ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

The auxin/indole-3-acetic acid (Aux/IAA) gene family is an important regulator for plant growth hormone signaling, involved in plant growth, development, as well as response to environmental stresses. In the present study, we identified SmIAA7 which is potentially associated with Salvia miltiorrhiza leaf development through comparatively analyzed the transcriptome data from different pinnate leaves. SmIAA7 was successfully isolated from S. miltiorrhiza using the specific primers. Then subsequent bioinformatic analysis, prokaryotic expression and purification, subcellular localization, and induction expression analysis under auxin and abiotic stress were performed. The full-length of SmIAA7 contained an ORF of 684 bp encoding a protein of 227 amino acid with a molecular weight of 25.3 kD. Conserved domain analysis showed that SmIAA7 contains the conserved Aux_IAA domain (pfam02309). Sequence analysis and phylogenetic tree analysis results indicated SmIAA7 was phylogenetically close to IAA7 and IAA14 from other plants, suggesting SmIAA7 involved in plant growth and development as well as response to environmental stresses. The prokaryotic expression vector pET28a-SmIAA7 was constructed and SmIAA7 recombinant protein was successfully expressed in E. coli Rosetta (DE3) strain. Subcellular localization experiment demonstrated that SmIAA7 localized in the nucleus of plant cells. Real-time fluorescence quantitative PCR results showed that the expression level of SmIAA7 was upregulated in response to auxin. Drought, low temperature, and salt stress significantly increased the transcript level of SmIAA7 gene. This study lays a foundation for further elucidating the role of SmIAA7 in leaf development, signal transduction, and stress defense in S. miltiorrhiza.

Six compounds were isolated from the roots of Ephedra sinica Stapf using various chromatographic techniques such as silica gel column chromatography, thin layer chromatography and semi-preparative HPLC. Their chemical structures were identified by analysis of physicochemical properties and spectral data, and determined as (Z)-docosanylferulate (1), (E)-docosanylferulate (2), bis (2-ethylheptyl) phythalate (3), 2,2′-oxybis (1,4-di-tert-butylbenzene) (4), diisobutyl phthalate (5), bis (2-ethylhexyl) phthalate (6). Among them, compound 1 is a new compound, compounds 2-4 were first isolated from Ephedra. A corticosterone-induced PC-12 cell injury model was used for compound activity screening. The results showed that compounds 1 and 5 significantly improved corticosterone-induced PC-12 cell injury and significantly increased 5-HT₇ receptor protein expression in the cells, indicating potential antidepressant activity.

Influenza virus causes serious threat to human life and health. Due to the inherent high variability of influenza virus, clinically resistant mutant strains of currently approved anti-influenza virus drugs have emerged. Therefore, it is urgent to develop antiviral drugs with new targets or mechanisms of action. RNA-dependent RNA polymerase is directly responsible for viral RNA transcription and replication, and plays key roles in the viral life cycle, which is considered an important target of anti-influenza drug design. From the point of view of medicinal chemistry, this review summarizes current advances in diverse small-molecule inhibitors targeting influenza virus RNA-dependent RNA polymerase, hoping to provide valuable reference for development of novel antiviral drugs.

Platycodonis Radix is the dry root of Platycodon grandiflorum of Campanulaceae, which has a variety of pharmacological effects and is a commonly used bulk Chinese medicine. In this study, the chloroplast genome sequences of six P. grandiflorum from different producing areas has been sequenced with Illumina HiSeq X Ten platform. The specific DNA barcodes were screened, and the germplasm resources and genetic diversity were analyzed according to the specific barcodes. The total length of the chloroplast genome of 6 P. grandiflorum samples was 172 260-172 275 bp, and all chloroplast genomes showed a typical circular tetrad structure and encoded 141 genes. The comparative genomics analysis and results of amplification efficiency demonstrated that trnG-UCC and ndhG_ndhF were the potential specific DNA barcodes for identification the germplasm resources of P. grandiflorum. A total of 305 P. grandiflorum samples were collected from 15 production areas in 9 provinces, for which the fragments of trnG-UCC and ndhG_ndhF were amplificated and the sequences were analyzed. The results showed that trnG-UCC and ndhG_ndhF have 5 and 11 mutation sites, respectively, and 5 and 7 haplotypes were identified, respectively. The combined analysis of the two sequences formed 13 haplotypes (named Hap1-Hap13), and Hap4 is the main genotype, followed by Hap1. The unique haplotypes possessed by the three producing areas can be used as DNA molecular tags in this area to distinguish from the germplasm resources of P. grandiflorum from other areas. The haplotype diversity, nucleotide diversity and genetic distance were 0.94, 4.79×10^-3 and 0.000 0-0.020 3, respectively, suggesting that the genetic diversity was abundant and intraspecific kinship was relatively close. This study laid a foundation for the identification of P. grandiflorum, the protection and utilization of germplasm resources, and molecular breeding.

