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New Index Demonstrates Association between Social Vulnerability, Environmental Burden, and Kidney Failure Risk among Individuals with Glomerular Disease.
Get PDFBACKGROUND: The Centers for Disease Control and Prevention (CDC) Environmental Justice Index Social-Environmental Ranking (EJI-SER) combines a Social Vulnerability Module (SV) with an Environmental Burden Module (EB) to characterize cumulative environmental and social burden at the census tract level. This analysis evaluates the association between EJI-SER and kidney outcomes in glomerular disease (GD) patients. METHODS: Cure Glomerulopathy (CureGN) is an observational cohort study of adults and children with biopsy-proven GD. EJI-SER is a percentile ranking by census tract, with a higher score indicating a more severe burden. Associations between EJI-SER and its components with kidney failure (initiation of kidney replacement therapy, transplant, or two estimated glomerular filtration rates [eGFRs] <15ml/min/1.73m2) and longitudinal eGFR were tested using multivariable Cox regression and linear mixed models, respectively, adjusted for demographics, histologic diagnosis, eGFR and urine protein to creatinine ratio at enrollment, and time from biopsy to enrollment. RESULTS: Among 1,149 participants with census tract data, the median (IQR) follow-up was 5.4 (3.0-7.0) years, the median (IQR) age at biopsy was 24 (10-48), and self-identified racial distribution was 5% Asian, 18% Black, and 70% White. Median (IQR) EJI-SER was 0.49 (0.26-0.75). EJI-SER scores in the lowest two quartiles were associated with a lower hazard of kidney failure compared to the highest quartile (adjusted HR [95% CI] 0.62 [0.36-1.08] and 0.43 [0.25-0.76] for EJI-SER 0-25% and >25-50% vs. >75%, respectively) and higher eGFR at enrolllment (adjusted mean 90.1 vs. 87.1 ml/min/1.73m2 for 0-25% vs. >75%, p=0.08). CONCLUSION: As captured by EJI-SER, higher environmental and social burdens are associated with lower eGFR and a higher risk of kidney failure in the CureGN cohort. This first use of the EJI-SER in GD demonstrates the need for additional investigation into social drivers of disparities in GD and policies and resources that address these structural inequities
Post-Discharge Sequelae of Severe Malaria Among Pediatric Patients in Western Uganda
Get PDFUganda bears the third highest malaria mortality rate globally, with the burden falling disproportionately on children. Most malaria-related deaths occur after progression to severe malaria (SM), which is often fatal without timely treatment. Compared to in-hospital care of SM, the post-discharge period has received relatively little attention. A study by the MUST-UNC collaboration found 25.6% of children hospitalized for SM in western Uganda reported persistent symptoms after discharge. Therefore, we modified the study to extend follow-ups to be biweekly and incorporate home visits contingent on symptom persistence. We sought to (i) estimate the prevalence and characterize persistent symptoms at 14-, 28, and 42-days post-discharge, and (ii) identify demographic, household, and clinical risk factors associated with these outcomes. We found that 85.25% of participants had at least one additional diagnosis to malaria. All children with comorbidities reported persistent symptom at 14 days, with sickle cell disease (SCD) emerging as a significant risk factor – possibly due to either increased complication risk or underrecognized regional prevalence of SCD. Additionally, leukocytosis in half of participants and the association of respiratory distress with symptom persistence suggests respiratory infections, including bacterial coinfections, contribute. These findings highlight the need for integrated post-discharge care that addresses not only the initial illness, but also comorbidities and coinfections. In high-burden settings, targeted interventions for children with chronic illnesses or nutrient deficiencies may be particularly effective in reducing post-discharge morbidity. Bachelor of Scienc
KEYS TO UNLOCKING HUMANE AND AFFORDABLE HOUSING FOR SINGLE MOTHERS IN GRANVILLE AND VANCE COUNTIES, NORTH CAROLINA: A CROSS-SECTORAL COALITION PROPOSAL
Get PDFHumane housing is a Vital Condition essential for stability, health, and long-term opportunity. This analysis examines housing challenges in Vance and Granville Counties, where aging structures, severe cost burden, and limited family-sized units disproportionately affect mothers in single-parent households. Using quantitative data from County Health Rankings, the Granville–Vance Community Health Assessment, and state dashboards, along with qualitative perspectives from community partners, the analysis identifies disparities in affordability, housing quality, and access. Findings show that housing instability contributes to chronic disease, respiratory illness, stress, school disruption, and reduced economic mobility. Community insights also reveal fragmented services and limited awareness of available resources among families. Because the drivers of housing instability are multilayered and many community assets remain disconnected, this analysis supports establishing a multi-sector coalition to coordinate resources and guide future policy investigation. Prioritizing this Vital Condition will enhance health and stability across both counties.Master of Public Healt
