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Development of a patient-centered outcome tool for blepharospasm: A stepwise modified Delphi study
Full text linkBlepharospasm (BSP) is characterized by excessive orbicularis oculi muscle activity leading to abnormal blinking and involuntary eyelid closure. Botulinum neurotoxin (BoNT) injections are the main treatment for BSP, but they only partially and transiently relieve symptoms, leading to a waxing and waning therapeutic response. A patient-centered outcome (PCO) tool that measures BSP symptoms in a simple and efficient way could inform the development of better treatments. Using a stepwise modified Delphi approach, potential PCO items were first identified using the Dystonia Coalition Database with data from over 200 individuals with BSP who had provided responses to existing clinical assessment scales. These items were then analyzed for contribution to overall severity using a Random Forests approach, and redundant items were merged and revised in a series of iterative meetings with a specialist panel along with input from patient advocacy group representatives and focus groups. An online survey was conducted with 330 individuals with BSP to validate and verify the items\u27 relevance. Finally, the specialist panel provided content validity ratio, which was repeated until it showed good agreement for relevance and clarity of all items. In the end, an easy-to-use PCO tool designed for smartphones and tablets containing 17 items covering three symptom domains (motor, disability, and psychosocial/quality of life) was created. This novel PCO tool for BSP may be used to characterize the cyclical response that an individual patient experiences from BoNT treatments and provide a vital tool for future investigations of longer-acting BoNT preparations or adjunctive therapies
Lysosomal LRRC8 complex impacts lysosomal pH, morphology, and systemic glucose metabolism
Full text linkThe lysosome integrates anabolic signaling and nutrient sensing to regulate intracellular growth pathways. The leucine-rich repeat-containing 8 (LRRC8) channel complex forms a lysosomal anion channel and regulates PI3K-AKT-mTOR signaling, skeletal muscle differentiation, growth, and systemic glucose metabolism. Here, we define the endogenous LRRC8 subunits localized to a subset of lysosomes in differentiated myotubes. We show that LRRC8A affects leucine-stimulated mTOR; lysosome size; number; pH; expression of lysosomal proteins LAMP2, P62, and LC3B; and lysosomal function. Mutating an LRRC8A lysosomal targeting dileucine motif sequence (LRRC8A-L706A;L707A) in myotubes recapitulates the abnormal AKT signaling and altered lysosomal morphology and pH observed in LRRC8A knockout cells. In vivo, LRRC8A-L706A;L707A knock-in mice exhibit increased adiposity, impaired glucose tolerance and insulin resistance associated with reduced skeletal muscle PI3K-AKT-mTOR signaling, glucose uptake, and impaired incorporation of glucose into glycogen. These data reveal a lysosomal LRRC8-mediated metabolic signaling function regulating lysosomal function, systemic glucose homeostasis, and insulin sensitivity
Importance of outbreak response research in bridging knowledge gaps on emerging infectious diseases
Full text linkAn important outcome of the devastating 2014 West African Ebola virus disease outbreak and the 2020 COVID-19 pandemic has been the growing promotion of conducting research during outbreaks of emerging infectious diseases (EIDs) as a valuable and acceptable process of acquiring knowledge to enhance our ability to better prevent and control these diseases in the future. Recognising the unique opportunity during outbreaks to leverage increases in cases over a short time interval and in a circumscribed area, we articulate a systematic process of conducting EID outbreak response research, highlighting knowledge gaps that should be prioritised, and measures that can be applied to mitigate numerous barriers commonly experienced during such times. We also highlight ethical considerations that must be addressed to minimise practices that continue to erode global confidence in sharing specimens and data
Endothelial cell Piezo1 promotes vascular smooth muscle cell differentiation on large arteries