Objective:To explore the factors influencing the supportive communication ability of medical undergraduates, and to propose strategies to improve supportive communication.Methods:By cluster sampling, we selected 388 medical undergraduates of grades 2017 and 2018 from Harbin Medical University for a questionnaire survey on supportive communication, general self-efficacy, and health education abilities. SPSS 22.0 was used for descriptive statistical analysis. AMOS 22.0 was used to construct a structural equation model to verify the relationship between the three variables. Mediating effects were also tested.Results:The students showed good supportive communication ability, with a total score of (74.28±10.84) points. The general self-efficacy score was (27.81±5.58) points, and the total score of health education ability was (25.50±4.76) points. General self-efficacy had direct positive effects on supportive communication and health education abilities ( β=0.75, 0.31, both P<0.001). Health education ability had a direct positive effect on supportive communication ability ( β=0.14, P<0.001). Health education ability played a significant mediating role in the influence of general self-efficacy on supportive communication ability (standardized mediating effect value=0.042, P<0.01), with the mediating effect accounting for 5.1%. Conclusions:The health education competency of medical undergraduates can mediate the effect of general self-efficacy on supportive communication ability. By strengthening medical humanities education to increase general self-efficacy and also emphasizing the cultivation of health education competency, the supportive communication ability of students can be improved.

This paper outlines the common aspects of constructing integrated urban medical groups,focusing on governance,organizational restructuring,operational modes,and mechanism synergy.It then delves into the challenges in China's group construction,highlighting issues with power-responsibility alignment,capacity evolution,incentive alignment,and performance evaluation.Finally,the paper suggests strategies to enhance China's compact urban medical groups,focusing on governance reform,capacity building,benefit integration,and performance evaluation.

A GC-LSTM model is proposed based on the characteristics of global optimization of genetic algorithm.The model automatically and iteratively searches the optimal hyper-parameter configuration of the C-LSTM model through the genetic algorithm of a specific genetic strategy,and it is configured using the genetic iteration results and validated on the MIT-BIH arrhythmia database according to the classification criteria of the Association for the Advancement of Medical Instrumentation.The testing shows that the classification accuracy,sensitivity,accuracy and F1 value of GC-LSTM model are 99.37%,95.62%,95.17%and 95.39%,respectively,higher than those of the manually established model,and it is also advantageous over the existing mainstream methods.Experimental results demonstrate that the proposed method can achieve better classification performance while avoiding a large number of experimental parameters.

AIM To study the neoflavonoids from Dalbergia cochinchinensis Pierre ex Laness and their anti-hypoxia/reoxygenation injury activities on H9c2 myocardial cells.METHODS The 70%ethanol extract from D.cochinchinensis was isolated and purified by silica gel,Sephadex LH-20 and reverse-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The CCK-8 method was used to detect their activities on H9c2 cells and protective effects on hypoxia-reoxygenation injury of H9c2 cells,and their structure-activity relationship was analyzed.RESULTS Twelve compounds were isolated and identified as latifolin(1),5-O-methyllatifolin(2),mimosifoliol(3),5-O-methydalbergiphenol(4),dalbergiphenol(5),cearoin(6),2,4-dihydroxy-5-methoxy-benzophenone(7),2-hydroxy-4,5-dimethoxybenzophenone(8),melannoin(9),2,2′,5-trihydroxy-4-methoxybenzophenone(10),dalbergin(11),4-methoxydalbergione(12).The dalbergiphenols and dalbergins had little toxicity to H9c2 cells,and dalbergiphenols had strong activity against hypoxia-reoxygenation injury of H9c2 cells.CONCLUSION Compound 8 is a new natural product.Compounds 4,9 are isolated from this plant for the first time.Dalbergiphenols may be the main neoflavonoids against hypoxia-reoxygenation injury of H9c2 cells.

Objective To construct and apply a management plan for microaspiration of oropharyngeal secretions in ICU intubated patients.Methods Based on evidence summaries and expert consultation,a management plan for microaspiration of oropharyngeal secretions in ICU intubated patients was constructed,consisting of 19 items covering 7 aspects including identification of risk factors,position management,tube and cuff selection,cuff management,mechanical ventilation management,pain and sedation management,removal of oropharyngeal and subglottic secretions,and oral care.Convenience sampling was used to select 141 ICU intubated patients from a tertiary A comprehensive hospital in Suzhou from June,2022 to September,2023.Patients were divided into an experimental group(n=72)and a control group(n=69)according to the wards.The experimental group received the management plan for microaspiration of oropharyngeal secretions in ICU intubated patients.The control group received the nursing bundle for ventilator associated pneumonia(VAP).The incidence and time from intubation to microaspiration and VAP,duration of mechanical ventilation,ICU length of stay,and disease outcome were compared between the 2 groups.Results The incidence of microaspiration of oropharyngeal secretions,the duration of mechanical ventilation,time from intubation to microaspiration showed significant differences between the 2 groups(P<0.05).There were no significant differences in the incidence of ventilator associated pneumonia,ICU length of stay,and disease outcome between the 2 groups(P>0.05).The time from intubation to VAP in the experimental group was 7.5 days,and that in the control group was 3.8 days.Conclusion The application of the management plan for microaspiration of orophaiyngeal secretions in ICU intubated patients is beneficial for reducing the incidence of microaspiration,delaying the time from intubation to microaspiration and VAP,and shortening the duration of mechanical ventilation.