Search Results (1,350)

Background: Tularemia is a rarely identified disease in Slovenia. In summer 2024, we detected a tularemia outbreak in the Kranjsko-Sorško polje, located in North-Western part of Slovenia. Aim: To describe the epidemiological investigations and preventive measures to contain the outbreak. Methods: The patients with confirmed tularemia were interviewed. Serology and PCR was used for microbiological confirmation of tularemia and in some patients by isolation from blood or by RT-PCR. Results: The majority of confirmed tularemia cases in 2024 were infected in the geographically limited area in North-Western part of Slovenia (38/46). Tularemia was confirmed in two patients by isolation Francisella tularensis subsp. holarctica from blood or wound, in one by blood PCR, and in the others by serology. Most cases were associated with mowing or harvesting hay with intensive dusting. Twenty-eight (75.7%) out of 37 cases developed pulmonary tularemia. Sixteen cases were hospitalized. After confirming the outbreak, we alerted medical professionals in the region and the general public using the regional and national media and website of National Institute of Public Health. Conclusions: Endemic tularemia in Slovenia is associated with handling wild life and presents in ulceroglandular form. In the localized outbreak in year 2024 there was an extraordinary upsurge of pulmonary tularemia, with many of the cases initially investigated for lung cancer based on the radiology reports. Due to dry weather condition in summer 2024, excessive dusting associated with mowing the grass and handling hay resulted in inhalation of infective aerosols leading to the infection with F. tularensis. Full article

Background: Delayed diagnosis of Chronic Obstructive Pulmonary Disease (COPD) in primary care is common and contributes to preventable morbidity. A deeper understanding of pre-diagnostic patterns is needed to develop targeted detection strategies. We aimed to characterize diagnostic delay and missed diagnostic opportunities (MDOs) and identify high-risk clinical profiles. Methods: We conducted a retrospective cohort study of 167 patients newly diagnosed with COPD in primary care centers in Madrid, Spain. Healthcare utilization in the 12 months preceding diagnosis was analyzed. Multivariable logistic regression was used to identify predictors of MDOs, and K-means clustering was used to identify patient phenotypes. Results: Diagnostic delay (>30 days) was present in 45.5% of patients, and MDOs in 47.3%. MDO-positive patients had significantly worse lung function (mean FEV₁: 1577 vs. 1898 mL, p = 0.008), greater symptom burden (CAT score ≥ 10: 79.7% vs. 59.1%, p = 0.003), and more frequent pre-diagnostic exacerbations (mean: 1.24 vs. 0.71, p = 0.032). After multivariable adjustment, diagnostic delay remained a powerful independent predictor of MDOs (OR 10.25, 95% CI 4.39–24.88; p < 0.001). Cluster analysis identified three distinct clinical phenotypes: ‘Paucisymptomatic–Preserved’, ‘Frequent Attenders/High-Risk’, and ‘Silent Decliners’. Conclusions: The pre-diagnostic period in COPD is a dynamic window of detectable, and potentially preventable, clinical deterioration driven by diagnostic inertia. The identification of distinct patient phenotypes suggests that a proactive, stratified, and personalized approach, rather than a one-size-fits-all strategy, is required to improve early diagnosis in primary care. Full article

Tuberculosis (TB) continues to be the world’s deadliest infectious disease, with an estimated 10.8 million new cases reported in 2023, of which India alone accounted for 28% of the global burden. This study aims to evaluate the impact of tuberculosis on pulmonary function and exercise tolerance, and to examine how these impairments affect health-related quality of life (HRQoL). In a cross-sectional design, 96 bacteriologically confirmed TB patients and 96 age- and sex-matched community controls underwent spirometry, six-minute-walk test (6 MWT), and HRQoL evaluation. DR-TB was detected in 27 patients (28.1%): Isoniazid monoresistance 59.3%, rifampicin monoresistance 11.1%, and XDR-TB 29.6%. Dyspnoea (70.8%) and cough (37.5%) were the most commonly reported symptoms among TB patients. Mean values of FEV₁, FVC, and FEV₁/FVC were significantly lower in TB patients compared to controls (62.8%, 65.97%, and 70.08% vs. 82.55%, 80.09%, and 78.08%, respectively; p < 0.001). Recurrent or DR-TB was associated with reduced spirometric indices and 6 MWT distances (241 m vs. 358 m in drug-sensitive TB). St. George’s respiratory questionnaire (SGRQ) scores indicated significantly poorer health-related quality of life (HRQoL) in patients compared to controls across all domains—symptoms (23.7 vs. 10.7), activity (33.3 vs. 14.2), and impact (20.6 vs. 9.4; p < 0.05). SGRQ scores were inversely correlated with lung function parameters (r = −0.42 to −0.56). These findings underscore the persistent health burden TB poses post-therapy, highlighting the need for routine post-TB functional screening and robust DR-TB control to achieve End-TB goals. Full article

