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Acute graft-versus-host disease (aGvHD) is a severe and often life-threatening complication of allogeneic hematopoietic cell transplantation (allo-HCT). AGvHD is mediated by alloreactive donor T-cells targeting predominantly the gastrointestinal tract, liver, and skin. Recent work in mice and patients undergoing allo-HCT showed that alloreactive T-cells can be identified by the expression of α4β7 integrin on T-cells even before manifestation of an aGvHD. Here, we investigated whether the detection of a combination of the expression of T-cell surface markers on peripheral blood (PB) CD8\(^+\) T-cells would improve the ability to predict aGvHD. To this end, we employed two independent preclinical models of minor histocompatibility antigen mismatched allo-HCT following myeloablative conditioning. Expression profiles of integrins, selectins, chemokine receptors, and activation markers of PB donor T-cells were measured with multiparameter flow cytometry at multiple time points before the onset of clinical aGvHD symptoms. In both allo-HCT models, we demonstrated a significant upregulation of α4β7 integrin, CD162E, CD162P, and conversely, a downregulation of CD62L on donor T-cells, which could be correlated with the development of aGvHD. Other surface markers, such as CD25, CD69, and CC-chemokine receptors were not found to be predictive markers. Based on these preclinical data from mouse models, we propose a surface marker panel on peripheral blood T-cells after allo-HCT combining α4β7 integrin with CD62L, CD162E, and CD162P (cutaneous lymphocyte antigens, CLA, in humans) to identify patients at risk for developing aGvHD early after allo-HCT.
The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 % for the whole dataset, 81.8 % for the trained runners, and 86.1 % for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions.
Background
Radioligand therapy (RLT) with \(^{177}\)Lu-labeled prostate-specific membrane antigen (PSMA) ligands is associated with prolonged overall survival (OS) in patients with advanced, metastatic castration-resistant prostate cancer (mCRPC). A substantial number of patients, however, are prone to treatment failure. We aimed to determine clinical baseline characteristics to predict OS in patients receiving [\(^{177}\)Lu]Lu-PSMA I&T RLT in a long-term follow-up.
Materials and methods
Ninety-two mCRPC patients treated with [\(^{177}\)Lu]Lu-PSMA I&T with a follow-up of at least 18 months were retrospectively identified. Multivariable Cox regression analyses were performed for various baseline characteristics, including laboratory values, Gleason score, age, prior therapies, and time interval between initial diagnosis and first treatment cycle (interval\(_{Diagnosis-RLT}\), per 12 months). Cutoff values for significant predictors were determined using receiver operating characteristic (ROC) analysis. ROC-derived thresholds were then applied to Kaplan–Meier analyses.
Results
Baseline C-reactive protein (CRP; hazard ratio [HR], 1.10, 95% CI 1.02–1.18; P = 0.01), lactate dehydrogenase (LDH; HR, 1.07, 95% CI 1.01–1.11; P = 0.01), aspartate aminotransferase (AST; HR, 1.16, 95% CI 1.06–1.26; P = 0.001), and interval\(_{Diagnosis-RLT}\) (HR, 0.95, 95% CI 0.91–0.99; P = 0.02) were identified as independent prognostic factors for OS. The following respective ROC-based thresholds were determined: CRP, 0.98 mg/dl (area under the curve [AUC], 0.80); LDH, 276.5 U/l (AUC, 0.83); AST, 26.95 U/l (AUC, 0.73); and interval\(_{Diagnosis-RLT}\), 43.5 months (AUC, 0.68; P < 0.01, respectively). Respective Kaplan–Meier analyses demonstrated a significantly longer median OS of patients with lower CRP, lower LDH, and lower AST, as well as prolonged interval\(_{Diagnosis-RLT}\) (P ≤ 0.01, respectively).
Conclusion
In mCRPC patients treated with [\(^{177}\)Lu]Lu-PSMA I&T, baseline CRP, LDH, AST, and time interval until RLT initiation (thereby reflecting a possible indicator for tumor aggressiveness) are independently associated with survival. Our findings are in line with previous findings on [\(^{177}\)Lu]Lu-PSMA-617, and we believe that these clinical baseline characteristics may support the nuclear medicine specialist to identify long-term survivors.
