Hepatitis B Birth Dose Vaccination Improvement Initiative: The 24-Hour Baby Bundle.

BACKGROUND: An estimated 25,000 infants are born to mothers diagnosed with hepatitis B virus (HBV) each year in the United States. Administration of the birth dose HBV vaccine prevents transmission during delivery. Despite national guidelines promoting vaccination within 24 hours of birth, fewer than 70% of infants receive the dose in their first 3 days of life. To improve compliance with national recommendations, Northwestern Medicine implemented a bundled care initiative in the well newborn nursery, entitled the 24-hour baby bundle (24-HBB). PURPOSE: Evaluate the 24-HBB's effect on improving time to HBV vaccine administration. METHODS: The 24-HBB was created by an interdisciplinary team and implemented on February 17, 2020. Bundled care begins at 23 hours of life, starting with the HBV vaccine, followed by bath, weight, and congenital heart disease screening, and ending with metabolic screening. We conducted a retrospective cohort study of 22,057 infants born at Northwestern Medicine Prentice Women's Hospital in Chicago, Illinois. Our sample included preintervention birthdates between February 16, 2019, and January 16, 2020, and postintervention birthdates between March 17, 2020, and February 16, 2021, with a 2-month washout education period between January 17, 2020, and March 16, 2020. RESULTS: Hepatitis B virus immunization within 24 hours increased significantly from 43.83% to 66.90% (P < .0001). In addition, overall hepatitis B immunization prior to discharge significantly increased after implementation of the 24-HBB (98.18% vs 98.82%, P < .0001). IMPLICATIONS FOR PRACTICE AND RESEARCH: The 24-HBB is effective at increasing rates of HBV immunization within 24 hours of birth. Newborn nurseries may benefit from similar initiatives to prevent hepatitis B infection, satisfy national recommendations, and promote childhood vaccination compliance.

Deep learning from latent spatiotemporal information of the heart: Identifying advanced bioimaging markers from echocardiograms.

A key strength of echocardiography lies in its integration of comprehensive spatiotemporal cardiac imaging data in real-time, to aid frontline or bedside patient risk stratification and management. Nonetheless, its acquisition, processing, and interpretation are known to all be subject to heterogeneity from its reliance on manual and subjective human tracings, which challenges workflow and protocol standardization and final interpretation accuracy. In the era of advanced computational power, utilization of machine learning algorithms for big data analytics in echocardiography promises reduction in cost, cognitive errors, and intra- and inter-observer variability. Novel spatiotemporal deep learning (DL) models allow the integration of temporal arm information based on unlabeled pixel echocardiographic data for convolution of an adaptive semantic spatiotemporal calibration to construct personalized 4D heart meshes, assess global and regional cardiac function, detect early valve pathology, and differentiate uncommon cardiovascular disorders. Meanwhile, data visualization on spatiotemporal DL prediction models helps extract latent temporal imaging features to develop advanced imaging biomarkers in early disease stages and advance our understanding of pathophysiology to support the development of personalized prevention or treatment strategies. Since portable echocardiograms have been increasingly used as point-of-care imaging tools to aid rural care delivery, the application of these new spatiotemporal DL techniques show the potentials in streamlining echocardiographic acquisition, processing, and data analysis to improve workflow standardization and efficiencies, and provide risk stratification and decision supporting tools in real-time, to prompt the building of new imaging diagnostic networks to enhance rural healthcare engagement.

Deep learning from atrioventricular plane displacement in patients with Takotsubo syndrome: lighting up the black-box.

