Microneedle patches integrated with lateral flow cassettes for blood-free chronic kidney disease point-of-care testing during a pandemic.

Chronic kidney disease (CKD) is the most neglected chronic disease affecting over 750 million persons in the world. Currently, many patients with cancers or other chronic diseases (i.e., CKD) struggle to receive clinical treatment or examination due to hospitals cancelling or delaying in the COVID-19 pandemic, which may increase the risk of death. Cystatin C (Cys C) has been proposed as a potential glomerular filtration rate (GFR) marker for the early detection of acute kidney injury and CKD. However, most traditional methods for Cys C detection are immunoassays using serum as a sample and are tedious to perform and economically burdensome. To diagnose the disease in the early stage and carry out daily management during the current pandemic, we developed an integration of hydrogel microneedle patch (HMNP) and lateral flow cassette (LFC) to rapidly detect Cys C in skin interstitial fluid (ISF) in 25 min for blood-free CKD management anytime and anywhere by the naked eye that can reduce the impact of an individual's quality of life and life expectancy. Conceivably, this strategy presents a wide scope in the application of numerous other diseases if corresponding analytes are available in skin ISF.

Modeling potential impact of COVID-19 pandemic on global electric vehicle supply chain.

The on-going COVID-19 pandemic and consequent lockdowns cast significant impacts on global economy in the short run. Their impact on stability of global electric vehicles (EVs) supply chain and thus our climate ambition in the long run, however, remains hitherto largely unexplored. We aim to address this gap based on an integrated model framework, including assessing supply risks of 17 selected core commodities throughout the EV supply chain and further applying the supply constraints to project future EV sales until 2030. Our model results under three pandemic development scenarios indicate that if the pandemic is effectively contained before 2024, the global EV industry will recover without fundamentally scathed and thus can maintain the same growth trend as in the no-pandemic scenario by 2030. We suggest that fiscal stimulus in the postpandemic era should be directed more toward upgrading the quality of battery products, rather than expanding the production capacity.

Artificial Intelligence for COVID-19 Risk Classification in Kidney Disease: Can Technology Unmask an Unseen Disease? (preprint)

Background: We developed two unique machine learning (ML) models that predict risk of: 1) a major COVID-19 outbreak in the service county of a local HD population within following week, and 2) a hemodialysis (HD) patient having an undetected SARS-CoV-2 infection that is identified after following 3 or more days. Methods: We used county-level data from United States population (March 2020) and HD patient data from a network of clinics (February-May 2020) to develop two ML models. First was a county-level model that used data from general and HD populations (21 variables); outcome of a COVID-19 outbreak in a dialysis service area was defined as a clinic being located in one of the national counties with the highest growth in COVID-19 positive cases (number and people per million (ppm)) in general population during 22-28 Mar 2020. Second was a patient-level model that used HD patient data (82 variables) to predict an individual having an undetected SARS-CoV-2 infection that is identified in subsequent [≥]3 days. Results: Among 1682 counties with dialysis clinics, 82 (4.9%) had a COVID-19 outbreak during 22-28 Mar 2020. Area under the receiver operating characteristic curve (AUROC) for the county-level model was 0.86 in testing dataset. Top predictor of a county experiencing an outbreak was the COVID-19 positive ppm in the general population in the prior week. In a select group (n=11,664) used to build the patient-level model, 28% of patients had COVID-19; prevalence was by design 10% in the testing dataset. AUROC for the patient-level model was 0.71 in the testing dataset. Top predictor of an HD patient having a SARS-CoV-2 infection was mean pre-HD body temperature in the prior week. Conclusions: Developed ML models appear suitable for predicting counties at risk of a COVID-19 outbreak and HD patients at risk of having an undetected SARS-CoV-2 infection.