A Deep Learning Approach for Basal Cell Carcinomas and Bowen’s Disease Recognition in Dermatopathology Image

Basal cell carcinomas and Bowen’s disease (squamous cell carcinoma in situ) are the most common cutaneous tumors. The early diagnoses of these diseases are very important due to their better prognosis. But it is a heavy workload for the pathologists to recognize a large number of pathological images and diagnose these diseases. So, there is an urgent need to develop an automatic method for detecting and classifying the skin cancers. This paper presents a recognition system of dermatopathology images based on the deep convolutional neural networks (CNN). The dermatopathology images are collected from the hospital. The deep learning model is trained using different image datasets. It can be found that the recognition accuracy of the system can be improved by using data augmentation even if the number of the clinical samples are not increased. But the recognition accuracy of the system is the highest when the number of the original histological image is increased. The experimental results that the system can correctly recognize 88.5% of patients with basal cell carcinoma and 86.5% of patients with Bowen’s disease.

Metabolic Enzyme Triosephosphate Isomerase 1 and Nicotinamide Phosphoribosyltransferase, Two Independent Inflammatory Indicators in Rheumatoid Arthritis: Evidences From Collagen-Induced Arthritis and Clinical Samples

Metabolic intervention is a novel anti-rheumatic approach. The glycolytic regulator NAMPT has been identified as a therapeutic target of rheumatoid arthritis (RA), while other metabolic regulators coordinating NAMPT to perpetuate inflammation are yet to be investigated. We continuously monitored and validated expression changes of Nampt and inflammatory indicators in peripheral while blood cells from rats with collagen-induced arthritis (CIA). Gene transcriptional profiles of Nampt+ and Nampt++ samples from identical CIA rats were compared by RNA-sequencing. Observed gene expression changes were validated in another batch of CIA rats, and typical metabolic regulators with persistent changes during inflammatory courses were further investigated in human subjects. According to expression differences of identified genes, RA patients were assigned into different subsets. Clinical manifestation and cytokine profiles among them were compared afterwards. Nampt overexpression typically occurred in CIA rats during early stages, when iNos and Il-1β started to be up-regulated. Among differentially expressed genes between Nampt+ and Nampt++ CIA rat samples, changes of Tpi1, the only glycolytic enzyme identified were sustained in the aftermath of acute inflammation. Similar to NAMPT, TPI1 expression in RA patients was higher than general population, which was synchronized with increase in RFn as well as inflammatory monocytes-related cytokines like Eotaxin. Meanwhile, RANTES levels were relatively low when NAMPT and TPI1 were overexpressed. Reciprocal interactions between TPI1 and HIF-1α were observed. HIF-1α promoted TPI1 expression, while TPI1 co-localized with HIF-1α in nucleus of inflammatory monocytes. In short, although NAMPT and TPI1 dominate different stages of CIA, they similarly provoke monocyte-mediated inflammation.

Recent Developments in Phenotypic and Molecular Diagnostic Methods for Antimicrobial Resistance Detection in Staphylococcus aureus: A Narrative Review

Staphylococcus aureus is an opportunistic pathogen responsible for a wide range of infections in humans, such as skin and soft tissue infections, pneumonia, food poisoning or sepsis. Historically, S. aureus was able to rapidly adapt to anti-staphylococcal antibiotics and become resistant to several classes of antibiotics. Today, methicillin-resistant S. aureus (MRSA) is a multidrug-resistant pathogen and is one of the most common bacteria responsible for hospital-acquired infections and outbreaks, in community settings as well. The rapid and accurate diagnosis of antimicrobial resistance in S. aureus is crucial to the early initiation of directed antibiotic therapy and to improve clinical outcomes for patients. In this narrative review, I provide an overview of recent phenotypic and molecular diagnostic methods for antimicrobial resistance detection in S. aureus, with a particular focus on MRSA detection. I consider methods for resistance detection in both clinical samples and isolated S. aureus cultures, along with a brief discussion of the advantages and the challenges of implementing such methods in routine diagnostics.

