Nek2A prevents centrosome clustering and induces cell death in cancer cells via KIF2C interaction.

Unlike normal cells, cancer cells frequently exhibit supernumerary centrosomes, leading to formation of multipolar spindles that can trigger cell death. Nevertheless, cancer cells with supernumerary centrosomes escape the deadly consequences of unequal segregation of genomic material by coalescing their centrosomes into two poles. This unique trait of cancer cells presents a promising target for cancer therapy, focusing on selectively attacking cells with supernumerary centrosomes. Nek2A is a kinase involved in mitotic regulation, including the centrosome cycle, where it phosphorylates linker proteins to separate centrosomes. In this study, we investigated if Nek2A also prevents clustering of supernumerary centrosomes, akin to its separation function. Reduction of Nek2A activity, achieved through knockout, silencing, or inhibition, promotes centrosome clustering, whereas its overexpression results in inhibition of clustering. Significantly, prevention of centrosome clustering induces cell death, but only in cancer cells with supernumerary centrosomes, both in vitro and in vivo. Notably, none of the known centrosomal (e.g., CNAP1, Rootletin, Gas2L1) or non-centrosomal (e.g., TRF1, HEC1) Nek2A targets were implicated in this machinery. Additionally, Nek2A operated via a pathway distinct from other proteins involved in centrosome clustering mechanisms, like HSET and NuMA. Through TurboID proximity labeling analysis, we identified novel proteins associated with the centrosome or microtubules, expanding the known interaction partners of Nek2A. KIF2C, in particular, emerged as a novel interactor, confirmed through coimmunoprecipitation and localization analysis. The silencing of KIF2C diminished the impact of Nek2A on centrosome clustering and rescued cell viability. Additionally, elevated Nek2A levels were indicative of better patient outcomes, specifically in those predicted to have excess centrosomes. Therefore, while Nek2A is a proposed target, its use must be specifically adapted to the broader cellular context, especially considering centrosome amplification. Discovering partners such as KIF2C offers fresh insights into cancer biology and new possibilities for targeted treatment.

Epigenetic-focused CRISPR/Cas9 screen identifies (absent, small, or homeotic)2-like protein (ASH2L) as a regulator of glioblastoma cell survival.

BACKGROUND: Glioblastoma is the most common and aggressive primary brain tumor with extremely poor prognosis, highlighting an urgent need for developing novel treatment options. Identifying epigenetic vulnerabilities of cancer cells can provide excellent therapeutic intervention points for various types of cancers. METHOD: In this study, we investigated epigenetic regulators of glioblastoma cell survival through CRISPR/Cas9 based genetic ablation screens using a customized sgRNA library EpiDoKOL, which targets critical functional domains of chromatin modifiers. RESULTS: Screens conducted in multiple cell lines revealed ASH2L, a histone lysine methyltransferase complex subunit, as a major regulator of glioblastoma cell viability. ASH2L depletion led to cell cycle arrest and apoptosis. RNA sequencing and greenCUT&RUN together identified a set of cell cycle regulatory genes, such as TRA2B, BARD1, KIF20B, ARID4A and SMARCC1 that were downregulated upon ASH2L depletion. Mass spectrometry analysis revealed the interaction partners of ASH2L in glioblastoma cell lines as SET1/MLL family members including SETD1A, SETD1B, MLL1 and MLL2. We further showed that glioblastoma cells had a differential dependency on expression of SET1/MLL family members for survival. The growth of ASH2L-depleted glioblastoma cells was markedly slower than controls in orthotopic in vivo models. TCGA analysis showed high ASH2L expression in glioblastoma compared to low grade gliomas and immunohistochemical analysis revealed significant ASH2L expression in glioblastoma tissues, attesting to its clinical relevance. Therefore, high throughput, robust and affordable screens with focused libraries, such as EpiDoKOL, holds great promise to enable rapid discovery of novel epigenetic regulators of cancer cell survival, such as ASH2L. CONCLUSION: Together, we suggest that targeting ASH2L could serve as a new therapeutic opportunity for glioblastoma. Video Abstract.

Higher rates of cefiderocol resistance among NDM producing Klebsiella bloodstream isolates applying EUCAST over CLSI breakpoints.

