Bidirectional Information Exchange Research Articles

Testing the Feasibility of a Digital Point of Care Solution for the Trusted Near Real-Time Bidirectional Exchange of Novel and Informative Adverse Event Information

AbstractA digital point-of-care solution was implemented to test the feasibility of near-real-time bi-directional communication between pharmacovigilance experts (PVEs) and healthcare professionals (HCPs) for exchanging unique and informative adverse event (AE) information. The solution was implemented in a commercially available electronic health record (EHR) system/platform, no direct contact between PVEs and the HCPs was possible. The Clinical Affairs team of the EHR vendor was used as an intermediary to ensure appropriate information was exchanged while protecting HCP and patient privacy. The study yielded 9 drug-event pairs of interest (AEI), 2 of which were confirmed as AEs by the HCP. On average it took 20.6 h to receive initial AEI information and 58.8 h to receive follow-up information, which represents a 96% reduction in time compared to current methods. Both interactions provided unique data that would not have been collected otherwise leading to the PVE being able to appropriately determine a potential causal association. This study successfully demonstrated the feasibility of using a compliant, bi-directional, digitally enabled clinical communication channel at the point of care to complement existing pharmacovigilance activities.

Physician perspectives on communication quality in pediatric care.

We aimed to explore physician perspectives on communication quality across pediatric contexts. We conducted semi-structured interviews over a 4-month period. Purposive sampling was conducted to ensure a broad sampling of pediatricians from multiple subspecialties and practice settings. Interviews were conducted until thematic saturation was reached. An interview guide was created based on existing literature. Interviews were transcribed and analyzed for key themes. Eleven pediatricians enrolled in our study. Following thematic analysis, results were organized into four primary themes: Communication Education, High Quality Communication; Low Quality Communication; and Communication Factors and Barriers. Participants prioritized information transfer as a key aspect of communication quality. They identified communication quality as having wellness implications for clinicians. Participants prioritize information transfer and state that clinicians often neglect bidirectional information exchange. Participants often lacked formal communication skills training which may make it difficult to navigate common barriers such as language and cultural differences, lack of time, and interprofessional dysfunction. Formal communication skills training may help overcome challenges and may enhance physician wellness while improving patient care. While the importance of clinician-parent communication in pediatric settings has been established, little is known about provider perceptions of communication quality. Participants identified information transfer as a key aspect of communication quality, often neglecting bidirectional information exchange. Participants often lacked formal communication skills training which may make it difficult to navigate common barriers such as language and cultural differences, lack of time, and interprofessional dysfunction. Formal communication skills training may help with these challenges and may enhance physician wellness while improving patient care. More work is needed to explore the impact of communication skills training on each of these factors.

MFH‐Net: A Hybrid CNN‐Transformer Network Based Multi‐Scale Fusion for Medical Image Segmentation

ABSTRACTIn recent years, U‐Net and its variants have gained widespread use in medical image segmentation. One key aspect of U‐Net's design is the skip connection, facilitating the retention of detailed information and leading to finer segmentation results. However, existing research often concentrates on enhancing either the encoder or decoder, neglecting the semantic gap between them, and resulting in suboptimal model performance. In response, we introduce Multi‐Scale Fusion module aimed at enhancing the original skip connections and addressing the semantic gap. Our approach fully incorporates the correlation between outputs from adjacent encoder layers and facilitates bidirectional information exchange across multiple layers. Additionally, we introduce Channel Relation Perception module to guide the fused multi‐scale information for efficient connection with decoder features. These two modules collectively bridge the semantic gap by capturing spatial and channel dependencies in the features, contributing to accurate medical image segmentation. Building upon these innovations, we propose a novel network called MFH‐Net. On three publicly available datasets, ISIC2016, ISIC2017, and Kvasir‐SEG, we perform a comprehensive evaluation of the network. The experimental results show that MFH‐Net exhibits higher segmentation accuracy in comparison with other competing methods. Importantly, the modules we have devised can be seamlessly incorporated into various networks, such as U‐Net and its variants, offering a potential avenue for further improving model performance.

Military-Civilian Partnership to Improve Combat Casualty Care Readiness Among Non-physician Providers.

