An Optimized Uncertainty-Aware Training Framework for Neural Networks

Uncertainty quantification (UQ) for predictions generated by neural networks (NNs) is of vital importance in safety-critical applications. An ideal model is supposed to generate low uncertainty for correct predictions and high uncertainty for incorrect predictions. The main focus of state-of-the-art training algorithms is to optimize the NN parameters to improve the accuracy-related metrics. Training based on uncertainty metrics has been fully ignored or overlooked in the literature. This article introduces a novel uncertainty-aware training algorithm for classification tasks. A novel predictive uncertainty estimate-based objective function is defined and optimized using the stochastic gradient descent method. This new multiobjective loss function covers both accuracy and uncertainty accuracy (UA) simultaneously during training. The performance of the proposed training framework is compared from different aspects with other UQ techniques for different benchmarks. The obtained results demonstrate the effectiveness of the proposed framework for developing the NN models capable of generating reliable uncertainty estimates.© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Indicators of Quality of Care in Individuals With Traumatic Spinal Cord Injury: A Scoping Review

Study Design: Scoping review. Objectives: To identify a practical and reproducible approach to organize Quality of Care Indicators (QoCI) in individuals with traumatic spinal cord injury (TSCI). Methods: A comprehensive literature review was conducted in the Cochrane Central Register of Controlled Trials (CENTRAL) (Date: May 2018), MEDLINE (1946 to May 2018), and EMBASE (1974 to May 2018). Two independent reviewers screened 6092 records and included 262 full texts, among which 60 studies were included for qualitative analysis. We included studies, with no language restriction, containing at least 1 quality of care indicator for individuals with traumatic spinal cord injury. Each potential indicator was evaluated in an online, focused group discussion to define its categorization (healthcare system structure, medical process, and individuals with Traumatic Spinal Cord Injury related outcomes), definition, survey options, and scale. Results: A total of 87 indicators were identified from 60 studies screened using our eligibility criteria. We defined each indicator. Out of 87 indicators, 37 appraised the healthcare system structure, 30 evaluated medical processes, and 20 included individuals with TSCI related outcomes. The healthcare system structure included the impact of the cost of hospitalization and rehabilitation, as well as staff and patient perception of treatment. The medical processes included targeting physical activities for improvement of health-related outcomes and complications. Changes in motor score, functional independence, and readmission rates were reported as individuals with TSCI-related outcomes indicators. Conclusion: Indicators of quality of care in the management of individuals with TSCI are important for health policy strategists to standardize healthcare assessment, for clinicians to improve care, and for data collection efforts including registries

