With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

ObjectiveTo investigate the therapeutic effect of paeoniflorin on metabolic dysfunction-associated fatty liver disease （MASLD） combined with insulin resistance （IR）， and to elucidate its regulatory mechanisms on the Toll-like receptor 4 （TLR4）/nuclear transcription factor （NF）-κB-insulin receptor substrate 1 （IRS1） inflammation-insulin signaling axis. MethodsMASLD mouse model was established using a 60% high-fat diet. Mice were randomly assigned to a normal control group， a MASLD model group， and paeoniflorin groups （low dose： 25 mg·kg^-1·d^-1， medium dose： 50 mg·kg^-1·d^-1， high dose： 100 mg·kg^-1·d^-1）， receiving intragastric administration for consecutive 12 weeks. Concurrently， a palmitic acid/oleic acid （PA/OA）-induced lipid deposition model was established in HepG2 cells. Cell counting kit-8 （CCK-8） assay was performed to determine the optimal concentrations of PA/OA and paeoniflorin for intervention. Mice body weight， liver index， and serum levels of alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， triglycerides （TG）， and total cholesterol （TC） were measured. Fasting blood glucose （FBG） and fasting insulin （FINS） levels were measured， and the homeostatic model assessment of insulin resistance （HOMA-IR） was calculated， while an oral glucose tolerance test （OGTT） was performed to evaluate glucose metabolism. Hematoxylin and eosin （HE） staining was used to observe pathological changes and inflammatory cell infiltration in liver tissue， and oil red O staining was used to evaluate lipid droplet deposition in liver tissue and HepG2 cells. Potential targets were screened using network pharmacology and molecular docking. Western blot analysis was performed to detect the expression of HSP90AA1， TLR4， p-NF-κB p65， p-IRS1， and p-Akt proteins in liver tissue and HepG2 cells. ResultsCompared with the normal control group， mice in the MASLD model group exhibited significantly increased body weight， liver index， and serum levels of ALT， AST， TG， and TC， as well as markedly elevated FBG， FINS， and HOMA-IR， and impaired glucose tolerance （P<0.01）. HE staining revealed marked hepatic steatosis and inflammatory cell infiltration， while oil red O staining showed a significant increase in lipid droplet deposition. Compared with the MASLD model group， after intervention with various doses of paeoniflorin， the increase in body weight and liver index was reduced， serum ALT， AST， TG， and TC levels decreased， and FBG， FINS， and HOMA-IR were significantly lowered， with glucose tolerance markedly improved （P<0.05， P<0.01）， showing a certain dose-dependent trend. HE and oil red O staining results showed a marked reduction in hepatic steatosis and lipid deposition. In vitro experiments demonstrated that， compared with the control group， PA/OA treatment significantly induced increased lipid deposition in HepG2 cells. Compared with the model group， paeoniflorin intervention significantly reduced intracellular lipid droplets， and lipid deposition showed a dose-dependent decreasing trend. Mechanism studies indicated that， compared with the normal control group， the MASLD model group exhibited significantly elevated expression of TLR4， HSP90AA1， and p-NF-κB p65， while p-IRS1 （Ser307） expression was elevated and p-Akt （Ser473） expression was reduced （P<0.01）. Compared with the model group， the expression of the aforementioned proteins was significantly reversed in all treatment groups following intervention （P<0.05， P<0.01）. ConclusionPaeoniflorin significantly alleviates lipid deposition and insulin resistance in MASLD mice. Its mechanism of action may involve targeting HSP90AA1， TLR4， and NF-κB1 to regulate the TLR4/NF-κB-IRS1 inflammation-insulin signaling axis. This regulation suppresses inflammatory responses and restores insulin signaling， thereby ameliorating abnormalities in glucose and lipid metabolism.

