Integrin α _v β ₃ is overexpressed in various tumor cells and angiogenesis. To date, no drug has been proven to target it for therapy. A first-in-human study was designed to investigate the safety, pharmacokinetics, and dosimetry of ¹⁷⁷Lu-AB-3PRGD2, a novel integrin α _v β ₃-targeting radionuclide drug with an albumin-binding motif to optimize the pharmacokinetics. Ten patients (3 men, 7 women; aged 45 ± 16 years) with integrin α _v β ₃-avid tumors were recruited to accept ¹⁷⁷Lu-AB-3PRGD2 injection in a dosage of 1.57 ± 0.08 GBq (42.32 ± 2.11 mCi), followed by serial scans to obtain its dynamic distribution in the body. Safety tests were performed before and every 2 weeks after the treatment for 6-8 weeks. No adverse event over grade 3 was observed. ¹⁷⁷Lu-AB-3PRGD2 was excreted mainly through the urinary system, with intense radioactivity in the kidneys and bladder. Moderate distribution was found in the liver, spleen, and intestines. The estimated blood half-life was 2.85 ± 2.17 h. The whole-body effective dose was 0.251 ± 0.047 mSv/MBq. The absorbed doses were 0.157 ± 0.032 mGy/MBq in red bone marrow and 0.684 ± 0.132 mGy/MBq in kidneys. This first-in-human study of ¹⁷⁷Lu-AB-3PRGD2 treatment indicates its promising potential for targeted radionuclide therapy of integrin α _v β ₃-avid tumors. It merits further studies in more patients with escalating doses and multiple treatment courses.

Objective:To explore the relationship between the differential genes derived from transcriptome mRNA sequencing and prognosis among heart failure patients with mildly reduced ejection fraction (HFmrEF) and preserved ejection fraction (HFpEF).Methods:This was a case-control study. Ten patients with HFmrEF and 10 patients with HFpEF treated at TEDA International Cardiovascular Disease Hospital from November 2021 to January 2022 were selected and differentially expressed genes were screened by transcriptome mRNA sequencing. Ten healthy people served as control group. In addition, 50 patients with HFmrEF, 62 patients with HFpEF, who were treated at TEDA International Cardiovascular Disease Hospital at the same period, were selected as validation groups, 57 healthy people served as control validation group. Real-time quantitative PCR (RT-qPCR) was used to detect the expression of differential genes in each group. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to assess the differential diagnosis and prognostic value of differential genes in these patients. Patients were followed up regularly to document adverse events within 1 year after discharge including cardiac death and readmission for heart failure. Survival analysis was performed using Kaplan-Meier curves and tested by log rank test. Cox regression analysis was used to explore whether differential mRNA was risk factors for poor prognosis in HFmrEF and HFpEF patients.Results:A total of four genes were differentially expressed (three upregulated and one downregulated gene) between the HFmrEF group and HFpEF group (adjust P<0.05). SLC12A1, C15orf48 and SPP1 were associated with the progress of cardiovascular disease, and selected for validation in the clinical cohort. RT-qPCR results showed that the gene expression of SLC12A1 in the HFmrEF group was significantly higher than that in the HFpEF group ( P<0.001). The AUC for the adjunctive differential diagnostic value of SLC12A1 for HFmrEF and HFpEF was 0.802 ( P<0.001) and the AUC of SLC12A1 with a cut-off value of 6.634 was 0.737 ( P=0.003) in determining poor prognosis in patients with HFpEF. Kaplan-Meier survival analysis showed that patients with SLC12A1≤6.634 had a higher incidence of adverse cardiac events than patients with SLC12A1 >6.634 ( P=0.001). Cox regression analysis showed that the risk of adverse cardiac events in the SLC12A1 ≤6.634 group was 6.787 times higher than in the SLC12A1 >6.634 group ( HR=6.787, P=0.011). Conclusions:Transcriptome mRNA sequencing analysis is valuable for detecting clinical relevant differentially expressed genes in HFmrEF and HFpEF patients, among which SLC12A1 can be used as a novel molecular biomarker to aid the differential diagnosis of HFmrEF and HFpEF. In addition, SLC12A1 may be used as an adjunctive biomarker for the prognosis evaluation in patients with HFpEF.

