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.

In 2022， Hainan provincial government launched the project for the prevention and control of viral hepatitis with the goals of a hepatitis B screening rate of 90%， a diagnostic rate of 90%， and a treatment rate of 80% among people aged 18 years and above by the year 2025， and the main intervention measures include population-based prevention， case screening， antiviral therapy， and health management. As of December 31， 2024， a total of 6.875 million individuals in the general population had been screened for hepatitis B， with a screening rate of 95.6%. A total of 184 710 individuals with positive HBsAg were identified， among whom 156 772 were diagnosed through serological reexamination， resulting in a diagnostic rate of 84.9%. A total of 50 742 patients with chronic hepatitis B were identified， among whom 42 921 had hepatitis B-specific health records established for health management， with a file establishment rate of 84.6%. A total of 31 553 individuals received antiviral therapy， with a treatment rate of 62.2%. A total of 2.503 million individuals at a high risk of hepatitis C were screened， among whom 4 870 tested positive for HCV antibody and 3 858 underwent HCV RNA testing， resulting in a diagnostic rate of 79.2%， and 1 824 individuals with positive HCV RNA were identified， among whom 1 194 received antiviral therapy， with a treatment rate of 65.5%. In addition， 159 301 individuals with negative HBsAg and anti-HBs and an age of 20 — 40 years were inoculated with hepatitis B vaccine free of charge. Through the implementation of the project for the prevention and control of viral hepatitis， a large number of hepatitis patients have been identified， treated， and managed in the province within a short period of time， which significantly accelerates the efforts to eliminate the crisis of viral hepatitis.

Objective:To investigate the value of coronary CTA (CCTA)-based traditional features and radiomics of plaque in the identification of culprit lesions that caused acute myocardial infarction (AMI).Methods:This was a retrospective multicenter study. From July 2016 to November 2023, a total of 344 patients from the Affiliated Hospital of Qingdao University (training cohort, n=184), Shandong Provincial Hospital Affiliated to Shandong First Medical University (validation cohort, n=88) and Qilu Hospital of Shandong University (test cohort, n=72) who received percutaneous coronary intervention (PCI) due to AMI and underwent CCTA within 48 hours of AMI were enrolled. The culprit plaques and non-culprit plaques were identified using a combination of electrocardiogram, CCTA, and angiographic findings. The vessel, plaque location, plaque type, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score, high-risk plaque characteristics, plaque length, plaque volume, and burden were analyzed, and 1 904 radiomics features were extracted for each plaque. The traditional imaging model, the radiomics model, and the combined model were established by using multivariate Logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model in identifying culprit lesions. The DeLong test was used for the comparison of AUC between every two models. The net reclassification index (NRI) was used to evaluate the incremental value of the combined model to the traditional imaging model and the radiomics model. The decision curve analysis (DCA) was used to assess the clinical net benefit of these models. A correlation heatmap was used to evaluate the correlation between the radiomics score and traditional CCTA factors. The interpretable analysis of the decision process of the combined model was performed by the Shapley Additive exPlanations (SHAP). Results:In the validation cohort and the test cohort, the AUC of the traditional imaging model developed by the vessel, plaque type, positive remodeling and CAD-RADS score was 0.898 (95% CI 0.869-0.922) and 0.881 (95% CI 0.848-0.910), respectively. The radiomics model developed by six radiomics features was 0.863 (95% CI 0.831-0.891) and 0.863 (95% CI 0.827-0.864), respectively. The AUC of the combined model was 0.930 (95% CI 0.905-0.950)and 0.919 (95% CI 0.889-0.942), respectively. In the validation cohort and the test cohort, the AUC of the combined model was higher than that of the traditional imaging model ( Z=4.013, 4.272, P<0.001) and that of the radiomics model ( Z=4.819, 3.784, P<0.001), respectively. In the validation cohort, the combined model yielded an NRI of 20.43% (95% CI 10.43%-30.44%, P<0.001) and 20.21% (95% CI 9.62%-30.80%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. In the test cohort, the combined model yielded an NRI of 28.05% (95% CI 16.72%-39.38%, P<0.001) and 23.57% (95% CI 13.58%-33.56%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. DCA showed the combined model had the highest clinical net benefit. The correlation heatmap showed the radiomics score was not correlated or only weakly correlated with traditional CCTA factors. SHAP indicated the radiomics and CAD-RADS score contributed significantly to the model. Conclusion:The CCTA-based traditional features and radiomics of plaque have favorable performance for the identification of culprit plaques in patients with AMI.

