Objective To investigate the impact of intraoperative red blood cell (RBC) transfusion volume on the postoperative oxygenation index in lung transplant recipients. Methods This retrospective study analyzed the clinical data of patients who underwent lung transplantation at Wuxi People's Hospital Affiliated to Nanjing Medical University from 2021 to 2023. Patients were divided into a non-severe primary graft dysfunction (PGD) group and a severe PGD group based on whether their postoperative oxygenation index was>200 mm Hg at 0, 24, and 48 h. General patient data and intraoperative RBC transfusion volumes were compared between the two groups. A binary logistic regression model was constructed to explore the effect size (OR and its 95%CI) of RBC transfusion volume on postoperative oxygenation status at different time points (0, 24, and 48 h). The area under the receiver operating characteristic curve was calculated to evaluate the model's diagnostic performance. Results A total of 351 patients were included (260 males, 91 females), with ages ranging from 20 to 77 years. The OR for the effect of intraoperative RBC transfusion on poor oxygenation was 1.486 (95%CI 0.982 to 2.248, P=0.061) at 0 h postoperatively, 3.111 (95%CI 1.793 to 5.399, P<0.001) at 24 h, and 1.583 (95%CI 1.026 to 2.442, P=0.038) at 48 h. This indicated that as time progressed, the postoperative oxygenation status of lung transplant recipients was affected by the intraoperative transfusion volume. Furthermore, an RBC transfusion volume>975 mLhad a significant impact on patient oxygenation at 24 and 48 h postoperatively. Conclusion The volume of intraoperative RBC transfusion has a significant impact on the oxygenation status at 24 and 48 h postoperatively. Intraoperative RBC transfusion volume is associated with the occurrence of severe PGD after lung transplantation. Controlling the volume of RBC transfusion during lung transplantation may help reduce the incidence of severe PGD.

Objective To investigate the expression and prognostic value of uncoupling protein 2(UCP2)and ubiquitin associated protein 2-like(UBAP2L)in non-small cell lung cancer(NSCLC).Methods A total of 94 patients with NSCLC who received surgical treatment in a hospital from March 2019 to March 2021 were selected as the study objects.The expressions of UCP2 protein,UBAP2L protein,UCP2 mRNA and UBAP2L mRNA in cancer tissues and adjacent tissues of NSCLC patients were detected by real-time fluorescence quan-titative polymerase chain reaction and immunohistochemistry.The correlation between UCP2 mRNA and UBAP2L mRNA in NSCLC was analyzed by Pearson correlation.Kaplan-Meier curve was used to analyze the differences in survival rates of NSCLC patients with different UCP2 mRNA and UBAP2L mRNA expres-sions,and multivariate Cox regression was used to analyze the prognostic factors of NSCLC patients.Results The positive rate of UCP2 protein and UBAP2L protein in cancer tissues of NSCLC patients was higher than that in adjacent tissues,and the difference was statistically significant(P＜0.05).The expressions of UCP2 mRNA and UBAP2L mRNA in cancer tissues of NSCLC patients were significantly higher than those in adjacent tissues,and the differences were statistically significant(P＜0.05).There was a positive cor-relation between UCP2 mRNA and UBAP2L mRNA expression in NSCLC patients(r=0.721,P＜0.001).The expression of UCP2 mRNA and UBAP2L mRNA in cancer tissues of NSCLC patients was related to TNM stage and lymph node metastasis.There was significant difference in 3-year survival rate between UCP2 mRNA high expression group and UCP2 mRNA low expression group(P＜0.05).There was significant difference in 3-year survival rate between UBAP2L mRNA high expression group and UBAP2L mRNA low expression group(P＜0.05).TNM stage Ⅲ A,lymph node metastasis,UCP2 mRNA high expression and UBAP2L mRNA high expression were risk factors for prognosis of NSCLC patients(P＜0.05).Conclusion UCP2 protein,UBAP2L protein,UCP2 mRNA and UBAP2L mRNA are up-regulated in NSCLC patients,and UCP2 mRNA and UBAP2L mRNA are helpful to evaluate the survival prognosis of NSCLC patients.

