A Greater Platelet Dose May Yield Better Clinical Outcomes for Platelet-Rich Plasma in the Treatment of Knee Osteoarthritis: A Systematic Review.

PURPOSE: To determine whether the platelet dose administered during a platelet-rich plasma (PRP) injection for knee osteoarthritis (OA) affects clinical outcomes. METHODS: A systematic review was performed by searching PubMed, Cochrane Library, and Embase for randomized controlled trials with at least 1 study arm using PRP for knee OA. Only studies that provided a platelet count, concentration, or dose with a minimum of 6-month outcome scores were included. Studies in which the PRP group had statistically significant positive outcomes were separated from those without statistical significance. The average platelet doses for studies with positive outcomes in the PRP group were compared with those without positive outcomes. RESULTS: After exclusion criteria were applied, 29 studies were analyzed. Of the 29, there were 31 arms that used PRP as a treatment method, of which 28 had statistically significant positive outcomes at 6 months compared with the control group. The mean platelet dose in the 28 with a positive outcome was 5,500 ± 474 × 106, whereas the 3 that had no positive difference had a mean platelet dose of 2,302 ± 437 × 106 (P < .01). There were 18 studies with 12-month outcomes, with 16 of 18 having positive outcomes. The positive studies had an average platelet dose of 5,464 ± 511, whereas the studies that had no statistical difference had an average platelet dose of 2,253 ± 753 × 106 (P < .05). CONCLUSIONS: Improved clinical outcomes from PRP injections for knee OA may be related to a greater platelet dose. LEVEL OF EVIDENCE: Level II, systematic review of Level I and II studies.

Bacterial virulence plays a crucial role in MRSA sepsis.

Bacterial sepsis is a major global cause of death. However, the pathophysiology of sepsis has remained poorly understood. In industrialized nations, Staphylococcus aureus represents the pathogen most commonly associated with mortality due to sepsis. Because of the alarming spread of antibiotic resistance, anti-virulence strategies are often proposed to treat staphylococcal sepsis. However, we do not yet completely understand if and how bacterial virulence contributes to sepsis, which is vital for a thorough assessment of such strategies. We here examined the role of virulence and quorum-sensing regulation in mouse and rabbit models of sepsis caused by methicillin-resistant S. aureus (MRSA). We determined that leukopenia was a predictor of disease outcome during an early critical stage of sepsis. Furthermore, in device-associated infection as the most frequent type of staphylococcal blood infection, quorum-sensing deficiency resulted in significantly higher mortality. Our findings give important guidance regarding anti-virulence drug development strategies for the treatment of staphylococcal sepsis. Moreover, they considerably add to our understanding of how bacterial sepsis develops by revealing a critical early stage of infection during which the battle between bacteria and leukocytes determines sepsis outcome. While sepsis has traditionally been attributed mainly to host factors, our study highlights a key role of the invading pathogen and its virulence mechanisms.

Prevalence of Moral Injury, Burnout, Anxiety, and Depression in Healthcare Workers 2 Years in to the COVID-19 Pandemic.

ABSTRACT: COVID-19 has led to marked increases in healthcare worker distress. Studies of these phenomena are often limited to a particular element of distress or a specific subset of healthcare workers. We administered the Moral Injury Symptom Scale for Healthcare Professionals, Copenhagen Burnout Inventory, Patient Health Questionnaire-9, and Generalized Anxiety Disorder-7 via online survey to 17,000 employees of a large academic medical center between December 2021 and February 2022. A total of 1945 participants completed the survey. Across all roles, the prevalence of moral injury, burnout, depression, and anxiety were 40.9%, 35.3%-60.6%, 25.4%, and 24.8%, respectively. Furthermore, 8.1% had been bothered by thoughts that they would be better off dead or of hurting themselves for "several days" or more frequently. Healthcare workers across all roles and practice settings are experiencing unsustainable levels of distress, with 1 in 12 regularly experiencing thoughts of self-harm.

Meningitis in the Guise of Dementia: Lyme-Induced Normal Pressure Hydrocephalus.

