Objective To analyze and preliminarily verify key genes and pathways in the transcriptome of peripheral blood of lung adenocarcinoma patients with metastasis bone pain (MBP), and to explore its underlying mechanism. Methods Nine lung adenocarcinoma patients with bone metastasis treated in the First Affiliated Hospital of Sun Yat-sen University from May 2020 to May 2021 were selected for retrospective analysis, including 4 patients with typical MBP clinical manifestations and visual analogue scale (VAS) ≥4 (MBP group) and 5 patients without suffering any pain (control group). Peripheral blood mRNA sequencing was performed to identify differentially expressed genes (DEGs), followed by functional pathways analysis and protein-protein interaction (PPI) network analysis. The most significant modules and hub genes were confirmed and visualized using Cytoscape software. The target miRNAs regulating these hub genes were predicted using Targetscan database, and long non-coding RNA (lncRNA) interacting with these miRNAs were also predicted using lncBase database. The relationships among lncRNA, miRNA and mRNA were visualized to construct a competing endogenous RNA (ceRNA) network through Cytoscape software, and the nodes of this network were verified using quantitative PCR (qPCR). Results A total of 1466 DEGs were identified, including 666 up-regulated genes and 800 down-regulated genes. Chemokine receptor 3 (CXCR3), pro-opiomelanocortin (POMC), neuromedin U receptor 1 (NMUR1), chemokine ligand 2 (CCL2) and endocannabinoid receptor 1 (CNR1) were identified as hub genes. The most significant enriched processes and pathways of DEGs included osteoclast differentiation, NOD like receptor signal transduction pathway, type Ⅰ interferon signal pathway, nuclear factor kappa-B (NF-κB) signal pathway, apoptosis/autophagy pathway, chemokine signal pathway, interleukin (IL)-1β pathway. Two ceRNA networks were identified: MALAT1-hsa-miR-124-3p.2-CCL2 and NEAT1-hsa-miR-325-3p-CXCR3. qPCR results showed that the expression levels of CCL2, CXCR3, MALAT1, NEAT1 and hsa-miR-325 were higher in MBP group than these in control group (P<0.05). Conclusions CXCR3, POMC, NMUR1, CCL2 and CNR1 may serve as key genes in the occurrence of MBP and could be important regulatory targets for MBP. The development of MBP in lung adenocarcinoma may be associated with the dysregulation of the networks: MALAT1-hsa-miR-124-3p.2-CCL2 and NEAT1-hsa-miR-325-3p-CXCR3.

Persistent inflammation, immuno-suppression and catabolism syndrome (PICS) occurs at the later stage of sepsis, characterized by T cell dysfunction with severe poor outcome. Recent studies found that T cell function be largely affected by its metabolic status. In sepsis, a variety of signaling molecules, including the nutrients, could trigger T cell to undergo metabolic reprogramming that from oxidative phosphorylation to aerobic glycolysis via phosphatidylinositol 3-kinase (PI3K)-serine/threonine kinases (Akt)-mammalian target of rapamycin (mTOR) pathway, leading to severe alterations of its immune phenotype. Glucagon-like peptide-1 (GLP-1) is a kind of incretin hormone that could regulate nutrients and energy metabolism in the body. It can reduce blood glucose level, suppress the immune and inflammatory responses. Plasma GLP-1 levels were rapidly elevated in sepsis and correlated closely with the outcome in critical care. GLP-1 receptor (GLP-1R) agonist could block the glycolysis of T cells, reduce glucose transporter type 1 mRNA expression, and inhibit T cell proliferation. Therefore, the elevated GLP-1 level may represent the metabolic switch toward "aerobic glycolysis", reflecting the pathological status of PICS. Here, the review elucidates the regulation of GLP-1 on the immune function and metabolic reprogramming of T cells and provides strategies for the prevention and treatment of T cell immune dysfunction in sepsis via GLP-1 receptor.