Objective To construct and validate machine learning-based models to predict the risk of acute kidney injury(AKI)following percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 2 315 STEMI patients who underwent PCI between January 2020 and June 2023 were included;306(13.2％)of them developed AKI.Baseline variables were screened using LASSO regression,with the optimal λ value selected via 10-fold cross-validation to identify AKI-associated features.Subsequently,eight distinct machine learning models were constructed and evaluated for their predictive performance.SHAP value analysis was employed to assess the impact of key variables on model predictions.Results LASSO regression identified seven variables significantly associated with AKI,including age,multivessel disease,preoperative creatinine,heart failure,white blood cell count,hemoglobin,and albumin levels.Among all the models,the light gradient boosting machine(LGBM)and extreme gradient boosting(XGB)demonstrated the best predictive performance,with training set AUCs being 0.899(95％CI:0.877-0.921)and 0.893(95％CI:0.868-0.918),and validation set AUCs being 0.809(95％CI:0.763-0.856)and 0.871(95％CI:0.833-0.909),respectively.SHAP analysis revealed that albumin,age,preoperative creatinine,and white blood cell count were the primary contributors to AKI risk.Conclusion This study successfully developed and validated machine learning-based predictive models capable of effectively identifying the risk of AKI following PCI in STEMI patients,thus providing valuable support for clinical decision-making.

The aim of this study is to explore the immunogenicity of African swine fever virus p37 recombinant protein in mice.C57BL/6J mice were immunized subcutaneously in the abdomen using p37 recombinant protein as antigen.The second immunization was performed 21 d after the first immunization.Serum-specific antibody levels were detected by ELISA;serum cytokine levels were detected using a multifactor assay technique;mice splenic lymphocytes were isolated 7 d after sec-ondary immunization,and the number of splenic lymphocytes secreting IFN-γ after recombinant protein stimulation was detected by ELISpot;and the ratio of CD4+T cells to CD8+T cells was detected by flow cytometry.The results of indirect ELISA showed that p37 recombinant protein could stimulate mice to produce high levels of specific antibodies;ELISpot showed that p37 recom-binant protein could significantly stimulate splenic lymphocytes to produce IFN-γ(P＜0.001)and activate cellular immune responses;the results of flow cytometry showed that it could signifi-cantly stimulate the differentiation of T-lymphocytes to CD4+T-lymphocytes(P＜0.001).In ad-dition,serum levels of IL-2,IL-4,IFN-γ,and TNF-α immune-related cytokines were significantly higher after the second immunization.Immunization of mice with p37 recombinant protein induced strong humoral and cellular immune responses with good immunogenicity,providing reference for the subsequent epitope identification and functional study of p37 protein and the antigen screening of ASF mRNA vaccine.

In order to establish a highly efficient,convenient,and effective circular RNA(circRNA)vaccine preparation system,enhanced green fluorescent protein(EGFP)circRNA was constructed using permuted intron exon(PIE)strategy based on type Ⅰ introns.Then,circRNA circularization rates of RNA after in vitro transcription(IVT),primary circularization(IVC1),and secondary circularization(IVC2)were compared after purification.The constructed circRNA system was fur-ther applied to porcine reproductive and respiratory syndrome virus(PRRSV),and two circRNAs based on PRRSV GP5 protein were constructed and developed for in vitro expression.Results showed that circularization rates and protein expression effects of EGFP circRNA in IVC1 RNA and IVC2 RNA were similar,but both were significantly better than those of IVT RNA.Purity of EGFP circRNA reached 74％,and purities of two PRRSV GP5 protein circRNAs constructed using this preparation system were 71％and 64％,respectively.Western blot and indirect immunofluo-rescence assay(IFA)results indicated that both of the PRRSV GP5 protein circRNAs were suc-cessfully expressed.The results demonstrated that an easy-to-operate,low-cost circRNA prepara-tion system with high circularization rate was successfully constructed.Two PRRSV GP5 protein circRNA vaccines were successfully prepared using this system and expressed efficiently,which provides a reference for the development of animal circRNA vaccines and novel candidate vaccines against PRRSV.

