Process evaluation of two large randomized controlled trials to understand factors influencing family physicians' use of antibiotic audit and feedback reports.

BACKGROUND: Unnecessary antibiotic prescriptions in primary care are common and contribute to antimicrobial resistance in the population. Audit and feedback (A&F) on antibiotic prescribing to primary care can improve the appropriateness of antibiotic prescribing, but the optimal approach is uncertain. We performed two pragmatic randomized controlled trials of different approaches to audit and feedback. The trial results showed that A&F was associated with significantly reducing antibiotic prescribing. Still, the effect size was small, and the modifications to the A&F interventions tested in the trials were not associated with any change. Herein, we report a theory-informed qualitative process evaluation to explore potential mechanisms underlying the observed effects. METHODS: Ontario family physicians in the intervention arms of both trials who were sent A&F letters were invited for one-on-one interviews. Purposive sampling was used to seek variation across interested participants in personal and practice characteristics. Qualitative analysis utilized inductive and deductive techniques informed by the Clinical Performance Feedback Intervention Theory. RESULTS: Modifications to the intervention design tested in the trial did not alter prescribing patterns beyond the changes made in response to the A&F overall for various reasons. Change in antibiotic prescribing in response to A&F depended on whether it led to the formation of specific intentions and whether those intentions translated to particular behaviours. Those without intentions to change tended to feel that their unique clinical context was not represented in the A&F. Those with intentions but without specific actions taken tended to express a lack of self-efficacy for avoiding a prescription in contexts with time constraints and/or without an ongoing patient relationship. Many participants noted that compared to overall prescribing, A&F on antibiotic prescription duration was perceived as new information and easily actionable. CONCLUSION: Our findings indicate that contextual factors, including the types of patients and the setting where they are seen, affect how clinicians react to audit and feedback. These results suggest a need to test tailored feedback reports that reflect the context of how, where, and why physicians prescribe antibiotics so that they might be perceived as more personal and more actionable. TRIAL REGISTRATION: Clinical Trial registration IDs: NCT04594200, NCT05044052.

Development and validation of a deep learning algorithm for the prediction of serum creatinine in critically ill patients.

Objectives: Serum creatinine (SCr) is the primary biomarker for assessing kidney function; however, it may lag behind true kidney function, especially in instances of acute kidney injury (AKI). The objective of the work is to develop Nephrocast, a deep-learning model to predict next-day SCr in adult patients treated in the intensive care unit (ICU). Materials and Methods: Nephrocast was trained and validated, temporally and prospectively, using electronic health record data of adult patients admitted to the ICU in the University of California San Diego Health (UCSDH) between January 1, 2016 and June 22, 2024. The model features consisted of demographics, comorbidities, vital signs and laboratory measurements, and medications. Model performance was evaluated by mean absolute error (MAE) and root-mean-square error (RMSE) and compared against the prediction day's SCr as a reference. Results: A total of 28 191 encounters met the eligibility criteria, corresponding to 105 718 patient-days. The median (interquartile range [IQR]) MAE and RMSE in the internal test set were 0.09 (0.085-0.09) mg/dL and 0.15 (0.146-0.152) mg/dL, respectively. In the prospective validation, the MAE and RMSE were 0.09 mg/dL and 0.14 mg/dL, respectively. The model's performance was superior to the reference SCr. Discussion and Conclusion: Our model demonstrated good performance in predicting next-day SCr by leveraging clinical data routinely collected in the ICU. The model could aid clinicians in in identifying high-risk patients for AKI, predicting AKI trajectory, and informing the dosing of renally eliminated drugs.

The impact of laboratory data missingness on sepsis diagnosis timeliness.

Objective: To investigate the impact of missing laboratory measurements on sepsis diagnostic delays. Materials and Methods: In adult patients admitted to 2 University of California San Diego (UCSD) hospitals from January 1, 2021 to June 30, 2024, we evaluated the relative time of organ failure (T OF) and time of clinical suspicion of sepsis (T suspicion) in patients with sepsis according to the Centers for Medicare & Medicaid Services (CMS) definition. Results: Of the patients studied, 48.7% (n = 2017) in the emergency department (ED), 30.8% (n = 209) in the wards, and 14.4% (n = 167) in the intensive care unit (ICU) had T OF after T suspicion. Patients with T OF after T suspicion had significantly higher data missingness of 1 or more of the 5 laboratory components used to determine organ failure. The mean number of missing labs was 4.23 vs 2.83 in the ED, 4.04 vs 3.38 in the wards, and 3.98 vs 3.19 in the ICU. Discussion: Our study identified many sepsis patients with missing laboratory results vital for the identification of organ failure and the diagnosis of sepsis at or before the time of clinical suspicion of sepsis. Addressing data missingness via more timely laboratory assessment could precipitate an earlier recognition of organ failure and potentially earlier diagnosis of and treatment initiation for sepsis. Conclusions: More prompt laboratory assessment might improve the timeliness of sepsis recognition and treatment.