Objective:To develop a deep learning model based on 3D super-resolution reconstruction technology and to analyze its feasibility and effectiveness in predicting paraglottic space invasion in hypopharyngeal cancer.Methods:A retrospective study was conducted involving 382 patients with hypopharyngeal squamous cell carcinoma treated at Qilu Hospital of Shandong University between January 2014 and December 2020. The cohort included 364 males and 18 females, with a mean age of 62±7 years. Patients were divided into a training set ( n=300) and a test set ( n=82) based on enrollment time. A generative adversarial network was used to perform 3D super-resolution reconstruction on contrast-enhanced CT images, improving spatial resolution by 16 times. A 2.5D deep learning strategy was employed to construct Resnet-NR and Resnet-SR models based on conventional and super-resolution images, respectively, to predict whether the paraglottic space was invaded. Model performance was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC). A multi-reader multi-case study was conducted to assess the impact of the artificial intelligence (AI) model on clinicians′ diagnostic capabilities. Results:The super-resolution model Resnet-SR achieved the highest accuracy in both the training set (AUC=0.87, 95% CI: 0.84-0.90) and the test set (AUC=0.88, 95% CI: 0.81-0.96), significantly outperforming traditional clinical indicators (T stage, N stage, tumor diameter, and pathological differentiation degree) (AUC range: 0.55-0.70, all P<0.05). In comparison, the conventional-resolution model Resnet-NR achieved AUCs of 0.81 (95% CI: 0.77-0.84, P=0.005) and 0.80 (95% CI: 0.71-0.89, P=0.184) in the training and test sets, respectively. Using Resnet-SR to assist clinical decision-making improved the diagnostic accuracy of junior physicians (AUC=0.793 without AI assistance vs. AUC=0.871 with AI assistance, P=0.012) and significantly reduced diagnosis time for clinicians of all experience levels (86.5 s without AI assistance vs. 82.5 s with AI assistance, t=2.01, P=0.032). Conclusion:This study successfully develops a deep learning model based on 3D super-resolution reconstruction technology, which can assist in preoperative prediction of paraglottic space invasion in hypopharyngeal cancer. The AI-assisted tool improves diagnostic accuracy for junior physicians and enhances diagnostic efficiency for clinicians across all experience levels.

Objective To analyze the acupoint selection law of acupuncture and moxibustion for postherpetic neuralgia(PHN)with R language data mining technology.Methods The clinical research literature on acupuncture and moxibustion treatment of PHN included in CNKI,Wanfang Data,VIP and CBM from January 1,2010 to July 1,2023 was retrieved,and the database was established by Excel 2016.R language was used to statistically analyze the frequency of acupoint usage,meridians,locations,specific acupoints,etc.Through association rule analysis and clustering analysis,the characteristics and law of acupoint selection for acupuncture and moxibustion treatment of PHN were obtained.Results A total of 198 articles were included,including 83 acupoints,with a total frequency of 714 times.The high-frequency acupoints include Ashi acupoint,Jiaji acupoint and Yanglingquan.The commonly used meridians were gallbladder meridian,spleen meridian and large intestine meridiam.The acupoints were mostly in the upper and lower limbs,with the Wushu acupoints,Yuan acupoints and Xiahe acupoints being the most common.The core acupoint was Ashi acupoint,Jiaji acupoint,Hegu,Quchi,and 9 sets of association rules and 5 effective clusters were obtained.Conclusion The most commonly used acupoints for acupuncture and moxibustion treatment of PHN are Ashi acupoint,Jiaji acupoint,Hegu and Quchi,which mainly follow the principle of combining local acupoint selection with distal acupoint selection.

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.

Objective:This study analyzed the clinical characteristics and early mortality risk factors in patients with hyperleukocytic acute leukemia (HAL) to provide a basis for predicting early prognosis.Methods:Data were retrospectively collected from 211 patients with primary HAL who visited the Emergency Center of the Hematology Hospital, Chinese Academy of Medical Sciences, between July 1, 2019 and November 30, 2021. The value of each indicator in early risk stratification and prognosis was analyzed.Results:The early-death group exhibited higher WBC, peripheral blood immature cell proportions, prothrombin times (PT), fibrinogen degradation products (FDP), and D-dimer levels than the non-early death group ( P<0.05). Mortality in hyperleukocytic AML (20.5% ) was significantly higher than that in hyperleukocytic ALL (9.3% ) ( P<0.05). There were significant differences in age, creatinine, PT, fibrinogen (FIB) levels, WBC, lactic dehydrogenase (LDH), uric acid, blood potassium, blood calcium, and blood phosphorus levels between the two groups of patients ( P<0.05). A WBC threshold of 255.96×10?/L predicted early mortality with 65.6% sensitivity and 69.0% specificity, with higher WBC levels associated with a 5.164-fold increased mortality risk ( P<0.05). The age, WBC, LDH, urea, PT, FDP and D-dimer of patients at the time of consultation are risk factors affecting the survival of HAL ( P<0.05) . Conclusion:HAL is a life-threatening condition with a high early mortality. Age, WBC, LDH, urea, PT, FDP and D-dimer are risk factors for early death in HAL.