BACKGROUND: Cefiderocol is generally active against carbapenem-resistant Klebsiella spp. (CRK) with higher MICs against metallo-beta-lactamase producers. There is a variation in cefiderocol interpretive criteria determined by EUCAST and CLSI. Our objective was to test CRK isolates against cefiderocol and compare cefiderocol susceptibilities using EUCAST and CLSI interpretive criteria. METHODS: A unique collection (n = 254) of mainly OXA-48-like- or NDM-producing CRK bloodstream isolates were tested against cefiderocol with disc diffusion (Mast Diagnostics, UK). Beta-lactam resistance genes and multilocus sequence types were identified using bioinformatics analyses on complete bacterial genomes. RESULTS: Median cefiderocol inhibition zone diameter was 24 mm (interquartile range [IQR] 24-26 mm) for all isolates and 18 mm (IQR 15-21 mm) for NDM producers. We observed significant variability between cefiderocol susceptibilities using EUCAST and CLSI breakpoints, such that 26% and 2% of all isolates, and 81% and 12% of the NDM producers were resistant to cefiderocol using EUCAST and CLSI interpretive criteria, respectively. CONCLUSIONS: Cefiderocol resistance rates among NDM producers are high using EUCAST criteria. Breakpoint variability may have significant implications on patient outcomes. Until more clinical outcome data are available, we suggest using EUCAST interpretive criteria for cefiderocol susceptibility testing.

MOKPE: drug-target interaction prediction via manifold optimization based kernel preserving embedding.

BACKGROUND: In many applications of bioinformatics, data stem from distinct heterogeneous sources. One of the well-known examples is the identification of drug-target interactions (DTIs), which is of significant importance in drug discovery. In this paper, we propose a novel framework, manifold optimization based kernel preserving embedding (MOKPE), to efficiently solve the problem of modeling heterogeneous data. Our model projects heterogeneous drug and target data into a unified embedding space by preserving drug-target interactions and drug-drug, target-target similarities simultaneously. RESULTS: We performed ten replications of ten-fold cross validation on four different drug-target interaction network data sets for predicting DTIs for previously unseen drugs. The classification evaluation metrics showed better or comparable performance compared to previous similarity-based state-of-the-art methods. We also evaluated MOKPE on predicting unknown DTIs of a given network. Our implementation of the proposed algorithm in R together with the scripts that replicate the reported experiments is publicly available at https://github.com/ocbinatli/mokpe .

Effectiveness of tocilizumab in non-intubated cases with COVID-19: a systematic review and meta-analysis.

PURPOSE: Tocilizumab, a monoclonal IL-6 receptor blocker, is an effective agent for severe-to-critical cases of COVID-19; however, its target patients for the optimum use need to be detailed. We performed a systematic review and meta-analysis to define its effect among severely ill but non-intubated cases with COVID-19. METHODS: We searched PubMed, Scopus, Web of Science, MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), Medrxiv, and Biorxiv until February 13, 2022, for non-intubated cases, and included randomized-controlled trials (RCT) based on bias assessment. The primary outcomes were the requirement of invasive mechanical ventilation and mortality. Random effect and fixed-effect models were used. The heterogeneity was measured using the χ2 and I2 statistics, with χ2 p ≤ 0.05 and I2 ≥ 50% indicating the presence of significant heterogeneity. We registered the study to the International Prospective Register of Systematic Reviews (PROSPERO) with the registration number CRD42021232575. RESULTS: Among 261 articles, 11 RCTs were included. The pooled analysis of the 11 RCTs demonstrated that the rate of mortality was significantly lower in the tocilizumab group than in the control group (20.0% and 24.2%, OR: 0.84, 95% CI 0.73-0.96, and heterogeneity I2 = 0%. p = 0.82.). The mechanical ventilation rate was lower in the tocilizumab group than the control group (27% vs 35.2%, OR: 0.76, 95% CI 0.67-0.86, and heterogeneity I2 = 6%. p = 0.39). CONCLUSION: Among non-intubated severe COVID-19 cases, tocilizumab reduces the risk of invasive mechanical ventilation and mortality compared to standard-of-care treatment.

Identifying Tissue- and Cohort-Specific RNA Regulatory Modules in Cancer Cells Using Multitask Learning.

