Industry perspective on regulatory authority (RA) quality reviews of biosimilar applications - an evaluation of RA guidance and expectations for chemical, manufacturing, and controls information through in-depth query analysis.

PURPOSE: Evaluate the type and quantity of quality information (i.e. Chemistry, Manufacturing, and Control) requested by the US FDA and EMA in queries pertaining to biosimilar applications. METHODS: Numbers/types of queries received following regulatory submissions (FDA/EMA, n = 7/n = 5) for seven biosimilars (PF-filgrastim [Nivestym], PF-rituximab [Ruxience®], PF-trastuzumab [Trazimera®], PF-bevacizumab [Zirabev®], PF-pegfilgrastim [Nyvepria®], PF-adalimumab [Abrilada™/Amsparity®], PF-infliximab [Ixifi]) from a single product portfolio were analyzed considering published regulatory authority (RA) guidance and in relation to sections/subsections of Module 3: Quality from the Common Technical Document regulatory dossier and topics based on keyword assignment. RESULTS: Queries were most frequently assigned (FDA/EMA %, range) to Drug Substance Manufacture (subsection 3.2.S.2; 21-35%/13-50%), Control of Drug Substance (3.2.S.4; 3-11%/5-17%), Drug Product Pharmaceutical Development (3.2.P.2; 1-12%/1-15%) and Manufacture (3.2.P.3; 17-41%/2-13%), and Analytical Similarity (3.2.R; 4-21%/4-20%). The proportion of Drug Substance and Drug Product queries was significantly different between RAs (n1 = 952, n2 = 468, p-value <0.001; two-sample proportion z-test). Topic assignments included: Control (12-27%/12-28%), Manufacturing (56-72%/34-66%), Stability (1-12%/2-24%), Biosimilarity (5-16%/5-25%), and Container Closure (0-3%/0-9%). CONCLUSION: The focus of both RAs on topics related to manufacturing and controls is valuable in understanding expectations for scientific and technical content related to gaining biosimilar approval.

The ReCoDe addiction research consortium: Losing and regaining control over drug intake-Findings and future perspectives.

Substance use disorders (SUDs) are seen as a continuum ranging from goal-directed and hedonic drug use to loss of control over drug intake with aversive consequences for mental and physical health and social functioning. The main goals of our interdisciplinary German collaborative research centre on Losing and Regaining Control over Drug Intake (ReCoDe) are (i) to study triggers (drug cues, stressors, drug priming) and modifying factors (age, gender, physical activity, cognitive functions, childhood adversity, social factors, such as loneliness and social contact/interaction) that longitudinally modulate the trajectories of losing and regaining control over drug consumption under real-life conditions. (ii) To study underlying behavioural, cognitive and neurobiological mechanisms of disease trajectories and drug-related behaviours and (iii) to provide non-invasive mechanism-based interventions. These goals are achieved by: (A) using innovative mHealth (mobile health) tools to longitudinally monitor the effects of triggers and modifying factors on drug consumption patterns in real life in a cohort of 900 patients with alcohol use disorder. This approach will be complemented by animal models of addiction with 24/7 automated behavioural monitoring across an entire disease trajectory; i.e. from a naïve state to a drug-taking state to an addiction or resilience-like state. (B) The identification and, if applicable, computational modelling of key molecular, neurobiological and psychological mechanisms (e.g., reduced cognitive flexibility) mediating the effects of such triggers and modifying factors on disease trajectories. (C) Developing and testing non-invasive interventions (e.g., Just-In-Time-Adaptive-Interventions (JITAIs), various non-invasive brain stimulations (NIBS), individualized physical activity) that specifically target the underlying mechanisms for regaining control over drug intake. Here, we will report on the most important results of the first funding period and outline our future research strategy.

From anticipation to action: A RCT on mental imagery exercises in daily life as a motivational amplifier for individuals with depressive symptoms.

