Objective:To investigate the efficacy of a predictive model for differentiating benign and malignant renal tumors based on CT radiomic features and clinical features.Methods:A retrospective study was conducted on 1 395 patients with renal tumors admitted to the First Affiliated Hospital of Zhengzhou University from December 2011 to December 2021, including 842 males and 553 females. The median age was 55 (44, 59) years, and the median tumor diameter was 3.6 (2.7, 4.6) cm. All patients underwent contrast-enhanced CT scaning before surgery, and radiomic features were extracted from non-contrast, arterial, and venous phase images. Prediction models for distinguishing benign and malignant renal tumors were constructed using five machine learning algorithms (logistic regression, support vector machine, neural network, random forest, and extreme gradient boosting), and these models were then ensembled to construct a stacking classifier. All patients underwent partial nephrectomy, and they were divided into a training group (941 cases, December 2011 to June 2020) and a validation group (454 cases, July 2020 to December 2021) based on the date of surgery. A clinical-radiomic model was developed by combining the result of stacking classifier, clinical features and CT report results, and its predictive performance was evaluated in the validation group.Results:The radiomic signature based on the combined features and five machine learning algorithms(AUC 0.835-0.844) showed higher accuracy in predicting benign and malignant renal tumors compared to single phases (AUC 0.744-0.831). After integrating the five machine learning algorithms, the AUC of the three-phase combined radiomic model in the validation group improved to 0.847(95% CI 0.802-0.892). The clinical-radiomic model, incorporating radiomic features, clinical features, and CT report results, achieved a significantly higher AUC in the validation group compared to radiologists [0.919(95% CI 0.889-0.950)vs. 0.835(95% CI 0.786-0.883), P<0.01]. Conclusions:The predictive model integrating CT radiomics features, clinical characteristics, and CT report results demonstrates excellent discriminative ability in distinguishing benign and malignant renal tumors.

Background and purpose:Single-week ultra-hypofractionated whole breast irradiation(WBI)after breast-conserving surgery could shorten the treatment duration while ensuring efficacy and safety,making it a viable option for WBI.However,ultra-hypofractionated WBI requires daily image-guided radiotherapy(IGRT),and its impact on setup errors remains unclear.This study aimed to identify factors associated with set-up errors in ultra-hypofractionated WBI guided with daily cone-beam computed tomography(CBCT)and calculate margin expanded from clinical target volume(CTV)to planning target volume(PTV).Methods:This study included patients enrolled in a prospective trial that explored the safety of single-week ultra-hypofractionated WBI(NCT04926766)in Shanghai Ruijin Hospital,which was approved by Shanghai Ruijin Hospital Ethics Committee(No.2020-352).All patients received CBCT1 after positioning.After correcting errors,patients received CBCT2.CBCT3 was conducted after radiotherapy was completed.The translational errors between CBCT1,CBCT2,and plan CT were initial and residual inter-fractional errors.The translational error between CBCT2 and CBCT3 was an intra-fractional error.The PTV margin was calculated according to the van Herk formula.Results:A total of 34 patients were enrolled in this study,and 510 CBCT images were collected.Daily CBCT significantly reduced set-up error in anterior-posterior(AP),superior-inferior(SI)and right-left(RL)directions(initial inter-fractional error vs residual inter-fractional error:AP,2.8 mm vs 0.4 mm;SI,1.6 mm vs 0.5 mm;RL,1.8 mm vs 0.3 mm,all P＜0.001).Higher CTV volume(＞402.5 cm3 vs≤402.5 cm3)was associated with larger residual inter-fractional error(0.5 mm vs 0.3 mm,P=0.023)and intra-fractional error(0.5 mm vs 0.2 mm,P=0.001)in AP direction.Higher CTV volume was also associated with larger residual inter-fractional error in the SI direction(0.6 mm vs 0.5 mm,P=0.037).Higher BMI(＞23.2 kg/m2 vs≤23.2 kg/m2)and larger weight(＞60.0 kg vs≤60.0 kg)were associated with larger intra-fractional error in AP direction:0.7 mm vs 0.2 mm(P＜0.001)and 0.5 mm vs 0.2 mm(P=0.033),respectively.Under guidance with daily CBCT,the recommended margins were 2.3 mm in AP direction,2.8 mm in SI direction,and 2.0 mm in RL direction.However,in patients with CTV volume＞402.5 cm3 and BMI＞23.2 kg/m2,a larger margin was recommended in SI direction:3.1 mm and 3.4 mm,respectively.Conclusion:The 3 mm margin was feasible under guidance with daily CBCT.The CTV to PTV margin should be larger in patients with higher BMI or CTV volume.

Background and purpose:Single-week ultra-hypofractionated whole breast irradiation(WBI)after breast-conserving surgery could shorten the treatment duration while ensuring efficacy and safety,making it a viable option for WBI.However,ultra-hypofractionated WBI requires daily image-guided radiotherapy(IGRT),and its impact on setup errors remains unclear.This study aimed to identify factors associated with set-up errors in ultra-hypofractionated WBI guided with daily cone-beam computed tomography(CBCT)and calculate margin expanded from clinical target volume(CTV)to planning target volume(PTV).Methods:This study included patients enrolled in a prospective trial that explored the safety of single-week ultra-hypofractionated WBI(NCT04926766)in Shanghai Ruijin Hospital,which was approved by Shanghai Ruijin Hospital Ethics Committee(No.2020-352).All patients received CBCT1 after positioning.After correcting errors,patients received CBCT2.CBCT3 was conducted after radiotherapy was completed.The translational errors between CBCT1,CBCT2,and plan CT were initial and residual inter-fractional errors.The translational error between CBCT2 and CBCT3 was an intra-fractional error.The PTV margin was calculated according to the van Herk formula.Results:A total of 34 patients were enrolled in this study,and 510 CBCT images were collected.Daily CBCT significantly reduced set-up error in anterior-posterior(AP),superior-inferior(SI)and right-left(RL)directions(initial inter-fractional error vs residual inter-fractional error:AP,2.8 mm vs 0.4 mm;SI,1.6 mm vs 0.5 mm;RL,1.8 mm vs 0.3 mm,all P＜0.001).Higher CTV volume(＞402.5 cm3 vs≤402.5 cm3)was associated with larger residual inter-fractional error(0.5 mm vs 0.3 mm,P=0.023)and intra-fractional error(0.5 mm vs 0.2 mm,P=0.001)in AP direction.Higher CTV volume was also associated with larger residual inter-fractional error in the SI direction(0.6 mm vs 0.5 mm,P=0.037).Higher BMI(＞23.2 kg/m2 vs≤23.2 kg/m2)and larger weight(＞60.0 kg vs≤60.0 kg)were associated with larger intra-fractional error in AP direction:0.7 mm vs 0.2 mm(P＜0.001)and 0.5 mm vs 0.2 mm(P=0.033),respectively.Under guidance with daily CBCT,the recommended margins were 2.3 mm in AP direction,2.8 mm in SI direction,and 2.0 mm in RL direction.However,in patients with CTV volume＞402.5 cm3 and BMI＞23.2 kg/m2,a larger margin was recommended in SI direction:3.1 mm and 3.4 mm,respectively.Conclusion:The 3 mm margin was feasible under guidance with daily CBCT.The CTV to PTV margin should be larger in patients with higher BMI or CTV volume.

