Methodological quality assessment is a pivotal link between primary studies and reliable evidence-based practice,and an essential pathway for operationalizing the core principles of the Guide on Methodological Standards in Pharmacoepidemiology(2nd edition).A prevalent challenge in practice,however,is the conflation of appraising methodological robustness(risk of bias assessment)with verifying reporting transparency(adherence to reporting guidelines).This paper systematically addresses this fundamental challenge,beginning with a clear distinction between the essence and boundaries of these two concepts.On this basis,the article provides a comprehensive review of mainstream quality assessment tools,covering the methodological features and evolutionary trajectory of numerous instruments for interventional(e.g.,RoB 2,ROBINS-I),observational(e.g.,NOS,the JBI/SIGN/NIH series),secondary(e.g.,AMSTAR 2),and other specific types of studies such as health economic evaluations.Furthermore,a complete case study is used to illustrate the practical application of the ROBINS-I tool.The paper's central thesis advocates for an"appraisal-informed design"philosophy,urging a conceptual shift from the retrospective critique of existing literature to the prospective quality control of new research by internalizing appraisal standards as design principles,while also exploring the emerging paradigm of artificial intelligence in assisting assessment.This paper provides a comprehensive methodological reference for researchers and practitioners to prudently select appropriate assessment tools and to conduct rigorous critical appraisals of pharmacoepidemiological evidence.

BACKGROUND:Early diagnosis of pulmonary fibrosis is the foundation for timely antifibrotic drug therapy.Therefore,exploring and discovering ideal biomarkers that can be effectively used for the early diagnosis of pulmonary fibrosis is crucial for the treatment of the disease.OBJECTIVE:To conduct an in-depth analysis of key autophagy-related genes involved in the process of pulmonary fibrosis by means of bioinformatics and machine learning techniques,in order to investigate whether autophagy-related core genes of pulmonary fibrosis can be used as reliable biomarkers in the assessment of the progression of pulmonary fibrosis.METHODS:Two datasets of pulmonary fibrosis,GSE24206 and GSE110147,were downloaded from the Gene Expression Omnibus(GEO)database(a public database developed and maintained by the U.S.National Center for Biotechnology Information to store and share bioinformatics data),and the gene expression matrices of these two datasets were normalized by using the"limma"package in R software.The autophagy-related genes were extracted from GeneCards database(a database created by the U.S.National Center for Biotechnology Information,which automatically integrates gene-centric data from about 200 Web sources,including genomic,transcriptomic,proteomic,genetic,clinical,and functional information).Differential gene analysis was performed on the pulmonary fibrosis dataset,and the common genes were extracted by cross-comparing the differential genes with the autophagy genes,so as to identify autophagy genes that may play a role in the process of pulmonary fibrosis.The intersecting genes were analyzed for functional enrichment and cellular immune infiltration by gene ontology and Kyoto Encyclopedia of Genes and Genomes.Core genes of pulmonary fibrosis associated with autophagy were screened by protein-protein interactions and machine learning,and core genes were subjected to the enrichment analysis.Diagnostic models were constructed from the identified core genes.Calibration curves were used to assess the predictive ability of the line graph model.An external dataset,GSE21369,was used to perform a receiver operating characteristic curve analysis to validate the expression profiles of pulmonary fibrosis genes associated with autophagy,as well as to predict Chinese herbs associated with the genes IL6 and COL1A2 via the Coremine database.Finally,human embryonic lung fibroblasts were cultured and modelled by transforming growth factor-β1 treatment,and the relative expression of genes in the model cells was verified using qRT-PCR.RESULTS AND CONCLUSION:(1)A total of 51 pulmonary fibrosis differential genes and 25 genes intersecting with autophagy genes were obtained.Gene ontology analysis showed that the 25 intersecting genes were related to extracellular matrix tissue,collagen metabolism,collagen pro-fibroblasts,and growth factor binding,etc.The results of Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that they were mainly related to the Phosphatidylinositol 3-kinase/protein kinase B signaling pathway and the signaling pathway of the extracellular matrix-receptor interactions.(2)Immunoinfiltration analysis revealed that the expression of activated memory CD4+T cells,M0 macrophages,and resting dendritic cells was significantly elevated in the pulmonary fibrosis group(P＜0.05),showing a strong correlation.(3)Two autophagy signature genes involved in the progression of pulmonary fibrosis were identified:COL1A2 and IL6.The column-line diagram model showed that the two core genes predicted the onset of pulmonary fibrosis more accurately,and the receiver operating characteristic curve analysis showed that the two characteristic genes had diagnostic significance.COL1A2 and IL6 were related to the cell-cycle pathway,mitogen-activated protein kinase signaling pathway,Janus kinase-signal transduction and activator of transcription signaling pathway and cytokine-cytokine receptor interactions.A total of 20 Chinese herbs were predicted to be related to COL1A2 and IL6 genes,and their efficacies were mainly to clear away heat and detoxify toxins and to invigorate blood and move qi.COL1A2 and IL6 were verified to be highly expressed in pulmonary fibrosis.To conclude,COL1A2 and IL6 may be potential diagnostic biomarkers for pulmonary fibrosis,but its specificity to pulmonary fibrosis needs to be further investigated.

