Accuracy and consistency of online large language model-based artificial intelligence chat platforms in answering patients' questions about heart failure.

BACKGROUND: Heart failure (HF) is a prevalent condition associated with significant morbidity. Patients may have questions that they feel embarrassed to ask or will face delays awaiting responses from their healthcare providers which may impact their health behavior. We aimed to investigate the potential of large language model (LLM) based artificial intelligence (AI) chat platforms in complementing the delivery of patient-centered care. METHODS: Using online patient forums and physician experience, we created 30 questions related to diagnosis, management and prognosis of HF. The questions were posed to two LLM-based AI chat platforms (OpenAI's ChatGPT-3.5 and Google's Bard). Each set of answers was evaluated by two HF experts, independently and blinded to each other, for accuracy (adequacy of content) and consistency of content. RESULTS: ChatGPT provided mostly appropriate answers (27/30, 90%) and showed a high degree of consistency (93%). Bard provided a similar content in its answers and thus was evaluated only for adequacy (23/30, 77%). The two HF experts' grades were concordant in 83% and 67% of the questions for ChatGPT and Bard, respectively. CONCLUSION: LLM-based AI chat platforms demonstrate potential in improving HF education and empowering patients, however, these platforms currently suffer from issues related to factual errors and difficulty with more contemporary recommendations. This inaccurate information may pose serious and life-threatening implications for patients that should be considered and addressed in future research.

Investigating the stretch-shortening cycle fatigue response to a high-intensity stressful phase of training in collegiate men's basketball.

Introduction: While using force-plate derived measures of vertical jump performance, reflective of stretch-shortening-cycle (SSC) efficiency is common practice in sport science, there is limited evidence as to which tests and measures may be most sensitive toward neuromuscular fatigue. The aim of this study was to explore the SSC fatigue response to a one-week high-intensity fatiguing phase of training in National Collegiate Athletic Association (NCAA) Division-I basketball players. Methods: The study timeline consisted of three weeks of baseline measures, one week of high-intensity training, and two weeks of follow-up testing. Countermovement jumps (CMJ) and 10-5 hop tests were performed at baseline, as well as at two time-points during, and three time-points following the fatiguing training period, allowing for performance-comparisons with baseline. Results: Compared to the weekly training sum at baseline, during the high intensity training phase, athletes were exposed to very large increases in selected external load metrics (ES = 1.44-3.16), suggesting that athletes experienced fatigue acutely, as well as potential longer lasting reductions in performance. Vertical jump data suggested that in the CMJ, traditional metrics such as jump height, as well as metrics reflecting kinetic outputs and movement strategies, were sensitive to the stark increase in high-intensity training exposure. The 10-5 hop test suggested a fatigue-induced loss of tolerance to ground impact reflected by performance reductions in metrics related to jump height and reactive strength qualities. Discussion: These findings emphasize that when monitoring neuromuscular fatigue, variables and assessments may not be looked at individually, but rather as part of a more global monitoring approach.

Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.

BACKGROUND: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems. OBJECTIVE: To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score. DESIGN: Retrospective, cohort study. PARTICIPANTS: Data from 6764 adults with HF were abstracted from EHRs at a large integrated health system from 1/1/10 to 12/31/19. MAIN MEASURES: One-year survival from time of first cardiology or primary care visit was estimated using MARKER-HF, SHFM, and MAGGIC. Discrimination was measured by the area under the receiver operating curve (AUC). Calibration was assessed graphically. KEY RESULTS: Compared to MARKER-HF, both SHFM and MAGGIC required a considerably larger amount of data engineering and imputation to generate risk score estimates. MARKER-HF, SHFM, and MAGGIC exhibited similar discriminations with AUCs of 0.70 (0.69-0.73), 0.71 (0.69-0.72), and 0.71 (95% CI 0.70-0.73), respectively. All three scores showed good calibration across the full risk spectrum. CONCLUSIONS: These findings suggest that MARKER-HF, which uses readily available clinical and lab measurements in the EHR and required less imputation and data engineering than SHFM and MAGGIC, is an easier tool to identify high-risk patients in ambulatory clinics who could benefit from referral to a HF specialist.

Vertical Jump Neuromuscular Performance Characteristics Determining On-Court Contribution in Male and Female NCAA Division 1 Basketball Players.

