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Introduction: Coronavirus 2019 (COVID-19) has had a devastating impact on mental health and access to addiction treatment in the United States, including in California, which resulted in the highest rates of emergency department visits (ED) for opioid poisoning in 2020. As California slowly returns to pre-pandemic normalcy, it remains uncertain whether the rates of opioid-related events have slowed down over time. We hypothesized that the number of opioid-related ED visits were exacerbated after the period of the COVID-19 pandemic and continue at a high rate in the present.Methods: In this analysis we searched the University of California (UC) Health Data Warehouse—a database of electronic health records from six UC health centers—for opioid-related ED visits, identifiying using the following International Classification of Diseases, 10th Ed, Clinical Modification codes: F11 codes, and T40.0*, T40.1*, T40.2*, T40.3*, T40.4*, T40.6*. Opioid overdose-associated visits were classified by types of opioids involved: heroin (T40.1*); prescription opioids (T40.2* or T40.3*); and synthetic opioids (T40.4*). We performed interrupted time analysis to estimate the immediate (level) change and change-in-time trend (trend change), from before (January 2018–October 2019) and during the pandemic (April 2020–December 2022). Monthly visit rates were evaluated with negative binomial regression adjusted for first-order autoregression and using all-cause ED counts as the offset. We present effect sizes as rate ratios (RR) and 95% confidence intervals (CI), tested at α = .05.Results: Before COVID-19, a steadily increasing trend in ED visit rates was observed for all outcomes (P < 0.05) except synthetic opioids. Total opioid-related ED visit rates increased by 15% (RR 1.15, 95%CI 1.02–1.29, P = 0.20) immediately after March 2020 before decreasing by 0.5% every month, albeit without statistical significance (RR .995, 95% CI .991–1.00, P = 0.06). Similar trends were observed with prescription opioid overdoses, with a step increase of 44% (RR 1.44, 95% CI 1.10–1.89, P = .008) before plateauing after March 2020 (RR 1.01, 95% CI .998–1.02, P = 0.12). After March 2020, ED visit rates for synthetic opioid overdoses were increasing steadily by 4% every month (RR 1.04, 95% CI 1.02–1.06, P = .001), unlike with heroin, which was observed with an 8% monthly reduction (RR .92, 95% CI .90–.93, P < .001). No immediate increase in visit rates was observed for either opioid.Conclusion: While opioid-related ED admissions among the UC health centers showed an overall decrease, prescription and synthetic opioid overdoses remained significantly higher than pre-pandemic trends as of December 2022. A multilevel approach to improve awareness of new opioid health policies could ameliorate these alarming rises in the post-pandemic era.

BACKGROUND: Alzheimers disease (AD) is the most common neurodegenerative disease. Studying the effects of drug treatments on multiple health outcomes related to AD could be beneficial in demonstrating which drugs reduce the disease burden and increase survival. METHODS: We conducted a comprehensive causal inference study implementing doubly robust estimators and using one of the largest high-quality medical databases, the Oracle Electronic Health Records (EHR) Real-World Data. Our work was focused on the estimation of the effects of the two common Alzheimers disease drugs, Donepezil and Memantine, and their combined use on the five-year survival since initial diagnosis of AD patients. Also, we formally tested for the presence of interaction between these drugs. RESULTS: Here, we show that the combined use of Donepezil and Memantine significantly elevates the probability of five-year survival. In particular, their combined use increases the probability of five-year survival by 0.050 (0.021, 0.078) (6.4%), 0.049 (0.012, 0.085), (6.3%), 0.065 (0.035, 0.095) (8.3%) compared to no drug treatment, the Memantine monotherapy, and the Donepezil monotherapy respectively. We also identify a significant beneficial additive drug-drug interaction effect between Donepezil and Memantine of 0.064 (0.030, 0.098). CONCLUSIONS: Based on our findings, adopting combined treatment of Memantine and Donepezil could extend the lives of approximately 303,000 people with AD living in the USA to be beyond five-years from diagnosis. If these patients instead have no drug treatment, Memantine monotherapy or Donepezil monotherapy they would be expected to die within five years.

Osteosarcoma (OS) is the most common malignant bone tumor with a poor prognosis. Here, we show that the nuclear receptor RORγ may serve as a potential therapeutic target in OS. OS exhibits a hyperactivated oxidative phosphorylation (OXPHOS) program, which fuels the carbon source to promote tumor progression. We found that RORγ is overexpressed in OS tumors and is linked to hyperactivated OXPHOS. RORγ induces the expression of PGC-1β and physically interacts with it to activate the OXPHOS program by upregulating the expression of respiratory chain component genes. Inhibition of RORγ strongly inhibits OXPHOS activation, downregulates mitochondrial functions, and increases ROS production, which results in OS cell apoptosis and ferroptosis. RORγ inverse agonists strongly suppressed OS tumor growth and progression and sensitized OS tumors to chemotherapy. Taken together, our results indicate that RORγ is a critical regulator of the OXPHOS program in OS and provides an effective therapeutic strategy for this deadly disease.

