Assessment of patient life engagement in schizophrenia using items from the Positive and Negative Syndrome Scale.

BACKGROUND: Improved patient life engagement is a meaningful treatment goal in schizophrenia that cannot be satisfactorily measured using existing tools. This research aimed to determine whether certain items from the Positive and Negative Syndrome Scale (PANSS) can assess patient life engagement in schizophrenia. METHODS: Three approaches were used to identify PANSS items that reflect patient life engagement: (1) a panel discussion with expert psychiatrists (n = 4); (2) interviews with patients with schizophrenia (n = 20); and (3) a principal component analysis to explore clustering of items (n = 954 from three randomized controlled trials). Internal consistency was assessed by Cronbach's alpha and item-total correlations. A minimal clinically important difference (MCID) was determined by anchor- and distribution-based methods. RESULTS: Expert psychiatrists identified 11 relevant items, and patients rated 13 items as "very relevant" to patient life engagement, most of which clustered in the principal component analysis. Considering all results, a composite set of 14 PANSS items that may be relevant to patient life engagement in schizophrenia was devised: P2, N1, N2, N3, N4, N5, N6, N7, G6, G7, G11, G13, G15, G16 (Cronbach's alpha, 0.84; item-total correlations, 0.35-0.56, indicating acceptable correlation with the underlying concept; exception: G6 [depression], 0.19). An MCID of 5 points (small/moderate improvement) or 10 points (large improvement) may be appropriate. CONCLUSIONS: A subset of 14 PANSS items may be used to reflect patient life engagement in clinical practice/trials in schizophrenia, complementing the results of traditional psychiatric symptom scales with a patient-centered outcome that is relevant to real-world treatment goals.

Issues, Challenges and Opportunities for Economic Evaluations of Orphan Drugs in Rare Diseases: An Umbrella Review.

BACKGROUND AND OBJECTIVES: There are significant challenges when obtaining clinical and economic evidence for health technology assessments of rare diseases. Many of them have been highlighted in previous systematic reviews but they have not been summarised in a comprehensive manner. For all stakeholders working with rare diseases, it is important to be aware and understand these issues. The objective of this review is to identify the main challenges for the economic evaluation of orphan drugs in rare diseases. METHODS: An umbrella review of systematic reviews of economic studies concerned with orphan and ultra-orphan drugs was conducted. Studies that were not systematic reviews, or on advanced therapeutic medicinal products, personalised medicines or other interventions that were not considered orphan drugs were excluded. The database searches included publications from 2010 to 2023, and were conducted in MEDLINE, EMBASE and the Cochrane library using filters for systematic reviews, and economic evaluations and models. These filters were combined with search terms for rare diseases and orphan drugs. A hand search supplemented the literature searches. The findings were reported by a compliant Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram. RESULTS: Two hundred and eighty-two records were identified from the literature searches, of which 64 were duplicates, whereas five reviews were identified from the hand search. A total of 36 reviews were included after screening against inclusion/exclusion criteria, 35 from literature searches and one from hand searching. Of those studies 1, 27 and 8 were low, moderate and high quality, respectively. The reviews highlight the scarcity of evidence for health economic parameters, for example, clinical effectiveness, costs, quality of life and the natural history of disease. Health economic evaluations such as cost-effectiveness and budget-impact analyses were scarce, and generally low-to-moderate quality. The causes were limited health economic parameters, together with publications bias, especially for cost-effectiveness analyses. CONCLUSIONS: The results highlighted issues around a considerable paucity of evidence for economic evaluations and few cost-effectiveness analyses, supporting the notion that a paucity of evidence makes economic evaluations of rare diseases more challenging compared with more prevalent diseases. Furthermore, we provide recommendations for more sustainable approaches in economic evaluations of rare diseases.

Structural equation modeling for identifying the drivers of health-related quality of life improvement experienced by patients with migraine receiving eptinezumab.

