Anisha Pulinchani

High prevalence of post‐acute sequelae of COVID‐19 (PASC/long COVID) has led to the determination of the pathophysiology of PASC. In a hospital‐based cross‐sectional survey, we assessed the clinical and immunological parameters in a set of 67 PASC and 81 recovered individuals from COVID‐19 (N‐PASC), to identify the immune biomarkers associated with the mechanistic cornerstone of this condition. PASC had higher chronic comorbidities, hospitalization, and ICU admissions during the COVID‐19 pandemic compared to the N‐PASC group. Though comparable SARS–CoV–2–specific antibodies were detected across the groups, their functionality (NAbs) was significantly higher in the N‐PASC. PASC patients exhibited features of immune dysregulation, characterized by altered coordination between cellular and humoral immune responses despite broadly comparable cytokine profiles and T‐cell responses. ROC analysis in hospitalized PASC and hospitalized N‐PASC subjects sampled at 1, 2, and 3 years post COVID‐19 supported the utility of IL‐6 as a biomarker for long‐term inflammatory activity. Higher FGF‐basic levels in the hospitalized PASC compared to non‐hospitalized PASC highlighted a potential association between elevated growth factor signaling and severity‐related immune dysregulation, tissue damage that may have utility as a biomarker candidate. Our analysis supports a dysregulated crosstalk between humoral and cellular immunity in PASC patients, which could be leading to inflammation and persistent clinical symptoms associated with this debilitating condition. In conclusion, comparable T‐cell and cytokine responses among long COVID and recovered individuals suggest that persistent symptoms are unlikely to be driven by impaired antiviral immunity, underscoring the potential role of immune dysregulation.

We generated a recombinant lumpy skin disease virus (LSDV) using the Ranchi strain as backbone by targeted insertion of an enhanced green fluorescent protein (EGFP) expression cassette into the ORF130 locus. A pET28-derived donor plasmid carrying EGFP flanked by ORF130 homology arms was assembled by Gibson cloning and transfected into LSDV-infected Vero cells to mediate homology-directed recombination. Recombinant virus (LSDV-GFP) was isolated by fluorescence-guided cell sorting. Conventional PCR, quantitative RT-PCR, and whole-genome sequencing confirmed precise, single-copy integration of EGFP at the intended locus with no detectable off-target alterations. Serial passaging demonstrated stable EGFP expression and complete elimination of parental virus, indicating genetic stability and purity. A fluorescence-based neutralization assay using LSDV-GFP detected anti-LSDV neutralizing antibodies with accuracy comparable to the standard virus neutralization test while reducing assay turnaround from 72 to 12 hours. This study establishes an in-house Ranchi strain-based LSDV reporter system that provides a genetically stable, rapid, and reliable platform for the detection and quantification of LSDV-neutralizing antibodies. In addition, it identifies ORF130 as a suitable locus for heterologous gene insertion and support LSDV-GFP as a potential versatile platform for viral vector development.

Background: Highly Pathogenic Avian Influenza (HPAI) H5N1 and H5N8 viruses pose serious threats to resident, wild, migratory birds, poultry, wildlife conservation and public health globally. HPAI viruses have caused sporadic human infections, alarming their potential public health significance. Since 2006, more than 560 outbreaks of HPAI H5N1 and H5N8 viruses have been reported in India. Repeated outbreaks since 2006 highlight the need for predictive tools to understand transmission dynamics and to assess the effectiveness of control strategies. Examining the spatio-temporal dynamics of HPAI viruses and environmental drivers is essential for improving outbreak prediction. The objective of the present study was to explore the utility of various modelling approaches for HPAI risk prediction, including spatial models and advanced machine learning models, utilizing the reported outbreak data from 2006 to 2025.Methods: We integrated district-level outbreak data with environmental, hydrological and poultry density variables to model HPAI risk across India. Different machine learning approaches, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Boosted Regression Trees (BRT), Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) were employed. Model performance was assessed using AUC, accuracy, recall, precision and confusion matrices. Spatial risk maps were generated based on predicted probabilities.Findings: The GAT model demonstrated better performance compared to other models, achieving an AUC of 0·953, an accuracy of 89·6% and a recall of 82·9% with minimal false negatives. Ourresults demonstrat that the HPAI outbreak risk prediction improves by incorporating spatial connectivity through graph-based attention models.Interpretation: The proposed approach provides a robust tool for risk mapping, early warning and geographically targeted intervention strategies in India. A limitation of this study is the absence of detailed poultry movement data, which may influence transmission dynamics and model accuracy.

