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Association Between Neighborhood-Level Social Vulnerability and Hypertension Outcomes
No full textBackground
Neighborhood-level social vulnerability is associated with hypertension prevalence and severity and with cardiovascular complications in conditions other than hypertension, but its association with cardiovascular complications in patients with hypertension is understudied.
Objectives
The aim of the study was to examine how the neighborhood-level social vulnerability index (SVI) influences cardiovascular outcomes in a large, diverse cohort of patients with hypertension.
Methods
We used electronic health data to examine the association between census tract-level rankings for the SVI with a composite endpoint of incident myocardial infarction, congestive heart failure, or stroke.
Results
In a longitudinal cohort of 330,972 patients with hypertension followed for a median of 6.6 years, the neighborhood-level SVI was significantly associated with the composite endpoint after adjustment for demographics, baseline body mass index and blood pressure (BP), and comorbidities (HR for quartile 4 [most disadvantaged group] vs quartile 1 = 1.31 [95% CI: 1.25-1.38], P \u3c 0.001). Patients living in quartile 4 SVI areas had a significantly lower BP control rate compared with patients living in quartile 1 SVI areas (70.3% vs 74.8%, P \u3c 0.001). Patients living in SVI quartile 4 areas were disproportionately Black (53.8%). Compared with the White race, the Black race was negatively associated with the composite outcome after adjustment for the SVI quartile, and other clinical factors (HR: 0.89 [95% CI: 0.86-0.92], P \u3c 0.001).
Conclusions
Neighborhood-level social vulnerability was strongly associated with adverse cardiovascular outcomes and poorer BP control and may be a driver of racial disparities in hypertension. These findings highlight the potential of leveraging social vulnerability indices for tailored interventions in hypertension management
Mental Health Counselors’ Perceptions of Professional Identity as Correctional Counselors in an Integrated Behavioral Healthcare Setting
No full textThis interpretive phenomenological study of four individuals examined clinical mental health counselors (MHCs) in a correctional integrated behavioral healthcare (IBH) setting to the purpose of examining how the unique experiences and roles of MHCs employed as correctional counselors in an IBH setting impacted the perceptions of their professional identity/development as counselors. Findings indicated how environment, work relationships, and multiple foci of mental health roles contributed to professional growth and identity development. Participants experienced an adaptive transitional process when applying counseling styles to the setting and population as well as integrating experiences as correctional counselors into pre-existing professional identities. Practical implications are discussed
The Impact of Chromium Ion Implantation on ALD Lead Chalcogenide Thin Films
No full textInherently the synthesis of semiconducting materials by Atomic Layer Deposition ALD produces only intrinsic undoped films which require the introduction of small amounts of impurities for doping to change them into extrinsic semiconductors. Apart from various in-situ diffusion doping techniques like delta doping during the ALD process, post deposition doping by ion implantation affords the best control of dose and doping profile. The present study investigates the impact of 180 keV Cr+ ion implantation on the properties of semiconducting ALD lead chalcogenide thin films to improve their thermoelectric figure of merit. The implantation was accomplished with 180 keV Chromium ions at a given fluence of 5 × 10¹⁵ ions cm−² to reach a desired 1% Cr doping level. The energy of the incident ions was tuned using stopping and range of ions in matter (SRIM) simulations to produce an implant peak around the projected range centered on the ALD film thickness. The thermoelectric PbTe thin films have been synthesized on silicon substrates covered with native oxide by ALD using lead (II)bis(2,2,6,6-tetramethyl-3,5-heptanedionato) (Pb(C₁₁H₁₉O₂)₂), and (trimethylsilyl) telluride ((Me₃Si)₂Te) as ALD precursors for lead, and tellurium and Nitrogen as the carrier and purge gas. The Si native oxide surface was functionalized before ALD PbTe thin film deposition to ensure reproducible chemisorption of the ALD precursor compounds. The growth temperature during ALD was varied over a range from 130°C to 170°C. The Lead precursor was volatilized at a temperature of 170 °C and the Tellurium precursor was heated at 45 °C. The chamber base pressure was kept at 500 mTorr. Several physical characterization techniques among them SEM and EDS have been employed to determine the ALD PbTe thin film characteristics before and after Chromium ion implantation. X-ray diffraction analysis reveals that the films exhibit a polycrystalline structure with simple cubic crystallites. Atomic force microscopy analysis was employed to determine the surface properties of the films, including surface topology, root mean square (RMS) roughness, grain height, and average size. For the electrical characterization we report the effects of the ion implantation on the resistivity ρ(T) as a function of temperature, the electrical conductivity, the Hall mobility, and the Seebeck coefficient
Development of a Personalized Conversational Health Agent to Enhance Physical Activity for Blind and Low-Vision Individuals
No full textBackground: With the advancements in mobile health (mHealth) technologies, sighted individuals can benefit from mobile apps and wearable devices to more easily manage their physical activity (PA) and wellness data through intuitive touch gestures and effective data visualizations. However, for blind and low-vision (BLV) individuals, these conventional interaction methods are often challenging, not only limiting their ability to use these technologies but also potentially diminishing their motivation to adopt them to support health-promoting behaviors. We aimed to develop a health monitoring application called Personalized and Conversational Health Agent (PCHA) that supports BLV individuals with self-monitoring and management of their PA and wellness data (e.g., step count, exercise duration, calories burned, heart rate).
