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Alzheimer's Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.

Breast cancer is a leading cause of morbidity and mortality among women comprising about 12% females worldwide. The underlying alteration in the gene expression, molecular mechanism and metabolic pathways responsible for incidence and progression of breast tumorigenesis are yet not completely understood. In the present study, potential biomarker genes involved in the early progression for early diagnosis of breast cancer has been detailed. Regulation and Gene profiling of Ductal Carcinoma In-situ (DCIS), Invasive Ductal Carcinoma (IDC) and healthy samples have been analyzed to follow their expression pattern employing normalization, statistical calculation, DEGs annotation and Protein-Protein Interaction (PPI) network. We have performed a comparative study on differentially expressed genes among Healthy vs DCIS, Healthy vsIDC and DCIS vs IDC. We found MCM102 and SLC12A8as consistently over-expressed and LEP, SORBS1, SFRP1, PLIN1, FABP4, RBP4, CD300LG, ID4, CRYAB, ECRG4, G0S2, FMO2, ADAMTS5, CAV1, CAV2, ABCA8, MAMDC2, IGFBP6, CLDN11, TGFBR3as under-expressed genes in all the 3 conditions categorized for pre-invasive and invasive ductal breast carcinoma. These genes were further studied for the active pathways where PPAR(γ) signaling pathway was found to be significantly involved. The gene expression profile database can be a potential tool in the early diagnosis of breast cancer.

BACKGROUND: The cluster of differentiation (CD) 74 is known for its immunological functions and its elevated level was reported in various cancer cells. AIM: The aim of the present study was to investigate the expression and potential roles of CD74 in the proliferative and apoptotic activity of breast cancer. METHODS: Expression of CD74, macrophage migration inhibitory factor (MIF) and CD44 was assayed in CAMA-1 and MDA-MB-231 cell lines using flow cytometry. CD74 was knocked down using CD74 siRNA-transfection in CAMA-1, and MDA-MB-231 cells and proliferation and apoptosis were determined in the transfected breast cancer cells. RESULTS: The data showed that CD74, MIF and CD44 were expressed in breast cancer cell lines and were associated with cell proliferation and apoptosis. Correlation analysis revealed that CD74 was positively correlated and colocalised with MIF on the cell-surface of CAMA-1 and MDA-MB-231. The knockdown of CD74 significantly reduced CAMA-1 and MDA-MB-231 cell proliferation and increased the level of apoptotic cells. CONCLUSION: We concluded that the interactions of CD74 with MIF and CD74 with CD44 could be a potential tumour marker for breast cancer cells. Moreover, the level of co-expression of MIF and CD74 or CD44 could be a surrogate marker for the efficacy of anti-angiogenic drugs, particularly in breast cancer tumours. In short, the study revealed the potential roles of CD74 in the proliferation and apoptosis of breast cancer which may serve as a potential therapeutic target for breast cancer.

Aims: To investigate the susceptibility profile of bacterial isolates from hand dug wells and borehole waters within the Amai Kingdom, in Ukwuani Local Government Area of Delta State. Place and Duration of Study: Department of Microbiology, Novena University, between April 2016 and November 2016. Methodology: A total of 30 boreholes and 30 well water samples were randomly sourced using sterile tightly locked plastic containers and transported to the laboratory within 2 hours. The water samples were cultured using spread plate method and the pure bacterial isolates tested for susceptibility to some commercial antibiotic discs using the Kirby-Bauer method. Results: Bacteria were isolated respectively from 50% and 100% of the borehole and well water samples collected. A total of 45 bacterial isolates, distributed among the genera Staphylococcus (6.7%), Bacillus (10.0%), Escherichia (16.7%), Pseudomonas (11.7%) Enterobacter (8.3%), Proteus (10.0%), and Klebsiella (11.7%), was encountered. Majority of the isolates were sensitive to Amoxicillin-clavulanic acid, Ciprofloxacin, Ceftriaxone, Gentamicin, Ofloxacin, and resistant to Ampicillin, Cotrimoxazole and Tetracycline. Bacillus sp (56%) was more resistant (p=.05) than S. aureus (33%), while the most resistant Gram-negative bacteria was Pseudomonas sp. (54%), followed by both E. coli (44%) and Klebsiella sp (44%), and both Proteus sp., (33%) and Enterobacter sp. (33%). Drug resistance was observed in at least three of the tested antibiotic disks. Conclusion: Findings from this study suggest that the drinking water sources in the Amai Kingdom are bacteriologically poor, with multiple-drug resistant bacteria, and thus of public health importance. Public education on dangers associated with indiscriminate dumping of waste and drug abuse is strongly recommended.

Polycystic ovary syndrome (PCOS) is endocrine system disease which affect women ages 18 to 44 where the women's hormones are imbalance. Recently it has been reported to occur in early age. Alteration of normal gene expression in PCOS has shown negative effects on long-term health issues. PCOS has been the responsible factor for the infertility in women of reproductive age group. Early diagnosis and treatment can improve the women's health suffering from PCOS. Earlier Studies shows correlation of PCOS upon insulin resistance with significant outcome, Current study shows the linkage between PCOS with obesity and non-obese patients. Gene expression datasets has been downloaded from GEO (control and PCOS affected patients). Normalization of the datasets were performed using R based on RMA and differentially expressed gene (DEG) were selected on the basis of p-value 0.05 followed by functional annotation of selected gene using Enrich R and DAVID. The DEGs were significantly related to PCOS with obesity and other risk factors involved in disease. The Gene Enrichment Analysis suggests alteration of genes and associated pathway in case of obesity. Current study provides a productive groundwork for specific biomarkers identification for the accurate diagnosis and efficient target for the treatment of PCOS.