Improving both performance and stability of n-type organic semiconductors by vitamin C.

Organic semiconductors (OSCs) are one of the most promising candidates for flexible, wearable and large-area electronics. However, the development of n-type OSCs has been severely held back due to the poor stability of their most candidates, that is, the intrinsically high reactivity of negatively charged polarons to oxygen and water. Here we demonstrate a general strategy based on vitamin C to stabilize n-type OSCs, remarkably improving the performance and stability of their device, for example, organic field-effect transistors. Vitamin C scavenges reactive oxygen species and inhibits their generation by sacrificial oxidation and non-sacrificial triplet quenching in a cascade process, which not only lastingly prevents molecular structure from oxidation damage but also passivates the latent electron traps to stabilize electron transport. This study presents a way to overcome the long-standing stability problem of n-type OSCs and devices.

miR-145 inhibits aerobic glycolysis and cell proliferation of cervical cancer by acting on MYC.

Nearly half a million women are diagnosed with cervical cancer (CC) each year, with the incidence of CC stabilizing or rising in low-income and middle-income countries. Cancer cells use metabolic reprogramming to meet the needs of rapid proliferation, known as the Warburg effect, but the mechanism of the Warburg effect in CC remains unclear. microRNAs (miRNAs) have a wide range of effects on gene expression and diverse modes of action, and they regulate genes for metabolic reprogramming. Dysregulation of miRNA expression leads to metabolic abnormalities in tumor cells and promotes tumorigenesis and tumor progression. In this study, we found that miR-145 was negatively correlated with metabolic reprogramming-related genes and prevented the proliferation and metastasis of CC cell lines by impeding aerobic glycolysis. A dual-luciferase reporter assay showed that miR-145 can bind to the 3'-untranslated region (3'-UTR) of MYC. Chromatin Immunoprecipitation-quantitative real-time PCR indicated that MYC was involved in the regulation of glycolysis-related genes. In addition, miR-145 mimics significantly suppressed the growth of CC cell xenograft tumor, prolonged the survival time of mice, and dramatically silenced the expression of tumor proliferation marker Ki-67. Therefore, the results suggested that miR-145 affects aerobic glycolysis through MYC, which may be a potential target for the treatment of CC.

Prognostic significance of dynamic changes in liver stiffness measurement in patients with chronic hepatitis B and compensated advanced chronic liver disease.

BACKGROUND AND AIM: Liver stiffness measurements (LSMs) are promising for monitoring disease progression or regression. We assessed the prognostic significance of dynamic changes in LSM over time on liver-related events (LREs) and death in patients with chronic hepatitis B (CHB) and compensated advanced chronic liver disease (cACLD). METHODS: This retrospective study included 1272 patients with CHB and cACLD who underwent at least two measurements, including LSM and fibrosis score based on four factors (FIB-4). ΔLSM was defined as [(follow-up LSM - baseline LSM)/baseline LSM × 100]. We recorded LREs and all-cause mortality during a median follow-up time of 46 months. Hazard ratios (HRs) and confidence intervals (CIs) for outcomes were calculated using Cox regression. RESULTS: Baseline FIB-4, baseline LSM, ΔFIB-4, ΔLSM, and ΔLSM/year were independently and simultaneously associated with LREs (adjusted HR, 1.04, 95% CI, 1.00-1.07; 1.02, 95% CI, 1.01-1.03; 1.06, 95% CI, 1.03-1.09; 1.96, 95% CI, 1.63-2.35, 1.02, 95% CI, 1.01-1.04, respectively). The baseline LSM combined with the ΔLSM achieved the highest Harrell's C (0.751), integrated AUC (0.776), and time-dependent AUC (0.737) for LREs. Using baseline LSM and ΔLSM, we proposed a risk stratification method to improve clinical applications. The risk proposed stratification based on LSM performed well in terms of prognosis: low risk (n = 390; reference), intermediate risk (n = 446; HR = 3.38), high risk (n = 272; HR = 5.64), and extremely high risk (n = 164; HR = 11.11). CONCLUSIONS: Baseline and repeated noninvasive tests measurement allow risk stratification of patients with CHB and cACLD. Combining baseline and dynamic changes in the LSM improves prognostic prediction.

Patients' willingness to pay for naloxone: A national cross-sectional survey of prescription opioid users with chronic pain in the United States.

