Explore the vast range of titles available.

Face generation from natural language input has rapidly emerged as a pivotal research area in computer vision, bridging the gap between creative design and practical commercial applications. This review paper synthesizes findings from ten seminal works to map the methodological evolution of text-to-face generation, tracing the progression from encoder- decoder architectures and Generative Adversarial Networks (GANs) to the current state-of-the-art dominated by diffusion models. This paper examines how these paradigms address core challenges such as generation fidelity, controllability, and cross-modal alignment. The analysis reveals that while early GAN-based approaches pioneered conditional control, modern diffusion models, often integrated with autoencoders, offer superior stability and detail. Furthermore, this paper explores extensions into multi-modal conditioning using audio and pose, as well as applications in synthetic data generation for privacy-preserving facial recognition. Despite significant advances, critical challenges persist, including ensuring precise semantic alignment, maintaining identity across edits, achieving temporal consistency, and mitigating ethical risks associated with deepfakes. This review concludes by identifying key trends and outlining promising future directions for developing more robust, efficient, and ethically sound face generation systems.

Compassion is a vital component to the achievement of positive health outcomes, particularly in mental health care. The rise of digital technologies may influence the delivery of compassionate care, and thus this relationship between compassion and digital health care needs to be better understood. This scoping review aimed to identify existing digital technologies being used by patients and health professionals in the delivery of mental health care, understand how digital technologies are being used in the delivery of compassionate mental health care, and determine the facilitators of and barriers to digital technology use among patients and health professionals in the delivery of compassionate mental health care. We conducted this scoping review through a search of Cumulative Index to Nursing and Allied Health Literature, Medical Literature Analysis and Retrieval System Online (MEDLINE), MEDLINE In-Process and EPub Ahead of Print, PsycINFO, and Web of Science for articles published from 1990 to 2019. Of the 4472 articles screened, 37 articles were included for data extraction. Telemedicine was the most widely used technology by mental health professionals. Digital technologies were described as facilitating compassionate care and were classified using a conceptual model to identify each digital intersection with compassionate care. Facilitators of and barriers to providing compassionate care through digital technology were identified, including increased safety for providers, health care professional perceptions and abilities, and the use of picture-in-picture feedback to evaluate social cues. Implementing digital technology into mental health care can improve the current delivery of compassionate care and create novel ways to provide compassion. However, as this is a new area of study, mental health professionals and organizations alike should be mindful that compassionate human-centered care is maintained in the delivery of digital health care. Future research could develop tools to facilitate and evaluate the enactment of compassion within digital health care.

IntroductionAs digital technologies become an integral part of mental health care delivery, concerns have risen regarding how this technology may detract from health professionals’ ability to provide compassionate care. To maintain and improve the quality of care for people with mental illness, there is a need to understand how to effectively incorporate technologies into the delivery of compassionate mental health care. The objectives of this scoping review are to: (1) identify the digital technologies currently being used among patients and health professionals in the delivery of mental health care; (2) determine how these digital technologies are being used in the context of the delivery of compassionate care and (3) uncover the barriers to, and facilitators of, digital technology-driven delivery of compassionate mental health care.Methods and analysisSearches were conducted of five databases, consisting of relevant articles published in English between 1990 and 2019. Identified articles will be independently screened for eligibility by two reviewers, first at a title and abstract stage, and then at a full-text level. Data will be extracted and compiled from eligible articles into a data extraction chart. Information collected will include a basic overview of the publication including the article title, authors, year of publication, country of origin, research design and research question addressed. On completion of data synthesis, the authors will conduct a consultation phase with relevant experts in the field.Ethics and disseminationEthical approval is not required for this scoping review. With regards to the dissemination plan, principles identified from the relevant articles may be presented at conferences and an article will be published in an academic journal with study results. The authors also intend to engage interested mental health professionals, health professional educators and patients in a discussion about the study findings and implications for the future.

