Marine-Derived Cytosine Arabinoside (Ara-C) Inhibits Biofilm Formation by Inhibiting PEL Operon Proteins (Pel A and Pel B) of Pseudomonas aeruginosa: An In Silico Approach.

Pseudomonas aeruginosa (P. aeruginosa) is a gram-negative biofilm-forming opportunistic human pathogen whose vital mechanism is biofilm formation for better survival. PelA and PelB proteins of the PEL operon are essential for bacterial-synthesized pellicle polysaccharide (PEL), which is a vital structural component of the biofilm. It helps in adherence of biofilm on the surface and maintenance of cell-to-cell interactions and with other matrix components. Here, in-silico molecular docking and simulation studies were performed against PelA and PelB using ten natural bioactive compounds, individually [podocarpic acids, ferruginol, scopadulcic acid B, pisiferic acid, metachromin A, Cytarabine (cytosine arabinoside; Ara-C), ursolic acid, oleanolic acid, maslinic acid, and betulinic acid], those have already been established as anti-infectious compounds. The results obtained from AutoDock and Glide-Schordinger stated that a marine-derived cytosine arabinoside (Ara-C) among the ten compounds binds active sites of PelA and PelB, exhibiting strong binding affinity [Trp224 (hydrogen), Ser219 (polar), Val234 (hydrophobic) for PelA; Leu365 and Glu389 (hydrogen), Gln366 (polar) for PelB] with high negative binding energy - 5.518 kcal/mol and - 6.056 kcal/mol, respectively. The molecular dynamic and simulation studies for 100 ns showed the MMGBSA binding energy scores are - 16.4 kcal/mol (Ara-C with PelA), and - 22.25 kcal/mol (Ara-C with PelB). Further, ADME/T studies indicate the IC50 values of AraC are 6.10 mM for PelA and 18.78 mM for PelB, which is a comparatively very low dose. The zero violation of Lipinski's Rule of Five further established that Ara-C is a good candidate for drug development. Thus, Ara-C could be considered a potent anti-biofilm compound against PEL operon-dependent biofilm formation of P. aeruginosa.

Lipidomic changes in a novel sepsis outcome-based analysis reveals potent pro-inflammatory and pro-resolving signaling lipids.

The purpose of this study was to investigate changes in the lipidome of patients with sepsis to identify signaling lipids associated with poor outcomes that could be linked to future therapies. Adult patients with sepsis were enrolled within 24h of sepsis recognition. Patients meeting Sepsis-3 criteria were enrolled from the emergency department or intensive care unit and blood samples were obtained. Clinical data were collected and outcomes of rapid recovery, chronic critical illness (CCI), or early death were adjudicated by clinicians. Lipidomic analysis was performed on two platforms, the Sciex™ 5500 device to perform a lipidomic screen of 1450 lipid species and a targeted signaling lipid panel using liquid-chromatography tandem mass spectrometry. For the lipidomic screen, there were 274 patients with sepsis: 192 with rapid recovery, 47 with CCI, and 35 with early deaths. CCI and early death patients were grouped together for analysis. Fatty acid (FA) 12:0 was decreased in CCI/early death, whereas FA 17:0 and 20:1 were elevated in CCI/early death, compared to rapid recovery patients. For the signaling lipid panel analysis, there were 262 patients with sepsis: 189 with rapid recovery, 45 with CCI, and 28 with early death. Pro-inflammatory signaling lipids from ω-6 poly-unsaturated fatty acids (PUFAs), including 15-hydroxyeicosatetraenoic (HETE), 12-HETE, and 11-HETE (oxidation products of arachidonic acid [AA]) were elevated in CCI/early death patients compared to rapid recovery. The pro-resolving lipid mediator from ω-3 PUFAs, 14(S)-hydroxy docosahexaenoic acid (14S-HDHA), was also elevated in CCI/early death compared to rapid recovery. Signaling lipids of the AA pathway were elevated in poor-outcome patients with sepsis and may serve as targets for future therapies.

