Search Results (2,199)

Background/Objectives: To simplify the decision-making process in radiomics by employing RadiomiX, an algorithm designed to automatically identify the best model combination and validate them across multiple environments was developed, thus enhancing the reliability of results. Methods: RadiomiX systematically tests classifier and feature selection method combinations known to be suitable for radiomic datasets to determine the best-performing configuration across multiple train–test splits and K-fold cross-validation. The framework was validated on four public retrospective radiomics datasets including lung nodules, metastatic breast cancer, and hepatic encephalopathy using CT, PET/CT, and MRI modalities. Model performance was assessed using the area under the receiver-operating-characteristic curve (AUC) and accuracy metrics. Results: RadiomiX achieved superior performance across four datasets: LLN (AUC = 0.850 and accuracy = 0.785), SLN (AUC = 0.845 and accuracy = 0.754), MBC (AUC = 0.889 and accuracy = 0.833), and CHE (AUC = 0.837 and accuracy = 0.730), significantly outperforming original published models (p < 0.001 for LLN/SLN and p = 0.023 for MBC accuracy). When original published models were re-evaluated using ten-fold cross-validation, their performance decreased substantially: LLN (AUC = 0.783 and accuracy = 0.731), SLN (AUC = 0.748 and accuracy = 0.714), MBC (AUC = 0.764 and accuracy = 0.711), and CHE (AUC = 0.755 and accuracy = 0.677), further highlighting RadiomiX’s methodological advantages. Conclusions: Systematically testing model combinations using RadiomiX has led to significant improvements in performance. This emphasizes the potential of automated ML as a step towards better-performing and more reliable radiomic models. Full article

Background: Cognitive behavioral therapy (CBT) has shown consistent efficacy in individuals with psychosis, as supported by many trials. One classical distinction is that between affective and non-affective psychosis. Few studies have specifically examined the possible moderating role of substantial affective elements. In this systematic review and meta-regression analysis, we assess how CBT response differs across the affective spectrum in psychosis. Methods: We included studies assessing various CBT modalities, including third-wave therapies, administered in people with psychosis. The study protocol is published in the Open Science Framework. Meta-regression was conducted to assess whether the proportion of participants with affective psychosis (AP), as proxied by a documented diagnosis of schizoaffective (SZA) disorder, moderated CBT efficacy across positive, negative, and depressive symptom domains. Results: The literature search identified 4457 records, of which 39 studies were included. The median proportion of SZA disorder participants was 17%, with a total of 422 AP participants represented. Meta-regression showed a trend toward lower CBT efficacy for positive symptoms with a higher SZA disorder proportion (β = +0.10 SMD per 10% increase in AP; p = 0.12), though it was not statistically significant. No significant associations were found for negative (β = +0.05; p = 0.73) or depressive symptoms (β = −0.02; p = 0.78). Heterogeneity was substantial across all models (I² ranging from 54% to 80%), and funnel plot asymmetry was observed in negative and depressive symptoms, indicating possible publication bias. Risk of bias assessment showed the anticipated inherent difficulty of psychotherapies in blinding and possibly dropout rates affecting some studies. Conclusions: Affective symptoms may reduce the effectiveness of CBT for positive symptoms in psychotic disorders, although the findings did not reach statistical significance. Other patient-level characteristics in psychosis could indicate which patients can benefit most from CBT modalities. Full article

