Short- and Long-Term Chest-CT Findings after Recovery from COVID-19: A Systematic Review and Meta-Analysis.

While ground-glass opacity, consolidation, and fibrosis in the lungs are some of the hallmarks of acute SAR-CoV-2 infection, it remains unclear whether these pulmonary radiological findings would resolve after acute symptoms have subsided. We conducted a systematic review and meta-analysis to evaluate chest computed tomography (CT) abnormalities stratified by COVID-19 disease severity and multiple timepoints post-infection. PubMed/MEDLINE was searched for relevant articles until 23 May 2023. Studies with COVID-19-recovered patients and follow-up chest CT at least 12 months post-infection were included. CT findings were evaluated at short-term (1-6 months) and long-term (12-24 months) follow-ups and by disease severity (severe and non-severe). A generalized linear mixed-effects model with random effects was used to estimate event rates for CT findings. A total of 2517 studies were identified, of which 43 met the inclusion (N = 8858 patients). Fibrotic-like changes had the highest event rate at short-term (0.44 [0.3-0.59]) and long-term (0.38 [0.23-0.56]) follow-ups. A meta-regression showed that over time the event rates decreased for any abnormality (ß = -0.137, p = 0.002), ground-glass opacities (ß = -0.169, p < 0.001), increased for honeycombing (ß = 0.075, p = 0.03), and did not change for fibrotic-like changes, bronchiectasis, reticulation, and interlobular septal thickening (p > 0.05 for all). The severe subgroup had significantly higher rates of any abnormalities (p < 0.001), bronchiectasis (p = 0.02), fibrotic-like changes (p = 0.03), and reticulation (p < 0.001) at long-term follow-ups when compared to the non-severe subgroup. In conclusion, significant CT abnormalities remained up to 2 years post-COVID-19, especially in patients with severe disease. Long-lasting pulmonary abnormalities post-SARS-CoV-2 infection signal a future public health concern, necessitating extended monitoring, rehabilitation, survivor support, vaccination, and ongoing research for targeted therapies.

ConSQL: A Database Language for Contex-aware Conversational Cooperative Query Answering.

In conversational query answering systems, context plays a significant role in accurately and meaningfully carrying it forward. In many chatbots, such as in Expedia, the discussion quickly degenerates into circling back to restarting the conversation or to inviting a live agent to intervene because the bot could not grasp the context. Contexts shorten interactions by way of implied query constraints to narrow search and to not repeat them in subsequent queries. In this paper, we introduce a novel way of viewing contexts as a distance function via the concept of query relaxation. We demonstrate that a typed domain distance function is sufficient to model context in a conversation. Our approach is based on the idea of non-monotonic constraint inheritance in a context hierarchy.

The Impact of Anxiety and Depression on Academic Performance: A Cross-Sectional Study among Medical Students in Syria.

Background The National Medical Unified Examination (NMUE) is a milestone in the life of medical students in Syria. The selection for residency programs depends mainly on the NMUE score, where competitive specialties require higher scores. Therefore, preparation for the NMUE might be a source of anxiety and depression. This study aims at evaluating the impact of anxiety and depression on the NMUE score. A secondary objective is to determine the effect of some factors (i.e., exercise, having breakfast, adequate sleep, and social media) on anxiety and depression. Methods A cross-sectional study was conducted using an online questionnaire and included medical students who were preparing for the October 2019 NMUE exam. The Generalized Anxiety Disorder scale (GAD-7) and the Patient Health Questionnaire (PHQ-9) were used to screen for anxiety and depression, respectively. NMUE scores were obtained from the official score report. Demographics and other potential confounding factors, such as Cumulative Grade Point Average, were obtained through the questionnaire. Results One hundred and thirty ( n = 130) students participated in the study, 83 of them were women (63.8%). The prevalence of anxiety and depression were 59.2 and 58%, respectively, with no difference between men and women. Both anxiety and depression were negatively correlated with the NMUE score. However, this relationship did not persist after controlling for other important predictors through multiple regression. Only exercising was statically significant in reducing PHQ-9 scores. None of the studied factors were significant in reducing GAD-7 scores. Conclusion Although participants with higher anxiety/depression had lower NMUE scores, this association does not imply causation. The high prevalence of anxiety and depression (approximately two-thirds of the participants) is concerning and may pose a great threat to students' well-being and adversely affect the quality of care provided by them as future health care professionals.

Hepatitis A virus infection is complicated by both pancytopenia and autoimmune hemolytic anemia (AIHA).

