Intraputamenal Cerebral Dopamine Neurotrophic Factor in Parkinson's Disease: A Randomized, Double-Blind, Multicenter Phase 1 Trial.

BACKGROUND: Cerebral dopamine neurotrophic factor (CDNF) is an unconventional neurotrophic factor that protects dopamine neurons and improves motor function in animal models of Parkinson's disease (PD). OBJECTIVE: The primary objectives of this study were to assess the safety and tolerability of both CDNF and the drug delivery system (DDS) in patients with PD of moderate severity. METHODS: We assessed the safety and tolerability of monthly intraputamenal CDNF infusions in patients with PD using an investigational DDS, a bone-anchored transcutaneous port connected to four catheters. This phase 1 trial was divided into a placebo-controlled, double-blind, 6-month main study followed by an active-treatment 6-month extension. Eligible patients, aged 35 to 75 years, had moderate idiopathic PD for 5 to 15 years and Hoehn and Yahr score ≤ 3 (off state). Seventeen patients were randomized to placebo (n = 6), 0.4 mg CDNF (n = 6), or 1.2 mg CDNF (n = 5). The primary endpoints were safety and tolerability of CDNF and DDS and catheter implantation accuracy. Secondary endpoints were measures of PD symptoms, including Unified Parkinson's Disease Rating Scale, and DDS patency and port stability. Exploratory endpoints included motor symptom assessment (PKG, Global Kinetics Pty Ltd, Melbourne, Australia) and positron emission tomography using dopamine transporter radioligand [18 F]FE-PE2I. RESULTS: Drug-related adverse events were mild to moderate with no difference between placebo and treatment groups. No severe adverse events were associated with the drug, and device delivery accuracy met specification. The severe adverse events recorded were associated with the infusion procedure and did not reoccur after procedural modification. There were no significant changes between placebo and CDNF treatment groups in secondary endpoints between baseline and the end of the main and extension studies. CONCLUSIONS: Intraputamenally administered CDNF was safe and well tolerated, and possible signs of biological response to the drug were observed in individual patients. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Utilizing Grasp Monitoring to Predict Microsurgical Expertise.

INTRODUCTION: Most microsurgical procedures require the surgeon to use tools to grasp and hold fragile objects in the surgical site. Prior research on grasping in surgery has mostly either been in other surgical techniques or used grasping as an auxiliary metric. We focus on microsurgery and investigate what grasping can tell about microsurgical skill and suturing performance. This study lays groundwork for using automatic detection of grasps to evaluate surgical skill. METHODS: Five expert surgeons and six novices completed sutures on a microsurgical training board. Video recordings of the performance were annotated for the number of grasps, while an eye tracker recorded the participants' pupil dilations for cognitive workload assessment. Performance was measured with suturing duration and the University of Western Ontario Microsurgical Skills Assessment instrument (UWOMSA). Differences in skill, suturing performance and cognitive workload were compared with grasping behavior. RESULTS: Novices needed significantly more grasps to complete sutures and failed to grasp more often than the experts. The number of grasps affected the suturing duration more in novices. Decreasing suturing efficiency as measured by UWOMSA instrument was associated with increase in grasps, even when we controlled for overall skill differences. Novices displayed larger pupil dilations when averaged over a sufficiently large sample, and the difference increased after the grasp. CONCLUSIONS: Grasping action during microsurgical procedures can be used as a conceptually simple yet objective proxy in microsurgical performance assessment. If the grasps could be detected automatically, they could be used to aid in computational evaluation of surgical trainees' performance.

Movement-level process modeling of microsurgical bimanual and unimanual tasks.

PURPOSE: Microsurgical techniques require highly skilled manual handling of specialized surgical instruments. Surgical process models are central for objective evaluation of these skills, enabling data-driven solutions that can improve intraoperative efficiency. METHOD: We built a surgical process model, defined at movement level in terms of elementary surgical actions ([Formula: see text]) and targets ([Formula: see text]). The model also included nonproductive movements, which enabled us to evaluate suturing efficiency and bi-manual dexterity. The elementary activities were used to investigate differences between novice ([Formula: see text]) and expert surgeons ([Formula: see text]) by comparing the cosine similarity of vector representations of a microsurgical suturing training task and its different segments. RESULTS: Based on our model, the experts were significantly more efficient than the novices at using their tools individually and simultaneously. At suture level, the experts were significantly more efficient at using their left hand tool, but the differences were not significant for the right hand tool. At the level of individual suture segments, the experts had on average 21.0 % higher suturing efficiency and 48.2 % higher bi-manual efficiency, and the results varied between segments. Similarity of the manual actions showed that expert and novice surgeons could be distinguished by their movement patterns. CONCLUSIONS: The surgical process model allowed us to identify differences between novices' and experts' movements and to evaluate their uni- and bi-manual tool use efficiency. Analyzing surgical tasks in this manner could be used to evaluate surgical skill and help surgical trainees detect problems in their performance computationally.

