Search Results (644)

Antimicrobial resistance (AMR) represents one of the most urgent global health threats, significantly impacting patient outcomes, healthcare systems, and economic sustainability. Rapid and accurate antimicrobial susceptibility testing (AST) are essential to guide targeted therapy, reduce inappropriate antimicrobial use, and support antimicrobial stewardship programs. However, conventional phenotypic AST methods, including broth microdilution, disk diffusion, agar dilution, and gradient strip tests, remain labor-intensive and require prolonged turnaround times, often delaying optimal therapeutic decisions. Although automated commercial platforms such as VITEK 2, BD Phoenix, MicroScan WalkAway, and Sensititre ARIS have improved laboratory workflow and standardization, they still rely on culture-based approaches and typically require 16–36 h to generate minimum inhibitory concentration (MIC) results. In recent years, several innovative rapid phenotypic AST technologies have emerged, aiming to significantly shorten the time to susceptibility results while maintaining high accuracy. This review provides an overview of currently available rapid automated phenotypic platforms for MIC determination, including VITEK^® Reveal™, ASTar, FASTinov^®AST, QuickMIC^®, and the Accelerate Pheno^® system. These systems employ advanced technologies such as volatile organic compound detection, flow cytometry, microfluidics, real-time imaging, and morphokinetic cellular analysis to deliver susceptibility results within a few hours directly from positive blood cultures. We summarize their technical principles, antibiotics and pathogens included, performances, and current limitations. Overall, the implementation of rapid phenotypic AST tools has the potential to substantially improve clinical decisions, optimize antimicrobial therapy, and contribute to fight AMR. Full article

Background/Objectives: Pediatric HIV case-finding and monitoring remain constrained by delayed early infant diagnosis (EID), loss to follow-up, and limited viral load (VL) testing—challenges particularly consequential in the operationally diverse Asia–Pacific region. We systematically reviewed innovations in point-of-care (POC) and near-patient HIV diagnostics and digital monitoring relevant to children and adolescents. Methods: Following a registered protocol (INPLASY2025110058) and PRISMA 2020 guidance, we searched PubMed, EMBASE, Cochrane Library, and WHO Global Index Medicus for studies on POC/near-patient EID and VL testing, dried blood spot (DBS) workflows, and digital monitoring tools. Risk of bias was assessed using RoB 2, QUADAS-2, and MMAT. Results: Fifty-three primary studies were included (39 sub-Saharan Africa, 12 Asia–Pacific, 1 multi-country/global, 1 Americas/Caribbean). Patient selection and flow/timing were common limitations in diagnostic accuracy studies; sample representativeness and nonresponse bias were frequent concerns in implementation studies. The most consistent benefits of POC EID and near-patient VL testing were shorter turnaround times and improved cascade completion when paired with quality assurance and connectivity. Conclusions: POC diagnostics and digital monitoring can help close pediatric HIV cascade gaps, though evidence derives predominantly from sub-Saharan Africa. Impact depends on implementation design. Asia–Pacific programs should prioritize generating context-specific evidence alongside the adaptation of established lessons. Full article

Neonatal infections remain a leading cause of morbidity and mortality worldwide, particularly among preterm and low-birth-weight infants and in low- and middle-income countries. This burden has intensified with the global increase in multidrug-resistant (MDR) bacteria, especially in neonatal intensive care units, where prolonged hospitalization, invasive interventions, and exposure to broad-spectrum antibiotics promote colonization, transmission, and invasive infection. In this narrative review, we explore the epidemiology and microbiological characteristics of MDR bacterial infections in newborns, alongside their associated risk factors, diagnostic challenges, treatment outcomes, and prevention strategies. Across different settings, Gram-negative pathogens, particularly Klebsiella pneumoniae, Escherichia coli, and Acinetobacter baumannii, account for a substantial proportion of severe neonatal infections, whereas methicillin-resistant Staphylococcus aureus remains important in selected units. The risk of MDR infection is driven by a complex interplay of factors, ranging from maternal and perinatal exposures to the inherent immunological vulnerability of newborns, hospital-based transmission, antibiotic selection pressure, and structural deficiencies in healthcare infrastructure. Diagnosis remains challenging because clinical presentations are nonspecific and culture-based methods are constrained by low blood volumes, prior antimicrobial exposure, and delayed turnaround times. Treatment is increasingly complicated due to resistance to standard empirical regimens, substantial regional variation in susceptibility profiles, and limited neonatal pharmacokinetic and safety data for reserve agents. Current evidence mainly supports surveillance-informed empirical therapy, susceptibility-guided treatment adjustment, antimicrobial stewardship, and strict infection prevention measures. Future progress will require neonatal-specific clinical trials, harmonized surveillance systems, stronger molecular epidemiology, and more equitable access to microbiological diagnostics and effective treatment. Full article

