Central Data Repository Research Articles

Lies, damned lies, and statistics: The uncertainty over COVID‐19 numbers in India

This paper intends to ascertain the veracity of reported data on deaths and testing pertaining to the novel coronavirus in India. We use a widely used forensic audit technique called Benford's law to analyze the data, and our findings suggest anomalies in the reported numbers and the reported data for most of the states do not adhere to the Benford distribution. The implications of these findings are manifold, especially on the trajectory of policy‐making, vaccination strategy, and preparedness for future waves and new variants. We strongly argue for the need for a robust data collection and reporting mechanism, creating a central data repository, and instituting a data‐driven policy framework as key steps in the process management bulwark for managing such future pandemics and other events concerning public health.

Simulation Testbed for Evaluating Distributed Querying and Searching of Mass Spectrometry Big Data in a Network-based Infrastructure.

Advance access and reuse mechanisms for large-scale Mass Spectrometry (MS) data are essential for democratizing data for the omics research community and making it adhere to FAIR (Findable, Accessible, Interoperable, Reusable) principles. Although a number of centralized data repositories have been established, they have been limited to search mechanisms that depend on the meta-data associated with these MS datasets. Furthermore, they require constant influx of resources for maintenance. In this paper, we proposed an alternative novel distributed infrastructure for direct MS/MS spectral search. We designed and developed a simulation testbed using concepts from computer networks, queuing theory, and stochastic simulation methods. Results show that a distributed MS search based on raw MS/MS spectra can scale gracefully for up-to 2000 participating nodes, while simultaneously processing queries using the proposed networked infrastructure on the order of milliseconds to a few seconds for up-to a total of fifty billion MS/MS spectra.

Commentary: Strategies to Address COVID-19 Vaccine Hesitancy and Mitigate Health Disparities in Minority Populations.

Commentary: Strategies to Address COVID-19 Vaccine Hesitancy and Mitigate Health Disparities in Minority Populations.

Mapping sugarcane in Thailand using transfer learning, a lightweight convolutional neural network, NICFI high resolution satellite imagery and Google Earth Engine

Air pollution from burning sugarcane is an important environmental issue in Thailand. Knowing the location and extent of sugarcane plantations would help in formulating effective strategies to reduce burning. High resolution satellite imagery combined with deep-learning technologies can be effective to map sugarcane with high precision. However, land cover mapping using high resolution data and computationally intensive deep-learning networks can be computationally costly. In this study, we used high resolution satellite imagery from Planet that has been made available to the public through the Norway's International Climate and Forest Initiative (NICFI). We tested a U-Net deep-learning algorithm with a lightweight MobileNetV2 network as the encoder branch using the Google Earth Engine computational platform. We trained a model using the RGB channels with pre-trained network (RGBt), a RGB model with randomly initialized weights (RGBr) and a model with randomly initialized weights including the NIR channel (RGBN). We found an F1-score of 0.9550, 0.9262 and 0.9297 for the RGBt, RGBr and RGBN models, respectively. For an independent model evaluation we found F1-scores of 0.9141, 0.8681 and 0.8911. We also found a discrepancy in the recall values reported by the model and those from the independent validation. We found that lightweight deep-learning models produce satisfactory results while providing effective means to apply mapping efforts at scale with reduced computational costs. We highlight the importance of central data repositories with labeled data as pre-trained networks were found to be effective in improving the accuracy.

Patient experiences and strategies for coping with SLE: A qualitative study.

