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Randomized controlled trials (RCTs) are the hallmark of evidence-based medicine and form the basis for translating research data into clinical practice. This review summarizes commonly applied designs and quality indicators of RCTs to provide guidance in interpreting and critically evaluating clinical research data. It further reflects on the principle of equipoise and its practical applicability to clinical science with an emphasis on critical care and neurological research. We performed a review of educational material, review articles, methodological studies, and published clinical trials using the databases MEDLINE, PubMed, and ClinicalTrials.gov. The most relevant recommendations regarding design, conduction, and reporting of RCTs may include the following: 1) clinically relevant end points should be defined a priori, and an unbiased analysis and report of the study results should be warranted, 2) both significant and nonsignificant results should be objectively reported and published, 3) structured study design and performance as indicated in the Consolidated Standards of Reporting Trials statement should be employed as well as registration in a public trial database, 4) potential conflicts of interest and funding sources should be disclaimed in study report or publication, and 5) in the comparison of experimental treatment with standard care, preplanned interim analyses during an ongoing RCT can aid in maintaining clinical equipoise by assessing benefit, harm, or futility, thus allowing decision on continuation or termination of the trial.

Rapid coagulation assessment is essential in critical care to enable timely correction of coagulopathy. Viscoelastic testing (VET) supports this goal but may be affected by mechanical stress during transport by pneumatic tube systems (PTS). As PTS are widely used to expedite sample delivery, evaluating the robustness of next-generation VET and platelet function assays under these conditions is crucial for reliable, time-sensitive diagnostics in intensive care. This study investigated the impact of PTS transport on VET and platelet function testing in healthy individuals and septic patients, including quantitative analysis of acceleration forces. This randomized trial applied a non-systematic sample-level allocation of paired blood samples from 46 healthy volunteers and 45 septic patients to manual and PTS transport. Acceleration was quantified via three-axis accelerometry. Samples were analyzed using ClotPro, ROTEM, TEG PlateletMapping, and Multiplate. Primary objective was the difference in test results following both transport modes. Analyses were performed on paired datasets (manual vs. PTS) per participant and assay. As pre-specified in the protocol, logistic regression modeled the probability of a clinically relevant EX-test clotting time (CT) change (≥ 10 s) within each cohort. Given the absence of associations, secondary equivalence analyses (TOST [two one-sided tests] and bootstrap) assessed whether observed effects were within pre-specified bounds. Neither logistic regression nor correlation analysis indicated an effect of mechanical stress on variable changes (all ρ < 0.5; p  > 0.01). Across platforms, most viscoelastic and platelet function variables remained within predefined equivalence margins after PTS transport. Exceptions were TEG PlateletMapping HKH-R and, by bootstrap, ADP/AA-inhibition. In healthy volunteers, equivalence was confirmed for all variables (TOST p  < 0.001). In septic patients, minor shifts remained within clinically acceptable limits, with equivalence confirmed for ClotPro IN-test CT (± 16s, p lower = 0.036; p upper < 0.001), EX-test MCF (± 2 mm, both p  < 0.001), ROTEM INTEM CT (± 16s, both p  < 0.001), Multiplate TRAP (± 10U, p lower = 0.001; p upper < 0.001), and TEG PlateletMapping ADP/AA inhibition (± 5%, both p  < 0.05). Most next-generation viscoelastic and platelet assays are robust to PTS-induced stress. Coagulation diagnostics can include PTS transport without compromising validity. Only selected TEG PlateletMapping variables exhibited variability, indicating limited robustness. Trial registration: The study is retrospectively registered with the German Clinical Trials Register (DRKS00036231; https://drks.de/search/de/trial/DRKS00036231/details on 20.02.2025).

The accumulation of copper in steel scrap is becoming an increasingly problematic issue in the steelmaking industry. Accordingly, the present study was undertaken to investigate the removal of copper from a liquid Fe–Cu alloy via tapping under vacuum. Furthermore, the impact of surface-active components sulfur and oxygen was examined. For this purpose, four Fe–0.5 wt% Cu alloys with varying oxygen and sulfur contents were melted and subsequently poured at a pressure of 100 Pa. The findings indicate that alloys with low oxygen and sulfur content exhibited enhanced copper evaporation. Additionally, the evaporation of other tramp metals, including manganese, phosphorus, and tin, was observed, and the influence of sulfur and oxygen on this process was discussed. Furthermore, the vacuum treatment conditions for copper evaporation in industrial settings were explored.

