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.

Traditional Chinese medicine （TCM） offers a rich theoretical foundation and clinical experience for the prevention and treatment of cardiovascular-kidney-metabolic syndrome（CKM）， demonstrating unique advantage. Building on previous work in managing diabetes， its complications， and chronic kidney disease， our team has proposed a five-phase evolution theory of "constraint， heat， deficiency， stasis， and toxin" as the core pathogenesis. These phases correspond to the pathological progression of constraint of phlegm-dampness， constraint transforming into heat， heat damaging qi and yin， stasis accumulated in the collateral vessels， and toxin induced by deficiency and stasis. In the prevention and treatment of CKM by TCM， it is emphasized to integrate the concept of "treating disease before it arises" with constitution theory， and incorporate the "2-5-8" prevention and treatment strategy， which combines prevention with treatment， tailors interventions to different phases， and employs comprehensive treatment modalities. Our goal is to leverage TCM's holistic advantages in preventing and treating CKM.

Objective To explore the feasibility of long non-coding RNAs (lncRNAs) as biomarkers for radiation-induced intestinal injury. Methods Mice were exposed to 15 Gy of ⁶⁰Co γ-rays to the abdominal area. The pathological changes in intestinal tissues were analyzed at 72 h post-irradiation to confirm the successful establishment of the radiation-induced intestinal injury model. Real-time quantitative PCR was conducted to detect the expression of candidate radiation-responsive lncRNAs in the jejunum, jejunal crypts, colon tissues, and plasma of irradiated mice. Human intestinal epithelial cell line HIEC-6 and human colon epithelial cell line NCM460 were exposed to 0, 5, 10, and 15 Gy of ⁶⁰Co γ-rays. The expression levels of candidate lncRNAs were measured at 4, 24, 48, and 72 h post-irradiation to observe their changes with the irradiation dose. Results Pathological analysis showed that abdominal irradiation with 15 Gy successfully established an acute radiation-induced intestinal injury mouse model. Real-time quantitative PCR showed that Dino, Lncpint, Meg3, Dnm3os, Trp53cor1, Pvt1, and Neat1 were significantly upregulated following the occurrence of radiation-induced intestinal injury (P < 0.05). Among them, Meg3 and Dnm3os in mouse plasma were significantly upregulated (P < 0.05), while Gas5 was significantly downregulated (P < 0.05). In HIEC-6 and NCM460 cells, the expression levels of DINO, MEG3, DNM3OS, and GAS5 showed dose-dependent patterns at certain time points (P < 0.05). Conclusion The lncRNAs encoded by MEG3, DNM3OS, and GAS5 in intestinal epithelial cells are responsive to ionizing radiation. Consistent differential expression changes were detected in mouse plasma and intestinal tissues, indicating their potential as biomarkers for radiation-induced intestinal injury.

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.

This study aimed to investigate the effects of acupuncture on dynamic changes in Ca²⁺, Na⁺, and H₂O₂ flux following eccentric exercise-induced muscle injury. The total of 324 healthy male Wistar rats were randomly divided into 6 groups: control group (C), eccentric exercise group (E), eccentric exercise with acupuncture group (EA), EA with TRP channel blocker group (EAT), EA with NOX2 blocker group (EAN) and EA with placebo group (EAP). Gastrocnemius muscles were subject to lengthening contractions with percutaneous electrical stimulation, followed by immediate pretreatment with blocking agents. After 30 min, acupuncture needling was administered to the gastrocnemius muscle, and real-time dynamic changes of Ca²⁺, Na⁺ and H₂O₂ flux were measured with non-invasive micro-test technique during the needle retention period, immediately, 3 h, 6 h, and 24 h post-extraction respectively. Results showed that compared with the E group, acupuncture significantly increased net Ca²⁺ efflux (P < 0.05), extended the period of net Na⁺ influx, and significantly decreased net H₂O₂ efflux (P < 0.05). However, these effects were significantly attenuated in the EAT and EAN groups, where excessive net H₂O₂ efflux was observed (P < 0.001). These findings indicate that acupuncture regulates the dynamic changes of Ca²⁺, Na⁺ and H₂O₂ flux by activating the TRP channels and interacting with NOX2 activity following eccentric exercise-induced skeletal muscle injury.
Animals ; Muscle, Skeletal/metabolism* ; Rats, Wistar ; Rats ; Male ; Calcium/metabolism* ; Hydrogen Peroxide/metabolism* ; Physical Conditioning, Animal ; Sodium/metabolism* ; Acupuncture Therapy ; NADPH Oxidase 2

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