MicroRNA (miRNA) alterations significantly impact the formation and progression of human cancers. miRNAs interact with messenger RNAs (mRNAs) to facilitate degradation or translational repression. Thus, identifying miRNA-mRNA regulatory modules in cohorts of primary tumor tissues are fundamental for understanding the biology of tumor heterogeneity and precise diagnosis and treatment. We established a multitask learning sparse regularized factor regression (MSRFR) method to determine key tissue- and cohort-specific miRNA-mRNA regulatory modules from expression profiles of tumors. MSRFR simultaneously models the sparse relationship between miRNAs and mRNAs and extracts tissue- and cohort-specific miRNA-mRNA regulatory modules separately. We tested the model's ability to determine cohort-specific regulatory modules of multiple cancer cohorts from the same tissue and their underlying tissue-specific regulatory modules by extracting similarities between cancer cohorts (i.e., blood, kidney, and lung). We also detected tissue-specific and cohort-specific signatures in the corresponding regulatory modules by comparing our findings from various other tissues. We show that MSRFR effectively determines cancer-related miRNAs in cohort-specific regulatory modules, distinguishes tissue- and cohort-specific regulatory modules from each other, and extracts tissue-specific information from different cohorts of disease-related tissue. Our findings indicate that the MSRFR model can support current efforts in precision medicine to define tumor-specific miRNA-mRNA signatures.

Comparison of ceftazidime-avibactam susceptibility testing methods against OXA-48-like carrying Klebsiella blood stream isolates.

Ceftazidime-avibactam exhibits good in vitro activity against carbapenem resistant Klebsiella carrying OXA-48-like enzymes. We tested two hundred unique carbapenem resistant Klebsiella blood stream isolates (71% with single OXA-48-like carbapenemases, including OXA-48, n = 62; OXA-232, n = 57; OXA-244, n = 17; OXA-181, n = 5) that were collected as part of a multicentre study against ceftazidime-avibactam using Etest (bioMérieux, Marcyl'Étoile, France), 10/4 µg disc (Thermo Fisher) and Sensititre Gram Negative EURGNCOL Plates (Lyophilized panels, Sensititre, Thermo Fisher) with the aim of comparing the performances of the Etest and disc to that of Sensititre. Ceftazidime-avibactam MIC50/90 was 2/>16 mg/L for the entire collection and was 2/4 mg/L for single OXA-48-like producers. Categorical and essential agreements between the Etest and Sensititre were 100% and 97%, respectively. Categorical agreement between the disc and Sensititre was 100%. Etest and 10/4 µg discs are suitable alternatives to Sensititre for ceftazidime-avibactam sensitivity testing for OXA-48-like producers.

Fast and interpretable genomic data analysis using multiple approximate kernel learning.

MOTIVATION: Dataset sizes in computational biology have been increased drastically with the help of improved data collection tools and increasing size of patient cohorts. Previous kernel-based machine learning algorithms proposed for increased interpretability started to fail with large sample sizes, owing to their lack of scalability. To overcome this problem, we proposed a fast and efficient multiple kernel learning (MKL) algorithm to be particularly used with large-scale data that integrates kernel approximation and group Lasso formulations into a conjoint model. Our method extracts significant and meaningful information from the genomic data while conjointly learning a model for out-of-sample prediction. It is scalable with increasing sample size by approximating instead of calculating distinct kernel matrices. RESULTS: To test our computational framework, namely, Multiple Approximate Kernel Learning (MAKL), we demonstrated our experiments on three cancer datasets and showed that MAKL is capable to outperform the baseline algorithm while using only a small fraction of the input features. We also reported selection frequencies of approximated kernel matrices associated with feature subsets (i.e. gene sets/pathways), which helps to see their relevance for the given classification task. Our fast and interpretable MKL algorithm producing sparse solutions is promising for computational biology applications considering its scalability and highly correlated structure of genomic datasets, and it can be used to discover new biomarkers and new therapeutic guidelines. AVAILABILITY AND IMPLEMENTATION: MAKL is available at https://github.com/begumbektas/makl together with the scripts that replicate the reported experiments. MAKL is also available as an R package at https://cran.r-project.org/web/packages/MAKL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Machine learning as a clinical decision support tool for patients with acromegaly.

