Search Results (376)

Background: Undifferentiated arthritis is characterized by synovitis that does not meet the criteria for any specific rheumatic disease. However, a subset of chronic occult infectious arthritis, owing to atypical or overlapping clinical features, is often misclassified as undifferentiated oligoarthritis, potentially leading to diagnostic delays and suboptimal management. This study aimed to compare the clinical, laboratory, and imaging characteristics of these two types of oligoarthritis and to evaluate potential discriminatory markers. Methods: Patients older than 16 years with synovitis involving ≤2 joints at Beijing Jishuitan Hospital from September 2023 to December 2024 were included. Ultrasound-guided joint aspiration or synovial biopsy samples were analyzed by culture and next-generation sequencing, classifying patients as pathogen-positive or -negative. Clinical, laboratory, and imaging data (ultrasound, MRI, CT, X-ray) were compared, and multivariable logistic regression and ROC analyses were performed to identify predictors of infectious arthritis. Results: A total of 57 patients were included, with 20 (35.1%) categorized as pathogen-positive and 37 (64.9%) as pathogen-negative. The mean age was 41.7 ± 14.3 years, and 61.4% of the patients were female, with no significant demographic differences between groups. Monoarthritis was more common in pathogen-positive patients, accounting for 95% of cases (p = 0.02). Although the distribution of affected joints was similar between groups, ultrasound revealed a significantly higher bone erosion grade in pathogen-positive patients (p = 0.02), and CT/X-ray demonstrated articular surface destruction in 58.8% of infectious cases compared to 6.2% in pathogen-negative cases (p < 0.001). Serum albumin levels were significantly lower in the pathogen-negative group (20.7 ± 8.5 g/L vs. 41.1 ± 3.9 g/L, p < 0.001). ROC analysis determined that an albumin threshold >35.4 g/L predicted microbiological positivity with 100% sensitivity and 69.7% specificity. Multivariable logistic regression identified normal serum albumin levels, severe ultrasound-detected bone erosion, and imaging evidence of joint surface destruction as significant predictors of chronic occult infectious arthritis. Conclusions: Our findings suggest that, despite overlapping clinical and laboratory features, serum albumin levels, severe bone erosion on ultrasound and articular surface destruction on CT/X-ray may help differentiate chronic occult infectious arthritis from undifferentiated oligoarthritis. Further studies with larger cohorts are needed to confirm these preliminary results. Full article

The complexity of the biochemistry and the variety of possible environments make the subject of the no-growth limits of bacteria a very tough challenge. This present work addresses the problem of applying to the microbial cultures the polymer science approach, which is widespread in food technology. This requires the definition of a “dynamic state diagram” that reports the expected trends of the glass transition of two virtual polymers, which mimic the crowded cytoplasmic polymers and the polymeric meshwork of the cell envelope, respectively, versus the water content. At any given temperature, the water content at the glass transition represents the lowest limit for the relevant molecular mobility. This representation leads one to recognize that the lowest temperature to observe microbial growth coincides with that of the largest freeze-concentrated liquid phase, in line with the values predicted by the Ratkowsky empirical equation. In view of potential applications in predictive microbiology, this paper suggests an alternative interpretation for the highest tolerated temperature and the temperature of the largest specific growth rate. Full article

Background/Objectives: The diagnosis of prosthetic joint infection remains difficult. Microbiological cultures frequently have false-positive and false-negative results. This study investigates whether rapid nanopore sequencing can be used to aid the identification of bacteria causing prosthetic joint infection for more timely identification and treatment. Methods: Nineteen patients who had revision surgery following total joint arthroplasty were included in this study. Of these, 15 patients had an infected joint arthroplasty. All patients had joint fluid aspirated at the time of revision surgery. The DNA was extracted from these fluid aspirates, and rapid nanopore sequencing was performed using the MinION device from Oxford Nanopore Technologies. The sequencing data was trimmed to improve quality and filtered to remove human reads using bioinformatic tools. Genomic sequence classification was performed using the Basic Local Alignment Search Tool. The results were filtered by read length and sequence identity score. The European Bone and Joint Infection Society criteria were used as a standard to identify infected and not infected patients. Confusion tables were used to calculate accuracy and F1 score based on this criteria and the nanopore sequencing results. Results: Microbiological cultures and nanopore sequencing had an accuracy of 68% and 74%, respectively. However, combining both results predicted infection accurately in 94% of cases (F1 score 96%). Conclusions: Nanopore sequencing has the potential to aid identification of bacteria causing prosthetic joint infection and may be useful as a supplementary diagnostic tool. Full article

