Complete Tolerogenic Adjuvant Stimulates Regulatory T Cell Response to Immunization.

We have determined in mice the minimum composition required for forming a vaccine adjuvant that stimulates a regulatory T (Treg) cell response to immunization, and we named the adjuvant "complete tolerogenic adjuvant." This new kind of adjuvant may let us use the well-proven "Ag with adjuvant" form of immunization for inducing Treg cell-mediated Ag-specific immunosuppression. The minimum composition consists of dexamethasone, rapamycin, and monophosphoryl lipid A at a mass ratio of 8:20:3. By dissecting the respective role of each of these components during immunization, we have further shown why immunosuppressive and immunogenic agents are both needed for forming true adjuvants for Treg cells. This finding may guide the design of additional, and potentially more potent, complete tolerogenic adjuvants with which we may form numerous novel vaccines for treating immune diseases.

Yet more evidence that non-aqueous myelin lipids can be directly imaged with ultrashort echo time (UTE) MRI on a clinical 3T scanner: a lyophilized red blood cell membrane lipid study.

Direct imaging of semi-solid lipids, such as myelin, is of great interest as a noninvasive biomarker of neurodegenerative diseases. Yet, the short T2 relaxation times of semi-solid lipid protons hamper direct detection through conventional magnetic resonance imaging (MRI) pulse sequences. In this study, we examined whether a three-dimensional ultrashort echo time (3D UTE) sequence can directly acquire signals from membrane lipids. Membrane lipids from red blood cells (RBC) were collected from commercially available blood as a general model of the myelin lipid bilayer and subjected to D2O exchange and freeze-drying for complete water removal. Sufficiently high MR signals were detected with the 3D UTE sequence, which showed an ultrashort T2* of ∼77-271 µs and a short T1 of ∼189 ms for semi-solid RBC membrane lipids. These measurements can guide designing UTE-based sequences for direct in vivo imaging of membrane lipids.

Water phase transition and signal nulling in 3D dual-echo adiabatic inversion-recovery UTE (IR-UTE) imaging of myelin.

PURPOSE: The semisolid myelin sheath has very fast transverse relaxation and is invisible to conventional MRI sequences. UTE sequences can detect signal from myelin. The major challenge is the concurrent detection of various water components. METHODS: The inversion recovery (IR)-based UTE (IR-UTE) sequence employs an adiabatic inversion pulse to invert and suppress water magnetizations. TI plays a key role in water suppression, with negative water magnetizations (negative phase) before the null point and positive water magnetizations (positive phase) after the null point. A series of dual-echo IR-UTE images were acquired with different TIs to detect water phase transition. The effects of TR in phase transition and water suppression were also investigated using a relatively long TR of 500 ms and a short TR of 106 ms. The water phase transition in dual-echo IR-UTE imaging of myelin was investigated in five ex vivo and five in vivo human brains. RESULTS: An apparent phase transition was observed in the second echo at the water signal null point, where the myelin signal was selectively detected by the UTE data acquisition at the optimal TI. The water phase transition point varied significantly across the brain when the long TR of 500 ms was used, whereas the convergence of TIs was observed when the short TR of 106 ms was used. CONCLUSION: The results suggest that the IR-UTE sequence with a short TR allows uniform inversion and nulling of water magnetizations, thereby providing volumetric imaging of myelin.

Towards assessing and improving the reliability of ultrashort echo time quantitative magnetization transfer (UTE-qMT) MRI of cortical bone: In silico and ex vivo study.