Military-civilian partnerships (MCP) provide a bidirectional exchange of information and trauma best practices. In 2021, Penn Presbyterian Medical Center and the U.S. Navy signed a 3-year memorandum of understanding to embed active duty trauma providers into the Trauma Division to facilitate the training and sustainment of combat casualty care (CCC) skills. To date, there is little evidence to demonstrate the efficacy of military-civilian partnerships in maintaining combat casualty readiness in non-physician trauma providers. We evaluated the impact of combat casualty readiness for non-physician providers by mapping clinical experiences in an urban Level I trauma center against the Defense Health Agency's Joint Trauma Systems (JTS) Clinical Practice Guidelines (CPG). The JTS CPGs provide best practices for CCCand highlight the critical skills providers need to know before deploying to an austere environment. Patient acuity data and specific JTS CPG skills performed by a non-physician providers were collected in their respective specialties for each patient seen between January 2023 to January 2024. Analyses were performed using descriptive statistics via Redcap. A sample of 6 Navy personnel in different specialties: 1 Physician Assistant, 3 Registered Nurses (emergency medicine, perioperative, critical care), and 2 corpsmen (scrub tech and search & rescue/prehospital medic) completed 1299 records on patients treated. In all, 685 (52.7%) were trauma patients and 614 (47.3%) were non-trauma patients. Categories of injuries seen, listed from the most frequent to the least, were as follows: Other (764), Falls (250), Motor Vehicle Crashes (164), Gunshot Wound (126), Stab-related injuries (41). Category 1 skills, defined as "essential to know," were performed in 921 (36.1%) of the patients treated. In Category 2, skills described as "important to know" were performed in 889 (34.8%) of the patients treated. Category 3 skills, identified as "less urgent" as they are rare among trauma patients, were performed in 486 (19.0%) of the patients treated, and 252 (9.8%) required none of the JTS CPG skills. These categories were further broken down based on the frequency of the skills performed. Analysis revealed strengths and identified opportunities to direct clinical experience for underperforming skills. Military-civilian partnerships support CCC readiness. The data presented and the continuation of mapping personnel's clinical experience to military CPGs can gauge readiness in non-physician trauma providers. Notably, several skills in each category were identified as opportunities to modify the clinical exposure of the military provider. These findings indicate that modifications in clinical assignments could enhance active duty combat casualty readiness in these critical skills.

Tactile Sensing and Rendering Patch with Dynamic and Static Sensing and Haptic Feedback for Immersive Communication.

Wearable human-machine interface (HMI) with bidirectional and multimodal tactile information exchange is of paramount importance in teleoperation by providing more intuitive data interpretation and delivery of tactilely related signals. However, the current sensing and feedback devices still lack enough integration and modalities. Here, we present a Tactile Sensing and Rendering Patch (TSRP) that is made of a customized expandable array which consists of a piezoelectric sensing and feedback unit fused with an elastomeric triboelectric multidimensional sensor and its inner pneumatic feedback structure. The primary functional unit of TSRP is mainly featured with a soft silicone substrate with compact multilayer structure integrating static and dynamic multidimensional tactile sensing capabilities, which synergistically leverage both triboelectric and piezoelectric effects. Additionally, based on the air chamber created by the triboelectric sensor and the converse piezoelectric effect, it provides pneumatic and vibrational haptic feedback simultaneously for both static and dynamic perception regeneration. With the aid of the other variants of this unit, the array shaped TSRP is capable of simulating different terrains, geometries, sliding, collisions, and other critical interactive events during teleoperation via skin perception. Moreover, immediate manipulation can be done on TSRP through the tactile sensors. The preliminary demonstration of TSRP interface with a completed control module in robotic teleoperation is provided, which shows the feasibility of assisting certain tasks in a complex environment by direct tactile communication. The proposed device offers a potential method of enabling bidirectional tactile communication with enriched key information for improving interaction efficiency in the fields of robot teleoperation and training.

