Screening for orthostatic hypotension in the geriatric population in a real-world primary care setting reduces prescribed antihypertensive medications.

BACKGROUND: To determine if outpatient screening for orthostatic hypotension (OH) in the geriatric population results in fewer prescribed antihypertensive medications and if a relationship exists between OH and specific pharmacologic classes of antihypertensive medications. MATERIALS AND METHODS: Patients ≥ 65 years were screened for OH, defined as a decrease in systolic blood pressure (SBP) ≥ 20 mm Hg or a decrease in diastolic blood pressure (DBP) ≥ 10 mm Hg after standing for 3 minutes. Sitting blood pressure (BP) was measured after patients had been seated quietly in an exam room. Patients then stood for approximately 3 minutes at which time standing BP was recorded. RESULTS: OH prevalence was 18%. Standing DBP was significantly different between the two groups (70 mmHg ± 18, 80 mmHg ± 13, P  = 0.007). Compared to patients without OH, patients with OH were more likely to have been previously prescribed beta-blockers (56% vs. 32%, P  = 0.056) and potassium-sparing diuretics (11% vs. 1%, P  = 0.026). Physicians discontinued an antihypertensive medication more often in patients who screened positive for OH than in to those who did not (17% vs. 4%, P  = 0.037). Calcium channel blockers were the most frequently discontinued class of medication. CONCLUSION: Asymptomatic OH is prevalent in geriatric patients. Screening for OH may lead to de-escalation of antihypertensive regimen and a reduction in polypharmacy. Positive screening for OH was associated with de-prescribing of antihypertensive medications. Prior use of beta-blockers and potassium-sparing diuretics was most largely associated with OH.

Risk factors associated with higher mortality in patients with cardiac implantable electronic device infection.

INTRODUCTION: Cardiac Implantable Electronic Devices (CIEDs) are widely used for the management of advanced heart failure and ventricular arrhythmias. CIED-Infection (CIED-I) has very high mortality, especially in the subsets of patients with limited health-care access and delayed presentation. The purpose of this study is to identify the risk-predictors mortality in subjects with CIED-I. METHODS: We performed a retrospective cohort study of a regional database in patients presenting with CIED infections to tertiary care medical centers across Western New York, USA from 2012 to 2020. The clinical outcomes included recurrent device infection (any admission for CIED-I after the first hospitalization for device infection), septic complications (pulmonary embolism, respiratory failure, septic shock, decompensated HF, acute kidney injury) and mortality outcomes (death during hospitalization, within 30 days from CIED-I, and within 1 year from CIED-I). We studied associations between categorical variables and hard outcomes using χ2 tests and used one-way analysis of variance to measure between-groups differences. RESULTS: We identified 296 patients with CIED-I, among which 218 (74%) were male, 237 (80%) were white and the mean age at the time of infection was 69.2 ± 13.7 years. One-third of the patients were referred from the regional facilities. Staphylococcus aureus was responsible for most infections, followed by Enterococcus fecalis. On multivariate analysis, the covariates associated with significantly increased mortality risk included referral from regional facility (OR: 2.0;1.0-4.0), hypertension (Odds ratio, OR: 3.2;1.3-8.8), right ventricular dysfunction (OR: 2.6;1.2-5.1), end-stage renal disease (OR: 2.6;1.1-6.2), immunosuppression (OR: 11.4;2.5-53.3), and septic shock as a complication of CIED-I (OR: 3.9;1.3-10.8). CONCLUSION: Hypertension, right ventricular dysfunction, immunosuppression, and end-stage renal disease are associated with higher mortality after CIED-I. Disproportionately higher mortality was also noted in subjects referred from the regional facilities. This underscores the importance of early clinical risk-assessment, and the need for a robust referral infrastructure to improve patient outcomes.

A machine learning application for raising WASH awareness in the times of COVID-19 pandemic.

The COVID-19 pandemic has revealed the power of internet disinformation in influencing global health. The deluge of information travels faster than the epidemic itself and is a threat to the health of millions across the globe. Health apps need to leverage machine learning for delivering the right information while constantly learning misinformation trends and deliver these effectively in vernacular languages in order to combat the infodemic at the grassroot levels in the general public. Our application, WashKaro, is a multi-pronged intervention that uses conversational Artificial Intelligence (AI), machine translation, and natural language processing to combat misinformation (NLP). WashKaro uses AI to provide accurate information matched against WHO recommendations and delivered in an understandable format in local languages. The primary aim of this study was to assess the use of neural models for text summarization and machine learning for delivering WHO matched COVID-19 information to mitigate the misinfodemic. The secondary aim of this study was to develop a symptom assessment tool and segmentation insights for improving the delivery of information. A total of 5026 people downloaded the app during the study window; among those, 1545 were actively engaged users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot "Satya" increased thus proving the usefulness of a mHealth platform to mitigate health misinformation. We conclude that a machine learning application delivering bite-sized vernacular audios and conversational AI is a practical approach to mitigate health misinformation.

COVID-19 Mask Usage and Social Distancing in Social Media Images: Large-scale Deep Learning Analysis.

