Clinical microbiology study of diabetic foot ulcer in Iran; pathogens and antibacterial susceptibility

The aim of this study was to investigate microbial pathogens and their antibiotic susceptibility profile in infected diabetic foot ulcers in Iranian patients. This was a one-year cross sectional study on diabetic patients with infected diabetic foot ulcer at Shariati Teaching Hospital, Tehran, Iran. Grade of ulcer was determined by Wagner's criteria. Specimens were obtained from the base of ulcer, deep part of the wound or aspiration and were tested with gram staining and antibacterial susceptibility was determined with both disk diffusion and E-Test methods. Total of 546 pathogens were isolated from 165 ulcers of 149 patients. Gram positive aerobes including Enterococcal species and methicillin resistant Staphylococcus aureus (S. aureus) (21.4 and 19.4%, respectively) were identified as the most common pathogens followed by Gram negative isolates including Escherichia coli and Pseudomonas-aeruginosa (12.6 and 5.4%, respectively). The majority of wounds were classified as Wagner grades 2 and 3 (15.7 and 75.7%). Appropriate empiric treatment to cover both these Gram positive and Gram negative pathogens is crucially important

The Optimal Configuration of Wave Energy Conversions Respective to the Nearshore Wave Energy Potential

Ocean energy is one potential renewable energy alternative to fossil fuels that has a more significant power generation due to its better predictability and availability. In order to harness this source, wave energy converters (WECs) have been devised and used over the past several years to generate as much energy and power as is feasible. While it is possible to install these devices in both nearshore and offshore areas, nearshore sites are more appropriate places since more severe weather occurs offshore. Determining the optimal location might be challenging when dealing with sites along the coast since they often have varying capacities for energy production. Constructing wave farms requires determining the appropriate location for WECs, which may lead us to its correct and optimum design. The WEC size, shape, and layout are factors that must be considered for installing these devices. Therefore, this review aims to explain the methodologies, advancements, and effective hydrodynamic parameters that may be used to discover the optimal configuration of WECs in nearshore locations using evolutionary algorithms (EAs)

A Multi-criteria Decision-making Optimization Model for Flood Management in Reservoirs

Flood management in a reservoir-outlet system is a multi-criterion decision-making (MCDM) issue, in which preventing flood damage and flood overtopping, as well as fulfilling water demands, are often considered essential practices. However, although MCDM models can be used for flood control, there is a knowledge gap in hybrid modeling of the reservoirs and their outlets based on a coupled MCDM and optimization model during the flood. In this paper, an MCDM-optimization model was presented for reservoir systems' optimal designs in flood conditions based on a robust optimization technique, namely multi-objective particle swarm optimization (MOPSO), applying a powerful MCDM tool, so-called complex proportional assessment (COPRAS) for the first time in the literature, considering the weights generated by Shannon Entropy method. The objectives of this optimization model were defined based on the non-linear interval number programming (NINP) technique to optimize the orifice and triangular, rectangular, and proportional weirs specifications. This methodology was applied to a practical reservoir MCDM optimization problem in flood conditions to demonstrate its applicability and efficiency. Results indicated that the proposed framework could successfully and effectually provide the reservoirs and outlets with superior optimal design

A Stochastic Conflict Resolution Optimization Model for Flood Management in Detention Basins: Application of Fuzzy Graph Model

Floods are a natural disaster of significant concern because of their considerable damages to people’s livelihood. To this extent, there is a critical need to enhance flood management techniques by establishing proper infrastructure, such as detention basins. Although intelligent models may be adopted for flood management by detention basins, there is a literature gap on the optimum design of such structures while facing flood risks. The presented study filled this research gap by introducing a methodology to obtain the optimum design of detention basins using a stochastic conflict resolution optimization model considering inflow hydrographs uncertainties. This optimization model was developed by minimizing the conditional value-at-risk (CvaR) of flood overtopping, downstream flood damage, and deficit risk of water demand, as well as the deviation of flood overtopping and downstream damage based on non-linear interval number programming (NINP), for four different outlets types via a robust optimization tool, namely the non-dominated sorting genetic algorithm-III (NSGA-III). Conflict resolution was performed using the graph model for conflict resolution (GMCR) technique, enhanced by fuzzy preferences, to comply with the authorities’ priorities. Results indicated that the proposed framework could effectively design optimum detention basins consistent with the regional and hydrological standards

Historical Hazard Assessment of Climate and Land Use–Land Cover Effects on Soil Erosion Using Remote Sensing: Case Study of Oman

Human activities, climate change, and land-use alterations accelerated soil erosion in recent decades and imposed significant threats to soil fertility and stability worldwide. Understanding and quantifying the spatiotemporal variation of soil erosion risks is crucial for adopting the best management practices for surface soils conservation. Here, we present a novel high-resolution (30 m) soil erosion framework based on the G2 erosion model by integrating satellite and reanalysis datasets and Machine Learning (ML) models to assess soil erosion risks and hazards spatiotemporally. The proposed method reflects the impacts of climate change in 1 h time resolutions and land use in 30 m scales on soil erosion risks for almost 4 decades (between 1985 and 2017). The soil erosion hazardous maps were generated/evaluated using Extreme Value Analysis (EVA), utilizing long-term annual soil erosion estimations/projections to aid policymakers in developing management strategies to protect lands against extreme erosion. The proposed framework is evaluated in the Sultanate of Oman, which lacks soil erosion estimation/assessment studies due to data scarcity. Results indicate that soil erosion has increasing perilous trends in high altitudes of the Sultanate of Oman that may cause substantial risks to soil health and stability

