Psychological traits and public attitudes towards abortion: the role of empathy, locus of control, and need for cognition

In the summer of 2022, the U.S. Supreme Court overturned the historic Roe v. Wade ruling, prompting various states to put forth ballot measures regarding state-level abortion rights. While earlier studies have established associations between demographics, such as religious beliefs and political ideologies, and attitudes toward abortion, the current research delves into the role of psychological traits such as empathy, locus of control, and need for cognition. A sample of 294 U.S. adults was obtained via Amazon Mechanical Turk, and participants were asked to provide their attitudes on seven abortion scenarios. They also responded to scales measuring empathy toward the pregnant woman and the unborn, locus of control, and need for cognition. Principal Component Analysis divided abortion attitudes into two categories: traumatic abortions (e.g., pregnancies due to rape) and elective abortions (e.g., the woman does not want the child anymore). After controlling for religious belief and political ideology, the study found psychological factors accounted for substantial variation in abortion attitudes. Notably, empathy toward the pregnant woman correlated positively with abortion support across both categories, while empathy toward the unborn revealed an inverse relationship. An internal locus of control was positively linked to support for both types of abortions. Conversely, external locus of control and need for cognition only positively correlated with attitudes toward elective abortion, showing no association with traumatic abortion attitudes. Collectively, these findings underscore the significant and unique role psychological factors play in shaping public attitudes toward abortion. Implications for research and practice were discussed

Fast and Interpretable Mortality Risk Scores for Critical Care Patients

Prediction of mortality in intensive care unit (ICU) patients is an important task in critical care medicine. Prior work in creating mortality risk models falls into two major categories: domain-expert-created scoring systems, and black box machine learning (ML) models. Both of these have disadvantages: black box models are unacceptable for use in hospitals, whereas manual creation of models (including hand-tuning of logistic regression parameters) relies on humans to perform high-dimensional constrained optimization, which leads to a loss in performance. In this work, we bridge the gap between accurate black box models and hand-tuned interpretable models. We build on modern interpretable ML techniques to design accurate and interpretable mortality risk scores. We leverage the largest existing public ICU monitoring datasets, namely the MIMIC III and eICU datasets. By evaluating risk across medical centers, we are able to study generalization across domains. In order to customize our risk score models, we develop a new algorithm, GroupFasterRisk, which has several important benefits: (1) it uses hard sparsity constraint, allowing users to directly control the number of features; (2) it incorporates group sparsity to allow more cohesive models; (3) it allows for monotonicity correction on models for including domain knowledge; (4) it produces many equally-good models at once, which allows domain experts to choose among them. GroupFasterRisk creates its risk scores within hours, even on the large datasets we study here. GroupFasterRisk's risk scores perform better than risk scores currently used in hospitals, and have similar prediction performance to black box ML models (despite being much sparser). Because GroupFasterRisk produces a variety of risk scores and handles constraints, it allows design flexibility, which is the key enabler of practical and trustworthy model creation

Healthcare Professionals and Telehealth Usability during COVID-19

Objective: During the coronavirus disease 2019 (COVID-19) pandemic, many other health providers needed to rapidly adopt telehealth services to ensure continuity of patient care, without the opportunity to extensively evaluate the usability of the adopted technology. Therefore, this study aims to examine health professionals’ telehealth usability during COVID-19 in Florida. Design: This cross-sectional study employed the Telehealth Usability Questionnaire (TUQ) to licensed healthcare providers in Florida in June 2020. Setting and Participants: A total of 399,660 selected health professionals with Florida licensure were recruited from open-access Florida healthcare to participate in a Qualtrics web-based survey. A total of 1,868 health professionals completed the survey. Multiple linear and mixed regression models were applied to analyze the overall and subdomain scores from TUQ. Results: The analysis of the overall TUQ score showed younger, female healthcare professionals, and participants who reported an increase in telehealth usage during pandemic had a significantly higher overall TUQ score. Compared with the score from physicians and nurses, the scores from the mental health group and social work group were significantly higher, while the score rehabilitation group was significantly lower. Analysis of the subdomain scores was consistent with the overall scores. Conclusion: The findings from this study indicate that the health professionals’ telehealth usability is related to age, gender, and the change of telehealth usage during the COVID-19 Page 2 of 12 Telehealth and Medicine Today® ISSN 2471-6960 https://doi.org/10.30953/tmt.v6.270 pandemic. While pandemics represent only one possible impetus for the healthcare system to swiftly switch to telehealth platforms, each profession should consider providing adequate resources to accommodate the need for change

Biosynthesis of Zinc Oxide Nanoparticles on l-Carnosine Biofunctionalized Polyacrylonitrile Nanofibers: a Biomimetic Wound Healing Material

Multifunctional biohybrid nanofibers (NFs) that can simultaneously drive various cellular activities and confer antibacterial properties are considered desirable in producing advanced wound healing materials. In this study, a bionanohybrid formulation was processed as a NF wound dressing to stimulate the adhesion and proliferation of fibroblast and endothelial cells that play a major role in wound healing. Polyacrylonitrile (PAN) electrospun NFs were hydrolyzed using NaOH and biofunctionalized with l-carnosine (CAR), a dipeptide which could later biosynthesize zinc oxide (ZnO) nanoparticles (NPs) on the NFs surface. The morphological study verified that ZnO NPs are uniformly distributed on the surface of CAR/PAN NFs. Through EDX and XRD analysis, it was validated that the NPs are composed of ZnO and/or ZnO/Zn(OH)2. The presence of CAR and ZnO NPs brought about a superhydrophilicity effect and notably raised the elastic modulus and tensile strength of Zn-CAR/PAN NFs. While CAR ligands were shown to improve the viability of fibroblast (L929) and endothelial (HUVEC) cells, ZnO NPs lowered the positive impact of CAR, most likely due to their repulsive negative surface charge. A scratch assay verified that CAR/PAN NFs and Zn-CAR/PAN NFs aided HUVEC migration more than PAN NFs. Also, an antibacterial assay implied that CAR/PAN NFs and Zn-CAR/PAN NFs are significantly more effective in inhibiting Staphylococcus aureus (S. aureus) than neat PAN NFs are (1000 and 500%, respectively). Taken together, compared to the neat PAN NFs, CAR/PAN NFs with and without the biosynthesized ZnO NPs can support the cellular activities of relevance for wound healing and inactivate bacteria

