Investigation of the molecular basis of cisplatin sensitivity in testicular germ cell tumours

Over 80% of metastatic testicular germ cell tumours (TGCT) are cured using cisplatin- based chemotherapy. Five studies were undertaken to investigate the molecular basis for the hypersensitivity of testicular tumour cells to cisplatin. Firstly, to determine the number of genes involved in the hypersensitivity of testicular tumour cells to cisplatin, four cell lines were fused with each other and complementation analysis was performed. Hybrids between testicular tumour cell lines were sensitive to cisplatin, indicating that the sensitivity might be controlled by a common mechanism and possibly by one gene. Secondly, to try to identify the gene, a cDNA library carrying a G418-resistant marker from a cisplatin resistant human cell line was transfected into a cisplatin-sensitive mouse embryonal carcinoma cell line. G418 and cisplatin-resistant primary transfectants were selected and G418-resistant secondary transfectants were isolated. However, the secondary transfectants failed to show resistance to cisplatin indicating that the primary cisplatin and G418-resistant transfectants were non-specific. Thirdly, to characterize a cisplatin resistant secondary transfectant, which was isolated by transfection of a cDNA library into a human testicular tumour cell line, the plasmid carrying the DNA insert was amplified and transfected into 5 tumour cell lines. However, it failed to confer resistance to cisplatin suggesting that the cisplatin resistance in the primary transfectant was non-specific. Fourthly, to identify human chromosomes carrying genes controlling cisplatin sensitivity, a mouse embryonal carcinoma cell line was fused with a human bladder tumour cell line. However, in contrast to most of the human-mouse hybrids, the hybrids isolated in this study showed little loss of human chromosomes. Therefore, it was not possible to identify which individual human chromosomes are responsible for causing changes in the sensitivity to cisplatin in the hybrid cells. Lastly, to compare the gene expression of testicular and bladder tumour cell lines before and after exposure to cisplatin, differential display RT-PCR was performed. Ten differentially displayed bands were characterized and sequenced. A DNA fragment located on chromosome 5q31 was found to be upregulated by cisplatin in testicular tumour cells

Modeling of Causes of Sina Weibo Continuance Intention with Mediation of Gender Effects

Sina Weibo is a Twitter-like social networking site and one of the most popular microblogging services in China. This study aims to examine the factors that influence the intentions of users to continue using this site. This paper synthesizes the expectation confirmation model (ECM), constructs of habit and perceived critical mass, and the gender effect to construct a theoretical model to explain and predict these user intentions. The model is then tested via an online survey of 498 Sina Weibo users and partial least squares (PLS) modeling. The results indicate that the continuance intention of users is directly predicted by their perceived usefulness of the service (β=0.299), their satisfaction (β=0.208), and their habits (β=0.389), which jointly explain 65.9% of the variance in intention. In addition to the effects of these predictors on the continuance intentions of Sina Weibo users, an assessment of the moderating effect of gender suggests that habit plays a more important role for females than for males in continuance intention, but perceived usefulness seems to be more important for males than for females. The implications of these findings are then discussed

A Causal Intervention Scheme for Semantic Segmentation of Quasi-periodic Cardiovascular Signals

Precise segmentation is a vital first step to analyze semantic information of cardiac cycle and capture anomaly with cardiovascular signals. However, in the field of deep semantic segmentation, inference is often unilaterally confounded by the individual attribute of data. Towards cardiovascular signals, quasi-periodicity is the essential characteristic to be learned, regarded as the synthesize of the attributes of morphology (Am) and rhythm (Ar). Our key insight is to suppress the over-dependence on Am or Ar while the generation process of deep representations. To address this issue, we establish a structural causal model as the foundation to customize the intervention approaches on Am and Ar, respectively. In this paper, we propose contrastive causal intervention (CCI) to form a novel training paradigm under a frame-level contrastive framework. The intervention can eliminate the implicit statistical bias brought by the single attribute and lead to more objective representations. We conduct comprehensive experiments with the controlled condition for QRS location and heart sound segmentation. The final results indicate that our approach can evidently improve the performance by up to 0.41% for QRS location and 2.73% for heart sound segmentation. The efficiency of the proposed method is generalized to multiple databases and noisy signals.Comment: submitted to IEEE Journal of Biomedical and Health Informatics (J-BHI

Classification between normal and tumor tissues based on the pair-wise gene expression ratio

BACKGROUND: Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancer-related signals are generally lacking. METHOD: Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression to pair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset this transformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). The efficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioning with informative features (gene annotation) as discriminating axes (single gene expression or pair-wise gene expression ratio). Classification results were compared to the original datasets for up to 10-feature model classifiers. RESULTS: 82 and 262 genes that have high correlation to tissue phenotype were selected from the colon and prostate datasets respectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient of variation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed dataset exhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from 45.3% to 16.5%. In prostate cancer, comparable CV was achieved with and without transformation. This improvement in CV, coupled with the improved correlation between the pair-wise gene expression ratio and tissue phenotypes, yielded higher classification efficiency, especially with the colon dataset – from 87.1% to 93.5%. Over 90% of the top ten discriminating axes in both datasets showed significant improvement after data transformation. The high classification efficiency achieved suggested that there exist some cancer-related signals in the form of pair-wise gene expression ratio. CONCLUSION: The results from this study indicated that: 1) in the case when the pair-wise expression ratio transformation achieves lower CV and higher correlation to tissue phenotypes, a better classification of tissue type will follow. 2) the comparable classification accuracy achieved after data transformation suggested that pair-wise gene expression ratio between some pairs of genes can identify reliable markers for cancer

Synthesis and Characterization of an Amphiphilic Linoleic Acid-g-Quaternary Chitosan with Low Toxicity

A novel amphiphilic derivative of chitosan, namely, a linoleic acid-g-quaternary chitosan (LA-g-QC), was designed and synthesized as low toxic material for biomedical applications in this study. The chemical structure of LA-g-QC was characterized by Fourier transform infrared spectroscopy (FTIR), 1H nuclear magnetic resonance (1H-NMR), and elemental analysis. LA-g-QC could form nanosized micelles with self-assembly, which was confirmed by the results of critical micelle concentration (CMC) via fluorescence spectroscopy. The average size of LA-g-QC was 140 nm and its zeta potential was approximately +35.50 mV. CMC value was 31.00 mg/mL. Furthermore, LA-g-QC micelles, at final concentrations between 0.94 μg/mL and 30 μg/mL, did not inhibit the proliferation of HepG2 or SMMC 7721 cell lines. Taken together, LA-g-QC has low cytotoxicity and high potential for the preparation of novel drug-delivery micelles

A Delay Learning Algorithm Based on Spike Train Kernels for Spiking Neurons

Neuroscience research confirms that the synaptic delays are not constant, but can be modulated. This paper proposes a supervised delay learning algorithm for spiking neurons with temporal encoding, in which both the weight and delay of a synaptic connection can be adjusted to enhance the learning performance. The proposed algorithm firstly defines spike train kernels to transform discrete spike trains during the learning phase into continuous analog signals so that common mathematical operations can be performed on them, and then deduces the supervised learning rules of synaptic weights and delays by gradient descent method. The proposed algorithm is successfully applied to various spike train learning tasks, and the effects of parameters of synaptic delays are analyzed in detail. Experimental results show that the network with dynamic delays achieves higher learning accuracy and less learning epochs than the network with static delays. The delay learning algorithm is further validated on a practical example of an image classification problem. The results again show that it can achieve a good classification performance with a proper receptive field. Therefore, the synaptic delay learning is significant for practical applications and theoretical researches of spiking neural networks