OBJECTIVE: To assess and improve the reliability of the ultrashort echo time quantitative magnetization transfer (UTE-qMT) modeling of the cortical bone. MATERIALS AND METHODS: Simulation-based digital phantoms were created that mimic the UTE-qMT properties of cortical bones. A wide range of SNR from 25 to 200 was simulated by adding different levels of noise to the synthesized MT-weighted images to assess the effect of SNR on UTE-qMT fitting results. Tensor-based denoising algorithm was applied to improve the fitting results. These results from digital phantom studies were validated via ex vivo rat leg bone scans. RESULTS: The selection of initial points for nonlinear fitting and the number of data points tested for qMT analysis have minimal effect on the fitting result. Magnetization exchange rate measurements are highly dependent on the SNR of raw images, which can be substantially improved with an appropriate denoising algorithm that gives similar fitting results from the raw images with an 8-fold higher SNR. DISCUSSION: The digital phantom approach enables the assessment of the reliability of bone UTE-qMT fitting by providing the known ground truth. These findings can be utilized for optimizing the data acquisition and analysis pipeline for UTE-qMT imaging of cortical bones.

High contrast cartilaginous endplate imaging in spine using three dimensional dual-inversion recovery prepared ultrashort echo time (3D DIR-UTE) sequence.

PURPOSE: To investigate the feasibility and application of a novel imaging technique, a three-dimensional dual adiabatic inversion recovery prepared ultrashort echo time (3D DIR-UTE) sequence, for high contrast assessment of cartilaginous endplate (CEP) imaging with head-to-head comparisons between other UTE imaging techniques. METHOD: The DIR-UTE sequence employs two narrow-band adiabatic full passage (AFP) pulses to suppress signals from long T2 water (e.g., nucleus pulposus (NP)) and bone marrow fat (BMF) independently, followed by multispoke UTE acquisition to detect signals from the CEP with short T2 relaxation times. The DIR-UTE sequence, in addition to three other UTE sequences namely, an IR-prepared and fat-saturated UTE (IR-FS-UTE), a T1-weighted and fat-saturated UTE sequence (T1w-FS-UTE), and a fat-saturated UTE (FS-UTE) was used for MR imaging on a 3 T scanner to image six asymptomatic volunteers, six patients with low back pain, as well as a human cadaveric specimen. The contrast-to-noise ratio of the CEP relative to the adjacent structures-specifically the NP and BMF-was then compared from the acquired images across the different UTE sequences. RESULTS: For asymptomatic volunteers, the DIR-UTE sequence showed significantly higher contrast-to-noise ratio values between the CEP and BMF (CNRCEP-BMF) (19.9 ± 3.0) and between the CEP and NP (CNRCEP-NP) (23.1 ± 1.7) compared to IR-FS-UTE (CNRCEP-BMF: 17.3 ± 1.2 and CNRCEP-NP: 19.1 ± 1.8), T1w-FS-UTE (CNRCEP-BMF: 9.0 ± 2.7 and CNRCEP-NP: 10.4 ± 3.5), and FS-UTE (CNRCEP-BMF: 7.7 ± 2.2 and CNRCEP-NP: 5.8 ± 2.4) for asymptomatic volunteers (all P-values < 0.001). For the spine sample and patients with low back pain, the DIR-UTE technique detected abnormalities such as irregularities and focal defects in the CEP regions. CONCLUSION: The 3D DIR-UTE sequence is able to provide high-contrast volumetric CEP imaging for human spines on a clinical 3 T scanner.

Comparative analysis of asian carps parvalbumin reveals the divergence pattern of major fish allergen.

BACKGROUND: Asian carps, a popular freshwater fish globally, are valued for their flavor and serve as a crucial protein source, especially for infants. However, grass carp parvalbumin is highly allergenic, surpassing the allergenicity of fish like salmon and cod. The allergenic potential of parvalbumin in other Asian carps remains unknown, underscoring the need for allergen identification to improve the precision of fish allergy diagnosis and treatment. OBJECTIVE: To identify all parvalbumin homologs in Asian carps and investigate the role of gene divergence in allergenic homolog formation. METHODS: Three annotated genomes of Asian carp, including grass carp, black carp and bighead carp, were constructed using a hybrid assembly approach. Through sequence homology at the genomic level, all the homologs of major fish allergens were identified. Bioinformatics tools were then employed to reveal the gene structures, expression levels, and protein conformations of parvalbumin. RESULTS: Grass carp genome analysis showed nine parvalbumin homologs, with Cid_PV2 most similar to Cten i 1. Bighead and black carp genomes had ten homologs, including potentially allergenic Mpi_PV7 and Hno_PV7. Tissue-specific expression patterns revealed alternative usage of parvalbumin homologs. Gene duplication events expanded parvalbumin copies in bony fish, with two gene clusters identified in Asian carp genomes. CONCLUSION: All the homologs of Asian carps' parvalbumin were accurately identified and gene divergence contributed to the formation of allergenic homologs. Together with a comprehensive gene sequence profile of carps' parvalbumin, those could be applied to achieve a more precise clinical diagnostic test.

