BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

Panax species are mostly valuable medicinal plants. While some species' seeds are sensitive to dehydration, the dehydration tolerance of seeds from other Panax species remains unclear. The phospholipase D(PLD) gene plays an important role in plant responses to dehydration stress. However, the characteristics of the PLD gene family and their mechanisms of response to dehydration stress in seeds of Panax species with different dehydration tolerances are not well understood. This study used seeds from eight Panax species to measure the germination rates and PLD activity after dehydration and to analyze the correlation between dehydration tolerance and seed traits. Bioinformatics analysis was also conducted to characterize the PnPLD and PvPLD gene families and to evaluate their expression patterns under dehydration stress. The dehydration tolerance of Panax seeds was ranked from high to low as follows: P. ginseng, P. zingiberensis, P. quinquefolius, P. vietnamensis var. fuscidiscus, P. japonicus var. angustifolius, P. japonicus, P. notoginseng, and P. stipuleanatus. A significant negative correlation was found between dehydration tolerance and seed shape(three-dimensional variance), with flatter seeds exhibiting stronger dehydration tolerance(r=-0.792). Eighteen and nineteen PLD members were identified in P. notoginseng and P. vietnamensis var. fuscidiscus, respectively. These members were classified into five isoforms: α, β, γ, δ, and ζ. The gene structures, subcellular localization, physicochemical properties, and other characteristics of PnPLD and PvPLD were similar. Both promoters contained regulatory elements associated with plant growth and development, hormone responses, and both abiotic and biotic stress. During dehydration, the PLD enzyme activity in P. notoginseng seeds gradually increased as the water content decreased, whereas in P. vietnamensis var. fuscidiscus, PLD activity first decreased and then increased. The expression of PLDα and PLDδ in P. notoginseng seeds initially increased and then decreased, whereas in P. vietnamensis var. fuscidiscus, the expression of PLDα and PLDδ consistently decreased. In conclusion, the dehydration tolerance of Panax seeds showed a significant negative correlation with seed shape. The dehydration tolerance in P. vietnamensis var. fuscidiscus and dehydration sensitivity of P. notoginseng seeds may be related to differences in PLD enzyme activity and the expression of PLDα and PLDδ genes. This study provided the first systematic comparison of dehydration tolerance in Panax seeds and analyzed the causes of tolerance differences and the optimal water content for long-term storage at ultra-low temperatures, thus providing a theoretical basis for the short-term and ultra-low temperature long-term storage of medicinal plant seeds with varying dehydration tolerances.
Seeds/metabolism* ; Panax/physiology* ; Plant Proteins/metabolism* ; Gene Expression Regulation, Plant ; Phospholipase D/metabolism* ; Plants, Medicinal/enzymology* ; Germination ; Multigene Family ; Water/metabolism* ; Dehydration ; Phylogeny

