Variable Recovery Research Articles

Magnetic resonance classification proposal for medial gastrocnemius muscle injuries.

Calf muscle injuries are common among athletes and occupational populations, with highly variable recovery times that are challenging to be predicted at the initial clinical evaluation. Specifically, in distal gastrocnemius muscle injuries, an ultrasound-based severity classification has shown to be useful for estimating the recovery time. According to the location of lesions and the recognition of some US signs, four types of injuries of the distal gastrocnemius muscle were described. Since magnetic resonance imaging (MRI) has proven to be useful in diagnosing and prognosticating muscle injuries by assessing the extent of affected connective tissue, a specific MRI protocol involving T1-weighted and fluid-sensitive static and dynamic acquisitions has been developed aimed to characterize the four types of injuries. We here describe the characteristics of this new MRI protocol and the interpretation of images, which will be useful to improve the recognition of acute and delayed distal gastrocnemius muscle injuries. The proposed classification includes: myoaponeurotic muscle injury without aponeurotic discontinuities (type 1), myoaponeurotic muscle injury with aponeurotic discontinuities (type 2), isolated free aponeurosis discontinuity (type 3), and a mixed myoaponeurotic-aponeurotic injury pattern (type 4). A comprehensive understanding of the MRI features associated with each injury type, in conjunction with multidisciplinary team collaboration, is essential for optimizing the athlete's recovery and return to play.

Benchmarking Stroke Outcome Prediction through Comprehensive Data Analysis - NeuralCup 2023.

Stroke is a significant cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. The NeuralCup 2023 consortium was established to address these challenges by comparing the predictability of stroke outcome models through a collaborative, data-driven approach. This study presents the consortium's findings, which involved 15 participating teams worldwide. Using a comprehensive dataset, which included clinical and imaging data, we conducted an open competition to identify and compare predictors of motor, cognitive, and neuropsychological (emotional) outcomes one-year post-stroke. Analyses incorporated both traditional and novel methods, including machine learning algorithms. These efforts culminated in the search for 'optimal recipes' for predicting each domain through an exhaustive exploration of the features of all the approaches. Key predictors included lesion characteristics, T1-weighted MRI sequences, and demographic factors. Notably, integrating FLAIR imaging and white matter tract analysis emerged as crucial to improving the accuracy of cognitive and motor outcome predictions, respectively. These findings advocate for a tailored, multifaceted approach to stroke outcome prediction, underscoring the potential of collaborative data science in addressing complex neurological prognostication challenges. This study also sets a new benchmark methodology in stroke research, offering a foundational step toward personalized care strategies that could significantly impact recovery planning and quality of life for stroke survivors.

VarLifter: Recovering Variables and Types from Bytecode of Solidity Smart Contracts

Since funds or tokens in smart contracts are maintained through specific state variables, contract audit, an effective means for security assurance, particularly focuses on these variables and their related operations. However, the absence of publicly accessible source code for numerous contracts, with only bytecode exposed, hinders audit efforts. Recovering variables and their types from Solidity bytecode is thus a critical task in smart contract analysis and audit, yet this is a challenging task because the bytecode loses variable and type information, only with low-level data operated by stack manipulations and untyped memory/storage accesses. The state-of-the-art smart contract decompilers miss identifying many variables and incorrectly infer the types for many identified variables. To this end, we propose VarLifter, a lifter dedicated to the precise and efficient recovery of typed variables. VarLifter interprets every read or written field of a data region as at least one potential variable, and after discarding falsely identified variables, it progressively refines the variable types based on the variable behaviors in the form of operation sequences. We evaluate VarLifter on 34,832 real-world Solidity smart contracts. VarLifter attains a precision of 97.48% and a recall of 91.84% for typed variable recovery. Moreover, VarLifter finishes analyzing 77% of smart contracts in around 10 seconds per contract. If VarLifter is used to replace the variable recovery modules of the two state-of-the-art Solidity bytecode decompilers, 52.4%, and 74.6% more typed variables will be correctly recovered, respectively. The applications of VarLifter to contract decompilation, contract audit, and contract bytecode fuzzing illustrate that the recovered variable information improves many contract analysis tasks.

Early vs late ICU admission in patients with COVID-19 pneumonia.

