Explore the vast range of titles available.

The rise of infections associated with indwelling medical devices is a growing concern, often complicated by biofilm formation leading to persistent infections. This study investigates a novel approach to prevent Candida albicans attachment on the surface by altering surface topography. The research focuses on two distinct surface topographies: symmetry (squares) and non-symmetry (lines), created through a direct laser photolithography process on a Cyclic olefin copolymer (COC) surface. The wettability of these patterned surfaces was then examined immediately after fabrication and plasma treatment to mimic the sterilization process of indwelling devices through UV plasma. The results reveal directional wettability in the line pattern and size-dependent wettability in both square and line patterns. Candida albicans were cultured on these surfaces to assess the efficacy of the topography in preventing biofilm formation. The study demonstrates that symmetry and non-symmetry pattern topography inhibit biofilm formation, providing a promising strategy for mitigating Candida-associated infections on medical devices. The research sheds light on the potential of surface modification techniques to enhance the biocompatibility of medical devices and reduce the risk of biofilm-related infections.

Biofilm deposition on indwelling medical devices and implanted biomaterials is frequently attributed to the prevalence of resistant infections in humans. Further, the nature of persistent infections is widely believed to have a biofilm etiology. In this study, the wettability of commercially available indwelling medical devices was explored for the first time, and its effect on the formation of biofilm was determined in vitro . Surprisingly, all tested indwelling devices were found to be hydrophilic, with surface water contact angles ranging from 60° to 75°. First, we established a thriving Candida albicans biofilm growth at 24 hours. in YEPD at 30°C and 37°C plus serum in vitro at Cyclic olefin copolymer (COC) modified surface , which was subsequently confirmed via scanning electron microscopy, while their cellular metabolic function was assessed using the XTT cell viability assay. Surfaces with patterned wettability show that a contact angle of 110° (hydrophobic) inhibits C . albicans planktonic and biofilm formation completely compared to robust growth at a contact angle of 40° (hydrophilic). This finding may provide a novel antimicrobial strategy to prevent biofilm growth and antimicrobial resistance on indwelling devices and prosthetic implants. Overall, this study provides valuable insights into the surface characteristics of medical devices and their potential impact on biofilm formation, leading to the development of improved approaches to control and prevent microbial biofilms and re-infections.

The gut microbiome plays a significant role in the development of Type 2 Diabetes Mellitus (T2DM), but the functional mechanisms behind this association merit deeper investigation. Here, we used the nanopore sequencing technology for metagenomic analyses to compare the gut microbiome of individuals with T2DM from the United Arab Emirates (n = 40) with that of control (n = 44). DMM enterotyping of the cohort resulted concordantly with previous results, in three dominant groups Bacteroides (K1), Firmicutes (K2), and Prevotella (K3) lineages. The diversity analysis revealed a high level of diversity in the Firmicutes group (K2) both in terms of species richness and evenness (Wilcoxon rank-sum test, p value < 0.05 vs. K1 and K3 groups), consistent with the Ruminococcus enterotype described in Western populations. Additionally, functional enrichment analyses of KEGG modules showed significant differences in abundance between individuals with T2DM and controls (FDR < 0.05). These differences include modules associated with the degradation of amino acids, such as arginine, the degradation of urea as well as those associated with homoacetogenesis. Prediction analysis with the Predomics approach suggested potential biomarkers for T2DM, including a balance between a depletion of Enterococcus faecium and Blautia lineages with an enrichment of Absiella spp or Eubacterium limosum in T2DM individuals, highlighting the potential of metagenomic analysis in predicting predisposition to diabetic cardiomyopathy in T2DM patients.