Periodontal disease is a widespread chronic condition linked to systemic illnesses such as cardiovascular disease, diabetes, and adverse pregnancy outcomes. Despite its global burden, population-specific studies on its risk factors remain limited, particularly in Central and Eastern Europe. The SANA-biome Project is a cross-sectional, community-based study designed to investigate the biological and social determinants of periodontal disease in Romania, a country with disproportionately high oral disease rates and minimal microbiome data. This protocol will integrate metagenomic, proteomic, and metabolomic data of the oral microbiome from saliva and dental calculus samples with detailed sociodemographic and lifestyle data collected through a structured 44-question survey. This study is grounded in two complementary frameworks: the IMPEDE model, which conceptualizes inflammation as both a driver and a consequence of microbial dysbiosis, and Ecosocial Theory, which situates disease within social and structural contexts. Our aims are as follows: (1) to identify lifestyle and behavioral predictors of periodontal disease; (2) to characterize the oral microbiome in individuals with and without periodontal disease; and (3) to evaluate the predictive value of combined microbial and sociodemographic features using statistical and machine learning approaches. Power calculations based on pilot data indicate a target enrollment of 120 participants. This integrative approach will help disentangle the complex interplay between microbiological and structural determinants of periodontal disease and inform culturally relevant prevention strategies. By focusing on an underrepresented population, this work contributes to a more equitable and interdisciplinary model of oral health research and supports the development of future precision public health interventions. Full article

Background/Objectives: Recent evidence suggests that gut microbiota plays an important role in anxiety and stress-related disorders through interactions along the gut–brain axis. Our aim was to determine the microbiological diversity of intestinal microorganisms in individuals with acute and remission phases of PD when compared to healthy individuals. Another aim was also to analyze the differences in the metabolic pathways occurring in the intestinal microbiota of individuals from the three analyzed groups. Methods: A diagnosis was established using the Mini-International Neuropsychiatric Interview (M.I.N.I). The gut’s microbiota composition was analyzed through bacterial 16S rRNA gene sequencing (V1–V2 regions). The clinical evaluations included a BMI measurement, Short Form-36 Health Survey (SF-36), Hamilton Anxiety Scale (HAM-A), Montgomery–Åsberg Depression Rating Scale (MADRS), Columbia-Suicide Severity Rating Scale (C-SSRS), and State-Trait Anxiety Inventory (STAI). Results: We recruited 62 participants (31 PD and 31 controls). After conducting quality control filtering, data from 54 participants were analyzed (25 PD, 11 acute, 14 remission, and 29 controls). Observed richness was lower in the acute PD (63) group than in the control (74) and remission (66) (p = 0.038) groups, whereas the Shannon and Simpson indices and beta diversity (PERMANOVA) were not significantly different. The Ruminococcus gnavus group was enriched in acute PD; no other deconfounded differences in microbial composition were detected. Predicted functional differences were detected by edgeR only and included the pathways that are related to steroid biosynthesis and innate immune signaling. Conclusions: Distinct gut microbial signatures were associated with PD, implicating both the metabolic and inflammatory pathways in disease pathophysiology. Full article

