Developing better digital health measures of Parkinson's disease using free living data and a crowdsourced data analysis challenge.

One of the promising opportunities of digital health is its potential to lead to more holistic understandings of diseases by interacting with the daily life of patients and through the collection of large amounts of real-world data. Validating and benchmarking indicators of disease severity in the home setting is difficult, however, given the large number of confounders present in the real world and the challenges in collecting ground truth data in the home. Here we leverage two datasets collected from patients with Parkinson's disease, which couples continuous wrist-worn accelerometer data with frequent symptom reports in the home setting, to develop digital biomarkers of symptom severity. Using these data, we performed a public benchmarking challenge in which participants were asked to build measures of severity across 3 symptoms (on/off medication, dyskinesia, and tremor). 42 teams participated and performance was improved over baseline models for each subchallenge. Additional ensemble modeling across submissions further improved performance, and the top models validated in a subset of patients whose symptoms were observed and rated by trained clinicians.

Bioactive molecules from fungal endophytes and their applications in pharmaceutical industries: challenges and future scope.

Medicinal plants are an important source of bioactive compounds and have been used to isolate various bioactive compounds having industrial applications. The demand for plants derived bioactive molecules is increasing gradually. However, the extensive use of these plants to extract bioactive molecules has threatened many plant species. Moreover, extracting bioactive molecules from these plants is laborious, costly, and time-consuming. So, some alternative sources and strategies are urgently needed to produce these bioactive molecules similar to that of plant origin. However, the interest in new bioactive molecules has recently shifted from plants to endophytic fungi because many fungi produce bioactive molecules similar to their host plant. Endophytic fungi live in mutualistic association within the healthy plant tissue without causing disease symptoms to the host plant. These fungi are a treasure house of novel bioactive molecules having broad pharmaceutical, industrial, and agricultural applications. The rapid increase in publications in this domain over the last three decades proves that natural product biologists and chemists are paying great attention to the natural bioactive products from endophytic fungi. Though endophytes are source of novel bioactive molecules but there is need of advanced technologies like clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR-Cas9) and epigenetic modifiers to enhance the production of compounds having industrial applications. This review provides an overview of the various industrial applications of bioactive molecules produced by endophytic fungi and the rationale behind selecting specific plants for fungal endophyte isolation. Overall, this study presents the current state of knowledge and highlights the potential of endophytic fungi for developing alternative therapies for drug-resistant infections.

Integrating multimodal data through interpretable heterogeneous ensembles.

Motivation: Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems. Results: We propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling. Availability and implementation: Code and data are available at https://github.com/GauravPandeyLab/ensemble_integration. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Electrochemical and DFT studies of andrographolide on electrochemically reduced graphene oxide for anti-viral herbaceutical sensor.

Herbal extracts are re-emerging as potential remedies for various vector-borne diseases. Amongst several phytochemicals, active ingredients of Andrographis paniculata extract is regarded as promising for dengue fever, caused by Aedes species. However, fingerprinting the active phytochemicals from herbal extracts are often relies on sophisticated analytical techniques which are not universally accessible. Herein, an electrochemically reduced graphene oxide on glassy carbon electrode (ErGO/GCE) has been devised as user-friendly and cost-effective sensor platform for fingerprinting of andrographolide (AG) in anti-dengue polyherbal formulation, i.e., Nilavembu kudineer powder. Confocal laser Raman and X-ray photoelectron spectral analyses revealed that the ErGO surfaces exert structural defects augmenting the conductivity at the electrode interface. DFT investigations enabled that C-3 and C-18 OH groups in AG is involved in the electrooxidation and adsorption-diffusion at the ErGO interface, respectively. Complementary electrochemical studies revealed that the diffusion-controlled process follows 1e-/1H+ transfer. Under optimal experimental conditions, ErGO sensor platform exhibit an amplified current sensitivity of 13.3 µA µM-1. cm-2 in the studied analyte concentration range of 10-400 µM. From the polyherbal extract and clinical sample analysis, the proposed sensor system offers selective, and sensitive detection of target AG regardless of common interferents.

