Adult-onset hypophosphatemic osteomalacia as a cause of widespread musculoskeletal pain:A retrospective case series of single center experience

BACKGROUND Osteomalacia(OM)is frequently confused with various musculoskeletal or other rheumatic diseases,especially in patients with adult-onset widespread musculoskeletal pain because of its low prevalence and non-specific manifestations.AIM To facilitate the early diagnosis and etiology-specific treatment of adult-onset hypophosphatemic OM.METHODS A retrospective review of medical records was performed to screen adult patients who visited a physiatry locomotive medicine clinic(spine and musculoskeletal pain clinic)primarily presenting with widespread musculoskeletal pain at a single tertiary hospital between January 2011 and December 2019.We enrolled patients with hypophosphatemia,high serum bone-specific alkaline phosphatase levels,and at least one imaging finding suggestive of OM.RESULTS Eight patients with adult-onset hypophosphatemic OM were included.The back was the most common site of pain.Proximal dominant symmetric muscle weakness was observed in more than half of the patients.Bone scintigraphy was the most useful imaging modality for diagnosing OM because radiotracer uptake in OM showed characteristic patterns.Six patients were diagnosed with adefovir(ADV)-induced Fanconi syndrome,and the other two patients were diagnosed with tumor-induced OM and light-chain nephropathy,respectively.After phosphorus and vitamin D supplementation and treatment for the underlying etiologies,improvements in pain,muscle strength,and gait were observed in all patients.CONCLUSION Mechanical pain characteristics,hypophosphatemia,and distinctive bone scintigraphy patterns are the initial diagnostic indicators of adult-onset hypophosphatemic OM.ADV-induced Fanconi syndrome is the most common etiology of hypophosphatemic OM in hepatitis B virus-endemic countries.

A structure translation model for crystal compounds

High-throughput virtual screening for crystals aims to discover new materials by evaluating the property of every virtual candidate in the database exhaustively.During this process,the major computational bottleneck is the costly structural relaxation of each hypothetical material on the large-scale dataset using density functional theory(DFT)calculations.Here,we present a generative domain translation framework that maps the unrelaxed structural domains to the relaxed domains,enabling data-driven structural translations.The model predicts the materials formation energy with a small mean absolute error without DFT relaxations,and furthermore can produce the atomic coordinates consistent with the DFT relaxed structures.The utility of the proposed concept is not restricted to the structural domains,and we expect that it can be extended to translate the domain of easy-to-compute properties into the domain of more difficult properties.