Decisions regarding veno-venous extracorporeal membrane oxygenation (vv-ECMO) in patients with acute respiratory distress syndrome (ARDS) are often based solely on clinical and physiological parameters, which may insufficiently reflect severity and heterogeneity of lung injury. This study aimed to develop a predictive model integrating machine learning-derived quantitative features from admission chest computed tomography (CT) with selected clinical variables to support early individualized decision-making regarding vv-ECMO therapy. In this retrospective single-center cohort study, 375 consecutive patients with COVID-19-associated ARDS admitted to the ICU between March 2020 and April 2022 were included. Lung segmentation from initial CTs was performed using a convolutional neural network (CNN) to generate high-resolution, anatomically accurate masks of the lungs. Subsequently, 592 radiomic features, quantifying lung aeration, density and morphology, were extracted. Four clinical parameters – age, mean airway pressure, lactate, and C-reactive protein, were selected on the basis of clinical relevance. Three logistic regression models were developed: (1) Imaging Model, (2) Clinical Model, and (3) Combined Model integrating different features. Predictive performance was assessed via the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity. A total of 375 patients were included: 172 in the training and 203 in the validation cohort. In the training cohort, the AUROCs were 0.743 (Imaging), 0.828 (Clinical), and 0.842 (Combined). In the validation cohort, the Combined Model achieved the highest AUROC (0.705), outperforming the Clinical (0.674) and Imaging (0.639) Models. Overall accuracy in the validation cohort was 64.0% (Combined), 66.5% (Clinical), and 59.1% (Imaging). The Combined Model showed 68.1% sensitivity and 58.9% specificity. Kaplan-Meier analysis confirmed a significantly greater cumulative incidence of ECMO therapy in patients predicted as high risk ( p  < 0.001), underscoring its potential to support individualized, timely ECMO decisions in ARDS by providing clinicians with objective data-driven risk estimates. Quantitative CT features based on machine learning-derived lung segmentation allow early individualized prediction of the need for vv-ECMO in ARDS. While clinical data remain essential, radiomic markers enhance prognostic accuracy. The Combined Model demonstrates considerable potential to support timely and evidence-based ECMO initiation, facilitating individualized critical care in both specialized and general ICU environments. Trial registration : The study is registered with the German Clinical Trials Register under the number DRKS00027856. Registered 18.01.2022, retrospectively registered due to retrospective design of the study.

Fibroblast heterogeneity is increasingly recognised across cancer conditions. Given their important contribution to disease progression, mapping fibroblasts’ heterogeneity is critical to devise effective anti-cancer therapies. Cancer-associated fibroblasts (CAFs) represent the most abundant cell population in pancreatic ductal adenocarcinoma (PDAC). Whether CAF phenotypes are differently specified by PDAC cell lineages remains to be elucidated. Here, we reveal an important role for the MAPK signalling pathway in defining PDAC CAF phenotypes. We show that epithelial MAPK activity promotes the myofibroblastic differentiation of CAFs by sustaining the expression and secretion of TGF-β1. We integrate single-cell profiling of post-perturbation transcriptional responses from mouse models with cellular and spatial profiles of human tissues to define a MAPK high CAF (mapCAF) phenotype. We show that this phenotype associates with basal-like tumour cells and reduced frequency of CD8 + T cells. In addition to elevated MAPK activity, this mapCAF phenotype is characterized by TGF-β signalling, hypoxia responsive signatures, and immunoregulatory gene programs. Furthermore, the mapCAF signature is enriched in myofibroblastic CAFs from various cancer conditions and correlates with reduced response to immune checkpoint inhibition in melanoma. Altogether, our data expand our knowledge on CAF phenotype heterogeneity and reveal a potential strategy for targeting myofibroblastic CAFs in vivo. Phenotypic and functional heterogeneity of cancer associated fibroblasts (CAFs) has been reported in pancreatic ductal adenocarcinoma (PDAC). Here the authors show that epithelial MAPK activity promotes myofibroblastic differentiation of CAFs. Furthermore, the epithelial basal-like subtype is associated with a CAF phenotype characterized by elevated MAPK activity and TGF-β signalling, associated with T cell exclusion in PDAC.

