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Low to moderate doses of 3-methylmethcathinone (3-MMC) produce analgesic effects in healthy volunteers:a proof of principle study with a designer drug
No full text3-Methylmethcathinone (3-MMC) is a synthetic cathinone that has been scheduled in many jurisdictions after it appeared on the consumer market as a designer drug or "legal high". At present, there are no medical applications for synthetic cathinones, but in the past cathinone and other compounds that are structurally related to amphetamine have been evaluated and recognized for their intrinsic analgesic quality. The present study aimed to assess the analgesic effects of low to moderate doses (25, 50 and 100 mg) of 3-MMC in healthy volunteers (N = 14) in a cross-over, placebo-controlled study. Participants were repeatedly exposed to experimental pain for up to 5 h after dosing in pressure pain threshold (PPT) and cold pressor test (CPT) paradigms. A profile of mood states questionnaire was used to assess the subjective effects of 3-MMC. Overall, 3-MMC produced dose-related elevations in pressure pain threshold and reduced subjective painfulness and unpleasantness in both experimental pain models. The analgesic effects of 3-MMC were most prominent after the 50 and 100 mg dose and persisted consistently for up to 5 h after dosing. 3-MMC also produced dose-related increments in mood that were prominent at 1 h, but not at 5 h after dosing. It is concluded that 3-MMC produces prolonged analgesic effects at doses that appear low enough to avoid a challenging subjective experience and that have been associated with a benign side effect profile. The present data warrant further research into the analgesic effects of low to moderate doses of 3-MMC in patient populations
Current insights on temporary mechanical circulatory support in adults with post-cardiotomy cardiogenic shock
No full textPost-cardiotomy cardiogenic shock (PCCS) is a critical condition characterized by persistent low cardiac output syndrome (LCOS) that manifests either as an inability to wean from cardiopulmonary bypass (CPB) or as severe cardiac dysfunction in the immediate post-operative period despite optimal medical therapy. With an incidence of 2-20%, PCCS is associated with high morbidity, mortality, and healthcare resource utilization. This review explores the pathophysiology of PCCS while emphasizing mechanisms such as direct myocardial damage, ischaemia-reperfusion injury, and systemic effects of extracorporeal circulation. It also discusses key diagnostic tools for PCCS including echocardiography, pulmonary artery catheters, vasoactive inotropic scores (VIS), and lactate clearance, which facilitate early recognition and management. Treatment pathways centred on temporary mechanical circulatory support (tMCS), tailored to clinical scenarios such as the inability to wean from CPB or refractory LCOS. The pivotal role of the multi-disciplinary Heart Team in decision-making, collaboration, and patient-centred care is highlighted. Finally, weaning protocols and considerations for long-term outcomes are discussed, underscoring the need for timely interventions and a personalized approach. Advances in PCCS management continue to evolve, aiming to improve survival and long-term outcomes for this high-risk population
Predicting digital contact tracing tool adoption during COVID-19 from the perspective of TAM:The role of trust, fear, privacy, anxiety, and social media
No full textObjective The emergence of more contagious SARS-CoV-2 variants, such as EG.5 (Eris), has heightened the urgency of assessing associated risks and managing the spread of infections. Digital Contact Tracing (DCT) tools have been widely adopted to mitigate these risks, although the factors driving their acceptance are complex and multifaceted. However, there is a significant lack of research on the application of DCT within Saudi Arabia, despite its proactive use of such technologies in public health strategies. This study investigates the key determinants of DCT adoption and acceptance by integrating the Technology Acceptance Model (TAM) with psychological, social, and regulatory factors related to the context of the study.Methods Using a quantitative, cross-sectional design, data were collected from Saudi participants through an online survey and analysed using Structural Equation Modeling (SEM) with SmartPLS4.Results The results supported all the hypotheses except for the relationship between social media awareness and DCT tool usage. The findings revealed that COVID-19-induced anxiety significantly influenced technology acceptance, with social influence playing a mediating role. This study introduces a novel, context-specific model contributing to the technology acceptance field by exploring how pandemic-related factors, such as anxiety and social influence, affect DCT tool adoption. It also addresses a critical gap in the previous literature by examining the mediating role of social impact in the association between privacy and event-related fear and the moderating effect of COVID-19 anxiety on social media awareness and DCT usage. The findings offer valuable insights for governmental interventions, health institutions, and legislators in managing pandemics globally and within the Kingdom of Saudi Arabia.Conclusion We introduce a novel, context-specific model for understanding how pandemic-related psychological and social factors influence DCT adoption in this study. Those results provide insight into how policymakers, health institutions, and legislators can use DCT tools to manage pandemics globally and in Saudi Arabia
