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Backgroud and objectivesThe implementation of patient-specific quality assurance (PSQA) has become a crucial aspect of the radiation therapy process. Machine learning models have demonstrated their potential as virtual QA tools, accurately predicting the gamma passing rate (GPR) of volumetric modulated arc therapy (VMAT)plans, thereby ensuring safe and efficient treatment for patients. However, there is limited multi-center research dedicated to predicting the GPR. In this study, a dosiomics-based machine learning approach was employed to construct a prediction model for classifying GPR in multiple radiotherapy institutions. Additionally, the model’s performance was compared by evaluating the impact of two distinct feature selection methods.MethodsA retrospective data collection was conducted on 572 VMAT patients across three radiotherapy institutions. Utilizing a three-dimensional dose verification technique grounded in real-time measurements, γ analysis was conducted according to the criteria of 3%/2 mm and 2%/2 mm, employing a dose threshold of 10% along with absolute dose and global normalization mode. Dosiomics features were extracted from the dose files, and distinct subsets of features were selected as inputs for the model using the random forest (RF) and RF combined with SHapley Additive exPlanations (SHAP) methods. The data underwent training using the extreme gradient boosting (XGBoost) algorithm, and the model’s classification performance was assessed through F1-score and area under the curve (AUC) values.ResultsThe model exhibited optimal performance under the 3%/2 mm criteria, utilizing a subset of 20 features and attaining an AUC value of 0.88 and an F1-score of 0.89. Similarly, under the 2%/2 mm criteria, the model demonstrated superior performance with a subset of 10 features, resulting in an AUC value of 0.91 and an F1-score of 0.89. The feature selection methods of RF and RF + SHAP have achieved good model performance by selecting as few features as possible.ConclusionBased on the multi-center PSQA results, it is possible to utilize dosiomics features extracted from dose files to construct a machine learning predictive model. This model demonstrates excellent discriminative abilities, thus promoting the progress of gamma passing rate prognostic models in clinical application and implementation. Furthermore, it holds potential in providing patients with secure and efficient personalized QA management, while also reducing the workload of medical physicists.

The cumulative deformation properties of subgrade soil under cyclic traffic loads are critical for optimizing pavement structure design and ensuring long-term highway structural performance. This study aims to investigate the coupling effect of freeze-thaw cycles and cyclic loads on the cumulative deformation behaviors and meso-structure of coarse-grained saline soil (CGSS) subgrade filling in high-cold areas. Dynamic triaxial tests and computed tomography (CT) scanning were conducted to analyze the CGSS under different working conditions. The research focused on the dynamic deformation development and damage evolution under varying freeze-thaw cycles and load amplitudes. The research results show that the cumulative deformation behavior of CGSS under cyclic loading is relatively sensitive to the freeze-thaw process. The cumulative dynamic strain increases as the freeze-thaw cycles, with a critical freeze-thaw cycle number of five. The stable cumulative dynamic strain curve exhibits clear three-stage characteristics when plotted in semi-log coordination, with critical loading cycles at 20 and 1,000. After 10–100 loading cycles, the cumulative strain curve quickly shows failure. The CGSS’s low density and pore regions greatly increase after a freeze-thaw cycle. The rise in dynamic stress amplitude notably affects the bonding between soil particles and crystalline salts. The coupling effect of the freeze-thaw cycle and dynamic activity exacerbates the deterioration of soil structure, resulting in variations in CT values within the scanning layer in the final state.