Two-dose priming immunization amplifies humoral immunity by synchronizing vaccine delivery with the germinal center response.

Prolonging exposure to subunit vaccines during the primary immune response enhances humoral immunity. Escalating-dose immunization (EDI), administering vaccines every other day in an increasing pattern over 2 weeks, is particularly effective but challenging to implement clinically. Here, using an HIV Env trimer/saponin adjuvant vaccine, we explored simplified EDI regimens and found that a two-shot regimen administering 20% of the vaccine followed by the remaining 80% of the dose 7 days later increased TFH responses 6-fold, antigen-specific germinal center (GC) B cells 10-fold, and serum antibody titers 10-fold compared with bolus immunization. Computational modeling of TFH priming and the GC response suggested that enhanced activation/antigen loading on dendritic cells and increased capture of antigen delivered in the second dose by follicular dendritic cells contribute to these effects, predictions we verified experimentally. These results suggest that a two-shot priming approach can be used to substantially enhance responses to subunit vaccines.

Automated Uniform Spheroid Generation Platform for High Throughput Drug Screening Process.

Three-dimensional (3D) spheroid models are crucial for cancer research, offering more accurate insights into tumour biology and drug responses than traditional 2D cell cultures. However, inconsistent and low-throughput spheroid production has hindered their application in drug screening. Here, we present an automated high-throughput platform for a spheroid selection, fabrication, and sorting system (SFSS) to produce uniform gelatine-encapsulated spheroids (GESs) with high efficiency. SFSS integrates advanced imaging, analysis, photo-triggered fabrication, and microfluidic sorting to precisely control spheroid size, shape, and viability. Our data demonstrate that our SFSS can produce over 50 GESs with consistent size and circularity in 30 min with over 97% sorting accuracy while maintaining cell viability and structural integrity. We demonstrated that the GESs can be used for drug screening and potentially for various assays. Thus, the SFSS could significantly enhance the efficiency of generating uniform spheroids, facilitating their application in drug development to investigate complex biological systems and drug responses in a more physiologically relevant context.

The septin cytoskeleton is required for plasma membrane repair.

Plasma membrane repair is a fundamental homeostatic process of eukaryotic cells. Here, we report a new function for the conserved cytoskeletal proteins known as septins in the repair of cells perforated by pore-forming toxins or mechanical disruption. Using a silencing RNA screen, we identified known repair factors (e.g. annexin A2, ANXA2) and novel factors such as septin 7 (SEPT7) that is essential for septin assembly. Upon plasma membrane injury, the septin cytoskeleton is extensively redistributed to form submembranous domains arranged as knob and loop structures containing F-actin, myosin IIA, S100A11, and ANXA2. Formation of these domains is Ca2+-dependent and correlates with plasma membrane repair efficiency. Super-resolution microscopy revealed that septins and F-actin form intertwined filaments associated with ANXA2. Depletion of SEPT7 prevented ANXA2 recruitment and formation of submembranous actomyosin domains. However, ANXA2 depletion had no effect on domain formation. Collectively, our data support a novel septin-based mechanism for resealing damaged cells, in which the septin cytoskeleton plays a key structural role in remodeling the plasma membrane by promoting the formation of SEPT/F-actin/myosin IIA/ANXA2/S100A11 repair domains.

Evaluating Dental Monitoring effectiveness compared with conventional monitoring of clear aligner therapy using the Peer Assessment Rating index.

