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.

Objective To provide a scientific basis for vector prevention and control in port areas, vector monitoring has been conducted continuously to characterize the composition, density, and seasonal fluctuations of vectors across 21 port-areas of Shandong province from 2017 to 2023. Methods In accordance with the Regional Vector Monitoring Plan for Frontier Ports, vector surveillance of mosquitoes, flies, cockroaches, rodents, and other disease vectors was performed. Data were analyzed to describe vector community composition and identify seasonal fluctuations. Results A total of 601 rodents belonging to 1 family, 4 genera, and 4 species were captured. Three dominant rodent species inhabit port areas. In addition, 88 104 mosquitoes were captured and classified into 3 subfamilies, 4 genera, and 12 species; 38 959 flies were trapped and classified into 8 families,31 genera, and 51 species, including 9 dominant fly species in port-areas; and 54 672 captured cockroaches were classified into 2 families,4 genera, and 4 species, with Blattella germanica as the dominant species. Conclusions Significant vector community changes(P＜0.005)have been identified after the epidemic. To improve the pertinence and efficacy of port health quarantine work, continuous and strong attention should be paid to vector compositions and population densities at Shandong ports.

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.

OBJECTIVES: Lymph node metastasis in papillary thyroid cancer (PTC) is closely associated with tumor recurrence and patient survival. However, current technologies have limited sensitivity in detecting occult cervical lymph node metastases. Identifying accurate molecular markers for predicting PTC metastasis holds significant clinical value. This study aims to analyze WNT10A expression in PTC and its clinical significance, and to explore the role of WNT10A gene knockdown in PTC cell proliferation, invasion, and metastasis. METHODS: The expression of WNT10A in thyroid carcinoma was analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) and University of Alabama at Birminghara Cancer data analysis Portal (UALCAN) databases. Real-time RT-PCR was used to measure WNT10A mRNA levels in tumor and adjacent normal tissues from 32 PTC patients. Immunohistochemistry was conducted on 158 PTC specimens to assess WNT10A protein expression and its correlation with clinicopathological features. In vitro experiments were performed using K1 and TPC-1 cell lines. Cell proliferation was assessed using the Celigo system and methyl thiazolyl tetrazolium (MTT) assays; apoptosis was measured via flow cytometry; invasion and metastasis were evaluated using scratch and Transwell assays. A xenograft model was established in nude mice to observe tumor growth, and tumor weight and volume were compared between cell lines. Differentially expressed genes regulated by WNT10A were identified via mRNA sequencing, followed by Gene Ontology (GO) and ingenuity pathway analysis (IPA). Real-time PCR and Western blotting were used to validate the effects of WNT10A on key downstream mRNA and protein in the Tec kinase signaling pathway. RESULTS: WNT10A mRNA expression was significantly higher in thyroid cancer tissues compared to adjacent normal tissues according to GEPIA and UALCAN (both P<0.01). The real-time RT-PCR result showed that WNT10A mRNA expression in PTC tissues was high than that in adjacent tissues (P<0.01). Immunohistochemistry revealed significantly higher WNT10A protein expression in PTC tissues compared to adjacent tissues (P<0.01), and its expression correlated with multifocality, extrathyroidal invasion, and lymph node metastasis. WNT10A knockdown significantly inhibited proliferation, altered cell cycle distribution, and increased apoptosis in K1 and TPC-1 cells (all P<0.01). WNT10A silencing also reduced migration and invasion abilities in both cell lines. In vivo, WNT10A knockdown in TPC-1 cells suppressed tumor formation in nude mice. GO analysis and IPA suggested that the Tec kinase signaling pathway was a key downstream target of WNT10A. RT-PCR and Western blotting confirmed that WNT10A knockdown downregulated the expression of key genes (STAT3, MAPK8, TNFRSF21, and AKT2) in this pathway. CONCLUSIONS WNT10A is highly expressed in PTC and is associated with tumor proliferation, invasion, and metastasis. Its tumor-promoting effects may be mediated through suppression of the Tec kinase signaling pathway.
Humans ; Cell Proliferation ; Thyroid Cancer, Papillary/pathology* ; Thyroid Neoplasms/metabolism* ; Animals ; Wnt Proteins/metabolism* ; Neoplasm Invasiveness ; Mice ; Cell Line, Tumor ; Female ; Male ; Mice, Nude ; Apoptosis ; Lymphatic Metastasis ; Middle Aged ; Cell Movement ; Adult

Stearoyl-coenzyme A desaturase 1 (SCD1) catalyzes the rate-limiting step of de novo lipogenesis and modulates lipid homeostasis. Although numerous SCD1 inhibitors were tested for treating metabolic disorders both in preclinical and clinic studies, the tissue-specific roles of SCD1 in modulating obesity-associated metabolic disorders and determining the pharmacological effect of chemical SCD1 inhibition remain unclear. Here a novel role for intestinal SCD1 in obesity-associated metabolic disorders was uncovered. Intestinal SCD1 was found to be induced during obesity progression both in humans and mice. Intestine-specific, but not liver-specific, SCD1 deficiency reduced obesity and hepatic steatosis. A939572, an SCD1-specific inhibitor, ameliorated obesity and hepatic steatosis dependent on intestinal, but not hepatic, SCD1. Mechanistically, intestinal SCD1 deficiency impeded obesity-induced oxidative stress through its novel function of inducing metallothionein 1 in intestinal epithelial cells. These results suggest that intestinal SCD1 could be a viable target that underlies the pharmacological effect of chemical SCD1 inhibition in the treatment of obesity-associated metabolic disorders.

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

Deactivation of the mitochondrial pyruvate dehydrogenase complex (PDC) is important for the metabolic switching of cancer cell from oxidative phosphorylation to aerobic glycolysis. Studies examining PDC activity regulation have mainly focused on the phosphorylation of pyruvate dehydrogenase (E1), leaving other post-translational modifications largely unexplored. Here, we demonstrate that the acetylation of Lys 488 of pyruvate dehydrogenase complex component X (PDHX) commonly occurs in hepatocellular carcinoma, disrupting PDC assembly and contributing to lactate-driven epigenetic control of gene expression. PDHX, an E3-binding protein in the PDC, is acetylated by the p300 at Lys 488, impeding the interaction between PDHX and dihydrolipoyl transacetylase (E2), thereby disrupting PDC assembly to inhibit its activation. PDC disruption results in the conversion of most glucose to lactate, contributing to the aerobic glycolysis and H3K56 lactylation-mediated gene expression, facilitating tumor progression. These findings highlight a previously unrecognized role of PDHX acetylation in regulating PDC assembly and activity, linking PDHX Lys 488 acetylation and histone lactylation during hepatocellular carcinoma progression and providing a potential biomarker and therapeutic target for further development.
Humans ; Acetylation ; Carcinoma, Hepatocellular/genetics* ; Liver Neoplasms/genetics* ; Pyruvate Dehydrogenase Complex/genetics* ; Gene Expression Regulation, Neoplastic ; Animals ; Mice ; Cell Line, Tumor ; Protein Processing, Post-Translational ; Histones/metabolism* ; Disease Progression