Objective: To analyze the association between dietary diversity and cognitive function among the elderly using group-based trajectory model, so as to provide the basis for formulating dietary intervention strategies to prevent cognitive impairment. Methods: Based on the Chinese Longitudinal Healthy Longevity Survey (CLHLS) project, demographic information, lifestyle behaviors, psychological well-being, and activities of daily living of elderly individuals aged ≥65 years from 2008 to 2018 were collected. Dietary diversity was assessed using a food frequency questionnaire, with a score of <7 points defined as low dietary diversity. Cognitive function was evaluated using the Chinese version of the Mini-Mental State Examination (MMSE). A group-based trajectory model was established based on cognitive function scores from 2008 to 2018 to analyze the trajectories of cognitive function change. The association between dietary diversity and cognitive function was analyzed using a multinomial logistic regression model. Results: A total of 1 613 individuals were collected, with a median age was 72.00 (interquartile range, 10.00) years. There were 810 males (50.22%) and 803 females (49.78%). The group-based trajectory model analysis categorized the participants into three groups: the low-level normal group, the high-level normal group, and the slow-then-rapid decline group, comprising 796 (49.35%), 585 (36.27%), and 232 (14.38%) individuals, respectively. Among these groups, the numbers of individuals with low dietary diversity were 497 (62.44%), 311 (53.16%), and 166 (71.55%), respectively, with a statistically significant difference (P<0.05). Multinomial logistic regression analysis showed that after adjusting for demographic information, lifestyle behaviors, psychological well-being, and activities of daily living, compared with the high-level normal group, low dietary diversity was statistically associated with cognitive function in the slow-then-rapid decline group (OR=1.622, 95%CI: 1.103-2.384). Conclusion Low dietary diversity may increase the risk of cognitive impairment among the elderly.

Medical visual question answering (MVQA) plays a crucial role in the fields of computer-aided diagnosis and telemedicine. Due to the limited size and uneven annotation quality of the MVQA datasets, most existing methods rely on additional datasets for pre-training and use discriminant formulas to predict answers from a predefined set of labels. This approach makes the model prone to overfitting in low resource domains. To cope with the above problems, we propose an image-aware generative MVQA method based on image caption prompts. Firstly, we combine a dual visual feature extractor with a progressive bilinear attention interaction module to extract multi-level image features. Secondly, we propose an image caption prompt method to guide the model to better understand the image information. Finally, the image-aware generative model is used to generate answers. Experimental results show that our proposed method outperforms existing models on the MVQA task, realizing efficient visual feature extraction, as well as flexible and accurate answer outputs with small computational costs in low-resource domains. It is of great significance for achieving personalized precision medicine, reducing medical burden, and improving medical diagnosis efficiency.
Humans ; Image Processing, Computer-Assisted/methods* ; Diagnosis, Computer-Assisted/methods* ; Algorithms ; Telemedicine

This study aims to investigate the therapeutic efficacy of 50 Hz-0.6 mT low-frequency pulsed electromagnetic field (PEMF) on postmenopausal osteoporosis in ovariectomized rats. Thirty 3-month-old female SD rats were selected and divided into a sham operation group (Sham), an ovariectomized model group (OVX), and a low-frequency pulsed electromagnetic field (PEMF) treatment group, with 10 rats in each group. After 8 weeks, the whole-body bone mineral density (BMD) of each group of rats was measured. The treatment group began to receive PEMF stimulation for 90 minutes daily, while the OVX group only received a simulated placement without electricity. After 6 weeks of intervention, all rats were sacrificed and tested for in vitro BMD, micro-CT, biomechanics, serum biochemical indicators, and bone tissue-related proteins. The results showed that the BMD of the OVX group was significantly lower than that of the Sham group 8 weeks after surgery, indicating successful modeling. After 6 weeks of treatment, compared with the OVX group, the PEMF group exhibited significantly increased BMD in the whole body, femur, and vertebral bodies. Micro-CT analysis results showed improved bone microstructure, significantly increased maximum load and bending strength of the femur, elevated levels of serum bone formation markers, and increased expression of osteogenic-related proteins. In conclusion, this study demonstrates that daily 90-minute exposure to 50 Hz-0.6 mT PEMF effectively enhances BMD, improves bone biomechanical properties, optimizes bone microstructure, stimulates bone formation, and inhibits bone resorption in ovariectomized rats, highlighting its therapeutic potential for postmenopausal osteoporosis.
Female ; Animals ; Rats, Sprague-Dawley ; Osteoporosis, Postmenopausal/therapy* ; Rats ; Bone Density ; Ovariectomy ; Magnetic Field Therapy/methods* ; Electromagnetic Fields

Severe traumatic brain injury (sTBI) has a high mortality and disability rate, making it a difficult issue and hot topic in neurosurgery. Controlled decompression is an important technique in the treatment of sTBI combined with intracranial hypertension, which can reduce the ischemia-reperfusion injury to the nervous tissue and intracranial vessel and can significantly lower the incidence of complications related to decompressive craniectomy. However, the effects of the controlled decompression technique have been affected by different understandings of the technique and nonstandard surgical procedures in clinical practice. For this purpose, the authors discussed the concept of controlled decompression technique, its indications and the key problems during operation so as to standardize the surgical procedures and improve the therapeutic effects of controlled decompression technique in the treatment of sTBI.