Oncology physiotherapists frequently provide care for patients experiencing severe immunosuppression. Exercise immunology, the science that studies the effects of exercise on the immune system, is a rapidly evolving field with direct relevance to oncology physiotherapists. Understanding oncology physiotherapists’ perspectives on the subject of immune functioning is essential to explore its possible integration into clinical reasoning. This study aimed to assess the perspectives of oncology physiotherapists concerning immune functioning in oncology physiotherapy. For this qualitative research, semi-structured interviews were performed with Dutch oncology physiotherapists. Results were analyzed via inductive thematic analysis, followed by a validation step with participants. Fifteen interviews were performed. Participants’ ages ranged from 30 to 63 years. Emerging themes were (1) the construct ‘immune functioning’ (definition, and associations with this construct in oncology physiotherapy), (2) characteristics related to decreased immune functioning (in oncology physiotherapy), (3) negative and positive influences on immune functioning (in oncology physiotherapy), (4) tailored physiotherapy treatment, (5) treatment outcomes in oncology physiotherapy, (6) the oncology physiotherapist within cancer care, and (7) measurement and interpretation of immune functioning. In conclusion, oncology physiotherapists play an important role in the personalized and comprehensive care of patients with cancer. They are eager to learn more about immune functioning with the goal of better informing patients about the health effects of exercise and to tailor their training better. Future exercise-immunology research should clarify the effects of different exercise modalities on immune functioning, and how physiotherapists could evaluate these effects. Full article

Thermal Power Plants (TPPs), as widely used industrial facilities for electricity generation, represent a key task in remote sensing image interpretation. However, detecting TPPs remains a challenging task due to their complex and irregular composition. Many traditional approaches focus on detecting compact, small-scale objects, while existing composite object detection methods are mostly part-based, limiting their ability to capture the structural and textural characteristics of composite targets like TPPs. Moreover, most of them rely on single-modality data, failing to fully exploit the rich information available in remote sensing imagery. To address these limitations, we propose a novel Cross-Modal Frequency-Responsive Attention Network (CFRANet). Specifically, the Modality-Aware Fusion Block (MAFB) facilitates the integration of multi-modal features, enhancing inter-modal interactions. Additionally, the Frequency-Responsive Attention (FRA) module leverages both spatial and localized dual-channel information and utilizes Fourier-based frequency decomposition to separately capture high- and low-frequency components, thereby improving the recognition of TPPs by learning both detailed textures and structural layouts. Experiments conducted on our newly proposed AIR-MTPP dataset demonstrate that CFRANet achieves state-of-the-art performance, with a ${\mathrm{mAP}}_{50}$ of 82.41%. Full article

Malignant gastric outlet obstruction (MGOO) is a serious complication arising from advanced gastric or pancreatic head cancer, significantly impairing patients’ quality of life by disrupting oral intake and inducing severe gastrointestinal symptoms. With benign causes such as peptic ulcer disease on the decline, malignancies now account for 50–80% of gastric outlet obstruction (GOO) cases globally. This review outlines the pathophysiology, evolving epidemiology, and treatment modalities for MGOO. Therapeutic approaches include conservative management, endoscopic stenting, surgical gastrojejunostomy (GJ), stomach partitioning gastrojejunostomy (SPGJ), and endoscopic ultrasound-guided gastroenterostomy (EUS-GE). While endoscopic stenting offers rapid symptom relief with minimal invasiveness, it has higher rates of re-obstruction. Surgical options like GJ and SPGJ provide more durable palliation, especially for patients with longer expected survival. SPGJ, a modified surgical technique, demonstrates reduced incidence of delayed gastric emptying and may improve postoperative oral intake and survival compared to conventional GJ. EUS-GE represents a promising, minimally invasive alternative that combines surgical durability with endoscopic efficiency, although long-term data remain limited. Treatment selection should consider patient performance status, tumor characteristics, prognosis, and institutional resources. This comprehensive review underscores the need for individualized, multidisciplinary decision-making to optimize symptom relief, nutritional status, and overall outcomes in patients with MGOO. Full article

