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Advanced cancer is associated with emotional distress, especially depression and feelings of sadness. To date, it is unclear which is the most effective way to address these problems. This review focuses on the effects of psychosocial interventions on the quality of life (QoL) of patients with advanced cancer. It was hypothesised that patients will benefit from psychosocial interventions by improving QoL, especially in the domain of emotional functioning. The review was conducted using systematic review methodology involving a systematic search of the literature published between 1990 and 2002, quality assessment of included studies, systematic data extraction and narrative data synthesis. In all, 10 randomised controlled studies involving 13 trials were included. Overall interventions and outcome measures across studies were heterogeneous. Outcome measures, pertaining to the QoL dimension of emotional functioning, were most frequently measured. A total of 12 trials evaluating behaviour therapy found positive effects on one or more indicators of QoL, for example, depression. The results of the review support recommendation of behaviour therapy in the care of patients with advanced cancer.

Malate occupies a central role in plant metabolism. Its importance in plant mineral nutrition is reflected by the role it plays in symbiotic nitrogen fixation, phosphorus acquisition, and aluminum tolerance. In nitrogen-fixing root nodules, malate is the primary substrate for bacteroid respiration, thus fueling nitrogenase. Malate also provides the carbon skeletons for assimilation of fixed nitrogen into amino acids. During phosphorus deficiency, malate is frequently secreted from roots to release unavailable forms of phosphorus. Malate is also involved with plant adaptation to aluminum toxicity. To define the genetic and biochemical regulation of malate formation in plant nutrition we have isolated and characterized genes involved in malate metabolism from nitrogen-fixing root nodules of alfalfa and those involved in organic acid excretion from phosphorus-deficient proteoid roots of white lupin. Moreover, we have overexpressed malate dehydrogenase in alfalfa in attempts to improve nutrient acquisition. This report is an overview of our efforts to understand and modify malate metabolism, particularly in the legumes alfalfa and white lupin.

SOX2 expression in high-grade cervical intraepithelial neoplasia (CIN3) and cervical squamous cell carcinoma is increased compared to that in the normal cervical epithelium. However, data on the expression and histological distribution of SOX2 in squamous epithelium during progression of CIN are largely lacking. We studied SOX2 expression throughout the epithelium in 53 cases of CIN1, 2, and 3. In general, SOX2 expression increased and expanded from basal/parabasal to the intermediate/superficial compartment during early stages of progression of CIN. An unexpected, specific expression pattern was found in areas classified as CIN2 and CIN3. This pattern was characterized by the absence or low expression of SOX2 in the basal/parabasal compartment and variable levels in the intermediate and superficial compartments. It was significantly associated with CIN3 (p = 0.009), not found in CIN1 and only seen in part of the CIN2 lesions. When the different patterns were correlated with the genetic make-up and presence of HPV, the CIN3-related pattern contained HPV-positive cells in the basal/parabasal cell compartment that were disomic. This is in contrast to the areas exhibiting the CIN1 and CIN2 related patterns, which frequently exhibited aneusomic cells. Based on their SOX2 localisation pattern, CIN1 and CIN2 could be delineated from CIN3. These data shed new light on the pathogenesis and dynamics of progression in premalignant cervical lesions, as well as on the target cells in the epithelium for HPV infection.

Background Results from clinical trials are often slowly implemented. We studied whether participation in multicenter clinical trials improves reported dissemination, convincement, and subsequent implementation of its results. Methods We sent a web-based questionnaire to gynecologists, residents, nurses, and midwives in all obstetrics and gynecology departments in the Netherlands. For nine trials in perinatology, reproductive medicine, and gynecologic oncology, we asked the respondents whether they had knowledge of the results, were convinced by the results, and what percentage of their patients were treated according to the results of these trials. We compared the level of knowledge, convincement, and reported implementation of results in practice for the nine trials for respondents who worked in hospitals that had recruited for a trial with respondents who worked in a hospital that had not recruited for that trial. The reported implementation was restricted to six trials that showed decisive results. Results We analyzed 202 questionnaires from 83 departments in obstetrics and gynecology in the Netherlands (93% of all departments). The percentage of respondents who had worked in a hospital that recruited for a specific study varied between 8% and 71% per study and was 28% on average. The relative risk (RR) for knowledge of the study result for respondents who had worked in a recruiting hospital was for all studies positive and varied between 1.1 and 3.3 (pooled RR: 1.8, 95% confidence interval (CI): 1.7–1.9). In general, health-care workers were convinced of trial results, independent of whether they had worked in a hospital that recruited for a trial or not (pooled RR: 1.02, 95% CI: 0.99–1.05). Reported implementation of trial’s results, that is, less than 20% were treated with unfavorable treatment according to study results, was better in hospitals that had recruited for those trials (pooled RR: 1.1, 95% CI: 1.02–1.19). Conclusion Participation in these multicenter clinical trials was associated with better knowledge about the trial’s results, with a minor improvement of the reported implementation of the study results.

