Objective Evaluation Research Articles

Association between biological markers and somatic pain

Pain, as one of the major clinical and social problems, still lacks effective objective evaluation indicators. At present, nearly one in five adults suffer from different types and different parts of pain. An important feature of clinical pain is persistent pain, in addition to sensory discrimination processes, it involves a range of brain regions involved in top-down cognitive and emotional processing. However, clinical pain is often affected by multiple factors and is difficult to assess objectively. Recently, blood, urine, cerebrospinal fluid, and biopsy tissue have been identified using novel, sensitive, and specific protein analysis methods in an attempt to detect changes in biological markers associated with different pain types. This paper will summarize the biological markers related to different parts of pain in the latest relevant studies at home and abroad and focus on the application prospect of such biological markers in the diagnosis, treatment, and prognosis evaluation of the pain.

Effectiveness of Treating Obstructive Sleep Apnea by Surgeries and Continuous Positive Airway Pressure: Evaluation Using Objective Sleep Parameters and Patient-Reported Outcomes.

Background/Objectives: Uvulopalatopharyngoplasty (UPPP), palatal plus nasal surgery (PNS), and continuous positive airway pressure (CPAP) are widely implemented treatments for obstructive sleep apnea (OSA). This study aims to explore the long-term effects on objective sleep parameters and patient-reported outcomes (PROs) following different therapeutic interventions for OSA. Methods: Data from patients with moderate-to-severe OSA were retrospectively collected from a medical center and a regional hospital, spanning from December 2011 to August 2018. Objective evaluations included the Apnea-Hypopnea Index (AHI), minimum O2 saturation, and sleep efficiency. The PROs consisted of the Snore Outcomes Survey and Epworth Sleepiness Scale. Using mixed-effects models, we evaluated longitudinal changes in sleep parameters and PROs, accounting for repeated measures and variations within individuals over time. Results: Among 448 patients with moderate-to-severe OSA, follow-up data were collected for 42 patients undergoing UPPP surgery, 171 undergoing PNS, 127 using CPAP, and 108 in the non-treated group. The mean follow-up was 16.7 months (SD = 11.9, range: 1.6-77.3). Significant improvements were observed in AHI, minimum O2 saturation, and hypersomnia immediately following interventions with UPPP, PNS, and CPAP therapy (p < 0.05). Moreover, the analysis revealed no significant rate of change in these parameters over time, suggesting that the benefits of these treatments were sustained in the long term. Furthermore, all interventions exhibited a significant short-term effect on self-reported snoring when compared to the control group, with a p-value of less than 0.001. However, the magnitude of this improvement gradually decreased over time. The snore scores seemed to return to pre-treatment levels among the UPPP, PNS, and CPAP groups after averages of 46.4, 63.5, and 74.4 months, respectively (all p < 0.05). Conclusions: Surgical interventions and CPAP therapy showed potential long-term effectiveness in managing OSA. Snoring symptoms reappeared about 3.9-5.3 years after surgical treatments, which seemed earlier than the average of 6.2 years in patients receiving CPAP and should be considered in patient-participatory decision-making processes.

Objective performance indicators versus GEARS: an opportunity for more accurate assessment of surgical skill.

Surgical skill evaluation that relies on subjective scoring of surgical videos can be time-consuming and inconsistent across raters. We demonstrate differentiated opportunities for objective evaluation to improve surgeon training and performance. Subjective evaluation was performed using the Global evaluative assessment of robotic skills (GEARS) from both expert and crowd raters; whereas, objective evaluation used objective performance indicators (OPIs) derived from da Vinci surgical systems. Classifiers were trained for each evaluation method to distinguish between surgical expertise levels. This study includes one clinical task from a case series of robotic-assisted sleeve gastrectomy procedures performed by a single surgeon, and two training tasks performed by novice and expert surgeons, i.e., surgeons with no experience in robotic-assisted surgery (RAS) and those with more than 500 RAS procedures. When comparing expert and novice skill levels, OPI-based classifier showed significantly higher accuracy than GEARS-based classifier on the more complex dissection task (OPI 0.93 ± 0.08 vs. GEARS 0.67 ± 0.18; 95% CI, 0.16-0.37; p = 0.02), but no significant difference was shown on the simpler suturing task. For the single-surgeon case series, both classifiers performed well when differentiating between early and late group cases with smaller group sizes and larger intervals between groups (OPI 0.9 ± 0.08; GEARS 0.87 ± 0.12; 95% CI, 0.02-0.04; p = 0.67). When increasing the group size to include more cases, thereby having smaller intervals between groups, OPIs demonstrated significantly higher accuracy (OPI 0.97 ± 0.06; GEARS 0.76 ± 0.07; 95% CI, 0.12-0.28; p = 0.004) in differentiating between the early/late cases. Objective methods for skill evaluation in RAS outperform subjective methods when (1) differentiating expertise in a technically challenging training task, and (2) identifying more granular differences along early versus late phases of a surgeon learning curve within a clinical task. Objective methods offer an opportunity for more accessible and scalable skill evaluation in RAS.