Transforming smart homes via P2P energy trading using robust forecasting and scheduling framework

With the advent of smart grids, advanced information infrastructures, advanced metering facilities, bidirectional exchange of information, and battery storage home area networks have all transformed the electricity consumption and energy efficiency. There is a significant shift in the energy management structure from the traditional centralized infrastructure to the flexible demand side driven cyber-physical power systems with clean energy and energy storage system. These changes have significantly evolved the home energy management (HEM) space. Consequently, stakeholders must define their responsibilities, create efficient regulatory frameworks, and test out novel commercial strategies. P2P energy trading appears to be a feasible solution in these circumstances, allowing users to trade electricity with one another before becoming completely reliant on the utility. P2P energy trading offers a more stable platform for energy trade by facilitating the exchange of energy between prosumers and consumers. This research proposes a novel demand and generation prediction techniques of P2P HEMS for optimal energy using the Multi-Objective Optimization model. An enhanced Wild Horse Optimization technique was first used to summarize historical records' qualities. Then, the Bi-LSTM is used to predict the demand and generation values. Furthermore, a Grasshopper optimization (GHO) approach is employed to fine-tune the model's hyperparameters. The P2P HEMS demand and generation prediction framework is offered with a probabilistic and fault evaluation that upholds load flow balance between need and supply for continuous operations. It results in an intelligent community transforming cities into smart ones, opening new avenues for scientific research in terms of technological developments.

The mechanism of ITGB4 in tumor migration and invasion.

Integrin β4 (ITGB4) is a transmembrane protein that functions as a mechanosensor, mediating the bidirectional exchange of information between the intracellular and extracellular matrices. ITGB4 plays a critical role in cell adhesion, migration, and signaling. Numerous studies have implicated ITGB4 as a key facilitator of tumor migration and invasion. This review provides a foundational description of the mechanisms by which ITGB4 regulates tumor migration and invasion through pathways involving focal adhesion kinase (FAK), protein kinase B (AKT), and matrix metalloproteinases (MMPs). These mechanisms encompass epithelial-mesenchymal transition (EMT), phosphorylation, and methylation of associated molecules. Additionally, this review explores the role of ITGB4 in the migration and invasion of prevalent clinical tumors, including those of the digestive system, breast, and prostate.

Improving emergency and disaster response management through bidirectional communication between citizens and decision-makers – A field test on the coordination of spontaneous volunteers

Cooperation between different stakeholders plays an important role in managing crises and disasters. The software tools used must also support cooperation and the mutual exchange of information. The bidirectional exchange of information between the population and official agencies poses a particular challenge. In this paper, we present a software demonstrator for the coordination of spontaneous volunteers and its evaluation as part of an overarching research project to gain design knowledge for the development of interconnected systems in disaster management. We describe how the system demonstrator was instantiated on the basis of our existing design knowledge from technical requirements and reference architecture and show the respective interfaces for the stakeholders involved. As the main contribution of the paper, we describe the implementation of a field test to evaluate the demonstrator and present the results in terms of usability and perceived usefulness, which we obtained using a methodologically sound questionnaire.

Using mHealth to Improve Communication in Adult Day Services Around the Needs of People With Dementia: Mixed Methods Assessment of Acceptability and Feasibility.

Adult day services (ADS) provide community-based health care for older adults with complex chronic conditions but rely on outdated methods for communicating users' health information with providers. CareMOBI, a novel mobile health (mHealth) app, was developed to address the need for a technological platform to improve bidirectional information exchange and communication between the ADS setting and providers. This study aims to examine the feasibility and acceptability of CareMOBI in the ADS setting. A concurrent-triangulation mixed methods design was used, and participants were client-facing ADS staff members, including direct care workers (paid caregivers), nurses, and social workers. Interviews were conducted to describe barriers and facilitators to the adoption of the CareMOBI app. The acceptability of the app was measured using an adapted version of the Technology Acceptance Model questionnaire. Data were integrated into 4 themes as anchors of an informational matrix: ease of use, clinical value, fit within workflow, and likelihood of adoption. A mix of ADS staff (N=22) participated in the study. Participants reported high levels of acceptability across the 4 domains. Qualitative findings corroborated the questionnaire results; participants viewed the app as useful and were likely to implement CareMOBI in their practice. However, participants expressed a need for proper training and technical support throughout the implementation process. The CareMOBI app has the potential to improve care management in the ADS setting by promoting effective communication through an easy-to-use and portable method. While the integration of CareMOBI is acceptable and feasible, developing role-specific training modules and technical assistance programs is imperative for successful implementation within the ADS setting.

