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batteries is greatly limited by the highly insulating nature of S 8 /Li 2 S 2-x (x ≤ 1) and the dissolution of intermediate lithium polysulfides (Li 2 S n , 4 ≤ n ≤ 8) during charge/discharge process. [7][8][9] Over the past decades, massive efforts like encapsulating S 8 in conductive matrix, [10][11][12][13] protective coating layers, [14][15][16] and inducing interlayer between cathode and separator, [17][18][19] have been made to manipulate this deficiency, aiming to lighten shuttling and migration of Li 2 S n during long-term cycling and to improve the electrode kinetics. 2D materials with large specific area, such as graphene oxides (GOs), [20][21][22][23][24] MnO 2 , [25] Co 4 N, [26] MXene, [27] provide numerous anchoring sites and have been successfully employed as cathode hosts to suppress shuttling and migration of Li 2 S n in the Li-S batteries. Usually, heteroatoms doping is a general modification technique to further increase the polarity of 2D materials to adsorb Li 2 S n , giving birth to nitrogen-doped graphene, [28] nitrogen-doped MXene, [29] cobalt-doped porous carbon, [30] molybdenum-doped MoO 3 , [31] etc. However, the doping amount is very limited, which seriously restricts the improvement of their electrochemical performance. Comparing with the traditional doping strategy, intercalation can induce more heteroatoms in their van der Waals gap with good uniformity and change the properties of 2D materials more significantly. [32] For example, in our previous study, we proved that the n-type semiconducting SnS 2 can turn to a p-type semiconductor or metal after intercalation of different transition metal atoms. [33] Besides the electrical properties, the electrochemical properties of 2D materials might also be tuned effectively by this intercalation strategy.Here, ultrathin 2D layered α-MoO 3 nanoribbons with thickness of ≈10 nm have been synthesized and selected as the host. The strong polarity of MoO 3 together with its high specific surface area provides numerous active sites to bind sulfur species effectively, thus suppressing the "shuttle effect" obviously. Intercalation of metal tin (Sn) into van der Waals gap was further used to enhance the intrinsic conductivity of MoO 3 and improve the binding energy with sulfur species. Transmission electron microscopy (TEM) proved that Sn was inserted into the van der Waals gap of MoO 3 uniformly. First-principles calculations further certify that binding energy as large as 3.01 eV Heteroatom doping strategies have been widely developed to engineer the conductivity and polarity of 2D materials to improve their performance as the host for sulfur cathode in lithium-sulfur batteries. However, further improvement is limited by the inhomogeneity and the small amount of the doping atoms. An intercalation method to improve the conductivity and polarity of 2D-layered α-MoO 3 nanoribbons is developed here, thus, resulting in much improved electrochemical performance as sulfur host with better rate and cycle performance. The first principle calculations show t...

Summary Background Pioneering effort has been made to facilitate the recognition of pathology in malignancies based on whole‐slide images (WSIs) through deep learning approaches. It remains unclear whether we can accurately detect and locate basal cell carcinoma (BCC) using smartphone‐captured images. Objectives To develop deep neural network frameworks for accurate BCC recognition and segmentation based on smartphone‐captured microscopic ocular images (MOIs). Methods We collected a total of 8046 MOIs, 6610 of which had binary classification labels and the other 1436 had pixelwise annotations. Meanwhile, 128 WSIs were collected for comparison. Two deep learning frameworks were created. The ‘cascade’ framework had a classification model for identifying hard cases (images with low prediction confidence) and a segmentation model for further in‐depth analysis of the hard cases. The ‘segmentation’ framework directly segmented and classified all images. Sensitivity, specificity and area under the curve (AUC) were used to evaluate the overall performance of BCC recognition. Results The MOI‐ and WSI‐based models achieved comparable AUCs around 0·95. The ‘cascade’ framework achieved 0·93 sensitivity and 0·91 specificity. The ‘segmentation’ framework was more accurate but required more computational resources, achieving 0·97 sensitivity, 0·94 specificity and 0·987 AUC. The runtime of the ‘segmentation’ framework was 15·3 ± 3·9 s per image, whereas the ‘cascade’ framework took 4·1 ± 1·4 s. Additionally, the ‘segmentation’ framework achieved 0·863 mean intersection over union. Conclusions Based on the accessible MOIs via smartphone photography, we developed two deep learning frameworks for recognizing BCC pathology with high sensitivity and specificity. This work opens a new avenue for automatic BCC diagnosis in different clinical scenarios. What's already known about this topic? The diagnosis of basal cell carcinoma (BCC) is labour intensive due to the large number of images to be examined, especially when consecutive slide reading is needed in Mohs surgery. Deep learning approaches have demonstrated promising results on pathological image‐related diagnostic tasks. Previous studies have focused on whole‐slide images (WSIs) and leveraged classification on image patches for detecting and localizing breast cancer metastases. What does this study add? Instead of WSIs, microscopic ocular images (MOIs) photographed from microscope eyepieces using smartphone cameras were used to develop neural network models for recognizing BCC automatically. The MOI‐ and WSI‐based models achieved comparable areas under the curve around 0·95. Two deep learning frameworks for recognizing BCC pathology were developed with high sensitivity and specificity. Recognizing BCC through a smartphone could be considered a future clinical choice.

BackgroundSPARC (secreted protein, acidic and rich in cysteine) is closely related with the progress, invasion and metastasis of malignant tumor and angiogenesis.MethodsUsing human colon adenocarcinoma tissues (hereinafter referred to as colon cancer) and their corresponding non-diseased colon from 114 patients' biopsies, the expression of SPARC and vascular endothelial growth factor (VEGF) were investigated by immunohistochemistry staining to assessment the relationship between SPARC and VEGF, as well as their prognostic significance in patients. Evaluation of VEGF expression level with the same tissues was used to establish the antigenic profiles, and the marker of CD34 staining was used as an indicator of microvessel density (MVD).ResultsSPARC expression was mainly in the stromal cells surrounding the colon cancer, and was significant difference in those tissues with the lymph node metastasis and differentiation degree of tumor. Expression of SPARC was significantly correlated with the expression of VEGF and MVD in colon cancer tissues. Patients with low or absence expressing SPARC had significantly worse overall survival and disease-free survival in a Single Factor Analysis; Cox Regression Analysis, SPARC emerged as an overall survival and disease-free survival independent prognostic factor for colon cancer.ConclusionThe low expression or absence of stromal SPARC was an independent prognostic factor for poor prognosis of colon cancer. SPARC maybe involved in the regulation of anti-angiogenesis by which it may serve as a novel target for colon cancer treatment as well as a novel distinctive marker.