Search Results (44)

(1) Background and Aims: Patients with inflammatory bowel disease (IBD) and primary sclerosing cholangitis (PSC) face a significantly increased risk of malignancies, including a 10-fold higher risk for colorectal cancer (CRC) and a lifetime risk for cholangiocarcinoma (CCA) exceeding 20%. The mechanisms underlying this elevated risk remain elusive. This review consolidates recent findings on cancer risk in PSC-IBD patients, focusing on molecular pathways, diagnostic innovations, and prevention strategies. (2) Methods: A comprehensive PubMed search was performed to identify studies published through to March 2025 on oncogenic processes, molecular mechanisms, and advancements in diagnostic and preventive strategies for CRC and CCA in PSC-IBD patients. (3) Results: Surveillance guidelines recommend an annual colonoscopy for CRC and imaging combined with CA 19-9 monitoring for CCA. Recent studies highlight the role of molecular alterations, including epigenetic modifications, in tumorigenesis. Advances in molecular diagnostics, imaging, and endoscopic technologies are improving the accuracy and timeliness of cancer detection. (4) Conclusions: PSC-IBD patients remain at high risk for CRC and CCA, emphasizing the need for vigilant surveillance and advanced prevention strategies. Advances in early detection and precision diagnostics offer new opportunities to reduce the cancer burden in this high-risk population. Full article

Background: Bloodstream infections continue to pose a serious global health threat due to their high morbidity and mortality, further worsened by rising antimicrobial resistance and delays in starting targeted therapy. This study assesses the accuracy and timeliness of therapeutic recommendations produced by an artificial intelligence (AI)-driven and machine-learning (ML) clinical decision support system (CDSS), comparing results based on molecular diagnostics alone with those that combine molecular and phenotypic data (standard cultures). Methods: In a prospective cross-sectional study conducted in Lima, Peru, 117 blood cultures were analyzed using FilmArray/GeneXpert for molecular identification and MALDI-TOF/VITEK 2.0 for phenotypic profiling. The AI/ML-based CDSS provided treatment recommendations in two formats, which were assessed for concordance and turnaround time. Results: Therapeutic recommendations showed 80.3% consistency between data types, with 86.3% concordance in pathogen and resistance detection. Notably, molecular-only recommendations were delivered 29 h earlier than those incorporating phenotypic data. Escherichia coli was the most frequently isolated pathogen, with a 95% concordance in suggested therapy. A substantial agreement was observed in treatment consistency (Kappa = 0.80). Conclusions: These findings highlight the potential of using AI-powered CDSS in conjunction with molecular diagnostics to accelerate clinical decision-making in bacteremia, supporting more timely interventions and improved antimicrobial stewardship. Further research is warranted to assess scalability and impact across diverse clinical settings. Full article

Background: Pediatric growth assessment is crucial for early diagnosis and intervention in growth disorders. Traditional methods often lack accuracy and real-time decision-making capabilities This study explores the application of machine learning (ML), particularly logistic regression, to improve diagnostic precision and timeliness in pediatric growth assessment. Logistic regression is a reliable and easily interpretable model for detecting growth abnormalities in children. Unlike complex machine learning models, it offers parsimony in transparency, efficiency, and reproducibility, making it ideal for clinical settings where explainable, data-driven decisions are essential. Methods: A logistic regression model was developed using R to analyze biometric and demographic data from a cross-sectional dataset, including real-world data from public institucions. The study employed a bibliometric analysis to identify key trends and incorporated data preprocessing techniques such as cleaning, imputation, and feature selection to enhance model performance. Performance metrics, including accuracy, sensitivity, and the Receiver Operating Characteristic (ROC) curve, were utilized for evaluation. Results: The logistic regression model demonstrated an accuracy of 94.65% and a sensitivity of 91.03%, significantly improving the identification of growth anomalies compared to conventional assessment methods. The model’s ROC curve showed an area under the curve (AUC) of 0.96, indicating excellent predictive capability. Findings highlight ML’s potential in automating pediatric growth monitoring and supporting clinical decision-making, as it can be very simple and highly interpretable in clinical practice. Conclusions: ML, particularly logistic regression, offers a promising tool for pediatric healthcare by enhancing diagnostic precision and operational efficiency. Despite these advancements, challenges remain regarding data quality, clinical integration, and privacy concerns. Future research should focus on expanding dataset diversity, improving model interpretability, and conducting external validation to facilitate broader clinical adoption. Full article