Aims: The spatiotemporal deep convolutional neural network (DCNN) helps reduce echocardiographic readers' erroneous 'judgement calls' on Takotsubo syndrome (TTS). The aim of this study was to improve the interpretability of the spatiotemporal DCNN to discover latent imaging features associated with causative TTS pathophysiology. Methods and results: We applied gradient-weighted class activation mapping analysis to visualize an established spatiotemporal DCNN based on the echocardiographic videos to differentiate TTS (150 patients) from anterior wall ST-segment elevation myocardial infarction (STEMI, 150 patients). Forty-eight human expert readers interpreted the same echocardiographic videos and prioritized the regions of interest on myocardium for the differentiation. Based on visualization results, we completed optical flow measurement, myocardial strain, and Doppler/tissue Doppler echocardiography studies to investigate regional myocardial temporal dynamics and diastology. While human readers' visualization predominantly focused on the apex of the heart in TTS patients, the DCNN temporal arm's saliency visualization was attentive on the base of the heart, particularly at the atrioventricular (AV) plane. Compared with STEMI patients, TTS patients consistently showed weaker peak longitudinal displacement (in pixels) in the basal inferoseptal (systolic: 2.15 ± 1.41 vs. 3.10 ± 1.66, P < 0.001; diastolic: 2.36 ± 1.71 vs. 2.97 ± 1.69, P = 0.004) and basal anterolateral (systolic: 2.70 ± 1.96 vs. 3.44 ± 2.13, P = 0.003; diastolic: 2.73 ± 1.70 vs. 3.45 ± 2.20, P = 0.002) segments, and worse longitudinal myocardial strain in the basal inferoseptal (-8.5 ± 3.8% vs. -9.9 ± 4.1%, P = 0.013) and basal anterolateral (-8.6 ± 4.2% vs. -10.4 ± 4.1%, P = 0.006) segments. Meanwhile, TTS patients showed worse diastolic mechanics than STEMI patients (E'/septal: 5.1 ± 1.2 cm/s vs. 6.3 ± 1.5 cm/s, P < 0.001; S'/septal: 5.8 ± 1.3 cm/s vs. 6.8 ± 1.4 cm/s, P < 0.001; E'/lateral: 6.0 ± 1.4 cm/s vs. 7.9 ± 1.6 cm/s, P < 0.001; S'/lateral: 6.3 ± 1.4 cm/s vs. 7.3 ± 1.5 cm/s, P < 0.001; E/E': 15.5 ± 5.6 vs. 12.5 ± 3.5, P < 0.001). Conclusion: The spatiotemporal DCNN saliency visualization helps identify the pattern of myocardial temporal dynamics and navigates the quantification of regional myocardial mechanics. Reduced AV plane displacement in TTS patients likely correlates with impaired diastolic mechanics.

Predicting adverse cardiac events in sarcoidosis: deep learning from automated characterization of regional myocardial remodeling.

Recognizing early cardiac sarcoidosis (CS) imaging phenotypes can help identify opportunities for effective treatment before irreversible myocardial pathology occurs. We aimed to characterize regional CS myocardial remodeling features correlating with future adverse cardiac events by coupling automated image processing and data analysis on cardiac magnetic resonance (CMR) imaging datasets. A deep convolutional neural network (DCNN) was used to process a CMR database of a 10-year cohort of 117 consecutive biopsy-proven sarcoidosis patients. The maximum relevance - minimum redundancy method was used to select the best subset of all the features-24 (from manual processing) and 232 (from automated processing) left ventricular (LV) structural/functional features. Three machine learning (ML) algorithms, logistic regression (LogR), support vector machine (SVM) and multi-layer neural networks (MLP), were used to build classifiers to categorize endpoints. Over a median follow-up of 41.8 (inter-quartile range 20.4-60.5) months, 35 sarcoidosis patients experienced a total of 43 cardiac events. After manual processing, LV ejection fraction (LVEF), late gadolinium enhancement, abnormal segmental wall motion, LV mass (LVM), LVMI index (LVMI), septal wall thickness, lateral wall thickness, relative wall thickness, and wall thickness of 9 (out of 17) individual LV segments were significantly different between patients with and without endpoints. After automated processing, LVEF, end-diastolic volume, end-systolic volume, LV mass and wall thickness of 92 (out of 216) individual LV segments were significantly different between patients with and without endpoints. To achieve the best predictive performance, ML algorithms selected lateral wall thickness, abnormal segmental wall motion, septal wall thickness, and increased wall thickness of 3 individual segments after manual image processing, and selected end-diastolic volume and 7 individual segments after automated image processing. LogR, SVM and MLP based on automated image processing consistently showed better predictive accuracies than those based on manual image processing. Automated image processing with a DCNN improves data resolution and regional CS myocardial remodeling pattern recognition, suggesting that a framework coupling automated image processing with data analysis can help clinical risk stratification.