Hyperspectral Attention Network for Object Tracking

Hyperspectral video provides rich spatial and spectral information, which is crucial for object tracking in complex scenarios. Despite extensive research, existing methods often face an inherent trade-off between rich spectral information and redundant noisy information. This dilemma arises from the efficient utilization of hyperspectral image data channels. To alleviate this problem, this paper introduces a hierarchical spectral attention network for hyperspectral object tracking. We employ a spectral band attention mechanism with adaptive soft threshold to examine the correlations across spectral bands, which integrates the information available in various spectral bands and eliminates redundant information. Moreover, we integrate spectral attention into a hierarchical tracking network to improve the integration of spectral and spatial information. The experimental results on entire public hyperspectral competition dataset WHISPER2020 show the superior performance of our proposed method compared with that of several related methods in visual effects and objective evaluation.

Perceptual Computing Based Method of Evaluation in E-learning Systems

Introduction. Implementation of e-learning has transformed the education system, expanding access to knowledge and providing flexibility in learning for both students and teachers. Application of information technologies in the education system allows to improve the learning process by introducing novel methods and approaches not only to teaching but also to knowledge assessment. However, along with the growing popularity of e-learning, new challenges arise. Among them the problem of objective evaluation of students' achievements—one of the key components of learning— is particularly significant. In an e-learning environment, evaluation becomes more challenging due to the lack of physical contact and limited access to informal communication. Therefore, it is necessary to develop and apply effective evaluation methods that would ensure objectivity, reliability, and adaptability to special features of e-learning. Evaluation using fuzzy sets enables the teacher to account for uncertainty in numerical grades and to reduce their subjective influence. Of particular importance are methods based on type-2 fuzzy sets, including perceptual computing. They enable both verbal evaluation of works and aggregation of various verbal and numerical assessments to determine the final grade. Objective. To formalize a model of quiz in e-learning systems, to develop a perceptual computing based evaluation method, which will enhance the objectivity and flexibility of the evaluation process. Results. Existing fuzzy set based evaluation methods in e-learning systems are reviewed. A graph model of a quiz is proposed, consisting of problems, knowledge units, and chunks connected by arcs loaded verbally. A method is described for calculating an aggregate grade using linguistically weighted average. Conclusions. The proposed method for quiz evaluation in e-learning systems, based on perceptual computing, allows to reduce the impact of the human factor and subjectivity. In particular, verbal evaluation of open-ended questions and processing of such grades using perceptual computing based on type-2 fuzzy sets lead to objective evaluation and have significant potential for enhancing the efficiency of e-learning. Keywords: perceptual computing, e-learning, chunk, fuzzy sets.