Siglecs are sialic acid-recognising lectins expressed on the cell surface of immune cells that regulate cellular adhesion, antigen uptake and signalling.

Morphine is a widely used opioid analgesic, which shows large differences in clinical response in children, even when aiming for equivalent plasma drug concentrations. Age-dependent brain disposition of morphine could contribute to this variability, as developmental increase in blood-brain barrier (BBB) P-glycoprotein (Pgp) expression has been reported. In addition, age-related pharmacodynamics might also explain the variability in effect. To assess the influence of these processes on morphine effectiveness, a multi-compartment brain physiologically based pharmacokinetic/pharmacodynamic (PB-PK/PD) model was developed in R (Version 3.6.2). Active Pgp-mediated morphine transport was measured in MDCKII-Pgp cells grown on transwell filters and translated by an in vitro-in vivo extrapolation approach, which included developmental Pgp expression. Passive BBB permeability of morphine and its active metabolite morphine-6-glucuronide (M6G) and their pharmacodynamic parameters were derived from experiments reported in literature. Model simulations after single dose morphine were compared with measured and published concentrations of morphine and M6G in plasma, brain extracellular fluid (ECF) and cerebrospinal fluid (CSF), as well as published drug responses in children (1 day– 16 years) and adults. Visual predictive checks indicated acceptable overlays between simulated and measured morphine and M6G concentration-time profiles and prediction errors were between 1 and -1. Incorporation of active Pgp-mediated BBB transport into the PB-PK/PD model resulted in a 1.3-fold reduced brain exposure in adults, indicating only a modest contribution on brain disposition. Analgesic effect-time profiles could be described reasonably well for older children and adults, but were largely underpredicted for neonates. In summary, an age-appropriate morphine PB-PK/PD model was developed for the prediction of brain pharmacokinetics and analgesic effects. In the neonatal population, pharmacodynamic characteristics, but not brain drug disposition, appear to be altered compared to adults and older children, which may explain the reported differences in analgesic effect.

The Gleason score is the strongest correlating predictor of recurrence for prostate cancer, but has substantial inter-observer variability, limiting its usefulness for individual patients. Specialised urological pathologists have greater concordance; however, such expertise is not widely available. Prostate cancer diagnostics could thus benefit from robust, reproducible Gleason grading. We aimed to investigate the potential of deep learning to perform automated Gleason grading of prostate biopsies. In this retrospective study, we developed a deep-learning system to grade prostate biopsies following the Gleason grading standard. The system was developed using randomly selected biopsies, sampled by the biopsy Gleason score, from patients at the Radboud University Medical Center (pathology report dated between Jan 1, 2012, and Dec 31, 2017). A semi-automatic labelling technique was used to circumvent the need for manual annotations by pathologists, using pathologists' reports as the reference standard during training. The system was developed to delineate individual glands, assign Gleason growth patterns, and determine the biopsy-level grade. For validation of the method, a consensus reference standard was set by three expert urological pathologists on an independent test set of 550 biopsies. Of these 550, 100 were used in an observer experiment, in which the system, 13 pathologists, and two pathologists in training were compared with respect to the reference standard. The system was also compared to an external test dataset of 886 cores, which contained 245 cores from a different centre that were independently graded by two pathologists. We collected 5759 biopsies from 1243 patients. The developed system achieved a high agreement with the reference standard (quadratic Cohen's kappa 0·918, 95% CI 0·891–0·941) and scored highly at clinical decision thresholds: benign versus malignant (area under the curve 0·990, 95% CI 0·982–0·996), grade group of 2 or more (0·978, 0·966–0·988), and grade group of 3 or more (0·974, 0·962–0·984). In an observer experiment, the deep-learning system scored higher (kappa 0·854) than the panel (median kappa 0·819), outperforming 10 of 15 pathologist observers. On the external test dataset, the system obtained a high agreement with the reference standard set independently by two pathologists (quadratic Cohen's kappa 0·723 and 0·707) and within inter-observer variability (kappa 0·71). Our automated deep-learning system achieved a performance similar to pathologists for Gleason grading and could potentially contribute to prostate cancer diagnosis. The system could potentially assist pathologists by screening biopsies, providing second opinions on grade group, and presenting quantitative measurements of volume percentages. Dutch Cancer Society.

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training. International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Here, the authors present the results of a biomedical image segmentation challenge, showing that a method capable of performing well on multiple tasks will generalize well to a previously unseen task.

In patients with suspected lymphoma, the tissue biopsy provides lymphoma confirmation, classification, and prognostic factors, including genetic changes. We developed a deep learning algorithm to detect MYC rearrangement in scanned histological slides of diffuse large B-cell lymphoma. The H&E-stained slides of 287 cases from 11 hospitals were used for training and evaluation. The overall sensitivity to detect MYC rearrangement was 0.93 and the specificity 0.52, showing that prediction of MYC translocation based on morphology alone was possible in 93% of MYC-rearranged cases. This would allow a simple and fast prescreening, saving approximately 34% of genetic tests with the current algorithm.