Biofilm-producing antibiotic-resistant bacteria in Indian patients: a comprehensive review.

For the past few years, microbial biofilms have been emerging as a significant threat to the modern healthcare system, and their prevalence and antibiotic resistance threat gradually increase daily among the human population. The biofilm has a remarkable impact in the field of infectious diseases, in particular healthcare-associated infections related to indwelling devices such as catheters, implants, artificial heart valves, and prosthetic joints. Bacterial biofilm potentially adheres to any biotic or abiotic surfaces that give specific shelter to the microbial community, making them less susceptible to many antimicrobial agents and even resistant to the immune cells of animal hosts. Around thirty clinical research reports available in PUBMED have been considered to establish the occurrence of biofilm-forming bacteria showing resistance against several regular antibiotics prescribed against infection by clinicians among Indian patients. After the extensive literature review, our observation exhibits a high predominance of biofilm formation among bacteria such as Escherichia sp., Streptococcus sp., Staphylococcus sp., and Pseudomonas sp., those are the most common biofilm-producing antibiotic-resistant bacteria among Indian patients with urinary tract infections and/or catheter-related infections, respiratory tract infections, dental infections, skin infections, and implant-associated infections. This review demonstrates that biofilm-associated bacterial infections constantly elevate in several pathological conditions along with the enhancement of the multi-drug resistance phenomenon.

Association of air pollution with postmenopausal breast cancer risk in UK Biobank.

BACKGROUND: We investigated the association of several air pollution measures with postmenopausal breast cancer (BCa) risk. METHODS: This study included 155,235 postmenopausal women (of which 6146 with BCa) from UK Biobank. Cancer diagnoses were ascertained through the linkage to the UK National Health Service Central Registers. Annual exposure averages were available from 2005, 2006, 2007, and 2010 for NO2, from 2007 and 2010 for PM10, and from 2010 for PM2.5, NOX, PM2.5-10 and PM2.5 absorbance. Information on BCa risk factors was collected at baseline. Cox proportional hazards regression was used to evaluate the associations of year-specific and cumulative average exposures with BCa risk, overall and with 2-year exposure lag, while adjusting for BCa risk factors. RESULTS: PM10 in 2007 and cumulative average PM10 were positively associated with BCa risk (2007 PM10: Hazard ratio [HR] per 10 µg/m3 = 1.18, 95% CI 1.08, 1.29; cumulative average PM10: HR per 10 µg/m3 = 1.99, 95% CI 1.75, 2.27). Compared to women with low exposure, women with higher 2007 PM10 and cumulative average PM10 had greater BCa risk (4th vs. 1st quartile HR = 1.15, 95% CI 1.07, 1.24, p-trend = 0.001 and HR = 1.35, 95% CI 1.25, 1.44, p-trend < 0.0001, respectively). No significant associations were found for any other exposure measures. In the analysis with 2-year exposure lag, both 2007 PM 10 and cumulative average PM10 were positively associated with BCa risk (4th vs. 1st quartile HR = 1.19, 95% CI 1.10, 1.28 and HR = 1.29, 95% CI 1.19, 1.39, respectively). CONCLUSION: Our findings suggest a positive association of 2007 PM10 and cumulative average PM10 with postmenopausal BCa risk.

Inferring Cell-Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model.