Understanding personal preferences to promote exercise adherence in Parkinson's disease
No full textIntroduction: Exercise is a recognized treatment for people with Parkinson's Disease (PwP), but personal exercise preferences are rarely considered in exercise intervention studies. Yet these are critical for understanding motivation and to ascertain long-term exercise adherence in PwP. This study compared preferred exercise types among PwP with exercise types investigated in prior research. Method: 672 PwP participated in an online survey, answering questions about currently practiced and preferred exercise types. Besides, a brief review was performed, evaluating exercise types deployed in prior studies. The preferred exercise types of PwP were subsequently compared to exercise types used in prior research. Results: PwP mainly preferred the same exercises that they were currently practicing. These preferences did not necessarily align with exercise types reported in prior studies. The top 5 highly preferred sport categories but relatively little studied were walking, biking, swimming, boxing, and tennis. Conclusion: Future intervention studies may benefit from considering personal preferences to both strengthen and understand exercise adherence
Formation of tight junction-like structures of zonula occludens 2 in platelet–platelet interaction
No full textBackground: In tissues, cell–cell adhesion, barrier formation, and communication are regulated by gap, adherens, and tight junction (TJ) proteins. Platelets express several of these proteins. Platelets express key building blocks of gap junctions, the connexins, with known functions in integrin αIIbβ 3 regulation. While, for some expressed TJ proteins like junctional adhesion molecule A and endothelial cell-specific adhesion molecule, a role in platelets has been uncovered, for other TJ proteins, like zonula occludens (ZO)-2 a contribution to platelet function is still unknown. Objectives: This study aimed to elucidate the role of ZO-2 in the stabilization of tight interplatelet contacts. Methods: Isolated human platelets from healthy volunteers and a patient deficient in ZO-2 were spread on fibrinogen and laminin surfaces in the presence of platelet agonists and inhibitors. Samples were fixed and prepared for microscopy. Results: Confocal and superresolution fluorescence microscopy indicated a redistribution of ZO-2 molecules forming clusters at sites of stable interplatelet contacts that was dependent on the platelet activation status. In the tight contacts, ZO-2 colocalized with endothelial cell-specific adhesion molecule and junctional adhesion molecule A. Furthermore, platinum replica electron microscopy revealed that interplatelet contacts resulted in compaction, detected as interwoven circumferential actin filaments, of interacting platelets. These changes were antagonized by cyclic adenosine monophosphate elevation and inhibition of αIIbβ 3 integrins. In a blood sample from a patient deficient in ZO-2, we observed an increased thrombus stability, suggesting a potential regulation of thrombus stability by these TJ-like structures. Conclusion: Jointly, these data point to the assembly of TJ-like structures of interacting platelets, which enforce platelet adhesion contacts but lower 3-dimensional thrombus stability.</p
Multi-phase feature-aligned fusion model for automated colorectal cancer segmentation in contrast-enhanced CT scans
No full textColorectal cancer (CRC) is one of the most common cancers worldwide. Accurate segmentation of CRC in contrast-enhanced CT images can guide personalized design of surgical and radiotherapy plans. Current studies solely utilize venous-phase CT images for tumor segmentation, ignoring valuable information in multi-phase CT images. In this study, we propose a novel deep learning framework that fully leverages multi-phase information for more accurate CRC segmentation. Specifically, we introduce a multi-phase feature-aligned fusion module that adaptively aligns and aggregates multi-phase features using a cross-phase attention mechanism. Additionally, we develop an attention-guided multi-scale fusion module and a dual-path deep supervision strategy to utilize multi-scale features at various resolution stages to enhance segmentation performance. Extensive experimental results from three datasets demonstrate that our proposed method outperforms other state-of-the-art segmentation methods. Our code is available at https://github.com/Topicking/Multi-phase-CRC-Segmentation
Evaluating photon-counting computed tomography for quantitative material characteristics and material differentiation in radiotherapy
