TRAF6 and TRAF2/3 Binding Motifs in CD40 Differentially Regulate B Cell Function in T-Dependent Antibody Responses and Dendritic Cell Function in Experimental Autoimmune Encephalomyelitis.

Expression of the costimulatory molecule CD40 on both B cells and dendritic cells (DCs) is required for induction of experimental autoimmune encephalomyelitis (EAE), and cell-autonomous CD40 expression on B cells is required for primary T-dependent (TD) Ab responses. We now ask whether the function of CD40 expressed by different cell types in these responses is mediated by the same or different cytoplasmic domains. CD40 has been reported to possess multiple cytoplasmic domains, including distinct TRAF6 and TRAF2/3 binding motifs. To elucidate the in vivo function of these motifs in B cells and DCs involved in EAE and TD germinal center responses, we have generated knock-in mice containing distinct CD40 cytoplasmic domain TRAF-binding site mutations and have used these animals, together with bone marrow chimeric mice, to assess the roles that these motifs play in CD40 function. We found that both TRAF2/3 and TRAF6 motifs of CD40 are critically involved in EAE induction and demonstrated that this is mediated by a role of both motifs for priming of pathogenic T cells by DCs. In contrast, the TRAF2/3 binding motif, but not the TRAF6 binding motif, is required for B cell CD40 function in TD high-affinity Ab responses. These data demonstrate that the requirements for expression of specific TRAF-binding CD40 motifs differ for B cells or DCs that function in specific immune responses and thus identify targets for intervention to modulate these responses.

Effect of statins on the age of onset of age-related macular degeneration.

BACKGROUND: This study evaluated the relationship between statin use and the age of onset of age-related macular degeneration (AMD). METHODS: Electronic Health Records from 52,840 patients evaluated at University of California Los Angeles (UCLA) Ophthalmology Clinics and 9,977 patients evaluated at University of California San Francisco (UCSF) Ophthalmology Clinics were screened. Survival analysis was performed using Cox proportional hazards regression models and visualized using Kaplan Meier survival curves, with the following covariates-sex, ethnicity, smoking history, fluoxetine use, obesity, diabetes mellitus, and hypertension. RESULTS: 5,498 of 52,840 patients at UCLA were diagnosed with AMD. Statin use was associated with a later AMD onset (HR = 0.8823, p < 0.0001), while female sex (HR = 1.0852, p= 00,035), obesity (HR = 1.4555, p < 0.0001), and fluoxetine (HR = 1.3797, p= 0.0003) were associated with an earlier AMD onset. Non-hispanic black (HR = 0.5687, p < 0.0001) and hispanic ethnicities (HR = 0.8269, p= 0.0028) were associated with a later AMD onset. When stratifying for ethnicity, statins, fluoxetine, sex, and obesity were significant only within non-hispanic white subjects. Statin use was significant among patients with dry AMD (HR = 0.8410, p= 0.0001) but not wet AMD (0.9188, p= 0.0351). In the replication cohort, 526 of 9,977 patients at UCSF had AMD. Associations between statins (HR = 0.7643, p= 0.0033), non-hispanic black ethnicity (HR = 0.5043, p= 0.0035), and obesity (HR = 1.9602, p < 0.0001) on AMD onset were confirmed. CONCLUSIONS: In both cohorts, statin use and non-hispanic black ethnicity are associated with a later AMD onset, while obesity with an earlier AMD onset.

Transient induction of telomerase expression mediates senescence and reduces tumorigenesis in primary fibroblasts.

