Differences in visuospatial cognition among table tennis players of different skill levels: an event-related potential study.

This study aimed to examine the influence of sport skill levels on behavioural and neuroelectric performance in visuospatial attention and memory visuospatial tasks were administered to 54 participants, including 18 elite and 18 amateur table tennis players and 18 nonathletes, while event-related potentials were recorded. In all the visuospatial attention and memory conditions, table tennis players displayed faster reaction times than nonathletes, regardless of skill level, although there was no difference in accuracy between groups. In addition, regardless of task conditions, both player groups had a greater P3 amplitude than nonathletes, and elite players exhibited a greater P3 amplitude than amateurs players. The results of this study indicate that table tennis players, irrespective of their skill level, exhibit enhanced visuospatial capabilities. Notably, athletes at the elite level appear to benefit from an augmented allocation of attentional resources when engaging in visuospatial tasks.

Enhanced versus standard hydration in acute ischaemic stroke: REVIVE - a randomised clinical trial.

RATIONALE: Early neurological deterioration (END) within 72 hours of stroke onset is associated with poor prognosis. Optimising hydration might reduce the risk of END. AIMS: To determine in acute ischaemic stroke patients if enhanced hydration versus standard hydration reduced the incidence of major (primary) and minor (secondary) END, as whether it increased the incidence of early neurological improvement (secondary), at 72 hours after admissionSample Size Estimate: 244 participants per arm. METHODS AND DESIGN: A prospective, double-blinded, multicentre, parallel-group, randomised controlled trial conducted at 4 hospitals from April 2014 to July 2020, with data analysed in August 2020. The sample size estimated was 488 participants (244 per arm). Ischaemic stroke patients with measurable neurological deficits of onset within 12 hours of emergency department presentation and blood urea nitrogen/creatinine (BUN/Cr) ratio ≥15 at point of admission were enrolled and randomised to 0.9% sodium chloride infusions of varying rates - enhanced hydration (20 mL/kg body weight, one-third given via bolus and remainder over 8 hours) versus standard hydration (60 mL/hour for 8 hours), followed by maintenance infusion of 40-80 mL/hour for the subsequent 64 hours. The primary outcome measure was the incidence of major early neurological deterioration at 72 hours after admission, defined as an increase in National Institutes of Health Stroke Scale of ≥4 points from baseline. RESULTS: 487 participants were randomised (median age 67 years; 287 females). At 72 hours: 7 (2.9%) in the enhanced-hydration arm and 5(2.0%) in the standard-hydration developed major early neurological deterioration (p=0.54). The incidence of minor early neurological deterioration and early neurological improvement did not differ between treatment arms. CONCLUSIONS AND RELEVANCE: Enhanced hydration ratio did not reduce END or improve short term outcomes in acute ischaemic stroke. TRIAL REGISTRATION: ClinicalTrials.gov (NCT02099383, https://clinicaltrials.gov/study/NCT02099383).

Effect of human H3N2 influenza virus reassortment on influenza incidence and severity during the 2017-18 influenza season in the USA: a retrospective observational genomic analysis.