Encouraging engagement in rewarding or pleasant activities is one of the most important treatment goals for depression. Mental imagery exercises have been shown to increase the motivation for planned behaviour in the lab but it is unclear whether this is also the case in daily life. Therefore, we aimed to investigate the effect of mental imagery exercises on motivation and behaviour in daily life. Participants with depressive symptoms (N = 59) were randomly assigned to a group receiving mental imagery (MI) exercises or a control group receiving relaxation (RE) exercises via study phones. We employed an experience sampling design with 10 assessments per day for 10 days (three days baseline, four days with two exercises per day and three days post-intervention). Data was analysed using t-tests and multilevel linear regression analyses. As predicted, MI exercises enhanced motivation and reward anticipation during the intervention phase compared to RE. However, MI did not enhance active behaviour or strengthen the temporal association from reward anticipation (t-1) to active behaviour (t). Mental imagery exercises can act as a motivational amplifier but its effects on behaviour and real-life reward processes remain to be elucidated.

SEMA3: A free smartphone platform for daily life surveys.

Traditionally, behavioral, social, and health science researchers have relied on global/retrospective survey methods administered cross-sectionally (i.e., on a single occasion) or longitudinally (i.e., on several occasions separated by weeks, months, or years). More recently, social and health scientists have added daily life survey methods (also known as intensive longitudinal methods or ambulatory assessment) to their toolkit. These methods (e.g., daily diaries, experience sampling, ecological momentary assessment) involve dense repeated assessments in everyday settings. To facilitate research using daily life survey methods, we present SEMA3 ( http://www.SEMA3.com ), a platform for designing and administering intensive longitudinal daily life surveys via Android and iOS smartphones. SEMA3 fills an important gap by providing researchers with a free, intuitive, and flexible platform with basic and advanced functionality. In this article, we describe SEMA3's development history and system architecture, provide an overview of how to design a study using SEMA3 and outline its key features, and discuss the platform's limitations and propose directions for future development of SEMA3.

RSDNet: A New Multiscale Rail Surface Defect Detection Model.

The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network's attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications.

Fitbit's accuracy to measure short bouts of stepping and sedentary behaviour: validation, sensitivity and specificity study.

Objective: This study aims to assess the suitability of Fitbit devices for real-time physical activity (PA) and sedentary behaviour (SB) monitoring in the context of just-in-time adaptive interventions (JITAIs) and event-based ecological momentary assessment (EMA) studies. Methods: Thirty-seven adults (18-65 years) and 32 older adults (65+) from Belgium and the Czech Republic wore four devices simultaneously for 3 days: two Fitbit models on the wrist, an ActiGraph GT3X+ at the hip and an ActivPAL at the thigh. Accuracy measures included mean (absolute) error and mean (absolute) percentage error. Concurrent validity was assessed using Lin's concordance correlation coefficient and Bland-Altman analyses. Fitbit's sensitivity and specificity for detecting stepping events across different thresholds and durations were calculated compared to ActiGraph, while ROC curve analyses identified optimal Fitbit thresholds for detecting sedentary events according to ActivPAL. Results: Fitbits demonstrated validity in measuring steps on a short time scale compared to ActiGraph. Except for stepping above 120 steps/min in older adults, both Fitbit models detected stepping bouts in adults and older adults with sensitivities and specificities exceeding 87% and 97%, respectively. Optimal cut-off values for identifying prolonged sitting bouts achieved sensitivities and specificities greater than 93% and 89%, respectively. Conclusions: This study provides practical insights into using Fitbit devices in JITAIs and event-based EMA studies among adults and older adults. Fitbits' reasonable accuracy in detecting short bouts of stepping and SB makes them suitable for triggering JITAI prompts or EMA questionnaires following a PA or SB event of interest.

Ecological momentary assessment of physical activity and sedentary behaviour in shift workers and non-shift workers: Validation study.

