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Objective To explore the five-circuit and six-qi features of birth time and onset time of children with acute lymphoblastic leukemia(ALL).Methods A total of 877 cases of children with ALL from Children's Hospital of Soochow University from June 2021 to February 2023 were collected,and their five-circuit and six-qi features of birth time and onset time were analyzed.And then the correlation of five-circuit and six-qi features of birth time and onset time with ALL was explored preliminarily,and the pathogenic characteristics of congenital factors and acquired pathogenic factors were revealed.Results(1)The children who were born in the year with the heavenly stems being bing(the 3rd of the ten heavenly stems)and ding(the 4th of the ten heavenly stems)and with the earthly branches being shen(the 8th of the twelve earthly branches)and you(the 9th of the twelve earthly branches)are prone to suffer from ALL,and the birth year of children with ALL had the five-circuit and six-qi features of the joining of guest circuit with dominant circuit being rebellious.ALL is commonly seen in the year with the heavenly stems being geng(the 7th of the ten heavenly stems)and xin(the 8th of the ten heavenly stems)and with the earthly branches being zi(the 1st of the twelve earthly branches)and chou(the 2nd of the twelve earthly branches),and the onset year of ALL in children had the five-circuit and six-qi features of the yearly circuit being gold-circuit and water-circuit,sitian-zaiquan yearly circuit qi being shaoyin monarch-fire with yangming dryness-gold,taiyin damp-earth with taiyang cold-water,and the qi-circuit assimilation relationship being celestial correspondence,same celestial correspondence,celestial correspondence in convergent year,disharmony,mildly-rebellious,and celestial restriction.Conclusion Gold-dryness and water-cold are the congenital factors and acquired pathogenic factors of ALL.The onset of ALL in children is closely related to qi insufficiency and qi stagnation of wood and fire in five-circuit and six-qi theory.

Objective:To construct a deep learning model based on YOLOV8-Seg algorithm to conduct automatic segmentation for the central gland(CG)and peripheral zone(PZ)of prostate,so as to provide a reliable basis for clinical diagnosis and treatment.Methods:The sequence data of T2-weighted imaging(T2WI)of horizontal relaxation time of 158 patients were selected from a public data set of magnetic resonance imaging(MRI)for prostate MRI,which was provided by the Charité University Hospital in Berlin,were selected.The all data were divided into a training set(109 cases),a validation set(16 cases),and a test set(33 cases)as the ratio of 7 to1 to 2.A lightweight asymmetric decoupled head(LADH)structure and the large kernel UniRepLKNetBlock module were integrated into the YOLOV8-Seg algorithm to enhance the capabilities of model's extraction feature,and the new model was named as YOLOV8-URLK.The assessment model with mean Average Precision(mAP),Dice Similarity Coefficient(DSC),95%Hausdorff Distance(HD95),and Average Surface Distance(ASD)was adopted to segment performance of the detection at prostate CG and PZ.Comparative experiments were conducted among that and YOLOV8-Seg,TransU-Net,and U-Net network,so as to validate the effectiveness of YOLOV8-URLK for detection and segmentation at prostate zone.Results:On the test set,the mAP@0.5(box)of YOLOV8-URLK model was 0.878,and the mean Dice coefficients,the mean HD95 values and the ASD values of that at CG and PZ were respectively(0.867,17.123 and 1.461)and(14.902,0.898 and 1.112).On the test set,the mAP@0.5(box)of YOLOV8-Seg model was 0.860,and the mean Dice coefficients of that at CG and PZ were 0.851 and 0.884,the mean HD95 values of that at them were 19.174 and 15.298,and ASD values of that at them were 1.781 and 1.219,respectively.On test set,the mean Dice coefficients of TransU-Net model at CG and PZ were 0.864 and 0.824,and the mean HD95 values of that at them were 18.134 and 19.402,and ASD values of that at them were 1.698 and 1.717,respectively.On the test set,the mean Dice coefficients of the U-Net model at CG and PZ were 0.857 and 0.690,and the mean HD95 values of that at them were 18.976 and 26.934,and ASD values of that at them were 1.753 and 2.135.The YOLOV8-URLK model can better reappear the segmentation trend of manual annotations.Conclusion:The YOLOV8-URLK model demonstrates higher precision in the detection and segmentation of MRI images of prostate,which were superior to YOLOV8-Seg,TransU-Net and U-Net.It can enhance the efficiency of the detection and segmentation.