Telomerase is an enzymatic ribonucleoprotein complex that acts as a reverse transcriptase in the elongation of telomeres. Telomerase activity is well documented in embryonic stem cells and the vast majority of tumor cells, but its role in somatic cells remains to be understood. Here, we report an unexpected function of telomerase during cellular senescence and tumorigenesis. We crossed Tert heterozygous knockout mice (mTert+/- ) for 26 generations, during which time there was progressive shortening of telomeres, and obtained primary skin fibroblasts from mTert+/+ and mTert-/- progeny of the 26th cross. As a consequence of insufficient telomerase activities in prior generations, both mTert+/+ and mTert-/- fibroblasts showed comparable and extremely short telomere length. However, mTert-/- cells approached cellular senescence faster and exhibited a significantly higher rate of malignant transformation than mTert+/+ cells. Furthermore, an evident up-regulation of telomerase reverse-transcriptase (TERT) expression was detected in mTert+/+ cells at the presenescence stage. Moreover, removal or down-regulation of TERT expression in mTert+/+ and human primary fibroblast cells via CRISPR/Cas9 or shRNA recapitulated mTert-/- phenotypes of accelerated senescence and transformation, and overexpression of TERT in mTert-/- cells rescued these phenotypes. Taking these data together, this study suggests that TERT has a previously underappreciated, protective role in buffering senescence stresses due to short, dysfunctional telomeres, and preventing malignant transformation.

Distributed Code for Semantic Relations Predicts Neural Similarity during Analogical Reasoning.

The ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coarsely coded as links in a semantic network or finely coded as distributed patterns over some core set of abstract relations. The form and content of the conceptual and neural representations of semantic relations are yet to be empirically established. Using sequential presentation of verbal analogies, we compared neural activities in making analogy judgments with predictions derived from alternative computational models of relational dissimilarity to adjudicate among rival accounts of how semantic relations are coded and compared in the brain. We found that a frontoparietal network encodes the three relation types included in the design. A computational model based on semantic relations coded as distributed representations over a pool of abstract relations predicted neural activities for individual relations within the left superior parietal cortex and for second-order comparisons of relations within a broader left-lateralized network.

Discharge Clinical Characteristics and Post-Discharge Events in Patients with Severe COVID-19: A Descriptive Case Series.

BACKGROUND: As the SARS-CoV-2 pandemic continues, little guidance is available on clinical indicators for safely discharging patients with severe COVID-19. OBJECTIVE: To describe the clinical courses of adult patients admitted for COVID-19 and identify associations between inpatient clinical features and post-discharge need for acute care. DESIGN: Retrospective chart reviews were performed to record laboratory values, temperature, and oxygen requirements of 99 adult inpatients with COVID-19. Those variables were used to predict emergency department (ED) visit or readmission within 30 days post-discharge. PATIENTS (OR PARTICIPANTS): Age ≥ 18 years, first hospitalization for COVID-19, admitted between March 1 and May 2, 2020, at University of California, Los Angeles (UCLA) Medical Center, managed by an inpatient medicine service. MAIN MEASURES: Ferritin, C-reactive protein, lactate dehydrogenase, D-dimer, procalcitonin, white blood cell count, absolute lymphocyte count, temperature, and oxygen requirement were noted. KEY RESULTS: Of 99 patients, five required ED admission within 30 days, and another five required readmission. Fever within 24 h of discharge, oxygen requirement, and laboratory abnormalities were not associated with need for ED visit or readmission within 30 days of discharge after admission for COVID-19. CONCLUSION: Our data suggest that neither persistent fever, oxygen requirement, nor laboratory marker derangement was associated with need for acute care in the 30-day period after discharge for severe COVID-19. These findings suggest that physicians need not await the normalization of laboratory markers, resolution of fever, or discontinuation of oxygen prior to discharging a stable or improving patient with COVID-19.

Neural representations of magnitude for natural and rational numbers.

Humans have developed multiple symbolic representations for numbers, including natural numbers (positive integers) as well as rational numbers (both fractions and decimals). Despite a considerable body of behavioral and neuroimaging research, it is currently unknown whether different notations map onto a single, fully abstract, magnitude code, or whether separate representations exist for specific number types (e.g., natural versus rational) or number representations (e.g., base-10 versus fractions). We address this question by comparing brain metabolic response during a magnitude comparison task involving (on different trials) integers, decimals, and fractions. Univariate and multivariate analyses revealed that the strength and pattern of activation for fractions differed systematically, within the intraparietal sulcus, from that of both decimals and integers, while the latter two number representations appeared virtually indistinguishable. These results demonstrate that the two major notations formats for rational numbers, fractions and decimals, evoke distinct neural representations of magnitude, with decimals representations being more closely linked to those of integers than to those of magnitude-equivalent fractions. Our findings thus suggest that number representation (base-10 versus fractions) is an important organizational principle for the neural substrate underlying mathematical cognition.