Moyamoya disease (MMD) is a chronic progressive cerebrovascular disease, and extracranial-intracranial revascularization is the most commonly treatment for MMD. With the continuous development of imaging techniques, the perioperative monitoring methods for MMD patients have gradually become more diverse. However, Doppler ultrasound still holds an important position due to its convenience and non-invasiveness. This article reviews the role of Doppler ultrasound in the perioperative assessment of patients with MMD.

Hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH) is often associated with neurological deterioration and poor prognosis. Early and accurate identification of patients at high risk for HE is crucial for guiding clinical practice. This article summarizes the imaging predictors for HE in patients with sICH, primarily focusing on non-enhanced CT and CT angiography, and briefly introduces various prediction models based on imaging features, in order to provide references for the clinical diagnosis and treatment of sICH.

Chest low-dose computed tomography (LDCT) is a widely utilized modality for lung cancer screening and follow-up in high-risk populations, owing to its low radiation dose. However, the diagnostic accuracy of LDCT is significantly constrained by inherent limitations, including elevated image noise and reduced contrast resolution. The potential for deep learning technologies to address these challenges through data-driven LDCT image denoising approaches has been demonstrated. In this review, the advantages and limitations of deep learning models were introduced, including supervised, unsupervised, and self-supervised learning. The potential and challenges of these models in clinical applications were analyzed, thereby providing a reference for subsequent research and clinical practice.

Inflammatory bowel disease(IBD)is a chronic and recurrent intestinal disease,and has become a major global health issue.Individuals with IBD face an elevated risk of developing colorectal cancer(CRC),and recent studies have indicated that mitochondrial dysfunction plays a pivotal role in the pathogenesis of both IBD and CRC.This review covers the pathogenesis of IBD and CRC,focusing on mitochondrial dysfunction,and explores pharmacological targets and strategies for addressing both conditions by modulating mitochondrial function.Additionally,recent advancements in the phar-macological modulation of mitochondrial dysfunction for treating IBD and CRC,encompassing mitochondrial damage,release of mitochondrial DNA(mtDNA),and impairment of mitophagy,are thoroughly summarized.The review also provides a systematic overview of natural compounds(such as flavonoids,alkaloids,and diterpenoids),Chinese medicines,and intestinal microbiota,which can alleviate IBD and attenuate the progression of CRC by modulating mitochondrial function.In the future,it will be imperative to develop more practical methodologies for real-time monitoring and accurate detection of mitochondrial function,which will greatly aid scientists in identifying more effective agents for treating IBD and CRC through modulation of mitochondrial function.

Objective To observe the efficacy of nomogram model based on enhanced MRI radiomics,deep learning(DL)and clinical features for differentiating spinal tuberculosis and pyogenic spondylitis.Methods Totally 59 cases of spinal tuberculosis and 66 of pyogenic spondylitis were retrospectively enrolled.Radiomics,DL and clinical features relevant to differentiating spinal tuberculosis and pyogenic spondylitis were selected.Then a predictive model was constructed using logistic regression based on the selected optimal features,and a comprehensive nomogram model was developed through combination of the above features.The effectiveness of these models for distinguishing spinal tuberculosis from pyogenic spondylitis were visualized based on receiver operating characteristic curves,calidration curves and decision curves.Results The nomogram model demonstrated the highest area under the curve(AUC)in both training set and test set,with AUC of 0.997 and 0.920,respectively.In test set,DeLong test indicated that the difference of AUC between the nomogram model and clinical model was significant(P=0.002),while no significant difference was observed between the nomogram model and the other models(all P＞0.05).The nomogram model provided the highest overall net benefit and exhibited good calibration for distinguishing spinal tuberculosis from pyogenic spondylitis.Conclusion Nomogram model based on enhanced MRI radiomics,DL and clinical features demonstrated high efficacy for differentiating spinal tuberculosis from pyogenic spondylitis.

