With the scientific and modernization of acupuncture,various kinds of acupuncture medical equipment continue to innovate,especially with the emergence of intelligent acupuncture diagnosis and treatment units,automatic detection of acupuncture needles in the"needle retention"stage of acupuncture clinical practice has become a hot demand.Aiming at the problems that the input image size is too large,the acupuncture needles are slender,and the acupuncture needles are densely distributed,the Acupuncture Needle Object Detection Model(ANODM),an improved YOLOv5 model for acupuncture needles,is proposed in this paper.① In the preprocessing stage,the image is divided into multiple patches for prediction,respectively.② At the model structure level,a new small object detection layer is added to the original three detection layers to improve the recognition ability of small objects.The ordinary convolution of the backbone network is replaced by the dialated convolution to increase the sensitivity field.Features of different stages are fused.③ In the post-processing stage,Soft-NMS is used to reduce the miss rate of positive samples,and cosine similarity match is used to reduce the error rate of negative samples.The experimental results show that,compared with the original YOLOv5,the detection accuracy of the improved YOLOv5 in this paper is improved by 4.2%on the acupuncture needle small object dataset,which can better complete the acupuncture needle small target detection task.

Objective:To investigate the role and mechanism of TrxR1 in reprogramming tumor-associated macrophage in breast cancer,providing novel insights and theoretical foundations for clinical breast cancer treatment.Methods:TISIDB database was used to analyze the relationship between TXNRD1(encoding TrxR1)and tumor immunity.Mouse breast cancer 4T1 cells conditioned medium was collected and co-cultured with bone marrow-derived macrophage(BMDM)cells for 48 h to detect the expression of macrophage immunosuppression-related factors.TrxR1 secretion by tumor cells was measured using ELISA kits.TXNRD1 knockdown efficiency was verified via Western blot.Fluorescence quantitative PCR(qPCR)and flow cytometry were used to detect the expression levels of macrophage immunosuppressive factors after TXNRD1 knockdown in tumor cells.JASPAR database was used to analyze the potential regulatory factors,and Western blot was used to verify the expression of pathway-related proteins.Results:Database analysis found that TXNRD1 expression positively correlated with survival risk indices across multiple cancers,with the strongest association observed in breast cancer.Further analysis found that elevated TXNRD1 expression correlated with reduced infiltration of M1 macrophages and natural killer(NK)cells,but increased M2 macrophage infiltration.qPCR and flow cytometry demonstrated that tumor-conditioned medium enhanced macrophage immunosuppression,whereas medium from TXNRD1-knockdown tumor cells suppressed this effect.And TrxR1-neutralizing antibodies could also reversed this effect.JASPAR database analysis identified STAT3 and STAT6 as potential transcriptional regulators,and Western blot confirmed that TXNRD1-knockdown tumor cells conditioned medium inhibited STAT6 pathway activation in macrophages.Conclusion:In the tumor microenvironment,breast tumor-derived TrxR1 promotes macrophage immunosuppression,potentially through activation of the STAT6 signaling pathway.

Objective:To investigate the role and mechanism of TrxR1 in reprogramming tumor-associated macrophage in breast cancer,providing novel insights and theoretical foundations for clinical breast cancer treatment.Methods:TISIDB database was used to analyze the relationship between TXNRD1(encoding TrxR1)and tumor immunity.Mouse breast cancer 4T1 cells conditioned medium was collected and co-cultured with bone marrow-derived macrophage(BMDM)cells for 48 h to detect the expression of macrophage immunosuppression-related factors.TrxR1 secretion by tumor cells was measured using ELISA kits.TXNRD1 knockdown efficiency was verified via Western blot.Fluorescence quantitative PCR(qPCR)and flow cytometry were used to detect the expression levels of macrophage immunosuppressive factors after TXNRD1 knockdown in tumor cells.JASPAR database was used to analyze the potential regulatory factors,and Western blot was used to verify the expression of pathway-related proteins.Results:Database analysis found that TXNRD1 expression positively correlated with survival risk indices across multiple cancers,with the strongest association observed in breast cancer.Further analysis found that elevated TXNRD1 expression correlated with reduced infiltration of M1 macrophages and natural killer(NK)cells,but increased M2 macrophage infiltration.qPCR and flow cytometry demonstrated that tumor-conditioned medium enhanced macrophage immunosuppression,whereas medium from TXNRD1-knockdown tumor cells suppressed this effect.And TrxR1-neutralizing antibodies could also reversed this effect.JASPAR database analysis identified STAT3 and STAT6 as potential transcriptional regulators,and Western blot confirmed that TXNRD1-knockdown tumor cells conditioned medium inhibited STAT6 pathway activation in macrophages.Conclusion:In the tumor microenvironment,breast tumor-derived TrxR1 promotes macrophage immunosuppression,potentially through activation of the STAT6 signaling pathway.

