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宫癌诊断:非编码RNA和生物传感器对人工智能衍生的方法

Seyyed Navid Mousavinejad¹, Rania Lachouri², Felora Ferdosi³

¹Department of Clinical Biochemistry, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Clinica chimica acta; international journal of clinical chemistry

|October 10, 2025

概括

新型生物标志物,如非编码RNA和人工智能衍生方法,对早期宫癌检测和监测有前途. 这些进展为改善全球实验室诊断和患者结果提供了新的希望.

科学领域:

在瘤学瘤学.
生物技术是生物技术.
人工智能在医学中的应用

背景情况:

子宫癌是全球重要的健康问题,特别是在低收入和中等收入国家.
人类乳头瘤病毒 (HPV) 类型16和18是主要的危险因素.
早期检测和预防策略,包括疫苗接种和查,至关重要.

研究的目的:

审查最近宫癌诊断技术的进展.
突出非编码RNA和人工智能衍生方法在临床诊断中的作用.
评估这些创新对未来实验室诊断的潜力.

主要方法:

关于非编码RNAs (微RNAs,长非编码RNAs,圆形RNAs) 作为生物标记物的当前文献的综述.
分析人工智能 (AI) 应用,包括机器学习和深度学习,在细胞学和细胞病理学中.
对用于检测子宫癌的生物传感器技术的评估.

主要成果:

非编码RNA正在成为早期检测和监测子宫癌的有希望的生物标志物.
通过改进的图像分析和模式识别,人工智能驱动的方法显著提高了诊断准确性.
这些技术的整合为更有效的宫癌管理提供了潜力.

结论:

非编码RNA和人工智能衍生方法代表了宫癌实验室诊断的重要未来方向.
这些先进技术有可能提高诊断准确性和患者的治疗结果.
进一步的研究和临床验证对于充分发挥其潜力至关重要.

关键词:

人工智能的人工智能是人工智能.生物传感器是一种生物传感器.宫癌是发生在宫癌的原因之一.深度学习是一种深度学习.机器学习机器学习没有编码的RNAs.

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lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...