Improving gene network inference with graph wavelets and making insights about ageing-associated regulatory changes in lungs.

Using gene-regulatory-networks-based approach for single-cell expression profiles can reveal unprecedented details about the effects of external and internal factors. However, noise and batch effect in sparse single-cell expression profiles can hamper correct estimation of dependencies among genes and regulatory changes. Here, we devise a conceptually different method using graphwavelet filters for improving gene network (GWNet)-based analysis of the transcriptome. Our approach improved the performance of several gene network-inference methods. Most Importantly, GWNet improved consistency in the prediction of gene regulatory network using single-cell transcriptome even in the presence of batch effect. The consistency of predicted gene network enabled reliable estimates of changes in the influence of genes not highlighted by differential-expression analysis. Applying GWNet on the single-cell transcriptome profile of lung cells, revealed biologically relevant changes in the influence of pathways and master regulators due to ageing. Surprisingly, the regulatory influence of ageing on pneumocytes type II cells showed noticeable similarity with patterns due to the effect of novel coronavirus infection in human lung.

CellAtlasSearch: a scalable search engine for single cells.

Owing to the advent of high throughput single cell transcriptomics, past few years have seen exponential growth in production of gene expression data. Recently efforts have been made by various research groups to homogenize and store single cell expression from a large number of studies. The true value of this ever increasing data deluge can be unlocked by making it searchable. To this end, we propose CellAtlasSearch, a novel search architecture for high dimensional expression data, which is massively parallel as well as light-weight, thus infinitely scalable. In CellAtlasSearch, we use a Graphical Processing Unit (GPU) friendly version of Locality Sensitive Hashing (LSH) for unmatched speedup in data processing and query. Currently, CellAtlasSearch features over 300 000 reference expression profiles including both bulk and single-cell data. It enables the user query individual single cell transcriptomes and finds matching samples from the database along with necessary meta information. CellAtlasSearch aims to assist researchers and clinicians in characterizing unannotated single cells. It also facilitates noise free, low dimensional representation of single-cell expression profiles by projecting them on a wide variety of reference samples. The web-server is accessible at: http://www.cellatlassearch.com.

PRP and its benefit as an adjunctive therapy with subcision and microneedling in atrophic scars: a comparative study.

Context: Scarring is a biological process of wound repair which leads to a difference in the normal structure and function of the skin and manifests as a depressed or raised area. Treatment of scars is challenging. A number of therapeutic approaches like surgical techniques and non-surgical techniques are performed to improve scarring. Aims and Objectives: The aim of this study was to compare the outcome of subcision followed by microneedling versus subcision followed by microneedling and topical platelet-rich plasma (PRP) in atrophic scars. Materials and Methods: A comparative prospective study was conducted at a tertiary care hospital in North India to compare the efficacy of subcision followed by microneedling versus subcision followed by microneedling and topical PRP. A total of 40 cases were taken and were randomly divided into two groups, A and B of 20 patients in each group. Topical PRP was applied as an additional therapy in Group B in the same sitting. Minimum three sittings were done in each patient at an interval of 4 weeks and results were assessed after 1 month of the third session. The statistical software used is Microsoft Excel and SPSS software program, version 24.0 for analysis of data and Microsoft Word to generate graphs and tables. Results: Improvement in scar grading was more in Group B as compared to Group A with statistically significant difference (P = 0.032). There was an improvement in scar grading from grade 4 scar to grade 2 in 15% and 30% patients of Groups A and B, respectively, with improvement in skin texture and pigmentation more in Group B. Conclusion: PRP proved to add to the improvement of grade of atrophic scars when combined with subcision and microneedling.

Confluent and Reticulated Papillomatosis Resembling Pityriasis Versicolor and Acanthosis Nigricans: Case Report.

Confluent and reticulated papillomatosis (CARP) is a rare disorder mostly seen in young adults. It is characterized by persistent dull-brown, centrally confluent, peripherally reticulate macules and papules, which coalesce to form patches and plaques on the upper trunk and neck. It is commonly confused with pityriasis versicolor and acanthosis nigricans (AN). We report the case of a 15-year-old male with multiple pigmented confluent and reticulated patches and plaques on the neck, trunk, and chin for 3 years, which was successfully treated with oral minocycline, resulting in complete resolution of lesions within 2 weeks. The morphology of CARP resembles that of various other dermatological conditions such as AN and pityriasis versicolor, and, as a result, it is frequently misdiagnosed and mistreated, leading to social embarrassment for the patient. Therefore, it is prudent for dermatologists to carry out comprehensive clinical and histopathological assessments to facilitate prompt diagnosis and management of this condition.

A Comparative Study of the Efficacy of Methotrexate versus Methotrexate with Apremilast in Moderate to Severe Chronic Plaque Psoriasis.