Quantum dots (QDs), nanoscale semiconductor crystals, have emerged as a revolutionary class of nanomaterials with unique optical and electrochemical properties, making them highly promising for applications in disease diagnosis and treatment. Their tunable emission spectra, long-term photostability, high quantum yield, and excellent charge carrier mobility enable precise control over light emission and efficient charge utilization, which are critical for biomedical applications. This article provides a comprehensive review of recent advancements in the use of quantum dots for disease diagnosis and therapy, highlighting their potential and the challenges involved in clinical translation. Quantum dots can be classified based on their elemental composition and structural configuration. For instance, IB-IIIA-VIA group quantum dots and core-shell structured quantum dots are among the most widely studied types. These classifications are essential for understanding their diverse functionalities and applications. In disease diagnosis, quantum dots have demonstrated remarkable potential due to their high brightness, photostability, and ability to provide precise biomarker detection. They are extensively used in bioimaging technologies, enabling high-resolution imaging of cells, tissues, and even individual biomolecules. As fluorescent markers, quantum dots facilitate cell tracking, biosensing, and the detection of diseases such as cancer, bacterial and viral infections, and immune-related disorders. Their ability to provide real-time, in vivo tracking of cellular processes has opened new avenues for early and accurate disease detection. In the realm of disease treatment, quantum dots serve as versatile nanocarriers for targeted drug delivery. Their nanoscale size and surface modifiability allow them to transport therapeutic agents to specific sites, improving drug bioavailability and reducing off-target effects. Additionally, quantum dots have shown promise as photosensitizers in photodynamic therapy (PDT). When exposed to specific wavelengths of light, quantum dots interact with oxygen molecules to generate reactive oxygen species (ROS), which can selectively destroy malignant cells, vascular lesions, and microbial infections. This targeted approach minimizes damage to healthy tissues, making PDT a promising strategy for treating complex diseases. Despite these advancements, the translation of quantum dots from research to clinical application faces significant challenges. Issues such as toxicity, stability, and scalability in industrial production remain major obstacles. The potential toxicity of quantum dots, particularly to vital organs, has raised concerns about their long-term safety. Researchers are actively exploring strategies to mitigate these risks, including surface modification, coating, and encapsulation techniques, which can enhance biocompatibility and reduce toxicity. Furthermore, improving the stability of quantum dots under physiological conditions is crucial for their effective use in biomedical applications. Advances in surface engineering and the development of novel encapsulation methods have shown promise in addressing these stability concerns. Industrial production of quantum dots also presents challenges, particularly in achieving consistent quality and scalability. Recent innovations in synthesis techniques and manufacturing processes are paving the way for large-scale production, which is essential for their widespread adoption in clinical settings. This article provides an in-depth analysis of the latest research progress in quantum dot applications, including drug delivery, bioimaging, biosensing, photodynamic therapy, and pathogen detection. It also discusses the multiple barriers hindering their clinical use and explores potential solutions to overcome these challenges. The review concludes with a forward-looking perspective on the future directions of quantum dot research, emphasizing the need for further studies on toxicity mitigation, stability enhancement, and scalable production. By addressing these critical issues, quantum dots can realize their full potential as transformative tools in disease diagnosis and treatment, ultimately improving patient outcomes and advancing biomedical science.

OBJECTIVE: To explore the application value and clinical effect of 3D printing combined with customized bone plate in the treatment of acetabular fracture. METHODS: From June 2020 to June 2022, 11 patients with acetabular fractures underwent preoperative planning using 3D printing technology and were treated with customized bone plates including 8 males and 3 females, aged 25 to 66 years old. The fractures were classified according to Letournel-Judet:4 posterior wall fractures, 2 T-type fractures, 2 transverse posterior wall fractures, 2 double column fractures, and 1 anterior column with posterior semi-transverse fractures. The operative time, intraoperative blood loss, intraoperative fluoroscopy times, postoperative drainage volume, postoperative fracture healing time, and hip function score were recorded and analyzed. RESULTS: The operation time of 11 patients was 80 to 150 min, intraoperative blood volume was 150 to 700 ml, fluoroscopy frequency was 2 to 6, postoperative drainage flow was 60 to 195 ml, and the fracture healing time was 2.5 to 6.0 months. Fracture reduction was evaluated according to Matta score:anatomical reduction in 3 cases and satisfactory reduction in 8 cases. Eleven patients were followed up for 7 to 18 months. The hip Merle d'Aubigne function scores were excellent in 6 cases, good in 3 cases, fair in 1 case and poor in 1 case. Incision fat liquefaction occurred in 1 case and obturator nerve traction in 1 case. CONCLUSION The application of 3D printing technology combined with customized bone plates in the treatment of acetabular fracture is effective. In addition, the printed model can provide the operator with the results of the three-dimensional shape of the fracture, which is convenient for surgical reduction and effectively improves the efficiency of surgery.
Humans ; Female ; Male ; Middle Aged ; Acetabulum/surgery* ; Printing, Three-Dimensional ; Adult ; Aged ; Bone Plates ; Fractures, Bone/surgery* ; Fracture Fixation, Internal/methods*

