Diabetic cardiomyopathy （DCM）， a cardiovascular complication caused by diabetes mellitus， is a major cause of heart failure and even sudden cardiac death in diabetic patients. Traditional Chinese medicine （TCM） posits that the core pathogenesis of DCM lies in internal deficiency and superficial excess， characterized by deficiency of both Qi and Yin combined with phlegm and blood stasis. Modern medical treatments for DCM primarily focus on blood glucose control and symptom alleviation yet lack targeted therapeutic strategies. In contrast， TCM offers a wealth of practical experience and a complete theoretical system， demonstrating definite clinical efficacy and high medication safety in DCM management. As a classic formula for tonifying Qi and nourishing Yin， Shengmaisan comprises Ginseng Radix et Rhizoma， Ophiopogonis Radix， and Schisandrae Chinensis Fructus. It contains multiple bioactive components， including ginsenosides， ophiopogonin， schisandrins， and homoisoflavonoids， which exhibit cardioprotective properties. The therapeutic mechanisms of Shengmaisan-like formulae for DCM involve enhancing myocardial contractility， attenuating myocardial fibrosis， modulating mitochondrial quality control， regulating glucose metabolism， mitigating oxidative stress， and suppressing inflammatory responses. Clinically， Shengmaisan-like formulae not only manage hyperglycemic status but also ameliorate cardiac structural and functional impairments and enhance exercise tolerance in DCM patients， playing a vital role in the prevention， treatment， and rehabilitation of DCM. This paper analyzes the feasibility of Shengmaisan-like formulae in DCM management and synthesizes current research achievements regarding their chemical components， mechanisms of action， and clinical applications， aiming to provide a scientific foundation for the use of such formulae in the treatment of DCM.

BACKGROUND: Over 20 million colonoscopies are performed in China annually. An automatic clinical decision support system (CDSS) with accurate semantic recognition of colonoscopy reports and guideline-based is helpful to relieve the increasing medical burden and standardize the healthcare. In this study, the CDSS was built under a hierarchical-label interpretable classification framework, trained by a state-of-the-art transformer-based model, and validated in a multi-center style. METHODS: We conducted stratified sampling on a previously established dataset containing 302,965 electronic colonoscopy reports with pathology, identified 2041 patients' records representative of overall features, and randomly divided into the training and testing sets (7:3). A total of five main labels and 22 sublabels were applied to annotate each record on a network platform, and the data were trained respectively by three pre-training models on Chinese corpus website, including bidirectional encoder representations from transformers (BERT)-base-Chinese (BC), the BERT-wwm-ext-Chinese (BWEC), and ernie-3.0-base-zh (E3BZ). The performance of trained models was subsequently compared with a randomly initialized model, and the preferred model was selected. Model fine-tuning was applied to further enhance the capacity. The system was validated in five other hospitals with 3177 consecutive colonoscopy cases. RESULTS: The E3BZ pre-trained model exhibited the best performance, with a 90.18% accuracy and a 69.14% Macro-F1 score overall. The model achieved 100% accuracy in identifying cancer cases and 99.16% for normal cases. In external validation, the model exhibited favorable consistency and good performance among five hospitals. CONCLUSIONS The novel CDSS possesses high-level semantic recognition of colonoscopy reports, provides appropriate recommendations, and holds the potential to be a powerful tool for physicians and patients. The hierarchical multi-label strategy and pre-training method should be amendable to manage more medical text in the future.
Humans ; Colonoscopy/methods* ; Deep Learning ; Decision Support Systems, Clinical ; Female ; Male

