Evidence-based medicine is the methodology of modern clinical research and plays an important role in guiding clinical practice. It has become an integral part of medical education. In the digital age, evidence-based medicine has evolved to incorporate innovative research models that utilize multimodal clinical big data and artificial intelligence methods. These advancements aim to address the challenges posed by diverse research questions, data methods, and evidence sources. However, the current teaching content in medical schools often fails to keep pace with the rapidly evolving disciplines, impeding students' comprehensive understanding of the discipline's knowledge system, cutting-edge theories, and development directions. In this regard, this article takes the opportunity of graduate curriculum reform to incorporate real-world data research, artificial intelligence, and bioinformatics into the existing evidence-based medicine curriculum, and explores the reform of evidence-based medicine teaching in the information age. The aim is to enable students to truly understand the role and value of evidence-based medicine in the development of medicine, while possessing a solid theoretical foundation, a broad international perspective, and a keen research sense, in order to cultivate talents for the development of the evidence-based medicine discipline.
Transcranial magneto-acoustic electrical stimulation (TMAES) is a novel method of brain nerve regulation and research, which uses induction current generated by the coupling of ultrasound and magnetic field to regulate neural electrical activity in different brain regions. As the second special envoy of nerve signal, calcium plays a key role in nerve signal transmission. In order to investigate the effect of TMAES on prefrontal cortex electrical activity, 15 mice were divided into control group, ultrasound stimulation (TUS) group and TMAES group. The TMAES group received 2.6 W/cm2 and 0.3 T of magnetic induction intensity, the TUS group received only ultrasound stimulation, and the control group received no ultrasound and magnetic field for one week. The calcium ion concentration in the prefrontal cortex of mice was recorded in real time by optical fiber photometric detection technology. The new object recognition experiment was conducted to compare the behavioral differences and the time-frequency distribution of calcium signal in each group. The results showed that the mean value of calcium transient signal in the TMAES group was (4.84 ± 0.11)% within 10 s after the stimulation, which was higher than that in the TUS group (4.40 ± 0.10)% and the control group (4.22 ± 0.08)%, and the waveform of calcium transient signal was slower, suggesting that calcium metabolism was faster. The main energy band of the TMAES group was 0−20 Hz, that of the TUS group was 0−12 Hz and that of the control group was 0−8 Hz. The cognitive index was 0.71 in the TMAES group, 0.63 in the TUS group, and 0.58 in the control group, indicating that both ultrasonic and magneto-acoustic stimulation could improve the cognitive ability of mice, but the effect of the TMAES group was better than that of the TUS group. These results suggest that TMAES can change the calcium homeostasis of prefrontal cortex nerve clusters, regulate the discharge activity of prefrontal nerve clusters, and promote cognitive function. The results of this study provide data support and reference for further exploration of the deep neural mechanism of TMAES.
With the gradual standardization and improvement of the real-world study system, real-world evidence, as a supplement to evidence from classical randomized controlled trials, is increasingly used to evaluate the effectiveness and safety of pharmaceuticals and medical devices. High-quality real-world evidence is not only related to the quality of real-world data, but also depends on the type of study design. Therefore, as one of the important designs for pragmatic clinical trials, the Zelen design has received much attention from investigators in recent years. This paper discussed the implementation processes, subtypes of design, advantages, limitations, statistical concerns, and appropriate application scenarios of the Zelen design, on the basis of published papers, in order to clarify its application value, and to provide references for future research.
With the real-world study (RWS) becoming a hotspot for clinical research, health data collected from routine clinical practice have gained increasing attention worldwide, particularly the data related to the off-label use of drugs, which have been at the forefront of clinical research in recent years. The guidance from the National Medical Products Administration has proposed that real-world evidence (RWE) can be an important consideration in supporting label expansions where randomized controlled trials are unfeasible. Nevertheless, how to use the RWE to support the approval of new or expanded indications remains unclear. This study aims to explore the structured process for the use of RWE in supporting label expansions of approved drugs, and to discuss the key considerations in such process by reviewing the documents from relevant regulatory agencies and publications from public databases, which can inform future directions for studies in this area.
Assessing the clinical value of pharmaceuticals is crucial for comprehensive evaluation in clinical practice and plays a vital role in supporting decision-making for drug supply assurance. Real-world data (RWD) offers valuable insights into the actual diagnosis and treatment processes, serving as a significant data source for evaluating the clinical demand, effectiveness, and safety of drugs. This technical guidance aims to elucidate the scope of application of RWD for the clinical value assessment of pharmaceuticals, as well as the key considerations for conducting value assessment research. These considerations include identifying the dimensions of clinical value that necessitate RWD and effectively utilizing RWD for evaluation purposes. Additionally, this guidance provides essential points for implementing pharmaceutical clinical value assessment based on real-world data, with a specific focus on study design and statistical analysis. By doing so, this guidance assists researchers in accurately comprehending and standardizing the utilization of real-world research in conducting pharmaceutical clinical research.