Role of serum Cystatin C as a marker of early nephropathy in metabolic syndrome: a case control study

Background: The metabolic syndrome (MS) has become a significant public health problem and patients with MS are at higher risk for developing renal diseases. Serum Cystatin C suggested as a sensitive endogenous marker than creatinine for slight changes in GFR could be useful marker in MS.Methods: A total of 200 subjects were included. New International Diabetes Federation (IDF) definition of MS was used as inclusion criteria. Patients excluded were those with hypo/hyperthyroidism, on glucocorticoids, statins and fibrate, malignancy, cirrhosis, active liver disease and conditions affecting abdominal girth. Serum Cystatin C, insulin, creatinine, triglycerides, high density lipoproteins-cholesterol (HDL-C), fasting glucose, Urinary microalbumin and Urinary creatinine were estimated by standard method.eGFR and HOMA-IR (homeostasis model assessment of insulin resistance) were calculated. The primary outcome assessed was the occurrence of early nephropathy in MS and the secondary outcome included evaluation of early nephropathy by serum Cystatin C and eGFR. Appropriate statistical test was applied by using SPSS Version 21 software.Results: Fasting insulin levels and insulin resistance were significantly raised in MS cases. eGFR (MDRD) was lower in the MS cases (72.59±8.79mL/min/1.73m2) vs non-MS (130.34±40.75 mL/min/1.73m). Urinary microalbumin levels and serum cystatin C were significantly increased in MS and the cystatin c levels showed significant positive correlation with urinary microalbumin and negative correlation with eGFR.eGFR was found to be lower in the microalbuminuric than normoalbuminuric groups.Conclusions: Serum Cystatin C levels are higher in MS and can be useful, practical, non-invasive biomarker for evaluation of early renal involvement in MS.

Derivation of process control strategy for biosimilar: Is it different from the way a control strategy is derived for a novel biologic?

Quality based development (QbD) has become the preferred choice for developing manufacturing process for any biologic drug. A proponent for this approach has been the US Food and Drug Association (FDA). Recently, the first QbD applications have been successfully filed with FDA. Biosimilars have also gained popularity in the recent past. Development of these drugs are very different from the way a novel biologic is developed. In the last five years, many companies around the world have started working on Biosimilars of which some companies have been able to successfully develop and get approvals for Biosimilars in both FDA and European Medicenes agency (EMA). Application of QbD for a Novel and a Biosimilar drug is quite different. By nature of the requirement for developing a Biosimilar, quality of the ‘reference product’ against which the biosimilar is being developed is considered while making decisions during process development. Though the same concepts applies for a novel drug, the target quality profile is not as defined as one can write for a Biosimilar. This is because product quality information regarding the reference product is well-known and can be thoroughly analyzed and characterized. While the targets can be easily derived for a Biosimilar, deriving a process control strategy is tough. Critical Process Parameter (CPP) is defined as a process parameter that has significant impact on the safety and efficacy of the drug. While this definition for CPP is applicable for a Bisomilar also, another aspect which requires consideration for a Biosimilar drug is the impact of process parameters on ‘fingerprint biosimilarity’. Hence the classification of process parameters as those that are critical and those that are not is not as straight forward like for a Novel drug. Derivation of acceptance range for these parameters also is different – The acceptance range for CPPs when compared to that for a novel biologic is generally found to be narrow. This is because the desired range for the outputs (such as aggregates, glycan, charge, size variants etc.) is narrow owing to the product quality ranges observed for the reference product and not just the levels of the outputs which has an effect on safety and efficacy. These subtle differences make deriving the process control strategy for a Bisomilar different from a novel biologic. In this presentation, a detailed overview of scale down model qualification, process characterization experiments, and the control strategy for Biosimilar manufacturing processes is provided. A case study will be presented which showcases some of these concepts of deriving control strategy as how it is applied for a Biosimilar process

CFLCA: High Performance based Heart disease Prediction System using Fuzzy Learning with Neural Networks

