A British teacher's former student has thanked her in a most generous and selfless way for his education and helping him to become a radiologist.
Dr. Ali Golian donated one of his kidneys to 42-year-old Sonia Leonardo after seeing her sister's Facebook post about Leonardo undergoing treatment for kidney failure, according to an article in the 16 August edition of the Daily Mail.
Leonardo taught the 30-year-old radiologist in June 2010, before working together at King's College Hospital in London for two years. They became friends at the time, but lost touch and had not last spoken in the last five years.
Unfortunately, none of Leonardo's family or friends offered to be a donor, prompting the teacher to rely on five half-hour dialysis sessions every day to survive, according to the report. The treatments and her condition left her weak and uncomfortable.
Then this past January, Leonardo's sister posted a Facebook message thanking the teacher's colleagues at the hospital for sending flowers and cards of well wishes. It was then Golian saw the news about his former instructor.
His kidney proved to be a perfect, match, the Daily Mail article noted, and five months later the transplant took place. Today, Leonardo is doing very well; she and Golian have become like brother and sister, and talk to each other every day.









![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)






