AI-enabled Cardiac Chambers Volumetry and Calcified Plaque Characterization in Coronary Artery Calcium (CAC) Scans (AI-CAC) Significantly Improves on Agatston CAC Score for Predicting All Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis.

Background: Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) algorithms applied to CAC scans may provide significant improvement in prediction of all cardiovascular disease (CVD) events in addition to CHD, including heart failure, atrial fibrillation, stroke, resuscitated cardiac arrest, and all CVD-related deaths. Methods: We applied AI-enabled automated cardiac chambers volumetry and automated calcified plaque characterization to CAC scans (AI-CAC) of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). We used 15-year outcomes data and assessed discrimination using the time-dependent area under the curve (AUC) for AI-CAC versus the Agatston Score. Results: During 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow up for AI-CAC vs Agatston Score was (0.784 vs 0.701), (0.771 vs. 0.709), (0.789 vs.0.712) and (0.816 vs. 0.729) (p<0.0001 for all), respectively. The category-free Net Reclassification Index of AI-CAC vs. Agatston Score at 1-, 5-, 10-, and 15-year follow up was 0.31, 0.24, 0.29 and 0.29 (p<.0001 for all), respectively. AI-CAC plaque characteristics including number, location, and density of plaque plus number of vessels significantly improved NRI for CAC 1-100 cohort vs. Agatston Score (0.342). Conclusion: In this multi-ethnic longitudinal population study, AI-CAC significantly and consistently improved the prediction of all CVD events over 15 years compared with the Agatston score.

Mapping recurrent mosaic copy number variation in human neurons.

When somatic cells acquire complex karyotypes, they often are removed by the immune system. Mutant somatic cells that evade immune surveillance can lead to cancer. Neurons with complex karyotypes arise during neurotypical brain development, but neurons are almost never the origin of brain cancers. Instead, somatic mutations in neurons can bring about neurodevelopmental disorders, and contribute to the polygenic landscape of neuropsychiatric and neurodegenerative disease. A subset of human neurons harbors idiosyncratic copy number variants (CNVs, "CNV neurons"), but previous analyses of CNV neurons are limited by relatively small sample sizes. Here, we develop an allele-based validation approach, SCOVAL, to corroborate or reject read-depth based CNV calls in single human neurons. We apply this approach to 2,125 frontal cortical neurons from a neurotypical human brain. SCOVAL identifies 226 CNV neurons, which include a subclass of 65 CNV neurons with highly aberrant karyotypes containing whole or substantial losses on multiple chromosomes. Moreover, we find that CNV location appears to be nonrandom. Recurrent regions of neuronal genome rearrangement contain fewer, but longer, genes.

Development and validation of a deep-learning model to predict 10-year atherosclerotic cardiovascular disease risk from retinal images using the UK Biobank and EyePACS 10K datasets.

Background: Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of death globally, and early detection of high-risk individuals is essential for initiating timely interventions. The authors aimed to develop and validate a deep learning (DL) model to predict an individual's elevated 10-year ASCVD risk score based on retinal images and limited demographic data. Methods: The study used 89,894 retinal fundus images from 44,176 UK Biobank participants (96% non-Hispanic White, 5% diabetic) to train and test the DL model. The DL model was developed using retinal images plus age, race/ethnicity, and sex at birth to predict an individual's 10-year ASCVD risk score using the pooled cohort equation (PCE) as the ground truth. This model was then tested on the US EyePACS 10K dataset (5.8% non-Hispanic White, 99.9% diabetic), composed of 18,900 images from 8969 diabetic individuals. Elevated ASCVD risk was defined as a PCE score of ≥7.5%. Results: In the UK Biobank internal validation dataset, the DL model achieved an area under the receiver operating characteristic curve of 0.89, sensitivity 84%, and specificity 90%, for detecting individuals with elevated ASCVD risk scores. In the EyePACS 10K and with the addition of a regression-derived diabetes modifier, it achieved sensitivity 94%, specificity 72%, mean error -0.2%, and mean absolute error 3.1%. Conclusion: This study demonstrates that DL models using retinal images can provide an additional approach to estimating ASCVD risk, as well as the value of applying DL models to different external datasets and opportunities about ASCVD risk assessment in patients living with diabetes.