Objective:To explore the longitudinal predictive relationship between self-neglect and frailty among older adults.Methods:Data were drawn from the Chinese Longitudinal Healthy Longevity Survey conducted in 2011 (T1), 2014 (T2), and 2018 (T3). A total of 1 495 older adults aged≥65 years at T1 who participated in three consecutive surveys and had no missing key variables were included. General demographic information, self-neglect scores, and frailty status were extracted. Spearman correlation analysis was used to examine the association between self-neglect and frailty. Cross-lagged analysis was employed to investigate the potential causal relationship between the two variables.Results:The self-neglect scores for 1 495 older adults at T1, T2, and T3 were (2.84±1.39), (2.47±1.30), and (2.41±1.20), respectively, showing a declining trend. The frailty scores at T1, T2, and T3 were 0 (0, 1.00), 0 (0, 2.00), and 1.00 (0, 2.00), respectively, indicating an increasing trend. Cross-lagged analysis revealed that self-neglect at T1 positively predicted frailty at T2 (β=0.076, P=0.004). Frailty at both T1 and T2 positively predicted self-neglect at T2 (β=0.057, P=0.044) and T3 (β=0.058, P=0.029), respectively. Conclusions:Frailty among older adults positively predicts self-neglect, and self-neglect also has a certain predictive effect on frailty. Medical staff should strengthen early screening and intervention for frailty in older adults to delay the occurrence and progression of self-neglect.

To explore the interaction between ASFV capsid protein M1249L and host from the host cellular perspective,M1249L was selected for constructing the bait plasmid(pGBKT7-M1249L)to screen the bone marrow-derived macrophages(BMDMs)cDNA library.After again co-transform and sequence alignment,20 candidate interacting host proteins were screened,such as IL-1β,CTSB and DNAJA3.And then,co-immunoprecipitation assay was performed to verify the interaction be-tween M1249L and host proteins.GO ontology(GO)and KEGG pathway enrichment analyses re-vealed that biological regulation,cellular communication and response to stimulus and others were enriched in biological processes.And these host proteins could share some pathways,including toll-like receptor signaling pathway and Nod-like receptor signaling pathway.Therefore,the results provides the theoretical basis for further research on the mechanism of ASFV M1249L in viral in-fection and immune regulation.

Objective:To explore the longitudinal predictive relationship between self-neglect and frailty among older adults.Methods:Data were drawn from the Chinese Longitudinal Healthy Longevity Survey conducted in 2011 (T1), 2014 (T2), and 2018 (T3). A total of 1 495 older adults aged≥65 years at T1 who participated in three consecutive surveys and had no missing key variables were included. General demographic information, self-neglect scores, and frailty status were extracted. Spearman correlation analysis was used to examine the association between self-neglect and frailty. Cross-lagged analysis was employed to investigate the potential causal relationship between the two variables.Results:The self-neglect scores for 1 495 older adults at T1, T2, and T3 were (2.84±1.39), (2.47±1.30), and (2.41±1.20), respectively, showing a declining trend. The frailty scores at T1, T2, and T3 were 0 (0, 1.00), 0 (0, 2.00), and 1.00 (0, 2.00), respectively, indicating an increasing trend. Cross-lagged analysis revealed that self-neglect at T1 positively predicted frailty at T2 (β=0.076, P=0.004). Frailty at both T1 and T2 positively predicted self-neglect at T2 (β=0.057, P=0.044) and T3 (β=0.058, P=0.029), respectively. Conclusions:Frailty among older adults positively predicts self-neglect, and self-neglect also has a certain predictive effect on frailty. Medical staff should strengthen early screening and intervention for frailty in older adults to delay the occurrence and progression of self-neglect.

Objective To construct and validate machine learning-based models to predict the risk of acute kidney injury(AKI)following percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 2 315 STEMI patients who underwent PCI between January 2020 and June 2023 were included;306(13.2％)of them developed AKI.Baseline variables were screened using LASSO regression,with the optimal λ value selected via 10-fold cross-validation to identify AKI-associated features.Subsequently,eight distinct machine learning models were constructed and evaluated for their predictive performance.SHAP value analysis was employed to assess the impact of key variables on model predictions.Results LASSO regression identified seven variables significantly associated with AKI,including age,multivessel disease,preoperative creatinine,heart failure,white blood cell count,hemoglobin,and albumin levels.Among all the models,the light gradient boosting machine(LGBM)and extreme gradient boosting(XGB)demonstrated the best predictive performance,with training set AUCs being 0.899(95％CI:0.877-0.921)and 0.893(95％CI:0.868-0.918),and validation set AUCs being 0.809(95％CI:0.763-0.856)and 0.871(95％CI:0.833-0.909),respectively.SHAP analysis revealed that albumin,age,preoperative creatinine,and white blood cell count were the primary contributors to AKI risk.Conclusion This study successfully developed and validated machine learning-based predictive models capable of effectively identifying the risk of AKI following PCI in STEMI patients,thus providing valuable support for clinical decision-making.