Objective To investigate the predictive value of combined radiomic features derived from chest CT scans with clinical characteristics for epidermal growth factor receptor(EGFR)gene mutations in non-small cell lung cancer(NSCLC).Methods A multi-center case-control study was conducted on the clinical data and CT images of 1 070 NSCLC patients from the radiology departments of the 3 medical institutions between January 2013 and October 2023.The 719 NSCLC patients from the First Affiliated Hospital of Army Medical University were randomly divided into a training set and an internal validation set in a ratio of 7∶3;The 173 patients in the Eastern Theatre General Hospital and the 178 patients in Army Medical Centre of PLA were assigned into the external validation set 1 and 2,respectively.Least absolute shrinkage and selection operator(LASSO)regression was employed to identify the optimal radiomic features,which were subsequently used to construct a radiomics model.Univariate and multivariate logistic regression analyses were applied to identify clinical features associated with EGFR mutation,thereby developing a clinical model.The radiomic and clinical features were subsequently combined to develop a comprehensive model.All the 3 classification models were built using random forest(RF)machine learning.The area under curve(AUC),accuracy,sensitivity and specificity were utilized to evaluate the predictive performance of the models.Calibration curve was plotted to assess the goodness of fit of the comprehensive model,while decision curve analysis was performed to assess the clinical utility of the model.Results The AUC value of the radiomics model was 0.762 4(95%CI:0.692 4～0.825 1),0.745 4(95%CI:0.671 1～0.814 3),and 0.724 7(95%CI:0.639 7～0.801 6),respectively,in the internal validation set,external validation set 1,and external validation set 2;The AUC value of the clinical prediction model was 0.691 7(95%CI:0.627 9～0.757 6),0.652 5(95%CI:0.576 7～0.729 1),and 0.779 2(95%CI:0.712 5～0.847 3),respectively in the above sets in turn;The comprehensive model constructed based on clinical features and radiomic features showed the best predictive efficacy,with an AUC value of 0.818 0(95%CI:0.757 7～0.874 3),0.782 4(95%CI:0.703 1～0.848 2),and 0.796 6(95%CI:0.718 1～0.868 6),respectively in the above sets.Calibration curve analysis indicated that the comprehensive model had a good fit,while decision curve analysis revealed that the model provided a favorable net benefit.Conclusion Our comprehensive model constructed based on chest CT radiomic features and clinical characteristics shows superior predictive performance for EGFR gene mutations in NSCLC across multiple center datasets,which may be helpful for clinical decision-making for treatment strategies.

Objective To evaluate the predictive value of deep learning features from chest CT images combined with clinical and radiomics features for epidermal growth factor receptor(EGFR)mutations in non-small cell lung cancer(NSCLC).Methods This case-control study retrospectively analyzed clinical and imaging data of 1 070 NSCLC patients from radiology departments at three hospitals(January 2013 to October 2023).Patients were divided into:a training set(n=502)and internal validation set(n=217)via 7∶3 randomization of 719 cases from the First Affiliated Hospital of Army Medical University;external validation set 1(n=173)from General Hospital of Eastern Theater Command;external validation set 2(n=178)from Daping Hospital of Army Medical University.Deep learning features were extracted using a 2.5D convolutional neural network(CNN)with ResNet101 backbone,radiomics features were derived from CT images,and clinical risk factors were identified to construct models.An integrated model combined deep learning,clinical,and radiomics features.All four models were developed using random forest(RF)classifiers.Calibration curves assessed goodness-of-fit,and decision curve analysis(DCA)evaluated clinical utility.Results The deep learning model achieved AUCs of 0.833 7(95%CI:0.770 6～0.884 7),0.815 1(0.741 6～0.882 8),and 0.810 1(0.745 2～0.873 6)in the internal and two external validation sets,respectively.Clinical models yielded AUCs of 0.731 0(0.660 2～0.802 1),0.746 0(0.666 4～0.824 9),and 0.813 4(0.743 1～0.883 6);radiomics models showed AUCs of 0.762 4(0.692 4～0.825 1),0.745 4(0.671 1～0.814 3),and 0.724 7(0.639 7～0.801 6).The integrated model demonstrated optimal performance with AUCs of 0.905 5(0.857 0～0.945 4),0.832 7(0.763 3～0.896 4),and 0.889 0(0.834 4～0.934 3).DCA indicated significant net benefit for EGFR prediction at threshold probabilities of 0.15～0.85 using the integrated model.Conclusion Deep learning features from CT images effectively predict EGFR mutation status in NSCLC.The integrated model combining deep learning,clinical,and radiomics features further enhances predictive performance.

