Defining and Validating Criteria to Identify Populations Who May Benefit From Home-Based Primary Care.

BACKGROUND: Home-based primary care (HBPC) is an important care delivery model for high-need older adults. Currently, target patient populations vary across HBPC programs, hindering expansion and large-scale evaluation. OBJECTIVES: Develop and validate criteria that identify appropriate HBPC target populations. RESEARCH DESIGN: A modified Delphi process was used to achieve expert consensus on criteria for identifying HBPC target populations. All criteria were defined and validated using linked data from Medicare claims and the National Health and Aging Trends Study (NHATS) (cohort n=21,727). Construct validation involved assessing demographics and health outcomes/expenditures for selected criteria. SUBJECTS: Delphi panelists (n=29) represented diverse professional perspectives. Criteria were validated on community-dwelling Medicare beneficiaries (age ≥70) enrolled in NHATS. MEASURES: Criteria were selected via Delphi questionnaires. For construct validation, sociodemographic characteristics of Medicare beneficiaries were self-reported in NHATS, and annual health care expenditures and mortality were obtained via linked Medicare claims. RESULTS: Panelists proposed an algorithm of criteria for HBPC target populations that included indicators for serious illness, functional impairment, and social isolation. The algorithms Delphi-selected criteria applied to 16.8% of Medicare beneficiaries. These HBPC target populations had higher annual health care costs [Med (IQR): $10,851 (3316, 31,556) vs.$2830 (913, 9574)] and higher 12-month mortality [15% (95% CI: 14, 17) vs. 5% (95% CI: 4, 5)] compared with the total validation cohort. CONCLUSIONS: We developed and validated an algorithm to define target populations for HBPC, which suggests a need for increased HBPC availability. By enabling objective identification of unmet demands for HBPC access or resources, this algorithm can foster robust evaluation and equitable expansion of HBPC

An Integrated Framework for Infectious Disease Control Using Mathematical Modeling and Deep Learning.

Infectious diseases are a major global public health concern. Precise modeling and prediction methods are essential to develop effective strategies for disease control. However, data imbalance and the presence of noise and intensity inhomogeneity make disease detection more challenging. Goal: In this article, a novel infectious disease pattern prediction system is proposed by integrating deterministic and stochastic model benefits with the benefits of the deep learning model. Results: The combined benefits yield improvement in the performance of solution prediction. Moreover, the objective is also to investigate the influence of time delay on infection rates and rates associated with vaccination. Conclusions: In this proposed framework, at first, the global stability at disease free equilibrium is effectively analysed using Routh-Haurwitz criteria and Lyapunov method, and the endemic equilibrium is analysed using non-linear Volterra integral equations in the infectious disease model. Unlike the existing model, emphasis is given to suggesting a model that is capable of investigating stability while considering the effect of vaccination and migration rate. Next, the influence of vaccination on the rate of infection is effectively predicted using an efficient deep learning model by employing the long-term dependencies in sequential data. Thus making the prediction more accurate

Efficient separation of carbon dioxide and methane in high-pressure and wet gas mixtures using Zr-MOF-808

The capture and separation of carbon dioxide (CO2) has been the focus of a plethora of research in order to mitigate its emissions and contribute to global development. Given that CO2 is commonly found in natural gas streams, there have been efforts to seek more efficient materials to separate gaseous mixtures such as CO2/CH4. However, there are only a few reports regarding adsorption processes within pressurized systems. In the offshore scenario, natural gas streams still exhibit high moisture content, necessitating a greater understanding of processes in moist systems. In this article, a metal-organic framework synthesis based on zirconium (MOF-808) was carried out through a conventional solvothermal method and autoclave for the adsorption of CO2 and CH4 under different temperatures (45–65 °C) and pressures up to 100 bar. Furthermore, the adsorption of humid CO2 was evaluated using thermal analyses. The MOF-808 synthesized in autoclave showed a high surface area (1502 m2/g), a high capacity for CO2 adsorption at 50 bar and 45 °C and had a low selectivity to capture CH4 molecules. It also exhibited a fine stability after five cycles of CO2 adsorption and desorption at 50 bar and 45 °C − as confirmed by structural post-adsorption analyses while maintaining its adsorption capacity and crystallinity. Furthermore, it can be observed that the adsorption capacity increased in a humid environment, and that the adsorbent remained stable after adsorption cycles in the presence of moisture. Finally, it was possible to confirm the occurrence of physisorption processes through nuclear magnetic resonance (NMR) analyses, thus validating the choice of mild temperatures for regeneration and contributing to the reduction of energy consumption in processing plants

A Predictive Nomogram for Development of Lymph Node Metastasis in Muscle-Invasive Bladder Cancer Following Neoadjuvant Therapy.

