Noninvasive Pregestational Genetic Testing of Embryos Using Smart Sensors Array

Abstract

Pregestational genetic testing of embryos is the conventional tool in detecting genetic disorders (fetal aneuploidy and monogenic disorders) for in vitro fertilization (IVF) procedures. The accepted clinical practice for genetic testing still depends on biopsy, which has the potential to harm the embryo. Noninvasive genetic prenatal testing has not yet been achieved. In this study, embryos with common genetic disorders created through IVF were tested with an artificially intelligent nanosensor array. Volatile organic compounds emitted by the culture fluid of embryos were analyzed with chemical gas sensors. The obtained results showed significant discrimination between the embryos with different genetic diseases and their wild-types. Embryos were obtained from the same clinical center for avoiding differences based on clinical and demographical characteristics. The achieved discrimination accuracy was 81% for PKD disease, 90% for FRAX disease, 85% for HOCM disease, 90% for BRCA disease, and 100% for HSCR disease. These proof-of-concept findings might launch the development of a noninvasive approach for early assessment of embryos by examining the culture fluid of the embryos, potentially enabling noninvasive diagnosis and screening of genetic diseases for IVF.

Materials Matter: Advancing Sensor Science through Innovation in Materials Chemistry

“…An excellent example of an application comes from Shibli et al, who demonstrate the use of nanomaterial-based arrays for pregestational genetic testing of embryos. 15 Another recent example of an application comes from Zhong and co-workers who show the utility of a materials-based design and machine learning methodology in the fabrication of a biodegradable face mask and its applicability in the diagnosis of chronic respiratory disease. 16 We anticipate that application-driven approaches that provide demonstrations of sensors operating directly in complex samples relevant to a solution of an outstanding problem will play a major role in moving chemical sensors beyond research and development and into widely distributed implementation.…”

“…Although innovations in materials and devices provide conceptual advances for driving the progress in sensor science, novel applications of established materials to sensing in complex environments that aim to solve clearly articulated problems are valuable as well. An excellent example of an application comes from Shibli et al, who demonstrate the use of nanomaterial-based arrays for pregestational genetic testing of embryos . Another recent example of an application comes from Zhong and co-workers who show the utility of a materials-based design and machine learning methodology in the fabrication of a biodegradable face mask and its applicability in the diagnosis of chronic respiratory disease .…”

Artificial Intelligence‐Based Medical Sensors for Healthcare System

“…In recent years, with the development of biomedical science and micromachining technology, the length and quality of people's lives have been further improved. [1][2][3][4][5][6][7][8] However, contemporary medical testing still faces the challenges of reactive, preventive, and non-timely, which prevent effective and reliable real-time monitoring, diagnosis, and treatment. Currently, the common methods to monitor the individual health are still based on clinical observation and self-reported questionnaires for DOI: 10.1002/adsr.202300009 disease diagnosis and classification.…”