OSC-Net: a multi-fidelity machine learning model for organic solar cells
Get PDFOrganic solar cells (OSCs) have emerged as a promising renewable energy technology, offering advantages such as lightweight design, semitransparency, flexibility, and cost-effectiveness. Power conversion efficiency (PCE) is a key device performance parameter for OSCs, defined as the ratio of the electrical power output generated by the device to the incident solar power input. Despite significant advances, the development of high-performance OSCs remains a labor-intensive process, heavily dependent on expert experience, involving extensive synthesis, characterization, and iterative optimization. Data-driven methods offer a promising alternative for accelerating material discovery, but their effectiveness is often limited by the scarcity of high-quality experimental data. To overcome this challenge, we propose OSC-Net, a multi-fidelity machine learning framework that integrates a large volume of computational data with a smaller set of high-accuracy experimental measurements. This approach enables accurate prediction of key device performance parameters, including PCE, while simultaneously tackling the challenges associated with experimental data scarcity and uncertainty quantification, enabling efficient screening of OSC materials. Importantly, the predictive capability of OSC-Net was verified against published experimental data, confirming its accuracy and reliability. By leveraging both data sources, OSC-Net achieves superior predictive performance compared to conventional single-fidelity models. Furthermore, the uncertainty quantification captures variability in the model, enhancing the reliability of predictions. Finally, OSC-Net was employed for large-scale high-throughput screening, successfully identifying promising candidates with high predicted PCEs that were validated against literature-reported experimental data. Thus, OSC-Net presents a feasible approach for rapid and accurate inference of device performance parameters with limited experimental datasets, enabling efficient OSC material discovery
Sarcopenia in Interventional Radiology: An Opportunistic Imaging Biomarker for Patient Outcomes and Procedural Planning
Get PDFSarcopenia, the loss of skeletal muscle mass and function, is a common and critical comorbidity in patients with conditions frequently managed by interventional radiologists, such as liver cirrhosis and hepatocellular carcinoma (HCC). Interventional radiologists are well positioned to incorporate opportunistic screening for this condition during routine preprocedural cross-sectional imaging. This review summarizes the current evidence on how sarcopenia influences patient outcomes and informs procedural planning across a spectrum of interventional radiology (IR) procedures. In transarterial embolizations for HCC, sarcopenia is a robust independent predictor of increased mortality, with meta-analyses suggesting it may also predict a lower tumor response rate. Even earlier stages of muscle loss (pre-sarcopenia) are associated with worse survival, and dynamic changes in muscle mass post-treatment can serve as a biomarker for tumor progression. For patients undergoing transjugular intrahepatic portosystemic shunt, pre-procedural sarcopenia and myosteatosis are strong, independent predictors of both mortality and the development of post-procedural hepatic encephalopathy, with the presence of both conferring the highest risk. In the context of pre-surgical portal vein embolization, sarcopenia is consistently associated with impaired volumetric liver growth, although this does not always translate to worse short-term surgical outcomes, as functional liver regeneration may be preserved. Following percutaneous liver tumor ablation, sarcopenia is a powerful predictor of overall mortality, while its role in predicting tumor recurrence remains an area of active investigation. Finally, in non-oncologic interventions for peripheral arterial disease, sarcopenia is highly prevalent and is associated with worse functional status, higher mortality, and a significantly increased risk of major amputation after endovascular therapy. In conclusion, sarcopenia is a powerful and readily available biomarker that provides crucial prognostic information—often independent of standard clinical scores—across a wide spectrum of IR procedures. The consistent evidence supports integrating sarcopenia evaluation into routine practice to enhance risk stratification, improve patient counseling, and guide multidisciplinary treatment planning
Cognitive assessment in the Accelerating Medicines Partnership® Schizophrenia Program: harmonization priorities and strategies in a diverse international sample
