Search Results (454)

Background: The growing demand for automated microorganism classification in the context of Laboratory 4.0 highlights the potential of convolutional neural networks (CNNs) for accurate and efficient image analysis. However, their effectiveness remains limited by the scarcity of large, labeled datasets. This study addresses a key gap in the literature by investigating how commonly used image preprocessing techniques, such as lossy compression, non-uniform scaling (typically applied to fit input images to CNN input layers), and data augmentation, affect the performance of CNNs in automated microorganism classification. Methods: Using two well-established CNN architectures, AlexNet and DenseNet-121, both frequently applied in biomedical image analysis, we conducted a series of computational experiments on a standardized dataset of high-resolution bacterial images. Results: Our results demonstrate under which conditions these preprocessing strategies degrade or improve CNN performance. Using the findings from this research to optimize hyperparameters and train the CNNs, we achieved classification accuracies of 98.61% with AlexNet and 99.82% with DenseNet-121, surpassing the performance reported in current state-of-the-art studies. Conclusions: This study advances laboratory digitalization by reducing data preparation effort, training time, and computational costs, while improving the accuracy of microorganism classification with deep learning. Its contributions also benefit broader biomedical fields such as automated diagnostics, digital pathology, clinical decision support, and point-of-care imaging. Full article

Laboratory astrophysics is an emerging interdisciplinary field bridging high-energy-density plasma physics and astrophysics. Optical diagnostic techniques offer high spatiotemporal resolution and the unique capability for simultaneous multi-field measurements. These attributes make them indispensable for deciphering extreme plasma dynamics in laboratory astrophysics. This review systematically elaborates on the physical principles and inversion methodologies of key optical diagnostics, including Nomarski interferometry, shadowgraphy, and Faraday rotation. Highlighting frontier progress by our team, we showcase the application of these techniques in analyzing jet collimation mechanisms, turbulent magnetic reconnection, collisionless shocks, and particle acceleration. Future trajectories for optical diagnostic development are also discussed. Full article

Malaria remains a considerable challenge to international health, especially in returning travelers from endemic regions where exposure risk may be downplayed. Prompt and accurate diagnosis is crucial, especially when conventional diagnostic techniques are insufficient. This case report presents a 59-year-old man who developed fever, rash, and myalgia after returning from the Amazon rainforest. Initial laboratory tests demonstrated leukopenia, thrombocytopenia, transaminitis, and hyperbilirubinemia. Despite these abnormal results and a clinically suspicious presentation, malaria smears were negative. Since the symptoms did not resolve, a Karius test—a plasma-based microbial cell-free DNA sequencing assay—successfully detected the presence of Plasmodium vivax, thus establishing the diagnosis. The patient needed several treatment regimens for the recurrent attacks, including chloroquine and primaquine, artemether-lumefantrine, and eventually a combination of quinine and doxycycline together with a prolonged course of primaquine. His symptoms resolved completely after the last treatment regimen, along with the normalization of the blood counts and liver function tests. This case demonstrates the limitations of smear microscopy diagnosis in P. vivax infections, highlights the role of molecular diagnostics like the Karius test, and stresses the importance of preventing relapses with adequate hypnozoite clearance. It further highlights the importance of clinician awareness and diligent follow-up in cases of travel-related Malaria, especially those with unusual presentations or recurrent symptoms. Full article