Visual impairment affects millions worldwide, creating significant barriers to environmental interaction and independence. Existing assistive technologies often rely on cloud-based processing, raising privacy concerns and limiting accessibility in resource-constrained environments. This paper explores the integration and potential of open-source AI models in developing a fully offline assistive system that can be locally set up and operated to support visually impaired individuals. Built on a Raspberry Pi 5, the system combines real-time object detection (YOLOv8), optical character recognition (Tesseract), face recognition with voice-guided registration, and offline voice command control (VOSK), delivering hands-free multimodal interaction without dependence on cloud infrastructure. Audio feedback is generated using Piper for real-time environmental awareness. Designed to prioritize user privacy, low latency, and affordability, the platform demonstrates that effective assistive functionality can be achieved using only open-source tools on low-power edge hardware. Evaluation results in controlled conditions show 75–90% detection and recognition accuracies, with sub-second response times, confirming the feasibility of deploying such systems in privacy-sensitive or resource-constrained environments.

A GPCR, the parathyroid hormone receptor, can elicit a sustained signal from internal membranes after internalization. The signal was found to be terminated by a feedback mechanism where PKA activates the proton pump v-ATPase, which acidifies endosomes. The PTH receptor is to our knowledge one of the first G protein–coupled receptor (GPCR) found to sustain cAMP signaling after internalization of the ligand–receptor complex in endosomes. This unexpected model is adding a new dimension on how we think about GPCR signaling, but its mechanism is incompletely understood. We report here that endosomal acidification mediated by the PKA action on the v-ATPase provides a negative feedback mechanism by which endosomal receptor signaling is turned off.

Type 2 diabetes mellitus (T2DM) drastically affects the population of Middle East countries with an ever-increasing number of overweight and obese individuals. The precise links between T2DM and gut microbiome composition remain elusive in these populations. Here, we performed 16 S rRNA and ITS2- gene based microbial profiling of 50 stool samples from Emirati adults with or without T2DM. The four major enterotypes initially described in westernized cohorts were retrieved in this Emirati population. T2DM and non-T2DM healthy controls had different microbiome compositions, with an enrichment in Prevotella enterotype in non-T2DM controls whereas T2DM individuals had a higher proportion of the dysbiotic Bacteroides 2 enterotype. No significant differences in microbial diversity were observed in T2DM individuals after controlling for cofounding factors, contrasting with reports from westernized cohorts. Interestingly, fungal diversity was significantly decreased in Bacteroides 2 enterotype. Functional profiling from 16 S rRNA gene data showed marked differences between T2DM and non-T2DM controls, with an enrichment in amino acid degradation and LPS-related modules in T2DM individuals, whereas non-T2DM controls had increased abundance of carbohydrate degradation modules in concordance with enterotype composition. These differences provide an insight into gut microbiome composition in Emirati population and its potential role in the development of diabetes mellitus.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious virus with overwhelming demand on healthcare systems, which require advanced predictive analytics to strategize COVID-19 management in a more effective and efficient manner. We analyzed clinical data of 2017 COVID-19 cases reported in the Dubai health authority and developed predictive models to predict the patient's length of hospital stay and risk of death. A decision tree (DT) model to predict COVID-19 length of stay was developed based on patient clinical information. The model showed very good performance with a coefficient of determination R 2 of 49.8% and a median absolute deviation of 2.85 days. Furthermore, another DT-based model was constructed to predict COVID-19 risk of death. The model showed excellent performance with sensitivity and specificity of 96.5 and 87.8%, respectively, and overall prediction accuracy of 96%. Further validation using unsupervised learning methods showed similar separation patterns, and a receiver operator characteristic approach suggested stable and robust DT model performance. The results show that a high risk of death of 78.2% is indicated for intubated COVID-19 patients who have not used anticoagulant medications. Fortunately, intubated patients who are using anticoagulant and dexamethasone medications with an international normalized ratio of <1.69 have zero risk of death from COVID-19. In conclusion, we constructed artificial intelligence–based models to accurately predict the length of hospital stay and risk of death in COVID-19 cases. These smart models will arm physicians on the front line to enhance management strategies to save lives.