Temporal correspondence of selenium and mercury, among brine shrimp and water in Great Salt Lake, Utah, USA.

The specific source of high burdens of selenium (Se) and mercury (Hg) in several bird species at Great Salt Lake (GSL) remain unknown. Frequent co-located water and brine shrimp samples were collected during 2016 through 2017 to identify potential correlations of element concentrations among brines and brine shrimp, a keystone species in the GSL. Like many aquatic systems, GSL is characterized by elevated methylmercury (MeHg) in deep waters. However, in contrast to thermally-stratified aquatic systems, biota in the salinity-stratified GSL do not reside in its deep waters, obscuring the presumed relationship between elevated MeHg in biota and in the deep brine. Brine shrimp and water column (shallow and deep, filtered and unfiltered) samples were collected from six sites spanning the South Arm of GSL approximately every other month. Mercury concentrations in brine shrimp (on average 89% of which is MeHg) were correlated only with total mercury in surface filtered water, and displayed little spatial variability, but consistent seasonal trends across the two sampled years. In contrast to Hg, temporal correspondence was observed between Se concentrations in brine shrimp and those in all water samples regardless of filtering and depth, with maxima and minima at higher-than-seasonal frequency. The data suggest a spatially diffuse source of bioavailable mercury to the shallow brine that responds to seasonal influences, for which the underlying deep brine, surficial sediments, and overlying atmosphere were evaluated in terms of potential temporal correspondence to shallow brine and brine shrimp Hg concentrations, as well as potential to mix across the extent of the shallow brine. Bioaccumulation factors were at the low end of those reported for marine systems, and decreased at higher trace element concentrations in water.

Tissue-Resident PDGFRα+ Progenitor Cells Contribute to Fibrosis versus Healing in a Context- and Spatiotemporally Dependent Manner.

PDGFRα+ mesenchymal progenitor cells are associated with pathological fibro-adipogenic processes. Conversely, a beneficial role for these cells during homeostasis or in response to revascularization and regeneration stimuli is suggested, but remains to be defined. We studied the molecular profile and function of PDGFRα+ cells in order to understand the mechanisms underlying their role in fibrosis versus regeneration. We show that PDGFRα+ cells are essential for tissue revascularization and restructuring through injury-stimulated remodeling of stromal and vascular components, context-dependent clonal expansion, and ultimate removal of pro-fibrotic PDGFRα+-derived cells. Tissue ischemia modulates the PDGFRα+ phenotype toward cells capable of remodeling the extracellular matrix and inducing cell-cell and cell-matrix adhesion, likely favoring tissue repair. Conversely, pathological healing occurs if PDGFRα+-derived cells persist as terminally differentiated mesenchymal cells. These studies support a context-dependent "yin-yang" biology of tissue-resident mesenchymal progenitor cells, which possess an innate ability to limit injury expansion while also promoting fibrosis in an unfavorable environment.

Treatment algorithms for managing hepatocellular carcinoma.

Early diagnosis and aggressive therapy improves outcome in hepatocellular carcinoma (HCC). Several potentially curative as well as palliative treatment options are available for patients. The choice of therapy is influenced by factors such as extent of tumor and severity of underlying liver dysfunction as well as availability of resources and of expertise. A systematic, algorithmic approach would ensure optimal therapy for each patient and is likely to improve outcomes. Even after receiving therapy for HCC, patients remain at risk for recurrent HCC as well as progression of underlying cirrhosis. Proper assessment and monitoring is needed for the underlying liver disease, which may progress to liver failure and have a major impact on long-term survival. Comprehensive care for patients with cirrhosis includes interventions such as antiviral therapy for HBV and HCV, abstention from alcohol, management of fatty liver disease, endoscopic surveillance and treatment for complications of portal hypertension and, if indicated, immunization against HAV and HBV. An algorithmic approach is useful for choosing the most appropriate treatment option for the individual patient from among the various options that are available. The general consensus is that the BCLC system should be preferred for staging HCC as it is useful in predicting outcomes and planning treatment. The BCLC system classifies patients with HCC into five categories: very early, early, intermediate, advanced, and terminal. It incorporates data on tumor status (number and size of nodules, vascular invasion, extra-hepatic spread), liver function (CTP status, presence of portal hypertension) and overall health status (constitutional symptoms, cancer symptoms, performance status). Treatment allocation according to sub-class of patients is a merit of the BCLC system; a few limitations have been noted, particularly with respect to patients with BCLC stage B and C disease. The treatment algorithm as per BCLC system is summarized in this review.