Benchmarking software to predict antibiotic resistance phenotypes in shotgun metagenomes using simulated data

The use of shotgun metagenomics for AMR detection is appealing because data can be generated from clinical samples with minimal processing. Detecting antimicrobial resistance (AMR) in clinical genomic data is an important epidemiological task, yet a complex bioinformatic process. Many software tools exist to detect AMR genes, but they have mostly been tested in their detection of genotypic resistance in individual bacterial strains. It is important to understand how well these bioinformatic tools detect AMR genes in shotgun metagenomic data. We developed a software pipeline, hAMRoaster (https://github.com/ewissel/hAMRoaster), for assessing accuracy of prediction of antibiotic resistance phenotypes. For evaluation purposes, we simulated a short read (Illumina) shotgun metagenomics community of eight bacterial pathogens with extensive antibiotic susceptibility testing profiles. We benchmarked nine open source bioinformatics tools for detecting AMR genes that 1) were conda or Docker installable, 2) had been actively maintained, 3) had an open source license, and 4) took FASTA or FASTQ files as input. Several metrics were calculated for each tool including sensitivity, specificity, and F1 at three coverage levels. This study revealed that tools were highly variable in sensitivity (0.25 - 0.99) and specificity (0.2 - 1) in detection of resistance in our synthetic FASTQ files despite similar databases and methods implemented. Tools performed similarly at all coverage levels (5x, 50x, 100x). Cohen’s kappa revealed low agreement across tools.

ANALYSIS OF THE ANTIBIOGRAM PROFILES OF BIOFILM FORMING STAPHYLOCOCCUS AUREUS AND ESCHERICHIA COLI

Background and Objectives: Bacteria that attach to the surfaces and produce polymeric matrix resulting in the biofilms formation are involved in a wider range of human infections. Biofilms forming Staphylococcus aureus and Escherichia coli are considered to be highly antibiotics resistant. This study aimed to analysis the antibiogram profile of biofilm forming S. aureus and E. coli in Mukalla city, Hadhramaut,Yemen. Methods: Sixty clinical isolates of S. aureus and E. coli were isolated from different clinical samples, and identified by standard bacteriological methods, then subjected to biofilm formation detection by tissue culture plate (TCP) method. The antibiotics susceptibility test was performed by disc diffusion (Kirby-Bauer) method. Chi-square test was used to analyze the data and p value< 0.05 was taken as significant. Results: Among the total isolates S. aureus and E. coli, TCP method detected 55% of isolates as strong, 25% as moderate and 25% as weak/non-biofilm producers. Biofilm forming of S. aureus developed significantly higher degrees of antibiotic resistance of amoxicillin/clavulanic acid100%, ceftazidime 95.8%, cefotaxime62.5%, cefadroxil 45.8%, ciprofloxacin 41.7% and ceftriaxone 25% with significant statistics correlation the resistance of amoxicillin/clavulanic acid and ceftazidime and bacterial biofilm production (p-value< 0.05). The rates of antibiotics resistance biofilm E. coli were 100%, 91.7%, 75%, 70.8%, 66.7%, 62.5% and 33.3%for amoxicillin/clavulanic acid, cefadroxil, cefotaxime, ceftazidime, ceftriaxone, ciprofloxacin and co-trimoxazole respectively with statistically significant correlation of cefadroxil resistance (p-value < 0.05). Conclusion: TCP method showed that S. aureus and E. coli isolates have high degree of biofilm forming ability. A high antibiotics resistance found in biofilm producers isolates than non-biofilm producers. Peer Review History: Received: 23 October 2021; Revised: 1 December; Accepted: 17 December, Available online: 15 January 2022 Academic Editor: Dr. Nuray Arı, Ankara University, Turkiye, [email protected] UJPR follows the most transparent and toughest ‘Advanced OPEN peer review’ system. The identity of the authors and, reviewers will be known to each other. This transparent process will help to eradicate any possible malicious/purposeful interference by any person (publishing staff, reviewer, editor, author, etc) during peer review. As a result of this unique system, all reviewers will get their due recognition and respect, once their names are published in the papers. We expect that, by publishing peer review reports with published papers, will be helpful to many authors for drafting their article according to the specifications. Auhors will remove any error of their article and they will improve their article(s) according to the previous reports displayed with published article(s). The main purpose of it is ‘to improve the quality of a candidate manuscript’. Our reviewers check the ‘strength and weakness of a manuscript honestly’. There will increase in the perfection, and transparency. Received file: Reviewer's Comments: Average Peer review marks at initial stage: 5.0/10 Average Peer review marks at publication stage: 7.0/10 Reviewers: Dr. Wadhah Hassan Ali Edrees, Hajja University, Yemen, [email protected] Dr. Tamer Elhabibi, Suez Canal University, Egypt, [email protected] Dr. Rawaa Souhil Al-Kayali, Aleppo University, Syria, [email protected] Rola Jadallah, Arab American University, Palestine, [email protected] Similar Articles: PREVALENCE OF STAPHYLOCOCCUS AUREUS IN DENTAL INFECTIONS AND THE OCCURRENCE OF MRSA IN ISOLATES PREVALENCE OF METHICILLIN RESISTANT STAPHYLOCOCCUS AUREUS (MRSA) AND ANTIMICROBIAL SUSCEPTIBILITY PATTERNS AT A PRIVATE HOSPITAL IN SANA'A, YEMEN

