Camrelizumab versus placebo in combination with gemcitabine and cisplatin as first-line treatment for recurrent or metastatic nasopharyngeal carcinoma (CAPTAIN-1st): a multicentre, randomised, double-blind, phase 3 trial.

BACKGROUND: The addition of camrelizumab to gemcitabine and cisplatin showed promising activity as first-line therapy in patients with recurrent or metastatic nasopharyngeal carcinoma in a phase 1 trial. We therefore compared camrelizumab plus gemcitabine and cisplatin with placebo plus gemcitabine and cisplatin in a randomised phase 3 trial. METHODS: In this randomised, double-blind, phase 3 trial done at 28 hospitals in China, patients were eligible if they were aged 18-75 years, had an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1, and had previously untreated recurrent or metastatic nasopharyngeal carcinoma. Patients were randomly assigned (1:1; using an interactive web-response system with a block size of four) to receive either camrelizumab (200 mg on day 1) or matching placebo intravenously, plus gemcitabine and cisplatin (gemcitabine 1000 mg/m2 on days 1 and 8; cisplatin 80 mg/m2 on day 1) intravenously every 3 weeks for four to six cycles, followed by maintenance therapy with camrelizumab or placebo, until radiographic progression, unacceptable toxicity, start of new anticancer treatment, investigator decision, or withdrawal of consent. Stratification factors used in randomisation were liver metastases, previous radical concurrent chemoradiotherapy, and ECOG performance status. The allocation sequence was generated by an independent randomisation group. The primary endpoint was progression-free survival per independent review committee. The significance threshold for independent review committee-assessed progression-free survival was p=0·0086 (one-sided) at the interim analysis. Efficacy and safety analyses included all patients who received at least one dose of study drug. This trial is registered with ClinicalTrials.gov, NCT03707509, and is closed for enrolment but is ongoing. FINDINGS: Between Nov 13, 2018, and Nov 29, 2019, 343 patients were screened and 263 were eligible and were randomly assigned to the camrelizumab group (n=134) or placebo group (n=129). At the prespecified interim analysis (June 15, 2020), independent review committee-assessed progression-free survival was significantly longer in the camrelizumab group (median 9·7 months [95% CI 8·3-11·4]) than in the placebo group (median 6·9 months [5·9-7·3]; hazard ratio 0·54 [95% CI 0·39-0·76]; one-sided p=0·0002). As of Dec 31, 2020, the most common grade 3 or worse adverse events of any cause were decreased white blood cell count (89 [66%] of 134 patients in the camrelizumab group vs 90 [70%] of 129 patients in the placebo group), decreased neutrophil count (86 [64%] vs 85 [66%]), anaemia (53 [40%] vs 57 [44%]), and decreased platelet count (53 [40%] vs 52 [40%]). Serious adverse events were reported in 59 (44%) of 134 patients in the camrelizumab group and 48 (37%) of 129 patients in the placebo group. Treatment-related deaths occurred in five (4%) patients in the camrelizumab group (two unknown cause of death, one multiple organ dysfunction syndrome, one pharyngeal haemorrhage, and one arrhythmia) and one (<1%) patient in the placebo group (unknown cause of death). INTERPRETATION: Our findings suggest that camrelizumab plus gemcitabine and cisplatin could be a new standard of care for patients with recurrent or metastatic nasopharyngeal carcinoma in the first-line setting. Longer follow-up is needed to confirm this conclusion. FUNDING: Jiangsu Hengrui Pharmaceuticals (formerly Jiangsu Hengrui Medicine). TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

LiFePO4/C twin microspheres as cathode materials with enhanced electrochemical performance.

Self-assembled lithium iron phosphate (LiFePO4) with tunable microstructure is an effective way to improve the electrochemical performance of cathode materials for lithium ion batteries. Herein, self-assembled LiFePO4/C twin microspheres are synthesized by a hydrothermal method using a mixed solution of phosphoric acid and phytic acid as the phosphorus source. The twin microspheres are hierarchical structures composed of primary nano-sized capsule-like particles (about 100 nm in diameter and 200 nm in length). The uniform thin carbon layer on the surface of the particles improves the charge transport capacity. The channel between the particles facilitates the electrolyte infiltration, and the high electrolyte accessibility enables the electrode material to obtain excellent ion transport. The optimal LiFePO4/C-60 exhibits excellent rate performance with discharge capacity of 156.3 mA h g-1 and 118.5 mA h g-1 respectively at 0.2C and 10C, and low temperature performances with discharge capacity of 90.67 mA h g-1 and 66.7 mA h g-1 at -15 °C and -25 °C, respectively. This research may provide a new pathway to improve the performance of LiFePO4 by tuning the micro-structures by adjusting the relative content of phosphoric acid and phytic acid.