BACKGROUND: Millions of U.S. people have been heavily affected by opioids. In March 2023, the Food and Drug Administration approved naloxone as an over-the-counter medication. This has allowed more access to patients at high risk of opioid overdose. However, the patient's willingness to pay for naloxone at the pharmacy counter has not been assessed. OBJECTIVES: This study aimed to characterize factors associated with the willingness to pay for naloxone among the patient group. METHODS: A cross-sectional Qualtrics online panel survey instrument was developed. This survey was distributed to patients in the United States, aged ≥ 18 years, with any chronic pain and taking opioids. The survey included demographics, and clinical characteristics (pain assessment, opioid use, and knowledge of naloxone). In addition, willingness to pay was assessed using a 7-point Likert scale ranging from strongly disagree to strongly agree. An ordinal logistic regression model was used to examine demographic and clinical characteristics. RESULTS: A total of 549 subjects completed the survey (women [53.01%], white or Caucasian (83.61%), age mean [SD] 44 [13]). Women were associated with less willingness to pay (adjusted odds ratio [aOR] 0.685 [95% CI 0.478-0.983], P = 0.0403). Compared with the high household income group (≥ $150,000), low household income ≤ $25,000 (aOR 0.326 [95% CI 0.160-0.662], P = 0.0020) or income between $25,000 and 74,999 (aOR 0.369 [95% CI 0.207-0.657], P = 0.0007) was associated with less likelihood of willing to pay. Patients with a previous diagnosis of obstructive sleep apnea were associated with a higher likelihood of willingness to pay (aOR 1.685 [95% CI 1.138-2.496], P = 0.0092). Each unit increase in pain was also associated with a higher likelihood of willingness to pay (aOR 1.247 [95% CI 1.139-1.365], P < 0.0001). CONCLUSIONS: Demographics and clinical factors were associated with willingness to pay for naloxone. This study's findings are useful in the development of interventions to address pharmacy-based naloxone distribution programs.

Suppressing the Intrinsic Photoelectric Response of Organic Semiconductors for Highly-Photostable Organic Transistors.

Suppressing the photoelectric response of organic semiconductors (OSs) is of great significance for improving the operational stability of organic field-effect transistors (OFETs) in light environments, but it is quite challenging because of the great difficulty in precisely modulating exciton dynamics. In this work, photostable OFETs are demonstrated by designing the micro-structure of OSs and introducing an electrical double layer at the OS/polyelectrolyte dielectric interface, in which multiple exciton dynamic processes can be modulated. The generation and dissociation of excitons are depressed due to the small light-absorption area of the microstripe structure and the excellent crystallinity of OSs. At the same time, a highly efficient exciton quenching process is activated by the electrical double layer at the OS/polyelectrolyte dielectric interface. As a result, the OFETs show outstanding tolerance to the light irradiation of up to 306 mW·cm-2 , which far surpasses the solar irradiance value in the atmosphere (≈138 mW·cm-2 ) and achieves the highest photostability ever reported in the literature. The findings promise a general and practicable strategy for the realization of photostable OFETs and organic circuits.

General Spatial Confinement Recrystallization Method for Rapid Preparation of Thickness-Controllable and Uniform Organic Semiconductor Single Crystals.

Organic semiconductor single crystals (OSSCs) are ideal materials for studying the intrinsic properties of organic semiconductors (OSCs) and constructing high-performance organic field-effect transistors (OFETs). However, there is no general method to rapidly prepare thickness-controllable and uniform single crystals for various OSCs. Here, inspired by the recrystallization (a spontaneous morphological instability phenomenon) of polycrystalline films, a spatial confinement recrystallization (SCR) method is developed to rapidly (even at several second timescales) grow thickness-controllable and uniform OSSCs in a well-controlled way by applying longitudinal pressure to tailor the growth direction of grains in OSCs polycrystalline films. The relationship between growth parameters including the growth time, temperature, longitudinal pressure, and thickness is comprehensively investigated. Remarkably, this method is applicable for various OSCs including insoluble and soluble small molecules and polymers, and can realize the high-quality crystal array growth. The corresponding 50 dinaphtho[2,3-b:2″,3″-f]thieno[3,2-b]thiophene (DNTT) single crystals coplanar OFETs prepared by the same batch have the mobility of 4.1 ± 0.4 cm2 V-1 s-1 , showing excellent uniformity. The overall performance of the method is superior to the reported methods in term of growth rate, generality, thickness controllability, and uniformity, indicating its broad application prospects in organic electronic and optoelectronic devices.