An Integration of UPLC-Q-TOF-MS, GC-MS, Electronic Nose, Electronic Tongue, and Molecular Docking for the Study of the Chemical Properties and Flavor Profiles of Moringa oleifera Leaves

Moringa oleifera leaves (MOLs) have gained significant attention due to their nutritional and biological activity. Therefore, this study aimed to examine its flavor characteristics and underlying compositions. In this study, we used ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), gas chromatography-mass spectrometry (GC-MS), electronic nose, electronic tongue, and molecular docking to comprehensively investigate the chemical properties and flavor profiles of MOLs. UPLC-Q-TOF-MS and GC-MS were instrumental in identifying the 20 non-volatile and 19 volatile constituents of MOLs, respectively. The electronic nose and electronic tongue systems provided an objective evaluation of the sweet, bitter, and spicy attributes and flavor characteristics of MOLs. Concurrently, molecular docking was employed to elucidate the material basis of flavor profiles. It revealed that glucosinolates and flavonoids are probably the key components for the bitter taste of MOLs. The sweet taste may be attributed to glucosinolates and flavonoids. The spicy scent appears to be linked to the presence of glucosinolates and alkaloids. The integration of these techniques confers a thorough understanding of the chemical composition and sensory properties of MOLs. These findings have significant implications for innovative applications in the food industry as well as pharmaceuticals and agriculture sectors; furthermore, they contribute towards enhancing the perception of Moringa oleifera as a valuable natural resource.

Multicriteria selection practices in public tender evaluation process: the case of Sri Lanka

PurposeAwarding contracts based solely on the lowest price is unsuitable for every project. Consequently, most procurement systems in developed countries have progressed to the multicriteria selection practices (MSPs) for tender evaluation. MSPs consider a range of quality measures, such as completion time, life cycle cost, functional characteristics, environmental impact and innovation, alongside bid price. This study examines the prevailing MSPs in Sri Lankan public tender evaluations to enhance the effectiveness of the local tender evaluation process.Design/methodology/approachA desk study approach was employed to collect bidding documents, resulting in the identification of 66 documents. A systematic screening process was then applied to identify those bidding documents that incorporated MSPs. Subsequently, content analysis was conducted to determine the common features of the functions used in MSPs.FindingsThe study identified six primary functions related to MSPs incorporated in the bidding documents to procure building and substation projects. Three functions follow the price-to-quality method, while the remaining three follow the quality-to-price method. Among these identified functions, four functions employ objective evaluation criteria, such as thickness, capacity and operational loss. The other two functions utilize subjective evaluation criteria, such as the project’s design and technical specifications. Contract awarding will be based on either the highest score or the lowest bid, depending on the function type.Originality/valueThis study’s originality lies in exploring MSPs in the Sri Lankan public tender evaluation process and in disclosing their characteristics to promote the MSPs in Sri Lanka and developing countries.

FMB: A functional manipulation benchmark for generalizable robotic learning

In this paper, we propose a real-world benchmark for studying robotic learning in the context of functional manipulation: a robot needs to accomplish complex long-horizon behaviors by composing individual manipulation skills in functionally relevant ways. The core design principles of our Functional Manipulation Benchmark (FMB) emphasize a harmonious balance between complexity and accessibility. Tasks are deliberately scoped to be narrow, ensuring that models and datasets of manageable scale can be utilized effectively to track progress. Simultaneously, they are diverse enough to pose a significant generalization challenge. Furthermore, the benchmark is designed to be easily replicable, encompassing all essential hardware and software components. To achieve this goal, FMB consists of a variety of 3D-printed objects designed for easy and accurate replication by other researchers. The objects are procedurally generated, providing a principled framework to study generalization in a controlled fashion. We focus on fundamental manipulation skills, including grasping, repositioning, and a range of assembly behaviors. The FMB can be used to evaluate methods for acquiring individual skills, as well as methods for effectively combining and ordering such skills in order to solve complex, multi-stage manipulation tasks. We also offer an imitation learning framework that includes a suite of policies trained to solve the proposed tasks. This enables researchers to utilize our tasks as a versatile toolkit for examining various parts of the pipeline. For example, researchers could propose a better design for a grasping controller and evaluate it in combination with our baseline reorientation and assembly policies as part of a pipeline for solving multi-stage tasks. Our dataset, object CAD files, code, and evaluation videos can be found on our project website: https://functional-manipulation-benchmark.github.io .