Carbon microspheres built of La2O3 quantum dots-implanted nanorods: Superb hosts with ultra-long Li2Sn-catalysis durability.

Practical utilization of Li-sulfur batteries (LSBs) is still hindered by the sulfur cathode side due to its inferior electrical conductivity, huge volume expansion and adverse polysulfide shuttling effects. Though using polar catalysts coupled with mesoporous carbons may well surmount these barriers, such unsheltered catalysts rarely survive due to oversaturated polysulfide adsorption and extra sulfuration side reactions. To overcome above constrains, we herein propose to implant highly reactive nanocatalysts into carbon matrix with few nanometers insertion depth for mechanical protection. As a paradigm study, we have embedded La2O3-quantum dots (QDs) into carbon nanorods, which are then assembled into carbon microspheres (CMs). As evaluated, La2O3 QDs-CMs can help elevate the cathode redox reaction kinetics and sulfur utilization ratios, delivering a large capacity of 1392 mAh g-1 at 0.25C and high-capacity retention of 76% after total cycling. The thin carbon layers on La2O3 QDs exert a key role in impeding excess polysulfide accumulation on catalysts and thus prevent their deactivation/failure. Our strategy may guide a smart way to make catalysts-involved sulfur cathode systems with ultra-long working durability for LSBs applications.

Study on the difference and regularity of tongue images in 309 patients with different pathological stages of non-small cell lung cancer.

BACKGROUND: Tongue diagnosis is a crucial traditional Chinese medicine (TCM) inspection method for TCM syndrome differentiation and treatment. OBJECTIVE: The primary research focus was on tongue image characteristic parameters of patients with non-small cell lung cancer (NSCLC). Analysis of the tongue image parameters of various pathological stages of NSCLC provides technical support for establishing an integrated Chinese and Western auxiliary diagnosis and efficacy evaluation medicine system for lung cancer that integrates tongue image features. METHODS: Tongue image characteristics of 309 patients with NSCLC and 206 controls were collected and analyzed clinically. The T-test or rank sum test and logistic regression analysis were applied to analyze the characteristics of tongue image indicators of different pathological stages of NSCLC. RESULTS: There were differences in tongue image characteristics in the NSCLC group compared to the control group. The tongue quality and brightness of the tongue coating in the NSCLC group increased, the red component was reduced, the tongue coating thickened, and the yellow component increased compared to the healthy control group. A comparison of tongue image indexes of NSCLC in different pathological stages showed that stage IV had lower TB-b and higher TB-a than stage I. In addition, stage IV had lower TB-b than stage II + III, showing an increase in the blue and red components of the tongue in stage IV and the appearance of cyanotic tongue features. CONCLUSION: The tongue image characteristics of NSCLC patients differed from those of the control group. Tongue imaging indicators can reflect the characteristics of tongue images of patients with NSCLC. The tongue image characteristics of patients with stage IV lung cancer are bluish and purple compared with those with stage I, II, and III. It is suggested that the tongue's image characteristics can be used as a reference for the pathological classification of NSCLC and judgment of the disease process.

Identification and in silico Analysis of Nonsense SNPs of Human Colorectal Cancer Protein.