Special Issue: Vaccines against Antibiotic-Resistant Bacteria: From Bench to Bedside.

The emergence and global dissemination of bacterial strains from numerous species with resistance to multiple antibiotic classes has increased in recent years, both in the healthcare and the community setting [...].

The Echocardiographic Evaluation of the Right Heart: Current and Future Advances.

PURPOSE OF REVIEW: To discuss physiologic and methodologic advances in the echocardiographic assessment of right heart (RH) function, including the emergence of artificial intelligence (AI) and point-of-care ultrasound. RECENT FINDINGS: Recent studies have highlighted the prognostic value of right ventricular (RV) longitudinal strain, RV end-systolic dimensions, and right atrial (RA) size and function in pulmonary hypertension and heart failure. While RA pressure is a central marker of right heart diastolic function, the recent emphasis on venous excess imaging (VExUS) has provided granularity to the systemic consequences of RH failure. Several methodological advances are also changing the landscape of RH imaging including post-processing 3D software to delineate the non-longitudinal (radial, anteroposterior, and circumferential) components of RV function, as well as AI segmentation- and non-segmentation-based quantification. Together with recent guidelines and advances in AI technology, the field is shifting from specific RV functional metrics to integrated RH disease-specific phenotypes. A modern echocardiographic evaluation of RH function should focus on the entire cardiopulmonary venous unit-from the venous to the pulmonary arterial system. Together, a multi-parametric approach, guided by physiology and AI algorithms, will help define novel integrated RH profiles for improved disease detection and monitoring.

Genomic data resources of the Brain Somatic Mosaicism Network for neuropsychiatric diseases.

Somatic mosaicism is defined as an occurrence of two or more populations of cells having genomic sequences differing at given loci in an individual who is derived from a single zygote. It is a characteristic of multicellular organisms that plays a crucial role in normal development and disease. To study the nature and extent of somatic mosaicism in autism spectrum disorder, bipolar disorder, focal cortical dysplasia, schizophrenia, and Tourette syndrome, a multi-institutional consortium called the Brain Somatic Mosaicism Network (BSMN) was formed through the National Institute of Mental Health (NIMH). In addition to genomic data of affected and neurotypical brains, the BSMN also developed and validated a best practices somatic single nucleotide variant calling workflow through the analysis of reference brain tissue. These resources, which include >400 terabytes of data from 1087 subjects, are now available to the research community via the NIMH Data Archive (NDA) and are described here.

Personalized digital behaviour interventions increase short-term physical activity: a randomized control crossover trial substudy of the MyHeart Counts Cardiovascular Health Study.