This study explored challenges that patients with systemic lupus erythematosus (SLE) and childhood-onset SLE (cSLE) face to identify modifiable influences and coping strategies in patient experiences. Participants were recruited from two academic medical centers through a Lupus Registry of individuals ≥18 years old and ≥4 1997 ACR classification criteria for SLE and a centralized data repository of cSLE patients, and participated in three focus groups. Transcripts were coded thematically and adjudicated by two independent reviewers. Thirteen adults, 7 (54%) with cSLE, participated in focus groups. Themes were categorized into two domains: (1) challenges with SLE diagnosis and management; and (2) patient coping strategies and modifiable factors of the SLE experience. Participants identified five primary challenges: diagnostic odyssey, public versus private face of SLE, SLE-related stresses, medication adherence, and transitioning from pediatric to adult care. Coping strategies and modifiable factors included social support, open communication about SLE, and strong patient-provider relationships. Several participants highlighted positive lessons learned through their experiences with SLE, including empathy, resilience, and self-care skills. Patients with cSLE and SLE identified common challenges, modifying influences and coping strategies based on personal experiences. A strong patient-provider relationship and trust in the medical team emerged as key modifiable factors. Deriving optimism from experiences with SLE was unique to several patients diagnosed as children or young adults. Leveraging factors that improved the participants' experiences living with SLE may be used in future studies to address vulnerabilities in care.

Characteristics and Outcomes of COVID-19 in Patients on Left Ventricular Assist Device Support.

The coronavirus disease 2019 (COVID-19) pandemic continues to afflict millions of people worldwide. Patients with end-stage heart failure and left ventricular assist devices (LVADs) may be at risk for severe COVID-19 given a high prevalence of complex comorbidities and functional impaired immunity. The objective of this study is to describe the clinical characteristics and outcomes of COVID-19 in patients with end-stage heart failure and durable LVADs. The Trans-CoV-VAD registry is a multi-center registry of LVAD and cardiac transplant patients in the United States with confirmed COVID-19. Patient characteristics, exposure history, presentation, laboratory data, course, and clinical outcomes were collected by participating institutions and reviewed by a central data repository. This report represents the participation of the first 9 centers to report LVAD data into the registry. A total of 40 patients were included in this cohort. The median age was 56 years (interquartile range, 46-68), 14 (35%) were women, and 21 (52%) were Black. Among the most common presenting symptoms were cough (41%), fever, and fatigue (both 38%). A total of 18% were asymptomatic at diagnosis. Only 43% of the patients reported either subjective or measured fever during the entire course of illness. Over half (60%) required hospitalization, and 8 patients (20%) died, often after lengthy hospitalizations. We present the largest case series of LVAD patients with COVID-19 to date. Understanding these characteristics is essential in an effort to improve the outcome of this complex patient population.

The Worldwide Epidemiology of Acute Appendicitis: An Analysis of the Global Health Data Exchange Dataset.

This study aims to describe the worldwide epidemiology and changing disease trends of acute appendicitis (AA). Epidemiological data on the incidence of AA and deaths were collected from the Global Health Data Exchange repository from 1990 to 2019. Data were stratified by age, sex and Socio-Demographic Index (SDI). In 2019, there were an estimated 17.7 million cases (incidence 228/100,000) with over 33,400 deaths (0.43/100,000). Both the absolute number and the incidence had increased from 1990 to 2019 (+ 38.8% and + 11.4%, respectively). The number of deaths and deaths per 100,000 declined during this period (- 21.8% and - 46.2%, respectively). These trends were largely similar in all 5 SDI groups. There was a significant difference in the incidence of AA between the SDI groups, with low SDI group having the lowest and high SDI group having the highest. The high SDI group had the lowest mortality rate (Kruskall-Wallis test, p < 0.001). The peak incidence was in the 15-19-year age group. The mortality rate increased exponentially from the third decade of life. The mortality rate of AA is declining worldwide, while the incidence is increasing. The peak incidence is in the 15-19-year group.

Becoming data: biometric IDs and the individual in ‘Digital India’

AbstractAadhaar (literally ‘foundation’) is the largest national biometric identification drive the world has witnessed. An Aadhaar is a twelve‐digit ID number linked to its holder's iris scans, fingerprints, facial photograph, and demographic information in a centralized database. In but a decade, India has expeditiously enrolled over 90 per cent of its billion‐strong population into a Central Identities Data Repository. This essay is an ethnographic consideration of the processes by which Aadhaar enrollees become data, focusing on the sociopolitical valence of biometric data. It argues that the datafication of the body via Aadhaar occasions re‐examinations of – and contestations over – the idea of the individual in postcolonial India, a country often deemed sociocentric in popular and scholarly discourse alike. Further, it suggests that biometric socialization facilitates belonging in a ‘Digital India’, often rendered as a data cosmopolis in emergent technocratic imaginations.