Dear Editor, We previously developed a novel paradigm of cell activation and signaling-directed （CASD） lineage conversion for direct reprogramming of fibroblasts into cardiac, neural and endothelial precursor cells. This method is based on the transient overexpression of iPSC factors （cell activation, CA） in conjunction with lineage-specific solu ble signals （signaling directed, SD）.

Physician burnout has a direct impact on the delivery of high-quality health care, with health information technology tools such as electronic health records (EHRs) adding to the burden of practice inefficiencies. The aim of this study was to determine the extent of burnout among physicians and learners (residents and fellows); identify significant EHR-related contributors of physician burnout; and explore the differences between physicians and learners with regard to EHR-related factors such as time spent in EHR, documentation styles, proficiency, training, and perceived usefulness. In addition, the study aimed to address gaps in the EHR-related burnout research methodologies by determining physicians' patterns of EHR use through usage logs. This study used a cross-sectional survey methodology and a review of administrative data for back-end log measures of survey respondents' EHR use, which was conducted at a large Canadian academic mental health hospital. Chi-square and Fisher exact tests were used to examine the association of EHR-related factors with general physician burnout. The survey was sent out to 474 individuals between May and June 2019, including physicians (n=407), residents (n=53), and fellows (n=14), and we measured physician burnout and perceptions of EHR stressors (along with demographic and practice characteristics). Our survey included 208 respondents, including physicians (n=176) and learners (n=32). The response rate was 43.2% for physicians (full-time: 156/208, 75.0%; part-time: 20/199, 10.1%), and 48% (32/67) for learners. A total of 25.6% (45/176) of practicing physicians and 19% (6/32) of learners reported having one or more symptoms of burnout, and 74.5% (155/208) of all respondents who reported burnout symptoms identified the EHR as a contributor. Lower satisfaction and higher frustration with the EHRs were significantly associated with perceptions of EHR contributing toward burnout. Physicians' and learners' experiences with the EHR, gathered through open-ended survey responses, identified challenges around the intuitiveness and usability of the technology as well as workflow issues. Metrics gathered from back-end usage logs demonstrated a 13.6-min overestimation in time spent on EHRs per patient and a 5.63-hour overestimation of after-hours EHR time, when compared with self-reported survey data. This study suggests that the use of EHRs is a perceived contributor to physician burnout. There should be a focus on combating physician burnout by reducing the unnecessary administrative burdens of EHRs through efficient implementation of systems and effective postimplementation strategies.

Background Patient engagement strategies in health service delivery have become more common in recent years. However, many healthcare organizations are challenged in identifying the best methods to engage patients in health information technology (IT) initiatives. Engaging with important stakeholders to identify effective opportunities can inform the development of a resource that addresses this issue and supports organizations in their endeavors. The purpose of this paper is to share our experience and lessons learned from applying a novel consensus-building technique in order to identify key elements for effective patient engagement in health IT initiatives. This will be done through a case study approach. Methods Patients, family members of patients, health professionals, researchers, students, vendor representatives and individuals who work in health IT roles in health organizations were engaged through a one-day symposium in Toronto, Canada in September, 2018. During the symposium, the Group Priority Sort technique was used to obtain structured feedback from symposium attendees in the context of small group discussions. Descriptive statistics and a content analysis were undertaken to analyze the data collected through the Group Priority Sort as well as participant feedback following the symposium. Results A total of 37 participants attended the symposium from a variety of settings and organizations. Using the Group Priority Sort technique, 30 topics were classified by priority to be included in a future resource. Participant feedback pertaining to the symposium and research methods was largely positive. Several areas of improvement, such as clarity of items, were identified from this case study. Conclusions The Group Priority Sort technique was an efficient method for obtaining valuable suggestions from a diverse group of stakeholders, including patients and family members. The specific priorities and feedback obtained from the symposium will be incorporated into a resource for healthcare organizations to aid them in engaging patients in health IT initiatives. Additionally, five important considerations were identified when conducting future work with the Group Priority Sort technique and are outlined in this paper.