Background: Helcococcus kunzii, a facultative anaerobe and Gram-positive coccus, has been documented as a cunning pathogen, mainly in immunocompromised individuals, as evidenced by recent clinical and microbiological reports. It has been associated with a variety of polymicrobial infections, comprising diabetic foot ulcers, prosthetic joint infections, osteomyelitis, endocarditis, and bloodstream infections. Despite its emerging clinical relevance, no licensed vaccine or targeted immunotherapy currently exists for H. kunzii, and its rising resistance to conventional antibiotics presents a growing public health concern. Objectives: In this study, we employed an integrated subtractive proteomics and immunoinformatics pipeline to design a multi-epitope subunit vaccine (MEV) candidate against H. kunzii. Initially, pan-proteome analysis identified non-redundant, essential, non-homologous, and virulent proteins suitable for therapeutic targeting. Methods/Results: From these, two highly conserved and surface-accessible proteins, cell division protein FtsZ and peptidoglycan glycosyltransferase FtsW, were selected as promising vaccine targets. Comprehensive epitope prediction identified nine cytotoxic T-lymphocyte (CTL), five helper T-lymphocyte (HTL), and two linear B-cell (LBL) epitopes, which were rationally assembled into a 397-amino-acid-long chimeric construct. The construct was designed using appropriate linkers and adjuvanted with the cholera toxin B (CTB) subunit (NCBI accession: AND74811.1) to enhance immunogenicity. Molecular docking and dynamics simulations revealed persistent and high-affinity ties amongst the MEV and essential immune receptors, indicating a durable ability to elicit an immune reaction. In silico immune dynamic simulations predicted vigorous B- and T-cell-mediated immune responses. Codon optimization and computer-aided cloning into the E. coli K12 host employing the pET-28a(+) vector suggested high translational efficiency and suitability for bacterial expression. Conclusions: Overall, this computationally designed MEV demonstrates favorable immunological and physicochemical properties, and presents a durable candidate for subsequent in vitro and in vivo validation against H. kunzii-associated infections. Full article

Background: A short cervix in the second trimester significantly increases preterm birth risk, yet no reliable first-trimester prediction method exists. Current guidelines lack consensus on which women should undergo transvaginal ultrasound (TVUS) screening for cost-effective prevention. Therefore, it is vital to establish a highly accurate and economical method for use in the early stages of pregnancy to predict short cervix in mid-pregnancy. Methods: A total of 1480 pregnant women with singleton pregnancies and at least one risk factor for spontaneous preterm birth (<37 weeks) were recruited from January 2020 to December 2020 at the Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine. Cervical length was assessed at 20–24 weeks of gestation, with a short cervix defined as <25 mm. Feature selection employed tree models, regularization, and recursive feature elimination (RFE). Seven machine learning models (logistic regression, linear discriminant analysis, k-nearest neighbors, support vector machine, decision tree, random forest, XGBoost) were trained to predict mid-trimester short cervix. The XGBoost model—an ensemble method leveraging sequential decision trees—was analyzed using Shapley Additive Explanation (SHAP) values to assess feature importance, revealing consistent associations between clinical predictors and outcomes that align with known clinical patterns. Results: Among 1480 participants, 376 (25.4%) developed mid-trimester short cervix. The XGBoost-based prediction model demonstrated high predictive performance in the training set (Recall = 0.838, F1 score = 0.848), test set (Recall = 0.850, F1 score = 0.910), and an independent dataset collected in January 2025 (Recall = 0.708, F1 score = 0.791), with SHAP analysis revealing pre-pregnancy BMI as the strongest predictor, followed by second-trimester pregnancy loss history, peripheral blood leukocyte count (WBC), and positive vaginal microbiological culture results (≥10⁵ CFU/mL, measured between 11⁺⁰ and 13⁺⁶ weeks). Conclusions: The XGBoost model accurately predicts mid-trimester short cervix using first-trimester clinical data, providing a 6-week window for targeted interventions before the 20–24-week gestational assessment. This early prediction could help guide timely preventive measures, potentially reducing the risk of spontaneous preterm birth (sPTB). Full article

