Objective To explore the feasibility and reference value of allogeneic lung transplantation and postoperative monitoring in miniature pigs for lung transplantation research. Methods Two miniature pigs (R1 and R2) underwent left lung allogeneic transplantation. Complement-dependent cytotoxicity tests and blood cross-matching were performed before surgery. The main operative times and partial pressure of arterial oxygen (PaO₂) after opening the pulmonary artery were recorded during surgery. Postoperatively, routine blood tests, biochemical blood indicators and inflammatory factors were detected, and pathological examinations of multiple organs were conducted. Results The complement-dependent cytotoxicity test showed that the survival rate of lymphocytes between donors and recipients was 42.5%-47.3%, and no agglutination reaction occurred in the cross-matching. The first warm ischemia times of D1 and D2 were 17 min and 10 min, respectively, and the cold ischemia times were 246 min and 216 min, respectively. Ultimately, R1 and R2 survived for 1.5 h and 104 h, respectively. Postoperatively, in R1, albumin (ALB) and globulin (GLB) decreased, and alanine aminotransferase increased; in R2, ALB, GLB and aspartate aminotransferase all increased. Urea nitrogen and serum creatinine increased in both recipients. Pathological results showed that in R1, the transplanted lung had partial consolidation with inflammatory cell infiltration, and multiple organs were congested and damaged. In R2, the transplanted lung had severe necrosis with fibrosis, and multiple organs had mild to moderate damage. The expression levels of interleukin-1β and interleukin-6 increased in the transplanted lungs. Conclusions The allogeneic lung transplantation model in miniature pigs may systematically evaluate immunological compatibility, intraoperative function and postoperative organ damage. The data obtained may provide technical references for subsequent lung transplantation research.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

ObjectiveThe study explores the influence of voluntary wheel running on the behavioral abnormalities and the activation state of the hypothalamic-pituitary-adrenal (HPA) axis in autism spectrum disorder (ASD) rats through gut microbiota. MethodsSD female rats were selected and administered either400 mg/kg of valproic acid (VPA) solution or an equivalent volume of saline via intraperitoneal injection on day 12.5 of pregnancy. The resulting offspring were divided into 2 groups: the ASD model group (PASD, n=35) and the normal control group (PCON, n=16). Behavioral assessments, including the three-chamber social test, open field test, and Morris water maze, were conducted on postnatal day 23. After behavioral testing, 8 rats from each group (PCON, PASD) were randomly selected for serum analysis using enzyme-linked immunosorbent assay (ELISA) to measure corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and corticosterone (CORT) concentration, to evaluate the functional state of the HPA axis in rats. On postnatal day 28, the remaining 8 rats in the PCON group were designated as the control group (CON, n=8), and the remaining 27 rats in the PASD group were randomly divided into 4 groups: ASD non-intervention group (ASD, n=6), ASD exercise group (ASDE, n=8), ASD fecal microbiota transplantation group (FMT, n=8), and ASD sham fecal microbiota transplantation group (sFMT, n=5). The rats in the ASD group and the CON group were kept under standard conditions, while the rats in the ASDE group performed 6 weeks of voluntary wheel running intervention starting on postnatal day 28. The rats in the FMT group were gavaged daily from postnatal day 42 with 1 ml/100 g fresh fecal suspension from ASDE rats which had undergone exercise for 2 weeks, 5 d per week, continuing for 4 weeks. The sFMT group received an equivalent volume of saline. After the interventions were completed, behavioral assessments and HPA axis markers were measured for all groups. ResultsBefore the intervention, the ASD model group exhibited significantly reduced social ability, social novelty preference, spontaneous activity, and exploratory interest, as well as impaired spatial learning, memory, and navigation abilities compared to the normal control group (P<0.05). Serum concentration of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and corticosterone (CORT) in the PASD group were significantly higher than those in the PCON group (P<0.05). Following 6 weeks of voluntary wheel running, the ASDE group showed significant improvements in social ability, social novelty preference, spontaneous activity, exploratory interest, spatial learning, memory, and navigation skills compared to the ASD group (P<0.05), with a significant decrease in serum CORT concentration (P<0.05), and a downward trend in CRH and ACTH concentration. After 4 weeks of fecal microbiota transplantation in the exercise group, the FMT group showed marked improvements in social ability, social novelty preference, spontaneous activity, exploratory interest, as well as spatial learning, memory, and navigation abilities compared to both the ASD and sFMT groups (P<0.05). In addition, serum ACTH and CORT concentration were significantly reduced (P<0.05), and CRH concentration also showed a decreasing trend. ConclusionExercise may improve ASD-related behaviors by suppressing the activation of the HPA axis, with the gut microbiota likely playing a crucial role in this process.

