Treatment patterns of individualized real-life tapering approaches based on shared decision-making in rheumatoid arthritis.

OBJECTIVE: To provide real-world evidence on patient-individual tapering patterns of disease-modifying antirheumatic drugs (DMARDs) in rheumatoid arthritis (RA) patients in daily clinical practice. METHODS: Data obtained through a controlled prospective cohort study in Germany conducted from July 2018 to March 2021 were analyzed. Participants consist of RA patients in sustained remission who were eligible for DMARD tapering at enrolment. Data from RA patients who experienced tapering of DMARDs at least once during the observational period (n = 200) were used. Descriptive analyses of medical outcomes at baseline and at time of first tapering, time to first tapering, tapering patterns by substance group, and tapering intensity were documented. RESULTS: We did not observe meaningful differences in either disease activity or quality of life measures between substance groups at enrolment, time of first tapering, and at 6 or 12 months after tapering. Median time until first tapering varied between substance groups (csDMARDs: 108 days; bDMARDs: 189 days; combination: 119 days). Most patients received one iteration of tapering only (147/200 patients, 73.5%). Dose reduction was applied for patients treated with csDMARDs (79/86 patients, 91.8%), spacing of interval was the most frequent strategy for patients treated with bDMARDs only (43/48 patients, 89.5%). Necessity for increased DMARD dosage was observed in only 10% of patients (20/200). Tapering intensity by substance was overall heterogenous, indicating high individualization. CONCLUSION: We identify highly heterogeneous tapering patterns between substance groups and within substances. Identification and recognition of patient-individual approaches of tapering will help to further improve the management of RA for both patients and rheumatologists.

A Smart Visual Sensing Concept Involving Deep Learning for a Robust Optical Character Recognition under Hard Real-World Conditions.

In this study, we propose a new model for optical character recognition (OCR) based on both CNNs (convolutional neural networks) and RNNs (recurrent neural networks). The distortions affecting the document image can take different forms, such as blur (focus blur, motion blur, etc.), shadow, bad contrast, etc. Document-image distortions significantly decrease the performance of OCR systems, to the extent that they reach a performance close to zero. Therefore, a robust OCR model that performs robustly even under hard (distortion) conditions is still sorely needed. However, our comprehensive study in this paper shows that various related works can somewhat improve their respective OCR recognition performance of degraded document images (e.g., captured by smartphone cameras under different conditions and, thus, distorted by shadows, contrast, blur, etc.), but it is worth underscoring, that improved recognition is neither sufficient nor always satisfactory-especially in very harsh conditions. Therefore, in this paper, we suggest and develop a much better and fully different approach and model architecture, which significantly outperforms the aforementioned previous related works. Furthermore, a new dataset was gathered to show a series of different and well-representative real-world scenarios of hard distortion conditions. The new OCR model suggested performs in such a way that even document images (even from the hardest conditions) that were previously not recognizable by other OCR systems can be fully recognized with up to 97.5% accuracy/precision by our new deep-learning-based OCR model.

A Deep-Learning Based Visual Sensing Concept for a Robust Classification of Document Images under Real-World Hard Conditions.

This paper's core objective is to develop and validate a new neurocomputing model to classify document images in particularly demanding hard conditions such as image distortions, image size variance and scale, a huge number of classes, etc. Document classification is a special machine vision task in which document images are categorized according to their likelihood. Document classification is by itself an important topic for the digital office and it has several usages. Additionally, different methods for solving this problem have been presented in various studies; their respectively reached performance is however not yet good enough. This task is very tough and challenging. Thus, a novel, more accurate and precise model is needed. Although the related works do reach acceptable accuracy values for less hard conditions, they generally fully fail in the face of those above-mentioned hard, real-world conditions, including, amongst others, distortions such as noise, blur, low contrast, and shadows. In this paper, a novel deep CNN model is developed, validated and benchmarked with a selection of the most relevant recent document classification models. Additionally, the model's sensitivity was significantly improved by injecting different artifacts during the training process. In the benchmarking, it does clearly outperform all others by at least 4%, thus reaching more than 96% accuracy.

Emission factors of ammonia for on-road vehicles in urban areas from a tunnel study in south China with laser-absorption based measurements.