Non-functioning pituitary adenomas (NFPAs) are relatively common. Apart from hyperprolactinemia caused by pituitary compression, they typically lack overt hormonal hypersecretion and usually present with clinical symptoms due to mass effects. Previously considered a uniform entity, NFPAs are actually a highly heterogeneous group of tumors, including aggressive subtypes like silent corticotroph adenomas (SCA) and null cell adenomas. The 2022 WHO new classification of pituitary tumors employs transcription factors [e.‍g., pituitary-specific transcription factor 1 (PIT-1), T-box transcription factor 19 (TBX19, also known as TPIT), steroidogenic factor 1 (SF-1)] for detailed categorization, allowing precise subclassification of NFPAs into multiple subtypes derived from distinct cell lineages, including silent gonadotroph adenomas, SCA, and plurihormonal PIT-1-positive adenomas. This helps identify highly invasive subtypes with high recurrence risk, guiding clinical diagnosis and treatment, prognostic assessment, and individualized management. The new classification also provides a theoretical basis for targeted therapies of NFPAs (e.g., somatostatin analogs and temozolomide). This review comprehensively discusses the latest pathological classification of NFPAs and its clinical implications, aiming to enhance understanding of this disease and offer valuable insights for precise diagnosis, treatment, and prognostic assessment.

Objective To analyze the trends in incidence and mortality of breast cancer among Chinese women from 1992 to 2021, assess the impact of age, period, and cohort on its incidence and mortality rates, and predict future trends to provide a basis for developing effective intervention strategies. Methods Utilizing the 2021 Global Burden of Disease (GBD2021) database, the Joinpoint regression model was employed to analyze the trends in age-standardized incidence and mortality rates of breast cancer among Chinese women from 1992 to 2021. The age-period-cohort model was applied to estimate the age, period, and cohort effects on the incidence and mortality of breast cancer among Chinese women during the same period. The autoregressive integrated moving average (ARIMA) model was used to predict the age-standardized incidence and mortality rates of breast cancer among Chinese women from 2022 to 2026. A stratified analysis was conducted to explore the impact of different risk factors [including smoking, alcohol consumption, high body mass index (BMI), hyperglycemia, physical inactivity, and diet] on breast cancer mortality. Results From 1992 to 2021, the incidence and mortality rates of breast cancer among Chinese women showed an overall upward trend, with incidence rates rising from 15.95/100,000 in 1992 to 55.54/100,000 in 2021, and mortality rates increasing from 7.35/100,000 to 12.41/100,000. The age-standardized incidence rate also exhibited an upward trend, rising from 18.51/100,000 to 37.00/100,000, with an average annual percentage change (AAPC) of 2.43%. However, the age-standardized mortality rate showed an overall downward trend, decreasing from 9.05/100,000 to 8.24/100,000, with an AAPC of -0.35%. The APC model analysis revealed that the age, period, and cohort effects on incidence and mortality were statistically significant (P<0.001). Within the same birth cohort, breast cancer incidence increased in women aged 15-89 years but decreased in those≥90 years. Breast cancer mortality showed a steady increase with age. With the increase in years, the risk of breast cancer incidence gradually increased, reaching the highest between 2017 and 2021, with a relative risk (RR) value of 1.37. Conversely, the risk of breast cancer mortality decreased with the increase in years, with the lowest mortality between 2012 and 2016, and an RR value of 0.86. With the increase in the birth cohort year, the risk of breast cancer incidence gradually increased, while the risk of mortality gradually decreased. The ARIMA model prediction results showed that the age-standardized incidence rate of breast cancer among women would continue to rise from 2022 to 2026, reaching 40.25/100,000 by 2026, while the age-standardized mortality rate would tend to stabilize at 8.28/100,000 by 2026. Among the risk factors for breast cancer, diet was found to have the highest impact on breast cancer mortality. Conclusions The incidence rate of breast cancer among Chinese women continues to rise, indicating that the prevention and control situation remains severe. Future efforts should focus on developing precise screening programs for high-risk populations and optimizing early screening strategies and treatment resource allocation based on predicted trend.

