Objective:To establish a model based on the characteristics of breast cancer ultrasound images through deep learning methods to predict the risk of axillary lymph node metastasis（ALNM）in patients with breast cancer（maximum diameter ≤2.5 cm）before surgery.Methods:A total of 419 patients（3 433 breast tumor ultrasound images）with breast cancer（maximum diameter ≤2.5 cm）who underwent axillary lymph node dissection at Beijing Tiantan Hospital，Capital Medical University from January 2019 to December 2024 were retrospectively included. According to the pathological results of axillary lymph nodes，they were divided into 220 cases in the ALNM occurrence group（positive group）and 199 cases in the non-ALNM occurrence group（negative group）. The breast cancer ultrasound images of the two groups of cases were randomly classified into the training set（2 404 images），the validation set（687 images）and the test set（342 images）according to a ratio of 7∶2∶1. YOLOv8 was used as the basic model of You Only Look Once（YOLO）and optimized. The optimized model was applied to locate and capture the potential ultrasound features of breast cancer cases in the training set. A prediction model was constructed based on the captured ultrasound features. The model was adjusted and optimized through the validation set，and then matched with the case images in the test set. The confusion classification matrix graph and the curve graph for measuring the model performance were used to evaluate the model prediction performance and interpret the model，and the efficacy of this model in identifying breast cancer patients at risk of ALNM was analyzed.Results:There were statistically significant differences between the positive and negative groups in terms of the pathological maximum diameter of breast tumors，pathological T staging，the differentiation degree，the presence of distant metastasis，the maximum diameter measured by ultrasound，the quadrant of breast tumor occurrence，the Breast Imaging - Reporting and Data System（BI-RADS）classification of breast tumors，and the presence of abnormal ultrasound features of lymph node（all P<0.05）. The established deep learning model could automatically perform bounding box localization for the breast cancer of patients.The breast tumors in the positive group had potential ultrasound features that could be captured by the model compared with those in the negative group. The mean average precision（mAP）50 was 0.883，mAP 50-95 was 0.636，PR-AUC was 0.884 5，strict PR-AUC was 0.636 4，the sensitivity was 90.5%，and the specificity was 91.2%，and it had a good predictive efficacy. Conclusions:This prediction model based on the ultrasound characteristics of breast cancer through deep learning can effectively predict breast cancer（maximum diameter ≤ 2.5 cm）with the risk of ALNM，providing an effective basis for the clinical management of axillary lymph nodes in breast cancer patients.

T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.

A method for determination and targeted screening of diflorasone components in health products using ultra performance liquid chromatography-quadrupole time of flight mass spectrometry(UPLC-Q-TOF/MS)was established.Four representative diflorasone and esters(diflorasone,diflorasone diacetate,diflorasone-17-propionate,and diflorasone-21-propionate)were selected to optimize the pretreatment conditions,and 10 mL of extraction solvent dosage,15 min of extraction time and 5 g of salting-out agent as the optimal conditions were selected by response surface methodology.The results showed that the four analytes exhibited good linearity within the concentration range of 2.0?100 μg/L with the chromatographic peak area,and the correlation coefficients(R2)were all greater than 0.9990,while the results of recovery and relative standard deviation could satisfy the requirements of determination.The common characteristic ions of diflorasone and esters werem/z121 andm/z335,and their specific structures were obtained by analyzing the cleavage pathway based on the optimized determination conditions.A targeted screening method for other esters of diflorasone based on characteristic ions guidance strategy was established.This method had many advantages such as high efficiency,high sensitivity and good reproducibility,and could be used for targeted screening of diflorasone and esters in health products.The developed characteristic ion guided strategy could be employed to construct mass spectral databases for various glucocorticoids,enabling comprehensive targeted screening across a broad range of compounds.

Lentiviral vectors(LVs)are key gene delivery tools for integrating target genes into the host genome,but they may also pose risks of insertional mutagenesis.The vector copy number(VCN)in cells is critical for determining the safety of gene modification.However,the reliability and accuracy of its quantification process are influenced by multiple factors.Developing cell reference materials with specific vector copy numbers represents a viable approach to enhance the reliability and consistency of measurement results,enabling quality control of the quantification process and traceability of outcomes.However,the preparation of such reference materials faces challenges in cell sample design,preparation protocols,and advanced quantification techniques.In this study,T lymphocyte cell line Jurkat-based reference materials with LV gene copy numbers of 1 and 2 copy/cell were developed.A high-precision duplex digital polymerase chain reaction(dPCR)method was established to quantify the LV gene and endogenous genes simultaneously.Additionally,the results of dPCR were cross-validated through next-generation sequencing and flow cytometric analysis.Ultimately,confocal microscopy characterization results showed that the developed cell reference materials had intact morphology.The quantification result of VCN-1 was(1.07±0.11)copy/cell,and that of VCN-2 was(2.09±0.21)copy/cell.These cell reference materials demonstrated compliance with stability and homogeneity requirements,and could be applied for quality control throughout the VCN measurement workflow and metrological traceability,improving the accuracy,comparability,and validity of copy number measurements.

