Publications by authors named "A Azhir"

Article Synopsis
  • Researchers developed an advanced algorithm for accurately identifying patients with post-acute sequelae of COVID-19 (PASC) using data from over 295,000 patients across various health facilities in Massachusetts.
  • The new phenotyping algorithm enhances precision in estimating the prevalence of PASC and reduces demographic bias, identifying over 24,000 patients with an accuracy of 79.9%.
  • This method paves the way for deeper studies into the complexities of PASC by providing reliable patient cohorts, surpassing limitations found in previous studies.
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Background/objective: Amyotrophic lateral sclerosis (ALS) is a diagnosis that incorporates a heterogeneous set of neurodegenerative processes into a single progressive and uniformly fatal disease making the development of a uniformly applicable therapeutic difficult. Recent multinational ALS natural history incidence studies have identified systemic chronic activation of the innate immune system as a major risk factor for developing ALS. Persistent immune activation in patients with ALS leads to loss of muscle and lowering of serum creatinine.

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Article Synopsis
  • The study evaluated the impact of a 6-month treatment with NP001, an immune modulator, on survival rates in patients with amyotrophic lateral sclerosis (ALS).
  • Based on data from 268 out of 273 participants in two clinical trials, the median overall survival (OS) increased by 4.8 months for those receiving NP001 compared to the placebo group, especially notable in patients aged 65 and under.
  • The results suggest that targeting inflammation via the innate immune system with NP001 could potentially offer new therapeutic approaches for ALS treatment.
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Scalable identification of patients with the post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms and the suboptimal accuracy, demographic biases, and underestimation of the PASC diagnosis code (ICD-10 U09.9). In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying research cohorts of PASC patients, defined as a diagnosis of exclusion.

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Background: Information on social media may affect peoples' contraceptive decision making. We performed an exploratory analysis of contraceptive content on Twitter (recently renamed X), a popular social media platform.

Methods: We selected a random subset of 1% of publicly available, English-language tweets related to reversible, prescription contraceptive methods posted between January 2014 and December 2019.

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