Objective: To develop a prediction model for mild cognitive impairment (MCI) risk among the elderly, so as to provide a tool for MCI early screening. Methods : From July 2022 to September 2024, a multi-stage stratified random cluster sampling method was used to recruit permanent residents aged ≥65 years from the Xinjiang Uygur Autonomous Region as study participants. Data on sociodemographic characteristics, nutritional status, body composition indices, bone mineral density, and handgrip strength were collected through questionnaires and physical examinations. Sarcopenia was defined based on appendicular skeletal muscle index and handgrip strength. MCI was assessed using the Mini-Mental State Examination, with adjustments for educational level. Participants were randomly divided into a training set and a validation set in a 7∶3 ratio. LASSO regression and multivariable logistic regression models were employed to screen for predictors and construct an MCI risk prediction model. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: A total of 1 641 participants were surveyed, including 755 males (46.01%) and 886 females (53.99%). The majority of participants were aged 65-<75 years, comprising 1 154 individuals (70.32%). MCI was detected in 517 participants, corresponding to a detection rate of 31.51%. Resultsfrom LASSO regression and multivariate logistic regression analysis showed that residence (rural, OR = 2.323, 95% CI: 1.682-3.210), age (75-<85 years, OR = 1.405, 95% CI: 1.019-1.937; ≥85 years, OR = 3.655, 95% CI: 1.696-7.875), educational level (primary school, OR = 0.341, 95% CI: 0.247-0.472; junior high school, OR = 0.255, 95% CI: 0.160-0.408; high school, OR = 0.286, 95% CI: 0.154-0.531; bachelor's degree or above, OR = 0.120, 95% CI: 0.041-0.351), history of alcohol consumption (yes, OR = 3.216, 95% CI: 2.164-4.779), risk of malnutrition (yes, OR = 1.464, 95% CI: 1.064-2.014), sarcopenia (yes, OR = 3.197, 95% CI: 2.332-4.385), and waist-to-hip ratio (abnormal, OR = 1.540, 95% CI: 1.159-2.048) were identified as predictive factors for MCI among the elderly. In the training set, the area under the ROC curve, sensitivity, and specificity were 0.788, 0.719, and 0.712, respectively. In the validation set, the corresponding values were 0.784, 0.913, and 0.542, respectively. DCA demonstrated that the model provided a higher clinical net benefit for predicting MCI risk when the risk threshold probability ranged from 0.124 to 0.764. Conclusion The prediction model developed in this study demonstrates good discriminative ability and clinical utility, indicating its substantial value for predicting the MCI risk among the elderly.

Colorectal cancer (CRC) is the third most commonly diagnosed malignancy and the second leading cause of cancer-related mortality worldwide. Despite therapeutic advancements over recent decades, the prognosis for patients with metastatic CRC (mCRC) remains poor. Approximately 2%-4% of mCRC cases exhibit human epidermal growth factor receptor 2 (HER2) amplification or overexpression, defining a distinct molecular subtype. This HER2-positive status is strongly associated with primary resistance to anti-epidermal growth factor receptor (EGFR) therapies, which are the standard of care for patients with RAS wild-type tumors. Beyond its well-established role in breast and gastric cancers, HER2 has emerged as a pivotal biomarker and actionable therapeutic target in mCRC. However, selecting appropriate treatment strategies remains challenging due to patient heterogeneity and diverse molecular subtypes. This review systematically summarizes the molecular biology, diagnostic strategies, and advances in targeted therapies for HER2-positive mCRC. On the diagnostic front, we discuss the applications of immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), next-generation sequencing (NGS), and circulating tumor DNA (ctDNA) detection technologies. We highlight discrepancies in diagnostic criteria across key clinical trials—such as HERACLES, DESTINY, and MOUNTAINEER—underscoring the urgent need for standardized, CRC-specific definitions to ensure consistent patient selection and comparability of efficacy data across studies. Although NGS enables comprehensive genomic profiling, its cost-effectiveness relative to traditional methods must be carefully considered. Therapeutically, we summarize clinical trial data for HER2-directed agents, including tyrosine kinase inhibitors (TKIs) such as tucatinib and lapatinib, monoclonal antibodies like trastuzumab, bispecific antibodies, and antibody-drug conjugates (ADCs) such as trastuzumab deruxtecan. We review dual-targeting strategies and note recent FDA approvals that represent significant milestones in second-line treatment. Additionally, we explore the potential of combining immune checkpoint inhibitors with HER2-targeted therapies to enhance antitumor immunity through mechanisms including antibody-dependent cellular cytotoxicity (ADCC) and modulation of the tumor microenvironment. ADCs enable precise delivery of cytotoxic payloads, reducing off-target toxicity while effectively inhibiting oncogenic pathways. A substantial portion of this review is dedicated to dissecting the molecular mechanisms underlying primary and acquired resistance to HER2-targeted therapies—persistent challenges that limit clinical benefit. These mechanisms include reactivation of downstream signaling pathways such as PI3K/AKT/mTOR and MAPK, concurrent mutations in genes like KRAS or BRAF, and alterations in HER2 expression that compromise treatment efficacy. For instance, specific HER2 mutations (e.g., L755S) can reduce drug binding affinity, while ctDNA monitoring facilitates early detection of emerging resistance clones during disease progression, thereby enabling timely therapeutic adjustments. Tumor heterogeneity and dynamic interactions with the microenvironment further complicate resistance patterns observed in clinical practice. HER2-targeted therapy represents a new frontier in precision oncology for mCRC, offering renewed hope for improving patient outcomes. Realizing this potential will require continued optimization of diagnostic algorithms and treatment workflows. Future efforts must focus on overcoming resistance, validating liquid biopsy approaches for dynamic monitoring, and establishing unified clinical guidelines. HER2 has become an essential biomarker for stratifying mCRC patients beyond traditional RAS and BRAF status, underscoring the shift from empiric treatment to biomarker-driven precision medicine. International, multidisciplinary collaboration will be critical to validate emerging biomarkers and refine treatment algorithms globally.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Ovarian tumor (OT) is the most lethal form of gynecologic malignancy, with minimal improvements in patient outcomes over the past several decades. Metastasis is the leading cause of ovarian cancer-related deaths, yet the underlying mechanisms remain poorly understood. Psychological stress is known to activate the glucocorticoid receptor (NR3C1), a factor associated with poor prognosis in OT patients. However, the precise mechanisms linking NR3C1 signaling and metastasis have yet to be fully elucidated. In this study, we demonstrate that chronic restraint stress accelerates epithelial-mesenchymal transition (EMT) and metastasis in OT through an NR3C1-dependent mechanism involving nuclear protein 1 (NUPR1). Mechanistically, NR3C1 directly regulates the transcription of NUPR1, which in turn increases the expression of snail family transcriptional repressor 2 (SNAI2), a key driver of EMT. Clinically, elevated NR3C1 positively correlates with NUPR1 expression in OT patients, and both are positively associated with poorer prognosis. Overall, our study identified the NR3C1/NUPR1 axis as a critical regulatory pathway in psychological stress-induced OT metastasis, suggesting a potential therapeutic target for intervention in OT metastasis.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