Objective
The admission interview in oncological inpatient rehabilitation might be a good opportunity to identify cancer patients' needs present after acute treatment. However, a relevant number of patients may not express their needs. In this study, we examined (a) the proportion of cancer patients with unexpressed needs, (b) topics of unexpressed needs and reasons for not expressing needs, (c) correlations of not expressing needs with several patient characteristics, and (d) predictors of not expressing needs.
Methods
We enrolled 449 patients with breast, prostate, and colon cancer at beginning and end of inpatient rehabilitation. We obtained self‐reports about unexpressed needs and health‐related variables (quality of life, depression, anxiety, adjustment disorder, and health literacy). We estimated frequencies and conducted correlation and ordinal logistic regression analyses.
Results
A quarter of patients stated they had “rather not” or “not at all” expressed all relevant needs. Patients mostly omitted fear of cancer recurrence. Most frequent reasons for not expressing needs were being focused on physical consequences of cancer, concerns emerging only later, and not knowing about the possibility of talking about distress. Not expressing needs was associated with several health‐related outcomes, for example, emotional functioning, adjustment disorder, fear of progression, and health literacy. Depression measured at the beginning of rehabilitation showed only small correlations and is therefore not sufficient to identify patients with unexpressed needs.
Conclusions
A relevant proportion of cancer patients reported unexpressed needs in the admission interview. This was associated with decreased mental health. Therefore, it seems necessary to support patients in expressing needs.
MicroRNAs (miRNAs) play regulatory roles in diverse processes in both eukaryotic hosts and their viruses, yet fundamental questions remain about which viruses code for miRNAs and the functions that they serve. Simian foamy viruses (SFVs) of Old World monkeys and apes can zoonotically infect humans and, by ill-defined mechanisms, take up lifelong infections in their hosts. Here, we report that SFVs encode multiple miRNAs via a noncanonical mode of biogenesis. The primary SFV miRNA transcripts (pri-miRNAs) are transcribed by RNA polymerase III (RNAP III) and take multiple forms, including some that are cleaved by Drosha. However, these miRNAs are generated in a context-dependent fashion, as longer RNAP II transcripts spanning this region are resistant to Drosha cleavage. This suggests that the virus may avoid any fitness penalty that could be associated with viral genome/transcript cleavage. Two SFV miRNAs share sequence similarity and functionality with notable host miRNAs, the lymphoproliferative miRNA miR-155 and the innate immunity suppressor miR-132. These results have important implications regarding foamy virus biology, viral miRNAs, and the development of retroviral-based vectors. IMPORTANCE Fundamental questions remain about which viruses encode miRNAs and their associated functions. Currently, few natural viruses with RNA genomes have been reported to encode miRNAs. Simian foamy viruses are retroviruses that are prevalent in nonhuman host populations, and some can zoonotically infect humans who hunt primates or work as animal caretakers. We identify a cluster of miRNAs encoded by SFV. Characterization of these miRNAs reveals evolutionarily conserved, unconventional mechanisms to generate small RNAs. Several SFV miRNAs share sequence similarity and functionality with host miRNAs, including the oncogenic miRNA miR-155 and innate immunity suppressor miR-132. Strikingly, unrelated herpesviruses also tap into one or both of these same regulatory pathways, implying relevance to a broad range of viruses. These findings provide new insights with respect to foamy virus biology and vectorology.
Background
Antidepressant medication is commonly used to treat depression. However, many patients do not respond to the first medication prescribed and improvements in symptoms are generally only detectable by clinicians 4–6 weeks after the medication has been initiated. As a result, there is often a long delay between the decision to initiate an antidepressant medication and the identification of an effective treatment regimen.
Previous work has demonstrated that antidepressant medications alter subtle measures of affective cognition in depressed patients, such as the appraisal of facial expression. Furthermore, these cognitive effects of antidepressants are apparent early in the course of treatment and can also predict later clinical response. This trial will assess whether an electronic test of affective cognition and symptoms (the Predicting Response to Depression Treatment Test; PReDicT Test) can be used to guide antidepressant treatment in depressed patients and, therefore, hasten treatment response compared to a control group of patients treated as usual.
