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Xugang Tang,1,2 Xiuchuan Li,1 Qian Xin,3 Qiang Wang,1 Shuang Li,1 Yongjian Yang1 1Department of Cardiology, The General Hospital of Western Theater Command, Chengdu, Sichuan, People’s Republic of China; 2Department of Cardiology, The No. 37 Hospital of Chinese PLA, Ya’an, Sichuan, People’s Republic of China; 3Department of Cardiology, The Sixth Medical Center of Chinese PLA General Hospital, Beijing, People’s Republic of ChinaCorrespondence: Yongjian Yang, Department of Cardiology, The General Hospital of Western Theater Command, Chengdu, Sichuan, People’s Republic of China, Tel +86 28 8657 1255, Fax +86 28 8657 1255, Email yyjcdzyy@sina.comPurpose: We aimed to explore whether anxiety is a risk factor for acute mountain sickness [AMS] in a young Chinese male population.Patients and Methods: A total of 143 young Chinese men with a median age of 23 years (IQR, 21– 25) were employed in the present study, and they were divided into the AMS+ and AMS- groups according to the Lake Louise AMS score [AMS-S] after exposure at 3800 m for two days. Participants’ pulse oximeter saturation [SpO2] and heart rate [HR] were measured. AMS was evaluated using the AMS-S. The anxiety and sleep quality of the subjects were assessed using the Zung Self-Rating Anxiety Scale [SAS] and the Athens Insomnia Scale [AIS], respectively. Outcomes were analysed using Spearman’s partial correlation and logistic regression analysis.Results: After two days of exposure at 3800 m, the overall prevalence of AMS was 54% in the whole group. The HR was significantly higher in the AMS+ group than in the AMS- group, as well as the SAS score and AIS score. A converse pattern was observed for SpO2. A significant difference was observed for the change in SAS and AIS score between the AMS+ and AMS- groups. Correlation analysis showed that AMS-S was positively correlated with SAS score, AIS score, HR, ΔSAS score, ΔAIS score, and ΔHR but negatively correlated with SpO2. AIS score was positively correlated with SAS score. After logistic regression analysis was adjusted for HR, SpO2, ΔAIS and ΔHR, SAS score (OR=1.446, 95% CI 1.200– 1.744, p< 0.001), AIS score (OR=1.216, 95% CI 1.033– 1.432) and ΔSAS score (OR=1.158, 95% CI 1.012– 1.327) were identified as independent risk factors for AMS.Conclusion: The present study suggests that anxiety is a risk factor for AMS among young Chinese men, and poor sleep quality may partially mediate the association.Keywords: anxiety, acute mountain sickness, high altitude, sleep

Qi Xin,1 Tonghui Xie,1 Rui Chen,1 Xing Zhang,1 Yingmu Tong,1 Hai Wang,1 Shufeng Wang,2 Chang Liu,1,3 Jingyao Zhang1,3 1Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, People’s Republic of China; 2Department of General Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, People’s Republic of China; 3Department of SICU, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, People’s Republic of ChinaCorrespondence: Chang Liu; Jingyao Zhang, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, People’s Republic of China, Tel +86-29-85323900, Fax +86-29-85324642, Email liuchangdoctor@163.com; you12ouy@163.comBackground: Sepsis-induced acute kidney injury (S-AKI) is associated with systemic inflammatory responses and coagulation system dysfunction, and it is associated with an increased risk of mortality. However, there was no study to explore the predictive value of inflammatory and coagulation indicators for S-AKI.Methods: In this retrospective study, 1051 sepsis patients were identified and divided into a training cohort (75%, n = 787) and a validation cohort (25%, n = 264) in chronological order according to the date they were admitted. Univariate analyses and multivariate logistic