Opioid Use 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) 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 (MLR) accounting for the complex survey design and adjusting for predisposing, enabling, and need factors within Andersen's Behavioral Model of Health Services Use. Another MLR examined the predictors associated with opioid prescribing in SARDs. RESULTS: Annually, an average of 5.20 (95% CI 3.58-6.82) million visits were made for SARDs, whereas 780.14 (95% CI 747.56-812.72) million 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%) (aOR 2.65 [95% CI 1.68-4.18]). Among the SARDs visits, adults aged 50-64 (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. CONCLUSIONS: Opioid prescribing in SARDs was higher compared to non-SARDs. Concerted efforts are needed to determine the appropriateness of opioid prescribing in SARDs.

Factors associated with adherence of cervical cancer screening in women with Systemic Lupus Erythematosus.

OBJECTIVES: To determine cervical cancer screening rates and factors associated with decreased cervical cancer screening in women with systemic lupus erythematosus (SLE). METHODS: We conducted a cross-sectional study that enrolled consecutive women (aged 21-64) with SLE. We collected demographics, clinical characteristics, constructs of the Health Beliefs Model (HBM) (i.e., susceptibility, severity, barriers, benefits, cues to action, and self-efficacy), and self-reported cervical cancer screening (confirmed with the electronic medical record). The primary outcome was adherence to cervical cancer screening according to current guidelines. Multivariable logistic regression models were used to examine the association between SLE disease activity and cervical cancer screening, and explore mediation effects from HBM constructs. RESULTS: We enrolled 130 women with SLE. The median age was 42 (IQR 32-52). The cervical cancer screening adherence rate was 61.5%. Women with high SLE disease activity were less likely to have cervical cancer screening versus those with low disease activity (OR 0.59, 0.39-0.89, p=0.01), which remained statistically significant after adjusting for baseline demographics and drug therapy in a multivariable model (OR 0.25, 95% CI 0.08-0.79, p=0.02). Regarding the HBM constructs, increased perceived barriers to cervical cancer screening (r=-0.30, p < 0.01) and decreased self-efficacy (r=-0.21, p=0.02) correlated with decreased cervical cancer screening. CONCLUSION: SLE patients with high disease activity undergo cervical cancer screening less frequently than those with low disease activated. Perceived barriers to cervical cancer screening are moderately correlated with decreased screening. These data highlight to need to develop strategies to increase cervical cancer screening in this high-risk patient population.

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.

Achieving Zero-Temperature Coefficient Point Behavior by Defect Passivation for Temperature-Immune Organic Field-Effect Transistors.

Organic field-effect transistors (OFETs) have broad prospects in biomedical, sensor, and aerospace applications. However, obtaining temperature-immune OFETs is difficult because the electrical properties of organic semiconductors (OSCs) are temperature-sensitive. The zero-temperature coefficient (ZTC) point behavior can be used to achieve a temperature-immune output current; however, it is difficult to achieve in organic devices with thermal activation characteristics, according to the existing ZTC point theory. Here, the Fermi pinning in OSCs is eliminated using the defect passivation strategy, making the Fermi level closer to the tail state at low temperatures; thus threshold voltage (VT) is negatively correlated with temperature. ZTC point behaviors in OFETs are achieved by compensation between VT and mobility at different temperatures to improve its temperature immunity. A temperature-immune output current can be realized in a variable-temperature bias voltage test over 50000 s by biasing the device at the ZTC point. This study provides an effective solution for temperature-immune OFETs and inspiration for their practical application.

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.

Unraveling the crucial role of trace oxygen in organic semiconductors.

Optoelectronic properties of semiconductors are significantly modified by impurities at trace level. Oxygen, a prevalent impurity in organic semiconductors (OSCs), has long been considered charge-carrier traps, leading to mobility degradation and stability problems. However, this understanding relies on the conventional deoxygenation methods, by which oxygen residues in OSCs are inevitable. It implies that the current understanding is questionable. Here, we develop a non-destructive deoxygenation method (i.e., de-doping) for OSCs by a soft plasma treatment, and thus reveal that trace oxygen significantly pre-empties the donor-like traps in OSCs, which is the origin of p-type characteristics exhibited by the majority of these materials. This insight is completely opposite to the previously reported carrier trapping and can clarify some previously unexplained organic electronics phenomena. Furthermore, the de-doping results in the disappearance of p-type behaviors and significant increase of n-type properties, while re-doping (under light irradiation in O2) can controllably reverse the process. Benefiting from this, the key electronic characteristics (e.g., polarity, conductivity, threshold voltage, and mobility) can be precisely modulated in a nondestructive way, expanding the explorable property space for all known OSC materials.