INTRODUCTION: This study aimed to evaluate the effectiveness of Dental Monitoring (DM) (Dental Monitoring SAS, Paris, France) compared with conventional monitoring (CM) during active orthodontic treatment. METHODS: The Peer Assessment Rating (PAR) index was used to evaluate the pretreatment and posttreatment records of 51 patients, with 26 in the CM group and 25 in the DM group. The change in weighted PAR was analyzed to assess the effectiveness of treatment. RESULTS: The chi-square test revealed that the CM group had a higher percentage of patients in the great improvement category compared with the DM group. However, this difference was not statistically significant (P = 0.192). A repeated measures general linear model demonstrated significant improvement over time (P <0.001), with no statistically significant group differences noted between CM and DM (P = 0.181) and no statistically significant time-by-group interaction (P = 0.299). CONCLUSIONS: Both CM and DM showed significant improvements in weighted PAR scores, but no statistically significant difference is present between the 2 groups.

Supramolecular assembly of amphiphilic platinum(ii) Schiff base complexes: diverse spectroscopic changes and nanostructures through rational molecular design and solvent control.

A new class of amphiphilic tetradentate platinum(ii) Schiff base complexes has been designed and synthesized. The self-assembly properties by exploiting the potential Pt⋯Pt interactions of amphiphilic platinum(ii) Schiff base complexes in the solution state have been systematically investigated. The presence of Pt⋯Pt interactions has further been supported by computational studies and non-covalent interaction (NCI) analysis of the dimer of the complex. The extent of the non-covalent Pt⋯Pt and π-π interactions could be regulated by a variation of the solvent compositions and the hydrophobicity of the complexes, which is accompanied by attractive spectroscopic and luminescence changes and leads to diverse morphological transformations. The present work represents a rare example of demonstration of directed cooperative assembly of amphiphilic platinum(ii) Schiff base complexes by intermolecular Pt⋯Pt interactions in solution with an in-depth mechanistic investigation, providing guiding principles for the construction of supramolecular structures with desirable properties using platinum(ii) Schiff base building blocks.

An ultrawide field-of-view pinhole compound eye using hemispherical nanowire array for robot vision.

Garnering inspiration from biological compound eyes, artificial vision systems boasting a vivid range of diverse visual functional traits have come to the fore recently. However, most of these artificial systems rely on transformable electronics, which suffer from the complexity and constrained geometry of global deformation, as well as potential mismatches between optical and detector units. Here, we present a unique pinhole compound eye that combines a three-dimensionally printed honeycomb optical structure with a hemispherical, all-solid-state, high-density perovskite nanowire photodetector array. The lens-free pinhole structure can be designed and fabricated with an arbitrary layout to match the underlying image sensor. Optical simulations and imaging results matched well with each other and substantiated the key characteristics and capabilities of our system, which include an ultrawide field of view, accurate target positioning, and motion tracking function. We further demonstrate the potential of our unique compound eye for advanced robotic vision by successfully completing a moving target tracking mission.

The adverse inpatient medication event and frailty (AIME-frail) risk prediction model.

BACKGROUND: Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable. AIM: To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients. METHODS: A retrospective cohort study was conducted at two tertiary Australian hospitals on patients discharged between 1st January and April 31, 2020. Data were extracted from electronic medical records (EMRs) and clinical coding databases. Medication harm was identified using ICD-10 Y-codes and confirmed by senior pharmacist review of medical records. The Hospital Frailty Risk Score (HFRS) was calculated for each patient. Logistic regression analysis was used to construct a modified AIME model. Candidate variables of the original AIME model, together with new variables including HFRS were tested. Performance of the final model was reported using area under the curve (AUC) and decision curve analysis (DCA). RESULTS: A total of 4089 patient admissions were included, with a mean age ± standard deviation (SD) of 64 years (±19 years), 2050 patients (50%) were males, and mean HFRS was 6.2 (±5.9). 184 patients (4.5%) experienced one or more medication harm events during hospitalisation. The new AIME-Frail risk model incorporated 5 of the original variables: length of stay (LOS), anti-psychotics, antiarrhythmics, immunosuppressants, and INR greater than 3, as well as 5 new variables: HFRS, anticoagulants, antibiotics, insulin, and opioid use. The AUC was 0.79 (95% CI: 0.76-0.83) which was superior to the original model (AUC = 0.70, 95% CI: 0.65-0.74) with a sensitivity of 69%, specificity of 81%, positive predictive value of 0.14 (95% CI: 0.10-0.17) and negative predictive value of 0.98 (95% CI: 0.97-0.99). The DCA identified the model as having potential clinical utility between the probability thresholds of 0.05-0.4. CONCLUSION: The inclusion of a frailty measure improved the predictive performance of the AIME model. Screening inpatients using the AIME-Frail tool could identify more patients at high-risk of medication harm who warrant timely clinician review.