regression analyses were performed to identify the independent predictors of S-AKI. The logistic regression analyses (enter methods) were used to conducted the prediction models. The ROC curves were used to determine the predictive value of the constructed models on S-AKI. To test whether the increase in the AUC is significant, we used a two-sided test for ROC curves available online (http://vassarstats.net/roc_comp.html). The secondary outcome was different AKI stages and major adverse kidney events within 30 days (MAKE30). Stage 3B of S-AKI was defined as both meeting the stage 3 criteria [increase of Cr level by > 300% (≥ 4.0 mg/dL with an acute increase of ≥ 0.5 mg/dL) and/or UO < 0.3 mL/kg/h for > 24 h or anuria for > 12 h and/or acute kidney replacement therapy] and having cystatin C positive. MAKE30 were a composite of death, new renal replacement therapy (RRT), or persistent renal dysfunction (PRD).Results: We discovered that cardiovascular disease, white blood cell (WBC), mean arterial pressure (MAP), platelet (PLT), serum procalcitonin (PCT), prothrombin time activity (PTA), and thrombin time (TT) were independent predictors for S-AKI. The predictive value (AUC = 0.855) of the simplest model 3 (constructed with PLT, PCT, and PTA), with a sensitivity of 77.6% and a specificity of 82.4%, had a similar predictive value comparing with the model 1 (AUC = 0.872) and the model 2 (AUC = 0.864) in the training cohort (P > 0.05). Compared with the model 1 (AUC = 0.888) and the model 2 (AUC = 0.887), the model 3 (AUC = 0.887) had a similar predictive value in the validation cohort. Moreover, model 3 had the best predictive power for predicting S-AKI in the stage 3 (AUC = 0.777), especially in stage 3B (AUC = 0.771). Finally, the model 3 (AUC = 0.843) had perfect predictive power for predicting MAKE30 in sepsis patients.Conclusion: Within 24 hours after admission, the simplest model 3 (constructed with PLT, PCT, and PTA) might be a robust predictor of the S-AKI in sepsis patients, providing information for timely and efficient intervention.Keywords: inflammation, coagulation, prediction model, sepsis, acute kidney injury

Abstract Background Effective screening is a desirable method for the early detection and successful treatment for diabetic retinopathy, and fundus photography is currently the dominant medium for retinal imaging due to its convenience and accessibility. Manual screening using fundus photographs has however involved considerable costs for patients, clinicians and national health systems, which has limited its application particularly in less-developed countries. The advent of artificial intelligence, and in particular deep learning techniques, has however raised the possibility of widespread automated screening. Main text In this review, we first briefly survey major published advances in retinal analysis using artificial intelligence. We take care to separately describe standard multiple-field fundus photography, and the newer modalities of ultra-wide field photography and smartphone-based photography. Finally, we consider several machine learning concepts that have been particularly relevant to the domain and illustrate their usage with extant works. Conclusions In the ophthalmology field, it was demonstrated that deep learning tools for diabetic retinopathy show clinically acceptable diagnostic performance when using colour retinal fundus images. Artificial intelligence models are among the most promising solutions to tackle the burden of diabetic retinopathy management in a comprehensive manner. However, future research is crucial to assess the potential clinical deployment, evaluate the cost-effectiveness of different DL systems in clinical practice and improve clinical acceptance.

