Coronavirus Disease 2019 (COVID-19) Vaccination for Children: Position Statement of Indian Academy of Pediatrics Advisory Committee on Vaccination and Immunization Practices.

JUSTIFICATION: Data generated after the first wave has revealed that some children with coronavirus 19 (COVID-19) can become seriously ill. Multi-inflammatory syndrome in children (MIS-C) and long COVID cause significant morbidity in children. Prolonged school closures and quarantine have played havoc with the psychosocial health of children. Many countries in the world have issued emergency use authorisation (EUA) of selected Covid-19 vaccines for use in children. In India, a Subject Expert Committee (SEC) has recommended the use of Covaxin (Bharat Biotech) for children from the ages of 2-18 years. The recommendation has been given to the Drugs Controller General of India (DCGI) for final approval. OBJECTIVE: To provide an evidence-based document to guide the pediatricians on the recommendation to administer COVID vaccines to children, as and when they are available for use. PROCESS: Formulation of key questions was done by the committee, followed by review of literature on epidemiology and burden of Covid-19 in children, review of the studies on COVID vaccines in children, and the IAP stand on Covid-19 vaccination in children. The available data was discussed in the ACVIP focused WhatsApp group followed by an online meeting on 24 October, 2021, wherein the document was discussed in detail and finalized. RECOMMENDATIONS: The IAP supports the Government of India's decision to extend the COVID-19 vaccination program to children between 2-18 years of age. Children with high-risk conditions may be immunized on a priority basis. The IAP and its members should be a partner with the Government of India, in the implementation of this program and the surveillance that is necessary following the roll-out.

Breastfeeding and Coronavirus Disease 2019 (COVID-19) Vaccination: Position Statement of Indian Academy of Pediatrics Advisory Committee on Vaccination and Immunization Practices.

JUSTIFICATION: In India, till recently, breastfeeding women have been excluded from the coronavirus disease (COVID-19) vaccination program, rendering a significant population of the country, including frontline workers, ineligible to derive the benefits of the COVID-19 vaccine rollout. OBJECTIVE: The objective of this recommendation is production of an evidence-based document to guide the pediatricians to give advice to breastfeeding mothers regarding the safety of COVID-19 vaccines in lactating women. PROCESS: Formulation of key question was done under the chairmanship of president of the IAP. It was followed by review of literature regarding efficacy and safety of COVID-19 vaccines in breastfeeding women. The recommendations of other international and national professional bodies were also deliberated in detail. The available data was discussed in the ACVIP focused WhatsApp group. Opinion of all members was taken and the final document was prepared after achieving consensus. RECOMMENDATIONS: The IAP/ACVIP recommends the administration of COVID-19 vaccines to all breastfeeding women. The IAP/ACVIP endorses the recent recommendation of the Government of India, to consider all breastfeeding women as eligible for COVID-19 vaccination.

Validation of Automated Screening for Referable Diabetic Retinopathy With an Autonomous Diagnostic Artificial Intelligence System in a Spanish Population.

PURPOSE: The purpose of this study is to compare the diagnostic performance of an autonomous artificial intelligence (AI) system for the diagnosis of referable diabetic retinopathy (RDR) to manual grading by Spanish ophthalmologists. METHODS: Subjects with type 1 and 2 diabetes participated in a diabetic retinopathy (DR) screening program in 2011 to 2012 in Valencia (Spain), and two images per eye were collected according to their standard protocol. Mydriatic drops were used in all patients. Retinal images-one disc and one fovea centered-were obtained under the Medical Research Ethics Committee approval and de-identified. Exams were graded by the autonomous AI system (IDx-DR, Coralville, Iowa, United States), and manually by masked ophthalmologists using adjudication. The outputs of the AI system and manual adjudicated grading were compared using sensitivity and specificity for diagnosis of both RDR and vision-threatening diabetic retinopathy (VTDR). RESULTS: A total of 2680 subjects were included in the study. According to manual grading, prevalence of RDR was 111/2680 (4.14%) and of VTDR was 69/2680 (2.57%). Against manual grading, the AI system had a 100% (95% confidence interval [CI]: 97%-100%) sensitivity and 81.82% (95% CI: 80%-83%) specificity for RDR, and a 100% (95% CI: 95%-100%) sensitivity and 94.64% (95% CI: 94%-95%) specificity for VTDR. CONCLUSION: Compared to manual grading by ophthalmologists, the autonomous diagnostic AI system had high sensitivity (100%) and specificity (82%) for diagnosing RDR and macular edema in people with diabetes in a screening program. Because of its immediate, point of care diagnosis, autonomous diagnostic AI has the potential to increase the accessibility of RDR screening in primary care settings.