Coronavirus disease 2019 (COVID-19) was first identified in respiratory samples and was found to commonly cause cough and pneumonia. However, non-respiratory symptoms including gastrointestinal disorders are also present and a big proportion of patients test positive for the virus in stools for a prolonged period. In this cross-sectional study, we investigated viral load trends in stools and nasopharyngeal swabs and their correlation with multiple demographic and clinical factors. The study included 211 laboratory-confirmed cases suffering from a mild form of the disease and completing their isolation period at a non-hospital center in the United Arab Emirates. Demographic and clinical information was collected by standardized questionnaire and from the medical records of the patient. Of the 211 participants, 25% tested negative in both sample types at the time of this study and 53% of the remaining patients had detectable viral RNA in their stools. A positive fecal viral test was associated with male gender, diarrhea as a symptom, and hospitalization during infection. A positive correlation was also observed between a delayed onset of symptoms and a positive stool test. Viral load in stools positively correlated with, being overweight, exercising, taking antibiotics in the last 3 months and blood type O. The viral load in nasopharyngeal swabs, on the other hand, was higher for blood type A, and rhesus positive (Rh factor). Regression analysis showed no correlation between the viral loads measured in stool and nasopharyngeal samples in any given patient. The results of this work highlight the factors associated with a higher viral count in each sample. It also shows the importance of stool sample analysis for the follow-up and diagnosis of recovering COVID-19 patients.

The expanding development of data mining and statistical learning techniques have enriched recent efforts to understand and identify metagenomics biomarkers in airways diseases. In contribution to the growing microbiota research in respiratory contexts, this study aims to characterize respiratory microbiota in asthmatic patients (pediatrics and adults) in comparison to healthy controls, to explore the potential of microbiota as a biomarker for asthma diagonosis and prediction. Analysis of 16 S-ribosomal RNA gene sequences reveals that respiratory microbial composition and diversity are significantly different between asthmatic and healthy subjects. Phylum Proteobacteria represented the predominant bacterial communities in asthmatic patients in comparison to healthy subjects. In contrast, a higher abundance of Moraxella and Alloiococcus was more prevalent in asthmatic patients compared to healthy controls. Using a machine learning approach, 57 microbial markers were identified and used to characterize notable microbiota composition differences between the groups. Among the selected OTUs, Moraxella and Corynebacterium genera were found to be more enriched on the pediatric asthmatics (p-values < 0.01). In the era of precision medicine, the discovery of the respiratory microbiota associated with asthma can lead to valuable applications for individualized asthma care.

Acute respiratory distress syndrome (ARDS) is a major complication of the respiratory illness coronavirus disease 2019, with a death rate reaching up to 40%. The main underlying cause of ARDS is a cytokine storm that results in a dysregulated immune response. This review discusses the role of cytokines and chemokines in SARS-CoV-2 and its predecessors SARS-CoV and MERS-CoV, with particular emphasis on the elevated levels of inflammatory mediators that are shown to be correlated with disease severity. For this purpose, we reviewed and analyzed clinical studies, research articles, and reviews published on PubMed, EMBASE, and Web of Science. This review illustrates the role of the innate and adaptive immune responses in SARS, MERS, and COVID-19 and identifies the general cytokine and chemokine profile in each of the three infections, focusing on the most prominent inflammatory mediators primarily responsible for the COVID-19 pathogenesis. The current treatment protocols or medications in clinical trials were reviewed while focusing on those targeting cytokines and chemokines. Altogether, the identified cytokines and chemokines profiles in SARS-CoV, MERS-CoV, and SARS-CoV-2 provide important information to better understand SARS-CoV-2 pathogenesis and highlight the importance of using prominent inflammatory mediators as markers for disease diagnosis and management. Our findings recommend that the use of immunosuppression cocktails provided to patients should be closely monitored and continuously assessed to maintain the desirable effects of cytokines and chemokines needed to fight the SARS, MERS, and COVID-19. The current gap in evidence is the lack of large clinical trials to determine the optimal and effective dosage and timing for a therapeutic regimen.