BACKGROUND: As new migraine therapies emerge, it is crucial for measures to capture the complexities of health-related quality of life (HRQoL) improvement beyond improvements in monthly migraine day (MMD) reduction. Investigations into the correlations between MMD reduction, symptom management, and HRQoL are lacking, particularly those that focus on improvements in canonical symptoms and improvement in patient-identified most-bothersome symptoms (PI-MBS), in patients treated with eptinezumab. This exploratory analysis identified efficacy measures mediating the effect of eptinezumab on HRQoL improvements in patients with migraine. METHODS: Data from the DELIVER study of patients with 2-4 prior preventive migraine treatment failures (NCT04418765) were inputted to two structural equation models describing sources of HRQoL improvement via Migraine-Specific Quality-of-Life Questionnaire (MSQ) scores. A single latent variable was defined to represent HRQoL and describe the sources of HRQoL in DELIVER. One model included all migraine symptoms while the second model included the PI-MBS as the only migraine symptom. Mediating variables capturing different aspects of efficacy included MMDs, other canonical symptoms, and PI-MBS. RESULTS: In the first model, reductions in MMDs and other canonical symptoms accounted for 35% (standardized effect size [SES] - 0.11) and 25% (SES - 0.08) of HRQoL improvement, respectively, with 41% (SES - 0.13) of improvement comprising "direct treatment effect," i.e., unexplained by mediators. In the second model, substantial HRQoL improvement with eptinezumab (86%; SES - 0.26) is due to MMD reduction (17%; SES - 0.05) and change in PI-MBS (69%; SES - 0.21). CONCLUSIONS: Improvements in HRQoL experienced by patients treated with eptinezumab can be substantially explained by its effect on migraine frequency and PI-MBS. Therefore, in addition to MMD reduction, healthcare providers should discuss PI-MBS improvements, since this may impact HRQoL. Health technology policymakers should consider implications of these findings in economic evaluation, as they point to alternative measurement of quality-adjusted life years to capture fully treatment benefits in cost-utility analyses. TRIAL REGISTRATION: ClinicalTrials.gov (Identifier: NCT04418765 ; EudraCT (Identifier: 2019-004497-25; URL: https://www.clinicaltrialsregister.eu/ctr-search/search?query=2019-004497-25 ).

Persistence to anti-CGRP monoclonal antibodies and onabotulinumtoxinA among patients with migraine: a retrospective cohort study.

BACKGROUND: To date, real-world evidence on persistence to anti-calcitonin gene-related peptide (anti-CGRP) monoclonal antibodies (mAbs) or onabotulinumtoxinA have excluded eptinezumab. This retrospective cohort study was performed to compare treatment persistency among patients with migraine on anti-CGRP mAbs (erenumab, fremanezumab, galcanezumab, or eptinezumab) or onabotulinumtoxinA. METHODS: This retrospective study used IQVIA PharmMetrics data. Adult patients with migraine treated with an anti-CGRP mAb or onabotulinumtoxinA who had 12 months of continuous insurance enrollment before starting treatment were included. A "most recent treatment episode" analysis was used in which the most recent episode was defined as the latest treatment period with the same drug (anti-CGRP mAb or onabotulinumtoxinA) without a ≥ 15-day gap in medication supply on/after June 25, 2020, to December 31, 2021. Patients were indexed at the start of their most recent episode. Patients were considered non-persistent and discontinued the therapy associated with their most recent episode if there was ≥ 15-day gap in medication supply. A Cox proportional-hazards model estimated the discontinuation hazard between treatments. The gap periods and cohort definition were varied in sensitivity analyses. RESULTS: The study included 66,576 patients (median age 46 years, 88.6% female). More eptinezumab-treated patients had chronic migraine (727/1074), ≥ 3 previous acute (323/1074) or preventive (333/1074) therapies, and more prior treatment episodes (3) than other treatment groups. Based on a 15-day treatment gap, patients on subcutaneous anti-CGRP mAbs had a 32% (95% CI: 1.19, 1.49; erenumab), 42% (95% CI: 1.27, 1.61; galcanezumab), and 58% (95% CI: 1.42, 1.80; fremanezumab) higher discontinuation hazard than those receiving eptinezumab, with this relationship attenuated, but still statistically significant based on 30-day and 60-day treatment gaps. There was no significant difference in the discontinuation hazard between eptinezumab and onabotulinumtoxinA. Based on a 15-day treatment gap among patients who newly initiated therapy, the discontinuation hazard of subcutaneous anti-CGRP mAbs remained significantly higher compared to eptinezumab and onabotulinumtoxinA. CONCLUSION: Patients treated with eptinezumab demonstrated persistency that was higher than subcutaneous anti-CGRP mAbs and similar to onabotulinumtoxinA.