Methodological quality assessment is a pivotal link between primary studies and reliable evidence-based practice,and an essential pathway for operationalizing the core principles of the Guide on Methodological Standards in Pharmacoepidemiology(2nd edition).A prevalent challenge in practice,however,is the conflation of appraising methodological robustness(risk of bias assessment)with verifying reporting transparency(adherence to reporting guidelines).This paper systematically addresses this fundamental challenge,beginning with a clear distinction between the essence and boundaries of these two concepts.On this basis,the article provides a comprehensive review of mainstream quality assessment tools,covering the methodological features and evolutionary trajectory of numerous instruments for interventional(e.g.,RoB 2,ROBINS-I),observational(e.g.,NOS,the JBI/SIGN/NIH series),secondary(e.g.,AMSTAR 2),and other specific types of studies such as health economic evaluations.Furthermore,a complete case study is used to illustrate the practical application of the ROBINS-I tool.The paper's central thesis advocates for an"appraisal-informed design"philosophy,urging a conceptual shift from the retrospective critique of existing literature to the prospective quality control of new research by internalizing appraisal standards as design principles,while also exploring the emerging paradigm of artificial intelligence in assisting assessment.This paper provides a comprehensive methodological reference for researchers and practitioners to prudently select appropriate assessment tools and to conduct rigorous critical appraisals of pharmacoepidemiological evidence.

BACKGROUND:Early diagnosis of pulmonary fibrosis is the foundation for timely antifibrotic drug therapy.Therefore,exploring and discovering ideal biomarkers that can be effectively used for the early diagnosis of pulmonary fibrosis is crucial for the treatment of the disease.OBJECTIVE:To conduct an in-depth analysis of key autophagy-related genes involved in the process of pulmonary fibrosis by means of bioinformatics and machine learning techniques,in order to investigate whether autophagy-related core genes of pulmonary fibrosis can be used as reliable biomarkers in the assessment of the progression of pulmonary fibrosis.METHODS:Two datasets of pulmonary fibrosis,GSE24206 and GSE110147,were downloaded from the Gene Expression Omnibus(GEO)database(a public database developed and maintained by the U.S.National Center for Biotechnology Information to store and share bioinformatics data),and the gene expression matrices of these two datasets were normalized by using the"limma"package in R software.The autophagy-related genes were extracted from GeneCards database(a database created by the U.S.National Center for Biotechnology Information,which automatically integrates gene-centric data from about 200 Web sources,including genomic,transcriptomic,proteomic,genetic,clinical,and functional information).Differential gene analysis was performed on the pulmonary fibrosis dataset,and the common genes were extracted by cross-comparing the differential genes with the autophagy genes,so as to identify autophagy genes that may play a role in the process of pulmonary fibrosis.The intersecting genes were analyzed for functional enrichment and cellular immune infiltration by gene ontology and Kyoto Encyclopedia of Genes and Genomes.Core genes of pulmonary fibrosis associated with autophagy were screened by protein-protein interactions and machine learning,and core genes were subjected to the enrichment analysis.Diagnostic models were constructed from the identified core genes.Calibration curves were used to assess the predictive ability of the line graph model.An external dataset,GSE21369,was used to perform a receiver operating characteristic curve analysis to validate the expression profiles of pulmonary fibrosis genes associated with autophagy,as well as to predict Chinese herbs associated with the genes IL6 and COL1A2 via the Coremine database.Finally,human embryonic lung fibroblasts were cultured and modelled by transforming growth factor-β1 treatment,and the relative expression of genes in the model cells was verified using qRT-PCR.RESULTS AND CONCLUSION:(1)A total of 51 pulmonary fibrosis differential genes and 25 genes intersecting with autophagy genes were obtained.Gene ontology analysis showed that the 25 intersecting genes were related to extracellular matrix tissue,collagen metabolism,collagen pro-fibroblasts,and growth factor binding,etc.The results of Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that they were mainly related to the Phosphatidylinositol 3-kinase/protein kinase B signaling pathway and the signaling pathway of the extracellular matrix-receptor interactions.(2)Immunoinfiltration analysis revealed that the expression of activated memory CD4+T cells,M0 macrophages,and resting dendritic cells was significantly elevated in the pulmonary fibrosis group(P＜0.05),showing a strong correlation.(3)Two autophagy signature genes involved in the progression of pulmonary fibrosis were identified:COL1A2 and IL6.The column-line diagram model showed that the two core genes predicted the onset of pulmonary fibrosis more accurately,and the receiver operating characteristic curve analysis showed that the two characteristic genes had diagnostic significance.COL1A2 and IL6 were related to the cell-cycle pathway,mitogen-activated protein kinase signaling pathway,Janus kinase-signal transduction and activator of transcription signaling pathway and cytokine-cytokine receptor interactions.A total of 20 Chinese herbs were predicted to be related to COL1A2 and IL6 genes,and their efficacies were mainly to clear away heat and detoxify toxins and to invigorate blood and move qi.COL1A2 and IL6 were verified to be highly expressed in pulmonary fibrosis.To conclude,COL1A2 and IL6 may be potential diagnostic biomarkers for pulmonary fibrosis,but its specificity to pulmonary fibrosis needs to be further investigated.

