OBJECTIVE: To investigate the antidepressant effects of Xiaoyao Pill (XYP) by exploring its interactions with gut microbiota and tryptophan metabolism. METHODS: Utilizing network pharmacology, the functional substance groups, key targets, and pathways of XYP in the treatment of depression were identified. The chronic unpredictable mild stress (CUMS) protocol was implemented in male Sprague-Dawley rats to establish depression model. Thirty rats were randomly divided into 3 groups according to their body weight (10 for each): control, CUMS and XYP groups (1.8 g/kg). After 28-day interventions, behavioral phenotyping including sucrose preference test (SPT) and open field test (OFT) were performed. Biochemical validation encompassed enzyme-linked immunosorbent assay for serum cortisol, hematoxylin-eosin histopathology, and immunohistochemistry. Liquid chromatography-mass spectrometry was utilized to profile serum metabolites, while fecal samples underwent metagenomic sequencing for gut microbiota characterization. RESULTS: Network pharmacology studies predicted that key components can protect the nervous system by regulating inflammatory pathways through the blood-brain barrier. SPT and OFT showed that XYP treatment significantly ameliorated depressive-like behaviors (all P<0.05). XYP treatment also restored hippocampal neuronal density, increased serum neurotransmitter levels of neurotransmitters such as 5-hydroxytryptamine and vasoactive intestinal peptide, and while suppressing inflammatory markers such as tumor necrosis factor-alpha, interleukin-1 beta (IL-1 β), and IL-6 (all P<0.05). Metagenomics revealed significant restructuring of gut microbiota, notably the regulation of Parabacteroides distasonis (P<0.05). Non-targeted metabolomics analysis showed that the level of metabolites in the tryptophan and kynurenine pathway significantly changed (variable importance in the projection >1, P<0.05), and the change of metabolic flux was significantly correlated with behavioral improvement (P<0.05). CONCLUSIONS XYP exerts antidepressant effects by increasing neurotransmitter levels, reducing inflammatory makers and modulating Parabacteroides distasonis. Through further exploration of metabolomics, we found that XYP may play a protective role in depression by regulating tryptophan metabolism.
Animals ; Tryptophan/metabolism* ; Drugs, Chinese Herbal/therapeutic use* ; Gastrointestinal Microbiome/drug effects* ; Rats, Sprague-Dawley ; Depression/blood* ; Male ; Stress, Psychological/drug therapy* ; Behavior, Animal/drug effects* ; Rats ; Chronic Disease ; Hippocampus/drug effects*

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.

This paper aimed to systematically evaluate the effects of hypoxic training at different fraction of inspired oxygen (FiO₂) on body composition, glucose metabolism, and lipid metabolism in obese individuals, and to determine the optimal oxygen concentration range to provide scientific evidence for personalized and precise hypoxic exercise prescriptions. A systematic search was conducted in the Cochrane Library, PubMed, Web of Science, Embase, and CNKI databases for randomized controlled trials and pre-post intervention studies published up to March 31, 2025, involving hypoxic training interventions in obese populations. Meta-analysis was performed using RevMan 5.4 software to assess the effects of different fraction of inspired oxygen (FiO₂≤14% vs. FiO₂>14%) on BMI, body fat percentage, waist circumference, fasting blood glucose, insulin, HOMA-IR, triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C), with subgroup analyses based on oxygen concentration. A total of 22 studies involving 292 participants were included. Meta-analysis showed that hypoxic training significantly reduced BMI (mean difference (MD)=-2.29,95%CI: -3.42 to -1.17, P<0.000 1), body fat percentage (MD=-2.32, 95%CI: -3.16 to -1.47, P<0.001), waist circumference (MD=-3.79, 95%CI: -6.73 to -0.85, P=0.01), fasting blood glucose (MD=-3.58, 95%CI: -6.23 to -0.93, P=0.008), insulin (MD=-1.60, 95%CI: -2.98 to -0.22, P=0.02), TG (MD=-0.18, 95%CI: -0.25 to -0.12, P<0.001), and LDL-C (MD=-0.25, 95%CI: -0.39 to -0.11, P=0.000 3). Greater improvements were observed under moderate hypoxic conditions with FiO₂>14%. Changes in HOMA-IR (MD=-0.74, 95%CI: -1.52 to 0.04,P=0.06) and HDL-C (MD=-0.09, 95%CI: -0.21 to 0.02, P=0.11) were not statistically significant. Hypoxic training can significantly improve body composition, glucose metabolism, and lipid metabolism indicators in obese individuals, with greater benefits observed under moderate hypoxia (FiO>14%). As a key parameter in hypoxic exercise interventions, the precise setting of oxygen concentration is crucial for optimizing intervention outcomes.

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.

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.