BACKGROUND: The adoption of nonpharmaceutical interventions and their surveillance are critical for detecting and stopping possible transmission routes of COVID-19. A study of the effects of these interventions can help shape public health decisions. The efficacy of nonpharmaceutical interventions can be affected by public behaviors in events, such as protests. We examined mask use and mask fit in the United States, from social media images, especially during the Black Lives Matter (BLM) protests, representing the first large-scale public gatherings in the pandemic. OBJECTIVE: This study assessed the use and fit of face masks and social distancing in the United States and events of large physical gatherings through public social media images from 6 cities and BLM protests. METHODS: We collected and analyzed 2.04 million public social media images from New York City, Dallas, Seattle, New Orleans, Boston, and Minneapolis between February 1, 2020, and May 31, 2020. We evaluated correlations between online mask usage trends and COVID-19 cases. We looked for significant changes in mask use patterns and group posting around important policy decisions. For BLM protests, we analyzed 195,452 posts from New York and Minneapolis from May 25, 2020, to July 15, 2020. We looked at differences in adopting the preventive measures in the BLM protests through the mask fit score. RESULTS: The average percentage of group pictures dropped from 8.05% to 4.65% after the lockdown week. New York City, Dallas, Seattle, New Orleans, Boston, and Minneapolis observed increases of 5.0%, 7.4%, 7.4%, 6.5%, 5.6%, and 7.1%, respectively, in mask use between February 2020 and May 2020. Boston and Minneapolis observed significant increases of 3.0% and 7.4%, respectively, in mask use after the mask mandates. Differences of 6.2% and 8.3% were found in group pictures between BLM posts and non-BLM posts for New York City and Minneapolis, respectively. In contrast, the differences in the percentage of masked faces in group pictures between BLM and non-BLM posts were 29.0% and 20.1% for New York City and Minneapolis, respectively. Across protests, 35% of individuals wore a mask with a fit score greater than 80%. CONCLUSIONS: The study found a significant drop in group posting when the stay-at-home laws were applied and a significant increase in mask use for 2 of 3 cities where masks were mandated. Although a positive trend toward mask use and social distancing was observed, a high percentage of posts showed disregard for the guidelines. BLM-related posts captured the lack of seriousness to safety measures, with a high percentage of group pictures and low mask fit scores. Thus, the methodology provides a directional indication of how government policies can be indirectly monitored through social media.

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays.

COVID-19 has emerged as a global crisis with unprecedented socio-economic challenges, jeopardizing our lives and livelihoods for years to come. The unavailability of vaccines for COVID-19 has rendered rapid testing of the population instrumental in order to contain the exponential rise in cases of infection. Shortage of RT-PCR test kits and delays in obtaining test results calls for alternative methods of rapid and reliable diagnosis. In this article, we propose a novel deep learning-based solution using chest X-rays which can help in rapid triaging of COVID-19 patients. The proposed solution uses image enhancement, image segmentation, and employs a modified stacked ensemble model consisting of four CNN base-learners along with Naive Bayes as meta-learner to classify chest X-rays into three classes viz. COVID-19, pneumonia, and normal. An effective pruning strategy as introduced in the proposed framework results in increased model performance, generalizability, and decreased model complexity. We incorporate explainability in our article by using Grad-CAM visualization in order to establish trust in the medical AI system. Furthermore, we evaluate multiple state-of-the-art GAN architectures and their ability to generate realistic synthetic samples of COVID-19 chest X-rays to deal with limited numbers of training samples. The proposed solution significantly outperforms existing methods, with 98.67% accuracy, 0.98 Kappa score, and F-1 scores of 100, 98, and 98 for COVID-19, normal, and pneumonia classes, respectively, on standard datasets. The proposed solution can be used as one element of patient evaluation along with gold-standard clinical and laboratory testing.

Similar requirement for clathrin in EGF- and HGF- stimulated Akt phosphorylation.

Receptor tyrosine kinases, such as the epidermal growth factor (EGF) receptor (EGFR) and Met lead to activation of intracellular signals including Akt, a critical regulator of cell survival, metabolism and proliferation. Upon binding their respective ligands, each of these receptors is recruited into clathrin coated pits (CCPs) eventually leading to endocytosis. We have recently shown that phosphorylation of Gab1 and Akt following EGFR activation requires clathrin, but does not require receptor endocytosis. We examined whether clathrin regulates Akt signaling downstream of Met, as it does for EGFR signaling. Stimulation with the Met ligand Hepatocyte Growth Factor (HGF) leads to enrichment of phosphorylated Gab1 (pGab1) within CCPs in ARPE-19 cells. Perturbation of clathrin using the inhibitor pitstop2 decreases HGF-stimulated Akt phosphorylation. These results indicate that clathrin may regulate Met signaling leading to Akt phosphorylation similarly as it does for EGFR signaling.

Epidermal growth factor-stimulated Akt phosphorylation requires clathrin or ErbB2 but not receptor endocytosis.

Epidermal growth factor (EGF) binding to its receptor (EGFR) activates several signaling intermediates, including Akt, leading to control of cell survival and metabolism. Concomitantly, ligand-bound EGFR is incorporated into clathrin-coated pits--membrane structures containing clathrin and other proteins--eventually leading to receptor internalization. Whether clathrin might regulate EGFR signaling at the plasma membrane before vesicle scission is poorly understood. We compared the effect of clathrin perturbation (preventing formation of, or receptor recruitment to, clathrin structures) to that of dynamin2 (allowing formation of clathrin structures but preventing EGFR internalization) under conditions in which EGFR endocytosis is clathrin dependent. Clathrin perturbation by siRNA gene silencing, with the clathrin inhibitor pitstop2, or knocksideways silencing inhibited EGF-simulated Gab1 and Akt phosphorylation in ARPE-19 cells. In contrast, perturbation of dynamin2 with inhibitors or by siRNA gene silencing did not affect EGF-stimulated Gab1 or Akt phosphorylation. EGF stimulation enriched Gab1 and phospho-Gab1 within clathrin structures. ARPE-19 cells have low ErbB2 expression, and overexpression and knockdown experiments revealed that robust ErbB2 expression bypassed the requirement for clathrin for EGF-stimulated Akt phosphorylation. Thus clathrin scaffolds may represent unique plasma membrane signaling microdomains required for signaling by certain receptors, a function that can be separated from vesicle formation.