Application of machine learning in predicting survival outcomes involving real-world data: a scoping review.

BACKGROUND: Despite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare. METHODS: PUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC). RESULTS: Of 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6-0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%). CONCLUSIONS: The ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.

Outpatient opioid prescribing by Alzheimer's diagnosis among older adults with pain in United States.

OBJECTIVE: To examine opioid prescribing practices for pain in older adults with and without Alzheimer's Disease and Related Dementias (ADRD). METHODS: This cross-sectional study used National Ambulatory Medical Care Survey data (2014-2016, and 2018). Adults aged ≥ 50 years with pain were analyzed. Prescribing of opioid and concomitant sedative prescriptions (including benzodiazepines, Z-drugs, and barbiturates) were identified by the Multum lexicon code. Multivariable logistic regression evaluated the risk of opioid prescribing or co-prescribing of opioid and sedative associated with ADRD in older adults with pain. RESULTS: There were 13,299 office visits in older adults with pain, representing 451.75 million visits. Opioid prescribing occurred in 27.19%; 30% involved co-prescribing of opioids and sedatives. ADRD was not associated with opioid prescribing or co-prescribing of opioid and sedative therapy. CONCLUSIONS: Opioid and sedatives are commonly prescribed in older adults with pain. Longitudinal studies need to understand the etiology and chronicity of opioid use in older patients, specifically with ADRD.

Identification and Alternative Splicing Profile of the Raffinose synthase Gene in Grass Species.

Raffinose synthase (Rafs) is an important enzyme in the synthesis pathway of raffinose from sucrose and galactinol in higher plants and is involved in the regulation of seed development and plant responses to abiotic stresses. In this study, we analyzed the Rafs families and profiled their alternative splicing patterns at the genome-wide scale from 10 grass species representing crops and grasses. A total of 73 Rafs genes were identified from grass species such as rice, maize, foxtail millet, and switchgrass. These Rafs genes were assigned to six groups based the phylogenetic analysis. We compared the gene structures, protein domains, and expression patterns of Rafs genes, and also unraveled the alternative transcripts of them. In addition, different conserved sequences were observed at these putative splice sites among grass species. The subcellular localization of PvRafs5 suggested that the Rafs gene was expressed in the cytoplasm or cell membrane. Our findings provide comprehensive knowledge of the Rafs families in terms of genes and proteins, which will facilitate further functional characterization in grass species in response to abiotic stress.

Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database.

BACKGROUND: Hospital readmissions for pneumonia are a growing concern in the US, with significant consequences for costs and quality of care. This study developed the rule-based model and other machine learning (ML) models to predict 30-day readmission risk in patients with pneumonia and compared model performance. METHODS: This population-based study involved patients aged ≥ 18 years hospitalized with pneumonia from January 1, 2016, through November 30, 2016, using the Healthcare Cost and Utilization Project-National Readmission Database (HCUP-NRD). Rule-based algorithms and other ML algorithms, specifically decision trees, random forest, extreme gradient descent boosting (XGBoost), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to model all-cause readmissions 30 days post-discharge from index pneumonia hospitalization. A total of 61 clinically relevant variables were included for ML model development. Models were trained on randomly partitioned 50% of the data and evaluated using the remaining dataset. Model hyperparameters were tuned using the ten-fold cross-validation on the resampled training dataset. The area under the receiver operating curves (AUROC) and area under precision-recall curves (AUPRC) were calculated for the testing set to evaluate the model performance. RESULTS: Of the 372,293 patients with an index hospital hospitalization for pneumonia, 48,280 (12.97%) were readmitted within 30 days. Judged by AUROC in the testing data, rule-based model (0.6591) significantly outperformed decision tree (0.5783, p value < 0.001), random forest (0.6509, p value < 0.01) and LASSO (0.6087, p value < 0.001), but was less superior than XGBoost (0.6606, p value = 0.015). The AUPRC of the rule-based model in the testing data (0.2146) was higher than the decision tree (0.1560), random forest (0.2052), and LASSO (0.2042), but was similar to XGBoost (0.2147). The top risk-predictive rules captured by the rule-based algorithm were comorbidities, illness severity, disposition locations, payer type, age, and length of stay. These predictive risk factors were also identified by other ML models with high variable importance. CONCLUSION: The performance of machine learning models for predicting readmission in pneumonia patients varied. The XGboost was better than the rule-based model based on the AUROC. However, important risk factors for predicting readmission remained consistent across ML models.