Prevalence and characterization of antimicrobial resistance among gram-negative bacteria isolated from febrile hospitalized patients in central Ethiopia

Background Infectious diseases are among the leading causes of death in many low-income countries, such as Ethiopia. Without reliable local data concerning causative pathogens and antimicrobial resistance, empiric treatment is suboptimal. The objective of this study was to characterize gram-negative bacteria (GNB) as pathogens and their resistance pattern in hospitalized patients with infections in central Ethiopia. Methods Patients ≥ 1 year of age with fever admitted to the Asella Referral and Teaching Hospital from April 2016 to June 2018 were included. Blood and other appropriate clinical specimens were collected and cultured on appropriate media. Antibiotic susceptibility testing (AST) was performed using the Kirby–Bauer method and VITEK® 2. Species identification and detection of resistance genes were conducted using MALDI-ToF MS (VITEK® MS) and PCR, respectively. Results Among the 684 study participants, 54.2% were male, and the median age was 22.0 (IQR: 14–35) years. Blood cultures were positive in 5.4% (n = 37) of cases. Among other clinical samples, 60.6% (20/33), 20.8% (5/24), and 37.5% (3/8) of swabs/pus, urine and other body fluid cultures, respectively, were positive. Among 66 pathogenic isolates, 57.6% (n = 38) were GNB, 39.4% (n = 26) were gram-positive, and 3.0% (n = 2) were Candida species. Among the isolated GNB, 42.1% (16/38) were Escherichia coli, 23.7% (9/38) Klebsiella pneumoniae and 10.5% (4/38) Pseudomonas aeruginosa. In total, 27/38 gram-negative isolates were available for further analysis. Resistance rates were as follows: ampicillin/sulbactam, 92.6% (n = 25); cefotaxime, 88.9% (n = 24); ceftazidime, 74.1% (n = 20); cefepime, 74.1% (n = 20); gentamicin, 55.6% (n = 15); piperacillin/tazobactam, 48.1% (n = 13); meropenem, 7.4% (n = 2); and amikacin, 3.7% (n = 1). The blaNDM-1 gene was detected in one K. pneumoniae and one Acinetobacter baumannii isolate, which carried an additional blaOXA-51 gene. The ESBL enzymes were detected in 81.5% (n = 22) of isolates as follows: TEM, 77.2% (n = 17); CTX-M-1 group, 68.2% (n = 15); SHV group, 27.3% (n = 6); and CTX-M-9 group, 9.1% (n = 2). Based on the in vitro antimicrobial susceptibility results, empiric treatment initiated in 13 of 18 (72.2%) patients was likely ineffective. Conclusion We report a high prevalence of ESBL-producing bacteria (81.5%) and carbapenem resistance (7.4%), with more than half of GNB carrying two or more ESBL enzymes resulting in suboptimal empiric antibiotic therapy. These findings indicate a need for local and national antimicrobial resistance surveillance and the strengthening of antimicrobial stewardship programs.

Establishing a Prognostic Signature Based on Epithelial–Mesenchymal Transition-Related Genes for Endometrial Cancer Patients