Automated tool detection with deep learning for monitoring kinematics and eye-hand coordination in microsurgery.

In microsurgical procedures, surgeons use micro-instruments under high magnifications to handle delicate tissues. These procedures require highly skilled attentional and motor control for planning and implementing eye-hand coordination strategies. Eye-hand coordination in surgery has mostly been studied in open, laparoscopic, and robot-assisted surgeries, as there are no available tools to perform automatic tool detection in microsurgery. We introduce and investigate a method for simultaneous detection and processing of micro-instruments and gaze during microsurgery. We train and evaluate a convolutional neural network for detecting 17 microsurgical tools with a dataset of 7500 frames from 20 videos of simulated and real surgical procedures. Model evaluations result in mean average precision at the 0.5 threshold of 89.5-91.4% for validation and 69.7-73.2% for testing over partially unseen surgical settings, and the average inference time of 39.90 ± 1.2 frames/second. While prior research has mostly evaluated surgical tool detection on homogeneous datasets with limited number of tools, we demonstrate the feasibility of transfer learning, and conclude that detectors that generalize reliably to new settings require data from several different surgical procedures. In a case study, we apply the detector with a microscope eye tracker to investigate tool use and eye-hand coordination during an intracranial vessel dissection task. The results show that tool kinematics differentiate microsurgical actions. The gaze-to-microscissors distances are also smaller during dissection than other actions when the surgeon has more space to maneuver. The presented detection pipeline provides the clinical and research communities with a valuable resource for automatic content extraction and objective skill assessment in various microsurgical environments.

Combined Gaze Metrics as Stress-Sensitive Indicators of Microsurgical Proficiency.

Background. Evaluation of microsurgical proficiency is conventionally subjective, time consuming, and unreliable. Eye movement-based metrics have been promising not only in detection of surgical expertise but also in identifying actual cognitive stress and workload. We investigated if pupil dilations and blinks could be utilized in parallel to accurately classify microsurgical proficiency and its moderating features, especially task-related stress. Methods. Participants (n = 11) were divided into groups based on prior experience in microsurgery: novices (n = 6) with no experience and trained microsurgeons (n = 5). All participants conducted standardized suturing tasks with authentic instruments and a surgical microscope. A support vector machine classifier was used to classify features of microsurgical expertise based on percentage changes in pupil size. Results. A total of 109 successful sutures with 1090 segments were recorded. Classification of expertise from sutures achieved accuracies between 74.3% and 76.0%. Classification from individual segments based on these same features was not feasible. Conclusions. Combined gaze metrics are applicable for classifying surgical proficiency during a defined task. Pupil dilation is also sensitive to external stress factors; however, the usefulness of blinks is impaired by low blink rates. The results can be translated to surgical education to improve feedback and should be investigated individually in the context of actual performance and in real patient operations. Combined gaze metrics may be ultimately utilized to help microsurgeons monitor their performance and workload in real time-which may lead to prevention of errors.

Production of recombinant HIV-1 nef protein using different expression host systems: a techno-economical comparison.

Three popular expression host systems Escherichia coli, Pichia pastoris and Drosophila S2 were analyzed techno-economically using HIV-1 Nef protein as the model product. On scale of 100 mg protein, the labor costs corresponded to 52-83% of the manufacturing costs. When analyzing the cost impact of the different phases (strain/cell line construction, bioreactor production, and primary purification), we found that with the microbial host systems the strain construction phase was most significant generating 56% (E. coli) and 72% (P. pastoris) of the manufacturing costs, whereas with the Drosophila S2 system the cell line construction and bioreactor production phases were equally significant (46 and 47% of the total costs, respectively). With different titers and production goal of 100 mg of Nef protein, the costs of P. pastoris and Drosophila S2 systems were about two and four times higher than the respective costs of the E. coli system. When equal titers and bioreactor working volumes (10 L) were assumed for all three systems, the manufacturing costs of the bioreactor production of the P. pastoris and Drosophila S2 systems were about two and 2.5 times higher than the respective costs of the E. coli system.