Objective To explore the predictive value of albumin, hemoglobin and multifactorial model for poor postoperative prognosis in elderly patients with meningioma. Methods A retrospective analysis was conducted on 253 elderly patients who underwent meningioma surgery and were transferred to the neurosurgical intensive care unit (NICU) at General Hospital of Northern Theater Command from January 2019 to September 2021, serving as the modeling cohort. Another 227 elderly patients who were treated in NICU after meningioma surgery from November 2021 to June 2023 were used as the validation cohort. Patients in the modeling cohort were categorized into good prognosis group [Glasgow Coma Scale (GCS) score>7, n=161] and poor prognosis group (GCS≤7, n=92) based on the GCS. Univariate and multifactorial logistic regression analyses were performed on the modeling cohort to identify independent risk factors, and a multifactorial model for predicting poor postoperative prognosis in elderly patients with meningioma was constructed based on these factors. The predictive efficacy and accuracy of the model were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, Hosmer-Lemeshow goodness-of-fit test, and calibration curves. The predictive value of postoperative albumin, hemoglobin, and the multifactorial models for postoperative prognosis in elderly meningioma patients was assessed using restricted cubic spline modeling (RCS), decision curves (DCA), and validated using an external validation cohort to assess the stability of the model. Results Meningioma WHO grade Ⅱ and Ⅲ (OR=3.994, 95%CI 1.963-8.126), postoperative hypoalbuminemia (OR=2.194, 95%CI 1.079-4.462), and postoperative anemia (OR=2.117, 95%CI 1.096-4.089) were identified as independent risk factors for poor postoperative prognosis in elderly meningioma patients (P<0.05), while the use of analgesic/sedative medications was a protective factor (OR=0.388, 95%CI 0.201-0.748, P<0.05). The Hosmer-Lemeshow test indicated that the constructed multifactorial model had a good fit accuracy (P=0.161). The AUC for predicting poor postoperative prognosis in elderly meningioma patients for postoperative albumin and hemoglobin were 0.545 (95%CI 0.472-0.617) and 0.632 (95%CI 0.561-0.702), respectively, and showed a nonlinear dose-response relationship with prognosis (P<0.01). DCA analysis results showed that the net benefit rate of multifactorial model was higher than that of postoperative albumin and hemoglobin when the threshold probabilities were between 0.10 and 0.90. The AUC for predicting postoperative prognosis in the elderly meningioma patients in the modeling and validation cohorts were 0.810 and 0.819, respectively, and their calibration curves suggested good discrimination and accuracy. Conclusions Meningioma WHO grades Ⅱ and Ⅲ, postoperative anemia and hypoalbuminemia are independent risk factors for poor postoperative prognosis in elderly meningioma patients, while the use of analgesic/sedative drugs is a protective factor. The multifactorial model constructed based on these factors has a good predictive efficacy and credibility, and can be used as a reference for clinical decision-making.

