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Aging is defined as the functional loss of tissues and organs over time. This is a biological, irreversible, progressive, and universal process that results from genetic and environmental factors, such as diet, physical activity, smoking, harmful alcohol consumption, and exposure to toxins, among others. Aging is a consequence of molecular and cellular damage built up over time. This damage begins with a gradual decrease in physical and mental capacity, thus increasing the risk of neurodegenerative diseases such as Alzheimer's and Parkinson's disease. Neuronal, functional, and structural damage can be explained by an imbalance among free radicals, reactive oxygen species, reactive nitrogen species, and antioxidants, which finally lead to oxidative stress. Due to the key role of free radicals, reactive oxygen species, and reactive nitrogen species, antioxidant therapy may reduce the oxidative damage associated with neurodegeneration. Exogenous antioxidants are molecules that may help maintain the balance between the formation and elimination of free radicals, thus protecting the cell from their toxicity. Among them, polyphenols are a broad group of secondary plant metabolites with potent antioxidant properties. Here, we review several studies that show the potential role of polyphenol consumption to prevent, or slow down, harmful oxidative processes linked to neurodegenerative disorders.Phenolic compounds (PC) have many health benefits such as antioxidant, anticarcinogenic, neuroprotective, and anti-inflammatory activities. All of these activities depend on their chemical structures and their interaction with biological targets in the body. PC occur naturally in polymerized form, linked to glycosides and requires metabolic transformation from their ingestion to their absorption. The gut microbiota can transform PC into more easily absorbed metabolites. The PC, in turn, have prebiotic and antimicrobial actions on the microbiota. Selleckchem GS-9674 Despite this, their low oral bioavailability still compromises biological performance. Therefore, the use of nanocarriers has been demonstrated to be a useful strategy to improve PC absorption and, consequently, their health effects. Nanotechnology is an excellent alternative able to overcome the limits of oral bioavailability of PC, since it offers protection from degradation during their passage through the gastrointestinal tract. Moreover, nanotechnology is also capable of promoting controlled PC release and modulating the interaction between PC and the microbiota. However, little is known about the impact of the nanotechnology on PC effects on the gut microbiota. This review highlights the use of nanotechnology for PC delivery on gut microbiota, focusing on the ability of such formulations to enhance oral bioavailability by applying nanocarriers (polymeric nanoparticles, nanostructured lipid carriers, solid lipid nanoparticles). In addition, the effects of free and nanocarried PC or nanocarriers per se on gut microbiota are also described.Poor performance among health service providers is a key barrier to high quality, adolescent-responsive health services. Collaborative learning has been shown to strengthen health service provider performance, but few studies have examined its implementation in adolescent health services. In this paper, we describe a collaborative learning approach for adolescent health service providers implemented as part of a project aiming to prevent HIV in adolescent girls and young women in the Democratic Republic of the Congo (DRC) and explore its feasibility, acceptability, benefits and challenges. To do so, we reviewed plans, budgets and progress reports, as well as nested implementation research related to the project. We also carried out a quantitative analysis of the number, location, participants and topics of collaborative learning sessions conducted as part of this initiative, and thematic analysis to synthesise findings on perceived benefits and challenges. Under the project, 32 collaborative learning sessionsability and uptake of health services.Advances in science and technology have allowed for incredible improvements in healthcare. Additionally, the digital revolution in healthcare provides new ways of collecting and storing large volumes of patient data, referred to as big healthcare data. As a result, healthcare providers are now able to use data to gain a deeper understanding of how to treat an individual in what is referred to as personalized healthcare. Regardless, there are several ethical challenges associated with big healthcare data that affect how personalized healthcare is delivered. To highlight these issues, this article will review the role of big data in personalized healthcare while also discussing the ethical challenges associated with it. The article will also discuss public health surveillance, its implications, and the challenges associated with collecting participants' information. The article will proceed by highlighting next generation technologies, including robotics and 3D printing. The article will conclude by providing recommendations on how patient privacy can be protected in next-generation personalized healthcare.Studies have shown that the sharing of big health data can improve patient management across primary and secondary care sectors. It can also reduce costs and can enhance the medical research process. Unfortunately, many big health data initiatives are being impeded because of a range of complex issues. This study was initiated to identify the said issues and develop a tool for health marketers to use to negate the barriers in big healthcare data projects. The study demonstrates how the Interactive Communication Technology Adoption Model can be operationalized to support qualitative researchers.The extracellular matrix (ECM) disruption and cytoskeleton reorganization are crucial events in tumor proliferation and invasion. E-Cadherin (E-CAD) is a member of cell adhesion molecules involved in cell-cell junctions and ECM stability. The loss of E-CAD expression is associated with cancer progression and metastasis. This retrospective study aimed to assess E-CAD protein expression in ovarian cancer (OC) tissues and to evaluate its prognostic value.