Calcitriol [1, 25[OH]2 D3] pre- and post-treatment suppresses inflammatory response to influenza A (H1N1) infection in human lung A549 epithelial cells.

PURPOSE: Influenza viruses infect airway epithelial cells, causing respiratory distress. Immune defense is maintained by chemokine/cytokine secretions from airway epithelial cells. While moderate inflammatory response protects from ill effects, hyper-inflammatory response promotes the pathogenesis. High circulating levels of vitamin D are known to mitigate effects of infectious diseases, including respiratory infectious diseases. The question whether and how vitamin D treatment pre-/post-viral exposure modulates inflammatory response is not clear. The present study was undertaken to understand autophagy/apoptosis balance and chemokine/cytokine response to influenza A (H1N1) infection by pre- and post-1, 25-dihydroxyvitamin D3 (1,25[OH]2 D3)[calcitriol] treatment of human lung A549 epithelial cells. METHODS: Influenza A (H1N1) virus was propagated in A549 cell line, titrated using hemagglutination assay, and was used to assess effect of calcitriol. After confirming that 100 nM of calcitriol fails to clear virus, A549 cells were either pre-treated (16 h) with 100 nM or post-treated with 30 nM of 1,25[OH]2 D3 of virus inoculation (1 h). Cells after incubation at 37 °C under 5 % CO2 for 48 h were collected and subjected to RNA and protein extraction. Measurements of viability, influenza M protein, and molecular parameters of cell death and inflammatory response were performed. RESULTS: We report that treatment of these cells with 100/30 nM of 1,25[OH]2 D3 prior to/or post-H1N1 exposure does not affect viral clearance but significantly reduces autophagy and restores increased apoptosis seen on H1N1 infection back to its constitutive level. However, it significantly decreases the levels of H1N1-induced TNF-α (tumor necrosis factor-alpha), IFN-ß (interferon-beta), and IFN-stimulated gene-15 (ISG15). 1,25[OH]2 D3 treatment prior to/or post-H1N1 infection significantly down-regulates IL-8 as well as IL-6 RNA levels. These results demonstrate that calcitriol treatment suppresses the H1N1-induced transcription of the chemokines RANTES and IL-8 in epithelial cells. CONCLUSION: The findings provide support for the initiation of vitamin D supplementation program to VDD populations in reducing the severity of influenza.

Extracting the hidden features in saline osmotic tolerance in Saccharomyces cerevisiae from DNA microarray data using the self-organizing map: biosynthesis of amino acids.

During saline stress, Saccharomyces cerevisiae changes its metabolic fluxes through the direct accumulation of metabolites such as glycerol and trehalose, which in turn provide tolerance to the cell against stress. Previous research shows that the various controls at both transcriptional and translational levels decide the phenomenon of stress, but details about such transition is still not very clear. This paper attempts to extract some hidden features through the information extraction approach from DNA microarray data during transition to osmotic tolerance, which are expected to be important in directing to the tolerance stage upon encountering osmotic stress in yeast. Time course of DNA microarray data during osmotic tolerance was analyzed by computational approach 'self-organizing map (SOM) extended with hierarchical clustering'. Since eukaryotic gene expression is governed by short regulatory sequences found upstream in promoter regions, therefore clusters containing the similar profiles obtained by SOM were further analyzed for overrepresentation of known regulatory binding sites in promoter region. It was found that apart from known and expected 'STRE' during osmotic stress, the 'GCN4' binding site is also found to be significant. Hence, it was suggested that the process of osmotic tolerance proceeds through a stage of amino acid starvation. The intracellular amino acids pool also found to be depleted during transition and restoration is faster in brewing strain than laboratory strain. Experiments involving supplementation of amino acids helps in reducing the lag time for recovery, which was found to be similar to that of brewing strain.