Backgroundand objectives: Implementation of patient-specific quality assurance (PSQA) is a crucial aspect of precise radiotherapy. Various machine learning-based models have showed potential as virtual quality assurance tools, being capable of accurately predicting the dose verification results of fixed-beam intensity-modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT) plans, thereby ensuring safe and efficient treatment for patients. However, there has been no research yet that simultaneously integrates different IMRT techniques to predict the gamma pass rate (GPR) and explain the model.Methods: Retrospective analysis of the 3D dosimetric verification results based on measurements with gamma pass rate criteria of 3%/2 mm and 10% dose threshold of 409 pelvic IMRT and VMAT plans was carried out. Radiomics features were extracted from the dose files, from which the XGBoost algorithm based on SHapley Additive exPlanations (SHAP) values was used to select the optimal feature subset as the input for the prediction model. The study employed four different machine learning algorithms, namely, random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), to construct predictive models. Sensitivity, specificity, F1 score, and AUC value were calculated to evaluate the classification performance of these models. The SHAP values were utilized to perform a related interpretive analysis on the best performing model.Results: The sensitivities and specificities of the RF, AdaBoost, XGBoost, and LightGBM models were 0.96, 0.82, 0.93, and 0.89, and 0.38, 0.54, 0.62, and 0.62, respectively. The F1 scores and area under the curve (AUC) values were 0.86, 0.81, 0.88, and 0.86, and 0.81, 0.77, 0.85, and 0.83, respectively. The explanation of the model output based on SHAP values can provide a reference basis for medical physicists when adjusting the plan, thereby improving the efficiency and quality of treatment plans.Conclusion: It is feasible to use a machine learning method based on radiomics to establish a gamma pass rate classification prediction model for IMRT and VMAT plans in the pelvis. The XGBoost model performs better in classification than the other three tree-based ensemble models, and global explanations and single-sample explanations of the model output through SHAP values may offer reference for medical physicists to provide high-quality plans, promoting the clinical application and implementation of GPR prediction models, and providing safe and efficient personalized QA management for patients.

As a Traditional Chinese Medicine prescription, Qingjin Yiqi Granules (QJYQ) provides an effective treatment for patients recovering from COVID-19. However, the pharmacokinetics characteristics of the main components of QJYQ in vivo are still unknown. An efficacious ultra-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) was developed and validated for the simultaneous determination of 33 components in rat plasma after oral administration of QJYQ. The plasma samples were precipitated with 400 µL methanol/acetonitrile (1/1, v/v) and analyzed in scheduled multiple reaction monitoring mode. The linear relationship of the 33 components was good (r > 0.9928). The lower limit of quantification for 33 components ranged from 0.4–60.5 ng/mL. The average recoveries and matrix effects of the analytes ranged from 72.9% to 115.0% with RSD of 1.4%–15.0%. All inter-day and intra-day RSDs were within 15.0%. After oral administration (3.15 g/kg), the validated approach was effectively applied to the pharmacokinetics of main components of QJYQ. Finally, fifteen main constituents of QJYQ with large plasma exposure were obtained, including baicalin, wogonoside, wogonin, apigenin-7-O-glucuronide, verbenalin, isoferulic acid, hesperidin, liquiritin, harpagide, protocatechuic acid, p-Coumaric acid, ferulic acid, sinapic acid, liquiritin apioside and glycyrrhizic acid. The present research lays a foundation for clarifying the therapeutic material basis of QJYQ and provides a reference for further scientific research and clinical application of QJYQ.

Accurate and complete diagnosis of nuclear accidents is the primary link to nuclear emergencies. The signed directed graph (SDG), as a common method of fault diagnosis, has good completeness. However, if there are feedforward and negative feedback control loops, compensation response and reverse response will be generated in the process of fault occurrence and propagation, which makes the state of the compensation variable and reverse variable uncertain, thus destroying the compatibility of the fault propagation path, leading to incomplete SDG reverse reasoning. In order to solve this problem, this paper proposes a SDG fault diagnosis method based on information flow. This method first finds out all the directed edges with the compensation variable or inverse variable node as the endpoint. By comparing the goodness of fit between the information flow on these directed edges and their total information flow curves, the path that contained the directed edge represented by sub-information flow with the highest R2 is obtained, and it is used as the fault propagation path with the maximum probability to determine the state of the compensation variable or inverse variable and fault location. At the same time, this paper extends the SDG model from the structure to build an FR-SDG model that can intuitively describe the node steady-state information under the final response and the fault propagation path with the maximum probability, which improves the diagnosis resolution. Finally, this paper carried out the relevant experiments in the subsystem of the nuclear reactor primary loop by using the simulation system PCTran AP1000, which verified the feasibility of this method.