Objective:To investigate the efficacy of a predictive model for differentiating benign and malignant renal tumors based on CT radiomic features and clinical features.Methods:A retrospective study was conducted on 1 395 patients with renal tumors admitted to the First Affiliated Hospital of Zhengzhou University from December 2011 to December 2021, including 842 males and 553 females. The median age was 55 (44, 59) years, and the median tumor diameter was 3.6 (2.7, 4.6) cm. All patients underwent contrast-enhanced CT scaning before surgery, and radiomic features were extracted from non-contrast, arterial, and venous phase images. Prediction models for distinguishing benign and malignant renal tumors were constructed using five machine learning algorithms (logistic regression, support vector machine, neural network, random forest, and extreme gradient boosting), and these models were then ensembled to construct a stacking classifier. All patients underwent partial nephrectomy, and they were divided into a training group (941 cases, December 2011 to June 2020) and a validation group (454 cases, July 2020 to December 2021) based on the date of surgery. A clinical-radiomic model was developed by combining the result of stacking classifier, clinical features and CT report results, and its predictive performance was evaluated in the validation group.Results:The radiomic signature based on the combined features and five machine learning algorithms(AUC 0.835-0.844) showed higher accuracy in predicting benign and malignant renal tumors compared to single phases (AUC 0.744-0.831). After integrating the five machine learning algorithms, the AUC of the three-phase combined radiomic model in the validation group improved to 0.847(95% CI 0.802-0.892). The clinical-radiomic model, incorporating radiomic features, clinical features, and CT report results, achieved a significantly higher AUC in the validation group compared to radiologists [0.919(95% CI 0.889-0.950)vs. 0.835(95% CI 0.786-0.883), P<0.01]. Conclusions:The predictive model integrating CT radiomics features, clinical characteristics, and CT report results demonstrates excellent discriminative ability in distinguishing benign and malignant renal tumors.

Objective:To evaluate the impact of bladder volume on dosimetric parameters of clinical target volume (CTV) and organs at risk (OAR) of intensity modulated proton therapy (IMPT) for localized prostate cancer during the treatment planning and daily treatment.Methods:Clinical data of 25 patients with localized prostate cancer admitted to Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine from November 2021 to June 2022 and enrolled in the "Proton Therapy System" (SAPT-PS-01) registered clinical trial were retrospectively analyzed. All patients were male and the median age was 72 years old. A total of 30 sets of IMPT plans were obtained. Based on the planning CT (30 sets) and weekly verification CT during treatment (172 sets), bladder volume, CTV and OAR dose parameters were collected. Spearman correlation analysis was used to evaluate the correlation between bladder volume in CT and the dosimetric parameters of CTV and OAR during IMPT plans, and Wilcoxon-Mann-Whitney test was adopted to compare the dosimetric parameters of CTV and OAR among different bladder volume change groups.Results:The V 95% of CTV1 and CTV2 were both 100.0%±0.0% in IMPT plans. Bladder volume was significantly negatively correlated with D mean, V 70 Gy(RBE), V 60 Gy(RBE), V 50 Gy(RBE), V 40 Gy(RBE) of the bladder ( P<0.001, 0.003, <0.001, <0.001,<0.001), and D mean, V 50 Gy(RBE) of the small intestine (both P<0.001). During treatment, bladder D mean, V 70 Gy(RBE), V 60 Gy(RBE), V 50 Gy(RBE), V 40 Gy(RBE)( P<0.001, 0.001, <0.001, <0.001, <0.001), rectal D mean, V 50 Gy(RBE), V 40 Gy(RBE) (all P<0.001), small intestine D mean, V 50 Gy (RBE) (both P<0.001) of patients with bladder volume increase >20% compared to baseline were significantly decreased compared to those in IMPT plans. But CTV1 V 100%, and CTV2 V 95% were significantly decreased too( P=0.029, 0.020). In the bladder volume decreased>20% patients, the D mean, V 70 Gy(RBE), V 60 Gy(RBE), V 50 Gy(RBE), V 40 Gy(RBE) of the bladder were significantly increased compared to those in IMPT plans (all P<0.001). However, a bladder volume reduction of ≤20% and increase of ≤20% from baseline had no significant impact on CTV and OAR dosimetric parameters during treatment. Conclusions:For patients with localized prostate cancer undergoing proton therapy, a certain bladder volume should be ensured during planning CT scans. During the daily treatment, the bladder volume should be maintained between 80%-120% of the baseline level to ensure CTV coverage and good dose sparing to OAR.