Comparative Genomics Reveals Insights into the Divergent Evolution of Astigmatic Mites and Household Pest Adaptations.

Highly diversified astigmatic mites comprise many medically important human household pests such as house dust mites causing ∼1-2% of all allergic diseases globally; however, their evolutionary origin and diverse lifestyles including reversible parasitism have not been illustrated at the genomic level, which hampers allergy prevention and our exploration of these household pests. Using six high-quality assembled and annotated genomes, this study not only refuted the monophyly of mites and ticks, but also thoroughly explored the divergence of Acariformes and the diversification of astigmatic mites. In monophyletic Acariformes, Prostigmata known as notorious plant pests first evolved, and then rapidly evolving Astigmata diverged from soil oribatid mites. Within astigmatic mites, a wide range of gene families rapidly expanded via tandem gene duplications, including ionotropic glutamate receptors, triacylglycerol lipases, serine proteases and UDP glucuronosyltransferases. Gene diversification after tandem duplications provides many genetic resources for adaptation to sensing environmental signals, digestion, and detoxification in rapidly changing household environments. Many gene decay events only occurred in the skin-burrowing parasitic mite Sarcoptes scabiei. Throughout the evolution of Acariformes, massive horizontal gene transfer events occurred in gene families such as UDP glucuronosyltransferases and several important fungal cell wall lytic enzymes, which enable detoxification and digestive functions and provide perfect drug targets for pest control. This comparative study sheds light on the divergent evolution and quick adaptation to human household environments of astigmatic mites and provides insights into the genetic adaptations and even control of human household pests.

Noninvasively differentiating acute and chronic nephropathies via multiparametric urea-CEST, nuclear Overhauser enhancement-CEST, and quantitative magnetization transfer MRI.

PURPOSE: Standardized blood tests often lack adequate sensitivity and specificity to capture the gradual progression of renal injuries. We suggest a multiparametric molecular MRI approach as a noninvasive tool for monitoring renal function loss and distinguishing different types of renal injuries. METHODS: CEST and quantitative magnetization transfer (qMT) imaging were performed on cisplatin (n = 16) and aristolochic acid (AA)-induced nephropathy (n = 22) mouse models at 7T with an infusion of either saline or urea. Seven-pool Lorentzian fitting was applied for the analysis of CEST Z-spectra, and the T1 -corrected CEST contrast apparent exchange-dependent relaxation (AREX) from urea (+1 ppm) and two nuclear Overhauser enhancement (NOE) pools (-1.6 and -3.5 ppm) were measured. Similarly, qMT spectra were fitted into two-pool Ramani equation and the relative semi-solid macromolecular pool-size ratio was measured. Histology of mouse kidneys was performed to validate the MR findings. RESULTS: AA model showed disrupted spatial gradients of urea in the kidney and significantly decreased NOE CEST and qMT contrast. The cisplatin model showed slightly decreased qMT contrast only. The orrelation of MR parameters to histological features showed that NOE CEST and qMT imaging are sensitive to both acute and chronic injuries, whereas urea CEST shows a significant correlation only to acute injuries. CONCLUSION: These results indicate that our multiparametric approach allows comprehensive and totally noninvasive monitoring of renal function and histological changes for distinguishing different nephropathies.

Automated diagnosis of flatfoot using cascaded convolutional neural network for angle measurements in weight-bearing lateral radiographs.

