We report the implantation of patient-derived midbrain dopaminergic progenitor cells, differentiated in vitro from autologous induced pluripotent stem cells (iPSCs), in a patient with idiopathic Parkinson’s disease. The patient-specific progenitor cells were produced under Good Manufacturing Practice conditions and characterized as having the phenotypic properties of substantia nigra pars compacta neurons; testing in a humanized mouse model (involving peripheral-blood mononuclear cells) indicated an absence of immunogenicity to these cells. The cells were implanted into the putamen (left hemisphere followed by right hemisphere, 6 months apart) of a patient with Parkinson’s disease, without the need for immunosuppression. Positron-emission tomography with the use of fluorine-18-L-dihydroxyphenylalanine suggested graft survival. Clinical measures of symptoms of Parkinson’s disease after surgery stabilized or improved at 18 to 24 months after implantation. (Funded by the National Institutes of Health and others.)

Parkinson's disease (PD) is a neurodegenerative disorder associated with loss of striatal dopamine, secondary to degeneration of midbrain dopamine (mDA) neurons in the substantia nigra, rendering cell transplantation a promising therapeutic strategy. To establish human induced pluripotent stem cell (hiPSC)-based autologous cell therapy, we report a platform of core techniques for the production of mDA progenitors as a safe and effective therapeutic product. First, by combining metabolism-regulating microRNAs with reprogramming factors, we developed a method to more efficiently generate clinical grade iPSCs, as evidenced by genomic integrity and unbiased pluripotent potential. Second, we established a “spotting”-based in vitro differentiation methodology to generate functional and healthy mDA cells in a scalable manner. Third, we developed a chemical method that safely eliminates undifferentiated cells from the final product. Dopaminergic cells thus produced express high levels of characteristic mDA markers, produce and secrete dopamine, and exhibit electrophysiological features typical of mDA cells. Transplantation of these cells into rodent models of PD robustly restores motor dysfunction and reinnervates host brain, while showing no evidence of tumor formation or redistribution of the implanted cells. We propose that this platform is suitable for the successful implementation of human personalized autologous cell therapy for PD.

Comprehensive and accurate detection of variants from whole-genome sequencing (WGS) is a strong prerequisite for translational genomic medicine; however, low concordance between analytic pipelines is an outstanding challenge. We processed a European and an African WGS samples with 70 analytic pipelines comprising the combination of 7 short-read aligners and 10 variant calling algorithms (VCAs), and observed remarkable differences in the number of variants called by different pipelines (max/min ratio: 1.3~3.4). The similarity between variant call sets was more closely determined by VCAs rather than by short-read aligners. Remarkably, reported minor allele frequency had a substantial effect on concordance between pipelines (concordance rate ratio: 0.11~0.92; Wald tests, P < 0.001), entailing more discordant results for rare and novel variants. We compared the performance of analytic pipelines and pipeline ensembles using gold-standard variant call sets and the catalog of variants from the 1000 Genomes Project. Notably, a single pipeline using BWA-MEM and GATK-HaplotypeCaller performed comparable to the pipeline ensembles for ‘callable’ regions (~97%) of the human reference genome. While a single pipeline is capable of analyzing common variants in most genomic regions, our findings demonstrated the limitations and challenges in analyzing rare or novel variants, especially for non-European genomes.

Artificial intelligence (AI) has transformed pediatric healthcare by supporting diagnostics, personalized treatment strategies, and prognosis predictions. Although it offers significant promise in these areas, its application in pediatric settings poses distinct challenges compared with that in adults due to variable developmental status, the limited availability of pediatric data, and ethical concerns regarding bias and transparency. This narrative review summarizes the key concepts of AI and its clinical applications across clinical fields in the treatment of children and adolescents. Here we highlight the emerging role of large language models in performing administrative tasks and clinical documentation and supporting decision-making. We also address the evolving impact of AI integration in surgical care as an example while exploring ongoing concerns regarding reliability and diagnostic safety. Furthermore, we survey AI-enabled medical devices and discuss the current regulatory frameworks relevant to pediatric care. This review provides a balanced overview of opportunities and challenges from a pediatrician's standpoint and aims to facilitate effective alignment and collaboration with key stakeholders in pediatric healthcare. Pediatricians must implement AI solutions cautiously and accountably to avoid unintended harm and realize their potential.

The COVID-19 pandemic highlighted healthcare disparities in multiple countries. As such morbidity and mortality vary significantly around the globe between populations and ethnic groups. Underlying medical conditions and environmental factors contribute higher incidence in some populations and a genetic predisposition may play a role for severe cases with respiratory failure. Here we investigated whether genetic variation in the key genes for viral entry to host cells—ACE2 and TMPRSS2—and sensing of viral genomic RNAs (i.e., TLR3/7/8) could explain the variation in incidence across diverse ethnic groups. Overall, these genes are under strong selection pressure and have very few nonsynonymous variants in all populations. Genetic determinant for the binding affinity between SARS-CoV-2 and ACE2 does not show significant difference between populations. Non-genetic factors are likely to contribute differential population characteristics affected by COVID-19. Nonetheless, a systematic mutagenesis study on the receptor binding domain of ACE2 is required to understand the difference in host-viral interaction across populations.