Aims: Physical activity is associated with decreased incidence of the chronic diseases associated with aging. We previously demonstrated that digital interventions delivered through a smartphone app can increase short-term physical activity. Methods and results: We offered enrolment to community-living iPhone-using adults aged ≥18 years in the USA, UK, and Hong Kong who downloaded the MyHeart Counts app. After completion of a 1-week baseline period, e-consented participants were randomized to four 7-day interventions. Interventions consisted of: (i) daily personalized e-coaching based on the individual's baseline activity patterns, (ii) daily prompts to complete 10 000 steps, (iii) hourly prompts to stand following inactivity, and (iv) daily instructions to read guidelines from the American Heart Association (AHA) website. After completion of one 7-day intervention, participants subsequently randomized to the next intervention of the crossover trial. The trial was completed in a free-living setting, where neither the participants nor investigators were blinded to the intervention. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in a modified intention-to-treat analysis (modified in that participants had to complete 7 days of baseline monitoring and at least 1 day of an intervention to be included in analyses). This trial is registered with ClinicalTrials.gov, NCT03090321. Conclusion: Between 1 January 2017 and 1 April 2022, 4500 participants consented to enrol in the trial (a subset of the approximately 50 000 participants in the larger MyHeart Counts study), of whom 2458 completed 7 days of baseline monitoring (mean daily steps 4232 ± 73) and at least 1 day of one of the four interventions. Personalized e-coaching prompts, tailored to an individual based on their baseline activity, increased step count significantly (+402 ± 71 steps from baseline, P = 7.1⨯10-8). Hourly stand prompts (+292 steps from baseline, P = 0.00029) and a daily prompt to read AHA guidelines (+215 steps from baseline, P = 0.021) were significantly associated with increased mean daily step count, while a daily reminder to complete 10 000 steps was not (+170 steps from baseline, P = 0.11). Digital studies have a significant advantage over traditional clinical trials in that they can continuously recruit participants in a cost-effective manner, allowing for new insights provided by increased statistical power and refinement of prior signals. Here, we present a novel finding that digital interventions tailored to an individual are effective in increasing short-term physical activity in a free-living cohort. These data suggest that participants are more likely to react positively and increase their physical activity when prompts are personalized. Further studies are needed to determine the effects of digital interventions on long-term outcomes.

Mutation Analysis in Regulator DNA-Binding Regions for Antimicrobial Efflux Pumps in 17,000 Pseudomonas aeruginosa Genomes.

Mutations leading to upregulation of efflux pumps can produce multiple drug resistance in the pathogen Pseudomonas aeruginosa. Changes in their DNA binding regions, i.e., palindromic operators, can compromise pump depression and subsequently enhance resistance against several antibacterials and biocides. Here, we have identified (pseudo)palindromic repeats close to promoters of genes encoding 13 core drug-efflux pumps of P. aeruginosa. This framework was applied to detect mutations in these repeats in 17,292 genomes. Eighty-nine percent of isolates carried at least one mutation. Eight binary genetic properties potentially related to expression were calculated for mutations. These included palindromicity reduction, mutation type, positioning within the repeat and DNA-bending shift. High-risk ST298, ST308 and ST357 clones commonly carried four conserved mutations while ST175 and the cystic fibrosis-linked ST649 clones showed none. Remarkably, a T-to-C transition in the fourth position of the upstream repeat for mexEF-oprN was nearly exclusive of the high-risk ST111 clone. Other mutations were associated with high-risk sublineages using sample geotemporal metadata. Moreover, 1.5% of isolates carried five or more mutations suggesting they undergo an alternative program for regulation of their effluxome. Overall, P. aeruginosa shows a wide range of operator mutations with a potential effect on efflux pump expression and antibiotic resistance.

Fecal carriage of extended-spectrum beta-lactamase-producing Enterobacterales in healthy Spanish schoolchildren.

Background: Extended-spectrum ß-lactamase-producing Enterobacterales (ESBL-E) are a serious threat among emerging antibiotic resistant bacteria. Particularly, the number of cases of ESBL-E infections reported in children has been increasing in recent years, and approved antibiotic treatments for this age group are limited. However, information regarding the prevalence of colonization in European children, risk factors associated with colonization, and the characteristics of the colonizing strains is scarce. The aims of this study were to determine the prevalence of ESBL-E colonization in fecal samples of apparently healthy schoolchildren, to identify lifestyle routines associated with colonization, and to characterize clonal relationships and mechanisms of resistance in ESBL-E isolates. Methods: A cohort of 887 healthy children (3-13 years old) from seven primary and secondary schools in the Madrid metropolitan area was recruited between April-June 2018, and sociodemographic information and daily habits were collected. Fecal samples were screened for ESBL-E carriage in selective medium. ESBL-E isolates were further characterized by assessing molecular epidemiology (PFGE and MLST), ESBL gene carriage, and antibiotic resistance profile. This information was analyzed in conjunction with the metadata of the participants in order to identify external factors associated with ESBL-E carriage. Results: Twenty four ESBL-E, all but one Escherichia coli, were detected in 23 children (prevalence: 2.6%; 95% CI: 1.6-3.6%). Of these, seven contained the blaCTX-M-14 allele, five the blaCTX-M-15, five the blaSHV-12, three the blaCTX-M-27, three the blaCTX-M-32, and one the blaCTX-M-9. Significant clonal diversity was observed among the isolates that grouped into 22 distinct clusters (at <85% similarity of PFGE profile). ESBL-producing E. coli isolates belonged to 12 different STs, with ST10 (25%) and ST131 (17%) being the most frequent. Apart from ß-lactams, resistance to trimethoprim/sulfamethoxazole (46%), ciprofloxacin (33%), levofloxacin (33%), tobramycin (21%), and gentamicin (8%) were the most frequently detected. Conclusion: The prevalence of ESBL-E in the studied cohort of children was lower than the average colonization rate previously detected in Europe for both children and adults. E. coli was the main ESBL-producing species detected and CTX-M were the most frequently identified ESBLs. High ST diversity suggests polyclonal dissemination. Compared to other STs, ST131 isolates were associated with resistance to various antimicrobials.