The Annual Wellness Visit Health Risk Assessment: Potential of Patient Portal-Based Completion and Patient-Oriented Education and Support.

Patient portals are secure online platforms that allow patients to perform electronic health management tasks and engage in bidirectional information exchange with their care team. Some health systems administer Medicare Annual Wellness Visit (AWV) health risk assessments through the patient portal. Scalable opportunities from portal-based administration of risk assessments are not well understood. Our objective is 2-fold-to understand who receives vs misses an AWV and health risk assessment and explore who might be missed with portal-based administration. This is an observational study of electronic medical record and patient portal data (10/03/2021-10/02/2022) for 12756 primary care patients 66+ years from a large academic health system. Two-thirds (n = 8420) of older primary care patients incurred an AWV; 81.0% of whom were active portal users. Older adults who were active portal users were more likely to incur AWV than those who were not, though portal use was high in both groups (81.0% with AWV vs 76.8% without; p < .001). Frequently affirmative health risk assessment categories included falls/balance concerns (44.2%), lack of a documented advanced directive (42.3%), sedentary behaviors (39.9%), and incontinence (35.1%). Mean number of portal messages over the 12-month observation period varied from 7.2 among older adults affirmative responses to concerns about safety at home to 13.8 for older adults who reported difficulty completing activities of daily living. Portal messaging varied more than 2-fold across affirmative health risk categories and were marginally higher with greater number affirmative (mean = 13.8 messages/year no risks; 19.6 messages/year 10+ risks). Most older adults were active portal users-a group more likely to have incurred a billed AWV. Efforts to integrate AWV risk assessments in the patient portal may streamline administration and scalability for dissemination of tailored electronically mediated preventive care but must attend to equity issues.

Mitigating healthcare supply chain challenges under disaster conditions: a holistic AI-based analysis of social media data

A key advantage of social media is the real-time exchange of views with large communities. In disaster situations, such bidirectional information exchange is most useful to victims and support teams, especially in communications with authorities, volunteers, and the public. This paper addresses challenges faced by the healthcare supply chain during the COVID-19 pandemic with analyses of Twitter data using an Artificial Intelligence-driven multi-step approach. We investigate tweets for information about healthcare supply chains, such as the scarcity of testing kits, oxygen cylinders, and hospital beds during the pandemic. We deployed machine learning to classify such tweets into imperative and non-imperative categories based on need severity. The study sought to predict the location of victims requesting help based on their imperative tweets if geo-tag information was missing. The proposed approach used four steps: (1) keyword-based informative tweet search, (2) raw tweet pre-processing, (3) content analysis to identify tweet trends, public sentiment, topics related to healthcare supply chain challenges, and crisis classification to label imperative and non-imperative tweets, (4) locating the point-of-crisis from imperative tweets to facilitate coordination of help operations. The pre-processing of tweets, trend analysis, and sentiment analysis relied on natural language processing and machine learning for topic modelling (K-mean clustering), crisis classification (random forest), and point-of-crisis detection (Markov chain). Results demonstrate the potential to capture significant, timely, and actionable information on healthcare supply chain challenges to respond quickly and appropriately in a pandemic.

Biology-Technology Interfaces – Refining the Core Principle of Biointelligent Systems

Biological transformation is a trend with high innovation potential and can be classified into three modes: bioinspiration, biointegration, and biointeraction. Particularly the latter describes the convergence of biology, computer science, and technology and aims to deploy so-called biointelligent systems that allow digital interaction between biological and technical subsystems. Biology-technology interfaces (BTI) form the core of biointelligent systems by connecting the living and the technical system. BTI include sensors and actuators that realize bidirectional information exchange. In this paper, we compare approaches found in literature and by conducting workshops with experts from different disciplines to refine the notion of BTI and illustrate this in examples. We found that the field has grown significantly in importance over the last 20 years and formed three application clusters from the technologies found, namely biocomputing, neuro-interfaces, and general control systems for (bio)production processes. Although many technologies are still at an early stage of development, great potential is apparent. In the future, their application in production should be evaluated from a technical and economic point of view and systematically pursued to foster biological transformation in the industry effectively.

