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Meerut woman, lover sentenced to death for killing husband, leaving snake on bed to fake accident
Prosecutors said the pair strangled Amit Kashyap, used 12 witnesses and call records to secure conviction, and the court ordered welfare for his three minor children.
- On Friday, Additional District and Sessions Judge Jaiveer Singh Nagar sentenced Ravita and her lover Amardeep to death for murdering her husband, Amit Kashyap, in Meerut by strangulation disguised as a snakebite.
- Ravita and Amardeep developed a nine-month relationship while he worked alongside her husband; when Amit discovered the affair, the two conspired to strangle him and stage his death as a snakebite.
- Post-Mortem examination revealed strangulation injuries rather than snakebite; prosecution linked the pair through snake charmer testimony and over 500 call records, securing conviction within 18 months under the Uttar Pradesh Police's 'Operation Conviction'.
- Following sentencing, authorities moved the convicts to separate barracks at the Chaudhary Charan Singh District Jail; Amardeep was placed with Sahil Shukla, who received a death sentence earlier this week.
- Defence Counsel Omkar Bhati stated the pair intends to file an appeal against the judgment, marking the second death penalty ruling in a Meerut murder case this week.
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Wife, Lover Strangle Man, Place Snake Beside Body To Pass Off Murder As Snakebite, Get Death Sentence
A court in Meerut has sentenced Ravita and her lover, Amardeep, to death for killing her husband, Amit Kashyap, and trying to make his death look like a snakebite. The court announced the punishment on Friday. Amit was found
·India
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Meerut woman, lover sentenced to death for killing husband, staging it as a snake-bite death
Amit Kashyap was found dead on a cot in his house in April last year and a live snake was found beside his body. His family initially suspected that he died due to a snakebite.
·New Delhi, India
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Coverage Details
Total News Sources38
Leaning Left4Leaning Right8Center1Last UpdatedBias Distribution61% Right
Bias Distribution
- 61% of the sources lean Right
61% Right
L 31%
R 61%
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