The type of admission to the Intensive Care Unit (ICU) influences the prognosis of patients with severe pneumonia and, in the case of patients with COVID-19 pneumonia, this is still unexplored. The objective of this study was to determine the differences between early and late ICU admission. A retrospective cohort study of patients with COVID-19 pneumonia at two high-complexity hospitals in Colombia. Early ICU admission (EICUA) was defined as direct admission from the emergency department or within the first 24 hours of admission. Late ICU admission (LICUA) was defined as admission from the hospitalization service after 24 hours of arrival. A robust Cox regression was performed for the variable recovery time, to determine the impact of the ICU admission type in the hazard rate. 68.2% were EICUA patients and 31.8% were LICUA patients. Recovery and duration of hospital stay were significantly lower in EICUA than in LICUA (9 vs 15 days, p = 0.0001, and 10 vs 15.5 days, p < 0.0001, respectively). However, the duration of ICU stay (7 vs 9 days, p = 0.131) and the invasive mechanical ventilation requirement (48.9% vs 54.9%, p = 0.374) were not statistically significant. The 30-day follow-up showed no difference between the EICUA and LICUA (alive 97% vs 94.6%, p = 0.705). Mortality between EICUA and LICUA patients with COVID-19 pneumonia showed no statistically significant differences. However, the recovery time, the probability intensity of instant recovery, and the duration of hospital stay were better in EICUA than in LICUA. Neither EICUA nor LICUA affects the final status (death) of patients.

Clinical Assessment and Management of Acute Spinal Cord Injury.

The information contained in this article is suitable for clinicians practicing in the United States desiring a general overview of the assessment and management of spinal cord injury (SCI), focusing on initial care, assessment, acute management, complications, prognostication, and future research directions. SCI presents significant challenges, affecting patients physically, emotionally, and financially, with variable recovery outcomes ranging from full functionality to lifelong dependence on caregivers. Initial care aims to minimize secondary injury through thorough neurological evaluations and imaging studies to assess the severity of the injury. Acute management prioritizes stabilizing respiratory and cardiovascular functions and maintaining proper spinal cord perfusion. Patients with unstable or progressive neurological decline benefit from timely surgical intervention to optimize neurological recovery. Subacute management focuses on addressing common complications affecting the respiratory, gastrointestinal, and genitourinary systems, emphasizing a holistic, multidisciplinary approach. Prognostication is currently based on neurological assessments and imaging findings, but emerging biomarkers offer the potential to refine outcome predictions further. Additionally, novel therapeutic interventions, such as hypothermia therapy and neuroprotective medications are being explored to mitigate secondary damage and enhance recovery. This paper serves as a high-yield refresher for clinicians for the assessment and management of acute spinal cord injury during index admission.

Application of Flores model considering variable recovery coefficient in dynamics analysis of ball bearings

PurposeThis paper aims to select an appropriate contact force model and apply it to the interaction model between the balls and the cage in the rolling bearings to describe the elastic–plastic collision phenomena between the two.Design/methodology/approachTaking the ball–disk collision mode as an example, several main contact force models were compared and analyzed through simulation and experiment. In addition, based on the consideration of yield strength of materials and initial collision velocity, a variable recovery coefficient model was proposed, and its validity and accuracy were verified by the ball–disk collision experiments. Then, respectively, the Flores model and the Hertz model were applied to the interaction between the balls and the cage, and the dynamics simulation results were compared.FindingsThe results indicate that the Flores model has good regression of recovery coefficient, indicating good applicability for both elastic and elastic–plastic contacts and can be applied to the contact collision situations of various materials. Under certain working conditions, there are significant differences in the dynamics results of rolling bearings simulated using the Flores model and Hertz model, respectively.Originality/valueThis paper applies the Flores model with variable recovery coefficients to the dynamics simulation analysis of ball bearings to solve the elastic–plastic collision problem between the rolling elements and the cage that cannot be reasonably handled by the Hertz model.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0138/

Location and function of TDP-43 in platelets, alterations in neurodegenerative diseases and arising considerations for current plasma biobank protocols

