Evidence map›Paper›PMID 40983860›Full record

ReviewJournal of applied genetics2026

Revolutionizing neonatal health: India's journey from assays to advanced genetics.

Akansha Bisht, Yasha Hasija

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of applied genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Akansha BishtDepartment of Biotechnology, Delhi Technological University (DTU), Shahbad Daulatpur, Main Bawana Road, Delhi, 110042, India.
Yasha HasijaDepartment of Biotechnology, Delhi Technological University (DTU), Shahbad Daulatpur, Main Bawana Road, Delhi, 110042, India. yashahasija@dtu.ac.in.ORCID http://orcid.org/0000-0003-0116-0711

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

According to UNICEF India, an estimated 67,385 neonates are born daily in India, each striving to survive the initial 28 days of life, which are pivotal in determining their future health prognosis. A significant number of these neonates succumb to inborn errors of metabolism (IEM), resulting in a spectrum of either manageable or severe clinical consequences. The evolution of techniques from basics to next-generation sequencing (NGS) and cutting-edge bioinformatics has enabled the prompt and precise identification of metabolic defects during the early stages of life. But the limited awareness, facilities, and access to the screening program necessitate the urgent need for establishing a state-of-the-art screening initiative all over India. The program holds the potential to substantially diminish infant mortality rates and alleviate the national health burden. This article delineates inborn errors of metabolism, investigates the advancements in diagnostic methodologies, outlines the NGS technique, underscores the role of computational biology, and advocates for the establishment of a centralized screening initiative in India specifically for treatable IEM. Furthermore, a few case studies have been included to showcase the notable discoveries of genes and associated disorders facilitated by NGS along with some studies highlighting the advantages of employing computational biology.

Indexed as

Genetic TestingInfant HealthMetabolism, Inborn ErrorsNeonatal ScreeningComputational BiologyHigh-Throughput Nucleotide SequencingHumansIndiaInfant, NewbornComputational toolsInborn errors of metabolismNewborn screeningNext-generation sequencing

Identifiers

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.