Objective:To compare the reliability between infrared thermal imaging technology(IRT) and colour Doppler ultrasound (CDU) in the detection of perforators for anterolateral femoral perforator flap(ALTPF).Methods:From September, 2017 to September, 2019, a total of 25 patients(16 males and 9 females, aged 12-53 years old with an average age of 38.7 years old) underwent ALTPF surgery for the purpose of resurfacing and reconstruction. All patients had pedicled or free ALTPF sized from 5.0 cm×8.0 cm to 7.5 cm×30.0 cm. Among all cases, 21 received free flaps and 4 received pedicled flaps. All patients underwent routine IRT and CDU examinations to locate the perforators preoperatively.Results:Of the 25 patients, CDU detected 53 perforators and IRT detected 51 "hot spots". There were 50 "hot spots" that corresponded to the findings of CDU with a κ index at 0.712( P<0.05), representing a high degree of consistency. The sensitivity and specificity were 94.3% and 85.7%, respectively. Conclusion:IRT has a high consistency in detecting perforators for ALTPF in comparison with CDU. IRT features portable, low cost, non-invasive, and easy to operate. At the same time, IRT has advantages in the patients who have thin subcutaneous tissue. With higher accuracy, IRT is expected to become an important part of preoperative perforator navigation.

Pathological scars cause both local physical discomforts and mental disorders for patients. With the development of research, botulinum toxin type A (BTX-A) has been gradually used in the clinical treatment of scars. This article summarizes three potential mechanisms of BTX-A, including early tension reduction, inhibition of connective tissue hyperplasia, and alleviation of skin inflammation, as well as the clinical application of BTX-A in treating different types of scars. However, at present there is no uniform standard for the time, method, and proportioning of BTX-A injection for scar treatment, and there is also a lack of long-term follow-up results. Further researches are needed in future to clarify the mechanism of BTX-A in inhibiting scars, establish a uniform protocol of BTX-A injection, and provide a more effective treatment plan for scars.

Brain signals refer to electrical signals or metabolic changes that occur as a consequence of brain cell activity.Among the various non-invasive measurement methods,electroencephalogram(EEG)stands out as a widely employed technique,providing valuable insights into brain patterns.The deviations observed in EEG reading serve as indicators of abnormal brain activity,which is associated with neurological diseases.Brain?computer interface(BCI)systems enable the direct extraction and transmission of information from the human brain,facilitating interaction with external devices.Notably,the emergence of artificial intelligence(AI)has had a profound impact on the enhancement of precision and accuracy in BCI technology,thereby broadening the scope of research in this field.AI techniques,encompassing machine learning(ML)and deep learning(DL)models,have demonstrated remarkable success in classifying and predicting various brain diseases.This comprehensive review investigates the application of AI in EEG-based brain disease diagnosis,highlighting advancements in AI algorithms.