Acute Respiratory Distress Syndrome (ARDS) is common in COVID-19 patients and is associated with high mortality. The aim of this observational study was to describe patients’ characteristics and outcome, identifying potential risk factors for in-hospital mortality and for developing Long-COVID symptoms. This retrospective study included all patients with COVID-19 associated ARDS (cARDS) in the period from March 2020 to March 2021 who were invasively ventilated at the intensive care unit (ICU) of the University Hospital Dresden, Germany. Between October 2021 and December 2021 patients discharged alive (at minimum 6 months after hospital discharge—midterm survival) were contacted and interviewed about persistent symptoms possibly associated with COVID-19 as well as the quality of their lives using the EQ-5D-5L-questionnaire. Long-COVID was defined as the occurrence of one of the symptoms at least 6 months after discharge. Risk factors for mortality were assessed with Cox regression models and risk factors for developing Long-COVID symptoms by using relative risk (RR) regression. 184 Patients were included in this study (male: n = 134 (73%), median age 67 (range 25–92). All patients were diagnosed with ARDS according to the Berlin Definition. 89% of patients (n = 164) had severe ARDS (Horovitz-index < 100 mmHg). In 27% (n = 49) extracorporeal membrane oxygenation was necessary to maintain gas exchange. The median length of in-hospital stay was 19 days (range 1–60). ICU mortality was 51%, hospital mortality 59%. Midterm survival (median 11 months) was 83% (n = 55) and 78% (n = 43) of these patients presented Long-COVID symptoms with fatigue as the most common symptom (70%). Extreme obesity (BMI > 40 kg/m 2 ) was the strongest predictor for in-hospital mortality (hazard ratio: 3.147, confidence interval 1.000–9.897) and for developing Long-COVID symptoms (RR 1.61, confidence interval 1.26–2.06). In-hospital mortality in severe cARDS patients was high, but > 80% of patients discharged alive survived the midterm observation period. Nonetheless, most patients developed Long-COVID symptoms. Extreme obesity with BMI > 40 kg/m 2 was identified as independent risk factor for in-hospital mortality and for developing Long-COVID symptoms. Trial registration DRKS-ID DRKS00027856.

Fine-grained spatio-temporal action detection in continuous, unconstrained field videos remains a formidable challenge due to severe background clutter, high inter-class similarity, and the scarcity of domain-specific benchmarks. To address these limitations, we first introduce a large-scale Wintering-Crane Benchmark, providing dense, individual-level bounding box annotations for six complex behaviors across diverse habitat scenes. Leveraging this data, we propose AviaTAD-LGH, a real-time multi-task framework that incorporates auxiliary motion supervision into a dual-pathway 3D backbone to enhance feature discriminability. A critical bottleneck in such multi-task settings is the negative transfer caused by conflicting optimization objectives. To resolve this, we present Lightweight Gradient Harmonization (LGH), a plug-and-play optimization strategy that dynamically modulates task weights based on the cosine similarity of gradient directions. This mechanism effectively aligns optimization trajectories without introducing inference latency. Extensive experiments demonstrate that AviaTAD-LGH achieves a state-of-the-art mAP of 68.60%, surpassing strong public baselines by 7.44% and improving upon the single-task baseline by 2.80%, with significant gains observed on ambiguous dynamic classes. The proposed pipeline enables efficient, scalable ecological monitoring suitable for edge deployment.