Growing interest in the future applications of nanotechnology in medicine has led to groundbreaking developments in nanosensors. Nanosensors are excellent platforms that provide reliable solutions for continuous monitoring and real-time detection of clinical targets. Nanosensors have attracted great attention due to their remarkable sensitivity, portability, selectivity, and automated data acquisition. The exceptional nanoscale properties of nanomaterials used in the nanosensors boost their sensing potential even at minimal concentrations of analytes present in a clinical sample. Along with applications in diverse sectors, the beneficial aspects of nanosensors have been exploited in healthcare systems to utilize their applications in diagnosing, treating, and preventing diseases. Hence, in this review, we have presented an overview of the disease-prognostic applications of nanosensors in chronic diseases through a detailed literature analysis. We focused on the advances in various nanosensors in the field of major diseases such as cancer, cardiovascular diseases, diabetes mellitus, and neurodegenerative diseases along with other prevalent diseases. This review demonstrates various categories of nanosensors with different nanoparticle compositions and detection methods suitable for specific diagnostic applications in clinical settings. The chemical properties of different nanoparticles provide unique characteristics to each nanosensors for their specific applications. This will aid the detection of potential biomarkers or pathological conditions that correlate with the early detection of various diseases. The potential challenges and possible recommendations of the applications of nanosensors for disease diagnosis are also discussed. The consolidated information present in the review will help to better understand the disease-prognostic potentials of nanosensors, which can be utilized to explore new avenues in improved therapeutic interventions and treatment modalities. Full article

Background/Objectives: Pneumatic dilation (PD) is a widely used treatment modality in the management of achalasia. It is particularly relevant in regions where many centers lack access to advanced therapeutic modalities. Therefore, we aimed to assess the effectiveness and safety of PD in our local region. Methods: This study retrospectively analyzed patients with achalasia that underwent PD from 1/2013 to 12/2019. The diagnosis of achalasia was established on the grounds of clinical symptoms, radiological and endoscopic findings, and esophageal manometry. Data on patient’s clinical characteristics, dilation technique and postprocedural follow-up were collected and statistically analyzed. Procedure effectiveness was defined as the postprocedural Eckardt score ≤ 3. Results: PD significantly reduced frequency of dysphagia, regurgitation, and retrosternal pain (p < 0.001). Body-weight increased significantly one month and one year after the procedure (p < 0.001). The procedural success rate was 100%. No severe complications were reported. Conclusions: PD is an effective and safe treatment modality in the management of achalasia. The study limitations include a single center design with the small number of participants, not all of whom underwent manometry, gender disproportion, absence of non-responders, and a short follow-up. Full article

Background: The influence of the initial ablation modality on pulmonary vein (PV) reconnection and substrate characteristics in redo procedures for recurrent atrial fibrillation (AF) remains unclear. We assessed how different thermal strategies—ablation index (AI)-guided radiofrequency (RF) versus cryoballoon (CB) ablation—affect remapping findings during redo pulmonary vein isolation (PVI). Methods: We included patients undergoing redo ablation between 2015 and 2024 with high-density electroanatomic mapping. Initial PVI modalities were retrospectively classified as low-power, long-duration (LPLD) RF; high-power, short-duration (HPSD) RF; or second-/third-generation CB. Reconnection sites were mapped using multielectrode catheters. Redo PVI was performed using AI-guided RF. Segments showing PV reconnection were reisolated; if all PVs remained isolated and AF persisted, posterior wall isolation was performed. Results: Among 195 patients (LPLD: 63; HPSD: 30; CB: 102), complete PVI at redo was observed in 0% (LPLD), 23.3% (HPSD), and 10.1% (CB) (p < 0.01 for LPLD vs. HPSD). Reconnection patterns varied by technique; LPLD primarily affected the right carina, while HPSD and CB showed reconnections at the LSPV ridge. Organized atrial tachycardia was least frequent after CB (12.7%, p < 0.002). Conclusion: Initial ablation strategy significantly influences PV reconnection and post-PVI arrhythmia patterns, with implications for redo procedure planning. Full article