The TAR DNA Binding Protein 43 (TDP-43) has been implicated in the pathogenesis of human neurodegenerative diseases and exhibits hallmark neuropathology in amyotrophic lateral sclerosis (ALS). Here, we explore its tractability as a plasma biomarker of disease and describe its localization and possible functions in the cytosol of platelets. Novel TDP-43 immunoassays were developed on three different technical platforms and qualified for specificity, signal-to-noise ratio, detection range, variation, spike recovery and dilution linearity in human plasma samples. Surprisingly, implementation of these assays demonstrated that biobank-archived plasma samples yielded considerable heterogeneity in TDP-43 levels. Importantly, subsequent investigation attributed these differences to variable platelet recovery. Fractionations of fresh blood revealed that ≥ 95% of the TDP-43 in platelet-containing plasma was compartmentalized within the platelet cytosol. We reasoned that this highly concentrated source of TDP-43 comprised an interesting substrate for biochemical analyses. Additional characterization of platelets revealed the presence of the disease-associated phosphoserine 409/410 TDP-43 proteoform and many neuron- and astrocyte-expressed TDP-43 mRNA targets. Considering these striking similarities, we propose that TDP-43 may serve analogous functional roles in platelets and synapses, and that the study of platelet TDP-43 might provide a window into disease-related TDP-43 dyshomeostasis in the central nervous system.

Assessment of sub-lethal effects of Celcron on Java barb through erythrocyte morphology and acetylcholinesterase activity: Implications for environmental health in aquatic ecosystems

Industrialization and the extensive use of chemicals have raised significant concerns about their environmental impacts, particularly on aquatic ecosystems. This study evaluated the sub-lethal effects of Celcron (Cec), an organophosphate insecticide, on the Java barb (Barbonymus gonionotus) through erythrocyte morphology and acetylcholinesterase (AChE) activity, aiming to refine biomarkers for environmental health assessments. We hypothesized that sub-lethal Cec exposure would induce significant erythrocyte abnormalities and decrease AChE activity in Java barb, with variable recovery rates between gill and kidney tissues. To test this, we exposed the juvenile Java barbs to two sub-lethal Cec concentrations - 0.01 ppm (10 % of the LC50) and 0.05 ppm (50 % of the LC50) -for 60 days. After the exposure period, the fish were placed in pesticide-free water to allow for recovery. Results indicated a significant decline in AChE activity in both liver and kidney tissues, with activity levels showing gradual recovery over time. Erythrocyte abnormalities, including nuclear and cellular changes, were significantly elevated in response to Cec exposure. The frequency of nuclear abnormalities such as micronuclei and binucleation increased in a concentration- and duration-dependent manner, with the gill blood exhibiting higher sensitivity and slower recovery compared to kidney blood. Cellular abnormalities such as twin, teardrop and spindle-shaped cells were also more prevalent in Cec-treated fish. Recovery from these abnormalities was observed but varied between gill and kidney blood, with gill blood showing higher sensitivity and slower recovery compared to kidney blood. This study underscores the utility of AChE activity and erythrocyte abnormalities as biomarkers for assessing pesticide impacts on aquatic organisms. The findings highlight the sensitivity of fish erythrocytes to environmental contaminants and emphasize the need for continued research to better understand the long-term effects of pesticide exposure on aquatic health and ecosystem stability.

Distinct trajectories of neuropsychiatric symptoms in the 12 months following traumatic brain injury (TBI): a TRACK-TBI study.

Neuropsychiatric symptoms are common after traumatic brain injury (TBI) and often resolve within 3 months post-injury. However, the degree to which individual patients follow this course is unknown. We characterized trajectories of neuropsychiatric symptoms over 12 months post-TBI. We hypothesized that a substantial proportion of individuals would display trajectories distinct from the group-average course, with some exhibiting less favorable courses. Participants were level 1 trauma center patients with TBI (n = 1943), orthopedic trauma controls (n = 257), and non-injured friend controls (n = 300). Trajectories of six symptom dimensions (Depression, Anxiety, Fear, Sleep, Physical, and Pain) were identified using growth mixture modeling from 2 weeks to 12 months post-injury. Depression, Anxiety, Fear, and Physical symptoms displayed three trajectories: Stable-Low (86.2-88.6%), Worsening (5.6-10.9%), and Improving (2.6-6.4%). Among symptomatic trajectories (Worsening, Improving), lower-severity TBI was associated with higher prevalence of elevated symptoms at 2 weeks that steadily resolved over 12 months compared to all other groups, whereas higher-severity TBI was associated with higher prevalence of symptoms that gradually worsened from 3-12 months. Sleep and Pain displayed more variable recovery courses, and the most common trajectory entailed an average level of problems that remained stable over time (Stable-Average; 46.7-82.6%). Symptomatic Sleep and Pain trajectories (Stable-Average, Improving) were more common in traumatically injured groups. Findings illustrate the nature and rates of distinct neuropsychiatric symptom trajectories and their relationship to traumatic injuries. Providers may use these results as a referent for gauging typical v. atypical recovery in the first 12 months post-injury.