OBJECTIVE: To explore the molecular mechanism of Shenmai Injection (SMI) against doxorubicin (DOX) induced cardiomyocyte apoptosis. METHODS: A total of 40 specific pathogen-free (SPF) male Sprague Dawley (SD) male rats were divided into 5 groups based on the random number table, including the control group, the model group, miR-30a agomir group, SMI low-dose (SMI-L) group, and SMI high-dose (SMI-H) group, with 8 rats in each group. Except for the control group, the rats were injected weekly with DOX (2 mg/kg) in the tail vein for 4 weeks to induce myocardial injury, and were given different regimens of continuous intervention for 2 weeks. Cardiac function was detected by echocardiography and myocardial pathological changes were observed by Van Gieson (VG) staining. Myocardial injury serum markers, including creatine kinase (CK), lactate dehydrogenase (LDH), troponin T (cTnT), N-terminal pro-brain natriuretic peptide (NT-proBNP), soluble ST2 (sST2), and growth differentiation factor-15 (GDF-15) were detected by enzyme linked immunosorbent assay (ELISA). Cardiomyocyte apoptosis was observed by terminal deoxynucleotidyl transferase-mediated biotinylated dUTP triphosphate nick end labeling (TUNEL) and transmission electron microscopy, and the expressions of target proteins and mRNA were detected by Western blot and quantitative real time polymerase chain reaction (qRT-RCR), respectively. RESULTS: The treatment with different doses of SMI reduced rat heart mass index and left ventricular mass index (P<0.05), significantly improved the left ventricular ejection fraction (P<0.05), decreased the levels of serum CK, LDH, cTnT, and NT-proBNP (P<0.05 or P<0.01), reduced the levels of serum sST2 and GDF-15 (P<0.05 or P<0.01), decreased the collagen volume fraction, reduced the expressions of rat myocardial type I and type III collagen (P<0.05 or P<0.01), and effectively alleviated myocardial fibrosis. And the study found that SMI promoted the expression levels of miR-30a and Bcl-2 in myocardium, and down-regulated the expression of Bax, which inhibited the activation of Caspase-3 and Caspase-9 (P<0.05 or P<0.01), and improved myocardial cell apoptosis. CONCLUSIONS SMI can alleviate myocardial injury and apoptosis caused by DOX, and its mechanism possibly by promoting the targeted expression of myocardial Bcl-2 protein through miR-30a.
Animals ; Myocytes, Cardiac/metabolism* ; Apoptosis/drug effects* ; MicroRNAs/genetics* ; Rats, Sprague-Dawley ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Doxorubicin/pharmacology* ; Proto-Oncogene Proteins c-bcl-2/genetics* ; Drug Combinations ; Injections ; Rats

Dendrobium moniliforme (D. moniliforme) is a traditional medicinal herb widely cultivated in Asia. Flavonoids, one of the largest groups of secondary metabolites in plants, are significant medicinal components in Dendrobium species. Several subgroups of R2R3-MYB proteins have been validated to directly regulate flavonoid biosynthesis. Using PacBio sequencing technology, we assembled a high-quality chromosome-level D. moniliforme genome with a total length of 1.20 Gb and a contig N50 of 3.97 Mb. The BUSCO assessment of genome annotation was 91.4%. By integrating the genome and transcriptome, we identified biosynthesis pathway enzyme genes related to flavonoids, polysaccharides, carotenoids, and alkaloids. A total of 90 R2R3-MYBs were identified in D. moniliforme and classified into 21 subgroups. Studies on the functions of R2R3-MYB transcription factors revealed that R2R3-MYB in SG6 can up-regulate flavonoid biosynthesis. Various validation experiments, including subcellular localization, transient overexpression, UPLC-MS/MS, HPLC, yeast one-hybrid, and dual-luciferase assays, demonstrated that DMYB69 directly up-regulates the expression of enzyme genes involved in flavonoid biosynthesis, increasing the content of flavonoids such as anthocyanin, flavone, and flavonol. Additionally, DMYB44 was shown to directly up-regulate the expression of carotenoid biosynthesis enzyme genes, thereby increasing carotenoid content. This study provides an essential genome resource and theoretical basis for molecular breeding research in D. moniliforme.