Introduction: Hepatitis A infection affects liver tissue primarily and might have some extrahepatic manifestations. Hematologically, the extrahepatic manifestations include aplastic anemia, red cell aplasia, and thrombocytopenia. There were reports about pancytopenia among patients with Hepatitis A infections, however, its association with autoimmune hemolytic anemia is rare as in our case. Case presentation: A 30-year-old male visited the emergency room with tiredness, unmeasured fever, and jaundice. He also mentioned that recently he had anorexia and weight loss without night sweating. Initial laboratory findings showed pancytopenia and marked elevation of AST and ALT. Direct Coombs and IgM anti-Hepatitis A virus were positive. Consequently, he was diagnosed with HAV complicated by both pancytopenia and AIHA and treated with prednisone (1 mg/kg) leading to significant improvement in his anemia. Discussion: This report describes a case of acute viral hepatitis A complicated with severe autoimmune hemolytic anemia and pancytopenia, which was successfully treated by high dose (1 mg/kg/day) prednisolone therapy. Conclusion: This case represents a rare case in the literature review that can increase the awareness of the wide range of complications of HAV and its association with pancytopenia and AIHA.

Integrating Spatial Modelling and Space-Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan.

The spatial-temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space-time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007-2016 as an example vector disease. The most significant clustering is evident during the years 2007-2008, 2010-2011, 2013, and 2016. Mostly, the clusters are found within the city's central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics.

Assessment of asthma-prone environment in Karachi, Pakistan using GIS modeling.

OBJECTIVE: To determine the association among number of factors influenced by asthma using geographic information system. METHODS: The cross-sectional study was conducted in Landhi and Korangi towns of Karachi from 2011 to 2013, and comprised ecological mapping and multi-criteria evaluation techniques to discover the relationship of local environmental settings with asthma. Additionally, exacerbating environment and the root causes within the local settings were assessed. Data was gathered using an extended version of the questionnaire developed by the International Union against Tuberculosis and Lung Disease. Data was analysed by using ArcGIS 10. RESULTS: The findings are very alarming as almost 40% (468,930 estimated pop 1998 census) of the study population lived in high asthma-prone environment, having a very high risk of respiratory disorders, including asthma. CONCLUSIONS: The integrated environmental effect in the form of respiratory disorders was appraised, focusing on asthma by using multi-criteria analysis.

A comprehensive assessment of environmental pollution by means of heavy metal analysis for oysters' reefs at Hab River Delta, Balochistan, Pakistan.

The heavy metal pollution status of oyster reefs has been assessed with respect to ten metals pollutants in seawater, sediments, and tissues of above two oysters (soft tissues and shells) for assessing the pollution status in a short food chain in Hab River Delta. The results showed that heavy metals accumulated in M. bilineata were higher than those in M. cuttackensis. Simultaneously, the population of M. bilineata species has been ironically decreasing as a results of high pollution. The determined concentrations revealed a significant differences in their profiles among sediments, seawater and bioaccumulation in tissues and shells of two native oysters. The present study also compared these metal concentrations with national and international database by applying different pollution indices. Heavy metals in all samples were above the national environmental quality standards (NEQS-Pakistan). High level of pollution with an alarming condition of Hab River Delta need more attention for coastal management.

Visual Life Sciences Workflow Design Using Distributed and Heterogeneous Resources.

Programming or querying usually presupposes some degree of technical familiarity with the syntax of a language and the peculiarity of the objects it manipulates to produce useful information. The degree of abstractions supported in a language helps lessen the depth of such familiarity needed, and aids in improving access to and usability of these resources. To help biologists concentrate more on their science questions and not on how to compute it, several successful workflow orchestration languages and systems have been proposed. Despite their popularity, significant limitations reduce their usability and limit applicability in novel applications. In this paper, we present a visual language, called VisFlow, for workflow orchestration using heterogeneous and distributed resources. We advance the idea that once resources are minimally described and abstracted, arbitrary workflows can be designed solely using query primitives supported in VisFlow. Its capabilities can be augmented by including computational artifacts in the form of library functions written in R, Python, and Java, or even in SQL and XQuery, making it a truly extensible system. We discuss its salient features and illustrate its capabilities using a substantial set of examples.

Optimizing Phylogenetic Queries for Performance.