Vehicle emission is an important source of ammonia (NH3) in urban areas. To better address the role of vehicle emission in urban NH3 sources, the emission factor of NH3 (NH3-EF) from vehicles running on roads under real-world conditions (on-road vehicles) needs to update accordingly with the increasingly tightened vehicle emission standards. In this study, laser-absorption based measurements of NH3 were conducted during a six-day campaign in 2019 at a busy urban tunnel with a daily traffic flow of nearly 40,000 vehicles in south China's Pearl River Delta (PRD) region. The NH3-EF was measured to be 16.6 ± 6.3 mg km-1 for the on-road vehicle fleets and 19.0 ± 7.2 mg km-1 for non-electric vehicles, with an NH3 to CO2 ratio of 0.27 ± 0.09 ppbv ppmv-1. Multiple linear regression revealed that the average NH3-EFs for gasoline vehicles (GVs), liquefied petroleum gas vehicles, and heavy-duty diesel vehicles (HDVs) were 18.8, 15.6, and 44.2 mg km-1, respectively. While NH3 emissions from GVs were greatly reduced with enhanced performance of engines and catalytic devices to meet stricter emission standards, the application of urea selective catalytic reduction (SCR) in HDVs makes their NH3 emission an emerging concern. Based on results from this study, HDVs may contribute over 11% of the vehicular NH3 emissions, although they only share ∼4% by vehicle numbers in China. With the updated NH3-EFs, NH3 emission from on-road vehicles was estimated to be 9 Gg yr-1 in the PRD region in 2019, contributing only 5% of total NH3 emissions in the region, but still might be a dominant NH3 source in the urban centers with little agricultural activity.

Real-World Efficacy and Safety of Lenvatinib in Korean Patients with Advanced Hepatocellular Carcinoma: A Multicenter Retrospective Analysis.

INTRODUCTION/OBJECTIVE: Lenvatinib demonstrated efficacy and safety in patients with advanced hepatocellular carcinoma (HCC) in the randomized phase III REFLECT trial. Considering the discrepancies in patients between clinical trial data and daily practice, an account of practical experience is needed. METHODS: We conducted a multicenter retrospective analysis in which 3 tertiary referral centers participated. A total of 92 patients with advanced HCC treated with lenvatinib between September 2018 and January 2020 were analyzed. RESULTS: Lenvatinib was used as the first-line therapy for 67 (72.8%) patients, and for 25 (27.2%) patients previously treated with other systemic therapy including immune checkpoint inhibitors. At the time of initiation of lenvatinib, 74 (80.4%) and 18 (19.6%) patients were classified as Child-Pugh A and B, respectively. Thirty-five patients (38.0%) had extensive disease that would have excluded them from the REFLECT trial. In the Child-Pugh A group, the response rate graded according to the Response Evaluation Criteria in Solid Tumors v1.1 was 21.1%, median progression-free survival (PFS) was 4.6 (95% confidence interval [CI] 3.1-6.1) months, and overall survival (OS) was 10.7 (95% CI 4.8-16.5) months for patients treated with first-line lenvatinib (n = 57). With second- or later-line lenvatinib (n = 17), median PFS and OS were 4.1 (95% CI 3.1-5.1) and 6.4 (95% CI 5.1-7.7) months, respectively. In the Child-Pugh B group (n = 18), median PFS and OS were 2.6 (95% CI 0.6-4.6) and 5.3 (95% CI 2.0-8.5) months, respectively. The most common grade 3-4 toxicities were hyperbilirubinemia (n = 8; 8.7%), AST elevation (n = 6; 6.5%), and diarrhea (n = 5; 5.4%) across all study patients. CONCLUSIONS: In this real-world study, lenvatinib was found to be well tolerated and effective in more heterogeneous HCC patient populations.

Accelerometric Gait Analysis Devices in Children-Will They Accept Them? Results From the AVAPed Study.

