miR-339 Promotes the Recovery of the Bone Marrow Mesenchymal Stem Cells (BMSCs)-Induced Corneal Epithelium Damage

This study intends to identify the expression profiles of micoRNAs during the recovery of damaged corneal epithelium induced by BMSCs. Differential expressions of miRNA after damage of corneal epithelium stimulated by BMSCs were analyzed based on micro-array and validated by qRT-PCR. The miRNA’s effect on cell proliferative and apoptotic activity was evaluated through transfection of plasmid with over presentation of miRNA and inhibitor of miRNA. miR-339 was significantly down-regulated in the process of recovery of the damaged corneal epithelium induced by BMSCs. Importin 13 and EGF expression was reduced after transfection of plasmid with over presentation of miR-339, which were reversed by transfection of the inhibitor of miR-339. Importin 13 was a target of miR-339. The cell proliferation and apoptosis could be restrained by miR-339 through regulation of the expression of Importin 13. In conclusion, the damaged corneal epithelium induced by BMSCs could be recovered by miR-339 through restraining Importin 13 expression, indicating that it might be a novel target for amelioration of corneal epithelium damage.

Genes and Weightlifting Performance

A recent case-control study identified 28 DNA polymorphisms associated with strength athlete status. However, studies of genotype-phenotype design are required to support those findings. The aim of the present study was to investigate both individually and in combination the association of 28 genetic markers with weightlifting performance in Russian athletes and to replicate the most significant findings in an independent cohort of Japanese athletes. Genomic DNA was collected from 53 elite Russian (31 men and 22 women, 23.3 ± 4.1 years) and 100 sub-elite Japanese (53 men and 47 women, 21.4 ± 4.2 years) weightlifters, and then genotyped using PCR or micro-array analysis. Out of 28 DNA polymorphisms, LRPPRC rs10186876 A, MMS22L rs9320823 T, MTHFR rs1801131 C, and PHACTR1 rs6905419 C alleles positively correlated (p < 0.05) with weightlifting performance (i.e., total lifts in snatch and clean and jerk in official competitions adjusted for sex and body mass) in Russian athletes. Next, using a polygenic approach, we found that carriers of a high (6–8) number of strength-related alleles had better competition results than carriers of a low (0–5) number of strength-related alleles (264.2 (14.7) vs. 239.1 (21.9) points; p = 0.009). These findings were replicated in the study of Japanese athletes. More specifically, Japanese carriers of a high number of strength-related alleles were stronger than carriers of a low number of strength-related alleles (212.9 (22.6) vs. 199.1 (17.2) points; p = 0.0016). In conclusion, we identified four common gene polymorphisms individually or in combination associated with weightlifting performance in athletes from East European and East Asian geographic ancestries.

Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images

Histological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properly guide therapy. Morphological evaluation remains the basis for subtyping and is completed by additional immunohistochemistry labelling to confirm the diagnosis, which delays molecular analysis and utilises precious sample. Therefore, we tested the capacity of convolutional neural networks (CNNs) to classify NSCLC based on pathologic HES diagnostic biopsies. The model was estimated with a learning cohort of 132 NSCLC patients and validated on an external validation cohort of 65 NSCLC patients. Based on image patches, a CNN using InceptionV3 architecture was trained and optimized to classify NSCLC between squamous and non-squamous subtypes. Accuracies of 0.99, 0.87, 0.85, 0.85 was reached in the training, validation and test sets and in the external validation cohort. At the patient level, the CNN model showed a capacity to predict the tumour histology with accuracy of 0.73 and 0.78 in the learning and external validation cohorts respectively. Selecting tumour area using virtual tissue micro-array improved prediction, with accuracy of 0.82 in the external validation cohort. This study underlines the capacity of CNN to predict NSCLC subtype with good accuracy and to be applied to small pathologic samples without annotation.

Co-Expression of Nogo-A in Dopaminergic Neurons of the Human Substantia Nigra Pars Compacta Is Reduced in Parkinson’s Disease

Parkinson’s disease is mainly characterized by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta. Together with the small number, the high vulnerability of the dopaminergic neurons is a major pathogenic culprit of Parkinson’s disease. Our previous findings of a higher survival of dopaminergic neurons in the substantia nigra co-expressing Nogo-A in an animal model of Parkinson’s disease suggested that Nogo-A may be associated with dopaminergic neurons resilience against Parkinson’s disease neurodegeneration. In the present study, we have addressed the expression of Nogo-A in the dopaminergic neurons in the substantia nigra in postmortem specimens of diseased and non-diseased subjects of different ages. For this purpose, in a collaborative effort we developed a tissue micro array (TMA) that allows for simultaneous staining of many samples in a single run. Interestingly, and in contrast to the observations gathered during normal aging and in the animal model of Parkinson’s disease, increasing age was significantly associated with a lower co-expression of Nogo-A in nigral dopaminergic neurons of patients with Parkinson’s disease. In sum, while Nogo-A expression in dopaminergic neurons is higher with increasing age, the opposite is the case in Parkinson’s disease. These observations suggest that Nogo-A might play a substantial role in the vulnerability of dopaminergic neurons in Parkinson’s disease.

Pearson’s Redundancy Multi-Filtering with BAT Algorithm for Selecting High Dimensional Imbalanced Features

Feature selection plays a vital role for every data analysis application. Feature selection aims to choose prominent set of features after removing redundant and irrelevant features from original set of features. High Dimensional dataset poses a challenging task for Machine Learning algorithms. Many state-of-art solutions were developed to handle this issue. High dimensionality in addition to imbalance ratio in the dataset becomes a tedious task. To overcome the issue, this paper introduces a novel method namely Pearson’s Redundancy Based Multi Filter algorithm with improved BAT algorithm (PRBMF-iBAT) to obtain multiple feature subsets. PRBMF is implemented using multiple filters to obtain highly relevant features. iBAT algorithm uses these features to find best subset of features for classification. The results prove that PRBMF-iBAT perform better for the classifier in terms of Accuracy, Precision, Recall and F- Measure for three micro array datasets with SVM classifier. The proposed system achieves 97.99% of accuracy as highest compared to the existing rCBR-BGOA algorithm.

CKM Gene rs8111989 Polymorphism and Power Athlete Status

Multiple genetic variants are known to influence athletic performance. These include polymorphisms of the muscle-specific creatine kinase (CKM) gene, which have been associated with endurance and/or power phenotypes. However, independent replication is required to support those findings. The aim of the present study was to determine whether the CKM (rs8111989, c.*800A>G) polymorphism is associated with power athlete status in professional Russian and Lithuanian competitors. Genomic DNA was collected from 693 national and international standard athletes from Russia (n = 458) and Lithuania (n = 235), and 500 healthy non-athlete subjects from Russia (n = 291) and Lithuania (n = 209). Genotyping for the CKM rs8111989 (A/G) polymorphism was performed using PCR or micro-array analysis. Genotype and allele frequencies were compared between all athletes and non-athletes, and between non-athletes and athletes, segregated according to population and sporting discipline (from anaerobic-type events). No statistically significant differences in genotype or allele frequencies were observed between non-athletes and power athletes (strength-, sprint- and speed/strength-oriented) athletes. The present study reports the non-association of the CKM rs8111989 with elite status in athletes from sports in which anaerobic energy pathways determine success.