OBJECTIVE: To develop machine learning (ML) models that predict postoperative remission, remission at last visit, and resistance to somatostatin receptor ligands (SRL) in patients with acromegaly and to determine the clinical features associated with the prognosis. METHODS: We studied outcomes using the area under the receiver operating characteristics (AUROC) values, which were reported as the performance metric. To determine the importance of each feature and easy interpretation, Shapley Additive explanations (SHAP) values, which help explain the outputs of ML models, are used. RESULTS: One-hundred fifty-two patients with acromegaly were included in the final analysis. The mean AUROC values resulting from 100 independent replications were 0.728 for postoperative 3 months remission status classification, 0.879 for remission at last visit classification, and 0.753 for SRL resistance status classification. Extreme gradient boosting model demonstrated that preoperative growth hormone (GH) level, age at operation, and preoperative tumor size were the most important predictors for early remission; resistance to SRL and preoperative tumor size represented the most important predictors of remission at last visit, and postoperative 3-month insulin-like growth factor 1 (IGF1) and GH levels (random and nadir) together with the sparsely granulated somatotroph adenoma subtype served as the most important predictors of SRL resistance. CONCLUSIONS: ML models may serve as valuable tools in the prediction of remission and SRL resistance.

Characteristics and outcomes of carbapenemase harbouring carbapenem-resistant Klebsiella spp. bloodstream infections: a multicentre prospective cohort study in an OXA-48 endemic setting.

A prospective, multicentre observational cohort study of carbapenem-resistant Klebsiella spp. (CRK) bloodstream infections was conducted in Turkey from June 2018 to June 2019. One hundred eighty-seven patients were recruited. Single OXA-48-like carbapenemases predominated (75%), followed by OXA-48-like/NDM coproducers (16%). OXA-232 constituted 31% of all OXA-48-like carbapenemases and was mainly carried on ST2096. Thirty-day mortality was 44% overall and 51% for ST2096. In the multivariate cox regression analysis, SOFA score and immunosuppression were significant predictors of 30-day mortality and ST2096 had a non-significant effect. All OXA-48-like producers remained susceptible to ceftazidime-avibactam.

A meta-analysis for the role of aminoglycosides and tigecyclines in combined regimens against colistin- and carbapenem-resistant Klebsiella pneumoniae bloodstream infections.

We aimed to describe the effect of aminoglycosides and tigecycline to reduce the mortality in colistin- and carbapenem-resistant Klebsiella pneumoniae (ColR-CR-Kp) infections. We included the studies with defined outcomes after active or non-active antibiotic treatment of ColR-CR-Kp infections. The active treatment was defined as adequate antibiotic use for at least 3 days (72 h) after the diagnosis of ColR-CR-Kp infection by culture. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement and the checklist of PRISMA 2020 was applied. Crude and adjusted odds ratios (OR) with 95% confidence interval (CI) were calculated and pooled in the random effects model. Adding aminoglycosides to the existing treatment regimen reduced overall mortality significantly (OR 0.34, 95% CI 0.20-0.58). Overall mortality was 34% in patients treated with aminoglycoside-combined regimens and was 60% in patients treated with non-aminoglycoside regimens. Treatment with tigecycline is not found to reduce mortality (OR: 0.76, 95% CI: 0.47-1.23). Our results suggest that aminoglycoside addition to the existing regimen of colistin- and carbapenem-resistant Klebsiella pneumoniae infections reduces mortality significantly.

High prevalence of ArmA-16S rRNA methyltransferase among aminoglycoside-resistant Klebsiella pneumoniae bloodstream isolates.

