Low-Cost Zinc-Alginate-Based Hydrogel-Polymer Electrolytes for Dendrite-Free Zinc-Ion Batteries with High Performances and Prolonged Lifetimes.

Aqueous zinc-ion batteries (ZIBs) represent an attractive choice for energy storage. However, ZIBs suffer from dendrite growth and an irreversible consumption of Zn metal, leading to capacity degradation and a low lifetime. In this work, a zinc-alginate (ZA) hydrogel-polymer electrolyte (HGPE) with a non-porous structure was prepared via the solution-casting method and ion displacement reaction. The resulting ZA-based HGPE exhibits a high ionic conductivity (1.24 mS cm-1 at room temperature), excellent mechanical properties (28 MPa), good thermal and electrochemical stability, and an outstanding zinc ion transference number (0.59). The ZA-based HGPE with dense structure is proven to benefit the prevention of the uneven distribution of ion current and facilitates the reduction of excessive interfacial resistance within the battery. In addition, it greatly promotes the uniform deposition of zinc ions on the electrode, thereby inhibiting the growth of zinc dendrites. The corresponding zinc symmetric battery with ZA-based HGPE can be cycled stably for 800 h at a current density of 1 mA cm-2, demonstrating the stable and reversible zinc plating/stripping behaviors on the electrode surfaces. Furthermore, the quasi-solid-state ZIB with zinc, ZA-based HGPE, and Ca0.24V2O5 (CVO) as the anode, electrolyte, and cathode materials, respectively, show a stable cyclic performance for 600 cycles at a large current density of 3 C (1 C = 400 mA g-1), in which the capacity retention rate is 88.7%. This research provides a new strategy for promoting the application of the aqueous ZIBs with high performance and environmental benignity.

Machine Learning-Based Decision Model to Distinguish Between COVID-19 and Influenza: A Retrospective, Two-Centered, Diagnostic Study.

BACKGROUND: Considering the current situation of the novel coronavirus disease (COVID-19) epidemic control, it is highly likely that COVID-19 and influenza may coincide during the approaching winter season. However, there is no available tool that can rapidly and precisely distinguish between these two diseases in the absence of laboratory evidence of specific pathogens. METHODS: Laboratory-confirmed COVID-19 and influenza patients between December 1, 2019 and February 29, 2020, from Zhongnan Hospital of Wuhan University (ZHWU) and Wuhan No.1 Hospital (WNH) located in Wuhan, China, were included for analysis. A machine learning-based decision model was developed using the XGBoost algorithms. RESULTS: Data of 357 COVID-19 and 1893 influenza patients from ZHWU were split into a training and a testing set in the ratio 7:3, while the dataset from WNH (308 COVID-19 and 312 influenza patients) was preserved for an external test. Model-based decision tree selected age, serum high-sensitivity C-reactive protein and circulating monocytes as meaningful indicators for classifying COVID-19 and influenza cases. In the training, testing and external sets, the model achieved good performance in identifying COVID-19 from influenza cases with a corresponding area under the receiver operating characteristic curve (AUC) of 0.94 (95% CI 0.93, 0.96), 0.93 (95% CI 0.90, 0.96), and 0.84 (95% CI: 0.81, 0.87), respectively. CONCLUSION: Machine learning provides a tool that can rapidly and accurately distinguish between COVID-19 and influenza cases. This finding would be particularly useful in regions with massive co-occurrences of COVID-19 and influenza cases while limited resources for laboratory testing of specific pathogens.

Feasibility of a 5G-Based Robot-Assisted Remote Ultrasound System for Cardiopulmonary Assessment of Patients With Coronavirus Disease 2019.

BACKGROUND: Traditional methods for cardiopulmonary assessment of patients with coronavirus disease 2019 (COVID-19) pose risks to both patients and examiners. This necessitates a remote examination of such patients without sacrificing information quality. RESEARCH QUESTION: The goal of this study was to assess the feasibility of a 5G-based robot-assisted remote ultrasound system in examining patients with COVID-19 and to establish an examination protocol for telerobotic ultrasound scanning. STUDY DESIGN AND METHODS: Twenty-three patients with COVID-19 were included and divided into two groups. Twelve were nonsevere cases, and 11 were severe cases. All patients underwent a 5G-based robot-assisted remote ultrasound system examination of the lungs and heart following an established protocol. Distribution characteristics and morphology of the lung and surrounding tissue lesions, left ventricular ejection fraction, ventricular area ratio, pericardial effusion, and examination-related complications were recorded. Bilateral lung lesions were evaluated by using a lung ultrasound score. RESULTS: The remote ultrasound system successfully and safely performed cardiopulmonary examinations of all patients. Peripheral lung lesions were clearly evaluated. Severe cases of COVID-19 had significantly more diseased regions (median [interquartile range], 6.0 [2.0-11.0] vs 1.0 [0.0-2.8]) and higher lung ultrasound scores (12.0 [4.0-24.0] vs 2.0 [0.0-4.0]) than nonsevere cases of COVID-19 (both, P < .05). One nonsevere case (8.3%; 95% CI, 1.5-35.4) and three severe cases (27.3%; 95% CI, 9.7-56.6) were complicated by pleural effusions. Four severe cases (36.4%; 95% CI, 15.2-64.6) were complicated by pericardial effusions (vs 0% of nonsevere cases, P < .05). No patients had significant examination-related complications. INTERPRETATION: Use of the 5G-based robot-assisted remote ultrasound system is feasible and effectively obtains ultrasound characteristics for cardiopulmonary assessment of patients with COVID-19. By following established protocols and considering medical history, clinical manifestations, and laboratory markers, this system might help to evaluate the severity of COVID-19 remotely.