Molecular and Entomological Characterization of 2023 Dengue Outbreak in Dhading District, Central Nepal.

In 2023, Nepal faced its second largest dengue outbreak ever, following a record-breaking number of dengue cases in 2022, characterized by the expansion of infections into areas of higher altitudes. However, the characteristics of the 2023 circulating dengue virus (DENV) and the vector density remain poorly understood. Therefore, we performed DENV serotyping, clinical and laboratory assessment, and entomological analysis of the 2023 outbreak in central Nepal. A total of 396 fever cases in Dhading hospital suspected of being DENV positive were enrolled, and blood samples were collected and tested by different techniques including PCR. Of these, 278 (70.2%) had confirmed DENV infection. Multiple serotypes (DENV-1, -2, and -3) were detected. DENV-2 (97.5%) re-emerged after six years in Dhading while DENV-3 was identified for the first time. Dengue inpatients had significantly higher frequency of anorexia, myalgia, rash, diarrhea, nausea, vomiting, abdominal pain, and thrombocytopenia (p < 0.05). In this area, Aedes mosquitoes largely predominated (90.7%) with the majority being A. aegypti (60.7%). We also found high levels of Aedes index (20.0%) and container index (16.7%). We confirmed multiple DENV serotype circulation with serotype re-emergence and new serotype introduction, and high vector density in 2023. These findings call for the urgent initiation and scaling up of DENV molecular surveillance in human and mosquito populations for dengue control and prevention in Nepal.

Employing deep learning and transfer learning for accurate brain tumor detection.

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis.

Assessment of Biotransformed Silica Nanoparticle on Blood Glucose Level in Human: An In Vitro Investigation.

Diabetes has affected nearly half a billion people worldwide. According to current guidelines, glycemic control is essential to mitigate diabetic complications. The antihyperglycemic effects of various chemically synthesized nanoparticles have been reported in animal models. However, their impact on humans has not been previously reported. This study was conducted to biosynthesize and assess the antihyperglycemic property of silica nanoparticles (SiO2-NPs) since they are non-toxic and biocompatible. SiO2-NPs biosynthesized using the endophytic fungus Fusarium oxysporum. In this collaborative study, 26 people, either hyperglycemic or euglycemic, diagnosed at the Endocrinology Outpatients, according to the American Diabetes Association, USA, were recruited. Silica nanoparticles were characterized and assessed for in vitro antihyperglycemic property using blood samples. Particle size distribution based on TEM images confirms that the average size of silica nanoparticle is 25 nm and is monodispersed in nature. The XRD pattern shows that only one broad peak at 2θ = 220 corresponds to the plane (101) of silica nanoparticles. UV Visible spectra show the λmax at 270 nm, peaks in FTIR at 1536 cm-1, 1640 cm-1, and 3420 cm-1 for the protein cap. The mean blood glucose was 120.2 mg/dL in the 'SiO2-NP untreated' group and decreased to 97.24 mg/dL in the 'SiO2-NP treated' group. A paired t-test (P-value < 0.0001) indicates a strong relationship between antihyperglycemia and silica NP. In our study, it has been observed that the biosynthesized silica nanoparticles using the endophytic fungus Fusarium oxysporum show antihyperglycemic property in vitro.

A hybrid deep CNN model for brain tumor image multi-classification.

The current approach to diagnosing and classifying brain tumors relies on the histological evaluation of biopsy samples, which is invasive, time-consuming, and susceptible to manual errors. These limitations underscore the pressing need for a fully automated, deep-learning-based multi-classification system for brain malignancies. This article aims to leverage a deep convolutional neural network (CNN) to enhance early detection and presents three distinct CNN models designed for different types of classification tasks. The first CNN model achieves an impressive detection accuracy of 99.53% for brain tumors. The second CNN model, with an accuracy of 93.81%, proficiently categorizes brain tumors into five distinct types: normal, glioma, meningioma, pituitary, and metastatic. Furthermore, the third CNN model demonstrates an accuracy of 98.56% in accurately classifying brain tumors into their different grades. To ensure optimal performance, a grid search optimization approach is employed to automatically fine-tune all the relevant hyperparameters of the CNN models. The utilization of large, publicly accessible clinical datasets results in robust and reliable classification outcomes. This article conducts a comprehensive comparison of the proposed models against classical models, such as AlexNet, DenseNet121, ResNet-101, VGG-19, and GoogleNet, reaffirming the superiority of the deep CNN-based approach in advancing the field of brain tumor classification and early detection.

