Introduction: Primary hyperoxaluria type-1 (PH1) is an autosomal recessive disorder caused by impaired activity of the hepatic peroxisomal alanine-glyoxilate aminotransferase, which leads to end-stage renal disease (ESRD) and requires combined liver-kidney transplantation (CLKT). Herein, we have reported 3 children diagnosed with PH1 who received CLKT. CASE 1: A 4.5-year-old boy with an elder brother diagnosed with PH1 was diagnosed during family screening when the sonography showed multiple calculi. Within 5 years he experienced flank pain, hematuria attacks, and anuric phases due to obstruction and received hemodialysis (HD) when ESRD appeared. CLKT was performed from his full-match sister at the age of 9.5. He is doing well at 5.5 years. CASE 2: A 7-year-old boy was admitted with polyuria, polydypsia, and stomach pain with renal stones on sonography. PD was instituted when serum creatinine and BUN levels were measured as high values. At the age of 10, CKLT was performed from his mother. His liver and renal function tests are well at 14 months after CKLT. CASE 3: A 2.5-year-old girl had attacks of dark urine without any pain; renal stones were imaged on sonography. She was diagnosed with PH1 and operated on several times due to obstruction. She received peritoneal dialysis and a cadaveric CLKT was performed when she was 9 years old. At the age of 16, she experienced chronic allograft nephropathy requiring HD and subsequent cadaveric donor renal transplantation at 1.5 years after initiation of HD.
Conclusion: Herein, we have presented the favorable clinical outcomes of patients with CKLT to indicate the validity of this treatment choice for PH1.
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http://dx.doi.org/10.1016/j.transproceed.2007.11.003 | DOI Listing |
Kidney Int Rep
January 2025
Division of Pediatric Nephrology, Rosenheim Hospital, Germany.
Introduction: Newborn screening (NBS) programs for a defined set of eligible diseases have been enormously successful, but genomic NBS allowing for detection of additional treatable disorders has not been broadly implemented. All 3 types of primary hyperoxaluria (PH1-3) are rare autosomal recessive diseases caused by distinct defects of glyoxylate metabolism that are diagnosed genetically with certainty. Early diagnosis and treatment are mandatory to avoid renal failure or sequalae associated with persistent hyperoxaluria.
View Article and Find Full Text PDFNephrology (Carlton)
January 2025
Center for Genetics and Inherited Diseases, Taibah University Medina, Madinah, Kingdom of Saudi Arabia.
Aim: Autosomal recessive primary hyperoxalurias (PH) are genetic disorders characterised by elevated oxalate production. Mutations in genes involved in glycoxylate metabolism are the underlying cause of PH. Type 1 PH (PH1) results in malfunctioning of alanine-glyoxylate aminotransferase enzymes of liver due to a change in the genetic sequence of alanine-glyoxylate aminotransferase (AGXT) gene.
View Article and Find Full Text PDFSmall
December 2024
School of Materials Science &Engineering, Tongji University, Shanghai, 201804, P. R. China.
Hydrogel-based flexible electronic components have become the optimal solution to address the rigidity problem of traditional electronics in health management. In this study, a multipurpose hydrogel is introduced, which is formed by combining a dual-network consisting of physical (chitosan, polyvinyl alcohol (PVA)) and chemical (poly(isopropyl acrylamide (NIPAM)-co-acrylamide (AM))) cross-linking, along with signal conversion fillers (eutectic gallium indium (EGaIn), TiC MXene, polyaniline (PANI)) for responding to external stimuli. Multiple sensing of dynamic and static signals is permissible for it.
View Article and Find Full Text PDFNano Lett
January 2025
Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Guangzhou 510632, P. R. China.
Comput Biol Med
December 2024
Linyi People's Hospital Health and Medical Big Data Center, Linyi City, Shandong Province, Linyi, 276034, Linyi, China; Linyi City People's Hospital, Linyi People's Hospital of Shandong Province, Linyi, 276034, Linyi, China. Electronic address:
Background: This work developed an interpretable deep learning model to automatically annotate landmarks and calculate the hallux valgus angle (HVA) and the intermetatarsal angle (IMA), reducing the time and error of manual calculations by medical experts and improving the efficiency and accuracy of hallux valgus (HV) diagnosis.
Methods: A total of 2,000 foot X-ray images were manually labeled with 12 landmarks by two surgical specialists as training data for the deep learning model. The important parts of the foot X-ray images centered on the proximal phalanx of the bunion (PH1), the first metatarsal (MT1), and the second metatarsal (MT2) were segmented using the proposed AG-UNet in the study.
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