Measure by measure: Resting heart rate across the 24-hour cycle.

BACKGROUND: Photoplethysmography (PPG) sensors, typically found in wrist-worn devices, can continuously monitor heart rate (HR) in large populations in real-world settings. Resting heart rate (RHR) is an important biomarker of morbidities and mortality, but no universally accepted definition nor measurement criteria exist. In this study, we provide a working definition of RHR and describe a method for accurate measurement of this biomarker, recorded using PPG derived from wristband measurement across the 24-hour cycle. METHODS: 433 healthy subjects wore a wrist device that measured activity and HR for up to 3 months. HR during inactivity was recorded and the duration of inactivity needed for HR to stabilise was ascertained. We identified the lowest HR during each 24-hour cycle (true RHR) and examined the time of day or night this occurred. The variation of HR during inactivity through the 24-hour cycle was also assessed. The sample was also subdivided according to daily activity levels for subset analysis. FINDINGS: Adequate data was obtained for 19,242 days and 18,520 nights. HR stabilised in most subjects after 4 minutes of inactivity. Mean (SD) RHR for the sample was 54.5 (8.0) bpm (day) and 50.5 (7.6) bpm (night). RHR values were highest in the least active group (lowest MET quartile). A circadian variation of HR during inactivity was confirmed, with the lowest values being between 0300 and 0700 hours for most subjects. INTERPRETATION: RHR measured using a PPG-based wrist-worn device is significantly lower at night than in the day, and a circadian rhythm of HR during inactivity was confirmed. Since RHR is such an important health metric, clarity on the definition and measurement methodology used is important. For most subjects, a minimum rest time of 4 minutes provides a reliable measurement of HR during inactivity and true RHR in a 24-hour cycle is best measured between 0300 and 0700 hours. Funding: This study was funded by Google.

Mapping the Complex Genetic Landscape of Human Neurons.

When somatic cells acquire complex karyotypes, they are removed by the immune system. Mutant somatic cells that evade immune surveillance can lead to cancer. Neurons with complex karyotypes arise during neurotypical brain development, but neurons are almost never the origin of brain cancers. Instead, somatic mutations in neurons can bring about neurodevelopmental disorders, and contribute to the polygenic landscape of neuropsychiatric and neurodegenerative disease. A subset of human neurons harbors idiosyncratic copy number variants (CNVs, "CNV neurons"), but previous analyses of CNV neurons have been limited by relatively small sample sizes. Here, we developed an allele-based validation approach, SCOVAL, to corroborate or reject read-depth based CNV calls in single human neurons. We applied this approach to 2,125 frontal cortical neurons from a neurotypical human brain. This approach identified 226 CNV neurons, as well as a class of CNV neurons with complex karyotypes containing whole or substantial losses on multiple chromosomes. Moreover, we found that CNV location appears to be nonrandom. Recurrent regions of neuronal genome rearrangement contained fewer, but longer, genes.