Neural adaptation in pSTS correlates with perceptual aftereffects to biological motion and with autistic traits.

The adaptive nature of biological motion perception has been documented in behavioral studies, with research showing that prolonged viewing of an action can bias judgments of subsequent actions towards the opposite of its attributes. However, the neural mechanisms underlying action adaptation aftereffects remain unknown. We examined adaptation-induced changes in brain responses to an ambiguous action after adapting to walking or running actions within two bilateral regions of interest: 1) human middle temporal area (hMT+), a lower-level motion-sensitive region of cortex, and 2) posterior superior temporal sulcus (pSTS), a higher-level action-selective area. We found a significant correlation between neural adaptation strength in right pSTS and perceptual aftereffects to biological motion measured behaviorally, but not in hMT+. The magnitude of neural adaptation in right pSTS was also strongly correlated with individual differences in the degree of autistic traits. Participants with more autistic traits exhibited less adaptation-induced modulations of brain responses in right pSTS and correspondingly weaker perceptual aftereffects. These results suggest a direct link between perceptual aftereffects and adaptation of neural populations in right pSTS after prolonged viewing of a biological motion stimulus, and highlight the potential importance of this brain region for understanding differences in social-cognitive processing along the autistic spectrum.

Thalamic and extrathalamic mechanisms of consciousness after severe brain injury.

OBJECTIVE: What mechanisms underlie the loss and recovery of consciousness after severe brain injury? We sought to establish, in the largest cohort of patients with disorders of consciousness (DOC) to date, the link between gold standard clinical measures of awareness and wakefulness, and specific patterns of local brain pathology-thereby possibly providing a mechanistic framework for patient diagnosis, prognosis, and treatment development. METHODS: Structural T1-weighted magnetic resonance images were collected, in a continuous sample of 143 severely brain-injured patients with DOC (and 96 volunteers), across 2 tertiary expert centers. Brain atrophy in subcortical regions (bilateral thalamus, basal ganglia, hippocampus, basal forebrain, and brainstem) was assessed across (1) healthy volunteers and patients, (2) clinical entities (eg, vegetative state, minimally conscious state), (3) clinical measures of consciousness (Coma Recovery Scale-Revised), and (4) injury etiology. RESULTS: Compared to volunteers, patients exhibited significant atrophy across all structures (p < 0.05, corrected). Strikingly, we found almost no significant differences across clinical entities. Nonetheless, the clinical measures of awareness and wakefulness upon which differential diagnosis rely were systematically associated with tissue atrophy within thalamic and basal ganglia nuclei, respectively; the basal forebrain was atrophied in proportion to patients' response to sensory stimulation. In addition, nontraumatic injuries exhibited more extensive thalamic atrophy. INTERPRETATION: These findings provide, for the first time, a grounding in pathology for gold standard behavior-based clinical measures of consciousness, and reframe our current models of DOC by stressing the different links tying thalamic mechanisms to willful behavior and extrathalamic mechanisms to behavioral (and electrocortical) arousal.

Histone deacetylase inhibitors are neuroprotective and preserve NGF-mediated cell survival following traumatic brain injury.