OBJECTIVES: Diagnosis of flatfoot using a radiograph is subject to intra- and inter-observer variabilities. Here, we developed a cascade convolutional neural network (CNN)-based deep learning model (DLM) for an automated angle measurement for flatfoot diagnosis using landmark detection. METHODS: We used 1200 weight-bearing lateral foot radiographs from young adult Korean males for the model development. An experienced orthopedic surgeon identified 22 radiographic landmarks and measured three angles for flatfoot diagnosis that served as the ground truth (GT). Another orthopedic surgeon (OS) and a general physician (GP) independently identified the landmarks of the test dataset and measured the angles using the same method. External validation was performed using 100 and 17 radiographs acquired from a tertiary referral center and a public database, respectively. RESULTS: The DLM showed smaller absolute average errors from the GT for the three angle measurements for flatfoot diagnosis compared with both human observers. Under the guidance of the DLM, the average errors of observers OS and GP decreased from 2.35° ± 3.01° to 1.55° ± 2.09° and from 1.99° ± 2.76° to 1.56° ± 2.19°, respectively (both p < 0.001). The total measurement time decreased from 195 to 135 min in observer OS and from 205 to 155 min in observer GP. The absolute average errors of the DLM in the external validation sets were similar or superior to those of human observers in the original test dataset. CONCLUSIONS: Our CNN model had significantly better accuracy and reliability than human observers in diagnosing flatfoot, and notably improved the accuracy and reliability of human observers. KEY POINTS: • Development of deep learning model (DLM) that allows automated angle measurements for landmark detection based on 1200 weight-bearing lateral radiographs for diagnosing flatfoot. • Our DLM showed smaller absolute average errors for flatfoot diagnosis compared with two human observers. • Under the guidance of the model, the average errors of two human observers decreased and total measurement time also decreased from 195 to 135 min and from 205 to 155 min.

Deep-learning-based personalized prediction of absolute neutrophil count recovery and comparison with clinicians for validation.

Neutropenia and its complications are major adverse effects of cytotoxic chemotherapy. The time to recovery from neutropenia varies from patient to patient, and cannot be easily predicted even by experts. Therefore, we trained a deep learning model using data from 525 pediatric patients with solid tumors to predict the day when patients recover from severe neutropenia after high-dose chemotherapy. We validated the model with data from 99 patients and compared its performance to those of clinicians. The accuracy of the model at predicting the recovery day, with a 1-day error, was 76%; its performance was better than those of the specialist group (58.59%) and the resident group (32.33%). In addition, 80% of clinicians changed their initial predictions at least once after the model's prediction was conveyed to them. In total, 86 prediction changes (90.53%) improved the recovery day estimate.

Preliminary Attainability Assessment of Real-World Data for Answering Major Clinical Research Questions in Breast Cancer Brain Metastasis: Framework Development and Validation Study.

BACKGROUND: In recent decades, real-world evidence (RWE) in oncology has rapidly gained traction for its potential to answer clinical questions that cannot be directly addressed by randomized clinical trials. Integrating real-world data (RWD) into clinical research promises to contribute to more sustainable research designs, including extension, augmentation, enrichment, and pragmatic designs. Nevertheless, clinical research using RWD is still limited because of concerns regarding the shortage of best practices for extracting, harmonizing, and analyzing RWD. In particular, pragmatic screening methods to determine whether the content of a data source is sufficient to answer the research questions before conducting the research with RWD have not yet been established. OBJECTIVE: We examined the PAR (Preliminary Attainability Assessment of Real-World Data) framework and assessed its utility in breast cancer brain metastasis (BCBM), which has an unmet medical need for data attainability screening at the preliminary step of observational studies that use RWD. METHODS: The PAR framework was proposed to assess data attainability from a particular data source during the early research process. The PAR framework has four sequential stages, starting with clinical question clarification: (1) operational definition of variables, (2) data matching (structural/semantic), (3) data screening and extraction, and (4) data attainability diagramming. We identified 5 clinical questions to be used for PAR framework evaluation through interviews and validated them with a survey of breast cancer experts. We used the Samsung Medical Center Breast Cancer Registry, a hospital-based real-time registry implemented in March 2021, leveraging the institution's anonymized and deidentified clinical data warehouse platform. The number of breast cancer patients in the registry was 45,129; it covered the period from June 1995 to December 2021. The registry consists of 24 base data marts that represent disease-specific breast cancer characteristics and care pathways. The outcomes included screening results of the clinical questions via the PAR framework and a procedural diagram of data attainability for each research question. RESULTS: Data attainability was tested for study feasibility according to the PAR framework with 5 clinical questions for BCBM. We obtained data sets that were sufficient to conduct studies with 4 of 5 clinical questions. The research questions stratified into 3 types when we developed data fields for clearly defined research variables. In the first, only 1 question could be answered using direct data variables. In the second, the other 3 questions required surrogate definitions that combined data variables. In the third, the question turned out to be not feasible for conducting further analysis. CONCLUSIONS: The adoption of the PAR framework was associated with more efficient preliminary clinical research using RWD from BCBM. Furthermore, this framework helped accelerate RWE generation through clinical research by enhancing transparency and reproducibility and lowering the entry barrier for clinical researchers.

