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Facial symmetry evaluation has always been a hot topic of concern for doctors who engage in the study of facial beauty disciplines such as orthodontics, dentistry, and plastic surgery. Although scholars at home and abroad have carried out much research on the evaluation of facial symmetry with a variety of emerging technologies and methods, there is still a lack of unified standards for the evaluation of facial asymmetry due to the complexity of the content and methods and individual subjectivity. Facial asymmetry involves changes in the length, width and height of the face. It is a complex dental and maxillofacial malformation whose early identification and accurate evaluation are particularly important. Clinically, in addition to the necessary dental and maxillofacial examinations, it is also necessary to evaluate facial asymmetry with the help of corresponding auxiliary methods. This paper gives a summary of the commonly used three-dimensional evaluation methods. The evaluation methods of facial asymmetry can be divided into 5 categories: qualitative analysis, quantitative analysis, dynamic analysis, mathematical analysis, and artificial intelligence analysis. After the analysis and summarization of the characteristics, advantages and limitations of each method in clinical applications, it is found that although these methods vary in accuracy, evaluation scope, diagnosis nature and calculation method, etc., the three-dimensional evaluation methods are more objective, more accurate and more convenient and will become the mainstream evaluation method for facial asymmetry with further development of three-dimensional measurement technologies.

Cerebral cavernous malformations (CCMs) are a cause of stroke and seizure for which no effective medical therapies yet exist. CCMs arise from the loss of an adaptor complex that negatively regulates MEKK3-KLF2/4 signalling in brain endothelial cells, but upstream activators of this disease pathway have yet to be identified. Here we identify endothelial Toll-like receptor 4 (TLR4) and the gut microbiome as critical stimulants of CCM formation. Activation of TLR4 by Gram-negative bacteria or lipopolysaccharide accelerates CCM formation, and genetic or pharmacologic blockade of TLR4 signalling prevents CCM formation in mice. Polymorphisms that increase expression of the TLR4 gene or the gene encoding its co-receptor CD14 are associated with higher CCM lesion burden in humans. Germ-free mice are protected from CCM formation, and a single course of antibiotics permanently alters CCM susceptibility in mice. These studies identify unexpected roles for the microbiome and innate immune signalling in the pathogenesis of a cerebrovascular disease, as well as strategies for its treatment.

With advancements in computing technology and the rapid progress of data science, machine learning has been widely applied in various fields, showing great potential, especially in digital healthcare. In recent years, conversational diagnostic systems have been used to predict diseases through symptom checking. Early systems predicted the likelihood of a single disease by minimizing the number of questions asked. However, doctors typically perform differential diagnoses in real medical practice, considering multiple possible diseases to address diagnostic uncertainty. This requires systems to ask more critical questions to improve diagnostic accuracy. Nevertheless, such systems in acute medical situations need to process information quickly and accurately, but the complexity of differential diagnosis increases the system's computational cost. To improve the efficiency and accuracy of telemedicine diagnostic systems, this study developed an optimized algorithm for the Top-K algorithm. This algorithm dynamically adjusts the number of the most likely diseases and symptoms by real-time monitoring of case progress, optimizing the diagnostic process, enhancing accuracy (99.81%), and increasing the exclusion rate of severe pathologies. Additionally, the Top-K algorithm optimizes the diagnostic model through a policy network loss function, effectively reducing the number of symptoms and diseases processed and improving the system's response speed by 1.3–1.9 times compared to the state-of-the-art differential diagnosis systems. [ABSTRACT FROM AUTHOR]

In view of problems in existing rolling bearing fault diagnosis methods for coal mine rotating machinery, such as incomplete signal feature extraction, low fault diagnosis accuracy and low efficiency, a rolling bearing fault diagnosis method based on wavelet packet decomposition and particle swarm optimization BP neural network was proposed. The method includes signal feature extraction and fault type recognition. In the signal feature extraction part, the collected vibration signals of rolling bearing are decomposed by wavelet packet to obtain energy of each sub-frequency band and total energy of the signal. After normalization processing, feature vector representing state of rolling bearing is obtained. In the fault type recognition part, initial weight and threshold of BP neural network are optimized by particle swarm optimization to accelerate convergence speed of the network and avoid falling into local minimum. The experimental results show that the method improves fault diagnosis efficiency and accuracy of rolling bearing.

Researchers have typically studied the brain by monitoring characteristic signals, such as electrophysiology and neurotransmitters by implanted electronics. Here, real‐time monitoring of dynamic deformations of the brain tissue in vivo, is demonstrated, as a new characteristic parameter that is reflective of brain states. As a proof of concept, a thin capacitive deformation sensor is fabricated and implanted between the skull and cortex, and the sensor is shown to effectively monitor dynamic deformations of the cortical surface in the rat brain as induced by respiration and heartbeat under different degrees of anesthesia. This brain monitoring approach based on deformation signals opens up a new direction for understanding the brain. [ABSTRACT FROM AUTHOR]

In view of problems that existing coal mine machinery online monitoring and diagnosis technology did not realize online extraction of fault characteristics and automatic identification of fault types, an online fault diagnosis and early warning system for coal mine rotating machinery based on LabVIEW was designed. The system analyzes vibration signal by means of spectrum analysis, power spectrum analysis, envelope spectrum analysis and cepstrum analysis, and obtains characteristic parameters of each component in running process of the rotating machine, and compares it with characteristic parameters in fault type database to realize fault diagnosis. Two kinds of fault diagnosis modes are designed including fine diagnosis and rough diagnosis, the two modes are associated by interlocking. If structural parameters of the main components of rotating machine are known, the fine diagnosis mode can be selected,otherwise, the rough diagnosis mode can be selected. Performance of the system is verified by simulating rotor unbalance fault of rotating machine, and the results show that the system can accurately identify faults and issue prompts with simple operation and high reliability.