Clinical and laboratory evidence of Lyme disease in North India, 2016-2019

Hard Ticks as Vectors: The Emerging Threat of Tick-Borne Diseases in India

Hard ticks (Ixodidae) play a critical role in transmitting various tick-borne diseases (TBDs), posing significant global threats to human and animal health. Climatic factors influence the abundance, diversity, and vectorial capacity of tick vectors. It is imperative to have a comprehensive understanding of hard ticks, pathogens, eco-epidemiology, and the impact of climatic changes on the transmission dynamics of TBDs. The distribution and life cycle patterns of hard ticks are influenced by diverse ecological factors that, in turn, can be impacted by changes in climate, leading to the expansion of the tick vector's range and geographical distribution. Vector competence, a pivotal aspect of vectorial capacity, involves the tick's ability to acquire, maintain, and transmit pathogens. Hard ticks, by efficiently feeding on diverse hosts and manipulating their immunity through their saliva, emerge as competent vectors for various pathogens, such as viruses, parasites and bacteria. This ability significantly influences the success of pathogen transmission. Further exploration of genetic diversity, population structure, and hybrid tick vectors is crucial, as they play a substantial role in influencing vector competence and complicating the dynamics of TBDs. This comprehensive review deals with important TBDs in India and delves into a profound understanding of hard ticks as vectors, their biology, and the factors influencing their vector competence. Given that TBDs continue to pose a substantial threat to global health, the review emphasizes the urgency of investigating tick control strategies and advancing vaccine development. Special attention is given to the pivotal role of population genetics in comprehending the genetic diversity of tick populations and providing essential insights into their adaptability to environmental changes.

Lyme borreliosis in a French expatriate in India

Lyme borreliosis is known to be endemic in temperate countries of the Northern hemisphere but is rarely reported in other regions of the world. Here, we describe a rare diagnosis in a traveller returning from India, who presented with two erythema migrans.

Epidemiological, clinical, and laboratory characteristics of human granulocytic anaplasmosis in North India

Human granulocytic anaplasmosis (HGA) is an emerging, rickettsial tick-borne disease caused by Anaplasma phagocytophilum. Sero-epidemiological data demonstrate that this pathogen has a worldwide distribution. The diagnosis of HGA requires a high index of clinical suspicion, even in endemic areas. In recent years, HGA has increasingly been reported from Asia and described in China, Japan, and Korea. We serologically and molecularly screened 467 patients with clinical suspicion of Anaplasmosis. The present study describes the epidemiology, clinical, and laboratory details of 6 confirmed and 43 probable cases of human granulocytic anaplasmosis. One of the HGA patients developed secondary invasive opportunistic Aspergillus fumigatus and Acinetobacter baumanii infection during the illness, which resulted in a fatal infection. The HGA patients without severe complications had excellent treatment responses to doxycycline. The emergence of this newly recognized tick-borne zoonotic HGA in North India is a significant concern for public health and is likely underdiagnosed, underreported, and untreated. Hence, it is also essential to establish a well-coordinated system for actively conducting tick surveillance, especially in the forested areas of the country.IMPORTANCEThe results of the present study show the clinical and laboratory evidence of autochthonous cases of Anaplasma phagocytophilum in North India. The results suggest the possibility of underdiagnosis of HGA in this geographical area. One of the HGA patients developed secondary invasive opportunistic Aspergillus fumigatus and Acinetobacter baumanii infection during the illness, which resulted in a fatal infection.

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

BACKGROUND

Lyme disease is among the most reported tick-borne diseases worldwide, making it a major ongoing public health concern. An effective Lyme disease case reporting system depends on timely diagnosis and reporting by health care professionals, and accurate laboratory testing and interpretation for clinical diagnosis validation. A lack of these can lead to delayed diagnosis and treatment, which can exacerbate the severity of Lyme disease symptoms. Therefore, there is a need to improve the monitoring of Lyme disease by using other data sources, such as web-based data.

