Background and purpose:Diffuse large B-cell lymphoma(DLBCL)is subclassified into germinal center B-cell-like(GCB)and non-GCB subtypes,which differ in prognosis and treatment response.However,current distinction still relies on invasive pathological assays.This study developed radiomics and deep-learning models based on multiparametric magnetic resonance imaging(MRI)to non-invasively differentiate the two subtypes preoperatively,thereby reducing dependence on histopathological examination.Methods:This study retrospectively included patients with pathologically confirmed DLBCL diagnosed at Huashan Hospital,Fudan University,and other institutions between March 2013 and December 2024.Using multiparametric MRI data,we developed DLBCL-subtype classification models that combined 4 radiomics-based machine-learning algorithms:support vector machine(SVM),logistic regression(LR),Gaussian process(GP)and Naive Bayes(NB),with 3 deep-learning architectures[densely-connected convolutional networks 121(DenseNet121),residual network 101(ResNet101)and EfficientNet-b5].Additionally,two radiologists with different experience levels independently classified DLBCL on MRI in a blinded fashion.Model and radiologist performance were quantified using the area under the receiver operating characteristic curve(AUC),accuracy(ACC),and F1-score to evaluate their ability to distinguish GCB from non-GCB subtypes.This study was approved by the Ethics Committee of Huashan Hospital of Fudan University(No.KY2024-663),and all patients signed informed consents.Results:A total of 173 patients were enrolled(55 with GCB subtype and 118 with non-GCB subtype).Radiomics and deep learning methods effectively distinguished DLBCL subtypes.Among these,the GP radiomics model(based on T1-CE+T2-FLAIR+ADC sequences)and DenseNet121 deep learning model(based on T1-CE+T2-FLAIR+ADC sequences)demonstrated optimal performance.Both achieved excellent results on the internal validation set(GP:AUC=0.900,ACC=0.896,F1=0.840;DenseNet121:AUC=0.846,ACC=0.854,F1=0.774)and maintained robustness on the external validation set.Furthermore,the classification efficacy of the optimal AI model surpassed that of experienced radiologists(highest physician AUC=0.678).Conclusion:Radiomics and deep-learning models based on multiparametric MRI features can effectively differentiate GCB from non-GCB subtypes of DLBCL.Among them,GP and DenseNet121 exhibit outstanding performance,especially when integrating multi-sequence feature sets for classifying DLBCL subtypes on complex imaging data.

Background and purpose:Diffuse large B-cell lymphoma(DLBCL)is subclassified into germinal center B-cell-like(GCB)and non-GCB subtypes,which differ in prognosis and treatment response.However,current distinction still relies on invasive pathological assays.This study developed radiomics and deep-learning models based on multiparametric magnetic resonance imaging(MRI)to non-invasively differentiate the two subtypes preoperatively,thereby reducing dependence on histopathological examination.Methods:This study retrospectively included patients with pathologically confirmed DLBCL diagnosed at Huashan Hospital,Fudan University,and other institutions between March 2013 and December 2024.Using multiparametric MRI data,we developed DLBCL-subtype classification models that combined 4 radiomics-based machine-learning algorithms:support vector machine(SVM),logistic regression(LR),Gaussian process(GP)and Naive Bayes(NB),with 3 deep-learning architectures[densely-connected convolutional networks 121(DenseNet121),residual network 101(ResNet101)and EfficientNet-b5].Additionally,two radiologists with different experience levels independently classified DLBCL on MRI in a blinded fashion.Model and radiologist performance were quantified using the area under the receiver operating characteristic curve(AUC),accuracy(ACC),and F1-score to evaluate their ability to distinguish GCB from non-GCB subtypes.This study was approved by the Ethics Committee of Huashan Hospital of Fudan University(No.KY2024-663),and all patients signed informed consents.Results:A total of 173 patients were enrolled(55 with GCB subtype and 118 with non-GCB subtype).Radiomics and deep learning methods effectively distinguished DLBCL subtypes.Among these,the GP radiomics model(based on T1-CE+T2-FLAIR+ADC sequences)and DenseNet121 deep learning model(based on T1-CE+T2-FLAIR+ADC sequences)demonstrated optimal performance.Both achieved excellent results on the internal validation set(GP:AUC=0.900,ACC=0.896,F1=0.840;DenseNet121:AUC=0.846,ACC=0.854,F1=0.774)and maintained robustness on the external validation set.Furthermore,the classification efficacy of the optimal AI model surpassed that of experienced radiologists(highest physician AUC=0.678).Conclusion:Radiomics and deep-learning models based on multiparametric MRI features can effectively differentiate GCB from non-GCB subtypes of DLBCL.Among them,GP and DenseNet121 exhibit outstanding performance,especially when integrating multi-sequence feature sets for classifying DLBCL subtypes on complex imaging data.