Acute traumatic brain injury (TBI) is associated with long-term cognitive and behavioral dysfunction. In vivo studies have shown histone deacetylase inhibitors (HDACis) to be neuroprotective following TBI in rodent models. HDACis are intriguing candidates because they are capable of provoking widespread genetic changes and modulation of protein function. By using known HDACis and a unique small-molecule pan-HDACi (LB-205), we investigated the effects and mechanisms associated with HDACi-induced neuroprotection following CNS injury in an astrocyte scratch assay in vitro and a rat TBI model in vivo. We demonstrate the preservation of sufficient expression of nerve growth factor (NGF) and activation of the neurotrophic tyrosine kinase receptor type 1 (TrkA) pathway following HDACi treatment to be crucial in stimulating the survival of CNS cells after TBI. HDACi treatment up-regulated the expression of NGF, phospho-TrkA, phospho-protein kinase B (p-AKT), NF-κB, and B-cell lymphoma 2 (Bcl-2) cell survival factors while down-regulating the expression of p75 neurotrophin receptor (NTR), phospho-JNK, and Bcl-2-associated X protein apoptosis factors. HDACi treatment also increased the expression of the stem cell biomarker nestin, and decreased the expression of reactive astrocyte biomarker GFAP within damaged tissue following TBI. These findings provide further insight into the mechanisms by which HDACi treatment after TBI is neuroprotective and support the continued study of HDACis following acute TBI.

Antibiotic Switches in Urinary Tract Infection Are Associated With Atypical Symptoms and Emergent Care.

IMPORTANCE: Given worsening global antibiotic resistance, antimicrobial stewardship aims to use the shortest effective duration of the most narrow-spectrum, effective antibiotic for patients with specific urinary symptoms and laboratory testing consistent with urinary tract infection (UTI). Inappropriate treatment and unnecessary antibiotic switching for UTIs harms patients in a multitude of ways. OBJECTIVE: This study sought to analyze antibiotic treatment failures as measured by antibiotic switching for treatment of UTI in emergent and ambulatory care. STUDY DESIGN: For this retrospective cohort study, 908 encounters during July 2019 bearing a diagnostic code for UTI/cystitis in a single health care system were reviewed. Urinary and microbiological testing, symptoms endorsed at presentation, and treatments prescribed were extracted from the medical record. RESULTS: Of 908 patients diagnosed with UTI, 64% of patients (579/908) received antibiotics, 86% of which were empiric. All patients evaluated in emergent care settings were prescribed antibiotics empirically in contrast to 71% of patients in ambulatory settings (P < 0.001). Of patients given antibiotics, 89 of 579 patients (15%, 10% of all 908 patients) were switched to alternative antibiotics within 28 days. Emergent care settings and positive urine cultures were significantly associated with increased antibiotic switching. Patients subjected to switching tended to have higher rates of presenting symptoms inconsistent with UTI. CONCLUSIONS: Empiric treatment, particularly in an emergent care setting, was frequently inappropriate and associated with increasing rates of antibiotic switching. Given the profound potential contribution to antibiotic resistance, these findings highlight the need for improved diagnostic and prescribing accuracy for UTI.

An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method.

Background and Objective: Detection of the dicrotic notch (DN) within a cardiac cycle is essential for assessment of cardiac output, calculation of pulse wave velocity, estimation of left ventricular ejection time, and supporting feature-based machine learning models for noninvasive blood pressure estimation, and hypotension, or hypertension prediction. In this study, we present a new algorithm based on the iterative envelope mean (IEM) method to detect automatically the DN in arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms. Methods: The algorithm was evaluated on both ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. The analysis involved a total of 1,171,288 cardiac cycles for ABP waveforms and 3,424,975 cardiac cycles for PPG waveforms. To evaluate the algorithm's performance, the systolic phase duration (SPD) was employed, which represents the duration from the onset of the systolic phase to the DN in the cardiac cycle. Correlation plots and regression analysis were used to compare the algorithm with an established DN detection technique (second derivative). The marking of the DN temporal location was carried out by an experienced researcher using the help of the 'find_peaks' function from the scipy PYTHON package, serving as a reference for the evaluation. The marking was visually validated by both an engineer and an anesthesiologist. The robustness of the algorithm was evaluated as the DN was made less visually distinct across signal-to-noise ratios (SNRs) ranging from -30 dB to -5 dB in both ABP and PPG waveforms. Results: The correlation between SPD estimated by the algorithm and that marked by the researcher is strong for both ABP (R2(87343) =.99, p<.001) and PPG (R2(86764) =.98, p<.001) waveforms. The algorithm had a lower mean error of dicrotic notch detection (s): 0.0047 (0.0029) for ABP waveforms and 0.0046 (0.0029) for PPG waveforms, compared to 0.0693 (0.0770) for ABP and 0.0968 (0.0909) for PPG waveforms for the established 2nd derivative method. The algorithm has high accuracy of DN detection for SNR of >= -9 dB for ABP waveforms and >= -12 dB for PPG waveforms indicating robust performance in detecting the DN when it is less visibly distinct. Conclusion: Our proposed IEM- based algorithm can detect DN in both ABP and PPG waveforms with low computational cost, even in cases where it is not distinctly defined within a cardiac cycle of the waveform ('DN-less signals'). The algorithm can potentially serve as a valuable, fast, and reliable tool for extracting features from ABP and PPG waveforms. It can be especially beneficial in medical applications where DN-based features, such as SPD, diastolic phase duration, and DN amplitude, play a significant role.

