Search Results (32)

(1) Acute pancreatitis (AP) is a medical emergency associated with high mortality rates. Early and accurate prognosis assessment during admission is crucial for optimizing patient management and outcomes. This study seeks to develop robust radiomics-based machine learning (ML) models to classify the severity of AP using contrast-enhanced computed tomography (CECT) scans. (2) Methods: A retrospective cohort of 287 AP patients with CECT scans was analyzed, and clinical data were collected within 72 h of admission. Patients were classified as mild or moderate/severe based on the Revised Atlanta classification. Two radiologists manually segmented the pancreas and peripancreatic regions on CECT scans, and 234 radiomic features were extracted. The performance of the ML algorithms was compared with that of traditional scoring systems, including Ranson and Glasgow-Imrie scores. (3) Results: Traditional severity scoring systems produced AUC values of 0.593 (Ranson, Admission), 0.696 (Ranson, 48 h), 0.677 (Ranson, Cumulative), and 0.663 (Glasgow-Imrie). Using LASSO regression, 12 radiomic features were selected for the ML classifiers. Among these, the best-performing ML classifier achieved an AUC of 0.876 in the training set and 0.777 in the test set. (4) Conclusions: Radiomics-based ML classifiers significantly enhanced the prediction of AP severity in patients undergoing CECT scans within 72 h of admission, outperforming traditional severity scoring systems. This research is the first to successfully predict prognosis by analyzing radiomic features from both pancreatic and peripancreatic tissues using multiple ML algorithms applied to early CECT images. Full article

Background and Objectives: Acute pancreatitis (AP) is an inflammatory disorder of the pancreas, with severe cases linked to a higher mortality rate. The prognosis of AP is influenced by factors such as necrosis, secondary infections, and organ failure. Tissue damage in AP is driven by the activation of leukocytes and the release of inflammatory mediators. Angiopoietin-like protein 8 (ANGPTL8), also known as betatrophin, is a recently discovered protein that regulates lipid metabolism. This study aimed to investigate the relationship between ANGPTL8 levels and disease severity in AP patients, and to explore the potential of ANGPTL8 as a biomarker. Materials and Methods: This prospective study included 50 patients diagnosed with AP who were admitted to the Department of Internal Medicine at Dr. Cemil Taşcıoğlu City Hospital between September 2021 and February 2022. Additionally, 39 healthy volunteers who underwent a check-up at the same hospital served as the control group. The Glasgow–Imrie (GI) score was used to assess the severity of pancreatitis. Results: ANGPTL8 levels were found to be significantly lower in the AP group compared to the control group, with a statistically significant correlation between ANGPTL8 levels and the severity of AP (p < 0.05). The cut-off level of ANGPTL8 based on the GI score was determined to be 70.9 ng/L. The GI score for ANGPTL8 was 0.749 (95% CI: 0.606–0.861) (p < 0.001). The overall cut-off value for ANGPTL8 was 179.2 ng/L, with an overall classification rate of 0.936 (95% CI: 0.864–0.977) (p < 0.001). Conclusions: This study demonstrates that ANGPTL8 levels vary between patients with and without AP, with lower levels observed in AP patients. Our research is the first to identify decreased ANGPTL8 levels as an independent predictor of AP severity. ANGPTL8 may play a crucial role in regulating inflammation or metabolic dysfunction in AP. However, further studies are needed to confirm these findings in larger populations and investigate ANGPTL8’s mechanistic role in AP. Longitudinal studies could help determine whether ANGPTL8 levels act as a biomarker for disease progression or treatment response, potentially paving the way for targeted therapies to improve outcomes for AP patients. Full article

The need for psychological treatment in the community, specifically in times of crisis and for those in isolation, calls for finding suitable interventions, especially for older adults. The present study examined the effect of a short mindfulness-based intervention emphasizing a ’decentering’ component and an equivalent guided-imagery intervention on cognitive and emotional measures in seniors living in the community. Thirty community seniors (M_age = 74.7) performed either ’decentering’ or matched guided-imagery intervention, or care as usual as a control. The 8-week interventions included weekly 20 min sessions and daily 10 min home practice. Participants underwent a cognitive and emotional assessment before and after the interventions, which included filling out questionnaires and performing the cognitive Simon task. The results showed improvements only for intervention groups: cognitively, reduced response time and improved accuracy rate were found in the Simon task. Emotionally, reported depression levels were decreased and an increase in reported positive relationships was found. Our study, hence, introduces two intervention protocols, with promising positive effects on psychological and cognitive status. This contributes evidence-based treatments, easy to deliver in nursing homes or retirement communities, for improving the life quality of older adults. Full article

