Diagnostics最新文献

Background/Objectives: Prolonged prelabour rupture of membranes (PROMs), and the resulting inflammatory response, can contribute to the occurrence of adverse neonatal outcomes, especially for early-preterm neonates. This prospective study aimed to measure neonates' inflammatory markers in the first 72 h of life based on ROM duration. The second aim was to examine the relationship between PROMs, serum inflammatory markers, and composite adverse neonatal outcomes after controlling for gestational age (GA). Methods: Data from 1026 patients were analyzed considering the following groups: group 1 (ROM < 18 h, n = 447 patients) and group 2 (ROM > 18 h, n = 579 patients). These groups were further segregated depending on the GA at the moment of membranes' rupture into subgroup 1 (<33 weeks of gestation and 6 days, n = 168 patients) and subgroup 2 (at least 34 completed weeks of gestation, n = 858 patients). Multiple logistic regressions and interaction analyses adjusted for GA considering five composite adverse neonatal outcomes and predictors were employed. Results: PROMs and high c-reactive protein (CRP) values significantly increased the risk of composite outcome 1 occurrence by 14% (95%CI: 1.03-1.57, p < 0.001). PROMs and high CRP values increased the risk of composite outcome 5 by 14% (95%CI: 1.07-1.78, p < 0.001), PROM and leukocytosis by 11% (95%CI: 1.02-1.59, p = 0.001), and PROMs and high PCT values by 21% (95%CI: 1.04-2.10, p < 0.001). Conclusions: The combination of PROMs and high CRP values significantly increased the risk of all evaluated adverse composite outcomes in early-preterm neonates and should point to careful monitoring of these patients.

Background: Sepsis is a major cause of patient death in intensive care units (ICUs). Rapid diagnosis of sepsis assists in optimizing treatments and improves outcomes. Several biomarkers are employed to aid in the diagnosis, prognostication, severity grading, and sub-type discrimination of severe septic infections (SSIs), including current diagnostic parameters, hemostatic measures, and specific organ dysfunction markers. Methods: This study involved 129 critically ill adults categorized into three groups: sepsis (Se = 48), pneumonia (Pn = 48), and Se/Pn (33). Concentrations of five plasma markers (IL-6, IL-8, TREM1, uPAR, and presepsin) were compared with 13 well-established measures of SSI in critically ill patients. These measures were heart rate (HR), white blood count (WBC), C-reactive protein (CRP), procalcitonin (PCT), lactate plasma concentrations, and measures of hemostasis status (platelets count (PLT), fibrinogen, prothrombin time (PT), activated partial thromboplastin time (APTT), international normalization ratio (INR) and D-dimer). Plasma bilirubin and creatinine served as indicators of liver and kidney dysfunction, respectively. Results: Promising roles for these biomarkers were found. The best results were for presepsin, which scored 10/13, followed by IL-6 and IL-8 (each scored 7/13), and the worst were for TREM-1 and uPAR (scored 3/13). Presepsin, IL-6, and IL-8 discriminated between the SSI sub-types, whilst only presepsin correlated with bilirubin and creatinine. uPAR was positive for kidney dysfunction, and TREM-1 was the only indicator of artificial ventilation (AV). Conclusions: Presepsin is an important potential biomarker in SSIs. However, further work is needed to define this marker's diagnostic and prognostic cutoff values.

Objectives: This study aimed to compare ultrasound (US) findings between automated and handheld breast ultrasound (ABUS and HHUS, respectively) in small breast cancers, based on the breast imaging reporting and data system (BI-RADS) category. Methods: We included 51 women (mean age: 52 years; range: 39-66 years) with breast cancer (invasive or DCIS), all of whom underwent both ABUS and HHUS. Patients with tumors measuring ≤1 cm on either modality were enrolled. Two breast radiologists retrospectively evaluated multiple imaging features, including shape, orientation, margin, echo pattern, and posterior characteristics and assigned BI-RADS categories. Lesion sizes were compared between US and pathological findings. Statistical analyses were performed using Bowker's test of symmetry, a paired t-test, and a cumulative link mixed model. Results: ABUS assigned lower BI-RADS categories than HHUS while still maintaining malignancy suspicion in categories 4A or higher (54.8% consistent with HHUS; 37.3% downcategorized in ABUS, p = 0.005). While ABUS demonstrated less aggressive margins in some cases (61.3% consistent with HHUS; 25.8% showing fewer suspicious margins in ABUS), this difference was not statistically significant (p = 0.221). Similarly, ABUS exhibited slightly greater height-width ratios compared to HHUS (median, interquartile range: 0.98, 0.7-1.12 vs. 0.86, 0.74-1.10, p = 0.166). No significant differences were observed in other US findings or tumor sizes between the two modalities (all p > 0.05). Conclusions: Small breast cancers exhibited suspicious US features on both ABUS and HHUS, yet they were assigned lower BI-RADS assessment categories on ABUS compared to HHUS. Therefore, when conducting breast cancer screening with ABUS, it is important to remain attentive to even subtle suspicious findings, and active consideration for biopsy may be warranted.

Drug-induced gingival overgrowth is associated with various systemic diseases, including epilepsy. Among antiepileptic medications, phenytoin is commonly reported to cause this condition. In contrast, sodium valproate (VPA), another widely used antiepileptic drug, rarely induces gingival overgrowth. This difference in side effects highlights the variability in drug-induced oral complications among different antiepileptic medications. This case study presents a patient who developed significant gingival overgrowth after using VPA for over 10 years. The study aims to identify VPA as the causative agent and observe changes during long-term administration and after dose reduction. Our findings demonstrate that even long-standing gingival overgrowth can improve rapidly following discontinuation of the causative medication, providing valuable insights for managing similar cases in the future.