Enhancing Traceability of Infectious Diseases: A Blockchain-Based Approach

The global pandemic of COVID-19 has brought significant attentions to three important features of disease direct reporting systems: traceability, reliability, and effectiveness. A traditional disease direct reporting system has a central node of control, with a hierarchical structure that goes up from locals (cities and counties) to regions and eventually reaches a central data repository. Such systems are often prone to easy data loss, arbitrary or unauthorized data changes, and unreliable traceability to individual nodes. Blockchain, as a new disruptive technology, provides a potential solution. Leveraging blockchain's features of decentralization, unforgeability, whole-process traceability, we develop a method for disease information tracing with key components including infectious disease information collection, information chain-style storage, and information query. Our blockchain-based infectious disease traceability method can promptly collect disease information and form the disease information time series blockchain. We demonstrate that the information chain constructed is authentic and transparent, and it can be queried and maintained at any node in the system. Consequently, the infectious disease information on the blockchain can be monitored and queried any time, thereby greatly facilitating the tracing of the propagation paths of infectious diseases.

Theoretical and Experimental Perspectives of Quantum Verification

In this perspective we discuss verification of quantum devices in the context of specific examples, formulated as proposed experiments. Our first example is verification of analog quantum simulators as Hamiltonian learning, where the input Hamiltonian as design goal is compared with the parent Hamiltonian for the quantum states prepared on the device. The second example discusses cross-device verification on the quantum level, i.e. by comparing quantum states prepared on different quantum devices. We focus in particular on protocols using randomized measurements, and we propose establishing a central data repository, where existing experimental devices and platforms can be compared. In our final example, we address verification of the output of a quantum device from a computer science perspective, addressing the question of how a user of a quantum processor can be certain about the correctness of its output, and propose minimal demonstrations on present day devices.

Defining Benchmarks in Pediatric Burn Care: Inception of the Pediatric Injury Quality Improvement Collaborative

Pediatric burn care is highly variable nationwide. Standardized quality and performance benchmarks are needed for guiding performance improvement within pediatric burn centers. A network of pediatric burn centers was established to develop and evaluate pediatric-specific best practices. A multi-disciplinary team including pediatric surgeons, nurses, advanced practice providers, pediatric intensivists, rehabilitation staff, and child psychologists from five pediatric burn centers established a collaborative to share and compare performance improvement data, evaluate outcomes, and exchange best care practices. In December 2016, the Pediatric Injury Quality Improvement Collaborative (PIQIC) was established. PIQIC members chose quality improvement indicators, drafted and approved a memorandum of understanding (MOU), data use agreement (DUA) and charter, formalized the multidisciplinary membership, and established a steering committee. Since inception, PIQIC has conducted monthly teleconferences and biannual in-person or virtual group meetings. A centralized data repository has been established where data is collated and analyzed for benchmarking in a blinded fashion. PIQIC has shown the feasibility of multi-institutional data collection, implementation of performance improvement metrics, publication of research, and enhancement of aggregate and institution-specific pediatric burn care.

Testing Novel New Drip Emitter with Variable Diameters for a Variable Rate Drip Irrigation

This research presents a new variable rate drip irrigation (VRDI) emitter design that can monitor individual water drops. Conventional drip systems cannot monitor the individual water flow rate per emitter. Application uniformity for conventional drip emitters can be decreased by clogged emitters, irregular emitter orifices, and decreases in pressure. A VRDI emitter can overcome the irrigation challenges in the field by increasing water application uniformity for each plant and reducing water losses. Flow rate is affected by the diameter of the delivery pipe and the pressure of the irrigation delivery system. This study compares the volumetric water flow rate for conventional drip emitters and new VRDI emitters with variable diameters inner (1 mm, 1.2 mm, 1.4 mm, and 1.6 mm) and outside (3 mm, 3.5 mm, 4 mm, and 4.5 mm) with three pressures (34 kPa, 69 kPa, and 103 kPa). The tests revealed that the new VRDI emitter had flow rates that increased as the operating pressure increased similar to a conventional drip tube. The flow rate was slightly increased in the VRDI with pressure, but even this increase did not show large changes in the flow rate. The flow rate of the conventional drip tube was 88% larger than the VRDI emitter for all pressures (p &lt; 0.05). However, operating pressure did not affect the drop sizes at the VRDI emitter, but the generalized linear mixed models (GLM) results show that volume per drop was impacted by the outside diameter of the VRDI outlet (p &lt; 0.05). The interaction between the inner and outside diameter was also significant at p &lt; 0.01, and the interaction between outside diameter and pressure was statistically significant at p &lt; 0.01. The electronic components used to control our VRDI emitter are readily compatible with off-the-shelf data telemetry solutions; thus, each emitter could be controlled remotely and relay data to a centralized data repository or decision-maker, and a plurality of these emitters could be used to enable full-field scale VRDI.

Sex differences in structural brain asymmetry of children with autism spectrum disorders

Previous studies have confirmed the sex difference of gray matter asymmetry in typically developing controls and the abnormal gray matter asymmetry in autism spectrum disorders. However, whether and how sex differences of gray matter asymmetry exist in autism spectrum disorders remains studied. This paper analyzes the above issues and explores correlations between gray matter asymmetry and autistic symptoms. Data from 72 children (36 males and 36 females) with autism spectrum disorders and 72 typically developing-controls (36 males and 36 females) between 8 and 14 years were included and obtained from the autism brain imaging data exchange repository (autism brain imaging data exchange I and autism brain imaging data exchange II). The voxel-based morphometry approach was used to assess gray matter asymmetry in T1-weighted brain data, and gray matter asymmetry was quantified as asymmetry index. A 2 × 2 analysis of covariance was used to identify asymmetry index differences among the four groups. Pearson correlation analysis was performed for asymmetry index values extracted from the clusters with significant differences between the four groups and autistic symptoms (social impairments, communication difficulties, and restricted, repetitive behaviors) measured by the revised autism diagnostic interview scale. Results showed that specific brain regions showed significant main effects for diagnosis in which autism spectrum disorders patients had more leftward asymmetry than typically developing-controls for the parahippocampal gyrus and the postcentral gyrus; specific brain regions showed significant main effects for sex in which females showed more rightward asymmetry for the middle temporal gyrus, inferior frontal gyrus, angular gyrus, and postcentral gyrus and minor rightward asymmetry for the superior frontal gyrus than males; significant diagnosis × sex interaction effects were identified in the angular gyrus and middle occipital gyrus. Pearson correlation analysis showed that males with autism spectrum disorders had a positive association between the asymmetry index value in the middle occipital gyrus and more significant verbal impairment measured by the revised autism diagnostic interview (r = 0.387, p = 0.026). The asymmetry index value in the parahippocampal gyrus was positively associated with more severe social impairment in females with autism spectrum disorders (r = 0.422, p = 0.020). We identified that the sex difference of gray matter asymmetry in children with autism spectrum disorders is qualitative rather than quantitative, which is relatively novel. Our findings provide the theoretical basis for conducting separate studies and using sex-specific diagnostic methods and treatments for males and females children with autism spectrum disorders.

Use and Implementation of an ERP to Enhance Security

ERP Systems are the fundamental approach and main use in every organization and business worldwide. To facilitate the use of the user, so that the term ERP is defined as software that is being intended to sustenance and automates the different processes of any business at every level of an organization. ERP is join up of three important components: Business Management Rehearses, Information Technology and Specific Business Objectives. At the primary of ERP is a well managed centralized data repository which acquires information from and amount information into the fragmented applications operating on a universal computing platform. To shield all data within the ERP system’s database, data security has conventionally been implemented by providing a centralized security infrastructure or subsystem within the ERP system.