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".

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.

Ultrahigh-gain organic transistors based on van der Waals metal-barrier interlayer-semiconductor junction.

Intrinsic gain is a vital figure of merit in transistors, closely related to signal amplification, operation voltage, power consumption, and circuit simplification. However, organic thin-film transistors (OTFTs) targeted at high gain have suffered from challenges such as narrow subthreshold operating voltage, low-quality interface, and uncontrollable barrier. Here, we report a van der Waals metal-barrier interlayer-semiconductor junction-based OTFT, which shows ultrahigh performance including ultrahigh gain of ~104, low saturation voltage, negligible hysteresis, and good stability. The high-quality van der Waals-contacted junctions are mainly attributed to patterning EGaIn liquid metal electrodes by low-energy microfluidic processes. The wide-bandgap semiconductor Ga2O3 as barrier interlayer is achieved by in situ surface oxidation of EGaIn electrodes, allowing for an adjustable barrier height and expected thermionic emission properties. The organic inverters with a high gain of 5130 and a simplified current stabilizer are further demonstrated, paving a way for high-gain and low-power organic electronics.

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.

Oxygen-Induced Lattice Strain for High-Performance Organic Transistors with Enhanced Stability.

Integrating the merits of low cost, flexibility, and large-area processing, organic semiconductors (OSCs) are promising candidates for the next-generation electronic materials. The mobility and stability are the key figures of merit for its practical application. However, it is greatly challenging to improve the mobility and stability simultaneously owing to the weak interactions and poor electronic coupling between OSCs molecules. Here, an oxygen-induced lattice strain (OILS) strategy is developed to achieve OSCs with both high mobility and high stability. Utilizing the strategy, the maximum mobility of dinaphtho[2,3-b:2',3'-f]thieno[3,2-b]thiophene (DNTT) organic field-effect transistor (OFET) rises to 15.3 cm2  V-1  s-1 and the contact resistance lowers to 25.5 Ω cm. Remarkably, the thermal stability of DNTT is much improved, and a record saturated power density of ≈3.4 × 104  W cm-2 is obtained. Both the experiments and theoretical calculations demonstrate that the lattice compressive strain induced by oxygen is responsible for their high performance and stability. Furthermore, the universality of the strategy is manifested in both n-type and p-type small OSCs. This work provides a novel strategy to improve both the mobility and the stability of OSCs, paving the way for the practical applications of organic devices.

Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysis.

Objectives: Machine learning algorithms are being increasingly used for predicting hospital readmissions. This meta-analysis evaluated the performance of logistic regression (LR) and machine learning (ML) models for the prediction of 30-day hospital readmission among patients in the US. Methods: Electronic databases (i.e., Medline, PubMed, and Embase) were searched from January 2015 to December 2019. Only studies in the English language were included. Two reviewers performed studies screening, quality appraisal, and data collection. The quality of the studies was assessed using the Quality in Prognosis Studies (QUIPS) tool. Model performance was evaluated using the Area Under the Curve (AUC). A random-effects meta-analysis was performed using STATA 16. Results: Nine studies were included based on the selection criteria. The most common ML techniques were tree-based methods such as boosting and random forest. Most of the studies had a low risk of bias (8/9). The AUC was greater with ML to predict 30-day all-cause hospital readmission compared with LR [Mean Difference (MD): 0.03; 95% Confidence Interval (CI) 0.01-0.05]. Subgroup analyses found that deep-learning methods had a better performance compared with LR (MD 0.06; 95% CI, 0.04-0.09), followed by neural networks (MD: 0.03; 95% CI, 0.03-0.03), while the AUCs of the tree-based (MD: 0.02; 95% CI -0.00-0.04) and kernel-based (MD: 0.02; 95% CI 0.02 (-0.13-0.16) methods were no different compared to LR. More than half of the studies evaluated heart failure-related rehospitalization (N = 5). For the readmission prediction among heart failure patients, ML performed better compared with LR, with a mean difference in AUC of 0.04 (95% CI, 0.01-0.07). The leave-one-out sensitivity analysis confirmed the robustness of the findings. Conclusion: Multiple ML methods were used to predict 30-day all-cause hospital readmission. Performance varied across the ML methods, with deep-learning methods showing the best performance over the LR.

EGR1 Promotes Ovarian Hyperstimulation Syndrome Through Upregulation of SOX9 Expression.