This study examined the criterion validity of an ecological momentary assessment (EMA)-reported physical activity and sedentary time compared with accelerometry in shift workers and non-shift workers. Australian workers (n = 102) received prompts through a mobile EMA app and wore the Actigraph accelerometer on the right hip for 7-10 days. Participants received five EMA prompts per day at 3-hour intervals on their mobile phones. EMA prompts sent to shift workers (SW-T) were tailored according to their work schedule. Non-shift workers (NSW-S) received prompts at standardised times. To assess criterion validity, the association of EMA-reported activities and the Actigraph accelerometer activity counts and number of steps were used. Participants were 36 ± 11 years and 58% were female. On occasions where participants reported physical activity, acceleration counts per minute (CPM) and steps were significantly higher (ß = 1184 CPM, CI 95%: 1034, 1334; ß = 20.9 steps, CI 95%: 18.2, 23.6) than each of the other EMA activities. Acceleration counts and steps were lower when sitting was reported than when no sitting was reported by EMA. Our study showed that EMA-reported physical activity and sedentary time was significantly associated with accelerometer-derived data. Therefore, EMA can be considered to assess shift workers' movement-related behaviours with accelerometers to provide rich contextual data.

Towards a coordinated approach for managing accelerated patient access to potentially beneficial medicines: reporting the perspectives of a multi-stakeholder, international workshop.

Accelerated and conditional regulatory pathways for drug approvals are intended to enable earlier patient access to potentially life-saving treatments, or treatments that provide benefits in addressing a significant unmet need. However, there are questions about how well such pathways work, how appropriately they are applied, and how the work of regulators can be better coordinated with that of health technology assessment (HTA) and payer bodies, providers and health systems, and other stakeholders. In June 2023, a multi-stakeholder, international workshop was convened in Adelaide, Australia, to deliberate the challenges, goals, and opportunities to improve accelerated access pathways. Workshop attendees included representatives from patient organizations, regulators, HTA/payer bodies, universities (ethicists, health economists), and companies developing and marketing new medicines from Australia, Asia, Europe, and North America. We reviewed the contents of this workshop to identify areas of agreement and disagreement, report the key themes of the discussion, and delineate next steps for improving accelerated access pathways. We found that there was general agreement among workshop attendees that accelerated access could be improved significantly by strengthening processes for stakeholder coordination, and that coordinated efforts will be required to implement meaningful change moving forward.

Clinical development and marketing application review times for novel orphan-designated drugs.

Development of an orphan-designated drug has been more challenging and financially less attractive than that of other drugs due to low prevalence of the condition, poorly defined biomarkers and lack of experience of healthcare providers in diagnosing and treating the condition. Guidance and incentives in some countries support the sponsors in developing orphan-designated drugs despite the challenges. Expedited regulatory programs as offered by the United States (US) Food and Drug Administration (FDA) and the European Medicines Agency (EMA) support the development of drugs, provide shorter marketing application review times or provide preliminary approval. In this study, we analyze marketing application review times in the US and in the European Union (EU) and clinical development times for novel, i.e., containing new molecular entity, orphan-designated drugs that were approved in the US between 1 June 2020 and 31 May 2023, and their correlation with expedited regulatory programs. Seventy-three marketing applications for novel orphan-designated drugs were approved by the FDA, and 39 also received a positive opinion from the EMA. The marketing application review time by the FDA for the 73 novel orphan-designated drugs approved in the US was 244 days (n = 73, median), and the marketing application review time by the EMA for the 39 drugs that were also approved in the EU was 353 days (n = 39, median). The typical clinical development time for a novel orphan-designated drug was 7.2 years (n = 72).

EU's Medical Device Expert Panels: Analysis of Membership and Published Clinical Evaluation Consultation Procedure (CECP) Results.