Background: Dengue and chikungunya, the major Aedes- Borne Diseases [ABDs] frequently cause outbreaks in the Southeast Asian region. The evaluation of the determinants of transmission at fine spatial scales are unavailable. We aimed to analyse the ward- level spatio-temporal patterns of reported dengue and chikungunya cases and their association with entomological, climatic and population density data using geospatial and machine learning techniques.Methods: We analysed secondary data reported during 2021-24 of 5891 confirmed cases of dengue and chikungunya, from 115 wards of Pune district in western India. Clustering of cases and vector density in spatial scales were assessed using Moran’s I and LISA statistics. ABDs, population density, location, meteorological and entomological factors along with their lagged variables (for 3months) were considered for correlation, LASSO and GAM regression models. For predictive analysis machine learning approaches (RF, XGBoost, LSTM) were applied. SHAP analysis was employed for feature importance.Findings: Reported cases showed inter annual variability with maximum number of cases during 2022 (1875 cases) and from the urban areas (4344 cases). Although ABD (dengue and chikungunya together) did not show any geographical clustering, vector indices demonstrated significant clustering. Across the various statistical model temporal persistence (case load of the previous month) emerged as the strongest predictor. Among the ML models analysed, LSTM performed the best (RMSE=0.0432) in predicting ABD outbreaks. SHAP analysis revealed higher feature importance to recent case burden, rural location, relative humidity and population density, as compared to larval indices. Interpretation: Our finding suggests the role of recent case burden, population density and meteorological variables in predicting ABD risk, more than conventional vector indices. Integrated modelling at fine administrative scales, using advanced analytical methods can inform better outbreak prediction. This in turn will help in undertaking targeted preparedness activities

This paper presents a family of non-parametric tests designed to assess exponentiality against alternatives with a decreasing mean time to failure. The exact null distribution of the test statistic is derived, and critical values are provided for various sample sizes. The asymptotic behavior of the test statistics is also analyzed. Furthermore, the test is extended to handle right-censored data. A simulation study is performed to evaluate the effectiveness of the proposed test, and its application is demonstrated using two real-life datasets.

Chandipura virus, a Vesiculovirus , was first identified in India during an outbreak investigation of acute febrile illness in 1965. The virus first gained global attention after being implicated as the causative agent of an outbreak of acute viral encephalitis characterized by rapid clinical deterioration and death, in 2003. Since then, the virus has caused multiple outbreaks in central and western Indian states. During July 2024, an increased incidence of acute encephalitis was reported from Gujarat state. In response an outbreak investigation was carried out and Chandipura virus was identified as the major etiology, accounting for 23.45% of the cases. Clinically, the disease was characterized by rapid progression of illness, with total duration of illness ranging from 24 to 72 h in majority of the cases. Vomiting was found to be significantly more in cases of Chandipura viral encephalitis. In the current outbreak, multiorgan dysfunction was identified as a complication in Chandipura infections. Serum glutamic‐pyruvic transaminase and creatinine were found to be significantly elevated in cases of Chandipura encephalitis, compared to other etiologies of acute viral encephalitis. The case fatality rate in the current outbreak was 47.36%. Among the samples collected during the investigations, 69% humans and 8.76% animal samples tested positive for neutralizing antibodies against Chandipura virus. Fever surveillance during the outbreak also identified Chandipura IgM positive cases without neurological involvement, raising the possibility of Chandipura febrile illness. Phylogenetic analysis of the isolate from 2024 indicates similarity with isolates obtained during 2012 and 2014 from Gujarat.

In patients with thalassemia major (TM), cardiac magnetic resonance feature-tracking (CMR-FT) has been shown to be an effective method for diagnosing subclinical left ventricular (LV) dysfunction. This study aimed to determine whether CMR-FT could detect abnormal RV dysfunction in patients with a normal right ventricular ejection fraction (RVEF). We performed a retrospective analysis of TM patients admitted to Dubai’s Rashid Hospital between July 2019 and March 2021. The inclusion criteria were TM patients with SSFP cine with T2* (T2*-weighted imaging), while exclusion criteria included any other cardiovascular disease. When there was no myocardial iron overload (MIO) (T2* ≥ 20 ms) and when there was significant MIO (T2* < 20 ms), the CMR-FT was used to correlate with EF. Among the 89 participants, there were 46 men (51.7%) and 43 women (48.3%), with a mean age of 26.14 ± 7.4 years (range from 10 to 48 years). Forty-six patients (51.69%) did not have MIO, while 43 individuals did (48.31%). Thirty-nine patients (32.6%) were diagnosed with severe MIO, while seventeen (19.1%) were diagnosed with mild to moderate MIO. A significant correlation existed between RVEF and T2* values (r = 0.274, p = 0.014) and between left ventricular ejection fraction (LVEF) and T2* values (r = 0.256, p = 0.022). Using a multiple logistic regression model with predictors such as right ventricular longitudinal strain (RVGLS), LV ejection fraction (LV EF), and hemoglobin, abnormal myocardial iron overload can be predicted. This model demonstrates an AUC of 78.3%, a sensitivity of 72%, and a specificity of 76%. In the group with preserved RVEF > 53%, the left ventricular radial strain (LVGRS) (p = 0.001), right ventricular radial strain (RVGRS) (p = 0.000), and right ventricular basal circumferential strain (RVGCS-basal) (p = 0.000) CMR-FT strain values are significantly lower than those of the control group (p > 0.05). There was no significant correlation between the LVGLS and T2*. RVGLS was ranked among the most accurate predictors of abnormal myocardial iron overload. The LVGRS, RVGRS, and RVGCS-basal CMR-FT strain values were the best predictors of subclinical RV dysfunction in the group with preserved RVEF. The most accurate way to diagnose MIO is still T2*, but FT-strain can help us figure out how MIO affects the myocardium from a pathophysiological point of view.

