Clinico-epidemiological presentations and management of Nipah virus infection during the outbreak in Kozhikode district, Kerala state, India 2023.

India experienced its sixth Nipah virus (NiV) outbreak in September 2023 in the Kozhikode district of Kerala state. The NiV is primarily transmitted by spillover events from infected bats followed by human-to-human transmission. The clinical specimens were screened using real-time RT-PCR, and positive specimens were further characterized using next-generation sequencing. We describe here an in-depth clinical presentation and management of NiV-confirmed cases and outbreak containment activities. The current outbreak reported a total of six cases with two deaths, with a case fatality ratio of 33.33%. The cases had a mixed presentation of acute respiratory distress syndrome and encephalitis syndrome. Fever was a persistent presentation in all the cases. The Nipah viral RNA was detected in clinical specimens until the post-onset day of illness (POD) 14, with viral load in the range of 1.7-3.3 × 104 viral RNA copies/mL. The genomic analysis showed that the sequences from the current outbreak clustered into the Indian clade similar to the 2018 and 2019 outbreaks. This study highlights the vigilance of the health system to detect and effectively manage the clustering of cases with clinical presentations similar to NiV, which led to early detection and containment activities.

Infections in Inborn Errors of Immunity with Combined Immune Deficiency: A Review.

Enhanced susceptibility to microbes, often resulting in severe, intractable and frequent infections due to usually innocuous organisms at uncommon sites, is the most striking feature in individuals with an inborn error of immunity. In this narrative review, based on the International Union of Immunological Societies' 2022 (IUIS 2022) Update on phenotypic classification of human inborn errors of immunity, the focus is on commonly encountered Combined Immunodeficiency Disorders (CIDs) with susceptibility to infections. Combined immune deficiency disorders are usually commensurate with survival beyond infancy unlike Severe Combined Immune Deficiency (SCID) and are often associated with clinical features of a syndromic nature. Defective humoral and cellular immune responses result in susceptibility to a broad range of microbial infections. Although disease onset is usually in early childhood, mild defects may present in late childhood or even in adulthood. A precise diagnosis is imperative not only for determining management strategies, but also for providing accurate genetic counseling, including prenatal diagnosis, and also in deciding empiric treatment of infections upfront before investigation reports are available.

Nipah Virus Outbreak in Kerala State, India Amidst of COVID-19 Pandemic.

We report here a Nipah virus (NiV) outbreak in Kozhikode district of Kerala state, India, which had caused fatal encephalitis in a 12-year-old boy and the outbreak response, which led to the successful containment of the disease and the related investigations. Quantitative real-time reverse transcription (RT)-PCR, ELISA-based antibody detection, and whole genome sequencing (WGS) were performed to confirm the NiV infection. Contacts of the index case were traced and isolated based on risk categorization. Bats from the areas near the epicenter of the outbreak were sampled for throat swabs, rectal swabs, and blood samples for NiV screening by real-time RT-PCR and anti-NiV bat immunoglobulin G (IgG) ELISA. A plaque reduction neutralization test was performed for the detection of neutralizing antibodies. Nipah viral RNA could be detected from blood, bronchial wash, endotracheal (ET) secretion, and cerebrospinal fluid (CSF) and anti-NiV immunoglobulin M (IgM) antibodies from the serum sample of the index case. Rapid establishment of an onsite NiV diagnostic facility and contact tracing helped in quick containment of the outbreak. NiV sequences retrieved from the clinical specimen of the index case formed a sub-cluster with the earlier reported Nipah I genotype sequences from India with more than 95% similarity. Anti-NiV IgG positivity could be detected in 21% of Pteropus medius (P. medius) and 37.73% of Rousettus leschenaultia (R. leschenaultia). Neutralizing antibodies against NiV could be detected in P. medius. Stringent surveillance and awareness campaigns need to be implemented in the area to reduce human-bat interactions and minimize spillover events, which can lead to sporadic outbreaks of NiV.

Initial Insights Into the Genetic Epidemiology of SARS-CoV-2 Isolates From Kerala Suggest Local Spread From Limited Introductions.

Coronavirus disease 2019 (COVID-19) rapidly spread from a city in China to almost every country in the world, affecting millions of individuals. The rapid increase in the COVID-19 cases in the state of Kerala in India has necessitated the understanding of SARS-CoV-2 genetic epidemiology. We sequenced 200 samples from patients in Kerala using COVIDSeq protocol amplicon-based sequencing. The analysis identified 166 high-quality single-nucleotide variants encompassing four novel variants and 89 new variants in the Indian isolated SARS-CoV-2. Phylogenetic and haplotype analysis revealed that the virus was dominated by three distinct introductions followed by local spread suggesting recent outbreaks and that it belongs to the A2a clade. Further analysis of the functional variants revealed that two variants in the S gene associated with increased infectivity and five variants mapped in primer binding sites affect the efficacy of RT-PCR. To the best of our knowledge, this is the first and most comprehensive report of SARS-CoV-2 genetic epidemiology from Kerala.

Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion.