While the cause of altered mentation in the elderly may be multifactorial, infectious etiologies may be missed. This case report aims to detail an account of a patient with dementia, found to have Lyme meningitis in the setting of a normal pressure hydrocephalus (NPH). The patient smelled of urine and presented with ambulatory dysfunction, fitting the "wet, wacky, and wobbly" triad of NPH while also having subjective chills and leukocytosis. Non-contrast brain CT scan showed dilated ventricles. Cerebrospinal fluid (CSF) studies suggested aseptic meningitis. Serum studies using a modified two-tiered algorithm confirmed the diagnosis of Lyme disease. Treatment of the underlying condition with a prolonged course of doxycycline improved symptoms and clinical course. Review of the literature on the association between Lyme meningitis and NPH reveals that few cases of Lyme-related NPH have been reported worldwide and further research into the pathophysiology, diagnostic approach, treatment modalities, and management of NPH secondary to Lyme meningitis may be warranted.

Contribution of Staphylococcal Enterotoxin B to Staphylococcus aureus Systemic Infection.

Staphylococcal enterotoxin B (SEB), which is produced by the major human pathogen, Staphylococcus aureus, represents a powerful superantigenic toxin and is considered a bioweapon. However, the contribution of SEB to S. aureus pathogenesis has never been directly demonstrated with genetically defined mutants in clinically relevant strains. Many isolates of the predominant Asian community-associated methicillin-resistant S. aureus lineage sequence type (ST) 59 harbor seb, implying a significant role of SEB in the observed hypervirulence of this lineage. We created an isogenic seb mutant in a representative ST59 isolate and assessed its virulence potential in mouse infection models. We detected a significant contribution of seb to systemic ST59 infection that was associated with a cytokine storm. Our results directly demonstrate that seb contributes to S. aureus pathogenesis, suggesting the value of including SEB as a target in multipronged antistaphylococcal drug development strategies. Furthermore, they indicate that seb contributes to fatal exacerbation of community-associated methicillin-resistant S. aureus infection.

Greenhouse Gas Emissions of Western Canadian Natural Gas: Proposed Emissions Tracking for Life Cycle Modeling.

Natural gas (NG) produced in Western Canada is a major and growing source of Canada's energy and greenhouse gas (GHG) emissions portfolio. Despite recent progress, there is still only limited understanding of the sources and drivers of Western Canadian greenhouse gas (GHG) emissions. We conduct a case study of a production facility based on Seven Generation Energy Ltd.'s Western Canadian operations and an upstream NG emissions intensity model. The case study upstream emissions intensity is estimated to be 3.1-4.0 gCO2e/MJ NG compared to current best estimates of British Columbia (BC) emissions intensities of 6.2-12 gCO2e/MJ NG and a US average estimate of 15 gCO2e/MJ. The analysis reveals that compared to US studies, public GHG emissions data for Western Canada is insufficient as current public data satisfies only 50% of typical LCA model inputs. Company provided data closes most of these gaps (∼80% of the model inputs). We recommend more detailed data collection and presentation of government reported data such as a breakdown of vented and fugitive methane emissions by source. We propose a data collection template to facilitate improved GHG emissions intensity estimates and insight about potential mitigation strategies.

A Real-Time Collision Avoidance Framework of MASS Based on B-Spline and Optimal Decoupling Control.

Real-time collision-avoidance navigation of autonomous ships is required by many application scenarios, such as carriage of goods by sea, search, and rescue. The collision avoidance algorithm is the core of autonomous navigation for Maritime autonomous surface ships (MASS). In order to realize real-time and free-collision under the condition of multi-ship encounter in an uncertain environment, a real-time collision avoidance framework is proposed using B-spline and optimal decoupling control. This framework takes advantage to handle the uncertain environment with limited sensing MASS which plans dynamically feasible, highly reliable, and safe feasible collision avoidance. First, owing to the collision risk assessment, a B-spline-based collision avoidance trajectory search (BCATS) algorithm is proposed to generate free-collision trajectories effectively. Second, a waypoint-based collision avoidance trajectory optimization is proposed with the path-speed decoupling control. Two benefits, a reduction of control cost and an improvement in the smoothness of the collision avoidance trajectory, are delivered. Finally, we conducted an experiment using the Electronic Chart System (ECS). The results reveal the robustness and real-time collision avoidance trajectory planned by the proposed collision avoidance system.