Abstract A054: Leveraging the power of Hope Leaders for enhancing diversity in clinical trials via a bi-directional cancer health information model

Abstract Introduction: Despite increasing recognition of the need for diversity in clinical trials, participation from racial and ethnic minorities, particularly from low socioeconomic communities, remains dismally low. This underrepresentation hinders the generalizability of trial results and perpetuates health disparities. Low socioeconomic environments, characterized by economic instability, restricted healthcare access, food scarcity, and deficient educational resources, exacerbate these challenges. Residents of such communities often lean on "Hope Leaders" - trusted community figures - as a strategy to navigate through these adversities. Recognizing the pivotal role of these Hope Leaders, we advocate for a community-centered bi-directional cancer health information flow model, which positions these Leaders at the core of community engagement endeavors. This model fosters a bi-directional health information exchange that synergizes Hope Leaders with the scientific community, thereby promoting improved health equity within clinical intervention research. The implementation of this model commences with a pre-engagement phase, a respectful approach where we familiarize ourselves with the community and its dynamics, without imposing any specific demands or expectations. Approach: The University of Illinois Cancer Center has had 6 community engagement specialists that have engaged with community members through numerous pre-engagement events that encompass activities ranging from mental health, cancer prevention, survivorship events, education, health fairs and community meetings. Based on these insights, we established a dedicated working group to identify hope leaders and to create targeted engagement strategies for specific areas of our catchment region through the bi-directional cancer health information flow model. These strategies are tailored to address the unique challenges and barriers these communities face regarding enrollment in cancer clinical trials. Additionally, the community-driven health communication model was implemented to identify Hope Leaders and engage them with cancer researchers as a part of our phase I activities for this model. Conclusion: This innovative approach aims to leverage the unique position of Hope Leaders in underserved communities to amplify community voices, improve engagement, and ultimately, increase minority representation in cancer clinical trials. The model provides a promising strategy to address the persistent disparity in clinical trial participation, facilitating more equitable and inclusive research outcomes. Citation Format: Joseph Dufraine, Yamile Molina, Jeanette Gonzalez, Maria Olivero, Theresa Mobley, Saria Lofton, Dedra Reis. Leveraging the power of Hope Leaders for enhancing diversity in clinical trials via a bi-directional cancer health information model [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A054.

Validation of a measure to assess decision-making autonomy in family planning services in three low- and middle-income countries: The Family Planning Autonomous Decision-Making scale (FP-ADM).

Integrating measures of respectful care is an important priority in family planning programs, aligned with maternal health efforts. Ensuring women can make autonomous reproductive health decisions is an important indicator of respectful care. While scales have been developed and validated in family planning for dimensions of person-centered care, none focus specifically on decision-making autonomy. The Mothers Autonomy in Decision-Making (MADM) scale measures autonomy in decision-making during maternity care. We adapted the MADM scale to measure autonomy surrounding a woman's decision to use a contraceptive method within the context of contraceptive counselling. This study presents a psychometric validation of the Family Planning Autonomous Decision-Making (FP-ADM) scale using data from Argentina, Ghana, and India. We used cross-sectional data from women in four subnational areas in Argentina (n = 890), Ghana (n = 1,114), and India (n = 1,130). In each area, 20 primary sampling units (PSUs) were randomly selected based on probability proportional to size. Households were randomly selected in Ghana and India. In Argentina, all facilities providing reproductive and maternal health services within selected PSUs were included and women were randomly selected upon exiting the facility. Interviews were conducted with a sample of 360 women per district. In total, 890 women completed the FP-ADM in Argentina, 1,114 in Ghana and 1,130 in India. To measure autonomous decision-making within FP service delivery, we adapted the items of the MADM scale to focus on family planning. To assess the scale's psychometric properties, we first examined the eigenvalues and conducted a parallel analysis to determine the number of factors. We then conducted exploratory factor analysis to determine which items to retain. The resulting factors were then identified based on the corresponding items. Internal consistency reliability was assessed with Cronbach's alpha. We assessed both convergent and divergent construct validity by examining associations with expected outcomes related to the underlying construct. The Eigenvalues and parallel analysis suggested a two-factor solution. The two underlying dimensions of the construct were identified as "Bidirectional Exchange of Information" (Factor 1) and "Empowered Choice" (Factor 2). Cronbach's alpha was calculated for the full scale and each subscale. Results suggested good internal consistency of the scale. There was a strong, significant positive association between whether a woman expressed satisfaction with quality of care received from the healthcare provider and her FP-ADM score in all three countries and a significant negative association between a woman's FP-ADM score and her stated desire to switch contraceptive methods in the future. Our results suggest the FP-ADM is a valid instrument to assess decision-making autonomy in contraceptive counseling and service delivery in diverse low- and middle-income countries. The scale evidenced strong construct, convergent, and divergent validity and high internal consistency reliability. Use of the FP-ADM scale could contribute to improved measurement of person-centered family planning services.