Background: Interstitial lung disease (ILD) is a frequent and potentially progressive manifestation in patients with connective tissue diseases (CTDs). Accurate and reproducible quantification of parenchymal abnormalities on high-resolution computed tomography (HRCT) is essential for evaluating treatment response and monitoring disease progression, particularly in complex cases undergoing antifibrotic therapy. Artificial intelligence (AI)-based tools may improve consistency in visual assessment and assist less experienced radiologists in longitudinal follow-up. Methods: In this retrospective study, 48 patients with CTD-related ILD receiving antifibrotic treatment were included. Each patient underwent four HRCT scans, which were evaluated independently by two radiologists (one expert, one non-expert) using a semi-quantitative scoring system. Percentage estimates of lung involvement were assigned for four parenchymal patterns: hyperlucency, ground-glass opacity (GGO), reticulation, and honeycombing. AI-based analysis was performed using the Imbio Lung Texture Analysis platform, which generated continuous volumetric percentages for each pattern. Concordance between AI and human interpretation was assessed, along with mean absolute error (MAE) and inter-reader differences. Results: The AI-based system demonstrated high concordance with the expert radiologist, with an overall agreement of 81% across patterns. The MAE between AI and the expert ranged from 1.8% to 2.6%. In contrast, concordance between AI and the non-expert radiologist was significantly lower (60–70%), with higher MAE values (3.9% to 5.2%). McNemar’s and Wilcoxon tests confirmed that AI aligned more closely with the expert than the non-expert reader (p < 0.01). AI proved particularly effective in detecting subtle changes in parenchymal burden during follow-up, especially when visual interpretation was inconsistent. Conclusions: AI-driven quantitative imaging offers performance comparable to expert radiologists in assessing ILD patterns on HRCT and significantly outperforms less experienced readers. Its reproducibility and sensitivity to change support its role in standardizing follow-up evaluations and enhancing multidisciplinary decision-making in patients with CTD-related ILD, particularly in progressive fibrosing cases receiving antifibrotic therapy. Full article

Following the COVID-19 pandemic, public interest in healthcare has significantly in-creased, emphasizing the importance of early disease detection through lung sound analysis. Lung sounds serve as a critical biomarker in the diagnosis of pulmonary diseases, and numerous deep learning-based approaches have been actively explored for this purpose. Existing lung sound classification models have demonstrated high accuracy, benefiting from recent advances in artificial intelligence (AI) technologies. However, these models often rely on transmitting data to computationally intensive servers for processing, introducing potential security risks due to the transfer of sensitive medical information over networks. To mitigate these concerns, on-device AI has garnered growing attention as a promising solution for protecting healthcare data. On-device AI enables local data processing and inference directly on the device, thereby enhancing data security compared to server-based schemes. Despite these advantages, on-device AI is inherently limited by computational constraints, while conventional models typically require substantial processing power to maintain high performance. In this study, we propose a lightweight lung sound classification model designed specifically for on-device environments. The proposed scheme extracts audio features using Mel spectrograms, chromagrams, and Mel-Frequency Cepstral Coefficients (MFCC), which are converted into image representations and stacked to form the model input. The lightweight model performs convolution operations tailored to both temporal and frequency–domain characteristics of lung sounds. Comparative experimental results demonstrate that the proposed model achieves superior inference performance while maintaining a significantly smaller model size than conventional classification schemes, making it well-suited for deployment on resource-constrained devices. Full article