Introduction. Aminoglycosides are used for the treatment of carbapenemase-producing Klebsiella pneumoniae (CPK) infections. 16S rRNA methyltransferases (RMTs) confer resistance to all aminoglycosides and are often cocarried with NDM.Hypothesis/Gap Statement. There is a dart of studies looking at the aminoglycoside resistance mechanisms for invasive CPK isolates, particularly in OXA-48 endemic settings.Aim. We aimed to determine the prevalence of RMTs and their association with beta lactamases and MLSTs amongst aminoglycoside-resistant CPK bloodstream isolates in an OXA-48 endemic setting.Methodology. CPK isolates (n=181), collected as part of a multicentre cohort study, were tested for amikacin, gentamicin and tobramycin susceptibility using custom-made sensititre plates (GN2XF, Thermo Fisher Scientific). All isolates were previously subjected to whole-genome sequencing. Carbapenemases, RMTs, MLSTs and plasmid incompatibility groups were detected on the assembled genomes.Results. Of the 181 isolates, 109(60 %) were resistant to all three aminoglycosides, and 96 of 109(88 %) aminoglycoside-resistant isolates carried an RMT (85 ArmA, 10 RmtC, 4 RmtF1; three isolates cocarried ArmA and RmtC). Main clonal types associated with ArmA were ST2096 (49/85, 58 %) and ST14 (24/85, 28 %), harbouring mainly OXA-232 and OXA-48 +NDM, respectively. RmtC was cocarried with NDM (5/10) on ST395, and NDM +OXA-48 or NDM +KPC (4/10) on ST14, ST15 and ST16. All RMT producers also carried CTX-M-15, and the majority cocarried SHV-106, TEM-150 and multiple other antibiotic resistance genes. The majority of the isolates harboured a combination of IncFIB, IncH and IncL/M type plasmids. Non-NDM producing isolates remained susceptible to ceftazidime-avibactam.Conclusion. Aminoglycoside resistance amongst CPK bloodstream isolates is extremely common and mainly driven by clonal spread of ArmA carried on ST2096 and ST14, associated with OXA-232 and OXA48 +NDM carriage, respectively.

Efficient Multitask Multiple Kernel Learning With Application to Cancer Research.

Multitask multiple kernel learning (MKL) algorithms combine the capabilities of incorporating different data sources into the prediction model and using the data from one task to improve the accuracy on others. However, these methods do not necessarily produce interpretable results. Restricting the solutions to the set of interpretable solutions increases the computational burden of the learning problem significantly, leading to computationally prohibitive run times for some important biomedical applications. That is why we propose a multitask MKL formulation with a clustering of tasks and develop a highly time-efficient solution approach for it. Our solution method is based on the Benders decomposition and treating the clustering problem as finding a given number of tree structures in a graph; hence, it is called the forest formulation. We use our method to discriminate early-stage and late-stage cancers using genomic data and gene sets and compare our algorithm against two other algorithms. The two other algorithms are based on different approaches for linearization of the problem while all algorithms make use of the cutting-plane method. Our results indicate that as the number of tasks and/or the number of desired clusters increase, the forest formulation becomes increasingly favorable in terms of computational performance.

Elimination of healthcare-associated Acinetobacter baumannii infection in a highly endemic region.

This paper describes the elimination of healthcare-associated Acinetobacter baumannii infections in a highly endemic region. A prospective, observational study was performed between October 2012 and October 2017. Acinetobacter baumannii were isolated from 59 patients, and >95% similarity was demonstrated among isolates of seven patients (DiversiLab™, BioMérieux). Carbapenemase activity was detected in 15 of 17 (88%) isolates, and all were OXA-23 type. The control of Acinetobacter baumannii outbreaks can be achieved by close follow-up supported by molecular techniques, strict application of infection control measures, and isolation of transferred patients.

PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning.

BACKGROUND: Identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning models is promising, extracting significant and meaningful knowledge from the data simultaneously during the learning process is a difficult task considering the high-dimensional and highly correlated nature of genomic datasets. Thus, there is a need for models that not only predict tumour volume from gene expression data of patients but also use prior information coming from pathway/gene sets during the learning process, to distinguish molecular mechanisms which play crucial role in tumour progression and therefore, disease prognosis. RESULTS: In this study, instead of initially choosing several pathways/gene sets from an available set and training a model on this previously chosen subset of genomic features, we built a novel machine learning algorithm, PrognosiT, that accomplishes both tasks together. We tested our algorithm on thyroid carcinoma patients using gene expression profiles and cancer-specific pathways/gene sets. Predictive performance of our novel multiple kernel learning algorithm (PrognosiT) was comparable or even better than random forest (RF) and support vector regression (SVR). It is also notable that, to predict tumour volume, PrognosiT used gene expression features less than one-tenth of what RF and SVR algorithms used. CONCLUSIONS: PrognosiT was able to obtain comparable or even better predictive performance than SVR and RF. Moreover, we demonstrated that during the learning process, our algorithm managed to extract relevant and meaningful pathway/gene sets information related to the studied cancer type, which provides insights about its progression and aggressiveness. We also compared gene expressions of the selected genes by our algorithm in tumour and normal tissues, and we then discussed up- and down-regulated genes selected by our algorithm while learning, which could be beneficial for determining new biomarkers.