Advancing Breast Cancer Research Through Collaborative Computing: Harnessing Google Colab for Innovation.

This investigation explores the potential efficacy of machine learning algorithms (MLAs), particularly convolutional neural networks (CNNs), in distinguishing between benign and malignant breast cancer tissue through the analysis of 1000 breast cancer images gathered from Kaggle.com, a domain of publicly accessible data. The dataset was meticulously partitioned into training, validation, and testing sets to facilitate model development and evaluation. Our results reveal promising outcomes, with the developed model achieving notable precision (92%), recall (92%), accuracy (92%), sensitivity (89%), specificity (96%), an F1 score of 0.92, and an area under the curve (AUC) of 0.944. These metrics underscore the model's ability to accurately identify malignant breast cancer images. Because of limitations such as sample size and potential variations in image quality, further research, data collection, and integration of theoretical models in a real-world clinical setting are needed to expand the reliability and generalizability of these MLAs. Nonetheless, this study serves to highlight the potential use of artificial intelligence models as supporting tools for physicians to utilize in breast cancer detection.

Full-color fiber light-emitting diodes based on perovskite quantum wires.

Fiber light-emitting diodes (Fi-LEDs), which can be used for wearable lighting and display devices, are one of the key components for fiber/textile electronics. However, there exist a number of impediments to overcome on device fabrication with fiber-like substrates, as well as on device encapsulations. Here, we uniformly grew all-inorganic perovskite quantum wire arrays by filling high-density alumina nanopores on the surface of Al fibers with a dip-coating process. With a two-step evaporation method to coat a surrounding transporting layer and semitransparent electrode, we successfully fabricated full-color Fi-LEDs with emission peaks at 625 nanometers (red), 512 nanometers (green), and 490 nanometers (sky-blue), respectively. Intriguingly, additional polydimethylsiloxane packaging helps instill the mechanical bendability, stretchability, and waterproof feature of Fi-LEDs. The plasticity of Al fiber also allows the one-dimensional architecture Fi-LED to be shaped and constructed for two-dimensional or even three-dimensional architectures, opening up a new vista for advanced lighting with unconventional formfactors.

Nucleosome reorganisation in breast cancer tissues.

BACKGROUND: Nucleosome repositioning in cancer is believed to cause many changes in genome organisation and gene expression. Understanding these changes is important to elucidate fundamental aspects of cancer. It is also important for medical diagnostics based on cell-free DNA (cfDNA), which originates from genomic DNA regions protected from digestion by nucleosomes. RESULTS: We have generated high-resolution nucleosome maps in paired tumour and normal tissues from the same breast cancer patients using MNase-assisted histone H3 ChIP-seq and compared them with the corresponding cfDNA from blood plasma. This analysis has detected single-nucleosome repositioning at key regulatory regions in a patient-specific manner and common cancer-specific patterns across patients. The nucleosomes gained in tumour versus normal tissue were particularly informative of cancer pathways, with ~ 20-fold enrichment at CpG islands, a large fraction of which marked promoters of genes encoding DNA-binding proteins. The tumour tissues were characterised by a 5-10 bp decrease in the average distance between nucleosomes (nucleosome repeat length, NRL), which is qualitatively similar to the differences between pluripotent and differentiated cells. This effect was correlated with gene activity, differential DNA methylation and changes in local occupancy of linker histone variants H1.4 and H1X. CONCLUSIONS: Our study offers a novel resource of high-resolution nucleosome maps in breast cancer patients and reports for the first time the effect of systematic decrease of NRL in paired tumour versus normal breast tissues from the same patient. Our findings provide a new mechanistic understanding of nucleosome repositioning in tumour tissues that can be valuable for patient diagnostics, stratification and monitoring.

Assertive community treatment as an alternative to incarceration for American pretrial detainees.