While various quantifiable physical attributes have been found to contribute to athletes' performance, there is a lack of scientific literature focused on examining how they relate to success during competition performance. The aim of this study was to investigate different countermovement jump (CMJ)-derived force-time characteristics and their utility in distinguishing high from low performers within a measure of on-court contribution (i.e., minutes per game played). Twenty-nine collegiate athletes (n = 15 males and n = 14 females) volunteered to participate in this investigation and performed CMJs on dual force plates sampling at 1000 Hz, weekly over the course of their basketball season. The athletes' average of their three best test-days across the season was used for further analysis. To identify their on-court contribution, athletes were divided into groups with high and low minutes per game, based on a median-split analysis. The findings suggest that at the overall group level (i.e., both genders), the modified reactive strength index (mRSI) and braking rate of force development (RFD) revealed the greatest between-group magnitudes of difference, with athletes playing more minutes per game showing greater performance. At the team-specific level, the braking RFD, average braking velocity, and mRSI were shown to be the greatest differentiators between groups for the men's team. The women's high-minutes group displayed greater magnitudes of mRSI and jump height. By identifying the neuromuscular qualities seen in top performers within their respective populations, the attributed physical performance underpinning these qualities may be identified, providing practitioners with insights into physical performance qualities and training methodologies that have the potential to influence basketball performance.

Changes in countermovement jump force-time characteristic in elite male basketball players: A season-long analyses.

Basketball is a sport that is characterized by various physical performance parameters and motor abilities such as speed, strength, and endurance, which are all underpinned by an athlete's efficient use of the stretch-shortening cycle (SSC). A common assessment to measure SSC efficiency is the countermovement jump (CMJ). When performed on a force plate, a plethora of different force-time metrics may be gleaned from the jump task, reflecting neuromuscular performance characteristics. The aim of this study was to investigate how different CMJ force-time characteristics change across different parts of the athletic year, within a sample of elite collegiate male basketball players. Twelve basketball players performed CMJ's on near-weekly basis, combining for a total of 219 screenings. The span of testing was broken down into four periods: pre-season, non-conference competitive period, conference competitive period, and post-season competitive period. Results suggest that basketball players were able to experience improvements and maintenance of performance with regards to various force-time metrics, transitioning from the pre-season period into respective later phases of the in-season period. A common theme was a significant improvement between the pre-season period and the non-conference period. Various force-time metrics were subject to change, while outcome metrics such as jump height remained unchanged, suggesting that practitioners are encouraged to more closely monitor how different force-time characteristics change over extended periods of time.

Deep Learning for Cardiovascular Imaging: A Review.

Importance: Artificial intelligence (AI), driven by advances in deep learning (DL), has the potential to reshape the field of cardiovascular imaging (CVI). While DL for CVI is still in its infancy, research is accelerating to aid in the acquisition, processing, and/or interpretation of CVI across various modalities, with several commercial products already in clinical use. It is imperative that cardiovascular imagers are familiar with DL systems, including a basic understanding of how they work, their relative strengths compared with other automated systems, and possible pitfalls in their implementation. The goal of this article is to review the methodology and application of DL to CVI in a simple, digestible fashion toward demystifying this emerging technology. Observations: At its core, DL is simply the application of a series of tunable mathematical operations that translate input data into a desired output. Based on artificial neural networks that are inspired by the human nervous system, there are several types of DL architectures suited to different tasks; convolutional neural networks are particularly adept at extracting valuable information from CVI data. We survey some of the notable applications of DL to tasks across the spectrum of CVI modalities. We also discuss challenges in the development and implementation of DL systems, including avoiding overfitting, preventing systematic bias, improving explainability, and fostering a human-machine partnership. Finally, we conclude with a vision of the future of DL for CVI. Conclusions and Relevance: Deep learning has the potential to meaningfully affect the field of CVI. Rather than a threat, DL could be seen as a partner to cardiovascular imagers in reducing technical burden and improving efficiency and quality of care. High-quality prospective evidence is still needed to demonstrate how the benefits of DL CVI systems may outweigh the risks.

Microporogen-Structured Collagen Matrices for Embedded Bioprinting of Tumor Models for Immuno-Oncology.

Embedded bioprinting enables the rapid design and fabrication of complex tissues that recapitulate in vivo microenvironments. However, few biological matrices enable good print fidelity, while simultaneously facilitate cell viability, proliferation, and migration. Here, a new microporogen-structured (µPOROS) matrix for embedded bioprinting is introduced, in which matrix rheology, printing behavior, and porosity are tailored by adding sacrificial microparticles composed of a gelatin-chitosan complex to a prepolymer collagen solution. To demonstrate its utility, a 3D tumor model is created via embedded printing of a murine melanoma cell ink within the µPOROS collagen matrix at 4 °C. The collagen matrix is subsequently crosslinked around the microparticles upon warming to 21 °C, followed by their melting and removal at 37 °C. This process results in a µPOROS matrix with a fibrillar collagen type-I network akin to that observed in vivo. Printed tumor cells remain viable and proliferate, while antigen-specific cytotoxic T cells incorporated in the matrix migrate to the tumor site, where they induce cell death. The integration of the µPOROS matrix with embedded bioprinting opens new avenues for creating complex tissue microenvironments in vitro that may find widespread use in drug discovery, disease modeling, and tissue engineering for therapeutic use.