BACKGROUND:The combination of traditional Chinese medicine syndrome and semen quality-related parameters can jointly predict the occurrence of abnormal increase in sperm DNA fragmentation index (DFI) and draw a column chart,which can significantly improve clinical practicality and application efficiency,provide a basis for comprehensive evaluation of semen quality in clinical practice,take active intervention measures to improve clinical outcomes,and formulate personalized medical plans.OBJECTIVE:To explore the prediction model and verification of sperm DNA fragments based on traditional Chinese medicine syndrome and semen quality-related parameters.METHODS:Retrospective analysis was made on 420 infertile patients who received traditional Chinese medicine syndrome diagnosis and sperm DNA fragment rate examination in the Department of Traditional Chinese Medicine Andrology,Nanxishan Hospital of Guangxi Zhuang Autonomous Region from July 2019 to July 2021.According to the Manual of Human Semen Examination and Treatment Laboratories (6th Edition),137 patients with sperm DFI>30％ were included in the group of abnormally high sperm DFI,and 283 patients with sperm DFI ≤ 30％ were taken as the control group.First,univariate analysis was used to screen the influencing factors of the abnormal increase of sperm DFI.Then,the best matching factor was selected by using the collinearity problem of LASSO correction factors.Then,it was included in the multifactor forward stepwise logistic regression to find out its independent influencing factors and draw a nomogram.Finally,the receiver operating characteristic curve,calibration curve,decision curve analysis and clinical impact curve were used to verify the differentiation and accuracy of the prediction model and its clinical application effectiveness.RESULTS AND CONCLUSION:(1) The results of the univariate analysis showed that age,body mass index,forward motion rate,total sperm motility,sperm concentration,sperm morphology,kidney yang deficiency syndrome,damp heat downpour syndrome,and kidney sperm deficiency syndrome were the influencing factors for the abnormal increase of sperm DFI (P<0.05).(2) The best matching factors further screened by LASSO regression were age,body mass index,total sperm motility,sperm concentration,sperm morphology,kidney yang deficiency syndrome,damp heat downpour syndrome,and kidney essence deficiency syndrome (P<0.05).(3) Multifactor forward stepwise Logistic regression showed that age,body mass index,sperm concentration,total sperm motility,damp heat downpour syndrome,and kidney yang deficiency syndrome were six independent factors that caused the abnormal increase in sperm DFI.(4) Receiver operating characteristic curve showed that the area under the curve of the model group was 0.760(0.713,0.806),and the area under the curve of the validation group was 0.745(0.714,0.776).It showed that the prediction model had good discrimination.(5) The average absolute error of the calibration curve was 0.040,and the Hosmer Lemeshow test (P>0.05),suggesting that there was no significant statistical difference between the probability of the abnormal increase in DFI of spermatozoa predicted by the model and the probability of the abnormal increase in DFI of spermatozoa actually occurred,which confirmed that the model had good accuracy.(6) Decision curve analysis and clinical impact curve showed that the model group and validation group had the maximum clinical net benefit when the threshold probability values were (0.08-0.84) and (0.09-0.78) respectively,and had good clinical application efficiency within the threshold probability range.(7) These findings conclude that age,body mass index,sperm concentration,total sperm viability,damp heat downpour syndrome and kidney yang deficiency syndrome are independent factors that cause the abnormal increase in sperm DFI.The nomogram of the clinical prediction model constructed by them has good clinical prediction value and clinical application efficiency,and can provide the basis for comprehensive clinical evaluation of semen quality and individualized medical service.

