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BackgroundThis investigation seeks to ascertain the efficacy of various machine learning models in forecasting early neurological deterioration (END) following thrombolysis in patients with acute ischemic stroke (AIS).MethodsEmploying data from the Shenyang Stroke Emergency Map database, this multicenter study compiled information on 7,570 AIS patients from 29 comprehensive hospitals who received thrombolytic therapy between January 2019 and December 2021. An independent testing cohort was constituted from 2,046 patients at the First People’s Hospital of Shenyang. The dataset incorporated 15 pertinent clinical and therapeutic variables. The principal outcome assessed was the occurrence of END post-thrombolysis. Model development was executed using an 80/20 split for training and internal validation, employing classifiers like logistic regression with lasso regularization (lasso regression), support vector machine (SVM), random forest (RF), gradient-boosted decision tree (GBDT), and multi-layer perceptron (MLP). The model with the highest area under the curve (AUC) was utilized to delineate feature significance.ResultsBaseline characteristics showed variability in END incidence between the training (n = 7,570; END incidence 22%) and external validation cohorts (n = 2,046; END incidence 10%; p

The global coronavirus disease 2019 (COVID-19) pandemic originating from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has exerted profound damage to millions of lives. Baicalein is a flavonoid that has gotten a lot of attention as a possible SARS-CoV-2 main protease (Mpro) inhibitor because it can fight off many different viruses. We prepared and screened three sets of databases, each containing 2563 baicalein analogues, against Mpro using molecular docking simulation. The data showed that several baicalein analogues exhibited stable binding energies relative to standard baicalein, indicating that they have some selectivity against Mpro. The binding properties of the top three stable analogues from each database were further analyzed with respect to their binding properties, such as binding mode, binding energy, and binding interaction of putative stable ligand confirmations at the target binding site region.

BackgroundTimely intravenous thrombolysis (IVT) is crucial for improving outcomes in acute ischemic stroke (AIS) patients. This study evaluates the effectiveness of the Acute Stroke Care Map (ASCaM) initiative in Shenyang, aimed at reducing door-to-needle times (DNT) and thus improving the timeliness of care for AIS patients.MethodsAn retrospective cohort study was conducted from April 2019 to December 2021 in 30 hospitals participating in the ASCaM initiative in Shenyang. The ASCaM bundle included strategies such as EMS prenotification, rapid stroke triage, on-call stroke neurologists, immediate neuroimaging interpretation, and the innovative Pre-hospital Emergency Call and Location Identification feature. An interrupted time series analysis (ITSA) was used to assess the impact of ASCaM on DNT, comparing 9 months pre-intervention with 24 months post-intervention.ResultsData from 9,680 IVT-treated ischemic stroke patients were analyzed, including 2,401 in the pre-intervention phase and 7,279 post-intervention. The ITSA revealed a significant reduction in monthly DNT by −1.12 min and a level change of −5.727 min post-ASCaM implementation.ConclusionThe ASCaM initiative significantly reduced in-hospital delays for AIS patients, demonstrating its effectiveness as a comprehensive stroke care improvement strategy in urban settings. These findings highlight the potential of coordinated care interventions to enhance timely access to reperfusion therapies and overall stroke prognosis.

Abstract Nucleophosmin 1 (NPM1) is a multifunctional protein that promotes tumor progression in various cancers and is associated with a poor prognosis of prostate cancer (PCa). However, the mechanism by which NPM1 exerts its malignant potential in PCa remains elusive. Here, we showed that NPM1 is overexpressed in PCa cell lines and tissues and that the dysregulation of NPM1 promotes PCa proliferation. We also demonstrated that NPM1 transcriptionally upregulates c-Myc expression in PCa cells that is diminished by blockade of bromodomain-containing protein 4 (BRD4). Furthermore, we detected a correlation between NPM1 and c-Myc in patient PCa specimens. Mechanistically, NPM1 influences and cooperates with BRD4 to facilitate c-Myc transcription to promote PCa progression. In addition, JQ1, a bromodomain and extra-terminal domain (BET) inhibitor, in combination with NPM1 inhibition suppresses PCa progression in vitro and in vivo. These results indicate that NPM1 promotes PCa progression through a c-Myc -mediated pathway via BRD4, and blockade of the NPM1–c-Myc oncogenic pathway may be a therapeutic strategy for PCa.

In this paper, by making use of some advanced tools from variational analysis and generalized differentiation, we establish necessary optimality conditions for a class of robust fractional minimax programming problems. Sufficient optimality conditions for the considered problem are also obtained by means of generalized convex functions. Additionally, we formulate a dual problem to the primal one and examine duality relations between them. In our results, by using the obtained results, we obtain necessary and sufficient optimality conditions for a class of robust fractional multi-objective optimization problems.

