Objective To explore the prevalence of depressive symptoms in postoperative patients with ovarian cancer and to analyze its influencing factors from multiple dimensions, including clinical characteristics, psychological factors, and laboratory indicators. Methods A cross-sectional study was conducted, which enrolled 235 postoperative patients with ovarian cancer. Depressive status was assessed using the patient health questionnaire, and the demographic, pathological, and medical record data of the patients were collected using the generalized anxiety disorder scale, Pittsburgh sleep quality index, European organization for research and treatment of cancer quality of life questionnaire core 30, and ECOG performance status score. Peripheral blood tumor marker (CA125), routine blood test, lymphocyte subsets, and serum cytokine levels were measured. Univariate and multivariate binary logistic regression analysis were used for statistical analysis. Results The prevalence of depression in postoperative patients with ovarian cancer was 39.15% (92/235). Univariate analysis showed that ECOG score ≥ 2 points, pain, anxiety, poor sleep quality, low quality of life, low life satisfaction, tumor recurrence, six or more cycles of chemotherapy, as well as higher levels of CA125, NLR, and NAR, and lower hemoglobin levels were significantly associated with depression (all P<0.05). Multivariate binary Logistic regression analysis showed that anxiety (OR=1.975, 95%CI: 1.231-3.170), sleep efficiency (OR=4.181, 95%CI: 1.211-14.43), sleep latency (OR=34.806, 95%CI: 4.258-284.542), ECOG performance status score, cognitive function (OR=0.918, 95%CI: 0.868-0.97), and life satisfaction were independent risk factors for depression (all P<0.05). Laboratory indicators were not independent influencing factors in the multivariate Logistic regression model. Conclusion Depression in postoperative patients with ovarian cancer is influenced by physiological, psychological, and social factors. Clinical management should focus on patients with anxiety, sleep disorders, poor physical condition, and low life satisfaction, and a comprehensive prevention and treatment strategy centered on psychological intervention and taking into account symptom management and social support should be implemented.

Plant-derived extracellular vesicles (PDEVs) are nanoscale extracellular vesicles secreted by plant cells, characterized by a lipid bilayer structure. These vesicles carry a variety of bioactive molecules, including proteins, nucleic acids, and lipids, and play essential roles in intercellular communication and physiological regulation in plants. Compared to animal-derived extracellular vesicles, PDEVs offer several advantages, such as a broad range of sources, high biocompatibility, low immunogenicity, and low production costs. Furthermore, PDEVs have demonstrated remarkable potential as natural nanocarriers for drug delivery, due to their ability to efficiently traverse biological barriers, such as the blood-brain barrier, making them promising candidates for drug delivery systems. This review systematically elaborates on the complex composition of PDEVs, which consists of lipids, proteins, and nucleic acids, the typical structural characteristics of their lipid bilayers ranging from 30 to 150 nm, and their versatile loading capabilities as drug carriers, efficiently encapsulating various types of therapeutic agents such as hydrophilic small molecules, hydrophobic drugs, nucleic acids, and proteins. We systematically summarize the recent advancements in strategies for enhancing the loading efficiency of PDEVs, which include methods such as co-incubation, ultrasound-assisted loading, electroporation, freeze-thaw cycles, and microfluidic technology. These techniques are evaluated based on their underlying principles, suitable drug types, and their respective advantages. In addition to loading strategies, we focus on the engineered approaches to achieve targeted delivery using PDEVs, such as genetic engineering modifications, chemical ligand conjugation, membrane fusion technology, and polyethylene glycol (PEG) modification. We discuss the mechanisms of these strategies in enhancing targeting efficiency, prolonging in vivo circulation time, and improving therapeutic efficacy. Further, this review highlights the application of PDEVs in various disease models, including tumor, skin inflammation, metabolic disorders, and neurodegenerative diseases, showcasing their therapeutic potential as multifunctional delivery platforms. The ability of PDEVs to encapsulate diverse therapeutic agents and target specific tissues or cells opens up new avenues for the treatment of complex diseases, offering advantages over conventional drug delivery systems. However, despite the promising applications of PDEVs, several challenges remain in their development and clinical translation. These challenges include variability in source materials, standardization of preparation processes, quality control, scalability of production, and the need for clinical validation. To overcome these obstacles, the integration of advanced technologies such as artificial intelligence-assisted design and multi-omics analysis is proposed as a way to facilitate the precise development of PDEVs. These emerging technologies hold the potential to further enhance the precision and effectiveness of plant-based drug delivery systems, ultimately advancing the field of precision medicine. In conclusion, the use of PDEVs as a platform for drug delivery represents a promising area of research with the potential to revolutionize therapeutic strategies. Their ability to encapsulate and deliver a wide variety of bioactive molecules, along with their inherent advantages in biocompatibility and versatility, makes them a valuable tool in the development of more efficient and targeted therapeutic interventions. Continued research and innovation in this field will pave the way for the clinical implementation of PDEVs in the treatment of various diseases, offering new hope for more effective and sustainable therapeutic options.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Ergopeptines or their derivatives are widely used for treating neurodegenerative and cerebrovascular diseases. The nonribosomal peptide synthetase-d-lysergyl peptide synthetase A (LPSA) determines ergopeptine formation but the detailed mechanism remains to be elucidated. Here, we characterized two LPSAs from Claviceps purpurea Cp-1 strain through heterologous expression in Aspergillus nidulans feeding with d-lysergic acid. We proved that Cp-LPSA1 catalyzed the formation of ergocornine, α-ergocryptine, and β-ergocryptine, precisely controlled by the substrate specificity of its three modules. Cp-LPSA2 was initially inactive but could be restored to catalyze α-ergosine formation. Using this platform, we validated that P1-LPSA1 and P1-LPSA2 from the reported C. purpurea P1 strain catalyzed ergotamine and α-ergocryptine formation, respectively. Typically, the non-ribosomal peptide codes implicated in every module of the LPSAs were defined and elucidated, in which certain key residues could play a switched role for substrate specificity and product interconversion. By constructing chimeric LPSAs through module assembly, the production of the desired ergopeptines was achieved. Notably, 1.46 mg/L of α-ergocryptine and 1.09 mg/L of ergotamine were produced respectively by mixed-culture of C. paspali No. 24 (fermentation supernatant) and the recombinants of A. nidulans. Our findings provide insights into the biosynthetic mechanism of ergopeptines and lay a foundation for directed ergopeptine biosynthesis.