Cellular communication through biochemical signaling is fundamental to every biological activity. Investigating cell signaling diffusions across cell types can further help understand biological mechanisms. In recent years, this has become an important research topic as single-cell sequencing technologies have matured. However, cell signaling activities are spatially constrained, and single-cell data cannot provide spatial information for each cell. This issue may cause a high false discovery rate, and using spatially resolved transcriptomics data is necessary. On the other hand, as far as we know, most existing methods focus on providing an ad hoc measurement to estimate intercellular communication instead of relying on a statistical model. It is undeniable that descriptive statistics are straightforward and accessible, but a suitable statistical model can provide more accurate and reliable inference. In this way, we propose a generalized linear regression model to infer cellular communications from spatially resolved transcriptomics data, especially spot-based data. Our BAyesian Tweedie modeling of COMmunications (BATCOM) method estimates the communication scores between cell types with the consideration of their corresponding distances. Due to the properties of the regression model, BATCOM naturally provides the direction of the communication between cell types and the interaction of ligands and receptors that other approaches cannot offer. We conduct simulation studies to assess the performance under different scenarios. We also employ BATCOM in a real-data application and compare it with other existing algorithms. In summary, our innovative model can fill gaps in the inference of cell-cell communication and provide a robust and straightforward result.

DHCR7 Expression Predicts Poor Outcomes and Mortality From Sepsis.

This is a study of lipid metabolic gene expression patterns to discover precision medicine for sepsis. OBJECTIVES: Sepsis patients experience poor outcomes including chronic critical illness (CCI) or early death (within 14 d). We investigated lipid metabolic gene expression differences by outcome to discover therapeutic targets. DESIGN SETTING AND PARTICITPANTS: Secondary analysis of samples from prospectively enrolled sepsis patients (first 24 hr) and a zebrafish endotoxemia model for drug discovery. Patients were enrolled from the emergency department or ICU at an urban teaching hospital. Enrollment samples from sepsis patients were analyzed. Clinical data and cholesterol levels were recorded. Leukocytes were processed for RNA sequencing and reverse transcriptase polymerase chain reaction. A lipopolysaccharide zebrafish endotoxemia model was used for confirmation of human transcriptomic findings and drug discovery. MAIN OUTCOMES AND MEASURES: The derivation cohort included 96 patients and controls (12 early death, 13 CCI, 51 rapid recovery, and 20 controls) and the validation cohort had 52 patients (6 early death, 8 CCI, and 38 rapid recovery). RESULTS: The cholesterol metabolism gene 7-dehydrocholesterol reductase (DHCR7) was significantly up-regulated in both derivation and validation cohorts in poor outcome sepsis compared with rapid recovery patients and in 90-day nonsurvivors (validation only) and validated using RT-qPCR analysis. Our zebrafish sepsis model showed up-regulation of dhcr7 and several of the same lipid genes up-regulated in poor outcome human sepsis (dhcr24, sqlea, cyp51, msmo1, and ldlra) compared with controls. We then tested six lipid-based drugs in the zebrafish endotoxemia model. Of these, only the Dhcr7 inhibitor AY9944 completely rescued zebrafish from lipopolysaccharide death in a model with 100% lethality. CONCLUSIONS: DHCR7, an important cholesterol metabolism gene, was up-regulated in poor outcome sepsis patients warranting external validation. This pathway may serve as a potential therapeutic target to improve sepsis outcomes.

Clustering single-cell multimodal omics data with jrSiCKLSNMF.

Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise. Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") that extracts latent factors shared across omics modalities within the same set of single cells. Results: We compare our clustering algorithm to several existing methods on four sets of data simulated from third party software. We also apply our algorithm to a real set of cell line data. Discussion: We show overwhelmingly better clustering performance than several existing methods on the simulated data. On a real multimodal omics dataset, we also find our method to produce scientifically accurate clustering results.

Adaptive Sparse Multi-Block PLS Discriminant Analysis: An Integrative Method for Identifying Key Biomarkers from Multi-Omics Data.

