Radix Astragali (RA) herb with warm property and significant “tonifying qi” effects is indicated for the syndrome of internal cold due to Yang deficiency. The purpose of this research was to explore effects of Radix Astragali (RA) through PPAR signaling pathway on gene expression profiles related to energy metabolism in rats with the Yang‐deficiency cold (YDC) syndrome, for identifying the pathological mechanism of Yang‐deficiency cold (YDC) syndrome and the effects mechanism of RA. The results indicated that RA could significantly increase body weight (BM), cold and heat tendency (CT), overall temperature (OT), rectum temperature (RT), toe temperature (TT), energy intake (EI), and V(O₂)/V(CO₂) ratio (which indicates basal metabolism, BM) (P<0.05), enhancing the depressed metabolic function in YDC syndrome model rat. Our data also indicated differentially expressed genes (DEGs) related to energy metabolism involving lipids, carbohydrates, and amino acids metabolic process; the expression of CPT‐1 and FABP4 (ap2) was improved; PPAR, Glycolysis, Wnt, cAMP, MAPK, AMPK, and fatty acid degradation signaling pathway may be related to energy metabolism. However, the Chinese herbal medicine RA plays a certain role in promoting the metabolism of substances and energy in rats by its warming and beneficial effect. Our results suggest that the mechanism underlying the function of RA may take effect through the regulation of PPAR signaling pathway and related gene expression. Lipids, carbohydrates, and amino acids metabolic process may be affected to adjust the reduced metabolic function in the model animals. In general, results indicate that RA could promote energy metabolism in rats with the YDC syndrome via PPAR signaling pathway regulating the expression of CPT‐1 and FABP4 (ap2), which reflected the warm and qi tonifying properties of RA.

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The prevalence of diabetes and its complications is an important global health problem. Diabetic retinopathy (DR), a common and specific microvascular complication of diabetes, has seriously affected socioeconomic factors and individual quality of life. This cross-sectional study utilized data from NHANES 1999–2020 to investigate the association between family income to poverty ratio (PIR) and DR prevalence among U.S. adults aged 20 years and above. Data from the National Health and Nutrition Examination Survey (NHANES) in 1999–2020, which included 39,210 participants, were utilized for this study. They were divided into two groups according to PIR < 5 or PIR ≥ 5. The baseline characteristics were described to show the distribution of each characteristic. Multiple linear regression and curve fitting were used to study the linear and nonlinear correlations between PIR and DR prevalence. Subgroup analysis and interaction tests were performed to test the stability of the relationship between PIR and DR prevalence. ROC analysis was performed to evaluate predictive performance. Among 39,210 adults aged 20 years and above, 31,907 (81.37%) were in Group 1 (PIR < 5), and 7,303 (18.63%) were in Group 2 (PIR ≥ 5). Compared with the prevalence of DR in Group 1 (2.7%), the prevalence of DR in Group 2 (1.5%) was lower. After adjusting for all covariates, a significant negative correlation was found between PIR and the prevalence of DR (OR: 0.91, 95% CI: [0.86 ~ 0.97], p = 0.002). Non-Hispanic Whites showing significant benefit (OR: 0.87, 95% CI: [0.80 ~ 0.96], p = 0.005, P for interaction = 0.017). After adjusting for age, the ROC analysis demonstrated a discriminative ability of the model with AUC of 0.747(95% CI: [0.74 ~ 0.76]). The results of this study indicate that an increase in PIR is correlated with a decrease in the likelihood of DR occurrence, suggesting its potential application value as an indicator for predicting the likelihood of DR occurrence. More screening and attention should be provided to low-income populations to reduce preventable social burdens.

Empty sella (ES) involves herniation of the pituitary fossa, leading to pituitary flattening. While typically associated with central hypothyroidism, its co-occurrence with hyperthyroidism is rarely reported and often overlooked. We report a rare case of hyperthyroidism in a patient with ES. The patient was diagnosed with ES combined with Graves’ disease (GD) and Hashimoto’s thyroiditis (HT). Her pituitary gland appeared flattened with a depressed upper edge. The gland height was approximately 2.3 mm. Abnormal thyroid function in this case may result from the combined effects of all three conditions. A literature search on PubMed revealed a possible association between ES and autoimmune thyroid disease; however, only seven relevant studies were identified, and no standardized diagnostic or treatment protocols exist. Hyperthyroidism may mask the diagnosis of ES. In patients whose thyroid function does not normalize with conventional oral antithyroid medication, the possibility of ES should be considered. When ES is associated with primary hyperthyroidism, antithyroid drug dosages should be lower than conventional doses. Thyroid function should be monitored more frequently, and medication dosages should be adjusted promptly.

To evaluate the diagnostic accuracy of machine learning‐assisted magnetic resonance imaging (MRI) in detecting cognitive impairment among Parkinson’s disease (PD) patients through a systematic review and meta‐analysis. We systematically searched for studies that applied machine learning algorithms to MRI data for diagnosing PD with mild cognitive impairment (PD‐MCI). Data were extracted and synthesized to calculate pooled sensitivity, specificity, positive likelihood ratio (PLR) and negative diagnostic likelihood ratio (NLR), and diagnostic odds ratios (DOR). A bivariate random‐effects model and summary receiver operating characteristic (SROC) curves were employed for statistical analysis. The quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS‐2) instrument. The publication bias was investigated through Deeks’ funnel plot. All statistical analyses were conducted using Stata 14.0. The pooled sensitivity and specificity for diagnosing PD‐MCI using machine learning‐assisted MRI were 0.82 (95% CI: 0.75–0.87) and 0.81 (95% CI: 0.73–0.87), respectively. The PLR was 4.28 (95% CI: 2.93–6.27), and the NLR was 0.23 (95% CI: 0.16–0.32), indicating a high diagnostic accuracy. The area under the curve (AUC) for the SROC was 0.85 (95% CI: 0.82–0.88). Quality assessment using the QUADAS‐2 tool showed a predominantly low risk of bias among the studies, and the Deeks’ funnel plot suggested no significant publication bias ( p = 0.30). In summary, the MRI combined with machine learning for diagnosing PD‐MCI achieved high accuracy with the pooled sensitivity of 82% and specificity of 81%.