Optimization of unmanned aerial vehicle inspection strategy for infrastructure based on model-enabled diagnostics and prognostics

The use of unmanned aerial vehicles (UAVs) for structural health inspection has become a promising technique to perform labor-intensive, accessibility-challenged, and sometimes dangerous inspection tasks. This paper presents a novel physics-informed UAV inspection planning framework for infrastructure structural health assessment based on model-based diagnostics and prognostics enabled by physics-based probabilistic analysis. It bridges the gap between UAV mission planning and inspection with model-based probabilistic analysis, by allowing bidirectional information exchange, namely (1) structural damage state diagnostics using UAV inspection data and (2) UAV inspection optimization through model-based failure prognostics. Based on the bidirectional communication, the impacts of the three key UAV inspection parameters (i.e., inspection distance, inspection interval, and critical maintenance threshold) on structural life-cycle cost are analyzed. The optimization of key UAV inspection parameters is achieved by minimizing the cost per unit time (CPUT) through model-based pre-posterior analysis. In this analysis, synthetic observations are generated using predictive models according to the prior distributions of various uncertainty sources (e.g., detection rate, damage state evolution, etc.). The generated synthetic observations are then used to obtain the posterior distributions of uncertain parameters, enabling the integration of prior information and Bayesian model updating into inspection optimization through a cost function. The proposed model offers a robust method to accommodate the inherent uncertainties in failure prognostics, leading to a more effective optimization of the UAV inspection parameters. The practical application of the framework is demonstrated through a miter gate example. The results show that the proposed method is able to efficiently determine the optimal UAV inspection parameters and continuously update the information model.

Frozen Section Timeout: Pilot Study to Reconcile Margins Using 3D Resected Specimen and Defect Scans.

Opportunities exist to improve intraoperative communication and documentation of resection margin details. We instituted a "frozen section timeout" that centers around visualization of the paired resection specimen and surgical defect-facilitating effective, bidirectional exchange of information. We designed an interactive form for use during the "frozen section timeout" including annotated 3D virtual models of the resected specimen and surgical defect, plus a "line-item" table for primary and supplemental margin results. The "timeout" was conducted over a Zoom call between the operating room and frozen section laboratory. The form was simultaneously projected and discussed while all members of the surgical care team stopped activities. Nurses, co-surgeons, and all other members of the surgical team were encouraged to take part in this process. Twenty-six frozen section timeouts were conducted during head and neck surgeries in the Department of Otolaryngology at Mount Sinai West Hospital. These timeouts were facilitated by the lead surgeon, and all other activities were halted to ensure that critical information was shared, documented, and agreed upon. During the timeout, the annotated specimen and defect scans were displayed, clearly demonstrating the at-risk margins and the corresponding location and breadth of supplemental margins harvested. Incorporating a frozen section timeout can improve intraoperative communication, increase transparency, and potentially eliminate uncertainty regarding margin status and tumor clearance. Visualization of at-risk margins and the corresponding location and breadth of supplemental margins promises an unprecedented level of documentation and understanding. This novel technique can establish a new and improved standard of care. NA Laryngoscope, 134:725-731, 2024.

