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Dendrobium is known for its pharmacological actions including anti-cancer effect, anti-fatigue effect, gastric ulcer protective effect, and so on. At present, only studies on endophytic fungi of Dendrobium affecting the metabolites of host plants have been reported, very little research has been done on endophytic bacteria. In this study, we have demonstrated the great diversity of endophytic bacteria in 6 Dendrobium samples from different origins and cultivars. According to the results of the culture-independent method, the endophytic bacterial community in Dendrobium stems showed obvious different in the 6 samples and was influenced by origin and cultivar. Some bacteria including Ralstonia , Comamonas and Lelliottia were first detected in Dendrobium in this study. Based on the culture-dependent method, a total of 165 cultivable endophytic bacteria isolates were isolated from the sterilized Dendrobium stems, and were classified into 43 species according to the 16S rRNA gene sequence analysis. Moreover, 14 of the 43 strains showed antimicrobial activity against phytopathogen using the Kirby-Bauer method. Strain NA-HTong-7 ( Bacillus megaterium , 99.12%) showed the highest antimicrobial activity. This study was the first comprehensive study on endophytic bacteria of Dendrobium from different origins and cultivars, which provides new insights into the endophytic bacteria from Dendrobium .

The growth of endophytic bacteria is influenced by the host plants and their secondary metabolites and activities. In this study, P. megaterium P-NA14 and P. megaterium D-HT207 were isolated from potato tuber and dendrobium stem respectively. They were both identified as Priestia megaterium . The antimicrobial activities and metabolites of both strains were explored. For antimicrobial activities, results showed that P. megaterium P-NA14 exhibited a stronger inhibition effect on the pathogen of dendrobium, while P. megaterium D-HT207 exhibited a stronger inhibition effect on the pathogen of potato. The supernatant of P. megaterium P-NA14 showed an inhibition effect only on Staphylococcus aureus , while the sediment of P. megaterium D-HT207 showed an inhibition effect only on Escherichia coli . For metabolomic analysis, the content of L -phenylalanine in P. megaterium P-NA14 was higher than that of P. megaterium D-HT207, and several key downstream metabolites of L -phenylalanine were associated with inhibition of S. aureus including tyrosine, capsaicin, etc. Therefore, we speculated that the different antimicrobial activities between P. megaterium P-NA14 and P. megaterium D-HT207 were possibly related to the content of L -phenylalanine and its metabolites. This study preliminarily explored why the same strains isolated from different hosts exhibit different activities from the perspective of metabolomics.

To validate a radiomics model based on multi-sequence magnetic resonance imaging (MRI) in predicting the ki-67 expression levels in early-stage endometrial cancer, 131 patients with early endometrial cancer who had undergone pathological examination and preoperative MRI scan were retrospectively enrolled and divided into two groups based on the ki-67 expression levels. The radiomics features were extracted from the T2 weighted imaging (T2WI), dynamic contrast enhanced T1 weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) map and screened using the Pearson correlation coefficients (PCC). A multi-layer perceptual machine and fivefold cross-validation were used to construct the radiomics model. The receiver operating characteristic (ROC) curves analysis, calibration curves, and decision curve analysis (DCA) were used to assess the models. The combined multi-sequence radiomics model of T2WI, DCE-T1WI, and ADC map showed better discriminatory powers than those using only one sequence. The combined radiomics models with multi-sequence fusions achieved the highest area under the ROC curve (AUC). The AUC value of the validation set was 0.852, with an accuracy of 0.827, sensitivity of 0.844, specificity of 0.773, and precision of 0.799. In conclusion, the combined multi-sequence MRI based radiomics model enables preoperative noninvasive prediction of the ki-67 expression levels in early endometrial cancer. This provides an objective imaging basis for clinical diagnosis and treatment.

