Circular RNA circ_KIAA1429 accelerates hepatocellular carcinoma progression via the miR-133a-3p/high mobility group AT-hook 2 (HMGA2) axis in an m6A-dependent manner.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide with high mortality rate, and the N6-methyladenosine (m6A) epigenetic modifications have been reported to be closely associated with the pathogenesis of HCC, but the detailed molecular mechanisms by which m6A regulates HCC progression have not been fully delineated. In this study, we evidenced that the m6A methyltransferase-like 3 (METTL3)-mediated m6A modification contributed to HCC aggressiveness through modulating a novel circ_KIAA1429/miR-133a-3p/HMGA2 axis. Specifically, circ_KIAA1429 was aberrantly overexpressed in HCC tissues and cells, and the expression levels of circ_KIAA1429 was positively regulated by METTL3 in HCC cells in a m6A-dependent manner. Then, functional experiments confirmed that deletion of both circ_KIAA1429 and METTL3 suppressed HCC cell proliferation, migration and cell mitosis in vitro and in vivo, and conversely, circ_KIAA1429 overexpression had opposite effects to accelerate HCC development. Furthermore, the downstream mechanisms by which circ_KIAA1429 regulated HCC progression were uncovered, and we validated that silencing of circ_KIAA1429 restrained the malignant phenotypes in HCC cells through modulating the miR-133a-3p/high mobility group AT-hook 2 (HMGA2) axis. To summarize, our study firstly investigated the involvement of a novel METTL3/m6A/circ_KIAA1429/miR-133a-3p/HMGA2 axis in regulating HCC development, which provided novel indicators for HCC diagnosis, therapy and prognosis.

Machine learning is an effective method to predict the 90-day prognosis of patients with transient ischemic attack and minor stroke.

OBJECTIVE: We aimed to investigate factors related to the 90-day poor prognosis (mRS≥3) in patients with transient ischemic attack (TIA) or minor stroke, construct 90-day poor prognosis prediction models for patients with TIA or minor stroke, and compare the predictive performance of machine learning models and Logistic model. METHOD: We selected TIA and minor stroke patients from a prospective registry study (CNSR-III). Demographic characteristics,smoking history, drinking history(≥20g/day), physiological data, medical history,secondary prevention treatment, in-hospital evaluation and education,laboratory data, neurological severity, mRS score and TOAST classification of patients were assessed. Univariate and multivariate logistic regression analyses were performed in the training set to identify predictors associated with poor outcome (mRS≥3). The predictors were used to establish machine learning models and the traditional Logistic model, which were randomly divided into the training set and test set according to the ratio of 70:30. The training set was used to construct the prediction model, and the test set was used to evaluate the effect of the model. The evaluation indicators of the model included the area under the curve (AUC) of the discrimination index and the Brier score (or calibration plot) of the calibration index. RESULT: A total of 10967 patients with TIA and minor stroke were enrolled in this study, with an average age of 61.77 ± 11.18 years, and women accounted for 30.68%. Factors associated with the poor prognosis in TIA and minor stroke patients included sex, age, stroke history, heart rate, D-dimer, creatinine, TOAST classification, admission mRS, discharge mRS, and discharge NIHSS score. All models, both those constructed by Logistic regression and those by machine learning, performed well in predicting the 90-day poor prognosis (AUC >0.800). The best performing AUC in the test set was the Catboost model (AUC=0.839), followed by the XGBoost, GBDT, random forest and Adaboost model (AUCs equal to 0.838, 0, 835, 0.832, 0.823, respectively). The performance of Catboost and XGBoost in predicting poor prognosis at 90-day was better than the Logistic model, and the difference was statistically significant(P<0.05). All models, both those constructed by Logistic regression and those by machine learning had good calibration. CONCLUSION: Machine learning algorithms were not inferior to the Logistic regression model in predicting the poor prognosis of patients with TIA and minor stroke at 90-day. Among them, the Catboost model had the best predictive performance. All models provided good discrimination.

Linguistic Characteristics of Mandarin-Speaking Huntington's Disease Patients.