Angiogenesis is strongly associated with ovarian hyperstimulation syndrome (OHSS) progression. Early growth response protein 1 (EGR1) plays an important role in angiogenesis. This study aimed to investigate the function and mechanism of EGR1 involved in OHSS progression. RNA-sequencing was used to identify differentially expressed genes. In vitro OHSS cell model was induced by treating KGN cells with human chorionic gonadotropin (hCG). In vivo OHSS model was established in mice. The expression levels of EGR1, SOX1, and VEGF were determined by Quantitative Real-Time polymerase chain reaction (qRT-PCR), Western blot, immunofluorescence staining, and immunochemistry assay. The content of VEGF in the culture medium of human granulosa-like tumor cell line (KGN) cells was accessed by the ELISA assay. The regulatory effect of EGR1 on SRY-box transcription factor 9 (SOX9) was addressed by luciferase reporter assay and chromatin immunoprecipitation. The ERG1 and SOX9 levels were significantly upregulated in granulosa cells from OHSS patients and there was a positive association between EGR1 and SOX9 expression. In the ovarian tissues of OHSS mice, the levels of EGR1 and SOX9 were also remarkedly increased. Treatment with hCG elevated the levels of vascular endothelial growth factor (VEGF), EGR1, and SOX9 in KGN cells. Silencing of EGR1 reversed the promoting effect of hCG on VEGF and SOX9 expression in KGN cells. EGR1 transcriptionally regulated SOX9 expression through binding to its promoter. In addition, administration of dopamine decreased hCG-induced VEGF in KGN cells and ameliorated the progression of OHSS in mice, which were companied with decreased EGR1 and SOX9 expression. EGR1 has a promoting effect on OHSS progression and dopamine protects against OHSS through suppression of EGR1/SOX9 cascade. Our findings may provide new targets for the treatment of OHSS.

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.

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.

Assessing treatment switch among patients with multiple sclerosis: A machine learning approach.

Background: Patients with multiple sclerosis (MS) frequently switch their Disease-Modifying Agents (DMA) for effectiveness and safety concerns. This study aimed to develop and compare the random forest (RF) machine learning (ML) model with the logistic regression (LR) model for predicting DMA switching among MS patients. Methods: This retrospective longitudinal study used the TriNetX data from a federated electronic medical records (EMR) network. Between September 2010 and May 2017, adults (aged ≥18) MS patients with ≥1 DMA prescription were identified, and the earliest DMA date was assigned as the index date. Patients prescribed any DMAs different from their index DMAs were considered as treatment switch. . The RF and LR models were built with 72 baseline characteristics and trained with 70% of the randomly split data after up-sampling. Area Under the Curves (AUC), accuracy, recall, G-measure, and F-1 score were used to evaluate the model performance. Results: In this study, 7258 MS patients with ≥1 DMA were identified. Within two years, 16% of MS patients switched to a different DMA. The RF model obtained significantly better discrimination than the LR model (AUC = 0.65 vs. 0.63, p < 0.0001); however, the RF model had a similar predictive performance to the LR model with respect to F- and G-measures (RF: 72% and 73% vs. LR: 72% and 73%, respectively). The most influential features identified from the RF model were age, type of index medication, and year of index. Conclusions: Compared to the LR model, RF performed better in predicting DMA switch in MS patients based on AUC measures; however, judged by F- and G-measures, the RF model performed similarly to LR. Further research is needed to understand the role of ML techniques in predicting treatment outcomes for the decision-making process to achieve optimal treatment goals.

Real-world Comparative Effectiveness of Methotrexate-based Combinations for Rheumatoid Arthritis: A Retrospective Cohort Study.