BACKGROUND: During the 2017-18 influenza season in the USA, there was a high incidence of influenza illness and mortality. However, no apparent antigenic change was identified in the dominant H3N2 viruses, and the severity of the season could not be solely attributed to a vaccine mismatch. We aimed to investigate whether the altered virus properties resulting from gene reassortment were underlying causes of the increased case number and disease severity associated with the 2017-18 influenza season. METHODS: Samples included were collected from patients with influenza who were prospectively recruited during the 2016-17 and 2017-18 influenza seasons at the Johns Hopkins Hospital Emergency Departments in Baltimore, MD, USA, as well as from archived samples from Johns Hopkins Health System sites. Among 647 recruited patients with influenza A virus infection, 411 patients with whole-genome sequences were available in the Johns Hopkins Center of Excellence for Influenza Research and Surveillance network during the 2016-17 and 2017-18 seasons. Phylogenetic trees were constructed based on viral whole-genome sequences. Representative viral isolates of the two seasons were characterised in immortalised cell lines and human nasal epithelial cell cultures, and patients' demographic data and clinical outcomes were analysed. FINDINGS: Unique H3N2 reassortment events were observed, resulting in two predominant strains in the 2017-18 season: HA clade 3C.2a2 and clade 3C.3a, which had novel gene segment constellations containing gene segments from HA clade 3C.2a1 viruses. The reassortant re3C.2a2 viruses replicated with faster kinetics and to a higher peak titre compared with the parental 3C.2a2 and 3C.2a1 viruses (48 h vs 72 h). Furthermore, patients infected with reassortant 3C.2a2 viruses had higher Influenza Severity Scores than patients infected with the parental 3C.2a2 viruses (median 3·00 [IQR 1·00-4·00] vs 1·50 [1·00-2·00]; p=0·018). INTERPRETATION: Our findings suggest that the increased severity of the 2017-18 influenza season was due in part to two intrasubtypes, cocirculating H3N2 reassortant viruses with fitness advantages over the parental viruses. This information could help inform future vaccine development and public health policies. FUNDING: The Center of Excellence for Influenza Research and Response in the US, National Science and Technology Council, and Chang Gung Memorial Hospital in Taiwan.

Physiologically-based pharmacokinetic model of sparsentan to evaluate drug-drug interaction potential.

Sparsentan is a dual endothelin/angiotensin II receptor antagonist indicated to reduce proteinuria in patients with primary IgA nephropathy at high risk of disease progression. In vitro data indicate that sparsentan is likely to inhibit or induce various CYP enzymes at therapeutic concentrations. Sparsentan as a victim and perpetrator of CYP3A4 mediated drug-drug interactions (DDIs) has been assessed clinically. A mechanistic, bottom-up, physiologically-based pharmacokinetic (PK) model for sparsentan was developed based on in vitro data of drug solubility, formulation dissolution and particle size, drug permeability, inhibition and induction of metabolic enzymes, and P-glycoprotein (P-gp) driven efflux. The model was verified using clinical PK data from healthy adult volunteers administered single and multiple doses in the fasted and fed states for a wide range of sparsentan doses. The model was also verified by simulation of clinically observed DDIs. The verified model was then used to test various DDI simulations of sparsentan as a perpetrator and victim of CYP3A4 using an expanded set of inducers and inhibitors with varying potency. Additional perpetrator and victim DDI simulations were performed using probes for CYP2C9 and CYP2C19. Simulations were conducted to predict the effect of complete inhibition of P-gp inhibition on sparsentan absorption and clearance. The predictive simulations indicated that exposure of sparsentan could increase greater than two-fold if co-administered with a strong CYP3A4 inhibitor, such as itraconazole. Other potential DDI interactions as victim or perpetrator were all within two-fold of control. The effect of complete P-gp inhibition on sparsentan PK was negligible.

Real-world application of PBPK in drug discovery.

The utility of PBPK models in support of drug development has been well documented. During the discovery stage, PBPK has increasingly been applied for early risk assessment, prediction of human dose, toxicokinetic dose projection and early formulation assessment. Previous review articles have proposed model building and application strategies for PBPK-based first in human predictions with comprehensive descriptions of the individual components of PBPK models. This includes the generation of decision trees, based on comprehensive literature reviews, to guide the application of PBPK in the discovery setting. The goal of this mini review is to provide additional guidance on the real-world application of PBPK, in support of the discovery stage of drug development. In this mini review, our goal is to provide guidance on the typical steps involved in the development and application of a PBPK model during drug discovery to assist in decision making. We have illustrated our recommended approach through description of case examples, where PBPK has been successfully applied to aid in human PK projection, candidate selection and prediction of drug interaction liability for parent and metabolite. Through these case studies, we have highlighted fundamental issues, including pre-verification in preclinical species, the application of empirical scalars in the prediction of in vivo clearance from in vitro systems, in silico prediction of permeability and the exploration of aqueous and biorelevant solubility data to predict dissolution. In addition, current knowledge gaps have been highlighted and future directions proposed. Significance Statement Through description of three case studies, we have highlighted the fundamental principles of PBPK application during drug discovery. These include pre-verification of the model in preclinical species, application of empirical scalars where necessary in the prediction of clearance, in silico prediction of permeability, and the exploration of aqueous and biorelevant solubility data to predict dissolution. In addition, current knowledge gaps have been highlighted and future directions proposed.

