Search Results (437)

Background and Clinical Significance: Serratus Anterior Muscle Pain Syndrome (SAMPS) is an underdiagnosed cause of anterior chest wall pain, often attributed to myofascial trigger points of the serratus anterior muscle (SAM) or dysfunction of the Long Thoracic Nerve (LTN), leading to significant disability and affecting ipsilateral upper limb movement and quality of life. Current diagnosis relies on exclusion and physical examination, with limited treatment options beyond conservative approaches. This case report presents a novel approach to chronic SAMPS, successfully diagnosed using Sonoguided Digital Palpation (SDP) and treated with ultrasound-guided hydrodissection of the LTN using 5% dextrose in water (D5W) without local anesthetic (LA), in a patient where conventional treatments had failed. Case Presentation: A 72-year-old male presented with a three-year history of persistent left chest pain radiating to the upper back, exacerbated by activity and mimicking cardiac pain. His medical history included two percutaneous coronary interventions. Physical examination revealed tenderness along the anterior axillary line and a positive hyperirritable spot at the mid axillary line at the 5th rib level. SDP was used to visualize the serratus anterior fascia (SAF) and LTN, and to reproduce the patient’s concordant pain by palpating the LTN. Ultrasound-guided hydrodissection of the LTN was then performed using 20–30cc of D5W without LA to separate the nerve from the surrounding tissues, employing a “fascial unzipping” technique. The patient reported immediate pain relief post-procedure, with the pain reducing from 9/10 to 1/10 on the Numeric Rating Scale (NRS), and sustained relief and functional improvement at the 12-month follow-up. Conclusions: Sonoguided Digital Palpation (SDP) of the LTN can serve as a valuable diagnostic adjunct for visualizing and diagnosing SAMPS. Ultrasound-guided hydrodissection of the LTN with D5W without LA may provide a promising and safe treatment option for patients with chronic SAMPS refractory to conservative management, resulting in rapid and sustained pain relief. Further research, including controlled trials, is warranted to evaluate the long-term efficacy and generalizability of these findings and to compare D5W to other injectates. Full article

Background: Community-acquired pneumonia (CAP) is the leading cause of mortality in children from middle- to low-income countries; diagnosing CAP includes clinical evaluation, laboratory testing and pulmonary imaging. Lung ultrasound (LUS) is a sensitive, accessible, non-invasive, non-radiant method for accurately evaluating the lung involvement in acute diseases. Whether LUS findings can be correlated with CAP’s severity or sepsis risk remains debatable. This study aimed to increase the importance of LUS in diagnosing and monitoring CAP. We analyzed 102 children aged 1 month up to 18 years, hospital admitted with CAP. Mean age was 5.71 ± 4.85 years. Underweight was encountered in 44.11% of children, especially below 5 years, while overweight was encountered in 11.36% of older children and adolescents. Patients with CAP presented with fever (79.41%), cough (97.05%), tachypnea (18.62%), respiratory failure symptoms (20.58%), chest pain (12.74%) or poor feeding. Despite the fact that 21.56% had clinically occult CAP and six patients (5.88%) experienced radiologically occult pneumonia, CAP diagnosis was established based on anomalies detected using LUS. Conclusions: Detailed clinical examination with abnormal/modified breath sounds and/or tachypnea is suggestive of acute pneumonia. LUS is a sensitive diagnostic tool. A future perspective of including LUS in the diagnosis algorithm of CAP should be taken into consideration. Full article

Sleep paralysis (SP), an REM parasomnia, can be characterized as one of the symptoms of narcolepsy. The SP phenomenon involves regaining meta-consciousness by the dreamer during REM, when the physiological atonia of skeletal muscles is accompanied by visual and auditory hallucinations that are perceived as vivid and distressing nightmares. Sensory impressions include personification of an unknown presence, strong chest pressure sensation, and intense fear resulting from subjective interaction with the unfolding nightmare. While the mechanism underlying skeletal muscle atonia is known, the physiology of hallucinations remains unclear. Their complex etiology involves interactions among various membrane receptor systems and neurotransmitters, which leads to altered neuronal functionality and disruptions in sensory perception. According to current knowledge, serotonergic activation of 5-hydroxytryptamine-receptor-2A (5-HT2A)-associated pathways plays a critical role in promoting hallucinogenesis during SP. Furthermore, they share similarities with psychedelic-substance-induced ones (i.e., LSD, psilocybin, and 2,5-dimethoxy-4-iodoamphetamine). These compounds also target the 5-HT2A receptor; however, their molecular mechanism varies from serotonin-induced ones. The current review discusses the intracellular signaling pathways responsible for promoting hallucinations in SP, highlighting the critical role of β-arrestin-2. We propose that the β-arrestin-2 signaling pathway does not directly induce hallucinations but creates a state of network susceptibility that facilitates their abrupt emergence in sensory areas. Understanding the molecular basis of serotonergic hallucinations and gaining better insight into 5-HT2A-receptor-dependent pathways may prove crucial in the treatment of multifactorial neuropsychiatric disorders associated with the dysfunctional activity of serotonin receptors. Full article