Designing privacy-friendly data repositories: a framework for a blockchain that follows the GDPR

PurposeThe paper posits that a solution for businesses to use privacy-friendly data repositories for its customers’ data is to change from the traditional centralized repository to a trusted, decentralized data repository. Blockchain is a technology that provides such a data repository. However, the European Union’s General Data Protection Regulation (GDPR) assumed a centralized data repository, and it is commonly argued that blockchain technology is not usable. This paper aims to posit a framework for adopting a blockchain that follows the GDPR.Design/methodology/approachThe paper uses the Levy and Ellis’ narrative review of literature methodology, which is based on constructivist theory posited by Lincoln and Guba. Using five information systems and computer science databases, the researchers searched for studies using the keywords GDPR and blockchain, using a forward and backward search technique. The search identified a corpus of 416 candidate studies, from which the researchers applied pre-established criteria to select 39 studies. The researchers mined this corpus for concepts, which they clustered into themes. Using the accepted computer science practice of privacy by design, the researchers combined the clustered themes into the paper’s posited framework.FindingsThe paper posits a framework that provides architectural tactics for designing a blockchain that follows GDPR to enhance privacy. The framework explicitly addresses the challenges of GDPR compliance using the unimagined decentralized storage of personal data. The framework addresses the blockchain–GDPR tension by establishing trust between a business and its customers vis-à-vis storing customers’ data. The trust is established through blockchain’s capability of providing the customer with private keys and control over their data, e.g. processing and access.Research limitations/implicationsThe paper provides a framework that demonstrates that blockchain technology can be designed for use in GDPR compliant solutions. In using the framework, a blockchain-based solution provides the ability to audit and monitor privacy measures, demonstrates a legal justification for processing activities, incorporates a data privacy policy, provides a map for data processing and ensures security and privacy awareness among all actors. The research is limited to a focus on blockchain–GDPR compliance; however, future research is needed to investigate the use of the framework in specific domains.Practical implicationsThe paper posits a framework that identifies the strategies and tactics necessary for GDPR compliance. Practitioners need to compliment the framework with rigorous privacy risk management, i.e. conducting a privacy risk analysis, identifying strategies and tactics to address such risks and preparing a privacy impact assessment that enhances accountability and transparency of a blockchain.Originality/valueWith the increasingly strategic use of data by businesses and the contravening growth of data privacy regulation, alternative technologies could provide businesses with a means to nurture trust with its customers regarding collected data. However, it is commonly assumed that the decentralized approach of blockchain technology cannot be applied to this business need. This paper posits a framework that enables a blockchain to be designed that follows the GDPR; thereby, providing an alternative for businesses to collect customers’ data while ensuring the customers’ trust.

Multiple imputation for analysis of incomplete data in distributed health data networks

Distributed health data networks (DHDNs) leverage data from multiple sources or sites such as electronic health records (EHRs) from multiple healthcare systems and have drawn increasing interests in recent years, as they do not require sharing of subject-level data and hence lower the hurdles for collaboration between institutions considerably. However, DHDNs face a number of challenges in data analysis, particularly in the presence of missing data. The current state-of-the-art methods for handling incomplete data require pooling data into a central repository before analysis, which is not feasible in DHDNs. In this paper, we address the missing data problem in distributed environments such as DHDNs that has not been investigated previously. We develop communication-efficient distributed multiple imputation methods for incomplete data that are horizontally partitioned. Since subject-level data are not shared or transferred outside of each site in the proposed methods, they enhance protection of patient privacy and have the potential to strengthen public trust in analysis of sensitive health data. We investigate, through extensive simulation studies, the performance of these methods. Our methods are applied to the analysis of an acute stroke dataset collected from multiple hospitals, mimicking a DHDN where health data are horizontally partitioned across hospitals and subject-level data cannot be shared or sent to a central data repository.

Early Follow-up After Colorectal Surgery Reduces Postdischarge Emergency Department Visits.