The vast majority of phylogenetic databases do not support declarative querying using which their contents can be flexibly and conveniently accessed and the template based query interfaces they support do not allow arbitrary speculative queries. They therefore also do not support query optimization leveraging unique phylogeny properties. While a small number of graph query languages such as XQuery, Cypher, and GraphQL exist for computer savvy users, most are too general and complex to be useful for biologists, and too inefficient for large phylogeny querying. In this paper, we discuss a recently introduced visual query language, called PhyQL, that leverages phylogeny specific properties to support essential and powerful constructs for a large class of phylogentic queries. We develop a range of pruning aids, and propose a substantial set of query optimization strategies using these aids suitable for large phylogeny querying. A hybrid optimization technique that exploits a set of indices and "graphlet" partitioning is discussed. A "fail soonest" strategy is used to avoid hopeless processing and is shown to produce dividends. Possible novel optimization techniques yet to be explored are also discussed.

A Visual Interface for Querying Heterogeneous Phylogenetic Databases.

Despite the recent growth in the number of phylogenetic databases, access to these wealth of resources remain largely tool or form-based interface driven. It is our thesis that the flexibility afforded by declarative query languages may offer the opportunity to access these repositories in a better way, and to use such a language to pose truly powerful queries in unprecedented ways. In this paper, we propose a substantially enhanced closed visual query language, called PhyQL, that can be used to query phylogenetic databases represented in a canonical form. The canonical representation presented helps capture most phylogenetic tree formats in a convenient way, and is used as the storage model for our PhyloBase database for which PhyQL serves as the query language. We have implemented a visual interface for the end users to pose PhyQL queries using visual icons, and drag and drop operations defined over them. Once a query is posed, the interface translates the visual query into a Datalog query for execution over the canonical database. Responses are returned as hyperlinks to phylogenies that can be viewed in several formats using the tree viewers supported by PhyloBase. Results cached in PhyQL buffer allows secondary querying on the computed results making it a truly powerful querying architecture.

Ranking Novel Regulatory Genes in Gene Expression Profiles using NetExpress.

Understanding gene regulation by identifying gene products and determining their roles in regulatory networks is a complex process. A common computational method is to reverse engineer a regulatory network from gene expression profile, and sanitize the network using known information about the genes, their interactions and other properties to filter out unlikely interactors. Unfortunately, due to limited resources most gene expression studies have a limited and small number of time points, and most reverse engineering tools are unable to handle large numbers of genes. Both of these factors play significant roles in influencing the accuracy of the process. In this paper, we present a new gene ranking algorithm from gene expression profiles with a small number of time points so that the most relevant genes can be selected for reverse engineering. We also present a graphical interface called NetExpress, which adopts this algorithm and allows users to set control parameters to effect the desired outcome, and visualize the analysis for iterative fine tuning.

Improving Integration Effectiveness of ID Mapping Based Biological Record Linkage.

Traditionally, biological objects such as genes, proteins, and pathways are represented by a convenient identifier, or ID, which is then used to cross reference, link and describe objects in biological databases. Relationships among the objects are often established using non-trivial and computationally complex ID mapping systems or converters, and are stored in authoritative databases such as UniGene, GeneCards, PIR and BioMart. Despite best efforts, such mappings are largely incomplete and riddled with false negatives. Consequently, data integration using record linkage that relies on these mappings produces poor quality of data, inadvertently leading to erroneous conclusions. In this paper, we discuss this largely ignored dimension of data integration, examine how the ubiquitous use of identifiers in biological databases is a significant barrier to knowledge fusion using distributed computational pipelines, and propose two algorithms for ad hoc and restriction free ID mapping of arbitrary types using online resources. We also propose two declarative statements for ID conversion and data integration based on ID mapping on-the-fly.

Querying KEGG pathways in logic.

Understanding the interaction patterns among biological entities in a pathway can potentially reveal the role of the entities in biological systems. Although considerable effort has been contributed to this direction, querying biological pathways remained relatively unexplored. Querying is principally different in which we retrieve pathways satisfying a given property in terms of its topology, or constituents. One such property is subnetwork matching using various constituent parameters. In this paper, we introduce a logic based framework for querying biological pathways using a novel and generic subgraph isomorphism computation technique. We develop a graphical interface called IsoKEGG to facilitate flexible querying of KEGG pathways based on isomorphic pathway topologies as well as matching any combination of node names, types, and edges. It allows editing KGML represented query pathways and returns all isomorphic patterns in KEGG pathways satisfying a given query condition for further analysis.

Designing integrated computational biology pipelines visually.

