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The reaction-diffusion system is naturally used in chemistry to represent substances reacting and diffusing over the spatial domain. Its solution illustrates the underlying process of a chemical reaction and displays diverse spatial patterns of the substances. Numerical methods like finite element method (FEM) are widely used to derive the approximate solution for the reaction-diffusion system. However, these methods require long computation time and huge computation resources when the system becomes complex. In this paper, we study the physics of a two-dimensional one-component reaction-diffusion system by using machine learning. An encoder-decoder based convolutional neural network (CNN) is designed and trained to directly predict the concentration distribution, bypassing the expensive FEM calculation process. Different simulation parameters, boundary conditions, geometry configurations and time are considered as the input features of the proposed learning model. In particular, the trained CNN model manages to learn the time-dependent behaviour of the reaction-diffusion system through the input time feature. Thus, the model is capable of providing concentration prediction at certain time directly with high test accuracy (mean relative error <3.04%) and 300 times faster than the traditional FEM. Our CNN-based learning model provides a rapid and accurate tool for predicting the concentration distribution of the reaction-diffusion system.

Background: The SPOTLIGHT trial demonstrated that zolbetuximab plus mFOLFOX6 (ZOL-FO) as a first-line regimen compared with placebo plus mFOLFOX6 (PLB-FO) conferred clinical benefits to patients with CLDN18.2-positive, HER2-negative advanced gastric or gastroesophageal junction (G/GEJ) adenocarcinoma. However, due to the high cost of zolbetuximab, whether ZOL-FO is cost-effective compared with PLB-FO is unclear. This study aimed to evaluate the cost-effectiveness of ZOL-FO as a first-line treatment option for CLDN18.2-positive, HER2-negative advanced G/GEJ adenocarcinoma from the perspective of the Chinese healthcare system. Methods: Markov models with three different health states were developed to assess the cost-effectiveness of ZOL-FO as a first-line treatment option for CLDN18.2-positive, HER2-negative advanced G/GEJ adenocarcinoma. Clinical efficacy data were obtained from the SPOTLIGHT trial; the drug’s cost was calculated at national bid prices, and other costs and utility values were obtained from the published literature. Outcomes included total costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs). The model’s robustness was verified using one-way sensitivity and probabilistic sensitivity analyses. Results: The ZOL-FO group gained 1.64 QALYs at$87,746.35, while the PLB-FO group gained 1.23 QALYs at $ 11,947.81. The ICER for ZOL-FO versus PLB-FO was$185,353.28 per QALY gained. The parameters exerting an important impact on the model results were the price of zolbetuximab, body surface area, and progression-free survival utility. At a willingness-to-pay threshold of $ 38,201/QALY, ZOL-FO had a 0% probability of cost-effectiveness compared with PLB-FO. Conclusion: From the perspective of the Chinese healthcare system, ZOL-FO is unlikely to be cost-effective as the first-line treatment option for CLDN18.2-positive, HER2-negative advanced G/GEJ adenocarcinoma.

Background: The findings of the CAPSTONE-1 trial showed that adebrelimab in combination with chemotherapy (etoposide-carboplatin) (ADCHM) is clinically beneficial as a first-line treatment for patients with extensive-stage small cell lung cancer (ES-SCLC), compared with placebo plus chemotherapy (PLCHM, etoposide-carboplatin). However, owing to the higher cost of adebrelimab, it is unclear whether ADCHM is cost-effective compared with PLCHM. This study aimed to evaluate the cost-effectiveness of ADCHM as a first-line treatment for patients with ES-SCLC from the perspective of the Chinese healthcare system. Methods: A Markov model with three health states was developed to assess the cost-effectiveness of ADCHM as a first-line treatment option with ES-SCLC. Clinical data were obtained from the CAPSTONE-1 trial. Costs of the drug were calculated at national tender prices, and other costs and utility values were obtained from published literature. The outcomes included life years (LYs), quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs). One-way sensitivity analysis and probabilistic sensitivity analysis were used to validate the robustness of the model. Results: The ADCHM group achieved 1.21 QALYs (2.47 LYs) for$25,312, whereas the PLCHM group achieved 0.81 QALYs (1.59 LYs) for $ 14,846. The ICER for ADCHM versus PLCHM was$25914 per QALY gained. The variables with the greatest impact on the model results were the utility value of progressive disease, the utility value of progression-free survival, and the price of adebrelimab (100 mg). At a willingness-to-pay threshold of $ 37,653/QALY, ADCHM had an 89.1% probability of being cost-effective compared with PLCHM. Conclusion: ADCHM may be a cost-effective first-line treatment strategy for ES-SCLC from the perspective of the Chinese healthcare system.