Anti-entzünliche Wirkung von Milch bei Makrophagen

Zielsetzung: Milch und fermentierte Milchprodukte wirken entzündungshemmend auf Darmzellen. Die Rolle von Muttermilch und Kuhmilch bei der Polarisation von RAW 264.7 Makrophagenzellen wurde bisher nicht beobachtet. Hintergrund der vorliegende Studie war es im Hinblick auf die orale Mundgesundheit die Wirkung von Kuhmilch und Muttermilch auf die Beeinflussung der Entzündungsreaktionen zu analysieren. Material und Methode: Um dieses Ziel zu erreichen, wurden hier RAW 264.7 Makrophagenzellen mit Speichel, IL1 und TNF und IL4 inkubiert, um einen M1- und M2-Phänotypen mit einem wässrigen Anteil an Kuhmilch und Muttermilch zu erzeugen Die Genexpression der M1-Gene (IL1 und IL8) und der M2-Gene (ARG1 und YM1) mit der positiven Kontrolle IL4 wurden durch RT-PCR nachgewiesen. ELISA wurde für IL1 verwendet. Ergebnisse: Die erhaltenen RT-PCR-Ergebnisse zeigen, dass Milch (Kuhmilch und erhitzte Muttermilch) die provozierte Entzündung in Makrophagenzellen durch Verringerung der genetischen Expression von M1-Genen (IL1 und IL8) gesenkt hat. Wenn Muttermilch oder Kuhmilch hinzugefügt wurden (beides in pasteurisiertem Zustand), kam es zu einer signifikanten Reduktion der Entzündungsantwort. Darüber hinaus wurde die genetische Expression von M2-Genen (ARG1, YM1) mit positiver Kontrolle der IL4 durch Zusatz von Milch (Kuhmilch und erhitzte Muttermilch) erhöht, was die entzündungshemmende Wirkung von Milch bei Makrophagen bestätigt. Die erhaltenen ELISA-Ergebnisse haben gezeigt, dass die IL1-Konzentration im Überstand durch Zusatz von erhitzter Muttermilch verringert wurde, was auch die entzündungshemmende Wirkung von Milch auf die RAW264.7 Makrophagen bestätigt. Schlussfolgerung: Milch bewirkt eine Verschiebung der Makrophagen von M1 in Richtung M2-Phänotyp in vitro.2. Abstract Objective: Milk and fermented milk products have an anti-inflammatory effect on intestinal cells. The role of human milk and cows milk in the polarization of RAW 264.7 macrophage cells has not previously been reported. The background of the present study was to analyze the effects of cows milk and human milk on the effects of inflammatory reactions on oral health. Material and Methods: To achieve this goal, RAW 264.7 macrophage cells were incubated with saliva, IL1 and TNF and IL4 to produce M1 and M2 phenotypes with an aqueous content of cow's milk and heated Mother milk Gene expression of M1 genes (IL1 and IL8) and the M2 genes (ARG1 and YM1) with the positive control IL4 were detected by RT-PCR. ELISA was used for IL1. Results: The obtained RT-PCR results show that milk (cow's milk and heated Mother milk) has lowered the provoked inflammation in macrophage cells by reducing the genetic expression of M1 genes (IL1 and IL8). When Mother milk or cow's milk was added (both in pasteurized state), there was a significant reduction in the inflammatory response. In addition, the genetic expression of M2 genes (ARG1, YM1) with positive control of IL4 was increased by the addition of milk (cow's milk and heated Mother milk), confirming the anti-inflammatory effect of milk on macrophages. The ELISA results obtained showed that the IL1 concentration in the supernatant was reduced by the addition of heated breast milk, which also confirmed the anti-inflammatory effect of milk on the RAW264.7 macrophages. Conclusion: Milk causes a shift of macrophages from M1 towards the M2 phenotype in vitro.Paralleltitel laut Übersetzung des VerfassersMedizinische Universität Wien, Diplomarbeit, 2019(VLID)336474

The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving significant genes. To determine the impact of different imputation methods on the identification of important genes, the chi-squared test was used to compare the proportions of overlaps between significant genes detected from original data and those detected from the imputed datasets. Additionally, the significant genes are tested for their enrichment in important pathways, using the ConsensusPathDB. Our results showed that almost all the significant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no significant difference in the performance of various imputation methods tested. The source code and selected datasets are available on http://profiles.bs.ipm.ir/softwares/imputation_methods/

Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error. In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer. In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry (IHC) staining scores of bladder and breast cancers. Our classification pipeline includes a basic CNN architecture, Google's Inceptions with three training strategies, and an ensemble of two state-of-the-art algorithms, Inception and ResNet. Training strategies include training the last layer of Google's Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Google's Inception architectures, Inception-V1 and Inception-V3. We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google's Inceptions even in presence of extensive tumor heterogeneity. On average, our pipeline achieved accuracies of 100%, 92%, 95%, and 69% for discrimination of various cancer tissues, subtypes, biomarkers, and scores, respectively. Our pipeline and related documentation is freely available at https://github.com/ih-_lab/CNN_Smoothie