In the United States and elsewhere around the world, people with serious mental illness (SMI) are overrepresented in the criminal justice system. Clinical interventions to divert such individuals out of correctional settings, including Assertive Community Treatment (ACT), have been shown to reduce rates of criminal justice recidivism when modified to allow for the use of court sanctions to encourage treatment adherence. However, these interventions are noted to be underutilized as alternative to incarceration (ATI) programs. This paper summarizes the results of a retrospective cohort study conducted in a New York State forensic psychiatric hospital of 87 pretrial detainees admitted after being found incompetent to stand trial between January 2019 and January 2022. Of these, 49 patients were referred to an ACT team that served as an ATI program. The study outcomes noted that patients referred to this ACT team were 20% less likely to remain in pretrial detention than those that were not. Moreover, patients referred to the ACT program were also 34% more likely to be granted an ATI plea bargain in the community that did not involve serving a prison term. These results suggest that pretrial detainees with SMI are more likely to be granted an ATI program that offers more intensive treatment services such as ACT, due to the capability of such programs to also provide more intensive outreach and community supervision than traditional outpatient mental health service providers.

Ultra-Sensitive and Stable Multiplexed Biosensors Array in Fully Printed and Integrated Platforms for Reliable Perspiration Analysis.

Electrochemical biosensors have emerged as one of the promising tools for tracking human body physiological dynamics via non-invasive perspiration analysis. However, it remains a key challenge to integrate multiplexed sensors in a highly controllable and reproducible manner to achieve long-term reliable biosensing, especially on flexible platforms. Herein, a fully inkjet printed and integrated multiplexed biosensing patch with remarkably high stability and sensitivity is reported for the first time. These desirable characteristics are enabled by the unique interpenetrating interface design and precise control over active materials mass loading, owing to the optimized ink formulations and droplet-assisted printing processes. The sensors deliver sensitivities of 313.28 µA mm-1 cm-2 for glucose and 0.87 µA mm-1 cm-2 for alcohol sensing with minimal drift over 30 h, which are among the best in the literature. The integrated patch can be used for reliable and wireless diet monitoring or medical intervention via epidermal analysis and would inspire the advances of wearable devices for intelligent healthcare applications.

The Septin Cytoskeleton is Required for Plasma Membrane Repair.

Mammalian cells are frequently exposed to mechanical and biochemical stressors resulting in plasma membrane injuries. Repair mechanisms reseal the plasma membrane to restore homeostasis and prevent cell death. In the present work, a silencing RNA screen was performed to uncover plasma membrane repair mechanisms of cells exposed to a pore-forming toxin (listeriolysin O). This screen identified molecules previously known to repair the injured plasma membrane such as annexin A2 (ANXA2) as well as novel plasma membrane repair candidate proteins. Of the novel candidates, we focused on septin 7 (SEPT7) because the septins are an important family of conserved eukaryotic cytoskeletal proteins. Using diverse experimental approaches, we established for the first time that SEPT7 plays a general role in plasma membrane repair of cells perforated by pore-forming toxins and mechanical wounding. Remarkably, upon cell injury, the septin cytoskeleton is extensively redistributed in a Ca 2+ -dependent fashion, a hallmark of plasma membrane repair machineries. The septins reorganize into subplasmalemmal domains arranged as knob and loop (or ring) structures containing F-actin, myosin II, and annexin A2 (ANXA2) and protrude from the cell surface. Importantly, the formation of these domains correlates with the plasma membrane repair efficiency. Super-resolution microscopy shows that septins and actin are arranged in intertwined filaments associated with ANXA2. Silencing SEPT7 expression prevented the formation of the F-actin/myosin II/ANXA2 domains, however, silencing expression of ANXA2 had no observable effect on their formation. These results highlight the key structural role of the septins in remodeling the plasma membrane and in the recruitment of the repair molecule ANXA2. Collectively, our data support a novel model in which the septin cytoskeleton acts as a scaffold to promote the formation of plasma membrane repair domains containing contractile F-actin and annexin A2.

Assembly of Luminescent Chiral Gold(I)-Sulfido Clusters via Chiral Self-Sorting.

Due to the ubiquity of chirality in nature, chiral self-assembly involving self-sorting behaviors has remained as one of the most important research topics of interests. Herein, starting from a racemic mixture of SEG-based (SEG=SEGPHOS) chlorogold(I) precursors, a unique chiral butterfly-shape hexadecanuclear gold(I) cluster (Au16 ) with different ratios of RSEG and SSEG ligands is obtained via homoleptic and heterochiral self-sorting. More interestingly, by employing different chlorogold(I) precursors of opposite chirality (such as RSEG -Au2 and SBIN -Au2 (BIN=BINAP)), an unprecedented heteroleptic and heterochiral self-sorting strategy has been developed to give a series of heteroleptic chiral decanuclear gold(I) clusters (Au10 ) with propellor-shape structures. Heterochiral and heteroleptic self-sorting have also been observed between enantiomers of homoleptic chiral Au10 clusters to result in the heteroleptic chiral Au10 clusters via cluster-to-cluster transformation. Incorporation of heteroleptic ligands is found to decrease the symmetry from S4 of homoleptic meso Au10 to C2 of heteroleptic chiral Au10 clusters. The chirality has been transferred from the axial chiral ligands and stored in the heteroleptic gold(I) clusters.