To effectively diagnose open-circuit (OC) faults in the insulated gate bipolar transistor (IGBT) of a cascaded H-bridge rectifier (CHBR) in real-time, this paper uses a single-phase three-cell CHBR as an example. Through mechanism analysis, the variation patterns of the capacitor voltage and grid current due to OC faults are defined. Based on this, the DC capacitor voltage threshold (VT) and the grid current coefficient of variation (CCV) are introduced as fault diagnosis indices, and a real-time OC fault diagnosis method for CHBR is established. The robustness, accuracy, timeliness, and universality of the proposed method are validated through simulations. The results show that the proposed method exhibits strong robustness when the grid voltage fluctuates, either dropping from 3 kV to 2.85 kV or rising from 3 kV to 3.15 kV. Compared to existing diagnostic methods, the proposed approach requires less diagnostic time, with the faulty IGBT being identified in as little as 3.09 ms under optimal conditions. Additionally, the diagnostic performance remains unaffected by changes in control strategies, making it universally applicable for OC fault diagnosis in CHBR under various control strategies (such as dq current decoupling control, PR current control, and transient current control), with comparable diagnosis results and speeds. Full article

Dry-type air-core reactors (DARs) often have inter-turn short circuit (ITSC) faults. However, traditional fault detection methods for DARs generally demonstrate poor timeliness and low sensitivity, and few methods combine intelligent algorithms for objective and accurate diagnosis. Therefore, a novel online diagnosis method for ITSC faults was proposed. First, the “field-circuit” coupling 2D model of reactors was established to simulate the impact of ITSC faults on the characteristics of various state parameters; accordingly, the Lissajous graph was introduced to characterize the short circuit fault. Then, the variation law of the Lissajous graph under different inter-turn fault layers, turns, and degrees was explored to verify the feasibilities of the proposed method. Finally, to achieve rapid diagnosis and fulfill the requirements of edge computing, a lightweight network model named MobileNetV3-Small was used and combined as a classifier to achieve accurate diagnosis of ITSC faults. The results robustly validate that the Lissajous graphical method can significantly reflect ITSC faults through observing the variation in the graph and feature parameters. Furthermore, the MobileNetV3-Small model achieves a diagnostic accuracy of up to 95.91%, which can further enhance the diagnostic accuracy of the ITSC fault degree. Full article

Deep learning methods based on neural network models have been widely applied to bearing fault classification. Although they can achieve high accuracy, they also come with significant complexity. Bearing faults often generate impact vibrations, which produce regular fault characteristic peaks on the envelope spectrum. This paper utilizes the differences in frequency and intensity of the envelope spectrum characteristic peaks under different bearing fault conditions as fault features. By combining these features with the simple and efficient Naive Bayes classifier for fault diagnosis, the algorithm complexity is reduced from the perspective of feature extraction and fault identification. The proposed method was validated using bearing fault data from the Case Western Reserve University (CWRU) dataset and the Machinery Fault Prediction Technology (MFPT) dataset. The results show that the method can classify bearing faults and achieve accurate diagnostic results. The average diagnostic accuracy for the four groups from these two datasets was 99.90% and 99.65%, respectively. The Naive Bayes classification algorithm was compared with classic algorithms in terms of classification accuracy and classification time. Additionally, the algorithm was compared with recent bearing fault diagnosis methods using the CWRU dataset in terms of algorithm complexity. The complexity of the proposed algorithm is only O(N(2280)), which is lower than that of other bearing fault diagnosis methods, where N represents the number of fault samples. This demonstrates that the method significantly reduces algorithm complexity while ensuring accuracy, improving diagnostic efficiency, enhancing the timeliness of real-time industrial bearing fault diagnosis, and reducing hardware setup and operating costs. Full article

In the area of biomedical diagnostics, microwave imaging techniques have been recently proposed for performing brain stroke detection and monitoring. Indeed, theoretically, these techniques make it possible to meet the timeliness requirements of such a diagnosis with portable systems. Moreover, relying on the use of microwaves, they are noninvasive and allow continuous monitoring of critical patients. In this paper, the microwave imaging problem is solved by exploiting multifrequency data by an inexact-Newton method formulated in the framework of non-constant exponent Lebesgue spaces. First, the method is numerically validated with three-dimensional head models affected by anatomically-realistic strokes. Then, a further assessment through experimental data obtained with a cylindrical phantom is conducted. A quite accurate reconstruction of the variations of dielectric properties inside the patient’s head due to the insurgence of stroke is obtained in both numerical and experimental cases, showing the potentiality of the proposed approach. Full article