Meta-regression to explain the placebo effects in clinical trials of anti-CGRP monoclonal antibodies for migraine prevention.

Background: Commonly used methods of comparison (e.g. network meta-analyses) require common comparator(s) across trials, such as placebo in placebo-controlled trials. Recent literature indicates that route of administration differences across placebo arms of clinical trials in pain disorders may contribute to differences in placebo effect.Methods: We conducted a meta-regression on placebo data from pivotal clinical trials of anti-calcitonin gene-related peptide (anti-CGRP) monoclonal antibodies for migraine prevention to quantify the impact of route of administration, migraine type (episodic/chronic), and number of prior treatment failures on placebo reduction in monthly migraine days (MMDs) across weeks 1-12 of treatment. A systematic literature review of Embase, MEDLINE, the Cochrane Library, and grey literature conducted in June 2021 identified 14 relevant, randomized placebo-controlled trials for analysis.Results: After testing models with different covariates, a meta-regression was fitted to the extracted placebo data with the covariates of route of administration, migraine type, and proportion of patients with ≥2 prior preventive treatment failures. An intravenous route of administration for the placebo arm was a predictor for higher MMD reduction. Predictors of lower MMD reduction were migraine type (episodic migraine) and a higher proportion of patients having ≥2 failed preventive treatments.Conclusions: The efficacy of intravenous anti-CGRP monoclonal antibodies are likely underestimated, and differences in the route of administration of placebo may necessitate use of alternative methods that do not assume the presence of a common comparator when comparing anti-CGRP monoclonal antibodies in migraine prevention. Further research into the contextual effects of the placebo effect is warranted.

Comparison of indirect treatment methods in migraine prevention to address differences in mode of administration.

Aim: Indirect treatment comparisons (ITCs) are anchored on a placebo comparator, and the placebo response may vary according to drug administration route. Migraine preventive treatment studies were used to evaluate ITCs and determine whether mode of administration influences placebo response and the overall study findings. Materials & methods: Change from baseline in monthly migraine days produced by monoclonal antibody treatments (subcutaneous, intravenous) was compared using fixed-effects Bayesian network meta-analysis (NMA), network meta-regression (NMR), and unanchored simulated treatment comparison (STC). Results: NMA and NMR provide mixed, rarely differentiated results between treatments, whereas unanchored STC strongly favors eptinezumab over other preventive treatments. Conclusion: Further investigations are needed to determine which ITC best reflects the impact of mode of administration on placebo.

Estimating treatment effects on health utility scores for patients living with migraine: a post hoc analysis of the DELIVER trial.

BACKGROUND: This post hoc analysis aimed to estimate eptinezumab's therapeutic effect on health utilities and determined to which extent monthly migraine days (MMDs) explain changes in health utilities. RESEARCH DESIGN/METHODS: DELIVER, a randomized, double-blind, placebo-controlled phase 3b trial (NCT04418765), investigated eptinezumab efficacy and safety in patients with 2-4 prior migraine treatment failures. Regression analysis explored the relationship between utility scores and MMDs, with eptinezumab treatment as a covariate along with MMDs to identify any MMD-independent effect on utilities. Path analysis quantified eptinezumab's impact as mediated through MMD reduction. RESULTS: The base case model showed that each reduction in MMD was associated with a mean utility score increase (0.0189; 95% CI: 0.0180, 0.0198; P < 0.001). Mean utility score was generally higher for eptinezumab versus placebo, justifying addition of treatment effect to the base case model. Patients administered eptinezumab had on average 0.0562 (95% CI: 0.0382, 0.0742; P < 0.001) higher utility versus placebo when controlling for number of MMDs. From path analysis, MMD reduction resulting from eptinezumab treatment accounted for 53% additional utility gain observed in patients. CONCLUSIONS: Changes in MMDs alone inadequately captured migraine's impact on patient utility, as there was also a positive eptinezumab-driven, treatment-specific impact on utility score. TRIAL REGISTRATION: The trial is registered at ClinicalTrials.gov (CT.gov identifier: NCT04418765).