Multimodal review helpfulness prediction (MRHP) aims to identify the most helpful reviews by leveraging both textual and visual information. However, prior studies have primarily focused on modeling interactions between these modalities, often overlooking the consistency between review content and ratings, which is a key indicator of review credibility. To address this limitation, we propose CRCNet (Content–Rating Consistency Network), a novel MRHP model that jointly captures the semantic consistency between review content and ratings while modeling the complementary characteristics of text and image modalities. CRCNet employs RoBERTa and VGG-16 to extract semantic and visual features, respectively. A co-attention mechanism is applied to capture the consistency between content and rating, and a Gated Multimodal Unit (GMU) is adopted to integrate consistency-aware representations. Experimental results on two large-scale Amazon review datasets demonstrate that CRCNet outperforms both unimodal and multimodal baselines in terms of MAE, MSE, RMSE, and MAPE. Further analysis confirms the effectiveness of content–rating consistency modeling and the superiority of the proposed fusion strategy. These findings suggest that incorporating semantic consistency into multimodal architectures can substantially improve the accuracy and trustworthiness of review helpfulness prediction. Full article

Multimodal object detection leverages the complementary characteristics of visible (RGB) and infrared (IR) imagery, making it well-suited for challenging scenarios such as low illumination, occlusion, and complex backgrounds. However, most existing fusion-based methods rely on static or heuristic strategies, limiting their adaptability to dynamic environments. To address this limitation, we propose CLSANet, a cognitive learning-based self-adaptive network that enhances detection performance by dynamically selecting and integrating modality-specific features. CLSANet consists of three key modules: (1) a Dominant Modality Identification Module that selects the most informative modality based on global scene analysis; (2) a Modality Enhancement Module that disentangles and strengthens shared and modality-specific representations; and (3) a Self-Adaptive Fusion Module that adjusts fusion weights spatially according to local scene complexity. Compared to existing methods, CLSANet achieves state-of-the-art detection performance with significantly fewer parameters and lower computational cost. Ablation studies further demonstrate the individual effectiveness of each module under different environmental conditions, particularly in low-light and occluded scenes. CLSANet offers a compact, interpretable, and practical solution for multimodal object detection in resource-constrained settings. Full article

Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low clinical value in diagnosis. Interpretation of the acoustic characteristics of BSs, i.e., using a phonoenterogram (PEG), may aid in diagnosing various GI conditions non-invasively. Use of artificial intelligence (AI) and improvements in computational analysis can enhance the use of PEGs in different GI diseases and lead to a non-invasive, cost-effective diagnostic modality that has not been explored before. The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. A total of 110 2-minute PEGs sampled at 44.1 kHz were recorded using the Eko DUO^® stethoscope from eight healthy volunteers at two locations, namely, left upper quadrant (LUQ) and right lower quadrant (RLQ) after IRB approval. The datasets were annotated by trained physicians, categorizing BSs as prominent or obscure using version 1.7 of Label Studio Software^®. Each BS recording was split up into 375 ms segments with 200 ms overlap for real-time BS detection. Each segment was binned based on whether it contained a prominent BS, resulting in a dataset of 36,149 non-prominent segments and 6435 prominent segments. Our dataset was divided into training, validation, and test sets (60/20/20% split). A 1D-CNN augmented transformer was trained to classify these segments via the input of Mel-frequency cepstral coefficients. The developed AI model achieved area under the receiver operating curve (ROC) of 0.92, accuracy of 86.6%, precision of 86.85%, and recall of 86.08%. This shows that the 1D-CNN augmented transformer with Mel-frequency cepstral coefficients achieved creditable performance metrics, signifying the YOLO model’s capability to classify prominent bowel sounds that can be further analyzed for various GI diseases. This proof-of-concept study in healthy volunteers demonstrates that automated BS detection can pave the way for developing more intuitive and efficient AI-PEG devices that can be trained and utilized to diagnose various GI conditions. To ensure the robustness and generalizability of these findings, further investigations encompassing a broader cohort, inclusive of both healthy and disease states are needed. Full article

