Grief and Bereavement in Fathers After the Death of a Child: A Systematic Review.

CONTEXT: The death of a child is devastating, and complicated grief adversely impacts parental physical and psychosocial well-being. Most research currently is centered on bereaved mothers, and the experiences of fathers remains underexplored. OBJECTIVE: We systematically reviewed the literature to characterize the grief and bereavement experiences of fathers after the death of a child. DATA SOURCES: We searched Medline, PsycInfo, Embase, and Cumulative Index to Nursing and Allied Health Literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. STUDY SELECTION: Inclusion criteria encompassed English language articles published between 2007 and 2019 that evaluated the grief and bereavement experiences of fathers after the death of their child. We excluded studies describing paternal bereavement after the death of a child aged older than 21 years, stillbirth, miscarriage, or studies that did not specify age of death. DATA EXTRACTION: Extracted domains included study design, demographics, findings, and quality assessment. RESULTS: We screened 1848 deduplicated titles and abstracts and 139 full articles, yielding 21 articles for inclusion in this analysis. Fathers often avoided discussing their grief with others, returned to work earlier, and used goal-oriented tasks as coping strategies. Intense grief reactions and posttraumatic psychological sequelae diminished over time in mothers yet persisted in fathers. LIMITATIONS: Included studies were primarily descriptive in nature, without ability to ascertain causality. Limited paternal data exists in the literature compared with maternal data. CONCLUSIONS: Despite evolving gender roles, many fathers navigate loss through stoicism, self-isolation, and hard work. For some fathers, these coping mechanisms may be inadequate for navigating grief.

Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design.

One of the main challenges in drug discovery is predicting protein-ligand binding affinity. Recently, machine learning approaches have made substantial progress on this task. However, current methods of model evaluation are overly optimistic in measuring generalization to new targets, and there does not exist a standard data set of sufficient size to compare performance between models. We present a new data set for structure-based machine learning, the CrossDocked2020 set, with 22.5 million poses of ligands docked into multiple similar binding pockets across the Protein Data Bank, and perform a comprehensive evaluation of grid-based convolutional neural network (CNN) models on this data set. We also demonstrate how the partitioning of the training data and test data can impact the results of models trained with the PDBbind data set, how performance improves by adding more lower-quality training data, and how training with docked poses imparts pose sensitivity to the predicted affinity of a complex. Our best performing model, an ensemble of five densely connected CNNs, achieves a root mean squared error of 1.42 and Pearson R of 0.612 on the affinity prediction task, an AUC of 0.956 at binding pose classification, and a 68.4% accuracy at pose selection on the CrossDocked2020 set. By providing data splits for clustered cross-validation and the raw data for the CrossDocked2020 set, we establish the first standardized data set for training machine learning models to recognize ligands in noncognate target structures while also greatly expanding the number of poses available for training. In order to facilitate community adoption of this data set for benchmarking protein-ligand binding affinity prediction, we provide our models, weights, and the CrossDocked2020 set at https://github.com/gnina/models.

Advancing the field of communication research in pediatric oncology: A systematic review of the literature analyzing medical dialogue.

Historically, communication research in pediatric oncology has relied on surveys and interviews, resulting in cross-sectional and retrospective studies constrained by selection, recognition, and recall biases. This systematic review identifies and synthesizes the published literature analyzing primary data from recorded conversations between pediatric oncologists, patients with cancer, and their families, with the following objectives: (1) to identify the extent and content of the evidence base, (2) to describe methodological strategies utilized in the analysis of recorded medical dialogue, (3) to aggregate salient findings, and (4) to generate recommendations for future prospective research related to analysis of medical dialogue in pediatric oncology.

A proposed mechanism for detergent-assisted foam fractionation of lysozyme and cellulase restored with beta-cyclodextrin.

Foam fractionation by itself cannot effectively concentrate hydrophilic proteins such as lysozyme and cellulase. However, the addition of a detergent to a protein solution can increase the foam volume, and thus, the performance of the foam fractionation process. In this article, we propose a possible protein concentration mechanism of this detergent-assisted foam fractionation: A detergent binds to an oppositely charged protein, followed by the detergent-protein complex being adsorbed onto a bubble during aeration. The formation of this complex is inferred by a decrease in surface tension of the detergent-protein solution. The surface tension of a solution with the complex is lower than the surface tension of a protein or a detergent solution alone. The detergent can then be stripped from the adsorbed protein, such as cellulase, by an artificial chaperone such as beta-cyclodextrin. Stripping the detergent from the protein allows the protein to return to its original conformation and to potentially retain all of its original activity following the foam fractionation process. Low-cost alternatives to beta-cyclodextrin such as corn dextrin were tested experimentally to restore the protein activity through detergent stripping, but without success.