Get PDFCognitive impairment occurs at higher rates in individuals at clinical high risk (CHR) for psychosis relative to healthy peers, and it contributes unique variance to multivariate prediction models of transition to psychosis. Such impairment is considered a core biomarker of schizophrenia. Thus, cognition is a key domain measured in the Accelerating Medicines Partnership® program for Schizophrenia (AMP SCZ initiative). The aim of this paper is to describe the rationale, processes, considerations, and final harmonization of the cognitive battery used in AMP SCZ across the two data collection networks. This battery comprises tests of general intellect and specific cognitive domains. We estimate premorbid intelligence at baseline and measure current intelligence at baseline and 2 years. Eight tests from the Penn Computerized Neurocognitive Battery (PennCNB), which measure verbal learning and memory, sensorimotor ability, attention, emotion recognition, working memory, processing speed, verbal memory, visual memory, and motor speed are administered repeatedly at baseline, and four follow-up timepoints over 2 years
Sample ascertainment and clinical outcome measures in the Accelerating Medicines Partnership® Schizophrenia Program
Get PDFClinical ascertainment and clinical outcome are key features of any large multisite study. In the ProNET and PRESCIENT research networks, the Accelerating Medicines Partnership® Schizophrenia (AMP®SCZ) Clinical Ascertainment and Outcome Measures Team aimed to establish a harmonized clinical assessment protocol across these two research networks and to define ascertainment criteria and primary and secondary endpoints. In addition to developing the assessment protocol, the goals of this aspect of the AMP SCZ project were: (1) to implement and monitor clinical training, ascertainment of participants, and clinical assessments; (2) to provide expert clinical input to the Psychosis Risk Evaluation, Data Integration and Computational Technologies: Data Processing, Analysis, and Coordination Center (PREDICT-DPACC) for data collection, quality control, and preparation of data for the analysis of the clinical measures; and (3) to provide ongoing support to the collection, analysis, and reporting of clinical data. This paper describes the (1) protocol clinical endpoints and outcomes, (2) rationale for the selection of the clinical measures, (3) extensive training of clinical staff, (4) preparation of clinical measures for a multisite study which includes several sites where English is not the native language; and (5) the assessment of measure stability over time in the AMP SCZ observational study comparing clinical ratings at baseline and at the 2-month follow up. Watch Dr. Jean Addington discuss her work and this article: https://vimeo.com/1040425281
Disentangling the Reproductive From the Liberatory in Urban School Contexts: Trauma-Informed Practice for Racially Just Systemic Teaching
Get PDFThe writing in this issue is inspired and springs from what hooks and Duncan-Andrade have called critical hope. A transgressive hope invoked by critical scholars and practitioners for centuries. Hope that strives, at every turn, to break systems of oppression that produce and reproduce trauma. Hope for the ways in which we can leverage trauma-informed practice (TIP) as a practice of liberation. In this special issue, authors illuminate how TIP can advance collective goals of urban education as liberation, speaking to multiple facets of urban education, as advanced by Milner and Lomotey, with particular attention to youth voice, multidisciplinary perspectives, policy, teaching and teacher education, and families and communities
A Deep Learning Model Leveraging Time-Series System Call Data to Detect Malware Attacks in Virtual Machines
Get PDFA Tenant Virtual Machine (TVM) user in the cloud may misuse its computing power to launch malware attack against other tenant VMs, Host OS, Hypervisor, or any other computing devices/resources inside the cloud environment of a Cloud Service Provider. The security solutions deployed within the TVM may not be reliable, as malware can disable them or remain undetected due to its hidden nature. Therefore, security solutions deployed outside the virtual machine are necessary. This research proposes deploying an Intrusion Detection System (IDS) at the Hypervisor layer, utilizing time series system call data and employing a Convolutional Neural Network (CNN) model to accurately detect the presence of malicious (malware) computer programs within virtual machines. The raw VMM system call traces are transformed into novel Time Series System Call patterns and utilized by a deep learning algorithm for training and building the classifier model. A deep learning model, CNN, is used to build the classifier model for detecting intrusions with high accuracy. It is capable of detecting both known and unknown malware. The CNN model is compared with machine learning algorithms for the results and discussions, and it outperforms ML algorithms in terms of intrusion detection accuracy when utilizing novel time series system call data.
[LJMU Drug Deaths Conf] Are overdoses in the United States down and why?
No full textPresentation to virtual confernece in Liverpool