NASHmap: clinical utility of a machine learning model to identify patients at risk of NASH in real-world settings.

The NASHmap model is a non-invasive tool using 14 variables (features) collected in standard clinical practice to classify patients as probable nonalcoholic steatohepatitis (NASH) or non-NASH, and here we have explored its performance and prediction accuracy. The National Institute of Diabetes and Digestive Kidney Diseases (NIDDK) NAFLD Adult Database and the Optum Electronic Health Record (EHR) were used for patient data. Model performance metrics were calculated from correct and incorrect classifications for 281 NIDDK (biopsy-confirmed NASH and non-NASH, with and without stratification by type 2 diabetes status) and 1,016 Optum (biopsy-confirmed NASH) patients. NASHmap sensitivity in NIDDK is 81%, with a slightly higher sensitivity in T2DM patients (86%) than non-T2DM patients (77%). NIDDK patients misclassified by NASHmap had mean feature values distinct from correctly predicted patients, particularly for aspartate transaminase (AST; 75.88 U/L true positive vs 34.94 U/L false negative), and alanine transaminase (ALT; 104.09 U/L vs 47.99 U/L). Sensitivity was slightly lower in Optum at 72%. In an undiagnosed Optum cohort at risk for NASH (n = 2.9 M), NASHmap predicted 31% of patients as NASH. This predicted NASH group had AST and ALT mean levels above normal range of 0-35 U/L, and 87% had HbA1C levels > 5.7%. Overall, NASHmap demonstrates good sensitivity in predicting NASH status in both datasets, and NASH patients misclassified as non-NASH by NASHmap have clinical profiles closer to non-NASH patients.

Assessment of patient life engagement in major depressive disorder using items from the Inventory of Depressive Symptomatology Self-Report (IDS-SR).

BACKGROUND: Patient-reported outcomes can measure domains that are personally meaningful, such as life engagement, which reflects motivation, pleasure, and well-being. This study explored whether certain items from the Inventory of Depressive Symptomatology Self-Report (IDS-SR) can capture patient life engagement in major depressive disorder (MDD). METHODS: IDS-SR life engagement items were identified by a) a panel of expert psychiatrists (n = 4), b) patient interviews (n = 20), and c) a principal component analysis (PCA) to explore clustering of items. Psychometric analyses were performed on potential subscales, and a minimal clinically important difference (MCID) was estimated by anchor- and distribution-based methods. IDS-SR data were obtained from three randomized controlled trials of adjunctive brexpiprazole in MDD. RESULTS: Expert psychiatrists selected 10 items by consensus from the IDS-SR that might capture patient life engagement (Cronbach's alpha, 0.82; item-total correlations, 0.36-0.58). Patient interviews identified 13 items as moderately to very relevant to life engagement (Cronbach's alpha, 0.85; item-total correlations, 0.35-0.61). The PCA revealed a cluster that included all 10 items selected by psychiatrists and 11 items identified by patients. Expert psychiatrists intentionally distinguished life engagement and core depressive symptoms, although patient insights and the PCA indicated that these aspects of MDD are strongly linked. The 10-item IDS-SR life engagement subscale had an MCID of 3-5 points. CONCLUSIONS: Different approaches consistently identified a subset of 10 IDS-SR items that can measure life engagement in MDD, which may be suitable to group into an IDS-SR life engagement subscale.

Mendelian randomization: estimation of inpatient hospital costs attributable to obesity.

BACKGROUND: Mendelian Randomization is a type of instrumental variable (IV) analysis that uses inherited genetic variants as instruments to estimate causal effects attributable to genetic factors. This study aims to estimate the impact of obesity on annual inpatient healthcare costs in the UK using linked data from the UK Biobank and Hospital Episode Statistics (HES). METHODS: UK Biobank data for 482,127 subjects was linked with HES inpatient admission records, and costs were assigned to episodes of care. A two-stage least squares (TSLS) IV model and a TSLS two-part cost model were compared to a naïve regression of inpatient healthcare costs on body mass index (BMI). RESULTS: The naïve analysis of annual cost on continuous BMI predicted an annual cost of £21.61 [95% CI £20.33 - £22.89] greater cost per unit increase in BMI. The TSLS IV model predicted an annual cost of £14.36 [95% CI £0.31 - £28.42] greater cost per unit increase in BMI. Modelled with a binary obesity variable, the naïve analysis predicted that obese subjects incurred £205.53 [95% CI £191.45 - £219.60] greater costs than non-obese subjects. The TSLS model predicted a cost £201.58 [95% CI £4.32 - £398.84] greater for obese subjects compared to non-obese subjects. CONCLUSIONS: The IV models provide evidence for a causal relationship between obesity and higher inpatient healthcare costs. Compared to the naïve models, the binary IV model found a slightly smaller marginal effect of obesity, and the continuous IV model found a slightly smaller marginal effect of a single unit increase in BMI.