OBJECTIVE

We analyzed global Twitter data to understand its potential and limitations as a tool for Lyme disease surveillance. We propose a transformer-based classification system to identify potential Lyme disease cases using self-reported tweets.

METHODS

Our initial sample included 20,000 tweets collected worldwide from a database of over 1.3 million Lyme disease tweets. After preprocessing and geolocating tweets, tweets in a subset of the initial sample were manually labeled as potential Lyme disease cases or non-Lyme disease cases using carefully selected keywords. Emojis were converted to sentiment words, which were then replaced in the tweets. This labeled tweet set was used for the training, validation, and performance testing of DistilBERT (distilled version of BERT [Bidirectional Encoder Representations from Transformers]), ALBERT (A Lite BERT), and BERTweet (BERT for English Tweets) classifiers.

RESULTS

The empirical results showed that BERTweet was the best classifier among all evaluated models (average F1-score of 89.3%, classification accuracy of 90.0%, and precision of 97.1%). However, for recall, term frequency-inverse document frequency and k-nearest neighbors performed better (93.2% and 82.6%, respectively). On using emojis to enrich the tweet embeddings, BERTweet had an increased recall (8% increase), DistilBERT had an increased F1-score of 93.8% (4% increase) and classification accuracy of 94.1% (4% increase), and ALBERT had an increased F1-score of 93.1% (5% increase) and classification accuracy of 93.9% (5% increase). The general awareness of Lyme disease was high in the United States, the United Kingdom, Australia, and Canada, with self-reported potential cases of Lyme disease from these countries accounting for around 50% (9939/20,000) of the collected English-language tweets, whereas Lyme disease-related tweets were rare in countries from Africa and Asia. The most reported Lyme disease-related symptoms in the data were rash, fatigue, fever, and arthritis, while symptoms, such as lymphadenopathy, palpitations, swollen lymph nodes, neck stiffness, and arrythmia, were uncommon, in accordance with Lyme disease symptom frequency.

CONCLUSIONS

The study highlights the robustness of BERTweet and DistilBERT as classifiers for potential cases of Lyme disease from self-reported data. The results demonstrated that emojis are effective for enrichment, thereby improving the accuracy of tweet embeddings and the performance of classifiers. Specifically, emojis reflecting sadness, empathy, and encouragement can reduce false negatives.

Recombinant protein immunoblots for differential diagnosis of tick-borne relapsing fever and Lyme disease

Lyme disease (LD) is caused by a group of tick-borne bacteria of the genus Borrelia termed Lyme disease Borreliae (LDB). The detection of serum antibodies to specific LDB antigens is widely used to support diagnosis of LD. Recent findings highlight a need for serological tests that can differentiate LD from tick-borne relapsing fever (TBRF) caused by a separate group of Borrelia species termed relapsing fever Borreliae. This is because LD and TBRF share some clinical symptoms and can occur in overlapping locations. The development of serological tests for TBRF is at an early stage compared with LD. This article reviews the application of line immunoblots (IBs), where recombinant proteins applied as lines on nitrocellulose membrane strips are used to detect antibodies in patient sera, for the diagnosis and differentiation of LD and TBRF.

Epidemiology of Lyme Disease

Lyme disease is the most common vector-borne illness in North America and Europe. The etiologic agent, Borrelia burgdorferi sensu lato, is transmitted to humans by certain species of Ixodes ticks, which are found widely in temperate regions of the Northern hemisphere. Clinical features are diverse but death is rare. The risk of human infection is determined by the distribution and abundance of vector ticks, ecologic factors influencing tick infection rates, and human behaviors that promote tick bite. Rates of infection are highest among children aged 5 to 15 years and adults aged more than 50 years. In the northeastern United States where disease is most common, exposure occurs primarily in areas immediately around the home. Knowledge of disease epidemiology is important for patient management and proper diagnosis.