The impact of a Telehealth-based Behavioral Lifestyle Program on hypertension control in African American participants: results from the HEALS Med-Tech randomized controlled trial.

Introduction: The HEALS Med-Tech program offers a unique blend of lifestyle changes and technology to improve hypertension (HTN) management in African Americans (AAs), a group disproportionately affected by cardiovascular disease (CVD).Methods: A randomized controlled trial compared HEALS Med-Tech against usual care for uncontrolled hypertension in AAs, focusing on diet, medication adherence, and telehealth. Results: In a study of 61 participants, HEALS Med-Tech significantly reduced systolic blood pressure by 12.95 mm Hg (p = 0.008) and 9.19 mm Hg (p = 0.013) at 3 and 12 months, respectively, demonstrating improved hypertension control. Conclusions: HEALS Med-Tech demonstrates potential in HTN management for AAs, advocating for culturally tailored, tech-integrated healthcare expansion.

Diatrizoate (Gastrograffin®) Small Bowel Follow Through for Small Bowel Obstructions: Timing and Outcomes.

INTRODUCTION: The advent of the Gastrograffin® small bowel follow through (G-SBFT) has resulted in a decreased rate of operative intervention of small bowel obstructions (SBO); however, there is no data to suggest when G-SBFT should be performed. METHODS: We retrospectively reviewed 548 patients, admitted to 1 of 9 hospitals with a diagnosis of SBO. Patients were divided into two categories with regards to timing of G-SBFT: before (early) or after (late) 48 hours from admission. Primary outcomes were length of stay (LOS) and total cost. Secondary outcomes were operative interventions and mortality. RESULTS: Of the reviewed patients, 71% had the G-SBFT ordered early. Comparing early versus late, there were no differences in patient characteristics with regards to age, sex, or BMI. There was a significant difference between LOS (4 vs 8 days, P < 0.05) and total cost ($17,056.19 vs $33,292.00, P < 0.05). There was no difference in mortality (1.3% vs 2.6%, P = 0.239) or 30-day readmission rates (15.6% vs 15.9%, P = 0.509). Patients in the early group underwent fewer operations (20.7% vs 31.9%, P = 0.05). DISCUSSION: Patients that had a G-SBFT ordered early had a decreased LOS, total cost, and operative intervention. This suggests there is a benefit to ordering G-SBFT earlier in the hospital stay to reduce the overall disease burden, and that it is safe to do so with regards to mortality and readmissions. We therefore recommend ordering a G-SBFT within 48 hours to reduce LOS, cost, and need for an operation.

Assessment of the Banff Working Group classification of definitive BK polyomavirus nephropathy.

Polyomavirus associated nephropathy (PyVAN) continues to be a burden in renal transplantation leading to allograft insufficiency or graft failure. A presumptive diagnosis of PyVAN is made based on the presence of BK polyomavirus in patients' plasma; however, kidney biopsy remains the gold standard to establish a definitive diagnosis. The Banff Working Group on PyVAN proposed a novel classification of definitive PyVAN based on polyomavirus replication/load level and the extent of interstitial fibrosis. The aim of our study was to test the newly defined classes of PyVAN using independent cohorts of 124 kidney transplant patients with PyVAN with respect to the initial presentation and outcome, and to compare our analysis to that previously reported. Detailed analysis of our cohort revealed that the proposed classification of PyVAN did not stratify or identify patients at increased risk of allograft failure. Specifically, while class 3 was associated with the worst prognosis, there was no significant difference between the outcomes in classes 1 and 2. We also found that the timing post-transplantation and inflammation in areas of interstitial fibrosis and tubular atrophy might be additional factors contributing to an unfavorable allograft outcome in patients with PyVAN.

Feature selection and extraction for class prediction in dysphonia measures analysis:A case study on Parkinson's disease speech rehabilitation.

BACKGROUND: Speech disorders such as dysphonia and dysarthria represent an early and common manifestation of Parkinson's disease. Class prediction is an essential task in automatic speech treatment, particularly in the Parkinson's disease case. Many classification experiments have been performed which focus on the automatic detection of Parkinson's disease patients from healthy speakers but results are still not optimistic. A major problem in accomplishing this task is high dimensionality of speech data. OBJECTIVE: In this work, the potential of Principal Component Analysis (PCA) based modeling in dimensionality reduction is taken into consideration as the data smoothening tool with multiclass target expression data. METHODS: On the basis of suggested PCA-based modeling, the power of class prediction using logistic regression (LR) and C5.0 in numeric data is investigated in publicly available Parkinson's disease dataset Silverman voice treatment (LSVT) to develop an advanced classification model. RESULTS: The main advantage of our model is the effective reduction of the number of factors from p= 309 to k= 32 for LSVT Voice Rehabilitation dataset, with a fine classification accuracy of 100% and 99.92% for PCA-LR and PCA-C5.0 respectively. In addition, using only 9 dysphonia features, classification accuracy was (99.20%) and (99.11%) for PCA-LR, and PCA-C5.0 respectively. CONCLUSIONS: Our combined dimension reduction and data smoothening approaches have significant potential to minimize the number of features and increase the classification accuracy and then automatically classify subjects into Parkinson's disease patients or healthy speakers.