Antitumor Agents. 272. Structure−Activity Relationships and In Vivo Selective Anti-Breast Cancer Activity of Novel Neo-tanshinlactone Analogues

Neo-tanshinlactone (1) and its previously reported analogs, such as 2, are potent and selective in vitro anti-breast cancer agents. The synthetic pathway to 2 was optimized from seven to five steps, with a better overall yield. Structure–activity relationships studies on these compounds revealed some key molecular determinants for this family of anti-breast agents. Several derivatives (19-21 and 24) exerted potent and selective anti-breast cancer activity with IC50 values of 0.3, 0.2, 0.1 and 0.1 μg/mL, respectively, against the ZR-75-1 cell lines. Compound 24 was two- to three-fold more potent than 1 against SK-BR-3 and ZR-75-1. Importantly, 21 exhibited high selectivity; it was 23 times more active against ZR-75-1 than MCF-7. Compound 20 had an approximately 12-fold ratio of SK-BR-3/MCF-7 selectivity. In addition, analog 2 showed potent activity against a ZR-75-1 xenograft model, but not PC-3 and MDA-MB-231 xenografts, as well as high selectivity against breast cancer cell line compared with normal breast tissue-derived cell lines. Further development of lead compounds 19-21 and 24 as clinical trial candidates is warranted

Force-Velocity Relationship in three Different Variations of Prone Row Exercises

This study examined the force-velocity relationship and tested the possibility of determining the relative loading intensity (% 1RM) in three different variations of prone row exercises. Thirty male top-level athletes from two different sports (National Team rugby union players and professional mixed martial arts fighters) were submitted to maximum dynamic strength assessments in the free prone bench pull, bent over barbell row, and bent over Smith-machine row, following standard procedures encompassing lifts performed from 40 to 100% of 1RM. The mean velocity, mean propulsive velocity, and peak velocity were measured in all attempts. Linear regression analyses were performed to establish the relationships between the different measures of bar-velocities and %1RM. The actual (obtained during the assessments) and predicted 1RM values (based on the predictive equations) for each exercise were compared using a paired t-test. In all exercises, the predicted 1RM scores - based on all velocity variables- were not different from their respective actual values. The close linear relationships between bar-velocities and distinct %1RM (coefficient of determination ≥ 80%, in all experimental conditions) allow precise determination of relative load and maximum dynamic strength, and enable coaches and sports scientists to use the different velocity outputs to rapidly and accurately monitor their athletes on a daily basis

The Use of Convolutional Neural Networks and Digital Camera Images in Cataract Detection

Cataract is one of the major causes of blindness in the world. Its early detection and treatment could greatly reduce the risk of deterioration and blindness. Instruments commonly used to detect cataracts are slit lamps and fundus cameras, which are highly expensive and require domain knowledge. Thus, the problem is that the lack of professional ophthalmologists could result in the delay of cataract detection, where medical treatment is inevitable. Therefore, this study aimed to design a convolutional neural network (CNN) with digital camera images (CNNDCI) system to detect cataracts efficiently and effectively. The designed CNNDCI system can perform the cataract identification process accurately in a user-friendly manner using smartphones to collect digital images. In addition, the existing numerical results provided by the literature were used to demonstrate the performance of the proposed CNNDCI system for cataract detection. Numerical results revealed that the designed CNNDCI system could identify cataracts effectively with satisfying accuracy. Thus, this study concluded that the presented CNNDCI architecture is a feasible and promising alternative for cataract detection

The Use of Convolutional Neural Networks and Digital Camera Images in Cataract Detection

Cataract is one of the major causes of blindness in the world. Its early detection and treatment could greatly reduce the risk of deterioration and blindness. Instruments commonly used to detect cataracts are slit lamps and fundus cameras, which are highly expensive and require domain knowledge. Thus, the problem is that the lack of professional ophthalmologists could result in the delay of cataract detection, where medical treatment is inevitable. Therefore, this study aimed to design a convolutional neural network (CNN) with digital camera images (CNNDCI) system to detect cataracts efficiently and effectively. The designed CNNDCI system can perform the cataract identification process accurately in a user-friendly manner using smartphones to collect digital images. In addition, the existing numerical results provided by the literature were used to demonstrate the performance of the proposed CNNDCI system for cataract detection. Numerical results revealed that the designed CNNDCI system could identify cataracts effectively with satisfying accuracy. Thus, this study concluded that the presented CNNDCI architecture is a feasible and promising alternative for cataract detection