Inhomogeneous magnetization transfer (ihMT) imaging reveals variable recovery profiles of active MS lesions according to size and localization

Abstract This work aims at exploiting the unique myelin specificity of the inhomogeneous magnetization transfer (ihMT) technique to characterize the recovery dynamics of active multiple sclerosis (MS) lesions. IhMT and three other myelin-sensitive techniques, conventional MT, T1-weighted, and diffusion tensor imaging, were applied in a 12-month longitudinal study performed on relapsing-remitting MS patients. An exponential recovery model was used to fit the variations over time of the metrics derived from each MR technique within new active lesions. A principal component analysis was performed on the model parameters obtained for all MR myelin-sensitive techniques across all active lesions of all patients to identify specific recovery profiles. The results show that the recovery profiles of myelin-sensitive MR metrics in active MS lesions vary according to the localization and size of lesions. The distance of lesions from the ventricles is positively associated with the recovery rates of ihMTR and T1w-MPRAGE: the further the lesion is from the ventricles, the higher the recovery rate of these metrics. Lesion size is positively associated with initial loss and negatively associated with final recovery of ihMTR and other MR metrics: small lesions have lower initial loss and greater final recovery of MR metrics than large lesions. Thanks to the specificity of the ihMT technique for myelin, these features can be interpreted in terms of remyelination. This study thus provides longitudinal in vivo support for the pathological observations of higher remyelination in small lesions compared with large ones and faster remyelination in lesions away from the ventricles. These results support the use of ihMT and other measures for quantifying remyelination rates in clinical studies of remyelination therapies.

Opioid-Associated Nervous System Injuries.

With the rise of the opioid epidemic, the practicing neurologist must recognize the patterns of a growing number of opioid-associated neurological injuries. This is in addition to the classic toxidrome of miosis, altered mental status, and respiratory depression, which must never be overlooked, as it is reversible and potentially lifesaving. Several other idiosyncratic syndromes due to opioid-related nervous system insults are defined by their characteristic imaging findings and portend variable functional recovery. Opioid toxicity can not only lead to brain injury, but also spinal cord and, rarely, peripheral nerve injury. As several newer synthetic opioids are undetectable by most assays, a low threshold to suspect opioid exposure must be maintained.

Dynamics of a Model of Coronavirus Disease with Fear Effect, Treatment Function, and Variable Recovery Rate

In this work, we developed, validated, and analysed the behaviour of a compartmental model of COVID-19 transmission in Saudi Arabia. The population was structured into four classes: susceptible (S), exposed (E), infectious (I), and removed (R) individuals. This SEIR model assumes a bilinear incidence rate and a nonlinear recovery rate that depends on the quality of health services. The model also considers a treatment function and incorporates the effect of fear due to the disease. We derived the expression of the basic reproduction number and the equilibrium points of the model and demonstrated that when the reproduction number is less than one, the disease-free equilibrium is stable, and the model predicts a backward bifurcation. We further found that when the reproduction number is larger than one, the model predicts stable periodic behaviour. Finally, we used numerical simulations with parameter values fitted to Saudi Arabia to analyse the effects of the model parameters on the model-predicted dynamic behaviours.