BackgroundAcute Ischemic Stroke (AIS) presents significant challenges in evaluating the effectiveness of Endovascular Treatment (EVT). This study develops a novel prognostic model to predict 6-month mortality post-EVT, aiding in identifying patients likely to benefit less from this intervention, thus enhancing therapeutic decision-making.MethodsWe employed a cohort of AIS patients from Shenyang First People’s Hospital, serving as the Validation set, to develop our model. LASSO regression was used for feature selection, followed by logistic regression to create a prognostic nomogram for predicting 6-month mortality post-EVT. The model’s performance was validated using a dataset from PLA Northern Theater Command General Hospital, assessing discriminative ability (C-index), calibration (calibration plot), and clinical utility (decision curve analysis). Statistical significance was set at p

BackgroundThis study aimed to compare the performance of different machine learning models in predicting symptomatic intracranial hemorrhage (sICH) after thrombolysis treatment for ischemic stroke.MethodsThis multicenter study utilized the Shenyang Stroke Emergency Map database, comprising 8,924 acute ischemic stroke patients from 29 comprehensive hospitals who underwent thrombolysis between January 2019 and December 2021. An independent testing cohort was further established, including 1,921 patients from the First People’s Hospital of Shenyang. The structured dataset encompassed 15 variables, including clinical and therapeutic metrics. The primary outcome was the sICH occurrence post-thrombolysis. Models were developed using an 80/20 split for training and internal validation. Performance was assessed using machine learning classifiers, including logistic regression with lasso regularization, support vector machine (SVM), random forest, gradient-boosted decision tree (GBDT), and multilayer perceptron (MLP). The model boasting the highest area under the curve (AUC) was specifically employed to highlight feature importance.ResultsBaseline characteristics were compared between the training cohort (n = 6,369) and the external validation cohort (n = 1,921), with the sICH incidence being slightly higher in the training cohort (1.6%) compared to the validation cohort (1.1%). Among the evaluated models, the logistic regression with lasso regularization achieved the highest AUC of 0.87 (95% confidence interval [CI]: 0.79–0.95; p

Side-chain alkylation of toluene with methanol is a green pathway to realize the one-step production of styrene under mild conditions, but the low selectivity of styrene is difficult to be improved with by-products of ethylbenzene and xylene. In this study, a new way is introduced to improve the catalytic performance by means of assisting basic compounds as co-catalysts during the toluene side-chain alkylation with methanol to styrene. As a result, high activity of side-chain alkylation appears over the basic Cs-modified zeolite catalysts prepared by ion exchange and impregnation methods. This high performance should be mainly attributed to two co-catalysis actions: (1) the promotion of basic compounds for methanol dehydrogenation to formaldehyde as the intermediate for side-chain alkylation; (2) the suppression of the styrene transfer hydrogenation on basic Cs-modified zeolites to avoid the formation of ethylbenzene. Especially for Cs2O/CsX-ex catalyst, the addition of 2% mol/mol 2-picoline in reaction mixture could achieve both 12.3% toluene conversion and 84.1% styrene selectivity. Whereas the higher concentration of 2-picoline (>6% mol/mol) caused an inhibition to the catalytic activity because the excessive basic compound poisoned the combined acid-base pathway required for the side-chain alkylation process. In addition, two possible side-chain alkylation reaction routes on Cs-modified zeolite under the different 2-picoline absorption were described.

Abstract Traditional diagnostic strategies for infectious disease detection require benchtop instruments that are inappropriate for point-of-care testing (POCT). Emerging microfluidics, a highly miniaturized, automatic, and integrated technology, are a potential substitute for traditional methods in performing rapid, low-cost, accurate, and on-site diagnoses. Molecular diagnostics are widely used in microfluidic devices as the most effective approaches for pathogen detection. This review summarizes the latest advances in microfluidics-based molecular diagnostics for infectious diseases from academic perspectives and industrial outlooks. First, we introduce the typical on-chip nucleic acid processes, including sample preprocessing, amplification, and signal read-out. Then, four categories of microfluidic platforms are compared with respect to features, merits, and demerits. We further discuss application of the digital assay in absolute nucleic acid quantification. Both the classic and recent microfluidics-based commercial molecular diagnostic devices are summarized as proof of the current market status. Finally, we propose future directions for microfluidics-based infectious disease diagnosis.

