Human Cytomegalovirus Oncoprotection across Diverse Populations, Tumor Histologies, and Age Groups: The Relevance for Prospective Vaccinal Therapy.

The oncogenicity of the human cytomegalovirus (CMV) is currently being widely debated. Most recently, mounting clinical evidence suggests an anti-cancer effect via CMV-induced T cell-mediated tumor destruction. However, the data were mostly obtained from single-center studies and in vitro experiments. Broad geographic coverage is required to offer a global perspective. Our study examined the correlation between country-specific CMV seroprevalence (across 73 countries) and the age-standardized incidence rate (of 34 invasive tumors). The populations studied were stratified according to decadal age periods as the immunologic effects of CMV seropositivity may depend upon age at initial infection. The International Agency for Research on Cancer of the World Health Organization (IARC WHO) database was used. The multivariate linear regression analysis revealed a worldwide inverse correlation between CMV seroprevalence and the incidences of 62.8% tumors. Notably, this inverse link persists for all cancers combined (Spearman's ρ = -0.732, p < 0.001; ß = -0.482, p < 0.001, adjusted R2 = 0.737). An antithetical and significant correlation was also observed in particular age groups for the vast majority of tumors. Our results corroborate the conclusions of previous studies and indicate that this oncopreventive phenomenon holds true on a global scale. It applies to a wide spectrum of cancer histologies, additionally supporting the idea of a common underlying mechanism-CMV-stimulated T cell tumor targeting. Although these results further advance the notion of CMV-based therapies, in-depth investigation of host-virus interactions is still warranted.

Familial occurrence of seizure disorders across MRI defined structural focal epilepsy etiology.

BACKGROUND: Previous studies have established familial occurrence of epilepsy and seizure disorders and early age of epilepsy onset as predictors of genetic epilepsy, but have not evaluated the rate of their occurrence in patients with different epilepsy etiology. Our study determines the distribution of familial occurrence and age of epilepsy onset across structural focal epilepsy (FE) etiology in a large FE cohort. METHODS: Records of 1354 consecutive patients evaluated for epilepsy and seizure disorders in The Neurology Clinic, University Clinical Center of Serbia from 2008 to 2019 were screened for FE. Structural etiology, lobar diagnosis, familial occurrence, and age at epilepsy onset were determined. Patients with a. nonlesional focal epilepsy (NLFE), b. hippocampal sclerosis (HS) and c. congenital or perinatal etiology (CPE) were classified as NAFE, while patients with an identified acquired focal epilepsy (AFE) constituted the control group. RESULTS: We identified 965 patients with FE, 329 (34.1 %) with NLFE, 213 (22.1 %) with HS, 174 (18.0 %) with CPE and 249 (25.8 %) with AFE. Familial occurrence was identified in 160 (16.6 %), 19.1 % of patients with NAFE and 9.2 % of AFE (p = 0.003). Patients with NAFE had a younger age of epilepsy onset (13 vs. 18 years, p < 0.001). The highest proportion of familial occurrence was found in patients with NLFE (23.7 %), while the youngest median age of epilepsy onset was identified in patients with HS (12 years) and CPE (11 years). CONCLUSION: Patients with NAFE frequently have familial occurrence of epilepsy and have an earlier age of epilepsy onset than patients with AFE.

MRI Tumor Regression Grade Combined with T2-Weighted Volumetry May Predict Histopathological Response in Locally Advanced Rectal Cancer following Neoadjuvant Chemoradiotherapy-A New Scoring System Proposal.

Modern studies focus on the discovery of innovative methods to improve the value of post-treatment magnetic resonance imaging (MRI) in the prediction of pathological responses to preoperative neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). The aim of this study was to assess the potential benefits of combining magnetic resonance tumor regression grade (mrTRG) with T2-weighted volumetry in the prediction of pathological responses to nCRT in LARC. This was a cohort study conducted on patients with histopathologically confirmed LARC in a period from 2020 to 2022. After histopathological verification, all patients underwent initial MRI studies, while the follow-up MRI was performed after nCRT. Tumor characteristics, MRI estimated tumor regression grade (mrTRG) and tumor volumetry were evaluated both initially and at follow-up. All patients were classified into responders and non-responders according to pathological tumor regression grade (pTRG) and mrTRG. A total of 71 patients, mostly male (66.2%) were included in the study. The median tumor volume reduction rate was significantly higher in nCRT-responders compared to non-responders (79.9% vs. 63.3%) (p = 0.003). Based on ROC analysis, optimal cut-off value for tumor volume reduction rate was determined with an area under the curve (AUC) value of 0.724 (p = 0.003). Using the tumor volume reduction rate ≥75% with the addition of response to nCRT according to mrTRG, a new scoring system for prediction of pTRG to preoperative nCRT in LARC was developed. Diagnostic performance of prediction score was tested and the sensitivity, PPV, specificity, and NPV were 81.8%, 56.3%, 71.4%, and 89.7%, respectively. The combination of mrTRG and T2-weighted volumetry increases the MRI-based prediction of pTRG to preoperative nCRT in LARC. The proposed scoring system could aid in distinguishing responders to nCRT, as these patients could benefit from organ-preserving treatment and a "watch and wait" strategy.