Stem cells play a crucial role in maintaining tissue regenerative capacity and homeostasis. However, mechanisms associated with stem cell senescence require further investigation. In this study, we conducted a proteomic analysis of human dental pulp stem cells (HDPSCs) obtained from individuals of various ages. Our findings showed that the expression of NUP62 was decreased in aged HDPSCs. We discovered that NUP62 alleviated senescence-associated phenotypes and enhanced differentiation potential both in vitro and in vivo. Conversely, the knocking down of NUP62 expression aggravated the senescence-associated phenotypes and impaired the proliferation and migration capacity of HDPSCs. Through RNA-sequence and decoding the epigenomic landscapes remodeled induced by NUP62 overexpression, we found that NUP62 helps alleviate senescence in HDPSCs by enhancing the nuclear transport of the transcription factor E2F1. This, in turn, stimulates the transcription of the epigenetic enzyme NSD2. Finally, the overexpression of NUP62 influences the H3K36me2 and H3K36me3 modifications of anti-aging genes (HMGA1, HMGA2, and SIRT6). Our results demonstrated that NUP62 regulates the fate of HDPSCs via NSD2-dependent epigenetic reprogramming.
Humans ; Dental Pulp/cytology* ; Nuclear Pore Complex Proteins/genetics* ; Cellular Senescence/genetics* ; Stem Cells/metabolism* ; Epigenesis, Genetic ; Cell Proliferation ; Cell Differentiation ; Histone-Lysine N-Methyltransferase/metabolism* ; Cells, Cultured ; Cellular Reprogramming ; Cell Movement ; Proteomics

Functional dyspepsia (FD), characterized by persistent or recurrent dyspeptic symptoms without identifiable organic, systemic or metabolic causes, is an increasingly recognized global health issue. The objective of this guideline is to equip clinicians and nursing professionals with evidence-based strategies for the management and treatment of adult patients with FD using traditional Chinese medicine (TCM). The Guideline Development Group consulted existing TCM consensus documents on FD and convened a panel of 35 clinicians to generate initial clinical queries. To address these queries, a systematic literature search was conducted across PubMed, EMBASE, the Cochrane Library, China National Knowledge Infrastructure (CNKI), VIP Database, China Biology Medicine (SinoMed) Database, Wanfang Database, Traditional Medicine Research Data Expanded (TMRDE), and the Traditional Chinese Medical Literature Analysis and Retrieval System (TCMLARS). The evidence from the literature was critically appraised using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. The strength of the recommendations was ascertained through a consensus-building process involving TCM and allopathic medicine experts, methodologists, pharmacologists, nursing specialists, and health economists, leveraging their collective expertise and empirical knowledge. The guideline comprises a total of 43 evidence-informed recommendations that span a range of clinical aspects, including the pathogenesis according to TCM, diagnostic approaches, therapeutic interventions, efficacy assessments, and prognostic considerations. Please cite this article as: Zhang SS, Zhao LQ, Hou XH, Bian ZX, Zheng JH, Tian HH, Yang GH, Hong WS, He YY, Liu L, Shen H, Li YP, Xie S, Shu J, Zeng BF, Li JX, Liu Z, Xiao ZH, Xiao JD, Zheng PY, Huang SG, Chen SL, Fei GJ. International clinical practice guideline on the use of traditional Chinese medicine for functional dyspepsia (2025). J Integr Med. 2025; 23(5):502-518.
Dyspepsia/drug therapy* ; Humans ; Medicine, Chinese Traditional/methods* ; Practice Guidelines as Topic ; Drugs, Chinese Herbal/therapeutic use*