Development of a novel machine learning model to predict presence of nonalcoholic steatohepatitis.

OBJECTIVE: To develop a computer model to predict patients with nonalcoholic steatohepatitis (NASH) using machine learning (ML). MATERIALS AND METHODS: This retrospective study utilized two databases: a) the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) nonalcoholic fatty liver disease (NAFLD) adult database (2004-2009), and b) the Optum® de-identified Electronic Health Record dataset (2007-2018), a real-world dataset representative of common electronic health records in the United States. We developed an ML model to predict NASH, using confirmed NASH and non-NASH based on liver histology results in the NIDDK dataset to train the model. RESULTS: Models were trained and tested on NIDDK NAFLD data (704 patients) and the best-performing models evaluated on Optum data (~3,000,000 patients). An eXtreme Gradient Boosting model (XGBoost) consisting of 14 features exhibited high performance as measured by area under the curve (0.82), sensitivity (81%), and precision (81%) in predicting NASH. Slightly reduced performance was observed with an abbreviated feature set of 5 variables (0.79, 80%, 80%, respectively). The full model demonstrated good performance (AUC 0.76) to predict NASH in Optum data. DISCUSSION: The proposed model, named NASHmap, is the first ML model developed with confirmed NASH and non-NASH cases as determined through liver biopsy and validated on a large, real-world patient dataset. Both the 14 and 5-feature versions exhibit high performance. CONCLUSION: The NASHmap model is a convenient and high performing tool that could be used to identify patients likely to have NASH in clinical settings, allowing better patient management and optimal allocation of clinical resources.

Mapping the Kansas City Cardiomyopathy Questionnaire (KCCQ) Onto EQ-5D-3L in Heart Failure Patients: Results for the Japanese and UK Value Sets.

Background. Health technology assessment bodies in several countries, including Japan and the United Kingdom, recommend mapping techniques to obtain utility scores in clinical trials that do not have a preference-based measure of health. This study sought to develop mapping algorithms to predict EQ-5D-3L scores from the Kansas City Cardiomyopathy Questionnaire (KCCQ) in patients with heart failure (HF). Methods. Data from the randomized, double-blind PARADIGM-HF trial were analyzed, and EQ-5D-3L scores were calculated using the Japanese and UK value sets. Several different model specifications were explored to best fit EQ-5D data collected at baseline with KCCQ scores, including ordinary least square regression, two-part, Tobit, and three-part models. Generalized estimating equations models were also fitted to analyze longitudinal EQ-5D data. To validate model predictions, the data set was split into a derivation (n = 4,465) from which the models were developed and a separate sample (n = 1,892) for validation. Results. There were only small differences between the different model classes tested. Model performance and predictive power was better for the item-level models than for the models including KCCQ domain scores. R 2 statistics for the item-level models ranged from 0.45 to 0.52. Mean absolute error in the validation sample was 0.10 for the models using the Japanese value set and 0.114 for the UK models. All models showed some underprediction of utility above 0.75 and overprediction of utility below 0.5, but performed well for population-level estimates. Conclusions. Using data from a large clinical trial in HF, we found that EQ-5D-3L scores can be estimated from responses to the KCCQ and can facilitate cost-utility analysis from existing HF trials where only the KCCQ was administered. Future validation in other HF populations is warranted.

Is the St. George's Respiratory Questionnaire an Appropriate Measure of Symptom Severity and Activity Limitations for Clinical Trials in COPD? Analysis of Pooled Data from Five Randomized Clinical Trials.