Nile red staining for rapid screening of plastic-suspect particles in edible seafood tissues

Concerns regarding microplastic (MP) contamination in aquatic ecosystems and its impact on seafood require a better understanding of human dietary MP exposure including extensive monitoring. While conventional techniques for MP analysis like infrared or Raman microspectroscopy provide detailed particle information, they are limited by low sample throughput, particularly when dealing with high particle numbers in seafood due to matrix-related residues. Consequently, more rapid techniques need to be developed to meet the requirements of large-scale monitoring. This study focused on semi-automated fluorescence imaging analysis after Nile red staining for rapid MP screening in seafood. By implementing RGB-based fluorescence threshold values, the need for high operator expertise to prevent misclassification was addressed. Food-relevant MP was identified with over 95% probability and differentiated from natural polymers with a 1% error rate. Comparison with laser direct infrared imaging (LDIR), a state-of-the-art method for rapid MP analysis, showed similar particle counts, indicating plausible results. However, highly variable recovery rates attributed to inhomogeneous particle spiking experiments highlight the need for future development of certified reference material including sample preparation. The proposed method demonstrated suitability of high throughput analysis for seafood samples, requiring 0.02–0.06 h/cm2 filter surface compared to 4.5–14.7 h/cm with LDIR analysis. Overall, the method holds promise as a screening tool for more accurate yet resource-intensive MP analysis methods such as spectroscopic or thermoanalytical techniques.Graphical

A fast-running physics-based wake model for a semi-infinite wind farm

This paper presents a new generation of fast-running physics-based models to predict the wake of a semi-infinite wind farm, extending infinitely in the lateral direction but with finite size in the streamwise direction. The assumption of a semi-infinite wind farm enables concurrent solving of the laterally averaged momentum equations in both streamwise and spanwise directions. The developed model captures important physical phenomena such as vertical top-down transport of energy into the farm, variable wake recovery rate due to the farm-generated turbulence and also wake deflection due to turbine yaw misalignment and Coriolis force. Of special note is the model's capability to predict and shed light on the counteracting effect of Coriolis force causing wake deflections in both positive and negative directions. Moreover, the impact of wind farm layout configuration on the flow distribution is modelled through a parameter called the local deficit coefficient. Model predictions were validated against large-eddy simulations extending up to 45 km downstream of wind farms. Detailed analyses were performed to study the impacts of various factors such as incoming turbulence, wind farm size, inter-turbine spacing and wind farm layout on the farm wake.

Observations from a decade of oligonucleotide bioanalysis by LC-MS.

There is a growing need for efficient bioanalysis of oligonucleotide therapeutics. This broad class of molecules presents numerous challenges relative to traditional small molecule therapeutics. Methodologies including ligand-binding assays or polymerase chain reaction may be fit-for-purpose in many instances, but liquid chromatography coupled to mass spectrometry (LC-MS) often delivers the best balance of sensitivity and selectivity. Over the last decade, we have engaged with many such molecules and derived insights into challenges and solutions. Herein, we provide four case studies illustrating challenges we have encountered. These issues include low or variable analyte recovery, poor resolution from related species, chromatographic abnormalities or challenging sensitivity. We present a summary of considerations, based on these experiences, to assist others working in the area.

Effect of Spaceflight and Microgravity on the Musculoskeletal System: A Review.

With National Aeronautics and Space Administration's plans for longer distance, longer duration spaceflights such as missions to Mars and the surge in popularity of space tourism, the need to better understand the effects of spaceflight on the musculoskeletal system has never been more present. However, there is a paucity of information on how spaceflight affects orthopaedic health. This review surveys existing literature and discusses the effect of spaceflight on each aspect of the musculoskeletal system. Spaceflight reduces bone mineral density at rapid rates because of multiple mechanisms. While this seems to be recoverable upon re-exposure to gravity, concern for fracture in spaceflight remains as microgravity impairs bone strength and fracture healing. Muscles, tendons, and entheses similarly undergo microgravity adaptation. These changes result in decreased muscle mass, increased tendon laxity, and decreased enthesis stiffness, thus decreasing the strength of the muscle-tendon-enthesis unit with variable recovery upon gravity re-exposure. Spaceflight also affects joint health; unloading of the joints facilitates changes that thin and atrophy cartilage similar to arthritic phenotypes. These changes are likely recoverable upon return to gravity with exercise. Multiple questions remain regarding effects of longer duration flights on health and implications of these findings on terrestrial medicine, which should be the target of future research.