BACKGROUND: The new EU Medical Device Regulation (MDR) places greater importance on the role of clinical evidence to establish safety and performance. Article 54 of the MDR calls for expert committees to independently review the scientific, technical, and clinical evidence supporting the market authorization of certain novel devices independently from the established process of Notified Body reviews. These experts provide a review and opinion that ultimately is taken into consideration alongside the information reviewed by the Notified Body during the review process. Four expert committees (General and Plastic Surgery and Dentistry; Orthopaedics, Traumatology, Rehabilitation, Rheumatology; Circulatory System; and Neurology) have published at least one Scientific Opinion (SO) under the Clinical Evaluation Consultation Procedure (CECP) in 2021-2022. METHODS: The four expert committees with published CECP opinions were reviewed to assess the academic backgrounds and professional expertise of each member with respect to clinical, technical, and biological domains on a 0-2 scale for each domain. A content review was conducted on the 10 CECP opinions published by these committees to assess their consistency with the goals and outcome expectations set by the MDR. The extent of content related to each of the clinical, technical, and biological domains was also assessed on a 0-2 scale. RESULTS: All committees were composed primarily by members with strong clinical expertise, but only a few had strong technical and biological expertise. Across committees, the average scores of members related to academic background and professional expertise both ranged from 1.64 to 2.00 in the clinical domain, but only 0-0.15 and 0.15-0.69, respectively, in the biological domain, and 0.12-0.55 and 0.23-0.73, respectively, in the technical domain. A content review for the 10 SOs showed that all opinions focused exclusively or primarily on the clinical evidence. Three contained a modest amount of additional text directed at technical/engineering issues and five at biological issues. CONCLUSION: Expert committees are composed predominantly of expert clinical reviewers but have many fewer members with significant technical or biological expertise. This may limit the ability of the committees to evaluate the significant technical and biological risks that are often best understood by preclinical testing. Broadening the expertise across the committees may improve the depth of their benefit/risk critiques.

Regulatory histories of recently withdrawn ovarian cancer treatment indications of 3 PARP inhibitors in the US and Europe: lessons for the accelerated approval pathway.

Background: Withdrawals of drug indications may reveal potential inadequacies in the regulatory approval processes of new drugs. Understanding potential weaknesses of the regulatory approval process is paramount given the increasing use of expedited pathways. In this paper, we focus on three poly-ADP-ribose polymerase inhibitors (olaparib, rucaparib and niraparib) for the treatment of women with heavily pretreated, recurrent ovarian cancer, which were eventually withdrawn. Methods: We use a comparative case study approach to evaluate the regulatory histories of these drug indications in the US and Europe. Results: Two drug indications benefited from the FDA's accelerated approval pathway, which explicitly lowers the bar for evidence of efficacy at the time of approval. Following accelerated approval, manufacturers are mandated to conduct post-marketing studies to confirm clinical benefit. The FDA granted accelerated approval to olaparib and rucaparib based on data on surrogate endpoints and converted the approval to regular approval after the submission of additional data on surrogate endpoints from one of two required confirmatory trials, that is, without data on clinical benefit. Niraparib directly received regular approval based only on data on a surrogate endpoint. By contrast, the EMA granted conditional marketing authorisation to rucaparib and was quicker to restrict usage than the FDA. Conclusion: The regulatory histories of these drug indications highlight the need to reform the accelerated approval pathway by ensuring that post-marketing requirements are followed, and that regular approval is only based on evidence of clinical benefit.

Examining early adherence measures as predictors of subsequent adherence in an intensive longitudinal study of individuals in mutual help groups: One day at a time.