Effectiveness of favipiravir in COVID-19: a live systematic review.

We performed a systematic review and meta-analysis for the effectiveness of Favipiravir on the fatality and the requirement of mechanical ventilation for the treatment of moderate to severe COVID-19 patients. We searched available literature and reported it by using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Until June 1, 2021, we searched PubMed, bioRxiv, medRxiv, ClinicalTrials.gov, Cochrane Central Register of Controlled Trials (CENTRAL), and Google Scholar by using the keywords "Favipiravir" and terms synonymous with COVID-19. Studies for Favipiravir treatment compared to standard of care among moderate and severe COVID-19 patients were included. Risk of bias assessment was performed using Revised Cochrane risk of bias tool for randomized trials (RoB 2) and ROBINS-I assessment tool for non-randomized studies. We defined the outcome measures as fatality and requirement for mechanical ventilation. A total of 2702 studies were identified and 12 clinical trials with 1636 patients were analyzed. Nine out of 12 studies were randomized controlled trials. Among the randomized studies, one study has low risk of bias, six studies have moderate risk of bias, and 2 studies have high risk of bias. Observational studies were identified as having moderate risk of bias and non-randomized study was found to have serious risk of bias. Our meta-analysis did not reveal any significant difference between the intervention and the comparator on fatality rate (OR 1.11, 95% CI 0.64-1.94) and mechanical ventilation requirement (OR 0.50, 95% CI 0.13-1.95). There is no significant difference in fatality rate and mechanical ventilation requirement between Favipiravir treatment and the standard of care in moderate and severe COVID-19 patients.

Assessment of quarter billion primary care prescriptions from a nationwide antimicrobial stewardship program.

We described the significance of systematic monitoring nationwide antimicrobial stewardship programs (ASPs) in primary care. All the prescriptions given by family physicians were recorded in Prescription Information System established by the Turkish Medicines and Medical Devices Agency of Ministry of Health. We calculated, for each prescription, "antibiotics amount" as number of boxes times number of items per box for medicines that belong to antiinfectives for systemic use (i.e., J01 block in the Anatomical Therapeutic Chemical Classification System). We compared the antibiotics amount before (2015) and after (2016) the extensive training programs for the family physicians. We included 266,389,209 prescriptions from state-operated family healthcare units (FHUs) between January 1, 2015 and December 31, 2016. These prescriptions were given by 26,313 individual family physicians in 22,518 FHUs for 50,713,181 individual patients. At least one antimicrobial was given in 37,024,232 (28.31%) prescriptions in 2015 and 36,154,684 (26.66%) prescriptions in 2016. The most common diagnosis was "acute upper respiratory infections (AURI)" (i.e., J00-J06 block in the 10th revision of the International Statistical Classification of Diseases and Related Health Problems) with 28.05%. The average antibiotics amount over prescriptions with AURI decreased in 79 out of 81 provinces, and overall rate of decrease in average antibiotics amount was 8.33%, where 28 and 53 provinces experienced decreases (range is between 28.63% and -3.05%) above and below this value, respectively. In the most successful province, the highest decrease in average amount of "other beta-lactam antibacterials" per prescription for AURI was 49.63% in January. Computational analyses on a big data set collected from a nationwide healthcare system brought a significant contribution in improving ASPs.

The seroprevalence of SARS-CoV-2 antibodies among health care workers before the era of vaccination: a systematic review and meta-analysis.