Thirty-day readmissions, emergency department visits, and observation stays are common after colorectal surgery (9%-25%, 8%-12%, and 3%-5%), yet it is unknown to what extent planned postdischarge care can decrease the frequency of emergency department visits. This study's aim was to determine whether early follow-up with the surgical team reduces 30-day emergency department visits. This retrospective cohort study used a central data repository of clinical and administrative data for 2013 through 2018. This study was conducted in a large statewide health care system (10 affiliated hospitals, >300 practices). All adult patients undergoing colorectal surgery were included unless they had a length of stay <1 day or died during the index hospitalization. Early (<10 days after discharge) versus late (≥10 days) follow-up at the outpatient surgery clinic, or no outpatient surgery clinic follow-up, was assessed. The primary outcome measured was the time to 30-day postdischarge emergency department visit. Our cohort included 3442 patients undergoing colorectal surgery; 38% of patients had an early clinic visit. Overall, 11% had an emergency department encounter between 11 and 30 days after discharge. Those with early follow-up had decreased emergency department encounters (adjusted HR 0.13; 95% CI, 0.08-0.22). An early clinic visit within 10 days, compared to 14 days, prevented an additional 142 emergency department encounters. Nationwide, this could potentially prevent 8433 unplanned visits each year with an estimated cost savings of $49 million annually. We used retrospective data and were unable to assess for health care utilization outside our health system. Early follow-up within 10 days of adult colorectal surgery is associated with decreased subsequent emergency department encounters. See Video Abstract at http://links.lww.com/DCR/B330. EL SEGUIMIENTO TEMPRANO DESPUÉS DE LA CIRUGÍA COLORRECTAL REDUCE LAS VISITAS AL SERVICIO DE URGENCIAS POSTERIOR AL ALTA: Los readmisión a los treinta días, las visitas al servicio de urgencias y las estancias de observación son comunes después de la cirugía colorrectal, 9-25%, 8-12% y 3-5%, respectivamente. Sin embargo, se desconoce en qué medida la atención planificada posterior al alta puede disminuir la frecuencia de las visitas al servicio de urgencias.Determinar si el seguimiento temprano con el equipo quirúrgico reduce las visitas a 30 días al servicio de urgencias.Este estudio de cohorte retrospectivo utilizó un depósito central de datos clínicos y administrativos para 2013-2018.Gran sistema de salud estatal (10 hospitales afiliados,> 300 consultorios).Se incluyeron todos los pacientes adultos de cirugía colorrectal a menos que tuvieran una estadía <1 día o murieran durante el índice de hospitalización.Temprano (<10 días después del alta) versus tardío (≥10 días) o sin seguimiento en la clínica de cirugía ambulatoria.Tiempo para la visita al servicio de urgencias a 30 días después del alta.Nuestra cohorte incluyó 3.442 pacientes de cirugía colorrectal; El 38% de los pacientes tuvieron una visita temprana a clínica. En total, el 11% tuvo un encuentro con el servicio de urgencias entre 11 y 30 días después de ser dado de alta. Aquellos con seguimiento temprano disminuyeron las visitas al servicio de urgencias (HR 0,13; IC del 95%: 0,08 a 0,22). Además, una visita temprana a la clínica en un plazo de 10 días, en comparación con 14 días, evitó 142 encuentros adicionales en el servicio de urgencias. A nivel nacional, esto podría prevenir 8.433 visitas no planificadas cada año con un ahorro estimado de $ 49 millones anuales.Utilizamos datos retrospectivos y no pudimos evaluar la utilización de la atención médica fuera de nuestro sistema de salud.El seguimiento temprano dentro de los 10 días de la cirugía colorrectal en adultos se asocia con una disminución de los encuentros posteriores en el servicio de urgencias. Consulte Video Resumen en http://links.lww.com/DCR/B330. (Traducción-Dr. Gonzalo Hagerman).