Background/Objectives: Uveal melanoma (UM) is the most common primary intraocular malignant tumor among adults and has a high risk of metastasis. Recently, artificial intelligence (AI) tools have been developed to support the management of UM across different clinical tasks. The definition of ground truth, the reference standard that models use in training and development, greatly influences the performance and clinical relevance of the models. Currently, there is limited consensus regarding which ground truth methods are most appropriate for each clinical application. This review aims to evaluate the advantages and limitations of available ground truth options in UM and proposes task-specific recommendations based on clinical utility, feasibility, and cost. Methods: A narrative review of the existing literature was conducted to identify and evaluate commonly used ground truth methods for UM AI applications based on factors such as time, cost, invasiveness, and required level of expertise. Results: Each ground truth method offers distinct benefits and drawbacks in relation to biological precision, invasiveness, availability, cost, and turnaround time. No single ground truth is universally optimal across all applications. Instead, the ideal choice depends on the intended clinical task, and practical alternatives exist to mitigate the constraints that result from limited time and institutional resources. Conclusions: The selection of ground truth for AI models in UM should be chosen based on the specific clinical task to balance predictive relevance with feasibility of implementation. The adoption of task-specific ground truth standards may improve the development of clinically meaningful AI tools and facilitate their integration into real-world practice. Full article

Objectives: This study evaluated the diagnostic performance of the lateral flow immunochromatographic assay (RESIST-ACINETO, Coris BioConcept) for the rapid detection of the major carbapenemases in Acinetobacter baumannii. Methods: Blood culture isolates collected between 2014 and 2024 with meropenem MIC ≥ 4 mg/L were retrieved, re-identified by MALDI-TOF MS, and susceptibility was confirmed by broth microdilution. Carbapenemase genes (bla_OXA-23, bla_OXA-40, bla_NDM) were detected using multiplex PCR, which served as the reference standard. All isolates were tested using the RESIST ACINETO assay, and diagnostic accuracy parameters were calculated. Results: A total of 114 isolates were recovered and confirmed as A. baumannii. Among 93 carbapenem-non-susceptible isolates, 97.8% (91/93) were correctly identified by the assay. The test showed 99.1% sensitivity and 99.1% specificity, with most positive results appearing within 3–10 min. Two discrepant results were observed (one false positive, one false negative), while all meropenem-susceptible isolates tested negative. Conclusions: The RESIST ACINETO assay provides rapid, accurate detection of carbapenemases in A. baumannii, significantly reducing turnaround time compared with conventional workflows. Its performance supports integration into routine diagnostics to enhance timely resistance confirmation and infection-control interventions. Full article

Background: Urinary tract infections (UTIs) are prevalent bacterial infections associated with significant morbidity and healthcare burden. Traditional diagnosis relies on urine culture, which is limited by long turnaround times and potential contamination. Automated urine flow cytometry, particularly the UF-5000 (Sysmex Corporation, Kobe, Japan), offers rapid and precise screening through bacterial and white blood cell (WBC) quantification. This systematic review and meta-analysis evaluates the diagnostic accuracy of the UF-5000 for UTI screening. Methods: We searched PubMed, Embase, Science Direct, and Web of Science for studies assessing the UF-5000’s performance, including sensitivity and specificity, with a minimum sample size of 40 and at least 10 UTI cases. Quality assessment was performed using QUADAS-2. Pooled estimates for sensitivity, specificity, and agreement (kappa) were calculated using random-effects models. Results: Eighteen studies, encompassing 25,337 samples, were included in the analysis. Pooled sensitivity and specificity for bacterial count (nine studies) were 0.927 (95% CI, 0.872–0.959) and 0.751 (95% CI, 0.558–0.878), respectively. For WBC count (four studies), sensitivity was 0.897 (95% CI, 0.755–0.961) and specificity was 0.600 (95% CI, 0.293–0.844). The UF-5000 also demonstrated moderate agreement (pooled kappa 0.52, 95% CI, 0.08–0.79) in distinguishing Gram-negative bacteria. Conclusions: Despite heterogeneity across studies, the UF-5000 demonstrates high diagnostic accuracy, particularly high sensitivity, supporting its role as a useful UTI screening tool to rule out infection in clinical settings. The device further provides clinical value through its ability to assist in the differentiation of Gram-negative bacteria. Full article