Transcriptome from Paired Samples Improves the Power of Comprehensive COVID-19 Host-Viral Characterization.

Previous transcriptome profiling studies showed significantly upregulated genes and altered biological pathways in acute COVID-19. However, changes in the transcriptional signatures during a defined time frame are not yet examined and described. The aims of this study included viral metagenomics and evaluation of the total expression in time-matched and tissue-matched paired COVID-19 samples with the analysis of the host splicing profile to reveal potential therapeutic targets. Prospective analysis of paired nasopharyngeal swabs (NPS) and blood (BL) samples from 18 COVID-19 patients with acute and resolved infection performed using Kallisto, Suppa2, Centrifuge, EdgeR, PantherDB, and L1000CDS2 tools. In NPS, we discovered 6 genes with changed splicing and 40 differentially expressed genes (DEG) that yielded 88 altered pathways. Blood samples yielded 15 alternatively spliced genes. Although the unpaired DEG analysis failed, pairing identified 78 genes and 242 altered pathways with meaningful clinical interpretation and new candidate drug combinations with up to 65% overlap. Metagenomics analyses showed SARS-CoV-2 dominance during and after the acute infection, with a significant reduction in NPS (0.008 vs. 0.002, p = 0.019). Even though both NPS and BL give meaningful insights into expression changes, this is the first demonstration of how the power of blood analysis is vastly maximized by pairing. The obtained results essentially showed that pairing is a determinant between a failed and a comprehensive study. Finally, the bioinformatics results prove to be a comprehensive tool for full-action insights, drug development, and infectious disease research when designed properly.

Novel variants in established epilepsy genes in focal epilepsy.

INTRODUCTION: Next generation sequencing (NGS) has greatly expanded our understanding of genetic contributors in multiple epilepsy syndromes, including focal epilepsy. Describing the genetic architecture of common syndromes promises to facilitate the diagnostic process as well as aid in the identification of patients who stand to benefit from genetic testing, but most studies to date have been limited to examining children or adults with intellectual disability. Our aim was to determine the yield of targeted sequencing of 5 established epilepsy genes (DEPDC5, LGI1, SCN1A, GRIN2A, and PCHD19) in an extensively phenotyped cohort of focal epilepsy patients with normal intellectual function or mild intellectual disability, as well as describe novel variants and determine the characteristics of variant carriers. PATIENTS AND METHODS: Targeted panel sequencing was performed on 96 patients with a strong clinical suspicion of genetic focal epilepsy. Patients had previously gone through a comprehensive diagnostic epilepsy evaluation in The Neurology Clinic, University Clinical Center of Serbia. Variants of interest (VOI) were classified using the American College of Medical Genetics and the Association for Molecular Pathology criteria. RESULTS: Six VOI in eight (8/96, 8.3%) patients were found in our cohort. Four likely pathogenic VOI were determined in six (6/96, 6.2%) patients, two DEPDC5 variants in two patients, one SCN1A variant in two patients and one PCDH19 variant in two patients. One variant of unknown significance (VUS) was found in GRIN2A in one (1/96, 1.0%) patient. Only one VOI in GRIN2A was classified as likely benign. No VOI were detected in LGI1. CONCLUSION: Sequencing of only five known epilepsy genes yielded a diagnostic result in 6.2% of our cohort and revealed multiple novel variants. Further research is necessary for a better understanding of the genetic basis in common epilepsy syndromes in patients with normal intellectual function or mild intellectual disability.

Molecular diagnostics and inhibition of cross-reactive carbohydrate determinants in Hymenoptera venom allergy.