An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method.

BACKGROUND AND OBJECTIVE: Detection of the dicrotic notch (DN) within a cardiac cycle is essential for assessment of cardiac output, calculation of pulse wave velocity, estimation of left ventricular ejection time, and supporting feature-based machine learning models for noninvasive blood pressure estimation, and hypotension, or hypertension prediction. In this study, we present a new algorithm based on the iterative envelope mean (IEM) method to detect automatically the DN in arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms. METHODS: The algorithm was evaluated on both ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. The analysis involved a total of 1,171,288 cardiac cycles for ABP waveforms and 3,424,975 cardiac cycles for PPG waveforms. To evaluate the algorithm's performance, the systolic phase duration (SPD) was employed, which represents the duration from the onset of the systolic phase to the DN in the cardiac cycle. Correlation plots and regression analysis were used to compare the algorithm against marked DN detection, while box plots and Bland-Altman plots were used to compare its performance with both marked DN detection and an established DN detection technique (second derivative). The marking of the DN temporal location was carried out by an experienced researcher using the help of the 'find_peaks' function from the scipy Python package, serving as a reference for the evaluation. The marking was visually validated by both an engineer and an anesthesiologist. The robustness of the algorithm was evaluated as the DN was made less visually distinct across signal-to-noise ratios (SNRs) ranging from -30 dB to -5 dB in both ABP and PPG waveforms. RESULTS: The correlation between SPD estimated by the algorithm and that marked by the researcher is strong for both ABP (R2(87,343) =0.99, p<.001) and PPG (R2(86,764) =0.98, p<.001) waveforms. The algorithm had a lower mean error of DN detection (s): 0.0047 (0.0029) for ABP waveforms and 0.0046 (0.0029) for PPG waveforms, compared to 0.0693 (0.0770) for ABP and 0.0968 (0.0909) for PPG waveforms for the established 2nd derivative method. The algorithm has high rate of detectability of DN detection for SNR of >= -9 dB for ABP waveforms and >= -12 dB for PPG waveforms indicating robust performance in detecting the DN when it is less visibly distinct. CONCLUSION: Our proposed IEM- based algorithm can detect DN in both ABP and PPG waveforms with low computational cost, even in cases where it is not distinctly defined within a cardiac cycle of the waveform ('DN-less signals'). The algorithm can potentially serve as a valuable, fast, and reliable tool for extracting features from ABP and PPG waveforms. It can be especially beneficial in medical applications where DN-based features, such as SPD, diastolic phase duration, and DN amplitude, play a significant role.

Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach.

Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre (training cohort). Demographics, medical comorbidities, vitals, and labs were collected and were used to construct random forest machine learning models to predict in-hospital mortality. Models were compared with logistic regression, and to commonly used heart failure risk scores. The models were subsequently validated in patients hospitalized with a diagnosis of heart failure from a second academic, community medical centre (validation cohort). The entire cohort comprised 21 802 patients, of which 14 539 were in the training cohort and 7263 were in the validation cohort. The median age (25th-75th percentile) was 70 (58-82) for the entire cohort, 43.2% were female, and 6.7% experienced inpatient mortality. In the overall cohort, 7621 (35.0%) patients had heart failure with reduced ejection fraction (EF ≤ 40%), 1271 (5.8%) had heart failure with mildly reduced EF (EF 41-49%), and 12 910 (59.2%) had heart failure with preserved EF (EF ≥ 50%). Random forest models in the validation cohort demonstrated a c-statistic (95% confidence interval) of 0.96 (0.95-0.97), sensitivity (SN) of 87.3%, and specificity (SP) of 90.6% for the prediction of in-hospital mortality. Models for those with HFrEF demonstrated a c-statistic of 0.96 (0.94-0.98), SN 88.2%, and SP 91.0%, and those for patients with HFpEF showed a c-statistic of 0.95 (0.93-0.97), SN 87.4%, and SP 89.5% for predicting in-hospital mortality. The random forest model significantly outperformed logistic regression (c-statistic 0.87, SN 75.9%, and SP 86.9%), and current existing risk scores including the Acute Decompensated Heart Failure National Registry risk score (c-statistic of 0.70, SN 69%, and SP 62%), and the Get With the Guidelines-Heart Failure risk score (c-statistic 0.69, SN 67%, and SP 63%); P < 0.001 for comparison. Machine learning models built from commonly recorded patient information can accurately predict in-hospital mortality among patients hospitalized with a diagnosis of heart failure.

Decreased glucocerebrosidase activity in Gaucher disease parallels quantitative enzyme loss due to abnormal interaction with TCP1 and c-Cbl.

Gaucher disease (GD), the most common lysosomal storage disorder of humans, is caused by mutations in the gene coding for the enzyme glucocerebrosidase (GCase). Clinical manifestations vary among patients with the three types of GD, and phenotypic heterogeneity occurs even among patients with identical mutations. To gain insight into why phenotypic heterogeneity occurs in GD, we investigated mechanisms underlying the net loss of GCase catalytic activity in cultured skin fibroblasts derived from patients with the three types of GD. The findings indicate that the loss of catalytic activity of GCase correlates with its quantitative reduction, rather than a decrease in functional capacity of mutant enzyme. Use of a proteasome inhibitor, lactacystin, resulted in increased expression of GCase, suggesting a mechanism of protein degradation in GD. Furthermore, reduced binding of GCase to TCP1 ring complex (TRiC), a regulator of correct protein folding, may result in defective maturation of nascent GCase in GD cells. Additionally, increased interaction between GCase and c-Cbl, an E3 ubiquitin ligase, may be involved in the degradation and loss of GCase in GD. The findings suggest that specific molecular mediators involved in GCase maturation and degradation could be responsible for phenotypic variation among patients with the same genotypes and that these mediators could be therapeutically targeted to increase GCase activity in patients with GD.

Ice Cores as a Source for Antimicrobials: From Bioprospecting to Biodesign.

The golden age has passed for antibiotic discovery, and while some antibiotics are currently in various phases of clinical trials in the United States, many pharmaceutical companies have abandoned antibiotic research. With the need for antibiotics, we should expand our horizon for therapeutic mining and can look toward understudied sources such as ice cores. Ice cores contain microorganisms and genetic material that have been frozen in time for thousands of years. The antibiotics used by these organisms are encoded in their genomes, which can be unlocked, identified, and characterized with modern advances in molecular biology, genetic sequencing, various computational approaches, and established natural product discovery pipelines. While synthetic biology can be used in natural product discovery approaches, synthetic biology and bioengineering efforts can also be leveraged in the selection and biodesign of increased compound yields, potency, and stability. Here, we provide the perspective that ice cores can be a source of novel antibiotic compounds and that the tools of synthetic biology can be used to design better antimicrobials.

Association of red blood cell distribution width with hospital admission and in-hospital mortality across all-cause adult emergency department visits.