Aims: To assess children's acceptance to wear a 3D-accelerometer which is attached to the waist under real-world conditions, and also to compare gait speed during supervised testing with the non-supervised gait speed in every-day life. Methods: In a controlled observational, cross sectional study thirty subjects with cerebral palsy (CP), with level I&II of the Gross Motor Function Classification System (GMFCS) and 30 healthy control children (Ctrl), aged 3-12 years, were asked to perform a 1-min-walking test (1 mwt) under laboratory conditions, and to wear an accelerometric device for a 1-week wearing home measurement (1 WHM). Acceptance was measured via wearing time, and by a questionnaire in which subjects rated restrictions in their daily living and wearing comfort. In addition, validity of 3D-accelerometric gait speed was checked through gold standard assessment of gait speed with a mobile perambulator. Results: Wearing time amounted to 10.3 (SD 3.4) hours per day, which was comparable between groups (T = 1.10, P = 0.3). Mode for wearing comfort [CP 1, Range (1,4), Ctrl 1, Range (1,6)] and restriction of daily living [CP 1, Range (1,3), Ctrl 1, Range (1,4)] was comparable between groups. Under laboratory conditions, Ctrl walked faster in the 1 mwt than CP (Ctrl 1.72 ± 0.29 m/s, CP 1.48 ± 0.41 m/s, P = 0.018). Similarly, a statistically significant difference was found when comparing real-world walking speed and laboratory walking speed (CP: 1 mwt 1.48 ± 0.41 m/s, 1 WHM 0.89 ± 0.09 m/s, P = 0.012; Ctrl: 1mwt 1.72 ± 0.29, 1 WHM 0.97 ± 0.06, P < 0.001). Conclusion: 3D-accelerometry is well-enough accepted in a pediatric population of patients with CP and a Ctrl group to allow valid assessments. Assessment outside the laboratory environment yields information about real world activity that was not captured by routine clinical tests. This suggests that assessment of habitual activities by wearable devices reflects the functioning of children in their home environment. This novel information constitutes an important goal for rehabilitation medicine. The study is registered at the German Register of Clinical Trials with the title "Acceptance and Validity of 3D Accelerometric Gait Analysis in Pediatric Patients" (AVAPed; DRKS00011919).

A Review on Automatic Facial Expression Recognition Systems Assisted by Multimodal Sensor Data.

Facial Expression Recognition (FER) can be widely applied to various research areas, such as mental diseases diagnosis and human social/physiological interaction detection. With the emerging advanced technologies in hardware and sensors, FER systems have been developed to support real-world application scenes, instead of laboratory environments. Although the laboratory-controlled FER systems achieve very high accuracy, around 97%, the technical transferring from the laboratory to real-world applications faces a great barrier of very low accuracy, approximately 50%. In this survey, we comprehensively discuss three significant challenges in the unconstrained real-world environments, such as illumination variation, head pose, and subject-dependence, which may not be resolved by only analysing images/videos in the FER system. We focus on those sensors that may provide extra information and help the FER systems to detect emotion in both static images and video sequences. We introduce three categories of sensors that may help improve the accuracy and reliability of an expression recognition system by tackling the challenges mentioned above in pure image/video processing. The first group is detailed-face sensors, which detect a small dynamic change of a face component, such as eye-trackers, which may help differentiate the background noise and the feature of faces. The second is non-visual sensors, such as audio, depth, and EEG sensors, which provide extra information in addition to visual dimension and improve the recognition reliability for example in illumination variation and position shift situation. The last is target-focused sensors, such as infrared thermal sensors, which can facilitate the FER systems to filter useless visual contents and may help resist illumination variation. Also, we discuss the methods of fusing different inputs obtained from multimodal sensors in an emotion system. We comparatively review the most prominent multimodal emotional expression recognition approaches and point out their advantages and limitations. We briefly introduce the benchmark data sets related to FER systems for each category of sensors and extend our survey to the open challenges and issues. Meanwhile, we design a framework of an expression recognition system, which uses multimodal sensor data (provided by the three categories of sensors) to provide complete information about emotions to assist the pure face image/video analysis. We theoretically analyse the feasibility and achievability of our new expression recognition system, especially for the use in the wild environment, and point out the future directions to design an efficient, emotional expression recognition system.

Efficacy and effectiveness trials have different goals, use different tools, and generate different messages.

The discussion about the optimal design of clinical trials reflects the perspectives of theory-based scientists and practice-based clinicians. Scientists compare the theory with published results. They observe a continuum from explanatory to pragmatic trials. Clinicians compare the problem they want to solve by completing a clinical trial with the results they can read in the literature. They observe a mixture of what they want and what they get. None of them can solve the problem without the support of the other. Here, we summarize the results of discussions with scientists and clinicians. All participants were interested to understand and analyze the arguments of the other side. As a result of this process, we conclude that scientists tell what they see, a continuum from clear explanatory to clear pragmatic trials. Clinicians tell what they want to see, a clear explanatory trial to describe the expected effects under ideal study conditions and a clear pragmatic trial to describe the observed effects under real-world conditions. Following this discussion, the solution was not too difficult. When we accept what we see, we will not get what we want. If we discuss a necessary change of management, we will end up with the conclusion that two types of studies are necessary to demonstrate efficacy and effectiveness. Efficacy can be demonstrated in an explanatory, ie, a randomized controlled trial (RCT) completed under ideal study conditions. Effectiveness can be demonstrated in an observational, ie, a pragmatic controlled trial (PCT) completed under real-world conditions. It is impossible to design a trial which can detect efficacy and effectiveness simultaneously. The RCTs describe what we may expect in health care, while the PCTs describe what we really observe.