Lung disease diagnosis from chest X-ray images is a critical task in clinical care, especially in resource-constrained settings where access to radiology expertise and computational infrastructure is limited. Recent advances in deep learning have shown promise, yet most studies focus solely on either classification or detection in isolation, rarely exploring their combined potential in an embedded, real-world setting. To address this, we present a dual deep learning approach that combines five-class disease classification and multi-label thoracic abnormality detection, optimized for embedded edge deployment. Specifically, we evaluate six state-of-the-art CNN architectures—ResNet101, DenseNet201, MobileNetV3-Large, EfficientNetV2-B0, InceptionResNetV2, and Xception—on both base (2020 images) and augmented (9875 images) datasets. Validation accuracies ranged from 55.3% to 70.7% on the base dataset and improved to 58.4% to 72.0% with augmentation, with MobileNetV3-Large achieving the highest accuracy on both. In parallel, we trained a YOLOv8n model for multi-label detection of 14 thoracic diseases. While not deployed in this work, its lightweight architecture makes it suitable for future use on embedded platforms. All classification models were evaluated for end-to-end inference on a Raspberry Pi 4 using a high-resolution chest X-ray image (2566 × 2566, PNG). MobileNetV3-Large demonstrated the fastest latency at 429.6 ms, and all models completed inference in under 2.4 s. These results demonstrate the feasibility of combining classification for rapid triage and detection for spatial interpretability in real-time, embedded clinical environments—paving the way for practical, low-cost AI-based decision support systems for surgery rooms and mobile clinical environments. Full article

Background: Bladder leiomyosarcoma is an extremely rare non-urothelial malignancy, accounting for less than 0.1% of all bladder tumors. It presents significant diagnostic and therapeutic challenges due to its aggressive nature and the absence of standardized treatment protocols. Case presentation: We report the case of a 61-year-old woman who presented with hematuria, dysuria, and suprapubic pain. Imaging revealed a large, locally invasive bladder mass, and histopathological examination following transurethral resection confirmed leiomyosarcoma. The patient underwent radical cystectomy with resection of adjacent bowel segments and urinary diversion. Histology showed a high-grade leiomyosarcoma (pT3N0) with extensive necrosis and a high mitotic index. Two months postoperatively, peritoneal dissemination was detected. Systemic chemotherapy with dacarbazine and doxorubicin initially led to the regression of metastases, but disease progression occurred within months, including lung, liver, and bone metastases. Palliative radiotherapy and second-line chemotherapy were initiated. As of now, 16 months have elapsed since surgery. Conclusions: This case underscores the aggressive clinical course of bladder leiomyosarcoma despite multimodal therapy and the urgent need for individualized management strategies. Given its rarity, this case contributes to the limited literature and highlights the importance of vigilant follow-ups and further studies to establish evidence-based treatment protocols. Full article

Perioperative/neoadjuvant chemo-immunotherapy is a standard treatment for patients with resectable non-small cell lung cancer (NSCLC). However, several key clinical questions remain unresolved, including the monitoring of tumor response during neoadjuvant treatment, detection of residual disease after neoadjuvant treatment or after surgery, stratification of recurrence risk, and earlier detection of disease recurrence. Circulating tumor DNA (ctDNA) has emerged as a promising biomarker to address these challenges. Data from several recent clinical trials of perioperative/neoadjuvant chemo-immunotherapy demonstrated that ctDNA clearance before surgery was associated with higher rates of major pathological response. Additionally, landmark ctDNA positivity after surgery identified patients at high risk of disease recurrence, and longitudinal ctDNA monitoring enabled earlier detection of recurrence compared with radiographic surveillance. Several ongoing trials are incorporating ctDNA as a biomarker to guide treatment decisions, including optimizing the duration of neoadjuvant therapy, evaluating the need for surgery, and tailoring adjuvant strategies. These trials, together with further development of ctDNA detection technologies, will clarify the role of ctDNA analysis in refining perioperative treatment strategies and may ultimately enable individualized care in patients with resectable NSCLC. In this review, we discuss the current research data on ctDNA analysis in NSCLC in this era of perioperative chemo-immunotherapy. Full article

