ObjectiveTo observe the effect of Huayu Mingmu prescription on retinal angiogenesis and phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin （PI3K/Akt/mTOR）-hypoxia inducible factor-1α/vascular endothelial growth factor A （HIF-1α/VEGFA） signaling pathway in diabetic retinopathy （DR） rats. MethodsSixty-four SPF-grade male SD rats were used in the study. Eleven rats were randomly selected as the normal group， while the remaining 53 rats were fed a high-sugar， high-fat diet combined with low-dose streptozotocin （STZ） intraperitoneal injection to establish a type 2 diabetes mellitus （T2DM） rat model. DR model evaluation was performed after 12 weeks of diabetes. The rats were then divided into model， low-dose， medium-dose， and high-dose groups of Huayu Mingmu prescription （9.29， 18.57， 37.14 g·kg^-1）， and a calcium dobesilate group （0.16 g·kg^-1）， with 10 rats in each group. The rats were orally administered the corresponding doses of Huayu Mingmu prescription and calcium dobesilate. The normal and model groups received equal volumes of physiological saline via gavage for 8 consecutive weeks. Retinal vascular changes were observed through fundus photography， and pathological changes in retinal tissue were evaluated using hematoxylin-eosin （HE） staining. Retinal microvascular pathological changes were examined through retinal vascular network preparation and periodic acid-Schiff （PAS） staining. Immunofluorescence （IF） was used to detect the expression of VEGFA and angiopoietin-2 （Ang-2） in retinal tissue. Western blot was employed to detect the protein expression of PI3K， Akt， mTOR， HIF-1α， VEGFA， and VEGFR2 in retinal tissue. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to assess the mRNA expression of PI3K， Akt， mTOR， HIF-1α， VEGFA， and VEGFR2 in retinal tissue. ResultsCompared with the normal group， the model group exhibited significant pathological changes in retinal tissue， including the appearance of acellular capillaries， as well as significant endothelial cell （E） proliferation and pericyte （P） loss （P<0.01）. The E/P was significantly elevated （P<0.01）. Protein and mRNA expression levels of PI3K， Akt， mTOR， HIF-1α， VEGFA， and VEGFR2 in retinal tissue were significantly increased （P<0.01）， and the expression of Ang-2 protein was significantly elevated （P<0.01）. In contrast， retinal tissue in the treatment groups showed alleviated pathological changes， with reduced endothelial cell proliferation and pericyte loss （P<0.05， P<0.01）. Among the treatment groups， the high-dose Huayu Mingmu prescription and the calcium dobesilate group exhibited a decreased E/P （P<0.01）. Protein and mRNA expression levels of PI3K， Akt， mTOR， HIF-1α， VEGFA， and VEGFR2 in retinal tissue were significantly reduced （P<0.05， P<0.01）， and the expression of Ang-2 protein was significantly decreased （P<0.01）. ConclusionHuayu Mingmu prescription can intervene in retinal neovascularization in DR rats， delay the progression of DR， and its mechanism may be related to antagonizing the PI3K/Akt/mTOR-HIF-1α/VEGFA signaling pathway.

OBJECTIVE: To observe the clinical efficacy of Daoqi (directing qi flowing) acupuncture technique in Huangdi Neijing (Yellow Emperor's Inner Classic) for moderate-to-severe obstructive sleep apnea-hypopnea syndrome (OSAHS). METHODS: Sixty patients with moderate-to-severe OSAHS were randomly divided into a Daoqi acupuncture group (30 cases) and a conventional acupuncture group (30 cases, 1 case dropped out). In the Daoqi acupuncture group, Daoqi acupuncture technique in Huangdi Neijing was applied at Shanglianquan (Extra), Fengfu (GV16), and bilateral Lieque (LU7), Zhaohai (KI6); in the conventional acupuncture group, conventional acupuncture was applied at Shanglianquan (Extra), Fengfu (GV16), Yamen (GV15), and bilateral Lieque (LU7), Zhaohai (KI6), Zusanli (ST36), Fenglong (ST40). The treatment was adopted once every other day, 3 times a week, 4 weeks as one course and 3 courses were required totally in both groups. Before and after treatment, the polysomnography (PSG) indexes [apnea-hypopnea index (AHI), hypopnea index (HI), apnea index (AI), longest apnea duration, lowest nocturnal SaO₂ (LSaO₂)], and scores of Epworth sleepiness scale (ESS), Pittsburgh sleep quality index (PSQI), World Health Organization quality of life-BREF (WHOQOL-BREF) were observed, and the clinical efficacy was evaluated after treatment in the two groups. RESULTS: After treatment, the AHI, HI, AI and longest apnea duration were reduced compared with those before treatment in the two groups (P<0.01), the LSaO₂ was increased in the Daoqi acupuncture group (P<0.01); in the Daoqi acupuncture group, the AHI, HI, AI and longest apnea duration were lower than those in the conventional acupuncture group (P<0.05), and the LSaO₂ was higher than that in the conventional acupuncture group (P<0.05). After treatment, the ESS and PSQI scores were decreased compared with those before treatment (P<0.01), while the WHOQOL-BREF scores were increased compared with those before treatment (P<0.01) in the two groups; in the Daoqi acupuncture group, the ESS and PSQI scores were lower than those in the conventional acupuncture group (P<0.05, P<0.01), and the WHOQOL-BREF score was higher than that in the conventional acupuncture group (P<0.05). The total effective rate was 93.3% (28/30) in the Daoqi acupuncture group, which was higher than 82.8% (24/29) in the conventional acupuncture group (P<0.01). CONCLUSION Daoqi acupuncture technique in Huangdi Neijing can effectively treat moderate-to-severe OSAHS patients, improve the clinical symptoms and quality of life, and has the advantages i.e. simpler acupoints selection and gentler stimulation.
Humans ; Sleep Apnea, Obstructive/physiopathology* ; Male ; Female ; Middle Aged ; Acupuncture Therapy ; Adult ; Acupuncture Points ; Treatment Outcome ; Aged ; Quality of Life