Indian Academy of Pediatrics (IAP) Advisory Committee on Vaccines and Immunization Practices (ACVIP): Recommended Immunization Schedule (2020-21) and Update on Immunization for Children Aged 0 Through 18 Years.

JUSTIFICATION: In view of new developments in vaccinology and the availability of new vaccines, there is a need to revise/review the existing immunization recommendations. PROCESS: Advisory Committee on Vaccines and Immunization Practices (ACVIP) of Indian Academy of Pediatrics (IAP) had a physical meeting in March, 2020 followed by online meetings (September-October, 2020), to discuss the updates and new recommendations. Opinion of each member was sought on the various recommendations and updates, following which an evidence-based consensus was reached. OBJECTIVES: To review and revise the IAP recommendations for 2020-21 and issue recommendations on existing and new vaccines. RECOMMENDATIONS: The major changes include recommendation of a booster dose of injectable polio vaccine (IPV) at 4-6 years for children who have received the initial IPV doses as per the ACVIP/IAP schedule, re-emphasis on the importance of IPV in the primary immunization schedule, preferred timing of second dose of varicella vaccine at 3-6 months after the first dose, and uniform dosing recommendation of 0.5 mL (15 µg HA) for inactivated influenza vaccines.

Immunization During the COVID-19 Pandemic: Recommendations From Indian Academy of Pediatrics Advisory Committee on Vaccines and Immunization Practices.

During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, immunization practices of all age groups, especially routine childhood vaccines, have been interrupted. Immunization is considered an essential health activity, which needs to be resumed as early as possible. This pandemic has created several unique issues related to routine immunization of individual children at clinics, which needs to be addressed. In this communication, the Advisory Committee on Vaccines and Immunization Practices (ACVIP) of Indian Academy of Pediatrics addresses the common questions and issues related to SARS-CoV-2 and routine immunization services. This also includes the recommendations for routine immunization of SARS-CoV-2 suspect and positive children, and for the logistics to be followed for immunization services.

Optimal surface segmentation with convex priors in irregularly sampled space.

Optimal surface segmentation is a state-of-the-art method used for segmentation of multiple globally optimal surfaces in volumetric datasets. The method is widely used in numerous medical image segmentation applications. However, nodes in the graph based optimal surface segmentation method typically encode uniformly distributed orthogonal voxels of the volume. Thus the segmentation cannot attain an accuracy greater than a single unit voxel, i.e. the distance between two adjoining nodes in graph space. Segmentation accuracy higher than a unit voxel is achievable by exploiting partial volume information in the voxels which shall result in non-equidistant spacing between adjoining graph nodes. This paper reports a generalized graph based multiple surface segmentation method with convex priors which can optimally segment the target surfaces in an irregularly sampled space. The proposed method allows non-equidistant spacing between the adjoining graph nodes to achieve subvoxel segmentation accuracy by utilizing the partial volume information in the voxels. The partial volume information in the voxels is exploited by computing a displacement field from the original volume data to identify the subvoxel-accurate centers within each voxel resulting in non-equidistant spacing between the adjoining graph nodes. The smoothness of each surface modeled as a convex constraint governs the connectivity and regularity of the surface. We employ an edge-based graph representation to incorporate the necessary constraints and the globally optimal solution is obtained by computing a minimum s-t cut. The proposed method was validated on 10 intravascular multi-frame ultrasound image datasets for subvoxel segmentation accuracy. In all cases, the approach yielded highly accurate results. Our approach can be readily extended to higher-dimensional segmentations.