No full textObjective. Photon-counting computed tomography (PCCT) counts the individual photons and measures their energy, which allows for energy binning and thereby multi-energy CT imaging. It is expected that quantitative data can be accurately extracted from the images and enable accurate material separation, yet its potential in radiotherapy is mostly unexplored. In this study, PCCT was assessed by evaluating estimation accuracies for relative electron density (RED), effective atomic number (Z eff), and proton stopping-power ratio (SPR), as well as the potential for material differentiation. Approach. PCCT images of a Gammex Advanced Electron Density phantom (Sun Nuclear) with tissue-equivalent materials were acquired in a small and large phantom setup on a NAEOTOM Alpha PCCT scanner (Siemens Healthineers). The scans were performed at 120 and 140 kVp, and virtual monoenergetic images (VMIs) were generated. These VMIs were used to estimate RED, Z eff, and SPR based on two calibration methods for each of the two phantom sizes. These results were compared to findings obtained based on dual-energy CT (DECT) scans acquired on a SOMATOM Confidence scanner (Siemens Healthineers) at 80 and 140 kVp, by using the low and high energy pair and VMIs. Calibration accuracy was quantified by the root-mean-squared error. Additional, material differentiation was assessed for both tissue-equivalent and calcium/iodine inserts by creating [RED/Z eff]-space plots. Main results. There was minimal differences between the two PCCT x-ray spectra, with SPR errors below 0.8% for the large phantom and 0.7% for the small phantom, which was comparable to DECT using VMIs. Material differentiation showed similar results for DECT and PCCT using VMIs, and resulted in less Z eff spread, than the regular DECT kVp pair, possibly due to denoising. Significance. This study showed the ability of PCCT to retrieve material characteristics and possibility for material differentiation between tissue-equivalent material and calcium/iodine, with results comparable to DECT.</p
FaceAge, a deep learning system to estimate biological age from face photographs to improve prognostication:a model development and validation study
No full textBackground: As humans age at different rates, physical appearance can yield insights into biological age and physiological health more reliably than chronological age. In medicine, however, appearance is incorporated into medical judgements in a subjective and non-standardised way. In this study, we aimed to develop and validate FaceAge, a deep learning system to estimate biological age from easily obtainable and low-cost face photographs. Methods: FaceAge was trained on data from 58 851 presumed healthy individuals aged 60 years or older: 56 304 individuals from the IMDb–Wiki dataset (training) and 2547 from the UTKFace dataset (initial validation). Clinical utility was evaluated on data from 6196 patients with cancer diagnoses from two institutions in the Netherlands and the USA: the MAASTRO, Harvard Thoracic, and Harvard Palliative cohorts FaceAge estimates in these cancer cohorts were compared with a non-cancerous reference cohort of 535 individuals. To assess the prognostic relevance of FaceAge, we performed Kaplan–Meier survival analysis and Cox modelling, adjusting for several clinical covariates. We also assessed the performance of FaceAge in patients with metastatic cancer receiving palliative treatment at the end of life by incorporating FaceAge into clinical prediction models. To evaluate whether FaceAge has the potential to be a biomarker for molecular ageing, we performed a gene-based analysis to assess its association with senescence genes. Findings: FaceAge showed significant independent prognostic performance in various cancer types and stages. Looking older was correlated with worse overall survival (after adjusting for covariates per-decade hazard ratio [HR] 1·151, p=0·013 in a pan-cancer cohort of n=4906; 1·148, p=0·011 in a thoracic cohort of n=573; and 1·117, p=0·021 in a palliative cohort of n=717). We found that, on average, patients with cancer looked older than their chronological age (mean increase of 4·79 years with respect to non-cancerous reference cohort, p<0·0001). We found that FaceAge can improve physicians’ survival predictions in patients with incurable cancer receiving palliative treatments (from area under the curve 0·74 [95% CI 0·70–0·78] to 0·8 [0·76–0·83]; p<0·0001), highlighting the clinical use of the algorithm to support end-of-life decision making. FaceAge was also significantly associated with molecular mechanisms of senescence through gene analysis, whereas age was not. Interpretation: Our results suggest that a deep learning model can estimate biological age from face photographs and thereby enhance survival prediction in patients with cancer. Further research, including validation in larger cohorts, is needed to verify these findings in patients with cancer and to establish whether the findings extend to patients with other diseases. Subject to further testing and validation, approaches such as FaceAge could be used to translate a patient's visual appearance into objective, quantitative, and clinically valuable measures. Funding: US National Institutes of Health and EU European Research Council.</p
