Reimagining Urban Spaces: Green Spaces, Obesity, and Health Resilience in an Era of Extreme Heat.

Record-breaking heat waves intensified by climate change pose both environmental and health threats, necessitating a balance between urban sustainability and well-being. Extreme heat and limited green space access are drivers of obesity prevalence, with decreased proximity to green spaces correlating with higher rates of obesity in nearby communities. In contrast, access to such green spaces fosters physical activity, well-being, and community cohesion, especially crucial in marginalized communities facing health disparities due to historical policies like redlining and underinvestment in social gathering spaces. Despite challenges, green space investment offers healthcare savings and environmental gains, necessitating a shift in perception towards viewing green spaces as essential for urban living. As heat waves persist, integrating health and sustainability in urban planning is paramount. Health and medical communities must play an active role in advocating for equitable access to urban green spaces, as they possess influential positions to address climate-related health disparities through localized advocacy.

Entropy removal of medical diagnostics.

Shannon entropy is a core concept in machine learning and information theory, particularly in decision tree modeling. To date, no studies have extensively and quantitatively applied Shannon entropy in a systematic way to quantify the entropy of clinical situations using diagnostic variables (true and false positives and negatives, respectively). Decision tree representations of medical decision-making tools can be generated using diagnostic variables found in literature and entropy removal can be calculated for these tools. This concept of clinical entropy removal has significant potential for further use to bring forth healthcare innovation, such as quantifying the impact of clinical guidelines and value of care and applications to Emergency Medicine scenarios where diagnostic accuracy in a limited time window is paramount. This analysis was done for 623 diagnostic tools and provided unique insights into their utility. For studies that provided detailed data on medical decision-making algorithms, bootstrapped datasets were generated from source data to perform comprehensive machine learning analysis on these algorithms and their constituent steps, which revealed a novel and thorough evaluation of medical diagnostic algorithms.

Tailoring Hydrophobicity and Pore Environment in Physisorbents for Improved Carbon Dioxide Capture under High Humidity.

CALF-20, a Zn-triazolate-based metal-organic framework (MOF), is one of the most promising adsorbent materials for CO2 capture. However, competitive adsorption of water severely limits its performance when the relative humidity (RH) exceeds 40%, limiting the potential implementation of CALF-20 in practical settings where CO2 is saturated with moisture, such as postcombustion flue gas. In this work, three newly designed MOFs related to CALF-20, denoted as NU-220, CALF-20M-w, and CALF-20M-e that feature hydrophobic methyltriazolate linkers, are presented. Inclusion of methyl groups in the linker is proposed as a strategy to improve the uptake of CO2 in the presence of water. Notably, both CALF-20M-w and CALF-20M-e retain over 20% of their initial CO2 capture efficiency at 70% RH─a threshold at which CALF-20 shows negligible CO2 uptake. Grand canonical Monte Carlo simulations reveal that the methyl group hinders water network formation in the pores of CALF-20M-w and CALF-20M-e and enhances their CO2 selectivity over N2 in the presence of a high moisture content. Moreover, calculated radial distribution functions indicate that introducing the methyl group into the triazolate linker increases the distance between water molecules and Zn coordination bonds, offering insights into the origin of the enhanced moisture stability observed for CALF-20M-w and CALF-20M-e relative to CALF-20. Overall, this straightforward design strategy has afforded more robust sorbents that can potentially meet the challenge of effectively capturing CO2 in practical industrial applications.

Network Analysis of Academic Medical Center Websites in the United States.

Healthcare resources are published annually in repositories such as the AHA Annual Survey DatabaseTM. However, these data repositories are created via manual surveying techniques which are cumbersome in collection and not updated as frequently as website information of the respective hospital systems represented. Also, this resource is not widely available to patients in an easy-to-use format. Network analysis techniques have the potential to create topological maps which serve to aid in pathfinding for patients in their search for healthcare services. This study explores the topological structure of forty United States academic health center websites. Network analysis is utilized to analyze and visualize 48,686 webpages. Several elements of network structure are examined including basic network properties, and centrality measures distributions. The Louvain community detection algorithm is used to examine the extent to which these techniques allow identification of healthcare resources within networks. The results indicate that websites with related healthcare services tend to form observable clusters useful in mapping key resources within a hospital system.

Emoji Education: How Students Can Help Increase Health Awareness by Making Emojis.

Emojis can improve health communication, especially when incorporating emojis into traditionally word-only texts. Beyond improving communication, emojis also offer greater access to health care, especially for vulnerable and marginalized populations with limited health literacy. A recent study found that 94% of patients with limited health literacy preferred health reports with emojis. Moreover, health officials are considering adding emojis to cardiopulmonary resuscitation guidelines and public health guidelines for handwashing. As the world evolves with new technology and new methods of communication, we must also evolve the language and method we use to communicate health information to patients. In this viewpoint, we aim to discuss the methods health care professionals can use to develop novel communication methods using emojis and the benefits of their incorporation into health care communication.

