Linking IL-6 and hsCRP among Indian patients with myocardial infarction.

The association between serum interleukin-6 (IL-6) and highly sensitive C - reactive protein (hsCRP) as predictors of the risk factors for Myocardial Infarction. The study included a total of 50 patients with Myocardial Infarction, aged between 25 to 74 years. The levels of hsCRP were measured using the immunoturbidimetry method, while Interleukin 6 was estimated using the sandwich ELISA method. Statistical analysis was conducted using SPSS version 21.0, with p values calculated using Quartile ratio, ANOVA unpaired t-test, and Kaplan-Meier Curve Method. A p-value of less than 0.05 was considered statistically significant. All participants underwent a questionnaire, physical examination, medical history assessment, and laboratory tests. The results of the study showed that there was a significant correlation between IL-6 and hsCRP levels in the Quartile groups, as well as with lipid profiles. The Kaplan-Meier method also demonstrated a significant association between IL-6 and hsCRP levels in participants. The comparison of biomarkers further supported these findings. Thus, data shows that elevated levels of hsCRP and IL-6 could serve as valuable diagnostic markers for predicting Acute Myocardial Infarction. Our study strongly suggests that IL-6 could be a powerful marker in evaluating the Myocardial Infarction.

Force Control Issues in Upper and Lower Limbs in Parkinson's Disease and Freezing of Gait.

Parkinson's Disease (PD) has been found to cause force control deficits in upper and lower limbs. About 50% of patients with advanced PD develop a debilitating symptom called freezing of gait (FOG), which has been linked to force control problems in the lower limbs, and some may only have a limited response to the gold standard pharmaceutical therapy, levodopa, resulting in partially levodopa-responsive FOG (PLR-FOG). There has been limited research on investigating upper-limb force control in people with PD with PLR-FOG, and without FOG. In this pilot study, force control was explored using an upper-and-lower-limb haptics-enabled robot in a reaching task while people with PD with and without PLR-FOG were on their levodopa medication. A healthy control group was used for reference, and each cohort completed the task at three different levels of assistance provided by the robot. Similar significant proportional force control deficits were found in the upper and lower limbs in patients with PLR-FOG versus those without FOG. Some aspects of force control were found to be retained, including an ability to increase or decrease force in response to changes in resistance while completing a reaching task. Overall, these results suggest there are force control deficits in both the upper and lower limbs in people with PLR-FOG.

Image-guided surgical planning of percutaneous nephrolithotomy with patient-specific CTRs.

PURPOSE: We propose the utilization of patient-specific concentric-tube robots (CTRs) whose designs are optimized to enhance their volumetric reachability of the renal stone, thus reducing the morbidities associated with percutaneous nephrolithotomy procedures. By employing a nested optimization-driven scheme, this work aims to determine a single surgical tract through which the patient-tailored CTR is deployed. We carry out a sensitivity analysis on the combined percutaneous access and optimized CTR design with respect to breathing-induced excursion of the kidneys based on preoperative images. Further, an investigation is also performed of the appropriateness and effectiveness of the percutaneous access provided by the proposed algorithm compared to that of an expert urologist. METHODS: The method is based on an ellipsoidal approximation to the renal calculi and a grid search over candidate skin areas and available renal calyces using an anatomically constrained kinematic mapping of the CTR. Percutaneous access is selected for collision-free CTR deployment to the centroid of the stones with minimal positional error at the renal calyx. Further optimization of the CTR design results in a robot tailored to the therapeutic anatomical features of each clinical case. The study examined 14 sets of clinical data of PCNL patients, analyzing stone reachability using preoperative images and breathing-induced motions of the kidney. An experienced urologist qualitatively assessed the adequacy of percutaneous access generated by the algorithm. RESULTS: An assessment conducted by an expert urologist found that the percutaneous accesses produced by the proposed approach were found to be comparable to those chosen by the expert surgeon in most clinical cases. The simulated results demonstrated a mean volume coverage of [Formula: see text] for static anatomy and [Formula: see text] and [Formula: see text] when considering a 1 cm excursion of the kidney in the craniocaudal directions due to respiration or tool-tissue interaction. CONCLUSION: The optimization-driven scheme for determining a single tract surgical plan, coupled with the use of a patient-specific CTR, shows promising results for improving percutaneous access in PCNL procedures. This approach clearly shows the potential for enhancing the quality and suitability of percutaneous accesses, addressing the challenges posed by staghorn and non-staghorn stones during PCNL procedures. Further research involving clinical validation is necessary to confirm these findings and explore the potential clinical benefits of the approach.