Mortality Correlates in Patients With Takotsubo Syndrome During the COVID-19 Pandemic.

We completed a systematic review of Takotsubo syndrome (TTS) cases reported during the coronavirus disease 2019 (COVID-19) pandemic and performed clustering and feature importance analysis and statistical testing for independence on the demographic, clinical, and imaging parameters. Compared with the data before the COVID-19 pandemic, TTS was increasingly diagnosed in physical stress (mostly COVID-19 pneumonia)-triggered male patients without psychiatric/neurologic disorders, warranting further investigation to establish new reference criteria to improve diagnostic specificity. In clustering analysis, sex and inpatient mortality primarily contributed to the automated classification of the TTS. Both sex and inpatient mortality had essential correlations with COVID-19 infection/pneumonia. There is effect modification of sex on outcomes in patients with COVID-19 infection and TTS, with male patients having significantly worse inpatient mortality. Meanwhile, significantly more male patients with TTS were classified as high risk according to International Takotsubo Registry prognostic scores, suggesting that male COVID-19/TTS survivors will likely have worse long-term outcome.

Spatio-temporal hybrid neural networks reduce erroneous human "judgement calls" in the diagnosis of Takotsubo syndrome.

BACKGROUND: We investigate whether deep learning (DL) neural networks can reduce erroneous human "judgment calls" on bedside echocardiograms and help distinguish Takotsubo syndrome (TTS) from anterior wall ST segment elevation myocardial infarction (STEMI). METHODS: We developed a single-channel (DCNN[2D SCI]), a multi-channel (DCNN[2D MCI]), and a 3-dimensional (DCNN[2D+t]) deep convolution neural network, and a recurrent neural network (RNN) based on 17,280 still-frame images and 540 videos from 2-dimensional echocardiograms in 10 years (1 January 2008 to 1 January 2018) retrospective cohort in University of Iowa (UI) and eight other medical centers. Echocardiograms from 450 UI patients were randomly divided into training and testing sets for internal training, testing, and model construction. Echocardiograms of 90 patients from the other medical centers were used for external validation to evaluate the model generalizability. A total of 49 board-certified human readers performed human-side classification on the same echocardiography dataset to compare the diagnostic performance and help data visualization. FINDINGS: The DCNN (2D SCI), DCNN (2D MCI), DCNN(2D+t), and RNN models established based on UI dataset for TTS versus STEMI prediction showed mean diagnostic accuracy 73%, 75%, 80%, and 75% respectively, and mean diagnostic accuracy of 74%, 74%, 77%, and 73%, respectively, on the external validation. DCNN(2D+t) (area under the curve [AUC] 0·787 vs. 0·699, P = 0·015) and RNN models (AUC 0·774 vs. 0·699, P = 0·033) outperformed human readers in differentiating TTS and STEMI by reducing human erroneous judgement calls on TTS. INTERPRETATION: Spatio-temporal hybrid DL neural networks reduce erroneous human "judgement calls" in distinguishing TTS from anterior wall STEMI based on bedside echocardiographic videos. FUNDING: University of Iowa Obermann Center for Advanced Studies Interdisciplinary Research Grant, and Institute for Clinical and Translational Science Grant. National Institutes of Health Award (1R01EB025018-01).

Machine Learning in Electrocardiography and Echocardiography: Technological Advances in Clinical Cardiology.

PURPOSE OF REVIEW: Electrocardiography (ECG) and echocardiography are the most widely used diagnostic tools in clinical cardiology. This review focuses on recent advancements in applying machine learning (ML) in ECG and echocardiography and potential synergistic ML integration of ECG and echocardiography. RECENT FINDINGS: ML algorithms have been used in ECG for technical quality assurance, arrhythmia identification, and prognostic predictions, and in echocardiography to recognize image views, quantify measurements, and identify pathologic patterns. Synergistic application of ML in ECG and echocardiograph has demonstrated the potential to optimize therapeutic response, improve risk stratification, and generate new disease classification. There is mounting evidence that ML potentially outperforms in disease diagnoses and outcome prediction with ECG and echocardiography when compared with trained healthcare professionals. The applications of ML in ECG and echocardiography are playing increasingly greater roles in medical research and clinical practice, particularly for their contributions to developing novel diagnostic/prognostic prediction models. The automation in data acquisition, processing, and interpretation help streamline the workflows of ECG and echocardiography in contemporary cardiology practice.