The minimally invasive cardiovascular surgery developed rapidly in last decades. In order to promote the development of minimally invasive cardiovascular surgery in China, the Chinese Minimally Invasive Cardiovascular Surgery Committee (CMICS) has gradually standardized the collection and report of the data of Chinese minimally invasive cardiovascular surgery since its establishment. The total operation volume of minimally invasive cardiovascular surgery in China has achieved substantial growth with a remarkable popularization of concepts of minimally invasive medicine in 2019. The data of Chinese minimally invasive cardiovascular surgery in 2019 was reported as a paper for the first time, which may provide reference to cardiovascular surgeons and related professionals.

Objective To investigate association of vitamin D receptor (VDR) polymorphisms with susceptibility to psoriasis vulgaris and clinical response to calcipotriol in patients with psoriasis vulgaris.Methods A total of 110 patients with psoriasis vulgaris and 183 healthy controls were enrolled into this study,and they were all of Han nationality from Hainan province.Ligase detection reaction (LDR) was conducted to determine the genotypes of VDR gene polymorphisms rs2228570,rs731236,rs1544410 and rs7975232.Single nucleotide polymorphism (SNP)-based association analysis in genotypic and allelic models,and haplotype-based association analysis were then performed.Then,75 patients with psoriasis area and severity index (PASI) scores less than 10 were topically treated with calcipotriol ointment alone.After 6-week treatment,the efficacy of calcipotriol ointment was evaluated,and the correlation between the efficacy and individual genotypes was analyzed.Results The frequency of A allele of rs7975232 in the psoriasis group and control group was 39.09％ and 27.05％ respectively,and the risk of developing psoriasis in rs7975232 A allele carriers was significantly higher than that in non-carriers (OR =1.731,95％ CI:1.213-2.471,P ＜ 0.05).Additionally,the risk of developing psoriasis in individuals with AA genotype (OR =2.404,95％ CI:1.085-5.328,P ＜ 0.05),as well as in individuals with AC genotype (OR =2.143,95％ CI:1.283-3.579,P ＜ 0.05),was significantly higher than that in patients with CC genotype.CTGA haplotype carriers (rs2228570,rs731236,rs1544410,rs7975232,respectively) had significantly higher risk of developing psoriasis compared with non-carriers (OR =1.907,95％ CI:1.132-3.214,P ＜ 0.05).Among 72 patients with mild-to-moderate psoriasis whose PASI scores were less than 10,patients with CC genotype of rs7975232 showed better response to calcipotriol ointment compared with those with AC genotype (OR =3.798,95％ CI:1.061-13.590,P ＜ 0.05) and those with AA genotype (OR =9.667,95％CI:1.556-60.040,P ＜ 0.05).Conclusion VDR polymorphisms are associated with psoriasis susceptibility and clinical response to calcipotriol in patients with psoriasis individuals.

To explore the mechanisms of Xiaotan Sanjie Decoction (XTSJD), a compound traditional Chinese herbal medicine, in inhibiting the tumor growth and preventing recurrence by testing the protein expressions of interleukin-8 (IL-8) and its receptors chemokine receptor 1 (CXCR1) and chemokine receptor 2 (CXCR2) in gastric tumor xenografts and gastric tissue adjacent to the tumor in mice.

Human beta defensin 2 (HBD-2) may play an important role in human defense against infection. Its antimicrobial capacity has been fully documented in in vitro study. In order to evaluae its in vivo effects, we developed an HBD-2 transgenic mouse model. The HBD-2 minigene containing CMV promoter, full length of HBD-2 cDNA and BGH polyA tail was generated by PCR amplification and introduced into the fertilized oocytes of C57 X ICR hybridized mouse by microinjection, and offspring were produced. DNA was isolated from the tails of the mouse pups, and the HBD-2 minigene incorporation was analyzed by PCR using HBD-2 specific primers. The HBD-2 gene expression in the multi-tissues of transgenic mice was determined at mRNA level by RT-PCR and at peptide level by immunohistological staining with the use of HBD-2 monoclonal antibody. The results showed that among 17 F0 transgenic mice, HBD-2 positive signal was determined by PCR in 4 mice, suggesting that HBD-2 minigene has been incorporated into the offspring mice. Meanwhile, a widespread expression of HBD-2 mRNA and peptide was detected in the F1 transgenic mice's multi-tissues such as trachea, lung, intestine, esophagus, testis, spleen, skin, endothelium and brain.
Animals ; Anti-Infective Agents ; Humans ; Mice ; Mice, Inbred C57BL ; Mice, Inbred ICR ; Mice, Transgenic ; Models, Animal ; Polymerase Chain Reaction ; RNA, Messenger ; analysis ; biosynthesis ; genetics ; beta-Defensins ; biosynthesis ; genetics