Robotics-Based Characterization of Sensorimotor Integration in Parkinson's Disease and the Effect of Medication.

Integration of multi-modal sensory inputs and modulation of motor outputs based on perceptual estimates is called Sensorimotor Integration (SMI). Optimal functioning of SMI is essential for perceiving the environment, modulating the motor outputs, and learning or modifying motor skills to suit the demands of the environment. Growing evidence suggests that patients diagnosed with Parkinson's Disease (PD) may suffer from an impairment in SMI that contributes to perceptual deficits, leading to motor abnormalities. However, the exact nature of the SMI impairment is still unclear. This study uses a robot-assisted assessment tool to quantitatively characterize SMI impairments in PD patients and how they affect voluntary movements. A set of assessment tasks was developed using a robotic manipulandum equipped with a virtual-reality system. The sensory conditions of the virtual environment were varied to facilitate the assessment of SMI. A hundred PD patients (before and after medication) and forty-three control subjects completed the tasks under varying sensory conditions. The kinematic measures obtained from the robotic device were used to evaluate SMI. The findings reveal that across all sensory conditions, PD patients had 36% higher endpoint error, 38% higher direction error in reaching tasks, and 43% higher number of violations in tracing tasks than control subjects due to impairment in integrating sensory inputs. However, they still retained motor learning ability and the ability to modulate motor outputs. The medication worsened the SMI deficits as PD patients, after medication, performed worse than before medication when encountering dynamic sensory environments and exhibited impaired motor learning ability.

Visual velocity perception dysfunction in Parkinson's disease.

OBJECTIVE: Compared with motor deficits, sensory information processing in Parkinson's disease (PD) is relatively unexplored. While there is increasing interest in understanding the sensory manifestations of PD, the extent of sensory abnormality in PD has remained relatively unexplored. Furthermore, most investigations on the sensory aspects of PD involve motor aspects, causing confounding results. As sensory deficits often arise in early PD development stages, they present a potential technological target for diagnosis and disease monitoring that is affordable and accessible. Considering this, the current study's aim is to assess visual spatiotemporal perception independent of goal directed movements in PD by designing and using a scalable computational tool. METHODS: A flexible 2-D virtual reality environment was created to evaluate various cases of visual perception. Using the tool, an experimental task quantifying the visual perception of velocity was tested on 37 individuals with PD and 17 age-matched control participants. RESULTS: PD patients, both ON and OFF PD therapy, displayed perceptual impairments (p = 0.001 and p = 0.008, respectively) at slower tested velocity magnitudes. These impairments were even observed in early stages of PD (p = 0.015). CONCLUSION: Visual velocity perception is impaired in PD patients, which suggests impairments in visual spatiotemporal processing occur in PD and provides a promising modality to be used with disease monitoring software. SIGNIFICANCE: Visual velocity perception shows high sensitivity to PD at all stages of the disease. Dysfunction in visual velocity perception may contribute to observed motor dysfunction in PD.

Robot-assisted investigation of sensorimotor control in Parkinson's disease.

