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Endothelial dysfunction plays a key role in the development of liver cirrhosis. Among the biomarkers of endothelial dysfunction, the soluble form of Vascular Adhesion Protein-1 (sVAP-1) is an unconventional and less known adhesion molecule endowed also with amine oxidase activity. The aim of this study was to explore and correlate the behavior of sVAP-1 with that of the soluble vascular cell adhesion molecule-1 (sVCAM-1) and intercellular adhesion molecule-1 (sICAM-1) and with the severity of liver cirrhosis. A cross-sectional study was carried out by enrolling 28 controls, 59 cirrhotic patients without hepatocellular carcinoma, and 56 patients with hepatocellular carcinoma (HCC), mainly caused by alcohol abuse. The levels of adhesion molecules and of the pro-inflammatory cytokines (IL-6 and TNF-αα) were determined by immunoassay and the enzymatic activity of sVAP-1 by a fluorometric assay. In non-diabetic patients without HCC, a specific behavior of sVAP-1 was highlighted. Differently from sVCAM-1, sICAM-1, and cytokines, the sVAP-1 level was significantly increased only in the early stage of disease, and then, it decreased in the last stage (866 ± 390 ng/mL vs. 545 ± 316 ng/mL, in Child–Pugh class A vs. C, respectively, p < 0.05). Bivariate analysis correlates sVAP-1 to sVCAM-1, in the absence of HCC (Spearman's rho = 0.403, p < 0.01). Multiple linear regression analysis revealed that sVCAM-1 appears to be a predictor of sVAP-1 (β coefficient = 0.374, p = 0.021). In conclusion, in non-diabetic and non-HCC cirrhotic patients, sVAP-1 may be a potential prognostic biomarker that, together with sVCAM-1 and pro-inflammatory cytokines, may provide information on the progression of sinusoidal liver endothelium damage. [ABSTRACT FROM AUTHOR]

Background: An aberrant cellular microenvironment characterized by pathological cells or inflammation represents an added risk factor across various cancer types. While the significance of chronic inflammation in the development of most diffuse tumors has been extensively studied, an exception to this analysis exists in the context of chondrosarcomas. Chondrosarcomas account for 20–30% of all bone sarcomas, with an estimated global incidence of 1 in 100,000. The average age at diagnosis is 50, and over 70% of patients are over 40. This retrospective study aimed to examine the role of C-reactive protein (CRP) as a prognostic factor in relation to the histopathological findings in chondrosarcoma. Methods: In this retrospective study, 70 patients diagnosed with chondrosarcoma and treated between 2004 and 2019 were included. Preoperative CRP levels were measured in mg/dL, with non-pathological values defined as below 0.5 mg/dL. Disease-free survival time was calculated from the initial diagnosis to events such as local recurrence or metastasis. Follow-up status was categorized as death from disease, no evidence of disease, or alive with disease. Patients were excluded if they had insufficient laboratory values, missing follow-up information, or incomplete histopathological reports. Results: The calculated risk estimation of a reduced follow-up time was 2.25 timed higher in the patients with a CRP level >0.5 mg/dL (HR 2.25 and 95% CI 1.13–4.45) and 3 times higher in patients with a tumor size > pT2 (HR 3 and 95% CI 1.59–5.92). We can easily confirm that risk factors for reduced prognosis lie in chondrosarcoma high grading, preoperative pathological CRP- level, and a size > 8 cm. Conclusions: A pretreatment CRP value greater than 0.5 mg/dL can be considered a sensitive prognostic and risk factor for distant metastasis for chondrosarcoma patients. [ABSTRACT FROM AUTHOR]

The challenges associated with conventional methods of COVID-19 detection have prompted the exploration of alternative approaches, including the analysis of lung X-ray images. This paper introduces a novel algorithm designed to identify abnormalities in X-ray images indicative of COVID-19 by combining the maximally stable extremal regions (MSER) method with metaheuristic algorithms. The MSER method is efficient and effective under various adverse conditions, utilizing symmetry as a key property to detect regions despite changes in scaling or lighting. However, calibrating the MSER method is challenging. Our approach transforms this calibration into an optimization task, employing metaheuristic algorithms such as Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Firefly (FF), and Genetic Algorithms (GA) to find the optimal parameters for MSER. By automating the calibration process through metaheuristic optimization, we overcome the primary disadvantage of the MSER method. This innovative combination enables precise detection of abnormal regions characteristic of COVID-19 without the need for extensive datasets of labeled training images, unlike deep learning methods. Our methodology was rigorously tested across multiple databases, and the detection quality was evaluated using various indices. The experimental results demonstrate the robust capability of our algorithm to support healthcare professionals in accurately detecting COVID-19, highlighting its significant potential and effectiveness as a practical and efficient alternative for medical diagnostics and precise image analysis. [ABSTRACT FROM AUTHOR]