Background/Objectives: Plastic and reconstructive surgery (PRS) is characterized by its wide range of techniques and procedures, aiming to address the need for individualized treatment approaches. As PRS is predominantly performed in an elective and non-emergency setting, a thorough preoperative risk assessment through standardized diagnostics remains essential. Lab testing is often routinely performed, yet its overall role and how specific parameters are currently used to stratify risk in PRS is poorly understood. We herein aim to explore the role of preoperative lab value testing and characterize current practices, perceived importance, and variability in their use for risk stratification. Methods: We conducted an anonymous, web-based cross-sectional survey of international PRS surgeons. Survey items captured demographics, routine preoperative assessment methods, ordering frequency of laboratory tests, and their perceived importance. Group comparisons were stratified by work setting, years of experience, and PRS subspecialization. Results: A total of 140 PRS surgeons from 24 countries completed the survey. Clinical evaluation (97.9%) and laboratory testing (84.3%) were the most common risk assessment methods investigated in our study; 70.7% agreed that preoperative lab values are important for surgical risk stratification while 64.3% would adopt a standardized lab-based risk assessment tool. The most ordered lab tests were hemoglobin (80.0%), hematocrit (76.4%), platelets (69.3%), creatinine (68.6%), and white blood cell count (67.1%). Hospital-based PRS surgeons ordered creatinine, WBC, INR and albumin more often and rated albumin of greater importance compared with PRS surgeons based in private practice. Conclusions: Preoperative labs are widely used in PRS with emphasis on hematologic and coagulation parameters, in both hospitals and private practices. Broad consensus on the importance of preoperative lab values in surgical risk stratification and a willingness to adopt a standardized, lab-based risk assessment tool highlight the need to harmonize current practice and integrate specific labs into standardized procedure-specific risk stratification. Full article

Ovarian cancer’s high mortality is primarily due to late-stage diagnosis, underscoring the critical need for improved early detection tools. This study develops and validates explainable artificial intelligence (XAI) models to discriminate malignant from benign ovarian masses using readily available demographic and laboratory data. A dataset of 309 patients (140 malignant, 169 benign) with 47 clinical parameters was analyzed. The Boruta algorithm selected 19 significant features, including tumor markers (CA125, HE4, CEA, CA19-9, AFP), hematological indices, liver function tests, and electrolytes. Five ensemble machine learning algorithms were optimized and evaluated using repeated stratified 5-fold cross-validation. The Gradient Boosting model achieved the highest performance with 88.99% (±3.2%) accuracy, 0.934 AUC-ROC, and 0.782 Matthews correlation coefficient. SHAP analysis identified HE4, CEA, globulin, CA125, and age as the most globally important features. Unlike black-box approaches, our XAI framework provides clinically interpretable decision pathways through LIME and SHAP visualizations, revealing how feature values push predictions toward malignancy or benignity. Partial dependence plots illustrated non-linear risk relationships, such as a sharp increase in malignancy probability with CA125 > 35 U/mL. This explainable approach demonstrates that ensemble models can achieve high diagnostic accuracy using routine lab data alone, performing comparably to established clinical indices while ensuring transparency and clinical plausibility. The integration of state-of-the-art XAI techniques highlights established biomarkers and reveals potential novel contributors like inflammatory and hepatic indices, offering a pragmatic, scalable triage tool to augment existing diagnostic pathways, particularly in resource-constrained settings. Full article

Reovirus infections pose a significant threat to waterfowl health and productivity globally. This review provides a comprehensive update on various aspects of waterfowl reoviruses (WRVs) affecting domestic duck and goose species. We outline the genetic diversity and evolution of circulating strains. The paper details the array of clinical signs and pathologies observed in infected birds. Most advanced laboratory diagnostic methods, including molecular techniques, are reviewed for their role in rapid and accurate detection, forming the cornerstone of effective surveillance programs. Furthermore, we explore the advancements in WRV vaccine development, covering traditional as well as promising novel approaches. The ongoing challenge of managing WRV infections necessitates integrated surveillance-control programs, prioritizing enhanced diagnostic capabilities and the development of more efficacious and broadly protective vaccines to safeguard populations of domestic ducks and geese. Full article

