Malavika Shankar

Dr. Malavika Shankar

orcid

Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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8 July 2022 in Vaccines
Verified
Journal Article
Open access
PDF file
APAshwaghosha Parthasarathi
Ashwaghosha Parthasarathi
RPRahul Krishna Puvvada
Rahul Krishna Puvvada
MSMalavika Shankar
Malavika Shankar
JSJayaraju Biligere Siddaiah
Jayaraju Biligere Siddaiah
Koustav GangulyKoustav Ganguly
Koustav Ganguly
Mahesh Padukudru AnandMahesh Padukudru Anand
Mahesh Padukudru Anand
To achieve herd immunity to a disease, a large portion of the population needs to be vaccinated, which is possible only when there is broad acceptance of the vaccine within the community. Thus, policymakers need to understand how the general public will perceive the vaccine. This study focused on the degree of COVID-19 vaccine hesitancy and refusal and explored sociodemographic correlations that influence vaccine hesitancy and refusal. A cross-sectional online survey was conducted among the adult population of India. The survey consisted of basic demographic questions and questions from the Vaccination Attitudes Examination (VAX) Scale. Multinomial logistical regression was used to identify correlates of vaccine hesitancy and refusal. Of the 1582 people in the study, 9% refused to become vaccinated and 30.8% were hesitant. We found that both hesitancy and refusal predictors were nearly identical (lower socioeconomic status, female gender, and older age groups), except for three groups (subjects aged 45–64 years, those with approximate income <10,000 INR/month, and those residing in rural households) that showed slightly higher odds of vaccine hesitancy than refusal. We need to address the underlying sociodemographic determinants and formulate public awareness programs to address specific subgroups that are at higher risk of rejecting the vaccine and convert those who are undecided or hesitant into those willing to accept the vaccine.

27 November 2025 in Diagnostics
Verified
Journal Article
Open access
PDF file
SRSukanya Ravindran
Sukanya Ravindran
MUMohammed Kaleem Ullah
Mohammed Kaleem Ullah
MKMedha Karnik
Medha Karnik
GVGreeshma M V
Greeshma M V
NBNidhi Bansal
Nidhi Bansal
Mahesh Padukudru AnandMahesh Padukudru Anand
Mahesh Padukudru Anand
Background/Objectives: Asthma is a heterogeneous inflammatory airway disease. Periostin, a matricellular protein induced by interleukin-13, contributes to airway inflammation and remodeling. This study evaluated serum periostin as a diagnostic biomarker and explored multidimensional phenotypes in adult asthma. Methods: A cross-sectional study included 76 adults, with 25 healthy controls, 25 moderate, and 26 severe asthma patients, classified per Global Initiative for Asthma (GINA)-2020 guidelines. Serum periostin was measured using an enzyme-linked immunosorbent assay (ELISA). Diagnostic accuracy was assessed using receiver operating characteristic (ROC) analysis, Firth-penalized logistic regression, bootstrap calibration (1000 resamples), decision curve analysis (DCA), and gradient boosting machine (GBM) validation. Principal component analysis (PCA) followed by k-means clustering identified distinct phenotypes based on clinical, functional, and inflammatory variables. Results: Asthma patients had higher serum periostin than controls (median 52.9 vs. 32.5 pg/mL; p < 0.01), with excellent diagnostic accuracy (AUC = 0.987; sensitivity = 94.1%, specificity = 100%). Firth regression identified periostin as the only independent predictor of asthma diagnosis (β = 0.387; OR = 1.47; 95% CI 1.23–2.08; p < 0.001). Calibration showed minimal error (MAE = 0.042) and DCA demonstrated clear net benefit. GBM confirmed periostin as the dominant diagnostic predictor. PCA revealed three clusters: Cluster 1: younger, lower periostin, preserved lung function, good symptom control; Cluster 2: intermediate periostin, greater airflow limitation, poorer control; and Cluster 3: highest periostin, elevated systemic inflammation (NLR, PLR, SII), with moderate functional impairment. Conclusions: Serum periostin is a reliable diagnostic biomarker for asthma. Multidimensional clustering highlights clinically relevant phenotypes linked to periostin, inflammatory burden, and lung function, supporting its role in personalized asthma management.

17 November 2025 in Diagnostics
Verified
Journal Article
Open access
PDF file
VSVENKATESHKUMAR CHANDANNA SERI
VENKATESHKUMAR CHANDANNA SERI
MUMohammed Kaleem Ullah
Mohammed Kaleem Ullah
JSJayaraju Biligere Siddaiah
Jayaraju Biligere Siddaiah
SCSindaghatta Krishnarao Chaya
Sindaghatta Krishnarao Chaya
KLKomarla Sundararaja Lokesh
Komarla Sundararaja Lokesh
Mahesh Padukudru AnandMahesh Padukudru Anand
Mahesh Padukudru Anand
Background: Chronic Obstructive Pulmonary Disease (COPD) remains a leading cause of morbidity and mortality worldwide, particularly in low- and middle-income countries (LMICs), where both tobacco and biomass smoke exposure are major risk factors. While spirometry is the diagnostic gold standard, reliable non-invasive biomarkers are needed for early detection and disease monitoring. The soluble receptor for advanced glycation end-products (sRAGE), a circulating decoy receptor with anti-inflammatory activity, has shown potential in this context. Methods: In this prospective, exposure-stratified, cross-sectional study, 150 adults were enrolled into four groups of 25 each—tobacco-smoke COPD, male tobacco-exposed controls, biomass-smoke COPD, and female biomass-exposed controls—along with 50 healthy controls (25 males, 25 females). Participants underwent clinical evaluation, spirometry, and serum sRAGE quantification (ELISA). Systemic inflammation was assessed using neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR). Correlation and receiver operating characteristic (ROC) analyses determined diagnostic performance. Results: Serum sRAGE levels were significantly lower in tobacco-induced (545 ng/mL) and biomass-induced COPD (540 ng/mL) versus controls (1207–1462 ng/mL; p < 0.001). sRAGE correlated positively with FEV1, FVC, and FEV1/FVC (r = 0.54–0.75, p < 0.001), and negatively with CAT, mMRC, and SGRQ-C. ROC analysis showed excellent discrimination (AUC = 0.990; 94% sensitivity; 96% specificity at 946 ng/mL cutoff). Conclusions: Serum sRAGE is a robust, non-invasive biomarker for COPD diagnosis and severity assessment across exposure phenotypes. Its integration into clinical practice may enhance early detection and risk stratification, particularly in LMICs.