MEFSR-GAN: A Multi-Exposure Feedback and Super-Resolution Multitask Network via Generative Adversarial Networks

In applications such as satellite remote sensing and aerial photography, imaging equipment must capture brightness information of different ground scenes within a restricted dynamic range. Due to camera sensor limitations, captured images can represent only a portion of such information, which results in lower resolution and lower dynamic range compared with real scenes. Image super resolution (SR) and multiple-exposure image fusion (MEF) are commonly employed technologies to address these issues. Nonetheless, these two problems are often researched in separate directions. In this paper, we propose MEFSR-GAN: an end-to-end framework based on generative adversarial networks that simultaneously combines super-resolution and multiple-exposure fusion. MEFSR-GAN includes a generator and two discriminators. The generator network consists of two parallel sub-networks for under-exposure and over-exposure, each containing a feature extraction block (FEB), a super-resolution block (SRB), and several multiple-exposure feedback blocks (MEFBs). It processes low-resolution under- and over-exposed images to produce high-resolution high dynamic range (HDR) images. These images are evaluated by two discriminator networks, driving the generator to generate realistic high-resolution HDR outputs through multi-goal training. Extensive qualitative and quantitative experiments were conducted on the SICE dataset, yielding a PSNR of 24.821 and an SSIM of 0.896 for 2× upscaling. These results demonstrate that MEFSR-GAN outperforms existing methods in terms of both visual effects and objective evaluation metrics, thereby establishing itself as a state-of-the-art technology.

Cost, risk, landscape context, and potential treatments vary with biological value for conservation of declining grassland birds

AbstractSpatially explicit models are an important component of systematic conservation planning, enabling the depiction of biodiversity metrics across landscapes and objective evaluation of candidate sites for conservation delivery. However, sites considered “best” for conservation are typically viewed from the standpoint of biological value and may not be the most effective or efficient when risk of habitat loss, cost of conservation, intended conservation treatments, and overall conservation strategy are considered. We evaluated risk of habitat loss, land cost, and landscape context for geographic areas harboring most‐dense to least‐dense population quartiles for 16 species of grassland birds in the US northern Great Plains. Differences in land cost, risk of grassland conversion, and landscape context among quartiles and species indicated that a minimum‐area strategy may be inefficient and even ineffective. Priority zones for western species were generally associated with lower agricultural land cost, more protected land, and landscape characteristics associated with intact grasslands; eastern species were generally associated with higher agricultural land cost, tillage probability, grass loss, cropland, development, forest, Conservation Reserve Program grasslands, and distance to grass. Our results indicate that addressing areas outside of population cores increases conservation options and may provide substantial benefits to portions of populations that are most vulnerable to habitat loss or other stressors.

Evaluating the Application of an EMS Clinical Judgment Theoretical Framework

ABSTRACT OBJECTIVES Clinical judgment (CJ) encompasses clinical reasoning (process of evaluating a problem) and clinical decision-making (choice made). A theoretical model to better define emergency medical services (EMS) CJ has been developed but its use has not been evaluated in EMS training and assessments. Our objective was to evaluate the performance of this EMS CJ model to assess clinical reasoning and decision-making in a simulated environment. METHODS In this evaluation, EMS clinician teams (2-3 members) were directed to care for a simulated older adult patient in their home following a fall. Simulations were video recorded, clinician team actions coded, and evaluated for whether proper CJ reasoning and decisions were made. We evaluated CJ in two ways: 1) EMS medical directors' (MD) determination of whether the CJ questions were addressed (MD score) and 2) objective rubric evaluation of CJ questions using the EMS CJ model focused on recognition of appropriate cues, performance of actions, and revaluation after action (rubric score). The CJ questions addressed in this simulation included: 1) Is the patient stable/unstable?, 2) Are interventions necessary before movement?, 3) How should the patient be transferred from the floor?, and 4) Does the cause of the fall require hospital evaluation? Descriptive statistics were calculated, and concordance between the two assessments was evaluated (mean, 95% CI). Percent concordance was calculated with a validity threshold set at 70%. RESULTS Four EMS MDs reviewed 20 videos addressing 80 clinical judgment decisions. Overall concordance between MD score and rubric score for CJ decisions was above the threshold at 88.1% (85.0, 91.2). Concordance between MD score and rubric score for each CJ decision was 92.0% (87.3, 96.7) for question 1, 79.9% (71.5, 88.3) for question 2, 95.0% (90.4, 99.6) for question 3, and 85.4% (79.5, 91.2) for question 4. CONCLUSION An objective evaluation of CJ decisions using a rubric derived from an EMS CJ theoretical framework demonstrated high concordance to subjective evaluations of CJ made by EMS MDs. This approach may allow for reproducible and objective CJ evaluations that could be used for competency assessment in EMS.