Osteoporotic proximal humeral fracture (OPHF) is one of the common osteoporotic fractures in the aged, with an incidence only lower than vertebral compression fracture, hip fracture, and distal radius fracture. OPHF, secondary to osteoporosis and characterized by poor bone quality, comminuted fracture pattern, slow healing, and severely impaired shoulder joint function, poses a big challenge to the current clinical diagnosis and treatment. In the field of diagnosis, treatment, and rehabilitation of OPHF, traditional Chinese and Western medicine have accumulated rich experience and evidence from evidence-based medicine and achieved favorable outcomes. However, there is still a lack of guidance from a relevant consensus as to how to integrate the advantages of the two medical systems and achieve the integrated diagnosis and treatment. To promote the diagnosis and treatment of OPHF with integrated traditional Chinese and Western medicine, relevant experts from Orthopedic Expert Committee of Geriatric Branch of Chinese Association of Gerontology and Geriatrics, Youth Osteoporosis Group of Orthopedic Branch of Chinese Medical Association, Osteoporosis Group of Orthopedic Surgeon Branch of Chinese Medical Doctor Association, and Osteoporosis Committee of Shanghai Association of Integrated Traditional Chinese and Western Medicine have been organized to formulate Expert consensus on the diagnosis and treatment of osteoporotic proximal humeral fracture with integrated traditional Chinese and Western medicine ( version 2024) by searching related literatures and based on the evidences from evidence-based medicine. This consensus consists of 13 recommendations about the diagnosis, treatment and rehabilitation of OPHF with integrated traditional Chinese medicine and Western medicine, aimed at standardizing, systematizing, and personalizing the diagnosis and treatment of OPHF with integrated traditional Chinse and Western medicine to improve the patients ′ function.

Objective To determine the effect of isopsoralen(ISO)on the healing of tibia fracture in mice and explore its underlying mechanism.Methods Fifty male C57BL/6 mice(2 month old,20±2 g)were randomly divided into model group and ISO treatment group,with 25 animals in each group.From the 3rd day after modeling,the mice from the ISO group were given an intragastric gavage of 40 mg/kg ISO,once per day for 28 consecutive days,while those of the model group was given same volume of normal saline in same way.On the 7th,14th,21st,and 28th day after gavage,the tibia on the surgical side was taken,and the fracture area was quantified by bone volume/total volume(BV/TV)after micro-CT scanning.The healing and shaping of the fracture end were observed through HE staining.ELISA was used to detect the serum contents of bone alkaline phosphatase(BALP)and procollagen type I N-terminal peptide(PINP)on the 14th day of gavage.Western blotting was employed to determine the expression levels of Collagen Ⅰ,Runx2,BMP2,OSX,and VEGF in the tibial callus tissue in 7 and 14 d after gavage.Vascular perfusion was applied to observe the callus microvessels in 28 d to quantitatively analyze the vascular volume fraction and vessel diameter.Immunohistochemical staining was conducted to observe the expression of VEGF in the callus in 14 d after gavage.Results HE staining displayed that the ISO group had faster healing process than the model group.Micro-CT quantification results showed that the ISO group had higher BV/TV ratio in 7 d after gavage though no statistical difference,significantly higher ratio in 14 d(P＜0.05),but obviously lower ratio in 21 and 28 d after gavage(both P＜0.05)when compared with the model group.The serum contents of BALP and PINP were also remarkably higher in the ISO group than the model group(P＜0.05).Western blotting results indicated that the expression levels of Collagen Ⅰ,Runx2,BMP2,OSX and VEGF in the ISO group were higher than those in the model group(P＜0.05).The results of angiography revealed that the vascular volume fraction and vessel diameter were notably increased in the ISO group than the model group(both P＜0.05).Immunohistochemical assay showed that the expression of VEGF was higher in the ISO group than the model group(P＜0.05).Conclusion ISO can improve the activity of osteoblasts,increase the expression of osteogenesis-related proteins,and accelerate the angiogenesis to promote fracture healing.

The incidence of intracranial aneurysms is higher in population, and the death and disability rates after rupture are extremely high. Early detection of intracranial aneurysms, prediction of rupture risk, operation program, and evaluation of treatment outcomes are all the clinical concerns. With the continuously deepening integration of artificial intelligence technology and medicine, its application in the diagnosis and treatment of intracranial aneurysms is becoming increasingly widespread. This article reviews the current application status of artificial intelligence technology in the diagnosis and treatment of intracranial aneurysms.

