Examining psychedelic-induced changes in social functioning and connectedness in a naturalistic online sample using the five-factor model of personality.

The present study examines prospective changes in personality traits relevant to social functioning as well as perceived social connectedness in relation to the naturalistic use of psychedelic compounds in an online volunteer sample. The study also examined the degree to which demographic characteristics, social setting, baseline personality, and acute subjective factors (e.g., emotional breakthrough experiences) influenced trajectories of personality and perceived social connectedness. Participants recruited online completed self-report measures of personality and social connectedness at three timepoints (baseline, 2weeks post-experience, 4weeks post-experience). Linear mixed models were used to examine changes in outcomes and the moderation of these outcomes by covariates. The most substantive changes were reductions in the personality domains Neuroticism, and increases in Agreeableness and social connectedness. Notably, reductions in Neuroticism and increases in Agreeableness covaried over time, which may be suggestive of common processes involving emotion regulation. Preliminary evidence was found for a specific effect on a component of Agreeableness involving a critical and quarrelsome interpersonal style. Although moderation by demographic characteristics, social setting, baseline personality, and acute factors generally found limited support, baseline standing on Neuroticism, perspective taking, and social connectedness showed tentative signs of amplifying adaptive effects on each trait, respectively. Our findings hold implications for the potential use of psychedelics for treating interpersonal elements of personality pathology as well as loneliness

A semi-Markov model for stroke with piecewise-constant hazards in the presence of left, right and interval censoring.

This paper presents a parametric method of fitting semi-Markov models with piecewise-constant hazards in the presence of left, right and interval censoring. We investigate transition intensities in a three-state illness-death model with no recovery. We relax the Markov assumption by adjusting the intensity for the transition from state 2 (illness) to state 3 (death) for the time spent in state 2 through a time-varying covariate. This involves the exact time of the transition from state 1 (healthy) to state 2. When the data are subject to left or interval censoring, this time is unknown. In the estimation of the likelihood, we take into account interval censoring by integrating out all possible times for the transition from state 1 to state 2. For left censoring, we use an Expectation-Maximisation inspired algorithm. A simulation study reflects the performance of the method. The proposed combination of statistical procedures provides great flexibility. We illustrate the method in an application by using data on stroke onset for the older population from the UK Medical Research Council Cognitive Function and Ageing Study

Prediction of the in situ coronal mass ejection rate for solar cycle 25: Implications for parker solar probe in situ observations

The Parker Solar Probe (PSP) and Solar Orbiter missions are designed to make groundbreaking observations of the Sun and interplanetary space within this decade. We show that a particularly interesting in situ observation of an interplanetary coronal mass ejection (ICME) by PSP may arise during close solar flybys (<0.1 au). During these times, the same magnetic flux rope inside an ICME could be observed in situ by PSP twice, by impacting its frontal part as well as its leg. Investigating the odds of this situation, we forecast the ICME rate in solar cycle 25 based on two models for the sunspot number (SSN): (1) the forecast of an expert panel in 2019 (maximum SSN = 115), and (2) a prediction by McIntosh et al. (2020, maximum SSN = 232). We link the SSN to the observed ICME rates in solar cycles 23 and 24 with the Richardson and Cane list and our own ICME catalog, and calculate that between one and seven ICMEs will be observed by PSP at heliocentric distances <0.1 au until 2025, including 1σ uncertainties. We then model the potential flux rope signatures of such a double-crossing event with the semiempirical 3DCORE flux rope model, showing a telltale elevation of the radial magnetic field component BR, and a sign reversal in the component BN normal to the solar equator compared to field rotation in the first encounter. This holds considerable promise to determine the structure of CMEs close to their origin in the solar corona

An assessment of pulse transit time for detecting heavy blood loss during surgical operation

Copyright @ Wang et al.; Licensee Bentham Open. This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.The main contribution of this paper is the use of non-invasive measurements such as electrocardiogram (ECG) and photoplethysmographic (PPG) pulse oximetry waveforms to develop a new physiological signal analysis technique for detecting blood loss during surgical operation. Urological surgery cases were considered as the control group due to its generality, and cardiac surgery as experimental group since it involves blood loss and water supply. Results show that the control group has the tendency of a reduction of the pulse transient time (PTT), and this indicates an increment in the blood flow velocity changes from slow to fast. While for the experimental group, the PTT indicates high values during blood loss, and low values during water supply. Statistical analysis shows considerable differences (i.e., P <0.05) between both groups leading to the conclusion that PTT could be a good indicator for monitoring patients' blood loss during a surgical operation.The National Science Council (NSC) of Taiwan and the Centre for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan

Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet

Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed visually employing clinical screening, a biopsy, dermoscopic analysis, and histopathological examination. It has been demonstrated that the dermoscopic analysis in the hands of inexperienced dermatologists may cause a reduction in diagnostic accuracy. Early detection and screening of skin cancer have the potential to reduce mortality and morbidity. Previous studies have shown Deep Learning ability to perform better than human experts in several visual recognition tasks. In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists. We used a pretrained MobileNet model to train over HAM10000 dataset using transfer learning. The model classifies skin lesion image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36 percent and top3 accuracy of 95.34 percent. The weighted average of precision, recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The model has been deployed as a web application for public use at (https://saketchaturvedi.github.io). This fast, expansible method holds the potential for substantial clinical impact, including broadening the scope of primary care practice and augmenting clinical decision-making for dermatology specialists.Comment: This is a pre-copyedited version of a contribution published in Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R., Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The definitive authentication version is available online via https://doi.org/10.1007/978-981-15-3383-9_1

A statistical model for the identification of genes governing the incidence of cancer with age

The cancer incidence increases with age. This epidemiological pattern of cancer incidence can be attributed to molecular and cellular processes of individual subjects. Also, the incidence of cancer with ages can be controlled by genes. Here we present a dynamic statistical model for explaining the epidemiological pattern of cancer incidence based on individual genes that regulate cancer formation and progression. We incorporate the mathematical equations of age-specific cancer incidence into a framework for functional mapping aimed at identifying quantitative trait loci (QTLs) for dynamic changes of a complex trait. The mathematical parameters that specify differences in the curve of cancer incidence among QTL genotypes are estimated within the context of maximum likelihood. The model provides testable quantitative hypotheses about the initiation and duration of genetic expression for QTLs involved in cancer progression. Computer simulation was used to examine the statistical behavior of the model. The model can be used as a tool for explaining the epidemiological pattern of cancer incidence

Effect of Body Mass Index on pregnancy outcomes in nulliparous women delivering singleton babies

Peer reviewedPublisher PD

Health literacy, health status, and healthcare utilization of Taiwanese adults: results from a national survey

Abstract Background Low health literacy is considered a worldwide health threat. The purpose of this study is to assess the prevalence and socio-demographic covariates of low health literacy in Taiwanese adults and to investigate the relationships between health literacy and health status and health care utilization. Methods A national survey of 1493 adults was conducted in 2008. Health literacy was measured using the Mandarin Health Literacy Scale. Health status was measured based on self-rated physical and mental health. Health care utilization was measured based on self-reported outpatient clinic visits, emergency room visits, and hospitalizations. Results Approximately thirty percent of adults were found to have low (inadequate or marginal) health literacy. They tended to be older, have fewer years of schooling, lower household income, and reside in less populated areas. Inadequate health literacy was associated with poorer mental health (OR, 0.57; 95% CI, 0.35-0.91). No association was found between health literacy and health care utilization even after adjusting for other covariates. Conclusions Low (inadequate and marginal) health literacy is prevalent in Taiwan. High prevalence of low health literacy is not necessarily indicative of the need for interventions. Systematic efforts to evaluate the impact of low health literacy on health outcomes in other countries would help to illuminate features of health care delivery and financing systems that may mitigate the adverse health effects of low health literacy.http://deepblue.lib.umich.edu/bitstream/2027.42/78252/1/1471-2458-10-614.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78252/2/1471-2458-10-614.pdfPeer Reviewe

Validation of Self-Reported Health Literacy Questions Among Diverse English and Spanish-Speaking Populations

BackgroundLimited health literacy (HL) contributes to poor health outcomes and disparities, and direct measurement is often time-intensive. Self-reported HL questions have not been validated among Spanish-speaking and diverse English-speaking populations.ObjectiveTo evaluate three self-reported questions: 1 "How confident are you filling out medical forms?"; 2 "How often do you have problems learning about your medical condition because of difficulty understanding written information?"; and 3 "How often do you have someone help you read hospital materials?" Answers were based on a 5-point Likert scale.DesignThis was a validation study nested within a trial of diabetes self-management support in the San Francisco Department of Public Health.ParticipantsEnglish and Spanish-speaking adults with type 2 diabetes receiving primary care.MethodsUsing the Test of Functional Health Literacy in Adults (s-TOFHLA) in English and Spanish as the reference, we classified HL as inadequate, marginal, or adequate. We calculated the C-index and test characteristics of the three questions and summative scale compared to the s-TOFHLA and assessed variations in performance by language, race/ethnicity, age, and education.Key resultsOf 296 participants, 48% were Spanish-speaking; 9% were White, non-Hispanic; 47% had inadequate HL and 12% had marginal HL. Overall, 57% reported being confident with forms "somewhat" or less. The "confident with forms" question performed best for detecting inadequate (C-index = 0.82, (0.77-0.87)) and inadequate plus marginal HL (C index = 0.81, (0.76-0.86); p&lt;0.01 for differences from other questions), and performed comparably to the summative scale. The "confident with forms" question and scale also performed best across language, race/ethnicity, educational attainment, and age.ConclusionsA single self-reported HL question about confidence with forms and a summative scale of three questions discriminated between Spanish and English speakers with adequate HL and those with inadequate and/or inadequate plus marginal HL. The "confident with forms" question or the summative scale may be useful for estimating HL in clinical research involving Spanish-speaking and English-speaking, chronically-ill, diverse populations