PURPOSE: Pelvic lymph node metastases (ypN+) after multiagent neoadjuvant chemotherapy (NAC) is a poor prognostic sign in nonmetastatic muscle-invasive bladder cancer (nmMIBC). We sought to create a nomogram predicting probability of ypN+ after NAC for cN0 nmMIBC and determine association with overall survival (OS). METHODS AND MATERIALS: We reviewed the National Cancer Database for patients with cT2-4N0M0 urothelial carcinoma of the bladder receiving multiagent NAC and surgery from 2004 to 2020. Following a data split, univariate logistic regression identified variables associated with ypN+ at P < .05. Eligible variables were used for multivariate logistic regression and nomogram generation. A threshold for 95% sensitivity defined high- and low-risk groups for ypN+. Fine-Gray models assessed ypN+ risk group and OS, accounting for competing risks of surgical mortality. RESULTS: A total of 6194 patients were identified with a median follow-up of 39.5 months (interquartile range [IQR], 20.5-67.2 months). Most patients had high-grade (97.7%) cT2 disease (70.8%) with nonpapillary urothelial histology (67.3%) and initiated NAC at a median of 41.0 days after diagnosis (IQR, 28.0-59.0 days).The nomogram included age in decades (odds ratio [OR], 0.94; 95% confidence interval [CI], 0.87-1.03; P = .172), weeks from diagnosis to NAC (OR, 1.02; 95% CI, 1.01-1.04; P = .004), nonpapillary histology (OR, 1.17; 95% CI, 0.99-1.39; P = .068), and clinical T-stage. Within the testing cohort, ypN+ was found in 392 (22.8%) high-risk and 12 (8.0%) low-risk patients (P < .001), with median OS of 36.1 and 74.0 months, respectively (P < .001). High-risk patients had worse OS despite competing risks of 30-day (subdistribution hazard ratio [SHR], 1.80; 95% CI, 1.49-2.18; P < .001) and 90-day surgical mortality (SHR, 1.68; 95% CI, 1.39-2.04; P < .001). CONCLUSIONS: This is the first study to provide a tool for predicting ypN+ and prognosticate worse OS in primarily high-grade nmMIBC and could select patients for alternative neoadjuvant therapy and facilitate future study

Latent profiles of home behaviour problems in Trinidad and Tobago.

Caregivers who interact with children at home can provide a critical, complementary perspective on a childs behaviour functioning. This research used a parent-administered measure of problem behaviours to study perceptions of child behaviours across home situations. We applied latent profile analysis to identify subgroups of children with common behavioural tendencies in a nationally representative sample (N = 709) of 4- to 13-year-old children in Trinidad and Tobago. This study (a) identified latent profiles of childrens over- and underactive behaviour problems in varied home settings and (b) examined how profile membership predicted academic skills and teacher-observed problem behaviours. The best-fitting four-profile model included one profile of adjusted behaviours (56%), one of the elevated attention-seeking behaviours (21%), a profile featuring withdrawn and disengaged behaviours (15%) and a relatively rare profile emphasising aggressive behaviours (8%). Children classified in the last profile displayed the poorest academic outcomes and the highest levels of teacher-observed behaviour problems

AI for Green Spaces: Leveraging Autonomous Navigation and Computer Vision for Park Litter Removal

There are 50 billion pieces of litter in the U.S. alone. Grass fields contribute to this problem because picnickers tend to leave trash on the field. We propose building a robot that can autonomously navigate, identify, and pick up trash in parks. To autonomously navigate the park, we used a Spanning Tree Coverage (STC) algorithm to generate a coverage path the robot could follow. To navigate this path, we successfully used Real-Time Kinematic (RTK) GPS, which provides a centimeter-level reading every second. For computer vision, we utilized the ResNet50 Convolutional Neural Network (CNN), which detects trash with 94.52% accuracy. For trash pickup, we tested multiple design concepts. We select a new pickup mechanism that specifically targets the trash we encounter on the field. Our solution achieved an overall success rate of 80%, demonstrating that autonomous trash pickup robots on grass fields are a viable solution

Warm Parenting Throughout Adolescence Predicts Basal Parasympathetic Activity Among Mexican‐Origin Youths