The long-term cost of developing and maintaining a computational pipeline that depends upon data integration and sophisticated workflow logic is too high to even contemplate "what if" or ad hoc type queries. In this paper, we introduce a novel application building interface for computational biology research, called VizBuilder, by leveraging a recent query language called BioFlow for life sciences databases. Using VizBuilder, it is now possible to develop ad hoc complex computational biology applications at throw away costs. The underlying query language supports data integration and workflow construction almost transparently and fully automatically, using a best effort approach. Users express their application by drawing it with VizBuilder icons and connecting them in a meaningful way. Completed applications are compiled and translated as BioFlow queries for execution by the data management system LifeDB, for which VizBuilder serves as a front end. We discuss VizBuilder features and functionalities in the context of a real life application after we briefly introduce BioFlow. The architecture and design principles of VizBuilder are also discussed. Finally, we outline future extensions of VizBuilder. To our knowledge, VizBuilder is a unique system that allows visually designing computational biology pipelines involving distributed and heterogeneous resources in an ad hoc manner.

A natural language interface plug-in for cooperative query answering in biological databases.

BACKGROUND: One of the many unique features of biological databases is that the mere existence of a ground data item is not always a precondition for a query response. It may be argued that from a biologist's standpoint, queries are not always best posed using a structured language. By this we mean that approximate and flexible responses to natural language like queries are well suited for this domain. This is partly due to biologists' tendency to seek simpler interfaces and partly due to the fact that questions in biology involve high level concepts that are open to interpretations computed using sophisticated tools. In such highly interpretive environments, rigidly structured databases do not always perform well. In this paper, our goal is to propose a semantic correspondence plug-in to aid natural language query processing over arbitrary biological database schema with an aim to providing cooperative responses to queries tailored to users' interpretations. RESULTS: Natural language interfaces for databases are generally effective when they are tuned to the underlying database schema and its semantics. Therefore, changes in database schema become impossible to support, or a substantial reorganization cost must be absorbed to reflect any change. We leverage developments in natural language parsing, rule languages and ontologies, and data integration technologies to assemble a prototype query processor that is able to transform a natural language query into a semantically equivalent structured query over the database. We allow knowledge rules and their frequent modifications as part of the underlying database schema. The approach we adopt in our plug-in overcomes some of the serious limitations of many contemporary natural language interfaces, including support for schema modifications and independence from underlying database schema. CONCLUSIONS: The plug-in introduced in this paper is generic and facilitates connecting user selected natural language interfaces to arbitrary databases using a semantic description of the intended application. We demonstrate the feasibility of our approach with a practical example.

Top-k similar graph matching using TraM in biological networks.

Many emerging database applications entail sophisticated graph-based query manipulation, predominantly evident in large-scale scientific applications. To access the information embedded in graphs, efficient graph matching tools and algorithms have become of prime importance. Although the prohibitively expensive time complexity associated with exact subgraph isomorphism techniques has limited its efficacy in the application domain, approximate yet efficient graph matching techniques have received much attention due to their pragmatic applicability. Since public domain databases are noisy and incomplete in nature, inexact graph matching techniques have proven to be more promising in terms of inferring knowledge from numerous structural data repositories. In this paper, we propose a novel technique called TraM for approximate graph matching that off-loads a significant amount of its processing on to the database making the approach viable for large graphs. Moreover, the vector space embedding of the graphs and efficient filtration of the search space enables computation of approximate graph similarity at a throw-away cost. We annotate nodes of the query graphs by means of their global topological properties and compare them with neighborhood biased segments of the datagraph for proper matches. We have conducted experiments on several real data sets, and have demonstrated the effectiveness and efficiency of the proposed method

In silico analysis of combinatorial microRNA activity reveals target genes and pathways associated with breast cancer metastasis.

Aberrant microRNA activity has been reported in many diseases, and studies often find numerous microRNAs concurrently dysregulated. Most target genes have binding sites for multiple microRNAs, and mounting evidence indicates that it is important to consider their combinatorial effect on target gene repression. A recent study associated the coincident loss of expression of six microRNAs with metastatic potential in breast cancer. Here, we used a new computational method, miR-AT!, to investigate combinatorial activity among this group of microRNAs. We found that the set of transcripts having multiple target sites for these microRNAs was significantly enriched with genes involved in cellular processes commonly perturbed in metastatic tumors: cell cycle regulation, cytoskeleton organization, and cell adhesion. Network analysis revealed numerous target genes upstream of cyclin D1 and c-Myc, indicating that the collective loss of the six microRNAs may have a focal effect on these two key regulatory nodes. A number of genes previously implicated in cancer metastasis are among the predicted combinatorial targets, including TGFB1, ARPC3, and RANKL. In summary, our analysis reveals extensive combinatorial interactions that have notable implications for their potential role in breast cancer metastasis and in therapeutic development.

