Does serum kidney injury molecule-1 predict early diabetic nephropathy: A comparative study with microalbuminuria.

Introduction: Diabetic nephropathy (DN) is a multifactorial disease, one of the most common complications of diabetes and a major cause of chronic kidney disease. Kidney injury molecule-1 (KIM-1) is a sensitive and specific marker of kidney injury as well as a predictor of prognosis. Objective: The present study aimed to investigate the usefulness of serum KIM-1 as an early marker of DN. Patients and Methods: The present study included total 75 participants, among whom 25 nondiabetic participants were chosen as controls. The 50 diabetic participants were divided into two groups according to urine protein/creatinine ratio (UPCR) as participants with normoalbuminuria (T2DM patients without nephropathy) and microalbuminuria (T2DM patients with nephropathy). The complete blood count, blood glucose, HbA1c, serum electrolytes, and creatinine levels were measured using standard laboratory techniques, and serum KIM-1 levels were measured by sandwich enzyme-linked immunosorbent assay. Results: There was a significant difference in the mean serum KIM-1 between the control and diabetics without microalbuminuria (P = 0.0001). Patients with longer duration of diabetes had a higher serum KIM-1 values (P = 0.05 in DM without microalbuminuria; P = 0.007 for DM with microalbuminuria). Serum KIM-1 did not correlate with UPCR in controls (P = ‒0.167), in diabetics with microalbuminuria (P = 0.487). However, there was a significant correlation observed between UPCR and serum KIM-1 in diabetics without microalbuminuria (P = 0.04). Conclusion: The present study observed significantly increased levels of serum KIM-1 in both the diabetic groups compared to controls. Moreover, serum KIM-1 positively correlated with the duration of diabetes. Therefore, serum KIM-1 may be used as an early diagnostic marker to predict nephropathy among diabetes in our population.

Ontology-Enabled Emotional Sentiment Analysis on COVID-19 Pandemic-Related Twitter Streams.

The exponential growth of social media users has changed the dynamics of retrieving the potential information from user-generated content and transformed the paradigm of information-retrieval mechanism with the novel developments on the concept of "web of data". In this regard, our proposed Ontology-Based Sentiment Analysis provides two novel approaches: First, the emotion extraction on tweets related to COVID-19 is carried out by a well-formed taxonomy that comprises possible emotional concepts with fine-grained properties and polarized values. Second, the potential entities present in the tweet can be analyzed for semantic associativity. The extraction of emotions can be performed in two cases: (i) words directly associated with the emotional concepts present in the taxonomy and (ii) words indirectly present in the emotional concepts. Though the latter case is very challenging in processing the tweets to find the hidden patterns and extract the meaningful facts associated with it, our proposed work is able to extract and detect almost 81% of true positives and considerably able to detect the false negatives. Finally, the proposed approach's superior performance is witnessed from its comparison with other peer-level approaches.