Infectious Disease Reports最新文献

{"title":"Associations of Influenza Illness During Pregnancy with Pregnancy Loss and Program-Defined Child Health Monitoring Indicator: A Matched Cohort Study.","authors":"Yuanyuan Zhang, Yan Shao, Suizan Zhou, Qianlan Wu, Ningning Du, Mingzhi Zhang, Qian Feng, Lin Bao, Yuanyuan Pang, Yayun Tan, Pengwei Cui, Jun Zhang, Haibing Yang, Suping Zhang, Liling Chen, Ying Song, W William Schluter","doi":"10.3390/idr18040082","DOIUrl":"10.3390/idr18040082","url":null,"abstract":"<p><strong>Background/objectives: </strong>This prospective cohort study assessed the impact of seasonal influenza during pregnancy on fetal and infant health outcomes.</p><p><strong>Methods: </strong>Pregnant women with laboratory-confirmed influenza were matched (1:4) by age and pregnancy loss history with women without influenza from annual cohorts in Suzhou, China, during 2015-2018. Participants underwent twice-weekly follow-up for influenza illness, and medical records were linked to ascertain outcomes. Multivariable regression models were used to estimate associations.</p><p><strong>Results: </strong>We included 441 pregnant women with influenza and 1764 without it. Four (0.9%) in the influenza group had late pregnancy loss, whereas one (0.1%) in the non-influenza group did. Influenza was associated with late pregnancy loss (adjusted hazard ratio [aHR] 31.1, 95% Confidence Interval [CI]: 1.3-756.8, <i>p</i> = 0.035). Four (0.9%) in the influenza group experienced early pregnancy loss, while five (0.3%) in the non-influenza group did. Influenza was not significantly associated with early pregnancy loss (aHR 2.1, 95% CI: 0.4-10.4, <i>p</i> = 0.374). By 8 months of age, infants born to 198 of the 441 mothers (44.9%) in the influenza group met the criteria for the program-defined child health monitoring indicator, compared with infants born to 650 of the 1764 mothers (36.8%) in the non-influenza group. Overweight/obesity was the largest contributor: 142 (32.2%) versus 470 (26.6%). Maternal influenza was associated with higher odds of meeting program-defined child health monitoring indicator in offspring (adjusted odds ratio [aOR] 1.4, 95% CI: 1.1-1.8, <i>p</i> = 0.006), with a significant contribution from overweight/obesity (aOR 1.3, 95% CI: 1.0-1.7, <i>p</i> = 0.045).</p><p><strong>Conclusions: </strong>In this cohort, influenza during pregnancy was associated with an increased risk of late pregnancy loss and program-defined child health monitoring indicator in offspring, particularly overweight/obesity. Because the observed association with pregnancy loss was based on a small number of events and had a wide confidence interval, it should be interpreted cautiously and confirmed in larger studies. These findings are consistent with current recommendations supporting influenza vaccination during pregnancy to protect maternal and infant health.</p>","PeriodicalId":13579,"journal":{"name":"Infectious Disease Reports","volume":"18 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13512286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148827597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maternal and Perinatal Outcomes Associated with Maternal HIV Infection in a Tertiary Hospital in Eastern Cape Province, South Africa.","authors":"Viwe Sodo-Mbotya, Ntandazo Dlatu, Geoffrey A B Buga","doi":"10.3390/idr18040081","DOIUrl":"10.3390/idr18040081","url":null,"abstract":"&lt;p&gt;&lt;p&gt;&lt;b&gt;Background&lt;/b&gt;: Despite substantial progress in prevention of mother-to-child transmission (PMTCT) programmes and widespread access to antiretroviral therapy (ART), maternal HIV infection remains associated with adverse maternal and neonatal outcomes in many high HIV-burden settings. This study compared maternal and perinatal outcomes among women living with HIV and HIV-negative women delivering at a tertiary referral hospital in the Eastern Cape Province, South Africa. &lt;b&gt;Methods&lt;/b&gt;: A retrospective comparative cohort study was conducted using routinely collected clinical records of 600 women (300 HIV-positive and 300 HIV-negative) who delivered at Nelson Mandela Academic Hospital between January and December 2019. Maternal, obstetric, and neonatal characteristics were compared according to maternal HIV status. Associations were evaluated using chi-square tests, multivariable logistic regression, Kaplan-Meier survival analysis, and Cox proportional hazards regression models. &lt;b&gt;Results&lt;/b&gt;: Women living with HIV were older, had higher parity, and were more likely to have documented anaemia and delayed antenatal care attendance than HIV-negative women. HIV-exposed pregnancies had higher frequencies