HEXIM1 controls P-TEFb processing and regulates drug sensitivity in triple-negative breast cancer.

The positive transcription elongation factor b (P-TEFb), composed of CDK9 and cyclin T, stimulates transcriptional elongation by RNA polymerase (Pol) II and regulates cell growth and differentiation. Recently, we demonstrated that P-TEFb also controls the expression of EMT regulators to promote breast cancer progression. In the nucleus, more than half of P-TEFb are sequestered in the inactive-state 7SK snRNP complex. Here, we show that the assembly of the 7SK snRNP is preceded by an intermediate complex between HEXIM1 and P-TEFb that allows transfer of the kinase active P-TEFb from Hsp90 to 7SK snRNP for its suppression. Down-regulation of HEXIM1 locks P-TEFb in the Hsp90 complex, keeping it in the active state to enhance breast cancer progression, but also rendering the cells highly sensitive to Hsp90 inhibition. Because HEXIM1 is often down-regulated in human triple-negative breast cancer (TNBC), these cells are particularly sensitive to Hsp90 inhibition. Our study provides a mechanistic explanation for the increased sensitivity of TNBC to Hsp90 inhibition.

trans-Trismethoxy resveratrol decreased fat accumulation dependent on fat-6 and fat-7 in Caenorhabditis elegans.

trans-Trismethoxy resveratrol (TMR) is a methyl analog of resveratrol. It is found to exhibit enhanced biological effects compared to resveratrol, such as inhibition of cancer cell growth and pro-apoptotic activities. However, the role of TMR in lipid metabolism is not fully understood. In this study, we used Caenorhabditis elegans, an in vivo nematode model which has been widely applied in disease research, including research on obesity, to investigate the effect of TMR on lipid metabolism. Treatment with TMR (100 and 200 µM) for 4 days significantly reduced triglyceride accumulation (14% and 20% reduction over the control, respectively) of C. elegans, without affecting nematode growth, food intake and reproduction. Treatment with TMR significantly downregulated stearoyl-CoA desaturase genes, fat-6 and fat-7, accompanied by a decrease in the desaturation index of fatty acids, the ratio of oleic acid to stearic acid. These results suggest that TMR inhibits fat accumulation by downregulating stearoyl-CoA desaturase in C. elegans.

Ectopic fat and aerobic fitness are key determinants of glucose homeostasis in nonobese Asians.

BACKGROUND: The importance of ectopic fat deposition and physical fitness in the pathogenesis of insulin resistance and beta cell dysfunction in subjects from the nonobese Asians is not known. MATERIALS AND METHODS: We conducted a cross-sectional study and measured insulin sensitivity (M value; 4-hour hyperinsulinaemic-euglycaemic clamp), insulin secretion rate (3-hour mixed meal tolerance test with oral minimal modelling), percent body fat, visceral adipose tissue, intramyocellular and intrahepatic lipid contents (magnetic resonance imaging and spectroscopy), cardiorespiratory fitness (VO2 max; graded exercise test) and habitual physical activity (short International Physical Activity Questionnaire) in 60 healthy nonobese Asian subjects (BMI = 21.9 ± 1.7 kg/m2 , age = 41.8 ± 13.4 years). RESULTS: M was inversely associated with percent body fat (r = -0.460, P < 0.001), visceral fat (r = -0.623, P < 0.001) and liver fat (r = -0.601, P < 0.001), whereas insulin secretion correlated positively with these adiposity indices (percent body fat: r = 0.303, P = 0.018; visceral fat: r = 0.409, P = 0.010; hepatic fat: r = 0.393, P = 0.002). VO2 max correlated negatively with insulin secretion rate (r = -0.420, P < 0.001) and positively with M (r = 0.658, P < 0.001). The amount of vigorous physical activity was positively associated with VO2 max (r = 0.682, P < 0.001). Multiple stepwise linear regression analyses indicated that VO2 max, age, and IHTG or VAT were independent determinants of insulin sensitivity and secretion (adjusted R2  = 69% and 33%, respectively, P < 0.001). CONCLUSIONS: Increased ectopic fat deposition is associated with reduced insulin sensitivity and increased insulin secretion in healthy nonobese Asians. Poor cardiorespiratory fitness, likely due to inadequate participation in vigorous exercise, is strongly related to suboptimal metabolic function. Interventions to encourage engagement in physical activity may thus be important for improving metabolic health in nonobese Asians.