Intention to get naloxone among patients prescribed opioids for chronic pain.

BACKGROUND: Prescription opioids have been increasingly prescribed for chronic pain while the opioid-related death rates grow. Naloxone, an opioid antagonist, is increasingly recommended in these patients, yet there is limited research that investigates the intention to get naloxone. This study aimed to investigate intention toward getting naloxone in patients prescribed opioids for chronic pain and to assess the predictive utility of the theory of reasoned action (TRA) constructs in explaining intention to get naloxone. METHODS: This was a cross-sectional study of a panel of U.S. adult patients prescribed opioids for chronic pain using a Qualtrics®XM survey. These patients participated in the study during February to March 2020. The online internet survey assessed the main outcome of intention to get naloxone and constructs of TRA (attitudes and subjective norms); additional measures assessed the characteristics of patients' opioid overdose risk factors, knowledge of naloxone, and their demographics. The relationship between TRA constructs, namely, attitudes and subjective norms, and the intention variable was examined using logistic regression analyses with the intention outcome contrasted as follows: high intention (scores ≥ 5) and non-high intention (scores < 5). RESULTS: A total of 549 participants completed the survey. Most of them were female (53.01%), White or Caucasian (83.61%), non-Hispanic (87.57%) and had a mean age of 44.16 years (SD = 13.37). Of these, 167 (30.42%) had high intention to get naloxone. The TRA construct of subjective norm was significantly associated with increased likelihood of higher intentions to get naloxone (OR 3.04, 95% CI 2.50-3.70, P < 0.0001). CONCLUSIONS: Our study provides empirical support of the TRA in predicting intention to get naloxone among chronic pain patients currently taking opioids. Subjective norms significantly predicted intention to get naloxone in these patients. The interventions targeting important reference groups of these patients would have greater impact on increasing intention to get naloxone in this population. Future studies should test whether theory-based interventions focusing on strengthening subjective norms increase intention to get naloxone in this population.

Application of machine learning in predicting hospital readmissions: a scoping review of the literature.

BACKGROUND: Advances in machine learning (ML) provide great opportunities in the prediction of hospital readmission. This review synthesizes the literature on ML methods and their performance for predicting hospital readmission in the US. METHODS: This review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) Statement. The extraction of items was also guided by the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Electronic databases PUBMED, MEDLINE, and EMBASE were systematically searched from January 1, 2015, through December 10, 2019. The articles were imported into COVIDENCE online software for title/abstract screening and full-text eligibility. Observational studies using ML techniques for hospital readmissions among US patients were eligible for inclusion. Articles without a full text available in the English language were excluded. A qualitative synthesis included study characteristics, ML algorithms utilized, and model validation, and quantitative analysis assessed model performance. Model performances in terms of Area Under the Curve (AUC) were analyzed using R software. Quality in Prognosis Studies (QUIPS) tool was used to assess the quality of the reviewed studies. RESULTS: Of 522 citations reviewed, 43 studies met the inclusion criteria. A majority of the studies used electronic health records (24, 56%), followed by population-based data sources (15, 35%) and administrative claims data (4, 9%). The most common algorithms were tree-based methods (23, 53%), neural network (NN) (14, 33%), regularized logistic regression (12, 28%), and support vector machine (SVM) (10, 23%). Most of these studies (37, 85%) were of high quality. A majority of these studies (28, 65%) reported ML algorithms with an AUC above 0.70. There was a range of variability within AUC reported by these studies with a median of 0.68 (IQR: 0.64-0.76; range: 0.50-0.90). CONCLUSIONS: The ML algorithms involving tree-based methods, NN, regularized logistic regression, and SVM are commonly used to predict hospital readmission in the US. Further research is needed to compare the performance of ML algorithms for hospital readmission prediction.

Opioid Prescribing Among Outpatients with Rheumatoid Arthritis.