of preterm birth (26.3% vs. 20.3%) and low birthweight (LBW). In adjusted analyses, maternal HIV-positive status remained independently associated with increased odds of LBW (AOR = 1.88; 95% CI: 1.18-3.00; &lt;i&gt;p&lt;/i&gt; = 0.008). LBW was independently associated with neonatal intensive care unit (NICU) admission (AOR = 2.45; 95% CI: 1.46-4.11; &lt;i&gt;p&lt;/i&gt; &lt; 0.001) and an increased hazard of in-hospital neonatal mortality (HR = 2.40; 95% CI: 1.55-3.70; &lt;i&gt;p&lt;/i&gt; &lt; 0.001). Maternal HIV-positive status (HR = 1.75; 95% CI: 1.12-2.71; &lt;i&gt;p&lt;/i&gt; = 0.015) and unsuppressed maternal viral load (HR = 2.05; 95% CI: 1.13-3.73; &lt;i&gt;p&lt;/i&gt; = 0.018) were also associated with increased hazards of neonatal mortality. However, these findings should be interpreted cautiously, given the limited number of neonatal mortality events (&lt;i&gt;n&lt;/i&gt; = 32). Among HIV-exposed infants with documented HIV test results, the observed mother-to-child transmission rate was 1.7%. However, incomplete infant follow-up and HIV testing data limited the precision of this estimate. Among women living with HIV, birthweight did not differ significantly according to the timing of ART initiation. &lt;b&gt;Conclusions&lt;/b&gt;: In this tertiary referral hospital cohort, maternal HIV infection was associated with adverse maternal and neonatal outcomes, particularly anemia, preterm birth, and LBW. LBW emerged as an important predictor of neonatal morbidity and mortality. These findings support continued efforts to strengthen integrated HIV and maternal healthcare services, promote early antenatal care engagement, maintain maternal viral suppression, and improve monitoring and care of high-risk neonates. Given the retrospective observational design, incomplete follow-up for selected outcomes, limited numbers of neona","PeriodicalId":13579,"journal":{"name":"Infectious Disease Reports","volume":"18 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13512250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148827766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human Infections Caused by Flavobacteriales: Clinical Characteristics and Antimicrobial Susceptibility Patterns.","authors":"Grant Whitmer, Maria Joyce","doi":"10.3390/idr18040080","DOIUrl":"10.3390/idr18040080","url":null,"abstract":"<p><strong>Background/objectives: </strong>Bacteria of the order Flavobacteriales are environmental Gram-negative bacilli. Several organisms within the order represent rare but clinically important causes of infection in human hosts, particularly in immunocompromised and hospitalized individuals. Eight genera have been implicated in human infections: <i>Elizabethkingia</i>, <i>Capnocytophaga</i>, <i>Chryseobacterium</i>, <i>Myroides</i>, <i>Bergeyella</i>, <i>Flavobacterium</i>, <i>Empedobacter</i>, and <i>Weeksella</i>. Despite their clinical relevance, there has been, as yet, no consolidated summary of clinical characteristics or resistance patterns across pathogens within the order.</p><p><strong>Methods: </strong>This narrative review examines the currently published data on human infections caused by organisms within Flavobacteriales. 63 sources, including 57 peer-reviewed articles, were included.</p><p><strong>Results: </strong>The clinical associations of infections caused by each of the above genera are discussed. In addition, available antimicrobial resistance data for the same organisms are summarized. Knowledge gaps are identified, including the lack of dedicated antimicrobial susceptibility breakpoints and limited data on rare categories of organisms.</p><p><strong>Conclusions: </strong>Human pathogens within Flavobacteriales display a number of commonalities, including a predilection for immunocompromised hosts, and frequently display resistance to carbapenems and aminoglycosides. Additional studies to better characterize these pathogens and optimize the approach to their treatment are strongly warranted.</p>","PeriodicalId":13579,"journal":{"name":"Infectious Disease Reports","volume":"18 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13512226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148827803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Rare Case of Acute Rheumatic Fever Diagnosed After Recent Travel Abroad.","authors":"Debora Agreiter, Stefan Fuchs, Franziska Marti","doi":"10.3390/idr18040079","DOIUrl":"10.3390/idr18040079","url":null,"abstract":"<p><strong>Background: </strong>Acute rheumatic fever (ARF) is a clinical syndrome triggered by <i>group A streptococcal</i> (<i>GAS</i>) infections and characterized by fever, carditis, and arthritis as its main manifestations. Diagnosis relies on the revised Jones criteria. We report a rare case of ARF diagnosed in Switzerland.