Tuberculosis of the heart is relatively rare and presents a significant diagnostic difficulty for physicians. It is the leading cause of death from infectious illness. It is one of the top 10 leading causes of death worldwide, with a disproportionate impact in low- and middle-income nations. The radiologist plays a pivotal role as CMR is a non-invasive radiological method that can aid in identifying potential overlap and differential diagnosis between tuberculosis, mass lesions, pericarditis, and myocarditis. Regardless of similarities or overlap in observations, the combination of clinical and certain particular radiological features, which are also detected by comparison to earlier and follow-up CMR scans, may aid in the differential diagnosis. CMR offers a significant advantage over echocardiography for detecting, characterizing, and assessing cardiovascular abnormalities. In conjunction with clinical presentation, knowledge of LGE, feature tracking, and parametric imaging in CMR may help in the early detection of tuberculous myopericarditis and serve as a surrogate for endomyocardial biopsy resulting in a quicker diagnosis and therapy. This article aims to explain the current state of cardiac tuberculosis, the diagnostic utility of CMR in tuberculosis (TB) patients, and offer an overview of the various imaging and laboratory procedures used to detect cardiac tuberculosis.

Background: Chest radiographs are frequently used to evaluate pediatric patients with COVID-19 infection during the current pandemic. Despite the minimal radiation dose associated with chest radiography, children are far more sensitive to ionizing radiation's carcinogenic effects than adults. This study aimed to examine whether serum biochemical markers could be potentially used as a surrogate for imaging findings to reduce radiation exposure. Methods: The retrospective posthoc analysis of 187 pediatric patients who underwent initial chest radiographs and serum biochemical parameters on the first day of emergency department admission. The cohort was separated into two groups according to whether or not the initial chest radiograph revealed evidence of pneumonia. Spearman's rank correlation was used to connect serum biochemical markers with observations on chest radiographs. The Student's t-test was employed for normally distributed data, and for non-normally distributed data, the Mann–Whitney U test was used. A simple binary logistic regression was used to determine the importance of LDH in predicting chest radiographs. The discriminating ability of LDH in predicting chest radiographs was determined using receiver operating characteristics (ROC) analysis. The cut-off value was determined using Youden's test. Interobserver agreement was quantified using the Cohen k coefficient. Results: 187 chest radiographs from 187 individual pediatric patients (95 boys and 92 girls; mean age ± SD, 10.1 ± 6.0 years; range, nine months–18 years) were evaluated. The first group has 103 patients who did not have pneumonia on chest radiographs, while the second group contains 84 patients who had evidence of pneumonia on chest radiographs. GGO, GGO with consolidation, consolidation, and peri-bronchial thickening were deemed radiographic evidence of pneumonia in group 2 patients. Individuals in group 2 with radiological indications of pneumonia had significantly higher LDH levels (p = 0.001) than patients in group 1. The Spearman's rank correlation coefficient between LDH and chest radiography score is 0.425, showing a significant link. With a p-value of < 0.001, the simple binary logistic regression analysis result validated the relevance of LDH in predicting chest radiography. An abnormal chest radiograph was related to LDH > 200.50 U/L (AUC = 0.75), according to the ROC method. Interobserver agreement between the two reviewers was almost perfect for chest radiography results in both groups (k = 0.96, p = 0.001). Conclusion: This study results show that, compared to other biochemical indicators, LDH has an 80.6% sensitivity and a 62% specificity for predicting abnormal chest radiographs in a pediatric patient with confirmed COVID-19 infection. It also emphasizes that biochemical measures, rather than chest radiological imaging, can detect the pathogenic response to COVID-19 infection in the chest earlier. As a result, we hypothesized LDH levels might be potentially used instead of chest radiography in children with COVID-19, reducing radiation exposure.