Objective To investigate the application value of the quantitative parameters of collateral circulation based on computed tomography perfusion(CTP)in evaluating the clinical prognosis of patients with acute ischemic stroke(AIS).Methods A total of 126 patients with AIS caused by middle cerebral artery occlusion were enrolled,collateral circulation were reconstructed and scored via multiphase computed tomography angiography(mCTA)based on CTP.The hypoperfusion intensity ratio(HIR)and collateral vessel density(CVD)were calculated.All patients were divided into good prognosis group(72 cases)and poor prognosis group(54 cases)based on 90 d modified Rankin scale(mRS)scores.Differences in cardiovascular risk factors,National Institutes of Health Stroke Scale(NIHSS)scores,Albert stroke program early CT(ASPECT)scores,Tmax＞10 s volume,Tmax＞6 s volume,core infarct area volume,and final infarct volume between the two groups were compared.Binary logistic regression was employed to identify independent predictors of the clinical prognosis,and the DeLong test was used to compare the efficacy of different predictors in predicting clinical prognosis.Results The good prognosis group had significantly higher ASPECT scores,mCTA scores,and CVD,but significantly lower HIR,Tmax＞10 s volume,and baseline NIHSS scores compared to the poor prognosis group(P＜0.05).Binary logistic regression analysis showed that ASPECT scores[odds ratio(OR)=0.780],mCTA scores(OR=0.669),CVD(OR=0.595),and HIR(OR=28.968)were independent predictors of clinical prognosis(P＜0.05).DeLong test found no significant difference in area under the curve(AUC)values between mCTA scores,CVD and HIR in predicting the clinical prognosis of AIS patients(P＞0.05).Conclusion Quantitative parameters such as CVD and HIR related to collateral circulation have a strong diagnostic efficacy in predicting the 90 d clinical prognosis in patients with AIS.

OBJECTIVE: To investigate the effect of Akt2 inhibitor on macrophage polarization in the periapical tissue in a rat model of periapical inflammation. METHODS: Rat models of periapical inflammation were established in 28 normal SD rats by opening the pulp cavity of the mandibular first molars, followed by injection of normal saline and Akt2 inhibitor into the left and right medullary cavities, respectively. Four rats without any treatment served as the healthy control group. At 7, 14, 21 and 28 days after modeling, 7 rat models and 1 control rat were randomly selected for observation of inflammatory infiltration in the periapical tissues by X-ray and HE staining. Immunohistochemistry was used to detect the expression and localization of Akt2, macrophages and the inflammatory mediators. RT-PCR was performed to detect the mRNA expressions of Akt2, CD86, CD163, inflammatory mediators, miR-155-5p and C/EBPβ to analyze the changes in macrophage polarization. RESULTS: X-ray and HE staining showed that periapical inflammation was the most obvious at 21 days after modeling in the rats. Immunohistochemistry and RT-PCR showed that compared with those in the control rats, the expressions of Akt2, CD86, CD163, miR-155-5p, C/EBPβ, and IL-10 increased significantly in the rat models at 21 days (P < 0.05). Compared with saline treatment, treatment with the Akt2 inhibitor significantly decreased the expression levels of Akt2, CD86, miR-155-5p and IL-6 and the ratio of CD86⁺M1/CD163⁺M2 macrophages (P < 0.05) and increased the expression levels of CD163, C/EBPβ and IL-10 in the rat models (P < 0.05). CONCLUSION Inhibition of Akt2 can delay the progression of periapical inflammation in rats and promote M2 macrophage polarization in the periapical inflammatory microenvironment possibly by reducing miR-155-5p expression and activating the expression of C/EBPβ in the Akt signaling pathway.
Rats ; Animals ; Proto-Oncogene Proteins c-akt/metabolism* ; MicroRNAs/genetics* ; Interleukin-10 ; Rats, Sprague-Dawley ; Macrophages/metabolism* ; Inflammation/metabolism*

Pain is one of the most prevalent health problems. Current medications for pain are mainly anticonvulsants， tricyclic antidepressants， and opioidergic drugs. However， their therapeutic effectiveness is limited during application， and some even have severe side effects. In recent years， research on natural ingredients from Chinese herbal medicine has been extensively conducted for their analgesic activities. A series of natural ingredients represented by alkaloids， coumarins， flavonoids， and terpenoids have shown great analgesic activity， and further studies on their analgesic mechanism have found that most natural products have multi-target analgesic mechanisms. It can exert analgesic effects by blocking ion channels， regulating related receptors， or inducing anti-inflammatory or antioxidant effects. In addition， many traditional Chinese medicine （TCM） formulas have shown great analgesic ability after clinical application and have multiple complex analgesic mechanisms. The drug cloud （dCloud） theory can better describe the mechanisms， and it can represent the complete therapeutic spectrum of multi-target analgesics from two dimensions， namely the "direct efficacy" that directly inhibits pain signals and the "background efficacy" that targets the root causes of pain. The authors summarized the research progress of natural ingredients with analgesic effects found in Chinese herbal medicine so far， as well as the analgesic efficacy and potential mechanisms of TCM formulas with great analgesic effects in clinical applications， so as to provide a new basis for searching for new analgesic drugs from TCM.

Recent advancement in natural language processing (NLP) and medical imaging empowers the wide applicability of deep learning models. These developments have increased not only data understanding, but also knowledge of state-of-the-art architectures and their real-world potentials. Medical imaging researchers have recognized the limitations of only targeting images, as well as the importance of integrating multimodal inputs into medical image analysis. The lack of comprehensive surveys of the current literature, however, impedes the progress of this domain. Existing research perspectives, as well as the architectures, tasks, datasets, and performance measures examined in the present literature, are reviewed in this work, and we also provide a brief description of possible future directions in the field, aiming to provide researchers and healthcare professionals with a detailed summary of existing academic research and to provide rational insights to facilitate future research.
Humans ; Natural Language Processing ; Surveys and Questionnaires