Our previous investigation suggested that faster seventh cervical nerve (C7) regeneration occurs in patients with cerebral injury undergoing contralateral C7 transfer. This finding needed further verification, and the mechanism remained largely unknown. Here, Tinel's test revealed faster C7 regeneration in patients with cerebral injury, which was further confirmed in mice by electrophysiological recordings and histological analysis. Furthermore, we identified an altered systemic inflammatory response that led to the transformation of macrophage polarization as a mechanism underlying the increased nerve regeneration in patients with cerebral injury. In mice, we showed that, as a contributing factor, serum amyloid protein A1 (SAA1) promoted C7 regeneration and interfered with macrophage polarization in vivo. Our results indicate that altered inflammation promotes the regenerative capacity of the C7 nerve by altering macrophage behavior. SAA1 may be a therapeutic target to improve the recovery of injured peripheral nerves.
Animals ; Brachial Plexus ; Brachial Plexus Neuropathies/surgery* ; Humans ; Mice ; Nerve Transfer ; Peripheral Nerves ; Spinal Nerves

Our previous investigation suggested that faster seventh cervical nerve (C7) regeneration occurs in patients with cerebral injury undergoing contralateral C7 transfer. This finding needed further verification, and the mechanism remained largely unknown. Here, Tinel’s test revealed faster C7 regeneration in patients with cerebral injury, which was further confirmed in mice by electrophysiological recordings and histological analysis. Furthermore, we identified an altered systemic inflammatory response that led to the transformation of macrophage polarization as a mechanism underlying the increased nerve regeneration in patients with cerebral injury. In mice, we showed that, as a contributing factor, serum amyloid protein A1 (SAA1) promoted C7 regeneration and interfered with macrophage polarization in vivo. Our results indicate that altered inflammation promotes the regenerative capacity of the C7 nerve by altering macrophage behavior. SAA1 may be a therapeutic target to improve the recovery of injured peripheral nerves.

Objective To discuss the effect of in vitro fertilization ( IVF) and mouse sperm cryopreservation , to establish a simple and economic frozen system for the genetically engineering mice preservation .Methods Sperm from genetically engineering mice were cryopreserved , IVF was performed using post-thawed sperm, then embryo transfer, to compare the effects of cryopreservation medium、age of male mice and sperm preincubation medium .Results Using CPA as sperm cryopreservation medium , when PM was used thawed-sperm preincubation in IVF , the fertility rates were from 82.49%to 91.43%, when HTF was used thawed-sperm preincubation in IVF , the fertility rates were from14.46%to 27.38%, there was a signification difference between PM and HTF sperm preincubation medium;10 to 35 weeks male genetically engineering mice sperm were succeed cryopreservation , and positive mice were procreated after 2-cell embryos were transferred;R18S3、CPM and CPA was used to freeze sperm , the fertility rates were 75.85%、88.89%to 94.27%, positive mice were procreated after 2-cell embryos were transferred;2-cell embryos after IVF were freezed , then thawed and positive mice were procreated after 2-cell embryos were transferred .Conclusion Using CPA as sperm cryopreservation medium , when PM was used thawed-sperm preincubation in IVF , genetically engineering mice sperm were succeed cryopreservation .

To elucidate the molecular characteristics of the hemagglutinin (HA) genes of H5N1 subtype of avian influenza viruses in the boundary region of Yunnan province. Of 420 samples were collected from the foreign poultry in boundary region of Yunnan province during 2003 to 2008 and these samples were subjected to screening by H5/N1 subtype-specific and multiplex RT-PCR. testing. The HA genes of H5N1 viruses from positive samples were amplified by RT-PCR and cloned into vector pMD18 T for subsequent sequencing. The alignment with sequences of the known reference strains and phylogenetic analysis were then performed. The genes from 21 representative positive samples with 4 different sequences at the cleavage site were obtained and all of them possessed the molecular characteristic of highly pathogenic avian influenza virus. The mutation of key amino acids had been found among receptor-binding sites, potential glycosylation sites and neutralizing epitopes.-Phylogenetic analysis showed those positive samples could be divided into 5 distinct clades, including clade 1, 2.4. 2.3.2, 2.3.4 and 7. It is evident that H5N1 viruses from the foreign boundary region of Yunnan province in 2003 to 2008 show genetic divergence and clade 2,3,4 is the dominant clade in this region.