Organochlorine pesticides disrupt T helper cell regulation and reduce IL-2 and IFN-γ favoring infection and production of autoantibodies among pemphigus patients.

The list of environmental factors that trigger autoimmune diseases in genetically susceptible individuals has grown in the recent years and is far from complete. The possible intervention of the environment in triggering these diseases is ever more perceived by the clinicians. This study investigated the effect of environmental factors like organochlorine pesticides (OCPs) on proportions of different T lymphocyte subsets and their cytokine secretion in-vitro among pemphigus patients, before and after specific immunosuppressive therapy. Higher levels of OCPs like ß-HCH (isoform of hexachlorohexane), α-endosulfan (a form of endosulfan) and p,p΄-DDE (a metabolite of o,p'-dichlorodiphenyltrichloroethane) were observed in the blood of pemphigus patients as compared to healthy controls. HCH and DDT exposure caused specific reduction in CD8+CD45RA+ and CD4+CD25+ T lymphocyte subpopulations in these patient PBMCs. A strong reduction in Th1 (IL-2 and IFN-γ) cytokines upon exposure to these OCPs in-vitro was also observed. These findings indicate that HCH and DDT have a significant impact on Th1 lymphocytes. Impaired production of these cytokines might favor infections and production of autoantibodies. We therefore speculate that the systemic absorption of the pesticide after the topical contact may be one of the factors triggering the immunological mechanism among pemphigus patients.

Evaluating active leprosy case identification methods in six districts of Nepal.

BACKGROUND: Nepal has achieved and sustained the elimination of leprosy as a public health problem since 2009, but 17 districts and 3 provinces with 41% (10,907,128) of Nepal's population have yet to eliminate the disease. Pediatric cases and grade-2 disabilities (G2D) indicate recent transmission and late diagnosis, respectively, which necessitate active and early case detection. This operational research was performed to identify approaches best suited for early case detection, determine community-based leprosy epidemiology, and identify hidden leprosy cases early and respond with prompt treatment. METHODS: Active case detection was undertaken in two Nepali provinces with the greatest burden of leprosy, Madhesh Province (40% national cases) and Lumbini Province (18%) and at-risk prison populations in Madhesh, Lumbini and Bagmati provinces. Case detection was performed by (1) house-to-house visits among vulnerable populations (n = 26,469); (2) contact examination and tracing (n = 7608); in Madhesh and Lumbini Provinces and, (3) screening prison populations (n = 4428) in Madhesh, Lumbini and Bagmati Provinces of Nepal. Per case direct medical and non-medical costs for each approach were calculated. RESULTS: New case detection rates were highest for contact tracing (250), followed by house-to-house visits (102) and prison screening (45) per 100,000 population screened. However, the cost per case identified was cheapest for house-to-house visits [Nepalese rupee (NPR) 76,500/case], followed by contact tracing (NPR 90,286/case) and prison screening (NPR 298,300/case). House-to-house and contact tracing case paucibacillary/multibacillary (PB:MB) ratios were 59:41 and 68:32; female/male ratios 63:37 and 57:43; pediatric cases 11% in both approaches; and grade-2 disabilities (G2D) 11% and 5%, respectively. Developing leprosy was not significantly different among household and neighbor contacts [odds ratios (OR) = 1.4, 95% confidence interval (CI): 0.24-5.85] and for contacts of MB versus PB cases (OR = 0.7, 95% CI 0.26-2.0). Attack rates were not significantly different among household contacts of MB cases (0.32%, 95% CI 0.07-0.94%) and PB cases (0.13%, 95% CI 0.03-0.73) (χ2 = 0.07, df = 1, P = 0.9) and neighbor contacts of MB cases (0.23%, 0.1-0.46) and PB cases (0.48%, 0.19-0.98) (χ2 = 0.8, df = 1, P = 0.7). BCG vaccination with scar presence had a significant protective effect against leprosy (OR = 0.42, 0.22-0.81). CONCLUSIONS: The most effective case identification approach here is contact tracing, followed by house-to-house visits in vulnerable populations and screening in prisons, although house-to-house visits are cheaper. The findings suggest that hidden cases, recent transmission, and late diagnosis in the community exist and highlight the importance of early case detection.