(1) Background: Comprehensive and timely lung cancer (LC) staging is essential for prognosis and management. The Lung Diagnostic Assessment Program (LDAP) in Southeastern (SE) Ontario aims to provide rapid, guideline-concordant care for suspected LC patients. We evaluated factors affecting the completeness and timeliness of staging for stage I–III LC patients in SE Ontario, including the impact of LDAP management. (2) Methods: This was a population-based retrospective cohort study using the LDAP database (January 2017–December 2019), linked with the Ontario Cancer Registry, to identify newly diagnosed LC patients. A Cox model approach identified variables associated with staging completeness and timeliness. (3) Results: Among 755 patients, 459 (60.8%) were managed through LDAP. Optimal staging was achieved in 596 patients (78.9%), 23 (3.0%) had alternative staging, and 136 (18.0%) had incomplete staging. In the adjusted analyses, LDAP management was associated with a higher likelihood of complete staging (OR 2.29, p < 0.0001) and faster staging completion (β = −18.53, p < 0.0001). Increased distance to PET centres was associated with a longer time to complete staging (β = 8.95 per 100 km, p = 0.0007), as was longer time to diagnosis (β = 21.63 per 30 days, p < 0.0001). (4) Conclusions: LDAP management in SE Ontario significantly improved staging completeness and shortened staging time for stage I–III LC patients. Full article

Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby compensating for the limitations of in-situ observations and satellite remote sensing. However, previous research has primarily focused on SMC diagnostics for the entire crop growth period, often neglecting the development of targeted soil moisture modeling paradigms that account for the specific characteristics of the canopy and root zone at different growth stages. Furthermore, the variations in soil moisture status between fields, resulting from the hysteresis of water flow in irrigation channels at different levels, may influence the development of soil moisture modeling schemes, an area that has been seldom explored. In this study, SMC models based on UAV spectral information were constructed using Random Forest (RF) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithms. The soil moisture modeling paradigms (i.e., input–output mapping) under different growth stages and soil moisture conditions of summer maize were systematically compared and discussed, along with the corresponding physical interpretability. Our results showed that (1) the SMC modeling schemes differ significantly across the various growth stages, with distinct input–output mappings recommended for the early (i.e., jointing, tasselling, and silking stages), middle (i.e., blister and milk stages), and late (i.e., maturing stage) periods. (2) these machine learning-based models performed best at the jointing stage, while subsequently, their accuracy generally exhibited a downward trend as the maize grew. (3) the RF model demonstrates superior robustness in estimating soil moisture status across different fields (moisture conditions), achieving optimal estimation accuracy in fields with overall higher SMC in line with the PSO-SVM model. (4) unlike the RF model’s robustness in spatial SMC diagnostics, the PSO-SVM model more reliably captured the temporal dynamics of SMC across different growth stages of summer maize. This study offers technical references for future modelers in UAV-based SMC modeling across various spatial and temporal conditions, addressing both the types of models as well as their input features. Full article

Neonatal epilepsy is an early postnatal brain disorder, and automatic seizure detection is crucial for timely diagnosis and treatment to reduce potential brain damage. This work proposes a novel Lightweight Multi-Attention Network, LMA-EEGNet, for diagnosing neonatal epileptic seizures from multi-channel EEG signals employing dilated depthwise separable convolution (DDS Conv) for feature extraction and using pointwise convolution followed by global average pooling for classification. The proposed approach substantially reduces the model size, number of parameters, and computational complexity, which are crucial for real-time detection and clinical diagnosis of neonatal epileptic seizures. LMA-EEGNet integrates temporal and spectral features through distinct temporal and spectral branches. The temporal branch uses DDS Conv to extract temporal features, enhanced by a channel attention mechanism. The spectral branch utilizes similar convolutions alongside a spatial attention mechanism to highlight key frequency components. Outputs from both branches are merged and processed through a pointwise convolution layer and a global average pooling layer for efficient neonatal seizure detection. Experimental results show that our model, with only 2471 parameters and a size of 23 KB, achieves an accuracy of 95.71% and an AUC of 0.9862, demonstrating its potential for practical deployment. This study provides an effective deep learning solution for the early detection of neonatal epileptic seizures, improving diagnostic accuracy and timeliness. Full article

Mpox, caused by viruses of the genus Orthopoxvirus, is an emerging threat to human and animal health. With increasing urbanization and more frequent interaction between humans and wild animals, the risk of Mpox transmission to humans has increased significantly. This review aims to examine in depth the epidemiology, pathogenesis, and diagnosis of Mpox, with a special focus on recent discoveries and advances in understanding the disease. Molecular mechanisms involved in viral replication will be examined, as well as risk factors associated with interspecific transmission and spread of the disease in human populations. Currently available diagnostic methods will also be discussed, with a critical analysis of their limitations and possible future directions for improving the accuracy and timeliness of diagnosis. Finally, this review will explore the public health implications associated with Mpox, emphasizing the importance of epidemiological surveillance, vaccination, and emergency preparedness to prevent and manage possible outbreaks. Understanding the epidemiology and control strategies for Mpox is critical to protecting the health of human and animal communities and mitigating the risk of interspecific transmission and spread of the disease. Full article

