Autism spectrum disorders (ASD) are lifelong heterogeneous set of neurodevelopmental conditions with strikingly profound male prevalence. Differences in sex biology and hormones are thought to play key roles in ASD prevalence and outcome, but the underlying molecular mechanisms responsible for ASD sex-differential risk are not well understood. Two recent studies reported a significant association between shortened telomere length (TL) and autistic children. However, the role of gender bias has been overlooked. Here, we carefully examined the status of average TL among non-syndromic male and female children with autism, and we also took a close look at the data from earlier reports. A total of 58 children were recruited for this project, including 24 apparently non-syndromic autistic children (14 males and 10 females), their healthy siblings (n = 10), and 24 sex-, age, and location-matched healthy controls. Relative TLs (RTL) were assessed by the monochrom multiplex quantitative polymerase chain reaction (MMQPCR) technique, using genomic DNA extracted from saliva samples. Data analysis showed that gender and age had strong impacts on average RTLs among the study groups. In a sex stratified manner, autistic male children had significantly shorter average RTL than their female counterparts. Only male children with autism showed a homogeneous pattern of shorter RTLs compared with their respective healthy controls. Our findings are indicative of a sexually dimorphic pattern of TL in childhood autism. The data presented here have important implications for the role of telomere biology in the molecular mechanisms responsible for ASD male bias prevalence and etiology.

Optimized levels of glial cell line-derived neurotrophic factor (GDNF) are critical for protection of dopaminergic neurons against parkinsonian cell death. Recombinant lentiviruses harboring GDNF coding sequence were constructed and used to infect astrocytoma cell line 1321N1. The infected astrocytes overexpressed GDNF mRNA and secreted an average of 2.2 ng/mL recombinant protein as tested in both 2 and 16 weeks post-infection. Serial dilutions of GDNF-enriched conditioned medium from infected astrocytes added to growing neuroblastoma cell line SK-N-MC resulted in commensurate resistance against 6-OHDA toxicity. SK-N-MC cell survival rate rose from 51% in control group to 84% in the cells grown with astro-CM containing 453 pg secreted GDNF, an increase that was highly significant (P < 0.0001). However, larger volumes of the GDNF-enriched conditioned medium failed to improve cell survival and addition of volumes that contained 1,600 pg or more GDNF further reduced survival rate to below 70%. Changes in cell survival paralleled to changes in the percent of apoptotic cell morphologies. These data demonstrate the feasibility of using astrocytes as minipumps to stably oversecrete neurotrophic factors and further indicate that GDNF can be applied to neuroprotection studies in PD pending the optimization of its concentrations.

Background: Metastasis is the main cause of death toll among breast cancer patients. Since current approaches for diagnosis of lymph node metastases are time-consuming, deep learning (DL) algorithms with more speed and accuracy are explored for effective alternatives. Methods: A total of 220025 whole-slide pictures from patients’ lymph nodes were classified into two cohorts: testing and training. For metastatic cancer identification, we employed hybrid convolutional network models. The performance of our diagnostic system was verified using 57458 unlabeled images that utilized criteria that included accuracy, sensitivity, specificity, and P-value. Results: The DL-based system that was automatically and exclusively capable of quantifying and identifying metastatic lymph nodes was engineered. Quantification was made with 98.84% accuracy. Moreover, the precision of VGG16 and Recall was 92.42% and 91.25%, respectively. Further experiments demonstrated that metastatic cancer differentiation levels could influence the recognition performance. Conclusion: Our engineered diagnostic complex showed an elevated level of precision and efficiency for lymph node diagnosis. Our innovative DL-based system has a potential to simplify pathological screening for metastasis in breast cancer patients.

Background: Breast cancer (BC) is a prevalent disease and a major cause of mortality among women worldwide. A substantial number of BC patients experience metastasis which in turn leads to treatment failure and death. The survival rate has been significantly increased due to more rapid detection and substantial improvements in adjuvant therapies including newer chemotherapeutic and targeted agents, and better radiotherapy techniques.Methods: In this study, we cross-compared the application of advanced artificial intelligence algorithms such as Logistic Regression, K-Nearest Neighbors, Discrete Cosine Transform, Random Forest Classifier, Support Vector Machines, Multilayer Perceptron, and Ensemble to diagnose BC metastasis. We further combined MLP with genetic algorithm (GA) as a hybrid method of intelligent analysis. The core data we used for comparison belonged to the images of both benign and malignant tumors collected from Wisconsin Breast Cancer dataset from the UCI repository.Results: The application of several different algorithms to the collection of BC data indicated that these algorithms have comparable accuracy rate in detecting and predicting cancer. However, our hybrid algorithm showed superior accuracy, sensitivity and specificity compared to the individual algorithms. Two methods of comparison (Cross-Validation and Holdout) were applied to this study which produced consistent results.Conclusion: Our findings indicate that our MLP-GA hybrid algorithm can speed up diagnosis with higher accuracy rate than the individual patterns of algorithm.