ObjectiveTo study the pharmacological substances and mechanisms through which sesquiterpene ZH-13 from Aquilariae Lignum Resinatum improves neuroinflammation. MethodsBV-2 microglial cells were stimulated with lipopolysaccharide （LPS） to induce neuroinflammation. The cells were divided into the normal group， the model group， and the ZH-13 low- and high-dose treatment groups （10， 20 μmol·L^-1）. The model group was treated with 1 μmol·L^-1 LPS. Cell viability was assessed using the cell proliferation and activity assay （CCK-8 kit）. Nitric oxide （NO） release in the cell supernatant was measured using a nitric oxide kit （Griess method）. The mRNA expression levels of interleukin-1β （IL-1β）， tumor necrosis factor-α （TNF-α）， inducible nitric oxide synthase （iNOS）， and interleukin-6 （IL-6） were detected by real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）. The phosphorylation of mitogen-activated protein kinase （MAPK） pathway proteins was assessed by Western blot. ResultsCompared with the model group， ZH-13 dose-dependently reduced NO release from BV-2 cells under LPS stimulation （P<0.05， P<0.01）. In the 20 μmol·L^-1 ZH-13 treatment group， the mRNA expression levels of IL-1β， TNF-α， iNOS， and IL-6 were significantly reduced compared to the model group （P<0.05， P<0.01）. In both the low- and high-dose ZH-13 groups， the expression of the inflammatory factor TNF-α and the phosphorylation of c-Jun N-terminal kinase （JNK） in the upstream MAPK pathway were significantly reduced （P<0.05）. After stimulation with the JNK agonist anisomycin （Ani）， both low- and high-dose ZH-13 treatment groups showed reduced phosphorylation of JNK proteins compared to the Ani-treated group （P<0.01）. ConclusionThe sesquiterpene compound ZH-13 from Aquilariae Lignum Resinatum significantly ameliorates LPS-induced neuroinflammatory responses in BV-2 cells by inhibiting excessive JNK phosphorylation and reducing TNF-α expression. These findings elucidate the pharmacological substances and mechanisms underlying the sedative and calming effects of Aquilariae Lignum Resinatum.

BACKGROUND: Neoadjuvant chemoradiotherapy followed by radical surgery has been a common practice for patients with locally advanced rectal cancer, but the response rate varies among patients. This study aimed to develop a ResNet-Vision Transformer based magnetic resonance imaging (MRI)-endoscopy fusion model to precisely predict treatment response and provide personalized treatment. METHODS: In this multicenter study, 366 eligible patients who had undergone neoadjuvant chemoradiotherapy followed by radical surgery at eight Chinese tertiary hospitals between January 2017 and June 2024 were recruited, with 2928 pretreatment colonic endoscopic images and 366 pelvic MRI images. An MRI-endoscopy fusion model was constructed based on the ResNet backbone and Transformer network using pretreatment MRI and endoscopic images. Treatment response was defined as good response or non-good response based on the tumor regression grade. The Delong test and the Hanley-McNeil test were utilized to compare prediction performance among different models and different subgroups, respectively. The predictive performance of the MRI-endoscopy fusion model was comprehensively validated in the test sets and was further compared to that of the single-modal MRI model and single-modal endoscopy model. RESULTS: The MRI-endoscopy fusion model demonstrated favorable prediction performance. In the internal validation set, the area under the curve (AUC) and accuracy were 0.852 (95% confidence interval [CI]: 0.744-0.940) and 0.737 (95% CI: 0.712-0.844), respectively. Moreover, the AUC and accuracy reached 0.769 (95% CI: 0.678-0.861) and 0.729 (95% CI: 0.628-0.821), respectively, in the external test set. In addition, the MRI-endoscopy fusion model outperformed the single-modal MRI model (AUC: 0.692 [95% CI: 0.609-0.783], accuracy: 0.659 [95% CI: 0.565-0.775]) and the single-modal endoscopy model (AUC: 0.720 [95% CI: 0.617-0.823], accuracy: 0.713 [95% CI: 0.612-0.809]) in the external test set. CONCLUSION The MRI-endoscopy fusion model based on ResNet-Vision Transformer achieved favorable performance in predicting treatment response to neoadjuvant chemoradiotherapy and holds tremendous potential for enabling personalized treatment regimens for locally advanced rectal cancer patients.
Humans ; Rectal Neoplasms/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Middle Aged ; Neoadjuvant Therapy/methods* ; Aged ; Adult ; Chemoradiotherapy/methods* ; Endoscopy/methods* ; Treatment Outcome

In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties, and this is particularly true for the real industrial application scenario. Therefore, the desired testing datasets, will be small-size (∼10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking. Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. We evaluate 23 computational models across various methodological paradigms, such as sequence-based, structure-informed and evolutionary approaches. This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.