P-glycoprotein(P-gp)is an ATP-dependent efflux transporter that is distributed in many tissues and organs.P-gp can selectively pump endogenous substrates and exogenous chemicals from the cell to the outside of the cell to maintain a stable endo-environment.However,it meanwhile restricts the entry of therapeutic drug into tissues and organs,and in particular,mediates the multidrug resistance of tumor cells to chemotherapeutic drugs.Therefore,understanding the localization of P-gp in different tissues and organs may be an important breakthrough point for disease treatment.In this paper,we mainly review the molecular structure,transport mechanism,localization,and regulation of P-gp in different tissues and organs,providing reference for the subsequent treatment of diseases.
Humans ; ATP Binding Cassette Transporter, Subfamily B, Member 1/chemistry* ; Animals ; Drug Resistance, Multiple

BACKGROUND: Neoadjuvant chemoradiotherapy followed by radical surgery has been a common practice for patients with locally advanced rectal cancer, but the response rate varies among patients. This study aimed to develop a ResNet-Vision Transformer based magnetic resonance imaging (MRI)-endoscopy fusion model to precisely predict treatment response and provide personalized treatment. METHODS: In this multicenter study, 366 eligible patients who had undergone neoadjuvant chemoradiotherapy followed by radical surgery at eight Chinese tertiary hospitals between January 2017 and June 2024 were recruited, with 2928 pretreatment colonic endoscopic images and 366 pelvic MRI images. An MRI-endoscopy fusion model was constructed based on the ResNet backbone and Transformer network using pretreatment MRI and endoscopic images. Treatment response was defined as good response or non-good response based on the tumor regression grade. The Delong test and the Hanley-McNeil test were utilized to compare prediction performance among different models and different subgroups, respectively. The predictive performance of the MRI-endoscopy fusion model was comprehensively validated in the test sets and was further compared to that of the single-modal MRI model and single-modal endoscopy model. RESULTS: The MRI-endoscopy fusion model demonstrated favorable prediction performance. In the internal validation set, the area under the curve (AUC) and accuracy were 0.852 (95% confidence interval [CI]: 0.744-0.940) and 0.737 (95% CI: 0.712-0.844), respectively. Moreover, the AUC and accuracy reached 0.769 (95% CI: 0.678-0.861) and 0.729 (95% CI: 0.628-0.821), respectively, in the external test set. In addition, the MRI-endoscopy fusion model outperformed the single-modal MRI model (AUC: 0.692 [95% CI: 0.609-0.783], accuracy: 0.659 [95% CI: 0.565-0.775]) and the single-modal endoscopy model (AUC: 0.720 [95% CI: 0.617-0.823], accuracy: 0.713 [95% CI: 0.612-0.809]) in the external test set. CONCLUSION The MRI-endoscopy fusion model based on ResNet-Vision Transformer achieved favorable performance in predicting treatment response to neoadjuvant chemoradiotherapy and holds tremendous potential for enabling personalized treatment regimens for locally advanced rectal cancer patients.
Humans ; Rectal Neoplasms/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Middle Aged ; Neoadjuvant Therapy/methods* ; Aged ; Adult ; Chemoradiotherapy/methods* ; Endoscopy/methods* ; Treatment Outcome