Parenting that is warm and supportive has been consistently linked to better emotion regulation in children, but less is known about this association in adolescents. Adolescence is thought to be an important period for emotion regulation development given that it coincides with the emergence of mental health issues. Respiratory sinus arrhythmia (RSA) is a measure of parasympathetic regulation linked to emotion and behavior regulation. Despite the well-documented links between parenting practices and emotion regulation, and between RSA and emotion regulation, few studies have focused on the association between positive parenting and adolescent RSA or included both mothers and fathers. The current study analyzed the influence of warm parenting throughout adolescence (ages 10-16) on basal RSA at age 17 in 229 Mexican-origin youths. Latent-growth curve models were used to analyze associations between maternal and paternal warmth and baseline RSA. Changes in maternal, but not paternal, warmth from age 10 to 16 were related to youths' basal RSA at age 17. Specifically, youths who perceived increasing (or less decreasing) maternal warmth across adolescence had higher basal RSA. This finding suggests that positive maternal parenting experiences during adolescence "get under the skin" to enhance parasympathetic functioning that supports youths' emotion regulation capacities

Epigenetic modifications control CYP1A1 Inducibility in human and rat keratinocytes

Serially passaged rat keratinocytes exhibit dramatically attenuated induction of Cyp1a1 by aryl hydrocarbon receptor ligands such as TCDD. However, the sensitivity to induction can be restored by protein synthesis inhibition. Previous work revealed that the functionality of the receptor was not affected by passaging. The present work explored the possibility of epigenetic silencing on CYP1A1 inducibility in both rat and human cells. Use of an array of small molecule epigenetic modulators demonstrated that inhibition of histone deacetylases mimicked the effect of protein synthesis inhibition. Consistent with this finding, cycloheximide treatment also reduced histone deacetylase activity. More importantly, when compared to human CYP1A1, rat Cyp1a1 exhibited much greater sensitivity toward epigenetic modulators, particularly inhibitors of histone deacetylases. Other genes in the aryl hydrocarbon receptor domain showed variable and less dramatic responses to histone deacetylase inhibitors. These findings highlight a potential species difference in epigenetics that must be considered when extrapolating results from rodent models to humans and has implications for xenobiotic- or drug-drug interactions where CYP1A1 activity plays an important role

The Role of Gender Norms on Intimate Partner Violence Among Newly Married Adolescent Girls and Young Women in India: A Longitudinal Multilevel Analysis.

Gender norms have been posited to impact intimate partner violence (IPV), but there is scant evidence of the longitudinal association between community-level gender norms and IPV. Using longitudinal data on 3,965 married girls surveyed in India, we fitted mixed-effects ordinal and binary logistic regression models for physical IPV intensity and occurrence of sexual IPV. We found a 26% increase in the odds that women experience frequent physical IPV per one unit increase in greater community-level equitable gender norms. We did not find an association between community-level equitable gender norms and sexual IPV. Findings suggest that the relationship between gender norms and physical and sexual IPV differs, indicating the need for tailored interventions for different types of IPV

Decoding Depth of Meditation: Electroencephalography Insights From Expert Vipassana Practitioners.

BACKGROUND: Meditation practices have demonstrated numerous psychological and physiological benefits, but capturing the neural correlates of varying meditative depths remains challenging. In this study, we aimed to decode self-reported time-varying meditative depth in expert practitioners using electroencephalography (EEG). METHODS: Expert Vipassana meditators (n = 34) participated in 2 separate sessions. Participants reported their meditative depth on a personally defined 1 to 5 scale using both traditional probing and a novel spontaneous emergence method. EEG activity and effective connectivity in theta, alpha, and gamma bands were used to predict meditative depth using machine/deep learning, including a novel method that fused source activity and connectivity information. RESULTS: We achieved significant accuracy in decoding self-reported meditative depth across unseen sessions. The spontaneous emergence method yielded improved decoding performance compared with traditional probing and correlated more strongly with postsession outcome measures. Best performance was achieved by a novel machine learning method that fused spatial, spectral, and connectivity information. Conventional EEG channel-level methods and preselected default mode network regions fell short in capturing the complex neural dynamics associated with varying meditation depths. CONCLUSIONS: This study demonstrates the feasibility of decoding personally defined meditative depth using EEG. The findings highlight the complex, multivariate nature of neural activity during meditation and introduce spontaneous emergence as an ecologically valid and less obtrusive experiential sampling method. These results have implications for advancing neurofeedback techniques and enhancing our understanding of meditative practices