Noncanonical role of singleminded-2s in mitochondrial respiratory chain formation in breast cancer.

Dysregulation of cellular metabolism is a hallmark of breast cancer progression and is associated with metastasis and therapeutic resistance. Here, we show that the breast tumor suppressor gene SIM2 promotes mitochondrial oxidative phosphorylation (OXPHOS) using breast cancer cell line models. Mechanistically, we found that SIM2s functions not as a transcription factor but localizes to mitochondria and directly interacts with the mitochondrial respiratory chain (MRC) to facilitate functional supercomplex (SC) formation. Loss of SIM2s expression disrupts SC formation through destabilization of MRC Complex III, leading to inhibition of electron transport, although Complex I (CI) activity is retained. A metabolomic analysis showed that knockout of SIM2s leads to a compensatory increase in ATP production through glycolysis and accelerated glutamine-driven TCA cycle production of NADH, creating a favorable environment for high cell proliferation. Our findings indicate that SIM2s is a novel stabilizing factor required for SC assembly, providing insight into the impact of the MRC on metabolic adaptation and breast cancer progression.

A comparative study of pretrained language models for long clinical text.

OBJECTIVE: Clinical knowledge-enriched transformer models (eg, ClinicalBERT) have state-of-the-art results on clinical natural language processing (NLP) tasks. One of the core limitations of these transformer models is the substantial memory consumption due to their full self-attention mechanism, which leads to the performance degradation in long clinical texts. To overcome this, we propose to leverage long-sequence transformer models (eg, Longformer and BigBird), which extend the maximum input sequence length from 512 to 4096, to enhance the ability to model long-term dependencies in long clinical texts. MATERIALS AND METHODS: Inspired by the success of long-sequence transformer models and the fact that clinical notes are mostly long, we introduce 2 domain-enriched language models, Clinical-Longformer and Clinical-BigBird, which are pretrained on a large-scale clinical corpus. We evaluate both language models using 10 baseline tasks including named entity recognition, question answering, natural language inference, and document classification tasks. RESULTS: The results demonstrate that Clinical-Longformer and Clinical-BigBird consistently and significantly outperform ClinicalBERT and other short-sequence transformers in all 10 downstream tasks and achieve new state-of-the-art results. DISCUSSION: Our pretrained language models provide the bedrock for clinical NLP using long texts. We have made our source code available at https://github.com/luoyuanlab/Clinical-Longformer, and the pretrained models available for public download at: https://huggingface.co/yikuan8/Clinical-Longformer. CONCLUSION: This study demonstrates that clinical knowledge-enriched long-sequence transformers are able to learn long-term dependencies in long clinical text. Our methods can also inspire the development of other domain-enriched long-sequence transformers.

A Simple Score to Identify Increased Risk of Transthyretin Amyloid Cardiomyopathy in Heart Failure With Preserved Ejection Fraction.