Sensorimotor control (SMC) is a complex function that involves sensory, cognitive, and motor systems working together to plan, update and execute voluntary movements. Any abnormality in these systems could lead to deficits in SMC, which would negatively impact an individual's ability to execute goal-directed motions. Recent studies have shown that patients diagnosed with Parkinson's disease (PD) have dysfunctions in sensory, motor, and cognitive systems, which could give rise to SMC deficits. However, SMC deficits in PD and how they affect a patient's upper-limb movements have not been well understood. The objective of the study was to investigate SMC deficits in PD and how they affect the planning and correction of upper-limb motions. This was accomplished using a robotic manipulandum equipped with a virtual-reality system. Twenty age-matched healthy controls and fifty-six PD patients (before and after medication) completed an obstacle avoidance task under dynamic conditions (target and obstacles in moving or stationary form, with and without mechanical perturbations). Kinematic information from the robot was used to extract eighteen features that evaluated the SMC functions of the participants. The findings show that the PD patients before medication were 32% slower, reached 16% fewer targets, hit 41% more obstacles, and were 26% less efficient than the control participants, and the difference in these features was statistically significant under dynamic conditions. In addition to the motor deficits, the PD patients also showed deficits in handling high cognitive loads and interpreting sensory cues. Further, the PD patients after medication exhibited worse sensory and cognitive performance than before medication under complex testing conditions. The PD patients also showed deficits in following the computational models leading to poor motor planning.

On Surgical Planning of Percutaneous Nephrolithotomy with Patient-Specific CTRs.

Percutaneous nephrolithotomy (PCNL) is considered a first-choice minimally invasive procedure for treating kidney stones larger than 2 cm. It yields higher stone-free rates than other minimally invasive techniques and is employed when extracorporeal shock wave lithotripsy or uteroscopy are, for instance, infeasible. Using this technique, surgeons create a tract through which a scope is inserted for gaining access to the stones. Traditional PCNL tools, however, present limited maneuverability, may require multiple punctures and often lead to excessive torquing of the instruments which can damage the kidney parenchyma and thus increase the risk of hemorrhage. We approach this problem by proposing a nested optimization-driven scheme for determining a single tract surgical plan along which a patient-specific concentric-tube robot (CTR) is deployed so as to enhance manipulability along the most dominant directions of the stone presentations. The approach is illustrated with seven sets of clinical data from patients who underwent PCNL. The simulated results may set the stage for achieving higher stone-free rates through single tract PCNL interventions while decreasing blood loss.

Video Context Improves Performance in Identifying Operative Planes on Static Surgical Images.

BACKGROUND: Correct identification of the surgical tissue planes of dissection is paramount at the operating room, and the needed skills seem to be improved with realistic dynamic models rather than mere still images. The objective is to assess the role of adding video prequels to still images taken from operations on the precision and accuracy of tissue plane identification using a validated simulation model, considering various levels of surgeons' experience. METHODS: A prospective observational study was conducted involving 15 surgeons distributed to three equal groups, including a consultant group [C], a senior group [S], and a junior group [J]. Subjects were asked to identify and draw ideal tissue planes in 20 images selected at suitable operative moments of identification before and after showing a 10- second videoclip preceding the still image. A validated comparative metric (using a modified Hausdorff distance [%Hdu] for object matching) was used to measure the distance between lines. A precision analysis was carried out based on the difference in %Hdu between lines drawn before and after watching the videos, and between-group comparisons were analyzed using a one-way analysis of variance (ANOVA). The analysis of accuracy was done on the difference in %Hdu between lines drawn by the subjects and the ideal lines provided by an expert panel. The impact of videos on accuracy was assessed using a repeated-measures ANOVA. RESULTS: The C group showed the highest preciseness as compared to the S and J groups (mean Hdu 9.17±11.86 versus 12.1±15.5 and 20.0±18.32, respectively, p <0.001) and significant differences between groups were found in 14 images (70%). Considering the expert panel as a reference, the interaction between time and experience level was significant ( F (2, 597) = 4.52, p <0.001). Although the subjects of the J group were significantly less accurate than other surgeons, only this group showed significant improvements in mean %Hdu values after watching the lead-in videos ( F (1, 597) = 6.04, p = 0.014). CONCLUSIONS: Adding video context improved the ability of junior trainees to identify tissue planes of dissection. A realistic model is recommended considering experience-based differences in precision in training programs.

Colonic displacement as a marker of endoscopic skill: development of a novel tool for endoscopy training.

