Инновационные стратегии установления давности наступления смерти в экспертной практике: мультиомика, искусственный интеллект и комбинированные модели (обзор)
- Авторы: Мустафина Г.Р.1, Кузнецов К.О.2,3, Кособуцкая С.А.4, Соколовский М.А.5, Семёнова А.И.5, Коротун В.Н.1
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Учреждения:
- Башкирский государственный медицинский университет
- Уфимский университет науки и технологий
- Бюро судебно-медицинской экспертизы
- Первый Московский государственный медицинский университет имени И.М. Сеченова (Сеченовский Университет)
- Российский национальный исследовательский медицинский университет имени Н.И. Пирогова
- Выпуск: Том 11, № 3 (2025)
- Страницы: 276-288
- Раздел: Научные обзоры
- URL: https://journals.rcsi.science/2411-8729/article/view/355562
- DOI: https://doi.org/10.17816/fm16307
- EDN: https://elibrary.ru/DSMDDR
- ID: 355562
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Аннотация
Определение давности наступления смерти является одной из ключевых задач судебно-медицинской практики, от точности решения которой зависит объективность экспертных заключений и эффективность следственных действий. Традиционные методы оценки давности наступления смерти, основанные на морфологических признаках и термометрии, обладают ограниченной эффективностью, особенно в позднем постмортальном периоде. Современные исследования сосредоточены на разработке инновационных подходов, основанных на молекулярных технологиях, анализе микробиоты, применении мультиомных стратегий и интеграции методов искусственного интеллекта для обработки больших массивов данных.
В обзоре представлены современные методы оценки давности наступления смерти, включая анализ нуклеиновых кислот (ДНК и РНК), протеомные и метаболомные подходы, а также изучение микробиомных изменений. Особое внимание уделено применению иммуногистохимических маркёров, масс-спектрометрии и ядерного магнитного резонанса для количественного анализа биохимических процессов в тканях и биологических жидкостях. Отмечены перспективы использования молекулярных и химических методов в судебной энтомологии в позднем постмортальном периоде. Отдельный раздел посвящён применению методов машинного и глубокого обучения для построения предиктивных моделей на основе мультимодальных данных, включая микробиомные профили, визуализационные признаки и климатические параметры. Описаны примеры комбинированных подходов, сочетающих биомолекулярные маркёры и вычислительные технологии, что позволяет повысить точность оценки давности наступления смерти как в раннем, так и в позднем постмортальном периоде.
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Гульгена Раисовна Мустафина
Башкирский государственный медицинский университет
Email: gulgenarm@mail.ru
ORCID iD: 0000-0003-2534-6385
SPIN-код: 8904-2046
канд. мед. наук, доцент
Россия, УфаКирилл Олегович Кузнецов
Уфимский университет науки и технологий; Бюро судебно-медицинской экспертизы
Автор, ответственный за переписку.
Email: kuznetsovarticles@mail.ru
ORCID iD: 0000-0002-2405-1801
SPIN-код: 3053-3773
MD
Россия, Уфа; УфаСветлана Александровна Кособуцкая
Первый Московский государственный медицинский университет имени И.М. Сеченова (Сеченовский Университет)
Email: fotinia78@mail.ru
ORCID iD: 0000-0002-5484-9574
SPIN-код: 2589-3752
канд. мед. наук
Россия, МоскваМаксим Александрович Соколовский
Российский национальный исследовательский медицинский университет имени Н.И. Пирогова
Email: maks_sokolovskiy@internet.ru
ORCID iD: 0009-0005-4998-3532
SPIN-код: 8809-6895
Россия, Москва
Альвина Ивановна Семёнова
Российский национальный исследовательский медицинский университет имени Н.И. Пирогова
Email: semyonowaalvina@yandex.ru
ORCID iD: 0009-0009-7823-9322
Россия, Москва
Валерий Николаевич Коротун
Башкирский государственный медицинский университет
Email: korotun_vn@mail.ru
ORCID iD: 0000-0001-9654-3269
SPIN-код: 5855-3161
канд. мед. наук, доцент
Россия, УфаСписок литературы
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