Unsupervised Machine Learning Organization of the Functional Dark Proteome of Gram-Negative "Superbugs": Six Protein Clusters Amenable for Distinct Scientific Applications.

Uncharacterized proteins have been underutilized as targets for the development of novel therapeutics for difficult-to-treat bacterial infections. To facilitate the exploration of these proteins, 2819 predicted, uncharacterized proteins (19.1% of the total) from reference strains of multidrug Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa species were organized using an unsupervised k-means machine learning algorithm. Classification using normalized values for protein length, pI, hydrophobicity, degree of conservation, structural disorder, and %AT of the coding gene rendered six natural clusters. Cluster proteins showed different trends regarding operon membership, expression, presence of unknown function domains, and interactomic relevance. Clusters 2, 4, and 5 were enriched with highly disordered proteins, nonworkable membrane proteins, and likely spurious proteins, respectively. Clusters 1, 3, and 6 showed closer distances to known antigens, antibiotic targets, and virulence factors. Up to 21.8% of proteins in these clusters were structurally covered by modeling, which allowed assessment of druggability and discontinuous B-cell epitopes. Five proteins (4 in Cluster 1) were potential druggable targets for antibiotherapy. Eighteen proteins (11 in Cluster 6) were strong B-cell and T-cell immunogen candidates for vaccine development. Conclusively, we provide a feature-based schema to fractionate the functional dark proteome of critical pathogens for fundamental and biomedical purposes.

Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study.

BACKGROUND: Morbidity from undiagnosed atrial fibrillation (AF) may be preventable with early detection. Many consumer wearables contain optical photoplethysmography (PPG) sensors to measure pulse rate. PPG-based software algorithms that detect irregular heart rhythms may identify undiagnosed AF in large populations using wearables, but minimizing false-positive detections is essential. METHODS: We performed a prospective remote clinical trial to examine a novel PPG-based algorithm for detecting undiagnosed AF from a range of wrist-worn devices. Adults aged ≥22 years in the United States without AF, using compatible wearable Fitbit devices and Android or iOS smartphones, were included. PPG data were analyzed using a novel algorithm that examines overlapping 5-minute pulse windows (tachograms). Eligible participants with an irregular heart rhythm detection (IHRD), defined as 11 consecutive irregular tachograms, were invited to schedule a telehealth visit and were mailed a 1-week ambulatory ECG patch monitor. The primary outcome was the positive predictive value of the first IHRD during ECG patch monitoring for concurrent AF. RESULTS: A total of 455 699 participants enrolled (median age 47 years, 71% female, 73% White) between May 6 and October 1, 2020. IHRDs occurred for 4728 (1%) participants, and 2070 (4%) participants aged ≥65 years during a median of 122 (interquartile range, 110-134) days at risk for an IHRD. Among 1057 participants with an IHRD notification and subsequent analyzable ECG patch monitor, AF was present in 340 (32.2%). Of the 225 participants with another IHRD during ECG patch monitoring, 221 had concurrent AF on the ECG and 4 did not, resulting in an IHRD positive predictive value of 98.2% (95% CI, 95.5%-99.5%). For participants aged ≥65 years, the IHRD positive predictive value was 97.0% (95% CI, 91.4%-99.4%). CONCLUSIONS: A novel PPG software algorithm for wearable Fitbit devices exhibited a high positive predictive value for concurrent AF and identified participants likely to have AF on subsequent ECG patch monitoring. Wearable devices may facilitate identifying individuals with undiagnosed AF. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04380415.

Corrigendum: Brief Research Report: Virus-Specific Humoral Immunity at Admission Predicts the Development of Respiratory Failure in Unvaccinated SARS-CoV-2 Patients.

[This corrects the article DOI: 10.3389/fimmu.2022.878812.].

Towards Control and Oversight of SARS-CoV-2 Diagnosis and Monitoring through Multiplexed Quantitative Electroanalytical Immune Response Biosensors.

