Experimental research based on robot-assisted surgery: Lower limb fracture reduction surgery planning navigation system.

Background and Aims: Lower extremity fracture reduction surgery is a key step in the treatment of lower extremity fractures. How to ensure high precision of fracture reduction while reducing secondary trauma during reduction is a difficult problem in current surgery. Methods: First, segmentation and three-dimensional reconstruction are performed based on fracture computed tomography images. A cross-sectional point cloud extraction algorithm based on the normal filtering of the long axis of the bone is designed to obtain the cross-sectional point clouds of the distal bone and the proximal bone, and the optimal reset target pose of the broken bone is obtained by using the iterative closest point algorithm. Then, the optimal reset sequence of reset parameters was determined, combined with the broken bone collision detection algorithm, a surgical planning algorithm for lower limb fracture reset was proposed, which can effectively reduce the reset force while ensuring the accuracy of the reset process without collision. Results: The average error of the reduction of the model bone was within 1.0 mm. The reduction operation using the planning and navigation system of lower extremity fracture reduction surgery can effectively reduce the reduction force. At the same time, it can better ensure the smooth change of the reduction force. Conclusion: Planning and navigation system of lower extremity fracture reduction surgery is feasible and effective.

Machine learning enabled self-calibration single fiber endoscopic imaging.

Single fiber scanners (SFSs), with the advantages of compact size, versatility, large field of view, and high resolution, have been applied in many areas. However, image distortions persistently impair the imaging quality of the SFS, although many efforts have been made to address the problem. In this Letter, we propose a simple and complete solution by combining the piezoelectric (PZT) self-induction sensor and machine learning algorithms. The PZT tube was utilized as both the actuator and the fiber position sensor. Additionally, the feedback sensor signal was interrogated by a convolution neural network to eliminate the noise. The experimental results show that the predicted fiber trajectory error was below 0.1%. Moreover, this self-calibration SFS has an excellent robustness to temperature changes (20-50°C). It is believed that the proposed solution has removed the biggest barrier for the SFS and greatly improved its performance and stability in complex environments.

In Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods.

Environmental chemicals may affect endocrine systems through multiple mechanisms, one of which is via effects on aromatase (also known as CYP19A1), an enzyme critical for maintaining the normal balance of estrogens and androgens in the body. Therefore, rapid and efficient identification of aromatase-related endocrine disrupting chemicals (EDCs) is important for toxicology and environment risk assessment. In this study, on the basis of the Tox21 10K compound library, in silico classification models for predicting aromatase binders/nonbinders were constructed by machine learning methods. To improve the prediction ability of the models, a combined classifier (CC) strategy that combines different independent machine learning methods was adopted. Performances of the models were measured by test and external validation sets containing 1336 and 216 chemicals, respectively. The best model was obtained with the MACCS (Molecular Access System) fingerprint and CC method, which exhibited an accuracy of 0.84 for the test set and 0.91 for the external validation set. Additionally, several representative substructures for characterizing aromatase binders, such as ketone, lactone, and nitrogen-containing derivatives, were identified using information gain and substructure frequency analysis. Our study provided a systematic assessment of chemicals binding to aromatase. The built models can be helpful to rapidly identify potential EDCs targeting aromatase.

Computational Investigation of Ligand Binding to the Peripheral Site in CYP3A4: Conformational Dynamics and Inhibitor Discovery.

Human cytochrome P450 3A4 (CYP3A4) is a major drug-metabolizing enzyme responsible for the metabolism of ∼50% of clinically used drugs and is often involved in drug-drug interactions. It exhibits atypical binding and kinetic behavior toward many ligands. Binding of ligands to CYP3A4 is a complex process. Recent studies from both crystallography and biochemistry suggested the existence of a peripheral ligand-binding site at the enzyme surface. However, the stability of the ligand bound at this peripheral site and the possibility of discovering new CYP3A4 ligands based on this site remain unclear. In this study, we employed a combination of molecular docking, multiparalleled molecular dynamics (MD) simulations, virtual screening, and experimental bioassay to investigate these issues. Our results revealed that the binding mode of progesterone (PGS), a substrate of CYP3A4, in the crystal structure was not stable and underwent a significant conformational change. Through Glide docking and MD refinement, it was found that PGS was able to stably bind at the peripheral site via contacts with Phe215, Phe219, Phe220, and Asp214. On the basis of the refined peripheral site, virtual screening was then performed against the Enamine database. A total of three compounds were finally found to have inhibitory activity against CYP3A4 in both human liver microsome and recombinant human CYP3A4 enzyme assays, one of which showed potent inhibitory activity with IC50 lower than 1 µM and two of which exhibited moderate inhibitory activity with IC50 values lower than 10 µM. The findings not only presented the dynamic behavior of PGS at the peripheral site but also demonstrated the first indication of discovering CYP3A4 inhibitors based on the peripheral site.

