A Robust UWSN Handover Prediction System Using Ensemble Learning.

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models.

Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep-sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors' knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model

Aprepitant, An Antiemetic Agent, Interferes with Metal Ion Homeostasis of Candida Auris and Displays Potent Synergistic Interactions With Azole Drugs

With the rapid increase in the frequency of azole-resistant species, combination therapy appears to be a promising tool to augment the antifungal activity of azole drugs against resistant Candida species. Here, we report the effect of aprepitant, an antiemetic agent, on the antifungal activities of azole drugs against the multidrug-resistant Candida auris. Aprepitant reduced the minimum inhibitory concentration (MIC) of itraconazole in vitro, by up to eight-folds. Additionally, the aprepitant/itraconazole combination interfered significantly with the biofilm-forming ability of C. auris by 95 ± 0.13%, and significantly disrupted mature biofilms by 52 ± 0.83%, relative to the untreated control. In a Caenorhabditis elegans infection model, the aprepitant/itraconazole combination significantly prolonged the survival of infected nematodes by ~90% (five days postinfection) and reduced the fungal burden by ~92% relative to the untreated control. Further, this novel drug combination displayed broad-spectrum synergistic interactions against other medically important Candida species such as C. albicans, C. krusei, C. tropicalis, and C. parapsilosis (ƩFICI ranged from 0.08 to 0.31). Comparative transcriptomic profiling and mechanistic studies indicated aprepitant/itraconazole interferes significantly with metal ion homeostasis and compromises the ROS detoxification ability of C. auris. This study presents aprepitant as a novel, potent, and broadspectrum azole chemosensitizing agent that warrants further investigation

Repurposing Approach IdentifiesPitavastatin as a Potent AzoleChemosensitizing Agent EffectiveAgainst Azole-Resistant CandidaSpecies

The limited number of antifungals and the rising frequency of azole-resistant Candida species are growing challenges to human medicine. Drug repurposing signifies an appealing approach to enhance the activity of current antifungal drugs. Here, we evaluated the ability of Pharmakon 1600 drug library to sensitize an azole-resistant Candida albicans to the effect of fluconazole. The primary screen revealed 44 non-antifungal hits were able to act synergistically with fluconazole against the test strain. Of note, 21 compounds, showed aptness for systemic administration and limited toxic effects, were considered as potential fluconazole adjuvants and thus were termed as “repositionable hits”. A follow-up analysis revealed pitavastatin displaying the most potent fluconazole chemosensitizing activity against the test strain (ΣFICI 0.05) and thus was further evaluated against 18 isolates of C. albicans (n = 9), C. glabrata (n = 4), and C. auris (n = 5). Pitavastatin displayed broad-spectrum synergistic interactions with both fluconazole and voriconazole against ~89% of the tested strains (ΣFICI 0.05–0.5). Additionally, the pitavastatin-fluconazole combination significantly reduced the biofilm-forming abilities of the tested Candida species by up to 73%, and successfully reduced the fungal burdens in a Caenorhabditis elegans infection model by up to 96%. This study presents pitavastatin as a potent azole chemosensitizing agent that warrant further investigation

Identification of a PhenylthiazoleSmall Molecule with DualAntifungal and Antibiofilm ActivityAgainst Candida albicans andCandida auris

Candida species are a leading source of healthcare infections globally. The limited number of antifungal drugs combined with the isolation of Candida species, namely C. albicans and C. auris, exhibiting resistance to current antifungals necessitates the development of new therapeutics. The present study tested 85 synthetic phenylthiazole small molecules for antifungal activity against drug-resistant C. albicans. Compound 1 emerged as the most potent molecule, inhibiting growth of C. albicans and C. auris strains at concentrations ranging from 0.25–2μg/mL. Additionally, compound 1 inhibited growth of other clinically-relevant yeast (Cryptococcus) and molds (Aspergillus) at a concentration as low as 0.50μg/mL. Compound 1 exhibited rapid fungicidal activity, reducing the burden of C. albicans and C. auris below the limit of detection within 30 minutes. Compound 1 exhibited potent antibiofilm activity, similar to amphotericin B, reducing the metabolic activity of adherent C. albicans and C. auris biofilms by more than 66% and 50%, respectively. Furthermore, compound 1 prolonged survival of Caenorhabditis elegans infected with strains of C. albicans and C. auris, relative to the untreated control. The present study highlights phenylthiazole small molecules, such as compound 1, warrant further investigation as novel antifungal agents for drug-resistant Candida infections

Ospemifene Displays Broad-Spectrum Synergistic Interactions with Itraconazole Through Potent Interference with Fungal Efflux Activities

Azole antifungals are vital therapeutic options for treating invasive mycotic infections. However, the emergence of azole-resistant isolates combined with limited therapeutic options presents a growing challenge in medical mycology. To address this issue, we utilized microdilution checkerboard assays to evaluate nine stilbene compounds for their ability to interact synergistically with azole drugs, particularly against azole-resistant fungal isolates. Ospemifene displayed the most potent azole chemosensitizing activity, and its combination with itraconazole displayed broad-spectrum synergistic interactions against Candida albicans, Candida auris, Cryptococcus neoformans, and Aspergillus fumigatus (ΣFICI = 0.05–0.50). Additionally, in a Caenorhabditis elegans infection model, the ospemifene-itraconazole combination significantly reduced fungal CFU burdens in infected nematodes by ~75–96%. Nile Red efflux assays and RT-qPCR analysis suggest ospemifene interferes directly with fungal efflux systems, thus permitting entry of azole drugs into fungal cells. This study identifies ospemifene as a novel antifungal adjuvant that augments the antifungal activity of itraconazole against a broad range of fungal pathogens

Non‐Toxic Glycosylated Gold Nanoparticle‐Amphotericin B Conjugates Reduce Biofilms and Intracellular Burden of Fungi and Parasites

Infections by intracellular pathogens cause significant morbidity and mortality due to lack of efficient drug delivery. Amphotericin B, currently used to treat leish maniasis and cryptococcosis, is very toxic and cannot eradicate intracellular Cryptococcus neoformans (C. neoformans). Glycosylated gold nanoparticles are water dispersible and biocompatible with very little toxici ty. While amphotericin B is insoluble in water at neutral pH, conjugates of amphotericin B and ultra-small gold nanoparticles (AuNP) are better dispersible in water. Amphotericin B conjugated glycosylated gold nanoparticles (AmpoB@AuNP) are more efficacious in treating both extracellular and intracellular forms of Leishmania mexicana (L. mexicana) than amphotericin B alone. In addition, AmpoB@AuNP are effective in reducing C. neoformans biofilms by 80% and intracellular C. neoformans burden by >90%. Furthermore, AmpoB@AuNP are not haemolytic at 50 mu g mL(-1) and are significantly less toxic to murine macrophages than amphotericin B. Ultra-small AuNPs are attractive delivery agents to treat intracellular infections and AmpoB@AuNP may be useful for treating C. neoformans infections in immunocompromised patients

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020