BACKGROUND:Neuregulin 1 has been shown to be characterized in cell proliferation,differentiation,and vascular growth.Human amniotic mesenchymal stem cells are important seed cells in the field of tissue engineering,and have been shown to be involved in tissue repair and regeneration. OBJECTIVE:To construct human amniotic mesenchymal stem cells overexpressing neuregulin 1 and investigate their proliferation and migration abilities,as well as their effects on wound healing. METHODS:(1)Human amniotic mesenchymal stem cells were in vitro isolated and cultured and identified.(2)A lentivirus overexpressing neuregulin 1 was constructed.Human amniotic mesenchymal stem cells were divided into empty group,neuregulin 1 group,and control group,and transfected with empty lentivirus and lentivirus overexpressing neuregulin 1,or not transfected,respectively.(3)Edu assay was used to detect the proliferation ability of the cells of each group,and Transwell assay was used to detect the migration ability of the cells.(4)The C57 BL/6 mouse trauma models were constructed and randomly divided into control group,empty group,neuregulin 1 group,with 8 mice in each group.Human amniotic mesenchymal stem cells transfected with empty lentivirus or lentivirus overexpressing neuregulin-1 were uniformly injected with 1 mL at multiple local wound sites.The control group was injected with an equal amount of saline.(5)The healing of the trauma was observed at 1,7,and 14 days after model establishment.Histological changes of the healing of the trauma were observed by hematoxylin-eosin staining.The expression of CD31 on the trauma was observed by immunohistochemistry. RESULTS AND CONCLUSION:(1)Human amniotic mesenchymal stem cells overexpressing neuregulin-1 were successfully constructed.The mRNA and protein expression of intracellular neuregulin 1 was significantly up-regulated compared with the empty group(P<0.05).(2)The overexpression of neuregulin 1 promoted the migratory ability(P<0.01)and proliferative ability of human amniotic mesenchymal stem cells(P<0.05).(3)Human amniotic mesenchymal stem cells overexpressing neuregulin 1 promoted wound healing in mice(P<0.05)and wound angiogenesis(P<0.05).The results showed that overexpression of neuregulin 1 resulted in an increase in the proliferative and migratory capacities of human amniotic mesenchymal stem cells,significantly promoting wound healing and angiogenesis.

Antibiotic adjuvants offer a promising strategy for restoring antibiotic sensitivity, expanding antibacterial spectra, and reducing required dosages. Previously, compound 15 was identified as a potential adjuvant for Polymyxin B (PB) against multidrug-resistant (MDR) Pseudomonas aeruginosa DK2; however, its clinical utility was hindered by high cytotoxicity, uncertain in vivo efficacy, and an unclear synergetic mechanism. To address these challenges, we synthesized and evaluated a series of novel benzamide derivatives, with A22 emerging as a particularly promising candidate. A22 demonstrated potent synergistic activity to PB, minimal cytotoxicity, improved water solubility, and broad-spectrum synergism of polymyxins against various clinically isolated MDR Gram-negative strains. In vivo studies using Caenorhabditis elegans and mouse models further confirmed the efficacy of A22. Moreover, A22 effectively suppressed the development of PB resistance in Pseudomonas aeruginosa DK2. Mechanistic investigations revealed that A22 enhances polymyxins activity by inducing reactive oxygen species production, reducing ATP levels, increasing NOX activity, and inhibiting biofilm formation, leading to bacterial death. These findings position A22 as a highly promising candidate for the development of polymyxin adjuvants, offering a robust approach to combating MDR Gram-negative bacterial infections.

