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Platinum-based chemotherapy in combination with anti-PD-L1 antibodies has shown promising results in mesothelioma. However, the immunological mechanisms underlying its efficacy are not well understood and there are no predictive biomarkers to guide treatment decisions.
A robust understanding of the cellular mechanisms underlying diseases sets the foundation for the effective design of drugs and other interventions. The wealth of existing single-cell atlases offers the opportunity to uncover high-resolution information on expression patterns across various cell types and
Timo Lassmann BSc (Hons) MSc PhD Feilman Fellow; Head, Precision Health Research and Head, Translational Intelligence timo.lassmann@thekids.org.au
The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments.
Infants with KMT2A-rearranged B-cell acute lymphoblastic leukemia (ALL) have a dismal prognosis. Survival outcomes have remained static in recent decades despite treatment intensification and novel therapies are urgently required.
Computer vision technology is advancing rare disease diagnosis to address unmet needs of the more than 300 million individuals affected globally; one in three rare diseases have a known facial phenotype. 3D face model reconstruction is a key driver of these advances.
People living with rare diseases (PLWRD) still face huge unmet needs, in part due to the fact that care systems are not sufficiently aligned with their needs and healthcare workforce (HWF) along their care pathways lacks competencies to efficiently tackle rare disease-specific challenges. Level of rare disease knowledge and awareness among the current and future HWF is insufficient.
Accurate knowledge of the relationship between craniofacial anomalies (CFA), intellectual disability (ID) and autism spectrum disorder (ASD) is essential to improve services and outcomes. The aim is to describe the association between CFA, ID and ASD using linked population data.
Identifying the causal variant for diagnosis of genetic diseases is challenging when using next-generation sequencing approaches and variant prioritization tools can assist in this task. These tools provide in silico predictions of variant pathogenicity, however they are agnostic to the disease under study. We previously performed a disease-specific benchmark of 24 such tools to assess how they perform in different disease contexts.
To describe trends, age, and sex-specific patterns of population hospital admissions with a diagnosis of craniosynostosis (CS) in Australia. Population data for hospital separations (in-patient) from public and private hospitals (July 1996-June 2018) were obtained from the publicly available Australian Institute of Health and Welfare (AIHW) National Hospital Morbidity Database.