Objective To investigate the risk factors associated with recurrence of high-grade non-functioning pituitary neuroendocrine tumors (NF-PitNETs) following total resection. Methods A retrospective study was conducted on the clinical data of 252 patients with high-grade NF-PitNETs who underwent surgical treatment at the Department of Neurosurgery, Beijing Tiantan Hospital from January 2012 to December 2023, and met the inclusion criteria. High-grade NF-PitNETs included Knosp 3A, 3B, and 4-grade subtypes. Kaplan-Meier curves and Log-rank tests were employed to compare the progression-free survival (PFS) of Knosp 3A, 3B, and 4-grade patients. Cox regression analysis was applied to identify the risk factors associated with the recurrence of high-grade NF-PitNETs. Receiver operating characteristic (ROC) curve was used to calculate the area under the curve (AUC) of each recurrence-related factor to evaluate the diagnostic efficiency. Results Survival analysis revealed that there were significant differences in PFS among Knosp 3A, 3B, and 4-grade patients (P<0.001). The PFS of Knosp 3A was significantly better than that of grade 3B and 4-grade (P<0.05), while there was no significant difference between Knosp 3B and 4-grade (P=0.118). After integrating the three groups into Knosp 3A group and 3B-4 group, there were significant differences in PFS and some clinical features between the two groups (P<0.05). Cox regression analysis indicated that age <55 years old (HR=2.883, 95%CI 1.253-6.634; P=0.013), T₂ heterogeneous signal (HR=1.842, 95%CI 1.061-3.197; P=0.030), Knosp 3B-4 (HR=2.190, 95%CI 1.069-4.488; P=0.032), and Ki-67 ≥3% (HR=2.266, 95%CI 1.265-4.061; P=0.006) were risk factors related to tumor recurrence. ROC curve analysis showed that the AUCs of the above-mentioned risk factors were 0.682, 0.706, 0.709 and 0.750, respectively, and the AUC of the multi-factor combined model (age+T₂ signal+Knosp grade+Ki-67) was 0.838, which was significantly larger than that of each single risk factor (P<0.05). Conclusion High-grade NF-PitNETs patients with age <55 years old, T₂ heterogeneous signal, Knosp 3B-4 and Ki-67≥3% have a higher recurrence risk. The combined application of multiple risk factors can improve the predictive value of recurrence.

Spinal cord injury (SCI) is a central nervous system disease with high morbidity and disability rates, bringing serious economic and psychological burdens to families and society worldwide. AMP-activated protein kinase (AMPK) is an important sensor in the energy metabolism process in living organisms, which plays a central role in maintaining energy balance. It is currently considered a key target for the prevention and treatment of multiple diseases. Studies have shown that AMPK signaling can regulate autophagy, neuroinflammation, oxidative stress, mitochondrial function and other processes after SCI, thus affecting the pathological process of SCI. This review summarizes the research progress on AMPK signaling pathway involved in the regulation of SCI, in order to provide new ideas for the treatment and drug development of SCI.

Objective To explore the value of a combined model based on high-kilovoltage CT radiomics and clinical factors for predicting monosodium urate (MSU) crystal deposition in gouty arthritis. Methods The clinical data of 136 patients with MSU crystal deposition adjacent to joints confirmed by dual-energy CT (DECT) and 79 patients with non-MSU calcifications adjacent to joints were retrospectively analyzed. The dataset was randomly divided into a training set (n=150) and a validation set (n=65) at a ratio of 7:3 for the construction of predictive models. Radiomic features were extracted from high-kilovolt (135 kV) images, and 20 radiomic features were selected using minimum redundancy-maximum relevance and least absolute shrinkage and selection operator (LASSO) regression. Logistic regression, light gradient boosting machine (LightGBM), and support vector machine models were built based on the selected features, and the best-performing model was identified. Multivariate logistic regression analysis was used to screen for risk factors associated with MSU crystal deposition adjacent to joints. A nomogram model was then constructed by integrating radiomic features and clinical variables. The diagnostic performance of the models was evaluated by means of the receiver operating characteristics (ROC) area under the curve (AUC). Results Multivariate logistic regression analysis revealed that CT value was an independent risk factor for MSU crystal deposition adjacent to joints (P<0.001). Among the three machine-learning models, the LightGBM model demonstrated the best predictive performance and good dataset robustness. Therefore, the nomogram was constructed using the LightGBM model. The AUCs of the nomogram model for predicting MSU crystal deposition in the training and validation sets were 0.932 and 0.856, respectively, both exceeding 0.85 and significantly higher than those of the clinical model (De-long test, P<0.05). No statistically significant difference was observed between nomogram model and radiomics model (De-long test, P>0.05). Conclusions High-kilovoltage CT radiomics analysis can predict MSU crystal deposition adjacent to joints. The nomogram model and the radiomics model both demonstrate high diagnostic performance, which can provide valuable references for clinical decision-making.

