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LiCoO2 coated with graphene and PSS:PEDOT（GP-LCO）is synthesized by a facile and scalable method. The structure and morphology of LiCoO2（LCO）and GP-LCO have been investigated by X-ray diffraction（XRD）and scanning electron microscopy（SEM）. Transmission electron microscopy（TEM）further demonstrate the existence of graphene and PSS:PEDOT. The electrochemical properties of the composites are investigated by cyclic voltammetry and AC impedance measurements, which show that the conductive film of graphene（1%）and PSS:PEDOT（2%）on the surface significantly decreases the charge transfer resistance and improves the cycle stability of LiCoO2. Galvanostatic charge-discharge test reveals that with only 2% Super P as conductive additive, the GP-LCO delivers a capacity of 173.9 mAh/g at 0.1 C and 118.0 mAh/g at 10 C, which are higher than the pristine LCO in the potential range of 3.0-4.5 V（vs. Li/Li+）. Moreover, it is clearly noticed that the GP-LCO exhibits a capacity retention of 76.6% after 100 cycles at 0.1C, while the LCO only presents a capacity retention of 41.1%.

As a new type of energy storage device, aqueous zinc-ion capacitors (AZICs) have excellent performance, such as high power density, high energy density, long cycle life and high safety.AZICs have a high application prospect in the field of civil electronic equipment and military electrified weapons and equipment, and are expected to become a new generation of energy storage scheme instead of lithium-ion batteries.In this paper, the recent advances of carbon-based materials for zinc ion capacitors, such as activated carbon, carbon nanotubes, graphene and biomass derived carbon were summarized. The zinc ion storage performance of MXenes was analyzed in detail, and the energy storage mechanisms of transition metal oxides were revealed. It is pointed out that developing the cathode materials with high capacity, high voltage tolerance and low temperature adaptability is of great importance.

Objective To build a diagnostic model of gastric cancer based on deep learning and evaluate the performance of the model. Methods The pathological sections of patients diagnosed with normal gastric mucosa, chronic gastritis, high-grade intraepithelial neoplasia or gastric adenocarcinoma by endoscopic examination in Zhejiang Provincial People's Hospital from January 2015 to January 2020 were retrospectively selected. The pathology slides were scanned at ×20 magnification to generate whole slide images (WSIs). These WSIs were randomly divided into patch classification data set, slide classification training set and slide classification test set at a ratio of 2:2:1. After the lesion regions of the patch classification data set were annotated and the patches were selected, they were randomly divided into training set, test set and validation set at a ratio of 20:1:1. The deep learning model Efficientnet and ResNet were used to train and the convolutional neural network (CNN) model for cancer and non-cancer classification was constructed. Based on the patch classification test set and validation set, the performance of the model was evaluated. The results were evaluated by the patch classification accuracy and the area under the curve (AUC). This model was used for image stitching to generate the cancerous heat map of WSIs and extract the slide-level cancer and non-cancer classification features of the heat map. LightGBM slide-level classification algorithm were trained and evaluated, and the gastric cancer of WSIs were diagnosed and recognized. The results were evaluated by AUC, accuracy, sensitivity and specificity. Results A total of 500 pathological sections of benign gastric diseases (normal gastric mucosa, chronic gastritis) and 500 pathological sections of gastric cancer (high-grade intraepithelial neoplasia and gastric adenocarcinoma) that met the inclusion and exclusion criteria were selected. The patch classification data set, slide classification training set and slide classification test set were 400, 400 and 200, respectively. The patch classification training set, test set, validation set were 402 000, 20 000, 20 000, respectively. CNN model based on Efficientnet-b1 network structure for patch classification in test set and validation set achieved the highest accuracy[test set: 91.3% (95% CI: 88.2%-95.4%); validation set: 92.5%(95% CI: 89.0%-95.3%)]and the highest AUC[test set: 0.95(95% CI: 0.93-0.98); validation set: 0.96(95% CI: 0.92-0.98)]. The AUC of the model based on LightGBM algorithm was 0.98(95% CI: 0.89-0.98), with accuracy of 88.0%(95% CI: 81.6%-94.3%), sensitivity 100%(95% CI: 88.0%-100%), and specificity 67.0%(95% CI: 57.0%-85.0%). Conclusion The CNN diagnostic model based on the pathology slides of gastric biopsy can locate the cancerous tissues, classify patch-level and slide-level lesion natures accurately, identify gastric cancer accurately, which has the potential to improve the diagnosis efficiency.

As the development of society, the specific energy density of the commercial lithium-ion battery still cannot meet the requirements for practical applications, such as electric vehicles, 3C(computer, communication and consumer electronics) products and energy storage devices. The Li- and Mn-rich cathode materials are expected to be the key electrode materials for high energy density lithium-ion battery due to their high electrochemical capacity (≈250 mAh/g), high operating voltage(≈3.6 V) and low cost. However, several issues and challenges limit their widespread applications for commercial lithium-ion battery, including high irreversible capacity, poor cycle life and fast voltage/capacity fading. In this paper, the latest research progress of Li- and Mn-rich cathode materials is reviewed, with emphasis on material structure, electrochemical reaction mechanism, failure mechanism and modification technology. The results show that ion doping, surface coating, crystal structure regulation and other techniques can significantly improve the electrochemical performance of Li and Mn-rich cathode materials. Finally, the development direction of Li- and Mn-rich cathode materials is prospected.