A Novel, Comprehensive A129 Mouse Model for Investigating Dengue Vaccines and Evaluating Pathogenesis.

In search of a mouse model for use in evaluating dengue vaccines, we assessed A129 mice that lacked IFN-α/ß receptors, rendering them susceptible to dengue virus (DENV) infection. To our knowledge, no reports have evaluated dengue vaccine efficiency using A129 mice. A129 mice were given a single intraperitoneal (IP) or subcutaneous (SC) injection of the vaccine, Dengvaxia. After 14 days of immunization via the IP or SC injection of Dengvaxia, the A129 mice exhibited notably elevated levels of anti-DENV immunoglobulin G and neutralizing antibodies (NAb) targeting all four DENV serotypes, with DENV-4 displaying the highest NAb levels. After challenge with DENV-2, Dengvaxia and mock-immunized mice survived, while only the mock group exhibited signs of morbidity. Viral genome levels in the serum and tissues (excluding the brain) were considerably lower in the immunized mice compared to those in the mock group. The SC administration of Dengvaxia resulted in lower viremia levels than IP administration did. Therefore, given that A129 mice manifest dengue-related morbidity, including viremia in the serum and other tissues, these mice represent a valuable model for investigating novel dengue vaccines and antiviral drugs and for exploring dengue pathogenesis.

Seroprevalence of SARS-CoV-2 infection in the general population of Nepal during the first and second generalized waves of the COVID-19 pandemic-2020-2021.

Few seroprevalence studies have been conducted on coronavirus disease (COVID-19) in Nepal. Here, we aimed to estimate seroprevalence and assess risk factors for infection in the general population of Nepal by conducting two rounds of sampling. The first round was in October 2020, at the peak of the first generalized wave of COVID-19, and the second round in July-August 2021, following the peak of the wave caused by the delta variant of SARS-CoV-2. We used cross-sectional probability-to-size (PPS)-based multistage cluster sampling to estimate the seroprevalence in the general population of Nepal at the national and provincial levels. We tested for anti-SARS-CoV-2 total antibody using the WANTAI SARS-CoV-2 Ab ELISA kit. In Round 1, the overall national seroprevalence was 14.4%, with provincial estimates ranging from 5.3% in Sudurpaschim to 27.3% in Madhesh Province. In Round 2, the estimated national seroprevalence was 70.7%, with the highest in the Madhesh Province (84.8%) and the lowest in the Gandaki Province (62.9%). Seroprevalence was comparable between males and females (Round 1, 15.8% vs. 12.2% and Round 2, 72.3% vs. 68.7%). The seroprevalence in the ecozones-Terai, hills, and mountains-was 76.3%, 65.3%, and 60.5% in Round 2 and 17.7%, 11.7%, and 4.6% in Round 1, respectively. In Nepal, COVID-19 vaccination was introduced in January 2021. At the peak of the first generalized wave of COVID-19, most of the population of Nepal remained unexposed to SARS-CoV-2. Towards the end of the second generalized wave in April 2021, two thirds of the population was exposed.

Visualizing symmetry-breaking electronic orders in epitaxial Kagome magnet FeSn films.

Kagome lattice hosts a plethora of quantum states arising from the interplay of topology, spin-orbit coupling, and electron correlations. Here, we report symmetry-breaking electronic orders tunable by an applied magnetic field in a model Kagome magnet FeSn consisting of alternating stacks of two-dimensional Fe3Sn Kagome and Sn2 honeycomb layers. On the Fe3Sn layer terminated FeSn thin films epitaxially grown on SrTiO3(111) substrates, we observe trimerization of the Kagome lattice using scanning tunneling microscopy/spectroscopy, breaking its six-fold rotational symmetry while preserving the translational symmetry. Such a trimerized Kagome lattice shows an energy-dependent contrast reversal in dI/dV maps, which is significantly enhanced by bound states induced by Sn vacancy defects. This trimerized Kagome lattice also exhibits stripe modulations that are energy-dependent and tunable by an applied in-plane magnetic field, indicating symmetry-breaking nematicity from the entangled magnetic and charge degrees of freedom in antiferromagnet FeSn.

Possible high risk of transmission of the Nipah virus in South and South East Asia: a review.