Point of View on Outcome Prediction Models in Post-Stroke Motor Recovery

Stroke is a leading cause of disability worldwide which can cause significant and persistent upper limb (UL) impairment. It is difficult to predict UL motor recovery after stroke and to forecast the expected outcomes of rehabilitation interventions during the acute and subacute phases when using clinical data alone. Accurate prediction of response to treatment could allow for more timely and targeted interventions, thereby improving recovery, resource allocation, and reducing the economic impact of post-stroke disability. Initial motor impairment is currently the strongest predictor of post-stroke motor recovery. Despite significant progress, current prediction models could be refined with additional predictors, and an emphasis on the time dependency of patient-specific predictions of UL recovery profiles. In the current paper a panel of experts provide their opinion on additional predictors and aspects of the literature that can help advance stroke outcome prediction models. Potential strategies include close attention to post-stroke data collection timeframes and adoption of individual-computerized modeling methods connected to a patient’s health record. These models should account for the non-linear and the variable recovery pattern of spontaneous neurological recovery. Additionally, input data should be extended to include cognitive, genomic, sensory, neural injury, and function measures as additional predictors of recovery. The accuracy of prediction models may be further improved by including standardized measures of outcome. Finally, we consider the potential impact of refined prediction models on healthcare costs.

Statistical learning for maintenance optimization: modeling the hazard function with variable recovery factors

Abstract: This study aims to utilize advanced statistical methods in equipment maintenance within the machining and casting processes of an industrial company, focusing on cost optimization. Employing a methodology that combines literature review, mathematical formulation, and statistical treatment of historical data from preventive and corrective maintenance, the study stands out for the use of maximum likelihood estimation to determine not only the model parameters but also the recovery factor used during preventive maintenance. It also calculates the optimal maintenance time to minimize the total cost. This work not only underscores the importance of a data-driven maintenance approach, balancing reliability and cost to achieve effective optimization but also signals the need for future studies, especially in simulations to further enhance preventive maintenance scheduling, encompassing all aspects of the production process.

High-yield and rapid isolation of extracellular vesicles by flocculation via orbital acoustic trapping: FLOAT

Extracellular vesicles (EVs) have been identified as promising biomarkers for the noninvasive diagnosis of various diseases. However, challenges in separating EVs from soluble proteins have resulted in variable EV recovery rates and low purities. Here, we report a high-yield ( > 90%) and rapid ( < 10 min) EV isolation method called FLocculation via Orbital Acoustic Trapping (FLOAT). The FLOAT approach utilizes an acoustofluidic droplet centrifuge to rotate and controllably heat liquid droplets. By adding a thermoresponsive polymer flocculant, nanoparticles as small as 20 nm can be rapidly and selectively concentrated at the center of the droplet. We demonstrate the ability of FLOAT to separate urinary EVs from the highly abundant Tamm-Horsfall protein, addressing a significant obstacle in the development of EV-based liquid biopsies. Due to its high-yield nature, FLOAT reduces biofluid starting volume requirements by a factor of 100 (from 20 mL to 200 µL), demonstrating its promising potential in point-of-care diagnostics.

Assessing the precision of a detergent-assisted cartridge precipitation workflow for non-targeted quantitative proteomics.

Detergent-based workflows incorporating sodium dodecyl sulfate (SDS) necessitate additional steps for detergent removal ahead of mass spectrometry (MS). These steps may lead to variable protein recovery, inconsistent enzyme digestion efficiency, and unreliable MS signals. To validate a detergent-based workflow for quantitative proteomics, we herein evaluate the precision of a bottom-up sample preparation strategy incorporating cartridge-based protein precipitation with organic solvent to deplete SDS. The variance of data-independent acquisition (SWATH-MS) data was isolated from sample preparation error by modelling the variance as a function of peptide signal intensity. Our SDS-assisted cartridge workflow yield a coefficient of variance (CV) of 13%-14%. By comparison, conventional (detergent-free) in-solution digestion increased the CV to 50%; in-gel digestion provided lower CVs between 14% and 20%. By filtering peptides predicting to display lower precision, we further enhance the validity of data in global comparative proteomics. These results demonstrate the detergent-based precipitation workflow is a reliable approach for in depth, label-free quantitative proteome analysis.