Guided by the turbidity toxin theory， it is believed that the key pathogenesis of constipation-predominant irritable bowel syndrome is the obstruction of turbidity toxin and the disruption of intestinal function. Treatment is based on the principles of dispelling turbidity toxin and promoting intestinal function. The clinical patterns can be divided into three types， turbidity toxin heat accumulation pattern， turbidity toxin combined with liver depression and qi stagnation pattern， and turbidity toxin combined with qi and yin deficiency pattern. The treatment can respectively use self-prescribed Tongfu Jiangzhuo Formula （通腑降浊方） to clear heat and unblock the bowels， direct the turbid downward and resolve toxins； use self-prescribed Shugan Jiangzhuo Formula （疏肝降浊方） to soothe the liver and move qi， direct the turbid downward and resolve toxins； use self-prescribed Mazhi Jiangzhuo Formula （麻枳降浊方） to boost qi and nourish yin， moisten the intestines to remove turbidity and resolve toxins.

Human adenovirus (HAdV) is a common pathogen causing respiratory tract infections. Severe cases of HAdV infection clinically present as critical illness, with a potentially high mortality rate, and may also result in the sequelae of obstructive bronchiolitis, closely intertwined with the mechanism of immune injuries. Neutrophil infiltration plays a crucial role in the early inflammatory response. However, it is a double-edged sword as it plays the role of a " protector" for the body. When it is excessively activated, it can trigger respiratory bursts, degranulation, and other cell signaling pathway dysregulation, causing acute or chronic inflammatory damage to local tissues or even the whole body system. This review aims to elucidate the mechanisms of neutrophil-mediated damage to respiratory tract infections caused by HAdV, providing a reference for early clinical intervention and medication, in order to maximize the benefits and reduce complications for critically ill patients in the early stages of the disease.

Objective:To explore the value of difference value and relative value of pulmonary vascular quantitative parameters of biphasic CT in diagnosis of chronic obstructive pulmonary disease (COPD) and high-risk groups.Methods:The study was a cross-sectional study. A retrospective study of 624 patients who underwent biphasic chest CT scanning in the Second Affiliated Hospital of PLA Naval Medical University from August 2018 to December 2020. Subjects were divided into three groups according to pulmonary function test: normal group ( n=321), preserved ratio impaired spirometry (PRISm) ( n=204) and COPD group ( n=99). The pulmonary vascular quantitative parameters were obtained by the workstation, including the number of pulmonary vessels (N total), cross-sectional area less than that of 5 mm 2 (N CSA5), vessel area (VA total) of 9, 12, 15, 18, 21 mm from the pleura, total blood vessels volume (TBV) and total blood vessels volume under 5 mm 2 (BV5) at expiratory and inspiratory phase. The difference value, relative value of biphasic CT were calculated. ANOVA test or Kruskal-Wallis H test was used to compare the differences of pulmonary vascular quantitative parameters among the three groups, and LSD or Bonferroni tests were used for multiple comparisons. Results:The N total, N CSA5, VA total of the whole lung in the inspiratory phase at the level of 9, 12, 15, 18 and 21 mm from the pleura and TBV, BV5 in the normal group, PRISm group and COPD group, showed an overall trend of decreasing and then increasing, and the PRISm group was lower than those of the normal group ( P<0.05). The differences in N total, N CSA5, VA total between the COPD group and the normal group at the level of 9 and 21 mm from the pleura were not statistically significant ( P>0.05), while the N total, N CSA5, V Atotal of biphasic difference value and relative value in COPD group demonstrated a significant difference from those in normal group ( P<0.05). Conclusion:The difference value and relative value of pulmonary vascular quantitative parameters in biphasic CT are more useful than single inspiratory-phase vascular parameters to differentiate the changes in pulmonary vascular remodeling between COPD patients and the normal population, and also to complement the pulmonary vascular characteristics of the PRISm population, which can provide a basis for early warning of COPD and high-risk populations.