Background: Chemotherapy-induced peripheral neuropathy is a common and debilitating side effect of cancer treatment. While exercise has shown promise in alleviating this burden, it remains underutilised in clinical practice due to the lack of accessible, clinician-friendly guidance. Aim: This review aimed to synthesise current evidence on exercise interventions for managing chemotherapy-induced peripheral neuropathy and provide practical insights to support clinicians in integrating these approaches into patient care. Methods: A search was conducted across MEDLINE, CINAHL, and SPORTDiscus using keywords related to exercise and CIPN. Studies were included if they involved adults receiving neurotoxic chemotherapy and exercise-based interventions. Two authors independently screened studies and resolved conflicts with a third author. Study quality was assessed using the JBI Critical Appraisal Tools, and only studies meeting a minimum quality standard were included. A balanced sampling approach was employed. Data on study design, participant characteristics, interventions, and outcomes were extracted. Results: Eleven studies were included, covering various exercise modalities: multimodal (n = 5), yoga (n = 2), aerobic (n = 1), resistance (n = 1), balance (n = 1), and sensorimotor (n = 1). Exercise interventions, particularly multimodal exercise, significantly improved symptom severity, functionality, and quality of life (p < 0.05). The studies had high methodological quality, with randomised controlled trials scoring between 9/13 and 11/13, and quasi-experimental studies scoring 8/9 on JBI tools. Conclusions: This review highlights the significant benefits of exercise, especially multimodal exercise, for managing CIPN and provides guidance for integrating these strategies into clinical practice. Future research is needed to refine exercise prescriptions and develop standardised guidelines. Full article

Vitamin A deficiency (VAD) remains a significant cause of preventable blindness worldwide, with ocular surface changes representing early manifestations that require prompt recognition and treatment. Conventional examination methods are capable of detecting advanced changes; however, subtle conjunctival abnormalities may be overlooked, potentially delaying the administration of appropriate interventions. We herein present the case of a 5-year-old Japanese boy with severe VAD due to selective eating patterns. This case demonstrates the utility of infrared photography as a novel diagnostic approach for detecting and monitoring conjunctival surface abnormalities. The patient exhibited symptoms including corneal ulcers, night blindness, and reduced visual acuity. Furthermore, blood tests revealed undetectable levels of vitamin A (5 IU/dL), despite relatively normal physical growth parameters. Conventional slit-lamp examination revealed characteristic sandpaper-like conjunctival changes. However, infrared photography (700–900 nm wavelength) revealed distinct abnormal patterns of conjunctival surface folds and keratinization that were not fully appreciated on a routine examination. Following high-dose vitamin A supplementation (4000 IU/day), complete resolution of ocular abnormalities was achieved within 2 months, with infrared imaging objectively documenting treatment response and normalization of conjunctival surface patterns. This case underscores the potential for severe VAD in developed countries, particularly in the context of dietary restrictions, thereby underscoring the significance of a comprehensive dietary history and a meticulous ocular examination. Infrared photography provides a number of advantages, including the capacity for non-invasive assessment, enhanced visualization of subtle changes, objective monitoring of treatment response, and cost-effectiveness due to the use of readily available equipment. This technique represents an underutilized diagnostic modality with particular promise for screening programs and clinical monitoring of VAD-related ocular manifestations, potentially preventing irreversible visual loss through early detection and intervention. Full article

