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It is noteworthy that only 5% of more than 7000 described rare diseases are treated. In the era of big data, there is ever-increasing data for understanding biomedicine. The need for efficient and rapid data collection, analyses and characterization methods is pressing. Rare diseases can particularly benefit from artificial intelligence (AI) application. AI, with an emphasis on machine learning, creates a path for such efforts and is being applied to diagnosis and treatment. AI has demonstrated its potential to learn and analyze data from different sources with results in prediction。Presently, there are AI-driven technologies applied for rare diseases and this review aims to summarize these advances. Moreover, this review scrutinizes the limitation and identifies the pitfalls of AI applications in the diagnosis and treatment of rare diseases.

Rare disease (RD)is the general term for a class of diseases with very low prevalence.Due to the relatively small number of patients with rare diseases, the diagnosis and treatment, drug research and development, medical safeguard, and scientific research of rare diseases are poorly developed, which highlight the difficulty in diagnosis and treatment, lack of availability of treatment drugs, and lower rate of medical safeguard. In 2018, China's First Edition of Rare Diseases Catalogue was published, which for the first time defined the management scope of rare diseases and promoted research development in field of RD.Since the 13th Five-Year Plan, China has launched a series of policies and measures outlining prevention and treatment of rare diseases, drug research and development, registration, accessing healthcare, and scientific research.In order to further improve the diagnosis, treatment, and management of rare diseases, and better safeguard the health rights and interests of patients with RDs, this paper focuses on the policies and exploration and practice related to rare diseases in China in recent years, and makes suggestions for future policy reviews.

In 2020 and 2021, great progress was made in the field of rare diseases in China. In the aspect of rare disease related policies, China built the National Key Laboratory of Rare Diseases and prepared for the National Quality Control Center of Rare Diseases. China has granted zero tariffs on raw materials and drugs for rare diseases since 2021, and the number of rare diseases included in health insurance in local areas has increased. In the aspect of drugs for rare disease, during the past two years, 14 drugs for rare diseases were included in the system of national health insurance; 13 were approved by China National Medical Products Administration; and more gained the qualification for prior review. In the aspect of patient registration and epidemiological study of rare diseases, China enabled the National Rare Diseases Registry System and set up the registry regime. The first epidemiological study of myasthenia gravis in China was published. The screening programof lysosomal storage syndrome and the first cohort study on achondroplasia in China were started. Besides the official policies and channels, organizations and informational platforms for patients also played an important role in the field of rare diseases.

Aims: To explore the relevance of disease duration with axial skeletal lesions in synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome based on computed tomography (CT). Methods: Patients who had undergone whole‐spine CT were included from a previously reported single‐center cohort of SAPHO syndrome. Patients with previous treatments of bisphosphates, biological agents or surgeries were excluded. Sites of axial skeletal involvement were reviewed including the spine, sacroiliac joints, and anterior chest wall. Duration of osteoarticular involvement was defined as the interval between the onset of osteoarticular symptoms and the CT evaluation. Results: Eighty‐one patients (54 female and 27 male) were included in the study. The mean disease duration was 26.9 ± 34.9 months. Fifty (63.0%) patients showed spinal lesions on CT. The average numbers of involved vertebral segments for corner and endplate lesions were 3.16 ± 3.51 and 0.73 ± 1.85 respectively. Sixty‐eight (84.0%) patients exhibited abnormalities in the anterior chest wall. The number of involved vertebral segments showed poor correlation with disease duration for both corner lesions (r2 = 0.051, P = 0.043) and endplate lesions (r2 = 0.137, P = 0.001). Patients with costoclavicular involvement showed remarkably longer disease duration than those without (49.3 ± 47.7 months vs 18.0 ± 23.5 months, P = 0.006), while no significant difference was detected regarding other structures in the anterior chest wall. Conclusion: Costoclavicular involvement is a hallmark of long disease duration in SAPHO syndrome. Corner and endplate lesions in the vertebrae and sacroiliac involvement may not relate to disease course. [ABSTRACT FROM AUTHOR]

Amyloid transthyretin (ATTR) amyloidosis caused by transthyretin misfolded into amyloid deposits in nerve and heart is a progressive rare disease. The unknown pathogenesis and the lack of therapy make the 5-year survival prognosis extremely poor. Currently available ATTR drugs can only relieve symptoms and slow down progression, but no drug has demonstrated curable effect for this disease. The growing volume of pharmacological data and large-scale genome and transcriptome data bring new opportunities to find potential new ATTR drugs through computational drug repositioning. We collected the ATTR-related in the disease pathogenesis and differentially expressed (DE) genes from five public databases and Gene Expression Omnibus expression profiles, respectively, then screened drug candidates by a corrected protein–protein network analysis of the ATTR-related genes as well as the drug targets from DrugBank database, and then filtered the drug candidates on the basis of gene expression data perturbed by compounds. We collected 139 and 56 ATTR-related genes from five public databases and transcriptome data, respectively, and performed functional enrichment analysis. We screened out 355 drug candidates based on the proximity to ATTR-related genes in the corrected interactome network, refined by graph neural networks. An Inverted Gene Set Enrichment analysis was further applied to estimate the effect of perturbations on ATTR-related and DE genes. High probability drug candidates were discussed. Drug repositioning using systematic computational processes on an interactome network with transcriptome data were performed to screen out several potential new drug candidates for ATTR. [ABSTRACT FROM AUTHOR]