Wen-qiang Xin,1 Qi-qiang Xin,2 Xin-yu Yang1 1Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin 300052, Peoples Republic of China; 2Department of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, Jiangxi 330000, Peoples Republic of ChinaCorrespondence: Xin-yu YangDepartment of Neurosurgery, Tianjin Medical University General Hospital, Tianjin 300052, Peoples Republic of ChinaTel +86-18622766038Fax +86-02260362062Email yangxinyu@tmu.edu.cnBackground: Endovascular coiling and surgical clipping are routinely used to treat unruptured middle cerebral artery aneurysms (MCAAs). However, the optimal treatment for unruptured MCAAs is controversial. We aimed to systematically and comprehensively compare the clinical outcomes between endovascular coiling and surgical clipping for the treatment of MCAAs.Method: This meta-analysis retrieved academic articles comparing the clinical outcomes between endovascular coiling and surgical clipping for unruptured MCAAs from the Cochrane Library, Medline, PubMed, and EMBASE databases. The reference articles of the identified studies were carefully reviewed to ensure that all available articles were represented in the study. The meta-analysis was conducted in accordance with the acknowledged the prioritized reported items for systematic review and meta-analysis (PRISMA) guidelines.Results: A total of 6 studies, which enrolled a total of 789 participants, were included in our analysis. Of these 789 patients with MCAAs, 144 were assigned to an endovascular coiling group, and 645 were assigned to a surgical clipping group. Our results demonstrated that endovascular coiling was associated with a higher rate of retreatment (OR = 104.926; 95% CI: 12.931 to 851.379; P2, infarction, or bleeding.Conclusion: Although this study has inherent limitations, surgical clipping of unruptured MCAAs resulted in significantly higher complete aneurysm occlusion and GOS≥4 rates and was associated with a lower incidence of retreatment and complication, especially for postoperative thrombus without infarction. Therefore, the effect induced by surgical clipping of unruptured MCAAs remains superior to that induced by endovascular coiling; surgical clipping should be regarded as the first choice of treatment for unruptured MCAAs.Keywords: endovascular coiling, surgical clipping, unruptured cerebral aneurysms

Ping Xie,1 Qian Xin,2 Sheng-Tao Yang,3 Tiantian He,2,4 Yuanfang Huang,2 Guangfu Zeng,2,4 Maosheng Ran,2,5 Xiaohai Tang2 1State Key Laboratory of Oral Diseases, West China College of Stomatology, Sichuan University, Chengdu, 2Chongqing Lummy Pharmaceutical Co., Ltd, Chongqing, 3College of Chemistry & Environment Protection Engineering, Southwest University for Nationalities, 4College of Life Sciences, Sichuan Normal University, 5State Key Laboratory of Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, People’s Republic of China Abstract: Carbon nanoparticles (CNPs) have been widely used in tumor drainage lymph node (TDLN) imaging, drug delivery, photothermal therapy, and so on. However, during the theranostic applications, the accumulation efficiency of CNPs in target organs is unknown yet, which largely hinders the extension of CNPs into clinical uses. Herein, we prepared skeleton-labeled 13C-CNPs that had identical properties to commercial CNPs suspension injection (CNSI) for the imaging and quantification in TDLN. 13C-CNPs were prepared by arc discharge method, followed by homogenization with polyvinylpyrrolidone. The size distribution and morphology of 13C-CNPs were nearly the same as those of CNSI under transmission electron microscope. The hydrodynamic radii of both 13C-CNPs and CNSI were similar, too. According to X-ray photoelectron spectroscopy and infrared spectroscopy analyses, the chemical compositions and chemical states of elements were also nearly identical for both labeled and commercial forms. The skeleton labeling of 13C was reflected by the shift of G-band toward lower frequency in Raman spectra. 13C-CNPs showed competitive performance in TDLN imaging, where the three lymph nodes (popliteal lymph node, common iliac artery lymph node, and paraaortic lymph node) were stained black upon the injection into the hind extremity of mice. The direct quantification of 13C-CNPs indicated that 877 µg/g of 13C-CNPs accumulated in the first station of TDLN (popliteal lymph node). The second station of TDLN (common iliac artery lymph node) had even higher accumulation level (1,062 µg/g), suggesting that 13C-CNPs migrated efficiently along lymphatic vessel. The value decreased to 405 µg/g in the third station of TDLN (paraaortic lymph node). Therefore, the 13C-CNPs provided quantitative approach to image and quantify CNSI in biological systems. The implication in biomedical applications and biosafety evaluations of CNSI is discussed. Keywords: carbon nanoparticles suspension injection, 13C-labeling, isotope ratio mass spectroscopy, quantification, bioeffect of nanomaterials