Improved multi-focus image fusion using online convolutional sparse coding based on sample-dependent dictionary

Multi-focus image fusion merges multiple images captured from different focused regions of a scene to create a fully-focused image. Convolutional sparse coding (CSC) methods are commonly employed for accurate extraction of focused regions, but they often disregard computational costs. To overcome this, an online convolutional sparse coding (OCSC) technique was introduced, but its performance is still limited by the number of filters used, affecting overall performance negatively. To address these limitations, a novel approach called Sample-Dependent Dictionary-based Online Convolutional Sparse Coding (SCSC) was proposed. SCSC enables the utilization of additional filters while maintaining low time and space complexity for processing high-dimensional or large data. Leveraging the computational efficiency and effective global feature extraction of SCSC, we propose a novel method for multi-focus image fusion. Our method involves a two-layer decomposition of each source image, yielding a base layer capturing the predominant features and a detail layer containing finer details. The amalgamation of the fused base and detail layers culminates in the reconstruction of the final image. The proposed method significantly mitigates artifacts, preserves fine details at the focus boundary, and demonstrates notable enhancements in both visual quality and objective evaluation of multi-focus image fusion.

Validation of a generic finite element vehicle buck model for near-side crashes

Objective Finite element (FE) reconstructions of motor vehicle crashes using human body models are effective tools for developing a better understanding of occupant kinematics and injuries in real-world lateral crash conditions, but current near-side reconstruction methods are limited by the paucity of full-scale FE vehicle models. The objective of this study was to validate a generic vehicle model equipped with left-side airbags and intrusion capability by simulating a series of near-side crash tests for a range of vehicles and assessing model accuracy using objective evaluation methods. Methods Moving deformable barrier crash tests were reconstructed for five common vehicle classifications (compact passenger, mid-size passenger, sport utility vehicle, pickup truck, and van) using an updated version of a previously developed simplified vehicle model. Unknown vehicle and intrusion properties (pretensioner force, seatback airbag pressure, curtain airbag pressure, door panel stiffness, ratio of dynamic-to-static intrusion, intrusion velocity, and intrusion scaling factor) were estimated by parameterizing them across 224 simulations per crash test using a Latin hypercube design of experiments. Model accuracy was assessed for 13 anthropomorphic test device signals using the Correlation and Analysis (CORA) objective rating method and injury metric comparisons. Results Maximum ratings of 0.69, 0.67, 0.52, 0.52, and 0.62 were achieved for the compact passenger, midsize passenger, sport utility vehicle, pickup truck, and van classifications, respectively. On average, the abdomen displayed the most accurate behavior (0.51 ± 0.12), followed by the thorax (0.50 ± 0.10) and head (0.50 ± 0.07). The pelvis displayed the least accurate behavior (0.46 ± 0.18) of any region. Reconstructions overpraedicted injury metrics in all cases. Conclusions All vehicles achieved “fair” biofidelity ratings and the compact passenger and midsize passenger vehicles achieved “good” biofidelity ratings, validating them for kinematic evaluations with vehicle-to-vehicle nearside crash reconstructions. Regression models were developed for injuries and CORA ratings and can be used to optimize vehicle parameters in future studies.