Importance: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a form of heart failure (HF) with preserved ejection fraction (HFpEF). Technetium Tc 99m pyrophosphate scintigraphy (PYP) enables ATTR-CM diagnosis. It is unclear which patients with HFpEF have sufficient risk of ATTR-CM to warrant PYP. Objective: To derive and validate a simple ATTR-CM score to predict increased risk of ATTR-CM in patients with HFpEF. Design, Setting, and Participants: Retrospective cohort study of 666 patients with HF (ejection fraction ≥ 40%) and suspected ATTR-CM referred for PYP at Mayo Clinic, Rochester, Minnesota, from May 10, 2013, through August 31, 2020. These data were analyzed September 2020 through December 2020. A logistic regression model predictive of ATTR-CM was derived and converted to a point-based ATTR-CM risk score. The score was further validated in a community ATTR-CM epidemiology study of older patients with HFpEF with increased left ventricular wall thickness ([WT] ≥ 12 mm) and in an external (Northwestern University, Chicago, Illinois) HFpEF cohort referred for PYP. Race was self-reported by the participants. In all cohorts, both case patients and control patients were definitively ascertained by PYP scanning and specialist evaluation. Main Outcomes and Measures: Performance of the derived ATTR-CM score in all cohorts (referral validation, community validation, and external validation) and prevalence of a high-risk ATTR-CM score in 4 multinational HFpEF clinical trials. Results: Participant cohorts included were referral derivation (n = 416; 13 participants [3%] were Black and 380 participants [94%] were White; ATTR-CM prevalence = 45%), referral validation (n = 250; 12 participants [5%]were Black and 228 participants [93%] were White; ATTR-CM prevalence = 48% ), community validation (n = 286; 5 participants [2%] were Black and 275 participants [96%] were White; ATTR-CM prevalence = 6% ), and external validation (n = 66; 23 participants [37%] were Black and 36 participants [58%] were White; ATTR-CM prevalence = 39%). Score variables included age, male sex, hypertension diagnosis, relative WT more than 0.57, posterior WT of 12 mm or more, and ejection fraction less than 60% (score range -1 to 10). Discrimination (area under the receiver operating characteristic curve [AUC] 0.89; 95% CI, 0.86-0.92; P < .001) and calibration (Hosmer-Lemeshow; χ2 = 4.6; P = .46) were strong. Discrimination (AUC ≥ 0.84; P < .001 for all) and calibration (Hosmer-Lemeshow χ2 = 2.8; P = .84; Hosmer-Lemeshow χ2 = 4.4; P = .35; Hosmer-Lemeshow χ2 = 2.5; P = .78 in referral, community, and external validation cohorts, respectively) were maintained in all validation cohorts. Precision-recall curves and predictive value vs prevalence plots indicated clinically useful classification performance for a score of 6 or more (positive predictive value ≥25%) in clinically relevant ATTR-CM prevalence (≥10% of patients with HFpEF) scenarios. In the HFpEF clinical trials, 11% to 35% of male and 0% to 6% of female patients had a high-risk (≥6) ATTR-CM score. Conclusions and Relevance: A simple 6 variable clinical score may be used to guide use of PYP and increase recognition of ATTR-CM among patients with HFpEF in the community. Further validation in larger and more diverse populations is needed.

Hyponatremia Is a Powerful Predictor of Poor Prognosis in Left Ventricular Assist Device Patients.

Serum sodium is an established prognostic marker in heart failure (HF) patients and is associated with an increased risk of morbidity and mortality. We sought to study the prognostic value of serum sodium in left ventricular assist device (LVAD) patients and whether hyponatremia reflects worsening HF or an alternative mechanism. We identified HF patients that underwent LVAD implantation between 2008 and 2019. Hyponatremia was defined as Na ≤134 mEq/L at 3 months after implantation. We assessed for differences in hyponatremia before and after LVAD implantation. We also evaluated the association of hyponatremia with all-cause mortality and recurrent HF hospitalizations. There were 342 eligible LVAD patients with a sodium value at 3 months. Among them, there was a significant improvement in serum sodium after LVAD implantation compared to preoperatively (137.2 vs. 134.7 mEq/L, P < 0.0001). Patients with and without hyponatremia had no significant differences in echocardiographic and hemodynamic measurements. In a multivariate analysis, hyponatremia was associated with a markedly increased risk of all-cause mortality (HR 3.69, 95% CI, 1.93-7.05, P < 0.001) when accounting for age, gender, co-morbidities, use of loop diuretics, and B-type natriuretic peptide levels. Hyponatremia was also significantly associated with recurrent HF hospitalizations (HR 2.11, 95% CI, 1.02-4.37, P = 0.04). Hyponatremia in LVAD patients is associated with significantly higher risk of all-cause mortality and recurrent HF hospitalizations. Hyponatremia may be a marker of ongoing neurohormonal activation that is more sensitive than other lab values, echocardiography parameters, and hemodynamic measurements.

Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction.

Heart failure with preserved ejection fraction (HFpEF) represents a prototypical cardiovascular condition in which machine learning may improve targeted therapies and mechanistic understanding of pathogenesis. Machine learning, which involves algorithms that learn from data, has the potential to guide precision medicine approaches for complex clinical syndromes such as HFpEF. It is therefore important to understand the potential utility and common pitfalls of machine learning so that it can be applied and interpreted appropriately. Although machine learning holds considerable promise for HFpEF, it is subject to several potential pitfalls, which are important factors to consider when interpreting machine learning studies.

Quantifying Sub-Meter Surface Heterogeneity on Mars Using Off-Axis Thermal Emission Imaging System (THEMIS) Data.