The development of versatile and sensitive biotools to quantify specific SARS-CoV-2 immunoglobulins in SARS-CoV-2 infected and non-infected individuals, built on the surface of magnetic microbeads functionalized with nucleocapsid (N) and in-house expressed recombinant spike (S) proteins is reported. Amperometric interrogation of captured N- and S-specific circulating total or individual immunoglobulin (Ig) isotypes (IgG, IgM, and IgA), subsequently labelled with HRP-conjugated secondary antibodies, was performed at disposable single or multiplexed (8×) screen-printed electrodes using the HQ/HRP/H2O2 system. The obtained results using N and in-house expressed S ectodomains of five SARS-CoV-2 variants of concern (including the latest Delta and Omicron) allow identification of vulnerable populations from those with natural or acquired immunity, monitoring of infection, evaluation of vaccine efficiency, and even identification of the variant responsible for the infection.

Transcription-associated DNA DSBs activate p53 during hiPSC-based neurogenesis.

Neurons are overproduced during cerebral cortical development. Neural progenitor cells (NPCs) divide rapidly and incur frequent DNA double-strand breaks (DSBs) throughout cortical neurogenesis. Although half of the neurons born during neurodevelopment die, many neurons with inaccurate DNA repair survive leading to brain somatic mosaicism. Recurrent DNA DSBs during neurodevelopment are associated with both gene expression level and gene length. We used imaging flow cytometry and a genome-wide DNA DSB capture approach to quantify and map DNA DSBs during human induced pluripotent stem cell (hiPSC)-based neurogenesis. Reduced p53 signaling was brought about by knockdown (p53KD); p53KD led to elevated DNA DSB burden in neurons that was associated with gene expression level but not gene length in neural progenitor cells (NPCs). Furthermore, DNA DSBs incurred from transcriptional, but not replicative, stress lead to p53 activation in neurotypical NPCs. In p53KD NPCs, DNA DSBs accumulate at transcription start sites of genes that are associated with neurological and psychiatric disorders. These findings add to a growing understanding of how neuronal genome dynamics are engaged by high transcriptional or replicative burden during neurodevelopment.

Identification of Promoter Region Markers Associated With Altered Expression of Resistance-Nodulation-Division Antibiotic Efflux Pumps in Acinetobacter baumannii.

Genetic alterations leading to the constitutive upregulation of specific efflux pumps contribute to antibacterial resistance in multidrug resistant bacteria. The identification of such resistance markers remains one of the most challenging tasks of genome-level resistance predictors. In this study, 487 non-redundant genetic events were identified in upstream zones of three operons coding for resistance-nodulation-division (RND) efflux pumps of 4,130 Acinetobacter baumannii isolates. These events included insertion sequences, small indels, and single nucleotide polymorphisms. In some cases, alterations explicitly modified the expression motifs described for these operons, such as the promoter boxes, operators, and Shine-Dalgarno sequences. In addition, changes in DNA curvature and mRNA secondary structures, which are structural elements that regulate expression, were also calculated. According to their influence on RND upregulation, the catalog of upstream modifications were associated with "experimentally verified," "presumed," and "probably irrelevant" degrees of certainty. For experimental verification, DNA of upstream sequences independently carrying selected markers, three for each RND operon, were fused to a luciferase reporter plasmid system. Five out of the nine selected markers tested showed significant increases in expression with respect to the wild-type sequence control. In particular, a 25-fold expression increase was observed with the ISAba1 insertion sequence upstream the adeABC pump. Next, overexpression of each of the three multi-specific RND pumps was linked to their respective antibacterial substrates by a deep A. baumannii literature screen. Consequently, a data flow framework was then developed to link genomic upregulatory RND determinants to potential antibiotic resistance. Assignment of potential increases in minimal inhibitory concentrations at the "experimentally verified" level was permitted for 42 isolates to 7-8 unrelated antibacterial agents including tigecycline, which is overlooked by conventional resistome predictors. Thus, our protocol may represent a time-saving filter step prior to laborious confirmation experiments for efflux-driven resistance. Altogether, a computational-experimental pipeline containing all components required for identifying the upstream regulatory resistome is proposed. This schema may provide the foundational stone for the elaboration of tools approaching antibiotic efflux that complement routine resistome predictors for preventing antimicrobial therapy failure against difficult-to-threat bacteria.

Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms.

Background: Measuring vital signs plays a key role in both patient care and wellness, but can be challenging outside of medical settings due to the lack of specialized equipment. Methods: In this study, we prospectively evaluated smartphone camera-based techniques for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. HR was measured by placing the finger over the rear-facing camera, while RR was measured via a video of the participants sitting still in front of the front-facing camera. Results: In the HR study of 95 participants (with a protocol that included both measurements at rest and post exercise), the mean absolute percent error (MAPE) ± standard deviation of the measurement was 1.6% ± 4.3%, which was significantly lower than the pre-specified goal of 5%. No significant differences in the MAPE were present across colorimeter-measured skin-tone subgroups: 1.8% ± 4.5% for very light to intermediate, 1.3% ± 3.3% for tan and brown, and 1.8% ± 4.9% for dark. In the RR study of 50 participants, the mean absolute error (MAE) was 0.78 ± 0.61 breaths/min, which was significantly lower than the pre-specified goal of 3 breaths/min. The MAE was low in both healthy participants (0.70 ± 0.67 breaths/min), and participants with chronic respiratory conditions (0.80 ± 0.60 breaths/min). Conclusions: These results validate the accuracy of our smartphone camera-based techniques to measure HR and RR across a range of pre-defined subgroups.

Brief Research Report: Virus-Specific Humoral Immunity at Admission Predicts the Development of Respiratory Failure in Unvaccinated SARS-CoV-2 Patients.

Introduction: There is robust evidence indicating that the SARS-CoV-2-specific humoral response is associated with protection against severe disease. However, relatively little data exist regarding how the humoral immune response at the time of hospital admission correlates with disease severity in unimmunized patients. Our goal was toidentify variables of the humoral response that could potentially serve as prognostic markers for COVID-19 progressionin unvaccinated SARS-CoV-2 patients. Methods: A prospective cross-sectional study was carried out in a cohort of 160 unimmunized, adult COVID-19 patients from the Hospital Universitario 12Octubre. Participants were classified into four clinical groups based on disease severity: non-survivors with respiratory failure (RF), RF survivors, patients requiring oxygen therapy and those not receiving oxygen therapy. Serum samples were taken on admission and IgM, IgG, IgG subclass antibody titers were determined by ELISA, and neutralizing antibody titersusing a surrogate neutralization assay. The differences in the antibody titers between groups and the association between the clinical and analytical characteristics of the patients and the antibody titers were analyzed. Results: Patients that developed RF and survived had IgM titers that were 2-fold higher than non-survivors (p = 0.001), higher levels of total IgG than those who developed RF and succumbed to infection (p< 0.001), and than patients who required oxygen therapy (p< 0.05), and had 5-fold higher IgG1 titers than RF non-survivors (p< 0.001) and those who needed oxygen therapy (p< 0.001), and 2-fold higher than patients that did not require oxygen therapy during admission (p< 0.05). In contrast, RF non-survivorshad the lowest neutralizing antibodylevels, which were significantly lower compared those with RF that survived (p = 0.03). A positive correlation was found between IgM, total IgG, IgG1 and IgG3 titers and neutralizing antibody titers in the total cohort (p ≤ 0.0036). Conclusions: We demonstrate that patients with RF that survived infection had significantly higher IgM, IgG, IgG1 and neutralizing titers compared to patients with RF that succumb to infection, suggesting that using humoral response variables could be used as a prognostic marker for guiding the clinical management of unimmunized patients admitted to the hospital for SARS-CoV-2 infection.