BACKGROUND: Individuals with a substance use disorder complete ecological momentary assessments (EMA) at lower rates than community samples. Previous research in tobacco users indicates that early log-in counts to smoking cessation websites predicted subsequent smoking cessation website usage. We extended this line of research to examine individuals who are seeking to change their drinking behaviors through mutual support groups. We examined whether adherence in the first 7 days (1487 observations) of an intensive longitudinal study design could predict subsequent EMA protocol adherence (50% and 80% adherence separately) at 30 (5700 observations) and 60 days (10,750 observations). METHODS: Participants (n = 132) attending mutual-help groups for alcohol use completed two assessments per day for 6 months. We trained four classification models (logistic regression, recursive partitioning, support vector machines, and neural networks) using a training dataset (80% of the data) with each of the first 7 days' cumulative EMA assessment completion. We then tested these models to predict the remaining 20% of the data and evaluated model classification accuracy. We also used univariate receiver operating characteristic curves to examine the minimal combination of days and completion percentage to best predict subsequent adherence. RESULTS: Different modeling techniques can be used with early assessment completion as predictors to accurately classify individuals that will meet minimal and optimal adherence rates later in the study. Models ranged in their performance from poor to outstanding classification, with no single model clearly outperforming other models. CONCLUSIONS: Traditional and machine learning approaches can be used concurrently to examine several methods of predicting EMA adherence based on early assessment completion. Future studies could investigate the use of several algorithms in real time to help improve participant adherence rates by monitoring early adherence and using early assessment completion as features in predictive modeling.

Evaluating the effectiveness of a mobile app-based self-guided psychological interventions to reduce relapse in substance use disorder: protocol for a randomized controlled trial.

Background: Substance Use Disorder (SUD) persists as a significant public health challenge worldwide, with an estimated prevalence of approximately 10-15% across the global populace. This condition is characterized by a notably high risk of lapses and relapses, even subsequent to treatment interventions. Mobile health interventions, owing to their widespread accessibility, emerge as a promising approach to diminish the risk of relapse post-treatment and to broaden the scope of care, especially in regions with a scarcity of trained medical professionals. Method: This study is designed to assess the effectiveness of mobile interventions in mitigating cravings and preventing lapses among individuals diagnosed with SUD. Employing a two-armed, randomized controlled trial framework, the study will evaluate a self-administered psychological intervention delivered through a mobile application, Nalogometr 2.0. Over a period of three months, participants will engage with intervention modules that primarily incorporate mindfulness techniques and Cognitive Behavioral Therapy (CBT) principles. Ecological Momentary Assessment (EMA) will be utilized to gather longitudinal data on a range of variables that are indicative of craving intensity and the risk of lapse. In addition to this, a monthly-administered battery of questionnaires will be employed to gauge the severity of substance dependence, as well as to measure levels of anxiety, depression, and overall life satisfaction. Results: Results will be submitted for publication in peer-reviewed journals. Clinical trial registration: https://clinicaltrials.gov/, identifier [NCT05730504].

Pleasure and Satisfaction as Predictors of Future Cigarette and E-Cigarette Use: A Novel Two-Stage Modeling Approach.

INTRODUCTION: Subjective experience of e-cigarettes may be an important factor in helping people who use combustible cigarettes switch completely to e-cigarettes to reduce harms from smoking. This paper describes a novel two-stage analysis using pleasure and satisfaction responses from Ecological Momentary Assessments (EMA) of both cigarette and e-cigarette use to predict future cigarette and e-cigarette tobacco use. METHODS: This observational study included adult users of cigarettes and e-cigarettes who provided 7-days of EMA, capturing cigarette and e-cigarette use, followed by biweekly reports of cigarette and e-cigarette use over one year. Participants were 279 adults who provided both cigarette and e-cigarette responses during the EMA. We employed a two-stage analytic approach in which EMA data were used to predict subsequent levels of cigarette and e-cigarette use. In the first stage, EMA responses of cigarette and e-cigarette events were modeled via a mixed-effects location scale (MELS) model to yield summaries of participants' means and variability on event-related ratings of pleasure and satisfaction. These EMA summaries served as predictors in the second stage analysis of the biweekly post-EMA longitudinal cigarette and e-cigarette use data. RESULTS: EMA pleasure and satisfaction ratings were similar for both products and predicted both longitudinal cigarette and e-cigarette use, even after controlling for baseline cigarette and e-cigarette dependence. Relatively higher levels of satisfaction with e-cigarettes were associated with greater decreases in cigarette use over time. CONCLUSIONS: Pleasure and satisfaction are important predictors of subsequent cigarette and e-cigarette use. IMPLICATIONS: Experienced subjective pleasure and satisfaction from e-cigarettes relative to cigarettes may be an important factor in helping individuals who smoke to switch completely to e-cigarettes as a harm reduction approach. In order to help sustain complete product switching and reduce dual use or relapse to smoking, e-cigarettes may need to deliver more satisfaction to the user compared to that experienced from cigarettes.