OBJECTIVES: Injuries are the leading cause of death among children and adolescents. Although numerous risk assessment tools for unintentional injuries in children have been developed and published both domestically and internationally, there is currently no global consensus on standardized use. This study aims to systematically characterize existing unintentional injury risk assessment tools for children aged 0-6 years, with the goal of informing scientific tool selection and optimization. METHODS: Relevant literature published up to January 2025 was retrieved from CNKI, Wanfang, PubMed, and Web of Science. An information extraction form was developed to gather data on the basic features of each assessment tool, assessment format, scoring methods and criteria, dimensions assessed, reliability and validity, and types of unintentional injuries covered. RESULTS: A total of 50 risk assessment tools for unintentional injuries among children aged 0-6 years were included. Among them, 35 tools assessed two or more types of unintentional injuries. Regarding assessment format, 38 tools relied on caregiver self-report, 2 on investigator interviews, 3 on direct observation by investigators, and 7 used multiple methods. The tools covered four major dimensions: knowledge, attitude, behavior, and environment. Eleven tools covered 3 dimensions, while only one tool addressed all 4. Nineteen tools provided clear scoring methods, 14 included criteria for risk determination, and only 11 had both scoring methods and risk criteria. Twenty-eight tools lacked both. Twenty-two tools had been evaluated for reliability and/or validity. Among the 25 English-language tools, only 3 had been translated into Chinese. CONCLUSIONS Currently, no existing tool comprehensively assesses all major types of unintentional injuries for children under six years of age. It is recommended that practitioners select appropriate tools based on specific needs. In addition, improvements should be pursued, such as translating and validating English-language tools, developing quantitative scoring methods and criteria for tools tailored to Chinese children for important but underrepresented injury types (e.g., road traffic injuries, drowning).
Humans ; Infant ; Child, Preschool ; Child ; Risk Assessment/methods* ; Accidental Injuries/prevention & control* ; Infant, Newborn ; Wounds and Injuries/epidemiology* ; Reproducibility of Results

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Background and purpose:Breast cancer is a major global public health problem.Bone is the most common site of distant metastasis of breast cancer,accounting for about 70%of all metastatic cases.Bone metastasis of breast cancer can cause a series of complications,including severe pain,pathological fracture,hypercalcemia,spinal cord compression,etc.,which bring great inconvenience to patients'physical activities and affect their quality of life.Metastatic recurrence is the leading cause of death in breast cancer patients.Therefore,there is an urgent need to build a diagnostic model of bone metastasis in breast cancer to identify patients with a high risk of bone metastasis.The aim of this study was to develop a predictive model based on machine learning to predict the probability of breast cancer developing bone metastasis.Methods:Data of breast cancer patients diagnosed between 2010 and 2015 were extracted from The Surveillance,Epidemiology,and End Results(SEER)database.The variables were screened by least absolute shrinkage and selection operator(LASSO)regression,univariate and multivariate logistic regression analysis,and statistically significant risk factors were included to build a prediction model.In this study,nine machine learning algorithms,including decision tree,elastic network,K-nearest neighbor,lightweight gradient elevator,logistic regression,neural network,random forest,support vector machine and limit gradient lifting,were used to adjust the model hyperparameters through random search and 5x cross-validation to build a breast cancer bone metastasis prediction model.The area under the receiver operating characteristic(ROC)curve,calibration curve and decision curve were used to evaluate the model,the optimal model was obtained,and the importance of variables was analyzed based on the optimal model.Finally,a network calculator for predicting the risk of bone metastasis of breast cancer was established using the optimal model.Results:The study included 10 106 patients with breast cancer,7 073 patients in the training set,and 3 033 patients in the validation set.We found that 4 494(63.5%)patients in the training set and 1 927(63.5%)patients in the validation set developed bone metastases,respectively.Race,pathologic grade,estrogen receptor(ER)status,progesterone receptor(PR)status,human epidermal growth factor receptor 2(HER2)status,N stage,lung metastasis,radiotherapy,chemotherapy and surgery were independent predictors of bone metastasis.The training set and verification set were used to verify the model,and the limit gradient lifting algorithm was superior to other machine learning algorithms by integrating the evaluation indexes such as the area under the ROC curve,calibration curve and decision curve.Finally,we used limit gradient algorithm to build network calculator for prediction of breast cancer bone metastases(https://bcbm.shinyapps.io/DynNomapp/).Conclusion:This study developed a predictive model based on machine learning to predict the probability of bone metastases in breast cancer patients,hoping to help clinicians make more rational treatment decisions.