Vitrimers are an innovative class of polymers that boast a remarkable fusion of mechanical and dynamic features, complemented by the added benefit of end-of-life recyclability. This extraordinary blend of properties makes them highly attractive for a variety of applications, such as the automotive sector, soft robotics, and the aerospace industry. At their core, vitrimer materials consist of crosslinked covalent networks that have the ability to dynamically reorganize in response to external factors, including temperature changes, pressure variations, or shifts in pH levels. In this review, the aim is to delve into the latest advancements in the theoretical understanding and computational design of vitrimers. The review begins by offering an overview of the fundamental principles that underlie the behavior of these materials, encompassing their structures, dynamic behavior, and reaction mechanisms. Subsequently, recent progress in the computational design of vitrimers is explored, with a focus on the employment of molecular dynamics (MD)/Monte Carlo (MC) simulations and density functional theory (DFT) calculations. Last, the existing challenges and prospective directions for this field are critically analyzed, emphasizing the necessity for additional theoretical and computational advancements, coupled with experimental validation.

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

Brain function connectivity, derived from functional magnetic resonance imaging (fMRI), has enjoyed high popularity in studies of Autism Spectrum Disorder (ASD) diagnosis. Albeit rapid progress has been made, most studies still suffer from several knotty issues: (1) the hardship of effectively modeling the sophisticated brain neuronal connectivity;(2) the dimensionality explosion resulted from excessive voxels in each fMRI sample; (3) the poor interpretability giving rise to unpersuasive diagnosis. To ameliorate these issues, we propose a GCN-based model, namely ConResNet. Specifically, a convolutional restructuring attention is designed for efficiently ROI-interaction feature extraction. Besides, we embed a salient graph pooling method to sift salient nodes. Extensive experiments conducted on ABIDE dataset demonstrate ConResNet achieves state-of-the-art performance. Moreover, the most salient brain regions predicted by ConResNet are closely consistent with theoretical knowledge in the medical domain, providing potential biomarkers for ASD clinical diagnosis.

Metabolic and epigenetic reprogramming play important roles in cancer therapeutic resistance. However, their interplays are poorly understood. We report here that elevated TIGAR (TP53-induced glycolysis and apoptosis regulator), an antioxidant and glucose metabolic regulator and a target of oncogenic histone methyltransferase NSD2 (nuclear receptor binding SET domain protein 2), is mainly localized in the nucleus of therapeutic resistant tumor cells where it stimulates NSD2 expression and elevates global H3K36me2 mark. Mechanistically, TIGAR directly interacts with the antioxidant master regulator NRF2 and facilitates chromatin recruitment of NRF2, H3K4me3 methylase MLL1 and elongating Pol-II to stimulate the expression of both new (NSD2) and established (NQO1/2, PRDX1 and GSTM4) targets of NRF2, independent of its enzymatic activity. Nuclear TIGAR confers cancer cell resistance to chemotherapy and hormonal therapy in vitro and in tumors through effective maintenance of redox homeostasis. In addition, nuclear accumulation of TIGAR is positively associated with NSD2 expression in clinical tumors and strongly correlated with poor survival. These findings define a nuclear TIGAR-mediated epigenetic autoregulatory loop in redox rebalance for tumor therapeutic resistance.

BackgroundCardiovascular and other circulatory system diseases have been implicated in the severity of COVID-19 in adults. This study provides a super learner ensemble of models for predicting COVID-19 severity among these patients.MethodThe COVID-19 Dataset of the Cerner Real-World Data was used for this study. Data on adult patients (18 years or older) with cardiovascular diseases between 2017 and 2019 were retrieved and a total of 13 of these conditions were identified. Among these patients, 33,042 admitted with positive diagnoses for COVID-19 between March 2020 and June 2020 (from 59 hospitals) were identified and selected for this study. A total of 14 statistical and machine learning models were developed and combined into a more powerful super learning model for predicting COVID-19 severity on admission to the hospital.ResultLASSO regression, a full extreme gradient boosting model with tree depth of 2, and a full logistic regression model were the most predictive with cross-validated AUROCs of 0.7964, 0.7961, and 0.7958 respectively. The resulting super learner ensemble model had a cross validated AUROC of 0.8006 (range: 0.7814, 0.8163). The unbiased AUROC of the super learner model on an independent test set was 0.8057 (95% CI: 0.7954, 0.8159).ConclusionHighly predictive models can be built to predict COVID-19 severity of patients with cardiovascular and other circulatory conditions. Super learning ensembles will improve individual and classical ensemble models significantly.

Prostate cancer (PCa) patients who progress to metastatic castration-resistant PCa (mCRPC) mostly have poor outcomes due to the lack of effective therapies. Our recent study established the orphan nuclear receptor RORγ as a novel therapeutic target for CRPC. Here, we reveal that elaiophylin (Elai), an antibiotic from Actinomycete streptomyces, is a novel RORγ antagonist and showed potent antitumor activity against CRPC in vitro and in vivo. We demonstrated that Elai selectively binded to RORγ protein and potently blocked RORγ transcriptional regulation activities. Structure-activity relationship studies showed that Elai occupied the binding pocket with several key interactions. Furthermore, Elai markedly reduced the recruitment of RORγ to its genomic DNA response element (RORE), suppressed the expression of RORγ target genes AR and AR variants, and significantly inhibited PCa cell growth. Importantly, Elai strongly suppressed tumor growth in both cell line based and patient-derived PCa xenograft models. Taken together, these results suggest that Elai is novel therapeutic RORγ inhibitor that can be used as a drug candidate for the treatment of human CRPC.

Herein, we synthesized new manganese(I) complexes coordinated with the tetradentate ligand PNNP. The complexes show higher activity and excellent substituent tolerance in contrast to their manganese counterparts and are applicable in the hydrogenation of a wide range of aromatic, aliphatic and heterocyclic ketones to their corresponding alcohols. [ABSTRACT FROM AUTHOR]