Colorectal cancer (CRC) is the third most prevalent disease in the world, with an estimated 1.2 million new cases each year. Spontaneous CRCs account for around 70% of all CRCs, are caused by somatic mutations. Minor variations or single-nucleotide polymorphisms (SNPs) in oncogene or tumor-suppressor genes cause familial CRC. MSH2 and MSH6 genes are located on chromosome 2. These genes products are involved in the repair of DNA replication defects. If these proteins are changed, the replication errors are not rectified, resulting in damaged DNA leading to colorectal cancer. We employed a variety of computational methodologies to find nsSNPs that are harmful to the structure and function of the MSH6 protein and could be causing CRC in our study. SIFT, PROVEAN, Poly- Phen-2, PhD-SNP, and SNPs&GO were among the in silico methods used to do the computational research. According to the findings, mutations of G932Q, E1234Q, and F1104Q are important alterations in native MSH6 protein rs35717727 that may contribute to its dysfunction and, ultimately, disease. The study also provided three-dimensional structures of the native MSH6 protein and mutations. These nsSNPs should be considered as key target mutations in many disorders involving MSH6 dysfunction in future studies. This is the first thorough study to use in silico technologies to assess MSH6 gene variants, and it will be extremely useful in planning largescale investigations and developing precision medicines to treat disorders caused by these polymorphisms. Additionally, animal models of various autoimmune disorders with these mutations could aid in determining their precise involvement.

Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup.

Background: Research on intelligent tongue diagnosis is a main direction in the modernization of tongue diagnosis technology. Identification of tongue shape and texture features is a difficult task for tongue diagnosis in traditional Chinese medicine (TCM). This study aimed to explore the application of deep learning techniques in tongue image analyses. Methods: A total of 8676 tongue images were annotated by clinical experts, into seven categories, including the fissured tongue, tooth-marked tongue, stasis tongue, spotted tongue, greasy coating, peeled coating, and rotten coating. Based on the labeled tongue images, the deep learning model faster region-based convolutional neural networks (Faster R-CNN) was utilized to classify tongue images. Four performance indices, i.e., accuracy, recall, precision, and F1-score, were selected to evaluate the model. Also, we applied it to analyze tongue image features of 3601 medical checkup participants in order to explore gender and age factors and the correlations among tongue features in diseases through complex networks. Results: The average accuracy, recall, precision, and F1-score of our model achieved 90.67%, 91.25%, 99.28%, and 95.00%, respectively. Over the tongue images from the medical checkup population, the model Faster R-CNN detected 41.49% fissured tongue images, 37.16% tooth-marked tongue images, 29.66% greasy coating images, 18.66% spotted tongue images, 9.97% stasis tongue images, 3.97% peeled coating images, and 1.22% rotten coating images. There were significant differences in the incidence of the fissured tongue, tooth-marked tongue, spotted tongue, and greasy coating among age and gender. Complex networks revealed that fissured tongue and tooth-marked were closely related to hypertension, dyslipidemia, overweight and nonalcoholic fatty liver disease (NAFLD), and a greasy coating tongue was associated with hypertension and overweight. Conclusion: The model Faster R-CNN shows good performance in the tongue image classification. And we have preliminarily revealed the relationship between tongue features and gender, age, and metabolic diseases in a medical checkup population.

Facile method to produce sub-1 nm pore-rich carbon from biomass wastes for high performance supercapacitors.

Sub-1 nm pores can lead to an anomalous increase in the supercapacitive performance [1], but it still faces great challenges from its relatively low sub-1 nm pore content, complicated preparation process, low yield and high cost. Here we successfully prepared a sub-1 nm pore-rich carbon from biomass wastes using a facile method by pre-treating walnut shell powder at 380 ℃ in air for different times to delicately tailor carbon defects, followed by KOH activation at 700 ℃. The as-prepared optimal material delivers the highest sub-1 nm pore content (Vsub-1 nm = 0.57 cm3 g-1, Vsub-1 nm/Vt = 58.4 %) among all reported porous carbons. A supercapacitor made from the material accomplishes an ultrahigh specific capacitance of 298.7F g-1 at 1 A g-1 in a two-electrode device, excellent rate capability (78.8 % retention from 1 to 10 A g-1) and long-cyclic life (94 % retention after 10,000 cycles at 10 A g-1) in KOH. Even in Et4NBF4 electrolyte that is often used in commercial supercapacitors, a high energy density of 82.8 Wh kg-1 at 7 kW kg-1 and excellent cycling performance (90 % retention after 10,000 cycles at 5 A g-1) can be achieved, ranking the best among all reported carbon-based electrical double layer capacitors tested in the same electrolyte. More importantly, it drives a light-emitting-diode (LED) to operate for as long as 20 min, vividly demonstrating the great potential of sub-1 nm pore-rich carbon-based high performance supercapacitors in practical applications.