Development and application of a PBPK modeling strategy to support antimalarial drug development.

As part of a collaboration between Medicines for Malaria Venture (MMV), Certara UK and Monash University, physiologically-based pharmacokinetic (PBPK) models were developed for 20 antimalarials, using data obtained from standardized in vitro assays and clinical studies within the literature. The models have been applied within antimalarial drug development at MMV for more than 5 years. During this time, a strategy for their impactful use has evolved. All models are described in the supplementary material and are available to researchers. Case studies are also presented, demonstrating real-world development and clinical applications, including the assessment of the drug-drug interaction liability between combination partners or with co-administered drugs. This work emphasizes the benefit of PBPK modeling for antimalarial drug development and decision making, and presents a strategy to integrate it into the research and development process. It also provides a repository of shared information to benefit the global health research community.

Diagnostic accuracy of clinical signs and symptoms of COVID-19: A systematic review and meta-analysis to investigate the different estimates in a different stage of the pandemic outbreak.

Background: The coronavirus (COVID-19) pandemic caused enormous adverse socioeconomic impacts worldwide. Evidence suggests that the diagnostic accuracy of clinical features of COVID-19 may vary among different populations. Methods: We conducted a systematic review and meta-analysis of studies from PubMed, Embase, Cochrane Library, Google Scholar, and the WHO Global Health Library for studies evaluating the accuracy of clinical features to predict and prognosticate COVID-19. We used the National Institutes of Health Quality Assessment Tool to evaluate the risk of bias, and the random-effects approach to obtain pooled prevalence, sensitivity, specificity, and likelihood ratios. Results: Among the 189 included studies (53 659 patients), fever, cough, diarrhoea, dyspnoea, and fatigue were the most reported predictors. In the later stage of the pandemic, the sensitivity in predicting COVID-19 of fever and cough decreased, while the sensitivity of other symptoms, including sputum production, sore throat, myalgia, fatigue, dyspnoea, headache, and diarrhoea, increased. A combination of fever, cough, fatigue, hypertension, and diabetes mellitus increases the odds of having a COVID-19 diagnosis in patients with a positive test (positive likelihood ratio (PLR) = 3.06)) and decreases the odds in those with a negative test (negative likelihood ratio (NLR) = 0.59)). A combination of fever, cough, sputum production, myalgia, fatigue, and dyspnea had a PLR = 10.44 and an NLR = 0.16 in predicting severe COVID-19. Further updating the umbrella review (1092 studies, including 3 342 969 patients) revealed the different prevalence of symptoms in different stages of the pandemic. Conclusions: Understanding the possible different distributions of predictors is essential for screening for potential COVID-19 infection and severe outcomes. Understanding that the prevalence of symptoms may change with time is important to developing a prediction model.

Using time-course as an essential factor to accurately predict sepsis-associated mortality among patients with suspected sepsis.