With the growing use of high-throughput technologies, multi-omics data containing various types of high-dimensional omics data is increasingly being generated to explore the association between the molecular mechanism of the host and diseases. In this study, we present an adaptive sparse multi-block partial least square discriminant analysis (asmbPLS-DA), an extension of our previous work, asmbPLS. This integrative approach identifies the most relevant features across different types of omics data while discriminating multiple disease outcome groups. We used simulation data with various scenarios and a real dataset from the TCGA project to demonstrate that asmbPLS-DA can identify key biomarkers from each type of omics data with better biological relevance than existing competitive methods. Moreover, asmbPLS-DA showed comparable performance in the classification of subjects in terms of disease status or phenotypes using integrated multi-omics molecular profiles, especially when combined with other classification algorithms, such as linear discriminant analysis and random forest. We have made the R package called asmbPLS that implements this method publicly available on GitHub. Overall, asmbPLS-DA achieved competitive performance in terms of feature selection and classification. We believe that asmbPLS-DA can be a valuable tool for multi-omics research.

mTOR pathway candidate genes and energy intake interaction on breast cancer risk in Black women from the Women's Circle of Health Study.

BACKGROUND: Excessive energy intake has been shown to affect the mammalian target of the rapamycin (mTOR) signaling pathway and breast cancer risk. It is not well understood whether there are gene-environment interactions between mTOR pathway genes and energy intake in relation to breast cancer risk. METHODS: The study included 1642 Black women (809 incident breast cancer cases and 833 controls) from the Women's Circle of Health Study (WCHS). We examined interactions between 43 candidate single-nucleotide polymorphisms (SNPs) in 20 mTOR pathway genes and quartiles of energy intake in relation to breast cancer risk overall and by ER- defined subtypes using Wald test with a 2-way interaction term. RESULTS: AKT1 rs10138227 (C > T) was only associated with a decreased overall breast cancer risk among women in quartile (Q)2 of energy intake, odds ratio (OR) = 0.60, 95% confidence interval (CI) 0.40, 0.91 (p-interaction = 0.042). Similar results were found in ER- tumors. AKT rs1130214 (C > A) was associated with decreased overall breast cancer risk in Q2 (OR = 0.63, 95% CI 0.44, 0.91) and Q3 (OR = 0.65, 95% CI 0.48, 0.89) (p-interaction = 0.026). HIF-1α C1772T rs11549465 (C > T) was associated with decreased overall breast cancer risk in Q4 (OR = 0.29, 95% CI 0.14, 0.59, p-interaction = 0.007); the results were similar in ER+ tumors. These interactions became non-significant after correction for multiple comparisons. CONCLUSION: Our findings suggest that mTOR genetic variants may interact with energy intake in relation to breast cancer risk, including the ER- subtype, in Black women. Future studies should confirm these findings.

asmbPLS: Adaptive Sparse Multi-block Partial Least Square for Survival Prediction using Multi-Omics Data.

Background: As high-throughput studies advance, more and more high-dimensional multi-omics data are available and collected from the same patient cohort. Using multi-omics data as predictors to predict survival outcomes is challenging due to the complex structure of such data. Results: In this article, we introduce an adaptive sparse multi-block partial least square (asmbPLS) regression method by assigning different penalty factors to different blocks in different PLS components for feature selection and prediction. We compared the proposed method with several competitive algorithms in many aspects including prediction performance, feature selection and computation efficiency. The performance and the efficiency of our method were demonstrated using both the simulated and the real data. Conclusions: In summary, asmbPLS achieved a competitive performance in prediction, feature selection, and computation efficiency. We anticipate asmbPLS to be a valuable tool for multi-omics research. An R package called asmbPLS implementing this method is made publicly available on GitHub.

Association Between Recreational Physical Activity and mTOR Signaling Pathway Protein Expression in Breast Tumor Tissue.

Physical activity (PA) is associated with decreased signaling in the mTOR pathway in animal models of mammary cancer, which may indicate favorable outcomes. We examined the association between PA and protein expression in the mTOR signaling pathway in breast tumor tissue. Data on 739 patients with breast cancer, among which 125 patients had adjacent-normal tissue, with tumor expression for mTOR, phosphorylated (p)-mTOR, p-AKT, and p-P70S6K were analyzed. Self-reported recreational PA levels during the year prior to diagnosis were classified using the Centers for Disease Control and Prevention guideline as sufficient (for moderate or vigorous) PA or insufficient PA (any PA but not meeting the guideline) or no PA. We performed linear models for mTOR protein and two-part gamma hurdle models for phosphorylated proteins. Overall, 34.8% of women reported sufficient PA; 14.2%, insufficient PA; 51.0%, no PA. Sufficient (vs. no) PA was associated with higher expression for p-P70S6K [35.8% increase; 95% confidence interval (CI), 2.6-80.2] and total phosphoprotein (28.5% increase; 95% CI, 5.8-56.3) among tumors with positive expression. In analyses stratified by PA intensity, sufficient versus no vigorous PA was also associated with higher expression levels of mTOR (beta = 17.7; 95% CI, 1.1-34.3) and total phosphoprotein (28.6% higher; 95% CI, 1.4-65.0 among women with positive expression) in tumors. The study found that guideline-concordant PA levels were associated with increased mTOR signaling pathway activity in breast tumors. Studying PA in relation to mTOR signaling in humans may need to consider the complexity of the behavioral and biological factors. Significance: PA increases energy expenditure and limits energy utilization in the cell, which can influence the mTOR pathway that is central to sensing energy influx and regulating cell growth. We studied exercise-mediated mTOR pathway activities in breast tumor and adjacent-normal tissue. Despite the discrepancies between animal and human data and the limitations of our approach, the findings provide a foundation to study the mechanisms of PA and their clinical implications.

mTOR pathway candidate genes and physical activity interaction on breast cancer risk in black women from the women's circle of health study.

BACKGROUND: Physical activity has been shown to affect the mammalian target of rapamycin (mTOR) signaling pathway and consequently breast carcinogenesis. Given that Black women in the USA are less physically active, it is not well understood whether there are gene-environment interactions between mTOR pathway genes and physical activity in relation to breast cancer risk in Black women. METHODS: The study included 1398 Black women (567 incident breast cancer cases and 831 controls) from the Women's Circle of Health Study (WCHS). We examined interactions between 43 candidate single-nucleotide polymorphisms (SNPs) in 20 mTOR pathway genes with levels of vigorous physical activity in relation to breast cancer risk overall and by ER-defined subtypes using Wald test with 2-way interaction term and multivariable logistic regression. RESULTS: AKT1 rs10138227 (C > T) and AKT1 rs1130214 (C > A) were only associated with a decreased risk of ER + breast cancer among women with vigorous physical activity (odds ratio [OR] = 0.15, 95% confidence interval (CI) 0.04, 0.56, for each copy of the T allele, p-interaction = 0.007 and OR = 0.51, 95% CI 0.27, 0.96, for each copy of the A allele, p-interaction = 0.045, respectively). MTOR rs2295080 (G > T) was only associated with an increased risk of ER + breast cancer among women with vigorous physical activity (OR = 2.24, 95% CI 1.16, 4.34, for each copy of the G allele; p-interaction = 0.043). EIF4E rs141689493 (G > A) was only associated with an increased risk of ER- breast cancer among women with vigorous physical activity (OR = 20.54, 95% CI 2.29, 184.17, for each copy of the A allele; p-interaction = 0.003). These interactions became non-significant after correction for multiple testing (FDR-adjusted p-value > 0.05). CONCLUSION: Our findings suggest that mTOR genetic variants may interact with physical activity in relation to breast cancer risk in Black women. Future studies should confirm these findings.

DHCR7 Expression Predicts Poor Outcomes and Mortality from Sepsis.

