Elective musculoskeletal surgery is associated with postoperative weight changes in pediatric and adolescent patients.

Purpose: Patients of elective orthopedic surgeries often reduce activity levels during postoperative recovery. It is unclear whether these extended periods of modified activities lead to weight changes. The purpose of this study was to evaluate changes in body mass index percentile in pediatric patients over 2.5 years following primary musculoskeletal surgeries. Methods: Institutional records for utilized current procedural terminology codes were used to identify patients aged 21 years or younger who underwent elective surgery at a single pediatric orthopedic institution between October 2016 and December 2018. Non-primary surgeries and patients without preoperative body mass index measurements were excluded. Demographic characteristics, height, weight, and body mass index within 30 months of surgery were collected. Body mass index relative to age was calculated. Analysis of body mass index changes at follow-up intervals of 3-7, 9-18, and 24-30 months after surgery was performed for the overall sample, within surgical categories, and within preoperative weight classifications. Results: A total of 1566 patients (53.1% female, average age 12.4 years) were included. Over one-third of patients were overweight or obese at presentation. The average change in body mass index percentile relative to baseline was increased at all follow-up intervals. Values reached significance at 9-18 months (p = .002) and 24-30 months (p = .001). While underweight and normal-weight patients had increased body mass index at all three timepoints, overweight or obese patients decreased. Conclusions: Patients undergoing elective orthopedic procedures may experience significant changes in body mass index percentile postoperatively. At extremes of weight, patients experience improvement toward the mean, but most patients may undergo body mass index increases beyond what would be expected during normal growth. Level of evidence: Retrospective level III.

AI Chatbots in Clinical Laboratory Medicine: Foundations and Trends.

BACKGROUND: Artificial intelligence (AI) conversational agents, or chatbots, are computer programs designed to simulate human conversations using natural language processing. They offer diverse functions and applications across an expanding range of healthcare domains. However, their roles in laboratory medicine remain unclear, as their accuracy, repeatability, and ability to interpret complex laboratory data have yet to be rigorously evaluated. CONTENT: This review provides an overview of the history of chatbots, two major chatbot development approaches, and their respective advantages and limitations. We discuss the capabilities and potential applications of chatbots in healthcare, focusing on the laboratory medicine field. Recent evaluations of chatbot performance are presented, with a special emphasis on large language models such as the Chat Generative Pre-trained Transformer in response to laboratory medicine questions across different categories, such as medical knowledge, laboratory operations, regulations, and interpretation of laboratory results as related to clinical context. We analyze the causes of chatbots' limitations and suggest research directions for developing more accurate, reliable, and manageable chatbots for applications in laboratory medicine. SUMMARY: Chatbots, which are rapidly evolving AI applications, hold tremendous potential to improve medical education, provide timely responses to clinical inquiries concerning laboratory tests, assist in interpreting laboratory results, and facilitate communication among patients, physicians, and laboratorians. Nevertheless, users should be vigilant of existing chatbots' limitations, such as misinformation, inconsistencies, and lack of human-like reasoning abilities. To be effectively used in laboratory medicine, chatbots must undergo extensive training on rigorously validated medical knowledge and be thoroughly evaluated against standard clinical practice.

PERK Inhibition by HC-5404 Sensitizes Renal Cell Carcinoma Tumor Models to Antiangiogenic Tyrosine Kinase Inhibitors.

PURPOSE: Tumors activate protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK, also called EIF2AK3) in response to hypoxia and nutrient deprivation as a stress-mitigation strategy. Here, we tested the hypothesis that inhibiting PERK with HC-5404 enhances the antitumor efficacy of standard-of-care VEGF receptor tyrosine kinase inhibitors (VEGFR-TKI). EXPERIMENTAL DESIGN: HC-5404 was characterized as a potent and selective PERK inhibitor, with favorable in vivo properties. Multiple renal cell carcinoma (RCC) tumor models were then cotreated with both HC-5404 and VEGFR-TKI in vivo, measuring tumor volume across time and evaluating tumor response by protein analysis and IHC. RESULTS: VEGFR-TKI including axitinib, cabozantinib, lenvatinib, and sunitinib induce PERK activation in 786-O RCC xenografts. Cotreatment with HC-5404 inhibited PERK in tumors and significantly increased antitumor effects of VEGFR-TKI across multiple RCC models, resulting in tumor stasis or regression. Analysis of tumor sections revealed that HC-5404 enhanced the antiangiogenic effects of axitinib and lenvatinib by inhibiting both new vasculature and mature tumor blood vessels. Xenografts that progress on axitinib monotherapy remain sensitive to the combination treatment, resulting in ∼20% tumor regression in the combination group. When tested across a panel of 18 RCC patient-derived xenograft (PDX) models, the combination induced greater antitumor effects relative to monotherapies. In this single animal study, nine out of 18 models responded with ≥50% tumor regression from baseline in the combination group. CONCLUSIONS: By disrupting an adaptive stress response evoked by VEGFR-TKI, HC-5404 presents a clinical opportunity to improve the antitumor effects of well-established standard-of-care therapies in RCC.

Sinogram domain angular upsampling of sparse-view micro-CT with dense residual hierarchical transformer and attention-weighted loss.

Reduced angular sampling is a key strategy for increasing scanning efficiency of micron-scale computed tomography (micro-CT). Despite boosting throughput, this strategy introduces noise and extrapolation artifacts due to undersampling. In this work, we present a solution to this issue, by proposing a novel Dense Residual Hierarchical Transformer (DRHT) network to recover high-quality sinograms from 2×, 4× and 8× undersampled scans. DRHT is trained to utilize limited information available from sparsely angular sampled scans and once trained, it can be applied to recover higher-resolution sinograms from shorter scan sessions. Our proposed DRHT model aggregates the benefits of a hierarchical- multi-scale structure along with the combination of local and global feature extraction through dense residual convolutional blocks and non-overlapping window transformer blocks respectively. We also propose a novel noise-aware loss function named KL-L1 to improve sinogram restoration to full resolution. KL-L1, a weighted combination of pixel-level and distribution-level cost functions, leverages inconsistencies in noise distribution and uses learnable spatial weight maps to improve the training of the DRHT model. We present ablation studies and evaluations of our method against other state-of-the-art (SOTA) models over multiple datasets. Our proposed DRHT network achieves an average increase in peak signal to noise ratio (PSNR) of 17.73 dB and a structural similarity index (SSIM) of 0.161, for 8× upsampling, across the three diverse datasets, compared to their respective Bicubic interpolated versions. This novel approach can be utilized to decrease radiation exposure to patients and reduce imaging time for large-scale CT imaging projects.

Quantitative Geometric Modeling of Blood Cells from X-ray Histotomograms of Whole Zebrafish Larvae.

Tissue phenotyping is foundational to understanding and assessing the cellular aspects of disease in organismal context and an important adjunct to molecular studies in the dissection of gene function, chemical effects, and disease. As a first step toward computational tissue phenotyping, we explore the potential of cellular phenotyping from 3-Dimensional (3D), 0.74 µm isotropic voxel resolution, whole zebrafish larval images derived from X-ray histotomography, a form of micro-CT customized for histopathology. As proof of principle towards computational tissue phenotyping of cells, we created a semi-automated mechanism for the segmentation of blood cells in the vascular spaces of zebrafish larvae, followed by modeling and extraction of quantitative geometric parameters. Manually segmented cells were used to train a random forest classifier for blood cells, enabling the use of a generalized cellular segmentation algorithm for the accurate segmentation of blood cells. These models were used to create an automated data segmentation and analysis pipeline to guide the steps in a 3D workflow including blood cell region prediction, cell boundary extraction, and statistical characterization of 3D geometric and cytological features. We were able to distinguish blood cells at two stages in development (4- and 5-days-post-fertilization) and wild-type vs. polA2 huli hutu ( hht ) mutants. The application of geometric modeling across cell types to and across organisms and sample types may comprise a valuable foundation for computational phenotyping that is more open, informative, rapid, objective, and reproducible.

Home visiting impacts during the pandemic: Evidence from a randomized controlled trial of child first.

Existing research has found that home visiting programs for families with young children can improve children's development and strengthen caregiver and family well-being. However, the pandemic created numerous challenges for home visiting programs, forcing them to deliver services online or in a hybrid format to respond to pandemic-related challenges. Questions remain about the impacts of these programs when delivered at-scale via a hybrid model, especially during this uniquely challenging time. The present study reports 12-month impacts from a randomized controlled trial of Child First-an evidence-based home visiting program that provides psychotherapeutic, parent-child intervention (children ages 0-5) embedded in a coordinated system of care-when implemented as a hybrid service. This study estimates impacts within four domains: families' receipt of services, caregiver psychological well-being and parenting, child behavior, and family economic well-being. After randomly assigning families (N = 226) to receive Child First or typical community services, the research team surveyed caregivers (N = 183) about a year after study enrollment. Results from regression models with site fixed effects revealed suggestive evidence that Child First reduced caregivers' job loss, residential mobility, and self-reported substance abuse, and increased receipt of virtual services during the pandemic. There were null impacts on caregivers' psychological well-being, families' involvement with the child welfare system, children's behaviors, and other indicators of economic well-being. Implications for future research and policy are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

BBox-Guided Segmentor: Leveraging expert knowledge for accurate stroke lesion segmentation using weakly supervised bounding box prior.

Stroke is one of the leading causes of death and disability in the world. Despite intensive research on automatic stroke lesion segmentation from non-invasive imaging modalities including diffusion-weighted imaging (DWI), challenges remain such as a lack of sufficient labeled data for training deep learning models and failure in detecting small lesions. In this paper, we propose BBox-Guided Segmentor, a method that significantly improves the accuracy of stroke lesion segmentation by leveraging expert knowledge. Specifically, our model uses a very coarse bounding box label provided by the expert and then performs accurate segmentation automatically. The small overhead of having the expert provide a rough bounding box leads to large performance improvement in segmentation, which is paramount to accurate stroke diagnosis. To train our model, we employ a weakly-supervised approach that uses a large number of weakly-labeled images with only bounding boxes and a small number of fully labeled images. The scarce fully labeled images are used to train a generator segmentation network, while adversarial training is used to leverage the large number of weakly-labeled images to provide additional learning signals. We evaluate our method extensively using a unique clinical dataset of 99 fully labeled cases (i.e., with full segmentation map labels) and 831 weakly labeled cases (i.e., with only bounding box labels), and the results demonstrate the superior performance of our approach over state-of-the-art stroke lesion segmentation models. We also achieve competitive performance as a SOTA fully supervised method using less than one-tenth of the complete labels. Our proposed approach has the potential to improve stroke diagnosis and treatment planning, which may lead to better patient outcomes.

Forecasting User Interests Through Topic Tag Predictions in Online Health Communities.

The increasing reliance on online communities for healthcare information by patients and caregivers has led to the increase in the spread of misinformation, or subjective, anecdotal and inaccurate or non-specific recommendations, which, if acted on, could cause serious harm to the patients. Hence, there is an urgent need to connect users with accurate and tailored health information in a timely manner to prevent such harm. This article proposes an innovative approach to suggesting reliable information to participants in online communities as they move through different stages in their disease or treatment. We hypothesize that patients with similar histories of disease progression or course of treatment would have similar information needs at comparable stages. Specifically, we pose the problem of predicting topic tags or keywords that describe the future information needs of users based on their profiles, traces of their online interactions within the community (past posts, replies) and the profiles and traces of online interactions of other users with similar profiles and similar traces of past interaction with the target users. The result is a variant of the collaborative information filtering or recommendation system tailored to the needs of users of online health communities. We report results of our experiments on two unique datasets from two different social media platforms which demonstrates the superiority of the proposed approach over the state of the art baselines with respect to accurate and timely prediction of topic tags (and hence information sources of interest).

Semi-supervised body parsing and pose estimation for enhancing infant general movement assessment.

General movement assessment (GMA) of infant movement videos (IMVs) is an effective method for early detection of cerebral palsy (CP) in infants. We demonstrate in this paper that end-to-end trainable neural networks for image sequence recognition can be applied to achieve good results in GMA, and more importantly, augmenting raw video with infant body parsing and pose estimation information can significantly improve performance. To solve the problem of efficiently utilizing partially labeled IMVs for body parsing, we propose a semi-supervised model, termed SiamParseNet (SPN), which consists of two branches, one for intra-frame body parts segmentation and another for inter-frame label propagation. During training, the two branches are jointly trained by alternating between using input pairs of only labeled frames and input of both labeled and unlabeled frames. We also investigate training data augmentation by proposing a factorized video generative adversarial network (FVGAN) to synthesize novel labeled frames for training. FVGAN decouples foreground and background generation which allows for generating multiple labeled frames from one real labeled frame. When testing, we employ a multi-source inference mechanism, where the final result for a test frame is either obtained via the segmentation branch or via propagation from a nearby key frame. We conduct extensive experiments for body parsing using SPN on two infant movement video datasets; on these partially labeled IMVs, we show that SPN coupled with FVGAN achieves state-of-the-art performance. We further demonstrate that our proposed SPN can be easily adapted to the infant pose estimation task with superior performance. Last but not least, we explore the clinical application of our method for GMA. We collected a new clinical IMV dataset with GMA annotations, and our experiments show that our SPN models for body parsing and pose estimation trained on the first two datasets generalize well to the new clinical dataset and their results can significantly boost the convolutional recurrent neural network (CRNN) based GMA prediction performance when combined with raw video inputs.

Accurate virus identification with interpretable Raman signatures by machine learning.

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device, coupled with label-free Raman spectroscopy, holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning (ML) approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such an ML approach for analyzing Raman spectra of human and avian viruses. A convolutional neural network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A versus type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus [IBV]) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups­for example, amide, amino acid, and carboxylic acid­we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids, and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses.

Rapid Biomarker Screening of Alzheimer's Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy.

The study of Alzheimer's disease (AD), the most common cause of dementia, faces challenges in terms of understanding the cause, monitoring the pathogenesis, and developing early diagnoses and effective treatments. Rapid and accurate identification of AD biomarkers in the brain is critical to providing key insights into AD and facilitating the development of early diagnosis methods. In this work, we developed a platform that enables a rapid screening of AD biomarkers by employing graphene-assisted Raman spectroscopy and machine learning interpretation in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, the accuracy was increased from 77% to 98% in machine learning classification. Further, using a linear support vector machine (SVM), we identified a spectral feature importance map that reveals the importance of each Raman wavenumber in classifying AD and non-AD spectra. Based on this spectral feature importance map, we identified AD biomarkers including Aß and tau proteins and other potential biomarkers, such as triolein, phosphatidylcholine, and actin, which have been confirmed by other biochemical studies. Our Raman-machine learning integrated method with interpretability will facilitate the study of AD and can be extended to other tissues and biofluids and for various other diseases.

Understanding the Excitation Wavelength Dependence and Thermal Stability of the SARS-CoV-2 Receptor-Binding Domain Using Surface-Enhanced Raman Scattering and Machine Learning.

COVID-19 has cost millions of lives worldwide. The constant mutation of SARS-CoV-2 calls for thorough research to facilitate the development of variant surveillance. In this work, we studied the fundamental properties related to the optical identification of the receptor-binding domain (RBD) of SARS-CoV-2 spike protein, a key component of viral infection. The Raman modes of the SARS-CoV-2 RBD were captured by surface-enhanced Raman spectroscopy (SERS) using gold nanoparticles (AuNPs). The observed Raman enhancement strongly depends on the excitation wavelength as a result of the aggregation of AuNPs. The characteristic Raman spectra of RBDs from SARS-CoV-2 and MERS-CoV were analyzed by principal component analysis that reveals the role of secondary structures in the SERS process, which is corroborated with the thermal stability under laser heating. We can easily distinguish the Raman spectra of two RBDs using machine learning algorithms with accuracy, precision, recall, and F1 scores all over 95%. Our work provides an in-depth understanding of the SARS-CoV-2 RBD and paves the way toward rapid analysis and discrimination of complex proteins of infectious viruses and other biomolecules.

Comparison of Weightbearing and Nonweightbearing Juvenile Osteochondritis Dissecans Lesions of the Lateral Femoral Condyle.

BACKGROUND: Repetitive microtrauma may contribute to osteochondritis dissecans (OCD) lesions of the femoral condyle. The effect of differential loading between OCD weightbearing (WB) zones has not been studied. PURPOSE: To determine whether clinical and radiographic variables differ by WB zone in lateral femoral condyle OCD lesions. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: We retrospectively reviewed a consecutive series of patients aged <18 years with lateral femoral condyle OCD lesions presenting at a single institution between 2004 and 2018. Patients with OCD lesions outside of the lateral femoral condyle were excluded. Lesions were localized on radiographs using the Cahill and Berg classification, referencing the Blumensaat line and an extension of the posterior femoral cortex. Progeny bone characteristics evaluated at baseline and 24-month follow-up included ossification, distinct borders from parent bone, and displacement. Baseline lesion dimensions were measured on magnetic resonance imaging (MRI) scans. We evaluated posttreatment pain level, return-to-activity rate, and patient-reported outcome measures (PROMs) including the Pediatric International Knee Documentation Committee score, Knee injury and Osteoarthritis Outcome Score, and Pediatric Functional Activity Brief Scale. RESULTS: A total of 62 lateral femoral condyle OCD lesions (mean follow-up, 24.1 months) presented within the study period: 26 WB lesions and 36 nonweightbearing (NWB) lesions. At presentation, no differences between the lesion types were observed in symptom chronicity or symptomatology. NWB lesions were deeper on MRI scans (sagittal depth, 7.11 vs 5.96 mm; P = .046; coronal depth ratio, 0.05 vs 0.01 mm; P = .003), were more likely to develop progeny bone (69.4% vs 44%; P = .047), and demonstrated higher radiographic healing rates (52.8% vs 24%; P = .025) compared with WB lesions. PROMs at follow-up were available for 25 of 62 patients (40.3%), with no statistically significant differences between cohorts at any time. Return to full activity was observed in 72% of WB and 82.1% of NWB lesions (P = .378). CONCLUSION: Lateral femoral condyle OCD lesions of the knee in WB and NWB zones presented similarly at initial evaluation; however, NWB lesions demonstrated higher rates of progeny bone formation and radiographic healing at mean 2-year follow-up.

Hepatotoxicity and Liver-Related Mortality in Women of Childbearing Potential Living With Human Immunodeficiency Virus and High CD4 Cell Counts Initiating Efavirenz-Containing Regimens.

BACKGROUND: Severe hepatotoxicity in people with human immunodeficiency virus (HIV) receiving efavirenz (EFV) has been reported. We assessed the incidence and risk factors of hepatotoxicity in women of childbearing age initiating EFV-containing regimens. METHODS: In the Promoting Maternal and Infant Survival Everywhere (PROMISE) trial, ART-naive pregnant women with HIV and CD4 count ≥ 350 cells/µL and alanine aminotransferase ≤ 2.5 the upper limit of normal were randomized during the antepartum and postpartum periods to antiretroviral therapy (ART) strategies to assess HIV vertical transmission, safety, and maternal disease progression. Hepatotoxicity was defined per the Division of AIDS Toxicity Tables. Cox proportional hazards models were constructed with covariates including participant characteristics, ART regimens, and timing of EFV initiation. RESULTS: Among 3576 women, 2435 (68%) initiated EFV at a median 121.1 weeks post delivery. After EFV initiation, 2.5% (61/2435) had severe (grade 3 or higher) hepatotoxicity with an incidence of 2.3 (95% confidence interval [CI], 2.0-2.6) per 100 person-years. Events occurred between 1 and 132 weeks postpartum. Of those with severe hepatotoxicity, 8.2% (5/61) were symptomatic, and 3.3% (2/61) of those with severe hepatotoxicity died from EFV-related hepatotoxicity, 1 of whom was symptomatic. The incidence of liver-related mortality was 0.07 (95% CI, .06-.08) per 100 person-years. In multivariable analysis, older age was associated with severe hepatotoxicity (adjusted hazard ratio per 5 years, 1.35 [95% CI, 1.06-1.70]). CONCLUSIONS: Severe hepatotoxicity after EFV initiation occurred in 2.5% of women and liver-related mortality occurred in 3% of those with severe hepatotoxicity. The occurrence of fatal events underscores the need for safer treatments for women of childbearing age.

Tenofovir Diphosphate Concentrations in Dried Blood Spots From Pregnant and Postpartum Adolescent and Young Women Receiving Daily Observed Pre-exposure Prophylaxis in Sub-Saharan Africa.

BACKGROUND: Intracellular tenofovir diphosphate (TFV-DP) concentration in dried blood spots (DBSs) is used to monitor cumulative pre-exposure prophylaxis (PrEP) adherence. We evaluated TFV-DP in DBSs following daily oral PrEP (emtricitabine 200 mg/tenofovir diphosphate 300 mg) among pregnant and postpartum adolescent girls and young women (AGYW). METHODS: Directly observed PrEP was administered for 12 weeks in a pregnancy (14-24 weeks' gestation, n = 20) and postpartum (6-12 weeks postpartum, n = 20) group of AGYW aged 16-24 years in sub-Saharan Africa. Weekly DBS TFV-DP was measured by validated liquid chromatography-tandem mass spectrometry assay. Week 12 TFV-DP distributions were compared between groups with Wilcoxon test. Population pharmacokinetic models were fit to estimate steady-state concentrations and create benchmarks for adherence categories. Baseline correlates of TFV-DP were evaluated. RESULTS: Median age was 20 (IQR, 19-22) years. Of 3360 doses, 3352 (>99%) were directly observed. TFV-DP median (IQR) half-life was 10 (7-12) days in pregnancy and 17 (14-21) days postpartum, with steady state achieved by 5 and 8 weeks, respectively. Observed median (IQR) steady-state TFV-DP was 965 fmol/punch (691-1166) in pregnancy versus 1406 fmol/punch (1053-1859) postpartum (P = .006). Modeled median steady-state TFV-DP was 881 fmol/punch (667-1105) in pregnancy versus 1438 fmol/punch (1178-1919) postpartum. In pooled analysis, baseline creatinine clearance was associated with observed TFV-DP concentrations. CONCLUSIONS: TFV-DP in African AGYW was approximately one-third lower in pregnancy than postpartum. These Population-specific benchmarks can be used to guide PrEP adherence support in pregnant/postpartum African women. CLINICAL TRIALS REGISTRATION: NCT03386578.

Spatial mapping of the collagen distribution in human and mouse tissues by force volume atomic force microscopy.

Changes in the elastic properties of living tissues during normal development and in pathological processes are often due to modifications of the collagen component of the extracellular matrix at various length scales. Force volume AFM can precisely capture the mechanical properties of biological samples with force sensitivity and spatial resolution. The integration of AFM data with data of the molecular composition contributes to understanding the interplay between tissue biochemistry, organization and function. The detection of micrometer-size, heterogeneous domains at different elastic moduli in tissue sections by AFM has remained elusive so far, due to the lack of correlations with histological, optical and biochemical assessments. In this work, force volume AFM is used to identify collagen-enriched domains, naturally present in human and mouse tissues, by their elastic modulus. Collagen identification is obtained in a robust way and affordable timescales, through an optimal design of the sample preparation method and AFM parameters for faster scan with micrometer resolution. The choice of a separate reference sample stained for collagen allows correlating elastic modulus with collagen amount and position with high statistical significance. The proposed preparation method ensures safe handling of the tissue sections guarantees the preservation of their micromechanical characteristics over time and makes it much easier to perform correlation experiments with different biomarkers independently.

Risk Factors for Adverse Radiographic Outcomes After Elastic Stable Intramedullary Nailing of Unstable Diaphyseal Tibia Fractures in Children.

OBJECTIVES: Elastic stable intramedullary nails (ESIN) are commonly utilized to treat unstable pediatric tibia fractures but have been associated with complications. The purpose of this study was to identify risk factors for adverse radiographic outcomes after ESIN of pediatric tibia fractures. METHODS: A retrospective review of all patients who underwent diaphyseal tibia fracture stabilization with ESIN between 2010 and 2018 at 3 pediatric level 1 trauma centers was performed. Inclusion criteria were open growth plates, no intra-articular or physeal fracture involvement, and radiographic follow-up until union. Patient demographics, injury mechanism, fracture characteristics, and implant fill relative to the medullary canal were recorded. Radiographic outcome measures included achievement of and time to union, residual angular deformity, and additional procedures. RESULTS: One hundred seventy-two patients met inclusion criteria and were followed for a mean of 1.2 years. Nonunions were observed in 3% of the patient cohort. Another 10% required >6 months to heal, but did not require further surgical intervention. Angular deformities were common with 57% having a residual deformity ≥5 degrees and 14% having a residual deformity ≥10 degrees. Of the patients with a residual deformity between 5 and 10 degrees, 3% were symptomatic, where as 26% of the patients with a residual deformity ≥10 degrees were symptomatic. Greater angular deformities were associated with open fractures, compartment syndrome, and longer time to union. Patient age, weight, tibial comminution, and canal fill were not associated with nonunions or malunions. CONCLUSIONS: ESIN of pediatric tibia fractures results in reliable healing for a majority of patients, but poses risks for residual angular deformities and delayed healing. Open fractures and compartment syndrome were associated with adverse radiographic outcomes.

Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics.

Understanding the breakdown mechanisms of polymer-based dielectrics is critical to achieving high-density energy storage. Here a comprehensive phase-field model is developed to investigate the electric, thermal, and mechanical effects in the breakdown process of polymer-based dielectrics. High-throughput simulations are performed for the P(VDF-HFP)-based nanocomposites filled with nanoparticles of different properties. Machine learning is conducted on the database from the high-throughput simulations to produce an analytical expression for the breakdown strength, which is verified by targeted experimental measurements and can be used to semiquantitatively predict the breakdown strength of the P(VDF-HFP)-based nanocomposites. The present work provides fundamental insights to the breakdown mechanisms of polymer nanocomposite dielectrics and establishes a powerful theoretical framework of materials design for optimizing their breakdown strength and thus maximizing their energy storage by screening suitable nanofillers. It can potentially be extended to optimize the performances of other types of materials such as thermoelectrics and solid electrolytes.

Conversion from Vancomycin Trough Concentration-Guided Dosing to Area Under the Curve-Guided Dosing Using Two Sample Measurements in Adults: Implementation at an Academic Medical Center.

STUDY OBJECTIVE: The optimal pharmacodynamic parameter for the prediction of efficacy of vancomycin is the area under the concentration-time curve (AUC), and current published data indicate that dosing based on vancomycin trough concentrations is an inaccurate substitute. In this study, our objective was to compare the achievement of therapeutic target attainment after switching from a trough-based to an AUC-based dosing strategy as a part of our institution's vancomycin-per-pharmacy protocol. DESIGN: Prospective observational quality assurance study. SETTING: Academic medical center. PATIENTS: A total of 296 hospitalized adults who received vancomycin and monitoring under our institution's vancomycin-per-pharmacy protocol were included in the analysis. The preimplementation retrospective comparison group consisted of 179 patients in whom vancomycin was initiated using a trough-based dosing strategy between November 22, 2017, and January 22, 2018. The postimplementation group included 117 patients in whom vancomycin was initiated using an AUC-based dosing strategy using two-point sampling between June 19, 2018, and July 19, 2018, after hospital-wide implementation of this protocol on June 19, 2018. MEASUREMENTS AND MAIN RESULTS: AUC values were calculated from two vancomycin concentrations (peak and trough). The primary outcome was achievement of therapeutic AUC values (400-800 mg·hr/L) in the postimplementation group or therapeutic trough level values (10-20 mg/L) in the preimplementation group. Only 98 (55%) of 179 initial trough values were therapeutic in the preimplementation group (trough-only dosing method) versus 86 (73.5%) of 117 initial AUC values in the postimplementation group (AUC-based dosing method) (p=0.0014). A lower proportion of supratherapeutic AUC values was observed in the postimplementation group compared with supratherapeutic trough concentrations in the preimplementation group (1.7% vs 18%, p<0.0001). Overall, 62% of patients with initially therapeutic AUC values had subsequent trough value increases of 25% or greater, occurring at a median of 6 days of vancomycin therapy. Nephrotoxicity occurred in 11% of patients in the preimplementation versus 9.4% in the postimplementation group (p=0.70). CONCLUSION: Compared with a trough concentration-based dosing strategy, AUC-based dosing using two-point sampling improved therapeutic target attainment. Implementation is feasible at any hospital that performs vancomycin peak concentration testing and is a workable alternative to using Bayesian software for estimating AUC. This approach should also be directly compared with AUC-based dosing using Bayesian software.

Social inclusion among mental health service users: A reliability test of the Mini-SCOPE Scale.

AIM AND OBJECTIVES: To evaluate the reliability of the Mini-SCOPE scale through interitem consistency and test-retest consistency, as well as the initial correlation with outcome measures of recovery. BACKGROUND: Adapted from a UK, Social and Communities Opportunities Profile (Mini-SCOPE) is a short version of an social inclusion measurement for English-speaking persons in recovery (PIR) in Singapore. Prior concept mapping sets the stage for this reliability study. DESIGN: This study adopted a nonexperimental, pre- and postdesign to validate the psychosocial measurement tool for community services. METHOD: Convenient sampling was conducted at the various designated clinics. A total of 170 voluntary participants from psychiatric outpatient clinics were recruited for this study. It evaluated the 4-week interval test-retest reliability of the Mini-SCOPE. "AGREE" equator checklist was completed to guide the reporting of clinical practice. "See Supporting Information File S1." RESULT: Outcome demonstrated that the Mini-SCOPE scale has good strength of reliability. CONCLUSION: This study showed that the Mini-SCOPE measurement has the potential to be used for programme evaluation in mental health settings. RELEVANCE TO CLINICAL PRACTICE: Applicable to nurses and other mental health professionals to consider the social and wellness aspects of the patients in their care when planning appropriate services.