Tongue color clustering and visual application based on 2D information.

PURPOSE: Studies have shown the association between tongue color and diseases. To help clinicians make more objective and accurate decisions quickly, we take unsupervised learning to deal with the basic clustering of tongue color in a 2D way. METHODS: A total of 595 typical tongue images were analyzed. The 3D information extracted from the image was transformed into 2D information by principal component analysis (PCA). K-Means was applied for clustering into four diagnostic groups. The results were evaluated by clustering accuracy (CA), Jaccard similarity coefficient (JSC), and adjusted rand index (ARI). RESULTS: The new 2D information totally retained 89.63% original information in the L*a*b* color space. And our methods successfully classified tongue images into four clusters and the CA, ARI, and JSC were 89.04%, 0.721, and 0.890, respectively. CONCLUSIONS: The 2D information of tongue color can be used for clustering and to improve the visualization. K-Means combined with PCA could be used for tongue color classification and diagnosis. Methods in the paper might provide reference for the other research based on image diagnosis technology.

Tongue Image Database Construction Based on the Expert Opinions: Assessment for Individual Agreement and Methods for Expert Selection.

This study aims at introducing a method for individual agreement evaluation to identify the discordant raters from the experts' group. We exclude those experts and decide the best experts selection method, so as to improve the reliability of the constructed tongue image database based on experts' opinions. Fifty experienced experts from the TCM diagnostic field all over China were invited to give ratings for 300 randomly selected tongue images. Gwet's AC1 (first-order agreement coefficient) was used to calculate the interrater and intrarater agreement. The optimization of the interrater agreement and the disagreement score were put forward to evaluate the external consistency for individual expert. The proposed method could successfully optimize the interrater agreement. By comparing three experts' selection methods, the interrater agreement was, respectively, increased from 0.53 [0.32-0.75] for original one to 0.64 [0.39-0.80] using method A (inclusion of experts whose intrarater agreement>0.6), 0.69 [0.63-0.81] using method B (inclusion of experts whose disagreement score="0"), and 0.76 [0.67-0.83] using method C (inclusion of experts whose intrarater agreement>0.6& disagreement score="0"). In this study, we provide an estimate of external consistency for individual expert, and the comprehensive consideration of both the internal consistency and the external consistency for each expert would be superior to either one in the tongue image construction based on expert opinions.

A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave.

OBJECTIVE: In this study, machine learning was utilized to classify and predict pulse wave of hypertensive group and healthy group and assess the risk of hypertension by observing the dynamic change of the pulse wave and provide an objective reference for clinical application of pulse diagnosis in traditional Chinese medicine (TCM). METHOD: The basic information from 450 hypertensive cases and 479 healthy cases was collected by self-developed H20 questionnaires and pulse wave information was acquired by self-developed pulse diagnostic instrument (PDA-1). H20 questionnaires and pulse wave information were used as input variables to obtain different machine learning classification models of hypertension. This method was aimed at analyzing the influence of pulse wave on the accuracy and stability of machine learning model, as well as the feature contribution of hypertension model after removing noise by K-means. RESULT: Compared with the classification results before removing noise, the accuracy and the area under the curve (AUC) had been improved. The accuracy rates of AdaBoost, Gradient Boosting, and Random Forest (RF) were 86.41%, 86.41%, and 85.33%, respectively. AUC were 0.86, 0.86, and 0.85, respectively. The maximum accuracy of SVM increased from 79.57% to 83.15%, and the AUC stability increased from 0.79 to 0.83. In addition, the features of importance on traditional statistics and machine learning were consistent. After removing noise, the features with large changes were h1/t1, w1/t, t, w2, h2, t1, and t5 in AdaBoost and Gradient Boosting (top10). The common variables for machine learning and traditional statistics were h1/t1, h5, t, Ad, BMI, and t2. CONCLUSION: Pulse wave-based diagnostic method of hypertension has significant value in reference. In view of the feasibility of digital-pulse-wave diagnosis and dynamically evaluating hypertension, it provides the research direction and foundation for Chinese medicine in the dynamic evaluation of modern disease diagnosis and curative effect.