PURPOSE: Guidelines recommend using disease-modifying antirheumatic drugs (DMARDs) in combination with methotrexate (MTX) for patients with rheumatoid arthritis (RA) after monotherapy. Little is known about the real-world comparative effectiveness of these MTX-DMARD combinations. This study compared the effectiveness of various MTX-based DMARD combinations for patients with RA initiating MTX-DMARD combination therapy using administrative claims database. METHODS: This retrospective cohort study included adults (aged ≥18 years) with RA who initiated MTX combination treatment with conventional synthetic DMARDs (csDMARDs), tumor necrosis factor inhibitor (TNFi) biologic DMARDs (bDMARDs), non-TNFi bDMARDs, or targeted synthetic DMARDs (tsDMARDs) between July 1, 2012, and December 31, 2013 (index date), from the MarketScan Commercial Claims Data. Patients had continuous enrollment from the 6 months of preindex period until the 12 months of postindex period. The MTX-based DMARD combination therapy cohort was defined as ≥1 MTX prescription in the first 30 days from the index date and ≥14 days overlapping use of the prescription fills of the MTX and the index DMARD. Effectiveness was measured by using the claims algorithm (dosing, switching, addition, oral glucocorticoid use, or multiple glucocorticoid injection). Propensity score analysis with the inverse probability of treatment weighting (PS-IPTW), estimated by using the generalized boosted machine learning method, was used to balance the distribution of baseline variables between the combination groups. Multivariable logistic regression using PS-IPTW was conducted to compare the effectiveness of the combination groups. Sensitivity analysis evaluated the modified effectiveness algorithms or the time to the first treatment failure. FINDINGS: A total of 3174 adult patients with RA starting an MTX-DMARD combination therapy were identified (mean [SD] age, 50 [9] years), including 1568 (49%) initiating a csDMARD + MTX, 1343 (42%) initiating TNFi + MTX, and 240 (8%) initiating non-TNFi bDMARD + MTX, and 23 (1%) initiating tsDMARD + MTX. Owing to the small sample, the tsDMARD combination group was not included in the comparative analysis. Algorithm-based therapy effectiveness was found in 9.95% of the csDMARD + MTX, 20.48% of the TNFi + MTX, and 20.83% of the non-TNFi + MTX groups. PS-IPTW showed that the csDMARD combination is less effective (adjusted odds ratio, 0.422; 95% CI, 0.341-0.524) than the TNFi combination; however, the non-TNFi biologic combination had similar effectiveness (aOR, 1.063; 95% CI, 0.680-1.662) compared to the TNFi combination. Sensitivity analyses confirmed the main results. IMPLICATIONS: Among RA patients initiating MTX-DMARD combinations, both non-TNFi biologics and TNFi-based combinations with MTX were equally effective, but csDMARD + MTX was less effective than the TNFi plus MTX.

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.

Factors influencing prescribing the first add-on disease-modifying antirheumatic drugs in patients initiating methotrexate for rheumatoid arthritis.

Background: Advances in Disease-Modifying Antirheumatic Drugs (DMARDs) have expanded the treatment landscape for Rheumatoid Arthritis (RA). Guidelines recommend adding either conventional synthetic (cs), biologic (b), or targeted synthetic (ts) DMARDs to methotrexate (MTX) for managing RA. Limited evidence exists regarding the factors that contribute to adding a DMARD agent to the MTX regimen. This study examined the factors associated with adding the first DMARD in RA patients initiating MTX. Methods: This retrospective cohort study utilized the MarketScan data (2012-2014) involving adults (aged ≥18) with RA initiating an MTX (index date) between Jul 1, 2012 and Dec 30, 2013, and with continuous enrollment for the 6-month pre-index period. The combination therapy users received the first treatment addition of DMARD starting from day 30 after the index MTX over one year period. The study focused on the addition of csDMARDs, Tumor Necrosis Factor Inhibitors (TNFi) bDMARDs, non-TNFi bDMARDs, or tsDMARDs. Baseline covariates were measured in the 6-month pre-index and grouped into predisposing, enabling, and need factors, as per the Andersen Behavior Model. Multivariable logistic regression examined the factors associated with the addition of TNFi compared to adding a csDMARD. An additional regression model evaluated the factors associated with adding any biologic (combining TNFi and non-TNFi biologics). Results: Among 8350 RA patients starting MTX, 31.92% (n = 2665) initiated any DMARD within the 1-year post-index period. Among RA patients initiating a DMARD prescription after starting MTX, 945 (11.32%) received combination therapy with treatment addition of a DMARD to MTX regimen; majority added TNFi (550, 58%), followed by csDMARD (352, 37%); non-TNF biologic (40, 4%), or tsDMARD (3, 0.3%). The tsDMARD group was limited and was not included for further analysis. The multivariable model found Preferred Provider Organization insurance coverage (odds ratio [OR], 1.43; 95% confidence interval (CI), 1.06-1.93), chronic pulmonary disease (OR, 1.98; 95% CI, 1.14-3.44), liver disease (OR, 5.24; 95% CI, 1.77-15.49), and Elixhauser score (OR, 0.91; 95% CI, 0.86-0.97) were significantly associated with the addition of TNF-α inhibitors. The separate multivariable model additionally found that patients from metropolitan areas (OR, 1.50; 95% CI, 1.04-2.16) were positively associated with adding any biological agent. Conclusions: TNFi are often added to MTX for managing RA. Enabling and need factors contribute to the prescribing of a TNFi add-on therapy in RA. Future research should examine the impact of these combination therapies on RA management.