ObjectiveTo investigate the therapeutic effect of paeoniflorin on metabolic dysfunction-associated fatty liver disease （MASLD） combined with insulin resistance （IR）， and to elucidate its regulatory mechanisms on the Toll-like receptor 4 （TLR4）/nuclear transcription factor （NF）-κB-insulin receptor substrate 1 （IRS1） inflammation-insulin signaling axis. MethodsMASLD mouse model was established using a 60% high-fat diet. Mice were randomly assigned to a normal control group， a MASLD model group， and paeoniflorin groups （low dose： 25 mg·kg^-1·d^-1， medium dose： 50 mg·kg^-1·d^-1， high dose： 100 mg·kg^-1·d^-1）， receiving intragastric administration for consecutive 12 weeks. Concurrently， a palmitic acid/oleic acid （PA/OA）-induced lipid deposition model was established in HepG2 cells. Cell counting kit-8 （CCK-8） assay was performed to determine the optimal concentrations of PA/OA and paeoniflorin for intervention. Mice body weight， liver index， and serum levels of alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， triglycerides （TG）， and total cholesterol （TC） were measured. Fasting blood glucose （FBG） and fasting insulin （FINS） levels were measured， and the homeostatic model assessment of insulin resistance （HOMA-IR） was calculated， while an oral glucose tolerance test （OGTT） was performed to evaluate glucose metabolism. Hematoxylin and eosin （HE） staining was used to observe pathological changes and inflammatory cell infiltration in liver tissue， and oil red O staining was used to evaluate lipid droplet deposition in liver tissue and HepG2 cells. Potential targets were screened using network pharmacology and molecular docking. Western blot analysis was performed to detect the expression of HSP90AA1， TLR4， p-NF-κB p65， p-IRS1， and p-Akt proteins in liver tissue and HepG2 cells. ResultsCompared with the normal control group， mice in the MASLD model group exhibited significantly increased body weight， liver index， and serum levels of ALT， AST， TG， and TC， as well as markedly elevated FBG， FINS， and HOMA-IR， and impaired glucose tolerance （P<0.01）. HE staining revealed marked hepatic steatosis and inflammatory cell infiltration， while oil red O staining showed a significant increase in lipid droplet deposition. Compared with the MASLD model group， after intervention with various doses of paeoniflorin， the increase in body weight and liver index was reduced， serum ALT， AST， TG， and TC levels decreased， and FBG， FINS， and HOMA-IR were significantly lowered， with glucose tolerance markedly improved （P<0.05， P<0.01）， showing a certain dose-dependent trend. HE and oil red O staining results showed a marked reduction in hepatic steatosis and lipid deposition. In vitro experiments demonstrated that， compared with the control group， PA/OA treatment significantly induced increased lipid deposition in HepG2 cells. Compared with the model group， paeoniflorin intervention significantly reduced intracellular lipid droplets， and lipid deposition showed a dose-dependent decreasing trend. Mechanism studies indicated that， compared with the normal control group， the MASLD model group exhibited significantly elevated expression of TLR4， HSP90AA1， and p-NF-κB p65， while p-IRS1 （Ser307） expression was elevated and p-Akt （Ser473） expression was reduced （P<0.01）. Compared with the model group， the expression of the aforementioned proteins was significantly reversed in all treatment groups following intervention （P<0.05， P<0.01）. ConclusionPaeoniflorin significantly alleviates lipid deposition and insulin resistance in MASLD mice. Its mechanism of action may involve targeting HSP90AA1， TLR4， and NF-κB1 to regulate the TLR4/NF-κB-IRS1 inflammation-insulin signaling axis. This regulation suppresses inflammatory responses and restores insulin signaling， thereby ameliorating abnormalities in glucose and lipid metabolism.

ObjectiveThe controllability changes of structural brain network were explored based on the control and brain network theory in young smokers, this may reveal that the controllability indicators can serve as a powerful factor to predict the sleep status in young smokers. MethodsFifty young smokers and 51 healthy controls from Inner Mongolia University of Science and Technology were enrolled. Diffusion tensor imaging (DTI) was used to construct structural brain network based on fractional anisotropy (FA) weight matrix. According to the control and brain network theory, the average controllability and the modal controllability were calculated. Two-sample t-test was used to compare the differences between the groups and Pearson correlation analysis to examine the correlation between significant average controllability and modal controllability with Fagerström Test of Nicotine Dependence (FTND) in young smokers. The nodes with the controllability score in the top 10% were selected as the super-controllers. Finally, we used BP neural network to predict the Pittsburgh Sleep Quality Index (PSQI) in young smokers. ResultsThe average controllability of dorsolateral superior frontal gyrus, supplementary motor area, lenticular nucleus putamen, and lenticular nucleus pallidum, and the modal controllability of orbital inferior frontal gyrus, supplementary motor area, gyrus rectus, and posterior cingulate gyrus in the young smokers’ group, were all significantly different from those of the healthy controls group (P<0.05). The average controllability of the right supplementary motor area (SMA.R) in the young smokers group was positively correlated with FTND (r=0.393 0, P=0.004 8), while modal controllability was negatively correlated with FTND (r=-0.330 1, P=0.019 2). ConclusionThe controllability of structural brain network in young smokers is abnormal. which may serve as an indicator to predict sleep condition. It may provide the imaging evidence for evaluating the cognitive function impairment in young smokers.