Background: Intracerebral hemorrhage (ICH) is a severe form of stroke associated with high morbidity and mortality. While neurosurgical evacuation may offer theoretical benefits, its impact on survival and hospital course remains debated. We aimed to compare the outcomes of surgical versus conservative management in patients with lobar, capsulo-lenticular, and thalamic ICH and to identify factors influencing mortality and the surgical decision. Methods: This single-center, retrospective cohort study included adult patients admitted to the County Clinical Emergency Hospital of Sibiu (2017–2023) with spontaneous supratentorial ICH confirmed via CT (deepest affected structure determining lobar, capsulo-lenticular, or thalamic location). We collected data on demographics, clinical presentation (Glasgow Coma Scale [GCS], anticoagulant use), hematoma characteristics (volume, extension), treatment modality (surgical vs. conservative), and in-hospital outcomes (mortality, length of stay). Statistical analyses included t-tests, χ², correlation tests, and logistic regression to identify independent predictors of mortality and surgery. Results: A total of 445 patients were analyzed: 144 lobar, 150 capsulo-lenticular, and 151 thalamic. Surgical intervention was more common in patients with larger volumes and lower GCS. Overall, in-hospital mortality varied by location, reaching 13% in the lobar group, 20.7% in the capsulo-lenticular group, and 35.1% in the thalamic group. Within each location, surgical intervention did not significantly reduce overall in-hospital mortality despite the more severe baseline presentation in surgical patients. In lobar ICH specifically, no clear survival advantage emerged, although surgery may still benefit those most severely compromised. For capsulo-lenticular hematomas > 30 mL, surgery was associated with lower mortality (39.4% vs. 61.5%). In patients with large lobar ICH, surgical intervention was associated with mortality rates similar to those seen in less severe, conservatively managed cohorts. Multivariable adjustment confirmed GCS and hematoma volume as independent mortality predictors; age and volume predicted the likelihood of surgical intervention. Conclusions: Despite targeting more severe cases, neurosurgical evacuation did not uniformly lower in-hospital mortality. In lobar ICH, surgical patients with larger hematomas (~48 mL) and lower GCS (~11.6) had mortality rates (~13%) comparable to less severe, conservative cohorts, indicating that surgical intervention was associated with similar mortality rates despite higher baseline risk. However, these findings do not establish a causal survival benefit and should be interpreted in the context of non-randomized patient selection. For capsulo-lenticular hematomas > 30 mL, surgery was associated with lower observed mortality (39.4% vs. 61.5%). Thalamic ICH remained most lethal, highlighting the difficulty of deep-brain bleeds and frequent ventricular extension. Across locations, hematoma volume and GCS were the primary outcome predictors, indicating the need for timely intervention, better patient selection, and possibly minimally invasive approaches. Future prospective multicenter research is necessary to refine surgical indications and validate these findings. To our knowledge, this investigation represents the largest and most contemporary single-center cohort study of supratentorial intracerebral hemorrhage conducted in Romania. Full article

The equivalent source method (ESM) is a core algorithm in integrated radiation-propagation acoustic field modeling. However, in challenging marine environments, including deep-sea and polar regions, where sound speed profiles exhibit strong vertical gradients, the ESM must increase waveguide stratification to maintain accuracy. This causes computational costs to scale exponentially with the number of layers, compromising efficiency and limiting applicability. To address this, this paper proposes a modal equivalent source (MES) model employing normal modes as basis functions instead of free-field Green’s functions. This model constructs a set of normal mode bases using full-depth hydroacoustic parameters, incorporating water column characteristics into the basis functions to eliminate waveguide stratification. This significantly reduces the computational matrix size of the ESM and computes acoustic fields in range-dependent waveguides using a single set of normal modes, resolving the dual limitations of inadequate precision and low efficiency in such environments. Concurrently, for the construction of basis functions, this paper also proposes a fast computation method for eigenvalues and eigenmodes in waveguide contexts based on phase functions and difference equations. Furthermore, coupling the MES method with the Finite Element Method (FEM) enables integrated computation of underwater target radiation and propagation fields. Multiple simulations demonstrate close agreement between the proposed model and reference results (errors < 4 dB). Under equivalent accuracy requirements, the proposed model reduces computation time to less than 1/25 of traditional ESM, achieving significant efficiency gains. Additionally, sea trial verification confirms model effectiveness, with mean correlation coefficients exceeding 0.9 and mean errors below 5 dB against experimental data. Full article