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform.

Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these changes. In recent decades, there have been massive worldwide rollouts of smart meters that automatically capture the total electricity usage of houses and buildings. Electricity load disaggregation (ELD) helps to break down total electricity usage into that of individual appliances. Studies have implemented ELD models based on various artificial intelligence techniques using a single ELD dataset. In this paper, a powerline noise transformation approach based on optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD-WPT) is proposed to merge the ELD datasets. The practical implications are that the method increases the size of training datasets and provides mutual benefits when utilizing datasets collected from other sources (especially from different countries). To reveal the effectiveness of the proposed method, it was compared with CEEMD-WPT (fixed controlled coefficients), standalone CEEMD, standalone WPT, and other existing works. The results show that the proposed approach improves the signal-to-noise ratio (SNR) significantly.

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness.

Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers' future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2-13.6% and 10.2-12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9-12.7% and 6.9-8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account-namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.

Moving Towards Intelligent Transportation via Artificial Intelligence and Internet-of-Things.

One of the key smart city visions is to bring smarter transport networks, specifically intelligent/smart transportation [...].

A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM.

Driver drowsiness and stress are major causes of traffic deaths and injuries, which ultimately wreak havoc on world economic loss. Researchers are in full swing to develop various algorithms for both drowsiness and stress recognition. In contrast to existing works, this paper proposes a generic model using multiple-objective genetic algorithm optimized deep multiple kernel learning support vector machine that is capable to recognize both driver drowsiness and stress. This algorithm simplifies the research formulations and model complexity that one model fits two applications. Results reveal that the proposed algorithm achieves an average sensitivity of 99%, specificity of 98.3% and area under the receiver operating characteristic curve (AUC) of 97.1% for driver drowsiness recognition. For driver stress recognition, the best performance is yielded with average sensitivity of 98.7%, specificity of 98.4% and AUC of 96.9%. Analysis also indicates that the proposed algorithm using multiple-objective genetic algorithm has better performance compared to the grid search method. Multiple kernel learning enhances the performance significantly compared to single typical kernel. Compared with existing works, the proposed algorithm not only achieves higher accuracy but also addressing the typical issues of dataset in simulated environment, no cross-validation and unreliable measurement stability of input signals.

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis.

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations.

Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints.

Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t)-space measurements, commonly referred to as big data. The purpose of this work is to accelerate big medical data acquisition in dynamic MRI by developing a non-convex minimization framework. In particular, to overcome the inherent speed limitation, both non-convex low-rank and sparsity constraints were combined to accelerate the dynamic imaging. However, the non-convex constraints make the dynamic reconstruction problem difficult to directly solve through the commonly-used numerical methods. To guarantee solution efficiency and stability, a numerical algorithm based on Alternating Direction Method of Multipliers (ADMM) is proposed to solve the resulting non-convex optimization problem. ADMM decomposes the original complex optimization problem into several simple sub-problems. Each sub-problem has a closed-form solution or could be efficiently solved using existing numerical methods. It has been proven that the quality of images reconstructed from fewer measurements can be significantly improved using non-convex minimization. Numerous experiments have been conducted on two in vivo cardiac datasets to compare the proposed method with several state-of-the-art imaging methods. Experimental results illustrated that the proposed method could guarantee the superior imaging performance in terms of quantitative and visual image quality assessments.

Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring.

