Search Results (21)

Background: International travel has increased over recent decades, leading to a rise in the number of patients presenting to emergency departments (EDs) with fever after returning from abroad. Evaluating fever in returning travelers is challenging because the differential diagnosis is broad, and exposure to tropical diseases limited, among most ED clinicians. Objective: This article aims to provide a practical overview of the most common travel-related causes of fever. The tool is intended to support targeted diagnostics and timely treatment and/or timely specialist referral, while emphasizing that non-travel-related infections must also be considered. Methods: We created a clinical summary of the most common causes of fever in returning travelers based on epidemiology, incubation periods, clinical features, and diagnostic approaches. A practical overview was created to aid ED clinicians in evaluating stable patients, incorporating travel history, exposure risks, and key clinical findings. Results: Malaria, dengue and typhoid fever are among the most common diagnoses in travelers returning from abroad, excluding non-travel-related diseases. These conditions share overlapping symptoms. Diagnosis relies on clinician awareness and a combination of exposure history, clinical evaluation, and targeted laboratory testing. Treatment depends on the causative pathogen and disease severity, but often requires early empiric therapy and supportive care. Conclusions: This article presents a systematic, pragmatic approach to the evaluation of fever in the returning traveler. This overview is designed to help ED clinicians recognize and make appropriate initial management and referral decisions when assessing a stable traveler. Nevertheless, we recommend specialist advice for most cases. Full article

The diagnosis of enteric fever has become difficult due to the nonspecific and overlapping clinical syndrome of typhoid and paratyphoid infections with other febrile illnesses. Moreover, the rapid emergence of fluoroquinolone-resistant typhoidal Salmonella and the lack of robust diagnostic methods highlight the urgent need for highly sensitive molecular techniques. Here, we evaluated the performance of a rapid, reliable, and cost-effective molecular diagnostic approach for detecting Salmonella Typhi, including the globally dominant haplotype H58 lineage (H58), and Salmonella Paratyphi A. An in-house-built conventional polymerase chain reaction (PCR) was performed on a collection of blood-culture-positive strains, and the sensitivity and specificity were compared with those of the standard blood culture results. H58 and non-H58 Typhi lineages with distinct resistance patterns were confirmed from the previously reported sequencing data. Our PCR result showed that target genes SSPA2308, STY2513, and STY0307 demonstrated 100% sensitivity and specificity for Salmonella Paratyphi A, Salmonella Typhi, and H58 Salmonella Typhi, respectively. The PCR assay reliably detected bacterial DNA at 5.2 × 10⁴ colony-forming units (CFUs), with consistent amplification observed up to 10⁻¹ dilution. This single-nucleotide polymorphism (SNP)-based diagnostic approach has added a new dimension to designing unique markers for multidrug-resistant (MDR)-associated H58 lineage detection and has the potential to inform local treatment algorithms. Full article

Background/Objectives: Typhoid fever remains a major public health challenge in many low-income countries, where overlapping clinical symptoms and the limited reliability of conventional diagnostic procedures hinder accurate diagnosis. This study aims to develop a reliable and efficient diagnostic framework that automates typhoid fever detection from clinical data while preserving patient privacy. Methods: To achieve this objective, we propose a hybrid framework combining genetic algorithm (GA)–based feature selection, a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) deep learning classifier, and federated learning. The GA identifies the most informative clinical features, reducing redundancy and computational complexity. The selected features are then used to train a CNN–LSTM model in a federated learning setup using the Federated Averaging (FedAvg) algorithm, enabling collaborative model training across multiple clients without sharing raw patient data. Results: Experimental results show that the proposed framework achieves 92% accuracy, with a strong F1-score and satisfactory sensitivity. Compared to models trained on the full feature set, the proposed approach requires less memory and shorter training time, while maintaining balanced performance under class imbalance. Conclusions: These results demonstrate that integrating evolutionary feature selection, deep sequential learning, and federated training provides an effective and privacy-aware solution for multi-class typhoid fever diagnosis. The proposed framework is particularly suitable for clinical environments with limited data access and constrained resources. Full article

