Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes.

Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.

Molecular functions of ANKLE2 and its implications in human disease.

Ankyrin repeat and LEM domain-containing 2 (ANKLE2) is a scaffolding protein with established roles in cell division and development, the dysfunction of which is increasingly implicated in human disease. ANKLE2 regulates nuclear envelope disassembly at the onset of mitosis and its reassembly after chromosome segregation. ANKLE2 dysfunction is associated with abnormal nuclear morphology and cell division. It regulates the nuclear envelope by mediating protein-protein interactions with barrier to autointegration factor (BANF1; also known as BAF) and with the kinase and phosphatase that modulate the phosphorylation state of BAF. In brain development, ANKLE2 is crucial for proper asymmetric division of neural progenitor cells. In humans, pathogenic loss-of-function mutations in ANKLE2 are associated with primary congenital microcephaly, a condition in which the brain is not properly developed at birth. ANKLE2 is also linked to other disease pathologies, including congenital Zika syndrome, cancer and tauopathy. Here, we review the molecular roles of ANKLE2 and the recent literature on human diseases caused by its dysfunction.

mi-Mic: a novel multi-layer statistical test for microbiota-disease associations.

mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships. Finally, it performs a Mann-Whitney test on paths that are consistently significant along the cladogram or on the leaves. mi-Mic has much higher true to false positives ratios than existing tests, as measured by a new real-to-shuffle positive score.

Special Issue: "Genes and Human Diseases".

Studying mechanisms of development and the causes of various human diseases continues to be the focus of attention of various researchers [...].

Clonal relationship, virulence genes, and antimicrobial resistance of Morganella morganii isolated from community-acquired infections and hospitalized patients: a neglected opportunistic pathogen

Morganella morganii is a bacterium belonging to the normal intestinal microbiota and the environment; however, in immunocompromised individuals, this bacterium can become an opportunistic pathogen, causing a series of diseases, both in hospitals and in the community, being urinary tract infections more prevalent. Therefore, the objective of this study was to evaluate the prevalence, virulence profile, and resistance to antimicrobials and the clonal relationship of isolates of urinary tract infections (UTI) caused by M. morganii, both in the hospital environment and in the community of the municipality of Londrina-PR, in southern Brazil, in order to better understand the mechanisms for the establishment of the disease caused by this bacterium. Our study showed that M. morganii presents a variety of virulence factors in the studied isolates. Hospital strains showed a higher prevalence for the virulence genes zapA, iutA, and fimH, while community strains showed a higher prevalence for the ireA and iutA genes. Hospital isolates showed greater resistance compared to community isolates, as well as a higher prevalence of multidrug-resistant (MDR) and extended-spectrum beta lactamase (ESBL)-producing isolates. Several M. morganii isolates from both sources showed high genetic similarity. The most prevalent plasmid incompatibility groups detected were FIB and I1, regardless of the isolation source. Thus, M. morganii isolates can accumulate virulence factors and antimicrobial resistance, making them a neglected opportunistic pathogen. (AU)

Analyzing the role of computational clusters and artificial intelligence in the development of child mental health

In recent years, there has been a lot of research interest in the growing use of artificial intelligence (AI) in health and medicine. This study attempts to provide a global, verified picture of research on AI in medicine and health. There are vast informational resources available, but there are also devices that can't decide examples precisely or predict the future. The conventional methods for diagnosing illnesses are manual and prone to error. When compared to elite human ability, the use of artificial intelligence's predictive approaches improves auto determination and reduces identification errors. A thorough analysis of those articles convinced the ordering party to order the most complex AI processes for clinical symptomatic frameworks. This research report seeks to unearth some key information on the flow and pastof many AI techniques in the clinical setting used in the current clinical investigation, particularly in the areas of coronary disease prediction, brain illness, prostate, liver illness, and kidney infection. In order to ensure that Childs are well-informed and guided, this study uses the coordination examination calculation to distinguish Childs' mental health difficulties and applies the reconciliation examination calculation to Childs' mental health inquiry. A thorough analysis and exploration of children's mental health is completed in light of the framework design approach and information mining grouping technique. (AU)

Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs.