Deep learning approach to detection of colonoscopic information from unstructured reports.

BACKGROUND: Colorectal cancer is a leading cause of cancer deaths. Several screening tests, such as colonoscopy, can be used to find polyps or colorectal cancer. Colonoscopy reports are often written in unstructured narrative text. The information embedded in the reports can be used for various purposes, including colorectal cancer risk prediction, follow-up recommendation, and quality measurement. However, the availability and accessibility of unstructured text data are still insufficient despite the large amounts of accumulated data. We aimed to develop and apply deep learning-based natural language processing (NLP) methods to detect colonoscopic information. METHODS: This study applied several deep learning-based NLP models to colonoscopy reports. Approximately 280,668 colonoscopy reports were extracted from the clinical data warehouse of Samsung Medical Center. For 5,000 reports, procedural information and colonoscopic findings were manually annotated with 17 labels. We compared the long short-term memory (LSTM) and BioBERT model to select the one with the best performance for colonoscopy reports, which was the bidirectional LSTM with conditional random fields. Then, we applied pre-trained word embedding using large unlabeled data (280,668 reports) to the selected model. RESULTS: The NLP model with pre-trained word embedding performed better for most labels than the model with one-hot encoding. The F1 scores for colonoscopic findings were: 0.9564 for lesions, 0.9722 for locations, 0.9809 for shapes, 0.9720 for colors, 0.9862 for sizes, and 0.9717 for numbers. CONCLUSIONS: This study applied deep learning-based clinical NLP models to extract meaningful information from colonoscopy reports. The method in this study achieved promising results that demonstrate it can be applied to various practical purposes.

Compressive Sensing of Medical Images Based on HSV Color Space.

Recently, compressive sensing (CS) schemes have been studied as a new compression modality that exploits the sensing matrix in the measurement scheme and the reconstruction scheme to recover the compressed signal. In addition, CS is exploited in medical imaging (MI) to support efficient sampling, compression, transmission, and storage of a large amount of MI. Although CS of MI has been extensively investigated, the effect of color space in CS of MI has not yet been studied in the literature. To fulfill these requirements, this article proposes a novel CS of MI based on hue-saturation value (HSV), using spread spectrum Fourier sampling (SSFS) and sparsity averaging with reweighted analysis (SARA). An HSV loop that performs SSFS is proposed to obtain a compressed signal. Next, HSV-SARA is proposed to reconstruct MI from the compressed signal. A set of color MIs is investigated, such as colonoscopy, magnetic resonance imaging of the brain and eye, and wireless capsule endoscopy images. Experiments were performed to show the superiority of HSV-SARA over benchmark methods in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) index, and measurement rate (MR). The experiments showed that a color MI, with a resolution of 256×256 pixels, could be compressed by the proposed CS at MR of 0.1, and could be improved in terms of SNR being 15.17% and SSIM being 2.53%. The proposed HSV-SARA can be a solution for color medical image compression and sampling to improve the image acquisition of medical devices.