Healthcare 4.0 addresses modernization and digital transformation challenges, such as home-based care and precision treatments, by leveraging advanced technologies to enhance accessibility and efficiency. Semantic technologies, particularly knowledge graphs (KGs), have proven instrumental in representing interconnected medical data and improving clinical decision-support systems. We previously introduced a semantic framework to assist medical experts during patient interactions. Operating iteratively, the framework prompts medical experts with relevant questions based on patient input, progressing toward accurate diagnoses in time-constrained settings. It comprises two components: (a) a KG representing symptoms, diseases, and their relationships, and (b) algorithms that generate questions and prioritize hypotheses—a ranked list of symptom–disease pairs. An earlier extension enriched the KG with a symptom ontology, incorporating hierarchical structures and inheritance relationships to improve accuracy and question-generation capabilities. This paper further extends the framework by introducing strategies tailored to specific medical domains. Strategies integrate domain-specific knowledge and algorithms, refining decision making while maintaining the iterative nature of expert–patient interactions. We demonstrate this approach using an emergency medicine case study, focusing on life-threatening conditions. The KG is enriched with attributes tailored to emergency contexts and supported by dedicated algorithms. Boolean rules attached to graph edges evaluate to TRUE or FALSE at runtime based on patient-specific data. These enhancements optimize decision making by embedding domain-specific goal-oriented knowledge and inference processes, providing a scalable and adaptable solution for diverse medical contexts. Full article

Traditional and complementary medicine (T&CM) plays a significant role in healthcare practices among healthcare professionals and oncology patients in Mexico, reflecting its cultural importance. This study aimed to analyze the prevalence, frequency, and factors associated with T&CM use in these two groups, highlighting the differences in practices and perceptions. A total of 382 individuals participated, including 152 healthcare professionals and 230 oncology patients. The findings revealed that while T&CM use was similarly prevalent among healthcare professionals (85.7%) and oncology patients (90.8%), frequent use (≥2 times per week) was significantly higher among patients (46.3%) compared to healthcare professionals (19.1%, p < 0.001). Healthcare professionals showed a preference for non-conventional nutritional interventions (32.5%) and yoga (14.6%) while oncology patients favored plant-based remedies (73.6%) and the consumption of exotic animals and venoms (4.8%). Females were more likely to use T&CM across both groups, with a stronger association among healthcare professionals (AdOR 3.695, 95% CI 1.8–7.4). Oncology patients were less likely to understand T&CM concepts and were more commonly associated with lower socioeconomic status and educational attainment. These findings underscore the importance of considering cultural and demographic factors when integrating T&CM into conventional medical care, especially in regions where T&CM remains widely practiced and trusted. Full article

BACKGROUND: Maximal safe resection is the objective of most neuro-oncological operations. Intraoperative magnetic resonance imaging (iMRI) may guide the surgeon to improve the extent of safe resection. There is limited evidence comparing the impact of iMRI on the rates of further resection between tumour types. AIM: To investigate the impact of iMRI on the rate of further resection following visualisation of residual tumour. METHODS: A retrospective cohort study identified all intracranial tumour operations performed in the 1.5 T iMRI machine of a single centre (2007–2023). Patients were identified using SurgiNet and were grouped according to their histopathological diagnosis in accordance with the WHO 2021 classification. The primary outcome was the rate of reoperation due to iMRI visualisation of residual tumours. RESULTS: A total of 574 cases were identified, including 152 low-grade gliomas (LGG), 108 high-grade gliomas (HGG), 194 pituitary neuroendocrine tumours (PitNETs), 15 metastases, and 6 meningiomas. Further resection following iMRI visualisation occurred in 45% of LGG cases, 47% of HGG cases, 29% of PitNET cases, and no meningioma or metastasis cases. Chi-square analysis showed that the rate of further resection after iMRI use across 2018–2023 was significantly higher than that across 2007–2012 (46% versus 33%, p = 0.036). CONCLUSION: Intraoperative MRI for guiding further resection was most useful in cases of LGG and HGG, possibly reflecting the difficulty of differentiating these tumour types from normal brain tissue. In addition, there was increased reliance on iMRI over time, which may represent our surgeons becoming accustomed to its use. Full article