Enhancing Classification Through Multi-view Synthesis in Multi-Population Ensemble Genetic Programming

This study proposes a genetic programming (GP) approach for classification, integrating cooperative co-evolution with multi-view synthesis. Addressing the challenges of high-dimensional data, we enhance GP by distributing features across multiple populations, each evolving concurrently and cooperatively. Akin to multi-view ensemble learning, the segmentation of the feature set improves classifier performance by processing disparate data "views". Individuals comprise multiple genes, with a SoftMax function synthesizing gene outputs. An ensemble method combines decisions across individuals from different populations, augmenting classification accuracy and robustness. Instead of exploring the entire search space, this ensemble approach divides the search space to multiple smaller subspaces that are easier to explore and ensures that each population specializes in different aspects of the problem space. Empirical tests on multiple datasets show that the classifier obtained from proposed approach outperforms the one obtained from a single-population GP executed for the entire feature set

Systematic review on intentional non-medical fentanyl use among people who use drugs

ObjectivesFentanyl is a highly potent opioid and has, until recently, been considered an unwanted contaminant in the street drug supply among people who use drugs (PWUD). However, it has become a drug of choice for an increasing number of individuals. This systematic review evaluated intentional non-medical fentanyl use among PWUD, specifically by summarizing demographic variance, reasons for use, and resulting patterns of use.MethodsThe search strategy was developed with a combination of free text keywords and MeSH and non-MeSH keywords, and adapted with database-specific filters to Ovid MEDLINE, Embase, Web of Science, and PsychINFO. Studies included were human studies with intentional use of non-medical fentanyl or analogues in individuals older than 13. Only peer-reviewed original articles available in English were included.ResultsThe search resulted in 4437 studies after de-duplication, of which 132 were selected for full-text review. Out of 41 papers included, it was found that individuals who use fentanyl intentionally were more likely to be young, male, and White. They were also more likely to have experienced overdoses, and report injection drug use. There is evidence that fentanyl seeking behaviours are motivated by greater potency, delay of withdrawal, lower cost, and greater availability.ConclusionsAmong PWUD, individuals who intentionally use fentanyl have severe substance use patterns, precarious living situations, and extensive overdose history. In response to the increasing number of individuals who use fentanyl, alternative treatment approaches need to be developed for more effective management of withdrawal and opioid use disorder.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42021272111

Improving the endoscopic recognition of early colorectal carcinoma using artificial intelligence: current evidence and future directions

Background and study aims Artificial intelligence (AI) has great potential to improve endoscopic recognition of early stage colorectal carcinoma (CRC). This scoping review aimed to summarize current evidence on this topic, provide an overview of the methodologies currently used, and guide future research. Methods A systematic search was performed following the PRISMA-Scr guideline. PubMed (including Medline), Scopus, Embase, IEEE Xplore, and ACM Digital Library were searched up to January 2024. Studies were eligible for inclusion when using AI for distinguishing CRC from colorectal polyps on endoscopic imaging, using histopathology as gold standard, reporting sensitivity, specificity, or accuracy as outcomes. Results Of 5024 screened articles, 26 were included. Computer-aided diagnosis (CADx) system classification categories ranged from two categories, such as lesions suitable or unsuitable for endoscopic resection, to five categories, such as hyperplastic polyp, sessile serrated lesion, adenoma, cancer, and other. The number of images used in testing databases varied from 69 to 84,585. Diagnostic performances were divergent, with sensitivities varying from 55.0% to 99.2%, specificities from 67.5% to 100% and accuracies from 74.4% to 94.4%. Conclusions This review highlights that using AI to improve endoscopic recognition of early stage CRC is an upcoming research field. We introduced a suggestions list of essential subjects to report in research regarding the development of endoscopy CADx systems, aiming to facilitate more complete reporting and better comparability between studies. There is a knowledge gap regarding real-time CADx system performance during multicenter external validation. Future research should focus on development of CADx systems that can differentiate CRC from premalignant lesions, while providing an indication of invasion depth

Vaccination with human amniotic epithelial cells confer effective protection in a murine model of Colon adenocarcinoma

As a prophylactic cancer vaccine, human amniotic membrane epithelial cells (hAECs) conferred effective protection in a murine model of colon cancer. The immunized mice mounted strong cross-protective CTL and antibody responses. Tumor burden was significantly reduced in tumor-bearing mice after immunization with hAECs. Placental cancer immunotherapy could be a promising approach for primary prevention of cancer. In spite of being the star of therapeutic strategies for cancer treatment, the results of immunotherapeutic approaches are still far from expectations. In this regard, primary prevention of cancer using prophylactic cancer vaccines has gained considerable attention. The immunologic similarities between cancer development and placentation have helped researchers to unravel molecular mechanisms responsible for carcinogenesis and to take advantage of stem cells from reproductive organs to elicit robust anti-cancer immune responses. Here, we showed that vaccination of mice with human amniotic membrane epithelial cells (hAECs) conferred effective protection against colon cancer and led to expansion of systemic and splenic cytotoxic T cell population and induction of cross-protective cytotoxic responses against tumor cells. Vaccinated mice mounted tumor-specific Th1 responses and produced cross-reactive antibodies against cell surface markers of cancer cells. Tumor burden was also significantly reduced in tumor-bearing mice immunized with hAECs. Our findings pave the way for potential future application of hAECs as an effective prophylactic cancer vaccine. © 2017 UIC