With the scientific and modernization of acupuncture,various kinds of acupuncture medical equipment continue to innovate,especially with the emergence of intelligent acupuncture diagnosis and treatment units,automatic detection of acupuncture needles in the"needle retention"stage of acupuncture clinical practice has become a hot demand.Aiming at the problems that the input image size is too large,the acupuncture needles are slender,and the acupuncture needles are densely distributed,the Acupuncture Needle Object Detection Model(ANODM),an improved YOLOv5 model for acupuncture needles,is proposed in this paper.① In the preprocessing stage,the image is divided into multiple patches for prediction,respectively.② At the model structure level,a new small object detection layer is added to the original three detection layers to improve the recognition ability of small objects.The ordinary convolution of the backbone network is replaced by the dialated convolution to increase the sensitivity field.Features of different stages are fused.③ In the post-processing stage,Soft-NMS is used to reduce the miss rate of positive samples,and cosine similarity match is used to reduce the error rate of negative samples.The experimental results show that,compared with the original YOLOv5,the detection accuracy of the improved YOLOv5 in this paper is improved by 4.2%on the acupuncture needle small object dataset,which can better complete the acupuncture needle small target detection task.

OBJECTIVETo investigate the value of MRI with CUBE sequence in early evaluation of the efficacy of neoadjuvant therapy (NAT) for locally advanced rectal cancer.

METHODSInclusion criteria: (1) rectal cancer proven by biopsy; (2) locally advanced rectal cancer (T3-4 or positive lymph nodes) with distance from lower edge of tumor to anal verge within 12 cm diagnosed by MRI before NAT; (3) acceptance of NAT treatment regulations and completion of NAT; (4) completion of routine MRI scan and CUBE scan before and after 2-course NAT chemotherapy (including new neoadjuvant chemotherapy and chemoradiotherapy); (5) completion of surgery 6-8 weeks after NAT; (6) exclusion of any previous NAT due to rectal malignant tumor or other tumors; (7) exclusion of poor image quality of preoperative routine MRI insufficient for rectal cancer staging or of CUBE image insufficient for tumor volume measurement. Fifty patients with advanced local rectal cancer were recruited in our hospital from February 2014 to January 2016. All the patients underwent MRI before and after 2-course neoadjuvent therapy. Tumor volume on CUBE were measured and the volume changes were calculated: volume difference= pre-treatment volume-post-treatment volume, volume change rate= (pre-treatment volume-post-treatment volume)/ pre-treatment volume. All the patients were categorized into sensitive and non-sensitive group according to postsurgical pathology. Comparisons were made between both groups before and after therapy. ROC curve was used to evaluate the value of CUBE-associated parameters in predicting the efficacy of rectal cancer.

RESULTSAmong enrolled 50 patients with rectal cancer, 31 were male and 19 were female, with mean age of 49.1 years (range 21 to 70 years). T-staging by MRI before NAT was T2N1-2 in 1 case, T3 in 43 cases, T4 in 6 cases. The number of patients after NAT from tumor regression grading (TRG) 0 to TRG3 was 14, 13, 18, 5, respectively. The sensitive group and insensitive group were 45 cases and 5 cases. Mean tumor volume before and after preoperative 2-course NAT was 18.70 (4.14 to 91.77) cmand 9.26 (1.02 to 52.58) cm, respectively, whose difference was significant (U=-5.826, P<0.001). Both measure values of overall tumor volume before and after preoperative NAT between sensitive group and insensitive group did not show significant differences(all P>0.05). While sensitivity group had significantly higher volume difference and change rate compared to insensitive group [ (11.90±10.01) cmvs. (0.65±3.93) cm, P=0.005; 0.45±0.28 vs. 0.09±0.36, P=0.010]. ROC curve revealed that the optimal cutoff value of accurate identification of patients with NAT sensitive was 1.96 cmfor volume difference with sensitivity 86.7% and specificity 80%, and 0.06 for volume change rate with sensitivity 93.3% and specificity 60%.

CONCLUSIONMRI CUBE can predict the efficacy of NAT for early rectal cancer patients accurately and sensitively through the detection of tumor volume change before and after NAT.