Face swapping in seizure videos for patient deidentification

ObjectiveThis study aimed to test different AI-based face-swapping models applied to videos of epileptic seizures, with the goal of protecting patient privacy while retaining clinically useful seizure semiology. We hypothesized that specific models would show differences in semiologic fidelity compared to the original clinical videos. MethodsThree open-source models, SimSwap, MobileFaceSwap and GHOST were adopted for face-swapping. For every model, an AI generated male and female image were used to replace the original faces. One representative seizure per patient from three patients with epilepsy was chosen (3 seizure videos x 3 AI models x 2 M/F swap) and remade to 18 transformed video clips. To evaluate the performance of the three models, we used both objective (AI-based) and subjective (expert clinician) evaluation. The objective assessment included four metrics for facial appearance and four metrics for facial expression changes. Four experienced epileptologists reviewed the clips and scoring according to deidentification and preservation of semiology. Kruskal-Wallis H test was used for statistical analysis among the models. ResultsIn the reproduced videos, the swapped face cannot be recognized as the original face, with no significant difference in scores of deidentification either by objective or subjective assessment. Regarding semiology preservation, no significant differences between models were observed in the objective evaluations. The subjective evaluations revealed that the GHOST model outperformed the other two models (p=0.028). ConclusionThis is the first study evaluating AI face swapping models in epileptic seizure video clips. Optimization of AI face-swapping models could enhance the accessibility of seizure videos for education and research while protecting patient privacy and maintaining semiology.

A Light-Weight Self-Supervised Infrared Image Perception Enhancement Method

Convolutional Neural Networks (CNNs) have achieved remarkable results in the field of infrared image enhancement. However, the research on the visual perception mechanism and the objective evaluation indicators for enhanced infrared images is still not in-depth enough. To make the subjective and objective evaluation more consistent, this paper uses a perceptual metric to evaluate the enhancement effect of infrared images. The perceptual metric mimics the early conversion process of the human visual system and uses the normalized Laplacian pyramid distance (NLPD) between the enhanced image and the original scene radiance to evaluate the image enhancement effect. Based on this, this paper designs an infrared image-enhancement algorithm that is more conducive to human visual perception. The algorithm uses a lightweight Fully Convolutional Network (FCN), with NLPD as the similarity measure, and trains the network in a self-supervised manner by minimizing the NLPD between the enhanced image and the original scene radiance to achieve infrared image enhancement. The experimental results show that the infrared image enhancement method in this paper outperforms existing methods in terms of visual perception quality, and due to the use of a lightweight network, it is also the fastest enhancement method currently.

Cooperation objective evaluation in aviation: validation and comparison of two novel approaches in simulated environment.

In operational environments, human interaction and cooperation between individuals are critical to efficiency and safety. These states are influenced by individuals' cognitive and emotional states. Human factor research aims to objectively quantify these states to prevent human error and maintain constant performances, particularly in high-risk settings such as aviation, where human error and performance account for a significant portion of accidents. Thus, this study aimed to evaluate and validate two novel methods for assessing the degree of cooperation among professional pilots engaged in real-flight simulation tasks. In addition, the study aimed to assess the ability of the proposed metrics to differentiate between the expertise levels of operating crews based on their levels of cooperation. Eight crews were involved in the experiments, consisting of four crews of Unexperienced pilots and four crews of Experienced pilots. An expert trainer, simulating air traffic management communication on one side and acting as a subject matter expert on the other, provided external evaluations of the pilots' mental states during the simulation. The two novel approaches introduced in this study were formulated based on circular correlation and mutual information techniques. The findings demonstrated the possibility of quantifying cooperation levels among pilots during realistic flight simulations. In addition, cooperation time is found to be significantly higher (p < 0.05) among Experienced pilots compared to Unexperienced ones. Furthermore, these preliminary results exhibited significant correlations (p < 0.05) with subjective and behavioral measures collected every 30 s during the task, confirming their reliability.

Five-Year Outcomes from a Prospective Study on Safety and Efficacy of Phasix ST Mesh Use at the Hiatus During Paraesophageal Hernia Repair.