Foodborne protein hydrolysates exhibit biological activity that may be therapeutic in a number of human disease settings. Hemp peptides (HP) generated by controlled hydrolysis of hemp proteins have a number of health benefits and are of pharmaceutical value. In the present study, we produce small molecular weight HP from hemp seed and investigate its anticancer properties in Hep3B human liver cancer cells. We demonstrate that HP treatment increased apoptosis, reduced cell viability, and reduced cell migration in Hep3B human liver cancer cells without affecting the normal liver cell line L02. We correlate these phenotypes with increased cellular ROS levels, upregulation of cleaved caspase 3 and Bad, and downregulation of antiapoptotic Bcl‐2. HP treatment led to increased Akt and GSK‐3β phosphorylation, with subsequent downregulation of β‐catenin, suggesting β‐catenin signaling modulation as a critical mechanism by which HP exhibits anticancer properties. Our findings suggest HP are of potential therapeutic interest for liver cancer treatment. Anti‐cancer property of new hemp protein hydrolysates on human liver cancers

Background: A modified Morrow septal myectomy (MMSM) is one of the main treatment methods for obstructive hypertrophic cardiomyopathy (OHCM). Our aim was to study the impact of MMSM on the risk of AF and stroke in OHCM patients. Methods and Results: From 1 January 2014 to 31 December 2020, 6426 patients with obstructive HCM (OHCM) were selected from the Beijing Municipal Health Commission Information Center (BMHCIC) datasets (mean age: 54.3 years; 43.8% female). After propensity score matching, 3780 patients were selected, including 1890 who received MMSM (Group 1) and 1890 who did not receive any surgery (Group 2). During a median of 0.8 (interquartile range [IQR]: 0.1, 2.7) years of follow-up after discharge from the hospital, stroke risk was lower in Group 1 compared to Group 2 (aHR: 0.4, 95%CI: 0.2–0.6, p < 0.001), and the results were further confirmed by Kaplan–Meier analyses (p < 0.001). There was no statistically significant difference in the risk of AF (aHR: 1.0, 95%CI: 0.7–1.5, p = 0.991). The risk of AF decreased in the first 6 years since receiving MMSM and then sharply increased beyond 6 years after MMSM according to Kaplan–Meier analyses. Conclusions: MMSM is associated with a decreased risk of stroke in OHCM patients. The risk of AF decreased in the first 6 years since receiving MMSM and then sharply increased beyond 6 years after MMSM.

Objective To establish a machine learning-based radiomics model to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes, thereby achieving accurate preoperative classification. Materials and methods A retrospective analysis was conducted on MRI T1WI-enhanced images of 105 patients with glioma and 172 patients with solitary brain metastasis from lung cancer, which were confirmed pathologically. The patients were divided into the training group and validation group in an 8:2 ratio for image segmentation, extraction, and filtering; multiple layer perceptron (MLP), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used for modeling; fivefold cross-validation was used to train the model; the validation group was used to evaluate and assess the predictive performance of the model, ROC curve was used to calculate the accuracy, sensitivity, and specificity of the model, and the area under curve (AUC) was used to assess the predictive performance of the model. Results The accuracy and AUC of the MLP differentiation model for high-grade glioma and solitary brain metastasis in the validation group was 0.992, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.968, respectively. The accuracy and AUC for the MLP and SVM differentiation model for high-grade glioma and small cell lung cancer brain metastasis in the validation group was 0.966, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.929, respectively. The accuracy and AUC for the MLP differentiation model for high-grade glioma and non-small cell lung cancer brain metastasis in the validation group was 0.982, 0.999, respectively, while the sensitivity and specificity were 0.958, 1.000, respectively. Conclusion The application of machine learning-based radiomics has a certain clinical value in differentiating glioma from solitary brain metastasis from lung cancer and its subtypes. In the HGG/SBM and HGG/NSCLC SBM validation groups, the MLP model had the best diagnostic performance, while in the HGG/SCLC SBM validation group, the MLP and SVM models had the best diagnostic performance.