STUDY OBJECTIVES: To examine the outpatient opioid prescribing practices and the factors associated with opioid prescriptions in patient visits with rheumatoid arthritis (RA). DESIGN: This cross-sectional study used the 2011-2016 National Ambulatory Medical Care Survey. Descriptive weighted analyses were used to examine the trends in opioid prescribing practices for RA. Multivariable logistic regression was used to examine the factors associated with opioid prescriptions among RA visits. SUBJECTS: Adult patients (>18 years of age) with a primary diagnosis of RA based on the International Classification of Diseases. RESULTS: According to the national surveys, an average of 4.45 (95% confidence interval [CI], 2.30-6.60) million office visits were made annually for RA. Approximately 24.28% of these visits involved opioid prescriptions. The RA visits involving opioid prescriptions increased from 1.43 million in 2011-2012 to 3.69 million in 2015-2016 (P < .0001). Being in the age group of 50-64 years (odds ratio [OR] = 3.40; 95% CI, 1.29-9.00), being Hispanic or Latino (OR = 2.92, 95% CI, 1.10-7.74), visiting primary physician (OR = 4.67; 95% CI, 1.86-11.75), prescribing of muscle relaxants (OR = 64.32; 95% CI, 9.71-426.09), acetaminophen (OR = 93.40; 95% CI, 26.19-333.04), antidepressants (OR = 6.10; 95% CI, 2.63-14.14), and glucocorticoids (OR = 3.20; 95% CI, 1.61-6.38), were associated with an increased likelihood of receiving opioid prescriptions in RA. CONCLUSIONS: One in four adult RA visits resulted in opioid prescriptions, and the opioid visits more than doubled during the study period. Several patient and provider factors were associated with the opioid prescribing among RA visits. Understanding these prescribing practices can help to devise strategies for safe opioid prescribing practices in RA.

Value of Contrast-Enhanced Ultrasonography in Evaluating Rheumatoid Arthritis: Preliminary Research Based on an Animal Model.

BACKGROUND The aim of this study was to evaluate the effectiveness of grayscale ultrasound (GSUS), power Doppler imaging (PDI), and contrast-enhanced ultrasonography (CEUS) in early rheumatoid arthritis (RA) diagnosis through animal experiments. MATERIAL AND METHODS A rabbit RA model was constructed. The animals were randomly divided into 2 groups, namely, the RA model group and the control group. GSUS, PDI, and CEUS were performed in the model group during early RA and were compared with pathology of synovial biopsies. The consistency of 3 types of ultrasonography was evaluated in tandem with pathological grading. RESULTS 23 rabbits in the RA model group completed the experiment. GSUS showed that the synovial thickening of grades 1, 2 and 3 occurred in 12, 19, and 15 joints, respectively. The sensitivity, specificity, and accuracy of PDI in the diagnosis of knee joint synovitis in RA grades 1, 2, and 3 were 80.56% (29/36), 60.00% (6/10), and 76.09% (35/46), respectively, while those with CEUS were 94.44% (34/36), 90.00% (9/10), and 93.47% (43/46), respectively. The differences in diagnostic sensitivity, specificity, and accuracy of the 2 methods were statistically significant. Additionally, the thickness of the synovium measured with GSUS precontrast was greater than that of postcontrast. CONCLUSIONS RA evaluated with GSUS is often more hypertrophied than when evaluated with CEUS, while evaluation by PDI is less hypertrophied than that by CEUS. However, from a practical view point, GSUS and PDI are of sufficient practical value, except for in a few special cases.

Extra-Thyroid Extension Prediction by Ultrasound Quantitative Method Based on Thyroid Capsule Response Evaluation.