OBJECTIVE: To assess the value of chromosomal microarray analysis (CMA) and fluorescence in situ hybridization (FISH) for the prenatal diagnosis of chromosomal mosaicisms. METHODS: A total of 775 pregnant women who had visited the Prenatal Diagnosis Center of Yancheng Maternal and Child Health Care Hospital from January 2018 to December 2020 were selected as study subjects. Chromosome karyotyping analysis and CMA were carried out for all women, and FISH was used to validate the suspected mosaicism cases. RESULTS: Among the 775 amniotic fluid samples, karyotyping has identified 13 mosaicism cases, which yielded a detection rate of 1.55%. Respectively, there were 4, 3, 4 and 2 cases for sex chromosome number mosaicisms, abnormal sex chromosome structure mosaicisms, abnormal autosomal number mosaicisms and abnormal autosomal structure mosaicisms. CMA has only detected only 6 of the 13 cases. Among 3 cases verified by FISH, 2 cases were consistent with the karyotyping and CMA results, and clearly showed low proportion mosaicism, and 1 case was consistent with the result of karyotyping but with a normal result by CMA. Eight pregnant women had chosen to terminate the pregnancy (5 with sex chromosome mosaicisms and 3 with autosomal mosaicisms). CONCLUSION For fetuses suspected for chromosomal mosaicisms, CMA, FISH and G-banding karyotyping should be combined to determine the type and proportion of mosaicisms more precisely in order to provide more information for genetic counseling.
Female ; Pregnancy ; Humans ; Mosaicism ; In Situ Hybridization, Fluorescence ; Chromosome Disorders/genetics* ; Prenatal Diagnosis/methods* ; Chromosome Aberrations ; Sex Chromosome Aberrations ; Microarray Analysis/methods* ; Chromosomes

Objective:To conduct meta analysis to compare the effect of complete resection with or without postoperative radiotherapy (PORT) on survival in stage Ⅲ(N 2) non-small cell lung cancer (NSCLC). Methods:Relevant studies of the efficacy of PORT for stage Ⅲ(N 2) NSCLC were searched from Wanfang Data, PubMed, and Cochrane Library from January 2006 to January 2022. Literature screening, extraction of information and assessment of the risk of bias of the included literature was carried out by two independent researchers. Meta analysis was performed using R4.0.3 software. Results:A total of 12 publications consisting of 2992 patients were included, 1479 cases in the PORT group and 1513 cases in the control group. PORT improved the overall survival (OS) and disease free survival (DFS) compared to the control group. Fixed-effects model meta analysis of 6 randomized controlled trials showed that PORT did not significantly reduce the risk of death ( HR=0.98, 95% CI: 0.80-1.20). Fixed-effects model meta analysis of 6 retrospective studies showed that PORT improved prognosis ( HR=0.68, 95% CI: 0.59-0.79). PORT could improve OS of patients with multiple (station) metastasis of ipsilateral mediastinum and / or submandibular lymph nodes ( HR=0.89, 95% CI: 0.80-0.99). Conclusions:PORT could improve OS and DFS in stage Ⅲ(N 2) NSCLC. A trend towards benefit can be observed in the subgroup with multiple/multi-station N2 metastasis.