Elder abuse and psychological distress are both important geriatric syndromes and are independently associated with premature morbidity and mortality. Despite recent advances, there has been little systematic exploration of the association between elder abuse and psychological distress. This systematic review synthesizes the qualitative and quantitative studies on the relationship between elder abuse and psychological distress, namely psychological distress as a risk factor and/or a consequence of elder abuse. Moreover, through this review, future research directions for elder abuse and psychological distress and their implications for practice and policy to improve the health and aging of vulnerable populations are also highlighted.

Despite equivalent or lower lifetime and past-year prevalence of mental disorder among racial/ethnic minorities compared to non-Latino Whites in the United States, evidence suggests that mental disorders are more persistent among minorities than non-Latino Whites. But, it is unclear how nativity and socioeconomic status contribute to observed racial/ethnic differences in prevalence and persistence of mood, anxiety, and substance disorders. Data were examined from a coordinated series of four national surveys that together assessed 21,024 Asian, non-Latino Black, Latino, and non-Latino White adults between 2001 and 2003. Common DSM-IV mood, anxiety, and substance disorders were assessed using the Composite International Diagnostic Interview. Logistic regression analyses examined how several predictors (e.g., race/ethnicity, nativity, education, income) and the interactions between those predictors were associated with both 12-month disorder prevalence and 12-month prevalence among lifetime cases. For the second series of analyses, age of onset and time since onset were used as additional control variables to indirectly estimate disorder persistence. Non-Latino Whites demonstrated the highest unadjusted 12-month prevalence of all disorder types (p < 0.001), though differences were also observed across minority groups. In contrast, Asian, Latino, and Black adults demonstrated higher 12-month prevalence of mood disorders among lifetime cases than Whites (p < 0.001) prior to adjustments Once we introduced nativity and other relevant controls (e.g., age, sex, urbanicity), US-born Whites with at least one US-born parent demonstrated higher 12-month mood disorder prevalence than foreign-born Whites or US-born Whites with two foreign parents (OR = 0.51, 95% CI = [0.36, 0.73]); this group also demonstrated higher odds of past-year mood disorder than Asian (OR = 0.59, 95% CI = [0.42, 0.82]) and Black (OR = 0.70, 95% CI = [0.58, 0.83]) adults, but not Latino adults (OR = 0.89, 95% CI = [0.74, 1.06]). Racial/ethnic differences in 12-month mood and substance disorder prevalence were moderated by educational attainment, especially among adults without a college education. Additionally, racial/ethnic minority groups with no more than a high school education demonstrated more persistent mood and substance disorders than non-Latino Whites; these relationships reversed or disappeared at higher education levels. Nativity may be a particularly relevant consideration for diagnosing mood disorder among non-Latino Whites; additionally, lower education appears to be associated with increased relative risk of persistent mood and substance use disorders among racial/ethnic minorities compared to non-Latino Whites. •Non-Latino Whites most likely to have 12-month disorders, even with SES controls.•Link between race/ethnicity and mood disorder varied by nativity among Whites.•Race/ethnicity interacted with education, but not income, to predict prevalence.•Racial/ethnic minority groups had more persistent mood disorders than Whites.•Observed links to persistent mood and substance disorders varied by education level.

Cannabinoid metabolites have been reported to be more potent than their parent compounds. Among them, ajulemic acid (AJA) is a side-chain analog of Δ9-THC-11-oic acid, which would be a good template structure for the discovery of more potent analogues. Herein, we optimized the key allylic oxidation step to introduce the C-11 hydroxy group with a high yield. A series of compounds was prepared with this condition applied including HU-210, 11-nor-Δ8-tetrahydrocannabinol (THC)-carboxylic acid and Δ9-THC-carboxylic acid.