Objective:To explore the mechanism of Danxing Zhishuang Prescription in the treatment of Parkinson's disease (PD) by combining network pharmacology with animal models.Methods:TCMSP, BATMAN database, Genecards, and OMIM databases were retrieved to obtain the active components and action targets of Danxing Zhishuang Prescription. Venny 2.1.0 was used to intersect drug targets and PD related genes, and a protein interaction network of the intersection targets was constructed using the STRING 12.0 platform. Topology analysis was performed using Cytoscape 3.10.0 software to identify the key targets of Danxing Zhishuang Prescription on PD; GO functional and KEGG pathway enrichment analysis was performed on key targets using the WeChat platform, and molecular docking was validated through AutoDockTools 1.5.7. Using a random number table method, mice were divided into a blank control group, a model group, and a Danxing Zhishuang Prescription group, with 20 mice in each group; except for the blank group, all other groups of mice were orally administered fisetin to prepare PD models; Danxing Zhishuang Prescription group was orally administered with concentrated Danxing Zhishuang Prescription at a dosage of 10.5 g/kg, while the blank group and model group were orally administered with 0.2 ml of physiological saline for 21 days; Western blot was used to detect the expressions of Akt1, Bcl-2, Bax, and α-Syn proteins.Results:359 intersection targets, 69 core targets, and 185 active components were obtained the treatment of PD with Danxing Zhishuang Prescription. The main active components included quercetin, kaempferol, phenylalanine, etc., and the key targets were AKT1, TP53, TNF, ESR1, etc. KEGG analysis revealed several key signaling pathways, such as AGE-RAGE, PI3K-Akt, fluid shear stress and atherosclerosis signaling pathways. The validation experiment results showed that compared with the model group, the expression of Bcl-2 protein was up-regulated ( P<0.01), and the expressions of Bax, Akt1, and α-Syn proteins were down-regulated in the Danxing Zhishuang Prescription group ( P<0.01). Conclusions:Danxing Zhishuang Prescription has the advantages of multi target and multi pathway treatment for PD. Its mechanism may be related to down-regulating the expressions of Bax, Akt1, and α-Syn proteins, improving brain blood supply, regulating neurotransmitter balance, inhibiting oxidative stress response, and promoting nerve regeneration.

Objective:To evaluate the potential of IgG N-glycans as diagnostic biomarker for ankylosing spondylitis (AS) by comparing and analyzing the IgG N-glycan profiles with AS and healthy controls.Methods:A 1∶1 matched case-control study design was adopted, 81 AS patients who visited the Department of Rheumatology and Immunology at Taian City Central Hospital and the Second Affiliated Hospital of Shandong First Medical University between July 2020 and June 2021 were recruited. These patients were matched with 81 healthy individuals undergoing routine physical checkup. The levels of IgG N-glycosylation in human plasma were quantitatively measured using ultrahigh-performance liquid chromatography. Binomial logistic regression analysis was performed to identify IgG N-glycan biomarkers associated with AS.Results:A total of 14 primary glycans and 13 derived traits showed statistically significant differences between the AS case group and the control group. Binomial logistic regression analysis showed that glycan peak 4, agalactosylated glycans, fucosylated glycans, and fucosylated agalactosylated glycans were positively associated with AS[ OR(95% CI)=1.12(1.01, 1.42), 1.21(1.03, 1.43), 1.48(1.08, 2.03), and 1.27(1.04, 1.55); P=0.036, 0.022, 0.039, 0.020, respectively]. In terms of diagnostic performance, the single glycan GP4 exhibited the largest area under the ROC curve, with an AUC (95% CI) 0.751 (0.677, 0.826), while the combined glycan indicators (GP4+G0+F+FG0) achieved an AUC (95% CI) 0.768(0.697, 0.840). Conclusion:IgG N-glycans have the potentials to serve as candidate biomarkers for AS, and warrants further investigation.

