Identification of drug combinations on the basis of machine learning to maximize anti-aging effects.

Aging is a multifactorial process that involves numerous genetic changes, so identifying anti-aging agents is quite challenging. Age-associated genetic factors must be better understood to search appropriately for anti-aging agents. We utilized an aging-related gene expression pattern-trained machine learning system that can implement reversible changes in aging by linking combinatory drugs. In silico gene expression pattern-based drug repositioning strategies, such as connectivity map, have been developed as a method for unique drug discovery. However, these strategies have limitations such as lists that differ for input and drug-inducing genes or constraints to compare experimental cell lines to target diseases. To address this issue and improve the prediction success rate, we modified the original version of expression profiles with a stepwise-filtered method. We utilized a machine learning system called deep-neural network (DNN). Here we report that combinational drug pairs using differential expressed genes (DEG) had a more enhanced anti-aging effect compared with single independent treatments on leukemia cells. This study shows potential drug combinations to retard the effects of aging with higher efficacy using innovative machine learning techniques.

Load-Bearing Capacity and Retention of Newly Developed Micro-Locking Implant Prosthetic System: An In Vitro Pilot Study.

The aim of this study was to introduce the newly developed micro-locking implant prosthetic system and to evaluate the resulting its characteristics. To evaluate load-bearing capacity, 25 implants were divided into five groups: external-hexagon connection (EH), internal-octagon connection (IO), internal-hexagon connection (IH), one-body implant (OB), micro-locking implant system (ML). The maximum compressive load was measured using a universal testing machine (UTM) according to the ISO 14801. Retention was evaluated in two experiments: (1) a tensile test of the structure modifications of the components (attachment and implant) and (2) a tensile test after cyclic loading (total 5,000,000 cycles, 100 N, 2 Hz). The load-bearing capacity of the ML group was not significantly different from the other groups (p > 0.05). The number of balls in the attachment and the presence of a hexagonal receptacle did not show a significant correlation with retention (p > 0.05), but the shape of the retentive groove in the implant post had a statistically significant effect on retention (p < 0.05). On the other hand, the retention loss was observed during the initial 1,000,000 cycles, but an overall constant retention was maintained afterward. Various preclinical studies on this novel micro-locking implant prosthetic system should continue so that it can be applied in clinical practice.