Extract of Aster koraiensis Nakai Leaf Ameliorates Memory Dysfunction via Anti-inflammatory Action.

Aster koraiensis Nakai (AK) leaf reportedly ameliorates health problems, such as diabetes. However, the effects of AK on cognitive dysfunction or memory impairment remain unclear. This study investigated whether AK leaf extract could attenuate cognitive impairment. We found that AK extract reduced the production of nitric oxide (NO), tumour necrosis factor (TNF)-α, phosphorylated-tau (p-tau), and the expression of inflammatory proteins in lipopolysaccharide- or amyloid-ß-treated cells. AK extract exhibited inhibitory activity of control specific binding on N-methyl-D-aspartate (NMDA) receptors. Scopolamine-induced AD models were used chronically in rats and acutely in mice. Relative to negative controls (NC), hippocampal choline acetyltransferase (ChAT) and B-cell lymphoma 2 (Bcl2) activity was increased in rats chronically treated with scopolamine and fed an AK extract-containing diet. In the Y-maze test, spontaneous alterations were increased in the AK extract-fed groups compared to NC. Rats administered AK extract showed increased escape latency in the passive avoidance test. In the hippocampus of rats fed a high-AK extract diet (AKH), the expression of neuroactive ligand-receptor interaction-related genes, including Npy2r, Htr2c, and Rxfp1, was significantly altered. In the Morris water maze assay of mice acutely treated with scopolamine, the swimming times in the target quadrant of AK extract-treated groups increased significantly to the levels of the Donepezil and normal groups. We used Tg6799 Aß-overexpressing 5XFAD transgenic mice to investigate Aß accumulation in animals. In the AD model using 5XFAD, the administration of AK extract decreased amyloid-ß (Aß) accumulation and increased the number of NeuN antibody-reactive cells in the subiculum relative to the control group. In conclusion, AK extract ameliorated memory dysfunction by modulating ChAT activity and Bcl2-related anti-apoptotic pathways, affecting the expression of neuroactive ligand-receptor interaction-related genes and inhibiting Aß accumulation. Therefore, AK extract could be a functional material improving cognition and memory.

Evaluation of the Antiviral Activity of Tabamide A and Its Structural Derivatives against Influenza Virus.

Influenza viruses cause severe endemic respiratory infections in both humans and animals worldwide. The emergence of drug-resistant viral strains requires the development of new influenza therapeutics. Tabamide A (TA0), a phenolic compound isolated from tobacco leaves, is known to have antiviral activity. We investigated whether synthetic TA0 and its derivatives exhibit anti-influenza virus activity. Analysis of structure-activity relationship revealed that two hydroxyl groups and a double bond between C7 and C8 in TA0 are crucial for maintaining its antiviral action. Among its derivatives, TA25 showed seven-fold higher activity than TA0. Administration of TA0 or TA25 effectively increased survival rate and reduced weight loss of virus-infected mice. TA25 appears to act early in the viral infection cycle by inhibiting viral mRNA synthesis on the template-negative strand. Thus, the anti-influenza virus activity of TA0 can be expanded by application of its synthetic derivatives, which may aid in the development of novel antiviral therapeutics.

DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

PURPOSE: To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs. DESIGN: DeepLensNet was trained by applying deep learning models to the Age-Related Eye Disease Study (AREDS) dataset. PARTICIPANTS: A total of 18 999 photographs (6333 triplets) from longitudinal follow-up of 1137 eyes (576 AREDS participants). METHODS: Deep learning models were trained to detect and quantify nuclear sclerosis (NS; scale 0.9-7.1) from 45-degree slit-lamp photographs and cortical lens opacity (CLO; scale 0%-100%) and posterior subcapsular cataract (PSC; scale 0%-100%) from retroillumination photographs. DeepLensNet performance was compared with that of 14 ophthalmologists and 24 medical students. MAIN OUTCOME MEASURES: Mean squared error (MSE). RESULTS: On the full test set, mean MSE for DeepLensNet was 0.23 (standard deviation [SD], 0.01) for NS, 13.1 (SD, 1.6) for CLO, and 16.6 (SD, 2.4) for PSC. On a subset of the test set (substantially enriched for positive cases of CLO and PSC), for NS, mean MSE for DeepLensNet was 0.23 (SD, 0.02), compared with 0.98 (SD, 0.24; P = 0.000001) for the ophthalmologists and 1.24 (SD, 0.34; P = 0.000005) for the medical students. For CLO, mean MSE was 53.5 (SD, 14.8), compared with 134.9 (SD, 89.9; P = 0.003) for the ophthalmologists and 433.6 (SD, 962.1; P = 0.0007) for the medical students. For PSC, mean MSE was 171.9 (SD, 38.9), compared with 176.8 (SD, 98.0; P = 0.67) for the ophthalmologists and 398.2 (SD, 645.4; P = 0.18) for the medical students. In external validation on the Singapore Malay Eye Study (sampled to reflect the cataract severity distribution in AREDS), the MSE for DeepSeeNet was 1.27 for NS and 25.5 for PSC. CONCLUSIONS: DeepLensNet performed automated and quantitative classification of cataract severity for all 3 types of age-related cataract. For the 2 most common types (NS and CLO), the accuracy was significantly superior to that of ophthalmologists; for the least common type (PSC), it was similar. DeepLensNet may have wide potential applications in both clinical and research domains. In the future, such approaches may increase the accessibility of cataract assessment globally. The code and models are available at https://github.com/ncbi/deeplensnet.

Who is more likely to adopt and comply with the electronic patient-reported outcome measure (ePROM) mobile application? A real-world study with cancer patients undergoing active treatment.

PURPOSE: This study aims to identify factors associated with the adoption and compliance of electronic patient-reported outcome measure (ePROM) use among cancer patients in a real-world setting. METHODS: This prospective cohort study was conducted at the Samsung Medical Center in Seoul, Korea, from September 2018 to January 2019. Cancer patients aged 18 years or older who owned smartphones and who were receiving chemotherapy or radiation therapy were eligible for this study. Patients were asked to use the app to report their symptoms every 7 days for a total of 21 days (3 weeks). Logistic regression was performed to identify the factors associated with the adoption and compliance. RESULTS: Among 580 patients, 417 (71.9%) adopted the ePROM app and 159 (27.4%) out of 417 had good compliance. Patients who had greater expectations regarding the ease of use (adjusted odds ratio [aOR] 2.67, 95% CI: 1.28-5.57) and usefulness (aOR 1.69, 95% CI: 1.05-2.72) of the ePROM app were more likely to adopt the app than those who did not. Patients who had greater satisfaction with usefulness (aOR 1.89, 95% CI 1.10-3.25) were more likely to comply with using the app, but satisfaction with ease of use was not related to the compliance. CONCLUSION: While expectation regarding the ease of use and usefulness of the ePROM app was associated with the adoption of the app, satisfaction with ease of use was not related to compliance with the ePROM app. Satisfaction with usefulness was associated with the compliance of ePROM app use.

Sodium-Glucose Cotransporter-2 Inhibitor-Related Diabetic Ketoacidosis: Accuracy Verification of Operational Definition.

BACKGROUND: The most important aspect of a retrospective cohort study is the operational definition (OP) of the disease. We developed a detailed OP for the detection of sodium-glucose cotransporter-2 inhibitors (SGLT2i) related to diabetic ketoacidosis (DKA). The OP was systemically verified and analyzed. METHODS: All patients prescribed SGLT2i at four university hospitals were enrolled in this experiment. A DKA diagnostic algorithm was created and distributed to each hospital; subsequently, the number of SGLT2i-related DKAs was confirmed. Then, the algorithm functionality was verified through manual chart reviews by an endocrinologist using the same OP. RESULTS: A total of 8,958 patients were initially prescribed SGLT2i. According to the algorithm, 0.18% (16/8,958) were confirmed to have SGLT2i-related DKA. However, based on manual chart reviews of these 16 cases, there was only one case of SGLT2i-related DKA (positive predictive value = 6.3%). Even after repeatedly narrowing the diagnosis range of the algorithm, the effect of a positive predictive value was insignificant (6.3-10.0%, P > 0.999). CONCLUSION: Owing to the nature of electronic medical record data, we could not create an algorithm that clearly differentiates SGLT2i-related DKA despite repeated attempts. In all retrospective studies, a portion of the samples should be randomly selected to confirm the accuracy of the OP through chart review. In retrospective cohort studies in which chart review is not possible, it will be difficult to guarantee the reliability of the results.

Development and Validation of Digital Health Technology Literacy Assessment Questionnaire.

In clinical practice, assessing digital health literacy is important to identify patients who may encounter difficulties adapting to digital health using digital technology and service. We developed the Digital Health Technology Literacy Assessment Questionnaire (DHTL-AQ) to assess the ability to use digital health technology, services, and data. The DHTL-AQ was developed in three phases. In the first phase, the conceptual framework and domains and items were generated from a systematic literature review using relevant theory and surveys. In the second phase, a cross-sectional survey with 590 adults age ≥ 18 years was conducted at an academic hospital in Seoul, Korea in January and February 2020 to test face validity of the items. Then, psychometric validation was conducted to determine the final items and cut-off scores of the DHTL-AQ. The eHealth literacy scale, the Newest Vital Sign, and 10 mobile app task ability assessments were examined to test validity. The final DHTL-AQ includes 34 items in two domains (digital functional and digital critical literacy) and 4 categories (Information and Communications Technology terms, Information and Communications Technology icons, use of an app, evaluating reliability and relevance of health information). The DHTL-AQ had excellent internal consistency (overall Cronbach's α = 0.95; 0.87-0.94 for subtotals) and acceptable model fit (CFI = 0.821, TLI = 0.807, SRMR = 0.065, RMSEA = 0.090). The DHTL-AQ was highly correlated with task ability assessment (r = 0.7591), and moderately correlated with the eHealth literacy scale (r = 0.5265) and the Newest Vital Sign (r = 0.5929). The DHTL-AQ is a reliable and valid instrument to measure digital health technology literacy.

Delayed urea differential enhancement CEST (dudeCEST)-MRI with T1 correction for monitoring renal urea handling.

PURPOSE: We demonstrate a method of delayed urea differential enhancement CEST for probing urea recycling action of the kidney using expanded multi-pool Lorentzian fitting and apparent exchange-dependent relaxation compensation. METHODS: T1 correction of urea CEST contrast by apparent exchange-dependent relaxation was tested in phantoms. Nine mice were scanned at 7 Tesla following intraperitoneal injection of 2M 150 µL urea, and later saline. T1 maps and Z-spectra were acquired before and 20 and 40 min postinjection. Z-spectra were fit to a 7-pool Lorentzian model for CEST quantification and compared to urea assay of kidney homogenate. Renal injury was induced by aristolochic acid in 7 mice, and the same scan protocol was performed. RESULTS: Apparent exchange-dependent relaxation corrected for variable T1 times in phantoms. Urea CEST contrast at +1 ppm increased significantly at both time points following urea injection in the inner medulla and papilla. When normalizing the postinjection urea CEST contrast to the corresponding baseline value, both urea and saline injection resulted in identical fold changes in urea CEST contrast. Urea assay of kidney homogenate showed a significant correlation to both apparent exchange-dependent relaxation (R2 = 0.4687, P = .0017) and non-T1 -corrected Lorentzian amplitudes (R2 = 0.4964, P = .0011). Renal injury resulted in increased T1 time in the cortex and reduced CEST contrast change upon urea and saline infusion. CONCLUSION: Delayed urea enhancement following infusion can provide insight into renal urea handling. In addition, changes in CEST contrast at 1.0 ppm following saline infusion may provide insight into renal function.