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OBJECTIVE: Perioperative anxiety and depression syndrome (PADS) is a common clinical concern among women with systemic tumors. Esketamine has been considered for its potential to alleviate anxiety and depressive symptoms. However, its specific application and effectiveness in PADS among women with systemic tumors remain unclear. This study aimed to analyze the utility of Machine Learning (ML) algorithms based on electroencephalogram (EEG) signals in evaluating perioperative anxiety and depression in women with systemic tumors treated with Esketamine, utilizing a large-scale medical data background. PATIENTS AND METHODS: A single-center, randomized, placebo-controlled (SC-RPC) trial design was adopted. A total of 112 female patients with systemic tumors and PADS who received Esketamine treatment were included as study participants. A moderate dose (0.7 mg/kg) of Esketamine was administered through intravenous infusion over a duration of 60 minutes. EEG signals were collected from all patients, and the EEG signal features of individuals with depression were compared to those without depression. In this study, a Support Vector Machine (SVM)-K-Nearest Neighbour (KNN) hybrid classifier was constructed based on SVM and KNN algorithms. Using the EEG signals, the classifier was utilized to assess the anxiety and depression status of the patients. The predictive performance of the classifier was evaluated using accuracy, sensitivity, and specificity measures. RESULTS: The C2 correntropy feature of the delta rhythm in the left-brain EEG signal was significantly higher in individuals with depression compared to those without depression (p<0.05). Moreover, the C2 correntropy feature of the Alpha, Beta, and Gamma rhythms in the left-brain EEG signal was significantly lower in individuals with depression compared to those without depression (p<0.05). In the right brain EEG signal, the C2 correntropy feature of the delta rhythm was significantly higher in individuals with depression (p<0.05), while the C2 correntropy feature of the alpha and gamma rhythms was significantly lower in individuals with depression compared to those without depression (p<0.05). Additionally, the C1 correntropy feature of the Gamma rhythm in the right brain EEG signal was significantly higher in individuals with depression compared to those without depression (p<0.05). The SVM classifier achieved accuracy, sensitivity, and specificity of 98.23%, 98.10%, and 98.56%, respectively, in recognizing the left-brain EEG signals, with a correlation coefficient of 0.95. In recognizing the right brain EEG signals, the SVM classifier achieved accuracy, sensitivity, and specificity of 98.74%, 98.43%, and 99.03%, respectively, with a correlation coefficient of 0.96. The improved SVM-KNN approach yielded an accuracy, recall, precision, F-score, area over the curve (AOC), and Receiver Operation Characteristics (ROC) of 0.829, 0.811, 0.791, 0.853, 0.787, and 0.877, respectively, in predicting anxiety. For predicting depression, the accuracy, recall, precision, F-score, AOC, and ROC were 0.869, 0.842, 0.831, 0.893, 0.827, and 0.917, respectively. CONCLUSIONS: Significant differences were observed in the brain EEG signals between individuals with depression and those without depression. The improved SVM-KNN algorithm developed in this study demonstrates good predictive capability for anxiety and depression.
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Macrodatos , Ketamina , Neoplasias , Femenino , Humanos , Depresión/diagnóstico , Depresión/tratamiento farmacológico , Ritmo Gamma , Ansiedad/diagnóstico , Ansiedad/tratamiento farmacológico , SíndromeRESUMEN
OBJECTIVE: To reveal the anti-tumor effect of micro ribonucleic acid (miR)-127-3p on epithelial ovarian cancer (EOC). PATIENTS AND METHODS: The expression of miR-127-3p in 7 kinds of EOC cell lines and 10 cases of clinical samples of EOC patients was detected via quantitative Reverse Transcription-Polymerase Chain Reaction (qRT-PCR). OVCAR-3 and Caov-3 cell lines were transfected with lentiviruses to overexpress endogenous miR-127-3p. Then, the anti-tumor effect of miR-127-3p on EOC cells was explored through the in vitro cell proliferation assay, bufalin sensitivity assay, wound healing assay, and invasion assay. In addition, whether the mitogen-activated protein kinase 4 (MAPK4) gene is a downstream target of miR-127-3p in EOC was verified via Dual-Luciferase reporter assay and qRT-PCR. The involvement of MAPK4 in regulating phenotypes of OVCAR-3 and Caov-3 cells was finally explored. RESULTS: MiR-127-3p was downregulated in both EOC cell lines and EOC tissues (p<0.05). After lentivirus-mediated overexpression of miR-127-3p, in vitro proliferation and invasion of EOC cells were inhibited, and the sensitivity to bufalin was enhanced (p<0.05). MiR-127-3p directly regulated MAPK4 gene in EOC. Moreover, the upregulation of MAPK4 inhibited the anti-tumor effect of miR-127-3p on EOC, manifested as the remarkably enhanced cell proliferation and migration (p<0.05), and the weakened sensitivity to bufalin (p<0.01). CONCLUSIONS: MiR-127-3p exerts an inhibitory effect on EOC cells via regulating MAPK4 level.
