Background Hypermanganesemia with dystonia 1 and 2 (HMNDYT1 and 2) are rare, inherited disorders of manganese transport. Objectives We aimed to describe clinical, laboratory features, and outcomes among children with HMNDYT. Methods We conducted a retrospective multicenter study involving tertiary centers across India. We enrolled children between 1 month to 18 years of age with genetically confirmed/clinically probable HMNDYT. Clinical, laboratory profile, genetic testing, treatment details, and outcomes scored by treating physicians on a Likert scale were recorded. Results We enrolled 27 children (19 girls). Fourteen harbored SLC30A10 mutations; nine had SLC39A14 mutations. The SLC39A14 cohort had lower median age at onset (1.3 [interquartile range (IQR), 0.7–5.5] years) versus SLC30A10 cohort (2.0 [IQR, 1.5–5.1] years). The most frequent neurological features were dystonia (100%; n = 27), gait abnormality (77.7%; n = 21), falls (66.7%; n = 18), and parkinsonism (59.3%; n = 16). Median serum manganese (Mn) levels among SLC39A14 (44.9 [IQR, 27.3–147.7] mcg/L) cohort were higher than SLC30A10 (29.4 [17.1–42.0] mcg/L); median hemoglobin was higher in SLC30A10 (16.3 [IQR, 15.2–17.5] g/dL) versus SLC39A14 cohort (12.5 [8.8–13.2] g/dL). Hepatic involvement and polycythaemia were observed exclusively in SLC30A10 variants. A total of 26/27 children underwent chelation with disodium calcium edetate. Nine demonstrated some improvement, three stabilized, two had marked improvement, and one had normalization. Children with SLC39A14 mutations had poorer response. Two children died and nine were lost to follow‐up. Conclusions We found female predominance. Children with SLC39A14 mutations presented at younger age and responded less favorably to chelation compared to SLC30A10 mutations. There is emerging need to better define management strategies, especially in low resource settings.

This study reports the clinical, laboratory profile and outcome in seven patients with biotinidase deficiency. The serum biotinidase activity was assayed using spectrophotometric analysis. The age at presentation varied from day 1 of life to the 5 th month. Seizures were the presenting complaint in six patients and clonic seizures were the predominant seizure type. Sparse hair was seen in four patients, while three did not have any cutaneous manifestation. None of the patients had acidosis or hyperammonemia. The clinical response to biotin was dramatic with seizure control in all patients. One patient had neurological deficit at follow-up, while none had optic atrophy or sensorineural hearing loss. Biotinidase deficiency, a potentially treatable condition, should be thought of in any child presenting with neurological symptoms, especially seizures, even in the absence of cutaneous or laboratory manifestations.

L-2-hydroxyglutaric aciduria (L-2-HGA), a neurometabolic disorder caused by mutations in the L-2 hydroxyglutarate dehydrogenase (L-2-HGDH) gene, presents with psychomotor retardation, cerebellar ataxia, extrapyramidal symptoms, macrocephaly and seizures. Characteristic magnetic resonance imaging findings include subcortical cerebral white matter abnormalities with T2 hyperintensities of the dentate nucleus, globus pallidus, putamen and caudate nucleus. The diagnosis can be confirmed by elevated urinary L-2 hydroxyglutaric acid and mutational analysis of the L-2-HGDH gene. We report two siblings with dystonia diagnosed by classical neuroimaging findings with elevated urinary 2 hydroxyglutaric acid. Riboflavin therapy has shown promising results in a subset of cases, thus highlighting the importance of making the diagnosis in these patients.

In this paper, the proposed work tests the computer vision application to perform the skill and emotion assessment of children with Autism Spectrum Disorder (ASD) by extracting various bio-behaviors, human activities, child-therapist interactions, and joint pose estimations from the video-recorded interactive singleor two-person play-based intervention sessions. A comprehensive data set of 300 videos are amassed from ASD children engaged in social interaction and developed three novel deep learning-based computer vision models which are explained as follows: 1) activity comprehension to analyze child-play partner interactions (Activity Comprehension model); 2) an automatic joint attention recognition framework using pose, and 3) emotion and facial expression recognition. We tested models on children's real-world unseen 68 videos captured from the clinic and public datasets. The activity comprehension model has an overall accuracy of 72.32%, the joint attention models have an accuracy of 97% for following eye gaze and 93.4% for hand pointing and the facial expression recognition model has an overall accuracy of 95.1%. The proposed models could extract activities and behaviors of interest from free-play and intervention session videos, empowering clinicians with data useful in diagnosis, assessment, treatment formulation, and monitoring of ASD children with limited supervision.

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