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 analyze the prevalence status of cardiometabolic risk factor (CMRF) and its aggregation among workers engaged in new forms of employment. Methods A total of 5 429 new employment workers (including couriers, online food delivery workers, and ride hailing drivers) who underwent health medical examinations at a tertiary hospital in Beijing City were selected as the research subjects using the judgment sampling method. Data on waist circumference, blood pressure, blood glucose, and blood lipid levels were collected to analyze their CMRF [central obesity, elevated blood pressure, elevated blood glucose, elevated triglycerides, and reduced high-density lipoprotein cholesterol (HDL-C)] and their aggregation (with ≥ 2 of the above 5 risk factors) status. Results The detection rates of central obesity, elevated blood pressure, elevated blood glucose, elevated triglycerides, and reduced HDL-C were 61.2%, 38.2%, 29.5%, 40.9% and 22.6%, respectively. The detection rates of CMRF aggregation was 57.8%. The result of multivariable logistic regression analysis showed that male, age ≥45 years, smoking, overweight, and obesity were risk factors for CMRF aggregation (all P<0.05). Conclusion The detection rate of CMRF and its aggregation among workers with new forms of employment in Beijing City is relatively high. Targeted prevention and control efforts should be strengthened for high-risk populations, especially males, workers aged ≥45 years, smokers, and those who are overweight or obese.

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.

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.

BACKGROUND: Previous evidence showed that ambient air pollution and cardiovascular mortality are related. However, there is a lack of evidence towards the modification effect of long-term lifestyle on the association between short-term ambient air pollution and death from cardiovascular events. METHOD: A total of 14,609 death from major adverse cardiovascular events (MACE) were identified among the China Kadoorie Biobank participants from 2013 to 2018. Ambient air pollution exposure including particulate matter 2.5 (PM_2.5), SO₂, NO₂, CO, and O₃ from the same period were obtained from space-time model reconstructions based on remote sensing data. Case-crossover design and conditional logistic regression was applied to estimate the effect of short-term exposure to air pollutants on MACE mortality. RESULTS: We found MACE mortality was significantly associated with PM_2.5 (relative percent increase 2.91% per 10 µg/m³ increase, 95% CI 1.32-4.53), NO₂ (5.37% per 10 µg/m³ increase, 95% CI 1.56-9.33), SO₂ (6.82% per 10 µg/m³ increase, 95% CI 2.99-10.80), and CO (2.24% per 0.1 mg/m³ increase, 95% CI 1.02-3.48). Stratified analyses indicated that drinking was associated with elevated risk of MACE mortality with NO₂ and SO₂ exposure; physical inactivity was associated with higher risk of death from MACE when exposed to PM_2.5; and people who had balanced diet had lower risk of MACE mortality when exposed to CO and NO₂. CONCLUSIONS The study results showed that short-term exposure to ambient PM_2.5, NO₂, SO₂, and CO would aggravate the risk of cardiovascular mortality, yet healthy lifestyle conduct might mitigate such negative impact to some extent.
Humans ; Cardiovascular Diseases/epidemiology* ; China/epidemiology* ; Male ; Female ; Air Pollution/adverse effects* ; Middle Aged ; Air Pollutants/analysis* ; Particulate Matter/analysis* ; Environmental Exposure/adverse effects* ; Life Style ; Aged ; Adult ; Risk Factors ; Cross-Over Studies ; East Asian People