Summary: Background: Radical measures are required to identify and reduce blindness due to diabetes to achieve the Sustainable Development Goals by 2030. Therefore, we evaluated the accuracy of an artificial intelligence (AI) model using deep learning in a population-based diabetic retinopathy screening programme in Zambia, a lower-middle-income country. Methods: We adopted an ensemble AI model consisting of a combination of two convolutional neural networks (an adapted VGGNet architecture and a residual neural network architecture) for classifying retinal colour fundus images. We trained our model on 76 370 retinal fundus images from 13 099 patients with diabetes who had participated in the Singapore Integrated Diabetic Retinopathy Program, between 2010 and 2013, which has been published previously. In this clinical validation study, we included all patients with a diagnosis of diabetes that attended a mobile screening unit in five urban centres in the Copperbelt province of Zambia from Feb 1 to June 31, 2012. In our model, referable diabetic retinopathy was defined as moderate non-proliferative diabetic retinopathy or worse, diabetic macular oedema, and ungradable images. Vision-threatening diabetic retinopathy comprised severe non-proliferative and proliferative diabetic retinopathy. We calculated the area under the curve (AUC), sensitivity, and specificity for referable diabetic retinopathy, and sensitivities of vision-threatening diabetic retinopathy and diabetic macular oedema compared with the grading by retinal specialists. We did a multivariate analysis for systemic risk factors and referable diabetic retinopathy between AI and human graders. Findings: A total of 4504 retinal fundus images from 3093 eyes of 1574 Zambians with diabetes were prospectively recruited. Referable diabetic retinopathy was found in 697 (22·5%) eyes, vision-threatening diabetic retinopathy in 171 (5·5%) eyes, and diabetic macular oedema in 249 (8·1%) eyes. The AUC of the AI system for referable diabetic retinopathy was 0·973 (95% CI 0·969–0·978), with corresponding sensitivity of 92·25% (90·10–94·12) and specificity of 89·04% (87·85–90·28). Vision-threatening diabetic retinopathy sensitivity was 99·42% (99·15–99·68) and diabetic macular oedema sensitivity was 97·19% (96·61–97·77). The AI model and human graders showed similar outcomes in referable diabetic retinopathy prevalence detection and systemic risk factors associations. Both the AI model and human graders identified longer duration of diabetes, higher level of glycated haemoglobin, and increased systolic blood pressure as risk factors associated with referable diabetic retinopathy. Interpretation: An AI system shows clinically acceptable performance in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, and diabetic macular oedema in population-based diabetic retinopathy screening. This shows the potential application and adoption of such AI technology in an under-resourced African population to reduce the incidence of preventable blindness, even when the model is trained in a different population. Funding: National Medical Research Council Health Service Research Grant, Large Collaborative Grant, Ministry of Health, Singapore; the SingHealth Foundation; and the Tanoto Foundation.

During the past decades, GMZ bentonite has been widely investigated for its use as buffer/backfill materials in China. Based on a comprehensive review of the former studies, achievements on experimental and theoretic works on the hydraulic aspects of compacted GMZ bentonite with consideration of temperature effects are presented in this paper. Water retention property of compacted GMZ bentonite depends on constraint conditions. Temperature effects on water-retention depend on constraint conditions and suction. The hysteresis behaviour is not obvious. Based on the test results, a revised water retention model was developed for considering the temperature effect. The saturated hydraulic conductivity of the densely compacted GMZ bentonite increases as dry density and temperature increases. A revised model, which considers temperature influence on water viscosity and the effective flow cross-sectional area of porous channels, for prediction of saturated hydraulic conductivity have been developed and verified. The unsaturated hydraulic conductivity of confined densely compacted GMZ bentonite samples decreases first and then increases with suction decrease from an initial value of 80 MPa to zero. With consideration of temperature effects and microstructure changes, a revised model for prediction of unsaturated hydraulic conductivity of compacted GMZ01 bentonite was proposed.