20 research outputs found
Deep learning of HIV field-based rapid tests
Although deep learning algorithms show increasing promise for disease diagnosis, their use with rapid diagnostic tests performed in the field has not been extensively tested. Here we use deep learning to classify images of rapid human immunodeficiency virus (HIV) tests acquired in rural South Africa. Using newly developed image capture protocols with the Samsung SM-P585 tablet, 60βfieldworkers routinely collected images of HIV lateral flow tests. From a library of 11,374βimages, deep learning algorithms were trained to classify tests as positive or negative. A pilot field study of the algorithms deployed as a mobile application demonstrated high levels of sensitivity (97.8%) and specificity (100%) compared with traditional visual interpretation by humans-experienced nurses and newly trained community health worker staff-and reduced the number of false positives and false negatives. Our findings lay the foundations for a new paradigm of deep learning-enabled diagnostics in low- and middle-income countries, termed REASSURED diagnostics1, an acronym for real-time connectivity, ease of specimen collection, affordable, sensitive, specific, user-friendly, rapid, equipment-free and deliverable. Such diagnostics have the potential to provide a platform for workforce training, quality assurance, decision support and mobile connectivity to inform disease control strategies, strengthen healthcare system efficiency and improve patient outcomes and outbreak management in emerging infections
Π‘ΡΡΡΠΊΡΡΡΠ° ΠΈ ΡΡΠ½ΠΊΡΠΈΠΈ Π°ΡΠΎΠΌΠ°ΡΠ°Π·Ρ ΠΈ Π΅Π΅ Π½Π΅ΡΡΠ΅ΡΠΎΠΈΠ΄Π½ΡΠ΅ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΡΡ
The analysis of the structure and function of aromatase (SYP19) - enzyme from the family of cytochrome P-450 that catalyzes the aromatization of six-membered ring A of the steroidal skeleton, namely transformation of androgens into estrogens peripheral and tumor tissues in the body, has been performed, and data in its non-steroidal inhibitors have been summarized.ΠΡΠΎΠ²Π΅Π΄Π΅Π½ Π°Π½Π°Π»ΠΈΠ· ΡΡΡΡΠΊΡΡΡΡ ΠΈ ΡΡΠ½ΠΊΡΠΈΠΈ Π°ΡΠΎΠΌΠ°ΡΠ°Π·Ρ (Π‘YP19) - ΡΠ΅ΡΠΌΠ΅Π½ΡΠ° ΡΠ΅ΠΌΠ΅ΠΉΡΡΠ²Π° ΡΠΈΡΠΎΡ
ΡΠΎΠΌΠΎΠ² Π -450, ΠΊΠΎΠ΄ΠΈΡΡΠ΅ΠΌΠΎΠ³ΠΎ ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½Π½ΡΠΌ Π½Π° ΠΊΠΎΡΠΎΡΠΊΠΎΠΌ ΠΏΠ»Π΅ΡΠ΅ 15-ΠΉ Ρ
ΡΠΎΠΌΠΎΡΠΎΠΌΡ (Π»ΠΎΠΊΡΡ 15q21) Π³Π΅Π½ΠΎΠΌ CYP19A1, ΠΊΠ°ΡΠ°Π»ΠΈΠ·ΠΈΡΡΡΡΠ΅Π³ΠΎ Π°ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΡ ΡΠ΅ΡΡΠΈΡΠ»Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠΊΠ»Π° Π ΡΡΠ΅ΡΠΎΠΈΠ΄Π½ΠΎΠ³ΠΎ ΡΠΊΠ΅Π»Π΅ΡΠ° ΠΈ ΠΎΠ±ΡΡΠ»Π°Π²Π»ΠΈΠ²Π°ΡΡΠ΅Π³ΠΎ ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΈΠ·ΠΌ ΡΡΠ΅ΡΠΎΠΈΠ΄Π½ΡΡ
Π³ΠΎΡΠΌΠΎΠ½ΠΎΠ², Π° ΠΈΠΌΠ΅Π½Π½ΠΎ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΠΈΡ Π°Π½Π΄ΡΠΎΠ³Π΅Π½ΠΎΠ² Π² ΡΡΡΡΠΎΠ³Π΅Π½Ρ ΠΏΠ΅ΡΠΈΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΈ ΠΎΠΏΡΡ
ΠΎΠ»Π΅Π²ΡΠΌΠΈ ΡΠΊΠ°Π½ΡΠΌΠΈ ΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌΠ°. ΠΠ±ΠΎΠ±ΡΠ΅Π½Ρ Π΄Π°Π½Π½ΡΠ΅ ΠΏΠΎ Π΅Π΅ Π½Π΅ΡΡΠ΅ΡΠΎΠΈΠ΄Π½ΡΠΌ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΡΠ°ΠΌ. ΠΠ±ΡΡΠΆΠ΄Π΅Π½Ρ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΠΎΠΉ ΠΈ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΡΡΡΡΠΊΡΡΡΡ Π‘YP19, ΠΎΠ±ΡΡΠ»Π°Π²Π»ΠΈΠ²Π°ΡΡΠΈΠ΅ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ Ρ ΠΌΠ΅ΠΌΠ±ΡΠ°Π½ΠΎΠΉ ΠΊΠ»Π΅ΡΠΊΠΈ ΠΈ ΡΡΠ±ΡΡΡΠ°ΡΠΎΠΌ, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΠΈΠ΅ ΡΡΠ°Π½ΡΠΏΠΎΡΡ ΠΏΠΎΡΠ»Π΅Π΄Π½Π΅Π³ΠΎ ΠΌΠ΅ΠΆΠ΄Ρ Π»ΠΈΠΏΠΈΠ΄Π½ΡΠΌ Π±ΠΈΡΠ»ΠΎΠ΅ΠΌ ΠΌΠ΅ΠΌΠ±ΡΠ°Π½Ρ ΠΈ Π°ΠΊΡΠΈΠ²Π½ΡΠΌ ΡΠ΅Π½ΡΡΠΎΠΌ ΡΠ΅ΡΠΌΠ΅Π½ΡΠ°. ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠΌΠΈ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΡΠ°ΠΌΠΈ Π‘YP19 Π² Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ ΡΠ²Π»ΡΡΡΡΡ ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡ, ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΠ΅ Π² ΡΠΎΡΡΠ°Π²Π΅ ΠΌΠΎΠ»Π΅ΠΊΡΠ»Ρ Π°Π·ΠΎΠ»ΡΠ½ΡΠ΅ ΠΈ Π°Π·ΠΈΠ½ΠΎΠ²ΡΠ΅ Π³Π΅ΡΠ΅ΡΠΎΡΠΈΠΊΠ»ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΡΠ°Π³ΠΌΠ΅Π½ΡΡ