Background: The World Health Organization recommends a package of pregnancy care that includes obstetric ultrasound scans. There are significant barriers to universal access to antenatal ultrasound, particularly because of the cost and need for maintenance of ultrasound equipment and a lack of trained personnel. As low-cost, handheld ultrasound devices have become widely available, the current roadblock is the global shortage of health care providers trained in obstetric scanning. Objective: The aim of this study is to improve pregnancy and risk assessment for women in underserved regions. Therefore, we are undertaking the Computer-Assisted Low-Cost Point-of-Care UltraSound (CALOPUS) project, bringing together experts in machine learning and clinical obstetric ultrasound. Methods: In this prospective study conducted in two clinical centers (United Kingdom and India), participating pregnant women were scanned and full-length ultrasounds were performed. Each woman underwent 2 consecutive ultrasound scans. The first was a series of simple, standardized ultrasound sweeps (the CALOPUS protocol), immediately followed by a routine, full clinical ultrasound examination that served as the comparator. We describe the development of a simple-to-use clinical protocol designed for nonexpert users to assess fetal viability, detect the presence of multiple pregnancies, evaluate placental location, assess amniotic fluid volume, determine fetal presentation, and perform basic fetal biometry. The CALOPUS protocol was designed using the smallest number of steps to minimize redundant information, while maximizing diagnostic information. Here, we describe how ultrasound videos and annotations are captured for machine learning. Results: Over 5571 scans have been acquired, from which 1,541,751 label annotations have been performed. An adapted protocol, including a low pelvic brim sweep and a well-filled maternal bladder, improved visualization of the cervix from 28% to 91% and classification of placental location from 82% to 94%. Excellent levels of intra- and interannotator agreement are achievable following training and standardization. Conclusions: The CALOPUS study is a unique study that uses obstetric ultrasound videos and annotations from pregnancies dated from 11 weeks and followed up until birth using novel ultrasound and annotation protocols. The data from this study are being used to develop and test several different machine learning algorithms to address key clinical diagnostic questions pertaining to obstetric risk management. We also highlight some of the challenges and potential solutions to interdisciplinary multinational imaging collaboration. International Registered Report Identifier (IRRID): RR1-10.2196/37374

Clinical and epidemiological characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are now widely available, but there are few data regarding longitudinal serology in large cohorts, particularly those from low-income and middle-income countries. We established an ongoing prospective cohort of 3,840 SARS-CoV-2-positive individuals according to RT-PCR in the Delhi-National Capital Region of India to document clinical and immunological characteristics during illness and convalescence. The immunoglobulin G (IgG) responses to the receptor binding domain (RBD) and nucleocapsid were assessed at 0 to 7 days, 10 to 28 days, and 6 to 10 weeks after infection. The clinical predictors of seroconversion were identified by multivariable regression analysis. The seroconversion rates during the postinfection windows of 0 to 7 days, 10 to 28 days, and 6 to 10 weeks were 46%, 84.7%, and 85.3%, respectively (N = 743). The proportion with a serological response increased with the severity of coronavirus disease 2019 (COVID-19). All participants with severe disease, 89.6% with mild to moderate infection, and 77.3% of asymptomatic participants had IgG antibodies to the RBD antigen. The threshold values for the nasopharyngeal viral RNA RT-PCR of a subset of asymptomatic and symptomatic seroconverters were comparable (P = 0.48) to those of nonseroconverters (P = 0.16) (N = 169). This is the first report of longitudinal humoral immune responses to SARS-CoV-2 over a period of 10 weeks in South Asia. The low seropositivity of asymptomatic participants and differences between assays highlight the importance of contextualizing the understanding of population serosurveys.