Background/Objectives: Bloodstream infections are among the most severe infectious diseases, with mortality rates up to 25%. Delays as short as one hour in the diagnosis or initiation of the appropriate antimicrobial therapy can significantly worsen patient outcomes. Methods: This retrospective study, in an Italian 900-bed hospital from January 2019 to December 2024, evaluates the impact of a 24/7 reorganization of the clinical microbiology laboratory, adding a night shift to ensure around-the-clock processing and introducing a fast-track diagnostic pathway to prioritize the blood cultures from critically ill patients (called urgent blood cultures) in terms of turnaround times for Gram staining, microorganism identification, and resistance marker detection. Results: A total of 194,171 blood cultures were processed. Following the implementation of the 24/7 model, the median Gram stain turnaround time decreased from 4.46 to 1.40 h, microorganism identification turnaround time decreased from 5.75 to 2.35 h, and resistance marker turnaround time from 6.97 to 2.68 h. Significant reductions were observed especially during night shifts. Urgent blood cultures yielded a higher positivity rate (16.22% vs. 13.04%) and included the isolation of time-critical bacteria that can cause meningitis, such as Streptococcus pneumoniae. Conclusions: The continuous around-the-clock processing of blood culture and prioritized blood cultures for critically ill patients significantly reduced reporting times, particularly overnight. This model enhances early sepsis management and exemplifies how tailored and precision microbiology, supported by strong interdisciplinary collaboration and effective communication, can enhance earlier targeted antimicrobial treatment. Full article

Campylobacter is the primary bacterial cause of foodborne gastroenteritis globally. While international standards recommend a tiered approach for detection, emerging evidence suggests that selective protocols may introduce species-specific recovery biases. This systematic review and meta-analysis evaluated the diagnostic performance of established culture-based detection protocols across diverse food matrices. Following PRISMA 2020 guidelines, we searched multiple databases for studies reporting 2 × 2 diagnostic accuracy data through October 2024. Ten studies comprising 43 method comparisons and 4599 samples met the inclusion criteria. The overall pooled sensitivity was 95.8% (95% CI: 93.6–97.4%), and the specificity was 90.2% (95% CI: 86.8–92.9%). Even with a limited number of comparisons (n = 2), direct culture demonstrated high diagnostic sensitivity (99.1%) and significantly faster turnaround times. Crucially, selective enrichment exhibited a profound species-specific bias: C. jejuni showed 59.4 percent lower recovery than C. coli in Bolton broth, likely due to differential polymyxin B susceptibility. These findings highlight the importance of context-dependent method selection within the ISO 10272-1:2017 framework, suggesting that direct culture (Procedure C) should be prioritized for high-contamination matrices to ensure unbiased recovery of C. jejuni. Large-scale multicenter validation is warranted to confirm these exploratory findings for direct culture. Full article

Extrahepatic cholangiocarcinoma (eCCA) is a highly aggressive malignancy arising from the biliary epithelium, with surgical resection representing the only potentially curative treatment. The predominant periductal infiltrating growth pattern, characterized by subepithelial tumor spread and desmoplastic stromal reaction, severely limits the diagnostic sensitivity of conventional endoscopic sampling techniques, which primarily assess the luminal mucosal surface. This review provides a histomorphology-oriented diagnostic framework for indeterminate extrahepatic biliary strictures, integrating advanced endoscopic technologies with emerging optical diagnostic approaches. ERCP combined with cholangioscopy demonstrates superior sensitivity for perihilar strictures, while EUS-guided tissue acquisition shows higher diagnostic yield in distal cholangiocarcinoma, also providing locoregional staging. Advanced EUS technologies—including elastography, contrast harmonic EUS, and Detective Flow Imaging—further improve characterization of indeterminate strictures by evaluating tissue stiffness, microvascular architecture, and periductal infiltration. Ex vivo fluorescence confocal laser microscopy (FCM) enables real-time microscopic evaluation of biopsy specimens, reducing diagnostic turnaround time and minimizing inadequate sampling. A location-adapted diagnostic algorithm integrating cross-sectional imaging, ERCP, cholangioscopy, and EUS is proposed. An integrated, biology-informed endoscopic approach tailored to tumor location and ductal wall involvement may significantly improve early eCCA detection and guide patient selection for curative treatment. Full article