Protein tyrosine phosphatase nonreceptor type 2 (PTPN2) is a promising target for sensitizing solid tumors to immune checkpoint blockades. However, the highly polar active sites of PTPN2 hinder drug discovery efforts. Leveraging small interfering RNA (siRNA) technology, we developed a novel glutathione-responsive nano-platform HPssPT (HA/PEIss@siPtpn2) to silence PTPN2 and enhance immunotherapy efficacy in hepatocellular carcinoma (HCC). HPssPT showed potent transfection and favorable safety profiles. PTPN2 deficiency induced by HPssPT amplified the interferon γ signaling in HCC cells by increasing the phosphorylation of Janus-activated kinase 1 and signal transducer and activator of transcription 1, resulting in enhanced antigen presentation and T cell activation. The nano-platform was also able to promote the M1-like polarization of macrophages in vitro. The unique tropism of HPssPT towards tumor-associated macrophages, facilitated by hyaluronic acid coating and CD44 receptor targeting, allowed for simultaneous reprogramming of both tumor cells and tumor-associated macrophages, thereby synergistically reshaping tumor microenvironment to an immunostimulatory state. In HCC, colorectal cancer, and melanoma animal models, HPssPT monotherapy provoked robust antitumor immunity, thereby sensitizing tumors to PD-1 blockade, which provided new inspiration for siRNA-based drug discovery and tumor immunotherapy.

Objective To investigate the clinical prognosis value of the positivity rate of oligoclonal bands(OCB)and immunoglobulin G(IgG)level of cerebrospinal fluid(CSF)in severe encephalitis.Methods A total of 699 cases of encephalitis patients admitted to the Department of Neurology of the First Affiliated Hospital of Air Force Military Medical University,and Xijing 986 Hospital from January 2016 to October 2020 were enrolled.According to the severity of their diseases,these patients were divided into a mild(n=360)group and a severe(n=339)group.CSF and serum samples were collected from the patient at the time of admission,and the differences in cerebrocyte count,glucose contem,glucose content,chlorine content,IgG of CSF and OCB of CSF and serum were compared.According to the GOS score of patients with severe encephalitis at discharge,the patients were divided into good prognosis group(n=259)and poor prognosis group(n=80),and multivariate Logistic regression analysis was used to analyze factors that affected the prognosis of severe encephalitis patients,and the correlation between the OCB and IgG of CSF and prognosis of patients with severe encephalitis.The predictive value of CSF IgG for the prognosis of patients with severe encephalitis was tested,and receiver operating characteristic(ROC)curve was plotted.Results Compared to patients with mild encephalitis,patients with severe encephalitis had a higher proportion of fever,pulmonary infection,status epilepticus,and mechanical ventilation,and were more likely to be complicated by stroke and hydrocephalus,and the differences were statistically significant(χ2=5.319～245.179,all P<0.05).There were significant differences in the positive rate of cerebrocyte count,chlorine content,IgG content and OCB in cerebrospinal fluid between the two groups(Z=-3.623,-4.875,-3.518,χ2=6.581,all P<0.05).CSF OCB and CSF IgG were independent risk factors for poor prognosis in patients with severe encephalitis(Wald χ2=7.295,0.001,all P<0.05).A restrictive cubic spline plot showed a linear relationship between CSF IgG and poor prognosis.The AUC(95%CI)of CSF IgG was 0.754(0.632～0.876).Conclusion The CSF IgG content and positive rate of CSF OCB in patients with severe encephalitis with poor prognosis are higher than those in patients with good prognosis,and detecting these two indicators has certain reference value for the prognosis prediction of patients with severe encephalitis.

Ivonescimab is a humanized bispecific antibody targeting human vascular endothelial growth factor-A(VEGF-A)and programmed death protein-1(PD-1).It was approved by National Medi-cal Products Administration on May 24th,2024,and can be used in combination with pemetrexed and carboplatin for locally advanced or positive EG-FR gene mutation after treatment with epidermal growth factor receptor(EGFR)tyrosine kinase inhib-itor.This paper mainly introduces the research progress of the world's first PD-1/VEGF bispecific antibody ivonescimab,and summarizes the mecha-nism of action,pharmacokinetics,phase Ⅰ-Ⅲ clinical trials and drug safety.