BACKGROUND: Biomarker dynamics in different time-courses might be the primary reason why a static measurement of a single biomarker cannot accurately predict sepsis outcomes. Therefore, we conducted this prospective hospital-based cohort study to simultaneously evaluate the performance of several conventional and novel biomarkers of sepsis in predicting sepsis-associated mortality on different days of illness among patients with suspected sepsis. METHODS: We evaluated the performance of 15 novel biomarkers including angiopoietin-2, pentraxin 3, sTREM-1, ICAM-1, VCAM-1, sCD14 and 163, E-selectin, P-selectin, TNF-alpha, interferon-gamma, CD64, IL-6, 8, and 10, along with few conventional markers for predicting sepsis-associated mortality. Patients were grouped into quartiles according to the number of days since symptom onset. Receiver operating characteristic curve (ROC) analysis was used to evaluate the biomarker performance. RESULTS: From 2014 to 2017, 1,483 patients were enrolled, of which 78% fulfilled the systemic inflammatory response syndrome criteria, 62% fulfilled the sepsis-3 criteria, 32% had septic shock, and 3.3% developed sepsis-associated mortality. IL-6, pentraxin 3, sCD163, and the blood gas profile demonstrated better performance in the early days of illness, both before and after adjusting for potential confounders (adjusted area under ROC curve [AUROC]:0.81-0.88). Notably, the Sequential Organ Failure Assessment (SOFA) score was relatively consistent throughout the course of illness (adjusted AUROC:0.70-0.91). CONCLUSION: IL-6, pentraxin 3, sCD163, and the blood gas profile showed excellent predictive accuracy in the early days of illness. The SOFA score was consistently predictive of sepsis-associated mortality throughout the course of illness, with an acceptable performance.

Applying symptom dynamics to accurately predict influenza virus infection: An international multicenter influenza-like illness surveillance study.

BACKGROUND: Public health organizations have recommended various definitions of influenza-like illnesses under the assumption that the symptoms do not change during influenza virus infection. To explore the relationship between symptoms and influenza over time, we analyzed a dataset from an international multicenter prospective emergency department (ED)-based influenza-like illness cohort study. METHODS: We recruited patients in the US and Taiwan between 2015 and 2020 with: (1) flu-like symptoms (fever and cough, headache, or sore throat), (2) absence of any of the respiratory infection symptoms, or (3) positive laboratory test results for influenza from the current ED visit. We evaluated the association between the symptoms and influenza virus infection on different days of illness. The association was evaluated among different subgroups, including different study countries, influenza subtypes, and only patients with influenza. RESULTS: Among the 2471 recruited patients, 45.7% tested positive for influenza virus. Cough was the most predictive symptom throughout the week (odds ratios [OR]: 7.08-11.15). In general, all symptoms were more predictive during the first 2 days (OR: 1.55-10.28). Upper respiratory symptoms, such as sore throat and productive cough, and general symptoms, such as body ache and fatigue, were more predictive in the first half of the week (OR: 1.51-3.25). Lower respiratory symptoms, such as shortness of breath and wheezing, were more predictive in the second half of the week (OR: 1.52-2.52). Similar trends were observed for most symptoms in the different subgroups. CONCLUSIONS: The time course is an important factor to be considered when evaluating the symptoms of influenza virus infection.

Developing and validating clinical features-based machine learning algorithms to predict influenza infection in influenza-like illness patients.

BACKGROUND: Seasonal influenza poses a significant risk, and patients can benefit from early diagnosis and treatment. However, underdiagnosis and undertreatment remain widespread. We developed and compared clinical feature-based machine learning (ML) algorithms that can accurately predict influenza infection in emergency departments (EDs) among patients with influenza-like illness (ILI). MATERIAL AND METHODS: We conducted a prospective cohort study in five EDs in the US and Taiwan from 2015 to 2020. Adult patients visiting the EDs with symptoms of ILI were recruited and tested by real-time RT-PCR for influenza. We evaluated seven ML algorithms and compared their results with previously developed clinical prediction models. RESULTS: Out of the 2189 enrolled patients, 1104 tested positive for influenza. The eXtreme Gradient Boosting achieved superior performance with an area under the receiver operating characteristic curve of 0.82 (95% confidence interval [CI] = 0.79-0.85), with a sensitivity of 0.92 (95% CI = 0.88-0.95), specificity of 0.89 (95% CI = 0.86-0.92), and accuracy of 0.72 (95% CI = 0.69-0.76) in the testing set over cut-offs of 0.4, 0.6 and 0.5, respectively. These results were superior to those of previously proposed clinical prediction models. The model interpretation revealed that body temperature, cough, rhinorrhea, and exposure history were positively associated with and the days of illness and influenza vaccine were negatively associated with influenza infection. We also found the week of the influenza season, pulse rate, and oxygen saturation to be associated with influenza infection. CONCLUSIONS: The clinical feature-based ML model outperformed conventional models for predicting influenza infection.

Trends of Regional Anesthesia Studies in Emergency Medicine: An Observational Study of Published Articles.

INTRODUCTION: Regional anesthesia (RA) has become a prominent component of multimodal pain management in emergency medicine (EM), and its use has increased rapidly in recent decades. Nevertheless, there is a paucity of data on how RA practice has evolved in the specialty. In this study we sought to investigate how RA has been implemented in EM by analyzing trends of published articles and to describe the characteristics of the published research. METHODS: We retrieved RA-related publications from the SciVerse Scopus database from inception to January 13, 2022, focusing on studies associated with the use of RA in EM. The primary outcome was an analysis of trend based on the number of annual publications. Other outcomes included reports of technique diversity by year, trends in the use of individual techniques, and characteristics of published articles. We used linear regression analysis to analyze trends. RESULTS: In total, 133 eligible publications were included. We found that overall 23 techniques have been described and results published in the EM literature. Articles related to RA increased from one article in 1982 to 18 in 2021, and the rate of publication has increased more rapidly since 2016. Reports of lower extremity blocks (60.90%) were published most frequently in ranked-first aggregated citations. The use of thoracic nerve blocks, such as the erector spinae plane block, has increased exponentially in the past three years. The United States (41.35%) has published the most RA-related articles. Regional anesthesia administered by emergency physicians (52.63%) comprised the leading field in published articles related to RA. Most publications discussed single-shot (88.72%) and ultrasound-guided methods (55.64%). CONCLUSION: This study highlights that the number of published articles related to regional anesthesia in EM has increased. Although RA research has primarily focused on lower extremity blocks, clinical researchers continue to broaden the field of study to encompass a wide spectrum of techniques and indications.

Antigenic Characterization and Pandemic Risk Assessment of North American H1 Influenza A Viruses Circulating in Swine.

The first pandemic of the 21st century was caused by an H1N1 influenza A virus (IAV) introduced from pigs into humans, highlighting the importance of swine as reservoirs for pandemic viruses. Two major lineages of swine H1 circulate in North America: the 1A classical swine lineage (including that of the 2009 H1N1 pandemic) and the 1B human seasonal-like lineage. Here, we investigated the evolution of these H1 IAV lineages in North American swine and their potential pandemic risk. We assessed the antigenic distance between the HA of representative swine H1 and human seasonal vaccine strains (1978 to 2015) in hemagglutination inhibition (HI) assays using a panel of monovalent antisera raised in pigs. Antigenic cross-reactivity varied by strain but was associated with genetic distance. Generally, the swine 1A lineage viruses that seeded the 2009 H1 pandemic were antigenically most similar to the H1 pandemic vaccine strains, with the exception of viruses in the genetic clade 1A.1.1.3, which had a two-amino acid deletion mutation near the receptor-binding site, which dramatically reduced antibody recognition. The swine 1B lineage strains, which arose from previously circulating (pre-2009 pandemic) human seasonal viruses, were more antigenically similar to pre-2009 human seasonal H1 vaccine viruses than post-2009 strains. Human population immunity was measured by cross-reactivity in HI assays to representative swine H1 strains. There was a broad range of titers against each swine strain that was not associated with age, sex, or location. However, there was almost no cross-reactivity in human sera to the 1A.1.1.3 and 1B.2.1 genetic clades of swine viruses, and the 1A.1.1.3 and 1B.2.1 clades were also the most antigenically distant to the human vaccine strains. Our data demonstrate that the antigenic distances of representative swine strains from human vaccine strains represent an important part of the rational assessment of swine IAV for zoonotic risk research and pandemic preparedness prioritization. IMPORTANCE Human H1 influenza A viruses (IAV) spread to pigs in North America, resulting in a sustained circulation of two major groups of H1 viruses in swine. We quantified the genetic diversity of H1 in swine and measured antigenic phenotypes. We demonstrated that the swine H1 lineages were significantly different from the human vaccine strains and that this antigenic dissimilarity increased over time as the viruses evolved in swine. Pandemic preparedness vaccine strains for human vaccines also demonstrated a loss in similarity with contemporary swine strains. Human sera revealed a range of responses to swine IAV, including two groups of viruses with little to no immunity. The surveillance and risk assessment of IAV diversity in pig populations are essential to detect strains with reduced immunity in humans and provide critical information for pandemic preparedness.

Physiologically Based Pharmacokinetic Modeling To Predict Drug-Biologic Interactions with Cytokine Modulators: Are These Relevant and Is Interleukin-6 Enough?

Drugs that modulate cytokine levels are often used for the treatment of cancer as well as inflammatory or immunologic disorders. Pharmacokinetic drug-biologic interactions (DBIs) may arise from suppression or elevation of cytochrome P450 (P450) enzymes caused by the increase or decrease in cytokine levels after administration of these therapies. There is in vitro and in vivo evidence that demonstrates a clear link between raised interleukin (IL)-6 levels and P450 suppression, in particular CYP3A4. However, despite this, the changes in IL-6 levels in vivo rarely lead to significant drug interactions (area under the curve and Cmax ratios < 2-fold). The clinical significance of such interactions therefore remains questionable and is dependent on the therapeutic index of the small molecule therapy. Physiologically based pharmacokinetic (PBPK) modeling has been used successfully to predict the impact of raised IL-6 on P450 activities. Beyond IL-6, published data show little evidence that IL-8, IL-10, and IL-17 suppress P450 enzymes. In vitro data suggest that IL-1ß, IL-2, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ can cause suppression of P450 enzymes. Despite in vivo there being a link between IL-6 levels and P450 suppression, the evidence to support a direct effect of IL-2, IL-8, IL-10, IL-17, IFN-γ, TNF-α, or vascular endothelial growth factor on P450 activity is inconclusive. This commentary will discuss the relevance of such drug-biologic interactions and whether current PBPK models considering only IL-6 are sufficient. SIGNIFICANCE STATEMENT: This commentary summarizes the current in vitro and in vivo literature regarding cytokine-mediated cytochrome P450 suppression and compares the relative suppressive potential of different cytokines in reference to interleukin (IL)-6. It also discusses the relevance of drug-biologic interactions to therapeutic use of small molecule drugs and whether current physiologically based pharmacokinetic models considering only IL-6 are sufficient to predict the extent of drug-biologic interactions.

Risk factors associated with peri-intubation cardiac arrest in the emergency department.

BACKGROUND: Peri-intubation cardiac arrest is an uncommon, serious complication following endotracheal intubation in the emergency department. Although several risk factors have been previously identified, this study aimed to comprehensively identify risk factors associated with peri-intubation cardiac arrest. METHODS: This retrospective, nested case-control study conducted from January 1, 2016 to December 31, 2020 analyzed variables including demographic characteristics, triage, and pre-intubation vital signs, medications, and laboratory data. Univariate analysis and multivariable logistic regression models were used to compare clinical factors between the patients with peri-intubation cardiac arrest and patients without cardiac arrest. RESULTS: Of the 6983 patients intubated during the study period, 5130 patients met the inclusion criteria; 92 (1.8%) patients met the criteria for peri-intubation cardiac arrest and 276 were age- and sex-matched to the control group. Before intubation, systolic blood pressure and diastolic blood pressure were lower (104 vs. 136.5 mmHg, p < 0.01; 59.5 vs. 78 mmHg, p < 0.01 respectively) and the shock index was higher in the patients with peri-intubation cardiac arrest than the control group (0.97 vs. 0.83, p < 0.0001). Cardiogenic pulmonary edema as an indication for intubation (adjusted odds ratio [aOR]: 5.921, 95% confidence interval [CI]: 1.044-33.57, p = 0.04), systolic blood pressure < 90 mmHg before intubation (aOR: 5.217, 95% CI: 1.484-18.34, p = 0.01), and elevated lactate levels (aOR: 1.012, 95% CI: 1.002-1.022, p = 0.01) were independent risk factors of peri-intubation cardiac arrest. CONCLUSIONS: Patients with hypotension before intubation have a higher risk of peri-intubation cardiac arrest in the emergency department. Future studies are needed to evaluate the influence of resuscitation before intubation and establish airway management strategies to avoid serious complications.

Ultrasound-Guided Femoral Nerve Block in Geriatric Patients with Hip Fracture in the Emergency Department.

Background and Objectives: Systemic analgesics, including opioids, are commonly used for acute pain control in traumatic hip fracture patients in the emergency department (ED). However, their use is associated with high rates of adverse reactions in the geriatric population. As such, the aim of this study was to investigate the impact of lidocaine-based single-shot ultrasound-guided femoral nerve block (USFNB) on the standard care for acute pain management in geriatric patients with traumatic hip fracture in the ED. Methods: This retrospective, single-center, observational study included adult patients aged ≥60 years presenting with acute traumatic hip fracture in the ED between 1 January 2017 and 31 December 2020. The primary outcome measure was the difference in the amount of opioid use, in terms of morphine milligram equivalents (MME), between lidocaine-based single-shot USFNB and standard care groups. The obtained data were evaluated through a time-to-event analysis (time to meaningful pain relief), a time course analysis, and a multivariable analysis. Results: Overall, 607 adult patients (USFNB group, 66; standard care group, 541) were included in the study. The patients in the USFNB group required 80% less MME than those in the standard care group (0.52 ± 1.47 vs. 2.57 ± 2.53, p < 0.001). The multivariable Cox proportional hazards regression models showed that patients who received USFNB achieved meaningful pain relief 2.37-fold faster (hazard ratio (HR) = 2.37, 95% confidence intervals (CI) = 1.73−3.24, p < 0.001). Conclusions: In geriatric patients with hip fractures, a lidocaine-based single-shot USFNB can significantly reduce opioid consumption and provide more rapid and effective pain reduction.

Semantic segmentation guided detector for segmentation, classification, and lesion mapping of acute ischemic stroke in MRI images.

BACKGROUND AND PURPOSE: MRI images timely and accurately reflect ischemic injuries to the brain tissues and, therefore, can support clinical decision-making of acute ischemic stroke (AIS). To maximize the information provided by the MRI images, we leverage deep learning models to segment, classify, and map lesion distributions of AIS. METHODS: We evaluated brain MRI images of AIS patients from 2017 to 2020 at a tertiary teaching hospital and developed the Semantic Segmentation Guided Detector Network (SGD-Net), composed of the first U-shaped model for segmentation in diffusion-weighted imaging (DWI) and the second model for binary classification of lesion size (lacune vs. non-lacune) and circulatory territory of lesion location (anterior vs. posterior circulation). Next, we modified the two-stage deep learning model into SGD-Net Plus by automatically segmenting AIS lesions in DWI images and registering the lesion in T1-weighted images and the brain atlases. RESULTS: The final enrollment (216 patients with 4606 slices) was divided into 80% for model development and 20% for testing. S1 model segmented AIS lesions in DWI images accurately with a pixel accuracy > 99% (Dice 0.806-0.828 and IoU 0.675-707). In comprehensive evaluation of classification performance, the two-stage SGD-Net outperformed the traditional one-stage models in classifying AIS lesion size (accuracy 0.867-0.956 vs. 0.511-0.867, AUROC 0.962-0.992 vs. 0.528-0.937, AUPRC 0.964-0.994 vs. 0.549-0.938) and location (accuracy 0.860-0.930 vs. 0.326-0.721, AUROC 0.936-0.988 vs. 0.493-0.833, AUPRC 0.883-0.978 vs. 0.365-0.695). The precise lesion segmentation at the first stage of the deep learning model was the basis for further application. After that, the modified two-stage model SGD-Net Plus accurately reported the volume, region percentage, and lesion percentage of each region on the selected brain atlas. Its reports provided clear descriptions and quantifications of the AIS-related brain injuries on white matter tracts, Brodmann areas, and cytoarchitectonic areas. CONCLUSION: Domain knowledge-oriented design of artificial intelligence applications can deepen our understanding of patients' conditions and strengthen the use of MRI for patient care. SGD-Net precisely segments AIS lesions on DWI and accurately classifies the lesions. In addition, SGD-Net Plus maps the AIS lesions and quantifies their occupancy in each brain region. They are practical tools to meet the clinical needs and enrich educational resources of neuroimage.

Pharmacokinetics of 38 Percent Silver Diamine Fluoride in Children.

PURPOSE: The purpose of this study was to measure serum levels and characterize the pharmacokinetics of silver and fluoride in healthy children receiving silver diamine fluoride (SDF) treatment for dental caries lesions. METHODS: Children (three to 13 years old with at least one caries lesion) were recruited at the University of California, San Francisco Pediatric Dental Clinic from August 2019 through March 2020. Blood was obtained at one randomly selected timepoint up to 168 hours after SDF application. Serum fluoride and silver were measured, and population pharmacokinetic modeling was used to estimate pharmacokinetic parameters and simulate silver concentration versus time profiles in cohorts of children (15 to 50 kg). RESULTS: Fifty-five children completed the study. Serum fluoride had no discernable temporal pattern. Silver concentra- tions were best described by a one-compartment model with first-order absorption and elimination, and weight as a covariate. Simulated 15 kg children had higher predicted peak silver concentrations than simulated 50 kg children (22.0 ng/mL [95 percent confidence interval {95 percent CI} equals 19.4 to 24.6] versus 12.8 ng/mL [95 percent CI equals 11.3 to 14.3]), and a longer predicted silver half-life (15.5 days [95 percent CI equals 12.5 to 18.5] versus 4.0 days [95 percent CI equals 2.7 to 5.3]). CONCLUSIONS: Evidence presented indicate that topical silver diamine fluoride application in children is safe, and serum concentrations of fluoride and silver pose little risk of toxicity.

Using Machine Learning to Develop and Validate an In-Hospital Mortality Prediction Model for Patients with Suspected Sepsis.

BACKGROUND: Early recognition of sepsis and the prediction of mortality in patients with infection are important. This multi-center, ED-based study aimed to develop and validate a 28-day mortality prediction model for patients with infection using various machine learning (ML) algorithms. METHODS: Patients with acute infection requiring intravenous antibiotic treatment during the first 24 h of admission were prospectively recruited. Patient demographics, comorbidities, clinical signs and symptoms, laboratory test data, selected sepsis-related novel biomarkers, and 28-day mortality were collected and divided into training (70%) and testing (30%) datasets. Logistic regression and seven ML algorithms were used to develop the prediction models. The area under the receiver operating characteristic curve (AUROC) was used to compare different models. RESULTS: A total of 555 patients were recruited with a full panel of biomarker tests. Among them, 18% fulfilled Sepsis-3 criteria, with a 28-day mortality rate of 8%. The wrapper algorithm selected 30 features, including disease severity scores, biochemical parameters, and conventional and few sepsis-related biomarkers. Random forest outperformed other ML models (AUROC: 0.96; 95% confidence interval: 0.93-0.98) and SOFA and early warning scores (AUROC: 0.64-0.84) in the prediction of 28-day mortality in patients with infection. Additionally, random forest remained the best-performing model, with an AUROC of 0.95 (95% CI: 0.91-0.98, p = 0.725) after removing five sepsis-related novel biomarkers. CONCLUSIONS: Our results demonstrated that ML models provide a more accurate prediction of 28-day mortality with an enhanced ability in dealing with multi-dimensional data than the logistic regression model.