As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode the biological characteristics about lncRNA-disease associations, the topological features from the entire heterogeneous graph perspective, and the gated enhanced pairwise features. First, the strategy for constructing hyperedges is designed to reflect the biological characteristic that multiple lncRNAs are involved in multiple disease processes. Furthermore, each hyperedge has its own biological perspective, and multiple hyperedges are beneficial for revealing the diverse relationships among multiple lncRNAs and diseases. Second, we encode the biological features of each lncRNA (disease) node using a strategy based on dynamic hypergraph convolutional networks. The strategy may adaptively learn the features of the hyperedges and formulate the dynamically evolved hypergraph topological structure. Third, a group convolutional network is established to integrate the entire heterogeneous topological structure and multiple types of node attributes within an lncRNA-disease-miRNA graph. Finally, a gated convolutional strategy is proposed to enhance the informative features of the lncRNA-disease node pairs. The comparison experiments indicate that AGLDA outperforms seven advanced prediction methods. The ablation studies confirm the effectiveness of major innovations, and the case studies validate AGLDA's ability in application for discovering potential disease-related lncRNA candidates.

QNetDiff: a quantitative measurement of network rewiring.

Bacteria in the human body, particularly in the large intestine, are known to be associated with various diseases. To identify disease-associated bacteria (markers), a typical method is to statistically compare the relative abundance of bacteria between healthy subjects and diseased patients. However, since bacteria do not necessarily cause diseases in isolation, it is also important to focus on the interactions and relationships among bacteria when examining their association with diseases. In fact, although there are common approaches to represent and analyze bacterial interaction relationships as networks, there are limited methods to find bacteria associated with diseases through network-driven analysis. In this paper, we focus on rewiring of the bacterial network and propose a new method for quantifying the rewiring. We then apply the proposed method to a group of colorectal cancer patients. We show that it can identify and detect bacteria that cannot be detected by conventional methods such as abundance comparison. Furthermore, the proposed method is implemented as a general-purpose tool and made available to the general public.

Massive RNA sequencing effort proposed.

U.S. plan would harness the "RNome" for medicine and more-but funding is uncertain.

Age-related disgust responses to signs of disease.

Previous studies found similarities in adults' disgust responses to benign (e.g. obesity) and actual disease signs (e.g. influenza). However, limited research has compared visual (i.e. benign and actual) to cognitive (i.e. disease label) disease cues in different age groups. The current study investigated disgust responses across middle childhood (7-9 years), late childhood (10-12 years), adolescence (13-17 years), and adulthood (18+ years). Participants viewed individuals representing a benign visual disease (obese), sick-looking (staphylococcus), sick-label (cold/flu), and healthy condition. Disgust-related outcomes were: (1) avoidance, or contact level with apparel the individual was said to have worn, (2) disgust facial reactions, and (3) a combination of (1) and (2). Avoidance was greater for the sick-looking and sick-label than the healthy and obese conditions. For facial reaction and combination outcomes, middle childhood participants responded with greater disgust to the sick-looking than the healthy condition, while late childhood participants expressed stronger disgust towards the sick-looking and obese conditions than the healthy condition. Adolescents and adults exhibited stronger disgust towards sick-label and sick-looking than obese and healthy conditions. Results suggest visual cues are central to children's disgust responses whereas adolescents and adult responses considered cognitive cues.

Reference-Class Problems Are Real: Health-Adjusted Reference Classes and Low Bone Mineral Density.

Elselijn Kingma argues that Christopher Boorse's biostatistical theory (the BST) does not show how the reference classes it uses are objective and naturalistic. Recently, philosophers of medicine have attempted to rebut Kingma's concerns. I argue that these rebuttals are theoretically unconvincing, and that there are clear examples of physicians adjusting their reference classes according to their prior knowledge of health and disease. I focus on the use of age-adjusted reference classes to diagnose low bone mineral density in children. In addition to using the BST's age, sex, and species, physicians also choose to use other factors to define reference classes, such as pubertal status, bone age, body size, and muscle mass. I show that physicians calibrate the reference classes they use according to their prior knowledge of health and disease. Reference classes are also chosen for pragmatic reasons, such as to predict fragility fractures.

Phosphoinositide switches in cell physiology - From molecular mechanisms to disease.

Phosphoinositides are amphipathic lipid molecules derived from phosphatidylinositol that represent low abundance components of biological membranes. Rather than serving as mere structural elements of lipid bilayers, they represent molecular switches for a broad range of biological processes, including cell signaling, membrane dynamics and remodeling, and many other functions. Here, we focus on the molecular mechanisms that turn phosphoinositides into molecular switches and how the dysregulation of these processes can lead to disease.