Shallow mud detection algorithm for submarine channels based on improved YOLOv5s.

Submarine mud poses a risk to channel navigation safety. Traditional detection methods lack efficiency and accuracy. As a result, this paper proposed an enhanced shallow submarine mud detection algorithm, leveraging an improved YOLOv5s model to increase accuracy and effectiveness in identifying such hazards in marine channels. Firstly, the sub-bottom profiler was employed to assess the submarine channel of Lianyungang Port to acquire the image data of the shallow mud sound print. Concurrently, the analysis incorporated the characteristics of changes in sound intensity peaks to precisely identify the shallow mud's location. Furthermore, the incorporation of C2F feature module into the backbone module enhances the gradient flow of the algorithm, augments the feature extraction information, and improves the algorithm's detection performance. Subsequently, Efficient Multi-Scale Attention (EMA) mechanism is incorporated into the neck module, aiming to optimize the algorithm's channel dimensions, minimize computational overhead, and enhance its detection efficiency. Finally, the study introduced Normalized Wasserstein Distance (NWD) loss function into bounding box regression loss function. This integration effectively addresses the issue of multi-scale defects, emphasizes the transformation of target planar position deviation, and improves the accuracy of the algorithm's detection capabilities. The results indicate that the improved YOLOv5s-EF algorithm outperforms the original YOLOv5s algorithm and other widely used detection algorithms. It achieved a validation set precision rate of 97.8%, recall rate of 97.6%, F1 value of 97.7%, mean Average Precision (mAP)@0.5 of 98.2%, mAP@0.95 of 69.6%, and Frames Per Second (FPS) of 51.8. YOLOv5s-EF algorithm proposed in this study offers a novel technical approach for detecting mud in submarine channels, which is importance for ensuring the safe operation and maintenance of dredging in such channels.

Multi-level prediction of substance use: Interaction of white matter integrity, resting-state connectivity and inhibitory control measured repeatedly in every-day life.

Substance use disorders are characterized by inhibition deficits related to disrupted connectivity in white matter pathways, leading via interaction to difficulties in resisting substance use. By combining neuroimaging with smartphone-based ecological momentary assessment (EMA), we questioned how biomarkers moderate inhibition deficits to predict use. Thus, we aimed to assess white matter integrity interaction with everyday inhibition deficits and related resting-state network connectivity to identify multi-dimensional predictors of substance use. Thirty-eight patients treated for alcohol, cannabis or tobacco use disorder completed 1 week of EMA to report substance use five times and complete Stroop inhibition testing twice daily. Before EMA tracking, participants underwent resting state functional MRI and diffusion tensor imaging (DTI) scanning. Regression analyses were conducted between mean Stroop performances and whole-brain fractional anisotropy (FA) in white matter. Moderation testing was conducted between mean FA within significant clusters as moderator and the link between momentary Stroop performance and use as outcome. Predictions between FA and resting-state connectivity strength in known inhibition-related networks were assessed using mixed modelling. Higher FA values in the anterior corpus callosum and bilateral anterior corona radiata predicted higher mean Stroop performance during the EMA week and stronger functional connectivity in occipital-frontal-cerebellar regions. Integrity in these regions moderated the link between inhibitory control and substance use, whereby stronger inhibition was predictive of the lowest probability of use for the highest FA values. In conclusion, compromised white matter structural integrity in anterior brain systems appears to underlie impairment in inhibitory control functional networks and compromised ability to refrain from substance use.