The pathways for receiving a diagnosis of a rare type of dementia are poorly understood. Diagnostic challenges decrease access to relevant health promotion activities and post-diagnostic support. This study was focused on pathways experienced by people affected by rare dementia in Wales, United Kingdom (UK), considering the practical, emotional, and economic consequences. Semi-structured interviews were completed with 10 people affected by rare dementia across Wales, UK (nine family caregivers and one person living with rare dementia). The interview data were subject to a thematic analysis and a bottom-up costing approach was used to cost the pathway journeys. Five transitional points occurred across the diagnostic pathway (initial contact, initial referral, further referrals—provider, further referrals—private, and diagnosis) alongside two further themes (i.e., involved in the diagnostic process and disputes between stakeholders). The timeliness of the diagnosis was perceived to often be subject to ‘luck’, with access to private healthcare a personal finance option to expedite the process. Higher economic costs were observed when, in retrospect, inappropriate referrals were made, or multiple referrals were required. The confusion and disputes relating to individual diagnostic pathways led to further emotional burdens, suggesting that higher economic costs and emotional consequences are interlinked. Clearer diagnostic pathways for rare dementia may prevent unnecessary service contacts, waiting times, and associated distress. Prioritising appropriate and timely service contacts leads to diagnosis and support to families and enables people to increase control over their health. Appropriate diagnostic pathways may be less costly and reduce costs for families. Full article

Leptospirosis represents a public health problem in Colombia. However, the underreporting of the disease is an unfortunate reality, with a clear trend towards a decrease in cases since 2019, when the guidelines for its confirmatory diagnosis changed with the requirement of two paired samples. The purpose of this review is to highlight the importance of leptospirosis. While the access to rapid diagnosis is available at practically all levels of care for dengue and malaria, leptospirosis—a doubly neglected disease—deserves recognition as a serious public health problem in Colombia. In this manner, it is proposed that molecular tests are a viable diagnostic alternative that can improve the targeted treatment of the patient and the timeliness of data and case reporting to SIVIGILA, and reduce the underreporting of the disease. Taking advantage of the strengthened technological infrastructure derived from the SARS-CoV-2 pandemic for molecular diagnosis in Colombia, with a network of 227 laboratories distributed throughout the national territory, with an installed capacity for PCR testing, it is proposed that molecular diagnosis can be used as an alternative for early diagnosis. This would allow case confirmation through the public health network in Colombia, and, together with the microagglutination (MAT) technique, the epidemiological surveillance of this disease in this country would be strengthened. Full article

With the miniaturization of ultrasound and the development of artificial intelligence, its application in disaster scenes and pre-hospital emergency care has become more and more common. This study summarizes the literature on portable ultrasound in pre-hospital emergency and disaster scene treatment in the past decade and reviews the development and application of portable ultrasound. Portable ultrasound diagnostic equipment can be used to diagnose abdominal bleeding, limb fracture, hemopneumothorax, pericardial effusion, etc., based on which trauma can be diagnosed pre-hospital and provide guiding suggestions for the next triage and rescue; in early rescue, portable ultrasound can guide emergency operations, such as tracheal intubation, pericardial cavity puncture, and thoracic and abdominal puncture as well as improve the accuracy and timeliness of operation techniques. In addition, with the development of artificial intelligence (AI), AI-assisted diagnosis can improve the diagnosis level of ultrasound at disaster sites. The portable ultrasound diagnosis system equipped with an AI robotic arm can maximize the pre-screening classification and fast and concise diagnosis and treatment of batch casualties, thus providing a reliable basis for batch casualty classification and evacuation at disaster accident sites. Full article

The COVID-19 pandemic has highlighted the urgent need for accurate, rapid, and cost-effective diagnostic methods to identify and track the disease. Traditional diagnostic methods, such as PCR and serological assays, have limitations in terms of sensitivity, specificity, and timeliness. To investigate the potential of using protein–peptide hybrid microarray (PPHM) technology to track the dynamic changes of antibodies in the serum of COVID-19 patients and evaluate the prognosis of patients over time. A discovery cohort of 20 patients with COVID-19 was assembled, and PPHM technology was used to track the dynamic changes of antibodies in the serum of these patients. The results were analyzed to classify the patients into different disease severity groups, and to predict the disease progression and prognosis of the patients. PPHM technology was found to be highly effective in detecting the dynamic changes of antibodies in the serum of COVID-19 patients. Four polypeptide antibodies were found to be particularly useful for reflecting the actual status of the patient’s recovery process and for accurately predicting the disease progression and prognosis of the patients. The findings of this study emphasize the multi-dimensional space of peptides to analyze the high-volume signals in the serum samples of COVID-19 patients and monitor the prognosis of patients over time. PPHM technology has the potential to be a powerful tool for tracking the dynamic changes of antibodies in the serum of COVID-19 patients and for improving the diagnosis and prognosis of the disease. Full article