The establishment and maintenance of pregnancy in humans proceed through a continuous change of biochemical and biophysical processes. It requires a constant interaction between the fetus and the maternal system. The present prospective study aims to elucidate changes in salivary proteome from the early to middle stages of term pregnancy, and establishing an expressional trajectory for modulated proteins. To date, a comprehensive characterization of the longitudinal salivary proteome in pregnancy has not been performed and it is our immediate interest. In the discovery phase, maternal saliva (N = 20) at 6–13, 18–21, and 26–29 weeks of gestation was analyzed using level-free proteomics (SWATH-MS) approach. The expression levels of 65 proteins were found to change significantly with gestational age and distributed into two distinct clusters with a unique expression trajectory. The results revealed that altered proteins are involved in maternal immune modulation, metabolism, and host defense mechanism. Further, verification of 12 proteins was employed using targeted mass spectrometry (MRM-MS) in a separate subset of saliva (N = 14). The MRM results of 12 selected proteins confirmed a similar expression pattern as in SWATH-MS analysis. Overall, the results not only demonstrate the longitudinal maternal saliva proteome for the first time but also set the groundwork for comparative analysis between term birth and adverse pregnancy outcomes.

Background The prevalence of preterm birth (PTB) is high in lower and middle-income countries (LMIC) such as India. In LMIC, since a large proportion seeks antenatal care for the first time beyond 14-weeks of pregnancy, accurate estimation of gestational age (GA) using measures derived from ultrasonography scans in the second and third trimesters is of paramount importance. Different models have been developed globally to estimate GA, and currently, LMIC uses Hadlock’s formula derived from data based on a North American cohort. This study aimed to develop a population-specific model using data from GARBH-Ini, a multidimensional and ongoing pregnancy cohort established in a district hospital in North India for studying PTB. Methods Data obtained by longitudinal ultrasonography across all trimesters of pregnancy was used to develop and validate GA models for second and third trimesters. The first trimester GA estimated by ultrasonography was considered the Gold Standard. The second and third trimester GA model named, Garbhini-GA2 is a multivariate random forest model using five ultrasonographic parameters routinely measured during this period. Garbhini-GA2 model was compared to Hadlock and INTERGROWTH-21st models in the TEST set by estimating root-mean-squared error, bias and PTB rate. Findings Garbhini-GA2 reduced the GA estimation error by 23-45% compared to the published models. Furthermore, the PTB rate estimated using Garbhini-GA2 was more accurate when compared to published formulae that overestimated the rate by 1·5-2·0 times. Interpretation The Garbhini-GA2 model developed is the first of its kind developed solely using Indian population data. The higher accuracy of GA estimation by Garbhini-GA2 emphasises the need to apply population-specific GA formulae to improve antenatal care and better PTB rate estimates. Funding Centre for Integrative Biology and Systems Medicine, IIT Madras; Department of Biotechnology, Government of India; Grand Challenges India, BIRAC. Evidence before this study The appropriate delivery of antenatal care and accurate delivery date estimation is heavily dependent on accurate pregnancy dating. Unlike GA estimation using crown-rump length in the first trimester, dating using foetal biometry during the second and third trimesters is prone to inaccuracies. This is a public health concern, particularly in low and middle-income countries like India, where nearly 40% of pregnant women seek their first antenatal care beyond 14 weeks of gestation. The dating formulae used in LMIC were developed using foetal biometry data from the Caucasian population, and these formulae are prone to be erroneous when used in ethnically different populations. Added value of this study This study developed a dating model, the Garbhini-GA2 model for second and third trimesters of pregnancy using multiple candidate biometric predictors measured in a North Indian population. When evaluated internally, this model outperformed the currently used dating models by reducing the errors in the estimation of gestational age by 25-40%. Further, Garbhini-GA2 estimated a PTB rate similar to that estimated by the Gold Standard in our population, while the published formulae overestimated the PTB rates. Implications of all the available evidence Our Garbhini-GA2 model, after due validations in independent cohorts across the Southeast Asian regions, has the potential to be quickly translated for clinical use across the region. A precise dating will benefit obstetricians and neonatologists to plan antenatal and neonatal care more exactly. From an epidemiologist standpoint, using the Garbhini-GA2 dating formulae will improve the precision of the estimates of pregnancy outcomes that heavily depend on gestational age, such as preterm birth, small for gestational age and stillbirth in our population. Additionally, our dating models will improve phenotyping by reducing the risk of misclassification between outcomes for mechanistic and biomarker research.

Preterm birth (PTB) is defined as a live-born baby delivered before 37 completed weeks of gestation [1,2]. It is one of the major global pregnancy complications with more than 60% of PTB occurring in lower-middle-income countries (LMICs) [3,4]. Preterm birth, small for gestational age (SGA) and low birth weight (LBW) newborns have been categorised as […]