1p36 deletion syndrome represents the most common terminal deletion observed in humans. Major clinical findings comprise developmental delay/intellectual disability, poor or absent expressive language, congenital central muscular hypotonia, brain anomalies, brachydactyly/camptodactyly, short feet, and characteristic facial features like straight eyebrows, deep-set eyes, and midface hypoplasia. So far, there is very limited knowledge about comorbid psychiatric disorders and their effective treatment in this special population. To fill this gap, this case report presents an initially four-year-old girl with 1p36.33-1p36.32 deletion, moderate intellectual disability, insomnia, oppositional-defiant disorder and attention deficit/hyperactivity disorder covering a period of time of about 1.5 years comprising initial psychological/psychiatric assessment, subsequent day clinic/outpatient treatment (amongst others including off-label use of melatonin and methylphenidate as well as parent-child interaction therapy) and follow-up assessment. Follow-up results indicated good efficacy of melatonin and methylphenidate medication without any adverse effects. Multidisciplinarity in diagnosis and treatment are mandatory to meet needs of patients with complex genetic disorders like 1p36 deletion syndrome. Off-label use of melatonin (for insomnia) and methylphenidate (for attention deficit/hyperactivity disorder) should be considered in young children with 1p36 deletion syndrome if behavioral interventions are not sufficient.
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http://dx.doi.org/10.3390/ijerph182212064 | DOI Listing |
BMC Cancer
January 2025
Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Background: Primary pulmonary lymphoepithelial carcinoma (pLEC) is a subtype of non-small cell lung cancer (NSCLC) characterized by Epstein-Barr virus (EBV) infection. However, the molecular pathogenesis of pLEC remains poorly understood.
Methods: In this study, we explored pLEC using whole-exome sequencing (WES) and RNA-whole-transcriptome sequencing (RNA-seq) technologies.
Transl Pediatr
October 2024
Department of General Surgery, Children's Hospital of Hebei Province, Shijiazhuang, China.
Background: Segmental chromosome aberrations, defined as presence of aberrations, deletion, or imbalance in the chromosomal arms, have long been considered as a predictor of poor prognosis of patients with neuroblastoma. The objective of this meta-analysis is to quantitively analyze the hazard ratios (HRs) of different whole or segmental chromosome aberrations for overall survival (OS) rate or event-free survival (EFS) rate of patients with neuroblastoma.
Methods: Relevant studies about chromosome, neuroblastoma, predictor, prognosis, and survival published from the inception to April 2023 in the databases of PubMed, Embase, and Web of Science were searched, screened, and reviewed.
Taiwan J Obstet Gynecol
November 2024
Department of Medical Research, MacKay Memorial Hospital, Taipei, Taiwan.
J Appl Genet
October 2024
Institute of Human Genetics, Polish Academy of Sciences, Strzeszynska 32, 60-479, Poznan, Poland.
In the monosomy 1p36 deletion syndrome, the role of DNA methylation in the genomic stability of the 1p36 region remains elusive. We hypothesize that changes in the methylation pattern at the 1p36 breakpoint hotspot region influenced the chromosomal breakage leading to terminal deletions. From the monosomy 1p36 material collection, four cases with 4.
View Article and Find Full Text PDFPLoS One
October 2024
Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Background: Glioma is a highly heterogeneous and poorly immunogenic malignant tumor, with limited efficacy of immunotherapy. The characteristics of the immunosuppressive tumor microenvironment (TME) are one of the important factors hindering the effectiveness of immunotherapy. Therefore, this study aims to reveal the immune microenvironment (IME) characteristics of glioma and predict different immune subtypes using machine learning methods, providing guidance for immune therapy in glioma.
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