BACKGROUND: The prevalence of SARS-CoV-2 infection among health care workers (HCWs) provides information about the spread of COVID-19 within health care facilities, and the risk groups. OBJECTIVES: We aimed to describe the rate of SARS-CoV-2 seroprevalence and its determinants among HCWs. DATA SOURCES: We used Web of Science, PubMed, Scopus, MEDLINE, EBSCOhost and Cochrane Library. STUDY ELIGIBILITY CRITERIA: We included the reports of SARS-CoV-2 seroprevalence with a sample size of minimum 1000 HCWs. METHODS: The study was registered at the International Prospective Register of Systematic Reviews (PROSPERO, no. CRD42021230456). We used PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. The keywords were "COVID-19", "SARS-CoV-2", "Coronavirus", "seroprevalence", "health care workers" and "risk factors". RESULTS: In total 4329 reports were retrieved, duplications were removed; after filtering according to the title and abstract, 25 studies were selected. Risk of bias was assessed in 25 studies; it was low in 13 studies, medium in four studies, and high in eight studies. In meta-analysis using the random effect model, the weighted average of seroprevalence was calculated as 8% (95% CI 6-10%). The pooled seroprevalence rates of the selected variables that have a rate above the average were male HCWs with 9% (95% CI 7-11%); HCWs from ethnic minorities with 13% (95% CI 9-17%); high exposure 9% (95% CI 6-13%); exposure to the virus outside the health care setting 22% (95% CI 14-32%). CONCLUSIONS: Our analysis indicates a SARS-CoV-2 seroprevalence rate of 8% among studies that included >1000 HCWs for the year 2020, before vaccinations started. The most common risk factors associated with higher seroprevalence rate were ethnicity, male gender and having a higher number of household contacts. Working as a frontline HCW was inconsistent in its association with higher seroprevalence.

Effectiveness of different types of mask in aerosol dispersion in SARS-CoV-2 infection.

OBJECTIVE: To compare the effectiveness of different mask types in limiting the dispersal of coughed air. METHOD: The Schlieren method with a single curved mirror was used in this study. Coughed air has a slightly higher temperature than ambient air, which generates a refractive index gradient. A curved mirror with a radius of curvature of 10 m and a diameter of 60 cm was used. The spread of the cough wavefront was investigated among five subjects wearing: (1) no mask; (2) a single surgical mask; (3) a double surgical mask; (4) a cloth mask; (5) a valveless N95 mask; and (6) a valved N95 mask. RESULTS: All mask types reduced the size of the contaminated region significantly. The percentage reduction in the cross-sectional area of the contaminated region for the same mask types on different subjects revealed by normalized data suggests that the fit of a mask plays an important role. CONCLUSIONS: No significant difference in the spread of coughed air was found between the use of a single surgical mask or a double surgical mask. Cloth masks may be effective, depending on the quality of the cloth. Valved N95 masks exclusively protect the user. The fit of a mask is an important factor to minimize the contaminated region.

Virulence Determinants of Colistin-Resistant K. pneumoniae High-Risk Clones.

We proposed the hypothesis that high-risk clones of colistin-resistant K. pneumoniae (ColR-Kp) possesses a high number of virulence factors and has enhanced survival capacity against the neutrophil activity. We studied virulence genes of ColR-Kp isolates and neutrophil response in 142 patients with invasive ColR-Kp infections. The ST101 and ST395 ColR-Kp infections had higher 30-day mortality (58%, p = 0.005 and 75%, p = 0.003). The presence of yersiniabactin biosynthesis gene (ybtS) and ferric uptake operon associated gene (kfu) were significantly higher in ST101 (99%, p ≤ 0.001) and ST395 (94%, p < 0.012). Being in ICU (OR: 7.9; CI: 1.43-55.98; p = 0.024), kfu (OR:27.0; CI: 5.67-179.65; p < 0.001) and ST101 (OR: 17.2; CI: 2.45-350.40; p = 0.01) were found to be predictors of 30-day mortality. Even the neutrophil uptake of kfu+-ybtS+ ColR-Kp was significantly higher than kfu--ybtS- ColR-Kp (phagocytosis rate: 78% vs. 65%, p < 0.001), and the kfu+-ybtS+ ColR-Kp survived more than kfu--ybtS- ColR-Kp (median survival index: 7.90 vs. 4.22; p = 0.001). The kfu+-ybtS+ ColR-Kp stimulated excessive NET formation. Iron uptake systems in high-risk clones of colistin-resistant K. pneumoniae enhance the success of survival against the neutrophil phagocytic defense and stimulate excessive NET formation. The drugs targeted to iron uptake systems would be a promising approach for the treatment of colistin-resistant high-risk clones of K. pneumoniae infections.