PandaX is a large upgradable liquid-xenon detector system that can be used for both direct dark-matter detection and 136 Xe double-beta decay search. It is located in the Jinping Deep-Underground Laboratory in Sichuan, China. The detector operates in dual-phase mode, allowing detection of both prompt scintillation, and ionization charge through proportional scintillation. The central time projection chamber will be staged, with the first stage accommodating a target mass of about 120 kg. In stage II, the target mass will be increased to about 0.5 ton. In the final stage, the detector can be upgraded to a multi-ton target mass. In this paper a detailed description of the stage-I detector design and performance results established during the commissioning phase is presented.

BackgroundIn 2012, the European Society of Intensive Care Medicine proposed a definition for acute gastrointestinal injury (AGI) based on current medical evidence and expert opinion. The aim of the present study was to evaluate the feasibility of using the current AGI grading system and to investigate the association between AGI severity grades with clinical outcome in critically ill patients.MethodsAdult patients at 14 general intensive care units (ICUs) with an expected ICU stay ≥24 h were prospectively studied. The AGI grade was assessed daily on the basis of gastrointestinal (GI) symptoms, intra-abdominal pressures, and feeding intolerance (FI) in the first week of admission to the ICU.ResultsAmong the 550 patients enrolled, 456 patients (82.9%) received mechanical ventilation, and 470 patients were identified for AGI. The distribution of the global AGI grade was 24.5% with grade I, 49.4% with grade II, 20.6% with grade III, and 5.5% with grade IV. AGI grading was positively correlated with 28- and 60-day mortality (P < 0.0001). Univariate Cox regression analysis showed that age, sepsis, diabetes mellitus, coronary artery disease, the use of vasoactive drugs, serum creatinine and lactate levels, mechanical ventilation, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and the global AGI grade were significantly (P ≤ 0.02) associated with 60-day mortality. In a multivariate analysis including these variables, diabetes mellitus (HR 1.43, 95% CI 1.03–1.87; P = 0.05), the use of vasoactive drugs (HR 1.56, 95% CI 1.12–2.11; P = 0.01), serum lactate (HR 1.15, 95% CI 1.06–1.24; P = 0.03), global AGI grade (HR 1.65, 95% CI 1.28–2.12; P = 0.008), and APACHE II score (HR 1.04, 95% CI 1.02–1.06; P < 0.001) were independently associated with 60-day mortality. In a subgroup analysis of 402 patients with 7-day survival, in addition to clinical predictors and the AGI grade on the first day of ICU stay, FI within the first week of ICU stay had an independent and incremental prognostic value for 60-day mortality (χ2 = 41.9 vs. 52.2, P = 0.007).ConclusionsThe AGI grading scheme is useful for identifying the severity of GI dysfunction and could be used as a predictor of impaired outcomes. In addition, these results support the hypothesis that persistent FI within the first week of ICU stay is an independent determinant for mortality.Trial registrationChinese Clinical Trial Registry identifier: ChiCTR-OCS-13003824. Registered on 29 September 2013.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-017-1780-4) contains supplementary material, which is available to authorized users.

Purpose: In prostate focal therapy, it is important to accurately localize malignant lesions in order to increase biological effect of the tumor region while achieving a reduction in dose to noncancerous tissue. In this work, we proposed a transfer learning–based deep learning approach, for classification of prostate lesions in multiparametric magnetic resonance imaging images. Methods: Magnetic resonance imaging images were preprocessed to remove bias artifact and normalize the data. Two state-of-the-art deep convolutional neural network models, InceptionV3 and VGG-16, were pretrained on ImageNet data set and retuned on the multiparametric magnetic resonance imaging data set. As lesion appearances differ by the prostate zone that it resides in, separate models were trained. Ensembling was performed on each prostate zone to improve area under the curve. In addition, the predictions from lesions on each prostate zone were scaled separately to increase the area under the curve for all lesions combined. Results: The models were tuned to produce the highest area under the curve on validation data set. When it was applied to the unseen test data set, the transferred InceptionV3 model achieved an area under the curve of 0.81 and the transferred VGG-16 model achieved an area under the curve of 0.83. This was the third best score among the 72 methods from 33 participating groups in ProstateX competition. Conclusion: The transfer learning approach is a promising method for prostate cancer detection on multiparametric magnetic resonance imaging images. Features learned from ImageNet data set can be useful for medical images.

Breast cancer (BC) is the most common malignant tumor in women worldwide, which seriously threatens women’s physical and mental health. In recent years, photodynamic therapy (PDT) has shown significant advantages in cancer treatment. PDT involves activating photosensitizers with appropriate wavelengths of light, producing transient levels of reactive oxygen species (ROS). Compared with free photosensitizers, the use of nanoparticles in PDT shows great advantages in terms of solubility, early degradation, and biodistribution, as well as more effective intercellular penetration and targeted cancer cell uptake. Under the current circumstances, researchers have made promising efforts to develop nanocarrier photosensitizers. Reasonably designed photosensitizer (PS) nanoparticles can be achieved through non-covalent (self-aggregation, interfacial deposition, interfacial polymerization or core-shell embedding and physical adsorption) or covalent (chemical immobilization or coupling) processes and accumulate in certain tumors through passive and/or active targeting. These PS loading methods provide chemical and physical stability to the PS payload. Among nanoparticles, metal nanoparticles have the advantages of high stability, adjustable size, optical properties, and easy surface functionalization, making them more biocompatible in biological applications. In this review, we summarize the current development and application status of photodynamic therapy for breast cancer, especially the latest developments in the application of metal nanocarriers in breast cancer PDT, and highlight some of the recent synergistic therapies, hopefully providing an accessible overview of the current knowledge that may act as a basis for new ideas or systematic evaluations of already promising results.

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