</p><p><strong>Case presentation: </strong>An 18-year-old woman presented to the emergency department five days after returning from a one-week stay in Egypt with intermittent fever and a transient sore throat, followed by migratory joint pain. Laboratory testing revealed leukocytosis, elevated C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR). Chest X-ray, urine analysis, and (travel-associated) pathogen-testing (<i>Epstein-Barr virus</i>, <i>Cytomegalovirus</i>, <i>human immunodeficiency virus</i>, <i>Malaria</i>, <i>Dengue</i>, <i>Zika</i>, <i>Chikungunya</i>) were unremarkable. Blood cultures remained negative. Initially, a viral respiratory infection was suspected and symptomatic treatment established. Days later the patient was re-evaluated due to persistent symptoms. Leukocyte count (Lc) and CRP further increased and subcutaneous nodules appeared. The antistreptolysin-O (ASO)-titer was elevated more than three times higher than the normal upper limit and proofed serological evidence of a preceding streptococcal infection (<i>GAS</i> throat culture had not been performed). ARF was diagnosed according to the Jones criteria. Treatment with amoxicillin and a high dose of acetylsalicylic acid (ASA) led to a rapid clinical improvement. Secondary prophylaxis with depot penicillin (benzathine penicillin G; 1.2 million units; once monthly) was initiated to prevent rheumatic heart disease.</p><p><strong>Conclusions: </strong>This case highlights a rare occurrence of ARF following travel to Egypt, with probable acquisition of <i>group A streptococcal</i> infection during the trip. It emphasizes consideration of ARF even in Western countries with low ARF-prevalence if clinical history is suggestive. Early recognition and initiation of antimicrobial and anti-inflammatory therapy is crucial to prevent cardiac involvement. To the best of our knowledge, this represents one of the first cases diagnosed in Switzerland in the past 20 years.</p>","PeriodicalId":13579,"journal":{"name":"Infectious Disease Reports","volume":"18 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13512616/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148827429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Malaria Parasite Image Classification on Real Microscopy Data.","authors":"Francesco Branda, Lilia Andriani, Riccardo Lucis, Annamaria Defilippo, Ugo Lomoio, Barbara Puccio, Giancarlo Ceccarelli, Massimo Ciccozzi, Fabio Scarpa, Pierangelo Veltri, Pietro Hiram Guzzi","doi":"10.3390/idr18040078","DOIUrl":"10.3390/idr18040078","url":null,"abstract":"<p><p><b>Background</b>: Accurate and timely malaria diagnosis with species-level identification of <i>Plasmodium</i> parasites is critical for guiding effective treatment and disease management. Although light microscopy remains the diagnostic gold standard, its dependence on highly trained personnel limits accessibility, particularly in resource-constrained settings. Recent advances in deep learning have enabled automated image-based diagnosis with promising performance; however, reliable differentiation among <i>Plasmodium</i> species continues to pose a major challenge. This study aims to evaluate and compare the effectiveness of different deep learning architectures for automated species identification from microscopy images. <b>Methods</b>: Three deep learning architectures were systematically assessed: a convolutional backbone (ResNet50), a Vision Transformer (ViT), and a hybrid ResNetViT model. All models were trained from scratch, without using pretrained weights, on a dataset comprising real-world thick blood smear images augmented with publicly available microscopy data from Kaggle. In the hybrid architecture, the ResNet module was used to extract robust local morphological features, while the ViT component captured long-range dependencies and contextual relationships within images. <b>Results</b>: In cross-validation experiments, all three architectures consistently achieved high diagnostic performance. ResNet50 attained the highest accuracy (96.9%), an F1-score of 96.3%, and an ROC-AUC of 0.997. The Vision Transformer achieved 93.1% accuracy, 91.7% F1-score, and an ROC-AUC of 0.989. The hybrid ResNetViT reached 95.2% accuracy, 94.2% F1-score, and an ROC-AUC of 0.995. These results confirm that all architectures can reliably distinguish among <i>Plasmodium</i> species. Although ResNet50 achieved the highest raw accuracy, the hybrid model showed the most stable calibration and the closest qualitative agreement between its attention maps and expert-annotated parasite locations. <b>Conclusions</b>: The findings demonstrate that convolutional, transformer-based, and hybrid deep learning architectures can be successfully trained on real microscopy data for species-level malaria diagnosis. These results support the feasibility of deploying scalable, automated diagnostic systems to improve both accuracy and accessibility of malaria detection, particularly in resource-limited healthcare settings.