Molecularly imprinted polymers (MIPs) have emerged as promising materials for selectively targeting biomolecules, including quorum sensing autoinducers that regulate bacterial communication and biofilm formation. In this study, both single-template and dual-template strategies were employed to design and synthesize MIPs capable of capturing autoinducer-2 analogs using (3R,4S)-tetrahydro-3,4-furandiol (T1) or (R/S) 2,2-dimethyl-1,3-dioxolane-4-methanol (T2) as the templates. This approach offers translational potential of a complementary or non-antibiotic strategy to conventional antimicrobial therapies in mitigating biofilm-associated infections. Computational modeling guided the rational selection of functional monomers, predicting favorable interaction energies (ΔE_C up to −135 kcal·mol⁻¹) and optimal hydrogen-bonding patterns to enhance template–polymer affinity. The synthesized MIPs were characterized using spectroscopic and microscopic techniques to confirm imprinting efficiency and structural integrity. The adsorption capacity measurements demonstrated higher adsorption capacity and selectivity of MIPs compared to non-imprinted polymers, with the highest selectivity equal to 3.36 for T1 and 3.14 for T2 on MIPs fabricated from methacrylic acid. Preliminary microbiological evaluations using Chromobacterium violaceum ATCC 12472 reveal that the MIPs prepared from 2-hydroxyethyl methacrylate effectively inhibited violacein production by up to 78.2% at 5.0 mg·mL⁻¹, consistent with quorum sensing interference. These findings highlight the feasibility of employing molecular imprinting to target autoinducer-2 analogs, introducing a novel synthetic strategy for disrupting bacterial communication. This further suggests that molecular imprinting can be leveraged to develop potent quorum-sensing inhibitors, an approach that offers translational potential as an alternative to conventional antimicrobial strategies to mitigate biofilm-associated infections. Full article

Background/Objectives: The SARS-CoV-2 and influenza A (H1N1)pdm09 pandemics have resulted in high numbers of ICU admissions, with high mortality. Identifying risk factors for ICU mortality at the time of admission can help optimize clinical decision making. However, the risk factors identified may differ, depending on the type of analysis used. Our aim is to compare the risk factors and performance of a linear model (multivariable logistic regression, GLM) with a non-linear model (random forest, RF) in a large national cohort. Methods: A retrospective analysis was performed on a multicenter database including 8902 critically ill patients with influenza A (H1N1)pdm09 or COVID-19 admitted to 184 Spanish ICUs. Demographic, clinical, laboratory, and microbiological data from the first 24 h were used. Prediction models were built using GLM and RF. The performance of the GLM was evaluated by area under the ROC curve (AUC), precision, sensitivity, and specificity, while the RF by out-of-bag (OOB) error and accuracy. In addition, in the RF, the im-portance of the variables in terms of accuracy reduction (AR) and Gini index reduction (GI) was determined. Results: Overall mortality in the ICU was 25.8%. Model performance was similar, with AUC = 76% for GLM, and AUC = 75.6% for RF. GLM identified 17 independent risk factors, while RF identified 19 for AR and 23 for GI. Thirteen variables were found to be important in both models. Laboratory variables such as procalcitonin, white blood cells, lactate, or D-dimer levels were not significant in GLM but were significant in RF. On the contrary, acute kidney injury and the presence of Acinetobacter spp. were important variables in the GLM but not in the RF. Conclusions: Although the performance of linear and non-linear models was similar, different risk factors were determined, depending on the model used. This alerts clinicians to the limitations and usefulness of studies limited to a single type of model. Full article

Background/Objectives: Clinical stability within 24 h prior to discharge is a key metric for safe care transitions in hospitalised patients with community-acquired pneumonia (CAP). However, its association with post-discharge outcomes, particularly readmissions, remains underexplored. This study assessed whether clinical instability before discharge is associated with 30-day mortality, readmissions, or a composite of both in hospitalised CAP patients. Methods: This retrospective cohort study included adults (≥18 years) admitted with CAP to two tertiary Australian hospitals between 1 January 2020 and 31 December 2023. Clinical instability was defined as abnormal vital signs (temperature, heart rate, respiratory rate, blood pressure, or oxygen saturation) within 24 h before discharge. Pneumonia severity was assessed using the CURB-65 score and frailty using the Hospital Frailty Risk Score. Multilevel logistic regression models were used to evaluate associations with outcomes, adjusting for age, sex, comorbidities, frailty, disease severity, microbiological aetiology, antibiotics prescribed during admission, and prior healthcare use. Competing risk regression accounted for death when analysing readmissions. Results: Of 3984 patients, 20.4% had clinical instability within 24 h before discharge. The composite outcome occurred in 21.9% patients, with 15.8% readmitted and 6.1% dying within 30 days. Clinical instability was significantly associated with the composite outcome (adjusted odds ratio [aOR] 1.73, 95% CI 1.42–2.09, p < 0.001), primarily driven by increased mortality risk (aOR 3.70, 95% CI 2.73–5.00, p < 0.001). However, no significant association was found between clinical instability and readmissions (aOR 1.16, 95% CI 0.93–1.44, p > 0.05). Conclusions: Clinical instability within 24 h before discharge predicts worse outcomes in CAP patients, driven by increased mortality risk rather than readmissions. Full article