BACKGROUND: Colonoscopy is a technically challenging procedure. The colonoscope is prone to forming loops in the colon, which can lead patient discomfort and even perforation. We hypothesized that expert endoscopists use techniques to avoid loop formation, identify and straighten loops earlier, and thus exert less force. METHODS: Using a commercially available physical colon simulator model (Kyoto Kagaku), electromagnetic tracking markers (NDI Medical) were placed along the mobile segments of the colon (sigmoid, transverse) to measure the degree of displacement of the colon as the scope was advanced to the cecum. The colon model was set for each participant to simulate a redundant alpha loop in the sigmoid colon. Gastroenterology and surgical trainees and attendings were assessed. Demographic data were collected for each participant. RESULTS: Seventy-five participants were enrolled in the study. There were 17 (22.7%) attending physicians, and 58 (77.3%) trainees. Attending physicians advanced the scope to the cecum faster. The mean time required for procedure completion was 360.5 s compared to 178.4 s for the trainee and attending groups respectively (mean difference: 182.1 s, 95% CI: 93.0, 269.7; p = 0.0002). Attending physicians exerted significantly lower mean colonic displacement than trainees. The mean colonic displacement was 79.8 mm for the trainee group and 57.9 mm for the attending group (mean difference: 21.9 mm, 95% CI: 2.6, 41.2; p = 0.04). Those who used torque steering caused lower maximum colonic displacement than those who used knob steering. CONCLUSION: Attending physicians advance the scope during colonoscopy in a manner that results in significantly less colonic displacement than resident trainees. Although prior studies have shown a difference in force application between endoscopists and inexperienced students, ours is the first to differentiate across varying degrees of endoscopic skill. Future studies will define metrics for incorporation into endoscopic training curricula, focusing on techniques that encourage safety and comfort for patients.

Texture-based Intraoperative Image Guidance for Tumor Localization in Minimally Invasive Surgery.

Intraoperative tumor localization in a deflated lung in minimally invasive surgery (MIS) is challenging as the lung cannot be manually palpated through small incisions. To do so remotely, an articulated multisensory imaging device combining tactile and ultrasound sensors was developed. It visualizes the surface tactile map and the depth of the tissue. However, with little maneuverability in MIS, localizing tumors using instrumented palpation is both tedious and inefficient. In this paper, a texture- based image guidance system that classifies tactile-guided ultrasound texture regions and provides beliefs on their types is proposed. The resulting interactive feedback allows directed palpation in MIS. A k-means classifier is used to first cluster gray-level co-occurrence matrix (GLCM)-based texture features of the ultrasound regions, followed by hidden Markov model-based belief propagation to establish confidence about the clustered features observing repeated patterns. When the beliefs converge, the system autonomously detects tumor and nontumor textures. The approach was tested on 20 ex vivo soft tissue specimens in a staged MIS. The results showed that with guidance, tumors in MIS could be localized with 98% accuracy, 99% sensitivity, and 97% specificity.Clinical Relevance- Texture-based image guidance adds efficiency and control to instrumented palpation in MIS. It renders fluidity and accuracy in image acquisition using a hand-held device where fatigue from prolonged handling affects imaging quality.

Abnormal Vision-Based Displacement Perception in Parkinson's Disease.

In this work, we investigate the effect of Parkinson's disease (PD), and common corresponding therapies on vision-based perception of motion, a critical perceptual ability required for performing a wide range of activities of daily livings. While PD has been recognized as mainly a motor disorder, sensory manifestation of PD can also play a major role in the resulting disability. In this paper, for the first time, the effect of disease duration and common therapies on vision-based perception of displacement were investigated. The study is conducted in a movement-independent manner, to reject the shadowing effects and isolate the targeted perceptual disorder to the maximum possible extent. Data was collected using a computerized graphical tool on 37 PD patients [6 early-stage de novo, 25 mid-stage using levodopa therapy, six later-stage using deep brain stimulation (DBS)] and 15 control participants. Besides the absolute measurement of perception through a psychometric analysis on two tested position reference magnitudes, we also investigated the linearity in perception using Weber's fraction. The results showed that individuals with PD displayed significant perceptual impairments compared to controls, though early-stage patients were not impaired. Mid-stage patients displayed impairments at the greater of the two tested reference magnitudes, while late-stage patients were impaired at both reference magnitudes. Levodopa and DBS use did not cause statistically significant differences in absolute displacement perception. The findings suggest abnormal visual processing in PD increasing with disease development, perhaps contributing to sensory-based impairments of PD such as bradykinesia, visuospatial deficits, and abnormal object recognition.