Background/Objectives: Elevated troponin levels are widely recognized as key biomarkers of myocardial injury and are frequently used in clinical decision making. However, not all instances of troponin elevation indicate true cardiac damage. In some cases, biochemical or immunological interferences may lead to false-positive results. These situations may lead to unnecessary diagnostic interventions and clinical uncertainty, ultimately impacting patient management negatively. This study aims to investigate the underlying mechanisms of false-positive troponin elevation in pediatric patients, focusing on factors such as macrotroponin formation, autoantibodies, and heterophile antibody interference. Methods: This retrospective study analyzed data from 13 pediatric patients who presented with elevated cardiac troponin levels between 2017 and 2024. Clinical evaluations included transthoracic echocardiography (TTE), electrocardiography (ECG), coronary computed tomography angiography (CTA), cardiac magnetic resonance imaging (MRI), and rheumatologic testing. Laboratory findings included measurements of cardiac troponins (cTnI and hs-cTnT) and pro-BNP levels. Results: Among 70 patients evaluated for elevated troponin levels, 13 (18.6%) were determined to have no identifiable cardiac etiology. The median age of these 13 patients was 13.0 years (range: 9–16), with 53.8% being female. The most common presenting complaints were chest pain (53.8%) and palpitations (30.8%). TTE findings were normal in 61.5% of the patients, and all patients had normal coronary CTA and cardiac MRI findings. Although initial troponin I levels were elevated in all cases, persistent positivity was observed up to 12 months. Median cTnI levels were 1.00 ng/mL (range: 0.33–7.19) at week 1 and 0.731 ng/mL (range: 0.175–4.56) at month 12. PEG precipitation testing identified macrotroponin in three patients (23.1%). No etiological explanation could be identified in 10 cases (76.9%), which were considered idiopathic. All patients had negative results for heterophile antibody and rheumatologic tests. Conclusions: When interpreting elevated troponin levels in children, biochemical interferences—especially macrotroponin—should not be overlooked. This study emphasizes the diagnostic uncertainty associated with non-cardiac troponin elevation. To better guide clinical practice and clarify false positivity rates, larger, multicenter prospective studies are needed. Full article

Coronary computed tomography angiography (CCTA) has emerged as the leading noninvasive imaging modality for the assessment of coronary artery disease (CAD), offering high-resolution visualization of the coronary anatomy and plaque characterization. The development of fractional flow reserve derived from CCTA (FFR-CT) has further transformed the diagnostic landscape by enabling the simultaneous evaluation of both anatomical stenosis and lesion-specific ischemia. FFR-CT has demonstrated diagnostic accuracy comparable to invasive FFR. The combined use of CCTA and FFR-CT is now pivotal in a broad range of clinical scenarios, including the evaluation of stable and acute chest pain, assessment of high-risk and complex plaque features, and preoperative planning. As evidence continues to mount, CCTA and FFR-CT are positioned to become the primary gatekeepers to the cardiac catheterization laboratory, potentially reducing the number of unnecessary invasive procedures. This review highlights the growing clinical utility of FFR-CT, its integration with advanced plaque imaging, and the future potential of these technologies in redefining the management of CAD, while also acknowledging current limitations, including image quality requirements, cost, and access. Full article