The continuous variability in the microbiological quality of surface waters presents significant challenges for ensuring the production of safe drinking water in compliance with public health regulations. Inadequate treatment of surface waters can lead to the presence of pathogenic microorganisms in the drinking water supply, posing serious risks to public health. This research presents an in-depth data analysis using machine learning tools for the induction of models to describe and predict microbiological water quality for the sustainable management of the Butoniga drinking water treatment facility in Istria (Croatia). Specifically, descriptive and predictive models for total coliforms and E. coli bacteria (i.e., classes), which are recognized as key sanitary indicators of microbiological contamination under both EU and Croatian water quality legislation, were developed. The descriptive models provided useful information about the main environmental factors that influence the microbiological water quality. The most significant influential factors were found to be pH, water temperature, and water turbidity. On the other hand, the predictive models were developed to estimate the concentrations of total coliforms and E. coli bacteria seven days in advance using several machine learning methods, including model trees, random forests, multi-layer perceptron, bagging, and XGBoost. Among these, model trees were selected for their interpretability and potential integration into decision support systems. The predictive models demonstrated satisfactory performance, with a correlation coefficient of 0.72 for total coliforms, and moderate predictive accuracy for E. coli bacteria, with a correlation coefficient of 0.48. The resulting models offer actionable insights for optimizing operational responses in water treatment processes based on real-time and predicted microbiological conditions in the Butoniga reservoir. Moreover, this research contributes to the development of predictive frameworks for microbiological water quality management and highlights the importance of further research and monitoring of this key aspect of the preservation of the environment and public health. Full article

Insulin-like growth factor-binding protein-3 (IGFBP-3) plays a vital role in cellular growth, development, and survival. Incorporating IGFBP-3 into baseline prognostic evaluations may enhance the prediction of mortality in patients with sepsis. In this study, serum levels of IGFBP-3, C-reactive protein, procalcitonin, lactate, interleukin-6, and mid-regional pro-adrenomedullin were measured upon admission to the internal medicine unit (IMU) in 139 patients with microbiologically confirmed sepsis. The objectives were as follows: (1) to classify septic patient phenotypes based on optimal thresholds of independent prognostic biomarkers and (2) to evaluate whether these biomarkers improve the predictive accuracy of a clinical model (Model 1), which includes the clinical predictors of 1-year mortality. Age, sequential organ failure assessment (SOFA) score, multiple sources of infection, and IGFBP-3 levels independently predicted 1-year mortality. Patients with IGFBP-3 levels below 10.64 had significantly lower median body temperature (p = 0.008), reduced lymphocyte count (p = 0.001), and higher 1-year mortality (p < 0.001). Model 1 included age, SOFA score, and the presence of multiple sources of sepsis as predictor variables. Model 2 incorporated the same variables as Model 1, with the addition of IGFBP-3 levels. When comparing their prognostic performance, Model 2 demonstrated superior predictive accuracy for mortality at 60, 90, and 365 days following admission to the IMU. Low IGFBP-3 levels at IMU admission are strongly associated with worse outcomes in septic patients, supporting its potential use as a prognostic biomarker. Full article