Diabetes mellitus (DM) is a fast-growing chronic metabolic disorder that leads to significant health, social, and economic problems worldwide. Chronic hyperglycemia caused by DM leads to multiple devastating complications, including macrovascular complications and microvascular complications, such as diabetic cardiovascular disease, diabetic nephropathy, diabetic neuropathy, and diabetic retinopathy. Numerous studies provide growing evidence that aberrant expression of and mutations in RNA-binding proteins (RBPs) genes are linked to the pathogenesis of diabetes and associated complications. RBPs are involved in RNA processing and metabolism by directing a variety of post-transcriptional events, such as alternative splicing, stability, localization, and translation, all of which have a significant impact on RNA fate, altering their function. Here, we purposed to summarize the current progression and underlying regulatory mechanisms of RBPs in the progression of diabetes and its complications. We expected that this review will open the door for RBPs and their RNA networks as novel therapeutic targets for diabetes and its related complications.

Purpose: To investigate the impact of different baseline characteristics on the efficacy of immune checkpoint inhibitors (ICIs) for advanced lung cancer.Methods: In order to identify eligible randomized controlled trials (RCTs), a systematic search was conducted in PubMed, Embase, Cochrane Library, Web of Science, and Scopus databases. The primary outcomes were hazard ratios (HRs) and 95% confidence intervals (CIs) for overall survival (OS). To explore the potential interaction during the administration of ICI, patients were stratified by baseline characteristics.Results: The meta-analysis included 24 RCTs. ① Compared with non-ICI therapy, patients with lung cancer benefitted more from immunotherapy (HR, 0.78; p < 0.0001). ② Patients without liver metastases could get more survival benefits than those with liver metastases (HR, 1.20; p = 0.0139). Similar outcomes were also observed in the following subgroups: small-cell lung cancer (HR, 1.20; p = 0.0433), subsequent line (HR, 1.40; p = 0.0147), and ICI monotherapy (HR, 1.40; p = 0.0147). ③ Subgroup analysis showed that tumor type affected the efficacy of immunotherapy in patients with brain metastases (HR, 0.72 vs. 1.41; interaction, p < 0.01). Among patients with smoking history (HR, 0.87 vs. 1.23; interaction, p = 0.05) and brain metastases (HR, 0.69 vs. 1.21; interaction, p = 0.05), the type of therapy (i.e., monotherapy or combination therapy) had potential influences on the efficacy of immunotherapy.Conclusion: Some critical baseline characteristics could indicate the efficacy of ICI therapy. Liver metastasis status could predict the efficacy of ICI therapy for lung cancer. Compared with small-cell lung cancer, patients with brain metastases might have durable OS in non-small-cell lung cancer. The smoking history or brain metastasis status of patients could indicate the potential clinical benefits of monotherapy or combination therapy.

The verification of nuclear design software commonly uses direct comparison methods. Benchmark questions, classical programs, experimental data, manual solutions, etc., would be used as expected results to compare with program outputs to evaluate the reliability of software coding and the accuracy of the numerical solution. Because nuclear power software numerically simulates complex physical processes, it involves many partial differential equations. It is usually challenging to construct analytical or accurate solutions and is expensive to develop benchmark questions and experimental data. Hence, the quantity of verification examples is small. By using the direct comparison method, verification is complicated, high cost, and inadequate. Entering the validation process without adequate proof will adversely impact the effectiveness and efficiency of validation. Metamorphic testing is an indirect verification technology that cleverly combines the nature of the model with software verification. It evaluates the correctness of the code by examining whether the program satisfies the metamorphic relation. Without manual solutions or benchmark examples, it has broad application prospects in the field of nuclear power. A lightweight verification method based on metamorphic relation has been produced here. Metamorphic relations are identified from physical equations, numerical algorithms, and program specifications. Next, they are explicitly used to system, integration, and unit tests to improve test adequacy. Because no need to develop verification examples, this method can detect code errors as soon as possible at a low cost, improve test efficiency, avoid mistakes remaining in subsequent stages and reduce the overall cost of verification.