Stapled peptides with significantly enhanced pharmacological profiles have emerged as promising therapeutic molecules due to their remarkable resistance to proteolysis and performance to penetrate cells. The all-hydrocarbon peptide stapling technique has already widely adopted with great success, yielding numerous potent peptide-based molecules. Based on our prior efforts, we conceived and prepared a double-stapled peptide in this study, termed FRNC-1, which effectively attenuated the bone resorption capacity of mature osteoclasts in vitro through specific inhibition of phosphorylated GSK-3β. The double-stapled peptide FRNC-1 displayed notably improved helical contents and resistance to proteolysis than its linear form. Additionally, FRNC-1 effectively prevented osteoclast activation and improved bone density for ovariectomized (OVX) mice after intravenous injection and importantly, after oral (intragastric) administration. The double-stapled peptide FRNC-1 is the first orally effective peptide that has been validated to date as a therapeutic candidate for postmenopausal osteoporosis (PMOP).

We reviewed and developed an indicator system framework for assessing teaching effect of laparoscopic simulation training through literature research, expert consultation, analytic hierarchy process and factor analysis. We also made an empirical study on the constructed index system. The system included 3 domains (A1: evaluation of laparoscopic simulator; A2: operation evaluation of experimental animals; A3: evaluation of clinical practice), 10 second-level indicators and 23 third-level indicators for assessing teaching effect of laparoscopic simulation training. The indicator system framework has good internal consistency (Cronbach α= 0.968) and external consistency (>0.72). The empirical study found that: in the results of A1-A3 in the first level indicator, the score of the experienced group was significantly higher than that of the inexperienced group ( P<0.05). In the evaluation results of the 10 secondary indicators in the secondary indicators B1-B10, the score of the experienced group was significantly higher than that of the inexperienced group ( P<0.01). For the first time, we have established and evaluated a comprehensive evaluation indicator system which is reliable and effective and can be used for further evaluation of teaching effect of laparoscopic simulation training. The following empirical studies have verified the effectiveness and practicability of the evaluation system.

Objective: To construct the BCMA-CAR using the B-cell maturation antigen (BCMA) specific ligand APRIL as antigen binding region and to validate the effect of BCMA-CAR modified T cells (BCMA-CAR-T) on myeloma cells. Methods: The BCMA-CAR was constructed using the BCMA specific ligand APRIL as antigen binding domain and 4-1BB as the costimulatory domain. The specific cytotoxicity against BCMA⁺ myeloma cell lines and primary multiple myeloma (MM) cells in vitro were evaluated. In addition, BCMA⁺ myeloma xenograft mouse model was established to assess the anti-tumor effect of BCMA-CAR-T cell therapy in vivo. Results: BCMA-CAR-T cells could specifically kill BCMA⁺ myeloma cell lines (For BCMA-CAR-T cells, BCMA⁺ cells are almost undetectable in the E∶T ratio of 1∶4) and MM patients’ bone marrow mononuclear cells (the proportion of residual cells in BCMA-CAR-T and vector-T groups was 16.0% vs 66.85%, P=0.003) with significant degranulation (CAR-T and vector-T cells cocultured with MM1.S, H929 and U266 had degranulation levels of 33.30% vs 5.62%, 16.97% vs 2.95% and 25.87% vs 2.97%, respectively, P<0.001) and cytokines release (P<0.01) in vitro. In a human BCMA⁺ myeloma xenograft mouse model, BCMA-CAR-T cells could significantly prolong the survival of mice (The median survival time of mice treated with BCMA-CAR-T and vector-T cells was 87.5 days and 67.5 days, respectively, P<0.001) . Conclusion The ligand-based BCMA-CAR-T cells could be a promising strategy for BCMA⁺ multiple myeloma treatment.