Background: Gonorrhoea is a common sexually transmitted infection with serious health consequences if not well-treated. Resistance to common therapeutic agents and limited diagnostics further heighten its burden on sexual and reproductive health. This study determined the positivity level, spatial distribution and factors influencing test positivity for Neisseria gonorrhoeae in Kampala, Uganda. Methods: Clinical data and urethral swabs were primarily collected from men with urethritis at 10 high-volume surveillance facilities. Laboratory analysis followed conventional microbiology techniques. Statistical analysis was conducted using R 4.4.3. Results: Among 1663 participants, 923 (56%, 95% CI: 53–58%) tested positive for N. gonorrhoeae, with comparable levels in Kampala divisions. Co-positivity of HIV and N. gonorrhoeae ranged from 5–27%. At bivariable analysis, there was a lower risk of testing positive for N. gonorrhoeae among participants aged above 24 years. Individuals who never use condoms or infrequently use them were marginally at a higher risk for positivity compared to routine users. Only age was the independent predictor for positivity with N. gonorrhoeae (aPR = 0.93, 95% CI: 0.87–0.99, p-value = 0.017), with men aged above 24 years being less likely to test positive for N. gonorrhoeae. Conclusions: Spatial distribution of N. gonorrhoeae positivity in Kampala was found not to be significantly influenced by location in any of the five divisions. Public health interventions should be tailored to focus on the high-risk groups such as men aged below 25 years, incorporating targeted education and prevention programs, particularly emphasizing consistent condom use among sexually active individuals to improve sexual and reproductive health in Kampala and greater Uganda. Full article

In horizontal well technology, hydraulic fracturing has been established as an essential technique for enhancing hydrocarbon production. However, the complex architecture of fracture networks challenges conventional monitoring methods. Micro-seismic monitoring, recognized for its superior resolution and sensitivity, enables precise fracture morphology characterization. This study advances diagnostic capabilities through integrated field–laboratory investigations and multi-domain signal processing. Hydraulic fracturing experiments under varied geological conditions generated critical micro-seismic datasets, with quantitative analyses revealing asymmetric propagation patterns (total length 312 ± 15 m, east wing 117 m/west wing 194 m) forming a 13.37 × 10⁴ m³ stimulated reservoir volume. Spatial event distribution exhibited density disparities correlating with geophone offsets (west wing 3.8 events/m vs. east 1.2 events/m at 420–794 m distances). Advanced time–frequency analyses and inversion algorithms differentiated signal characteristics demonstrating logarithmic SNR (Signal-to-Noise Ratio)–magnitude relationships (SNR 0.49–4.82, R² = 0.87), with near-field events (<500 m) showing 68% reduced magnitude variance compared to far-field counterparts. Coupled numerical simulations confirmed stress field interactions where fracture trajectories deviated 5–15° from principal stress directions due to prior-stage stress shadows. Branch fracture networks identified in Stages 4/7/9/10 with orthogonal/oblique intersections (45–65° dip angles) enhanced stimulation reservoir volume (SRV) by 37–42% versus planar fractures. These geometric parameters—including height (20 ± 3 m), width (44 ± 5 m), spacing, and complexity—were quantitatively linked to micro-seismic response patterns. The developed diagnostic framework provides operational guidelines for optimizing fracture geometry control, demonstrating how heterogeneity-driven signal variations inform stimulation strategy adjustments to improve reservoir recovery and economic returns. Full article

Urinary tract infections (UTIs) are among the most common pathologies, with a high incidence in women and hospitalized patients. Their diagnosis is based on the presence of clinical symptoms and signs in addition to the detection of microorganisms in urine trough urine cultures, a time-consuming and resource-intensive test. The goal was to optimize UTI detection through artificial intelligence (machine learning) using non-microbiological laboratory parameters, thereby reducing unnecessary cultures and expediting diagnosis. A total of 4283 urine cultures from patients with suspected UTIs were analyzed in the Microbiology Laboratory of the University Hospital Virgen de las Nieves (Granada, Spain) between 2016 and 2020. Various machine learning algorithms were applied to predict positive urine cultures and the type of isolated microorganism. Random Forest demonstrated the best performance, achieving an accuracy (percentage of correct positive and negative classifications) of 82.2% and an area under the ROC curve of 87.1%. Moreover, the Tree algorithm successfully predicted the presence of Gram-negative bacilli in urine cultures with an accuracy of 79.0%. Among the most relevant predictive variables were the presence of leukocytes and nitrites in the urine dipstick test, along with elevated white cells count, monocyte count, lymphocyte percentage in blood and creatinine levels. The integration of AI algorithms and non-microbiological parameters within the diagnostic and management pathways of UTI holds considerable promise. However, further validation with clinical data is required for integration into hospital practice. Full article