Reliable production forecasting is a critical task for evaluating asset valuation and commercial performance in oil and gas reservoirs. Conventional short-term forecasting methods, such as Arps’ decline curve analysis, rely on simple mathematical curve fitting and often oversimplify reservoir performance. On the other hand, long-term forecasting requires complex multidisciplinary models that integrate geophysics, reservoir engineering, and production engineering, but these approaches are time-consuming and have high turnaround times. To bridge the gap between long and short-term production forecasts, reduced-physics models such as Blasingame type curves have been developed, incorporating transient well behaviour derived from diffusivity equations and Darcy’s law. These models assume homogeneity and uniform reservoir properties, enabling faster results while honouring pressure performance. However, despite their efficiency, they still face limitations in reliability, particularly when extended to long-term forecasts. This paper proposes a hybrid modelling approach that integrates flowing material balance (FMB) concepts into physics-informed neural networks (PiNNs) and machine learning models to improve the accuracy and reliability of production forecasting. The proposed methodology introduces two hybrid strategies: physics-informed models enriched with FMB feature, and PiNNs. The first proposed hybrid model uses a created FMB-derived feature as input to neural networks. The second PiNN model embeds data-driven loss functions with a physics-based envelope to reflect reservoir response into the machine learning model. The primary loss function is mean squared error, ensuring minimization of data misfit between predicted and observed production rates. The study validates both proposed physically informed neural network models through performance metrics such as RMSE, MAE, MAPE, and R². Results application on field data shows that the integration of FMB into neural network models using the PiNN concept guides the neural network models to predict the production rates with higher reliability over the full span of the tested data period, which was the last year of unseen production data. Additionally, the proposed PiNN model is able to predict the well productivity index via hyper-tuning of the PiNN model. Furthermore, the PiNN is not improving the metric performance of conventional neural networks, as it has to satisfy an additional material balance equation. This is due to a lower degree of freedom in the PiNN models. Full article

The first influenza A(H5N1) human case associated with the A(H5N1) dairy cattle outbreak in the United States was identified in April 2024. The U.S. CDC response to this outbreak was activated days later and remained active until July 2025. During this time, 70 human cases of influenza A(H5N1) were detected with a range of epidemiological links to sources of exposure. Next-generation sequencing (NGS) of human samples was an effectual mechanism for tracking and analyzing the outbreak evolution throughout the response. Due to the specimens’ importance and their variable physical quality, an assortment of laboratory methods was utilized including influenza segment-specific amplification, enrichment capture, short-read, and long-read sequencing. Combining these methods allowed for high-quality genomic data production with rapid turnaround times—typically 2 days from sample receipt to public database submission. By leveraging replicate sequencing, enrichment capture, and sequencing of diagnostic amplicons, valuable genomic data could be produced directly from human clinical specimens that would have normally been considered too weak for routine virologic surveillance sequencing. The resulting assemblies were characterized and analyzed by CDC and shared with local and state public health authorities to facilitate case investigations and risk assessment. These data were further used for phylogenetic analyses of viruses from human cases to investigate likely animal-to-human transmission events and identify clusters within the outbreak that might indicate trends in the types of exposures. Through the adaptable laboratory workflow and the rapid release of viral genomic data, the public health risk mitigation strategies could be evaluated and adjusted in real time. Full article

Background: Turnaround time (TAT) is a key performance indicator in clinical microbiology, particularly for urine cultures, which represent a high-volume workload and directly impact antimicrobial stewardship. Methods: This retrospective observational study compared urine culture TAT before (2023, manual workflow) and after (2025, total laboratory automation using WASPLab™) implementation in a high-volume reference laboratory. A total of 16,210 cultures in 2023 and 60,474 in 2025 were included. TAT was defined as the time from laboratory receipt to final report validation. Results: Implementation of total laboratory automation significantly reduced median TAT for both negative cultures (from 49.68 to 34.38 h) and positive cultures (from 50.42 to 34.62 h) (p < 0.001). In addition, variability in reporting times decreased, indicating improved consistency. Laboratory productivity increased from 2316 to 7559 cultures per full-time equivalent, representing a 3.26-fold improvement. Conclusions: Total laboratory automation significantly improved the speed and consistency of urine culture reporting, supporting enhanced laboratory efficiency and facilitating timely clinical decision-making. Full article