This is a fatal case of multiple complicated congenital anomalies displaying several symptoms consistent with hydrolethalus syndrome. The newborn's phenotype is characterized by a combination of serious anatomical abnormalities such as open-book cerebral hemispheres, defective lobulation of the lungs (one lobe on the left, two on the right), a smaller right kidney, a smooth cerebral surface, and a specific keyhole-shaped defect in the skull base, primarily associated with hydrocephalus.

Background: Mental disorders are disturbances of brain functions that cause cognitive, affective, volitional, and behavioral functions to be disrupted to varying degrees. One of these disorders is depression, a significant factor contributing to the increase in suicide cases worldwide. Consequently, depression has become a significant public health issue globally. Electroencephalogram (EEG) data can be utilized to diagnose mild depression disorder (MDD), offering valuable insights into the pathophysiological mechanisms underlying mental disorders and enhancing the understanding of MDD. Methods: This survey emphasizes the critical role of EEG in advancing artificial intelligence (AI)-driven approaches for depression diagnosis. By focusing on studies that integrate EEG with machine learning (ML) and deep learning (DL) techniques, we systematically analyze methods utilizing EEG signals to identify depression biomarkers. The survey highlights advancements in EEG preprocessing, feature extraction, and model development, showcasing how these approaches enhance the diagnostic precision, scalability, and automation of depression detection. Results: This survey is distinguished from prior reviews by addressing their limitations and providing researchers with valuable insights for future studies. It offers a comprehensive comparison of ML and DL approaches utilizing EEG and an overview of the five key steps in depression detection. The survey also presents existing datasets for depression diagnosis and critically analyzes their limitations. Furthermore, it explores future directions and challenges, such as enhancing diagnostic robustness with data augmentation techniques and optimizing EEG channel selection for improved accuracy. The potential of transfer learning and encoder-decoder architectures to leverage pre-trained models and enhance diagnostic performance is also discussed. Advancements in feature extraction methods for automated depression diagnosis are highlighted as avenues for improving ML and DL model performance. Additionally, integrating Internet of Things (IoT) devices with EEG for continuous mental health monitoring and distinguishing between different types of depression are identified as critical research areas. Finally, the review emphasizes improving the reliability and predictability of computational intelligence-based models to advance depression diagnosis. Conclusions: This study will serve as a well-organized and helpful reference for researchers working on detecting depression using EEG signals and provide insights into the future directions outlined above, guiding further advancements in the field.

Background/Objectives: To develop a computer-aided diagnosis (CAD) method for the classification of late gadolinium enhancement (LGE) cardiac MRI images into myocardial infarction (MI), myocarditis, and healthy classes using a fine-tuned VGG16 model hybridized with multi-layer perceptron (MLP) (VGG16-MLP) and assess our model's performance in comparison to various pre-trained base models and MRI readers. Methods: This study included 361 LGE images for MI, 222 for myocarditis, and 254 for the healthy class. The left ventricle was extracted automatically using a U-net segmentation model on LGE images. Fine-tuned VGG16 was performed for feature extraction. A spatial attention mechanism was implemented as a part of the neural network architecture. The MLP architecture was used for the classification. The evaluation metrics were calculated using a separate test set. To compare the VGG16 model's performance in feature extraction, various pre-trained base models were evaluated: VGG19, DenseNet121, DenseNet201, MobileNet, InceptionV3, and InceptionResNetV2. The Support Vector Machine (SVM) classifier was evaluated and compared to MLP for the classification task. The performance of the VGG16-MLP model was compared with a subjective visual analysis conducted by two blinded independent readers. Results: The VGG16-MLP model allowed high-performance differentiation between MI, myocarditis, and healthy LGE cardiac MRI images. It outperformed the other tested models with 96% accuracy, 97% precision, 96% sensitivity, and 96% F1-score. Our model surpassed the accuracy of Reader 1 by 27% and Reader 2 by 17%. Conclusions: Our study demonstrated that the VGG16-MLP model permits accurate classification of MI, myocarditis, and healthy LGE cardiac MRI images and could be considered a reliable computer-aided diagnosis approach specifically for radiologists with limited experience in cardiovascular imaging.

Background/Objectives: Malignant pleural effusions (MPEs) pose a significant challenge in clinical practice and exert a considerable socio-economic burden on the healthcare system, affecting approximately 1 million individuals annually. These effusions are a leading cause of debilitating dyspnea and a diminished quality of life among cancer patients, with distant metastasis to the pleural layers occurring in about 20% of cases during treatment. Methods: A cross-sectional, observational case-control study was conducted on 151 Bulgarian patients with a hydrothorax. The control group included 72 patients with benign diseases, confirmed via biopsy, with 38 having inflammatory and 34 non-inflammatory pleural effusions. The other 79 patients had malignant pleural involvement. These groups are representative of the main types of pleural pathology. Results: The study found that all of the tumor markers, except for PIVKA-II (Protein induced by vitamin K absence-II), showed statistically significant differences between the malignant and non-malignant patient groups, with CAE (carcinoembryonic antigen) and CA19-9 showing the most notable differences. The Receiver Operating Characteristic (ROC) analysis revealed that CA72-4 had the best ability to distinguish between the two groups, while PIVKA was the weakest, with optimal cut-off values for all of the relevant tumor markers being derived using the Youden index. Conclusions: In conclusion, our study highlights the transformative potential of pleural fluid tumor markers as precise and minimally invasive resources for distinguishing malignant from non-malignant pleural effusions. These findings pave the way for improved diagnostic accuracy and personalized clinical management, addressing a critical gap in the care of patients with pleural pathologies.