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Objective This study was performed to explore the effective management of bleeding associated with radiofrequency ablation (RFA) of benign thyroid nodules. Methods Thirty-five patients with benign thyroid nodules who were treated with ultrasound-guided RFA from July 2015 to December 2016 at the Third Affiliated Hospital of Sun Yat-sen University were retrospectively reviewed. The technique efficacy, bleeding, and other complications were assessed during the follow-up period. Results The mean technique efficacy was 55.6%±22.8% at 1 month and 24.1%±17.1% at 6 months after the procedure. One case of an intranodular haematoma and two cases of voice change (>1 month) were observed. All patients recovered with corresponding treatment. Conclusion Although the incidence of haemorrhage is low, serious haematomas are life-threatening. Therefore, having a comprehensive understanding of the potential complications, an accurate clinical strategy, and adequate technical skills may prevent or help to properly manage these complications.

Peptic ulcer bleeding due to primary hyperparathyroidism is extremely rare. We report a case of a 42-year-old male with life-threatening acute upper gastrointestinal bleeding secondary to a duodenal ulcer and a history of kidney stones. Gastroscopic therapy, Billroth II gastrointestinal anastomosis and angiographic embolization were sequentially conducted to arrest the hemorrhage. A complete investigative work-up revealed that the duodenal ulcer bleeding was due to primary hyperparathyroidism coexisting with a parathyroid adenoma. Following this event, the patient developed a severe abdominal cavity infection and sepsis. An elective parathyroidectomy was performed, and the histology was confirmed to be a typical parathyroid adenoma. Postoperatively, the patient’s calcium and parathyroid levels were normalized. Attention should be paid to patients with an upper gastrointestinal ulcer, especially when it is accompanied by kidney stones.

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Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based on electroencephalography (EEG) are significant in the clinical diagnosis and treatment of epilepsy. In this paper, we first decompose the patient's EEG signal into multiple intrinsic modal functions (IMFs) using empirical modal decomposition, then compute the mean, standard deviation, fluctuation index, and sample entropy of IMF1, and finally classify them using a fusion algorithm of support vector machine and K‐nearest neighbor optimized by particle swarm algorithm. The results of validation using the epileptic EEG data set from Bonn University show that the auto‐detection and fast recognition method proposed in this paper can achieve a high seizure accuracy recognition rate (≥95%) with only a small number of training samples, which has a good clinical application value.