BACKGROUND: The composition of venom extracts, cross-reactive carbohydrate determinants (CCD) and the component-resolved diagnostics (CRD) are important fields of investigation. IgE-reactivity to CCD complicates the interpretation of IgE to Hymenoptera venoms, especially in patients with multiple-positivity. We analyzed the clinical importance of CRD and CCD-inhibition for selection of allergens for venom immunotherapy (VIT). METHODS: In 71 patients, we measured specific IgE (sIgE) to honeybee venom (HBV), wasp venom (WV), hornet venom (HV), CCD, and recombinant allergens: phospholipase A2 (rApi m 1), hyaluronidase (rApi m 2), icarapin (rApi m 10), antigen 5 (rVes v 5), and phospholipase A1 (Immunoblot). In 29/71 HBV/WV/HV/CCD-positive patients CCD-inhibition was performed. According to CRD and CCD-inhibition, we identified true sensitization and defined groups of multiple-positive patients who needed CCD-inhibition before starting VIT. RESULTS: sIgE-rApi m 1, sIgE-rApi m 2, and sIgE-rApi m 10 were detected in 65.7%, 68.4%, and 58%, respectively. In HBV allergic patients, CRD sensitivity was 86.8%. In WV allergic patients, sensitivity of sIgE-rVes v 5 was 94%. True multiple-sensitization was found in 44.8% of HBV/WV/HV/CCD-positive patients after CCD-inhibition. Patients with multiple venom- and CCD-positivity had more frequent severe allergic reactions (p < 0.001). CCD-inhibition was helpful in HBV/WV/HV/CCD-positive patients who were negative to all tested recombinant honeybee allergens. Persistence of HBV-positivity after CCD-inhibition requires CRD to other honeybee recombinant allergens. CONCLUSION: CRD, using a profile of five most important recombinant allergens and CCD, has a high sensitivity for the diagnosis of venom allergy, especially in patients positive to several venom extracts. CRD and CCD-inhibition are helpful to reveal the clinically relevant, true sensitization and improve the selection of venoms for long-lasting VIT.

Novel GATOR1 variants in focal epilepsy.

INTRODUCTION: Variants in GATOR1 genes are well established in focal epilepsy syndromes. A strong association of GATOR1 variants with drug-resistant epilepsy as well as an increased risk of sudden unexplained death in epilepsy warrants developing strategies to facilitate the identification of patients who could potentially benefit from genetic testing and precision medicine. We aimed to determine the yield of GATOR1 gene sequencing in patients with focal epilepsy typically referred for genetic testing, establish novel GATOR1 variants and determine clinical, electroencephalographic, and radiological characteristics of variant carriers. PATIENTS AND METHODS: Ninety-six patients with clinical suspicion of genetic focal epilepsy with previous comprehensive diagnostic epilepsy evaluation in The Neurology Clinic, University Clinical Center of Serbia, were included in the study. Sequencing was performed using a custom gene panel encompassing DEPDC5, NPRL2, and NPRL3. Variants of interest (VOI) were classified according to criteria proposed by the American College of Medical Genetics and the Association for Molecular Pathology. RESULTS: Four previously unreported VOI in 4/96 (4.2%) patients were found in our cohort. Three likely pathogenic variants were determined in 3/96 (3.1%) patients, one frameshift variant in DEPDC5 in a patient with nonlesional frontal lobe epilepsy, one splicogenic DEPDC5 variant in a patient with nonlesional posterior quadrant epilepsy, and one frameshift variant in NPRL2 in a patient with temporal lobe epilepsy associated with hippocampal sclerosis. Only one VOI, a missense variant in NPRL3, found in 1/96 (1.1%) patients, was classified as a variant of unknown significance. CONCLUSION: GATOR1 gene sequencing was diagnostic in 3.1% of our cohort and revealed three novel likely pathogenic variants, including a previously unreported association of temporal lobe epilepsy with hippocampal sclerosis with an NPRL2 variant. Further research is essential for a better understanding of the clinical scope of GATOR1 gene-associated epilepsy.

Elevated Transaminases as Predictors of COVID-19 Pneumonia Severity.