Transplantation of exogenous dopaminergic neuron （DA neurons） is a promising approach for treating Parkin- son＇s disease （PD）. However, a major stumbling block has been the lack of a reliable source of donor DA neurons. Here we show that a combination of five transcriptional factors Mashl, Ngn2, Sox2, Nurrl, and Pitx3 can directly and effectively reprogram human fibroblasts into DA neuron-like cells. The reprogrammed cells stained positive for various markers for DA neurons. They also showed characteristic DA uptake and production properties. Moreover, they exhibited DA neuron-specific electrophysiological profiles. Finally, they provided symptomatic relief in a rat PD model. Therefore, our directly reprogrammed DA neuron-like cells are a promising source of cell-replacement thera- py for PD.

Health-related virtual reality (VR) applications for patient treatment, rehabilitation, and medical professional training are on the rise. However, there is little guidance on how to select and perform usability evaluations for VR health interventions compared to the supports that exist for other digital health technologies. The purpose of this viewpoint paper is to present an introductory summary of various usability testing approaches or methods that can be used for VR applications. Along with an overview of each, a list of resources is provided for readers to obtain additionally relevant information. Six categories of VR usability evaluations are described using a previously developed classification taxonomy specific to VR environments: (1) cognitive or task walkthrough, (2) graphical evaluation, (3) post hoc questionnaires or interviews, (4) physical performance evaluation, (5) user interface evaluation, and (6) heuristic evaluation. Given the growth of VR in health care, rigorous evaluation and usability testing is crucial in the development and implementation of novel VR interventions. The approaches outlined in this paper provide a starting point for conducting usability assessments for health-related VR applications; however, there is a need to also move beyond these to adopt those from the gaming industry, where assessments for both usability and user experience are routinely conducted.

Probabilistic Logic Programs (PLPs) generalize traditional logic programs and allow the encoding of models combining logical structure and uncertainty. In PLP, inference is performed by summarizing the possible worlds which entail the query in a suitable data structure, and using this data structure to compute the answer probability. Systems such as ProbLog, PITA, etc., use propositional data structures like explanation graphs, BDDs, SDDs, etc., to represent the possible worlds. While this approach saves inference time due to substructure sharing, there are a number of problems where a more compact data structure is possible. We propose a data structure called Ordered Symbolic Derivation Diagram (OSDD) which captures the possible worlds by means of constraint formulas. We describe a program transformation technique to construct OSDDs via query evaluation, and give procedures to perform exact and approximate inference over OSDDs. Our approach has two key properties. Firstly, the exact inference procedure is a generalization of traditional inference, and results in speedup over the latter in certain settings. Secondly, the approximate technique is a generalization of likelihood weighting in Bayesian Networks, and allows us to perform sampling-based inference with lower rejection rate and variance. We evaluate the effectiveness of the proposed techniques through experiments on several problems.

We introduce the State Classification Problem (SCP) for hybrid systems, and present Neural State Classification (NSC) as an efficient solution technique. SCP generalizes the model checking problem as it entails classifying each state \\(s\\) of a hybrid automaton as either positive or negative, depending on whether or not \\(s\\) satisfies a given time-bounded reachability specification. This is an interesting problem in its own right, which NSC solves using machine-learning techniques, Deep Neural Networks in particular. State classifiers produced by NSC tend to be very efficient (run in constant time and space), but may be subject to classification errors. To quantify and mitigate such errors, our approach comprises: i) techniques for certifying, with statistical guarantees, that an NSC classifier meets given accuracy levels; ii) tuning techniques, including a novel technique based on adversarial sampling, that can virtually eliminate false negatives (positive states classified as negative), thereby making the classifier more conservative. We have applied NSC to six nonlinear hybrid system benchmarks, achieving an accuracy of 99.25% to 99.98%, and a false-negative rate of 0.0033 to 0, which we further reduced to 0.0015 to 0 after tuning the classifier. We believe that this level of accuracy is acceptable in many practical applications, and that these results demonstrate the promise of the NSC approach.