Background/Objectives: Febrile illness in returning travelers presents a diagnostic and operational challenge for emergency medicine clinicians as early symptoms of high-consequence tropical infections often overlap with common viral syndromes. This review synthesizes current evidence to guide frontline clinicians in the systematic evaluation, diagnosis, and management of internally acquired febrile illnesses with a focus on pathogen of greatest relevance to United States (US) emergency departments (ED). Methods: We conducted a narrative review of the literature addressing epidemiology, clinical presentation, diagnostic testing, and management strategies for key travel-associated infections. Special consideration was given to rapid diagnostic modalities, pediatric risk factors, and infections most frequently implicated in returning travelers, including chikungunya (CHIK), dengue virus (DENV) disease, Ebola virus (EBV) disease, malaria, Mpox, typhoid fever (TF), yellow fever (YF), and Zika virus (ZIKV) disease. Results: Effective evaluation begins with a detailed travel and exposure history, recognition of epidemiologic and clinical red flags, and targeted use of rapid diagnostic tests. Malaria remains the most common life-threatening cause of post-travel fever and the only pathogen with reliable Food and Drug Administration (FDA)-cleared rapid testing available in the ED. Arboviral infections such as DENV, CHIK, ZIKV, and YFrequire region-specific consideration and phase-appropriate molecular or serologic evaluation. Emerging and high-consequence pathogens, including Mpox and EBV, necessitate strict infection control measures and coordination with public health authorities. Pediatric travelers, particularly those visiting friends and relatives, face disproportionate risk for severe systemic infections and often require broader diagnostic testing. Conclusions: A structured approach integrating travel history, focused examination, rapid diagnostics, and early recognition of high-risk features is essential to improving outcomes for febrile returning travelers. Strengthened vector control, enhanced vaccination uptake, and global surveillance are critical to reducing future disease burden. Full article

Background: In recent years, there has been a significant increase in cases of tularemia, a rare zoonotic disease caused by Francisella tularensis, in Europe. Methods: To investigate the epidemiological, clinical, and therapeutic characteristics of tularemia patients in Germany, we performed a retrospective evaluation of tularemia cases treated between 2010 and 2025 at selected treatment centers of the Permanent Working Group of Competence and Treatment Centers for High Consequence Infectious Diseases (STAKOB) at the Robert Koch Institute. Results: A total of 65 patients (median age: 48.5 years; 66.2% male) were identified. Most common manifestation was ulceroglandular (70.7%), followed by oropharyngeal (13.8%), pulmonary (10.8%), oculoglandular (7.7%), typhoidal (4.6%), and meningitic (4.6%). Serological confirmation of the diagnosis was achieved in all patients (90.8% ELISA, 46.2% Western blot). PCR-based direct pathogen detection was successful in 26.2%. Bloodstream infection was detected in 4.6%. Median incubation period was 7 days (IQR: 4–10), with fever being the most common symptom in 96.9% and lymphadenopathy in 46.2%. Median time to recovery was 56 days (IQR: 37–80) in patients diagnosed and treated early (≤3 weeks after symptom onset), compared to 84 days (IQR: 66–182) in patients with late diagnosis (>3 weeks after symptom onset; p = 0.015). Empirical therapy with beta-lactam antibiotics was initiated in 49.2% of cases. Following suspicion of tularemia, 96.9% received recommended treatment with fluoroquinolones, tetracyclines, or aminoglycosides. Conclusions: Delayed diagnosis and inappropriate initial therapy can significantly prolong disease courses and increase morbidity. Early treatment with effective antibiotics, considering the intrinsic beta-lactam resistance of Francisella tularensis, leads to faster recovery. Full article

Background: Typhoid fever (TF) is a systemic infection caused by Salmonella enterica serovar Typhi, typically associated with regions where sanitation and access to clean water are inadequate. Although rare in non-endemic countries, TF remains a diagnostic consideration in travelers returning from endemic areas with febrile illness. Case report: We present the case of an 18-year-old female who developed TF following recent travel to Nigeria. The initial clinical presentation, including fever, dysuria, and abdominal pain, led to a misdiagnosis of acute pyelonephritis. Malaria, arboviral infections, acute viral hepatitis, and parasitic diseases were systematically ruled out through clinical evaluation, serological testing, and parasitological analysis. The clinical course was marked by fever, abdominal pain, somnolence, and hematological and hepatic abnormalities. Blood cultures confirmed the diagnosis, with the isolate verified and serotyped by the National Center of Infectious and Parasitic Diseases. Targeted antimicrobial treatment with ceftriaxone and levofloxacin resulted in full recovery, with no evidence of relapse or chronic carriage over a three-month follow-up period. Conclusions: This case highlights the critical importance of a structured differential diagnostic approach and microbiological confirmation in febrile patients with relevant travel history. In non-endemic settings, where TF may be underrecognized, early recognition, pathogen identification, and appropriate antimicrobial therapy remain essential to favorable outcomes and public health safety. Full article