In the clinical stage, suspected hemolytic plasma may cause hemolysis illness, manifesting as symptoms such as heart failure, severe anemia, etc. Applying a deep learning method to plasma images significantly improves recognition accuracy, so that this paper proposes a plasma quality detection model based on improved "You Only Look Once" 5th version (YOLOv5). Then the model presented in this paper and the evaluation system ‌were introduced‌ into the plasma datasets, and ‌the average accuracy of the final classification reached 98.7%‌. The results of this paper's experiment were obtained through the combination of several key algorithm modules including‌ omni-dimensional dynamic convolution, pooling with separable kernel attention, residual bi-fusion feature pyramid network, ‌and‌ re-parameterization convolution. The method of this paper‌ obtains the feature information of spatial mapping efficiently, and enhances the average recognition accuracy of plasma quality detection. This paper presents a high-efficiency detection method for plasma images, aiming to provide a practical approach to prevent hemolysis illnesses caused by external factors.
Algorithms ; Humans ; Hemolysis ; Plasma ; Deep Learning ; Image Processing, Computer-Assisted/methods*

OBJECTIVES: To investigate the structural changes of rat thoracic aorta and changes in expression levels of Bmal1 and cyclins in thoracic aorta endothelial cells following heat stress. METHODS: Twenty male SD rats were randomized equally into control group and heat stress group. After exposure to 32 ℃ for 2 weeks in the latter group, the rats were examined for histopathological changes and Bmal1 expression in the thoracic aorta using HE staining and immunohistochemistry. In the cell experiments, cultured rat thoracic aortic endothelial cells (RTAECs) were incubated at 40 ℃ for 12 h with or without prior transfection with a Bmal1-specific small interfering RNA (si-Bmal1) or a negative sequence. In both rat thoracic aorta and RTAECs, the expressions of Bmal1, the cell cycle proteins CDK1, CDK4, CDK6, and cyclin B1, and apoptosis-related proteins Bax and Bcl-2 were detected using Western blotting. TUNEL staining was used to detect cell apoptosis in rat thoracic aorta, and the changes in cell cycle distribution and apoptosis in RTAECs were analyzed with flow cytometry. RESULTS: Compared with the control rats, the rats exposed to heat stress showed significantly increased blood pressures and lowered heart rate with elastic fiber disruption and increased expressions of Bmal1, cyclin B1 and CDK1 in the thoracic aorta (P<0.05). In cultured RTAECs, heat stress caused significant increase of Bmal1, cyclin B1 and CDK1 protein expression levels, which were obviously lowered in cells with prior si-Bmal1 transfection. Bmal1 knockdown also inhibited heat stress-induced increase of apoptosis in RTAECs as evidenced by decreased expression of Bax and increased expression of Bcl-2. CONCLUSIONS Heat stress upregulates Bmal1 expression and causes alterations in expressions of cyclins to trigger apoptosis of rat thoracic aorta endothelial cells, which can be partly alleviated by suppressing Bmal1 expression.
Animals ; ARNTL Transcription Factors/genetics* ; Male ; Aorta, Thoracic/metabolism* ; Rats ; Rats, Sprague-Dawley ; Endothelial Cells/metabolism* ; Apoptosis ; Cells, Cultured ; Heat-Shock Response ; Cyclin B1/metabolism* ; CDC2 Protein Kinase/metabolism* ; Cyclins/metabolism* ; RNA, Small Interfering ; bcl-2-Associated X Protein/metabolism*