Nipah virus (NiV) is a zoonotic, single-stranded RNA virus from the family Paramyxoviridae, genus Henipavirus. NiV is a biosafety-level-4 pathogen that is mostly spread by Pteropus species, which serve as its natural reservoir host. NiV is one of the major public health challenges in South and South East Asia. However, few molecular studies have been conducted to characterise NiV in a specific region. The main objective of this review is to understand the epidemiology, pathogenesis, molecular surveillance, transmission dynamics, genetic diversity, reservoir host, clinical characteristics, and phylogenetics of NiV. South and South East Asian nations have experienced NiV outbreaks. Phylogenetic analysis confirmed that two primary clades of NiV are in circulation. In humans, NiV causes severe respiratory illness and/or deadly encephalitis. NiV is mainly diagnosed by ELISA along with PCR. Therefore, we recommend that the governments of the region support the One Health approach to reducing the risk of zoonotic disease transmission in their respective countries.

Deciphering Alloy Composition in Superconducting Single-Layer FeSe1-xSx on SrTiO3(001) Substrates by Machine Learning of STM/S Data.

Scanning tunneling microscopy (STM) is a powerful technique for imaging atomic structure and inferring information on local elemental composition, chemical bonding, and electronic excitations. However, a plain visual analysis of STM images can be challenging for such determination in multicomponent alloys, particularly beyond the diluted limit due to chemical disorder and electronic inhomogeneity. One viable solution is to use machine learning to analyze STM data and identify hidden patterns and correlations. Here, we apply this approach to determine the Se/S concentration in superconducting single-layer FeSe1-xSx alloys epitaxially grown on SrTiO3(001) substrates via molecular beam epitaxy. First, the K-means clustering method is applied to identify defect-related dI/dV tunneling spectra taken by current imaging tunneling spectroscopy. Then, the Se/S ratio is calculated by analyzing the remaining spectra based on the singular value decomposition method. Such analysis provides an efficient and reliable determination of alloy composition and further reveals the correlations of nanoscale chemical inhomogeneity to superconductivity in single-layer iron chalcogenide films.

Risk Factors of Incident Kidney Stones in Indian Adults: A Hospital-Based Cross-Sectional Study.

Background The diverse manifestations of urolithiasis provide very interesting epidemiological data. This has prompted various studies to look into the etiopathogenesis of renal stones, which is believed to be multifactorial, both exogenous and endogenous. VDR Fok1 is a risk factor for renal stone formation and could cause the formation of renal stones through the mechanism of crystal induction and crystallization in the urine. While a few recent studies have shown the role of heavy metals like cadmium and lead in the formation of renal stones, the current knowledge is still insufficient. Methods This case-control prospective study was conducted in Guru Teg Bahadur (GTB) Hospital, a tertiary care facility in Delhi with 30 cases and 30 controls. Patients visiting the department of surgery between November 2011 and April 2013 were enrolled in the study. Cases were defined as patients with renal stones diagnosed on the basis of history and radiological investigations. Controls were selected from the patients admitted to the department of surgery for reasons other than renal stones. The study protocol was approved by the Institutional Ethical Committee of the University College of Medical Sciences, GTB Hospital, Delhi. Written informed consent was obtained from all patients. A structured questionnaire was used to collect data. Metal levels were analyzed by an atomic absorption spectrophotometer (Shimadzu Flame AA-680, Shimadzu Corp., Kyoto, Japan) at Delhi University. The vitamin D receptor gene was measured using genomic DNA. Horizontal agarose gel electrophoresis was used for the quantification of the genomic DNA. Results There were 30 cases and 30 controls in the study. Stress was more prevalent among cases (63%) compared to controls (36%). Nearly 83% of cases had the ff allele of the Vitamin D receptor gene compared to 46% of controls. The median arsenic and lead levels were higher among cases compared to controls. In the unadjusted model of logistic regression, we found stressed patients had three times higher odds of developing renal stones compared to non-stressed patients (OR (95% CI): 2.98 (1.04-8.52); p=0.04). Similarly, patients with higher blood concentrations of arsenic and lead had higher odds of developing renal stones compared to those with lower concentrations.  Conclusions There was a definitive role of heavy metals, including lead, cadmium, and arsenic, seen with renal stones. A significant association was seen between the ff allele of VDR polymorphism (Fok1 enzymes) and patients with renal stones. Other parameters, including male and stress factors, seem to have an important role in renal stone formation.