As China accelerates its efforts in deep space exploration and long-duration space missions, including the operationalization of the Tiangong Space Station and the development of manned lunar missions, safeguarding astronauts’ physiological and cognitive functions under extreme space conditions becomes a pressing scientific imperative. Among the multifactorial stressors of spaceflight, microgravity emerges as a particularly potent disruptor of neurobehavioral homeostasis. Dopamine (DA) plays a central role in regulating behavior under space microgravity by influencing reward processing, motivation, executive function and sensorimotor integration. Changes in gravity disrupt dopaminergic signaling at multiple levels, leading to impairments in motor coordination, cognitive flexibility, and emotional stability. Microgravity exposure induces a cascade of neurobiological changes that challenge dopaminergic stability at multiple levels: from the transcriptional regulation of DA synthesis enzymes and the excitability of DA neurons, to receptor distribution dynamics and the efficiency of downstream signaling pathways. These changes involve downregulation of tyrosine hydroxylase in the substantia nigra, reduced phosphorylation of DA receptors, and alterations in vesicular monoamine transporter expression, all of which compromise synaptic DA availability. Experimental findings from space analog studies and simulated microgravity models suggest that gravitational unloading alters striatal and mesocorticolimbic DA circuitry, resulting in diminished motor coordination, impaired vestibular compensation, and decreased cognitive flexibility. These alterations not only compromise astronauts’ operational performance but also elevate the risk of mood disturbances and motivational deficits during prolonged missions. The review systematically synthesizes current findings across multiple domains: molecular neurobiology, behavioral neuroscience, and gravitational physiology. It highlights that maintaining DA homeostasis is pivotal in preserving neuroplasticity, particularly within brain regions critical to adaptation, such as the basal ganglia, prefrontal cortex, and cerebellum. The paper also discusses the dual-edged nature of DA plasticity: while adaptive remodeling of synapses and receptor sensitivity can serve as compensatory mechanisms under stress, chronic dopaminergic imbalance may lead to maladaptive outcomes, such as cognitive rigidity and motor dysregulation. Furthermore, we propose a conceptual framework that integrates homeostatic neuroregulation with the demands of space environmental adaptation. By drawing from interdisciplinary research, the review underscores the potential of multiple intervention strategies including pharmacological treatment, nutritional support, neural stimulation techniques, and most importantly, structured physical exercise. Recent rodent studies demonstrate that treadmill exercise upregulates DA transporter expression in the dorsal striatum, enhances tyrosine hydroxylase activity, and increases DA release during cognitive tasks, indicating both protective and restorative effects on dopaminergic networks. Thus, exercise is highlighted as a key approach because of its sustained effects on DA production, receptor function, and brain plasticity, making it a strong candidate for developing effective measures to support astronauts in maintaining cognitive and emotional stability during space missions. In conclusion, the paper not only underscores the centrality of DA homeostasis in space neuroscience but also reflects the authors’ broader academic viewpoint: understanding the neurochemical substrates of behavior under microgravity is fundamental to both space health and terrestrial neuroscience. By bridging basic neurobiology with applied space medicine, this work contributes to the emerging field of gravitational neurobiology and provides a foundation for future research into individualized performance optimization in extreme environments.