BACKGROUND The aim of this study was to assess the interaction between thyroid malignancies and thyroid anterior capsule by ultrasound quantification to determine extra-capsular invasion. MATERIAL AND METHODS A total of 145 patients preoperatively diagnosed with malignant nodules under the thyroid anterior capsule were selected and routinely examined by ultrasound. The length of the nodules (from the junction of the nodule capsule to the deepest point of the nodule, vertical diameter, V) and the distance between the nodule protruding from thyroid capsule and the highest protruding (ledge length, L) nodule were used to obtain the L/V ratio. These parameters where then used to compare the efficacy of predicting extra-thyroid extension (ETE) between L/V, the aspect ratio of the tumor, and manual judgment. RESULTS Out of 145 nodules, there were 63 ETEs and 82 non-ETEs determined by ultrasound. Extra-capsular invasion was associated with L//V ratio, but there was no significant correlation between capsular invasion and AR (aspect ratio), age, location, or presence of clustered calcification. The ability of the ratio of L/V to predict extra-capsular invasion was superior to the predictive ability of the AR ratio. With a Youden index of 0.593, the L/V ratio was 0.2325. The use of the L/V ratio to determine the presence of ETE was superior to subjective visual judgment. CONCLUSIONS The calculation of L/V ratio by ultrasound could more precisely predict the ETE compared with manual judgment, which indirectly reflects the interaction between thyroid capsule and malignant nodules. The above conclusions need to be confirmed by a range of cases.

Nucleolin mediates the binding of cancer cells to L-selectin under conditions of lymphodynamic shear stress.

Head and neck squamous cell carcinoma (HNSCC) cells bind to lymphocytes via L-selectin in a shear-dependent manner. This interaction takes place exclusively under low-shear stress conditions, such as those found within the lymph node parenchyma. This represents a novel functional role for L-selectin-selectin ligand interactions. Our previous work has characterized as-of-yet unidentified L-selectin ligands expressed by HNSCC cells that are specifically active under conditions of low shear stress consistent with lymph flow. Using an affinity purification approach, we now show that nucleolin expressed on the surface of HNSCC cells is an active ligand for L-selectin. Parallel plate chamber flow-based experiments and atomic force microscopy (AFM) experiments show that nucleolin is the main functional ligand under these low-force conditions. Furthermore, AFM shows a clear relationship between work of deadhesion and physiological loading rates. Our results reveal nucleolin as the first major ligand reported for L-selectin that operates under low-shear stress conditions.

Janus adhesive microneedle patch loaded with exosomes for intrauterine adhesion treatment.

Intrauterine adhesions (IUAs) are common sequelae of cervical mucosa damage caused by uterine curettage. Establishing an anti-adhesion barrier between the damaged endometrium with a sustained-release drug capability and hence promoting endogenous regeneration of the endometrium is an available treatment for IUA. However, current therapy lacks long-term intracavitary residence, drug-delivery permeability, and tissue anti-adhesion to the endometrium. Here, we report the design of a Janus microneedle patch consisting of two layers: an adhesive inner layer with an exosomes-loaded microneedle, which endows the patch with a tissue adhesive capability as well as transdermal drug-delivery capability; and an anti-adhesion outer layer, which prevents the intrauterine membrane from postoperative adhesion. This Janus adhesive microneedle patch firmly adhered to uterine tissue, and sustainedly released ∼80% of the total loaded exosomes in 7 days, hence promoting the expression of vascular- and endothelial-related cell signals. Furthermore, the anti-adhesive layer of the microneedle patch exhibited low cell and protein adhesion performance. In rats, the microneedle patch successfully prevented uterine adhesions, improved endometrial angiogenesis, proliferation, and hormone response levels. This study provides a stable anti-adhesion barrier as well as efficient drug-release capability treatment for intrauterine adhesion treatment.

Risk of incident cardiovascular events with disease-modifying anti-rheumatic drugs among adults with rheumatoid arthritis: a nested case-control study.