Full text linkVascular stabilization is a mechanosensitive process, in part driven by blood flow. Here, we demonstrate the involvement of the mechanosensitive ion channel, Piezo1, in promoting arterial accumulation of vascular smooth muscle cells (vSMCs) during zebrafish development. Using a series of small molecule antagonists or agonists to temporally regulate Piezo1 activity, we identified a role for the Piezo1 channel in regulating klf2a, a blood flow responsive transcription factor, expression levels and altered targeting of vSMCs between arteries and veins. Increasing Piezo1 activity suppressed klf2a and increased vSMC association with the cardinal vein, while inhibition of Piezo1 activity increased klf2a levels and decreased vSMC association with arteries. We supported the small molecule findings with in vivo genetic suppression of piezo1 and 2 in zebrafish, resulting in loss of transgelin+ vSMCs on the dorsal aorta. Further, endothelial cell (EC)-specific Piezo1 knockout in mice was sufficient to decrease vSMC accumulation along the descending dorsal aorta during development, thus phenocopying our zebrafish data, and supporting functional conservation of Piezo1 in mammals. To determine the underlying mechanism, we used in vitro modeling assays to demonstrate that differential sensing of pulsatile versus laminar flow forces across endothelial cells changes the expression of mural cell differentiation genes. Together, our findings suggest a crucial role for EC Piezo1 in sensing force within large arteries to mediate mural cell differentiation and stabilization of the arterial vasculature
Real-world outcomes and management considerations following surgical aortic valve replacement with the Trifecta valve
Full text linkBACKGROUND: Bioprosthetic surgical aortic valve replacement (SAVR) using the Trifecta valve was frequently chosen because of its large opening area and low transvalvular gradient. However, long-term follow-up revealed the potential for early structural valve deterioration. To further assess the long-term clinical outcomes and management considerations for patients implanted with the Trifecta valve, a real-world study using Medicare fee-for-service claims data was conducted with a focus on Trifecta valve reintervention.
METHODS: De-identified patients undergoing SAVR with the Trifecta™ valve (Abbott) in the U.S. between 1/1/2011-12/31/2021 were selected by ICD-9/10 procedure codes and then linked to a manufacturer device tracking database. All-cause mortality and freedom from Trifecta valve reintervention with repeat SAVR or valve-in-valve transcatheter aortic valve implantation (ViV-TAVI) were evaluated at 10-years using the Kaplan Meier method. Independent predictors for reintervention and clinical outcomes following reintervention were assessed.
RESULTS: Among 242,160 Medicare beneficiaries undergoing SAVR during the study period, 23,197 were implanted with the Trifecta valve. Mean age was 75.2 ± 7.4 years. At 10-years survival was 32.3 % (95 % CI, 31.4 %-33.3 %) and the freedom from valve reintervention was 82.4 % (95 % CI, 81.1 %-83.5 %). Independent predictors for reintervention included younger age, female, obesity, and implants with a small valve size (19 mm, 21 mm). Reintervention with ViV-TAVI (N = 796) was associated with better operative survival (3.8 % vs. 12.5 %, p \u3c 0.001) than repeat SAVR (N = 577).
CONCLUSION: This real-world nationwide study of Medicare beneficiaries receiving the Trifecta valve demonstrates \u3e80 % freedom from all-cause valve reintervention at 10-years post-implant with reintervention using ViV-TAVI having improved operative survival compared to repeat SAVR
Baseline clinical characterization of participants in the Accelerating Medicines Partnership Schizophrenia program
Full text linkBACKGROUND: This paper focuses on the baseline clinical characterization of the participants in the Accelerating Medicines Partnership Schizophrenia (AMP SCZ) program. The AMP SCZ program is designed to investigate a wide array of clinical variables and biomarkers in a total of 2040 clinical high-risk (CHR) participants and 652 community control (CC) participants.
METHODS: The dataset analyzed includes 1642 individuals at clinical high risk for psychosis and 519 CCs. Key measures include the Positive Symptoms and Diagnostic Criteria for the Comprehensive Assessment of At-Risk Mental States Harmonized with the Structured Interview for Psychosis-Risk Syndromes, which determined CHR criteria and the severity of attenuated psychotic symptoms (APS). Other measures included the Structured Clinical Interview for DSM-5, scales to assess negative symptoms, depression, suicidal ideation, substance use, social and role functioning, and a selection of patient-reported outcomes.