Interactions of omeprazole-based analogues with cytochrome P450 2C19: a computational study.

Cytochrome P450 2C19 (CYP2C19) is one of 57 drug metabolizing enzymes in humans and is responsible for the metabolism of ∼7-10% of drugs in clinical use. Recently omeprazole-based analogues were reported to be the potent inhibitors of CYP2C19 and have the potential to be used as the tool compounds for studying the substrate selectivity of CYP2C19. However, the binding modes of these compounds with CYP2C19 remain to be elucidated. In this study, a combination of molecular docking, molecular dynamics (MD), and MM/GBSA calculations was employed to systematically investigate the interactions between these compounds and CYP2C19. The binding modes of these analogues were analyzed in detail. The results indicated that the inclusion of explicit active site water molecules could improve binding energy prediction when the water molecules formed a hydrogen bonding network between the ligand and protein. We also found that the effect of active site water molecules on binding free energy prediction was dependent on the ligand binding modes. Our results unravel the interactions of these omeprazole-based analogues with CYP2C19 and might be helpful for the future design of potent CYP2C19 inhibitors with improved metabolic properties.

Effects of protein flexibility and active site water molecules on the prediction of sites of metabolism for cytochrome P450 2C19 substrates.

Structure-based prediction of sites of metabolism (SOMs) mediated by cytochrome P450s (CYPs) is of great interest in drug discovery and development. However, protein flexibility and active site water molecules remain a challenge for accurate SOM prediction. CYP2C19 is one of the major drug-metabolizing enzymes and has attracted considerable attention because of its polymorphism and capability of metabolizing ∼7% clinically used drugs. In this study, we systematically evaluated the effects of protein flexibility and active site water molecules on SOM prediction for CYP2C19 substrates. Multiple conformational sampling techniques including GOLD flexible residues sampling, molecular dynamics (MD) and tCONCOORD side-chain sampling were adopted for assessing the influence of protein flexibility on SOM prediction. The prediction accuracy could be significantly improved when protein flexibility was considered using the tCONCOORD sampling method, which indicated that the side-chain conformation was important for accurate prediction. However, the inclusion of the crystallographic or MD-derived water molecule(s) does not necessarily improve the prediction accuracy. Finally, a combination of docking results with SMARTCyp was found to be able to increase the SOM prediction accuracy.

Dynamic performance of the beam position monitor support at the SSRF.

Electron beam stability is very important for third-generation light sources, especially for the Shanghai Synchrotron Radiation Facility whose ground vibrations are much larger than those for other light sources. Beam position monitors (BPMs), used to monitor the position of the electron beam, require a greater stability than other mechanical structures. This paper concentrates on an investigation of the dynamic performance of the BPM support prototype. Modal and response analyses have been carried out by finite-element (FE) calculations and vibration measurements. Inconsistent results between calculation and measurement have motivated a change in the soft connections between the support and the ground from a ground bolt in the initial design to full grout. As a result the mechanical stability of the BPM support is greatly improved, showing an increase in the first eigenfrequency from 20.2 Hz to 50.2 Hz and a decrease in the ratio of the root-mean-square displacement (4-50 Hz) between the ground and the top of the support from 4.36 to 1.23 in the lateral direction. An example is given to show how FE analysis can guide the mechanical design and dynamic measurements (i.e. it is not just used as a verification method). Similar ideas can be applied to improve the stability of other mechanical structures.

Mechanical stability of the magnet girder assembly at the SSRF.

Electron beam stability is very important for third-generation light sources, especially for the Shanghai Synchrotron Radiation Facility (SSRF), whose ground vibration is much larger than other light sources. The mechanical stability of the magnet girder assemblies (MGAs) in a storage ring is essential for electron beam stability and performance. In order to improve the mechanical stability of the R&D MGAs in the storage ring of the SSRF, the number of MGAs in each lattice cell has been modified from three to five, and the girder structure has been optimized. Vibration measurements have been performed on the modified MGA prototype (the longest and heaviest MGA in the cell) to investigate mechanical stability and the influence of cooling water on magnet vibration. Measurement results show that the modified MGA has improved the first eigenfrequency from 5.9 Hz in R&D time to 21.9 Hz in the lateral and 22.5 Hz in the vertical direction; it has a good mechanical stability performance compared with other third-generation light source projects; and the influence of cooling water on the magnet vibration is about 4%, less than that of the ground vibration.