Objective: Sepsis patients experience poor outcomes including chronic critical illness (CCI) or early death (within 14 days). We investigated lipid metabolic gene expression differences by outcome to discover therapeutic targets. Design: Secondary analysis of samples from prospectively enrolled sepsis patients and a zebrafish sepsis model for drug discovery. Setting: Emergency department or ICU at an urban teaching hospital. Patients: Sepsis patients presenting within 24 hours. Methods: Enrollment samples from sepsis patients were analyzed. Clinical data and cholesterol levels were recorded. Leukocytes were processed for RNA sequencing (RNA-seq) and reverse transcriptase polymerase chain reaction (RT-qPCR). A lipopolysaccharide (LPS) zebrafish sepsis model was used for confirmation of human transcriptomic findings and drug discovery. Measurements and Main Results: There were 96 samples in the derivation (76 sepsis, 20 controls) and 52 in the validation cohort (sepsis only). The cholesterol metabolism gene 7-Dehydrocholesterol Reductase ( DHCR7) was significantly upregulated in both derivation and validation cohorts in poor outcome sepsis compared to rapid recovery patients and in 90-day non-survivors (validation only) and validated using RT-qPCR analysis. Our zebrafish sepsis model showed upregulation of dhcr7 and several of the same lipid genes upregulated in poor outcome human sepsis (dhcr24, sqlea, cyp51, msmo1 , ldlra) compared to controls. We then tested six lipid-based drugs in the zebrafish sepsis model. Of these, only the Dhcr7 inhibitor AY9944 completely rescued zebrafish from LPS death in a model with 100% lethality. Conclusions: DHCR7, an important cholesterol metabolism gene, was upregulated in poor outcome sepsis patients warranting external validation. This pathway may serve as a potential therapeutic target to improve sepsis outcomes.

mTOR pathway candidate genes and obesity interaction on breast cancer risk in black women from the Women's Circle of Health Study.

BACKGROUND: Obesity is known to stimulate the mammalian target of rapamycin (mTOR) signaling pathway and both obesity and the mTOR signaling pathway are implicated in breast carcinogenesis. We investigated potential gene-environment interactions between mTOR pathway genes and obesity in relation to breast cancer risk among Black women. METHODS: The study included 1,655 Black women (821 incident breast cancer cases and 834 controls) from the Women's Circle of Health Study (WCHS). Obesity measures including body mass index (BMI); central obesity i.e., waist circumference (WC) and waist/hip ratio (WHR); and body fat distribution (fat mass, fat mass index and percent body fat) were obtained by trained research staff. We examined the associations of 43 candidate single-nucleotide polymorphisms (SNPs) in 20 mTOR pathway genes with breast cancer risk using multivariable logistic regression. We next examined interactions between these SNPs and measures of obesity using Wald test with 2-way interaction term. RESULTS: The variant allele of BRAF (rs114729114 C > T) was associated with an increase in overall breast cancer risk [odds ratio (OR) = 1.81, 95% confidence interval (CI) 1.10-2.99, for each copy of the T allele] and the risk of estrogen receptor (ER)-defined subtypes (ER+ tumors: OR = 1.83, 95% CI 1.04,3.29, for each copy of the T allele; ER- tumors OR = 2.14, 95% CI 1.03,4.45, for each copy of the T allele). Genetic variants in AKT, AKT1, PGF, PRKAG2, RAPTOR, TSC2 showed suggestive associations with overall breast cancer risk and the risk of, ER+ and ER- tumors (range of p-values = 0.040-0.097). We also found interactions of several of the SNPs with BMI, WHR, WC, fat mass, fat mass index and percent body fat in relation to breast cancer risk. These associations and interactions, however, became nonsignificant after correction for multiple testing (FDR-adjusted p-value > 0.05). CONCLUSION: We found associations between mTOR genetic variants and breast cancer risk as well as gene and body fatness interactions in relation to breast cancer risk. However, these associations and interactions became nonsignificant after correction for multiple testing. Future studies with larger sample sizes are required to confirm and validate these findings.

Intraindividual Reliability of Opportunistic Computed Tomography-Assessed Adiposity and Skeletal Muscle Among Breast Cancer Patients.

