Socioeconomic inequality in hypertension and its determinants in people over 60 years in Fasa, southern Iran: a Blinder-Oaxaca decomposition.

Background: Hypertension (HTN) is well-known as a major risk factor for various noncommunicable diseases. Evidence indicates a link between socioeconomic status and the likelihood of developing HTN. A thorough comprehension of the inequalities in HTN is crucial for implementing evidence-based interventions.

Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.

Background: Breast cancer (BC), as a leading cause of cancer mortality in women, demands robust prediction models for early diagnosis and personalized treatment. Artificial Intelligence (AI) and Machine Learning (ML) algorithms offer promising solutions for automated survival prediction, driving this study's systematic review and meta-analysis.

Methods: Three online databases (Web of Science, PubMed, and Scopus) were comprehensively searched (January 2016-August 2023) using key terms ("Breast Cancer", "Survival Prediction", and "Machine Learning") and their synonyms.

NEMS generated electromechanical frequency combs.

This paper presents a novel technique for low-power generation of frequency combs (FC) over a wide frequency range. It leverages modal interactions between electrical and mechanical resonators in electrostatic NEMS operating in air to provide a simple architecture for FC generators. A biased voltage signal drives the electrical resonator at resonance which is set to match an integer submultiple of twice the mechanical resonator's resonance.

Investigation of the expression level of methylated septin 9 gene and serological carcinoembryonic antigen to diagnose colorectal cancer. A meta-analysis study.

Introduction: Colorectal cancer (CRC) is a rising threat, necessitating accurate early diagnosis.

Aim: This meta-analysis scrutinised methylated septin 9 (SEPT9) and carcinoembryonic antigen (CEA) in CRC.

Methods: From January 2012 to December 2022, databases including PubMed and Google Scholar were explored for English publications.

TinyML and edge intelligence applications in cardiovascular disease: A survey.

Tiny machine learning (TinyML) and edge intelligence have emerged as pivotal paradigms for enabling machine learning on resource-constrained devices situated at the extreme edge of networks. In this paper, we explore the transformative potential of TinyML in facilitating pervasive, low-power cardiovascular monitoring and real-time analytics for patients with cardiac anomalies, leveraging wearable devices as the primary interface. To begin with, we provide an overview of TinyML software and hardware enablers, accompanied by an examination of networking solutions such as Low-power Wide area network (LPWAN) that facilitate the seamless deployment of TinyML frameworks.