Radiation-induced gastrointestinal (GI) dose constraints are still a matter of concern with the ongoing evolution of patient outcomes and treatment-related toxicity in the era of image-guided intensity-modulated radiation therapy (IMRT), stereotactic ablative radiotherapy (SABR), and novel systemic agents. Small bowel (SB) dose constraints in pelvic radiotherapy (RT) are a critical aspect of treatment planning, and prospective data to support them are scarce. Previous and current guidelines are based on retrospective data and experts' opinions. Patient-related factors, including genetic, biological, and clinical features and systemic management, modulate toxicity. Omic and microbiome alterations between patients receiving RT to the SB may aid in the identification of patients at risk and real-time identification of acute and late toxicity. Actionable biomarkers may represent a pragmatic approach to translating findings into personalized treatment with biologically optimized dose escalation, given the mitigation of the understood risk. Biomarkers grounded in the genome, transcriptome, proteome, and microbiome should undergo analysis in trials that employ, R.T. Bioinformatic templates will be needed to help advance data collection, aggregation, and analysis, and eventually, decision making with respect to dose constraints in the modern RT era. [ABSTRACT FROM AUTHOR]

The link between mitochondria and major depressive disorder (MDD) is increasingly evident, underscored both by mitochondria's involvement in many mechanisms identified in depression and the high prevalence of MDD in individuals with mitochondrial disorders. Mitochondrial functions and energy metabolism are increasingly considered to be involved in MDD's pathogenesis. This study focused on cellular and mitochondrial (dys)function in two atypical cases: an antidepressant non-responding MDD patient ("Non-R") and another with an unexplained mitochondrial disorder ("Mito"). Skin biopsies from these patients and controls were used to generate various cell types, including astrocytes and neurons, and cellular and mitochondrial functions were analyzed. Similarities were observed between the Mito patient and a broader MDD cohort, including decreased respiration and mitochondrial function. Conversely, the Non-R patient exhibited increased respiratory rates, mitochondrial calcium, and resting membrane potential. In conclusion, the Non-R patient's data offered a new perspective on MDD, suggesting a detrimental imbalance in mitochondrial and cellular processes, rather than simply reduced functions. Meanwhile, the Mito patient's data revealed the extensive effects of mitochondrial dysfunctions on cellular functions, potentially highlighting new MDD-associated impairments. Together, these case studies enhance our comprehension of MDD. [ABSTRACT FROM AUTHOR]

Simple Summary: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death, and identifying effective treatments is crucial. In this study, we focused on patients with advanced liver cancer and explored how a specific protein in the blood, i.e., stem cell growth factor beta (SCGF-β), could indicate how well patients would respond to a common cancer drug combination. By analyzing blood samples from patients with HCC before and after treatment, we discovered that high SCGF-β levels predicted poorer outcomes. Accordingly, our research suggests that measuring SCGF-β levels could help physicians better understand patient prognosis and tailor treatment plans more effectively. The insights from our study could lead to more personalized and successful treatment strategies for patients with HCC. In this retrospective study, we investigated the potential application of serum stem cell growth factor beta (SCGF-β) as a biomarker for predicting the therapeutic response and prognosis in patients with hepatocellular carcinoma (HCC) undergoing atezolizumab and bevacizumab (Atz/Bev) combination therapy. Pre- and post-treatment serum SCGF-β levels were measured and analyzed in relation to treatment outcomes and overall survival (OS). Pretreatment SCGF-β levels were associated with treatment response. Patients with SCGF-β levels exceeding the 163,295 pg/mL cutoff experienced significantly reduced OS, with a median OS of 12.03 months, compared to 28.87 months in those with SCGF-β levels at or below this threshold. These findings suggest that SCGF-β can serve as a predictive marker for clinical outcomes in HCC treatment, highlighting the need for prospective studies to further validate these results and clarify the mechanisms underlying SCGF-β-related therapeutic resistance. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has profoundly altered global social dynamics through extensive containment measures. Understanding how individuals perceive the virus, distinguishing between realistic and symbolic threats, and considering the influence of personality traits is essential for effective public health messaging and interventions. This study explores the linkage between personality traits and perceived COVID-19 threats, mediated by conspiracy mentality, across diverse international contexts. Online survey data were collected in June 2021 from 13,645 participants across seven European countries, the US, and Colombia. The study explores how individuals with different Dark Triad traits perceive the threat of COVID-19, and whether conspiracy mentality mediates these perceptions. The findings reveal distinct associations. Machiavellianism and narcissism were linked to lower perceived COVID-19 threat, while psychopathy was associated with greater perceived threat. Additionally, individuals with higher conspiracy mentality endorsed greater perceived threat related to COVID-19. This study highlights the influence of Dark Triad personality traits on how individuals perceive the threats posed by COVID-19, with conspiracy mentality mediating this relationship. Understanding these associations provides valuable insights into psychological factors affecting COVID-19 perception, informing future research and public health strategies., (© 2023 Scandinavian Psychological Associations and John Wiley & Sons Ltd.)