Background/Objectives: Sepsis is a leading cause of hospital mortality and represents a time-sensitive medical emergency. Current diagnostic strategies rely on clinical assessment, severity scores, biomarkers, and blood cultures. However, blood cultures require 24–72 h for pathogen identification and demonstrate limited sensitivity, while biomarkers such as procalcitonin and C-reactive protein lack optimal specificity. These limitations support the widespread empirical use of broad-spectrum antibiotics and highlight the need for rapid, sensitive, and culture-independent diagnostic tools. Methods: A narrative literature review was conducted using PubMed and Google Scholar, including 28 studies published over the past 10 years, encompassing observational and preclinical investigations. Current evidence on the application of Raman spectroscopy in sepsis was summarized, with a dual focus on pathogen identification and the assessment of the host response. Results: Raman spectroscopy has demonstrated the ability to detect early molecular alterations in circulating immune cells and mitochondrial redox status, potentially preceding conventional biomarkers. For pathogen identification, Raman techniques have achieved diagnostic accuracies comparable to automated systems, but with significantly shorter turnaround times. Integration with microfluidics, optical tweezers, and deep learning algorithms has further enhanced performance, although these applications remain largely experimental. Conclusions: Despite these promising results, the lack of methodological standardization, spectral overlap among phylogenetically related species, limited large-scale validation, and challenges in interpreting certain spectral signatures remain unresolved. Most available evidence originates from preclinical, single-center, and controlled studies, underscoring the need for prospective multicenter trials and harmonized protocols. Full article

Prenatal whole-genome sequencing (WGS) is a comprehensive genetic test for fetal anomalies, enabling simultaneous detection of aneuploidies, copy number variants (CNVs), single-nucleotide variants (SNVs), small insertions/deletions, structural variants, and regions of absence of heterozygosity. However, its clinical performance, optimal sequencing strategies, and implementation challenges remain incompletely defined. We conducted a narrative review of PubMed-indexed studies (1966–December 2025) evaluating prenatal WGS in fetuses with structural anomalies. Across 29 studies, diagnostic yield ranged from approximately 20% to 40%, influenced by phenotype complexity, sequencing depth, and study design. Low-coverage WGS (≤5×) reliably detected large chromosomal abnormalities with a performance comparable to chromosomal microarray analysis. Moderate-coverage WGS (20–40×) additionally enabled detection of SNVs and structural variants, providing up to 30% incremental diagnostic yield after uninformative standard testing. Turnaround times were typically 14–21 days. Higher sequencing depth increases detection of variants of uncertain significance (0.6% to 35.7%) and secondary/incidental findings (1.6–30.8%). Prenatal WGS offers meaningful diagnostic value but requires careful patient selection, multidisciplinary expertise, and structured pre- and post-test genetic counseling to ensure responsible integration into routine clinical practice, with careful consideration of clinical benefit and economic feasibility. Full article

Background: Molecular diagnostics may detect several respiratory pathogens simultaneously with rapid turnaround times. The aim of this study was to determine the frequency and distribution of respiratory pathogens among symptomatic outpatients. Methods: All outpatients presented for testing due to suspected acute respiratory infection between 1 January and 31 December 2024 to the Teaching Institute for Public Health of Split-Dalmatia County, Croatia, and multiplex real-time PCRs for 13 respiratory pathogens were included. Results: Out of 15,437 analyzed panels, 8878 (57.5%) were positive. Single-pathogen infections dominated (82.6%), while co-infections were recorded in 17.4% of panels; therefore, a total of 10,546 individual pathogens were detected, which were mostly viruses (87.0%). The following distribution of pathogens was observed: rhinovirus/enterovirus in 38.9% of positive results, influenza A virus in 14.5%, SARS-CoV-2 in 9.5%, parainfluenza virus in 7.9%, respiratory syncytial virus in 7.3%, Mycoplasma pneumoniae in 4.9%, Bordetella pertussis in 4.6%, human metapneumovirus in 4.2%, adenovirus in 3.4%, Chlamydia pneumoniae in 3.4%, influenza B virus in 1.3%, Bordetella parapertussis in 0.1% and Legionella pneumophila had one positive result. The first trimester of the year had the highest number of positive test panels (47.0%). Conclusions: Our study demonstrates a predominance of viral pathogens across all age groups and seasons, further supporting guideline-based practice and highlighting the importance of confirming bacterial infection before initiating antibiotic therapy. This insight into the post-pandemic circulation of respiratory pathogens may help inform public health strategies, including improved surveillance, anticipation of seasonal outbreaks, and targeted interventions, thereby supporting future pandemic preparedness and mitigation efforts. Full article