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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 2007 flood in the Sahel: causes, characteristics and its presentation in the media and FEWS NET
(2012)
During the rainy season in 2007, reports about exceptional rains and floodings in the Sahel were published in the media, especially in August and September. Institutions and organizations like the World Food Programme (WFP) and FEWS NET put the events on the agenda and released alerts and requested help. The partly controversial picture was that most of the Sahel faced a crisis caused by widespread floodings. Our study shows that the rainy season in 2007 was exceptional with regard to rainfall amount and return periods. In many areas the event had a return period between 1 and 50 yr with high spatial heterogeneity, with the exception of the Upper Volta basin, which yielded return periods of up to 1200 yr. Despite the strong rainfall, the interpretation of satellite images show that the floods were mainly confined to lakes and river beds. However, the study also proves the difficulties in assessing the meteorological processes and the demarcation of flooded areas in satellite images without ground truthing. These facts and the somewhat vague and controversial reports in the media and FEWS NET demonstrate that it is crucial to thoroughly analyze such events at a regional and local scale involving the local population.
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.
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.
Background
Medical resource management can be improved by assessing the likelihood of prolonged length of stay (LOS) for head and neck cancer surgery patients. The objective of this study was to develop predictive models that could be used to determine whether a patient's LOS after cancer surgery falls within the normal range of the cohort.
Methods
We conducted a retrospective analysis of a dataset consisting of 300 consecutive patients who underwent head and neck cancer surgery between 2017 and 2022 at a single university medical center. Prolonged LOS was defined as LOS exceeding the 75th percentile of the cohort. Feature importance analysis was performed to evaluate the most important predictors for prolonged LOS. We then constructed 7 machine learning and deep learning algorithms for the prediction modeling of prolonged LOS.
Results
The algorithms reached accuracy values of 75.40 (radial basis function neural network) to 97.92 (Random Trees) for the training set and 64.90 (multilayer perceptron neural network) to 84.14 (Random Trees) for the testing set. The leading parameters predicting prolonged LOS were operation time, ischemia time, the graft used, the ASA score, the intensive care stay, and the pathological stages. The results revealed that patients who had a higher number of harvested lymph nodes (LN) had a lower probability of recurrence but also a greater LOS. However, patients with prolonged LOS were also at greater risk of recurrence, particularly when fewer (LN) were extracted. Further, LOS was more strongly correlated with the overall number of extracted lymph nodes than with the number of positive lymph nodes or the ratio of positive to overall extracted lymph nodes, indicating that particularly unnecessary lymph node extraction might be associated with prolonged LOS.
Conclusions
The results emphasize the need for a closer follow-up of patients who experience prolonged LOS. Prospective trials are warranted to validate the present results.
Background: Oro-antral communication (OAC) is a common complication following the extraction of upper molar teeth. The Archer and the Root Sinus (RS) systems can be used to classify impacted teeth in panoramic radiographs. The Archer classes B-D and the Root Sinus classes III, IV have been associated with an increased risk of OAC following tooth extraction in the upper molar region. In our previous study, we found that panoramic radiographs are not reliable for predicting OAC. This study aimed to (1) determine the feasibility of automating the classification (Archer/RS classes) of impacted teeth from panoramic radiographs, (2) determine the distribution of OAC stratified by classification system classes for the purposes of decision tree construction, and (3) determine the feasibility of automating the prediction of OAC utilizing the mentioned classification systems. Methods: We utilized multiple supervised pre-trained machine learning models (VGG16, ResNet50, Inceptionv3, EfficientNet, MobileNetV2), one custom-made convolutional neural network (CNN) model, and a Bag of Visual Words (BoVW) technique to evaluate the performance to predict the clinical classification systems RS and Archer from panoramic radiographs (Aim 1). We then used Chi-square Automatic Interaction Detectors (CHAID) to determine the distribution of OAC stratified by the Archer/RS classes to introduce a decision tree for simple use in clinics (Aim 2). Lastly, we tested the ability of a multilayer perceptron artificial neural network (MLP) and a radial basis function neural network (RBNN) to predict OAC based on the high-risk classes RS III, IV, and Archer B-D (Aim 3). Results: We achieved accuracies of up to 0.771 for EfficientNet and MobileNetV2 when examining the Archer classification. For the AUC, we obtained values of up to 0.902 for our custom-made CNN. In comparison, the detection of the RS classification achieved accuracies of up to 0.792 for the BoVW and an AUC of up to 0.716 for our custom-made CNN. Overall, the Archer classification was detected more reliably than the RS classification when considering all algorithms. CHAID predicted 77.4% correctness for the Archer classification and 81.4% for the RS classification. MLP (AUC: 0.590) and RBNN (AUC: 0.590) for the Archer classification as well as MLP 0.638) and RBNN (0.630) for the RS classification did not show sufficient predictive capability for OAC. Conclusions: The results reveal that impacted teeth can be classified using panoramic radiographs (best AUC: 0.902), and the classification systems can be stratified according to their relationship to OAC (81.4% correct for RS classification). However, the Archer and RS classes did not achieve satisfactory AUCs for predicting OAC (best AUC: 0.638). Additional research is needed to validate the results externally and to develop a reliable risk stratification tool based on the present findings.
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.
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.
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.