Single-image blind deblurring for imaging sensors in the Internet of Things (IoT) is a challenging ill-conditioned inverse problem, which requires regularization techniques to stabilize the image restoration process. The purpose is to recover the underlying blur kernel and latent sharp image from only one blurred image. Under many degraded imaging conditions, the blur kernel could be considered not only spatially sparse, but also piecewise smooth with the support of a continuous curve. By taking advantage of the hybrid sparse properties of the blur kernel, a hybrid regularization method is proposed in this paper to robustly and accurately estimate the blur kernel. The effectiveness of the proposed blur kernel estimation method is enhanced by incorporating both the L 1 -norm of kernel intensity and the squared L 2 -norm of the intensity derivative. Once the accurate estimation of the blur kernel is obtained, the original blind deblurring can be simplified to the direct deconvolution of blurred images. To guarantee robust non-blind deconvolution, a variational image restoration model is presented based on the L 1 -norm data-fidelity term and the total generalized variation (TGV) regularizer of second-order. All non-smooth optimization problems related to blur kernel estimation and non-blind deconvolution are effectively handled by using the alternating direction method of multipliers (ADMM)-based numerical methods. Comprehensive experiments on both synthetic and realistic datasets have been implemented to compare the proposed method with several state-of-the-art methods. The experimental comparisons have illustrated the satisfactory imaging performance of the proposed method in terms of quantitative and qualitative evaluations.

Pulsatile hemodynamics in patient-specific thoracic aortic dissection models constructed from computed tomography angiography.

PURPOSE: Thoracic aortic dissection (TAD) is considered one of the most catastrophic and non-traumatic cardiovascular diseases associated with high morbidity and mortality rates in clinical treatment. The purpose of this paper is to investigate the pulsatile hemodynamics changes throughout a cardiac cycle in a Stanford Type B TAD model with the aid of computational fluid dynamics (CFD) method. METHODS: A patient-specific dissected aorta geometry was reconstructed from the three-dimensional (3D) computed tomography angiography (CTA) scanning. The realistic time-dependent pulsatile boundary conditions were prescribed for our 3D patient-specific TAD model. Blood was considered to be an incompressible, Newtonian fluid. The aortic wall was assumed to be rigid, and a no-slip boundary condition was applied at the wall. CFD simulations were processed using the finite volume (FV) method to investigate the pulsatile hemodynamics in terms of blood flow velocity, aortic wall pressure, wall shear stress and flow vorticity. In the experiments, blood velocity, pressure, wall shear stress and vorticity distributions were analyzed qualitatively and quantitatively. RESULTS: The experimental results demonstrated a high wall shear stress and strong vertical flow at dissection initiation. The results also indicated that wall shear progressed along the false lumen, which is a possible cause of blood flow between aortic wall layers.

Methicillin Resistance Elements in the Canine Pathogen Staphylococcus pseudintermedius and Their Association with the Peptide Toxin PSM-mec.

Staphylococcus pseudintermedius is a frequent cause of infections in dogs. Infectious isolates of this coagulase-positive staphylococcal species are often methicillin- and multidrug-resistant, which complicates therapy. In staphylococci, methicillin resistance is encoded by determinants found on mobile genetic elements called Staphylococcal Chromosome Cassette mec (SCCmec), which, in addition to methicillin resistance factors, sometimes encode additional genes, such as further resistance factors and, rarely, virulence determinants. In this study, we analyzed SCCmec in a collection of infectious methicillin-resistant S. pseudintermedius (MRSP) isolates from predominant lineages in the United States. We found that several lineages characteristically have specific types of SCCmec elements and Agr types and harbor additional factors in their SCCmec elements that may promote virulence or affect DNA uptake. All isolates had SCCmec-encoded restriction-modification (R-M) systems of types I or II, and sequence types (STs) ST84 and ST64 had one type II and one type I R-M system, although the latter lacked a complete methylation enzyme gene. ST68 isolates also had an SCCmec-encoded CRISPR system. ST71 isolates had a psm-mec gene, which, in all but apparently Agr-dysfunctional isolates, produced a PSM-mec peptide toxin, albeit at relatively small amounts. This study gives detailed insight into the composition of SCCmec elements in infectious isolates of S. pseudintermedius and lays the genetic foundation for further efforts directed at elucidating the contribution of identified accessory SCCmec factors in impacting SCCmec-encoded and thus methicillin resistance-associated virulence and resistance to DNA uptake in this leading canine pathogen.

Top 25 Most-Cited Articles on Robotic-Assisted Lumbar Spine Surgery.