Background: Elastic image fusion (EIF) using an intraoperative CT (iCT) scan may enhance neuronavigation accuracy and compensate for brain shift. Objective: To evaluate the safety and reliability of the EIF algorithm (Virtual iMRI Cranial 4.5, Brainlab AG, Munich Germany, for the identification of residual tumour in glioblastoma surgery. Moreover, the impact of brain shift on software reliability is assessed. Methods: This ambispective study included 80 patients with a diagnosis of glioblastoma. Pre-operative MRI was elastically fused with an intraoperative CT scan (BodyTom; Samsung-Neurologica, Danvers, MA, USA) acquired at the end of the resection. Diagnostic specificity and the sensitivity of each tool was determined. The impact of brain shift on residual tumour was statistically analysed. An analysis of accuracy was performed through Target Registration Error (TRE) measurement after rigid image fusion (RIF) and EIF. A qualitative evaluation of each Virtual MRI image (VMRI) was performed. Results: VMRI identified residual tumour in 26/80 patients (32.5%), confirmed by post-operative MRI (true positive). Of these, 5 cases were left intentionally due to DES-positive responses, 8 cases underwent near maximal or subtotal resection, and 13 cases were not detected by iCT. However, in the other 27/80 cases (33.8%), VMRI reported residual tumour that was present neither on iCT nor on post-operative MRI (false positive). i-CT showed a sensitivity of 56% and specificity of 100%; VMRI demonstrated a sensitivity of 100% and specificity of 50%. Spearman correlation analysis showed a moderate correlation between pre-operative volume and VMRI tumour residual. Moreover, tumour involving insula or infiltrating more than one lobe displayed higher median values (p = 0.023) of virtual residual tumour. A statistically significant reduction towards lower TRE values after EIF was observed for test structures. Conclusions: Virtual iMRI was proven to be a feasible option to detect residual tumour. Its integration within a multimodal imaging protocol may provide neurosurgeons with intraoperatively updated imaging. Full article

Intraoperative magnetic resonance imaging (iMRI) has witnessed significant growth in the field of neurosurgery, particularly in glioma surgery, enhancing image-guided neuronavigation and optimizing the extent of resection (EOR). Despite its extensive use in the treatment of gliomas, its utility in brain metastases (BMs) remains unexplored. This study examined the effect of iMRI on BM resection. This retrospective study was conducted at the neurosurgical center of the University Hospital of the Technical University of Munich and involved 25 patients with BM who underwent resection using 3-Tesla iMRI between 2018 and 2022. Volumetric measurements of the resected contrast-enhancing metastases were performed using preoperative, intraoperative, and postoperative MRI images. The Karnofsky Performance Score (KPS) and neurological status of the patients were assessed pre- and postoperatively. Local recurrence and in-brain progression were reported in patients who underwent follow-up MRI at 3 and 6 months postoperatively. In this cohort (n = 25, mean age 63.6 years), non-small-cell lung cancer (NSCLC) was the most common origin (28%). The mean surgical duration was 219.9 min, and that of iMRI was 61.7 min. Indications for iMRI were primarily associated with preoperative imaging, suggesting an unclear entity that is often suspicious for glioma. Gross total resection (GTR) was achieved in 21 patients (84%). Continued resection was pursued after iMRI in six cases (24%), resulting in an improved EOR of 100% in five cases and 97.6% in one case. Neurological status postoperatively remained stable in 60%, improved in 24%, and worsened in 16% of patients. No wound healing or postoperative complications were observed. Among the thirteen patients who underwent follow-up MRI 3 months postoperatively, one patient showed local recurrence at the site of resection, and seven patients showed in-brain progression. Of the eight patients who underwent a 6-month follow-up MRI, two showed local recurrence, while three exhibited in-brain progression. The observed favorable profiles of GTR, coupled with the notable absence of wound-healing problems and acute postoperative complications, affirm the safety and feasibility of incorporating iMRI into the neurosurgical workflow for resecting BM with specific indications. The real-time imaging capabilities of iMRI offer unparalleled precision, aiding meticulous tumor delineation and informed decision-making, ultimately contributing to improved patient outcomes. Although our experience suggests the potential benefits of iMRI as a safe tool for enhancing EOR, we acknowledge the need for larger prospective clinical trials. Comprehensive investigations on a broader scale are imperative to further elucidate the specific indications for iMRI in the context of BMs and to study its impact on survival. Rigorous prospective studies will refine our understanding of the clinical scenarios in which iMRI can maximize its impact, guiding neurosurgeons toward more informed and tailored decision-making. Full article