Methods/design
The study is a randomised, two-arm, multi-centre, open-label, clinical investigation of a medical device, the PReDicT Test. It will be conducted in five European countries (UK, France, Spain, Germany and the Netherlands) in depressed patients who are commencing antidepressant medication. Patients will be randomised to treatment guided by the PReDicT Test (PReDicT arm) or to Treatment as Usual (TaU arm). Patients in the TaU arm will be treated as per current standard guidelines in their particular country. Patients in the PReDicT arm will complete the PReDicT Test after 1 (and if necessary, 2) weeks of treatment. If the test indicates non-response to the treatment, physicians will be advised to immediately alter the patient’s antidepressant therapy by dose escalation or switching to another compound. The primary outcome of the study is the proportion of patients showing a clinical response (defined as 50% or greater decrease in baseline scores of depressionmeasured using the Quick Inventory of Depressive Symptoms – Self-Rated questionnaire) at week 8. Health economic and acceptability data will also be collected and analysed.
Discussion
This trial will test the clinical efficacy, cost-effectiveness and acceptability of using the novel PReDicT Test to guide antidepressant treatment selection in depressed patients.
Trial registration
ClinicalTrials.gov, ID: NCT02790970. Registered on 30 March 2016.
The seasonal snow cover in the European Alps plays a crucial role in the region's climate, ecology, and economy. It affects the local climate through its high albedo, protects permafrost, provides habitats, and acts as a water reservoir that feeds European rivers. However, these functions are threatened by climate change. Analyzing snow cover dynamics is essential to predict future developments and assess related ecological and economic impacts.
This study explores the potential of long Earth Observation (EO) time series for modeling and predicting the snow line elevation (SLE) in the Alps. Based on approximately 15,000 Landsat satellite images, SLE time series were generated for the years 1985 to 2022. Various univariate forecasting models were evaluated, with the best results achieved by Random Forests, Telescope, and Seasonal ARIMA. A newly developed approach combines the best models into a robust ensemble, achieving an average Nash-Sutcliffe efficiency (NSE) of 0.8 in catchments with strong seasonal signals.
Forecasts for 2030 indicate significant upward shifts in the SLE, particularly in the Western and Southern Alps. Given the variability in results, a multivariate modeling approach using climate variables is recommended to improve prediction accuracy. This study lays the groundwork for future models that could potentially project SLE dynamics through the end of the 21st century under various climate scenarios, which is highly relevant for climate policy in the Alpine region.
Presence is often considered the most important quale describing the subjective feeling of being in a computer-generated and/or computer-mediated virtual environment. The identification and separation of orthogonal presence components, i.e., the place illusion and the plausibility illusion, has been an accepted theoretical model describing Virtual Reality (VR) experiences for some time. This perspective article challenges this presence-oriented VR theory. First, we argue that a place illusion cannot be the major construct to describe the much wider scope of virtual, augmented, and mixed reality (VR, AR, MR: or XR for short). Second, we argue that there is no plausibility illusion but merely plausibility, and we derive the place illusion caused by the congruent and plausible generation of spatial cues and similarly for all the current model’s so-defined illusions. Finally, we propose congruence and plausibility to become the central essential conditions in a novel theoretical model describing XR experiences and effects.
Schizophrenia (SCZ) is a severe mental disorder with immense personal and societal costs; identifying individuals at risk is therefore of utmost importance. Genomic risk profile scores (GRPS) have been shown to significantly predict cases-control status. Making use of a large-population based sample from Sweden, we replicate a previous finding demonstrating that the GRPS is strongly associated with admission frequency and chronicity of SCZ. Furthermore, we were able to show a substantial gap in prediction accuracy between males and females. In sum, our results indicate that prediction accuracy by GRPS depends on clinical and demographic characteristics.
Introduction: The aim of our study was to develop a reproducible murine model of elastase-induced aneurysm formation combined with aortic transplantation.
Methods: Adult male mice (n = 6-9 per group) underwent infrarenal, orthotopic transplantation of the aorta treated with elastase or left untreated. Subsequently, both groups of mice were monitored by ultrasound until 7 weeks after grafting.
Results: Mice receiving an elastase-pretreated aorta developed aneurysms and exhibited a significantly increased diastolic vessel diameter compared to control grafted mice at 7 week after surgery (1.11 +/- 0.10 mm vs. 0.75 +/- 0.03 mm; p <= 0.001). Histopathological examination revealed disruption of medial elastin, an increase in collagen content and smooth muscle cells, and neointima formation in aneurysm grafts.
Conclusions: We developed a reproducible murine model of elastase-induced aneurysm combined with aortic transplantation. This model may be suitable to investigate aneurysm-specific inflammatory processes and for use in gene-targeted animals.