Managing and querying gene expression data using Curray.

BACKGROUND: In principle, gene expression data can be viewed as providing just the three-valued expression profiles of target biological elements relative to an experiment at hand. Although complicated, gathering expression profiles does not pose much of a challenge from a query language standpoint. What is interesting is how these expression profiles are used to tease out information from the vast array of information repositories that ascribe meaning to the expression profiles. Since such annotations are inherently experiment specific functions, much the same way as queries in databases, developing a querying system for gene expression data appears to be pointless. Instead, developing tools and techniques to support individual assignment has been considered prudent in contemporary research. RESULTS: We propose a gene expression data management and querying system that is able to support pre-expression, expression and post-expression level analysis and reduce impedance mismatch between analysis systems. To this end, we propose a new, platform-independent and general purpose query language called Curray, for Custom Microarray query language, to support online expression data analysis using distributed resources. It includes features to design expression analysis pipelines using language constructs at the conceptual level. The ability to include user defined functions as a first-class language feature facilitates unlimited analysis support and removes language limitations. We show that Curray's declarative and extensible features nimbly allow flexible modeling and room for customization. CONCLUSIONS: The developments proposed in this article allow users to view their expression data from a conceptual standpoint - experiments, probes, expressions, mapping, etc. at multiple levels of representation and independent of the underlying chip technologies. It also allows transparent roll-up and drill-down along representation hierarchies from raw data to standards such as MIAME and MAGE-ML using linguistic constructs. Curray also allows seamless integration with distributed web resources through its LifeDB system of which it is a part.

Gene regulatory network reveals oxidative stress as the underlying molecular mechanism of type 2 diabetes and hypertension.

BACKGROUND: The prevalence of diabetes is increasing worldwide. It has been long known that increased rates of inflammatory diseases, such as obesity (OBS), hypertension (HT) and cardiovascular diseases (CVD) are highly associated with type 2 diabetes (T2D). T2D and/or OBS can develop independently, due to genetic, behavioral or lifestyle-related variables but both lead to oxidative stress generation. The underlying mechanisms by which theses complications arise and manifest together remain poorly understood. Protein-protein interactions regulate nearly every living process. Availability of high-throughput genomic data has enabled unprecedented views of gene and protein co-expression, co-regulations and interactions in cellular systems. METHODS: The present work, applied a systems biology approach to develop gene interaction network models, comprised of high throughput genomic and PPI data for T2D. The genes differentially regulated through T2D were 'mined' and their 'wirings' were studied to get a more complete understanding of the overall gene network topology and their role in disease progression. RESULTS: By analyzing the genes related to T2D, HT and OBS, a highly regulated gene-disease integrated network model has been developed that provides useful functional linkages among groups of genes and thus addressing how different inflammatory diseases are connected and propagated at genetic level. Based on the investigations around the 'hubs' that provided more meaningful insights about the cross-talk within gene-disease networks in terms of disease phenotype association with oxidative stress and inflammation, a hypothetical co-regulation disease mechanism model been proposed. The results from this study revealed that the oxidative stress mediated regulation cascade is the common mechanistic link among the pathogenesis of T2D, HT and other inflammatory diseases such as OBS. CONCLUSION: The findings provide a novel comprehensive approach for understanding the pathogenesis of various co-associated chronic inflammatory diseases by combining the power of pathway analysis with gene regulatory network evaluation.

In silico analysis of human Telomerase Reverse Transcriptase (hTERT) gene: identification of a distant homolog of Melanoma Antigen Family Gene (MAGE).

Melanoma antigen family (MAGE) genes are widely expressed in various tumor types but silent in normal cells except germ-line cells lacking human leukocyte antigen (HLA) expression. Over 25 MAGE genes have been identified in different tissues, mostly located in Xq28 of human chromosome and some of them in chromosome 3 and 15, containing either single or multiple-exons. This in silico study predicted the genes on hTERT location and identified a distant relative of MAGE gene located on chromosome 5. The study identified a single exon coding ~850 residues polypeptide sharing ~30% homology with Macfa-MAGE E1 and hMAGE-E1. dbEST search of the predicted transcript matches 5' and 3' flanking ESTs. The predicted protein showed sequence homology within the MAGE homology domain 2 (MHD2). UCSC genome annotation of CpG Island around the coding region reveals that this gene could be silent by methylation. Affymetrix all-exon track indicates the gene could be expressed in different tissues particularly in cancer cells as they widely undergo a genome wide demethylation process.