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*

This study investigates the pharmacokinetics and metabolic characteristics of three marine-derived piericidins as potential drug leads for kidney disease: piericidin A (PA) and its two glycosides (GPAs), glucopiericidin A (GPA) and 13-hydroxyglucopiericidin A (13-OH-GPA). The research aims to facilitate lead selection and optimization for developing a viable preclinical candidate. Rapid absorption of PA and GPAs in mice was observed, characterized by short half-lives and low bioavailability. Glycosides and hydroxyl groups significantly enhanced the absorption rate (13-OH-GPA > GPA > PA). PA and GPAs exhibited metabolic instability in liver microsomes due to Cytochrome P450 enzymes (CYPs) and uridine diphosphoglucuronosyl transferases (UGTs). Glucuronidation emerged as the primary metabolic pathway, with UGT1A7, UGT1A8, UGT1A9, and UGT1A10 demonstrating high elimination rates (30%-70%) for PA and GPAs. This rapid glucuronidation may contribute to the low bioavailability of GPAs. Despite its low bioavailability (2.69%), 13-OH-GPA showed higher kidney distribution (19.8%) compared to PA (10.0%) and GPA (7.3%), suggesting enhanced biological efficacy in kidney diseases. Modifying the C-13 hydroxyl group appears to be a promising approach to improve bioavailability. In conclusion, this study provides valuable metabolic insights for the development and optimization of marine-derived piericidins as potential drug leads for kidney disease.
Animals ; Male ; Mice ; Aquatic Organisms/chemistry* ; Biological Availability ; Cytochrome P-450 Enzyme System/metabolism* ; Glucuronosyltransferase/metabolism* ; Microsomes, Liver/metabolism* ; Molecular Structure ; Biological Products/pharmacokinetics* ; Pyridines/pharmacokinetics*

OBJECTIVE: Poxviruses are zoonotic pathogens that infect humans, mammals, vertebrates, and arthropods. However, the specific role of ticks in transmission and evolution of these viruses remains unclear. METHODS: Transcriptomic and metatranscriptomic raw data from 329 sampling pools of seven tick species across five continents were mined to assess the diversity and abundance of poxviruses. Chordopoxviral sequences were assembled and subjected to phylogenetic analysis to trace the origins of the unblasted fragments within these sequences. RESULTS: Fifty-eight poxvirus species, representing two subfamilies and 20 genera, were identified, with 212 poxviral sequences assembled. A substantial proportion of AT-rich fragments were detected in the assembled poxviral genomes. These genomic sequences contained fragments originating from rodents, archaea, and arthropods. CONCLUSION Our findings indicate that ticks play a significant role in the transmission and evolution of poxviruses. These viruses demonstrate the capacity to modulate virulence and adaptability through horizontal gene transfer, gene recombination, and gene mutations, thereby promoting co-existence and co-evolution with their hosts. This study advances understanding of the ecological dynamics of poxvirus transmission and evolution and highlights the potential role of ticks as vectors and vessels in these processes.
Animals ; Poxviridae/physiology* ; Ticks/virology* ; Phylogeny ; Transcriptome ; Evolution, Molecular ; Poxviridae Infections/virology* ; Genome, Viral