BACKGROUND: Robot-guided lumbar spine surgery has evolved rapidly with evidence to support its utility and feasibility compared with conventional freehand and fluoroscopy-based techniques. The objective of this study was to assess trends among the top 25 most-cited articles pertaining to robotic-guided lumbar spine surgery. METHODS: An "advanced document search" using Boolean search operator terms was performed on 16 November 2022 through the Web of Science and SCOPUS citation databases to determine the top 25 most-referenced articles on robotic lumbar spine surgery. The articles were compiled into a directory and hierarchically organized based on the total number of citations. RESULTS: Cumulatively, the "Top 25" list for robot-assisted navigation in lumbar spine surgery received 2240 citations, averaging 97.39 citations annually. The number of citations ranged from 221 to 40 for the 25 most-cited articles. The most-cited study, by Kantelhardt et al, received 221 citations, averaging 18 citations per year. CONCLUSIONS: As utilization of robot-guided modalities in lumbar spine surgery increases, this review highlights the most impactful studies to support its efficacy and implementation. Practical considerations such as cost-effectiveness, however, need to be better defined through further longitudinal studies that evaluate patient-reported outcomes and cost-utility. CLINICAL RELEVANCE: Through an overview of the top 25 most-cited articles, the present review highlights the rising prominence and technical efficacy of robotic-guided systems within lumbar spine surgery, with consideration to pragmatic limitations and need for additional data to facilitate cost-effective applications.

Native Circular RNA Pulldown Method to Simultaneously Profile RNA and Protein Interactions.

Circular RNAs (circRNAs) are a widespread, cell-, tissue-, and disease-specific class of largely non-coding RNA transcripts. These single-stranded, covalently-closed transcripts arise through non-canonical splicing of pre-mRNA, a process called back-splicing. Back-splicing results in circRNAs which are distinguishable from their cognate mRNA as they possess a unique sequence of nucleic acids called the backsplice junction (BSJ). CircRNAs have been shown to play key functional roles in various cellular contexts and achieve this through their interaction with other macromolecules, particularly other RNA molecules and proteins. To elucidate the molecular mechanisms underlying circRNA function, it is necessary to identify these interacting partners. Herein, we present an optimized strategy for the simultaneous purification of the circRNA interactome within eukaryotic cells, allowing the identification of both circRNA-RNA and circRNA-protein interactions.

Integration of Chronological Age Does Not Improve the Performance of a Mixed-Effect Model Using Comorbidity Burden and Frailty to Predict 90-Day Readmission After Surgery for Degenerative Scoliosis.

OBJECTIVE: We evaluated the contributions of chronological age, comorbidity burden, and/or frailty in predicting 90-day readmission in patients undergoing degenerative scoliosis surgery. METHODS: Patients were identified through the Healthcare Cost and Utilization Project Nationwide Readmissions Database. Frailty was assessed using the Johns Hopkins Adjusted Clinical Groups frailty-defining indicator. Comorbidity was assessed using the Elixhauser Comorbidity Index (ECI). Generalized linear mixed-effects models were created to predict readmission using age, frailty, and/or ECI. Area under the curve (AUC) was compared using DeLong's test. RESULTS: A total of 8104 patients were identified. Readmission rate was 9.8%, with infection representing the most common cause (3.5%). Our first model utilized chronological age, ECI, and/or frailty as primary predictors. The combination of ECI + frailty + age performed best, but the inclusion of chronological age did not significantly improve performance compared to ECI + frailty alone (AUC 0.603 vs. 0.599, P = 0.290). A second model using only chronological age and frailty as primary predictors performed better, however the inclusion of chronological age worsened performance when compared to frailty alone (AUC 0.747 vs. 0.743, P = 0.043). CONCLUSIONS: These data support frailty as a predictor of 90-day readmission within a nationally representative sample. Frailty alone performed better than combinations of ECI and age. Interestingly, the integration of chronological age did not dramatically improve the model's performance. Limitations include the use of a national registry and a single frailty index. This provides impetus to explore biological age, rather than chronological age, as a potential tool for surgical risk assessment.