Descriptive epidemiology of dermatophytosis in rodents

Abstract Introduction Dermatophytosis is a zoonotic disease caused by a group of keratinophilic fungi called dermatophytes. Objectives Since the epidemiology of diseases revolves over time, this research studies the incidence of dermatophytosis among rodents referred to mycology laboratory during 2019–2021. Methods A total of 163 rodents including rabbits, guinea pigs, and hamsters suspecting having dermatophytosis were sampled by scraping lesions. Direct microscopic examination, culture, and polymerase chain reaction were done for diagnosis of dermatophytosis and identification of the etiologic agent. Results The results of this study showed that 37.4% of rodents were involved with dermatophytosis, among which 41.13% of rabbits, 25% of guinea pigs, and 26.3% of hamsters were included. Microsporum canis (52.7%) was the most isolated agent. Incidence of dermatophytosis was higher in female in rabbits while in hamsters and guinea pigs male were mostly infected. Rodents less than 6 months were more susceptible for dermatophytosis except for hamsters in which 6–12 months animals had a higher prevalence. Conclusion In conclusion, it is significant to update our knowledge about the epidemiology of dermatophytosis in rodents and other animals every few years to define valid preventive strategies. Moreover, since dermatophytes are contagious and zoonotic, it is also a priority to apply preventing methods for dermatophytosis and treat infected rodents with appropriate antifungal agents to decrease the risk of infection

Bioremediation of heavy metals in food industry: Application of Saccharomyces cerevisiae

Heavy metals are natural elements in the Earth’s crust that can enter human food through industrial or agricultural processing, in the form of fertilizers and pesticides. These elements are not biodegradable. Some heavy metals are known as pollutants and are toxic, and their bioaccumulation in plant and animal tissues can cause undesirable effects for humans; therefore, their amount in water and food should always be under control. The aim of this study is to investigate the conditions for the bioremediation of heavy metals in foods. Various physical, chemical, and biological methods have been used to reduce the heavy metal content in the environment. During the last decades, bioremediation methods using plants and microorganisms have created interest to researchers for their advantages such as being more specific and environmentally friendly. The main pollutant elements in foods and beverages are lead, cadmium, arsenic, and mercury, which have their own permissible limits. Among the microorganisms that are capable of bioremediation of heavy metals, Saccharomyces cerevisiae is an interesting choice for its special characteristics and being safe for humans, which make it quite common and useful in the food industry. Its mass production as the byproduct of the fermentation industry and the low cost of culture media are the other advantages. The ability of this yeast to remove an individual separated element has also been widely investigated. In countries with high heavy metal pollution in wheat, the use of S. cerevisiae is a native solution for overcoming the problem of solution.This article summarizes the main conditions for heavy metal absorption by S. cerevisiae.How to cite: Massoud R, Hadiani MR, Khosravi Darani K, et al. Bioremediation of heavy metals in food industry: Application of Saccharomyces cerevisiae. Electron J Biotechnol 2019;37. https://doi.org/10.1016/j.ejbt.2018.11.003. Keywords: Arsenic, Bioaccumulation, Bioremediation, Biosorption, Cadmium, Foods, Heavy metals, Lead, Mercury, Pollutants, Yeas

Preparation of Transdermal Patch Containing Selenium Nanoparticles Loaded with Doxycycline and Evaluation of Skin Wound Healing in a Rat Model

The present study aimed to prepare and evaluate a controlled-release system based on a chitosan scaffold containing selenium nanoparticles loaded with doxycycline. Its topical application in skin wound healing in rats was investigated. Therefore, 80 female rats were used and, after creating experimental skin defects on their back, were randomly divided into four equal groups: the control group without any therapeutic intervention; the second group received a chitosan transdermal patch (Ch); the third group received chitosan transdermal patch loaded with selenium nanoparticles (ChSeN), and the last group received chitosan transdermal patch containing selenium nanoparticle loaded by doxycycline (ChSeND). Morphological and structural characteristics of the synthesized patches were evaluated, and in addition to measuring the skin wound area on days 3, 7, and 21, a histopathological examination was performed. On the third day of the study, less hemorrhage and inflammation and more neo-vascularization were seen in the ChSeND group. Moreover, on day 7, less inflammation and collagen formation were recorded in the ChSeN and ChSeND groups than in the other groups. At the same time, more neo-vascularization and re-epithelialization were seen in the ChSeND group on days 7 and 21. In addition, on day 21 of the study, the most collagen formation was in this group. Examination of the wound area also showed that the lowest area belonged to the ChSeND group. The results showed that the simultaneous presence of selenium nanoparticles and doxycycline in the ChSeND group provided the best repair compared to the control, Ch and ChSeN groups