BACKGROUND: This study aimed to calculate the frequency of elevated liver enzymes in hospitalized patients with coronavirus disease 2019 (COVID-19) infection and to test if liver enzyme biochemistry levels on admission could predict the computed tomography (CT) scan severity score of bilateral interstitial pneumonia. METHODS: This single-center study comprised of 323 patients including their demographic data, laboratory analyses, and radiological findings. All the information was taken from electronic health records, followed by statistical analysis. RESULTS: Out of 323 patients, 115 of them (35.60%) had aspartate aminotransferase (AST) and/or alanine aminotransferase (ALT) over 40 U/L on admission. AST was the best predictor of CT scan severity score of bilateral interstitial pneumonia (R2 = 0.313, Adjusted R2 = 0.299). CT scan severity score in the peak of the infection could be predicted with the value of AST, neutrophils, platelets, and monocytes count (R2 = 0.535, Adjusted R2 = 0.495). CONCLUSION: AST, neutrophils, platelets, and monocytes count on admission can account for almost half (49.5%) of the variability in CT scan severity score at peak of the disease, predicting the extensiveness of interstitial pneumonia related to COVID-19 infection. Liver enzymes should be closely monitored in order to stratify COVID-19 patients with a higher risk of developing severe forms of the disease and to plan the beforehand step-up treatment.

A New Interactive Tool to Visualize and Analyze COVID-19 Data: The PERISCOPE Atlas.

Since the start of the 21st century, the world has not confronted a more serious threat to global public health than the COVID-19 pandemic. While governments initially took radical actions in response to the pandemic to avoid catastrophic collapse of their health care systems, government policies have also had numerous knock-on socioeconomic, political, behavioral and economic effects. Researchers, thus, have a unique opportunity to forward our collective understanding of the modern world and to respond to the emergency situation in a way that optimizes resources and maximizes results. The PERISCOPE project, funded by the European Commission, brings together a large number of research institutions to collect data and carry out research to understand all the impacts of the pandemic, and create predictive models that can be used to optimize intervention strategies and better face possible future health emergencies. One of the main tangible outcomes of this project is the PERISCOPE Atlas: an interactive tool that allows to visualize and analyze COVID-19-related health, economic and sociopolitical data, featuring a WebGIS and several dashboards. This paper describes the first release of the Atlas, listing the data sources used, the main functionalities and the future development.

The correlation between genetic factors and freezing of gait in patients with Parkinson's disease.

BACKGROUND: Clinical-related risk factors to freezing of gait (FOG) in Parkinson's disease (PD) have been identified. Still, the influence of genetic variations on the FOG occurrence has been poorly studied thus far. AIM: We aimed to evaluate the association of six selected polymorphisms of DRD2, ANKK1, and COMT genes with the FOG occurrence and explore the influence of ANNK1/DRD2 haplotypes on the onset of FOG in the group of PD patients. METHOD: PD patients (n = 234), treated with levodopa for at least two years, were genotyped for the rs4680 in COMT, rs6277, rs1076560, and rs2283265 in DRD2, and rs1800497 and rs2734849 polymorphisms in ANKK1 genes. FOG was evaluated by posing a direct question. In addition, a comprehensive set of clinical scales was applied to all patients. RESULTS: FOG occurred in 132 (56.4%) PD patients in our cohort. Freezers were younger at PD onset, had longer disease duration, used higher levodopa daily doses and dopaminergic agents, and had higher motor and non-motor scales scores than non-freezers. FOG was more frequent among AA rs4680 COMT carriers than AG and GG rs4680 COMT carriers. Independent predictors of FOG were: disease duration of more than ten years, levodopa daily dose higher than 500 mg/day, motor status, and COMT AA genotype. AGGAA and GGAAA haplotypes were revealed as protective and vulnerability factors for FOG occurrence. CONCLUSION: In addition to previously identified disease- and therapy-related risk factors, our results suggested a possible contribution of dopamine-related genes to the FOG occurrence.

Low-Risk Surgical Aortic Valve Replacement in the Era of Transcatheter Aortic Valve Implantation.

Open surgical aortic valve replacement (SAVR) is a viable alternative to transcatheter implantation in low-risk patients. In this light, we evaluated the safety and effectiveness of SAVR performed through conventional and less invasive surgical approaches in a high-volume center. We retrospectively reviewed the records of 395 consecutive patients who underwent open SAVR from January 2019 through December 2019 in our center. We evaluated and compared the operative results and postoperative major adverse outcomes of 3 surgical approaches: full median sternotomy (n=267), upper ministernotomy (ministernotomy) (n=106), and right anterior thoracotomy (minithoracotomy) (n=22). Overall, the 30-day all-cause mortality rate was 0.8% (3 patients). Stroke occurred in 8 patients (2%), disabling stroke in 4 patients (1%), myocardial infarction in 1 (0.2%), and surgical site infection in 13 (3.2%). There was no difference in 30-day mortality rate or incidence of postoperative major adverse events among the 3 surgical groups. Stroke and surgical site infection occurred more frequently, but not significantly so, in the full-sternotomy group. The mean hospital stay was longer after full sternotomy (9.1 ± 5.5 d) than after ministernotomy (7.5 ± 2.9 d) or minithoracotomy (7.4 ± 1.9 d) (P=0.012). Our findings suggest that open SAVR performed in a high-volume center is associated with a low early mortality rate and that less invasive approaches result in faster postoperative recovery and shorter hospital stays.

