BACKGROUND: Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor's performance is still not satisfactory. METHODS: In this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and compositional-based (i.e., normalized qualitative characteristic) properties of protein sequence to generate features. Then a hierarchical deep forest model fuses these three encoding schemes to build the proposed model DPI_CDF. RESULTS: The empirical outcomes on 10-fold cross-validation demonstrate that the proposed model achieved 99.13 % accuracy and 0.982 of Matthew's-correlation-coefficient (MCC) on the training dataset. The generalization power of the trained model is further examined on an independent dataset and achieved 95.01% of maximum accuracy and 0.900 MCC. When compared to current state-of-the-art methods, DPI_CDF improves in terms of accuracy by 4.27% and 4.31% on training and testing datasets, respectively. We believe, DPI_CDF will support the research community to identify druggable proteins and escalate the drug discovery process. AVAILABILITY: The benchmark datasets and source codes are available in GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF .

We present a design of an inductively coupled radio frequency (ICRF) marker for magnetic resonance (MR)-based positional tracking, enabling the robust increase of tracking signal at all scanning orientations in quadrature-excited closed MR imaging (MRI). The marker employs three curved resonant circuits fully covering a cylindrical surface that encloses the signal source. Each resonant circuit is a planar spiral inductor with parallel plate capacitors fabricated monolithically on flexible printed circuit board (FPC) and bent to achieve the curved structure. Size of the constructed marker is Ø3-mm ×5 -mm with quality factor > 22, and its tracking performance was validated with 1.5 T MRI scanner. As result, the marker remains as a high positive contrast spot under 360° rotations in 3 axes. The marker can be accurately localized with a maximum error of 0.56 mm under a displacement of 56 mm from the isocenter, along with an inherent standard deviation of 0.1-mm. Accrediting to the high image contrast, the presented marker enables automatic and real-time tracking in 3D without dependency on its orientation with respect to the MRI scanner receive coil. In combination with its small form-factor, the presented marker would facilitate robust and wireless MR-based tracking for intervention and clinical diagnosis. This method targets applications that can involve rotational changes in all axes (X-Y-Z).

Image processing has significantly extended the practical value of the eye-in-hand camera, enabling and promoting its applications for quantitative measurement. However, fully vision-based pose estimation methods sometimes encounter difficulties in handling cases with deficient features. In this article, we fuse visual information with the sparse strain data collected from a single-core fiber inscribed with fiber Bragg gratings (FBGs) to facilitate continuum robot pose estimation. An improved extreme learning machine algorithm with selective training data updates is implemented to establish and refine the FBG-empowered (F-emp) pose estimatoronline. The integration of F-emp pose estimation can improve sensing robustness by reducing the number of times that visual tracking is lost given moving visual obstacles and varying lighting. In particular, this integration solves pose estimation failures under full occlusion of the tracked features or complete darkness. Utilizing the fused pose feedback, a hybrid controller incorporating kinematics and data-driven algorithms is proposed to accomplish fast convergence with high accuracy. The online-learning error compensator can improve the target tracking performance with a 52.3%–90.1% error reduction compared with constant-curvature model-based control, without requiring fine model-parameter tuning and prior data acquisition.

Soft robots have great potential in surgical applications due to their compliance and adaptability to their environment. However, their flexibility and nonlinearity bring challenges for precise modeling, sensing, and control, especially in constrained cavities. In this article, a simple, compact two-segment soft robot for flexible laser ablation is proposed. The proximal hydraulic-driven segment can offer omnidirectional bending so as to navigate toward lesions. The distal segment driven by tendons enables precise, fast steering of laser collimator for laser sweeping on lesion targets. The dynamics of such mechanical steering motion can be enhanced with a metal spring backbone integrated along the collimator, thus facilitating the control with certain linearity and responsiveness. A soft robot modeling and control scheme based on Koopman operators is proposed. We also design a disturbance observer so as to incorporate the controller feedback with real-time fiber optic shape sensing. Experimental validation is conducted on simulated orex-vivolaser ablation tasks, thus evaluating our control strategies in laser path following across various contours/patterns. As a result, such a simple compact laser manipulation can perform up to 6 Hz sweeping with precision of path following errors below 1 mm. Such modeling and control scheme could also be used on an endoscopic laser ablation robot with unsymmetric mechanism driven by two tendons.

Cardiac electrophysiology is an effective treatment for atrial fibrillation, in which a long, steerable catheter is inserted into the heart chamber to conduct radio frequency ablation. Magnetic resonance imaging (MRI) can provide enhanced intraoperative monitoring of the ablation progress as well as the localization of catheter position. However, accurate and real-time tracking of the catheter shape and its efficient manipulation under MRI remains challenging. In this article, we designed a shape tracking system that integrates a multicore fiber Bragg grating (FBG) fiber and tracking coils with a standard cardiac catheter. Both the shape and positional tracking of the bendable section could be achieved. A learning-based modeling method is developed for cardiac catheters, which uses FBG-reconstructed three-dimensional curvatures for model initialization. The proposed modeling method was implemented on an MRI-guided robotic platform to achieve feedback control of a cardiac catheter. The shape tracking performance was experimentally verified, demonstrating 2.33° average error for each sensing segment and 1.53 mm positional accuracy at the catheter tip. The feedback control performance was tested by autonomous targeting and path following (average deviation of 0.62 mm) tasks. The overall performance of the integrated robotic system was validated by a pulmonary vein isolation simulator withex-vivotissue ablation, which employed a left atrial phantom with pulsatile liquid flow. Catheter tracking and feedback control tests were conducted in an MRI scanner, demonstrating the capability of the proposed system under MRI.