General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.
Animals ; Machine Learning ; Electroencephalography/methods* ; Ketamine/administration & dosage* ; Rats ; Male ; Propofol/administration & dosage* ; Rats, Sprague-Dawley ; Anesthesia, General/methods* ; Brain/physiology* ; Intraoperative Neurophysiological Monitoring/methods*

AIM: To predict the core targets and related signaling pathways of Yi-xin-yin oral liquid for the treatment of arrhythmia, heart failure and myocarditis based on UHPLC-Q-TOF/MS, network pharmacology, molecular docking methods, cell experiments, according to the“homotherapy for heteropathy”theory in traditional Chinese medicine. METHODS: UHPLC-Q-TOF / MS was used to analyze and identify the chemical composition of Yi-xin-yin oral liquid Extract and the blood-absorbing components of rats oral administrated with Yi-xin-yin oral liquid extract, which compounds were applied in the databases searching for the potential targets (TCMSP, SwissTargetPrediction) and disease targets (OMIM, Genecard). Venn diagram was used for target intersection, and the subsequent protein-protein interaction network obtained core targets by STRING11.5 database, and then construct a "disease-component-target" network by cytoscape3.9.0. Finally, DAVID database was used to analysis GO function and KEGG enrichment analysis of core targets, and molecular docking validation was performed using Autodock vina software. And, validated with H9c2 cells for potential active ingredients and targets. RESULTS: A total of 156 compounds were identified from Yi - xin-yin Oral Liquid extract; 34 compounds were identified from rat serum, including 6-gin-gerol, isoliquiritigenin, glycyrrhizic acid and other compounds, and 139 intersecting targets were obtained. The KEGG pathway enrichment analysis mainly involved the TNF signaling pathway, IL-17 signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway and so on. The TNF and IL-6 targets were selected for molecular docking with the main compounds, and the docking results were good (less than -5 kcal/mol). In vitro cellular experiments have shown that Yi-xin-yin oral liquid can exert therapeutic effects by regulating TNF and IL-6. CONCLUSION: The main potential active ingredients of Yi-xin-yin oral liquid may be isoliquiritigenin, glycyrrhetinic acid, calycosin-7-glucoside, salvianolic acid B, and 6-gingerol, which mainly act on TNF, IL-6 and other targets to regulate specific signaling pathways and exert therapeutic effects.

Improving the reproducibility of biomedical research results is a major challenge. Transparent and accurate reporting of the research process enables readers to evaluate the reliability of the research results and further explore the experiment by repeating it or building upon its findings. The ARRIVE 2.0 guidelines, released in 2019 by the UK National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs), provide a checklist that is applicable to any in vivo animal research report. These guidelines aim to improve the standardization of experimental design, implementation, and reporting, as well as enhance the reliability, repeatability, and clinical translation of animal experimental results. The use of the ARRIVE 2.0 guidelines not only enriches the details of animal experimental research reports, ensuring that information on animal experimental results is fully evaluated and utilized, but also enables readers to understand the content expressed by the author accurately and clearly, promoting the transparency and completeness of the fundamental research review process. At present, the ARRIVE 2.0 guidelines have been widely adopted by international biomedical journals. This article is based on the best practices following the ARRIVE 2.0 guidelines in international journals, and it interprets, explains, and elaborates in Chinese the fifth part of the comprehensive version of the ARRIVE 2.0 guidelines published in PLoS Biology in 2020 (the original text can be found at https://arriveguidelines.org). This section includes the items 6-11 of Recommended 11 section, covering "Animal Care and Monitoring", "Interpretation/Scientific Implications", "Generalisability/Translation", "Protocol Registration", "Data Access" and "Declaration of Interests". Its aim is to promote a comprehensive understanding and use of the ARRIVE 2.0 guidelines among domestic researchers, to enhance the standardization of experimental animal research and reporting, and to promote high-quality development of experimental animal sciences and comparative medicine research in China.

AIM To study the secondary metabolites from the endophytic fungi Candida sp.of Berberis atrocarpa Schneid.METHODS The ethyl acetate fraction and petroleum ether fraction from the secondary metabolites of Candida sp.fermentation extract were separated and purified by silica gel,Sephadex LH-20 and preparative liquid chromatography,then the structures of obtained compounds were identified by physicochemical properties and spectral data.RESULTS Eighteen compounds were isolated and identified as 1-phenyl-1,2-ethanediol(1),4-hydroxyphenethyl alcohol(2),4-hydroxybenzoic acid(3),4-hydroxyphenylacetic acid(4),3-hydroxyphenylacetic acid(5),3-methylsulfinyl propionic acid(6),phenylacetic acid(7),(S)-N-nitroso-1-amino-p-hydroxy phenylethanol(8),2-phenylacetamide(9),p-hydroxybenzaldehyde(10),ethyl 2-(4-hydroxyphenyl)acetate(11),dibutyl phthalate(12),5,5'-dimethoxybiphenyl-2,2'-diol(13),3-indolealdehyde(14),N-acetyl-L-phenylalanine(15),9-hydroxy-10E,12Z-octadecadienoic acid(16),9-hydroxy-10E,12E-octadecadienoic acid(17),(6E)-5-methylene-6-tetradecenoic acid(18).CONCLUSION Compounds 1,3-8 and 10-18 are isolated from Candida sp for the first time.

