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UK Train Worker Who Saved Lives in Mass Stabbing Named
Samir Zitouni, a 48-year-old LNER rail worker, was seriously injured while protecting passengers during a mass stabbing on a train, credited with saving multiple lives.
- On Saturday, Samir Zitouni, a 48-year-old LNER rail worker, intervened on the 18:25 GMT Doncaster to London King's Cross service, saving passengers while suffering serious injuries.
- After the train passed through Peterborough, witnesses said one person brandishing a knife began stabbing passengers, and police charged Anthony Williams, 32, from Peterborough, on Monday while reviewing links to a Friday stabbing.
- David Horne, Managing Director at LNER, praised the staff's bravery and said the operator will support Zitouni and his family as he recovers after more than 20 years of service.
- Mr Zitouni remains critically unwell but stable in hospital, British Transport Police said, and people on board were taken to Addenbrooke's Hospital after the train stopped at Huntingdon, Cambridgeshire.
- Forensics officers remained at the scene on Sunday as investigations continued, raising 'serious questions' about how the suspected knifeman evaded police; this breaking story is updated shortly via @BBCBreaking and the BBC News App.
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Samir Zitouni, 48 years old, is in critical position after he tried to protect passengers during the attack with the Saturday box, from the train of the LNER railway company that circles...
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Muslim railway worker praised as ‘hero’ for saving passengers during stabbing attack
An Algerian Muslim railway employee has been praised by police for his ‘bravery’ in saving multiple lives during a mass stabbing attack aboard a train in Cambridgeshire. Samir Zitouni, 48, a “heroic” member of staff, was left with “life-threatening” injuries after a mass stabbing on a train in Cambridgeshire on Saturday November 1. He was praised as […] The post Muslim railway worker praised as ‘hero’ for saving passengers during stabbing attack…
Coverage Details
Total News Sources28
Leaning Left3Leaning Right1Center17Last UpdatedBias Distribution81% Center
Bias Distribution
- 81% of the sources are Center
81% Center
14%
C 81%
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