Objective To investigate the clinical application value of predicting the severity of acute pancreatitis(AP)based on MRI-T2WI radiomics nomogram.Methods A total of 375 patients with AP were analyzed retrospectively,who were divided into 281 cases in the training group and 94 cases in the validation group according to the ratio of 3∶1.Based on MRI-T2WI image,man-ual segmentation was performed for the pancreatic parenchyma.The radiomics feature were selected by feature extraction and dimen-sionality reduction,the support vector machine(SVM)classifier were used to construct the radiomics model.Logistic regression analysis was used to screen out independent risk factors,and an radiomics nomogram model was constructed in combined with the Radiomics score(Radscore),and the predictive performances of the models were evaluated.Results Receiver operating characteristic(ROC)curve analysis showed that the predictive efficacy of radiomics nomogram model[training group,area under the curve(AUC)=0.893;val-idation group,AUC=0.889]was higher than that of clinical model(training group,AUC=0.799;validation group,AUC=0.809)and radiomics model(training group,AUC=0.814;validation group,AUC=0.823).Conclusion The radiomics nomogram based on MRI-T2WI radiomics features and independent risk factors has high clinical application value for the prediction of AP severity.

Objective To establish an improved simple and efficient method for isolation and culture of primary lung fibroblasts(LFB)from neonatal mice,and study their growth characteristics in vitro.Methods The lungs of 3-days-old mice or 8-weeks-old mice were taken under sterile conditions,and the stromal tissue was cut into 1mm3 tissue mass.High-glucose DMEM culture medium containing 10g/dl fetal bovine serum(FBS)or 20g/dl FBS was used for tissue adhesion culture.The LFB was purified by the differential time adhesion method,and the growth morphology and adherence state of the cells were observed dynamically under an inverted microscope.The primary LFB was identified by flow cytometry.The activity of the third-generation cell after culture was detected by CCK-8 assay.Results Lung tissues in mice at 3 days of age and 20g/dl FBS concentration cultured by the improved tissue mass adherent cultured method began to grow radially to the periphery on the 2nd day.On the day 7th,the cells growth density reached 90%,and the cell morphology was as a spindle.The CD140a positive and CD45 negative cells reached more than 90%after purification by differential time adhesion method,and the cells still maintain good cell activity after cultured for 3 generations.Conclusion The improved tissue adhesion method can obtain a large number of purified mice LFB with good activity simply and efficiently,which lays a foundation for the study of lung inflammation,tumors and in vitro efficacy of drugs.

Purpose To investigate the clinical value of multi-center digital mammography radiomics nomogram model in predicting triple-negative breast cancer(TNBC).Materials and Methods The digital mammograms of 462 patients with pathologically confirmed breast cancer from November 2016 to March 2022 were retrospectively analyzed,including 243 cases from Yijishan Hospital of Wannan Medical College(institution 1),106 cases from Fuyang People's Hospital(institution 2)and 113 cases from Taihe People's Hospital(institution 3).According to the results of immunohistochemistry,a total of 349 breast cancer patients in institution 1 and institution 2 were randomly divided into the training group(244 cases,including 41 TNBC and 203 non-TNBC)and the validation group(105 cases,including 18 TNBC and 87 non-TNBC)according to the ratio of 7∶3,113 breast cancer patients(24 TNBC and 89 non-TNBC)from institution 3 were included in the external validation group.Comparing the mediolateral oblique and cranial cauda digital mammography images,the mammography imaging with larger lesion areas were selected,and the image segmentation and radiomics feature extraction were performed.The radiomics model was constructed by using Logistic regression.The clinicopathological parameters and radiomics scores were used to construct a nomogram.Receiver operating characteristic and decision curve analysis were used to evaluate the model performance.To compare The predictive performance between the models was compared.Results Finally,four radiomics features closely related to TNBC were selected to construct an radiomics model.The area under the curve,sensitivity and specificity of TNBC predicted by the radiomics model in training group,validation group and external test group were 0.868,90.24%and 72.91%,0.827,72.22%and 75.86%,0.837,70.83%and 78.65%,respectively.The area under the curve,sensitivity and specificity of TNBC predicted by the combined model in the training group,validation group and external test group were 0.903,80.49%and 86.70%,0.890,77.78%and 88.51%,0.870,62.50%and 85.39%,respectively.The combined model was better than the single image omics model in predicting TNBC,and the difference was statistically significant between the training group and the verification group(Z=2.061,2.064,both P<0.05),but not between the external test group(Z=1.223,P=0.221).In three group,decision curve analysis showed that the nomogram predicted a higher net benefit than the radiomics model for triple-negative breast cancer.Conclusion The radiomics model has high diagnostic efficiency in predicting TNBC,and the nomogram model combined with the radiomics score and histological grading can further improve the prediction efficiency.