Hypoculoside, a sphingoid base-like compound from Acremonium disrupts the membrane integrity of yeast cells.

We have isolated Hypoculoside, a new glycosidic amino alcohol lipid from the fungus Acremonium sp. F2434 belonging to the order Hypocreales and determined its structure by 2D-NMR (Nuclear Magnetic Resonance) spectroscopy. Hypoculoside has antifungal, antibacterial and cytotoxic activities. Homozygous profiling (HOP) of hypoculoside in Saccharomyces cerevisiae (budding yeast) revealed that several mutants defective in vesicular trafficking and vacuolar protein transport are sensitive to hypoculoside. Staining of budding yeast cells with the styryl dye FM4-64 indicated that hypoculoside damaged the vacuolar structure. Furthermore, the propidium iodide (PI) uptake assay showed that hypoculoside disrupted the plasma membrane integrity of budding yeast cells. Interestingly, the glycosidic moiety of hypoculoside is required for its deleterious effect on growth, vacuoles and plasma membrane of budding yeast cells.

The regulatory protein SnoN antagonizes activin/Smad2 protein signaling and thereby promotes adipocyte differentiation and obesity in mice.

Ski-related oncogene SnoN (SnoN or SKIL) regulates multiple signaling pathways in a tissue- and developmental stage-dependent manner and has broad functions in embryonic angiogenesis, mammary gland alveologenesis, cancer, and aging. Here, we report that SnoN also plays a critical role in white adipose tissue (WAT) development by regulating mesenchymal stem cell (MSC) self-renewal and differentiation. We found that SnoN promotes MSC differentiation in the adipocyte lineage by antagonizing activin A/Smad2, but not TGFß/Smad3 signaling. Mice lacking SnoN or expressing a mutant SnoN defective in binding to the Smads were protected from high-fat diet-induced obesity and insulin resistance, and MSCs lacking a functional SnoN exhibited defective differentiation. We further demonstrated that activin, via Smad2, appears to be the major regulator of WAT development in vivo We also noted that activin A is abundantly expressed in WAT and adipocytes through an autocrine mechanism and promotes MSC self-renewal and inhibits adipogenic differentiation by inducing expression of the gene encoding the homeobox transcription factor Nanog. Of note, SnoN repressed activin/Smad2 signaling and activin A expression, enabling expression of adipocyte-specific transcription factors and promoting adipogenic differentiation. In conclusion, our study has revealed that SnoN plays an important in vivo role in adipocyte differentiation and WAT development in vivo by decreasing activity in the activin/Smad2 signaling pathway.

Regulation of glucose metabolism in nondiabetic, metabolically obese normal-weight Asians.

