Systematic review of Lyme disease in Turkey

Aseptic Meningitis Linked to Borrelia afzelii Seroconversion in Northeastern Greece: An Emerging Infectious Disease Contested in the Region

Borreliosis (Lyme disease) is a zoonosis, mediated to humans and small mammals through specific vectors (ticks), with increasing global incidence. It is associated with a variety of clinical manifestations and can, if not promptly recognized and left untreated, lead to significant disability. In Europe, the main Borrelia species causing disease in humans are Borrelia burgdorferi s.s., Borrelia afzelii, Borrelia garinii, and Borrelia spielmanii. The Ixodes ricinus tick is their principal vector. Although Lyme disease is considered endemic in the Balkan region and Turkey, and all three main Lyme pathogens have been detected in ticks collected in these countries, autochthonous Lyme disease remains controversial in Greece. We report a case of aseptic meningitis associated with antibody seroconversion against Borrelia afzelii in a young female patient from the prefecture of Thasos without any relevant travel history. The patient presented with fever and severe headache, and the cerebrospinal fluid examination showed lymphocytic pleocytosis. Serum analysis was positive for specific IgG antibodies against Borrelia afzelii. In the absence of typical erythema migrans, serological evidence of infection is required for diagnosis. Although atypical in terms of clinical presentation, the seasonality and geographical location of potential disease transmission in the reported patient should raise awareness among clinicians for a still controversial and potentially underreported emerging infectious disease in Greece.

Is There a Role for Dark Field Microscopy in the Diagnosis of Lyme Disease?A Narrative Review

The diagnosis of Lyme disease is becoming more common in Turkey. Nonetheless, some physicians are not aware of the diagnostic principles that should be followed when faced with a suspected patient and could use tests that are not recommended, such as darkfield microscopy. Dark field microscopy is a diagnostic technique to visualize the spirochetes that cause Lyme disease; however, it is not recommended for the diagnosis of Lyme disease. One of the main limitations of dark field microscopy is its low sensitivity. Another limitation is its high false-positivity rate, as other microorganisms and cellular debris can be mistaken for spirochetes, leading to a misdiagnosis thatmay result in unnecessary treatment. Therefore, this study aimed to review the literature on the role of dark field microscopy as a diagnostic method for Lyme disease and inform physicians about recommended approaches in line with the recommendations of national or international guidelines. An electronic search of Pubmed, Scopus, and Web of Science was performed using the following medical subject headings (MeSH) search terms: Lyme borreliosis, Lyme disease, Borrelia burgdorferi, diagnosis, and microscopy. With this narrative review, we aimed to inform physicians better and improve patient care for patients with suspected Lyme disease.

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.

Serological evidence of possible Borrelia afzelii lyme disease in Greece

Suggestions that Lyme disease exists in Greece remain controversial and no study to date has definitively identified the presence of a Borrelia species that infects humans. We examined patients throughout Greece suspected for Lyme disease by enzyme-linked immunosorbent assay (ELISA) and by western blotting for Borrelia burgdorferi sensu lato species. We found one patient positive for Borrelia burgdorferi and two patients positive for Borrelia afzelii specific IgG antibodies. Both B. afzelii patients were suffering by neurological manifestations and had never traveled abroad. We provide serological evidence of two autochthonous Lyme disease cases in Greece, possibly caused by B. afzelii.

Infection of hard ticks in the Caspian Sea littoral of Iran with Lyme borreliosis and relapsing fever borreliae

The Caspian Sea littoral of Iran is home to various hard tick species, including Ixodes ricinus, the notorious vector of Lyme borreliosis (LB) in Eurasia. Here, in this area, we examined I. ricinus and other hard ticks, along with common rodents and small mammals for LB and relapsing fever (RF) borreliae infection. Ticks were collected from various mammalian hosts, including sheep, goats, cattle, camels, horses, dogs, donkeys, rodents, and hedgehogs. Rodents and small mammals were live-captured from different habitats. A real-time PCR for 16S rRNA sequence revealed borrelial DNA in 71 out of 501 (≈14 %) specimens belonging to I. ricinus and Rhipicephalus ticks. None of the rodents and small mammals showed borrelial infection in the viscera. PCR amplification and sequencing of a 600-bp sequence of the flaB identified Borrelia bavariensis, Borrelia garinii, Borrelia afzelii, and Borrelia valaisiana, and the RF Borrelia, B. miyamotoi in I. ricinus ticks. The RF-like Borrelia in Rhipicephalus ticks shared the highest identity (98.97 %) with an isolate infecting Haemaphysalis megaspinosa ticks in Japan. Our phylogeny and BLAST analysis suggest the range extension of the European I. ricinus-associated borreliae into the north of Iran.