Objectives: To test the association between the initial red blood cell distribution width (RDW) value in the emergency department (ED) and hospital admission and, among those admitted, in-hospital mortality. Materials and Methods: We perform a retrospective analysis of 210 930 adult ED visits with complete blood count results from March 2013 to February 2022. Primary outcomes were hospital admission and in-hospital mortality. Variables for each visit included demographics, comorbidities, vital signs, basic metabolic panel, complete blood count, and final diagnosis. The association of each outcome with the initial RDW value was calculated across 3 age groups (<45, 45-65, and >65) as well as across 374 diagnosis categories. Logistic regression (LR) and XGBoost models using all variables excluding final diagnoses were built to test whether RDW was a highly weighted and informative predictor for each outcome. Finally, simplified models using only age, sex, and vital signs were built to test whether RDW had additive predictive value. Results: Compared to that of discharged visits (mean [SD]: 13.8 [2.03]), RDW was significantly elevated in visits that resulted in admission (15.1 [2.72]) and, among admissions, those resulting in intensive care unit stay (15.3 [2.88]) and/or death (16.8 [3.25]). This relationship held across age groups as well as across various diagnosis categories. An RDW >16 achieved 90% specificity for hospital admission, while an RDW >18.5 achieved 90% specificity for in-hospital mortality. LR achieved a test area under the curve (AUC) of 0.77 (95% confidence interval [CI] 0.77-0.78) for hospital admission and 0.85 (95% CI 0.81-0.88) for in-hospital mortality, while XGBoost achieved a test AUC of 0.90 (95% CI 0.89-0.90) for hospital admission and 0.96 (95% CI 0.94-0.97) for in-hospital mortality. RDW had high scaled weights and information gain for both outcomes and had additive value in simplified models predicting hospital admission. Discussion: Elevated RDW, previously associated with mortality in myocardial infarction, pulmonary embolism, heart failure, sepsis, and COVID-19, is associated with hospital admission and in-hospital mortality across all-cause adult ED visits. Used alone, elevated RDW may be a specific, but not sensitive, test for both outcomes, with multivariate LR and XGBoost models showing significantly improved test characteristics. Conclusions: RDW, a component of the complete blood count panel routinely ordered as the initial workup for the undifferentiated patient, may be a generalizable biomarker for acuity in the ED.

Automated large-scale prediction of exudative AMD progression using machine-read OCT biomarkers.

Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss in individuals over 55 years old in the United States. One of the late-stage manifestations of AMD, and a major cause of vision loss, is the development of exudative macular neovascularization (MNV). Optical Coherence Tomography (OCT) is the gold standard to identify fluid at different levels within the retina. The presence of fluid is considered the hallmark to define the presence of disease activity. Anti-vascular growth factor (anti-VEGF) injections can be used to treat exudative MNV. However, given the limitations of anti-VEGF treatment, as burdensome need for frequent visits and repeated injections to sustain efficacy, limited durability of the treatment, poor or no response, there is a great interest in detecting early biomarkers associated with a higher risk for AMD progression to exudative forms in order to optimize the design of early intervention clinical trials. The annotation of structural biomarkers on optical coherence tomography (OCT) B-scans is a laborious, complex and time-consuming process, and discrepancies between human graders can introduce variability into this assessment. To address this issue, a deep-learning model (SLIVER-net) was proposed, which could identify AMD biomarkers on structural OCT volumes with high precision and without human supervision. However, the validation was performed on a small dataset, and the true predictive power of these detected biomarkers in the context of a large cohort has not been evaluated. In this retrospective cohort study, we perform the largest-scale validation of these biomarkers to date. We also assess how these features combined with other EHR data (demographics, comorbidities, etc) affect and/or improve the prediction performance relative to known factors. Our hypothesis is that these biomarkers can be identified by a machine learning algorithm without human supervision, in a way that they preserve their predictive nature. The way we test this hypothesis is by building several machine learning models utilizing these machine-read biomarkers and assessing their added predictive power. We found that not only can we show that the machine-read OCT B-scan biomarkers are predictive of AMD progression, we also observe that our proposed combined OCT and EHR data-based algorithm outperforms the state-of-the-art solution in clinically relevant metrics and provides actionable information which has the potential to improve patient care. In addition, it provides a framework for automated large-scale processing of OCT volumes, making it possible to analyze vast archives without human supervision.

Re-Imagining Public Health Education: Students' Perspectives and Considerations.