Objective:To explore the diagnostic value of CT pulmonary vascular quantitative parameters in patients with chronic obstructive pulmonary disease (COPD) and high-risk groups.Methods:A retrospective study of 1 126 patients who underwent chest CT examination and pulmonary function test in Shanghai Tongji Hospital from January 2015 to August 2020. According to lung function, they were divided into COPD group (471 cases), high-risk group (454 cases), and normal control group (201 cases). Pulmonary vascular parameters on chest CT, including the total number of vessels (N total), the number cross-sectional area of vessels under 5 mm 2 (N CSA<5), lung surface area (LSA), number of pulmonary blood vessels per unit lung surface area (N total/LSA) and the total area of vessels (VA total) at a 9, 15, 21 mm depth from the pleural surface, and the total blood vessel volume (TBV), blood vessel volume under 5 mm 2 and 10 mm 2(BV5 and BV10) were measured quantitatively. Kruskal-Wallis H test was used to compare the differences of quantitative parameters of pulmonary vascular in the three groups; Spearman rank test was used to analyze the correlation between CT pulmonary vascular parameters and pulmonary function. Results:There were significant differences in N total/LSA at a 9, 15, 21 mm depth from the pleural surface among three groups ( P<0.05). There were significant differences in N CSA<5, N total at a 9 mm depth from the pleural surface among three groups ( P<0.05). There were significant differences in LSA at a 9 mm depth from the pleural surface, N CSA<5, N total, LSA, VA total at a 15, 21 mm depth from the pleural surface and TBV, BV5 and BV10 among three groups ( P<0.05). In high-risk group, there were positive correlation between N total/LSA, VA total at a 9 mm depth from the pleural surface and some pulmonary function parameters ( r=0.095-0.139, P<0.05). N CSA<5, N total, LSA, N total/LSA, TBV, BV5 and BV10 at different depth from pleural surface were negatively correlated with some pulmonary function parameters ( r=-0.110--0.215, P<0.05). In COPD group, number of vessels at a 9 mm depth from the pleural surface was positively correlated with the diffusion capacity for carbon monoxide of the lung single breath ( r=0.105, 0.103, P<0.05). In addition to N total/LSA were positively correlated with lung function parameters ( r=0.181-0.324, P<0.05), the remaining pulmonary vascular parameters were negatively correlated with some pulmonary function parameters ( r=-0.092--0.431, P<0.05). Conclusion:Quantitative chest CT imaging are able to effectively evaluate pulmonary vascular changes in COPD patients and high-risk groups, and the quantitative parameters of pulmonary vascular CT may distinguish COPD from high-risk groups, providing a novel means for early diagnosis of COPD and prediction of high-risk groups.

Objective:To assess the effectiveness of a model created using clinical features and preoperative chest CT imaging features in predicting the chronic obstructive pulmonary disease (COPD) among patients diagnosed with lung cancer.Methods:A retrospective analysis was conducted on clinical (age, gender, smoking history, smoking index, etc.) and imaging (lesion size, location, density, lobulation sign, etc.) data from 444 lung cancer patients confirmed by pathology at the Second Affiliated Hospital of Naval Medical University between June 2014 and March 2021. These patients were randomly divided into a training set (310 patients) and an internal test set (134 patients) using a 7∶3 ratio through the random function in Python. Based on the results of pulmonary function tests, the patients were further categorized into two groups: lung cancer combined with COPD and lung cancer non-COPD. Initially, univariate analysis was performed to identify statistically significant differences in clinical characteristics between the two groups. The variables showing significance were then included in the logistic regression analysis to determine the independent factors predicting lung cancer combined with COPD, thereby constructing the clinical model. The image features underwent a filtering process using the minimum absolute value convergence and selection operator. The reliability of these features was assessed through leave-P groups-out cross-validation repeated five times. Subsequently, a radiological model was developed. Finally, a combined model was established by combining the radiological signature with the clinical features. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) curves were plotted to evaluate the predictive capability and clinical applicability of the model. The area under the curve (AUC) for each model in predicting lung cancer combined with COPD was compared using the DeLong test.Results:In the training set, there were 182 cases in the lung cancer combined with COPD group and 128 cases in the lung cancer non-COPD group. The combined model demonstrated an AUC of 0.89 for predicting lung cancer combined with COPD, while the clinical model achieved an AUC of 0.82 and the radiological model had an AUC of 0.85. In the test set, there were 78 cases in the lung cancer combined with COPD group and 56 cases in the lung cancer non-COPD group. The combined model yielded an AUC of 0.85 for predicting lung cancer combined with COPD, compared to 0.77 for the clinical model and 0.83 for the radiological model. The difference in AUC between the radiological model and the clinical model was not statistically significant ( Z=1.40, P=0.163). However, there were statistically significant differences in the AUC values between the combined model and the clinical model ( Z=-4.01, P=0.010), as well as between the combined model and the radiological model ( Z=-2.57, P<0.001). DCA showed the maximum net benifit of the combined model. Conclusion:The developed synthetic diagnostic combined model, incorporating both radiological signature and clinical features, demonstrates the ability to predict COPD in patients with lung cancer.