In the dynamic landscape of healthcare, decision support systems (DSS) confront continuous challenges, especially in the era of big data. Background: This study extends a Q&A-based medical DSS framework that utilizes semantic technologies for disease inference based on a patient’s symptoms. The framework inputs “evidential symptoms” (symptoms experienced by the patient) and outputs a ranked list of hypotheses, comprising an ordered pair of a disease and a characteristic symptom. Our focus is on advancing the framework by introducing ontology integration to semantically enrich its knowledgebase and refine its outcomes, offering three key advantages: Propagation, Hierarchy, and Range Expansion of symptoms. Additionally, we assessed the performance of the fully implemented framework in Python. During the evaluation, we inspected the framework’s ability to infer the patient’s disease from a subset of reported symptoms and evaluated its effectiveness in ranking it prominently among hypothesized diseases. Methods: We conducted the expansion using dedicated algorithms. For the evaluation process, we defined various metrics and applied them across our knowledge base, encompassing 410 patient records and 41 different diseases. Results: We presented the outcomes of the expansion on a toy problem, highlighting the three expansion advantages. Furthermore, the evaluation process yielded promising results: With a third of patient symptoms as evidence, the framework successfully identified the disease in 94% of cases, achieving a top-ranking accuracy of 73%. Conclusions: These results underscore the robust capabilities of the framework, and the enrichment enhances the efficiency of medical experts, enabling them to provide more precise and informed diagnostics. Full article

Advances in the field of machine learning (ML) have been reflected in the intensity of research studies exploiting these techniques for a better understanding of existing phenomena, and for predicting future ones, as a mean for promoting a more efficient and sustainable transportation system. The present study aims to understand the trends of utilizing diverse ML approaches to tackle issues within sub-domains of transportation and to identify underutilized potentials among them. This paper presents a methodology for the bi-dimensional classification of a large corpus of scientific articles. The articles are classified into six transport-related sub-domains, based on the definition of the Israeli Smart Transport Research Center, whose aim is a transportation system with zero externalities, and the ML techniques used in each of them is identified. A fuzzy KNN model is implemented for the multi-classification of articles into the transportation sub-domains and an ontology-based reasoning for identifying the share of each applied ML approach is employed. The application of these methodologies to a corpus of 1718 articles revealed, among other findings, an increasing share of artificial neural networks and deep learning techniques from 2018 until 2022, particularly in the traffic management sub-domain. A significant contribution of the development of these automatic methodologies is the ability to reuse them for ongoing exploration of trends regarding the use of ML techniques for transportation sub-domains. Full article

Brain malignancies, given their intricate nature and location, present significant challenges in both diagnosis and treatment. This review critically assesses a range of diagnostic and surgical techniques that have emerged as transformative tools in brain malignancy management. Fast biopsy techniques, prioritizing rapid and minimally invasive tissue sampling, have revolutionized initial diagnostic stages. Intraoperative flow cytometry (iFC) offers real-time cellular analysis during surgeries, ensuring optimal tumor resection. The advent of intraoperative MRI (iMRI) has seamlessly integrated imaging into surgical procedures, providing dynamic feedback and preserving critical brain structures. Additionally, 5-aminolevulinic acid (5-ALA) has enhanced surgical precision by inducing fluorescence in tumor cells, aiding in their complete resection. Several other techniques have been developed in recent years, including intraoperative mass spectrometry methodologies. While each technique boasts unique strengths, they also present potential limitations. As technology and research continue to evolve, these methods are set to undergo further refinement. Collaborative global efforts will be pivotal in driving these advancements, promising a future of improved patient outcomes in brain malignancy management. Full article