Objective Linguistic problem is common in Huntington's disease (HD) patients. It has been studied before in native speakers of alphabetic languages, such as English. As a hieroglyphic language, Chinese differs from alphabetic languages in terms of phonology, morphology, semantics and syntax. We aimed to investigate the linguistic characteristics of manifest HD in native speakers of Mandarin. Meanwhile, we expected to explore the linguistic differences associated with cortical or subcortical pathology.Methods Five HD patients and five Alzheimer's disease (AD) patients matched in age, gender, disease course and educational level were enrolled. All the participants were Mandarin native speakers. All finished history inquiry, physical examination, basic test, genetic test and neuropsychological assessment. Language evaluation was performed by Aphasia Battery of Chinese.Results HD patients had a mean disease course of 5.4±2.97 (range, 2-10) years. They showed a linguistic disorder close to transcortical motor aphasia. They exhibited prominent phonological impairment, as well as slight semantic and syntactic abnormality. Tonic errors were found in speech. Character structural errors and substitutions were detected in writing. In comparison, AD patients showed a more severe linguistic impairment, characterized by global aphasia with more semantic errors. Conclusion Mandarin-speaking HD patients have a transcortical motor aphasia-like disturbance with prominent phonological impairment, whereas AD patients have a more severe global aphasia with salient semantic impairment.

Evaluation of antibacterial and anti-biofilm properties of kojic acid against five food-related bacteria and related subcellular mechanisms of bacterial inactivation.

Although the antimicrobial properties of kojic acid have been recognized, the subcellular mechanism of bacterial inactivation caused by it has never been clearly elucidated. In the present study, the antibacterial and anti-biofilm activity of kojic acid was evaluated against five foodborne pathogens including Listeria monocytogenes, Bacillus subtilis, Staphylococcus aureus, Escherichia coli, and Salmonella typhimurium. The antibacterial activity was determined by minimum inhibitory concentration, minimum bactericidal concentration, and the time-kill assay. Among them, the susceptibility of Escherichia coli was significant with the lowest minimum inhibitory concentration and minimum bactericidal concentration values of 10 and 20 mM, respectively. Subcellular mechanism of bacterial inactivation related to kojic acid was revealed through comprehensive factors including cell morphology, membrane permeability, K+ leakage, zeta potential, intracellular enzyme, and DNA assay. Results demonstrated that bacterial inactivation caused by kojic acid, especially for Gram-negative bacteria, was primarily induced by the pronounced damage to the cell membrane integrity. Leakage of intracellular enzyme to the supernatants implied that the cell membrane permeability was compromised. Consequently, the release of K+ from the cytosol leads to the alterations of the zeta potential of cells, which would disturb the subcellular localization of some proteins and thereby cause the bacterial inactivation. The free -CH2OH group at the C-2 of kojic acid could play more significant role in the antimicrobial performance of kojic acid against Gram-negative bacteria. Moreover, remarkable interaction with DNA was also observed. Kojic acid at sub-minimum inhibitory concentration inhibited biofilm formation by these bacteria.

Lipase-Catalyzed Synthesis of Sucrose Monolaurate and Its Antibacterial Property and Mode of Action against Four Pathogenic Bacteria.

The aim of this work was to evaluate the antibacterial activities and mode of action of sucrose monolaurate (SML) with a desirable purity, synthesized by Lipozyme TL IM-mediated transesterification in the novel ionic liquid, against four pathogenic bacteria including L. monocytogenes, B. subtilis, S. aureus, and E. coli. The antibacterial activity was determined by minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and the time⁻kill assay. SML showed varying antibacterial activity against tested bacteria with MICs and MBCs of 2.5 and 20 mM for L. monocytogenes, 2.5 and 20 mM for B. subtilis, 10 and 40 mM for S. aureus, respectively. No dramatic inhibition was observed for E. coli at 80 mM SML. Mechanism of bacterial inactivation caused by SML was revealed through comprehensive factors including cell morphology, cellular lysis, membrane permeability, K⁺ leakage, zeta potential, intracellular enzyme, and DNA assay. Results demonstrated that bacterial inactivation against Gram-positive bacteria was primarily induced by the pronounced damage to the cell membrane integrity. SML may interact with cytoplasmic membrane to disturb the regulation system of peptidoglycan hydrolase activities to degrade the peptidoglycan layer and form a hole in the layer. Then, the inside cytoplasmic membrane was blown out due to turgor pressure and the cytoplasmic materials inside leaked out. Leakage of intracellular enzyme to the supernatants implied that the cell membrane permeability was compromised. Consequently, the release of K⁺ from the cytosol lead to the alterations of the zeta potential of cells, which would disturb the subcellular localization of some proteins, and thereby causing bacterial inactivation. Moreover, remarkable interaction with DNA was also observed. SML at sub-MIC inhibited biofilm formation by these bacteria.