Objective:To explore the differences of functional small airway and pulmonary vascular parameters in chronic obstructive pulmonary disease (COPD) of different imaging phenotypes.Methods:One hundred and thirty COPD patients underwent biphasic CT scanning in Shanghai Changzheng Hospital from August 2018 to August 2020 were analyzed retrospectively. The patients were classified into three phenotypes based on the presence of emphysema and bronchial wall thickening on CT images. Phenotype A: no emphysema or mild emphysema, with or without bronchial wall thickening; Phenotype E: obvious emphysema without bronchial wall thickening; phenotype M: significant emphysema and bronchial wall thickening were present. Parametric response map (PRM) and pulmonary vascular parameters were quantitatively measured at the whole lung level. PRM parameters included the volume of emphysema (PRMV Emphysema), the volume of functional small airway (PRMV fSAD), the volume of normal pulmonary parenchyma (PRMV Normal) and its volume percentage (%). Pulmonary vascular parameters included the number of vessels (N) and cross-sectional area vessels<5 mm 2 (N -CSA<5) at 6, 9, 12, 15, 18 21, 24 mm distance from the pleura. ANOVA or Kruskal-Wallis H tests were used to compare the differences for PRM and pulmonary vascular parameters among the three phenotypes, and LSD or Bonferroni tests were used for multiple comparisons. Results:There were significant differences among the three phenotypes for PRMV fSAD, PRMV Emphysema, PRMV fSAD%, PRMV Emphysema%, and PRMV Normal% at the whole lung level ( P<0.05). PRMV Emphysema, PRMV Emphysema%, PRMV Fsad, PRMV fSAD% of phenotype A were lower than those of phenotype E and M ( P<0.001), while there was no significant difference for PRMV Emphysema, PRMV Emphysema%, PRMV fSAD, PRMV fSAD% between phenotype E and phenotype M ( P>0.05). There were significant differences in N and N -CSA<5 that 6 mm distance from the pleura among the three groups( P<0.05). Among them, N and N -CSA<5 that 6 mm distance from pleura in phenotype M were significantly lower than those in phenotype A( P<0.001,0.002); No significant differences was found in N between phenotype M and phenotype E( P>0.05), while there was significant differences in N -CSA<5 between phenotype M and phenotype E( P=0.034). Conclusion:Biphasic quantitative CT analysis can reflect the heterogeneity of the functional small airways and pulmonary vascular abnormality in COPD with different phenotypes, and provide objective evidence for individualized diagnosis and treatment.

Objective:To evaluate the efficacy of individualized mini-fluid challenge test in determining the fluid responsiveness in the patients undergoing surgery in prone position.Methods:A total of 47 patients of either sex, aged > 18 yr, with boy mass index of 18-30 kg/m 2, of American Society of Anesthesiologists Physical Status classification Ⅰ or Ⅱ, undergoing elective spinal surgery in prone position, were included. The volume-controlled mode was used for mechanical ventilation, and the tidal volume was set at 8 ml/kg. The hemodynamic parameters were monitored by FloTrac/Vigileo system. The patient was changed to prone position at 5 min after endotracheal intubation (T 1), hydroxyethyl starch 130/0.4 sodium chloride injection 2 ml/kg was intravenously given at 5 min of prone position (T 2), and fluid 3 ml/kg was continuously infused at 1 min after 2 ml/kg fluid infusion (T 3), and both infusion rates were 0.5 ml·kg -1·min -1. A mini-fluid challenge test was performed during T 2-T 3 period, the standard volume therapy (total infusion of liquid 5 ml/kg) was carried out from T 2 to 1 min after infusion of liquid 3 ml/kg (T 4). The rate of change in SV at T 3 time point (ΔSVT 3) was calculated relative to T 2 time point, and the rate of change in SV at T 4 time point (ΔSVT 4) was calculated relative to T 2 time point. Positive fluid responsiveness test was defined as an increase in ΔSVT 4≥10%, and patients were divided into volume response group (Rs group) and non-volume response group (NRs group). ΔSVT 3, ΔSVT 4 and stroke volume variation and pulse pressure variation at T 3 and T 4 time points were selected, the receiver operating characteristic curve predicting fluid responsiveness was generated, and the area under the receiver operating characteristic curve (AUC) was calculated. Results:Forty-one patients were finally enrolled, including 18 cases in Rs group and 23 cases in NRs group. The AUC of ΔSVT 3 determining fluid responsiveness was 0.976, with the sensitivity 0.944 and specificity 0.957. The AUC of ΔSVT 4 determining fluid responsiveness was 0.971, with sensitivity 0.889 and specificity 0.95. The AUC of stroke volume variation at T 3 and T 4 in predicting fluid responsiveness was 0.632 and 0.609, respectively. The AUC of pulse pressure variation at T 3 and T 4 predicting fluid responsiveness was 0.470 and 0.380, respectively. Conclusions:Individualized mini-fluid challenge test (2 mg/kg colloidal solution) can accurately determine the fluid responsiveness in the patients undergoing surgery in prone position.

Objective:To explore the predictive value of random forest regression model for pulmonary function test.Methods:From August 2018 to December 2019, 615 subjects who underwent screening for three major chest diseases in Shanghai Changzheng Hospital were analyzed retrospectively. According to the ratio of forced expiratory volume in the first second to forced vital capacity (FEV 1/FVC) and the percentage of forced expiratory volume in the first second to the predicted value (FEV 1%), the subjects were divided into normal group, high risk group and chronic obstructive pulmonary disease (COPD) group. The CT quantitative parameter of small airway was parameter response mapping (PRM) parameters, including lung volume, the volume of functional small airways disease (PRMV fSAD), the volume of emphysema (PRMV Emph), the volume of normal lung tissue (PRMV Normal), the volume of uncategorized lung tissue (PRMV Uncategorized) and the percentage of the latter four volumes to the whole lung (%). ANOVA or Kruskal Wallis H was used to test the differences of basic clinical characteristics (age, sex, height, body mass), pulmonary function parameters and small airway CT quantitative parameters among the three groups; Spearman test was used to evaluate the correlation between PRM parameters and pulmonary function parameters. Finally, a random forest regression model based on PRM combined with four basic clinical characteristics was constructed to predict lung function. Results:There were significant differences in the parameters of whole lung PRM among the three groups ( P<0.001). Quantitative CT parameters PRMV Emph, PRMV Emph%, and PRMV Normal% showed a moderate correlation with FEV 1/FVC ( P<0.001). Whole lung volume, PRMV Normal,PRMV Uncategorized and PRMV Uncategorized% were strongly or moderately positively correlated with FVC ( P<0.001), other PRM parameters were weakly or very weakly correlated with pulmonary function parameters. Based on the above parameters, a random forest model for predicting FEV 1/FVC and a random forest model for predicting FEV 1% were established. The random forest model for predicting FEV 1/FVC predicted FEV 1/FVC and actual value was R 2=0.864 in the training set and R 2=0.749 in the validation set. The random forest model for predicting FEV 1% predicted FEV 1% and the actual value in the training set was R 2=0.888, and the validation set was R 2=0.792. The sensitivity, specificity and accuracy of predicting FEV 1% random forest model for the classification of normal group from high-risk group were 0.85(34/40), 0.90(65/72) and 0.88(99/112), respectively; and the sensitivity, specificity and accuracy of predicting FEV 1/FVC random forest model for differentiating non COPD group from COPD group were 0.89(8/9), 1.00 (112/112) and 0.99(120/121), respectively. While the accuracy of two models combination for subclassification of COPD [global initiative for chronic obstructive lung disease (GOLD) Ⅰ, GOLDⅡ and GOLD Ⅲ+Ⅳ] was only 0.44. Conclusions:Small airway CT quantitative parameter PRM can distinguish the normal population, high-risk and COPD population. The comprehensive regression prediction model combined with clinical characteristics based on PRM parameter show good performance differentiating normal group from high risk group, and differentiating non-COPD group from COPD group. Therefore, one-stop CT scan can evaluate the functional small airway and PFT simultaneously.