(1) Background: Murine typhus, caused by Rickettsia typhi, is a neglected rickettsial disease and an underdiagnosed cause of acute febrile illness (AFI), particularly in endemic regions such as Indonesia. (2) Case description: We report a case series of four patients presenting with AFI of less than seven days in duration. Three patients were admitted with moderate disease, while one presented with septic shock with the macrophage activation-like syndrome (MALS) phenotype. Common clinical features included myalgia and headache; additional symptoms included cough, sore throat, and abdominal pain. Laboratory findings revealed bicytopenia, elevated transaminases, and raised inflammatory and bacterial infection markers. Common tropical infections—dengue, typhoid fever, and leptospirosis—and other potential sources of infection were excluded early during hospitalization. Diagnosis was confirmed by nucleic acid amplification testing (NAAT), which detected R. typhi in all patients. Doxycycline was initiated following confirmation, leading to defervescence within 36–48 h. (3) Conclusions: Murine typhus remains an underrecognized cause of febrile illness in Indonesia. In the near future, the inclusion of rickettsial testing in the diagnostic protocol of AFI will be crucial, as it enables timely administration of effective, low-cost treatment. Full article

Background: Typhoid fever remains a significant public health challenge, especially in developing countries where diagnostic resources are limited. Accurate and timely diagnosis is crucial for effective treatment and disease containment. Traditional diagnostic methods, while effective, can be time-consuming and resource-intensive. This study aims to develop a lightweight machine learning-based diagnostic tool for the early and efficient detection of typhoid fever using clinical data. Methods: A custom dataset comprising 14 clinical and demographic parameters—including age, gender, headache, muscle pain, nausea, diarrhea, cough, fever range (°F), hemoglobin (g/dL), platelet count, urine culture bacteria, calcium (mg/dL), and potassium (mg/dL)—was analyzed. A machine learning metamodel, integrating Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Decision Tree classifiers with a Light Gradient Boosting Machine (LGBM), was trained and evaluated using k-fold cross-validation. Performance was assessed using precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results: The proposed metamodel demonstrated superior diagnostic performance, achieving a precision of 99%, recall of 100%, and an AUC of 1.00. It outperformed traditional diagnostic methods and other standalone machine learning algorithms, offering high accuracy and generalizability. Conclusions: The lightweight machine learning metamodel provides a cost-effective, non-invasive, and rapid diagnostic alternative for typhoid fever, particularly suited for resource-limited settings. Its reliance on accessible clinical parameters ensures practical applicability and scalability, potentially improving patient outcomes and aiding in disease control. Future work will focus on broader validation and integration into clinical workflows to further enhance its utility. Full article

Background/Objectives: Although a “forgotten” disease in developed countries, typhoid fever remains a significant global health problem, especially in regions with inadequate sanitation and overcrowding. Despite medical advances, this systemic bacterial infection, caused by Salmonella Typhi, continues to affect millions worldwide. Accurate diagnosis and timely treatment are crucial to prevent severe complications and mortality. Even though antibiotic therapy is effective, the emergence of drug-resistant strains is a growing challenge. Methods: We present a series of cases encountered in a tertiary infectious disease hospital in Romania over 15 years. Results: The hospitalised patients were mainly from Sub-Saharan Africa and the Indian subcontinent; the median time between the onset of the first symptoms and hospital admission was 15 days. The symptoms encountered along with fever were headache, chills, cough, diarrhoea and tachycardia, an unusual feature in the clinical picture of this disease. Aneosinophilia (the absence of peripheral eosinophilic granulocytes) was the most frequently encountered laboratory finding, followed by increased serum transaminases and inflammatory syndrome. Conclusions: S. Typhi was generally identified from blood culture, demonstrating, except in one case, resistance to ciprofloxacin and, in several cases, multi-drug resistance (MDR). In this series of cases, all strains were sensitive to ceftriaxone. Full article