Validation of R/S-Warfarin Analysis Method in Human Blood Plasma Using HPLC with Fluorescence Detection and its Application to Patient Samples.

Context: Warfarin is extensively used for venous thromboembolism and other coagulopathies. In clinical settings, warfarin is administered as a mixture of S-warfarin and R-warfarin, and both enantiomers are metabolized by multiple cytochrome P450 enzymes into many hydroxylation metabolites. Validation of analysis method and estimation of warfarin plasma levels are important, especially in narrow-index drugs such as warfarin. Aims: This study aimed to obtain a validated method for analyzing warfarin in patient plasma according to the European Medicines Agency (EMA) guidelines. Materials and Methods: A total of 77 patients were enrolled in this study. Five millimeters of venous blood was collected using sodium ethylenediaminetetraacetic acid (EDTA) tubes for pharmacokinetic analysis. Samples were prepared by the protein precipitation technique using acetonitrile. The optimum conditions for the analysis of warfarin in human plasma were tested using fluorescence detector (FLD) high-performance liquid chromatography (HPLC) with Chiralcel OD-RH column (4.6 × 150 mm i.d., 5 µm), Chiralcel OD-RH guard column (4.0 × 10 mm, 5 µm), and a column temperature of 45°C. The mobile phase used was acetonitrile: phosphate buffer pH 2 (40:60), with an isocratic flow rate of 1 ml/min and an injection volume of 20 µl. Excitation and emission wavelengths were 310 and 350 nm (warfarin) and 300 and 400 nm (griseofulvin). The retention time of griseofulvin was 6-7.5 minutes; R-warfarin was 10-11.5 minutes; and S-warfarin was 14-16 minutes. Results: The result of this validation obtained the optimum conditions. This method yielded the limit of detection (LOD) values of 0.0674 ppm (R-warfarin) and 0.0897 ppm (S-warfarin). The limit of quantification (LOQ) values were 0.225 ppm (R-warfarin) and 0.298 ppm (S-warfarin). Linearity existed at concentrations of 0.2-3 ppm with the line equation y = 0.0705x + 0.0704 with R² = 0.978 for R-warfarin and y = 0.0513x + 0.0297 with R² = 0.9924 for S-warfarin. At least 75% of the seven concentrations met the reverse concentration requirements, which were below ± 15%. This method met the requirements of accuracy and precision within and between runs, selectivity, and carryover where the %RSD and %diff values were below ± 15%. The mean plasma concentrations of R-warfarin and S-warfarin were found to be 0.76 ± 1.87 (SD) µg/ml and 0.59 ± 0.81 (SD) µg/ml, respectively. The mean standard dose group plasma concentration from the analysis of 77 samples was 0.68 ± 0.61 µg/mL for R-warfarin and 0.52 ± 0.42 µg/mL for S-warfarin. Conclusions: Based on these results, this analytical method can be declared valid and can be used for sample measurement in warfarin pharmacokinetic studies.

Optimizing Outcomes in Psychotherapy for Anxiety Disorders Using Smartphone-Based and Passive Sensing Features: Protocol for a Randomized Controlled Trial.