Background/Objetives: Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with poor prognosis. Nutritional disorders, particularly cachexia, significantly impact morbidity and mortality in IPF but remain under-investigated. This study aimed to characterize cachexia phenotypes in IPF through morphofunctional assessment (MFA) and to evaluate their prognostic relevance, including the role of mitochondrial biomarkers. Methods: In this prospective bicenter study, 85 IPF patients underwent MFA including bioelectrical impedance vector analysis (BIVA), nutritional ultrasound (NU), and T12-level computed tomography (T12-CT) for body composition. Functional and strength assessments included timed up and go test (TUG) and handgrip strength (HGS), respectively. Cachexia was defined by Evans’ criteria, Martin’s CT-based criteria, and our IPF-specific proposed definition. Serum GDF-15 and PGC-1α levels were also measured. Results: Cachexia prevalence varied by definition: 24.71% (Evans), 29.5% (Martin) and 42.4% (IPF Cachexia Syndrome). Cachectic patients showed significantly lower muscle mass, function, and quality (measured by reduced muscle attenuation at T12-CT), along with higher GDF-15 and lower PGC-1α levels. The presence of IPF Cachexia syndrome (HR 2.56; 95% CI, 1.08–6.07; p = 0.033), GDF-15 > 4412.0 pg/mL (HR 3.21; 95% CI, 1.04–9.90; p = 0.042) and impaired TUG (>8 s) (HR 3.77; 95% CI, 1.63–8.71; 0.002) were all independently associated with increased 24-month mortality. Conclusions: Cachexia is prevalent in IPF and showed strong concordance between the three diagnostic criteria. The IPF Cachexia syndrome, based on comprehensive morphofunctional phenotyping, demonstrated superior discriminatory capacity. The addition of mitochondrial biomarkers may improve early detection and support personalized interventions to improve patient outcomes. Full article

Background: Establishing a highly accurate regional medical collaboration (RMC) system for interstitial lung disease (ILD) may facilitate early disease detection, improve patient satisfaction, and enhance advanced-stage care. Methods: We investigated whether the lung conditions in patients cared for by our RMC system for ILD were detected earlier than those with usual care. Additionally, we investigated patients’ preferences regarding its use. Result: The time from respiratory symptoms onset to hospital referral did not differ significantly between patients cared for by the system and those with usual care. However, the number of patients referred to our hospital for suspected ILD before the onset of symptoms was significantly higher from the participating institutions than from other institutions (44.1% vs. 24.6%, p = 0.025). Additionally, 66.0% of patients preferred the medical care with the system. Conclusions: Establishing an RMC system for ILD may lead to earlier disease detection and contribute to improvement in medical care delivery to patients. Full article

Immune checkpoint inhibitors (ICIs) are a key treatment for advanced non-small cell lung cancer (NSCLC), but most patients will ultimately experience disease progression due to acquired resistance to ICI. Clinically, it is relevant to differentiate between systemic progression (SP) and oligoprogression (OP). Following SP, ICI treatment is usually discontinued, while in OP, patients are preferably treated with local ablative treatment with continuation of the ICI treatment. However, with progressive disease, it remains difficult to differentiate between true OP or SP. Circulating tumor DNA (ctDNA) analysis provides an accurate real-time reflection of the tumor burden. It remains elusive if ctDNA abundance and/or dynamics can discriminate between OP and SP. Therefore, the aim of this exploratory cohort study is to evaluate whether the sequential molecular tumor profiling of ctDNA is suitable for discriminating between true OP and SP in advanced NSCLC. Patients with stage III/IV NSCLC showing progression after ≥3 months of ICI were included. OP was defined retrospectively by RECIST response ≥ 6 months after local treatment and continued ICIs. Serial plasma samples were analyzed using the AVENIO ctDNA Expanded NGS assay targeting 77 cancer-related genes. Twenty patients (6 OP, 14 SP) were included. Somatic alterations were detected in 16 patients (median 4 mutations). No significant differences in baseline ctDNA levels, changes at progression, or mutation patterns were observed between OP and SP. Although ctDNA levels generally decreased early after the start of ICI treatment, and were increased at disease progression, mutational profiles of the 77 genes using the AVENIO Expanded ctDNA panel did not distinguish OP from SP. Full article