Objective:To establish a model based on the characteristics of breast cancer ultrasound images through deep learning methods to predict the risk of axillary lymph node metastasis（ALNM）in patients with breast cancer（maximum diameter ≤2.5 cm）before surgery.Methods:A total of 419 patients（3 433 breast tumor ultrasound images）with breast cancer（maximum diameter ≤2.5 cm）who underwent axillary lymph node dissection at Beijing Tiantan Hospital，Capital Medical University from January 2019 to December 2024 were retrospectively included. According to the pathological results of axillary lymph nodes，they were divided into 220 cases in the ALNM occurrence group（positive group）and 199 cases in the non-ALNM occurrence group（negative group）. The breast cancer ultrasound images of the two groups of cases were randomly classified into the training set（2 404 images），the validation set（687 images）and the test set（342 images）according to a ratio of 7∶2∶1. YOLOv8 was used as the basic model of You Only Look Once（YOLO）and optimized. The optimized model was applied to locate and capture the potential ultrasound features of breast cancer cases in the training set. A prediction model was constructed based on the captured ultrasound features. The model was adjusted and optimized through the validation set，and then matched with the case images in the test set. The confusion classification matrix graph and the curve graph for measuring the model performance were used to evaluate the model prediction performance and interpret the model，and the efficacy of this model in identifying breast cancer patients at risk of ALNM was analyzed.Results:There were statistically significant differences between the positive and negative groups in terms of the pathological maximum diameter of breast tumors，pathological T staging，the differentiation degree，the presence of distant metastasis，the maximum diameter measured by ultrasound，the quadrant of breast tumor occurrence，the Breast Imaging - Reporting and Data System（BI-RADS）classification of breast tumors，and the presence of abnormal ultrasound features of lymph node（all P<0.05）. The established deep learning model could automatically perform bounding box localization for the breast cancer of patients.The breast tumors in the positive group had potential ultrasound features that could be captured by the model compared with those in the negative group. The mean average precision（mAP）50 was 0.883，mAP 50-95 was 0.636，PR-AUC was 0.884 5，strict PR-AUC was 0.636 4，the sensitivity was 90.5%，and the specificity was 91.2%，and it had a good predictive efficacy. Conclusions:This prediction model based on the ultrasound characteristics of breast cancer through deep learning can effectively predict breast cancer（maximum diameter ≤ 2.5 cm）with the risk of ALNM，providing an effective basis for the clinical management of axillary lymph nodes in breast cancer patients.

Objective:To evaluate the potential of IgG N-glycans as diagnostic biomarker for ankylosing spondylitis (AS) by comparing and analyzing the IgG N-glycan profiles with AS and healthy controls.Methods:A 1∶1 matched case-control study design was adopted, 81 AS patients who visited the Department of Rheumatology and Immunology at Taian City Central Hospital and the Second Affiliated Hospital of Shandong First Medical University between July 2020 and June 2021 were recruited. These patients were matched with 81 healthy individuals undergoing routine physical checkup. The levels of IgG N-glycosylation in human plasma were quantitatively measured using ultrahigh-performance liquid chromatography. Binomial logistic regression analysis was performed to identify IgG N-glycan biomarkers associated with AS.Results:A total of 14 primary glycans and 13 derived traits showed statistically significant differences between the AS case group and the control group. Binomial logistic regression analysis showed that glycan peak 4, agalactosylated glycans, fucosylated glycans, and fucosylated agalactosylated glycans were positively associated with AS[ OR(95% CI)=1.12(1.01, 1.42), 1.21(1.03, 1.43), 1.48(1.08, 2.03), and 1.27(1.04, 1.55); P=0.036, 0.022, 0.039, 0.020, respectively]. In terms of diagnostic performance, the single glycan GP4 exhibited the largest area under the ROC curve, with an AUC (95% CI) 0.751 (0.677, 0.826), while the combined glycan indicators (GP4+G0+F+FG0) achieved an AUC (95% CI) 0.768(0.697, 0.840). Conclusion:IgG N-glycans have the potentials to serve as candidate biomarkers for AS, and warrants further investigation.

Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing. Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing. Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing. Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.

Objective To investigate the equivalent conversion of head injury criterion(HIC)under anterior-posterior(AP)and lateral-medial(LM)craniocerebral impact for mild craniocerebral injury in rats using motor evoked potential(MEP)and β-amyloid precursor protein(β-APP)immunohistochemistry(IHC).Methods Sixty healthy adult male SD rats were randomly divided into 0 m control group,0.5 m-AP and 0.5 m-LM injury groups,and 1 m-AP and 1 m-LM injury groups(12 rats in each group).The control group did not undergo any impact injury experiment.After the impact injury experiment,the injury and control groups were subjected to excessive anesthesia to produce β-APP immunohistochemical stained slices,and the percentage of positive area and integral optical density(IOD)in the brainstem pyramidal tract area of the slices were determined.The MEP groups were divided in the same manner as the IHC groups and the MEP amplitudes of the MEP and control groups were measured after the impact injury experiment.Results With an increase in the degree of injury,the decrease in MEP amplitude,percentage of positive areas,and IOD in the injury groups significantly increased.When the degree of injury was low,the sensitivity of IHC was higher than that of MEP.When the degree of injury was the same,the HIC in the LM direction was lower than that in the AP direction.When the HIC was the same,the degree of injury in the LM direction was greater than that in the AP direction.Conclusions The joint evaluation of MEP and β-APP can provide experimental references for the study of HIC equivalent conversion in AP-LM craniocerebral impact injury.