Time to Publication for Randomized Clinical Trials Presented as Abstracts at Three Gastroenterology and Hepatology Conferences in 2017.

Background & Aims: Results of randomized clinical trials are often first presented as conference abstracts, but these abstracts may be difficult to find, and trial results included in the abstract may not be followed by subsequent journal publications. In a review of abstracts submitted to eight major medical and surgical conferences in 2017, we identified 237 abstracts reporting primary results of randomized clinical trials accepted for presentation at three major gastroenterology and hepatology conferences. The aims of this new analysis were to determine the publication rate for these abstracts and the proportion of publications that included trial registration numbers in the publication abstract. Methods: Clinical trial registries, PubMed, Europe PMC, and Google Scholar were searched through November 1, 2021, for publications reporting trial results for the selected abstracts. Publications were reviewed to determine if they included a trial registration number and if the registration number was in the abstract. Results: Publications were found for 157 abstracts (66%) within four years of the conference. Publications were found more frequently for the 194 abstracts reporting results of registered trials (144, 74%) than for the 43 abstracts reporting unregistered trials (13, 30%), but only 67% of these 144 publications included the registration number in the publication abstract. Ten unpublished trials had summary results posted on ClinicalTrials.gov. Conclusions: Clinical trial results could be more accessible if all trials were registered, authors included registration numbers in both conference and journal abstracts, and journal editors required the inclusion of registration numbers in publication abstracts for registered clinical trials.

Genetic Expression of CYP2B6 Gene in Phenobarbitone Responder and Non- responder Neonates.

Quantitative real-time polymerase chain reaction for identifying CYP2B6 gene expression was done on blood samples of 30 phenobarbitone responder and 30 non-responder neonates with seizures. CYP2B6 was observed to be significantly down regulated among phenobarbitone non-responders as compared to phenobarbitone responders (Mean (SD) DCt 17.97 (1.19) vs 15.40 (1.83); P<0.001).

Co-Circulation of Dengue Virus Serotypes 1, 2, and 3 during the 2022 Dengue Outbreak in Nepal: A Cross-Sectional Study.

The largest dengue outbreak in the history of Nepal occurred in 2022, with a significant number of casualties. It affected all 77 districts, with the nation's capital, Kathmandu (altitude 1300 m), being the hardest hit. However, the molecular epidemiology of this outbreak, including the dengue virus (DENV) serotype(s) responsible for this epidemic, remain unknown. Here, we report the epidemic trends, clinico-laboratory features, and virus serotypes and their viral load profiles that are associated with this outbreak in Nepal. Dengue-suspected febrile patients were investigated by routine laboratory, serological, and molecular tools, including a real-time quantitative polymerase chain reaction (qRT-PCR). Of the 538 dengue-suspected patients enrolled, 401 (74.5%) were diagnosed with dengue. Among these dengue cases, 129 (32.2%) patients who required hospital admission had significant associations with myalgia, rash, diarrhea, retro-orbital pain, bleeding, and abdominal pain. DENV-1, -2, and -3 were identified during the 2022 epidemic, with a predominance of DENV-1 (57.1%) and DENV-3 (32.1%), exhibiting a new serotype addition. We found that multiple serotypes circulated in 2022, with a higher frequency of hospitalizations, more severe dengue, and more deaths than in the past. Therefore, precise mapping of dengue and other related infections through integrated disease surveillance, evaluation of the dynamics of population-level immunity and virus evolution should be the urgent plans of action for evidence-based policy-making for dengue control and prevention in the country.

Putative interactions between transthyretin and endosulfan II and its relevance in breast cancer.

The unregulated use of organochlorine pesticides (OCPs) has been linked to spread of breast cancer (BC), but the underlying biomolecular interactions are unknown. Using a case-control study, we compared OCP blood levels and protein signatures among BC patients. Five pesticides were found in significantly higher concentrations in breast cancer patients than in healthy controls: p',p' dichloro diphenyl trichloroethane (DDT), p'p' dichloro diphenyl dichloroethane (DDD), endosulfan II, delta-hexachlorocyclohexane (dHCH), and heptachlor epoxide A (HTEA). According to the odds ratio analysis, these OCPs, which have been banned for decades, continue to raise the risk of cancer in Indian women. Proteomic analysis of plasma from estrogen receptor-positive breast cancer patients revealed 17 dysregulated proteins, but transthyretin (TTR) was three times higher than in healthy controls, which is further validated by enzyme-linked immunosorbent assays (ELISA). Molecular docking and molecular dynamics studies revealed a competitive affinity between endosulfan II and the thyroxine-binding site of TTR, pointing towards the significance of the competition between thyroxin and endosulfan, resulting in endocrine disruption leading to breast cancer. Our study sheds light on the putative role of TTR in OCP-mediated BC, but more research is needed to decipher the underlying mechanisms that can be used to prevent the carcinogenic effects of these pesticides on women's health.