Purpose: The objective of this study was to examine the psychometric properties of the St. George's Respiratory Questionnaire (SGRQ) in patients with chronic obstructive pulmonary disease (COPD) using Rasch measurement theory (RMT) analyses. Materials and Methods: RMT analysis was conducted on the baseline SGRQ data from five multi-national, Phase III randomized trials investigating a fixed-dose combination of a long-acting ß2-agonist and a long-acting muscarinic antagonist in COPD patients. Analysis was performed for the SGRQ "Symptoms" and "Activity" domains. An exploratory analysis was also conducted using the different specific symptoms as defined in the reconceptualization of the SGRQ "Symptoms" domain. Differential item functioning (DIF) analysis was performed for geographical regions on the "Activity" domain, in order to explore cross-cultural validity of the SGRQ. Results: Overall, the SGRQ "Activity" domain showed good measurement property, but two items ("Sitting or lying still making feel breathless" and "Playing sports or game making feel breathless") showed very high fit residuals. The SGRQ "Symptoms" domain demonstrated good targeting; however, two items showed disordered thresholds ("Coughed" and "Brought up phlegm"). In an exploratory RMT analysis, measures for "Cough and Sputum", "Breathing difficulties" or "Wheezing attacks" showed unsatisfactory measurement properties with poor reliability (person separation index = 0.35, 0.66 and 0.16, respectively) and targeting issues. The examination of cross-cultural performances of the SGRQ "Activity" items showed a great variability in the responses to these items in different global regions. Conclusion: Our results indicated that SGRQ may not be an appropriate instrument to measure symptom severity or activity limitations in patients with COPD. Hence, there is a need to develop other relevant PRO instruments that can be used in conjunction with SGRQ to provide a holistic assessment of the health status of COPD patients in clinical research.

Optimal actuation of flagellar magnetic microswimmers.

We present an automated procedure for the design of optimal actuation for flagellar magnetic microswimmers based on numerical optimization. Using this method, a magnetic actuation method is provided which allows these devices to swim significantly faster compared to the usual sinusoidal actuation. This leads to a novel swimming strategy which makes the swimmer perform a three-dimensional figure-eight trajectory. This shows that a faster propulsion is obtained when the swimmer is allowed to go out of plane. This approach is experimentally validated on a scaled-up flexible swimmer.

Application of a Relative Visual Performance Model in a Virtual Reality Immersive System.

As part of an evaluation process of user experience realism in a Virtual Reality (VR) system, we focus in this paper on one of the core characteristics of vision: the relationship between contrast and luminance. The experiment aims at validating in VR reaction time predictions given by Rea and Ouellette's model. The subjects have to distinguish, as fast as they can, a target object from an uniform background. Our results did not match the predictions of the model. Our subjects showed higher performance in performing the task than expected. At low level of contrast, our subjects could easily perceive a target they should not have been able to see at all. This is explained by the size of the visual field surrounding the target: at low level of visibility, the larger the surrounding, the easier perception the is. We conclude that the Rea and Ouellette's model could be applied in VR if a specific visual field size factor was added.

Pre-Calibrated Visuo-Haptic Co-Location Improves Execution in Virtual Environments.

A common approach to visio-haptic human-machine interfaces adopts a simpler design by shifting grounded force feedback away from the virtual scene. The alternative design favors intuitiveness by displaying visual and grounded force feedback at the same location (i.e. visuo-haptic co-location) but requires a sensibly more complex implementation and a tedious kinematic conception. The benefits of one approach over the other had not been fully investigated. Notably, (i) while users seem to better operate under co-located condition, it's not always the case. (ii) In the case of a desktop interface, the cost of a complex implementation to achieve co-location is challenged. We aim here to resolve (i) by conducting a user-centered experiment in which participants performed two generic tasks in co-located and delocated configurations, and comparing their performances. Additionally, we intend to fill the gap (ii) by testing a design without continuous head tracking, i.e., with static co-location. Participants' performances are assessed in terms of execution time, accuracy and force variation, while their subjective experiences are collected via a survey. Findings indicate that co-located configurations lead to shorter execution times, more accurate motions, better management of forces and are largely preferred by users, even when the co-location is pre-calibrated statically.

Tele-Robotic Platform for Dexterous Optical Single-Cell Manipulation.