</p>","PeriodicalId":13579,"journal":{"name":"Infectious Disease Reports","volume":"18 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13512370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148827662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early Machine Learning-Based Identification of Hospitalized Patients at Low Risk of Respiratory Deterioration or Mortality in Community-Acquired Pneumonia: External Validation of a Multivariable Model.","authors":"Claudia Gyimah, Prasamsa Pudasaini, Allison LeMahieu, Phillip Schulte, Yewande E Odeyemi","doi":"10.3390/idr18040077","DOIUrl":"10.3390/idr18040077","url":null,"abstract":"<p><strong>Background/objective: </strong>To externally validate our previously published machine learning model for identifying hospitalized patients with community-acquired pneumonia (CAP) at low risk of respiratory deterioration or death using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.</p><p><strong>Methods: </strong>This is a retrospective cohort study of adult patients (≥18 years) who were admitted with CAP and acute hypoxemic respiratory failure using the publicly available MIMIC-IV critical care dataset (Beth Israel Deaconess Medical Center, Boston, MA). We conducted an external validation study of a previously developed gradient boosting machine (GBM) model without recalibration using data available within the first 6 h of hospital admission.</p><p><strong>Results: </strong>For the primary composite outcome (need for advanced respiratory support [high flow nasal cannula (HFNC), non-invasive mechanical ventilation (NIMV), invasive mechanical ventilation (IMV)] or in-hospital death), the gradient boosting model demonstrated comparable performance in the derivation and external validation cohorts. The area under the receiver operating characteristic curve (AUC) was 0.713 in the Mayo cohort (n = 4379) and 0.689 in the MIMIC-IV cohort. Accuracy was 0.612 (95% confidence interval [CI], 0.595-0.628) versus 0.606 (95% CI 0.600-0.611), specificity 0.574 versus 0.523, sensitivity 0.723 versus 0.754, NPV 0.860 versus 0.842, and PPV 0.364 versus 0.401, respectively. For secondary outcomes, model discrimination was comparable between cohorts. The AUC for in-hospital mortality was 0.727 in the Mayo Cohort versus 0.733 in MIMIC-IV; for IMV, 0.736 versus 0.724; and for NIMV, 0.732 versus 0.708.</p><p><strong>Conclusions: </strong>Our machine learning algorithm demonstrated good discrimination and a high negative predictive value for identifying low-risk hospitalized CAP patients for respiratory deterioration or death in the external MIMIC-IV dataset, supporting its potential utility for prognostic enrichment in pneumonia clinical trials.</p>","PeriodicalId":13579,"journal":{"name":"Infectious Disease Reports","volume":"18 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13512326/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148827650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medication Patterns as a Lens on Health Needs Among Migrant Agricultural Workers in Informal Settlements in Apulia: A Descriptive Outreach Study.","authors":"Cesare De Virgilio Suglia, Renato Laforgia, Marcella Schiavone, Anna Belfiore, Giacomo Guido, Rosa Buonamassa, Alba Cuxart-Graell, Martina Di Noto, Valeria Mele, Emanuele Costanza, Venilia Cocco, Alexandre Meduri, Lucia Raho, Nicole Laforgia, Roberta Iatta, Giovanni Putoto, Francesco Di Gennaro","doi":"10.3390/idr18040076","DOIUrl":"10.3390/idr18040076","url":null,"abstract":"<p><p><b>Background:</b> Migrant agricultural workers in Italy often experience social and health vulnerabilities, including unstable housing and limited access to primary care. In Southern Italy, many live in informal settlements and seek care through outreach services. This study describes patterns of pharmacological treatment in this population and examines how they relate to the clinical conditions managed in mobile clinics. <b>Methods:</b> We analyzed routinely collected data from 2928 unique patients (8547 clinical encounters; 8965 treatment occurrences) managed by Doctors with Africa CUAMM mobile clinics in 12 informal settlements in Apulia, Italy, between 2017 and 2026. Diagnoses were grouped into clinical categories, and pharmacological treatments were