Human Diseases are increasing rapidly in today’s generation mainly due to the life style of people like poor diet, lack of exercises, drugs and alcohol consumption etc. But the most spreading disease that is commonly around 80% of people death direct and indirectly heart disease basis. In future (approximately after 10 years) maximum number of people may expire cause of heart diseases. Due to these reasons, many of researchers providing enormous remedy, data analysis in various proposed technologies for diagnosing heart diseases with plenty of medical data which is related to heart disease. In field of Medicine regularly receives very wide range of medical data in the form of text, image, audio, video, signal pockets, etc. This database contains raw dataset which consist of inconsistent and redundant data. The health care system is no doubt very rich in aspect of storing data but at the same time very poor in fetching knowledge. Data mining (DM) methods can help in extracting a valuable knowledge by applying DM terminologies like clustering, regression, segmentation, classification etc. After the collection of data when the dataset becomes larger and more complex than data mining algorithms and clustering algorithms (D-Tree, Neural Networks, K-means, etc.) are used. To get accuracy and precision values improved with proposed method of Cognitive Fuzzy Learning based Clustering Algorithm (CFLCA) method. CFLCA methodology creates advanced meta indexing for n-dimensional unstructured data. The heart disease dataset used after data enrichment and feature engineering with UCI machine learning algorithm, attain high level accurate and prediction rate. Through this proposed CFLCA algorithm is having high accuracy, precision and recall values of data analysis for heart diseases detection

Glycemic Control and Its associated Determinants among Type II Diabetic Patients at Tertiary Care Hospital in North India

Introduction: Good glycemic control has been defined as achieving a target of fasting plasma glucose level of between 80 and 110 mg/dl, or glycosylated haemoglobin (HbA1C) of <7.0%. Poor glycemic control is highly correlated with chronic conditions related to the damaging effects of hyperglycaemia, resulting in serious complications. To restrict and delay the complications of diabetes mellitus, good glycemic control is essential. Objective: To identify the determinants associated with poor glycemic control among Type 2 diabetes mellitus patients. Method: A cross sectional study was conducted among 403 confirmed type 2 diabetic patients who attendedone of the tertiary care hospitals of North India over a period of six months (July- December 2021). The collected data was analysed using IBM SPSS version 28. Chi-square test was applied to compare various determinants of glycemic control. A p-value of <0.05 was considered to be statistically significant. Results: Out of 403 participants, 57.6% had poor glycemic control of diabetic condition. Higher age of participants, illiteracy, being overweight, having positive history of smoking and alcohol, longer duration of diabetes, participants taking both oral and insulin treatment for diabetes, taking medicine irregularly were the significant determinants of poor glycemic control. Conclusion: Higher percentage(57.6%) of poor glycemic control was observed in the study.To improve the glycemic control, efforts should be made towards improving modifiable factors like overweight, smoking, alcohol, regularity of medication etc. Good lifestyle interventions help in control of poor glycemic control

Relationship between CAD Risk Genotype in the Chromosome 9p21 Locus and Gene Expression. Identification of Eight New ANRIL Splice Variants

BACKGROUND: Several genome-wide association studies have recently linked a group of single nucleotide polymorphisms in the 9p21 region with cardiovascular disease. The molecular mechanisms of this link are not fully understood. We investigated five different expression microarray datasets in order to determine if the genotype had effect on expression of any gene transcript in aorta, mammary artery, carotid plaque and lymphoblastoid cells. METHODOLOGY/PRINCIPAL FINDINGS: After multiple testing correction, no genes were found to have relation to the rs2891168 risk genotype, either on a genome-wide scale or on a regional (8 MB) scale. The neighbouring ANRIL gene was found to have eight novel transcript variants not previously known from literature and these varied by tissue type. We therefore performed a detailed probe-level analysis and found small stretches of significant relation to genotype but no consistent associations. In all investigated tissues we found an inverse correlation between ANRIL and the MTAP gene and a positive correlation between ANRIL and CDKN2A and CDKN2B. CONCLUSIONS/SIGNIFICANCE: Investigation of relation of the risk genotype to gene expression is complicated by the transcript complexity of the locus. With our investigation of a range of relevant tissue we wish to underscore the need for careful attention to the complexity of the alternative splicing issues in the region and its implications to the design of future gene expression studies