Resultats A Court Terme De La Trabeculoplastie Selective Au Laser Chez Les Patients Togolais

Aim: To check the short-term tonometric results of SLT in the treatment of primary glaucoma at the open angle and in charge of ocular hypertonias in Togolese people. Methods: A retrospective study was carried out in an ophthalmology center. The first 130 eyes of 72 patients benefited from the SLT laser procedure. The tonometric controls work object focus on follow-up at 1, 3, and 6 months post laser treatment. Results: 130 eyes of 72 patients were collected. The average age of the patients was 49.74 years (± 17.45) and the ages vary between 10 and 85 years. The average IOP of the laser before the laser (J0) was (24.99 ± 8.41) mm Hg. The mean IOP at the post-laser control at 1 month was (18.79 ± 3.73) mm Hg. The average IOP for the post-laser control at 3 months was (18.44 ± 3.81) mm Hg. The mean IOP at the post-laser control at 6 months was (18.13 ± 3.63) mm Hg. The percentage reduction in intraocular pressure compared to IOP was pretreated from 20.2% to 1 month; 22.1% at 3 months; and 23.3% at 6 months. In 1 month, 49.2% of the eyes we treated showed a reduction in IOP of less than 20% compared to IOP pretreatment. After 3 months and 6 months, it was 55.4% higher. Also, 52.3% have a PIO reduction percentage which is greater than or equal to 20% compared to pre-treatment IOP. Discussion: Selective laser trabeculoplasty, most especially, is interesting in ocular hypertonies. Treatment of over 180 ° allows one patient out of two to have a pressure reduction that is greater than or equal to 20%. Conclusion: The SLT presents a significant advantage for our glaucomatous patients

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery