Objective To explore the correlation between vitamin A (VA) level andMycoplasma pneumonia (MP). Methods Children aged 0-12 years hospitalized with acute infectious respiratory diseases during March 2015 to December 2015 were randomly selected. The level of serum VA was detected by high performance liquid chromatography (HPLC). MP-DNA on nasopharyngeal swab was detected by polymerase chain reaction (PCR). The serum MP-IgM was detected by enzyme linked immunosorbent assay (ELISA). The MP infection rate among subclinical VA deficiency (SVAD) group, suspicious subclinical VA deifciency (SSVAD) group and VA normal group were analyzed and compared.Results Among 600 children, there were 83 cases of SVAD (13.83%) and 193 cases of SSVAD (32.17%). There were statistical differences of the incidences between SVAD and SSVAD in children younger than 1-year-old, 1-3 years old, 3-6 years old and≥6 years old (P all<0.001), among which SVAD and SSVAD groups had the highest incidence rates in infants younger than 1 year old (26.36% and 49.10%respectively). Among 600 children, MP was positive in 201 children (33.5%), in whom 57 children (28.35%) were SVAD and 70 children (34.83%) were SSVAD. The incidence rate of SVAD in children with MP positive was higher than that in children with MP negative (P<0.001). In 201 children with MP positive, there were signiifcant differences in the distribution of SVAD, SSVAD and VA among different age groups (P=0.003), and the incidence rate of SVAD in infants younger than 1 year old was higher (48.39%).Conclusions SSVAD and SVAD are common in infants younger than 1 year old; SVAD may be associated with MP infection in children.

OBJECTIVE To surveillance invasive fungal infection rate in SICU,in order to direct intervention to prevent invasive fungal infection.METHODS The samples collected from SICU patients in our hospital between Jan 2003-Nov 2008 were cultured.RESULTS According to the diagnosis standard of nosocomial infections,75 case of 3699 patients were isolated fungi.During 6-years invasive fungal infection rate is 2.027%,(1.05%-2.63%).Totally 86 fungi strains were isolated,the majority of them being Candida albicans,accounting for 46.51%;Candida glabrata 22.09%;Candida tropicalis 13.95%.CONCLUSIONS During 6-years,invasive fungal infection rate and incidence density do not increase.Candida are the major pathogens of fungal infections in SICU.

Doxorubicin (Dox) is widely used to the treatment of cancer, however, it could cause damage to gastric mucosa. To investigate the protective effects and related mechanisms of coenzyme Q10 (CoQ10) and vitamin C (VC) on Dox-induced gastric mucosal injury, we presented the survey of the 4 groups of the rats with different conditions. The results showed Dox treatment significantly induced GES-1 apoptosis, but preconditioning in GES-1 cells with VC or CoQ10 significantly inhibited the Dox-induced decrease and other harm effects, including the expression and of IκKβ, IκBα, NF-κB/p65 and tumor necrosis factor (TNF-α) in GES-1 cells. Moreover, high-throughput sequencing results showed Dox treatment increased the number of harmful gut microbes, and CoQ10 and VC treatment inhibited this effect. CoQ10 and VC treatment inhibits Dox-induced gastric mucosal injury by inhibiting the activation of the IkKB/IκBα/NF-κB/p65/TNF-α pathway, promoting anti-inflammatory effects of gastric tissue and regulating the composition of the intestinal flora.

Doxorubicin (Dox) is widely used to the treatment of cancer, however, it could cause damage to gastric mucosa. To investigate the protective effects and related mechanisms of coenzyme Q10 (CoQ10) and vitamin C (VC) on Dox-induced gastric mucosal injury, we presented the survey of the 4 groups of the rats with different conditions. The results showed Dox treatment significantly induced GES-1 apoptosis, but preconditioning in GES-1 cells with VC or CoQ10 significantly inhibited the Dox-induced decrease and other harm effects, including the expression and of IκKβ, IκBα, NF-κB/p65 and tumor necrosis factor (TNF-α) in GES-1 cells. Moreover, high-throughput sequencing results showed Dox treatment increased the number of harmful gut microbes, and CoQ10 and VC treatment inhibited this effect. CoQ10 and VC treatment inhibits Dox-induced gastric mucosal injury by inhibiting the activation of the IkKB/IκBα/NF-κB/p65/TNF-α pathway, promoting anti-inflammatory effects of gastric tissue and regulating the composition of the intestinal flora.

Objective:To explore the effects of bladder volumes from CT simulation on bladder volume consistency and set-up errors during radiotherapy for prostate cancer, aiming to provide a reference for clinical practice.Methods:A retrospective analysis was conducted for of 66 prostate cancer patients treated with intensity-modulated radiation therapy in the Sun Yat-sen University Cancer Center from August 2015 to November 2020. They underwent CT scan or radiotherapy after voluntarily holding in urine. Cone beam computed tomography (CBCT) scans were performed for them to measure their set-up errors in left-right (L-R), superior-inferior (S-I), and anterior-posterior (A-P) directions before each treatment. The bladder contours of the patients were delineated on CT simulation images and CBCT images. Accordingly, bladder volumes were calculated. Based on the calculated bladder volumes derived from the CT simulation images, the patients were divided into three groups: 18 cases in the 200-300 ml group, 24 cases in the 300-400 ml group, and 24 cases in the >400 ml group. Finally, this study analyzed the effects of bladder volumes derived from CT simulation on set-up errors and the changes of CBCT-derived bladder volumes relative to planned volumes during radiotherapy.Results:The bladder volumes in the 200-300 ml, 300-400 ml, and >400 ml groups during radiotherapy were reduced by 15%, 26%, and 32%, respectively. The pairwise comparison indicates statistically significant differences in the changes of bladder volumes among the three groups ( Z=3.43, 7.97, 4.83, P<0.05). Regarding the three-dimensional set-up errors, there were statistically significant differences in S-I set-up errors among the three groups ( H=26.72, P<0.05), but there was no statistically significant difference in L-R and A-P set-up errors ( P>0.05) among these groups. The 200-300 ml, 300-400 ml, and >400 ml groups exhibited S-I set-up errors of 0.00 (-0.20, 0.20) cm, 0.00 (-0.20, 0.30) cm, and -0.10 (-0.30, 0.20) cm, respectively. Therefore, the >400 ml group displayed larger the S-I set-up errors than other two groups, with statistically significant differences ( Z=4.17, 4.66, P< 0.05), while there was no statistically significant differences in S-I set-up errors between other two groups ( P> 0.05). Conclusions:Controlling the bladder filling volumes at 200-300 ml in CT simulation is beneficial for maintaining bladder volume consistency and reducing set-up errors of patients during radiotherapy.

Objective:To investigate the clinical value of magnetic resonance imaging (MRI) based integrated deep learning model for predicting the times of linear staplers used in double stapling technique for middle-low rectal cancer resection.Methods:The retrospective cohort study was conducted. The clinicopathological data of 263 patients who underwent low anterior resection (LAR) for middle-low rectal cancer in Ruijin Hospital of Shanghai Jiaotong University School of Medicine from January 2018 to December 2022 were collected as training dataset. There were 183 males and 80 females, aged 63(55,68)years. The clinicopathological data of 128 patients with middle-low rectal cancer were collected as validation dataset, including 83 males and 45 females, with age as 65(57,70)years. The training dataset was used to construct the prediction model, and the validation dataset was used to validate the prediction model. Observation indicators: (1) clinicopathological features of patients in the training dataset; (2) influencing factors for ≥3 times using of linear staplers in the operation; (3) prediction model construction; (4) efficiency evaluation of prediction model; (5) validation of prediction model. Measurement data with skewed distribution were represented as M( Q1, Q3), and Mann-Whitney U test was used for comparison between groups. Count data were expressed as absolute numbers, and comparison between groups was conducted using the chi-square test. Wilcoxon rank sum test was used for non-parametric data analysis. Univariate analysis was conducted using the Logistic regression model, and multivariate analysis was conducted using the Logistic stepwise regression model. The receiver operating characteristic (ROC) curve was draw and the area under the curve (AUC) was calculated. The AUC of the ROC curve >0.75 indicated the prediction model as acceptable. Comparison of AUC was conducted using the Delong test. Results:(1) Clinicopathological features of patients in the training dataset. Of the 263 patients, there were 48 cases with linear staplers used in the operation ≥3 times and 215 cases with linear staplers used in the operation ≤2 times. Cases with preoperative serum carcinoembryonic antigen (CEA) >5 μg/L, cases with anastomotic leakage, cases with tumor diameter ≥5 cm were 20, 12, 13 in the 48 cases with linear staplers used ≥3 times in the operation, versus 56, 26, 21 in the 215 cases with linear staplers used ≤2 times in the operation, showing significant differences in the above indicators between them ( χ2=4.66, 5.29, 10.45, P<0.05). (2) Influencing factors for ≥3 times using of linear staplers in the operation. Results of multivariate analysis showed that preoperative serum CEA >5 μg/L and tumor diameter ≥5 cm were independent risk factors for ≥3 times using of linear staplers in the operation ( odds ratio=2.26, 3.39, 95% confidence interval as 1.15-4.43, 1.50-7.65, P<0.05). (3) Prediction model construction. According to the results of multivariate analysis, the clinical prediction model was established as Logit(P)=-2.018+0.814×preoperative serum CEA (>5 μg/L as 1, ≤5 μg/L as 0)+ 1.222×tumor diameter (≥5 cm as 1, <5 cm as 0). The image data segmented by the Mask region convolutional neural network (MASK R-CNN) was input into the three-dimensional convolutional neural network (C3D), and the image prediction model was constructed by training. The image data segmented by the MASK R-CNN and the clinical independent risk factors were input into the C3D, and the integrated prediction model was constructed by training. (4) Efficiency evaluation of prediction model. The sensitivity, specificity and accuracy of the clinical prediction model was 70.0%, 81.0% and 79.4%, respectively, with the Yoden index as 0.51. The sensitivity, specificity and accuracy of the image prediction model was 50.0%, 98.3% and 91.2%, respectively, with the Yoden index as 0.48. The sensitivity, specificity and accuracy of the integrated prediction model was 70.0%, 98.3% and 94.1%, respectively, with the Yoden index as 0.68. The AUC of clinical prediction model, image prediction model and integrated prediction model was 0.72(95% confidence interval as 0.61-0.83), 0.81(95% confidence interval as 0.71-0.91) and 0.88(95% confidence interval as 0.81-0.95), respectively. There were significant differences in the efficacy between the integrated prediction model and the image prediction model or the clinical prediction model ( Z=2.98, 2.48, P<0.05). (5) Validation of prediction model. The three prediction models were externally validated by validation dataset. The sensitivity, specificity and accuracy of the clinical prediction model was 62.5%, 66.1% and 65.6%, respectively, with the Yoden index as 0.29. The sensitivity, specificity and accuracy of the image prediction model was 58.8%, 95.5% and 92.1%, respectively, with the Yoden index as 0.64. The sensitivity, specificity and accuracy of the integrated prediction model was 68.8%, 97.3% and 93.8%, respectively, with the Yoden index as 0.66. The AUC of clinical prediction model, image prediction model and integrated prediction model was 0.65(95% confidence interval as 0.55-0.75), 0.75(95% confidence interval as 0.66-0.84) and 0.84(95% confidence interval as 0.74-0.93), respec-tively. There was significant differences in the efficacy between the clinical prediction model and the integrated prediction model ( Z=3.24, P<0.05). Conclusion:The MRI-based deep-learning model can help predicting the high-risk population with ≥3 times using of linear staplers in resection of middle-low rectal cancer with double stapling technique.