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Objective To establish an automatic segmentation algorithm using convolution neural network, and to validate the consistency between the algorithm and manual segmentation. Methods One hundred andforty⁃six CT scans of intracerebral hemorrhage (ICH) were included from Chinese Intracranial Hemorrhage Image Database (CICHID). They were randomly divided into training set (n=90), testing set (n=26) and validation set(n=30). All CT scans were manual segmentation. Training set and testing set were used for algorithm training. The validation set was measured by four methods including manual segmentation, algorithm segmentation, accurate Tada formula and traditional Tada formula. The consistency test was performed. Results Compared with the Tada formula methods, the percentage error ofalgorithm values was the smallest 15.54 (8.41,23.18) %, and algorithm agreement with the manual reference was the strongest (correlation coefficient 0.983). Bland⁃Altman analysis showed that 93.33% of the data was within the 95% limits of agreement (95%LoA), and 95%LoA was narrow (⁃ 6.46-5.97 ml). No significant differences were found in size and shape (P > 0.05, for all). Conclusions The algorithm using convolutional neural network has a certain application prospect, but it needs still more validation in large sample research. DOI：10.3969/j.issn.1672⁃6731.2020.07.005

According to the high incidence of cerebral hemorrhage, the burden of disease is heavy. Both domestic and overseas built various diagnosis and treatment system. However, according to the lack of precious diagnosis and treatment in the traditional system, the patient's prognosis is quite different. Thank to the advances in technology such as artificial intelligence, it is possible to achieve the precision medicine in the diagnosis, evaluation, prediction and treatment of hemorrhagic stroke. The present paper intends to review the current technological progress, looking forward to achieve precision, individualization in hemorrhagic stroke. DOI：10.3969/j.issn.1672⁃6731.2019.09.003

Both manifestations and treatments of intracranial hemorrhage (ICH) are varied and the effect meets bottlenecks. Recently, the artificial intelligence (AI) technology has developed rapidly. This review aims to help clinicians understand AI technology regarding its application in ICH by systematically reviewing the historical and current examples. Hope to stimulate the AI progress and enhance the level of treatment in ICH in the future. Ultimately, the treatment of ICH would be precision and individualization. DOI:10.3969/j.issn.1672-6731.2019.09.004

Abstract Background Postoperative central nervous system infections (PCNSIs) represent a serious complication, and the timely use of antibiotics guided by the identification of the causative pathogens and their antibiotic sensitivities is essential for treatment. However, there are little data regarding the prevalence of PCNSI pathogens in China. The aim of this study is to investigate the features of pathogens in patients with PCNSIs, which could help clinicians to choose the appropriate empirical antibiotic therapy. Methods We retrospectively examined the positive CSF cultures in patients who underwent craniotomy between January 2010 and December 2015. We collected data, including demographic characteristics, type of neurosurgery, laboratory data, causative organisms and antimicrobial susceptibility testing results. Results A total of 62 patients with 90 isolates out of 818 patients with 2433 CSF culture samples were available for data analysis. The estimated incidence and culture-positive rate of PCNSIs were approximately 0.9 and 7.5%, respectively. The predominant organism was coagulase-negative staphylococci, of which most were methicillin-resistant coagulase-negative staphylococci (MRCoNS). All were susceptible to vancomycin, linezolid, rifampicin and amoxicillin-clavulanate. Acinetobacter baumannii was the most frequent causative Gram-negative agent and was resistant to 12 out of 18 antimicrobials tested. The sensitivity rates for tigecycline and minocycline were only 40 and 33%, respectively. Conclusion PCNSIs could lead to high mortality. Although the MRCoNS were the predominant organism, the management of Acinetobacter baumannii was a major clinical challenge with few effective antimicrobials in PCNSIs.

The article offers insight to a research in which Sinocyclocheilus sanxiaensis, a new blind fish from the Three Gorges of Yangtze River provides insights into speciation of Chinese cavefish. It mentions that mitochondrial cytochrome b was sequenced and analyzed to clarify the phylogenetic position of the new species, and to estimate divergence time from its putative sister species. It also mentions that DNA was extracted from the pectoral fin on the right side of the fish.

In the present study, Chiamydomonas reinhardtii was chosen as a representative green microalga to clarify the effect of copper, zinc, cadmium, and mercury on different parameters (growth, photosynthetic pigment chlorophyll a and the photosynthetic parameter ΦM) and to evaluate the potential application of bbe algae toximeter in biomonitoring of heavy metal pollution. The results indicate that the inhibitory effects of the Cd, Cu, Hg and Zn to C. reinhardtii varied significantly and the content of cholorophyll a was the most sensitive indicator parameter in the toxicity experiment of C. reinhardtii. Although ΦM was the least sensitive one, concentrations (mgfL) of heavy metal (inhibition = 50%, time = 2h) from toxicity experiments were the closest to those from bbe algae toximeter. This study tried to use bbe algae toximeter filled with C. reinhardtii to monitor the pollution of some heavy metals but the results seemed not so satisfactory because the in- hibition concentrations were still above the environmental quality standards for surface water in China, although it is possible to use the alga as bioindicator of serious heavy metal pollution (e.g. effluents from mines). Maybe future work is still needed to select a more sensitive alga or to optimize the toximeter like adding a concentration device to the toximeter. [ABSTRACT FROM AUTHOR]