ObjectiveThe function of Bcl-6 in T follicular helper (Tfh) cell maturation is indispensable, and Tfh cells play a pivotal role in asthma. This study investigated the impact of Bcl-6 on asthmatic traits.MethodsThe microscopic pathological alterations, airway resistance (AR), and lung compliance (LC) were determined in asthmatic mice and Bcl-6 interference mice. The surface molecular markers of Tfh cells and the Bcl-6 mRNA and protein expression were determined by flow cytometry, RT-qPCR, and Western blotting, respectively. The relationships between the Tfh cell ratio and the IgE and IgG1 concentrations in peripheral blood mononuclear cells (PBMCs) and bronchoalveolar lavage fluid (BALF) were determined.ResultsAsthmatic inflammatory changes were observed in the lung tissue and were attenuated by Bcl-6 siRNA and dexamethasone (DXM). Asthmatic mice exhibited an increased AR and a decreased LC, while Bcl-6 siRNA or DXM mitigated these changes. The percentages of Tfh cells and eosinophils were significantly increased in the asthmatic mice, and they significantly decreased after Bcl-6 inhibition or DXM treatment. RT-qPCR and Western blotting analyses revealed that the Bcl-6 expression level in PBMCs was significantly higher in asthmatic mice, and it decreased following Bcl-6 inhibition or DXM treatment. The IgE expression in the serum and BALF and the B cell expression in PBMCs exhibited a similar trend. In asthmatic mice, the ratio of Tfh cells in the peripheral blood showed a strong positive correlation with the IgE levels in the serum and BALF, but not with the IgG1 levels.ConclusionThe amelioration of airway inflammation and airway hyper-responsiveness is achieved through Bcl-6 suppression, which effectively hinders Tfh cell differentiation, ultimately resulting in a concurrent reduction in IgE production.

Aphids are agricultural pests of great economical interest. Alternatives to insecticides, using semiochemicals, are of difficult applications. In fact, sex pheromones are of little use as aphids reproduce partenogenetically most of the time. Besides, the alarm pheromone, (E)-ß-farnesene for a great number of species, is difficult to synthesize and unstable in the environment. The search for novel semiochemicals to be used in population control can be efficiently approached through the study of the olfactory system at the biochemical level. Recently odorant-binding proteins (OBPs) have been shown to play a central role in olfactory recognition, thus becoming the target of choice for designing new semiochemicals. To address the question of how the alarm message is recognised at the level of OBPs, we have tested 29 compounds, including (E)-ß-farnesene, in binding assays with 6 recombinant proteins and in behaviour experiments. We have found that good repellents bind OBP3 and/or OBP7, while non repellents present different spectra of binding. These results have been verified with two species of aphids, Acyrthosiphon pisum and Myzus persicae, both using (E)-ß-farnesene as the alarm pheromone. Our results represent further support to the idea (so far convincingly demonstrated only in Drosophila) that OBPs are involved in decoding the chemical information of odorants and pheromones, and for the first time provide such evidence in other insect species and using wild-type insects. Moreover, the data offer guidelines and protocols for the discovery of potential alarm pheromones, using ligand-binding assays as a preliminary screening before subjecting selected compounds to behaviour tests.

The fireworks algorithm (FA) is a new parallel diffuse optimization algorithm to simulate the fireworks explosion phenomenon, which realizes the balance between global exploration and local searching by means of adjusting the explosion mode of fireworks bombs. By introducing the grouping strategy of the shuffled frog leaping algorithm (SFLA), an improved FA-SFLA hybrid algorithm is put forward, which can effectively make the FA jump out of the local optimum and accelerate the global search ability. The simulation results show that the hybrid algorithm greatly improves the accuracy and convergence velocity for solving the function optimization problems.

Economic Load Dispatch (ELD) in power systems represents a core challenge in optimizing power system operations. Practical dispatch models typically exhibit nonlinearity, multiple constraints and even non-convexity. To address the high computational complexity and susceptibility to local optima encountered by traditional optimization methods, this paper introduces six swarm-based optimization algorithms, such as Arithmetic Optimization Algorithm (AOA) and Whale Optimization Algorithm (WOA) and systematically evaluates their solution capabilities. A mathematical model for minimizing fuel costs is constructed. Comparative experiments are conducted by using simulation cases with 6 units (small-scale), 20 units (medium-scale) and 40 units (large-scale), focusing on analyzing the convergence speed, optimization accuracy and result stability of each algorithm. Simulation results demonstrate that MPA and WOA exhibit outstanding optimization capabilities in most scenarios, while AOA and RRTO show greater stability in high-dimensional scheduling problems. ZOA and SOA achieve a good balance between the global exploration and the local exploitation. This study confirms the significant advantages of swarm intelligence algorithms in complex ELD problems, providing theoretical and engineering practice support for subsequent integration with hybrid optimization strategies and machine learning methods.