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Abstract Background The expression changes of some proteins are associated with cancer progression, and can be used as biomarkers in cancer diagnosis. Automated systems have been frequently applied in the large-scale detection of protein biomarkers and have provided a valuable complement for wet-laboratory experiments. For example, our previous work used an immunohistochemical image-based machine learning classifier of protein subcellular locations to screen biomarker proteins that change locations in colon cancer tissues. The tool could recognize the location of biomarkers but did not consider the effect of protein expression level changes on the screening process. Results In this study, we built an automated classification model that recognizes protein expression levels in immunohistochemical images, and used the protein expression levels in combination with subcellular locations to screen cancer biomarkers. To minimize the effect of non-informative sections on the immunohistochemical images, we employed the representative image patches as input and applied a Wasserstein distance method to determine the number of patches. For the patches and the whole images, we compared the ability of color features, characteristic curve features, and deep convolutional neural network features to distinguish different levels of protein expression and employed deep learning and conventional classification models. Experimental results showed that the best classifier can achieve an accuracy of 73.72% and an F1-score of 0.6343. In the screening of protein biomarkers, the detection accuracy improved from 63.64 to 95.45% upon the incorporation of the protein expression changes. Conclusions Machine learning can distinguish different protein expression levels and speed up their annotation in the future. Combining information on the expression patterns and subcellular locations of protein can improve the accuracy of automatic cancer biomarker screening. This work could be useful in discovering new cancer biomarkers for clinical diagnosis and research.

In recent years, there is a growing interest in developing ultralong organic room‐temperature phosphorescence (ORTP) with lifetimes in the range of seconds. As one of the important three primary colors, ultralong blue ORTP is an indispensable core component in RTP regulation and application, however, the large Stokes shift characteristics pose certain challenges in developing ultralong blue ORTP. Here, a new family of blue phosphors are synthesized and ultralong blue ORTP are realized through crystal assembly in water and organic phase. Remarkably, compared to crystal materials obtained in organic phases, assembly materials obtained in water enabled long‐lived blue ORTP with lifetimes up to 2.3 s and phosphorescence quantum yield reached as high as 29.27%. In addition to the efficient and green assembly form, such dark blue ORTP materials possessed high thermal stability and flexible tunability. Moreover, the superiority of the selected assembly components is demonstrated by comparing them with polymer assembly and the proposed blue ORTP materials show great potential in an organic programmable information encryption/anti‐counterfeiting. [ABSTRACT FROM AUTHOR]

Objective To explore the effect of cisplatin on the expression of axon guidance related gene in mouse dorsal root ganglion (DRG) neurons using transcriptome sequencing technique, and to further explore the neurotoxic of cisplatin. Methods The primary cultures of DRG neurons was isolated from Kunming mice and divided into control group and injury group. Neurons in the control group were cultured for 24 hours, and those in the injury group were pre-cultured for 4 hours and then treated with 20 μmol/L cisplatin for 20 hours. To observe the neurites and the growth cones in two groups by the NF200 immunofluorescence staining. Total RNA was extracted from the cells, and transcriptome sequencing was performed using RNA-seq technology. The axon guidance related genes were searched through literature and database (total 231), and the differential expression of related genes was analyzed. Real-time fluorescence quantitative PCR (qPCR) was used to verify the expression of related genes (Cxcl12,Ext1,Gdnf,Plxna4,Nrp2,Apbb2,Sema6a,Nrp1,Nrcam,Slit1,Ank3,Ncam1,Sema7a,Dag1,Klf7,Ntn1). Results Compared with the control group, the neurites of the injury group were significantly shortened and reduced, and the growthcones showed barb retraction. A large number of genes were down-regulated in the injury group as compared to the control group, among them, 49 genes related to axon guidance pathway were down-regulated. The proteins interacted closely and mainly focus on Ntn1,Nrp1,Nrp2,Sema6a,plxna2,plxna4,Slit2,Robo1.The qPCR verification of related genes expression was consistent with the trend of RNA-seq results. Conclusions The neurotoxicity mechanism of cisplatin may be related to the down-regulation of many axon guidance related genes.

Chinese nouns lack inflection and cannot reflect the quantitative relationship between singular and plural numbers. However, neural processes of picture naming are different from those of words. We assume that Chinese single and plural picture naming is different, and they may involve quantitative processing. Therefore, Experiment 1 was designed by picking picture naming as the task and Chinese as the target language and compared the accuracy, reaction time, and event-related potentials (ERPs) between single and plural picture naming, where two types of pictures were mixed. Although the T-test showed no significant differences in behavioral data, there were differences in ERPs. ERP differences involved two effects: P1 of 160–180 ms and P2 of 220–260 ms in the parietal-occipital lobe. These differences are suggested to reflect the neural differences in quantitative processing. Therefore, Chinese singular and plural picture naming consists of word production and implicit quantitative processing simultaneously. To explore the relationship between the two processings, we added a semantic factor (inanimate vs. animate items) to the quantity factor of Experiment 1 and carried out Experiment 2, with the observation indexes unchanged. There were no significant differences in behavioral data among the four conditions. After variance analysis, ERPs results indicated an interaction between semantic and quantitative factors in the central area at 180–280 ms. In summary, we suggest that Chinese singular and plural picture naming includes two simultaneous neural processing tasks: word production and quantitative processing, which interact in the central area at 180–280 ms.