Robotics and AI for Teleoperation, Tele-Assessment, and Tele-Training for Surgery in the Era of COVID-19: Existing Challenges, and Future Vision.

The unprecedented shock caused by the COVID-19 pandemic has severely influenced the delivery of regular healthcare services. Most non-urgent medical activities, including elective surgeries, have been paused to mitigate the risk of infection and to dedicate medical resources to managing the pandemic. In this regard, not only surgeries are substantially influenced, but also pre- and post-operative assessment of patients and training for surgical procedures have been significantly impacted due to the pandemic. Many countries are planning a phased reopening, which includes the resumption of some surgical procedures. However, it is not clear how the reopening safe-practice guidelines will impact the quality of healthcare delivery. This perspective article evaluates the use of robotics and AI in 1) robotics-assisted surgery, 2) tele-examination of patients for pre- and post-surgery, and 3) tele-training for surgical procedures. Surgeons interact with a large number of staff and patients on a daily basis. Thus, the risk of infection transmission between them raises concerns. In addition, pre- and post-operative assessment also raises concerns about increasing the risk of disease transmission, in particular, since many patients may have other underlying conditions, which can increase their chances of mortality due to the virus. The pandemic has also limited the time and access that trainee surgeons have for training in the OR and/or in the presence of an expert. In this article, we describe existing challenges and possible solutions and suggest future research directions that may be relevant for robotics and AI in addressing the three tasks mentioned above.

A deep explainable artificial intelligent framework for neurological disorders discrimination.

Pathological hand tremor (PHT) is a common symptom of Parkinson's disease (PD) and essential tremor (ET), which affects manual targeting, motor coordination, and movement kinetics. Effective treatment and management of the symptoms relies on the correct and in-time diagnosis of the affected individuals, where the characteristics of PHT serve as an imperative metric for this purpose. Due to the overlapping features of the corresponding symptoms, however, a high level of expertise and specialized diagnostic methodologies are required to correctly distinguish PD from ET. In this work, we propose the data-driven [Formula: see text] model, which processes the kinematics of the hand in the affected individuals and classifies the patients into PD or ET. [Formula: see text] is trained over 90 hours of hand motion signals consisting of 250 tremor assessments from 81 patients, recorded at the London Movement Disorders Centre, ON, Canada. The [Formula: see text] outperforms its state-of-the-art counterparts achieving exceptional differential diagnosis accuracy of [Formula: see text]. In addition, using the explainability and interpretability measures for machine learning models, clinically viable and statistically significant insights on how the data-driven model discriminates between the two groups of patients are achieved.

Intraoperative Localization of STN During DBS Surgery Using a Data-Driven Model.

A new approach is presented for localizing the Subthalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery based on microelectrode recordings (MERs). DBS is an accepted treatment for individuals living with Parkinson's Disease (PD). This surgery involves implantation of a permanent electrode inside the STN to deliver electrical current. Since the STN is a very small region inside the brain, accurate placement of an electrode is a challenging task for the surgical team. Prior to placement of the permanent electrode, microelectrode recordings of brain activity are used intraoperatively to localize the STN. The placement of the electrode and the success of the therapy depend on this location. In this paper, an objective approach is implemented to help the surgical team in localizing the STN. This is achieved by processing the MER signals and extracting features during the surgery to be used in a Machine Learning (ML) algorithm for defining the neurophysiological borders of the STN. For this purpose, a new classification approach is proposed with the goal of detecting both the dorsal and the ventral borders of the STN during the surgical procedure. Results collected from 100 PD patients in this study, show that by calculating and extracting wavelet transformation features from MER signals and using a data-driven computational deep neural network model, it is possible to detect the borders of the STN with an accuracy of 92%. The proposed method can be implemented in real-time during the surgery to model the neurophysiological nonlinearity along the path of the electrode trajectory during insertion.

PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models.

The global aging phenomenon has increased the number of individuals with age-related neurological movement disorders including Parkinson's Disease (PD) and Essential Tremor (ET). Pathological Hand Tremor (PHT), which is considered among the most common motor symptoms of such disorders, can severely affect patients' independence and quality of life. To develop advanced rehabilitation and assistive technologies, accurate estimation/prediction of nonstationary PHT is critical, however, the required level of accuracy has not yet been achieved. The lack of sizable datasets and generalizable modeling techniques that can fully represent the spectrotemporal characteristics of PHT have been a critical bottleneck in attaining this goal. This paper addresses this unmet need through establishing a deep recurrent model to predict and eliminate the PHT component of hand motion. More specifically, we propose a machine learning-based, assumption-free, and real-time PHT elimination framework, the PHTNet, by incorporating deep bidirectional recurrent neural networks. The PHTNet is developed over a hand motion dataset of 81 ET and PD patients collected systematically in a movement disorders clinic over 3 years. The PHTNet is the first intelligent systems model developed on this scale for PHT elimination that maximizes the resolution of estimation and allows for prediction of future and upcoming sub-movements.

Multi-Lateral Teleoperation Based on Multi-Agent Framework: Application to Simultaneous Training and Therapy in Telerehabilitation.

In this paper, a new scheme for multi-lateral remote rehabilitation is proposed. There exist one therapist, one patient, and several trainees, who are participating in the process of telerehabilitation (TR) in this scheme. This kind of strategy helps the therapist to facilitate the neurorehabilitation remotely. Thus, the patients can stay in their homes, resulting in safer and less expensive costs. Meanwhile, several trainees in medical education centers can be trained by participating partially in the rehabilitation process. The trainees participate in a "hands-on" manner; so, they feel like they are rehabilitating the patient directly. For implementing such a scheme, a novel theoretical method is proposed using the power of multi-agent systems (MAS) theory into the multi-lateral teleoperation, based on the self-intelligence in the MAS. In the previous related works, changing the number of participants in the multi-lateral teleoperation tasks required redesigning the controllers; while, in this paper using both of the decentralized control and the self-intelligence of the MAS, avoids the need for redesigning the controller in the proposed structure. Moreover, in this research, uncertainties in the operators' dynamics, as well as time-varying delays in the communication channels, are taken into account. It is shown that the proposed structure has two tuning matrices (L and D) that can be used for different scenarios of multi-lateral teleoperation. By choosing proper tuning matrices, many related works about the multi-lateral teleoperation/telerehabilitation process can be implemented. In the final section of the paper, several scenarios were introduced to achieve "Simultaneous Training and Therapy" in TR and are implemented with the proposed structure. The results confirmed the stability and performance of the proposed framework.

Differential Temporal Perception Abilities in Parkinson's Disease Patients Based on Timing Magnitude.

Non-motor symptoms in Parkinson's Disease (PD) predate motor symptoms and substantially decrease quality of life; however, detection, monitoring, and treatments are unavailable for many of these symptoms. Temporal perception abnormalities in PD are generally attributed to altered Basal Ganglia (BG) function. Present studies are confounded by motor control facilitating movements that are integrated into protocols assessing temporal perception. There is uncertainty regarding the BG's influence on timing processes of different time scales and how PD therapies affect this perception. In this study, PD patients using Levodopa (n = 25), Deep Brain Stimulation (DBS; n = 6), de novo patients (n = 6), and healthy controls (n = 17) completed a visual temporal perception task in seconds and sub-section timing scales using a computer-generated graphical tool. For all patient groups, there were no impairments seen at the smaller tested magnitudes (using sub-second timing). However, all PD groups displayed significant impairments at the larger tested magnitudes (using interval timing). Neither Levodopa nor DBS therapy led to significant improvements in timing abilities. Levodopa resulted in a strong trend towards impairing timing processes and caused a deterioration in perceptual coherency according to Weber's Law. It is shown that timing abnormalities in PD occur in the seconds range but do not extend to the sub-second range. Furthermore, observed timing deficits were shown to not be solely caused by motor deficiency. This provides evidence to support internal clock models involving the BG (among other neural regions) in interval timing, and cerebellar control of sub-second timing. This study also revealed significant temporal perception deficits in recently diagnosed PD patients; thus, temporal perception abnormalities might act as an early disease marker, with the graphical tool showing potential for disease monitoring.

