with better interpretability, avoids producing counter-intuitive and misleading results, and has better prediction accuracy.

Fe3O4 nanoparticles were embedded within a glauconite‑calcium alginate (G/CA) matrix to create magnetic hybrid spheres (MNPs-G/CA), with the aim of purifying water from methylene blue (MB) at temperatures of 25, 40, and 50 °C. MNPs-G/CA adsorbent was characterized using numerous techniques, including elemental mapping, zeta potential, FTIR, FESEM, XRD, EDX, and TEM. The greatest amount of the removed MB was achieved under definite conditions of solution pH 8.0, MNPs-G/CA mass (25 mg), interaction time (2 h), and 200 mg/L of MB concentration. The MB uptake process kinetic followed a pseudo-second-order equation (R2 > 0.99) at all tested temperatures. The equilibrium data were fitted to a statistical physics multilayer model in conjunction with the Langmuir and Freundlich equations. The steric n parameter reveals that MNPs-G/CA adsorbent possesses a mixed adsorption orientation (i.e., ranging from 0.69 to 0.93) across various temperatures. The amount of MNPs-G/CA active positions (the NM parameter) was progressively increased from 245 mg/g to 419 mg/g. The measured adsorption capacities (Qsat) ranged from 466.49 to 664.37 mg/g, and the removal of MB molecules was consistent with an endothermic interaction. The interface between the MNPs-G/CA-MB was principally dictated by electrostatic attractions, as evidenced by the values of adsorption energies (∆E), which varied from 16.75 to 21.52 kJ/mol. The regenerated MNPs-G/CA offered over 80 % of its adsorption strength after the fourth adsorption-desorption cycle. This study contributes to our understanding of the physicochemical parameters controlling the MB adsorption mechanism on multifunctional hybrid adsorbents, like the interface between glauconite, alginate, and MNPs.

Causal inference is inherently complex, often dependent on key assumptions that are sometimes overlooked. One such assumption is the potential for unidirectional or bidirectional causality, while another is population homogeneity, which suggests that the causal direction between two variables remains consistent across the study sample. Discerning these processes requires meticulous data collection through an appropriate research design and the use of suitable software to define and fit alternative models. In psychiatry, the co-occurrence of different disorders is common and can stem from various origins. A patient diagnosed with two disorders might have one recognized as primary and the other as secondary, suggesting the existence of two types of comorbidity within the population. For example, in some individuals, depression might lead to substance use, while in others, substance use could lead to depression. Identifying the primary disorder is crucial for developing effective treatment plans. This article explores the use of finite mixture models to depict within-sample heterogeneity. We begin with the Direction of Causation (DoC) model for twin data and extend it to a mixture distribution model. This extension allows for the calculation of the likelihood of each individual\'s data for the two alternate causal directions. Given twin data, there are four possible pairwise combinations of causal direction. Through simulations, we investigate the Direction of Causation Twin Mixture (mixCLPM) model\'s potential to detect and model heterogeneity due to varying causal directions.

In oncology drug development, tumor dynamics modeling is widely applied to predict patients\' overall survival (OS) via parametric models. However, the current modeling paradigm, which assumes a disease-specific link between tumor dynamics and survival, has its limitations. This is particularly evident in drug development scenarios where the clinical trial under consideration contains patients with tumor types for which there is little to no prior institutional data. In this work, we propose the use of a pan-indication solid tumor machine learning (ML) approach whereby all three tumor metrics (tumor shrinkage rate, tumor regrowth rate and time to tumor growth) are simultaneously used to predict patients\' OS in a tumor type independent manner. We demonstrate the utility of this approach in a clinical trial of cancer patients treated with the tyrosine kinase inhibitor, pralsetinib. We compared the parametric and ML models and the results showed that the proposed ML approach is able to adequately predict patient OS across RET-altered solid tumors, including non-small cell lung cancer, medullary thyroid cancer as well as other solid tumors. While the findings of this study are promising, further research is needed for evaluating the generalizability of the ML model to other solid tumor types.

Important foraging and nesting habitats for Caribbean green sea turtles (Chelonia mydas) exist within the Mesoamerican Reef System in the Mexican Caribbean. During the last 25 years, urban development and touristic activities have drastically increased in Quintana Roo, Mexico. Moreover, in the last decade, massive pelagic sargasso blooms have also afflicted this region; however, information about the biochemical responses of Caribbean green turtles to these inputs is absent. This study aimed to assess if the oxidative stress indicators in the red blood cells of green turtles are valuable biomarkers of the extent of the anthropic impact in this region. Persistent organic pollutants (POPs) were also measured in the plasma of free-living green turtles during 2015-2018 to characterize these habitats further. As biochemical biomarkers, the production rate of superoxide radical (O2•-), carbonylated protein content, and lipid peroxidation (TBARS) levels, and the activities of superoxide dismutase, glutathione S-transferase (GST), catalase, glutathione peroxidase were measured in erythrocytes. A 15 % occurrence of fibropapillomatosis (FP) was revealed, with tumor size being positively correlated with CAT activity in the affected individuals. A multivariate analysis embracing all oxidative stress markers discriminated green turtles between years of capture (p < 0.001), with those sampled during 2015 presenting the highest production of O2•- (p = 0.001), activities of GST (p < 0.001), levels of TBARS (p < 0.001) and carbonylated proteins (p = 0.02). These local and temporal biochemical responses coincided with the first massive Sargassum spp. bloom reported in the region. The results of this study corroborate the utility of the oxidative stress indicators as biomarkers of environmental conditions (sargasso blooms and POPs) in the green turtle as sentinel species.