Modulation of mTORC1 and IL-6 following mirror therapy and pregabalin in complex regional pain syndrome type 1.

Aim: The study was designed to evaluate the modulation of mTOR complex 1 (mTORC1) and IL-6 genes following the use of mirror therapy (MT) and pregabalin in complex regional pain syndrome type-1 patients. Materials & methods: Two groups of 20 patients: MT group received MT and pregabalin, control therapy group received pregabalin. Neuropathic pain symptom inventory (NPSI), numeric rating scale - pain, modified motor activity log, SF-12 questionnaire for quality of life and IL-6 and mTORC1 expression were evaluated. Results: Group MT demonstrated a statistically significant improvement in NPSI burning, NPSI allodynia and numeric rating scale pain scores, modified motor activity log and SF-12 scores. Significant downregulation of mTORC1 and IL-6 observed in both. Conclusion: MT is a significant adjunct to pregabalin in improving motor function, quality of life and alleviating pain in complex regional pain syndrome type 1. Clinical Trial Registration: CTRI/2019/01/017272 (ClinicalTrials.gov).

Complex regional pain syndrome is a form of long-term pain that involves an arm or a leg. It can develop after an injury, a surgery or a stroke. Although many drugs have been used for its treatment, the limited relief that these drugs produce along with their side effects have shifted focus to other physical and psychological modes of therapy. Mirror therapy is one such modality where the image of normal functioning limb seen in a mirror placed over the affected limb leads to pain relief in the affected limb. We have provided evidence that mirror therapy can reduce the pain of this syndrome and also decrease the levels of pain related genes in the body. This will help us to devise better treatment strategies for complex regional pain syndrome.

Deregulated phenotype of autoreactive Th17 and Treg clone cells in pemphigus vulgaris after in-vitro treatment with desmoglein antigen (Dsg-3).

The loss of balance between regulatory T (Treg) and T helper 17 (Th17) causes loss of tolerance against desmoglein (Dsg)-3 leading to pemphigus vulgaris (PV), an autoimmune bullous skin disorder associated with autoantibodies against Dsg-3. We aimed to elucidate the complex relationship of Th17 and Treg cells, their molecules, and the underlying mechanism in the development of PV disease. Using cytokine secretion assays, Th17 and Treg cells were sorted by FACS Aria-III within Dsg-3-responsive PBMC population and homogeneous T cell clones were generated in-vitro. Different cell surface molecules like CD25, GITR, CD122, CD152, CD45RO, IL-23R, STAT3, STAT5, CD127, HLA-DR, CCR4, CCR5, CCR6 and CCR7 were studied. The functional response of Th17 and Treg cells were elucidated by measuring the levels of various cytokines released by IL-10 and IL-17 T cells. The mRNA expression of transcription factors (FoxP3 and RORγt) was also analyzed. IL-17 secreting (Th17) cells with phenotype CD4+IL-17+ were greatly increased and IL-10 secreting (Treg) cells with phenotype CD4+IL-10+ were reduced in PV cases than healthy controls. The qPCR analysis showing high expression of retinoic acid receptor-related orphan receptor gamma (RORγt) mRNA in comparison to forkhead box P3 (FoxP3) mRNA confirmed the development of pro-inflammatory Th17 response in PV. Further, the cytokine profile of pro-inflammatory and anti-inflammatory cytokines suggested defective suppressive functions in Treg cells with high inflammatory response. Our findings indicate that autoantigen Dsg-3 specifically allows the proliferation of IL-17 secreting T cells though has a negative effect on IL-10 secreting T cells leading to dysregulation of immunity in PV patients. This antagonistic relationship between Dsg-3-specific Th17 and Treg cells may be critical for the onset and persistence of inflammation in PV cases.