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Background Chronic stage renal disease is a severe disease of the kidney which affects people globally. According to the global burden of diseases in 2010, this disease has caused more deaths worldwide and due to the high death rate, the ESRD (end-stage renal disease) is now ranked up from 27th to 18th range in the list. Methodology Dialysis samples were collected from the Haripur city and surrounding areas. Samples were inoculated on different selective media for bacterial growth. In addition, different biochemical tests were also performed for identification, where as the resistance genes were identified through a polymerase chain reaction. Result Out of the total 100 dialysis patient’s blood samples, only 17 showed the presence of gram-positive bacteria i.e., Staphylococcus aureus while two shown the presence of gram-negative bacteria i.e., Klebsiella pneumoniaeee and Pseudomonas aeruginosa. While in molecular identification two antibiotic resistance genes muc and mecA belong to the staphylococcus strain shown their presence. Conclusion A high infection rate has been observed in fistula-based hemodialysis (17(77.27%)) as compares to catheter-based hemodialysis (5(22.3%) with no significant difference of incidence between the groups (p > 0.05).

Breast cancer is a heterogeneous disease in which genetic factors are involved in disease worsening and higher mortality. Epidemiological and clinical research revealed that breast cancer incidence continues to rise. 100 histopathologically confirmed untreated newly diagnosed cases of invasive ductal carcinoma (IDC) of breast and 100 healthy subjects were involved and blood samples were collected in non-EDTA plain vials. Serum was separated by centrifugation, total RNA was extracted from serum, and cDNA synthesis was done to study the miRNA-495 and neurexin-1 (NRXN-1) and contactin 1 (CNTN-1) mRNA expression by QRT-PCR. The expression levels of miRNA-495, NRXN-1, and CNTN-1 were expressed in fold change. The present study observed decreased relative miRNA-495 expression (0.07-fold) while an increase in NRXN-1 (11.61-fold) and CNTN-1 (4.92-fold) was observed among breast cancer patients compared to healthy controls. A significant difference was observed in miRNA-495 expression with menopausal status ( p = 0.0001 ) and TNM stages ( p = 0.02 ). It was observed that NRXN-1 expression was significantly associated with menopausal status ( p = 0.03 ), lymph node involvement ( p < 0.0001 ), estrogen receptor (ER) status ( p = 0.03 ), progesterone receptor (PR) status ( p = 0.005 ), TNM stages ( p < 0.0001 ), and distant metastases ( p < 0.0001 ). CNTN-1 expression was also found to be associated with lymph node involvement ( p = 0.01 ), PR status ( p = 0.03 ), HER2 status ( p = 0.04 ), TNM stages ( p < 0.0001 ), and distant metastases ( p < 0.0001 ). ROC suggested that NRXN-1 and CNTN-1 could be the important predictive marker for disease advancement and distant organ metastases. The study concluded that the decreased expression of miR-495 observed in breast cancer patients showed a negative correlation with NRXN-1 while the increased expression of NRXN-1 and CNTN-1 was linked with disease advancement and distant metastases and could be the important predictive marker for breast cancer patients.

Accurate blood smear quantification with various blood cell samples is of great clinical importance. The conventional manual process of blood smear quantification is quite time consuming and is prone to errors. Therefore, this paper presents automatic detection of the most frequently occurring condition in human blood&mdash, microcytic hyperchromic anemia&mdash, which is the cause of various life-threatening diseases. This task has been done with segmentation of blood contents, i.e., Red Blood Cells (RBCs), White Blood Cells (WBCs), and platelets, in the first step. Then, the most influential features like geometric shape descriptors, Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and Gabor features (mean squared energy and mean amplitude) are extracted from each of the RBCs. To discriminate the cells as hypochromic microcytes among other RBC classes, scanning is done at angles (0∘, 45∘, 90∘, and 135∘). To achieve high-level accuracy, Adaptive Synthetic (AdaSyn) sampling for imbalance learning is used to balance the datasets and locality sensitive discriminant analysis (LSDA) technique is used for feature reduction. Finally, upon using these features, classification of blood cells is done using the multilayer perceptual model and random forest learning algorithms. Performance in terms of accuracy was 96%, which is better than the performance of existing techniques. The final outcome of this work may be useful in the efforts to produce a cost-effective screening scheme that could make inexpensive screening for blood smear analysis available globally, thus providing early detection of these diseases.

BackgroundType 2 diabetes mellitus (T2DM) is a multifactorial disorder that leads to alterations in gene regulation. Long non-coding RNAs (lncRNAs) have become a major research topic as they are involved in metabolic disorders.MethodsThis study included a total of 400 study subjects; 200 were subjects with T2DM and 200 were healthy subjects. Extracted RNA was used to synthesize cDNA by quantitative real time. Serum analysis was carried out to determine differences in biochemical parameters. Recorded data were used to evaluate associations with expression of lncRNAs NF-kappaB interacting lncRNA (NKILA), nuclear enriched abundant transcript 1 (NEAT1), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), and myocardial infarction-associated transcript (MIAT) in T2DM cases.ResultsCompared with healthy controls, patients with T2DM showed an overall increase in expression of lncRNAs NKILA, NEAT, MALAT1, and MIAT by 3.94-fold, 5.28-fold, 4.46-fold, and 6.35-fold, respectively. Among patients with T2DM, higher expression of lncRNA NKILA was associated with hypertension (p=0.001), smoking (pConclusionIncreased circulating NKILA, NEAT1, MALAT, and MIAT expression in patients with T2DM, which is linked with poor patient outcomes and significantly linked with alcoholism and smoking, may influence the degree and severity of disease among patients with T2DM. These lncRNAs may contribute to the progression of T2DM disease or other related diabetes-related complications.