Operationalizing and selecting outcome measures for the HEALing Communities Study

BackgroundThe Helping to End Addiction Long-termSM (HEALing) Communities Study (HCS) is a multisite, parallel-group, cluster randomized wait-list controlled trial evaluating the impact of the Communities That HEAL intervention to reduce opioid overdose deaths and associated adverse outcomes. This paper presents the approach used to define and align administrative data across the four research sites to measure key study outcomes. MethodsPriority was given to using administrative data and established data collection infrastructure to ensure reliable, timely, and sustainable measures and to harmonize study outcomes across the HCS sites. ResultsThe research teams established multiple data use agreements and developed technical specifications for more than 80 study measures. The primary outcome, number of opioid overdose deaths, will be measured from death certificate data. Three secondary outcome measures will support hypothesis testing for specific evidence-based practices known to decrease opioid overdose deaths: (1) number of naloxone units distributed in HCS communities; (2) number of unique HCS residents receiving Food and Drug Administration-approved buprenorphine products for treatment of opioid use disorder; and (3) number of HCS residents with new incidents of high-risk opioid prescribing. ConclusionsThe HCS has already made an impact on existing data capacity in the four states. In addition to providing data needed to measure study outcomes, the HCS will provide methodology and tools to facilitate data-driven responses to the opioid epidemic, and establish a central repository for community-level longitudinal data to help researchers and public health practitioners study and understand different aspects of the Communities That HEAL framework.

Consensus transcriptional regulatory networks of coronavirus-infected human cells

Establishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of MERS, SARS1 and SARS2 infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family HCTs encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository.

Association of Sex or Race With the Effect of Weight Loss on Physical Function

Consideration of differential treatment effects among subgroups in clinical trial research is a topic of increasing interest. This is an especially salient issue for weight loss trials. To determine whether stratification by sex and race is associated with meaningful differences in physical function response to weight loss among older adults. This pooled analysis used individual-level data from 8 completed randomized clinical trials of weight loss conducted at Wake Forest University or Wake Forest School of Medicine, Winston-Salem, North Carolina. Data were housed within the Wake Forest Older Americans Independence Center data repository and provided complete exposure, outcome, and covariate information. Data were collected from November 1996 to March 30, 2017, and analyzed from August 15, 2019, to June 10, 2020. Treatment arms within each study were collapsed into caloric restriction (CR [n = 734]) and non-CR (n = 583) categories based on whether caloric restriction was specified in the original study protocol. Objectively measured 6-month change in weight, fast-paced gait speed (meters per second), and Short Physical Performance Battery (SPPB) score. A total of 1317 adults (mean [SD] age, 67.7 [5.4] years; 920 [69.9%] female; 275 [20.9%] Black) with overweight or obesity (mean [SD] body mass index [calculated as weight in kilograms divided by height in meters squared], 33.9 [4.4]) were included at baseline. Six-month weight change achieved among those randomized to CR was -7.7% (95% CI, -8.3% to -7.2%), with no difference noted by sex; however, White individuals lost more weight than Black individuals assigned to CR (-9.0% [95% CI, -9.6% to -8.4%] vs -6.0% [95% CI, -6.9% to 5.2%]; P < .001), and all CR groups lost a significantly greater amount from baseline compared with non-CR groups (Black participants in CR vs non-CR groups, -5.3% [95% CI, -6.4% to -4.1%; P < .001]; White participants in CR vs non-CR groups, -7.2% [95% CI, -7.8% to -6.6%; P < .001]). Women experienced greater weight loss-associated improvement in SPPB score (CR group, 0.35 [95% CI, 0.18-0.52]; non-CR group, 0.08 [95% CI, -0.11 to 0.27]) compared with men (CR group, 0.23 [95% CI, 0.00-0.46]; non-CR group, 0.34 [95% CI, 0.09-0.58]; P = .03). Black participants experienced greater weight loss-associated improvement in gait speed (CR group, 0.08 [95% CI, 0.05-0.10] m/s; non-CR group, 0.02 [95% CI, -0.01 to 0.05] m/s) compared with White participants (CR group, 0.07 [95% CI, 0.06-0.09] m/s; non-CR group, 0.06 [95% CI, 0.04-0.08] m/s; P = .02). The association of weight loss on physical function in older adults appears to differ by sex and race. These findings affirm the need to consider biological variables in clinical trial design.