Background: Psoriasis is an inflammatory systemic disease with a chronic relapsing course. Methotrexate, a dihydrofolate reductase inhibitor, and Apremilast, an oral phosphodiesterase type 4 inhibitor, are currently the mainstay drugs in the treatment of psoriasis. Aims and Objectives: To compare the efficacy of Methotrexate with a combination of Methotrexate and Apremilast in treating chronic plaque psoriasis. Materials and Methods: The present study was a prospective comparative study conducted among 40 patients, aged above 18 years, with clinically diagnosed psoriasis attending Dermatology OPD of a tertiary care hospital in North India. The study utilised a pre-structured proforma to record a detailed demographic profile and clinical examination related to chronic plaque psoriasis. The patients were divided into two groups of 20 each. Group A was treated with oral Methotrexate, while Group B was treated with oral Apremilast and Methotrexate, and they were evaluated every 4 weeks for 12 weeks. Necessary investigations were done wherever indicated. Results: The male-to-female ratio was 1.35, and the majority (55.0%) of patients belonged to the age group of 31-50 years. 27.5% of patients had comorbidities like diabetes, hypertension, etc., The mean PASI score of group A at the first, second and third follow-ups was higher than that of group B. The reduction in mean PASI score was statistically significant in group B at successive follow-ups, with a percentage improvement of 89.4% at the end of 12 weeks. Conclusions: When comparing monotherapy with methotrexate and multidrug therapy with Methotrexate and Apremilast, multidrug therapy had better efficacy.

Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

MOTIVATION: Continuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of variants and huge scale of genomic data have added to the challenges of tracing the mutations/variants and their relationship to infection severity (if any). RESULTS: We explored the suitability of virus-genotype guided machine-learning in infection prognosis and identification of features/mutations-of-interest. Total 199,519 outcome-traced genomes, representing 45,625 nucleotide-mutations, were employed. Among these, post data-cleaning, Low and High severity genomes were classified using an integrated model (employing virus genotype, epitopic-influence and patient-age) with consistently high ROC-AUC (Asia:0.97 ± 0.01, Europe:0.94 ± 0.01, N.America:0.92 ± 0.02, Africa:0.94 ± 0.07, S.America:0.93 ± 03). Although virus-genotype alone could enable high predictivity (0.97 ± 0.01, 0.89 ± 0.02, 0.86 ± 0.04, 0.95 ± 0.06, 0.9 ± 0.04), the performance was not found to be consistent and the models for a few geographies displayed significant improvement in predictivity when the influence of age and/or epitope was incorporated with virus-genotype (Wilcoxon p_BH < 0.05). Neither age or epitopic-influence or clade information could out-perform the integrated features. A sparse model (6 features), developed using patient-age and epitopic-influence of the mutations, performed reasonably well (>0.87 ± 0.03, 0.91 ± 0.01, 0.87 ± 0.03, 0.84 ± 0.08, 0.89 ± 0.05). High-performance models were employed for inferring the important mutations-of-interest using Shapley Additive exPlanations (SHAP). The changes in HLA interactions of the mutated epitopes of reference SARS-CoV-2 were then subsequently probed. Notably, we also describe the significance of a 'temporal-modeling approach' to benchmark the models linked with continuously evolving pathogens. We conclude that while machine learning can play a vital role in identifying relevant mutations and factors driving the severity, caution should be exercised in using the genotypic signatures for predictive prognosis.

Corrigendum: "EviMass": A Literature Evidence-Based Miner for Human Microbial Associations.

[This corrects the article DOI: 10.3389/fgene.2019.00849.].

"EviMass": A Literature Evidence-Based Miner for Human Microbial Associations.

The importance of understanding microbe-microbe as well as microbe-disease associations is one of the key thrust areas in human microbiome research. High-throughput metagenomic and transcriptomic projects have fueled discovery of a number of new microbial associations. Consequently, a plethora of information is being added routinely to biomedical literature, thereby contributing toward enhancing our knowledge on microbial associations. In this communication, we present a tool called "EviMass" (Evidence based mining of human Microbial Associations), which can assist biologists to validate their predicted hypotheses from new microbiome studies. Users can interactively query the processed back-end database for microbe-microbe and disease-microbe associations. The EviMass tool can also be used to upload microbial association networks generated from a human "disease-control" microbiome study and validate the associations from biomedical literature. Additionally, a list of differentially abundant microbes for the corresponding disease can be queried in the tool for reported evidences. The results are presented as graphical plots, tabulated summary, and other evidence statistics. EviMass is a comprehensive platform and is expected to enable microbiome researchers not only in mining microbial associations, but also enriching a new research hypothesis. The tool is available free for academic use at https://web.rniapps.net/evimass.