Objective To investigate the effect and mechanism of miR-155-5p targeting suppressor of cytokine signaling 1(SOCS1)in regulating the Janus kinase 2(JAK2)/signal transducer and activator of transcrip-tion 3(STAT3)signaling pathway in renal injury associated with lupus nephritis(LN).Methods Thirty female MRL-faslpr lupus model mice were randomly divided into five groups(n=6 per group):the model group,the antagomir NC group,the miR-155-5p antagomir group,the miR-155-5p antagomir+shRNA control group,and the miR-155-5p antagomir+SOCS1 shRNA group.The mice were treated with adeno-associated virus vectors carrying miR-155-5p antagomir,antagomir NC,SOCS1 shRNA,or shRNA control.Additionally,six age-matched C57BL/6 mice served as a control group and received an equivalent volume of saline.Serum blood urea nitrogen(BUN)and creatinine(Scr)levels,renal histopathological changes,and the expression levels of miR-155-5p,SOCS1,phosphorylated JAK2(p-JAK2),and phosphorylated STAT3(p-STAT3)in renal tissues were evaluated.Results Compared with the normal group,the model group exhibited significantly elevated levels of BUN,Scr,miR-155-5p,p-JAK2,and p-STAT3 proteins in the kidneys(P<0.01),while the expression level of SOCS1 was markedly reduced(P<0.01).Compared with both the model group and the antagomir NC group,the miR-155-5p antagomir group showed decreased levels of BUN,Scr,miR-155-5p,p-JAK2,and p-STAT3 proteins(P<0.01),along with a significant increase in SOCS1 expression(P<0.01).Similarly,compared with the miR-155-5p antagomir group and the miR-155-5p antagomir+shRNA control group,the miR-155-5p antagomir+SOCS1 shRNA group demon-strated significantly higher levels of BUN,Scr,miR-155-5p,p-JAK2,and p-STAT3 proteins(P<0.01),while SOCS1 expression was notably decreased(P<0.01).Renal pathology analysis revealed that,compared to the normal group,the model group exhibited glomerular atrophy,extensive infiltration of inflammatory cells in the renal tubulointerstitial region,and partial renal tubular necrosis.In contrast,the miR-155-5p antagomir group showed marked improvements in glomerular atrophy,tubular necrosis,and inflammatory cell infiltration compared with the model group and antagomir NC group.Furthermore,compared with the miR-155-5p antagomir group and the miR-155-5p antagomir+shRNA control group,the miR-155-5p antagomir+SOCS1 shRNA group exhibited more severe glomerular atrophy,tubular necrosis,and inflammatory cell infiltration.Conclusion MiR-155-5p exacerbates renal damage in MRL-faslpr lupus model mice by targeting SOCS1,potentially through the activation of the JAK2/STAT3 signaling pathway.

Objective:To investigate the predictive value of transabdominal uterine electromyography for pre-term labor after tocolysis in women with threatened preterm labor.Methods:A total of 48 pregnant women at 28-34 weeks of gestation diagnosed with threatened preterm labor and admitted to The Fourth Affiliated Hospital of Soo-chow University from January to September 2023 were included.According to the response to tocolysis and whether the pregnancy was prolonged for at least 48 h,women were divided into two groups:non-preterm birth within 48 h(n=35)and preterm birth within 48 h(n=13).Uterine electromyography parameters and difference were compared before and after tocolytic therapy in two groups.Univariate Logistic regression was performed to predict the related factors of preterm birth within 48 h after the using of tocolysis in pregnant women with threat-ened preterm birth by uterine electromyography,and receiver operating characteristic(ROC)curve was per-formed to evaluate their performance.Results:Compared to before treatment with tocolysis,after therapy,in the non-preterm birth within 48 h group,significant reductions in contraction frequency,area,duration and amplitude were observed(P<0.05).In the preterm birth within 48 h group,only contraction frequency decreased significant-ly(P<0.05).Univariate Logistic regression indicated that contraction frequency,contraction duration,and contrac-tion area were predictive factors for premature birth within 48 h after tocolysis(P<0.05).When the duration of u-terine contractions lasting for 104.55 s or more the sensitivity and specificity of predicting premature birth within 48 h are 92.3％and 68.6％,respectively.Conclusions:Uterine electromyography may predict the premature birth within 48 h after tocolytic treatment in preterm labor,which may provide reference for subsequent corticosteroid therapy or transfer of high-risk pregnant patients.

