Prevalence of Borrelia burgdorferi in Ixodidae Tick around Asia: A Systematic Review and Meta-Analysis

Discrepancy between IDSA and ESGBOR in Lyme disease: Individual participant meta-analysis in Türkiye

BACKGROUND

The evidence on the prevalence of Lyme borreliosis (LB) is limited, but there is a suspicion of overdiagnosis of LB in recent years. We reviewed the LB diagnosis and treatment-related data in Türkiye, based on the Infectious Diseases Society of America (IDSA) 2020 and European Society of Clinical Microbiology and Infectious Diseases Study Group for Lyme Borreliosis (ESGBOR) 2018 guidelines. By detecting the disagreements between these two, we outlined the areas to be improved for future guidelines.

METHODS

We performed a literature search according to the PRISMA guidelines in PubMed, Ovid-Medline, Web of Science, Turkish Medline, Scopus, CINAHL, ULAKBIM TR Index, Google Scholar and Cochrane Library databases. We included the published cases in a database and evaluated according to IDSA and ESGBOR guidelines. We outlined the reasons for misdiagnoses and inappropriate uses of antibiotics.

RESULTS

We included 42 relevant studies with 84 LB cases reported from Türkiye between 1990 and December 2022. Among 84 cases, the most common clinical findings were nervous system findings (n = 37, 44.0%), erythema migrans (n = 29, 34.5%) and ophthalmologic findings (n = 15, 17.9%). The IDSA 2020 and ESGBOR 2018 guidelines agreed on the diagnosis of 71 (84.5%) cases; there was an agreement that 31 cases (36.9%) were misdiagnosed and 40 cases (47.6%) were correctly diagnosed, and there was disagreement for 13 cases (15.5%). Serum immunoglobulin M (IgM), IgG measurements by ELISA and western blot were widely performed, and they were effective in definitive diagnosis merely when used according to guidelines. Inappropriate use of antibiotics was detected in 42 (50.0%) of cases which were classified in the following categories: incorrect LB diagnosis, inappropriate choice of antibiotic, inappropriate route of drug administration and prolonged antibiotic treatment.

CONCLUSION

Overdiagnosis and non-adherence to guidelines is a common problem. The discordance between seroprevalence and clinical studies necessitates a consensus over the best clinical approach.

The Role of Environmental Factors in Lyme Disease Transmission in the European Union: A Systematic Review

BACKGROUND

Lyme disease (LD) is an emergent vector-borne disease caused by Borrelia spp. and transmitted through infected ticks, mainly Ixodes spp. Our objective was to determine meteorological and environmental factors associated with LD transmission in Europe and the effect of climate change on LD.

MATERIALS AND METHODS

A systematic review following the PRISMA guidelines was performed. We selected studies on LD transmission in the European Union (EU) and the European Economic Area (EEA) published between 2000 and 2022. The protocol was registered in the PROSPERO database.

RESULTS

We included 81 studies. The impact of environmental, meteorological or climate change factors on tick vectors was studied in 65 papers (80%), and the impact on human LD cases was studied in 16 papers (19%), whereas animal hosts were only addressed in one study (1%). A significant positive relationship was observed between temperature and precipitation and the epidemiology of LD, although contrasting results were found among studies. Other positive factors were humidity and the expansion of anthropized habitats.

CONCLUSIONS

The epidemiology of LD seems to be related to climatic factors that are changing globally due to ongoing climate change. Unfortunately, the complete zoonotic cycle was not systematically analyzed. It is important to adopt a One Health approach to understand LD epidemiology.

Efficacy and safety of antibiotics for treatment of leptospirosis: a systematic review and network meta-analysis

BACKGROUND

Leptospirosis, an important zoonotic bacterial disease, commonly affects resource-poor populations and results in significant morbidity and mortality worldwide. The value of antibiotics in leptospirosis remains unclear, as evidenced by the conflicting opinions published.

METHODS

We conducted a search in the PubMed, Web of Science, and Cochrane Library databases for studies. These studies included clinical trials and retrospective studies that evaluated the efficacy or safety of antibiotics for leptospirosis treatment. The primary outcomes assessed were defervescence time, mortality rate, and hospital stays. Subgroup analyses were performed based on whether there were cases involving children and whether there were cases of severe jaundice. Safety was defined as the prevalence of adverse events associated with the use of antibiotics. p scores were utilized to rank the efficacy of the antibiotics.

RESULTS

There are included 9 randomized controlled trials (RCTs), 1 control trial (CT), and 3 retrospective studies (RS) involving 920 patients and 8 antibiotics. Six antibiotics resulted in significantly shorter defervescence times compared to the control, namely cefotaxime (MD, - 1.88; 95% CI =  - 2.60 to - 1.15), azithromycin (MD, - 1.74; 95% CI =  - 2.52 to - 0.95), doxycycline (MD, - 1.53; 95% CI =  - 2.05 to - 1.00), ceftriaxone (MD, - 1.22; 95% CI =  - 1.89 to - 0.55), penicillin (MD, - 1.22; 95% CI =  - 1.80 to - 0.64), and penicillin or ampicillin (MD, - 0.08; 95% CI =  - 1.01 to - 0.59). The antibiotics were not effective in reducing the mortality and hospital stays. Common adverse reactions to antibiotics included Jarisch-Herxheimer reaction, rash, headache, and digestive reactions (nausea, vomiting, diarrhea, abdominal pain, and others).

CONCLUSIONS

Findings recommend that leptospirosis patients be treated with antibiotics, which significantly reduced the leptospirosis defervescence time. Cephalosporins, doxycycline, and penicillin are suggested, and azithromycin may be a suitable alternative for drug-resistant cases.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022354938.

Clinical Algorithm and Diagnostic Tools for Lyme Disease

Lyme disease (LD) is the prototype of tick-borne infections. The broad spectrum of LD symptomatology, together with the tremendous variety of sensibility and specificity of diagnostic tests, poses a complex challenge for LD diagnosis. Here, we propose a clinical algorithm for Lyme patients to prevent treatment delay in suspicious scenarios.

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.

Advances in the Epidemiological Surveillance of Tick-Borne Pathogens

Ticks are obligate blood-feeding ectoparasites of mammals, birds, and reptiles, which are globally important vectors of pathogens that impact both human and animal health [...].