Assessment and Diagnosis of Down Syndrome Regression Disorder: International Expert Consensus.

Objective: To develop standardization for nomenclature, diagnostic work up and diagnostic criteria for cases of neurocognitive regression in Down syndrome. Background: There are no consensus criteria for the evaluation or diagnosis of neurocognitive regression in persons with Down syndrome. As such, previously published data on this condition is relegated to smaller case series with heterogenous data sets. Lack of standardized assessment tools has slowed research in this clinical area. Methods: The authors performed a two-round traditional Delphi method survey of an international group of clinicians with experience in treating Down syndrome to develop a standardized approach to clinical care and research in this area. Thirty-eight potential panelists who had either previously published on neurocognitive regression in Down syndrome or were involved in national or international working groups on this condition were invited to participate. In total, 27 panelists (71%) represented nine medical specialties and six different countries reached agreement on preliminary standards in this disease area. Moderators developed a proposed nomenclature, diagnostic work up and diagnostic criteria based on previously published reports of regression in persons with Down syndrome. Results: During the first round of survey, agreement on nomenclature for the condition was reached with 78% of panelists agreeing to use the term Down Syndrome Regression Disorder (DSRD). Agreement on diagnostic work up and diagnostic criteria was not reach on the first round due to low agreement amongst panelists with regards to the need for neurodiagnostic testing. Following incorporation of panelist feedback, diagnostic criteria were agreed upon (96% agreement on neuroimaging, 100% agreement on bloodwork, 88% agreement on lumbar puncture, 100% agreement on urine studies, and 96% agreement on "other" studies) as were diagnostic criteria (96% agreement). Conclusions: The authors present international consensus agreement on the nomenclature, diagnostic work up, and diagnostic criteria for DSRD, providing an initial practical framework that can advance both research and clinical practices for this condition.

Synthesis, Characterization, and Hydrogen Evolution Activity of Metallo-meso-(4-fluoro-2,6-dimethylphenyl)porphyrin Derivatives.

Zn(II), Cu(II), and Ni(II) 5,10,15,20-tetrakis(4-fluoro-2,6-dimethylphenyl)porphyrins (TFPs) have been synthesized and characterized. The electronic spectroscopy and cyclic voltammetry of these compounds, along with the free-base macrocycle (2H-TFP), have been determined; 2H-TFP was also structurally characterized by X-ray crystallography. The Cu(II)TFP exhibits catalytic activity for the hydrogen evolution reaction (HER). The analysis of linear sweep voltammograms shows that the HER reaction of Cu(II)TFP with benzoic acid is first-order in proton concentration with an average apparent rate constant for HER catalysis of k app = 5.79 ± 0.47 × 103 M-1 s-1.

Screening of Parkinson's Disease Using Geometric Features Extracted from Spiral Drawings.

Conventional means of Parkinson's Disease (PD) screening rely on qualitative tests typically administered by trained neurologists. Tablet technologies that enable data collection during handwriting and drawing tasks may provide low-cost, portable, and instantaneous quantitative methods for high-throughput PD screening. However, past efforts to use data from tablet-based drawing processes to distinguish between PD and control populations have demonstrated only moderate classification ability. Focusing on digitized drawings of Archimedean spirals, the present study utilized data from the open-access ParkinsonHW dataset to improve existing PD drawing diagnostic pipelines. Random forest classifiers were constructed using previously documented features and highly-predictive, newly-proposed features that leverage the many unique mathematical characteristics of the Archimedean spiral. This approach yielded an AUC of 0.999 on the particular dataset we tested on, and more importantly identified interpretable features with good promise for generalization across diverse patient cohorts. It demonstrated the potency of mathematical relationships inherent to the drawing shape and the usefulness of sparse feature sets and simple models, which further enhance interpretability, in the face of limited sample size. The results of this study also inform suggestions for future drawing task design and data analytics (feature extraction, shape selection, task diversity, drawing templates, and data sharing).

Cohesin: behind dynamic genome topology and gene expression reprogramming.

Beyond its originally discovered role tethering replicated sister chromatids, cohesin has emerged as a master regulator of gene expression. Recent advances in chromatin topology resolution and single-cell studies have revealed that cohesin has a pivotal role regulating highly dynamic chromatin interactions linked to transcription control. The dynamic association of cohesin with chromatin and its capacity to perform loop extrusion contribute to the heterogeneity of chromatin contacts. Additionally, different cohesin subcomplexes, with specific properties and regulation, control gene expression across the cell cycle and during developmental cell commitment. Here, we discuss the most recent literature in the field to highlight the role of cohesin in gene expression regulation during transcriptional shifts and its relationship with human diseases.