Spontaneous bacterial peritonitis (SBP) is a life-threatening complication in patients with cirrhosis. We aimed to develop an explainable machine learning model to achieve the early prediction and outcome interpretation of SBP. We used CatBoost algorithm to construct MODEL-1 with 46 variables. After dimensionality reduction, we constructed MODEL-2. We calculated and compared the sensitivity and negative predictive value (NPV) of MODEL-1 and MODEL-2. Finally, we used the SHAP (SHapley Additive exPlanations) method to provide insights into the model’s outcome or prediction. MODEL-2 (AUROC: 0.822; 95% confidence interval [CI] 0.783–0.856), liked MODEL-1 (AUROC: 0.822; 95% CI 0.784–0.856), could well predict the risk of SBP in cirrhotic ascites patients. The 6 most influential predictive variables were total protein, C-reactive protein, prothrombin activity, cholinesterase, lymphocyte ratio and apolipoprotein A1. For binary classifier, the sensitivity and NPV of MODEL-1 were 0.894 and 0.885, respectively, while for MODEL-2 they were 0.927 and 0.904, respectively. We applied CatBoost algorithm to establish a practical and explainable prediction model for risk of SBP in cirrhotic patients with ascites. We also identified 6 important variables closely related to the occurrence of SBP.

Carfentanil is an ultra-potent synthetic opioid. The Russian police force used both carfentanil and remifentanil to resolve a hostage incident in Moscow. This reported use sparked an interest in the pharmacology and toxicology of carfentanil in the human body, and data on its metabolites were later published. However, there have been few studies on the synthesis of carfentanil metabolites, and biological extraction has also put forward large uncertainty in subsequent studies. The aim of the present study is to investigate the synthesis of biphasic metabolites that are unique to carfentanil. The purpose was to produce corresponding metabolites conveniently, quickly, and at low cost that can be used for comparison with published structures and to confirm the administration of carfentanil.

Purpose: Results from the LAUNCH trial suggest transarterial chemoembolization (TACE) in combination with lenvatinib is significantly more effective than lenvatinib as a first-line treatment option for advanced hepatocellular carcinoma (HCC). However, the cost of TACE is substantial. This study compares the cost-effectiveness of TACE in combination with lenvatinib (TACE-LEN) with that of lenvatinib alone as the first-line treatment for advanced HCC from the perspective of the Chinese healthcare system. Methods: Markov models of different health states were constructed to simulate first-line treatment, disease progression, and survival in patients with advanced HCC. Clinical efficacy was obtained from the LAUNCH trial. The cost of drugs was sourced from national tender prices, and the treatment cost of weight-decreased was obtained from the Fujian Provincial Bureau of Prices. Other costs and utility values were based on the published literature. Total costs, life years (LYs), quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs) comprised the model output. One-way and probabilistic sensitivity analyses were performed to validate model robustness and subgroup analyses were also conducted. Results: Analysis of the model showed that compared to lenvatinib, TACE-LEN improved effectiveness by 1.60 QALYs at a total cost increase of$48,874.69, with an ICER value of $ 30,482.13/QALY. A one-way sensitivity analysis found that the progression-free survival utility value per year had the greatest impact on the model. A probabilistic sensitivity analysis showed that TACE-LEN had a 97.9% probability of being cost-effective as the first-line treatment option for advanced HCC compared to lenvatinib when the willingness-to-pay (WTP) value was$38,201/QALY (three times the Chinese GDP per capita in 2022). Subgroup analysis showed that all subgroups of patients preferred TACE-LEN. However, when the WTP threshold was below $ 30,300/QALY, TACE-LEN is no longer cost-effective. Conclusion: Our study found TACE-LEN to be a cost-effective treatment option for patients with advanced HCC compared to lenvatinib from a Chinese healthcare system perspective, but not so in low-income provinces in China.

Stress exposures across the lifespan, such as childhood adversities and financial strain, may contribute to social disparities in cognitive outcomes. Individuals from socially disadvantaged backgrounds tend to be exposed to more stressors than their advantaged counterparts. Prior research has indicated that psychosocial stressors are key determinants of physical and mental health as well as health disparities. However, their roles in Alzheimer’s Disease and Related Dementias (ADRD) risk have received considerably less attention. In addition to understanding and intervening directly on stressors, elucidating modifiable factors, such as social networks, further along these pathways may offer additional intervention avenues. Greater social networks have been consistently noted as predictors of lower ADRD risk, yet the structure and impact of social networks may vary across racial and ethnic groups, making it essential to assess the associations between social networks and ADRD risk in diverse racial and ethnic groups. Psychosocial stressors may lead to sleep disturbances (e.g., sleep apnea and insomnia), biobehavioral factors that disproportionately affect racial and ethnic minorities. With the availability of effective treatments for some types of sleep disturbances, evaluating how these disturbances contribute to ADRD risk across different racial and ethnic groups could identify novel targets for reducing disparities in ADRD. This presentation will cover a series of studies applying advanced epidemiologic methods, including causal inference, to investigate the roles of life course stress and resilience factors in contributing to disparities in cognitive aging.