Determination of Passive Dry Powder Inhaler Aerodynamic Particle Size Distribution by Multi-Stage Cascade Impactor: International Pharmaceutical Aerosol Consortium on Regulation & Science (IPAC-RS) Recommendations to Support Both Product Quality Control and Clinical Programs.

The multi-stage cascade impactor (CI) is the mainstay method for the determination of the aerodynamic particle size distribution (APSD) of aerosols emitted from orally inhaled products (OIPs). CIs are designed to operate at a constant flow rate throughout the measurement process. However, it is necessary to mimic an inhalation maneuver to disperse the powder into an aerosol when testing passive dry powder inhalers (DPIs), which constitute a significant portion of available products in this inhaler class. Methods in the pharmacopeial compendia intended for product quality assurance initiate sampling by applying a vacuum to the measurement apparatus using a timer-operated solenoid valve located downstream of the CI, resulting in a period when the flow rate through the impactor rapidly increases from zero towards the target flow rate. This article provides recommendations for achieving consistent APSD measurements, including selection of the CI, pre-separator, and flow control equipment, as well as reviewing considerations that relate to the shape of the flow rate-sampling time profile. Evidence from comparisons of different DPIs delivering the same active pharmaceutical ingredients (APIs) is indicative that the compendial method for APSD measurement is insensitive as a predictor of pharmacokinetic outcomes. Although inappropriate for product quality testing, guidance is therefore provided towards adopting a more clinically realistic methodology, including the use of an anatomically appropriate inlet and mimicking patient inhalation at the DPI while operating the CI at constant flow rate. Many of these recommendations are applicable to the testing of other OIP classes.

Robotics-Assisted Surgical Skills Evaluation based on Electrocortical Activity.

Skills assessment in Robotics-Assisted Minimally Invasive Surgery (RAMIS) is mainly performed based on temporal, motion-based and outcome-based metrics. While these components are essential for the proper assessment of skills in RAMIS, they do not suffice for full representation of all underlying aspects of skilled performance. Besides such commonplace components of skills, there exist other elements to be taken into account for comprehensive skills assessment. Among such elements are cognitive states (such as levels of stress, attention, concentration) that can directly affect performance. Investigating the impact of electrocortical activity and cognitive states of RAMIS surgeons over their performance has, however, received little attention in the literature. Therefore, in this paper, novel performance metrics based on electroencephalography (EEG) signals are studied for potential augmentation into RAMIS training and its assessment platform. For this purpose, a user study was conducted involving 23 novices and 9 expert RAMIS surgeons. The participants were asked to perform two tasks on the dv-Trainer®, (Mimic Technologies) RAMIS simulator, while their brain EEG signals were being measured using the Muse EEG headband (InteraXon Inc.). The performance metrics were defined as mean values of band powers of EEG signals over various ranges of frequency. Statistical analysis was performed to evaluate metrics over 5 different ranges of frequency for 4 electrode locations and during 2 RAMIS training tasks. The results indicated statistically significant differences in electrocortical activity between novices and experts in temporoparietal and left frontal regions of their brain for mid to high-frequency ranges. Overall, RAMIS experts showed lower levels of electrocortical activity in those regions compared to novices. The results indicate that electrocortical activity measured by EEG signals have the potential to provide useful information for skills assessment in RAMIS.