Objective To explore the application value of dual-phase dual-energy CT (DECT) perfusion imaging in preoperative lung function assessment of lung cancer patients. Methods Data were collected from patients with stageⅠA non-small cell lung cancer who underwent surgical treatment in the Department of Thoracic Surgery, the First Affiliated Hospital of Nanjing Medical University, from November 2022 to June 2024. All patients underwent DECT perfusion imaging and pulmonary function testing (PFT) before surgery. PFT observation indicators included ventilation function indicators such as forced expiratory volume in one second (FEV1), forced vital capacity (FVC), 1-second rate (FEV1/FVC), maximal voluntary ventilation (MVV), and diffusion function indicators such as diffusing capacity for carbon monoxide (DLCO) and DLCO per liter of alveolar volume (DLCO/VA). The software eXamine was used to obtain quantitative parameters of DECT perfusion imaging, including volume parameters and perfusion parameters of both lungs and each lung lobe. The correlation between the volume parameters and perfusion parameters of both lungs and the ventilation and diffusion function indicators of the patients, as well as the differences in quantitative parameters of each lung lobe, was analyzed. Results The end-inspiration lung volume and biphasic volume difference were strongly positively correlated with FEV1 and FVC (r=0.636, r=0.682, r=0.614, r=0.624, P<0.001) and moderately positively correlated with MVV and DLCO (r=0.499, r=0.514, r=0.549, r=0.447, P<0.001); the end-expiration lung volume was weakly negatively correlated with DLCO/VA (r=−0.295, P=0.026); the volume ratio was positively correlated with FEV1, FVC, MVV, and MVV% (r=0.424, r=0.399, r=0.415, r=0.310, P<0.05); the end-inspiration iodine content was weakly positively correlated with DLCO/VA% (rs=0.292, P=0.030); the end-expiration iodine content was weakly positively correlated with FEV1, FVC, MVV, DLCO%, and DLCO/VA (r=0.307, r=0.299, r=0.295, r=0.366, r=0.320, P<0.05) and moderately positively correlated with DLCO (r=0.439, P<0.001); the end-inspiration iodine concentration was negatively correlated with FEV1, FVC, MVV, and MVV% (rs=−0.407, rs=−0.426, rs=−0.352, rs=−0.277, P＜0.05); the end-expiratory phase iodine concentration was moderately positively correlated with DLCO/VA (r=0.403, P=0.002); both the iodine concentration difference and the iodine concentration ratio were moderately positively correlated with FEV1, FEV1%, FVC, MVV, MVV% (P<0.05). The lung volume and iodine concentration ratio values were both highest in the left upper lung lobe and lowest in the right middle lung lobe; the differences in lung volume, lung volume ratio, intrapulmonary iodine content, and intrapulmonary iodine concentration were all highest in the lower lobes of both lungs and lowest in the middle lobe of the right lung. Conclusion Dual-phase DECT perfusion imaging can accurately assess overall lung function and quantify regional lung function.

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.

Birth cohort is an important platform to study the effect of early-life exposure on health outcome, but large cohorts to investigate the effect of preconception exposure, especially paternal exposure, on reproductive health and birth outcome are limited. The Preconception Reproductive Health and Birth Outcome Cohort (PREBIC) is a prospective birth cohort study which pays equal attention to the contribution of environmental, psychological, behavioral as well as other factors to reproductive health and adverse birth outcomes in both men and women in Chongqing, China. PREBIC started in 2019 and plans to recruit 20 800 reproductive-age couples with child-bearing willingness. Followed up was conducted to understand the conception status of the women within two years. Women in pregnancy would be visited at first, second, third trimesters and after delivery. The offspring would be monitored until 2 years old to understand the incidences of preterm birth, low birth weight, birth defects, neurodevelopmental disorders and other outcomes. Related information and biospecimen collections (including semen, peripheral blood, urine, placenta, umbilical cord, cord blood and oral swab) were scheduled in each period. By January 2022, PREBIC had recruited 8 698 participants from all 38 districts in Chongqing. The goal of PREBIC is to establish one of the largest prospective preconception birth cohorts covering both men and women, which might provide a unique insight to understand the effects of the full reproductive cycle on reproductive health and adverse outcomes, with especial emphasis on preconception exposures.