Interstitial lung diseases (ILD) significantly impact health and mortality, affecting millions of individuals worldwide. During the COVID-19 pandemic, lung ultrasonography (LUS) became an indispensable diagnostic and management tool for lung disorders. However, utilising LUS to diagnose ILD requires significant expertise. This research aims to develop an automated and efficient approach for diagnosing ILD from LUS videos using AI to support clinicians in their diagnostic procedures. We developed a binary classifier based on a state-of-the-art CSwin Transformer to discriminate between LUS videos from healthy and non-healthy patients. We used a multi-centric dataset from the Royal Melbourne Hospital (Australia) and the ULTRa Lab at the University of Trento (Italy), comprising 60 LUS videos. Each video corresponds to a single patient, comprising 30 healthy individuals and 30 patients with ILD, with frame counts ranging from 96 to 300 per video. Each video is annotated using the corresponding medical report as ground truth. The datasets used for training the model underwent selective frame filtering, including reduction in frame numbers to eliminate potentially misleading frames in non-healthy videos. This step was crucial because some ILD videos included segments of normal frames, which could be mixed with the pathological features and mislead the model. To address this, we eliminated frames with a healthy appearance, such as frames without B-lines, thereby ensuring that training focused on diagnostically relevant features. The trained model was assessed on an unseen, separate dataset of 12 videos (3 healthy and 9 ILD) with frame counts ranging from 96 to 300 per video. The model achieved an average classification accuracy of 91%, calculated as the mean of three testing methods: Random Sampling (92%), Key Featuring (92%), and Chunk Averaging (89%). In RS, 32 frames were randomly selected from each of the 12 videos, resulting in a classification with 92% accuracy, with specificity, precision, recall, and F1-score of 100%, 100%, 90%, and 95%, respectively. Similarly, KF, which involved manually selecting 32 key frames based on representative frames from each of the 12 videos, achieved 92% accuracy with a specificity, precision, recall, and F1-score of 100%, 100%, 90%, and 95%, respectively. In contrast, the CA method, where the 12 videos were divided into video segments (chunks) of 32 consecutive frames, with 82 video segments, achieved an 89% classification accuracy (73 out of 82 video segments). Among the 9 misclassified segments in the CA method, 6 were false positives and 3 were false negatives, corresponding to an 11% misclassification rate. The accuracy differences observed between the three training scenarios were confirmed to be statistically significant via inferential analysis. A one-way ANOVA conducted on the 10-fold cross-validation accuracies yielded a large F-statistic of 2135.67 and a small p-value of 6.7 × 10⁻²⁶, indicating highly significant differences in model performance. The proposed approach is a valid solution for fully automating LUS disease detection, aligning with clinical diagnostic practices that integrate dynamic LUS videos. In conclusion, introducing the selective frame filtering technique to refine the dataset training reduced the effort required for labelling. Full article

Respirable crystalline silica is a well-established occupational hazard in construction work. Despite increased awareness, consistent exposure control remains a challenge, particularly in dynamic and resource-constrained environments. Respirable crystalline silica exposure in construction environments challenges the achievement of the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-Being) and SDG 8 (Decent Work and Economic Growth). Respirable crystalline silica particles cause severe health complications, including silicosis, lung cancer, cardiovascular diseases, and autoimmune disorders, representing a significant barrier to achieving SDG 3.9’s target of reducing deaths and illnesses from hazardous chemical exposures by 2030. This systematic review evaluates two decades of advancements (2004–2024) in respirable crystalline silica identification, characterisation, and mitigation within construction, synthesising evidence from 143 studies to assess progress toward sustainable occupational health management. This review documents a paradigmatic shift from traditional exposure assessment toward sophisticated monitoring approaches incorporating real-time detection systems, virtual reality–Computational Fluid Dynamics simulations, and wearable sensor technologies. Engineering controls, including local exhaust ventilation, wet suppression methods, and modified tool designs, have achieved exposure reductions exceeding 90%, directly supporting SDG 8.8’s commitment to safe working environments for all workers, including migrants and those in precarious employment. However, substantial barriers persist, including prohibitive costs, inadequate infrastructure, and regional regulatory disparities that particularly disadvantage lower-resourced countries, contradicting the Sustainable Development Goals’ principles of leaving no one behind. The findings advocate holistic approaches integrating technological innovation with context-specific regulations, enhanced international cooperation, and culturally adapted worker education to achieve equitable occupational health protection supporting multiple Sustainable Development Goals’ objectives by 2030 and also highlighting potential areas for future research. Full article