BackgroundsEpithelial–mesenchymal transition (EMT) is a sequential process where tumor cells develop from the epithelial state to the mesenchymal state. EMT contributes to various tumor functions including initiation, propagating potential, and resistance to therapy, thus affecting the survival time of patients. The aim of this research is to set up an EMT-related prognostic signature for endometrial cancer (EC).MethodsEMT-related gene (ERG) expression and clinical data were acquired from The Cancer Genome Atlas (TCGA). The entire set was randomly divided into two sets, one for contributing the risk model (risk score) and the other for validating. Univariate and multivariate Cox proportional hazards regression analyses were applied to the training set to select the prognostic ERGs. The expression of 10 ERGs was confirmed by qRT-PCR in clinical samples. Then, we developed a nomogram predicting 1-/3-/5-year survival possibility combining the risk score and clinical factors. The entire set was stratified into the high- and low-risk groups, which was used to analyze the immune infiltrating, tumorigenesis pathways, and response to drugs.ResultsA total of 220 genes were screened out from 1,316 ERGs for their differential expression in tumor versus normal. Next, 10 genes were found to be associated with overall survival (OS) in EC, and the expression was validated by qRT-PCR using clinical samples, so we constructed a 10-ERG-based risk score to distinguish high-/low-risk patients and a nomogram to predict survival rate. The calibration plots proved the predictive value of our model. Gene Set Enrichment Analysis (GSEA) discovered that in the low-risk group, immune-related pathways were enriched; in the high-risk group, tumorigenesis pathways were enriched. The low-risk group showed more immune activities, higher tumor mutational burden (TMB), and higher CTAL4/PD1 expression, which was in line with a better response to immune checkpoint inhibitors. Nevertheless, response to chemotherapeutic drugs turned out better in the high-risk group. The high-risk group had higher N6-methyladenosine (m6A) RNA expression, microsatellite instability level, and stemness indices.ConclusionWe constructed the ERG-related signature model to predict the prognosis of EC patients. What is more, it might offer a reference for predicting individualized response to immune checkpoint inhibitors and chemotherapeutic drugs.

Poorer white matter microstructure predicts slower and more variable reaction time performance: evidence for the neural noise hypothesis in a large lifespan cohort

Most prior research in the neural and behavioral sciences has been focused on characterizing averages in cognition, brain characteristics, or behavior, and attempting to predict differences in these averages among individuals. However, this overwhelming focus on mean levels may leave us with an incomplete picture of what drives individual differences in behavioral phenotypes by ignoring the variability of behavior around an individual’s mean. In particular, better white matter (WM) structural microstructure has been hypothesized to support consistent behavioral performance by decreasing gaussian noise in signal transfer. In contrast, lower indices of white matter microstructure have been associated with greater within-subject variance in the ability to deploy performance-related resources, especially in clinical samples. We tested this ‘neural noise’ hypothesis in a large adult lifespan cohort (Cam-CAN) with over 2500 individuals in a (2681 behavioral sessions with 708 scans in adults aged 18–102) using measures of WM tract microstructure to predict mean levels and variability in reaction time performance on a simple behavioral task using a dynamic structural equation model (DSEM). We found broad support for neural noise hypothesis, such that lower WM microstructure predicted individual differences in separable components of behavioral performance estimated using DSEM, including slower mean responses and increased variability. These effects were robust when including age in the model, suggesting consistent effects of WM microstructure across the adult lifespan above and beyond concurrent effects of ageing. Crucially, these results demonstrate the utility of DSEM for modeling and predicting behavioral variability directly, and the promise of studying variability for understanding cognitive processes.

Detection of Enterovirus D68 in Wastewater Samples from the UK between July and November 2021

Infection with enterovirus D68 (EV-D68) has been linked with severe neurological disease such as acute flaccid myelitis (AFM) in recent years. However, active surveillance for EV-D68 is lacking, which makes full assessment of this association difficult. Although a high number of EV-D68 infections were expected in 2020 based on the EV-D68′s known biannual circulation patterns, no apparent increase in EV-D68 detections or AFM cases was observed during 2020. We describe an upsurge of EV-D68 detections in wastewater samples from the United Kingdom between July and November 2021 mirroring the recently reported rise in EV-D68 detections in clinical samples from various European countries. We provide the first publicly available 2021 EV-D68 sequences showing co-circulation of EV-D68 strains from genetic clade D and sub-clade B3 as in previous years. Our results show the value of environmental surveillance (ES) for the early detection of circulating and clinically relevant human viruses. The use of a next-generation sequencing (NGS) approach helped us to estimate the prevalence of EV-D68 viruses among EV strains from other EV serotypes and to detect EV-D68 minor variants. The utility of ES at reducing gaps in virus surveillance for EV-D68 and the possible impact of nonpharmaceutical interventions introduced to control the COVID-19 pandemic on EV-D68 transmission dynamics are discussed.