Corticotomy is a clinical procedure to accelerate orthodontic tooth movement characterized by the regional acceleratory phenomenon (RAP). Despite its therapeutic effects, the surgical risk and unclear mechanism hamper the clinical application. Numerous evidences support macrophages as the key immune cells during bone remodeling. Our study discovered that the monocyte-derived macrophages primarily exhibited a pro-inflammatory phenotype that dominated bone remodeling in corticotomy by CX3CR1^CreERT2; R26^GFP lineage tracing system. Fluorescence staining, flow cytometry analysis, and western blot determined the significantly enhanced expression of binding immunoglobulin protein (BiP) and emphasized the activation of sensor activating transcription factor 6 (ATF6) in macrophages. Then, we verified that macrophage specific ATF6 deletion (ATF6^f/f; CX3CR1^CreERT2 mice) decreased the proportion of pro-inflammatory macrophages and therefore blocked the acceleration effect of corticotomy. In contrast, macrophage ATF6 overexpression exaggerated the acceleration of orthodontic tooth movement. In vitro experiments also proved that higher proportion of pro-inflammatory macrophages was positively correlated with higher expression of ATF6. At the mechanism level, RNA-seq and CUT&Tag analysis demonstrated that ATF6 modulated the macrophage-orchestrated inflammation through interacting with Tnfα promotor and augmenting its transcription. Additionally, molecular docking simulation and dual-luciferase reporter system indicated the possible binding sites outside of the traditional endoplasmic reticulum-stress response element (ERSE). Taken together, ATF6 may aggravate orthodontic bone remodeling by promoting Tnfα transcription in macrophages, suggesting that ATF6 may represent a promising therapeutic target for non-invasive accelerated orthodontics.
Animals ; Mice ; Macrophages/metabolism* ; Tumor Necrosis Factor-alpha/genetics* ; Tooth Movement Techniques/methods* ; Activating Transcription Factor 6/metabolism* ; Bone Remodeling ; Flow Cytometry ; Blotting, Western

Five new flavan-4-ol glycosides jixueqiosides A-E (1-5) and two new flavan glycosides jixueqiosides F and G (6 and 7), along with twelve known flavan-4-ol glycosides (8-19), were isolated from the roots of Pronephrium penangianum. Comprehensive spectral analyses, X-ray single-crystal diffraction, and theoretical electronic circular dichroism (ECD) calculations established structures and absolute configurations. A single crystal structure of flavan-4-ol glycoside (14) was reported for the first time, while the characteristic ECD and NMR data for all isolated flavan-4-ol glycosides (1-5 , 8-19) were analyzed, establishing a set of empirical rules. Activity screening of these isolates showed that 8 and 9 could inhibit the proliferation of MDA-MB-231 and MCF-7 cells with IC₅₀ values of 7.93 ? 2.85 ?mol?L^-1 and 5.87 ? 1.58 ?mol?L^-1 (MDA-MB-231), and 2.21 ? 1.38 ?mol?L^-1 and 3.52 ? 1.55 ?mol?L^-1 (MCF-7), respectively. Western blotting and flow cytometry analyses demonstrated that 8 and 9 dose-dependently induced apoptosis in MDA-MB-231 cells by up-regulating BAX, activating caspase-3 and down-regulating BCL-2. Additionally, compound 8 affected autophagy-related proteins, increasing the ratio of LC3-II/LC3-I and Beclin-1 levels to inhibit MDA-MB-231 cell proliferation. Moreover, anti-inflammatory studies indicated that 2, 3, 7, 13, 14, and 18 moderately inhibited tumor necrosis factor-a (TNF-a), interleukin-6 (IL-6), and nitric oxide (NO) release.
Humans ; Plant Roots/chemistry* ; Glycosides/isolation & purification* ; Anti-Inflammatory Agents/isolation & purification* ; Flavonoids/isolation & purification* ; Cell Proliferation/drug effects* ; Antineoplastic Agents, Phytogenic/isolation & purification* ; Molecular Structure ; Apoptosis/drug effects* ; Cell Line, Tumor ; Tumor Necrosis Factor-alpha/immunology* ; Drugs, Chinese Herbal/pharmacology* ; Interleukin-6/immunology* ; Animals ; Mice