Intravenous immunoglobulin resistance in Kawasaki disease patients: prediction using clinical data.

BACKGROUND: About 10-20% of Kawasaki disease (KD) patients are resistant to the initial infusion of intravenous immunoglobin (IVIG). The aim of this study was to assess whether IVIG resistance in KD patients could be predicted using standard clinical and laboratory features. METHODS: Data were from two cohorts: a Korean cohort of 7101 KD patients from 2015 to 2017 and a cohort of 649 KD patients from San Diego enrolled from 1998 to 2021. Features included laboratory values, the worst Z-score from the initial echocardiogram or during hospitalization, and the five clinical KD signs at presentation. RESULTS: Five machine learning models achieved a maximum median AUC of 0.711 [IQR: 0.706-0.72] in the Korean cohort and 0.696 [IQR: 0.609-0.722] in the San Diego cohort during stratified 10-fold cross-validation using significant laboratory features identified from univariate analysis. Adding the Z-score, KD clinical signs, or both did not considerably improve the median AUC in either cohort. CONCLUSIONS: Using commonly measured clinical laboratory data alone or in conjunction with echocardiographic findings and clinical features is not sufficient to predict IVIG resistance. Further attempts to predict IVIG resistance will need to incorporate additional data such as transcriptomics, proteomics, and genetics to achieve meaningful predictive utility. IMPACT: We demonstrated that laboratory, echocardiographic, and clinical findings cannot predict intravenous immunoglobin (IVIG) resistance to a clinically meaningful extent using machine learning in a homogenous Asian or ethnically diverse population of patients with Kawasaki disease (KD). Visualizing these features using uniform manifold approximation and projection (UMAP) is an important step to evaluate predictive utility in a qualitative manner. Further attempts to predict IVIG resistance in KD patients will need to incorporate novel biomarkers or other specialized features such as genetic differences or transcriptomics to be clinically useful.

A Deep Learning Framework for Image-Based Screening of Kawasaki Disease.

Kawasaki disease (KD) is a leading cause of acquired heart disease in children and is characterized by the presence of a combination of five clinical signs assessed during the physical examination. Timely treatment of intravenous immunoglobin is needed to prevent coronary artery aneurysm formation, but KD is usually diagnosed when pediatric patients are evaluated by a clinician in the emergency department days after onset. One or more of the five clinical signs usually manifests in pediatric patients prior to ED admission, presenting an opportunity for earlier intervention if families receive guidance to seek medical care as soon as clinical signs are observed along with a fever for at least five days. We present a deep learning framework for a novel screening tool to calculate the relative risk of KD by analyzing images of the five clinical signs. The framework consists of convolutional neural networks to separately calculate the risk for each clinical sign, and a new algorithm to determine what clinical sign is in an image. We achieved a mean accuracy of 90% during 10-fold cross-validation and 88% during external validation for the new algorithm. These results demonstrate the algorithms in the proposed screening tool can be utilized by families to determine if their child should be evaluated by a clinician based on the number of clinical signs consistent with KD.Clinical Relevance- This screening framework has the potential for earlier clinical evaluation and detection of KD to reduce the risk of coronary artery complications.

Experiments with RouteNav, A Wayfinding App for Blind Travelers in a Transit Hub.

RouteNav is an iOS app designed to support wayfinding for blind travelers in an indoor/outdoor transit hub. It doesn't rely on external infrastructure (such as BLE beacons); instead, localization is obtained by fusing spatial information from inertial dead reckoning and GPS (when available) via particle filtering. Routes are expressed as sequences of "tiles", where each tile may contain relevant points of interest. Redundant modalities are used to guide users to switching goalposts within tiles. In this paper, we describe the different components of RouteNav, and report on a user study with seven blind participants, who traversed three challenging routes in a transit hub while receiving input from the app.