Cholangiocarcinoma has an extremely poor prognosis, and the efficacy of existing treatment methods is limited. In the highly desmoplastic tumor microenvironment of cholangiocarcinoma, cancer-associated fibroblasts (CAFs) are the core regulators. Their significant heterogeneity and complex intercellular crosstalk network are not only key factors driving cholangiocarcinoma progression and drug resistance, but also highly promising therapeutic targets. This review focuses on the characteristics of CAFs in cholangiocarcinoma and the key crosstalk mechanisms between CAFs and tumor cells as well as immune cells, and summarizes the research progress and limitations of current therapeutic strategies targeting CAFs.

Objective:To explore the impact of body constitutional metabolic phenotype on the outcomes of hypertensive intracerebral hemorrhage (HICH) patients one year after onset.Methods:This study retrospectively studied the clinical data of 467 HICH patients admitted to the First People's Hospital of Lianyungang City from May 2021 to May 2023. Based on telephone follow-up after one year, the patients were categorized into two groups: a good outcome group (287 cases) and a poor outcome group (180 cases). According to the patients' body mass index (BMI) and metabolic status, the population was divided into six phenotypes: metabolically healthy with normal weight (MH-NW), metabolically healthy with overweight (MH-OW), metabolically healthy obesity (MHO), metabolically unhealthy normal weight (MU-NW), metabolically unhealthy with overweight (MU-OW), and metabolically unhealthy with obesity (MUO). The baseline data of the two groups were compared between two groups. The influencing factors of adverse outcomes in patients with HICH one year after onset were analyzed. Quantitative data that conforms to normal distribution were represented by xˉ±s, and independent sample t-test was used for comparison between two groups; The measurement data of skewed distribution was represented by M ( Q1, Q3), and Mann Whitney U test was used for comparison between the two groups; Count data was presented as an example (%), and comparison between groups was conducted using the χ2 test. Multivariate logistic regression analysis was used to analyze the influencing factors of poor prognosis in HICH patients one year after onset. Results:BMI, high density lipoprotein cholesterol(HDL-C) levels and baseline Glasgow coma score(GCS) score in the poor outcome group were lower than those in the good outcome group [23.8 (22.4, 26.1) kg/m 2 vs. 25.0 (22.5, 27.4) kg/m 2, Z=-2.31, P=0.021; 1.1 (1.0,1.4) mmol/L vs. 1.3 (1.0,1.6) mmol/L, Z=-4.18, P<0.001; 14 (13,15) score vs. 10 (7,13) score, Z=-10.20, P<0.001]. The incidence of hemorrhage into the ventricle, cerebral hernia, pulmonary infection and hydrocephalus [43.3%(78/180) vs. 23.7% (68/287). 5.6%(10/180) vs. 0.7% (2/287), 48.9%(88/180) vs. 6.6% (19/287), 5.0%(9/180) vs. 1.4% (4/287), χ2=19.86, P<0.001, χ2=10.43, P<0.001, χ2=111.90, P<0.001, χ2=5.32, P=0.021], proportion of surgical removal of hematoma [41.1%(74/180) vs. 19.5% (56/287), χ2=25.69, P<0.001], systolic blood pressure [158 (141,173) mmHg vs. 152 (138,169) mmHg, Z=-2.18, P=0.029] and fasting blood glucose [6.9 (5.7,8.2) mmol/L vs. 6.3 (5.4,7.8) mmol/L, Z=-2.08, P=0.038] were higher than those in good outcome group. The metabolic phenotypes in the poor conversion group were as follows: 41 cases (22.8%) of MH-NW, 23 cases (12.8%) of MH-OW, 9 cases (5.0%) of MHO, 54 cases (30.0%) of MU-NW, 33 cases (18.3%) of MU-OW, and 20 cases (11.1%) of MUO. Conversely, the metabolic phenotypes in the good conversion group were as follows: 67 cases (23.3%) of MH-NW, 77 cases (26.8%) of MH-OW, 31 cases (10.8%) of MHO, 40 cases (13.9%) of MU-NW, 46 cases (16.0%) of MU-OW, and 26 cases (9.1%) of MUO. Regarding metabolic types, the poor conversion group comprised 73 healthy cases (40.6%) and 107 unhealthy cases (59.4%), whereas the good conversion group had 177 healthy cases (61.7%) and 110 unhealthy cases (38.3%). In terms of body mass, the poor conversion group included 94 cases (52.2%) of normal weight, 57 cases (31.7%) of overweight, and 29 cases (16.1%) of obesity. Conversely, the good conversion group had 108 cases (37.6%) of normal weight, 122 cases (42.5%) of overweight, and 57 cases (19.9%) of obesity.There were statistically significant differences in the composition ratios of physical metabolic phenotype, metabolic type, and xBMI type between the two groups of patients ( χ2=29.56, P<0.001, χ2=19.83, P<0.001, χ2=9.68, P=0.008). Multivariate Logistic regression analysis showed that after adjusting for other risk factors related to the prognosis of HICH, HDL-C ( OR=0.30, 95% CI: 0.12-0.75, P=0.010), admission GCS score ( OR=0.71, 95% CI:0.64-0.79, P<0.001), MH-OW ( OR=0.38, 95% CI: 0.17-0.82, P=0.013) and MHO ( OR=0.30, 95% CI:0.09-0.99, P=0.048) were all protective factors for adverse outcomes in patients with HICH 1 year after the onset of the disease, and hemorrhage into the ventricle ( OR=2.46, 95% CI:1.41-4.32, P=0.002) and pulmonary infection ( OR=9.13, 95% CI: 4.78- 17.44, P<0.001) were risk factors for adverse outcomes. Conclusions:MH-OW and MHO are beneficial to the prognosis of HICH patients 1 year after the onset of HICH. The secondary prevention of HICH patients should pay attention to the BMI level and comprehensive metabolic status of the patients.