Aquaculture has rapidly become a vital sector for ensuring global food security by meeting the growing demand for animal protein. Bangladesh, one of the world’s leading aquaculture producers, recorded a production of 4.91 million MT in 2022–2023, largely driven by inland farming systems. Despite this remarkable growth, the sector is highly vulnerable to disease outbreaks, which are aggravated by different factors. Pathogens such as bacteria, viruses, fungi, and parasites cause significant losses, while conventional disease diagnosis in Bangladesh still depends mainly on visual assessment and basic laboratory techniques, limiting early detection. This narrative review highlights recent advances in diagnostics as molecular tools, immunodiagnostics, nanodiagnostics, machine learning, and next-generation sequencing (NGS) that are widely applied globally but remain limited in Bangladesh due to infrastructure gaps, lack of skilled manpower, and resource constraints. Current treatment strategies largely rely on antibiotics and aquaculture medicinal products (AMPs), often misused without proper diagnosis, contributing to antimicrobial resistance (AMR). Promising alternatives, including probiotics, immunostimulants, vaccines, and enhanced biosecurity, require greater adoption and farmer awareness. The near-term priorities for Bangladesh include standardized disease and AMR surveillance, prudent antibiotic stewardship, phased adoption of validated rapid diagnostics, and investment in diagnostic and human capacity. Policy-level actions, including a national aquatic animal health strategy, stricter antimicrobial regulation, strengthening diagnostic infrastructure in institution, are crucial to achieve sustainable disease management and ensure long-term resilience of aquaculture in Bangladesh. Full article

The Mycobacterium tuberculosis complex (MTBC) represents one of the most significant bacterial pathogen groups affecting both animals and humans worldwide. This review provides a comprehensive analysis of MTBC species distribution across different animal hosts and evaluates current laboratory diagnostic methodologies for pathogen detection and identification. The complex comprises seven primary species: Mycobacterium bovis, M. caprae, M. tuberculosis, M. microti, M. canettii, M. africanum, and M. pinnipedii, each exhibiting distinct host preferences, geographical distributions, and pathogenic characteristics. Despite sharing >99% genetic homology, these species demonstrate variable biochemical properties, morphological features, and pathogenicity profiles across mammalian species. Current diagnostic approaches encompass both traditional culture-based methods and advanced molecular techniques, including whole genome sequencing. This review emphasises the critical importance of rapid, accurate detection methods for effective tuberculosis surveillance and control programmes in veterinary and public health contexts. Full article

Structural health monitoring (SHM) plays a critical role in ensuring the safety and performance of aerospace structures throughout their lifecycle. As aircraft and spacecraft systems grow in complexity, the integration of machine learning (ML) into SHM frameworks is revolutionizing how damage is detected, localized, and predicted. This review presents a comprehensive examination of recent advances in ML-based SHM methods tailored to aerospace applications. It covers supervised, unsupervised, deep, and hybrid learning techniques, highlighting their capabilities in processing high-dimensional sensor data, managing uncertainty, and enabling real-time diagnostics. Particular focus is given to the challenges of data scarcity, operational variability, and interpretability in safety-critical environments. The review also explores emerging directions such as digital twins, transfer learning, and federated learning. By mapping current strengths and limitations, this paper provides a roadmap for future research and outlines the key enablers needed to bring ML-based SHM from laboratory development to widespread aerospace deployment. Full article