Background: A new-onset adrenocortical insufficiency (NAI) is the most critical postoperative endocrinological complication after transsphenoidal surgery for macroadenomas. Because of increased mortality risk, arginine vasopressin deficiency (AVP-D) is also a relevant postoperative complication. This study aimed to identify easy-to-acquire magnet resonance imaging (MRI) aspects of the pituitary stalk to predict these insufficiencies after transsphenoidal surgery. Methods: Pituitary stalk morphology was reviewed intraoperatively and three months postoperatively in the MRIs of 48 transsphenoidal surgeries for macroadenomas. NAI was validated in endocrinological follow-up controls 10–14 months post-surgery. Results: Intraoperative pituitary stalk diameters were 0.5 mm larger in patients who developed NAI and AVP-D. The odds ratio was 29 for NAI and 6 for AVP-D in binary regression analysis. A value of 2.9 mm was identified as the optimal cut-off for the minimal pituitary stalk diameter regarding NAI, with a high specificity of 89%. There was no difference in pituitary stalk diameter regarding these insufficiencies three months post-surgery. Conclusions: We identified an increased pituitary stalk diameter in intraoperative MRIs as a predictive factor of NAI and AVP-D after transsphenoidal surgery. These findings might improve the early detection of NAI and, thus, optimal management. However, validating these retrospective findings in prospective studies is obligatory. Full article

In children with bladder/prostate (BP) and perianal rhabdomyosarcoma (RMS), we use a hybrid treatment concept for those suitable, combining organ-preserving tumor resection and high-dose rate brachytherapy (HDR-BT). This treatment concept has been shown to improve outcomes. However, it is associated with specific challenges for the clinicians. The exact position of the tubes for BT is a prerequisite for precise radiotherapy. It can finally be determined only with an MRI or CT scan. We evaluated the use of an intraoperative MRI (iMRI) to control the position of the BT tubes and for radiotherapy planning in all patients with BP and perianal RMS who received the above-mentioned combination therapy in our department since January 2021. iMRI was used in 12 children. All tubes were clearly localized. No adverse events occurred. In all 12 children, radiotherapy could be started on time. In a historical cohort without iMRI, this was not possible in 3 out of 20 children. The use of iMRI in children with BP and perianal RMS improved patient safety and treatment quality. This technology has proven to be successful for the patient population we have defined and has become a standard procedure in our institution. Full article

Surgical resection continues to be the primary initial therapeutic strategy in the treatment of patients with brain tumors. Computerized cranial neuronavigation based on preoperative imaging offers precision guidance during craniotomy and early tumor resection but progressively loses validity with brain shift. Intraoperative MRI (iMRI) and intraoperative ultrasound (iUS) can update the imaging used for guidance and navigation but are limited in terms of temporal and spatial resolution, respectively. We present a system that uses time-stamped tool-tip positions of surgical instruments to generate a map of resection progress with high spatial and temporal accuracy. We evaluate this system and present results from 80 cranial tumor resections. Regions of the preoperative tumor segmentation that are covered by the resection map (True Positive Tracking) and regions of the preoperative tumor segmentation not covered by the resection map (True Negative Tracking) are determined for each case. We compare True Negative Tracking, which estimates the residual tumor, with the actual residual tumor identified using iMRI. We discuss factors that can cause False Positive Tracking and False Negative Tracking, which underestimate and overestimate the residual tumor, respectively. Our method provides good estimates of the residual tumor when there is minimal brain shift, and line-of-sight is maintained. When these conditions are not met, surgeons report that it is still useful for identifying regions of potential residual. Full article

Application of a combined atmospheric plasma/UV laser to cotton fabrics impregnated with selected non-durable flame retardants (FRs) has shown evidence of covalent grafting of the latter species on to cotton fibre surfaces. As a result, an increase in their durability to water-soaking for 30 min at 40 °C has been recorded. Based on previous research plasma gases comprising Ar^80%/CO₂^20% or N₂^80%/O₂^20% were used to pre-expose cotton fabric prior to or after FR impregnation to promote the formation of radical species and increased –COOH groups on surface cellulosic chains, which would encourage formation of FR-cellulose bonds. Analysis by scanning electron microscopy (SEM/EDX), X-ray photoelectron spectroscopy (XPS) and thermal analysis (TGA) suggested that organophosphorus- and nitrogen- containing flame retarding species in the presence of the silicon-containing molecules such as 3-aminopropyltriethoxy silane (APTS) resulted in formation of FR-S-O-cellulose links, which gave rise to post-water-soaking FR retentions > 10%. Similarly, the organophosphorus FR, diethyl N, N bis (2-hydroxyethyl) aminomethylphosphonate (DBAP), after plasma/UV exposure produced similar percentage retention values possibly via (PO).O.cellulose bond formation, While none of the plasmas/UV-treated, FR-impregnated fabrics showed self-extinction behaviour, although burning rates reduced and significant char formation was evident, it has been shown that FR durability may be increased using plasma/UV treatments. Full article