RESULTS: CHR participants presented with more severe ratings on all clinical measures and poorer functioning relative to the CC. There were a few significant small associations between measures of APS and other clinical measures.
CONCLUSION: The results from this study support previous research indicating that CHR individuals face serious clinical challenges beyond the risk of developing psychosis. Findings indicate significant associations among various clinical measures, underscoring the complex nature of the CHR population. Limitations are acknowledged, including the preliminary nature of the data and the need for more in-depth analyses from AMP SCZ papers already in progress. Future work will focus on longitudinal data and further exploration of clinical variables and their relationship with biomarkers
An explainable AI approach for mapping multivariate regional brain age and clinical severity patterns in Alzheimer\u27s disease
Full text linkAge is a significant risk factor for mild cognitive impairment (MCI) and Alzheimer\u27s disease (AD) and identifying brain age patterns is critical for comprehending the normal aging and MCI/AD processes. Prior studies have widely established the univariate relationships between brain regions and age, while multivariate associations remain largely unexplored. Herein, various artificial intelligence (AI) models were used to perform brain age prediction using an MRI dataset
Increasing birth-dose hepatitis B vaccination in Nigeria: Qualitative analysis of data from a crowdsourcing open call
Full text linkBACKGROUND: Despite World Health Organization and Nigerian recommendations for hepatitis B birth-dose (HepB-BD) vaccination, only one-third of Nigerian newborns receive timely HepB-BD vaccination, This study identified facilitators to increasing HepB-BD vaccination based on data from a crowdsourcing open call in Nigeria.
METHODS: Our team conducted an open call across Nigeria for teams to submit ideas on how to increase HepB-BD vaccination. Independent judges evaluated the submissions based on predefined criteria. We analyzed textual data from the top 29 entries using iterative coding and thematic analysis within a socioecological model to identify priority facilitators.
RESULTS: The open call received 362 total submissions, and 58.5% (215/362) of submissions were from women. Analysis of the top 29 submissions revealed 6 priority facilitators for increasing HepB-BD vaccination: (1) engage religious and healthcare leaders to educate pregnant women; (2) strengthen national policies for vaccination support and tracking; (3) counter misconceptions to promote attitude and behavior changes; (4) utilize rural infrastructure like town halls and radio programs to disseminate information; (5) translate educational materials into local languages like pidgin; and (6) organize financial or social incentives.
CONCLUSIONS: Our crowdsourcing open call identified key facilitators and strategies for increasing HepB-BD vaccination among Nigerian newborns. The findings from this study can inform HepB-BD initiatives in Nigeria and other low- and middle-income countries
Sociodemographic factors predict outcomes and reveal spatial tumor patterns in glioblastoma
Full text linkBACKGROUND: Glioblastoma (GBM) is the deadliest malignant glioma of the central nervous system. Postsurgical functional impairment correlates with survival and is estimated using metrics such as extent and location of resection. This study uses machine learning to evaluate the predictive ability of baseline sociodemographic and lifestyle factors for forecasting postoperative functional outcomes in GBM patients.
METHODS: Glioblastoma patients (
RESULTS: Utilizing decision trees with age, SVI/SES, sex, tobacco use, alcohol use, obesity, and race as predictors, we achieved 88% accuracy in classifying median KPS and 85% accuracy in classifying KPS slope. Socioeconomic factors were the strongest predictors. Age, sex, and tobacco use were also strong predictors. Significant correlations in spatial tumor distributions were observed based on outcome measures and SVI/SES.
CONCLUSIONS: The current work demonstrates the utility of machine learning to predict functional outcomes in GBM patients prior to treatment using lifestyle and sociodemographic factors. Our results suggest that socioeconomic factors, age, tobacco use, and biological sex can be reliable predictors of functional outcomes. Incorporating these factors could improve therapeutic approaches tailored to individual patients