Lung cancer is one of the most common and deadly forms of cancer worldwide, despite sustained efforts to encourage smoking cessation and raise awareness of the risk factors. In Romania, lung cancer is a significant health challenge, being the leading cause of death caused by cancer, especially amongst men. The incidence of lung cancer in connective tissue disease (CTD) varies in different studies from 4.5% in rheumatoid arthritis (RA), to 4.4% in polymyositis or dermatomyositis, and up to 11.1% in systemic sclerosis. However, older studies have shown an increased risk of cancer in patients with rheumatoid arthritis (RA), ranging from 10% to 30% compared to the general population, particularly in those undergoing methotrexate therapy. Rheumatoid arthritis affects approximately 40 per 100,000 people annually worldwide, with a three- to four-fold higher incidence in women. Non-small cell lung cancer (NSCLC), the most common lung cancer subtype, has been linked to RA, yet the association remains poorly defined, with limited insight into the underlying molecular mechanisms. We present the case of a 61-year-old male with a 49-pack-year smoking history and a known diagnosis of rheumatoid arthritis, currently managed with methotrexate therapy. He was admitted for evaluation due to a progressive decline in general condition, characterized by worsening dyspnea and chest pain, symptoms that had been longstanding but had markedly exacerbated over the past two weeks. Based on a chest CT performed prior to the patient’s admission to our clinic, subsequent diagnostic investigations established the diagnosis of pulmonary adenocarcinoma. The diagnostic process proved to be particularly challenging due to the presence of multiple comorbidities, which significantly impacted both the diagnostic approach and the overall clinical trajectory. Full article

The use of temporary mechanical circulatory support in refractory heart failure cardiogenic shock (HFCS) has risen, leading to potential complications. Post-Cardiac Injury Syndrome (PCIS) from Impella insertion is rare but may result from subclavian artery manipulation and aortic irritation. We report the first case of pericarditis (PCIS) caused by Impella 5.5 insertion in an HFCS patient awaiting heart transplantation. The patient developed chest pain, tachycardia, and hypotension post-Impella insertion. Laboratory results and electrocardiograms confirmed PCIS. Treatment with Ibuprofen and Colchicine was successful. He received a heart transplant 14 days later. This case emphasizes recognizing iatrogenic pericarditis after Impella insertion and the need to avoid additional myocardial strain. Full article

Background: Pulmonary embolism (PE) increases right ventricular (RV) afterload, potentially leading to myocardial stress and electrocardiographic abnormalities. Although QTc prolongation has been suggested as a marker of RV dysfunction, its prevalence, clinical significance, and prognostic value in acute PE remain poorly defined. Objective: The objective of this study is to evaluate the prevalence and clinical implications of QTc prolongation in patients with intermediate–high and high-risk acute PE. Methods: We retrospectively analyzed 95 consecutive patients admitted with intermediate–high or high-risk PE between September 2021 and December 2023. QTc prolongation was defined as ≥470 ms in males and ≥480 ms in females. Clinical, imaging, and laboratory data were compared between patients with normal and prolonged QTc intervals. QTc was assessed at admission, after treatment, and prior to discharge. Results: QTc prolongation was observed in 28.4% of patients at presentation. This group had significantly higher lactate levels (2.3 vs. 1.8 mmol/L, p = 0.03) and a non-significant trend toward elevated troponin and lower oxygen saturation. No differences were observed in echocardiographic or CT-based RV dysfunction parameters. QTc values normalized by discharge irrespective of treatment modality. There was no association between QTc prolongation and in-hospital or long-term mortality. A trend toward more aspiration thrombectomy was noted in the prolonged QTc group (29.6% vs. 11.8%, p = 0.06). Conclusions: QTc prolongation is common in acute intermediate–high and high-risk PE and may reflect transient myocardial stress. While not predictive of clinical outcomes, it should be considered in the differential diagnosis of QTc prolongation in patients presenting with dyspnea and chest pain. Full article

Background: Chest X-rays are rapidly gaining prominence as a prevalent diagnostic tool, as recognized by the World Health Organization (WHO). However, interpreting chest X-rays can be demanding and time-consuming, even for experienced radiologists, leading to potential misinterpretations and delays in treatment. Method: The purpose of this research is the development of a RadAI model. The RadAI model can accurately detect four types of lung abnormalities in chest X-rays and generate a report on each identified abnormality. Moreover, deep learning algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable potential in automating medical image analysis, including chest X-rays. This work addresses the challenge of chest X-ray interpretation by fine tuning the following three advanced deep learning models: Feature-selective and Spatial Receptive Fields Network (FSRFNet50), ResNext50, and ResNet50. These models are compared based on accuracy, precision, recall, and F1-score. Results: The outstanding performance of RadAI shows its potential to assist radiologists to interpret the detected chest abnormalities accurately. Conclusions: RadAI is beneficial in enhancing the accuracy and efficiency of chest X-ray interpretation, ultimately supporting the timely and reliable diagnosis of lung abnormalities. Full article