Objective To explore the risk factors for medium- and long-term mortality in patients with severe community-acquired pneumonia (SCAP) based on the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ), construct a prognostic model and evaluate its predictive efficacy. Methods In this retrospective cohort study, 1943 SCAP patients from the U.S. MIMIC-Ⅳ database (2008-2019) were randomly divided into training (n=1363) and validation (n=580) sets (7:3 ratio). Primary and secondary endpoints were 1-year and 30-/90-day all-cause mortality, respectively. Prognostic factors were selected using LASSO regression and multivariable Cox proportional hazards modeling, and a visual nomogram model was built. Model performance was assessed via C-index, receiver operator characteristic (ROC) curves, and calibration curves, and compared with the CURB-65 score. Risk stratification was validated using Kaplan-Meier analysis. Results The 30-day, 90-day, and 1-year all-cause mortality rates for SCAP patients were 25.9%, 34.5%, and 42.6%, respectively. Seven independent risk factors were identified: age (HR=1.037), heart rate (HR=1.007), red blood cell distribution width (RDW, HR=1.092), Acute Physiology Score Ⅲ (APS-Ⅲ, HR=1.013), cerebrovascular disease (HR=1.453), liver disease (HR=1.272), and malignancy (HR=2.007). Based on these factors, Cox regression model was constructed and nomogram was drawn, C-indices of training set and validation set were 0.710 and 0.688, respectively. For 1-year mortality prediction, the model achieved superior area under the ROC curve (AUC) values (training set: 0.768; validation set: 0.738) compared with CURB-65 score (training set: 0.648; validation set: 0.616). Kaplan-Meier survival analysis revealed significantly worse survival in high-risk group than low-risk group (P<0.0001). Conclusions Age, heart rate, RDW, APS-Ⅲ, cerebrovascular disease, liver disease, and malignant tumor were medium- and long-term mortality risk factors in SCAP patients. The prognostic model constructed based on these factors has high predictive power and provides an important clinical diagnosis and treatment reference.

Objective To analyze the correlation between hemoglobin (Hb), anemia, and sarcopenia in the elderly population in Chongqing communities. Methods A cross-sectional study was conducted on elderly individuals who underwent healthy examinations at 5 community health service centers in Chongqing from March to August 2023. Demographic characteristics, social factors, body composition measurement, grip strength, 6-meter gait speed and blood tests were assessed. Receiver operating characteristic (ROC) curve was utilized to evaluate the accuracy of Hb in predicting sarcopenia, and the Youden index was employed to determine the optimal Hb cut-off value for diagnosing sarcopenia and its components. Both unadjusted and adjusted logistic regression analyses were performed to examine the relationship between Hb and anemia with sarcopenia and its components. Results A total of 531 elderly populations were included, with an average age of (71.1±6.5) years. The overall prevalence of sarcopenia was 13.6% (72/531), including 29 males (40.3%) and 43 females (59.7%). Unadjusted analyses showed that Hb was correlated with sarcopenia, decreased muscle mass, slower gait speed, and reduced grip strength (P<0.05). After adjusting for all potential risk factors, Hb was still significantly associated with sarcopenia and reduced grip strength (P<0.05). For every 10 g/L increase in Hb, the risk of sarcopenia decreased by 2.3%, and the risk of reduced grip strength decreased by 1.7% (P<0.05). Anemia was correlated with sarcopenia, reduced muscle mass, and decreased grip strength in unadjusted analyses (P<0.05), while the correlation between anemia and reduced grip strength remained significant after adjustment for all potential risk factors (P<0.05). The optimal Hb cut-off value for diagnosing sarcopenia in males and females were 148 g/L and 128 g/L, respectively. Conclusions Hb is an independent risk factor for sarcopenia and reduced grip strength. Anemia is associated with sarcopenia, but is not an independent risk factor for sarcopenia.