Type 2 diabetes in Asia occurs largely in the absence of obesity. The metabolically obese normal-weight (MONW) phenotype refers to lean subjects with metabolic dysfunction that is typically observed in people with obesity and is associated with increased risk for diabetes. Previous studies evaluated MONW subjects who had greater body mass index (BMI) or total body fat than respective control groups, making interpretation of the results difficult. We evaluated insulin sensitivity (hyperinsulinemic-euglycemic clamp); insulin secretion (mixed meal with oral minimal modeling); intra-abdominal, muscle, and liver fat contents (magnetic resonance); and fasting and postprandial glucose and insulin concentrations in 18 MONW subjects and 18 metabolically healthy controls matched for age (43 ± 3 and 40 ± 3 yr; P = 0.52), BMI (both 22 ± 1 kg/m2; P = 0.69), total body fat (17 ± 1 and 16 ± 1 kg; P = 0.33), and sex (9 men and 9 women in each group). Compared with controls, MONW subjects had an approximately twofold greater visceral adipose tissue volume and an approximately fourfold greater intrahepatic fat content (but similar muscle fat), 20-30% lower glucose disposal rates and insulin sensitivity, and 30-40% greater insulin secretion rates (all P < 0.05). The disposition index, fasting glucose, and HbA1c concentrations were not different between groups, whereas postprandial glucose and insulin concentrations were ~15% and ~65% greater, respectively, in MONW than control subjects (both P < 0.05). We conclude that the MONW phenotype is associated with accumulation of fat in the intra-abdominal area and the liver, profound insulin resistance, but also a robust ß-cell insulin secretion response that compensates for insulin resistance and helps maintain glucose homeostasis.

Evaluation of the applicability of the Immuno-solid-phase allergen chip (ISAC) assay in atopic patients in Singapore.

BACKGROUND/OBJECTIVE: Molecular-based allergy diagnostics are gaining popularity in clinical practice. Our aim was to evaluate their role in the tropics, given the inherent genetic and environmental differences. METHODS: We recruited subjects with history of atopy and collected data on demographics and atopic symptoms using validated questionnaires. Subjects underwent a series of skin prick tests (SPT). Serum total and specific IgE levels were measured using ImmunoCAP FEIA and ImmunoCAP ISAC®, respectively. We describe their pattern of sensitization and agreement between test methods. RESULTS: A total of 135 subjects were recruited; mean ± SD age of 31.18 ± 12.72 years, 52.7% female. Allergic rhinitis (AR) was the most prevalent clinical manifestation of atopy (70.7%), followed by atopic dermatitis (AD) (50.5%) and asthma (26.2%). Polysensitization was seen in 51.1% of subjects by both SPT and ISAC. House dust mites (HDM) were the dominant allergen, with sensitization in 67.8% and 62% of subjects on SPT and ISAC, respectively. A group of subjects with monosensitization to B. tropicalis was identified. HDM sensitization was strongly associated with AR, while AD and asthma were not associated with sensitization to any allergen. Agreement between SPT and ISAC was mostly suboptimal. Greatest agreement was documented for the measurement of HDM sensitization with both methods (κ = 0.64). Sensitization to the bulk of the remaining allergens in the ISAC panel was infrequent. CONCLUSION: Multiplex methods should not be used as a screening tool, especially in a population with lower rates of polysensitization and a dominant sensitizing allergen. There may be a role in adjusting the antigen spectrum in the ISAC panel to regional differences.

Divergence of iron metabolism in wild Malaysian yeast.

Comparative genomic studies have reported widespread variation in levels of gene expression within and between species. Using these data to infer organism-level trait divergence has proven to be a key challenge in the field. We have used a wild Malaysian population of S. cerevisiae as a test bed in the search to predict and validate trait differences based on observations of regulatory variation. Malaysian yeast, when cultured in standard medium, activated regulatory programs that protect cells from the toxic effects of high iron. Malaysian yeast also showed a hyperactive regulatory response during culture in the presence of excess iron and had a unique growth defect in conditions of high iron. Molecular validation experiments pinpointed the iron metabolism factors AFT1, CCC1, and YAP5 as contributors to these molecular and cellular phenotypes; in genome-scale sequence analyses, a suite of iron toxicity response genes showed evidence for rapid protein evolution in Malaysian yeast. Our findings support a model in which iron metabolism has diverged in Malaysian yeast as a consequence of a change in selective pressure, with Malaysian alleles shifting the dynamic range of iron response to low-iron concentrations and weakening resistance to extreme iron toxicity. By dissecting the iron scarcity specialist behavior of Malaysian yeast, our work highlights the power of expression divergence as a signpost for biologically and evolutionarily relevant variation at the organismal level. Interpreting the phenotypic relevance of gene expression variation is one of the primary challenges of modern genomics.