Awareness, Practices, Attitudes, And Barriers Of Telehealth In Saudi Arabia.

Objective: To assess awareness level, practices, attitudes and barriers regarding telehealth among healthcare providers (HCPs) in Saudi Arabia. METHODS: This cross-sectional study was approved by institutional review board (IRB) at King Fahad Medical City, Riyadh, Saudi Arabia, from June 28 to September 18, 2022, and comprised HCPs at different hospitals across the country. Data was collected using an online questionnaire exploring the awareness level, practices, attitudes and barriers regarding telehealth. Data was analysed using Statistical Analysis System 9.4. RESULTS: Of the 607 HCPs, 525(86.5%) were Saudi HCPs; 301(57.33%) males and 224(42.66%) females, with the largest age group being 31-40 years 218(41.52%). The remaining 82(13.5%) HCPs were non-Saudi HCPs; 43(52.43%) males and 39(47.56%) females, with the largest age group being 31-40 years 35(42.68%). Overall, there were 305(50.24%) health practitioners and most of the HCPs belonged to the Riyadh region 194(31.96%). 549(90.44%) of HCPs were aware of telehealth, while 360(59.30%) were actually practicing it. Also, 488(80.39%) of HCPs believed that telehealth is a valid technology, while 443(72.98%) believed telehealth is a reliable technology. Conclusion: Awareness regarding telehealth was high among HCPs meaning telehealth are progressively growing and spreading among HCPs and medical facilities in Saudi Arabia, who also found it valid and reliable.

Molecular identification, phylogeny and antifungal susceptibilities of dematiaceous fungi isolated from human keratomycosis.

PURPOSE: To investigate the dematiaceous fungal profile of patients with ocular mycoses attending a tertiary eye care hospital in Coimbatore, India METHODS: The identification of dematiaceous fungus based on their morphology, their genotypes, and the measurement of the minimum inhibitory concentrations (MICs) using microdilution method of routinely used antifungal drugs were all compared. RESULTS: A total of 148 dematiaceous fungi were isolated during a study period of 27 months. Isolates were confirmed as Curvularia spp. (n = 98), Exserohilum spp. (n = 32), Alternaria spp. (n = 14), Exophiala spp. (n = 2), Cladosporium sp. (n = 1) and Aureobasidium sp. (n = 1). Out of 50 well grown isolates characterized genotypically based on the amplification and sequencing of the ITS region of the ribosomal RNA gene cluster and subsequent BLAST analysis, Curvularia lunata (n = 24), C. aeria (n = 1), C. spicifera (n = 8), C. hawaiiensis (n = 1), C. maydis (n = 2), C. papendorfii (n = 2), C. geniculata (n = 3), C. tetramera (n = 2) and Exs. rostratum (n = 7) were identified. In vitro antifungal susceptibilities of the most tested dematiaceous isolates showed that voriconazole had a MIC50 of 0.25 µg ml-1, while amphotericin B had a MIC50 of 0.25 µg ml-1 for Curvularia spp. and Alternaria spp. CONCLUSION: Voriconazole proved to be the most effective drug against the pigmented filamentous fungi, followed by amphotericin B, itraconazole and econazole.

Deep Transfer Learning Based Unified Framework for COVID19 Classification and Infection Detection from Chest X-Ray Images.

The presentation of the COVID19 has endangered several million lives worldwide causing thousands of deaths every day. Evolution of COVID19 as a pandemic calls for automated solutions for initial screening and treatment management. In addition to the thermal scanning mechanisms, findings from chest X-ray imaging examinations are reliable predictors in COVID19 detection, long-term monitoring and severity evaluation. This paper presents a novel deep transfer learning based framework for COVID19 detection and segmentation of infections from chest X-ray images. It is realized as a two-stage cascaded framework with classifier and segmentation subnetwork models. The classifier is modeled as a fine-tuned residual SqueezeNet network, and the segmentation network is implemented as a fine-tuned SegNet semantic segmentation network. The segmentation task is enhanced with a bioinspired Gaussian Mixture Model-based super pixel segmentation. This framework is trained and tested with two public datasets for binary and multiclass classifications and infection segmentation. It achieves accuracies of 99.69% and 99.48% for binary and three class classifications, and a mean accuracy of 83.437% for segmentation. Experimental results and comparative evaluations demonstrate the superiority of this unified model and signify potential extensions for biomarker definition and severity quantization.

Proteomics approach and techniques in identification of reliable biomarkers for diseases.

Biomarkers, also called biological markers, are indicators to identify a biological case or situation as well as detecting any presence of biological activities and processes. Proteins are considered as a type of biomarkers based on their characteristics. Therefore, proteomics approach is one of the most promising approaches in this field. The purpose of this review is to summarize the use of proteomics approach and techniques to identify proteins as biomarkers for different diseases. This review was obtained by searching in a computerized database. So, different researches and studies that used proteomics approach to identify different biomarkers for different diseases were reviewed. Also, techniques of proteomics that are used to identify proteins as biomarkers were collected. Techniques and methods of proteomics approach are used for the identification of proteins' activities and presence as biomarkers for different types of diseases from different types of samples. There are three essential steps of this approach including: extraction and separation of proteins, identification of proteins, and verification of proteins. Finally, clinical trials for new discovered biomarker or undefined biomarker would be on.

In-vitro assessment of first-line antifungal drugs against Aspergillus spp. caused human keratomycoses.

BACKGROUND AND OBJECTIVES: Aspergillus keratitis are in the increasing trend and reported as the second most common cause of mycotic keratitis in developing countries. The present study was designed to isolate, identify Aspergillus spp. from the keratits/corneal ulcer patients attending a tertiary care eye hospital, Coimbatore, South India and to assess the minimum inhibitory concentrations (MICs) against ten clinically used first-line antifungal drugs. METHODS: A total of seventy-three Aspergillus strains isolated from corneal scrapings were included and assessed for a period of one year. All isolates were identified up to the species level by morphological observations. Antifungal drug susceptibilities were determined against a standard panel of antifungal agents. CONCLUSIONS: Five different species of aspergilli, A. flavus (n=53), A. fumigatus (n=14), A. terreus (n=9), A. tamarii (n=6) and A. niger (n=3) were identified based on morphological features. Minimum inhibitory concentration analyses indicated that, voriconazole, natamycin, itraconazole, clotrimazole, econazole followed by ketoconazole shall be the order of choices for the effective treatment for Aspergillus keratitis.