P-219 Single-follicle spatial proteomics of the whole ovary by MALDI mass spectrometry imaging: towards the identification of protein-markers of the follicle's quality

Abstract Study question Can we improve our knowledge of the single-follicle proteome signature by MALDI mass spectrometry imaging? Summary answer We identified follicle-type proteins that function during follicle growth (NUMA1, TPM2), GV-to-MII transition (SFPQ, ACTBL, MARCS, NUCL), ovulation (GELS, CO1A2) and preimplantation development (TIF1B, KHDC3). What is known already The acquisition of oocytes developmental competence occurs through a bidirectional exchange of molecular information between the oocyte, the companion follicle cells, the stroma, and the vascular network. This reciprocal relationship is still poorly understood and particularly scarce is our knowledge of the proteins that are playing a regulative role during folliculogenesis. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) is a powerful tool that allows the study of the proteomic landscape of small morphological structures inside a tissue, but in the field of reproduction has been only used to analyse rodent spermatogenesis. Study design, size, duration Nano-scale liquid chromatography-electrospray ionisation-tandem mass spectrometry (nLC-ESI-MS/MS) was combined with MALDI-MSI to identify the proteomic landscape and to map the changes occurring throughout folliculogenesis. We performed single-follicle spatial proteomics on all the follicles, from the secondary to the fully-grown preovulatory, present in histological serial sections of an entire prepubertal 25-day-old mouse ovary. Cross-referencing our results with those obtained by previous proteomics and transcriptomics analyses allowed to strengthen their significance. Participants/materials, setting, methods A single 25-day-old mouse ovary was fixed in 10% formalin, dehydrated, embedded in paraffin, and sectioned to obtain 133 6-μm serial sections. After paraffin removal, trypsin digestion and matrix deposition, mass spectra were acquired using a rapifleX MALDI Tissuetyper™ (Bruker) with a raster sampling of 20 μm in both x and y axes. Then, tryptic peptides were extracted from sections and analysed with nLC-ESI-MS/MS using a Dionex-UltiMate-3000 LC-nano-system coupled with an Impact HD™ UHR-QqToF (Bruker). Main results and the role of chance This study proposes a spatial proteomics workflow to investigate the proteome of a whole prepubertal 25-day-old mouse ovary, preserving the spatial relationship of the peptides in this complex histological context. A total of 401 proteins were identified by nLC-ESI-MS/MS, 69 with a known function in ovary biology. Enrichment analysis highlighted significant KEGG and Reactome pathways, with apoptosis, developmental biology, PI3K-Akt, epigenetic regulation of gene expression, and extracellular matrix organisation being well represented. Then, correlating these data with the spatial information provided by MALDI-MSI on 276 follicles highlighted 94 proteins that were detected throughout the secondary to the pre-ovulatory transition. Of these, 37 proteins showed a gradual quantitative change during follicle differentiation, comprising 10 with a known role in follicle growth (NUMA1, TPM2), oocyte GV-to-MII transition (SFPQ, ACTBL, MARCS, NUCL), ovulation (GELS, CO1A2) and preimplantation development (TIF1B, KHDC3). The proteome landscape identified includes molecules of known function in the ovary, but also those whose specific role is emerging. Altogether, this work demonstrates the utility of performing spatial proteomics in the context of the ovary and offers sound bases for more in-depth investigations that aim to further unravel its spatial proteome. Limitations, reasons for caution A limitation of MALDI-MSI is its 20 µm/pixel resolution which 1) leads to the acquisition of spectra comprising follicular and extra-follicular tissue, particularly with small follicles (15-30µm in diameter); and 2) does not distinguish among the different follicular cell types, thus precluding its application for single-cell in situ proteomics. Wider implications of the findings MALDI-MSI is a potent technology to study the proteome of ovary specimens, but it could also be used for the analysis of their lipidic or metabolic profiles. Our pipelline aim to identify protein-markers of the follicle's quality and, in turn, of the oocyte's developmental competence. Trial registration number not appplicable

MI-CAT: A transformer-based domain adaptation network for motor imagery classification