β-adrenoreceptor (ADRB) ligands are actively used in the therapy of bronchopulmonary and cardiovascular diseases. When using these drugs, it is important to assess changes in ADRB content in different tissues. In most cases, the direct measurement of ADRB content in lung and heart cells is not possible. ADRB2 content in peripheral blood lymphocytes (or mononuclear cells) was shown to correlate with that in myocardial cells. It has been suggested that blood lymphocytes can be used to monitor ADRB content in solid organs. However, the estimation of ADRB1 content in myocardium from its content in peripheral lymphocytes is not possible due to the low content of ADRB1 in lymphocytes. In the present study, we performed simultaneous determination of ADRB1 and ADRB2 both in the total population of PBMCs and in isolated subpopulations of monocytes, T-lymphocytes, and NK-cells from 23 healthy donors using the modified radioligand method. The highest amount of ADRB2 was detected in NK cells, followed by PBMCs, monocytes, and T cells. The content of these receptors in all blood cell subpopulations was significantly correlated with each other, suggesting the possibility of using PBMCs to monitor ADRB2 in solid organs. For the first time, ADRB1 was detected in monocytes and NK cells. Full article

Poorly differentiated thyroid malignancy, a rare histological type of aggressive thyroid malignancy with associated difficulties and gaps in its histological and molecular characterization, might lead to challenging clinical presentations that require a prompt multimodal approach. This case study involved a 56-year-old, non-smoking male with a rapidly developing goiter (within 2–3 months) in association with mild, non-specific neck compressive symptoms. His medical history was irrelevant. A voluminous goiter with substernal and posterior extension up to the vertebral bodies was detected using an ultrasound and computed tomography (CT) scan and required emergency thyroidectomy. He had normal thyroid function, as well as negative thyroid autoimmunity and serum calcitonin. The surgery was successful upon “Y” incision, which was used to give better access to the retrosternal component in order to avoid a sternotomy. Post-operatively, the subject developed hypoparathyroidism-related hypocalcemia and showed a very high serum thyroglobulin level (>550 ng/mL). The pathological report confirmed poorly differentiated, multifocal thyroid carcinoma (with an insular, solid, and trabecular pattern) against a background of papillary carcinoma (pT3b, pN0, and pM1; L1; V2; Pn0; R1; and stage IVB). The subject received 200 mCi of radioiodine therapy for 6 weeks following the thoracic surgery. Whole-body scintigraphy was performed before radioiodine therapy and showed increased radiotracer uptake at the thyroid remnants and pre-tracheal levels. Additionally, single-photon emission computed tomography combined with CT (SPECT/CT) was performed, and confirmed the areas of intense uptake, in addition to a moderate uptake in the right and left pulmonary parenchyma, suggesting lung metastasis. To conclude, an overall low level of statistical evidence exists regarding poorly differentiated malignancy in substernal goiters, and the data also remains scarce regarding the impact of genetic and molecular configurations, such as the BRAF-positive profile, in this specific instance. Furthermore, multimodal management includes additional diagnosis methods such as SPECT/CT, while long-term multilayered therapy includes tyrosine kinase inhibitors if the outcome shows an iodine-resistant profile with a poor prognosis. Awareness remains a key factor in cases of a poorly differentiated carcinoma presenting as a rapidly growing goiter with substernal extension in an apparently healthy adult. A surgical approach, while varying with the surgeon’s skills, represents a mandatory step to ensure a better prognosis. In addition to a meticulous histological characterization, genetic/molecular features provide valuable information regarding the outcome and can further help with the decision to use new anti-cancer drugs if tumor response upon radioiodine therapy is no longer achieved; such a development is expected in this disease stage in association with a BRAF-positive configuration. Full article