Objective: To compare the effects of robot-assisted therapy with conventional therapy for accelerating stratified intervention in poststroke patients with upper limb dysfunction. Background: For stroke survivors, recovery of upper extremity function remains a major challenge in rehabilitation. Literature has suggested that the rate of recovery may improve if treatments can be individualized to their clinical profiles. However, there still lack clinical evidence on how to create treatment tailored to individual patients. Robot-assisted Therapy (RT) provides a straightforward approach to adjustment of the assistance-resistance continuum for individual patients. In early Brunnstrom stages of recovery, patients benefit from assistance training, whereas in later stages the training is favored with resistance. Therefore, RT may enhance Conventional Therapy (CT) but the use of RT in stratified intervention has not been investigated. This study evaluated the possible benefit of adopting RT following a protocol of upper-limb training, which was stratified with the Brunnstrom stage of each individual. Methods: This study was a single-blinded randomized controlled trial. A total of 53 patients with stroke were recruited and randomized into 2 groups (CT, n = 27, 3 dropped out and RT, n = 26, 2 dropped out). Both groups were trained once per day, 5 days per week for 4 weeks. The CT group received 30 min of conventional therapy; the RT group received 30 min of upper limb robot-assisted training. Patients were assessed at the beginning, week-2, and week-4 of the treatment. The outcome measures included the Fugl-Meyer Assessment Upper-Extremity (FMA-UE) and the Modified Barthel Index (MBI). Results: Across the 4-week intervention, participants in the RT group recovered 1.979 points of FMA-UE per week, compared to 1.198 points per week in the CT group (t94 = 3.333, p < 0.01); the recovery rate was 0.781 points/week higher in the RT group than in the CT group. Moreover, the recovery of FMA-UE was faster in proximal joints (t94 = 3.199, p < 0.01), and for patients in Brunnstrom Stage III (t34 = 2.526, p < 0.05). The improvements in MBI were not significantly different between RT and CT. Conclusion: Robot-assisted therapy showed initial evidence for the acceleration of post-stroke recovery of motor function in the upper limb. Initial observations suggested that patients in Brunnstrom recovery stage III might benefit the most from the stratified intervention assisted by robotics. Clinical trial registration: https://www.chictr.org.cn/showproj.html?proj=61834, Identifier [ChiCTR2000039010]. Registered 13 March 2020. [ABSTRACT FROM AUTHOR]

The de-differentiation and proliferation of smooth muscle cells (SMCs) are widely accepted as the major contributor to vascular remodeling. However, recent studies indicate that vascular stem cells (VSCs) also play an important role, but their relative contribution remains to be elucidated. In this study, we used genetic lineage tracing approach to further investigate the contribution of SMCs and VSCs to neointimal thickening in response to endothelium denudation injury or artery ligation. In vitro and in vivo analysis of MYH11-cre/Rosa-loxP-RFP mouse artery showed that SMCs proliferated at a much slower rate than non-SMCs. Upon denudation or ligation injury, two distinct types of neointima were identified: Type-I neointimal cells mainly involved SMCs, while Type II mainly involved non-SMCs. Using Sox10-cre/Rosa-loxP-LacZ mice, we found that Sox10+ cells were one of the cell sources in neointima. In addition, lineage tracing using Tie2-cre/Rosa-LoxP-RFP showed that endothelial cells also contributed to the neointimal formation, but rarely transdifferentiated into mesenchymal lineages. These results provide a novel insight into the contribution of vascular cells to neointima formation, and have significant impact on the development of more effective therapies that target specific vascular cell types.

SummaryWe consider the problem of estimating the causal effect of drug exposure on clinical response in a randomized dose comparison trial where each group receives a different dose. Because exposure is not randomized, the exposure-response relationship is subject to confounding in this setting. Conventional statistical methods for confounding adjustment with a continuous exposure typically assume that there are no unmeasured confounders. This article provides an instrumental variable approach that does not require the assumption of no unmeasured confounders. Specifically, we use randomized dose assignment as an instrumental variable and characterize the causal exposure-response relationship using a control variable under a mild monotonicity assumption on individual dose-exposure profiles. Based on this characterization, we derive partial identification bounds for the causal exposure-response relationship, which are model-free but may be too wide to be useful in practice. For practicality, we further develop a simple estimation method based on a regression model for the mean response conditional on exposure and the control variable. Simulation results show that, in the presence of unmeasured confounding, the model-based estimation method reduces estimation bias effectively at the expense of increased variability, as compared to existing methods. The method is illustrated with real data from a study of chimeric antigen receptor T cell therapy for treating chronic lymphocytic leukemia. [ABSTRACT FROM AUTHOR]