Objective To investigate the effect and mechanism of miR-155-5p targeting suppressor of cytokine signaling 1(SOCS1)in regulating the Janus kinase 2(JAK2)/signal transducer and activator of transcrip-tion 3(STAT3)signaling pathway in renal injury associated with lupus nephritis(LN).Methods Thirty female MRL-faslpr lupus model mice were randomly divided into five groups(n=6 per group):the model group,the antagomir NC group,the miR-155-5p antagomir group,the miR-155-5p antagomir+shRNA control group,and the miR-155-5p antagomir+SOCS1 shRNA group.The mice were treated with adeno-associated virus vectors carrying miR-155-5p antagomir,antagomir NC,SOCS1 shRNA,or shRNA control.Additionally,six age-matched C57BL/6 mice served as a control group and received an equivalent volume of saline.Serum blood urea nitrogen(BUN)and creatinine(Scr)levels,renal histopathological changes,and the expression levels of miR-155-5p,SOCS1,phosphorylated JAK2(p-JAK2),and phosphorylated STAT3(p-STAT3)in renal tissues were evaluated.Results Compared with the normal group,the model group exhibited significantly elevated levels of BUN,Scr,miR-155-5p,p-JAK2,and p-STAT3 proteins in the kidneys(P<0.01),while the expression level of SOCS1 was markedly reduced(P<0.01).Compared with both the model group and the antagomir NC group,the miR-155-5p antagomir group showed decreased levels of BUN,Scr,miR-155-5p,p-JAK2,and p-STAT3 proteins(P<0.01),along with a significant increase in SOCS1 expression(P<0.01).Similarly,compared with the miR-155-5p antagomir group and the miR-155-5p antagomir+shRNA control group,the miR-155-5p antagomir+SOCS1 shRNA group demon-strated significantly higher levels of BUN,Scr,miR-155-5p,p-JAK2,and p-STAT3 proteins(P<0.01),while SOCS1 expression was notably decreased(P<0.01).Renal pathology analysis revealed that,compared to the normal group,the model group exhibited glomerular atrophy,extensive infiltration of inflammatory cells in the renal tubulointerstitial region,and partial renal tubular necrosis.In contrast,the miR-155-5p antagomir group showed marked improvements in glomerular atrophy,tubular necrosis,and inflammatory cell infiltration compared with the model group and antagomir NC group.Furthermore,compared with the miR-155-5p antagomir group and the miR-155-5p antagomir+shRNA control group,the miR-155-5p antagomir+SOCS1 shRNA group exhibited more severe glomerular atrophy,tubular necrosis,and inflammatory cell infiltration.Conclusion MiR-155-5p exacerbates renal damage in MRL-faslpr lupus model mice by targeting SOCS1,potentially through the activation of the JAK2/STAT3 signaling pathway.

Objective:To investigate the predictive value of transabdominal uterine electromyography for pre-term labor after tocolysis in women with threatened preterm labor.Methods:A total of 48 pregnant women at 28-34 weeks of gestation diagnosed with threatened preterm labor and admitted to The Fourth Affiliated Hospital of Soo-chow University from January to September 2023 were included.According to the response to tocolysis and whether the pregnancy was prolonged for at least 48 h,women were divided into two groups:non-preterm birth within 48 h(n=35)and preterm birth within 48 h(n=13).Uterine electromyography parameters and difference were compared before and after tocolytic therapy in two groups.Univariate Logistic regression was performed to predict the related factors of preterm birth within 48 h after the using of tocolysis in pregnant women with threat-ened preterm birth by uterine electromyography,and receiver operating characteristic(ROC)curve was per-formed to evaluate their performance.Results:Compared to before treatment with tocolysis,after therapy,in the non-preterm birth within 48 h group,significant reductions in contraction frequency,area,duration and amplitude were observed(P<0.05).In the preterm birth within 48 h group,only contraction frequency decreased significant-ly(P<0.05).Univariate Logistic regression indicated that contraction frequency,contraction duration,and contrac-tion area were predictive factors for premature birth within 48 h after tocolysis(P<0.05).When the duration of u-terine contractions lasting for 104.55 s or more the sensitivity and specificity of predicting premature birth within 48 h are 92.3％and 68.6％,respectively.Conclusions:Uterine electromyography may predict the premature birth within 48 h after tocolytic treatment in preterm labor,which may provide reference for subsequent corticosteroid therapy or transfer of high-risk pregnant patients.

BACKGROUND:The combination of traditional Chinese medicine syndrome and semen quality-related parameters can jointly predict the occurrence of abnormal increase in sperm DNA fragmentation index (DFI) and draw a column chart,which can significantly improve clinical practicality and application efficiency,provide a basis for comprehensive evaluation of semen quality in clinical practice,take active intervention measures to improve clinical outcomes,and formulate personalized medical plans.OBJECTIVE:To explore the prediction model and verification of sperm DNA fragments based on traditional Chinese medicine syndrome and semen quality-related parameters.METHODS:Retrospective analysis was made on 420 infertile patients who received traditional Chinese medicine syndrome diagnosis and sperm DNA fragment rate examination in the Department of Traditional Chinese Medicine Andrology,Nanxishan Hospital of Guangxi Zhuang Autonomous Region from July 2019 to July 2021.According to the Manual of Human Semen Examination and Treatment Laboratories (6th Edition),137 patients with sperm DFI>30％ were included in the group of abnormally high sperm DFI,and 283 patients with sperm DFI ≤ 30％ were taken as the control group.First,univariate analysis was used to screen the influencing factors of the abnormal increase of sperm DFI.Then,the best matching factor was selected by using the collinearity problem of LASSO correction factors.Then,it was included in the multifactor forward stepwise logistic regression to find out its independent influencing factors and draw a nomogram.Finally,the receiver operating characteristic curve,calibration curve,decision curve analysis and clinical impact curve were used to verify the differentiation and accuracy of the prediction model and its clinical application effectiveness.RESULTS AND CONCLUSION:(1) The results of the univariate analysis showed that age,body mass index,forward motion rate,total sperm motility,sperm concentration,sperm morphology,kidney yang deficiency syndrome,damp heat downpour syndrome,and kidney sperm deficiency syndrome were the influencing factors for the abnormal increase of sperm DFI (P<0.05).