PURPOSE: To comprehensively analyze the geographic and temporal trends of foot fracture, understand its health burden by age, sex, and sociodemographic index (SDI), and explore its leading causes from 1990 to 2019. METHODS: The datasets in the present study were generated from the Global Burden of Diseases Study 2019, which included foot fracture data from 1990 to 2019. We extracted estimates along with the 95% uncertainty interval (UI) for the incidence and years lived with disability (YLDs) of foot fracture by location, age, gender, and cause. The epidemiology and burden of foot fracture at the global, regional, and national level was exhibited. Next, we presented the age and sex patterns of foot fracture. The leading cause of foot fracture was another focus of this study from the viewpoint of age, sex, and location. Then, Pearson's correlations between age-standardized rate (ASR), SDI, and estimated annual percentage change were calculated. RESULTS: The age-standardized incidence rate was 138.68 (95% UI: 104.88 - 182.53) per 100,000 persons for both sexes, 174.24 (95% UI: 134.35 - 222.49) per 100,000 persons for males, and 102.19 (95% UI: 73.28 - 138.00) per 100,000 persons for females in 2019. The age-standardized YLDs rate was 5.91 (95% UI: 3.58 - 9.25) per 100,000 persons for both genders, 7.35 (95% UI: 4.45 - 11.50) per 100,000 persons for males, and 4.51 (95% UI: 2.75 - 7.03) per 100,000 persons for females in 2019. The global incidence and YLDs of foot fracture increased in number and decreased in ASR from 1990 to 2019. The global geographical distribution of foot fracture is uneven. The incidence rate for males peaked at the age group of 20 - 24 years, while that for females increased with advancing age. The incidence rate of older people was rising, as younger age incidence rate declined from 1990 to 2019. Falls, exposure to mechanical forces, and road traffic injuries were the 3 leading causes of foot fracture. Correlations were observed between ASR, estimated annual percentage change, and SDI. CONCLUSIONS The burden of foot fracture remains high globally, and it poses an enormous public health challenge, with population aging. It is necessary to allocate more resources to the high-risk populations. Targeted realistic intervention policies and strategies are warranted.
Humans ; Male ; Female ; Incidence ; Fractures, Bone/epidemiology* ; Middle Aged ; Adult ; Global Health ; Aged ; Global Burden of Disease ; Adolescent ; Child ; Young Adult ; Foot Injuries/epidemiology* ; Cost of Illness ; Child, Preschool ; Aged, 80 and over ; Infant

Objective To investigate the mechanism by which angiotensin Ⅱ type 1 receptor autoantibody(AT1-AA)promotes phenotypic switch of vascular smooth muscle cells(VSMCs)and vascular fibrosis through abnormal expression of circadian clock protein BMAL1.Methods Twelve male SD rats(6～8 weeks old,weighing 180～220 g)were randomly divided into(n=6)a control group and an AT1-AA-positive group[established by active immunization of SD rats with AT1R extracellular loop Ⅱ peptide(AT1R-ECLⅡ)].HE and Masson stainings were used to observe structural changes and fibrosis in the thoracic aorta(n=3).Western blotting was performed to detect the expression of Collagen I,phenotypic switch-related proteins(SM22,α-SMA,OPN and MMP2)in vascular tissues and primary VSMCs(n=4),as well as the expression of BMAL1 at CT0,CT4,CT8,CT12,CT16,and CT20.Transwell and scratch assays were used to assess the proliferation and migration of VSMCs(n=3).si-RNA was employed to knock down Bmal1,followed by detection of BMAL1,Collagen I,and phenotypic conversion-related protein expression(n=3).Additionally,AT1-AA-positive AT1R-knockout(AT1R-KO)rats were constructed to measure BMAL1 expression in thoracic aortic tissues(n=4).Results The AT1-AA-positive rats had significantly thickened thoracic aortic vessel wall[(140±9)%vs(120±5)%,P<0.05],badly arranged VSMCs,obvious blue Masson staining,and up-regulated Collagen I expression(P<0.05).In the thoracic aorta of AT1-AA-positive rats and AT1-AA-treated VSMCs,the expression of contractile phenotype-related proteins(α-SMA,SM22)was decreased(P<0.05),while the expression of synthetic phenotype-related proteins(OPN,MMP2)was increased(P<0.05).AT1-AA enhanced the scratch healing ability and migration ability of VSMCs.Furthermore,both mRNA and protein levels of Bmal1 were significantly up-regulated at CT12(P<0.05),and the rhythmicity of Bmal1 was lost.Knockdown of Bmal1 partially ameliorated AT1-AA-induced phenotypic switch of VSMCs.Compared with AT1-AA-positive WT rats,AT1-AA-positive AT1R-KO rats showed significantly reduced BMAL1 expression in the thoracic aorta(1.35±0.06 vs 0.86±0.07,P<0.001).At the cellular level,AT1-AA-induced phenotypic switch and high Collagen I expression in VSMCs were partially improved in AT1R-KO VSMCs.Conclusion AT1-AA promotes VSMCs phenotypic conversion and vascular fibrosis through the AT1R-Bmal1 axis.