COVID-19 severity determinants inferred through ecological and epidemiological modeling.

Understanding variations in the severity of infectious diseases is essential for planning proper mitigation strategies. Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease clinical severity and transmissibility, as more infected also lead to more deaths. Instead, we use epidemiological modeling to propose a disease severity measure that accounts for the underlying disease dynamics. The measure corresponds to the ratio of population-averaged mortality and recovery rates (m/r), is independent of the disease transmission dynamics (i.e., the basic reproduction number), and has a direct mechanistic interpretation. We use this measure to assess demographic, medical, meteorological, and environmental factors associated with the disease severity. For this, we employ an ecological regression study design and analyze different US states during the first disease outbreak. Principal Component Analysis, followed by univariate, and multivariate analyses based on machine learning techniques, is used for selecting important predictors. The usefulness of the introduced severity measure and the validity of the approach are confirmed by the fact that, without using prior knowledge from clinical studies, we recover the main significant predictors known to influence disease severity, in particular age, chronic diseases, and racial factors. Additionally, we identify long-term pollution exposure and population density as not widely recognized (though for the pollution previously hypothesized) significant predictors. The proposed measure is applicable for inferring severity determinants not only of COVID-19 but also of other infectious diseases, and the obtained results may aid a better understanding of the present and future epidemics. Our holistic, systematic investigation of disease severity at the human-environment intersection by epidemiological dynamical modeling and machine learning ecological regressions is aligned with the One Health approach. The obtained results emphasize a syndemic nature of COVID-19 risks.

Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values.

Amyotrophic lateral sclerosis (ALS) is a prototypical neurodegenerative disease characterized by progressive degeneration of motor neurons to severely effect the functionality to control voluntary muscle movement. Most of the non-additive genetic aberrations responsible for ALS make its molecular classification very challenging along with limited sample size, curse of dimensionality, class imbalance and noise in the data. Deep learning methods have been successful in many other related areas but have low minority class accuracy and suffer from the lack of explainability when used directly with RNA expression features for ALS molecular classification. In this paper, we propose a deep-learning-based molecular ALS classification and interpretation framework. Our framework is based on training a convolution neural network (CNN) on images obtained from converting RNA expression values into pixels based on DeepInsight similarity technique. Then, we employed Shapley additive explanations (SHAP) to extract pixels with higher relevance to ALS classifications. These pixels were mapped back to the genes which made them up. This enabled us to classify ALS samples with high accuracy for a minority class along with identifying genes that might be playing an important role in ALS molecular classifications. Taken together with RNA expression images classified with CNN, our preliminary analysis of the genes identified by SHAP interpretation demonstrate the value of utilizing Machine Learning to perform molecular classification of ALS and uncover disease-associated genes.

Inferring the Main Drivers of SARS-CoV-2 Global Transmissibility by Feature Selection Methods.

Identifying the main environmental drivers of SARS-CoV-2 transmissibility in the population is crucial for understanding current and potential future outbursts of COVID-19 and other infectious diseases. To address this problem, we concentrate on the basic reproduction number R 0, which is not sensitive to testing coverage and represents transmissibility in an absence of social distancing and in a completely susceptible population. While many variables may potentially influence R 0, a high correlation between these variables may obscure the result interpretation. Consequently, we combine Principal Component Analysis with feature selection methods from several regression-based approaches to identify the main demographic and meteorological drivers behind R 0. We robustly obtain that country's wealth/development (GDP per capita or Human Development Index) is the most important R 0 predictor at the global level, probably being a good proxy for the overall contact frequency in a population. This main effect is modulated by built-up area per capita (crowdedness in indoor space), onset of infection (likely related to increased awareness of infection risks), net migration, unhealthy living lifestyle/conditions including pollution, seasonality, and possibly BCG vaccination prevalence. Also, we argue that several variables that significantly correlate with transmissibility do not directly influence R 0 or affect it differently than suggested by naïve analysis.