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Carcinoma Epitelial de Ovario/metabolismo , Regulación hacia Abajo , MicroARNs/metabolismo , Neoplasias Ováricas/metabolismo , ARN Helicasas/metabolismo , Carcinoma Epitelial de Ovario/patología , Línea Celular , Movimiento Celular , Proliferación Celular , Femenino , Humanos , MicroARNs/genética , Neoplasias Ováricas/patología , ARN Helicasas/genéticaRESUMEN
Special AT-rich sequence-binding protein 2 (Satb2) is a transcriptional regulator and people with SATB2 mutation or duplication could display epilepsy. However, whether Satb2 is related with epilepsy and its mechanisms are largely unexplored. Here we found that the expression of Satb2 was decreased following the neuronal hyperactivities. Ablation of Satb2 in mice would decrease incidence and stage of seizure induced by intraperitoneal injection of pentylenetetrazol (PTZ). At cellular levels, we found pyramidal neuronal excitability and excitatory synaptic inputs in CA1 were decreased in Satb2 mutant mice. Taking together, we proved that deletion of Satb2 in mice increased PTZ seizure threshold probably by modulating neuronal excitability.
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Proteínas de Unión a la Región de Fijación a la Matriz/efectos de los fármacos , Pentilenotetrazol/farmacología , Células Piramidales/metabolismo , Convulsiones/metabolismo , Factores de Transcripción/efectos de los fármacos , Animales , Epilepsia/metabolismo , Hipocampo/efectos de los fármacos , Hipocampo/metabolismo , Proteínas de Unión a la Región de Fijación a la Matriz/metabolismo , Ratones Transgénicos , Células Piramidales/efectos de los fármacos , Convulsiones/inducido químicamente , Factores de Transcripción/metabolismoRESUMEN
Primary thyroid paraganglioma (PTPG) is rare,we report a case of 55 years old women with PTPG, which describes the clinical features, diagnosis and treatment. A possible diagnosis, treatment and follow-up strategy was proposed by reviewing relevant reports. It aims to improve the cognitive of PTPG and standardize its treatment.
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Paraganglioma , Neoplasias de la Tiroides , Femenino , Humanos , Inmunohistoquímica , Persona de Mediana Edad , Paraganglioma/diagnóstico por imagen , Paraganglioma/terapia , Neoplasias de la Tiroides/diagnóstico por imagen , Neoplasias de la Tiroides/terapiaRESUMEN
OBJECTIVE: To create a protocol that could be used to construct chemical information database from scientific literature quickly and automatically. METHODS: Scientific literature, patents and technical reports from different chemical disciplines were collected and stored in PDF format as fundamental datasets. Chemical structures were transformed from published documents and images to machine-readable data by using the name conversion technology and optical structure recognition tool CLiDE. In the process of molecular structure information extraction, Markush structures were enumerated into well-defined monomer molecules by means of QueryTools in molecule editor ChemDraw. Document management software EndNote X8 was applied to acquire bibliographical references involving title, author, journal and year of publication. Text mining toolkit ChemDataExtractor was adopted to retrieve information that could be used to populate structured chemical database from figures, tables, and textual paragraphs. After this step, detailed manual revision and annotation were conducted in order to ensure the accuracy and completeness of the data. In addition to the literature data, computing simulation platform Pipeline Pilot 7.5 was utilized to calculate the physical and chemical properties and predict molecular attributes. Furthermore, open database ChEMBL was linked to fetch known bioactivities, such as indications and targets. After information extraction and data expansion, five separate metadata files were generated, including molecular structure data file, molecular information, bibliographical references, predictable attributes and known bioactivities. Canonical simplified molecular input line entry specification as primary key, metadata files were associated through common key nodes including molecular number and PDF number to construct an integrated chemical information database. RESULTS: A reasonable construction protocol of chemical information database was created successfully. A total of 174 research articles and 25 reviews published in Marine Drugs from January 2015 to June 2016 collected as essential data source, and an elementary marine natural product database named PKU-MNPD was built in accordance with this protocol, which contained 3 262 molecules and 19 821 records. CONCLUSION: This data aggregation protocol is of great help for the chemical information database construction in accuracy, comprehensiveness and efficiency based on original documents. The structured chemical information database can facilitate the access to medical intelligence and accelerate the transformation of scientific research achievements.