classified by therapeutic class. We conducted a descriptive analysis of the distribution of diagnostic categories and associated treatments. <b>Results:</b> The population was predominantly male (96.5%), young (81% <45 years), and largely excluded from regular primary care (93.5% without a General Practitioner). Musculoskeletal disorders and fatigue were the leading diagnostic category (34.0%), followed by gastrointestinal (13.5%) and respiratory conditions (13.0%). Non-steroidal anti-inflammatory drugs (NSAIDs) and analgesics were the most frequently recorded treatments (32.6% of treatment occurrences). Among musculoskeletal presentations, NSAIDs were used in 76% of cases. Gastroprotective agents were documented in 52% of encounters with gastrointestinal symptoms. Among cardiovascular presentations, 87.7% of treatment occurrences involved chronic management with antihypertensives or beta-blockers. <b>Conclusions:</b> In this outreach setting, medication use provides a descriptive picture of common health problems and treatment responses among migrant agricultural workers living in informal settlements. The prominent use of symptomatic pharmacological relief, particularly NSAIDs in musculoskeletal conditions, suggests that care is often focused on managing pain and acute complaints in a population facing barriers to continuous primary care. These findings support the need for stronger inclusion of migrant workers in the National Health Service and for policies that address underlying social and structural determinants of health.</p>","PeriodicalId":13579,"journal":{"name":"Infectious Disease Reports","volume":"18 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148578519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facial Cellulitis Mimicking Ludwig's Angina in a Patient with Chronic Myelogenous Leukemia on Dasatinib Therapy.","authors":"Nicole Liang, Jenna DeTemple, Christopher E Potts, Usman Alizai, Charles Meadows","doi":"10.3390/idr18040075","DOIUrl":"10.3390/idr18040075","url":null,"abstract":"<p><strong>Background: </strong>Immunosuppressed patients are at an increased risk for developing odontogenic and orofacial infections, which can present with atypical processes and features that may mimic rare but life-threatening infections such as Ludwig's angina. Differentiating cellulitis from a deep neck space infection is often challenging in this population in acute settings due to a broad differential diagnosis and blunted inflammatory responses. This diagnostic uncertainty complicates acute risk stratification and may delay recognition of conditions requiring early airway evaluation and intervention.</p><p><strong>Case presentation: </strong>We present the case of a 28-year-old male with chronic myelogenous leukemia on immunosuppression with dasatinib who developed unilateral facial swelling and severe odontogenic pain that was refractory to empiric antibiotic therapy. The patient's presentation with rapid clinical progression, early trismus, submandibular involvement, and floor-of-mouth tenderness raised significant concern for evolving Ludwig's angina. Laboratory evaluation demonstrated elevated inflammatory markers, including erythrocyte sedimentation rate and C-reactive protein, further complicating early assessment. Imaging was promptly obtained to determine the nature of the infection, and the patient was admitted for intravenous antibiotic therapy and airway monitoring. Clinical improvement ensued.</p><p><strong>Conclusions: </strong>This case highlights the diagnostic overlap between facial cellulitis and Ludwig's angina and underscores the importance of prompt imaging, airway monitoring, and clinical vigilance for risk stratification of immunocompromised patients in the acute setting to prevent life-threatening complications.</p>","PeriodicalId":13579,"journal":{"name":"Infectious Disease Reports","volume":"18 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13397903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148578433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Typhus Group Rickettsiosis and the Dengue Diagnostic Paradox: Low Laboratory-Confirmed Dengue Amid the Americas' Largest Epidemic.","authors":"Manuel Sierra, Elsa Palou, Fernando Baires, Helen Hoffman, Heike Hesse, Miguel Sierra-Hoffman, Amy C Madril","doi":"10.3390/idr18040074","DOIUrl":"10.3390/idr18040074","url":null,"abstract":"<p><strong>Background: </strong>Rickettsial diseases, particularly typhus group rickettsioses, remain underrecognized across the Americas despite ecological conditions favorable for transmission. Concurrently, Central America is experiencing one of its largest dengue epidemics, with an unexpectedly low proportion of laboratory-confirmed cases.