143 formalin-fixed and paraffin-embedded (FFPE) blocks of primary advanced stages OC were retrieved and used to construct Tissue microarrays. Automated immunohistochemistry technique was performed to evaluate E-CAD protein expression patterns in OC.

E-CAD protein expression was significantly correlated with OC histological subtype (p<0.0001), while borderline significant correlations were observed with both tumor grade (p=0.06) and stage (p=0.07). Interestingly, Kaplan-Meier survival analysis showed that OC patients with membranous E-CAD expression survived longer than those with no E-CAD expression mainly those at advanced stages (p<0.009). Further in silico analysis confirms the key roles of E-CAD in OC molecular functions.

we reported a prognosis value of membranous E-CAD in advanced stage OC patients. Further validation using larger cohorts is recommended to extract clinically relevant outcomes towards better OC management and individualized oncology.

we reported a prognosis value of membranous E-CAD in advanced stage OC patients. Further validation using larger cohorts is recommended to extract clinically relevant outcomes towards better OC management and individualized oncology.This research investigates factors influencing the actual usage of wearable fitness devices. Based on the Unified Theory of Acceptance and Use of Technology, the authors propose that privacy concerns, social influence, data accuracy, device engagement, and user efficacy impact the actual usage of wearable fitness devices via performance and effort expectancy. Based on 124 responses using the structural equation approach, most hypotheses were supported. The social influence had the strongest indirect effect through performance expectancy, while user efficacy had the strongest indirect effect through effort expectancy. Data accuracy and device engagement had a positive influence on actual usage and privacy concerns negatively affected the device's use.This study considers Theory of Reasoned Action and Technology Acceptance Model frameworks to test the mediating role of attitude towards using E-health platforms. 224 medical practitioners' responses are collected in the online mode. The mediation analysis supports the full mediation role of attitude towards using the E-health platforms in the relationship between perceived usefulness and intentions to use with (indirect effect = .15, SE = 0.03, LLCI = 0.09, and ULCI = .22). E-health platform developers should take active measures to improve the attitude of medical practitioners towards using such platforms, to derive the best results of the added features.Most Ugandans live in rural, medically underserved communities where geography and poverty lead to reduced access to healthcare. We present a novel low-cost approach for supplemental primary care financing through 1) pooling community wealth to cover overhead costs for outreach clinic activities and 2) issuing microfinance loans to motorcycle taxi entrepreneurs to overcome gaps in access to transportation. The intervention described here, which leverages community participation as a means to extend the reach of government health service delivery, was developed and implemented by Health Access Connect (HAC), a non-governmental organization based in Uganda. HAC began its work in August 2015 in the Lake Victoria region and now serves over 40 sites in Uganda across 5 districts, helping government health-care workers to provide over 1,300 patient services per month (and over 35,000 since the program's inception) with an average administrative cost of $6.24 per patient service in 2020. In this article, we demonstrate how integrated and appropriately resourced monthly outreach clinics, based on a microfinance-linked model of wealth pooling and government cooperation, can expand the capacity of government-provided healthcare to reach more patients living in remote communities. This scalable, sustainable, and flexible model is responsive to shifting needs of patients and health systems and presents an alternative approach to healthcare financing in low-resource settings. More rigorous evaluation of health outcomes stemming from such community-based models of service delivery is warranted.The electroencephalogram (EEG) is the most promising and efficient technique to study epilepsy and record all the electrical activity going in our brain. Automated screening of epilepsy through data-driven algorithms reduces the manual workload of doctors to diagnose epilepsy. New algorithms are biased either towards signal processing or deep learning, which holds subjective advantages and disadvantages. The proposed pipeline is an end-to-end automated seizure prediction framework with a Fourier transform feature extraction and deep learning-based transformer model, a blend of signal processing and deep learning - this imbibes the potential features to automatically identify the attentive regions in EEG signals for effective screening. The proposed pipeline has demonstrated superior performance on the benchmark dataset with average sensitivity and false-positive rate per hour (FPR/h) as 98.46%, 94.83% and 0.12439, 0, respectively. The proposed work shows great results on the benchmark datasets and a big potential for clinics as a support system with medical experts monitoring the patients.