Methods: Drawing on social cognitive theory and insights from prior needs assessment research, five key design goals were established to guide the development of the app’s core features and functionalities. PCHA leverages a large language model (LLM) to enable a conversational health agent that can be installed on iPhone and Apple Watch devices. This conversational interface is designed to ensure accessibility and inclusivity, offering PA management tools through a voice user interface (VUI) that minimizes the navigation challenges often associated with traditional touchscreen-based systems. To ensure evidence-based PA guidance, a thorough review of scientific literature and published PA guidelines was conducted. Finally, two blind accessibility experts conducted the accessibility testing.
Results: Accessible user interface (UI) designs, featuring high color contrast, large buttons, and a simple layout, were created using Figma. The main features and functionalities include: (I) a voice health interview to assess users’ basic health information; (II) PA recommendations to guide users toward achieving their PA goals; (III) a chat feature enabling human-like conversations with the app; (IV) a PA scheduling and reminder feature with haptic feedback on the Apple Watch; and (V) an in-exercise mode that provides audible updates on heart rate, PA duration, and walking speed. The app’s mobile accessibility was found to be satisfactory.
Conclusions: A follow-up study involving BLV research participants will be conducted to improve the app’s accessibility and usability, and to update its features and functionalities. More research is needed to fully harness the potential of LLMs in the new mHealth system to motivate PA behaviors for BLV populations. To deliver truly personalized PA feedback for BLV individuals, mHealth app developer should incorporate PA and wellness data specific to the BLV population, along with their unique personal and contextual factors that influence PA behaviors
Predicting Mental Health Disparities Using Machine Learning for African Americans in Southeastern Virginia
No full textThis study examined mental health disparities among African Americans using AI and machine learning for outcome prediction. Analyzing data from African American adults (18–85) in Southeastern Virginia (2016–2020), we found Mood Affective Disorders were most prevalent (41.66%), followed by Schizophrenia Spectrum and Other Psychotic Disorders. Females predominantly experienced mood disorders, with patient ages typically ranging from late thirties to mid-forties. Medicare coverage was notably high among schizophrenia patients, while emergency admissions and comorbidities significantly impacted total healthcare charges. Machine learning models, including gradient boosting, random forest, neural networks, logistic regression, and Naive Bayes, were validated through 100 repeated 5-fold cross-validations. Gradient boosting demonstrated superior predictive performance among all models. Nomograms were developed to visualize risk factors, with gender, age, comorbidities, and insurance type emerging as key predictors. The study revealed higher mental health disorder prevalence compared to national averages, suggesting a potentially greater mental health burden in this population. Despite the limitations of its retrospective design and regional focus, this research provides valuable insights into mental health disparities among African Americans in Southeastern Virginia, particularly regarding demographic and clinical risk factors
In Situ Laser Fenestration for Zone 2 TEVAR: A 15 Year Experience Demonstrating Its Safety, Efficacy and Durability
No full textAssociation of Physical Activity and Neighborhood Deprivation with Extracellular Vesicle Characteristics in African American Women with Obesity
No full textAssociation of Physical Activity and Neighborhood Deprivation with Extracellular Vesicle Characteristics in African American Women with Obesit
Inferior Vena Cava Thrombosis in Nephrotic Syndrome: Two Cases Managed with Endovascular Thrombectomy
No full textBackground: Nephrotic syndrome (NS) confers a hypercoagulable state that can precipitate venous thromboembolism (VTE), rarely involving the inferior vena cava (IVC).
Cases: We describe two patients with NS who developed IVC thrombosis. One underwent mechanical thrombectomy using the ClotTriever®/Protrieve™ system (Inari Medical, Irvine, CA, USA); the other underwent aspiration thrombectomy using the AngioVac® system (AngioDynamics, Latham, NY, USA), with adjunct balloon maneuvers for renal vein thrombus.
Outcomes: Both patients achieved immediate IVC recanalization with symptom improvement and patent IVC on short‑term duplex follow‑up.
Conclusions: In NS‑associated IVC thrombosis, device‑based endovascular thrombectomy can be performed safely with restoration of caval patency. Further study is needed to define patient selection and long‑term outcomes
Vagally Mediated Heart Rate Variability Modulates the Association Between The Perceived Workload and the Stroop Effect on Behavioral Performance
No full textVagally mediated heart rate variability (vmHRV) reflects top-down cognitive processes involved in emotion-cognition integration. Using cognitive control can be effortful and increase negative affect. However, this intrinsic affective component of cognitive control has not been well studied, and the role of vmHRV in the association between subjective experience in using cognitive control and behavioral performance remains unknown. The current study aimed to examine the relationship of vmHRV with cognitive control and perceived workload in a cognitive task. Eighty-one participants performed the Stroop interference task. Participants rated subjective workload using the NASA Task Load Index (NASA-TLX) scale for congruent and incongruent trials separately. Moreover, cognitive performance was analyzed with the ex-Gaussian model, from which the parameters μ and τ were derived to reflect sensorimotor processing and inhibitory control, respectively. Multiple regressions were used to analyze the effects of TLX change score (incongruent–congruent), vmHRV, and their interaction on the Stroop effect. Results showed that vmHRV negatively predicted the Stroop effect on τ. Importantly, vmHRV moderated the association between perceived workload and the Stroop effect on τ. Our findings highlight the role of cardiac vagal control in emotion-cognition integration and have theoretical and practical implications
Multi-Modal MRI Based Segmentation of Brain Metastases Using Adaptive Self-Attention
No full textBrain metastases (BMs) are the most common adult central nervous system malignancy, affecting 20–40% of cancer patients. Accurate segmentation of metastatic lesions in multi-modal MRI is essential for treatment planning and prognosis however, manual delineation is time consuming and prone to variability. Traditional deep learning models such as U-Net, have improved segmentation accuracy but capture limited long-range dependencies and struggle with variations in metastasis size, shape, and distribution. This study introduces the Adaptive Integrated Multi-modal Segmentation (AIMS) model, an adaptive self-attention framework within a hybrid U-Net and Transformer architecture to enhance BM segmentation by leveraging multi-modal MRI integration. The proposed model integrates convolutional feature extraction with self-attention to capture both local and global contextual information while filtering out non-informative slices. The BraTS-METS dataset, consisting of 1303 cases with T1, T1Gd, T2, and FLAIR sequences, was used for training and evaluation. Preprocessing included bias field correction, intensity normalization, spatial resampling, and skull stripping. The encoder employs a U-Net backbone, while transformer based attention in the bottleneck refines feature interactions. Feature-wise attention maps guide the decoder to enhance segmentation accuracy, particularly for small and irregularly shaped metastases. The model was validated using a fivefold cross-validation approach and demonstrated superior segmentation performance, achieving higher Dice Similarity Coefficients (DSC) compared to state-of-the-art hybrid U-Net based models. Hausdorff Distance 95 (HD95) scores further indicated precise boundary delineation. By integrating adaptive self-attention with multi-modal MRI, the proposed model enhances segmentation accuracy and robustness in brain metastases. The findings highlight its potential for improving automated BM delineation, reducing manual inefficiencies, and assisting medical professionals in treatment planning and informed clinical decisions