BACKGROUND: Psychotherapies, such as cognitive behavioral therapy (CBT), currently have the strongest evidence of durable symptom changes for most psychological disorders, such as anxiety disorders. Nevertheless, only about half of individuals treated with CBT benefit from it. Predictive algorithms, including digital assessments and passive sensing features, could better identify patients who would benefit from CBT, and thus, improve treatment choices. OBJECTIVE: This study aims to establish predictive features that forecast responses to transdiagnostic CBT in anxiety disorders and to investigate key mechanisms underlying treatment responses. METHODS: This study is a 2-armed randomized controlled clinical trial. We include patients with anxiety disorders who are randomized to either a transdiagnostic CBT group or a waitlist (referred to as WAIT). We index key features to predict responses prior to starting treatment using subjective self-report questionnaires, experimental tasks, biological samples, ecological momentary assessments, activity tracking, and smartphone-based passive sensing to derive a multimodal feature set for predictive modeling. Additional assessments take place weekly at mid- and posttreatment and at 6- and 12-month follow-ups to index anxiety and depression symptom severity. We aim to include 150 patients, randomized to CBT versus WAIT at a 3:1 ratio. The data set will be subject to full feature and important features selected by minimal redundancy and maximal relevance feature selection and then fed into machine leaning models, including eXtreme gradient boosting, pattern recognition network, and k-nearest neighbors to forecast treatment response. The performance of the developed models will be evaluated. In addition to predictive modeling, we will test specific mechanistic hypotheses (eg, association between self-efficacy, daily symptoms obtained using ecological momentary assessments, and treatment response) to elucidate mechanisms underlying treatment response. RESULTS: The trial is now completed. It was approved by the Cantonal Ethics Committee, Zurich. The results will be disseminated through publications in scientific peer-reviewed journals and conference presentations. CONCLUSIONS: The aim of this trial is to improve current CBT treatment by precise forecasting of treatment response and by understanding and potentially augmenting underpinning mechanisms and personalizing treatment. TRIAL REGISTRATION: ClinicalTrials.gov NCT03945617; https://clinicaltrials.gov/ct2/show/results/NCT03945617. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42547.

Model-Based Practically Precise Fabrication of HgSe Quantum Dots toward Their Application in a Long-Wave Infrared Micro Spectrometer.

Quantum dot (QD) passive filters present a simple and low-cost strategy for the micromation of spectrometers. In this field, the consistency between ultranarrow band gap QD fabrication and the precise control of its absorption characteristics is the key-challenge to extend QD spectrometers into the long-wave infrared (LWIR) region. Here, we show the model-based, practically precise fabrication of HgSe QDs as well as their specific spectral responses. Both the theoretical and experimental models of the HgSe QDs r-λ are formulated, which reveals the variation of transmission spectrum with the size of HgSe QDs. Then, the HgSe QDs synthesis parameter-spectral response hyperplane model and neural network model were obtained by using traditional polynomial fitting and machine learning, respectively. We also demonstrate the model-based precise fabrication of HgSe QDs with transmission characteristic peaks within 14 µm. The further simulation also shows that the 255─element QD filter array has the signal-to-noise ratio up to 14.57 dB with detection resolution about 5 cm-1.

Molecular detection and characterization of prevailing Theileria equi genotype in equine from northern India.

Equine piroplasmosis caused by Theileria equi is a febrile, tick-borne disease of equids. However, there is limited literature about the genotyping of T. equi in India. Blood samples were collected from 202 horses and subjected to microscopy and PCR to detect T. equi. Initially, a universal screening primer pair targeting 18S ribosomal RNA genes common for Babesia caballi and T. equi was employed to amplify the DNA of both parasites. Thereafter additional primers were employed for species-specific detection resulting in amplification of approximately 435 bp specific for T. equi. T.equi was detected in 9.9% and 20.79% of horses screened by microscopy and PCR, respectively. The representative samples confirmed positive by PCR were sequenced, submitted to NCBI (OR651254, OR687254, OR685656, OR650830, OR650834), and used for genotype characterization and phylogenetic analysis. Employing Genetool and MEGA X software, the T. equi Indian isolates and across the globe were compared, and the results demonstrated 99.05-100% and 95.86-100% homologies, respectively. All the T. equi Indian isolates belonged to genotype A. Phylogeny based on the EMA-1 gene of five isolates (OR731831, OR731833, OR731829, OR731830, OR731832) were also characterized by sequencing and support the previous findings. Genotypes C and D, as well as genotypes B and E, exhibited lower levels of evolutionary divergence compared to other genotypes. The EMA-1 gene exhibited limited diversity and might not be the most suitable target for assessing variability within T. equi populations. The findings also reveal a significant association (p < 0.01) between T. equi infection and the presence of ticks.