</p><p><strong>Objective: </strong>We aimed to explore whether typhus group rickettsioses may contribute to dengue-like febrile illness in Central America in the context of low dengue diagnostic confirmation.</p><p><strong>Materials and methods: </strong>An analytical narrative review was conducted integrating secondary epidemiological data from the Pan American Health Organization (PAHO) Arbovirus Surveillance Portal and the peer-reviewed literature. Data from seven Central American countries (2024) were analyzed to estimate the proportion of laboratory-confirmed dengue cases. A focused literature review examined epidemiological, clinical, and ecological evidence supporting rickettsial transmission.</p><p><strong>Results: </strong>Of 543,006 reported dengue cases in Central America in 2024, only 62,285 (11%) were laboratory-confirmed. Evidence from Costa Rica and regional serologic studies suggests ongoing rickettsial transmission. Ecological and socioeconomic conditions-including vector abundance, peridomestic reservoirs, and climate variability-mirror those of South Texas, where murine typhus is endemic.</p><p><strong>Conclusions: </strong>The discrepancy between reported and confirmed dengue cases suggests a potential diagnostic gap. Typhus group rickettsioses represent a plausible, underrecognized contributor to febrile illness in Central America. Strengthening surveillance, diagnostics, and clinical awareness is essential to address this hidden burden.</p>","PeriodicalId":13579,"journal":{"name":"Infectious Disease Reports","volume":"18 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148578450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble Machine Learning for Malaria Diagnosis in Resource-Limited Settings Using Clinical and Demographic Features.","authors":"Panashe Nyengera, Hilary Takunda Takawira, Farai Fredric Mlambo","doi":"10.3390/idr18040072","DOIUrl":"10.3390/idr18040072","url":null,"abstract":"<p><strong>Background: </strong>Sub-Saharan Africa suffers the greatest impact of malaria, with the 2024 Health Organization (WHO )report stating that the region represents 94% of global cases and 95% of deaths. Challenges in malaria elimination stem from weak health systems and limitations of traditional diagnostic methods like microscopy and malaria Rapid Diagnostic Tests (mRDTs), which result in missed diagnoses, delays in treatment, and preventable fatalities in resource-limited settings. This paper addresses these diagnostic limitations by developing and systematically evaluating a machine learning (ML) framework for malaria diagnosis that leverages routine clinical symptoms and demographic information tailored for these environments.</p><p><strong>Methods: </strong>Examining 637 patient records from Gutu Mission Hospital and Gweru Provincial Hospital in Zimbabwe, the research analyzed clinical symptoms (fever, chills, abdominal pain, headache, diarrhea) and demographic data (age, gender, residence, travel history). Data preprocessing involved addressing class imbalance with the Synthetic Minority Oversampling Technique (SMOTE) and employing Recursive Feature Elimination (RFE) for feature selection. Seven ML models were trained: Logistic Regression, Random Forest, Decision Trees, Gradient Boosting, K-Nearest Neighbor, Naive Bayes, and XGBoost. These individual models were used to construct ensemble models like Bagging, Stacking, Soft Voting, and AdaBoost. Performance metrics included accuracy, precision, confusion matrices, recall, F1 score, and AUC-ROC.</p><p><strong>Results: </strong>Statistically significant predictors for malaria included chills (<i>p</i> = 0.001), fever (<i>p</i> = 0.003), diarrhea (<i>p</i> = 0.01), and abdominal pain (<i>p</i> < 0.001), with travel history showing significance among demographic factors (<i>p</i> = 0.02). The stacking ensemble model yielded superior performance, achieving an accuracy of 0.96, precision of 0.95, recall of 0.98, F1 score of 0.96, and AUC-ROC of 0.98.</p><p><strong>Conclusions: </strong>This study underscores the potential of ML, particularly ensemble techniques, to enhance malaria management in resource-limited settings, providing a scalable and cost-effective diagnostic alternative that utilizes accessible clinical and demographic data, thereby supporting healthcare workers and control programs in areas where traditional methods are inadequate.</p>","PeriodicalId":13579,"journal":{"name":"Infectious Disease Reports","volume":"18 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13398222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148578394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}