Optimal multi-object segmentation with novel gradient vector flow based shape priors.

Shape priors have been widely utilized in medical image segmentation to improve segmentation accuracy and robustness. A major way to encode such a prior shape model is to use a mesh representation, which is prone to causing self-intersection or mesh folding. Those problems require complex and expensive algorithms to mitigate. In this paper, we propose a novel shape prior directly embedded in the voxel grid space, based on gradient vector flows of a pre-segmentation. The flexible and powerful prior shape representation is ready to be extended to simultaneously segmenting multiple interacting objects with minimum separation distance constraint. The segmentation problem of multiple interacting objects with shape priors is formulated as a Markov Random Field problem, which seeks to optimize the label assignment (objects or background) for each voxel while keeping the label consistency between the neighboring voxels. The optimization problem can be efficiently solved with a single minimum s-t cut in an appropriately constructed graph. The proposed algorithm has been validated on two multi-object segmentation applications: the brain tissue segmentation in MRI images and the bladder/prostate segmentation in CT images. Both sets of experiments showed superior or competitive performance of the proposed method to the compared state-of-the-art methods.

Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images.

Automated segmentation of object boundaries or surfaces is crucial for quantitative image analysis in numerous biomedical applications. For example, retinal surfaces in optical coherence tomography (OCT) images play a vital role in the diagnosis and management of retinal diseases. Recently, graph based surface segmentation and contour modeling have been developed and optimized for various surface segmentation tasks. These methods require expertly designed, application specific transforms, including cost functions, constraints and model parameters. However, deep learning based methods are able to directly learn the model and features from training data. In this paper, we propose a convolutional neural network (CNN) based framework to segment multiple surfaces simultaneously. We demonstrate the application of the proposed method by training a single CNN to segment three retinal surfaces in two types of OCT images - normal retinas and retinas affected by intermediate age-related macular degeneration (AMD). The trained network directly infers the segmentations for each B-scan in one pass. The proposed method was validated on 50 retinal OCT volumes (3000 B-scans) including 25 normal and 25 intermediate AMD subjects. Our experiment demonstrated statistically significant improvement of segmentation accuracy compared to the optimal surface segmentation method with convex priors (OSCS) and two deep learning based UNET methods for both types of data. The average computation time for segmenting an entire OCT volume (consisting of 60 B-scans each) for the proposed method was 12.3 seconds, demonstrating low computation costs and higher performance compared to the graph based optimal surface segmentation and UNET based methods.

A rapid 3D fat-water decomposition method using globally optimal surface estimation (R-GOOSE).

PURPOSE: To improve the graph model of our previous work GOOSE for fat-water decomposition with higher computational efficiency and quantitative accuracy. METHODS: A modification of the GOOSE fat water decomposition algorithm is introduced while the global convergence guarantees of GOOSE are still inherited to minimize fat-water swaps and phase wraps. In this paper, two non-equidistant graph optimization frameworks are proposed as a single-step framework termed as rapid GOOSE (R-GOOSE), and a multi-step framework termed as multi-scale R-GOOSE (mR-GOOSE). Both frameworks contain considerably less graph connectivity than GOOSE, resulting in a great computation reduction thus making it readily applicable to multidimensional fat water applications. The quantitative accuracy and computational time of the novel frameworks are compared with GOOSE on the 2012 ISMRM Challenge datasets to demonstrate the improvement in performance. RESULTS: Both frameworks accomplish the same level of high accuracy as GOOSE among all datasets. Compared to 100 layers in GOOSE, only 8 layers were used in the new graph model. Computational time is lowered by an order of magnitude to around 5 s for each dataset in (mR-GOOSE), R-GOOSE achieves an average run-time of 8 s. CONCLUSION: The proposed method provides fat-water decomposition results with a lower run-time and higher accuracy compared to the previously proposed GOOSE algorithm. Magn Reson Med 79:2401-2407, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Indian Academy of Pediatrics (IAP) Advisory Committee on Vaccines and Immunization Practices (ACVIP) Recommended Immunization Schedule (2018-19) and Update on Immunization for Children Aged 0 Through 18 Years.