A systems biology approach to COVID-19 progression in population.

A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data but avoid overfitting in parameter inference. We here develop a simple time-dependent model, where social distancing effects are introduced analogous to coarse-grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts, observed between Hubei (Wuhan) and other Mainland China provinces. We find that these unintuitive data may be explained through an interplay of differences in transmissibility, effective protection, and detection efficiencies between Hubei and other provinces. More generally, our results demonstrate that regional differences may drastically shape infection outbursts. The obtained results demonstrate the applicability of our developed method to extract key infection parameters directly from publically available data so that it can be globally applied to outbreaks of COVID-19 in a number of countries. Overall, we show that applications of uncommon strategies, such as methods and approaches from molecular systems biology research to mathematical epidemiology, may significantly advance our understanding of COVID-19 and other infectious diseases.

The First Molecular Characterization of Serbian SARS-CoV-2 Isolates From a Unique Early Second Wave in Europe.

March 6, 2020 is considered as the official date of the beginning of the COVID-19 epidemic in Serbia. In late spring and early summer 2020, Europe recorded a decline in the rate of SARS-CoV-2 infection and subsiding of the first wave. This trend lasted until the fall, when the second wave of the epidemic began to appear. Unlike the rest of Europe, Serbia was hit by the second wave of the epidemic a few months earlier. Already in June 2020, newly confirmed cases had risen exponentially. As the COVID-19 pandemic is the first pandemic in which there has been instant sharing of genomic information on isolates around the world, the aim of this study was to analyze whole SARS-CoV-2 viral genomes from Serbia, to identify circulating variants/clade/lineages, and to explore site-specific mutational patterns in the unique early second wave of the European epidemic. This analysis of Serbian isolates represents the first publication from Balkan countries, which demonstrates the importance of specificities of local transmission especially when preventive measures differ among countries. One hundred forty-eight different genome variants among 41 Serbian isolates were detected in this study. One unique and seven extremely rare mutations were identified, with locally specific continuous dominance of the 20D clade. At the same time, amino acid substitutions of newly identified variants of concern were found in our isolates from October 2020. Future research should be focused on functional characterization of novel mutations in order to understand the exact role of these variations.

PM2.5 as a major predictor of COVID-19 basic reproduction number in the USA.

Many studies have proposed a relationship between COVID-19 transmissibility and ambient pollution levels. However, a major limitation in establishing such associations is to adequately account for complex disease dynamics, influenced by e.g. significant differences in control measures and testing policies. Another difficulty is appropriately controlling the effects of other potentially important factors, due to both their mutual correlations and a limited dataset. To overcome these difficulties, we will here use the basic reproduction number (R0) that we estimate for USA states using non-linear dynamics methods. To account for a large number of predictors (many of which are mutually strongly correlated), combined with a limited dataset, we employ machine-learning methods. Specifically, to reduce dimensionality without complicating the variable interpretation, we employ Principal Component Analysis on subsets of mutually related (and correlated) predictors. Methods that allow feature (predictor) selection, and ranking their importance, are then used, including both linear regressions with regularization and feature selection (Lasso and Elastic Net) and non-parametric methods based on ensembles of weak-learners (Random Forest and Gradient Boost). Through these substantially different approaches, we robustly obtain that PM2.5 is a major predictor of R0 in USA states, with corrections from factors such as other pollutants, prosperity measures, population density, chronic disease levels, and possibly racial composition. As a rough magnitude estimate, we obtain that a relative change in R0, with variations in pollution levels observed in the USA, is typically ~30%, which further underscores the importance of pollution in COVID-19 transmissibility.

How accurate are citations of frequently cited papers in biomedical literature?