Malaria and Typhoid fever are prevalent diseases in tropical regions, and both are exacerbated by unclear protocols, drug resistance, and environmental factors. Prompt and accurate diagnosis is crucial to improve accessibility and reduce mortality rates. Traditional diagnosis methods cannot effectively capture the complexities of these diseases due to the presence of similar symptoms. Although machine learning (ML) models offer accurate predictions, they operate as “black boxes” with non-interpretable decision-making processes, making it challenging for healthcare providers to comprehend how the conclusions are reached. This study employs explainable AI (XAI) models such as Local Interpretable Model-agnostic Explanations (LIME), and Large Language Models (LLMs) like GPT to clarify diagnostic results for healthcare workers, building trust and transparency in medical diagnostics by describing which symptoms had the greatest impact on the model’s decisions and providing clear, understandable explanations. The models were implemented on Google Colab and Visual Studio Code because of their rich libraries and extensions. Results showed that the Random Forest model outperformed the other tested models; in addition, important features were identified with the LIME plots while ChatGPT 3.5 had a comparative advantage over other LLMs. The study integrates RF, LIME, and GPT in building a mobile app to enhance the interpretability and transparency in malaria and typhoid diagnosis system. Despite its promising results, the system’s performance is constrained by the quality of the dataset. Additionally, while LIME and GPT improve transparency, they may introduce complexities in real-time deployment due to computational demands and the need for internet service to maintain relevance and accuracy. The findings suggest that AI-driven diagnostic systems can significantly enhance healthcare delivery in environments with limited resources, and future works can explore the applicability of this framework to other medical conditions and datasets. Full article

Medical diagnosis is the process of finding out what is the disease a person may be suffering from. From the symptoms and their gradation, the doctor can decide which the dominant disease is. Nevertheless, in the process of medical diagnosis, there is ambiguity, uncertainty, and a lack of medical knowledge that can adversely affect the doctor’s judgment. Thus, a tool of artificial intelligence, fuzzy logic, has come to enhance the decision-making of diagnosis in a medical environment. Fuzzy set theory uses the membership degree to characterize the uncertainty and, therefore, fuzzy sets are integrated into imperfect data in order to make a reliable diagnosis. The patient’s medical status is represented as q-rung orthopair fuzzy values. In this paper, many versions and methodologies were applied such as the composite fuzzy relation, fuzzy sets extensions (q-ROFS) with aggregation operators, and similarity measures, which were proposed as decision-making intelligent methods. The aim of this procedure was to find out which of the diseases (viral fever, malaria fever, typhoid fever, stomach problems, and chest problems), was the most influential for each patient. The work emphasizes the contribution of aggregation operators in medical data in order to contain more than one expert’s aspect. The performance of the methodology was quite good and interesting as most of the results were in agreement with previous works. Full article

Multi-criteria decision-making (MCDM) is now frequently utilized to solve difficulties in everyday life. It is challenging to rank possibilities from a set of options since this process depends on so many conflicting criteria. The current study focuses on recognizing symptoms of illness and then using an MCDM diagnosis to determine the potential disease. The following symptoms are considered in this study: fever, body aches, fatigue, chills, shortness of breath (SOB), nausea, vomiting, and diarrhea. This study shows how the generalised dual hesitant hexagonal fuzzy number $\left(GDH{H}_{\chi }FN\right)$ is used to diagnose disease. We also introduce a new de-fuzzification method for $GDH{H}_{\chi }FN$. To diagnose a given condition, $GDH{H}_{\chi }FN$ coupled with MCDM tools, such as the fuzzy criteria importance through inter-criteria correlation (FCRITIC) method, is used for finding the weight of criteria. Furthermore, the fuzzy weighted aggregated sum product assessment (FWASPAS) method and a fuzzy combined compromise solution (FCoCoSo) are used to rank the alternatives. The alternative diseases are chosen to be malaria, influenza, typhoid, dengue, monkeypox, ebola, and pneumonia. A sensitivity analysis is carried out on three patients affected by different diseases to assess the validity and reliability of our methodologies. Full article