Surface heterogeneities below the spatial resolution of thermal infrared (TIR) instruments result in anisothermality and can produce emissivity spectra with negative slopes toward longer wavelengths. Sloped spectra arise from an incorrect assumption of either a uniform surface temperature or a maximum emissivity during the temperature-emissivity separation of radiance data. Surface roughness and lateral mixing of different sub-pixel surface units result in distinct spectral slopes with magnitudes proportional to the degree of temperature mixing. Routine Off-nadir Targeted Observations (ROTO) of the Thermal Emission Imaging Spectrometer (THEMIS) are used here for the first time to investigate anisothermality below the spatial resolution of THEMIS. The southern flank of Apollinaris Mons and regions within the Medusae Fossae Formation are studied using THEMIS ROTO data acquired just after local sunset. We observe a range of sloped TIR emission spectra dependent on the magnitude of temperature differences within a THEMIS pixel. Spectral slopes and wavelength-dependent brightness temperature differences are forward-modeled for a series of two-component surfaces of varying thermal inertia values. Our results imply that differing relative proportions of rocky and unconsolidated surface units are observed at each ROTO viewing geometry and suggest a local rock abundance six times greater than published results that rely on nadir data. High-resolution visible images of these regions indicate a mixture of surface units from boulders to dunes, providing credence to the model.

Determining Emplacement Conditions and Vent Locations for Channelized Lava Flows Southwest of Arsia Mons.

The lava flow field southwest of Arsia Mons, Mars has complex volcanic geomorphology. Overlapping flows make observations of their total lengths and identification of their source vents impossible. Application of flow emplacement models, which rely upon physical parameters such as flow length, using only the exposed flow may produce inaccurate estimates of effusion rate, viscosity, and yield strength. We use an established terrestrial thermorheological model (PyFLOWGO), modified to Mars conditions, to estimate effusion rates, viscosities, yield strengths, and possible vent locations for five Mars flows. Our investigation found a range of effusion rates from 2,500 to 6,750 m3 s-1 (average of ∼4,960 m3 s-1). These results are an order of magnitude higher than terrestrial channelized basaltic flows. Corresponding modeled viscosities and yield strengths ranged from 9.4 × 103 to 6.6 × 105 Pa s (average of 5.5 × 104 Pa s) and 66 to 381 Pa (average of 209 Pa), respectively. A novel secondary application of PyFLOWGO that assumes upslope channel narrowing provided estimates of the entire channel length, which is on average four times longer than the exposed portions. Projecting these lengths upslope shows that four of the five flows may have a common vent location, which shares morphologic similarities to other Tharsis region vents. This modeling approach for partially-exposed lava flows makes it possible to not only determine eruptive parameters, but also to estimate total channel lengths and thereby identify possible source vents.

What Are the Contextual Enablers and Impacts of Using Digital Technology to Extend Maternal and Child Health Services to Rural Areas? Findings of a Qualitative Study From Nigeria.

Background: Strengthening health systems to improve access to maternity services remains challenging for Nigeria due partly to weak and irregular in-service training and deficient data management. This paper reports the implementation of digital health tools for video training (VTR) of health workers and digitization of health data at scale, supported by satellite communications (SatCom) technology and existing 3G mobile networks. Objective: To understand whether, and under what circumstances using digital interventions to extend maternal, newborn and child health (MNCH) services to remote areas of Nigeria improved standards of healthcare delivery. Methods: From March 2017 to March 2019, VTR and data digitization interventions were delivered in 126 facilities across three states of Nigeria. Data collection combined documents review with 294 semi-structured interviews of stakeholders across four phases (baseline, midline, endline, and 12-months post-project closedown) to assess acceptability and impacts of digital interventions. Data was analyzed using a framework approach, drawing on a modified Technology Acceptance Model to identify factors that shaped technology adoption and use. Results: Analysis of documents and interview transcripts revealed that a supportive policy environment, and track record of private-public partnerships facilitated adoption of technology. The determinants of technology acceptance among health workers included ease of use, perceived usefulness, and prior familiarity with technology. Perceptions of impact suggested that at the micro (individual) level, repeated engagement with clinical videos increased staff knowledge, motivation and confidence to perform healthcare roles. At meso (organizational) level, better-trained staff felt supported and empowered to provide respectful healthcare and improved management of obstetric complications, triggering increased use of MNCH services. The macro level saw greater use of reliable and accurate data for policymaking. Conclusions: Simultaneous and sustained implementation of VTR and data digitization at scale enabled through SatCom and 3G mobile networks are feasible approaches for supporting improvements in staff confidence and motivation and reported MNCH practices. By identifying mechanisms of impact of digital interventions on micro, meso, and macro levels of the health system, the study extends the evidence base for effectiveness of digital health and theoretical underpinnings to guide further technology use for improving MNCH services in low resource settings. Trial Registration: ISRCTN32105372.