Angiotensin II type 1 (AT1) receptor blockers (ARBs), as antihypertensive drugs, have drawn attention for their benefits to individuals with diabetes and prediabetes. However, the direct effects of ARBs on insulin secretion remain unclear. In this study, we aimed to investigate the insulinotropic effect of ARBs and the underlying electrophysiological mechanism. We found that only telmisartan among the three ARBs (telmisartan, valsartan, and irbesartan) exhibited an insulin secretagogue role in rat islets. Independent of AT1 receptor and peroxisome proliferator-activated receptor γ (PPARγ), telmisartan exerted effects on ion channels including voltage-dependent potassium (Kv) channels and L-type voltage-gated calcium channels (VGCCs) to promote extracellular Ca2+ influx, thereby potentiating insulin secretion in a glucose-dependent manner. Furthermore, we identified that telmisartan directly inhibited Kv2.1 channel on a Chinese hamster ovary cell line with Kv2.1 channel overexpression. Acute exposure of db/db mice to a telmisartan dose equivalent to therapeutic doses in humans resulted in lower blood glucose and increased plasma insulin concentration in OGTT. We further observed the telmisartan-induced insulinotropic and electrophysiological effects on pathological pancreatic islets or β-cells isolated from db/db mice. Collectively, our results establish an important insulinotropic function of telmisartan distinct from other ARBs in the treatment of diabetes.

Oriented external electric fields are now emerging as “smart effectors” of chemical changes. The key challenges in experimentally studying electrostatic catalysis are (i) controlling the orientation of fields along the reaction axis and (ii) finely adjusting the magnitudes of electrostatic stimuli. Surface models provide a versatile platform for addressing the direction of electric fields with respect to reactants and balancing the trade-off between the solubility of charged species and the intensity of electric fields. In this mini-review, we present the recent advances that have been investigated of the electrostatic effect on the chemical reaction on the monolayer-functionalized silicon surfaces. We mainly focus on elucidating the mediator/catalysis role of static electric fields induced from either solid/liquid electric double layers at electrode/electrolyte interfaces or space charges in the semiconductors, indicating the electrostatic aspects is of great significance in the semiconductor electrochemistry, redox electroactivity, and chemical bonding. Herein, the functionalization of silicon surfaces allows scientists to explore electrostatic catalysis from nanoscale to mesoscale; most importantly, it provides glimpses of the wide-ranging potentials of oriented electric fields for switching on/off the macroscale synthetic organic electrochemistry and living radical polymerization.

Glucagon-like peptide-1 receptor (GLP-1R) agonist-based therapeutics for type 2 diabetes mellitus have attracted worldwide attention. However, there are challenges in the development of small molecule GLP-1R agonists owing to the complexity of ligand recognition and signal induction mechanisms. Here, we attained S6 using virtual screening and fluorescent imaging plate reader (FLIPR)-based calcium assays. The purpose of this study was to identify and characterize S6, a novel small molecule GLP-1R agonist. Data from cellular thermal shift assay (CETSA) and Bio-Layer Interferometry (BLI) indicated that S6 could bind potently with GLP-1R. Radioimmunoassay data showed that S6 potentiated insulin secretion in a glucose-dependent manner and the insulinotropic effect was mediated by GLP-1R. Calcium imaging techniques suggested that S6 elevated the intracellular calcium concentration [(Ca2+)i] by activating GLP-1R. In patch-clamp experiments, we demonstrated that S6 inhibited voltage-dependent K+ (Kv) channels in a GLP-1R-dependent fashion. Besides, S6 significantly prolonged action potential duration but had no effect on voltage-dependent Ca2+ channels. In summary, these findings indicate that S6 stimulates glucose-dependent insulin secretion mainly by acting on GLP-1R, inhibiting Kv channels, increasing (Ca2+)i. This study will provide direction for the screening and development of novel small-molecule agents targeting GLP-1R in the future.

Dopamine (DA) has a vital role in the central nervous system and also modulates lipid and glucose metabolism. The present study aimed to investigate the effect of dopamine on insulin secretion and the underlying mechanisms in rat pancreatic β-cells. Data from the radioimmunoassay indicated that dopamine inhibited insulin secretion in a glucose- and dose-dependent manner. This inhibitory effect of dopamine was mediated mainly by D2-like receptors, but not D1-like receptors. Whole-cell patch-clamp recordings showed that dopamine decreased voltage-dependent Ca2+ channel currents, which could be reversed by inhibition of the D2-like receptor. Dopamine increased voltage-dependent potassium (KV) channel currents and shortened action potential duration, which was antagonized by inhibition of D2-like receptors. Further experiments showed that D2-like receptor activation by quinpirole increased KV channel currents. In addition, using calcium imaging techniques, we found that dopamine reduced intracellular Ca2+ concentration, which was also reversed by D2-like receptor antagonists. Similarly, quinpirole was found to decrease intracellular Ca2+ levels. Taken together, these findings demonstrate that dopamine inhibits insulin secretion mainly by acting on D2-like receptors, inhibiting Ca2+ channels, and activating Kv channels. This process results in shortened action potential duration and decreased intracellular Ca2+ levels in β-cells. This work offers new insights into a glucose-dependent mechanism whereby dopamine regulates insulin secretion.