Objective:To investigate the value of CT features in predicting visceral pleural invasion (VPI) in clinical stage ⅠA peripheral lung adenocarcinoma under the pleura.Methods:The CT signs of 274 patients with clinical stage ⅠA peripheral lung adenocarcinoma under the pleura diagnosed in Changzheng Hospital of Naval Medical University from January 2015 to November 2021 were retrospectively analyzed. According to the ratio of 6∶4, 164 patients collected from January 2015 to August 2019 were used as the training group, and 110 patients collected from August 2019 to November 2021 were used as the validation group. The maximum diameter of the tumor (T), the maximum diameter of the consolidation part (C), and the minimum distance between the lesion and the pleura (DLP) were quantitatively measured, and the proportion of the consolidation part was calculated (C/T ratio, CTR). The CT signs of the tumor were analyzed, such as the relationship between the tumor and the pleura classification, the presence of a bridge tag sign, the location of the lesion, density type, shape, margin, boundary and so on. Variables with significant difference in the univariate analysis were entered into multivariate logistic regression analysis to explore predictors for VPI, and a binary logistic regression model was established. The predictive performance of the model was analyzed by receiver operating characteristic curve in the training and validation group.Results:There were 121 cases with VPI and 153 cases without VPI among the 274 patients with lung adenocarcinoma. There were 79 cases with VPI and 85 cases without VPI in the training group. Univariate analysis found that the maximum diameter of the consolidation part, CTR, density type, spiculation sign, vascular cluster sign, relationship of tumor and pleura and bridge tag sign between patients with VPI and those without VPI were significantly different in the training group( P<0.05). Multivariate logistic regression analysis found the relationship between tumor and pleura [taking type Ⅰ as reference, type Ⅱ (OR=6.662, 95%CI 2.364-18.571, P<0.001), type Ⅲ (OR=34.488, 95%CI 8.923-133.294, P<0.001)] and vascular cluster sign (OR=4.257, 95%CI 1.334-13.581, P=0.014) were independent risk factors for VPI in the training group. The sensitivity, specifcity, and area under curve (AUC) for the logistic model in the training group were 62.03%, 89.41% and 0.826, respectively, using the optimal cutoff value of 0.504. The validation group obtained an sensitivity, specifcity, and AUC of 92.86%, 47.06%, and 0.713, respectively, using the optimal cutoff value of 0.449. Conclusion:The relationship between the tumor and the pleura and the vascular cluster sign in the CT features can help to predict visceral pleural invasion in the clinical stage ⅠA peripheral lung adenocarcinoma under the pleura.

This paper reports 3 cases of acute hepatitis of unknown cause in children who met the case definition of WHO. Human adenovirus group C was detected in case 3, and the quantity of viral DNA was relatively high, which may be related to the liver function damage in the patient, but its role in pathogenesis needs further study to confirm.