[Objective]To study the effect and mechanism of Chrysanthemi Indici Flos extract(CYM.E)on hyperuricemia in rats induced by"overeating greasy".[Methods]Sixty-four rats were randomly divided into normal control group,model control group,allopurinol 10mg·kg-1 group,Benzbromarone 5 mg·kg-1 group,and CYM.E 15,30,60 and 90 mg·kg-1 group,with 8 rats in each group.Except for the normal control group,the other groups used fat emulsion to establish a rat model of anthropomorphic hyperuricemia based on"overeating greasy".During the experiment,serum uric acid(UA),total cholesterol(TC),triglyceride(TG),high density lipoprotein cholesterol(HDL-c)and low density lipoprotein cholesterol(LDL-c)levels were measured.Interleukin-6(IL-6),tumor necrosis factor-α(TNF-α)and lipopolysaccharide(LPS)were determined by enzyme linked immunosorbent assay(ELISA).The mRNA expression of TNF-α and Toll like receptor 4(TLR4)in the small intestine was measured by Real-time quantitative polymerase chain reaction(qRT-PCR);and the protein expression of TLR4 in the small intestine was measured by Western blot.Intestinal microecology was classified with operational taxonomic units(OTU),and OTU was analyzed with structure,and LEfSe difference,etc.[Results]CYM.E significantly reduced the serum UA,TC,LDL-c,IL-6 and TNF-αlevels of model rats;and reduced plasma LPS level.Moreover,CYM.E could reduce the expression of TNF-α mRNA and TLR4 mRNA in the small intestine,and reduced the protein expression of TLR4 in the small intestine,thus improving the microinflammatory state of the intestinal tract,increased the abundance and species of Lactobacillales and Bifidobacteriales,and reduced the abundance and species of Bacteroidales and Escherichia coli.[Conclusion]CYM.E has the effects of decreasing serum UA levels,regulating blood lipids,decreasing inflammatory damage,and decreasing endotoxin release,which may exert an UA-lowering effect by regulating intestinal microecology.

Objective: To analyze and compare therapy responses, outcomes, and incidence of severe hematologic adverse events of flumatinib and imatinib in patients newly diagnosed with chronic phase chronic myeloid leukemia (CML) . Methods: Data of patients with chronic phase CML diagnosed between January 2006 and November 2022 from 76 centers, aged ≥18 years, and received initial flumatinib or imatinib therapy within 6 months after diagnosis in China were retrospectively interrogated. Propensity score matching (PSM) analysis was performed to reduce the bias of the initial TKI selection, and the therapy responses and outcomes of patients receiving initial flumatinib or imatinib therapy were compared. Results: A total of 4 833 adult patients with CML receiving initial imatinib (n=4 380) or flumatinib (n=453) therapy were included in the study. In the imatinib cohort, the median follow-up time was 54 [interquartile range (IQR), 31-85] months, and the 7-year cumulative incidences of CCyR, MMR, MR(4), and MR(4.5) were 95.2%, 88.4%, 78.3%, and 63.0%, respectively. The 7-year FFS, PFS, and OS rates were 71.8%, 93.0%, and 96.9%, respectively. With the median follow-up of 18 (IQR, 13-25) months in the flumatinib cohort, the 2-year cumulative incidences of CCyR, MMR, MR(4), and MR(4.5) were 95.4%, 86.5%, 58.4%, and 46.6%, respectively. The 2-year FFS, PFS, and OS rates were 80.1%, 95.0%, and 99.5%, respectively. The PSM analysis indicated that patients receiving initial flumatinib therapy had significantly higher cumulative incidences of CCyR, MMR, MR(4), and MR(4.5) and higher probabilities of FFS than those receiving the initial imatinib therapy (all P<0.001), whereas the PFS (P=0.230) and OS (P=0.268) were comparable between the two cohorts. The incidence of severe hematologic adverse events (grade≥Ⅲ) was comparable in the two cohorts. Conclusion: Patients receiving initial flumatinib therapy had higher cumulative incidences of therapy responses and higher probability of FFS than those receiving initial imatinib therapy, whereas the incidence of severe hematologic adverse events was comparable between the two cohorts.
Adult ; Humans ; Adolescent ; Imatinib Mesylate/adverse effects* ; Incidence ; Antineoplastic Agents/adverse effects* ; Retrospective Studies ; Pyrimidines/adverse effects* ; Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy* ; Treatment Outcome ; Benzamides/adverse effects* ; Leukemia, Myeloid, Chronic-Phase/drug therapy* ; Aminopyridines/therapeutic use* ; Protein Kinase Inhibitors/therapeutic use*

Humans ; Child ; Neurofibromatosis 1/drug therapy* ; Benzimidazoles/therapeutic use*