Since the onset of the COVID-19 pandemic, the shortcomings and neglected weaknesses of public health systems have risen to the surface, emphasizing the need for new approaches to designing and delivering public health training. Higher education institutions have a critical role in advocating for societal change and sufficiently prepare the next generations of public health. Therefore, this commentary shares the unique voices of current and recently completed graduate students from public health programs across the United States in identifying areas of improvement, so that proactive steps toward refining the current landscape of public health education and training may be taken. We speak upon the inaction and accountability of public health academic spaces in dismantling the various forms of systemic oppression, such as racism, colonialism, and epistemic injustice, while encouraging prospective and current graduate colleagues to be mindful and curious to re-imagine the role of such pedagogies of public health to reduce the progressing health inequities.

Automated Identification of Incomplete and Complete Retinal Epithelial Pigment and Outer Retinal Atrophy Using Machine Learning.

OBJECTIVE: To assess and validate a deep learning algorithm to automatically detect incomplete retinal pigment epithelial and outer retinal atrophy (iRORA) and complete retinal pigment epithelial and outer retinal atrophy (cRORA) in eyes with age-related macular degeneration. DESIGN: In a retrospective machine learning analysis, a deep learning model was trained to jointly classify the presence of iRORA and cRORA within a given B-scan. The algorithm was evaluated using 2 separate and independent datasets. PARTICIPANTS: OCT B-scan volumes from 71 patients with nonneovascular age-related macular degeneration captured at the Doheny-University of California Los Angeles Eye Centers and the following 2 external OCT B-scans testing datasets: (1) University of Pennsylvania, University of Miami, and Case Western Reserve University and (2) Doheny Image Reading Research Laboratory. METHODS: The images were annotated by an experienced grader for the presence of iRORA and cRORA. A Resnet18 model was trained to classify these annotations for each B-scan using OCT volumes collected at the Doheny-University of California Los Angeles Eye Centers. The model was applied to 2 testing datasets to assess out-of-sample model performance. MAIN OUTCOMES MEASURES: Model performance was quantified in terms of area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). Sensitivity, specificity, and positive predictive value were also compared against additional clinician annotators. RESULTS: On an independently collected test set, consisting of 1117 volumes from the general population, the model predicted iRORA and cRORA presence within the entire volume with nearly perfect AUROC performance and AUPRC scores (iRORA, 0.61; 95% confidence interval [CI] [0.45, 0.82]: cRORA, 0.83; 95% CI [0.68, 0.95]). On another independently collected set, consisting of 60 OCT B-scans enriched for iRORA and cRORA lesions, the model performed with AUROC (iRORA: 0.68, 95% CI [0.54, 0.81]; cRORA: 0.84, 95% CI [0.75, 0.94]) and AUPRC (iRORA: 0.70, 95% CI [0.55, 0.86]; cRORA: 0.82, 95% CI [0.70, 0.93]). CONCLUSIONS: A deep learning model can accurately and precisely identify both iRORA and cRORA lesions within the OCT B-scan volume. The model can achieve similar sensitivity compared with human graders, which potentially obviates a laborious and time-consuming annotation process and could be developed into a diagnostic screening tool.

SLIViT: a general AI framework for clinical-feature diagnosis from limited 3D biomedical-imaging data.

We present SLIViT, a deep-learning framework that accurately measures disease-related risk factors in volumetric biomedical imaging, such as magnetic resonance imaging (MRI) scans, optical coherence tomography (OCT) scans, and ultrasound videos. To evaluate SLIViT, we applied it to five different datasets of these three different data modalities tackling seven learning tasks (including both classification and regression) and found that it consistently and significantly outperforms domain-specific state-of-the-art models, typically improving performance (ROC AUC or correlation) by 0.1-0.4. Notably, compared to existing approaches, SLIViT can be applied even when only a small number of annotated training samples is available, which is often a constraint in medical applications. When trained on less than 700 annotated volumes, SLIViT obtained accuracy comparable to trained clinical specialists while reducing annotation time by a factor of 5,000 demonstrating its utility to automate and expedite ongoing research and other practical clinical scenarios.