OBJECTIVE: This study examined the risk of cardiovascular disease (CVD) associated with the disease-modifying anti-rheumatic drugs (DMARDs) in rheumatoid arthritis (RA). METHOD: This nested case-control study used the MarketScan database (2012-2014), involving adult RA patients (aged ≥18 years) initiating either a conventional synthetic (cs) DMARD, biologic DMARD, or targeted synthetic (ts) DMARD between January 1, 2013 and December 31, 2014 (cohort entry) and had no CVD history. Cases were individuals with incident CVD identified using diagnosis codes or procedure codes from medical claims. For each case, 10 age- and sex-matched controls were selected using the incident density sampling with replacement. Prescriptions of DMARDs were measured 90 days before the event date. Conditional logistic regression examined the association of risk of CVD with DMARDs in combination treatment or individual use, with reference to methotrexate (MTX) monotherapy, adjusting for baseline confounders. Subgroup analyses were performed separately in DMARD combination therapy users or individual DMARD users, respectively. RESULTS: In total, 270 cases of incident CVD and 2700 controls were included (mean [standard deviation (SD)] age: 54 [1]; 75.6% women). The commonly prescribed DMARD therapies were csDMARD monotherapy (n = 795, 27.04%), followed by  tumor necrosis factor inhibitors (TNFi) monotherapy (n = 367, 12.48%), and TNFi in combination with MTX (n = 314, 10.68%). Compared with MTX monotherapy, overall use of DMARD agents was not associated with the differential risk of CVD, including various types of DMARD combination regimens. The findings were similar across subgroup analyses. CONCLUSIONS: The study found no differential risk of CVD with DMARDs in combination therapy or monotherapy compared to MTX monotherapy in patients with RA. Key Points • This study evaluated the risk of cardiovascular disease (CVD) associated with the disease-modifying anti-rheumatic drugs (DMARDs) in rheumatoid arthritis (RA). • Findings suggest no differential CVD risk with DMARDs in combination with MTX or used individually compared with MTX monotherapy in patients with early RA. • Further efforts should focus on a better understanding of the mechanism of DMARD combination treatments with MTX in modifying CV risk.

Opioid Treatment for Adults With and Without Systemic Autoimmune/Inflammatory Rheumatic Diseases: Analysis of 2006-2019 United States National Data.

OBJECTIVES: This study compared opioid prescribing among ambulatory visits with systemic autoimmune/inflammatory rheumatic diseases (SARDs) or without and assessed factors associated with opioid prescribing in SARDs. METHODS: This cross-sectional study used the National Ambulatory Medical Care Survey between 2006 and 2019. Adult (≥18 years) visits with a primary diagnosis of SARDs, including rheumatoid arthritis, ankylosing spondylitis, psoriatic arthritis, or systemic lupus erythematosus were included in the study. Opioid prescribing was compared between those with vs without SARDs using multivariable logistic regression accounting for the complex survey design and adjusting for predisposing, enabling, and need factors within Andersen's Behavioral Model of Health Services Use. Another multivariable logistic regression examined the predictors associated with opioid prescribing in SARDs. RESULTS: Annually, an average of 5.20 million (95% confidence interval [CI] 3.58-6.82) visits were made for SARDs, whereas 780.14 million (95% CI 747.56-812.72) visits were made for non-SARDs. The SARDs group was more likely to be prescribed opioids (22.53%) than the non-SARDs group (9.83%) (adjusted odds ratio [aOR] 2.65; 95% CI 1.68-4.18). Among the SARDs visits, patient age from 50 to 64 (aOR 1.95; 95% CI 1.05-3.65 relative to ages 18-49) and prescribing of glucocorticoids (aOR 1.75; 95% CI 1.20-2.54) were associated with an increased odd of opioid prescribing, whereas private insurance relative to Medicare (aOR 0.50; 95% CI 0.31-0.82) was associated with a decreased odds of opioid prescribing. CONCLUSION: Opioid prescribing in SARDs was higher compared to non-SARDs. Concerted efforts are needed to determine the appropriateness of opioid prescribing in SARDs.

Effects of temperature-related changes on charred bone in soil: From P release to microbial community.

Phosphorus (P) is one of the most common limited nutrients in terrestrial ecosystems. Animal bones, with abundant bioapatite, are considerable P sources in terrestrial ecosystems. Heating significantly promotes P release from bone bioapatite, which may alleviate P limitation in soil. This study aimed to explore P release from charred bone (CB) under heating at various temperatures (based on common natural heating). It showed that heating at ∼300 °C significantly increased the P release (up to ∼30 mg/kg) from CB compared with other heating temperatures. Then, the subsequent changes of available P and pH induced evident alternation of soil microbial community composition. For instance, CB heated at ∼300 °C caused elevation of phosphate-solubilizing fungi (PSF) abundance. This further stimulated P mobility in the soil. Meanwhile, the fungal community assembly process was shifted from stochastic to deterministic, whereas the bacterial community was relatively stable. This indicated that the bacterial community showed fewer sensitive responses to the CB addition. This study hence elucidated the significant contribution of heated bone materials on P supply. Moreover, functional fungi might assist CB treated by natural heating (e.g., fire) to construct P "Hot Spots".