RE: https://bsky.app/profile/did:plc:cfqu6alt3sngvm3mir7vqtzy/post/3mdycyp34wk2c
On the Evolution of Life — Why...
#phototrophy #photosynthesis #chlorophyll #retinal #ATP #plants #microorganisms #evolution #biology #biochemistry #science
Original open access article
Burnetti et al. 02 February 2026, npj Complex 3, 9
Priority effects inhibit the repeated evolution of phototrophy
#FYI #PaulBeckwith video lecture #phototrophy #photosynthesis #chlorophyll #retinal #ATP #plants #microorganisms #evolution #biology #biochemistry #science
Paul & Newton introduce a highly interesting paper on how and why plants/microorganisms developed #energy production from #sunlight twice differently.
(And yes, we also have rhodopsin in our eyes, we "see" with it. From the incoming light it produces electric signals for the brain.)

¡Hola comunidad! Hoy hemos publicado un artículo muy esperado en el blog: la revolución de los #Retinoides de última generación. ¿Creías que el #Retinol era demasiado fuerte para el #ContornoDeOjos?
Te contamos todo sobre el #Retinal, cómo actúa y por qué es la solución perfecta para una #MiradaJoven y firme, sin la temida irritación. ¡Y con consejos sobre cómo usarlo!
Puedes leer el artículo completo aquí: https://todocontornodeojos.com/retinol-ultima-generacion-menos-irritacion/
This is a different breakthrough than I just posted. It uses #nanotechnogy
Optics.org: Brown University nanoparticles help restore lost vision
A project at #BrownUniversity has developed a new approach to the treatment of age-related #maculardegeneration (AMD) and other #retinal disorders.
#Blindness
“Our findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions.” #isogg #dna #retina #retinal #aging #epigenetics #geneadons
Aim To develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk. Methods A total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation. A total of 35 913 of the remaining 35 917 participants had available mortality data and were used to investigate the association between retinal age gap and mortality. Results The DL model achieved a strong correlation of 0.81 (p<0·001) between retinal age and chronological age, and an overall mean absolute error of 3.55 years. Cox regression models showed that each 1 year increase in the retinal age gap was associated with a 2% increase in risk of all-cause mortality (hazard ratio (HR)=1.02, 95% CI 1.00 to 1.03, p=0.020) and a 3% increase in risk of cause-specific mortality attributable to non-cardiovascular and non-cancer disease (HR=1.03, 95% CI 1.00 to 1.05, p=0.041) after multivariable adjustments. No significant association was identified between retinal age gap and cardiovascular- or cancer-related mortality. Conclusions Our findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions. Data are available in a public, open access repository.
#AI-screened #eye #pics #diagnose #childhood #autism with 100% #accuracy.
#Researchers have been able to accurately #diagnose #autism in #children using #AI to #screen #retinal #photographs The findings support using AI as an #objective #screeningtool for early #diagnosis, especially when #access to a #specialist #child #psychiatrist is limited.
#Women #Transgender #LGBTQ #LGBTQIA #Health #Healthcare #Autism
https://newatlas.com/medical/retinal-photograph-ai-deep-learning-algorithm-diagnose-child-autism/
Researchers have taken photographs of children’s retinas and screened them using a deep learning AI algorithm to diagnose autism with 100% accuracy. The findings support using AI as an objective screening tool for early diagnosis, especially when access to a specialist child psychiatrist is limited.