Accurately obtaining the crop quantity and density is not only crucial for the demand-based input of water and fertilizer in the field but also vital for ensuring the yield and quality of crops. Aerial photography by unmanned aerial vehicles (UAVs) can quickly acquire the distribution image information of crops over a large area. However, the accurate recognition of a single type of dense targets is a huge challenge for most recognition algorithms. Taking banana seedlings as an example in this study, we captured the images of banana plantations by UAVs from high altitudes to explore an efficient recognition method for dense targets. We proposed a strategy of "cut-recognition-stitch" and constructed a counting method based on the improved Faster R-CNN algorithm. First, the images containing highly dense targets were cropped into a large number of image tiles according to different sizes (simulating different flight altitudes), and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm was adopted to improve the image quality. A banana seedling dataset containing 36 000 image tiles was constructed. Then, the Faster R-CNN network with optimized parameters was used to train the banana seedling recognition model. Finally, the recognition results were reversely stitched together, and a boundary deduplication algorithm was designed to correct the final counting results to reduce the repeated recognition caused by image cropping. The results show that the recognition accuracy of the Faster R-CNN with optimized parameters for banana image datasets of different sizes can reach up to 0.99 at most. The deduplication algorithm can reduce the average counting error for the original aerial images from 1.60% to 0.60%, and the average counting accuracy of banana seedlings reaches 99.4%. The proposed method effectively addresses the challenge of recognizing dense small objects in high-resolution aerial images, providing an efficient and reliable technical solution for intelligent crop density monitoring in precision agriculture.
Musa/growth & development* ; Crops, Agricultural/growth & development* ; Algorithms ; Neural Networks, Computer ; Unmanned Aerial Devices ; Seedlings/growth & development* ; Image Processing, Computer-Assisted/methods* ; Photography ; Agriculture/methods*

Objective:To explore the relation of depressive and anxiety symptoms to defense mechanism in transgender population.Methods:Totally 451 transgender patients in the sexual and psychological outpatient depart-ment of a hospital were selected.They were assessed with the self-Rating Depression Scale(SDS),Self-Rating Anxiety Scale(SAS)and Defense Mechanism Scale(DSQ).The SDS standard score of ≥53 was classified as having depressive symptoms,and the SAS standard score of ≥50 was classified as having anxiety symptoms.Re-sults:The detection rates of depression and anxiety were 46.8％and 28.8％respectively.Multiple linear regression analysis showed that SDS scores were positively correlated with DSQ scores of projection,conceit,complaint,with-drawal,somatization,control,isolation and identity(β=0.08-0.22),while SDS scores were negatively correlated with DSQ scores of sublimation,depression,omnipotence with incompetence and denial(0=-0.09--0.19).The SAS scores were positively correlated with the DSQ scores of projection,latent manifestation,somatization,control,isolation,identity,and consumption tendency(0=0.09-0.26),while the SAS scores were negatively cor-related with the DSQ scores of sublimation,depression,omnipotence accompanied by incompetence,and denial(β=-0.09--0.15).Conclusion:The proportion of depression and anxiety symptoms detected in the transgender group is higher,which may be related to the use of some defenses.

Objective To construct a prognostic risk model for exploring the prognostic value of copper metabolism related genes(CMRGs)in lung adenocarcinoma(LUAD),thereby providing a reference for personalized treatment of LUAD patients.Methods The RNA-seq data of LUAD tissues and adjacent or normal lung tissues were downloaded from the Cancer Genome Atlas(TCGA)database and Genotype-tissue Expression(GTEx)database.The risk scoring model was established using univariate Cox regression analysis,Lasso analysis and multivariate Cox regression analysis,and the receiver operating characteristic(ROC)curves and nomogram were used to evaluate the model performance.The LUAD data in the Gene Expression Omnibus(GEO),the Tumor Immune Single-cell Hub(TISCH)single-cell sequencing analysis,and the Human Protein Atlas(HPA)immunohistochemistry analysis were used for external validation.Additionally,the immune microenvironment and drug sensitivity of high-and low-risk groups were analyzed.Results A risk model consisting of 6 genes was constructed.The overall survival rate of low-risk group was higher than that of high-risk group(P<0.001).ROC analysis showed that the area under curve of the risk model in training set reached 0.729,0.749 and 0.707 at 1-,3-and 5-year,respectively,and the C index of C-index curve was 0.721(95%CI:0.678-0.764).The immune microenvironment differed significantly between high-and low-risk groups(P<0.001),and the drug sensitivity analysis in high-and low-risk groups revealed that there was statistically significant for gemcitabine,gefitinib,crizotinib and savolitinib(P<0.001).Conclusion The risk model constructed with 6 CMRGs enable the prediction of the prognosis of LUAD patients.The immune microenvironment differs in high-and low-risk group,and high-risk patients are more sensitive to drugs such as gemcitabine,gefitinib,crizotinib and savolitinib,which provide a reference for the personalized treatment of LUAD patients.

