Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Implantable cardioverter defibrillator multisensor monitoring during home confinement caused by the covid-19 pandemic

Background: Utilization of remote monitoring platforms was recommended amidst the COVID-19 pandemic. The HeartLogic algorithm com bines data from multiple implantable cardioverter defibrillator (ICD) sensors (first and third heart sounds, intrathoracic impedance, respira tions, night heart rate, and patient activity) to provide integrated data that may allow for detection of early signs of worsening HF. Purpose: We examined whether the HeartLogic platform may elucidate behavioral changes that impact HF decompensation, and the possi ble consequences of home confinement caused by the COVID-19 pandemic. Methods: The Italian lockdown was imposed from March 8th to May 18th. On March 8th 2020, the HeartLogic feature was active in 349 ICD and cardiac resynchronization therapy ICD patients at 20 Italian centers. The period from January 1st to July 19th was divided in 3 phases: Pre-Lockdown (weeks 1-11), Lockdown (weeks 12-20), Post-Lockdown (weeks 21-29). Results: Immediately after the implementation of stay at home orders (week 12) we observed a significant drop in median activity level (65min [36-103] in week 12 vs. 101min [61-140] in Pre-Lockdown; p < 0.001), while there was no difference in the other contributing sensors. The median composite HeartLogic index increased at the end of Lockdown (4.7 [1.3-10.2] in week 20 vs. 2.5 [0.7-7.0] in Pre-Lockdown; p = 0.019). The weekly rate of HeartLogic alerts was significantly higher during Lockdown (1.56 alerts/week/100pts, 95%CI:1.15-2.06; IRR = 1.71, p = 0.014) and Post-Lockdown (1.37 alerts/week/100pts, 95%CI:0.99-1.84; IRR = 1.50, p = 0.072) than that reported in Pre-Lockdown (0.91 alerts/week/100pts, 95%CI:0.64-1.27). However, the median duration of alert state and the maximum index value did not change among phases, as well as the proportion of alerts followed by clinical actions at the centers (Pre-Lockdown: 31%, Lockdown: 22%, Post Lockdown: 28%), and the proportion of alerts fully managed remotely (i.e. no in-clinic visits) (Pre-Lockdown: 89%, Lockdown: 90%, Post Lockdown: 88%). Conclusions: The system was sensitive to the behavioral changes occurred during the lockdown, i.e. decrease in activity. However, the home confinement had no impact on the other sensors. The higher rate of HeartLogic alerts during lockdown and the increase in the median index after 8 weeks of home confinement suggest the worsening of the HF status, possibly explained by the behavioral changes. Nonethe less, the management of the HF detected events (actions performed and management strategy) was not impacted by the restrictions

Imaging Spectrometry of Inland and Coastal Waters: State of the Art, Achievements and Perspectives

Imaging spectrometry of non-oceanic aquatic ecosystems has been in development since the late 1980s when the first airborne hyperspectral sensors were deployed over lakes. Most water quality management applications were, however, developed using multispectral mid-spatial resolution satellites or coarse spatial resolution ocean colour satellites till now. This situation is about to change with a suite of upcoming imaging spectrometers being deployed from experimental satellites or from the International Space Station. We review the science of developing applications for inland and coastal aquatic ecosystems that often are a mixture of optically shallow and optically deep waters, with gradients of clear to turbid and oligotrophic to hypertrophic productive waters and with varying bottom visibility with and without macrophytes, macro-algae, benthic micro-algae or corals. As the spaceborne, airborne and in situ optical sensors become increasingly available and appropriate for aquatic ecosystem detection, monitoring and assessment, the science-based applications will need to be further developed to an operational level. The Earth Observation-derived information products will range from more accurate estimates of turbidity and transparency measures, chlorophyll, suspended matter and coloured dissolved organic matter concentration, to more sophisticated products such as particle size distributions, phytoplankton functional types or distinguishing sources of suspended and coloured dissolved matter, estimating water depth and mapping types of heterogeneous substrates. We provide an overview of past science, current state of the art and future directions so that early career scientists as well as aquatic ecosystem managers and associated industry groups may be prepared for the imminent deluge of imaging spectrometry data

Prediction of Solubility and Permeability Class Membership: Provisional BCS Classification of the World’s Top Oral Drugs

The Biopharmaceutics Classification System (BCS) categorizes drugs into one of four biopharmaceutical classes according to their water solubility and membrane permeability characteristics and broadly allows the prediction of the rate-limiting step in the intestinal absorption process following oral administration. Since its introduction in 1995, the BCS has generated remarkable impact on the global pharmaceutical sciences arena, in drug discovery, development, and regulation, and extensive validation/discussion/extension of the BCS is continuously published in the literature. The BCS has been effectively implanted by drug regulatory agencies around the world in setting bioavailability/bioequivalence standards for immediate-release (IR) oral drug product approval. In this review, we describe the BCS scientific framework and impact on regulatory practice of oral drug products and review the provisional BCS classification of the top drugs on the global market. The Biopharmaceutical Drug Disposition Classification System and its association with the BCS are discussed as well. One notable finding of the provisional BCS classification is that the clinical performance of the majority of approved IR oral drug products essential for human health can be assured with an in vitro dissolution test, rather than empirical in vivo human studies