Cardiovascular diseases remain one of the leading causes of mortality worldwide, highlighting the importance of effective monitoring and early diagnosis. While electrocardiography (ECG) is the standard technique for evaluating the heart’s electrical activity and detecting rhythm and conduction abnormalities, it alone is insufficient for identifying certain conditions, such as valvular disorders. Phonocardiography (PCG) allows the recording and analysis of heart sounds and improves the diagnostic accuracy when combined with ECG. In this study, ECG and PCG signals were simultaneously acquired from a resting adult subject using a compact system comprising an analog front-end (model AD8232, manufactured by Analog Devices, Wilmington, MA, USA) for ECG acquisition and a digital stethoscope built around a condenser electret microphone (model HM-9250, manufactured by HMYL, Anqing, China). Both the ECG electrodes and the microphone were positioned on the chest to ensure the spatial alignment of the signals. An adaptive segmentation algorithm was developed to segment PCG and ECG signals based on their morphological and temporal features. This algorithm identifies the onset and peaks of S1 and S2 heart sounds in the PCG and the Q, R, and S waves in the ECG, enabling the extraction of the systolic time intervals such as EMAT, PEP, LVET, and LVST parameters proven useful in the diagnosis and monitoring of cardiovascular diseases. Based on the segmented signals, the measured averages (EMAT = 74.35 ms, PEP = 89.00 ms, LVET = 244.39 ms, LVST = 258.60 ms) were consistent with the reference standards, demonstrating the reliability of the developed method. The proposed algorithm was validated on synchronized ECG and PCG signals from multiple subjects in an open-source dataset (BSSLAB Localized ECG Data). The systolic intervals extracted using the proposed method closely matched the literature values, confirming the robustness across different recording conditions; in detail, the mean Q–S1 interval was 40.45 ms (≈45 ms reference value, mean difference: −4.85 ms, LoA: −3.42 ms and −6.09 ms) and the R–S1 interval was 14.09 ms (≈15 ms reference value, mean difference: −1.2 ms, LoA: −0.55 ms and −1.85 ms). In conclusion, the results demonstrate the potential of the joint ECG and PCG analysis to improve the long-term monitoring of cardiovascular diseases. Full article

Pneumothorax is a potentially life-threatening condition defined as the collapse of the lung due to air leakage into the chest cavity. Delays in the diagnosis of pneumothorax can lead to severe complications and even mortality. A significant challenge in pneumothorax diagnosis is the shortage of radiologists, resulting in the absence of written reports in plain X-rays and, consequently, impacting patient care. In this paper, we propose an automatic triage system for pneumothorax detection in X-ray images based on deep learning. We address this problem from the perspective of multi-source domain adaptation where different datasets available on the Internet are used for training and testing. In particular, we use datasets which contain chest X-ray images corresponding to different conditions (including pneumothorax). A convolutional neural network (CNN) with an EfficientNet architecture is trained and optimized to identify radiographic signs of pneumothorax using those public datasets. We present the results using cross-dataset validation, demonstrating the robustness and generalization capabilities of our multi-source solution across different datasets. The experimental results demonstrate the model’s potential to assist clinicians in prioritizing and correctly detecting urgent cases of pneumothorax using different integrated deployment strategies. Full article

Tuberculosis (TB) is the most serious worldwide infectious disease and the leading cause of death among people with HIV. Early diagnosis and prompt treatment can cut off the rising number of TB deaths, and analysis of chest X-rays is a cost-effective method. We describe a deep learning-based cascade algorithm for detecting TB in chest X-rays. Firstly, the lung regions were segregated from other anatomical structures by an encoder–decoder with an atrous separable convolution network—DeepLabv3+ with an XceptionNet backbone, DLabv3+X, and then cropped by a bounding box. Using the cropped lung images, we trained several pre-trained Deep Convolutional Neural Networks (DCNNs) on the images with hyperparameters optimized by a Bayesian algorithm. Different combinations of trained DCNNs were compared, and the combination with the maximum accuracy was retained as the winning combination. The ensemble classifier was designed to predict the presence of TB by fusing DCNNs from the winning combination via weighted averaging. Our lung segmentation was evaluated on three publicly available datasets: it provided better Intercept over Union (IoU) values: 95.1% for Montgomery County (MC), 92.8% for Shenzhen (SZ), and 96.1% for JSRT datasets. For TB prediction, our ensemble classifier produced a better accuracy of 92.7% for the MC dataset and obtained a comparable accuracy of 95.5% for the SZ dataset. Finally, occlusion sensitivity and gradient-weighted class activation maps (Grad-CAM) were generated to indicate the most influential regions for the prediction of TB and to localize TB manifestations. Full article

