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Abstract Background A crucial aspect of continued senior care is the early detection and management of frailty. Developing reliable and secure electronic frailty assessment tools can benefit virtual appointments, a need especially relevant in the context of the COVID-19 pandemic. An emerging effort has targeted web-based software applications to improve accessibility and usage. The objectives of this scoping review are to identify and evaluate web-based frailty assessment tools currently available and to identify challenges and opportunities for future development. Methods We conducted a review with literature (e.g., using MEDLINE databases) and Google searches (last updated on October 10, 2021). Each of the identified web applications were assessed based on eight featured categories and assigned a rating score accordingly. Results Twelve web-based frailty assessment applications were found, chiefly provided by the USA (50%) or European countries (41%) and focused on frailty grading and outcome prediction for specific patient groups (59%). Categories that scored well among the applications included the User Interface (2.8/3) and the Cost (2.7/3). Other categories had a mean score of 1.6/3 or lower. The least developed feature was Data Saving. Conclusions Web-based applications represent a viable option for remote frailty assessments and multidisciplinary integrated care of older adults. Despite the available web-based frailty assessments on the Internet, many missed certain needed features for professional use in healthcare settings. This situation calls for fully comprehensive web-based applications, taking into consideration a number of key functions linking graphical user interface and functionalities, and paying special attention to secure data management.

Remote-sensing data are used extensively to monitor water quality parameters such as clarity, temperature, and chlorophyll-a (chl-a) content. This is generally achieved by collecting in situ data coincident with satellite data collections and then creating empirical water quality models using approaches such as multi-linear regression or step-wise linear regression. These approaches, which require modelers to select model parameters, may not be well suited for optically complex waters, where interference from suspended solids, dissolved organic matter, or other constituents may act as “confusers”. For these waters, it may be useful to include non-standard terms, which might not be considered when using traditional methods. Recent machine-learning work has demonstrated an ability to explore large feature spaces and generate accurate empirical models that do not require parameter selection. However, these methods, because of the large number of included terms involved, result in models that are not explainable and cannot be analyzed. We explore the use of Least Absolute Shrinkage and Select Operator (LASSO), or L1, regularization to fit linear regression models and produce parsimonious models with limited terms to enable interpretation and explainability. We demonstrate this approach with a case study in which chl-a models are developed for Utah Lake, Utah, USA., an optically complex freshwater body, and compare the resulting model terms to model terms from the literature. We discuss trade-offs between interpretability and model performance while using L1 regularization as a tool. The resulting model terms are both similar to and distinct from those in the literature, thereby suggesting that this approach is useful for the development of models for optically complex water bodies where standard model terms may not be optimal. We investigate the effect of non-coincident data, that is, the length of time between satellite image collection and in situ sampling, on model performance. We find that, for Utah Lake (for which there are extensive data available), three days is the limit, but 12 h provides the best trade-off. This value is site-dependent, and researchers should use site-specific numbers. To document and explain our approach, we provide Colab notebooks for compiling near-coincident data pairs of remote-sensing and in situ data using Google Earth Engine (GEE) and a second notebook implementing L1 model creation using scikitlearn. The second notebook includes data-engineering routines with which to generate band ratios, logs, and other combinations. The notebooks can be easily modified to adapt them to other locations, sensors, or parameters.

We present the development and testing of a web application called the historical validation tool (HVT) that processes and visualizes observed and simulated historical stream discharge data from the global GEOGloWS ECMWF streamflow services (GESS), performs seasonally adjusted bias correction, computes goodness-of-fit metrics, and performs forward bias correction on subsequent forecasts. The HVT corrects GESS output at a local scale using a technique that identifies and corrects model bias using observed hydrological data that are accessed using web services. HVT evaluates the performance of the GESS historic simulation data and provides more accurate historic simulation and bias-corrected forecast data. The HVT also allows users of the GEOGloWS historical streamflow data to use local observed data to both validate and improve the accuracy of local streamflow predictions. We developed the HVT using Tethys Platform, an open-source web application development framework. HVT presents data visualization using web mapping services and data plotting in the web map interface while functions related to bias correction, metrics reporting, and data generation for statistical analysis are computed by the back end. We present five case studies using the HVT in Australia, Brazil, Colombia, the Dominican Republic, and Peru. In these case studies, in addition to presenting the application, we evaluate the accuracy of the method we implemented in the HVT for bias correction. These case studies show that the HVT bias correction in Brazil, Colombia, and Peru results in significant improvement in historic simulation across the countries, while bias correction only resulted in marginal historic simulation improvements in Australia and the Dominican Republic. The HVT web application allows users to use local data to adjust global historical simulation and forecasts and validate the results, making the GESS modeling results more useful at a local scale.

Abstract Background Prostate cancer is a common disease in men and at present there is no effective therapy available due to its recurrence despite androgen deprivation therapy. The epidermal growth factor receptor family (EGFR/HER1, HER2/neu and HER3)/PI3K/Akt signaling axis has been implicated in prostate cancer development and progression. However, Erlotinib, an EGFR tyrosine kinase inhibitor, has less effect on proliferation and apoptosis in prostate cancer cell lines. In this study, we evaluate whether MP470, a novel receptor tyrosine kinase inhibitor alone or in combination with Erlotinib has inhibitory effect on prostate cancer in vitro and in vivo. Methods The efficacy of MP470 or MP470 plus Erlotinib was evaluated in vitro using three prostate cancer cell lines by MTS and apoptosis assays. The molecular mechanism study was carried out by phosphorylation antibody array, immunoblotting and immunohistochemistry. A LNCaP mouse xenograft model was also used to determine the tumor growth inhibition by MP470, Erlotinib or the combination treatments. Results MP470 exhibits low μM IC50 in prostate cancer cell lines. Additive effects on both cytotoxicity and induction of apoptosis were observed when LNCaP were treated with MP470 in combination with Erlotinib. This combination treatment completely inhibited phosphorylation of the HER family members (HER1, 2, 3), binding of PI3K regulatory unit p85 to HER3 and downstream Akt activity even after androgen depletion. Furthermore, in a LNCaP mouse xenograft model, the MP470-Erlotinib combination produced 30–65% dose-dependent tumor growth inhibition (TGI). Conclusion We propose that MP470-Erlotinib targets the HER family/PI3K/Akt pathway and may represent a novel therapeutic strategy for prostate cancer.