BACKGROUND: Adiposity and skeletal muscle levels assessed on computed tomography (CT) scans are prognostic indicators for patients with breast cancer. However, the intraindividual reliability of temporal changes in body composition assessed on opportunistic CT scans is unclear. METHODS: This retrospective study included 50 patients newly diagnosed with breast cancer who had archived CT scans pre- and postsurgery for breast cancer. The third lumbar CT image was segmented for areas of 3 types of adipose tissues and 5 different densities of skeletal muscles. Mean and percent changes in areas pre- vs postsurgery were compared using Wilcoxon signed rank tests. Intraclass correlation coefficients (ICCs) with 95% confidence intervals were assessed. A 2-sided P less than .05 was considered statistically significant. RESULTS: Mean (SD) age at diagnosis was 58.3 (12.5) years, and the interval between CT scans was 590.6 (536.8) days. Areas for body composition components were unchanged except for intermuscular adipose tissue (mean change = 1.45 cm2, 6.74% increase, P = .008) and very high-density muscle (mean change = -0.37 cm2, 11.08% decrease, P = .01) during the interval. There was strong intraindividual reliability in adipose tissue and skeletal muscle areas on pre- vs postsurgery scans overall (ICC = 0.763-0.998) and for scans collected 3 or less years apart (ICC = 0.802-0.999; 42 patients). CONCLUSIONS: Although some body composition components may change after breast cancer surgery, CT scan assessments of body composition were reliable for a 3-year interval including the surgery. These findings inform measurement characteristics of body composition on opportunistic CT scans of patients undergoing surgery for breast cancer.

Impact of Skin Biopsy and Clinical-Pathologic Correlation in Dermatology Inpatient Consults.

Background While studies of hospital dermatology have demonstrated diagnostic discordance between primary teams and dermatology consultants, little is known about the impact of biopsy and clinical-pathologic correlation (CPC) in consultation. This study compares biopsy performance based on diagnostic discordance and evaluates the impact of CPC on the diagnosis. Methods This was a retrospective review of 376 dermatologic consultations at a single academic medical center between July 1, 2017, and June 27, 2018. Results Biopsy was significantly less likely to be performed when the diagnosis by the referring primary team was unspecified (p < 0.001). In 24 percent of cases, the diagnosis based on histopathology alone differed from the diagnosis reached by formal CPC consensus review with either potential or significant impact on management. Conclusion Dermatologists who perform inpatient consultations and rely on hospital-based pathology services may consider a consensus review for CPC. Requests to perform a biopsy may be interpreted as a request for diagnostic assistance rather than pressure to perform a procedure.

SAREV: A review on statistical analytics of single-cell RNA sequencing data.

Due to the development of next-generation RNA sequencing (NGS) technologies, there has been tremendous progress in research involving determining the role of genomics, transcriptomics and epigenomics in complex biological systems. However, scientists have realized that information obtained using earlier technology, frequently called 'bulk RNA-seq' data, provides information averaged across all the cells present in a tissue. Relatively newly developed single cell (scRNA-seq) technology allows us to provide transcriptomic information at a single-cell resolution. Nevertheless, these high-resolution data have their own complex natures and demand novel statistical data analysis methods to provide effective and highly accurate results on complex biological systems. In this review, we cover many such recently developed statistical methods for researchers wanting to pursue scRNA-seq statistical and computational research as well as scientific research about these existing methods and free software tools available for their generated data. This review is certainly not exhaustive due to page limitations. We have tried to cover the popular methods starting from quality control to the downstream analysis of finding differentially expressed genes and concluding with a brief description of network analysis.

MarZIC: A Marginal Mediation Model for Zero-Inflated Compositional Mediators with Applications to Microbiome Data.

