Extraction, Identification, and Quantification of Polyphenols from the Theobroma cacao L. Fruit: Yield vs. Environmental Friendliness.

The cacao fruit is a rich source of polyphenols, including flavonoids and phenolic acids, which possess significant health benefits. The accurate identification and quantification of these bioactive compounds extracted from different parts of the cacao fruit, such as pods, beans, nibs, and cacao shells, require specific treatment conditions and analytical techniques. This review presents a comprehensive comparison of extraction processes and analytical techniques used to identify and quantify polyphenols from various parts of the cacao fruit. Additionally, it highlights the environmental impact of these methods, exploring the challenges and opportunities in selecting and utilizing extraction, analytical, and impact assessment techniques, while considering polyphenols' yield. The review aims to provide a thorough overview of the current knowledge that can guide future decisions for those seeking to obtain polyphenols from different parts of the cacao fruit.

Economic and Humanistic Burden of Rheumatoid Arthritis: Results From the US National Survey Data 2018-2020.

OBJECTIVE: Our objective was to estimate the economic and humanistic burden among US adults with rheumatoid arthritis (RA). METHODS: This study analyzed results from the Medical Expenditure Panel Survey from 2018 to 2020. Adults (aged ≥18 years) self-reporting with RA or with the presence of the International Classification of Disease, 10th Revision clinical modification codes were identified. Healthcare expenditures (inpatient care, outpatient care, emergency department, office visits, prescription medications, home health, and others) were measured. The Short Form 12 Health Survey physical component summary (PCS), mental component summary (MCS), activities of daily living (ADL), and instrumental ADL (IADL) were measured. Two-part models assessed the incremental increase in the health care expenditures for the RA group compared to the non-RA group. In addition, the multivariable linear regression was used to evaluate the marginal difference in PCS and MCS between those with RA and those without RA, whereas the multivariable logistic regression models were used to evaluate the association between ADL and IADL by RA status. RESULTS: Annually, 4.27 million adults with RA were identified. The two-part model showed significantly higher total annual healthcare expenditures in the RA group than non-RA group (mean $3,382.971 [95% confidence interval (CI) $1,816.50-$4,949.44]). Compared to the non-RA group, the RA group was associated with lower PCS scores (mean 4.78 [95% CI 3.47-6.09]) and similarly lower MCS scores (mean -0.84 [95% CI -2.18 to 0.50]), as well as increased odds of requesting ADL (adjusted odds ratio [aOR] 2.02 [95% CI 1.59-2.56]) and IADL assistance (aOR 2.11 [95% CI 1.57-2.84]). CONCLUSION: RA was associated with higher health care expenditures, particularly prescription medication costs, and was associated with suboptimal quality of life.

Interactive roles of co-solvents and lemon-oil composition in the fabrication of dilutable clear emulsions.

Clear emulsions are used as flavor carriers by the beverage industry because of their favorable optical properties. A transparent microemulsion with small droplets requires a high concentration of surfactants, and is often non-dilutable, posing a significant challenge to their application in the food industry. The formation of dilutable microemulsions by modulating the compatibility of oil composition and co-solvents was studied. While single-fold lemon oil exhibited poor loading capacity overall, no precipitation occurred due to the stronger interaction between monoterpenes and sucrose monopalmitate (SMP). Conversely, emulsification of five-fold lemon oil with 20 % ethanol demonstrated a higher loading capacity and a stronger dilution stability than other lemon oils. This is likely due to the balanced composition of surface-active monoterpenes and other components in five-fold lemon oil which facilitated the effective use of micellar space and aided in the retention of both surfactants and co-solvents post-dilution. The emulsification of higher-folded lemon oil, however, was favored by the use of propylene glycol as a surfactant exhibiting stronger dilution stability than ethanol, though it required twice as much co-solvent. The high concentration of surface-active monoterpene in the lower-folded lemon oils competes with propylene glycol for interfacial incorporation. This study demonstrated that co-solvents and oil composition play interactive roles in producing dilutable optically clear emulsions, and it provides a blueprint for the food industry to design colloidal systems using a minimum of surfactants.

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.

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.

Follicular CD8+ T cells promote immunoglobulin production and demyelination in multiple sclerosis and a murine model.

Emerging evidence underscores the importance of CD8+ T cells in the pathogenesis of multiple sclerosis (MS), but the precise mechanisms remain ambiguous. This study intends to elucidate the involvement of a novel subset of follicular CD8+ T cells (CD8+CXCR5+ T) in MS and an experimental autoimmune encephalomyelitis (EAE) murine model. The expansion of CD8+CXCR5+ T cells was observed in both MS patients and EAE mice during the acute phase. In relapsing MS patients, higher frequencies of circulating CD8+CXCR5+ T cells were positively correlated with new gadolinium-enhancement lesions in the central nervous system (CNS). In EAE mice, frequencies of CD8+CXCR5+ T cells were also positively correlated with clinical scores. These cells were found to infiltrate into ectopic lymphoid-like structures in the spinal cords during the peak of the disease. Furthermore, CD8+CXCR5+ T cells, exhibiting high expression levels of ICOS, CD40L, IL-21, and IL-6, were shown to facilitate B cell activation and differentiation through a synergistic interaction between CD40L and IL-21. Transferring CD8+CXCR5+ T cells into naïve mice confirmed their ability to enhance the production of anti-MOG35-55 antibodies and contribute to the disease progression. Consequently, CD8+CXCR5+ T cells may play a role in CNS demyelination through heightening humoral immune responses.

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.

Factors Associated With Adherence of Cervical Cancer Screening in Women With Systemic Lupus Erythematosus.

OBJECTIVE: The objective is 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 (age 21-64 years) with SLE. We collected demographics, clinical characteristics, constructs of the Health Beliefs Model (HBM) (ie, 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 years (interquartile range 32-52 years). 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 (odds ratio 0.59, 95% confidence interval [CI] 0.39-0.89; P = 0.01), which remained statistically significant after adjusting for baseline demographics and drug therapy in a multivariable model (odds ratio 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: Patients with SLE with high disease activity undergo cervical cancer screening less frequently than those with low disease activity. Perceived barriers to cervical cancer screening are moderately correlated with decreased screening. These data highlight the need to develop strategies to increase cervical cancer screening in this high-risk patient population.

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.

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.

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.

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.

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.

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.