Objective:To deeply explore the thematic needs and characteristics of learners regarding course elements based on the review texts of nursing massive open online courses (MOOC), providing a reference for achieving effective alignment between digital nursing education content and learner needs.Methods:Data were collected from the review texts of 112 nursing courses on the Chinese University MOOC platform using a web crawler script written with Python′s Requests library. The collection period spanned from the course launch dates to December 31, 2023. Sentiment analysis and high-frequency words analysis were conducted using Chinese text Nature language processing library, and core themes of learners′ positive and negative reviews were extracted using the latent dirichlet allocation.Results:A corpus of 18 184 nursing MOOC review texts was constructed, with positive sentiment reviews accounting for 89.30% (16 238/18 184) and negative sentiment reviews making up 10.70% (1 946/18 184). Word frequency analysis revealed that most nursing MOOC serve as carriers for blended online and offline teaching models, with students being the primary target audience, though social participants were also involved. The reviews effectively mirrored real-world clinical nursing scenarios. The need of learners was categorized into three major themes: content design and assessment, course resources and teaching strategies, and software applications and platform functionality.Conclusions:This study, leveraging text mining technology, thoroughly investigated the three thematic characteristics of nursing MOOC needs of online learners and proposed targeted optimization recommendations. Future research could incorporate other online teaching platforms and comprehensively construct a sentiment lexicon for nursing online course reviews using big data modeling and machine learning algorithms. These would enable a holistic analysis of the digital nursing education landscape, allowing for precise improvements to address existing shortcomings.

Objective:To conduct performance tests on medical ultrasound probes repaired by the third party,and explore whether the key parameters of the probes of third-party repair can meet the requirements of clinical use for quality.Methods:A total of 79 ultrasound probes that had been repaired by the third party were selected from different medical institutions.The performance tests were conducted on multiple parameters of ultrasound probes of different models and brands in accordance with national technical standards and relevant industry norms.Then,the test results were analyzed,studied and evaluated.Results:The tested results of the temperature rise and the leakage current of the ultrasound probes,which were repaired by the third party,met the national standards.However,in the test for sound power,26.58%of the probes failed to meet the national standards,which outputted sound intensity that was calculated was higher than the specified value.Conclusion:The general performance of the probes that have been repaired by the third party is well,but the quality of the repair is uneven levels,and some indicators do not meet national standards or industry norms,which might lead to occur risks in ultrasound diagnosis of medical institutions.It is recommended to implement regular test for quality and performance of medical ultrasound equipment,and establish a method and system for quality monitoring and re-evaluation after sale of repair for medical ultrasound,so as to ensure the use and safety of the equipment.

Objective To investigate the clinical prognosis value of the positivity rate of oligoclonal bands(OCB)and immunoglobulin G(IgG)level of cerebrospinal fluid(CSF)in severe encephalitis.Methods A total of 699 cases of encephalitis patients admitted to the Department of Neurology of the First Affiliated Hospital of Air Force Military Medical University,and Xijing 986 Hospital from January 2016 to October 2020 were enrolled.According to the severity of their diseases,these patients were divided into a mild(n=360)group and a severe(n=339)group.CSF and serum samples were collected from the patient at the time of admission,and the differences in cerebrocyte count,glucose contem,glucose content,chlorine content,IgG of CSF and OCB of CSF and serum were compared.According to the GOS score of patients with severe encephalitis at discharge,the patients were divided into good prognosis group(n=259)and poor prognosis group(n=80),and multivariate Logistic regression analysis was used to analyze factors that affected the prognosis of severe encephalitis patients,and the correlation between the OCB and IgG of CSF and prognosis of patients with severe encephalitis.The predictive value of CSF IgG for the prognosis of patients with severe encephalitis was tested,and receiver operating characteristic(ROC)curve was plotted.Results Compared to patients with mild encephalitis,patients with severe encephalitis had a higher proportion of fever,pulmonary infection,status epilepticus,and mechanical ventilation,and were more likely to be complicated by stroke and hydrocephalus,and the differences were statistically significant(χ2=5.319～245.179,all P<0.05).There were significant differences in the positive rate of cerebrocyte count,chlorine content,IgG content and OCB in cerebrospinal fluid between the two groups(Z=-3.623,-4.875,-3.518,χ2=6.581,all P<0.05).CSF OCB and CSF IgG were independent risk factors for poor prognosis in patients with severe encephalitis(Wald χ2=7.295,0.001,all P<0.05).A restrictive cubic spline plot showed a linear relationship between CSF IgG and poor prognosis.The AUC(95%CI)of CSF IgG was 0.754(0.632～0.876).Conclusion The CSF IgG content and positive rate of CSF OCB in patients with severe encephalitis with poor prognosis are higher than those in patients with good prognosis,and detecting these two indicators has certain reference value for the prognosis prediction of patients with severe encephalitis.