Due to its convenience and safety, electroencephalography (EEG) data is one of the most widely used signals in motor imagery (MI) brain–computer interfaces (BCIs). In recent years, methods based on deep learning have been widely applied to the field of BCIs, and some studies have gradually tried to apply Transformer to EEG signal decoding due to its superior global information focusing ability. However, EEG signals vary from subject to subject. Based on Transformer, how to effectively use data from other subjects (source domain) to improve the classification performance of a single subject (target domain) remains a challenge. To fill this gap, we propose a novel architecture called MI-CAT. The architecture innovatively utilizes Transformer’s self-attention and cross-attention mechanisms to interact features to resolve differential distribution between different domains. Specifically, we adopt a patch embedding layer for the extracted source and target features to divide the features into multiple patches. Then, we comprehensively focus on the intra-domain and inter-domain features by stacked multiple Cross-Transformer Blocks (CTBs), which can adaptively conduct bidirectional knowledge transfer and information exchange between domains. Furthermore, we also utilize two non-shared domain-based attention blocks to efficiently capture domain-dependent information, optimizing the features extracted from the source and target domains to assist in feature alignment. To evaluate our method, we conduct extensive experiments on two real public EEG datasets, Dataset IIb and Dataset IIa, achieving competitive performance with an average classification accuracy of 85.26% and 76.81%, respectively. Experimental results demonstrate that our method is a powerful model for decoding EEG signals and facilitates the development of the Transformer for brain–computer interfaces (BCIs).

Smartphone-Based Remote Monitoring for Chronic Heart Failure: Mixed Methods Analysis of User Experience From Patient and Nurse Perspectives.

Community-based management by heart failure specialist nurses (HFSNs) is key to improving self-care in heart failure with reduced ejection fraction. Remote monitoring (RM) can aid nurse-led management, but in the literature, user feedback evaluation is skewed in favor of the patient rather than nursing user experience. Furthermore, the ways in which different groups use the same RM platform at the same time are rarely directly compared in the literature. We present a balanced semantic analysis of user feedback from patient and nurse perspectives of Luscii, a smartphone-based RM strategy combining self-measurement of vital signs, instant messaging, and e-learning. This study aims to (1) evaluate how patients and nurses use this type of RM (usage type), (2) evaluate patients' and nurses' user feedback on this type of RM (user experience), and (3) directly compare the usage type and user experience of patients and nurses using the same type of RM platform at the same time. We performed a retrospective usage type and user experience evaluation of the RM platform from the perspective of both patients with heart failure with reduced ejection fraction and the HFSNs using the platform to manage them. We conducted semantic analysis of written patient feedback provided via the platform and a focus group of 6 HFSNs. Additionally, as an indirect measure of tablet adherence, self-measured vital signs (blood pressure, heart rate, and body mass) were extracted from the RM platform at onboarding and 3 months later. Paired 2-tailed t tests were used to evaluate differences between mean scores across the 2 timepoints. A total of 79 patients (mean age 62 years; 35%, 28/79 female) were included. Semantic analysis of usage type revealed extensive, bidirectional information exchange between patients and HFSNs using the platform. Semantic analysis of user experience demonstrates a range of positive and negative perspectives. Positive impacts included increased patient engagement, convenience for both user groups, and continuity of care. Negative impacts included information overload for patients and increased workload for nurses. After the patients used the platform for 3 months, they showed significant reductions in heart rate (P=.004) and blood pressure (P=.008) but not body mass (P=.97) compared with onboarding. Smartphone-based RM with messaging and e-learning facilitates bilateral information sharing between patients and nurses on a range of topics. Patient and nurse user experience is largely positive and symmetrical, but there are possible negative impacts on patient attention and nurse workload. We recommend RM providers involve patient and nurse users in platform development, including recognition of RM usage in nursing job plans.

Regulative development as a model for origin of life and artificial life studies

Using the formal framework of the Free Energy Principle, we show how generic thermodynamic requirements on bidirectional information exchange between a system and its environment can generate complexity. This leads to the emergence of hierarchical computational architectures in systems that operate sufficiently far from thermal equilibrium. In this setting, the environment of any system increases its ability to predict system behavior by “engineering” the system towards increased morphological complexity and hence larger-scale, more macroscopic behaviors. When seen in this light, regulative development becomes an environmentally-driven process in which “parts” are assembled to produce a system with predictable behavior. We suggest on this basis that life is thermodynamically favorable and that, when designing artificial living systems, human engineers are acting like a generic “environment”.