The outpatient clinics suffer from various inefficiencies such as long queues, long waiting times and low utilisation of medical resources. Simulation modelling and process improvement techniques have been applied separately to tackle such inefficiencies. This paper demonstrates how simulation modelling and lean management can be integrated to improve the patient flow and performance of outpatient clinics. The proposed framework for integrating lean and simulation consists of three main phases where the first phase includes the system description, data collection and analysis, and simulation

The retina's high oxygen demands and the retinal vasculature's relatively sparse nature are assumed to contribute to the retina's specific vulnerability to vascular diseases. This study has been designed to model the oxygen transport in physiologically realistic retinal networks. A computational fluid dynamics study has been conducted to investigate the effect of topological changes on the oxygen partial pressure distribution in retinal blood vessels. The Navier Stokes equations for blood flow and the mass transport equation for oxygen have been coupled and solved simultaneously for the

An attractive feature of fractional calculus is its application in various interdisciplinary fields, extending from biomedical and biological notions to mechanical properties. For their description, fractional-order models have outperformed the corresponding integer-order models, resulting in a more realistic behavior, due to the additional degrees of freedom offered and the long-term memory effect that reflects the fractional order. These improved features are processed by appropriate circuit implementations, derived through several approximation methods, whose primary objective is to provide

2020 has witnessed a rapidly spread pandemic COVID-19 which is one of the worst in the history of mankind. Scientists believe that COVID-19 spreads mainly from a person to another. Recent researches consider bats as a vector for COVID-19. This paper suggests a variable fractional order model for COVID-19 to figure out how bats and hosts interact, and how the seafood market affect people. The proposed model assumes that infection cannot be recovered. The basic reproduction number R0 for real data on reported cases in Wuhan China was computed. Disease-free equilibrium points and proposed model

Gastric cancer (GC) remains a major public health challenge worldwide. Long non-coding RNAs (lncRNAs) play important roles in the development, progression, and resistance to the treatment of GC, as shown by recent developments in molecular characterization. Still, an in-depth investigation of the lncRNA landscape in GC is absent. However, The objective of this systematic review is to evaluate our present understanding of the role that lncRNA dysregulation plays in the etiology of GC and treatment resistance, with a focus on the underlying mechanisms and clinical implications. Research that

Gallbladder cancer (GBC) is an aggressive and lethal malignancy with a poor prognosis. Long noncoding RNAs (lncRNAs) and natural products have emerged as key orchestrators of cancer pathogenesis through widespread dysregulation across GBC transcriptomes. Functional studies have revealed that lncRNAs interact with oncoproteins and tumor suppressors to control proliferation, invasion, metastasis, angiogenesis, stemness, and drug resistance. Curcumin, baicalein, oleanolic acid, shikonin, oxymatrine, arctigenin, liensinine, fangchinoline, and dioscin are a few examples of natural compounds that

The global rise in Metabolic dysfunction-associated steatotic liver disease (MASLD)/Metabolic dysfunction-associated steatohepatitis (MASH) highlights the urgent necessity for noninvasive biomarkers to detect these conditions early. To address this, we endeavored to construct a diagnostic model for MASLD/MASH using a combination of bioinformatics, molecular/biochemical data, and machine learning techniques. Initially, bioinformatics analysis was employed to identify RNA molecules associated with MASLD/MASH pathogenesis and enriched in ferroptosis and exophagy. This analysis unveiled specific

Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control

Lung cancer (LC) represents most of the cancer incidences in the world. There are many types of LC, but Lung Adenocarcinoma (LUAD) is the most common type. Although RNA-seq and microarray data provide a vast amount of gene expression data, most of the genes are insignificant to clinical diagnosis. Feature selection (FS) techniques overcome the high dimensionality and sparsity issues of the large-scale data. We propose a framework that applies an ensemble of feature selection techniques to identify genes highly correlated to LUAD. Utilizing LUAD RNA-seq data from the Cancer Genome Atlas (TCGA)

Introduction: With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses. Method: In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic