Atomic Diffusivities of Yttrium, Titanium and Oxygen Calculated by Ab Initio Molecular Dynamics in Molten 316L Oxide-Dispersion-Strengthened Steel Fabricated via Additive Manufacturing.

Oxide-dispersion-strengthened (ODS) steels have long been viewed as a prime solution for harsh environments. However, conventional manufacturing of ODS steels limits the final product geometry, is difficult to scale up to large components, and is expensive due to multiple highly involved, solid-state processing steps required. Additive manufacturing (AM) can directly incorporate dispersion elements (e.g., Y, Ti and O) during component fabrication, thus bypassing the need for an ODS steel supply chain, the scale-up challenges of powder processing routes, the buoyancy challenges associated with casting ODS steels, and the joining issues for net-shape component fabrication. In the AM process, the diffusion of the dispersion elements in the molten steel plays a key role in the precipitation of the oxide particles, thereby influencing the microstructure, thermal stability and high-temperature mechanical properties of the resulting ODS steels. In this work, the atomic diffusivities of Y, Ti, and O in molten 316L stainless steel (SS) as functions of temperature are determined by ab initio molecular dynamics simulations. The latest Vienna Ab initio Simulation Package (VASP) package that incorporates an on-the-fly machine learning force field for accelerated computation is used. At a constant temperature, the time-dependent coordinates of the target atoms in the molten 316L SS were analyzed in the form of mean square displacement in order to obtain diffusivity. The values of the diffusivity at multiple temperatures are then fitted to the Arrhenius form to determine the activation energy and the pre-exponential factor. Given the challenges in experimental measurement of atomic diffusivity at such high temperatures and correspondingly the lack of experimental data, this study provides important physical parameters for future modeling of the oxide precipitation kinetics during AM process.

Engineered extracellular vesicles carrying let-7a-5p for alleviating inflammation in acute lung injury.

BACKGROUND: Acute lung injury (ALI) is a life-threatening respiratory condition characterized by severe inflammation and lung tissue damage, frequently causing rapid respiratory failure and long-term complications. The microRNA let-7a-5p is involved in the progression of lung injury, inflammation, and fibrosis by regulating immune cell activation and cytokine production. This study aims to use an innovative cellular electroporation platform to generate extracellular vesicles (EVs) carring let-7a-5p (EV-let-7a-5p) derived from transfected Wharton's jelly-mesenchymal stem cells (WJ-MSCs) as a potential gene therapy for ALI. METHODS: A cellular nanoporation (CNP) method was used to induce the production and release of EV-let-7a-5p from WJ-MSCs transfected with the relevant plasmid DNA. EV-let-7a-5p in the conditioned medium were isolated using a tangential flow filtration (TFF) system. EV characterization followed the minimal consensus guidelines outlined by the International Society for Extracellular Vesicles. We conducted a thorough set of therapeutic assessments, including the antifibrotic effects using a transforming growth factor beta (TGF-ß)-induced cell model, the modulation effects on macrophage polarization, and the influence of EV-let-7a-5p in a rat model of hyperoxia-induced ALI. RESULTS: The CNP platform significantly increased EV secretion from transfected WJ-MSCs, and the encapsulated let-7a-5p in engineered EVs was markedly higher than that in untreated WJ-MSCs. These EV-let-7a-5p did not influence cell proliferation and effectively mitigated the TGF-ß-induced fibrotic phenotype by downregulating SMAD2/3 phosphorylation in LL29 cells. Furthermore, EV-let-7a-5p regulated M2-like macrophage activation in an inflammatory microenvironment and significantly induced interleukin (IL)-10 secretion, demonstrating their modulatory effect on inflammation. Administering EVs from untreated WJ-MSCs slightly improved lung function and increased let-7a-5p expression in plasma in the hyperoxia-induced ALI rat model. In comparison, EV-let-7a-5p significantly reduced macrophage infiltration and collagen deposition while increasing IL-10 expression, causing a substantial improvement in lung function. CONCLUSION: This study reveals that the use of the CNP platform to stimulate and transfect WJ-MSCs could generate an abundance of let-7a-5p-enriched EVs, which underscores the therapeutic potential in countering inflammatory responses, fibrotic activation, and hyperoxia-induced lung injury. These results provide potential avenues for developing innovative therapeutic approaches for more effective interventions in ALI.

High-yield BMP2 expression in rice cells via CRISPR and endogenous αAmy3 promoter.

Plant cells serve as versatile platforms for the production of high-value recombinant proteins. This study explored the efficacy of utilizing an endogenous αAmy3 promoter for the expression of a bioactive pharmaceutical protein, specifically the mature region of human bone morphogenetic protein 2 (hBMP2m). Utilizing a refined CRISPR/Cas9-mediated intron-targeting insertion technique, which incorporates an artificial 3' splicing site upstream of the target gene, we achieved a transformation efficiency of 13.5% in rice calli that carried the rice-codon optimized mature region of hBMP2 cDNA (rhBMP2m) in the αAmy3 intron 1. Both homozygous and heterozygous rhBMP2m knock-in rice suspension cell lines were generated. These lines demonstrated the endogenous αAmy3 promoter regulated rhBMP2m mRNA and rhBMP2m recombinant protein expression, with strongly upregulation in respond to sugar depletion. The homozygous rhBMP2m knock-in cell line yielded an impressive 21.5 µg/mL of rhBMP2m recombinant protein, accounting for 1.03% of the total soluble protein. The high-yield expression was stably maintained across two generations, indicating the genetic stability of rhBMP2m gene knock-in at the αAmy3 intron 1 locus. Additionally, the rice cell-derived rhBMP2m proteins were found to be glycosylated, capable of dimer formation, and bioactive. Our results indicate that the endogenous rice αAmy3 promoter-signal peptide-based expression system is an effective strategy for producing bioactive pharmaceutical proteins. KEY POINTS: • The endogenous αAmy3 promoter-based expression system enhanced the yield of BMP2 • The increased yield of BMP2 accounted for 1.03% of the total rice-soluble proteins • The rice-produced BMP2 showed glycosylation modifications, dimer formation, and bioactivity.

Biobased polyester versus synthetic fiberglass casts for treating stable upper limb fractures in children: a randomized controlled trial.

BACKGROUND: Stable upper limb fractures, such as radius, ulna, or distal humerus fractures, are common pediatric orthopedic traumas that are traditionally managed with cast immobilization. The commonly used synthetic fiberglass cast is light and water resistant but may promote skin itchiness during casting, which is a common complaint of patients. In addition, these diisocyanate-based casts have been proven to be toxic and may cause asthma. Herein, we introduce a novel biobased polyester cast to compare its clinical outcomes and patient satisfaction with conventional synthetic fiberglass casts. METHODS: From Feb 2022 to Nov 2022, we undertook a single-center prospective randomized trial involving 100 children with cast-immobilized stable upper limb fractures. These patients were randomized into either biobased polyester or synthetic fiberglass groups. All patients were regularly followed up till the cast removal which occurred approximately 3-4 weeks after immobilizing. Objective clinical findings and subjective patient questionnaire were all collected and analyzed. RESULTS: According to the radiographs taken on the day of cast removal, there was no loss of reduction in both groups. The incidence of skin problems was 3.4 times higher in the synthetic fiberglass group than in the biobased polyester group. For the subjective questionnaire, the biobased polyester cast was preferred in every sub-item. CONCLUSIONS: Our study strongly suggested that the novel biobased polyester cast provides matching stability to conventional fiberglass casts and improves patient satisfaction in an eco-friendlier and safer way. TRIAL REGISTRATION: ClinicalTrials.gov Protocol Registration and Results System ( https://www. CLINICALTRIALS: gov/ ; ID: NCT06102603; Date: 26/10/2023).

Dual task measures in older adults with and without cognitive impairment: response to simultaneous cognitive-exercise training and minimal clinically important difference estimates.

BACKGROUND: Responsiveness and minimal clinically important difference (MCID) are critical indices to understand whether observed improvement represents a meaningful improvement after intervention. Although simultaneous cognitive-exercise training (SCET; e.g., performing memory tasks while cycling) has been suggested to enhance the cognitive function of older adults, responsiveness and MCID have not been established. Hence, we aimed to estimate responsiveness and MCIDs of two dual task performance involving cognition and hand function in older adults with and without cognitive impairment and to compare the differences in responsiveness and MCIDs of the two dual task performance between older adults with and without cognitive impairment. METHODS: A total of 106 older adults completed the Montreal Cognitive Assessment and two dual tasks before and after SCET. One dual task was a combination of Serial Sevens Test and Box and Block Test (BBT), and the other included frequency discrimination and BBT. We used effect size and standardized response mean to indicate responsiveness and used anchor- and distribution-based approaches to estimating MCID ranges. When conducting data analysis, all participants were classified into two cognitive groups, cognitively healthy (Montreal Cognitive Assessment ≥ 26) and cognitively impaired (Montreal Cognitive Assessment < 26) groups, based on the scores of the Montreal Cognitive Assessment before SCET. RESULTS: In the cognitively healthy group, Serial Seven Test performance when tasked with BBT and BBT performance when tasked with Serial Seven Test were responsive to SCET (effect size = 0.18-0.29; standardized response mean = 0.25-0.37). MCIDs of Serial Seven Test performance when tasked with BBT ranged 2.09-2.36, and MCIDs of BBT performance when tasked with Serial Seven Test ranged 3.77-5.85. In the cognitively impaired group, only frequency discrimination performance when tasked with BBT was responsive to SCET (effect size = 0.37; standardized response mean = 0.47). MCIDs of frequency discrimination performance when tasked with BBT ranged 1.47-2.18, and MCIDs of BBT performance when tasked with frequency discrimination ranged 1.13-7.62. CONCLUSIONS: Current findings suggest that a change in Serial Seven Test performance when tasked with BBT between 2.09 and 2.36 corrected number (correct responses - incorrect responses) should be considered a meaningful change for older adults who are cognitively healthy, and a change in frequency discrimination performance when tasked with BBT between 1.47 and 2.18 corrected number (correct responses - incorrect responses) should be considered a meaningful change for older adults who are cognitively impaired. Clinical practitioners may use these established MCIDs of dual tasks involving cognition and hand function to interpret changes following SCET for older adults with and without cognitive impairment. TRIAL REGISTRATION: NCT04689776, 30/12/2020.

Effects of computer-based cognitive training combined with physical training for older adults with cognitive impairment: A four-arm randomized controlled trial.

Objective: Combined physical (PHY) and cognitive (COG) training in sequential (SEQ) and simultaneous (SIMUL) sessions may delay the progression of cognitive impairment. To date, no study has directly compared in older adults with cognitive impairment the effects of COG training, PHY training, SEQ motor-cognitive training and SIMUL motor-cognitve training on specific indices of cognitive performance and activities of daily living (ADL). The purpose of this study was to determine whether SEQ and SIMUL motor-cognitive training can improve treatment outcomes compared with PHY or COG training alone. We also aimed to compare the effects of SEQ versus SIMUL motor-cognitive training on cognitive functions and instrumental ADL (IADL) in older adults with cognitive impairment. Methods: A cluster randomized controlled trial was conducted. Eighty older adults with cognitive impairment were randomly assigned to COG, PHY, SEQ or SIMUL training groups. The intervention consisted of 90-min training sessions, totaling 36 sessions. Outcome measures were the Montreal Cognitive Assessment, three subtests of the Wechsler Memory Scale (WMS) and the Lawton IADL scale. Results: Significant interaction effects between group and time were found in WMS-spatial span (p = 0.04) and WMS-word lists (p = 0.041). For WMS-spatial span, the SIMUL group showed outperformed the COG (p = 0.039), PHY (p = 0.010) and SEQ groups (p = 0.017). For WMS-word lists, the SEQ group improve more than COG (p = 0.013), PHY (p = 0.030) and SIMUL (p = 0.019) groups. No significant differences were found in IADL performance among four groups (p = 0.645). Conclusions: Our study showed SEQ and SIMUL motor-cognitive training led to more pronounced improvements in visuospatial working memory or verbal memory compared with isolated COG or PHY training for community-based older adults with cognitive impairment. For enhancing effects on IADL, we suggest the use of sensitive measurement tools and context-enriched cognitive training involving real-life task demands.

Distinct subsets of synovial fibroblasts control cartilage destruction in joint diseases.

Inflammation-induced bone destruction is the main cause of progressive joint damage in rheumatoid arthritis (RA) and osteoarthritis (OA). In addition, depending on the tissue microenvironment stimulators, the synovium transforms into a hyperplastic invasive tissue. The synovium includes two specific subsets of fibroblasts surrounding the joints: lining and sublining synovial fibroblasts (SFs). These SFs grow and interact with immune cells invading the bone and cartilage; specifically, SFs, which are the major mesenchymal cells in the joints, develop an aggressive phenotype, thereby producing cytokines and proteases involved in arthritis pathogeneses. Transcriptomic differences in the heterogeneity of SFs reflect the joint-specific origins of the SFs interacting with immune cells. To understand the subsets of SFs that lead to joint damage in arthritis, clarifying the distinct phenotypes and properties of SFs and understanding how they influence bone cells, such as osteoclasts and chondrocytes, is crucial. This review provides an overview of the advancements in the understanding of SF subsets and features, which may aid in identifying newer therapeutic targets.

A Review on Progress, Challenges, and Prospects of Material Jetting of Copper and Tungsten.

Copper (Cu) and tungsten (W) possess exceptional electrical and thermal conductivity properties, making them suitable candidates for applications such as interconnects and thermal conductivity enhancements. Solution-based additive manufacturing (SBAM) offers unique advantages, including patterning capabilities, cost-effectiveness, and scalability among the various methods for manufacturing Cu and W-based films and structures. In particular, SBAM material jetting techniques, such as inkjet printing (IJP), direct ink writing (DIW), and aerosol jet printing (AJP), present a promising approach for design freedom, low material wastes, and versatility as either stand-alone printers or integrated with powder bed-based metal additive manufacturing (MAM). Thus, this review summarizes recent advancements in solution-processed Cu and W, focusing on IJP, DIW, and AJP techniques. The discussion encompasses general aspects, current status, challenges, and recent research highlights. Furthermore, this paper addresses integrating material jetting techniques with powder bed-based MAM to fabricate functional alloys and multi-material structures. Finally, the factors influencing large-scale fabrication and potential prospects in this area are explored.

What quantifies good primary care in the United States? A review of algorithms and metrics using real-world data.

Primary care physicians (PCPs) play an indispensable role in providing comprehensive care and referring patients for specialty care and other medical services. As the COVID-19 outbreak disrupts patient access to care, understanding the quality of primary care is critical at this unprecedented moment to support patients with complex medical needs in the primary care setting and inform policymakers to redesign our primary care system. The traditional way of collecting information from patient surveys is time-consuming and costly, and novel data collection and analysis methods are needed. In this review paper, we describe the existing algorithms and metrics that use the real-world data to qualify and quantify primary care, including the identification of an individual's likely PCP (identification of plurality provider and major provider), assessment of process quality (for example, appropriate-care-model composite measures), and continuity and regularity of care index (including the interval index, variance index and relative variance index), and highlight the strength and limitation of real world data from electronic health records (EHRs) and claims data in determining the quality of PCP care. The EHR audits facilitate assessing the quality of the workflow process and clinical appropriateness of primary care practices. With extensive and diverse records, administrative claims data can provide reliable information as it assesses primary care quality through coded information from different providers or networks. The use of EHRs and administrative claims data may be a cost-effective analytic strategy for evaluating the quality of primary care.

Unusual capitate fracture with dorsal shearing pattern and concomitant carpometacarpal dislocation with a 6-year follow-up: A case report.

BACKGROUND: Isolated capitate fractures are rare carpal fractures. Following high-energy injuries, capitate fractures are usually associated with other carpal fractures or ligament injuries. The management of capitate fractures depends on the fracture pattern. Here, we report an unusual capitate fracture with a dorsal shearing pattern and concomitant carpometacarpal dislocation, with a 6-year follow-up. To the best of our knowledge, this fracture pattern and surgical management have not been previously reported. CASE SUMMARY: A 28-year-old man presented with left-hand volar tenderness and decreased grip strength that persisted for one month after a traffic accident. Radiography showed a distal capitate fracture with carpometacarpal joint incongruence. Computed tomography (CT) revealed a distal capitate fracture with carpometacarpal joint dislocation. The distal fragment was rotated by 90° in the sagittal plane, and an oblique shearing fracture pattern was noted. Open reduction and internal fixation (ORIF) with a locking plate were performed using the dorsal approach. The imaging studies performed 3 mo and 6 years following surgery revealed a healed fracture, and the Disabilities of the Arm, Shoulder, and Hand and visual analog scale scores were significantly improved. CONCLUSION: CT can detect capitate fractures with dorsal shearing pattern and concomitant carpometacarpal dislocation. ORIF using a locking plate are possible.

Repurposing Astragalus Polysaccharide PG2 for Inhibiting ACE2 and SARS-CoV-2 Spike Syncytial Formation and Anti-Inflammatory Effects.

The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a serious threat to global public health. In an effort to develop novel anti-coronavirus therapeutics and achieve prophylactics, we used gene set enrichment analysis (GSEA) for drug screening and identified that Astragalus polysaccharide (PG2), a mixture of polysaccharides purified from Astragalus membranaceus, could effectively reverse COVID-19 signature genes. Further biological assays revealed that PG2 could prevent the fusion of BHK21-expressing wild-type (WT) viral spike (S) protein and Calu-3-expressing ACE2. Additionally, it specifically prevents the binding of recombinant viral S of WT, alpha, and beta strains to ACE2 receptor in our non-cell-based system. In addition, PG2 enhances let-7a, miR-146a, and miR-148b expression levels in the lung epithelial cells. These findings speculate that PG2 has the potential to reduce viral replication in lung and cytokine storm via these PG2-induced miRNAs. Furthermore, macrophage activation is one of the primary issues leading to the complicated condition of COVID-19 patients, and our results revealed that PG2 could regulate the activation of macrophages by promoting the polarization of THP-1-derived macrophages into an anti-inflammatory phenotype. In this study, PG2 stimulated M2 macrophage activation and increased the expression levels of anti-inflammatory cytokines IL-10 and IL-1RN. Additionally, PG2 was recently used to treat patients with severe COVID-19 symptoms by reducing the neutrophil-to-lymphocyte ratio (NLR). Therefore, our data suggest that PG2, a repurposed drug, possesses the potential to prevent WT SARS-CoV-2 S-mediated syncytia formation with the host cells; it also inhibits the binding of S proteins of WT, alpha, and beta strains to the recombinant ACE2 and halts severe COVID-19 development by regulating the polarization of macrophages to M2 cells.

Psychometric Evaluation of the Perceived Research Burden Assessment (PeRBA) in Longitudinal Studies of Alzheimer Disease Using Rasch Analysis.

INTRODUCTION: The Perceived Research Burden Assessment (PeRBA) was developed to measure participant perceptions of burden in research studies. This study aimed to examine the psychometric properties of this assessment using Rasch analysis in participants in the longitudinal studies of the Alzheimer disease (AD) and their family members. METHODS: PeRBA was administered to 443 participants in studies of AD and 212 family members across 4 Alzheimer Disease Research Centers. We used Rasch analysis to examine PeRBA's psychometric properties, and data-model fit both at item and scale levels. RESULTS: PeRBA demonstrated good reliability and item and person fit for participants and family members. A few items did not fit the model for participants or family members. Areas of content redundancy were found in items assessing similar amounts of perceived research burden. Areas of content gaps were also found, with no items assessing certain levels of perceived research burden. CONCLUSION: Analysis results support the good overall psychometric properties of PeRBA among research participants in studies of AD and their family members. Recommendations have been provided to improve the assessment, including rewording items and adding items that could account for a broader range of perceived research burden.

Successful real-world application of an osteoarthritis classification deep-learning model using 9210 knees-An orthopedic surgeon's view.

This study aimed to evaluate the performance of a deep-learning model to evaluate knee osteoarthritis using Kellgren-Lawrence grading in real-life knee radiographs. A deep convolutional neural network model was trained using 8964 knee radiographs from the osteoarthritis initiative (OAI), including 962 testing set images. Another 246 knee radiographs from the Far Eastern Memorial Hospital were used for external validation. The OAI testing set and external validation images were evaluated by experienced specialists, two orthopedic surgeons, and a musculoskeletal radiologist. The accuracy, interobserver agreement, F1 score, precision, recall, specificity, and ability to identify surgical candidates were used to compare the performances of the model and specialists. Attention maps illustrated the interpretability of the model classification. The model had a 78% accuracy and consistent interobserver agreement for the OAI (model-surgeon 1 К = 0.80, model-surgeon 2 К = 0.84, model-radiologist К = 0.86) and external validation (model-surgeon 1 К = 0.81, model-surgeon 2 К = 0.82, model-radiologist К = 0.83) images. A lower interobserver agreement was found in the images misclassified by the model (model-surgeon 1 К = 0.57, model-surgeon 2 К = 0.47, model-radiologist К = 0.65). The model performed better than specialists in identifying surgical candidates (Kellgren-Lawrence Stages 3 and 4) with an F1 score of 0.923. Our model not only had comparable results with specialists with respect to the ability to identify surgical candidates but also performed consistently with open database and real-life radiographs. We believe the controversy of the misclassified knee osteoarthritis images was based on a significantly lower interobserver agreement.

Psychometric properties of the Center for Epidemiologic Studies Depression (CES-D) scale in stroke survivors.

PURPOSE: This study aimed to evaluate the psychometric properties of the Center for Epidemiologic Studies Depression (CES-D) scale in adults with stroke. METHODS: A secondary analysis of the Stroke Recovery in Underserved Populations Cohort Study. The CES-D was administrated to 828 stroke patients at discharge from inpatient rehabilitation facilities and at 3- and 12-month follow-ups. Data were analyzed using classical test theory (CTT) and Rasch measurement model. RESULTS: Confirmatory factor analyses of the CES-D items showed excellent fit of a four-factor model (CFI = 0.98; TLI = 0.98; RMSEA = 0.05). CTT analyses revealed satisfactory reliability and validity. Rasch analyses also supported the unidimensionality of each factor (subscale). Wright maps indicated a floor effect and item gaps. A few items displayed differential item functioning: 3 items (1 depressed affect and 2 somatic symptoms) across gender, 1 item (depressed affect) across time of assessment and all # somatic symptom items across time of assessment. CONCLUSION: The four-factor structure of the CES-D was confirmed and its psychometric properties were validated, supporting the use of four subscales to characterize depressive symptomatology in adults with stroke. Supplementary assessments are needed for evaluating and comparing somatic symptoms over time. A refinement of the CES-D was recommended to better differentiate stroke survivors with subtle depressive symptoms.

Mesenchymal Stem Cell Secreted-Extracellular Vesicles are Involved in Chondrocyte Production and Reduce Adipogenesis during Stem Cell Differentiation.

BACKGROUND: Extracellular vesicles (EVs) are derived from internal cellular compartments, and have potential as a diagnostic and therapeutic tool in degenerative disease associated with aging. Mesenchymal stem cells (MSCs) have become a promising tool for functional EVs production. This study investigated the efficacy of EVs and its effect on differentiation capacity. METHODS: The characteristics of MSCs were evaluated by flow cytometry and stem cell differentiation analysis, and a production mode of functional EVs was scaled from MSCs. The concentration and size of EVs were quantitated by Nanoparticle Tracking Analysis (NTA). Western blot analysis was used to assess the protein expression of exosome-specific markers. The effects of MSC-derived EVs were assessed by chondrogenic and adipogenic differentiation analyses and histological observation. RESULTS: The range of the particle size of adipose-derived stem cells (ADSCs)- and Wharton's jelly -MSCs-derived EVs were from 130 to 150 nm as measured by NTA, which showed positive expression of exosomal markers. The chondrogenic induction ability was weakened in the absence of EVs in vitro. Interestingly, after EV administration, type II collagen, a major component in the cartilage extracellular matrix, was upregulated compared to the EV-free condition. Moreover, EVs decreased the lipid accumulation rate during adipogenic induction. CONCLUSION: The results indicated that the production model could facilitate production of effective EVs and further demonstrated the role of MSC-derived EVs in cell differentiation. MSC-derived EVs could be successfully used in cell-free therapy to guide chondrogenic differentiation of ADSC for future clinical applications in cartilage regeneration.

Adipose-Derived Stem Cell Exosomes as a Novel Anti-Inflammatory Agent and the Current Therapeutic Targets for Rheumatoid Arthritis.

Patients with rheumatoid arthritis (RA), a chronic inflammatory joint disorder, may not respond adequately to current RA treatments. Mesenchymal stem cells (MSCs) elicit several immunomodulatory and anti-inflammatory effects and, thus, have therapeutic potential. Specifically, adipose-derived stem cell (ADSC)-based RA therapy may have considerable potency in modulating the immune response, and human adipose tissue is abundant and easy to obtain. Paracrine factors, such as exosomes (Exos), contribute to ADSCs' immunomodulatory function. ADSC-Exo-based treatment can reproduce ADSCs' immunomodulatory function and overcome the limitations of traditional cell therapy. ADSC-Exos combined with current drug therapies may provide improved therapeutic effects. Using ADSC-Exos, instead of ADSCs, to treat RA may be a promising cell-free treatment strategy. This review summarizes the current knowledge of medical therapies, ADSC-based therapy, and ADSC-Exos for RA and discusses the anti-inflammatory properties of ADSCs and ADSC-Exos. Finally, this review highlights the expanding role and potential immunomodulatory activity of ADSC-Exos in patients with RA.

Radiographic outcomes of the treatment of complex femoral shaft fractures (AO/OTA 32-C) with intramedullary nailing: a retrospective analysis of different techniques.

OBJECTIVES: To assess the results of open versus closed reduction in intramedullary nailing (IMN) for complex femoral fractures (Arbeitsgemeinschaft für Osteosynthesefragen Foundation/Orthopaedic Trauma Association [AO/OTA]: 32-C) and to determine the factors involved in bone healing. METHODS: This retrospective study involved 47 consecutive patients with complex femoral diaphyseal fractures who underwent reduction and fixation. RESULTS: All open-reduction and 12 closed-reduction patients (52.17%) had an anatomical-to-small gap. The closed-small group had the highest bone union rate (100%), followed by the open-reduction (79.17%) and closed-large groups (72.73%); intergroup differences were significant. The closed-small group had the shortest mean union time (7.31 months), followed by the open-reduction group (7.58 months). The closed-large group had a significantly longer union time (9.75 months) than those in the closed-small and open-reduction groups. Femoral radiographic union scores in the closed-small and open-reduction groups were similar at three timepoints; scores were higher than those in the closed-large group, with a significant difference 6 and 9 months post-operatively. CONCLUSION: IMN with closed reduction for complex femoral shaft fractures had better outcomes and fewer complications versus open reduction. For unsatisfactory closed reduction outcomes (i.e., residual gap >10 mm), minimally invasive techniques or open reduction with minimal stripping should be considered.

Temporal trend of opioid and nonopioid pain medications: results from a national in-home survey, 2001 to 2018.

Introduction: The opioid epidemic persists in the United States. The use of opioid medications is often assessed by claims data but potentially underestimated. Objectives: We evaluated the temporal trend in the use of opioid and nonopioid pain medications from a national survey. Methods: Using data from the 2001 to 2018 National Health and Nutrition Examination Survey (NHANES), we examined the current use of prescription analgesics in the past 30 days among 50,201 respondents aged 20 years or older. Joinpoint regressions were used to test statistically meaningful trends of opioid vs nonopioid analgesics. Results: The mean percentage of people who had pain medications in the past 30 days was 6.4% (5.3%-7.1%) for opioid and 11.3% (9.0%-14.8%) for nonopioid analgesics. The availability of opioid and nonopioid prescriptions at home has remained stable, except for the slight decline of opioids among cancer-free patients in 2005 to 2018. The most frequently used opioid analgesic medications included hydrocodone/acetaminophen, tramadol, and hydrocodone. Conclusion: We uniquely measured the proportion of people who had opioid and nonopioid pain medications at home in the United States and supplemented the previous knowledge of prescription rates mainly obtained from claims data.

Deep Learning-Based Pain Classifier Based on the Facial Expression in Critically Ill Patients.

Objective: Pain assessment based on facial expressions is an essential issue in critically ill patients, but an automated assessment tool is still lacking. We conducted this prospective study to establish the deep learning-based pain classifier based on facial expressions. Methods: We enrolled critically ill patients during 2020-2021 at a tertiary hospital in central Taiwan and recorded video clips with labeled pain scores based on facial expressions, such as relaxed (0), tense (1), and grimacing (2). We established both image- and video-based pain classifiers through using convolutional neural network (CNN) models, such as Resnet34, VGG16, and InceptionV1 and bidirectional long short-term memory networks (BiLSTM). The performance of classifiers in the test dataset was determined by accuracy, sensitivity, and F1-score. Results: A total of 63 participants with 746 video clips were eligible for analysis. The accuracy of using Resnet34 in the polychromous image-based classifier for pain scores 0, 1, 2 was merely 0.5589, and the accuracy of dichotomous pain classifiers between 0 vs. 1/2 and 0 vs. 2 were 0.7668 and 0.8593, respectively. Similar accuracy of image-based pain classifier was found using VGG16 and InceptionV1. The accuracy of the video-based pain classifier to classify 0 vs. 1/2 and 0 vs. 2 was approximately 0.81 and 0.88, respectively. We further tested the performance of established classifiers without reference, mimicking clinical scenarios with a new patient, and found the performance remained high. Conclusions: The present study demonstrates the practical application of deep learning-based automated pain assessment in critically ill patients, and more studies are warranted to validate our findings.