Search Results (28)

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: Non-typhoidal (NT) Salmonella spp. constitutes a major cause of foodborne illness. Culture is the gold standard, but it is time consuming, whereas multiplex nucleic acid amplification tests (NAATs)/Polymerase Chain Reaction (PCR) offer faster detection with variable reported performance. Objectives: To compare the diagnostic accuracy of multiplex NAAT/PCR and culture for Salmonella spp. using various statistical models with or without a gold standard assumption. Methods: A systematic search (PubMed, Web of Science, Scopus; up to April 2024) identified 44 studies (55 comparisons). Diagnostic performance was evaluated using the frequentists bivariate model (BM) and Split Component Synthesis (SCS) and the Bayesian bivariate models (BBMs) and hierarchical summary ROC (BHSROC). Results: Across models, multiplex NAAT/PCR demonstrated high specificity (>98%) but model-dependent variability in sensitivity (85.5–94.8%), consistently substantial between study heterogeneity and threshold variation. The BM and BBM yielded a higher sensitivity estimate with narrower non-overlapping confidence intervals while SCS and BHSROC models, which are more robust to threshold differences, produced more conservative estimates with wider uncertainty. In Bayesian latent class analyses, culture remained highly accurate (Se: 97.17%, 95% CrI: 70.3–99.99; Sp: 96.06%, 95% CrI: 78.9–99.99), but with wide credible intervals indicating variation between studies, perhaps due to the different protocols used. Conclusion: Model choice affects inferred diagnostic accuracy, particularly when high heterogeneity is present. Both multiplex NAAT/PCR and culture showed high accuracy; hence, a combination of the two tests could optimise rapid diagnosis and treatment. Future research should include cost effectiveness and decision analysis to update the diagnostic algorithms. 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/Objectives: This study presents a novel double-weighted Bayesian Ensemble Machine Learning (DW-EML) model aimed at improving the classification and prediction of metabolomics data. This discipline, which involves the comprehensive analysis of metabolites in a biological system, provides valuable insights into complex biological processes and disease states. As metabolomics assumes an increasingly prominent role in the diagnosis of human diseases and in precision medicine, there is a pressing need for more robust artificial intelligence tools that can offer enhanced reliability and accuracy in medical applications. The proposed DW-EML model addresses this by integrating multiple classifiers within a double-weighted voting scheme, which assigns weights based on the cross-validation accuracy and classification confidence, ensuring a more reliable prediction framework. Methods: The model was applied to publicly available datasets derived from studies on critical illness in children, chronic typhoid carriage, and early detection of ovarian cancer. Results: The results demonstrate that the DW-EML approach outperformed methods traditionally used in metabolomics, such as the Partial Least Squares Discriminant Analysis in terms of accuracy and predictive power. Conclusions: The DW-EML model is a promising tool for metabolomic data analysis, offering enhanced robustness and reliability for diagnostic and prognostic applications and potentially contributing to the advancement of personalized and precision medicine. 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

Background: Gastric diseases caused, in particular, by Campylobacter, non-typhoidal Salmonella, and Shigella resulting from food and/or water problems, are a disproportionately distributed burden in developing countries in Central Africa. The aim of this work was to compile a list of studies establishing the prevalence of the involvement of these bacterial genera in diarrheal syndromes in Central Africa from 1998 to 2022. Methods: The Preferred Reporting Articles for Systemic Reviews and Meta-Analyses, six (6) database (Pubmed, Google Scholar, Semantic Scholar, Freefullpdf, and Scinapse) were perused for research on the role of Campylobacter, Salmonella and Shigella diarrheal infections in humans and animals, in 9 country of Central Africa over from 1998 to 2022. Results: Seventeen articles were selected, including 16 on humans and one on animals. These data were recorded in 6 of the 9 countries of Central Africa, including Gabon (5), Angola (3), Cameroon (3), the Democratic Republic of Congo (3), Chad (2), and the Central African Republic (1). Mono-infections with Salmonella spp. were the most predominant (55.56%, n = 5/9), followed by an equal proportion of Campylobacter spp. and Shigella spp. with 44.44% (4/9), respectively and, co-infections with Campylobacter/Salmonella spp. and Salmonella/Shigella spp. with a prevalence of 11.11% (1/9) respectively. The most used diagnostic tool was conventional culture (82.35%) against 17.65% for PCR or real-time PCR. Conclusion: Despite the paucity of recorded data on the prevalence of diarrheal infections due to Campylobacter in this sub-region, it is crucial that scientific studies focus on the diagnosis and monitoring of this zoonotic bacterium. Also, improved diagnosis will necessarily involve the integration of molecular tools in the diagnosis of these diarrheic syndromes in both humans and animals. 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

Gallbladder carcinoma represents the most aggressive biliary tract cancer and the sixth most common gastrointestinal malignancy. The diagnosis is a challenging clinical task due to its clinical presentation, which is often non-specific, mimicking a heterogeneous group of diseases, as well as benign processes such as complicated cholecystitis, xanthogranulomatous cholecystitis, adenomyomatosis, porcelain gallbladder or metastasis to the gallbladder (most frequently derived from melanoma, renal cell carcinoma). Risk factors include gallstones, carcinogen exposure, porcelain gallbladder, typhoid carrier state, gallbladder polyps and abnormal pancreaticobiliary ductal junction. Typical imaging features on CT or MRI reveal three major patterns: asymmetric focal or diffuse wall-thickening of the gallbladder, a solid mass that replaces the gallbladder and invades the adjacent organs or as an intraluminal enhancement mass arising predominantly from the gallbladder fundus. The tumor can spread to the liver, the adjacent internal organs and lymph nodes. Depending on the disease stage, surgical resection is the curative treatment option in early stages and adjuvant combination chemotherapy at advanced stages. The purpose of this scientific paper is to fully illustrate and evaluate, through multimodality imaging findings (CT and MRI), different presentations and imaging scenarios of gallbladder cancer in six patients and thoroughly analyze the risk factors, patterns of spread and differential diagnosis regarding each particular case. Full article

Salmonella enterica subsp. enterica serovar Gallinarum (SG) has two distinct biovars, Pullorum and Gallinarum. They are bacterial pathogens that exhibit host specificity for poultry and aquatic birds, causing severe systemic diseases known as fowl typhoid (FT) and Pullorum disease (PD), respectively. The virulence mechanisms of biovars Gallinarum and Pullorum are multifactorial, involving a variety of genes and pathways that contribute to their pathogenicity. In addition, these serovars have developed resistance to various antimicrobial agents, leading to the emergence of multidrug-resistant strains. Due to their economic and public health significance, rapid and accurate diagnosis is crucial for effective control and prevention of these diseases. Conventional methods, such as bacterial culture and serological tests, have been used for screening and diagnosis. However, molecular-based methods are becoming increasingly important due to their rapidity, high sensitivity, and specificity, opening new horizons for the development of innovative approaches to control FT and PD. The aim of this review is to highlight the current state of knowledge on biovars Gallinarum and Pullorum, emphasizing the importance of continued research into their pathogenesis, drug resistance and diagnosis to better understand and control these pathogens in poultry farms. Full article