Objective To observe the correlations of chest CT quantitative parameters in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD)with blood eosinophil(EOS)level.Methods Chest CT data of 162 AECOPD patients with elevated eosinophils were retrospectively analyzed.The patients were divided into low EOS group(n=105)and high EOS group(n=57)according to the absolute counting of blood EOS.The quantitative CT parameters,including the number of whole lung bronchi and the volume of blood vessels,low-attenuation area percentage(LAA％)of whole lung,of left/right lung and each lobe of lung,as well as the luminal diameter(LD),wall thickness(WT),wall area(WA)and WA percentage of total bronchial cross-section(WA％)of grade 3 to 8 bronchi were compared between groups.Spearman correlations were performed to analyze the correlations of quantitative CT parameters with blood EOS level.Results LAA％of the whole lung,of the left/right lung and each lobe of lung,as well as of the upper lobe of right lung LDgrade4,middle lobe of right lung WTgrade5,upper lobe of right lung WAgrade4,middle lobe of right lung WAgrade5 and lower lobe of left lung WAgrade3 in low EOS group were all higher than those in high EOS group(all P＜0.05).Except for the upper lobe of right lung LDgrade4,the above quantitative CT indexes being significant different between groups were all weakly and negatively correlated with blood EOS level(r=-0.335 to-0.164,all P＜0.05).Conclusion Chest CT quantitative parameters of AECOPD patients were correlated with blood EOS level,among which LAA％,a part of WT and WA were all weakly negatively correlated with blood EOS level.

Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quantitative CT(QCT)BMD examination were retrospectively enrolled and divided into training set(n=304)and test set(n=76)at a ratio of 8∶2.The mean BMD of L1-L3 vertebrae were measured based on QCT.Spongy bones of T5-T10 vertebrae were segmented as RO1,radiomics(Rad)features were extracted,and machine learning(ML),Rad and deep learning(DL)models were constructed for classification of osteoporosis(OP)and evaluating BMD,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,MLBagging OP and RadBagging-OP had the best performances for classification of OP.In test set,AUC of MLBagging-OP,RadBagging-op and DLOP for classification of OP was 0.943,0.944 and 0.947,respectively,with no significant difference(all P＞0.05).BMD obtained with all the above models had good consistency with those measured with QCT(most of the differences were within the range of(x)±1.96s),which were highly positively correlated(r=0.910-0.974,all P＜0.001).Conclusion AI models based on non-contrast chest CT had high efficacy for classification of OP,and good consistency of BMD measurements were found between AI models and QCT.

Purpose To explore the application value of contrast enhancement boost(CE-Boost)technique in image quality of tumors and their feeding arteries in preoperative evaluation of renal cancer patients.Materials and Methods A total of 36 renal cancer patients in People's Hospital Affiliated to Shandong First Medical University from August 2022 to May 2023 with pathologically confirmed were retrospective collected.All patients underwent renal enhanced CT.The cortical phase images were post-processed using the CE-Boost technique to obtain CE-Boost images.The cortical phase images were set as group A and the CE-Boost images were set as group B.The CT value and image noise(SD)of abdominal aorta,renal artery,tumor and its adjacent renal cortex,and SD of the vertical spinal muscle on both sides of the spine of two groups were measured and recorded,and then the signal-to-noise ratio and contrast-to-noise ratio of tumor,abdominal aorta and renal artery were calculated.The image quality of the tumor,tumor feeding artery and renal artery was scored on 4 points by 2 doctors with double-blind method.Results The signal-to-noise ratio,contrast-to-noise ratio and CT value of group B were significantly higher than those of group A(t=-27.385--5.267,all P<0.05).The SD of tumor,abdominal aorta,right and left renal artery were not significantly different between group A and B(t=-1.849-0.993,all P>0.05).The subjective score of tumor in group A and B were no significant difference(Z=-1.490,P=0.136).However,the subjective score of tumor feeding arteries and renal arteries were significantly higher in group B than in group A(Z=-3.512,P=0.000;Z=-2.127,P=0.033).Conclusion The CT CE-Boost technique can improve the image quality of renal enhanced CT and provide visualization of tumor feeding arteries.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.