Objectives: Human Urinary Kallidinogenase (HUK) is a tissue kallikrein that plays neuroprotective role in ischemic conditions via different mechanisms. Mild hypothermia (MH) is another robust neuroprotectant that reduces mortality but does not profoundly ameliorate the neurological outcome in hypoxic-ischemic encephalopathy (HIE) patients. However, whether the combination of HUK and MH can be used as a promising neuroprotective treatment in HIE is unknown.Methods: One-hundred and forty-four adult Wistar rats were randomly divided into five groups: Sham, HIE, HUK, MH and a combination of HUK and MH treatment. The HIE rat model was established by right carotid dissection followed by hypoxia aspiration. The survival curve was created within 7 days, and the neurological severity scores (NSS) were assessed at days 0, 1, 3, and 7. Nissl staining, Terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling (TUNEL), immunofluorescent staining and western blotting were used to evaluate neuronal survival, apoptosis and necrosis, tight-junction proteins Claudin-1 and Zonula occludens-1 (ZO-1), vascular endothelial growth factor (VEGF), doublecortex (DCX), bradykinin receptor B1 (BDKRB1), BDKRB2 and Ki67 staining.Results: The combined treatment rescued all HIE rats from death and had a best survival curve compared to HIE. The Combination also reduced the NSS scores after HIE at days 7, better than HUK or MH alone. The combination of HUK and MH reserved more cells in Nissl staining and inhibited neuronal apoptosis and necrosis as well as significantly attenuated HIE-induced decreases in claudin-1, ZO-1, cyclin D1 and BDKRB1/B2 in comparison to HUK or MH treatment alone. Moreover, the combined treatment increased the expression of VEGF and DCX as well as the number of Ki67-labeled cells.Conclusions: This study demonstrates that both HUK and MH are neuroprotective after HIE insult; however, the combined therapy with HUK and MH enhanced the efficiency and efficacy of either therapy alone in the treatment of HIE, at least partially by promoting angiogenesis and regeneration and rescuing tight-junction loss. The combination of HUK and MH seems to be a feasible and promising clinical strategy to alleviate cerebral injury following HIE insult.Highlights: -The combination of HUK and MH distinctly reduces neurological dysfunction in HIE rats.-HUK enhances the neuroprotective effects of MH in HIE.-MH attenuates tight-junction disruption, upregulates the BDKR B1/2, DCX and cyclin D1.-The combination of MH and HUK enhances the expressions of MH/HUK mediated-BDKR B1/2, DCX, cyclin D1 and Ki67 positive cells.

MicroRNAs (miRNAs) are a class of endogenous, non-coding small RNAs that play an important role in transcriptional and post-transcriptional gene regulation. However, the sequence information and functions of miRNAs are still unexplored in Tulipa edulis. In this study, high-throughput sequencing was used to identify small RNAs in stolon formation stages (stage 1, 2, and 3) in T. edulis. A total of 12,890,912, 12,182,122, and 12,061,434 clean reads were obtained from stage 1, 2, and 3, respectively. Among the reads, 88 conserved miRNAs and 70 novel miRNAs were identified. Target prediction of 122 miRNAs resulted in 531 potential target genes. Nr, Swiss-Prot, GO, COG, and KEGG annotations revealed that these target genes participate in many biologic and metabolic processes. Moreover, qRT-PCR was performed to analyze the expression levels of the miRNAs and target genes in stolon formation. The results revealed that miRNAs play a key role in T. edulis stolon formation. [ABSTRACT FROM AUTHOR]