Objective: To investigate the effect of hypertensive disorder complicating pregnancy (HDCP) on the mortality and early complications of premature infants. Methods: The general clinical data of preterm infants with gestational age 24-36^{+ 6} weeks were collected from the cooperative units in the task group from January 1, 2013 to December 31, 2014.According to the severity of HDCP, the infants were divided into 4 groups: HDCP group, preeclampsia group, eclampsia group and non HDCP group, the mortality and major complications of preterm infants were compared, and the influencing factors were analyzed. Results: The mortality rate of preterm in the HDCP group was significantly higher than that of non HDCP group, and there was statistical significance (χ²=9.970, P=0.019). Eclampsia had a highest fatality rate (4.8%) in the early stage, compared with non HDCP group (2.2%), and the difference was statistically significant.Comparison of HDCP group (1.8%) and eclampsia group (3.2%) suggested that there was no statistically significant difference.The incidence of respiratory distress syndrome (RDS) in preterm in HDCP group was significantly higher than that of non HDCP group, and there was statistical significance (χ²=13.241, P=0.004). Eclampsia group showed the highest incidence (35.4%), compared with non HDCP group (16.2%), the difference was statistically significant, but compared with HDCP group (19.9%), preeclampsia group (17.1%), there was no significant diffe-rence.The incidence of bronchopulmonary dysplasia (BPD) in preterm in HDCP group was significantly higher than that of non HDCP group (χ²=9.592, P=0.022), the highest incidence showed up in eclampsia group (9.7%), compared with non HDCP group (2.0%) and HDCP group (1.7%), the difference was statistically significant.But there was no statistically significant difference, compared with preeclampsia group.As the degree of HDCP aggravated, the incidence of BPD gradually rose.There was no significant impact on necrotizing enterocolitis (NEC), retinopathy of prematurity (ROP), intraventricular hemorrhage (IVH) and sepsis of HDCP (χ²=7.054, 7.214, 0.358, 3.852; P=0.070, 0.065, 0.949, 0.278). Considering the overall outcome of the child, that was, whether the child died or survived, he had at least one complication, and HDCP had an effect on it (χ²=15.697, P=0.001), so the incidence increased while the degree of HDCP rose gradually.After adjusting gestational age, birth weight, sex, way of delivery, placental abruption and front placenta, prenatal hormonal, gestational diabetes, neonatal asphyxia and other factors, the results displayed that HDCP was the factor leading to the death of premature baby (OR=2.159, 95%CI: 1.093-4.266), and comparison between preeclampsia and eclampsia showed no statistical difference (P=0.714, 0.389); HDCP had no significant influence on RDS, BDP, ICH, NEC, ROP and sepsis. Conclusions HDCP leads to increased risk of premature death, but also leads to the increased incidence of RDS and BPD, but it had no obvious effect on NEC, ROP, IVH, sepsis and other complications.

Objective:To establish a preliminary diagnostic criteria of pathological internet use for field test in China.Methods:Through searching common "pathological internet use" diagnostic criteria or screen criteria in literature,a diagnostic criteria items pool and a preliminary diagnostic criteria of "pathological internet use" were established .According to the diagnostic criteria,each 2 senior professional psychiatrists in turn interviewed 79 patients and their parents,and then made diagnosis respectively. In addiction,44 high school students were each evaluated by one psychiatrist.The definite diagnosis was made when 2 evaluators make the same diagnosis for one patient.Result:In 123 patients,54 were pathological internet use.For diagnosis,the value of kappa for inter-rater reliability was 0.812( P<0.001).In the 12 criteria items,2 for poor inter-rater reliability and 4 for little contribution to diagnosis were eliminated.In the reserved 6 items,if 4 items(or more)were positive,the diagnostic sensitivity was 78.9% and specificity was 95.3%.Functional impairment criteria were made strictly.In the patients who were made the definite diagnosis,90.7% had duration of illness above 3 months,and 77.7% above 6 months.Conclusion:The preliminary diagnostic criteria of pathological internet use after revised includes 6 symptom criteria,3 functional impairment criteria,duration of illness and exclusive criteria.The criteria proposed is with high consistency on evaluations made by psychiatric raters,and with operational convenience.The criteria,after further revised,may fit the clinical application.