Poly(ADP-ribosyl)ation (PARylation) is a specific form of post-translational modification (PTM) predominantly triggered by the activation of poly-ADP-ribose polymerase 1 (PARP1). However, the role and mechanism of PARylation in the advancement of acute kidney injury (AKI) remain undetermined. Here, we demonstrated the significant upregulation of PARP1 and its associated PARylation in murine models of AKI, consistent with renal biopsy findings in patients with AKI. This elevation in PARP1 expression might be attributed to trimethylation of histone H3 lysine 4 (H3K4me3). Furthermore, a reduction in PARylation levels mitigated renal dysfunction in the AKI mouse models. Mechanistically, liquid chromatography-mass spectrometry indicated that PARylation mainly occurred in receptor for activated C kinase 1 (RACK1), thereby facilitating its subsequent phosphorylation. Moreover, the phosphorylation of RACK1 enhanced its dimerization and accelerated the ubiquitination-mediated hypoxia inducible factor-1α (HIF-1α) degradation, thereby exacerbating kidney injury. Additionally, we identified a PARP1 proteolysis-targeting chimera (PROTAC), A19, as a PARP1 degrader that demonstrated superior protective effects against renal injury compared with PJ34, a previously identified PARP1 inhibitor. Collectively, both genetic and drug-based inhibition of PARylation mitigated kidney injury, indicating that the PARylated RACK1/HIF-1α axis could be a promising therapeutic target for AKI treatment.

OBJECTIVES: To elucidate the anti-aging effect of β-sitosterol (BS), an important component in the fruits of Alpinia oxyphylla Miq., in C. elegans and its regulatory effect on ETS-5 gene to modulate ferroptosis. METHODS: C. elegans treated with 10 µg/mL BS were monitored for survival time and changes in body length, motility, and reproductive function. The effect of ETS-5 gene knockdown on survival time of C. elegans was observed, and the changes in fat accumulation and lipid redox homeostasis in the transfected C. elegans were assessed using Oil Red O staining and by detecting MDA levels and the GSH/GSSG ratio. The mRNA expression levels of ferroptosis-related genes (FTN-1, GPX-1 and AAT-9) were detected using qPCR. The effects of BS treatment and ETS-5 knockdown on AAT-9 enzyme activity in C. elegans were examined. The effect of BS on nuclear localization of FEV (the human homolog of ETS-5) was validated in cultured human umbilical venous endothelial cells (HUVECs). RESULTS: Both BS treatment and ETS-5 knockdown significantly prolonged the lifespan, promoted lipid accumulation and reduced lipid peroxidation in C. elegans. ETS-5 knockdown resulted in upregulated expressions of the ferroptosis repressors GPX-1, AAT-9 and FTN-1 and increased the GSH/GSSG ratio in C. elegans. CONCLUSIONS BS inhibits ferroptosis in C. elegans by suppressing the expression of ETS-5 transcription factor and hence the activity of AAT-9 enzyme, a key gene for ferroptosis, which in turn prolongs the lifespan of C. elegans.
Animals ; Caenorhabditis elegans/physiology* ; Ferroptosis/drug effects* ; Alpinia/chemistry* ; Sitosterols/pharmacology* ; Longevity/drug effects* ; Fruit/chemistry* ; Humans

Department of Kidney Transplantation, the First Affiliated Hospital of Xi'an Jiaotong University reported a case of vasoplegic syndrome (VS). During a livingdonor kidney transplantation, the patient developed abrupt hypotension immediately after allograft reperfusion, with blood pressure dropping to 50/30 mmHg (1 mmHg=0.133 kPa). The diagnosis of VS was confirmed through multidisciplinary consultation, integration of clinical indicators, and ultrasonographic assessment. Management included vasopressor therapy (dopamine, norepinephrine, and epinephrine), fluid resuscitation, blood transfusion, and albumin administration. Hemodynamics were subsequently stabilized, and kidney allograft function returned to normal. At more than three months of followup, both kidney function and blood pressure remained stable.