JUSTIFICATION: There is a need to revise/review recommendations regarding existing vaccines in view of current developments in vaccinology. PROCESS: Advisory Committee on Vaccines and Immunization Practices (ACVIP) of Indian Academy of Pediatrics (IAP) reviewed the new evidence, had two meetings, and representatives of few vaccine manufacturers also presented their data. The recommendations were finalized unanimously. OBJECTIVES: To revise and review the IAP recommendations for 2018-19 and issue recommendations on existing and certain new vaccines. RECOMMENDATIONS: The major changes in the IAP 2018-19 Immunization Timetable include administration of hepatitis B vaccine within 24 hours of age, acceptance of four doses of hepatitis B vaccine if a combination pentavalent or hexavalent vaccine is used, administration of DTwP or DTaP in the primary series, and complete replacement of oral polio vaccine (OPV) by injectable polio vaccine (IPV) as early as possible. In case IPV is not available or feasible, the child should be offered three doses of bivalent OPV. In such cases, the child should be advised to receive two fractional doses of IPV at a Government facility at 6 and 14 weeks or at least one dose of intramuscular IPV, either standalone or as a combination, at 14 weeks. The first dose of monovalent Rotavirus vaccine (RV1) can be administered at 6 weeks and the second at 10 weeks of age in a two-dose schedule. Any of the available rotavirus vaccine may be administered. Inactivated influenza vaccine (either trivalent or quadrivalent) is recommended annually to all children between 6 months to 5 years of age. Measles-containing vaccine (MMR/MR) should be administered after 9 months of age. Additional dose of MR vaccine may be administered during MR campaign for children 9 months to 15 years, irrespective of previous vaccination status. Single dose of Typhoid conjugate vaccine (TCV) is recommended from the age of 6 months and beyond, and can be administered with MMR vaccine if administered at 9 months. Four-dose schedule of anti-rabies vaccine for Post Exposure Prophylaxis as recommended by World Health Organization in 2018, is endorsed, and monoclonal rabies antibody can be administered as an alternative to Rabies immunoglobulin for post-exposure prophylaxis.

Gravitational self-force correction to the innermost stable circular equatorial orbit of a Kerr black hole.

For a self-gravitating particle of mass µ in orbit around a Kerr black hole of mass M ≫ µ, we compute the O(µ/M) shift in the frequency of the innermost stable circular equatorial orbit due to the conservative piece of the gravitational self-force acting on the particle. Our treatment is based on a Hamiltonian formulation of the dynamics in terms of geodesic motion in a certain locally defined effective smooth spacetime. We recover the same result using the so-called first law of binary black-hole mechanics. We give numerical results for the innermost stable circular equatorial orbit frequency shift as a function of the black hole's spin amplitude, and compare with predictions based on the post-Newtonian approximation and the effective one-body model. Our results provide an accurate strong-field benchmark for spin effects in the general-relativistic two-body problem.

Observables of a test mass along an inclined orbit in a post-Newtonian-approximated Kerr spacetime including the linear and quadratic spin terms.

The orbital motion is derived for a nonspinning test mass in the relativistic, gravitational field of a rotationally deformed body not restricted to the equatorial plane or spherical orbit. The gravitational field of the central body is represented by the Kerr metric, expanded to second post-Newtonian order including the linear and quadratic spin terms. The orbital period, the intrinsic periastron advance, and the precession of the orbital plane are derived with the aid of novel canonical variables and action-based methods.