Laparoscopic paraesophageal hernia (PEH) repair has a high hernia recurrence rate. The aim of this study was to assess the 5-year hernia recurrence rate after PEH repair using a combination of bioresorbable mesh and advanced surgical techniques to address tension as needed in a prospective group of patients. In 2016, a prospective database was established for 50 patients undergoing primary, elective PEH repair with a new bioresorbable mesh (Phasix ST). Intraoperatively, tension was addressed with Collis gastroplasty and diaphragm relaxing incisions as needed. All 50 patients from the initial study were tracked and asked to return for objective follow-up. Recurrence was considered present for any hernia more than 2 cm in size. Objective follow-up was conducted in 27 of the original 50 (54%) patients at a median of 5.25 years after their PEH repair. Before the 5-year follow-up, 5 patients had a known recurrent hernia. Objective evaluation at 5 years identified an additional 3 recurrences, for a total recurrence rate of 25% (8 of 32 patients). The hernia recurrence rate in patients with a Collis gastroplasty was significantly lower than in those without a Collis (7% vs 54%, p = 0.008). Two patients underwent reoperation for hernia recurrence. No patient had a mesh infection or mesh erosion. The combination of Phasix ST Mesh and tension-reducing techniques during PEH repair led to a 25% hernia recurrence rate at 5 years. The addition of a Collis gastroplasty led to significantly fewer hernia recurrences and is indicative of the potential for esophageal shortening in many patients with a PEH. The long-term safety and efficacy of Phasix ST Mesh in combination with surgical technique for PEH repair is confirmed.

Improving the Retention Rate of Fat Grafting by Botulinum Toxin A: A Randomized, Self-controlled, Clinical Trial.

Botulinum toxin A (BTX-A) can enhance the efficacy of fat grafting. However, most studies conducted animal experiments, lacked objective clinical data, or were non-randomized controlled trials. Thus, objective evaluation of the clinical effectiveness of BTX-A is still elusive. A randomized, self-controlled trial (2020-2022) on 16 patients who underwent bilateral autogenous fat breast augmentation was performed with each patient receiving autologous fat graft and BTX-A on one side and fat graft and equal volume of saline on the other side. All patients were followed. The effects of BTX-A were evaluated objectively by comparing the remaining bilateral fat graft volumes obtained through digital three-dimensional reconstruction. The improvement of each breast appearance and complication were assessed by the physician and patients who were blinded to the treatment. The outcome of fat breast augmentation was evident for both sides at follow-up with no evidence of fat embolism, vascular/nervous injury, infection, and prolonged bruising. The analysis of the three-dimensional reconstruction data and assessments from both physicians and patients showed significant differences in the fat graft retention volume between the BTX-A side and the control side. No significant difference was found in the incidence of complications between the two sides. Autogenous fat breast augmentation is safe and effective. This study shows that BTX-A can significantly improve the retention rate of fat transplantation, but cannot reduce complications. Trial registration This study was registered prior to patient enrollment (ClinicalTrials.gov identifier:ChiCTR2100054878). This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Evaluation of the Surface Downward Longwave Radiation Estimation Models over Land Surface

Surface downward longwave radiation (SDLR) is crucial for maintaining the global radiative budget balance. Due to their ease of practicality, SDLR parameterization models are widely used, making their objective evaluation essential. In this study, against comprehensive ground measurements collected from more than 300 globally distributed sites, four SDLR parameterization models, including three popular existing ones and a newly proposed model, were evaluated under clear- and cloudy-sky conditions at hourly (daytime and nighttime) and daily scales, respectively. The validation results indicated that the new model, namely the Peng model, originally proposed for SDLR estimation at the sea surface and applied for the first time to the land surface, outperformed all three existing models in nearly all cases, especially under cloudy-sky conditions. Moreover, the Peng model demonstrated robustness across various land cover types, elevation zones, and seasons. All four SDLR models outperformed the Global Land Surface Satellite product from Advanced Very High-Resolution Radiometer Data (GLASS-AVHRR), ERA5, and CERES_SYN1de-g_Ed4A products. The Peng model achieved the highest accuracy, with validated RMSE values of 13.552 and 14.055 W/m2 and biases of −0.25 and −0.025 W/m2 under clear- and cloudy-sky conditions at daily scale, respectively. Its superior performance can be attributed to the inclusion of two cloud parameters, total column cloud liquid water and ice water, besides the cloud fraction. However, the optimal combination of these three parameters may vary depending on specific cases. In addition, all SDLR models require improvements for wetlands, bare soil, ice-covered surfaces, and high-elevation regions. Overall, the Peng model demonstrates significant potential for widespread use in SDLR estimation for both land and sea surfaces.

A Novel Validation Study of a Wrist Orthosis for the Objective Evaluation of Rigidity in Parkinson’s Disease

A Novel Validation Study of a Wrist Orthosis for the Objective Evaluation of Rigidity in Parkinson’s Disease