(2) The best matching factors further screened by LASSO regression were age,body mass index,total sperm motility,sperm concentration,sperm morphology,kidney yang deficiency syndrome,damp heat downpour syndrome,and kidney essence deficiency syndrome (P<0.05).(3) Multifactor forward stepwise Logistic regression showed that age,body mass index,sperm concentration,total sperm motility,damp heat downpour syndrome,and kidney yang deficiency syndrome were six independent factors that caused the abnormal increase in sperm DFI.(4) Receiver operating characteristic curve showed that the area under the curve of the model group was 0.760(0.713,0.806),and the area under the curve of the validation group was 0.745(0.714,0.776).It showed that the prediction model had good discrimination.(5) The average absolute error of the calibration curve was 0.040,and the Hosmer Lemeshow test (P>0.05),suggesting that there was no significant statistical difference between the probability of the abnormal increase in DFI of spermatozoa predicted by the model and the probability of the abnormal increase in DFI of spermatozoa actually occurred,which confirmed that the model had good accuracy.(6) Decision curve analysis and clinical impact curve showed that the model group and validation group had the maximum clinical net benefit when the threshold probability values were (0.08-0.84) and (0.09-0.78) respectively,and had good clinical application efficiency within the threshold probability range.(7) These findings conclude that age,body mass index,sperm concentration,total sperm viability,damp heat downpour syndrome and kidney yang deficiency syndrome are independent factors that cause the abnormal increase in sperm DFI.The nomogram of the clinical prediction model constructed by them has good clinical prediction value and clinical application efficiency,and can provide the basis for comprehensive clinical evaluation of semen quality and individualized medical service.

Objective:To construct a deep learning model based on YOLOV8-Seg algorithm to conduct automatic segmentation for the central gland(CG)and peripheral zone(PZ)of prostate,so as to provide a reliable basis for clinical diagnosis and treatment.Methods:The sequence data of T2-weighted imaging(T2WI)of horizontal relaxation time of 158 patients were selected from a public data set of magnetic resonance imaging(MRI)for prostate MRI,which was provided by the Charité University Hospital in Berlin,were selected.The all data were divided into a training set(109 cases),a validation set(16 cases),and a test set(33 cases)as the ratio of 7 to1 to 2.A lightweight asymmetric decoupled head(LADH)structure and the large kernel UniRepLKNetBlock module were integrated into the YOLOV8-Seg algorithm to enhance the capabilities of model's extraction feature,and the new model was named as YOLOV8-URLK.The assessment model with mean Average Precision(mAP),Dice Similarity Coefficient(DSC),95%Hausdorff Distance(HD95),and Average Surface Distance(ASD)was adopted to segment performance of the detection at prostate CG and PZ.Comparative experiments were conducted among that and YOLOV8-Seg,TransU-Net,and U-Net network,so as to validate the effectiveness of YOLOV8-URLK for detection and segmentation at prostate zone.Results:On the test set,the mAP@0.5(box)of YOLOV8-URLK model was 0.878,and the mean Dice coefficients,the mean HD95 values and the ASD values of that at CG and PZ were respectively(0.867,17.123 and 1.461)and(14.902,0.898 and 1.112).On the test set,the mAP@0.5(box)of YOLOV8-Seg model was 0.860,and the mean Dice coefficients of that at CG and PZ were 0.851 and 0.884,the mean HD95 values of that at them were 19.174 and 15.298,and ASD values of that at them were 1.781 and 1.219,respectively.On test set,the mean Dice coefficients of TransU-Net model at CG and PZ were 0.864 and 0.824,and the mean HD95 values of that at them were 18.134 and 19.402,and ASD values of that at them were 1.698 and 1.717,respectively.On the test set,the mean Dice coefficients of the U-Net model at CG and PZ were 0.857 and 0.690,and the mean HD95 values of that at them were 18.976 and 26.934,and ASD values of that at them were 1.753 and 2.135.The YOLOV8-URLK model can better reappear the segmentation trend of manual annotations.Conclusion:The YOLOV8-URLK model demonstrates higher precision in the detection and segmentation of MRI images of prostate,which were superior to YOLOV8-Seg,TransU-Net and U-Net.It can enhance the efficiency of the detection and segmentation.