Scrub typhus is a mite-borne disease caused by a Gram-negative obligately intracellular bacillus, Orientia tsutsugamushi. The disease is endemic in the Asia–Australia–Pacific region, including Thailand. Scrub typhus generally manifests as acute undifferentiated febrile fever along with myalgia, rash, and lymphadenopathy. An eschar can be a valuable diagnostic clue, but this skin lesion may be missed in some patients. The disease symptoms resemble those of other febrile illnesses such as leptospirosis, typhoid, murine typhus, malaria, and dengue fever, making a laboratory diagnosis necessary for the definitive diagnosis. In this study, we expressed a recombinant protein derived from 56-kDa type-specific antigen of O. tsutsugamushi Karp serotype and tested its ability to detect and differentiate scrub typhus infection. IgM and IgG antibodies were determined in sera from scrub typhus (n = 92) and other febrile illness patients (murine typhus (n = 25), melioidosis (n = 36), leptospirosis (n = 42), and dengue (n = 35)) from Thailand. Sensitivities of 87.0% and 59.8% with a specified assay cut-off were obtained for IgM and IgG indirect ELISAs, respectively, with a specificity of 100% in both tests. The sensitivity was increased to 95.7% when a combination of IgM and IgG ELISAs results was considered. Our study suggested a potential of the 56-kDa recombinant protein for further development and evaluation for use in scrub typhus serodiagnosis. Full article

The aim of this systematic review is to distinguish the clinical features of immunocompetent children with non-typhoid Salmonella spondylodiscitis and summarize the diagnosis, diagnostic tools, and treatment methods to guide clinicians. The review was conducted according to the preferred PRISMA guidelines. We conducted a literature search in the PubMed, Embase, and Cochrane Library databases. Article screening, data extraction, and study evaluation were performed by two independent reviewers. A total of 20 articles, published between 1977 and 2020, were selected, which included 21 patients with average age of 12.76 years (range, 2–18) without comorbidities; in total, 19% of the patients had positive blood cultures for non-typhoid Salmonella, and 80.9% underwent either CT-guided or open biopsy, which were positive for NTS. All infections were monomicrobial, and 11 different serotypes of non-typhoid Salmonella were identified. Analyzing the reviewed cases, 52.4% of the patients presented with fever, 90.5% had localized pain, and only 19% had gastroenteritis. The most common level of discitis was the lumbar region, especially the L4/L5 level. Primarily, third-generation cephalosporin was administered, and antibiotic treatment was given for an average of 9.6 weeks. Non-typhoid Salmonella spondylodiscitis is a rare clinical entity in healthy and immunocompetent children. The identification of the responsible organism is essential to guide antibiotic therapy and define the treatment duration. A significant limiting factor in this systematic review was the lack of published research articles and case series due to the rarity of the disease. Full article

Typhoid fever, also known as typhoid, is a life-threatening bacterial infection that remains a global health concern. The infection is associated with a significant morbidity and mortality rate, resulting in an urgent need for specific and rapid detection tests to aid prevention and management of the disease. The present review aims to assess the specificity and sensitivity of the available literature on the immunodiagnostics of typhoid fever. A literature search was conducted using three databases (PubMed, ProQuest and Scopus) and manual searches through the references of identified full texts to retrieve relevant literature published between 1 January 2011 and 31 December 2020. Of the 577 studies identified in our search, 12 were included in further analysis. Lipopolysaccharides (LPS) and hemolysin E (HlyE) were the most frequently studied antigens. The specimens examined in these studies included serum and saliva. Using blood culture as the gold standard, anti-LPS IgA gave the highest sensitivity of 96% (95% CI: 93–99) and specificity of 96% (95% CI: 93–99) for distinguishing between typhoid cases and healthy controls, whereas the combination of anti-LPS and anti-flagellin total IgGAM gave the highest sensitivity of 93% (95% CI: 86–99) and specificity of 95% (95% CI: 89–100) for distinguishing typhoid cases and other febrile infections. A comparably high sensitivity of 92% (95% CI: 86–98) and specificity of 89% (95% CI: 78–100) were shown in testing based on detection of the combination of anti-LPS (IgA and IgM) and anti-HlyE IgG as well as a slightly lower sensitivity of 91% (95% CI: 74–100) in the case of anti-50kDa IgA. Anti-50kDa IgM had the lowest sensitivity of 36% (95% CI: 6–65) against both healthy and febrile controls. The development of a rapid diagnostic test targeting antibodies against lipopolysaccharides combined with flagellin appeared to be a suitable approach for the rapid detection test of typhoid fever. Saliva is added benefit for rapid typhoid diagnosis since it is less invasive. As a result, further studies could be done to develop additional approaches for adopting such samples. Full article