ObjectiveThis study aims to develop and validate a novel multimodal predictive model， termed criss-cross network（CCNet）-directed graph neural network（DGNN）（CGN）， for accurate assessment of pulmonary nodule progression in high-risk individuals for lung cancer， by integrating longitudinal chest computed tomography（CT） imaging with both traditional Chinese and western clinical evaluation data. MethodsA cohort of 4 432 patients with pulmonary nodules was retrospectively analyzed. A twin CCNet was employed to extract spatiotemporal representations from paired sequential CT scans. Structured clinical assessment and imaging-derived features were encoded via a multilayer perceptron， and a similarity-based alignment strategy was adopted to harmonize multimodal imaging features across temporal dimensions. Subsequently， a DGNN was constructed to integrate heterogeneous features， where nodes represented modality-specific embeddings and edges denoted inter-modal information flow. Finally， model optimization was performed using a joint loss function combining cross-entropy and cosine similarity loss， facilitating robust classification of nodule progression status. ResultsThe proposed CGN model demonstrated superior predictive performance on the held-out test set， achieving an area under the receiver operating characteristic curve（AUC） of 0.830， accuracy of 0.843， sensitivity of 0.657， specificity of 0.712， Cohen's Kappa of 0.417， and F₁ score of 0.544. Compared with unimodal baselines， the CGN model yielded a 36%-48% relative improvement in AUC. Ablation studies revealed a 2%-22% increase in AUC when compared to simplified architectures lacking key components， substantiating the efficacy of the proposed multimodal fusion strategy and modular design. Incorporation of traditional Chinese medicine （TCM）-specific symptomatology led to an additional 5% improvement in AUC， underscoring the complementary value of integrating TCM and western clinical data. Through gradient-weighted activation mapping visualization analysis， it was found that the model's attention predominantly focused on nodule regions and effectively captured dynamic associations between clinical data and imaging-derived features. ConclusionThe CGN model， by synergistically combining cross-attention encoding with directed graph-based feature integration， enables effective alignment and fusion of heterogeneous multimodal data. The incorporation of both TCM and western clinical information facilitates complementary feature enrichment， thereby enhancing predictive accuracy for pulmonary nodule progression. This approach holds significant potential for supporting intelligent risk stratification and personalized surveillance strategies in lung cancer prevention.

This study aims to investigate the effects of renin inhibitor aliskiren on kidney injury in human angiotensinogen-renin (AGT-REN) double transgenic hypertensive (dTH) mice and explore its possible mechanism. The dTH mice were divided into hypertension group (HT group) and aliskiren intervention group (HT+Aliskiren group), while wild-type C57BL/6 mice were served as the control group (WT group). Blood pressure data of mice in HT+Aliskiren group were collected after 28 d of subcutaneous penetration of aliskiren (20 mg/kg), and the damage of renal tissue structure and collagen deposition were observed by HE, Masson and PAS staining. The ultrastructure of kidney was observed by transmission electron microscope. Coomassie bright blue staining and biochemical analyzer were used to detect renal function injury. The expression of renin-angiotensin system (RAS) was determined by ELISA and immunohistochemistry. The contents of superoxide dismutase (SOD) and malondialdehyde (MDA) in kidney were determined by chemiluminescence method. The content of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase subunit p47^phox, inducible nitric oxide synthase (iNOS), 3-nitrotyrosine (3-NT), NADPH oxidase 2 (NOX2) and NADPH oxidase 4 (NOX4) were detected by Western blot analysis. The results showed that compared with WT group, the blood pressure of mice in HT group was significantly increased. The renal tissue structure in HT group showed glomerular sclerosis, severe interstitial tubular injury, and increased collagen deposition. In addition, 24 h urinary protein, serum creatinine and urea levels increased. Serum and renal tissue levels of angiotensin II (Ang II) were increased, serum angiotensin-(1-7) [Ang-(1-7)] expression was decreased, and renal Ang-(1-7) expression was elevated. The expressions of ACE, Ang II type 1 receptor (AT₁R) and MasR in renal tissue were increased, while the expression of ACE2 was decreased. MDA content increased, SOD content decreased, and the expressions of p47^phox, iNOS, 3-NT, NOX2 and NOX4 were increased. However, aliskiren reduced blood pressure in dTH mice, improved renal structure and renal function, reduced Ang II and Ang-(1-7) levels in serum and renal tissue, reduced the expression of ACE and AT₁R in renal tissue, increased the expression of ACE2 and MasR in renal tissue, and decreased the above levels of oxidative stress indexes in dTH mice. These results suggest that aliskiren may play a protective role in hypertensive renal injury by regulating the balance between ACE-Ang II-AT₁R and ACE2-Ang-(1-7)-MasR axes and inhibiting oxidative stress.
Animals ; Fumarates/therapeutic use* ; Mice ; Renin/antagonists & inhibitors* ; Amides/therapeutic use* ; Mice, Inbred C57BL ; Hypertension/physiopathology* ; Mice, Transgenic ; Kidney/pathology* ; Angiotensinogen/genetics* ; Renin-Angiotensin System/drug effects* ; NADPH Oxidases/metabolism* ; Male ; Antihypertensive Agents/pharmacology* ; Humans ; Superoxide Dismutase/metabolism* ; NADPH Oxidase 4