Single-cell manipulation is considered a key technology in biomedical research. However, the lack of intuitive and effective systems makes this technology less accessible. We propose a new tele-robotic solution for dexterous cell manipulation through optical tweezers. A slave-device consists of a combination of robot-assisted stages and a high-speed multi-trap technique. It allows for the manipulation of more than 15 optical traps in a large workspace with nanometric resolution. A master-device (6+1 degree of freedom (DoF)) is employed to control the 3D position of optical traps in different arrangements for specific purposes. Precision and efficiency studies are carried out with trajectory control tasks. Three state-of-the-art experiments were performed to verify the efficiency of the proposed platform. First, the reliable 3D rotation of a cell is demonstrated. Secondly, a six-DoF teleoperated optical-robot is used to transport a cluster of cells. Finally, a single-cell is dexterously manipulated through an optical-robot with a fork end-effector. Results illustrate the capability to perform complex tasks in efficient and intuitive ways, opening possibilities for new biomedical applications.

Patient-reported outcomes are important elements of psoriasis treatment decision making: A discrete choice experiment survey of dermatologists in the United States.

BACKGROUND: Psoriasis Area and Severity Index (PASI) response rates have been the benchmark for evaluating treatment efficacy in trials involving moderate-to-severe psoriasis. OBJECTIVE: To understand how dermatologists assess biologics and which trade-off rules they apply when planning psoriasis treatment. METHODS: Two online surveys of 130 and 129 US dermatologists (surveys 1 and 2, respectively) were conducted with use of direct and indirect elicitation via discrete choice experiment. Respondents were asked to choose hypothetical biologics on the basis of 6 attributes (a ≥75% reduction from baseline in PASI score or a ≥90% reduction from baseline in PASI score, infection risk, dosing frequency, and 3 patient-reported outcomes [PROs] [relief of depression, relief of itching, and impact on usual activities]). RESULTS: Most dermatologists (74% in survey 1 and 76% in survey 2) reported using both PASI and PROs when selecting a biologic. PASI response rate was the most important attribute (35%-38% of overall decision weight), whereas combined PRO attributes had similar importance (36% of decision weight). Infection risk and dosing frequency influenced the decision to a lesser extent. LIMITATIONS: Potential bias in considering 3 PROs versus 1 PASI rate and 1 safety attribute. CONCLUSION: PASI is most important for dermatologists selecting biologics, but PROs are also considered, especially when PASI response rate is similar between treatments. PRO data should be collected in trials involving moderate-to-severe psoriasis.

KiloHertz Bandwidth, Dual-Stage Haptic Device Lets You Touch Brownian Motion.

This paper describes a haptic interface that has a uniform response over the entire human tactile frequency range. Structural mechanics makes it very difficult to implement articulated mechanical systems that can transmit high frequency signals. Here, we separated the frequency range into two frequency bands. The lower band is within the first structural mode of the corresponding haptic device while the higher one can be transmitted accurately by a fast actuator operating from conservation of momentum, that is, without reaction forces to the ground. To couple the two systems, we adopted a channel separation approach akin to that employed in the design of acoustic reproduction systems. The two channels are recombined at the tip of the device to give a uniform frequency response from DC to one kHz. In terms of mechanical design, the high-frequency transducer was embedded inside the tip of the main stage so that during operation, the human operator has only to interact with a single finger interface. In order to exemplify the type of application that would benefit from this kind of interface, we applied it to the haptic exploration with microscopic scales objects which are known to behave with very fast dynamics. The novel haptic interface was bilaterally coupled with a micromanipulation platform to demonstrate its capabilities. Operators could feel interaction forces arising from contact as well as those resulting from Brownian motion and could manoeuvre a micro bead in the absence of vision.

Contributed Review: Quartz force sensing probes for micro-applications.

As self-sensing and self-exciting probes, quartz sensors present many advantages over silicon cantilevers for microscopy, micro-robotics, and other micro-applications. Their development and use is further bolstered by the fact that they can be manufactured from common quartz components. This paper therefore reviews applications of the increasingly popular quartz tuning fork probes as force sensors in the literature and examines the options for higher-frequency quartz probes using the other available types of flexional, thickness-shear or length-extensional resonators.