Objective:To analyze the differentially expressed messenger RNA(mRNA), microRNA(miRNA), and long non-coding RNA(lncRNA)during the browning of mouse subcutaneous adipose tissue, construct a competitive endogenous RNA(ceRNA)network, and provide a theoretical basis for investigating the regulatory mechanisms of white adipose tissue browning.Methods:A cold-stimulated mouse model was established for transcriptome sequencing.Bioinformatics tools were employed to screen for differentially expressed mRNAs, miRNAs, and lncRNAs.An integrated mRNA-miRNA-lncRNA analysis was performed to construct a ceRNA network.Gene Ontology(GO), Kyoto Encyclopedia of Genes and Genomes(KEGG), and Gene Set Enrichment Analysis(GSEA)were conducted on the differentially expressed mRNAs and ceRNA networks to explore transcriptional regulation during the cold-induced browning of subcutaneous adipose tissue.Results:Transcriptomic analysis of the cold-stimulated model identified 4, 256 differentially expressed RNAs, which include 3, 600 mRNAs, 588 lncRNAs, and 68 miRNAs.GO and KEGG analyses revealed that the browning of white adipose tissue involves immune-related processes, such as immune system processes, immune responses, adaptive and innate immune responses, and the positive regulation of T-cell activation.A ceRNA network associated with browning regulation was constructed, comprising 233 nodes(188 mRNAs, 34 miRNAs, and 11 lncRNAs)and 351 edges.Protein-protein interaction(PPI)analysis of the mRNAs within the ceRNA network highlighted pathways including apoptosis, intracellular signaling transduction, hypoxia-inducible factor-1(HIF-1), AMP-activated protein kinase(AMPK), Janus kinase-signal transducer and activators of transcription(JAK-STAT)signaling, carbon metabolism, glycolysis, and thyroid hormone pathways, all of which regulate lipid metabolism, hypoxia, and glycolysis.Cytohubba analysis identified the top 10 hub genes: Bcl2, Src, Cebpb, Creb1, Runx1, Foxo3, Ets1, Socs3, Slc2 a4, and Pkm. Conclusions:The ceRNA network that regulates the browning of white adipose tissue is involved in various pathways, including carbon metabolism, glycolysis, thyroid hormone signaling, growth hormone signaling, prolactin signaling, as well as the HIF-1, AMPK, and JAK-STAT pathways.Key regulatory miRNAs in this context include miR-30e-5p, miR-182-5p, miR-20b-5p, miR-144-3p, miR-363-3p, miR-141-3p, miR-203-3p, and miR-107-3p.These miRNAs may serve as critical targets for inducing browning in response to cold exposure.