BACKGROUND AND OBJECTIVE: Breast cancer is a frequently diagnosed cancer in women, contributing to significant mortality rates. Death rates are relatively higher in developing nations due to the shortage of early detection amenities and constraints on access to technical advances combating this disease. The only way to diagnose cancer with certainty is through biopsy performed by pathologists. Computer-aided diagnostic algorithms can assist pathologists in being more productive, objective and consistent in the diagnostic process. The focus of this work is to develop a reliable automated breast cancer diagnosis method which can operate in the prevailing clinical environment. METHODS: Nuclei overlap and complex structural organisation of the breast tissue in biopsy images make nuclei segmentation, feature extraction and classification challenging. In this work, a nucleus guided transfer learning (NucTraL) methodology is proposed as a simple and affordable breast tumor classification algorithm. The image feature is represented by fusion of local nuclei features that are extracted using convolutional neural network (CNN) models pretrained on the ImageNet database. The nucleus patch extraction strategy used in this work avoids fine segmentation of the nuclei boundary but provides features with good discriminative power for classification. Classification of the fused features into benign and malignant classes is performed using a support vector machine (SVM) classifier. A belief theory based classifier fusion (BCF) strategy is then employed to combine the outputs arising from the different CNN-SVM combinations to improve accuracy further. RESULTS: Evaluation of results is achieved by executing 100 random trials with 70%-30% train to test division on the publicly available BreaKHis dataset. The proposed framework achieved average accuracy of 96.91%, sensitivity of 97.24% and specificity of 96.18%. CONCLUSION: It is found that the proposed NucTraL+BCF framework outperforms several recent approaches and achieves results comparable to the state-of-the-art methods even without using high computational power. This qualitative framework based on transfer learning can contribute significantly for developing cost effective and low complexity CAD system for breast cancer diagnosis from histopathological images.

Computer assisted recognition of breast cancer in biopsy images via fusion of nucleus-guided deep convolutional features.

BACKGROUND AND OBJECTIVE: Breast cancer is a commonly detected cancer among women, resulting in a high number of cancer-related mortality. Biopsy performed by pathologists is the final confirmation procedure for breast cancer diagnosis. Computer-aided diagnosis systems can support the pathologist for better diagnosis and also in reducing subjective errors. METHODS: In the automation of breast cancer analysis, feature extraction is a challenging task due to the structural diversity of the breast tissue images. Here, we propose a nucleus feature extraction methodology using a convolutional neural network (CNN), 'NucDeep', for automated breast cancer detection. Non-overlapping nuclei patches detected from the images enable the design of a low complexity CNN for feature extraction. A feature fusion approach with support vector machine classifier (FF + SVM) is used to classify breast tumor images based on the extracted CNN features. The feature fusion method transforms the local nuclei features into a compact image-level feature, thus improving the classifier performance. A patch class probability based decision scheme (NucDeep + SVM + PD) for image-level classification is also introduced in this work. RESULTS: The proposed framework is evaluated on the publicly available BreaKHis dataset by conducting 5 random trials with 70-30 train-test data split, achieving average image level recognition rate of 96.66  ±  0.77%, 100% specificity and 96.21% sensitivity. CONCLUSION: It was found that the proposed NucDeep + FF + SVM model outperforms several recent existing methods and reveals a comparable state of the art performance even with low training complexity. As an effective and inexpensive model, the classification of biopsy images for breast tumor diagnosis introduced in this research will thus help to develop a reliable support tool for pathologists.

Prevalence of MRSA Nasal Carriage in Patients Admitted to a Tertiary Care Hospital in Southern India.

INTRODUCTION: Infections with MRSA, both community and hospital acquired, are well established and the source of infection is often a carrier. There are very few studies showing the magnitude of MRSA nasal colonization among healthy persons from the community. This study was conducted to detect the prevalence of MRSA nasal carriage in patients who did not have any known risk factors associated with HA- MRSA colonization, admitted to a tertiary care centre in Kerala. MATERIALS AND METHODS: Nasal swabs were collected from patients within 24 hours of admission. Specimen were inoculated on chromogenic agar (HiCrome MeReSa agar-HiMedia) for MRSA screening. Isolates were then subjected to antibiotic sensitivity tests, SCCmec typing and PVL gene detection. RESULTS: Out of 683 patients, 16 carried MRSA in their nares (2.3%). Of the 16 strains 13 (81.25 %) strain were SCCmec type III and one belonged to SCCmec type IV (6.25 %). Two strains failed to amplify SCCmec genes. Three strains carried genes for PVL toxin (18.75%). CONCLUSION: With a better understanding of the complex epidemiology of MRSA it is increasingly apparent that demarcations between the HA and CA phenotypes are not as clear cut as previously thought. In this study of nasal carriage of MRSA in the community we have demonstrated prevalence consistent with published data. Most isolates however were shown to belong to the type conventionally assigned to HA-MRSA.

Too close for comfort: screening strategy to detect methicillin-resistant Staphylococcus aureus conversion in exposed roommates.

All 899 roommates exposed to methicillin-resistant Staphylococcus aureus (MRSA) index cases were studied over 57 months. MRSA detection is better at approximately 3 days (50%-55%) or 7 days (56%) after contact has been broken than day 0 (30%). Polymerase chain reaction testing at day 3 performs similarly to culture at day 7. Nasal/rectal screening provides superior detection than nasal alone. Those exposed >48 hours are at significantly greater risk of colonization.