Detectable changes in the blood transcriptome are present after two weeks of antituberculosis therapy

Rationale: Globally there are approximately 9 million new active tuberculosis cases and 1.4 million deaths annually . Effective antituberculosis treatment monitoring is difficult as there are no existing biomarkers of poor adherence or inadequate treatment earlier than 2 months after treatment initiation. Inadequate treatment leads to worsening disease, disease transmission and drug resistance. Objectives To determine if blood transcriptional signatures change in response to antituberculosis treatment and could act as early biomarkers of a successful response. METHODS: Blood transcriptional profiles of untreated active tuberculosis patients in South Africa were analysed before, during (2 weeks and 2 months), at the end of (6 months) and after (12 months) antituberculosis treatment, and compared to individuals with latent tuberculosis. An active-tuberculosis transcriptional signature and a specific treatment-response transcriptional signature were derived. The specific treatment response transcriptional signature was tested in two independent cohorts. Two quantitative scoring algorithms were applied to measure the changes in the transcriptional response. The most significantly represented pathways were determined using Ingenuity Pathway Analysis. RESULTS: An active tuberculosis 664-transcript signature and a treatment specific 320-transcript signature significantly diminished after 2 weeks of treatment in all cohorts, and continued to diminish until 6 months. The transcriptional response to treatment could be individually measured in each patient. CONCLUSIONS: Significant changes in the transcriptional signatures measured by blood tests were readily detectable just 2 weeks after treatment initiation. These findings suggest that blood transcriptional signatures could be used as early surrogate biomarkers of successful treatment response

Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta

Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoracic aortic aneurysms (ATAA). To accurately reproduce hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements still makes researchers resort to idealized BCs. In this study we generated and thoroughly characterized a large dataset of synthetic 4D aortic velocity profiles suitable to be used as BCs for CFD simulations. 4D flow MRI scans of 30 subjects with ATAA were processed to extract cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors. We built a data-driven generative model of 4D aortic velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.Comment: 21 pages, 5 figures, 2 tables To be submitted to "Computer methods and programs in biomedicine" Scripts: https://github.com/saitta-s/flow4D Synthetic velocity profiles: //doi.org/10.5281/zenodo.725198

CCR5AS lncRNA variation differentially regulates CCR5, influencing HIV disease outcome.

Multiple genome-wide studies have identified associations between outcome of human immunodeficiency virus (HIV) infection and polymorphisms in and around the gene encoding the HIV co-receptor CCR5, but the functional basis for the strongest of these associations, rs1015164A/G, is unknown. We found that rs1015164 marks variation in an activating transcription factor 1 binding site that controls expression of the antisense long noncoding RNA (lncRNA) CCR5AS. Knockdown or enhancement of CCR5AS expression resulted in a corresponding change in CCR5 expression on CD4+ T cells. CCR5AS interfered with interactions between the RNA-binding protein Raly and the CCR5 3' untranslated region, protecting CCR5 messenger RNA from Raly-mediated degradation. Reduction in CCR5 expression through inhibition of CCR5AS diminished infection of CD4+ T cells with CCR5-tropic HIV in vitro. These data represent a rare determination of the functional importance of a genome-wide disease association where expression of a lncRNA affects HIV infection and disease progression

Difference in distribution functions:A new diffusion weighted imaging metric for estimating white matter integrity

Diffusion weighted imaging (DWI) is a widely recognized neuroimaging technique to evaluate the microstructure of brain white matter. The objective of this study is to establish an improved automated DWI marker for estimating white matter integrity and investigating ageing related cognitive decline. The concept of Wasserstein distance was introduced to help establish a new measure: difference in distribution functions (DDF), which captures the difference of reshaping one's mean diffusivity (MD) distribution to a reference MD distribution. This new DWI measure was developed using a population-based cohort (n=19,369) from the UK Biobank. Validation was conducted using the data drawn from two independent cohorts: the Sydney Memory and Ageing Study, a community-dwelling sample (n=402), and the Renji Cerebral Small Vessel Disease Cohort Study (RCCS), which consisted of cerebral small vessel disease (CSVD) patients (n=171) and cognitively normal controls (NC) (n=43). DDF was associated with age across all three samples and better explained the variance of changes than other established DWI measures, such as fractional anisotropy, mean diffusivity and peak width of skeletonized mean diffusivity (PSMD). Significant correlations between DDF and cognition were found in the UK Biobank cohort and the MAS cohort. Binary logistic analysis and receiver operator characteristic curve analysis of RCCS demonstrated that DDF had higher sensitivity in distinguishing CSVD patients from NC than the other DWI measures. To demonstrate the flexibility of DDF, we calculated regional DDF which also showed significant correlation with age and cognition. DDF can be used as a marker for monitoring the white matter microstructural changes and ageing related cognitive decline in the elderly