Personalized psychiatry represents an innovative therapeutic approach that integrates biological, genetic, and clinical data to optimize the treatment of mental disorders. Laboratory diagnostics play a fundamental role in this process by providing precise biomarkers that characterize pathophysiological mechanisms such as neuroinflammatory processes, oxidative stress, dysfunction of the Hypothalamic–Pituitary–Adrenal (HPA) axis, as well as disturbances in neuroplasticity and neurodegeneration. This article discusses the use of advanced analytical techniques, such as immunoenzymatic assays for pro-inflammatory cytokines (Interleukin-1β- IL-1β; Interleukin-6-IL-6; Interleukin-18-IL-18; and Tumor Necrosis Factor- α - TNF-α). It also emphasizes the role of pharmacogenomic diagnostics in the individualization of psychotropic therapy. Interdisciplinary collaboration between laboratory diagnosticians and clinicians supports the potential for multidimensional analysis of biomarker data in a clinical context, which supports precise patient profiling and monitoring of treatment responses. Despite progress, there are limitations, such as the lack of standardization in measurement methods, insufficient biomarker validation, and limited availability of tests in clinical practice. Development prospects include the integration of multi-marker panels, the use of point-of-care diagnostics, and the implementation of artificial intelligence tools for the analysis of multidimensional data. As a result, laboratory diagnostics are becoming an integral element of personalized psychiatry, enabling a better understanding of the neurobiology of mental disorders and the implementation of more effective therapeutic strategies. Full article

Background/Objectives: Thyroid-stimulating hormone (TSH), free thyroxine (FT4), and total triiodothyronine (TT3) are biomarkers for evaluating thyroid function. Although hormone levels are affected by many biological and environmental factors, most laboratories use manufacturer-provided reference intervals (RIs) without considering these factors. Thus, in this study we assessed RIs for TSH, FT4, and TT3 in a Korean population, using a direct selection technique. Methods: Serum samples from patients without a history of thyroid disease, medication, family history, or antibody-positive test results were collected after a review of medical records. TSH, FT4, and TT3 levels were measured using the Cobas e801 analyzer (Roche Diagnostics GmbH, Mannheim, Germany) with dedicated reagents. RIs were then established using a non-parametric method, using values at the 2.5th and 97.5th percentiles as reference limits, which were then verified in a validation cohort. Results: A total of 618 subjects were enrolled in this study. Because the distribution of reference values for the four subgroups divided by sex and age (65 years) showed insignificant differences, combined RIs were determined, with the established RIs being 0.38–5.46 mIU/L for TSH, 12.28–22.40 pmol/L for FT4, and 0.94–2.32 nmol/L for TT3. When compared to manufacturer-claimed RIs, the Korean RI for TSH showed higher upper limits, while that for TT3 showed lower upper limits. Additionally, when newly established RIs were applied to the validation cohort, the rate of test-positive results decreased significantly. Conclusions: Significant differences in RIs for TSH and TT3 in the Korean population, compared to manufacturer-claimed values, highlight the need for population-specific RIs. Thus, interpreting the results for the Korean population requires caution, and Korean population-based RIs are necessary. Full article

Liquid biopsies promise major advantages for cancer screening and diagnosis. By detecting biomarkers in peripheral blood samples, liquid biopsies reduce the need for invasive techniques and provide important genetic information integral to the emerging molecular classification of cancers. Unfortunately, the concentrations of most biomarkers, particularly circulating tumour nucleic acids, are vanishingly small—beyond the sensitivity and specificity of most assays. Clustered Regularly Interspaced Short Palindromic Repeats diagnostics (herein labelled ‘CRISPR-Dx’) use gene editing tools to detect, rather than modify, nucleic acids with extremely high specificity. These tools are commonly combined with isothermal nucleic acid amplification to also achieve sensitivities comparable to high-performance laboratory-based techniques, such as digital PCR. CRISPR assays, however, are inherently well suited to adaptation for point-of-care (POC) use, and unlike antigen-based POC assays, are significantly easier and faster to develop. In this review, we summarise current CRISPR-Dx platforms and their analytical potential for cancer biomarker discovery, with an emphasis on enhancing early diagnosis, disease monitoring, point-of-care testing, and supporting cancer therapy. Full article