A series of proof-of-concept models of polyoxomolybdates with different protonated disubstituted aniline counterions and the same β-Mo8O26 polyanion were synthesized to study the mechanism governing the formation of the intermolecular charge transfer (inter-CT) band. [ABSTRACT FROM AUTHOR]

Aims: To investigate the association of several single-nucleotide polymorphisms (SNPs) within methylenetetrahydrofolate reductase (MTHFR) gene, and additional gene–environment interaction with ischemic stroke (IS) risk. Methods: Testing for Hardy–Weinberg equilibrium in controls was conducted using SNPstats (online software: http://bioinfo.iconcologia.net/SNPstats). Generalized multifactor dimensionality reduction (GMDR) was used to screen the best interaction combination among four SNPs within MTHFR gene and smoking or alcohol drinking. Results: The frequency of the rs4846049-A allele was 28.6% in IS patients and 19.1% in normal controls, in addition, the frequency of the rs3737967-T allele was 27.9% in IS patients and 20.3% in normal controls, which was also indicating a statistically significant difference. The rs4846049-A and rs3737967-T were associated with an increased risk of IS risk; adjusted odds ratios (ORs) (95% confidence interval [CI]) were 1.76 (1.28–2.13) and 1.51 (1.13–1.97), respectively. GMDR model found significant gene–alcohol drinking interaction combination, but no significant gene–tobacco smoking interaction combinations. In order to obtain the odds ratios and 95% CI for the joint effects of gene–alcohol drinking on IS, we conducted stratified analysis for interaction effect using logistic regression. We found that alcohol drinkers with rs4846049-CA/AA genotype also have the highest IS risk, compared with never drinkers with rs4846049-CC genotype, OR (95% CI) = 3.12 (1.83–4.45), after adjustment for age, smoke, and smoking status. Conclusions: The rs4846049-A and rs3737967-T, gene–environment interaction between rs1764391 and rs918592, gene–environment interaction between rs4846049 and alcohol drinking were all associated with increased IS risk.

To explore the signature function of the tumor mutational burden (TMB) and potential biomarkers in prostate cancer (PCa), transcriptome profiles, somatic mutation data, and clinicopathologic feature information were downloaded from The Cancer Genome Atlas (TCGA) database. R software package was used to generate a waterfall plot to summarize the specific mutation information and calculate the TMB value of PCa. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to select the hub genes related to the TMB from the ImmPort network to build a risk score (RS) model to evaluate prognostic values and plot Kaplan–Meier (K-M) curves to predict PCa patients survival. The results showed that PCa patients with a high TMB exhibited higher infiltration of CD8+ T cells and CD4+ T cells and better overall survival (OS) than those with a low TMB. The anti-Mullerian hormone (AMH), baculoviral IAP repeat-containing 5 (BIRC5), and opoid receptor kappa 1 (OPRK1) genes were three hub genes and their copy number variation (CNV) was relatively likely to affect the infiltration of immune cells. Moreover, PCa patients with low AMH or BIRC5 expression had a longer survival time and lower cancer recurrence, while elevated AMH or BIRC5 expression favored PCa progression. In contrast, PCa patients with low OPRK1 expression had poorer OS in the early stage of PCa and a higher recurrent rate than those with high expression. Taken together, these results suggest that the TMB may be a promising prognostic biomarker for PCa and that AMH, OPRK1, and BIRC5 are hub genes affecting the TMB; AMH, OPRK1, and BIRC5 could serve as potential immunotherapeutic targets for PCa treatment.

Abstract Castration‐resistant prostate cancer (CRPC) is the latest stage of PCa, and there is almost no effective treatment available for the patients with CRPC when next‐generation androgen deprivation therapy drugs, such as enzalutamide (ENZ), fail. The androgen receptor (AR) plays key roles in PCa and CRPC progression and drug resistance. Histone acetyltransferase 1 (HAT1) has recently been reported to be highly expressed in some tumors, such as lung carcinoma. However, what relationship between the AR and HAT1, and whether or how HAT1 plays roles in CRPC progression and drug resistance remain elusive. In the present study, we found that HAT1 is highly expressed in PCa cells, and the overexpression of HAT1 is linked with CRPC cell proliferation. Moreover, the HAT1 expression is positively correlated with the expression of AR, including both AR‐FL (full‐length) and AR‐V7 (variant 7), which is mainly mediated by a bromodomain containing protein 4 (BRD4) ‐mediated pathway. Furthermore, knockdown of HAT1 can re‐sensitize the response of CRPC cells to ENZ treatment in cells and mouse models. In addition, ascorbate was observed to decrease AR expression through downregulation of HAT1 expression. Collectively, our findings reveal a novel AR signaling regulation pathway in PCa and CRPC and suggest that HAT1 serves as a critical oncoprotein and an ideal target for the treatment of ENZ resistance in CRPC patients.