Background: Invasive fungal infections (IFIs) after liver transplantation (LT) remain a concern. No universal protocol for antifungal prophylaxis in LT exists. Antifungal prophylaxis varies across European centers. Studies suggest risk stratification for prophylaxis. This study assessed IFI frequency and outcomes in adult LT recipients without antifungal prophylaxis and evaluated risk stratification for predicting IFIs. Method: A retrospective analysis of clinical and microbiological data from 244 liver transplant patients focused on IFI within 100 days post-transplantation. Of these, 225 (92%) had right liver transplants from living donors. We assessed two risk stratification models for predicting IFI: one categorizes patients into low- and high-risk groups, and the other divides patients into three categories, with two eligible for prophylaxis and one not. Results: Of 244 patients, 3% (seven individuals) developed invasive fungal infections (IFI), including two aspergillosis and five candidiasis. IFI occurred in 8% of high-risk and 2% of low-risk patients in the first stratification, with no significant difference between groups (p = 0.144). In the second stratification, IFI was found in 4% of the target and 2% of non-target groups, without a significant difference (p = 0.455). Patients with IFI showed higher mean MELD scores of 21.71 ± 2.35 versus 17.04 ± 6.48 in those without IFI (p < 0.05). Conclusions: This study evaluated IFI outcomes without systemic antifungal prophylaxis in LT recipients. Limited antifungal use in a major living liver donor transplantation (LDLT) group, with low MELD scores and immunosuppression protocols, could be feasible. Future multicenter studies can improve understanding and develop prophylaxis algorithms for LT settings. Full article

Waterborne illnesses, including those caused by Salmonella, are an increasing public health challenge, particularly in developing countries. Potential sources of salmonellosis include fruits and vegetables irrigated/treated with surface water, leading to human infections. Salmonella causes millions of gastroenteritis cases annually, but early detection through routine water quality surveillance is time-consuming, requires specialized equipment, and faces limitations, such as coverage gaps, delayed data, and poor accessibility. Climate change-driven extreme events such as floods and droughts further exacerbate variability in water quality. In this context, remote sensing offers an efficient and cost-effective alternative for environmental monitoring. This study evaluated the potential of Sentinel-2 satellite imagery to predict Salmonella occurrence in the Maipo and Mapocho river basins (Chile) by integrating spectral, microbiological, climatic, and land use variables. A total of 1851 water samples collected between 2019 and 2023, including 704 positive samples for Salmonella, were used to develop a predictive model. Predicting Salmonella in surface waters using remote sensing is challenging for several reasons. Satellite sensors capture environmental proxies (e.g., vegetation cover, surface moisture, and turbidity) but not pathogens. Our goal was to identify proxies that reliably correlate with Salmonella. Twelve spectral indices (e.g., NDVI, NDWI, and MNDWI) were used as predictors to develop a predictive model for the presence of the pathogen, which achieved 59.2% accuracy. By spatially interpolating the occurrences, it was possible to identify areas with the greatest potential for Salmonella presence. NDWI and AWEI were most strongly correlated with Salmonella presence in high-humidity areas, and spatial interpolation identified the higher-risk zones. These findings reveal the challenges of using remote sensing to identify environmental conditions conducive to the presence of pathogens in surface waters. This study highlights the methodological challenges that must be addressed to make satellite-based surveillance an accessible and effective public health tool. By integrating satellite data with environmental and microbiological analyses, this approach can potentially strengthen low-cost, proactive environmental monitoring for public health decision-making in the context of climate change. Full article

COVID-19 mortality is a complex phenomenon influenced by multiple factors. This study aimed to identify factors associated with death in COVID-19 patients by considering clinical, demographic, environmental, and socioeconomic conditions, using machine learning models and a national dataset from Mexico covering all pandemic waves. We integrated data from the national COVID-19 dataset, municipal-level socioeconomic indicators, and water quality contaminants (physicochemical and microbiological). Patients were assigned to one of four datasets (groundwater, lentic, lotic, and coastal) based on their municipality of residence. We trained XGBoost models to predict patient death or survival on balanced subsets of each dataset. Hyperparameters were optimized using a grid search and cross-validation, and feature importance was analyzed using SHAP values, point-biserial correlation, and XGBoost metrics. The models achieved strong predictive performance (F1 score > 0.97). Key risk factors included older age (≥50 years), pneumonia, intubation, obesity, diabetes, hypertension, and chronic kidney disease, while outpatient status, younger age (<40 years), contact with a confirmed case, and care in private medical units were associated with survival. Female sex showed a protective trend. Higher socioeconomic levels appeared protective, whereas lower levels increased risk. Water quality contaminants (e.g., manganese, hardness, fluoride, dissolved oxygen, fecal coliforms) ranked among the top 30 features, suggesting an association between environmental factors and COVID-19 mortality. Full article