Small-scale object detection remains a significant challenge in semi-supervised object detection (SSOD), particularly in panoramic dental X-rays. Due to the small lesion size, low contrast, and complex anatomical background, conventional teacher models often fail to extract accurate lesion features, leading to noisy pseudo labels and suboptimal detection performance. Additionally, most existing SSOD methods rely on high-confidence thresholds to select pseudo labels, which may mistakenly discard valuable predictions with low scores but accurate localization—especially for small-scale targets. To address these challenges, we propose Entropy Teacher, a novel SSOD framework specifically designed for small-scale dental disease detection. Our method introduces an Entropy-Guided Feature Pyramid that integrates entropy-guided representations to enhance fine-grained structural learning. Moreover, we develop a low-confidence pseudo-label mining (LCPLM) strategy with a class-adaptive thresholding mechanism to effectively recover high-quality pseudo labels below conventional confidence thresholds. Extensive experiments on the Dental Disease Dataset and ChestX-Det demonstrate that Entropy Teacher achieves state-of-the-art performance, surpassing the baseline Unbiased Teacher by +3.8 $A{P}_{50}$ and +4.5 $A{P}_S$. These results confirm the effectiveness of entropy-guided representations and low-confidence mining in improving small-scale lesion detection under limited supervision. Full article

Multiple sclerosis (MS) is a chronic progressive neurodegenerative disease that leads to disabilities such as difficulty moving and slowed cognitive processing. It is the leading non-traumatic cause of disability worldwide. MS also has a high potential to become a model for neurodegenerative diseases with a progression like Alzheimer’s or Parkinson’s. Cardiovascular diseases (CVDs) remain the leading cause of global deaths and have a considerable economic impact. The higher incidence of cardiovascular comorbidities in patients with MS compared to healthy individuals of the same age worsens the prognosis of neurological pathology, leading to a higher level of disability, poorer physical outcomes, higher depression scores, cognitive aging, and diminished quality of life. Classical observational studies often have questionable elements that can represent a source of error, making it difficult to establish a causal relationship between MS and CVD. Genetic studies, including genome-wide evaluation, may resolve this issue and may represent a topic for future research. We report the case of a 31-year-old male patient with a history of multiple sclerosis (MS) diagnosed seven years prior, who presented with acute chest pain upon returning from vacation. Despite the previous recommendation for disease-modifying therapy, the patient had discontinued treatment by personal choice. Electrocardiography (ECG) revealed ST-segment elevation in inferior leads, and emergent coronary angiography identified severe multi-vessel coronary artery disease (CAD), requiring immediate revascularization. This case highlights the potential cardiovascular risks in young patients with MS and the importance of continuous medical supervision. Full article

Pneumonia remains a leading cause of respiratory morbidity and mortality, underscoring the need for rapid and accurate diagnosis to enable timely treatment and prevent complications. This study introduces PELM (Pneumonia Ensemble Learning Model), a novel deep learning framework for automated pneumonia detection using chest X-ray (CXR) images. The model integrates four high-performing architectures—InceptionV3, VGG16, ResNet50, and Vision Transformer (ViT)—via feature-level concatenation to exploit complementary feature representations. A curated, large-scale dataset comprising 50,000 PA-view CXR images was assembled from NIH ChestX-ray14, CheXpert, PadChest, and Kaggle CXR Pneumonia datasets, including both pneumonia and non-pneumonia cases. To ensure fair benchmarking, all models were trained and evaluated under identical preprocessing and hyperparameter settings. PELM achieved outstanding performance, with 96% accuracy, 99% precision, 91% recall, 95% F1-score, 91% specificity, and an AUC of 0.91—surpassing individual model baselines and previously published methods. Additionally, comparative experiments were conducted using tabular clinical data from over 10,000 patients, enabling a direct evaluation of image-based and structured-data-based classification pipelines. These results demonstrate that ensemble learning with hybrid architectures significantly enhances diagnostic accuracy and generalization. The proposed approach is computationally efficient, clinically scalable, and particularly well-suited for deployment in low-resource healthcare settings, where radiologist access may be limited. PELM represents a promising advancement toward reliable, interpretable, and accessible AI-assisted pneumonia screening in global clinical practice. Full article