BACKGROUND: The human microbiome can contribute to pathogeneses of many complex diseases by mediating disease-leading causal pathways. However, standard mediation analysis methods are not adequate to analyze the microbiome as a mediator due to the excessive number of zero-valued sequencing reads in the data and that the relative abundances have to sum to one. The two main challenges raised by the zero-inflated data structure are: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are not zero (i.e., false zeros). METHODS: We develop a novel marginal mediation analysis method under the potential-outcomes framework to address the issues. We also show that the marginal model can account for the compositional structure of microbiome data. RESULTS: The mediation effect can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the application in a real microbiome study showcase our approach in comparison with existing approaches. CONCLUSIONS: When analyzing the zero-inflated microbiome composition as the mediators, MarZIC approach has better performance than standard causal mediation analysis approaches and existing competing approach.

Unraveling T Cell Responses for Long Term Protection of SARS-CoV-2 Infection.

Due to the COVID-19 pandemic, the global need for vaccines to prevent the disease is imperative. To date, several manufacturers have made efforts to develop vaccines against SARS-CoV-2. In spite of the success of developing many useful vaccines so far, it will be helpful for future vaccine designs, targetting long-term disease protection. For this, we need to know more details of the mechanism of T cell responses to SARS-CoV-2. In this study, we first detected pairwise differentially expressed genes among the healthy, mild, and severe COVID-19 groups of patients based on the expression of CD4+ T cells and CD8+ T cells, respectively. The CD4+ T cells dataset contains 6 mild COVID-19 patients, 8 severe COVID-19 patients, and 6 healthy donors, while the CD8+ T cells dataset has 15 mild COVID-19 patients, 22 severe COVID-19 patients, and 4 healthy donors. Furthermore, we utilized the deep learning algorithm to investigate the potential of differentially expressed genes in distinguishing different disease states. Finally, we built co-expression networks among those genes separately. For CD4+ T cells, we identified 6 modules for the healthy network, 4 modules for the mild network, and 1 module for the severe network; for CD8+ T cells, we detected 6 modules for the healthy network, 4 modules for the mild network, and 3 modules for the severe network. We also obtained hub genes for each module and evaluated the differential connectivity of each gene between pairs of networks constructed on different disease states. Summarizing the results, we find that the following genes TNF, CCL4, XCL1, and IFITM1 can be highly identified with SARS-CoV-2. It is interesting to see that IFITM1 has already been known to inhibit multiple infections with other enveloped viruses, including coronavirus. In addition, our networks show some specific patterns of connectivity among genes and some meaningful clusters related to COVID-19. The results might improve the insight of gene expression mechanisms associated with both CD4+ and CD8+ T cells, expand our understanding of COVID-19 and help develop vaccines with long-term protection.

Circulating Microbiota in Cardiometabolic Disease.

The rapid expansion of microbiota research has significantly advanced our understanding of the complex interactions between gut microbiota and cardiovascular, metabolic, and renal system regulation. Low-grade chronic inflammation has long been implicated as one of the key mechanisms underlying cardiometabolic disease risk and progression, even before the insights provided by gut microbiota research in the past decade. Microbial translocation into the bloodstream can occur via different routes, including through the oral and/or intestinal mucosa, and may contribute to chronic inflammation in cardiometabolic disease. Among several gut-derived products identifiable in the systemic circulation, bacterial endotoxins and metabolites have been extensively studied, however recent advances in microbial DNA sequencing have further allowed us to identify highly diverse communities of microorganisms in the bloodstream from an -omics standpoint, which is termed "circulating microbiota." While detecting microorganisms in the bloodstream was historically considered as an indication of infection, evidence on the circulating microbiota is continually accumulating in various patient populations without clinical signs of infection and even in otherwise healthy individuals. Moreover, both quantitative and compositional alterations of the circulating microbiota have recently been implicated in the pathogenesis of chronic inflammatory conditions, potentially through their immunostimulatory, atherogenic, and cardiotoxic properties. In this mini review, we aim to provide recent evidence on the characteristics and roles of circulating microbiota in several cardiometabolic diseases, such as type 2 diabetes, cardiovascular disease, and chronic kidney disease, with highlights of our emerging findings on circulating microbiota in patients with end-stage kidney disease undergoing hemodialysis.