Objective:To investigate the risk factors for proximal junctional kyphosis (PJK) in adult spinal deformity patients with concomitant osteoporosis undergoing long-segment spinal fusion surgery.Methods:A retrospective analysis was conducted on 76 adults spinal deformity patients with osteoporosis who underwent long-segment spinal fusion surgery at the Department of Orthopaedics, Beijing Chaoyang Hospital, between June 2013 and December 2019. The cohort included 19 males and 57 females, with a mean age of 66.26±6.10 years (range, 54-78 years). Patients were categorized into two groups based on the occurrence of PJK within a 2-year postoperative follow-up: the PJK group (21 cases) and the non-PJK group (55 cases). Comparative analyses were performed on baseline characteristics, surgical details, preoperative and postoperative spinal-pelvic parameters, Hounsfield Units (HU) of the vertebral bodies, and paraspinal muscle morphology between the groups. Spinal-pelvic parameters included the main Cobb angle, lumbar lordosis (LL), lumbosacral lordosis (LSL), sagittal vertical axis (SVA), T 1 pelvic angle (TPA), pelvic tilt (PT), sacral slope (SS), and pelvic incidence (PI). Preoperative CT was used to measure HU values at the upper instrumented vertebra (UIV), UIV+1, and UIV+2. Paraspinal muscle morphology, including the relative functional cross-sectional area (rFCSA) and functional muscle-fat index (FMFI) at the L 4 lower endplate level, was assessed using preoperative MRI. Optimal cutoff values for HU and paraspinal muscle parameters were determined using receiver operating characteristic curve analysis. Multivariable logistic regression was employed to identify independent risk factors for PJK. Results:Significant differences were observed between the PJK and non-PJK groups in preoperative PT (17.60°±8.39° vs. 24.12°±9.37°), postoperative LL (35.61°±10.62° vs. 42.22°±13.11°), LSL (30.24°±10.10° vs. 35.87°±11.12°), and SVA (37.82°±20.46° vs. 21.37°±17.35°). The differences were statistically significant ( P<0.05). The HU values of UIV (113.62±17.25 vs. 133.94±16.61), UIV+1 (123.14±16.03 vs. 138.27±13.69), and UIV+2 (121.00±15.91 vs. 134.47±15.53) were significantly lower in the PJK group ( P<0.05). Optimal cutoff values for HU at UIV, UIV+1, and UIV+2 were identified as 120.72, 127.51, and 121.50, respectively. Significant differences were also found in rFCSA (156.87±48.06 vs. 204.87±50.16) and FMFI (0.31±0.10 vs. 0.23±0.09). The differences were statistically significant( P<0.05), with optimal cutoff values of 175.43 for rFCSA and 0.24 for FMFI. Multivariable logistic regression analysis indicated that postoperative SVA [ OR=1.049, 95% CI (1.003, 1.097), P=0.037], HU of UIV [ OR=0.938, 95% CI (0.887, 0.991), P=0.024], and rFCSA of paraspinal muscles [ OR=0.883, 95% CI (0.792, 0.983), P=0.023] were independent risk factors for PJK. Conclusion:Reduced HU values of the UIV, decreased rFCSA of lumbar paraspinal muscles, and inadequate sagittal alignment correction are independent risk factors for PJK in adult spinal deformity patients with osteoporosis undergoing long-segment spinal fusion surgery.