Bleeding events in patients receiving direct oral anticoagulation (DOAC) can be life-threatening even at therapeutic DOAC plasma concentrations, as anticoagulation impairs hemostasis and should therefore be identified immediately after hospital admission. The anticoagulatory effects of DOAC are typically not measurable in standard coagulation tests, such as PT or aPTT. Specific calibrated anti-FXa-tests allow specific drug monitoring, but they are too time-consuming for critical bleeding events and are commonly not available for 24 h/7 days in routine care. However, recent advances in point-of-care (POC) viscoelastic testing (VET) have shown a promising approach for rapid and quantitative detection of DOAC plasma concentrations using the Russell viper venom factor V activator (RVV for FXa-inhibitors) test or the ecarin clotting time (thrombin inhibitors). In acute bleeding situations, direct FXa inhibitors can be reversed by specific antidote andexanet alfa or hemostasis can be improved by prothrombin complex factor concentrates (PCCs). After reversal, confirmation of reversal efficacy is often requested, but no routine assays are currently available. Thus, the emergency management of bleeding DOAC patients is usually “blinded” with regard to reversal efficacy. POC VET laboratory assays might therefore also be helpful for measuring DOAC effects after reversal. We present a case series demonstrating the usefulness of RVV-clotting time post-DOAC reversal with andexanet alfa.

Background: Improving precision medicine in chemotherapy requires highly sensitive and easily applicable diagnostic tools. In addition, non-invasive molecular real-time monitoring of cytotoxic response is highly desirable. Here, we employed the kinetics of DNA double-strand breaks (DSB) and cell-free DNA (cfDNA) in a cell model of topoisomerase II-inhibitors in T cell leukemia (Jurkat cells) compared to normal cells (peripheral blood mononuclear cells, PBMCs). Methods: We applied automated microscopy to quantify immuno-stained phosphorylated H2AX (γH2AX) as a marker for either DNA damage response (DDR) or cell death and quantitative PCR-based analysis of nuclear and mitochondrial cfDNA concentrations. Results: Jurkat cells displayed a DDR to cytotoxic drug treatment significantly earlier than PBMCs, and etoposide (ETP) induced DSB formation faster than doxorubicin (DOX) in both Jurkat and PBMCs. Jurkat cells exhibited an earlier cytotoxic response compared to PBMC, with a significantly increased mitochondrial cfDNA formation after 2 h of DOX application. In PBMCs, increased cell death was detected after 4 h of incubation with ETP, whereas DOX treatment was less effective. Conclusions: Both automated microscopy and mitochondrial cfDNA quantification analysis indicate that (malignant) Jurkat cells are more sensitive to DOX than (healthy) PBMC. Our real-time approach can improve DDR inducing drug selection and adaptation in cancer therapy and aids in decisions for optimal patient biosampling.

Background Personalized therapy planning remains a significant challenge in advanced colorectal cancer care, despite extensive research on prognostic and predictive markers. A strong correlation of sarcopenia or overall body composition and survival has been described. Here, we explore whether automated assessment of body composition and liver metastases from standard of care CT images can add to clinical parameters in personalized survival risk prognostication. Methods We retrospectively analysed clinical imaging data from 85 patients (50.6% female, mean age 58.9 SD 12.2 years) with colorectal cancer and synchronous liver metastases. Pretrained deep learning models were used to assess body composition and liver metastasis geometry from abdominal CT images before the initiation of systemic treatment. Abdominal muscle‐to‐bone ratio (MBR) was calculated by dividing abdominal muscle volume by abdominal bone volume. MBR was compared with body mass index (BMI), abdominal muscle volume, and abdominal muscle volume divided by height squared. Differences in overall survival based on body composition and liver metastasis parameters were compared using Kaplan–Meier survival curves. Results were correlated with clinical and biomarker data to develop a machine learning model for survival risk prognostication. Results The MBR, unlike abdominal muscle volume or BMI, was significantly associated with overall survival (HR 0.39, 95% CI: 0.19–0.80, P = 0.009). The MBR (P = 0.022), liver metastasis surface area (P = 0.01) and primary tumour sidedness (P = 0.007) were independently associated with overall survival in multivariate analysis. Body composition parameters did not correlate with KRAS mutational status or primary tumour sidedness. A prediction model based on MBR, liver metastasis surface area and primary tumour sidedness achieved a concordance index of 0.69. Conclusions Automated segmentation enables to extract prognostic parameters from routine imaging data for personalized survival modelling in advanced colorectal cancer patients.