In aging, physiologic networks decline in function at rates that differ between individuals, producing a wide distribution of lifespan. Though 70% of human lifespan variance remains unexplained by heritable factors, little is known about the intrinsic sources of physiologic heterogeneity in aging. To understand how complex physiologic networks generate lifespan variation, new methods are needed. Here, we present Asynch-seq, an approach that uses gene-expression heterogeneity within isogenic populations to study the processes generating lifespan variation. By collecting thousands of single-individual transcriptomes, we capture the Caenorhabditis elegans \"pan-transcriptome\"-a highly resolved atlas of non-genetic variation. We use our atlas to guide a large-scale perturbation screen that identifies the decoupling of total mRNA content between germline and soma as the largest source of physiologic heterogeneity in aging, driven by pleiotropic genes whose knockdown dramatically reduces lifespan variance. Our work demonstrates how systematic mapping of physiologic heterogeneity can be applied to reduce inter-individual disparities in aging.

UNASSIGNED: Two randomized trials (SPCG4 and PIVOT) have compared surgery to conservative management for localized prostate cancer. The applicability of these trials to contemporary practice remains uncertain. We aimed to develop an individualized prediction model for prostate cancer mortality comparing immediate surgery at a high-volume center to active surveillance.
UNASSIGNED: We determined whether the relative risk of prostate cancer mortality with surgery vs observation varied by baseline risk. We then used various estimates of relative risk to estimate 15-year mortality with and without surgery using, as a predictor, risk of biochemical recurrence calculated from a model.
UNASSIGNED: We saw no evidence that relative risk varied by baseline risk, supporting the use of a constant relative risk. Compared with observation, surgery was associated with negligible benefit for patients with Grade Group (GG) 1 disease (0.2% mortality reduction at 15 years) and small benefit for patients with GG2 with lower PSA and stage (≤5% mortality reduction). Benefit was greater (6%-9%) for patients with GG3 or GG4 though still modest, but effect estimates varied widely depending on choice of hazard ratio for surgery (6%-36% absolute risk reduction).
UNASSIGNED: Surgery should be avoided for men with low-risk (GG1) prostate cancer and for many men with GG2 disease. Surgical benefits are greater in men with higher-risk disease. Integration of findings with a life expectancy model will allow patients to make informed treatment decisions given their oncologic risk, risk of death from other causes, and estimated effects of surgery.

Virtual control groups (VCGs) created from historical control data (HCD) can reduce the number of concurrent control group animals needed in regulatory toxicity studies by up to 25%. This study investigates the performance of VCGs on statistical outcomes of body weight development between treatment and control groups in legacy studies. The objective is to reproduce the statistical outcomes of 28-day sub-chronic studies (legacy studies) after replacing the concurrent control group with virtual ones. In rodent toxicity studies initial body weight is used as surrogate for the age of animals. For the assessment of VCG-sampling methods three different approaches are explored: (i) sampling VCGs from the entire HCD ignoring initial body weight information of the legacy study, (ii) sampling from HCD matching the legacy study\'s initial body weights, and (iii) sampling from HCD with assigned statistical weights derived from legacy study initial body weight information. It is shown that the ability to reproduce statistical outcomes by virtual controls is mainly determined by the congruence between the legacy study and the HCD weight distribution: regardless of the chosen approach, the ability to reproduce statistical outcomes was well for VCGs when the legacy study\'s initial-body-weight distribution was similar to the HCD\'s. When the initial body weight range of the legacy study was at the extreme ends of the HCD\'s distribution, the weighted-sampling approach was superior. This article highlights the importance of proper HCD-matching by the legacy study\'s initial body weight and discusses required conditions to accurately reproduce body weight development.
Animal control data from past studies performed in a standardized manner can be used to create virtual control groups (VCGs) to use in new studies instead of control animals. This approach can reduce the number of study animals by up to 25%. This study assesses the performance of VCGs selected by body weight in rat studies. The objective was to reproduce the original study results as closely as possible after replacing the original control group values with VCGs from a pool of historical control values. Several methods for selecting control animal data to create VCGs were compared. Among these, assigning statistical weights to the sampling pool yielded the best performance. Ideally the body weight distributions on day 1 of the study should be similar between the VCG and the original study animals. This article shows that proper selection VCGs can yield reliable study data with fewer animals.