Citations are an important, but often overlooked, part of every scientific paper. They allow the reader to trace the flow of evidence, serving as a gateway to relevant literature. Most scientists are aware of citations' errors, but few appreciate the prevalence of these problems. The purpose of the present study was to examine how often frequently cited papers in biomedical scientific literature are cited inaccurately. The study included an active participation of the first authors of included papers; to first-hand verify the citations accuracy. Findings from feasibility study, where we reviewed 1540 articles containing 2526 citations of 14 most cited articles in which the authors were affiliated with the Faculty of Medicine University of Belgrade, were further evaluated for external confirmation in an independent verification set of articles. Verification set included 4912 citations identified in 2995 articles that cited 13 most cited articles published by authors affiliated with the Mayo Clinic Division of Nephrology and Hypertension. A citation was defined as being accurate if the cited article supported or was in accordance with the statement by citing authors. At least one inaccurate citation was found in 11 and 15% of articles in the feasibility study and verification set, respectively, suggesting that inaccurate citations are common in biomedical literature. The most common problem was the citation of nonexistent findings (38.4%), followed by an incorrect interpretation of findings (15.4%). One-fifth of inaccurate citations were due to chains of inaccurate citations. Based on these findings, several actions to reduce citation inaccuracies have been proposed.

Preeclamptic Women Have Disrupted Placental microRNA Expression at the Time of Preeclampsia Diagnosis: Meta-Analysis.

Introduction: Preeclampsia (PE) is a pregnancy-associated, multi-organ, life-threatening disease that appears after the 20th week of gestation. The aim of this study was to perform a systematic review and meta-analysis to determine whether women with PE have disrupted miRNA expression compared to women who do not have PE. Methods: We conducted a systematic review and meta-analysis of studies that reported miRNAs expression levels in placenta or peripheral blood of pregnant women with vs. without PE. Studies published before October 29, 2021 were identified through PubMed, EMBASE and Web of Science. Two reviewers used predefined forms and protocols to evaluate independently the eligibility of studies based on titles and abstracts and to perform full-text screening, data abstraction and quality assessment. Standardized mean difference (SMD) was used as a measure of effect size. Results: 229 publications were included in the systematic review and 53 in the meta-analysis. The expression levels in placenta were significantly higher in women with PE compared to women without PE for miRNA-16 (SMD = 1.51,95%CI = 0.55-2.46), miRNA-20b (SMD = 0.89, 95%CI = 0.33-1.45), miRNA-23a (SMD = 2.02, 95%CI = 1.25-2.78), miRNA-29b (SMD = 1.37, 95%CI = 0.36-2.37), miRNA-155 (SMD = 2.99, 95%CI = 0.83-5.14) and miRNA-210 (SMD = 1.63, 95%CI = 0.69-2.58), and significantly lower for miRNA-376c (SMD = -4.86, 95%CI = -9.51 to -0.20). An increased level of miRNK-155 expression was found in peripheral blood of women with PE (SMD = 2.06, 95%CI = 0.35-3.76), while the expression level of miRNA-16 was significantly lower in peripheral blood of PE women (SMD = -0.47, 95%CI = -0.91 to -0.03). The functional roles of the presented miRNAs include control of trophoblast proliferation, migration, invasion, apoptosis, differentiation, cellular metabolism and angiogenesis. Conclusion: miRNAs play an important role in the pathophysiology of PE. The identification of differentially expressed miRNAs in maternal blood creates an opportunity to define an easily accessible biomarker of PE.

Buffy Coat DNA Methylation Profile Is Representative of Methylation Patterns in White Blood Cell Types in Normal Pregnancy.

Background: We aimed to assess the extent to which the buffy coat DNA methylome is representative of methylation patterns in constitutive white blood cell (WBC) types in normal pregnancy. Methods: A comparison of differential methylation of buffy coat DNA vs DNA isolated from polymorphonuclear (PMN) and lymphocytic fractions was performed for each blood sample obtained within 24 h prior to delivery from 29 normotensive pregnant women. Methylation profiles were obtained using an Illumina Human Methylation 450 BeadChip and CHaMP bioinformatics pipeline. A subset of differentially methylated probes (DMPs) showing discordant methylation were further investigated using statistical modeling and enrichment analysis. Results: The smallest number of DMPs was found between the buffy coat and the PMN fraction (2.96%). Pathway enrichment analysis of the DMPs identified biological pathways involved in the particular leukocyte lineage, consistent with perturbations during isolation. The comparisons between the buffy coat and the isolated fractions as a group using linear modeling yielded a small number of probes (∼29,000) with discordant methylation. Demethylation of probes in the buffy coat compared to derived cell lines was more common and was prevalent in shelf and open sea regions. Conclusion: Buffy coat is representative of methylation patterns in WBC types in normal pregnancy. The differential methylations are consistent with perturbations during isolation of constituent cells and likely originate in vitro due to the physical stress during cell separation and are of no physiological relevance. These findings help the interpretation of DNA methylation profiling in pregnancy and numerous other conditions.