With the trend of population aging, the number of patients with comorbidities is increasing, who are the main subjects of general practice service in general hospitals. Tongji Hospital created a new service model and opened a multidisciplinary outpatient clinic based on general practice department (MDT general practic clinic) for patients with comorbidities in December 2021, which has improved the clinical outcomes, and the medical experience and satisfaction of patients. This article elaborates on the organizational structure, team building, and operational process of the MDT general practice clinic for comorbidities; analyzes the characteristics of patients and the implementation effects, to provide a reference for comorbidity patient service in general hospitals.

Objective:To develop machine learning models for interpreting brain maturity in preterm infants based on electroencephalographic (EEG) features and analyze factors affecting interpretation accuracy.Methods:This prospective study enrolled preterm infants requiring bedside EEG monitoring in the Department of Neonatology at the First Hospital of Jilin University from January 2023 to March 2024. Data from each integer-corrected gestational age (GA) group were randomly split into training and testing sets (7∶3 ratio) using Python's sklearn.model_selection.train_test_split function. Three machine learning models, including support vector regression (SVR), random forest, and decision tree, were employed to analyze EEG signals. Model performance was evaluated against manually interpreted GA as the gold standard using prediction deviation, mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient ( r). Accuracy was defined based on the difference between predicted and manually interpreted GA (categorized into accurate and inaccurate groups), with a difference less than one week considered accurate. Statistical analyses included Chi-square test (or Fisher's exact test), t-test, Mann-Whitney U test, and multivariate logistic regression. Results:Among 241 preterm infants (training set: n=168; testing set: n=73), the random forest model demonstrated optimal performance: concordance rate 90.4% (66/73) with MAE 0.378 weeks, RMSE 0.577 weeks, and r=0.932 ( P<0.001). The decision tree model achieved 87.7% concordance (64/73) with MAE 0.316 weeks, while SVR showed 64.2% concordance (47/73) and MAE 0.840 weeks. Stratified by GA, random forest performed best in the 34 weeks group [concordance 100.0% (52/52), MAE 0.269 weeks], followed by the 32-34 weeks group [89.0% (81/91), MAE 0.448 weeks] and <32 weeks group [88.8% (87/98), MAE 0.561 weeks]. Compared to the accurate group ( n=205), the inaccurate group ( n=36) had higher rates of vaginal delivery [41.7% (15/36) vs. 19.5% (40/205), χ2=8.53], grade ≥Ⅱ intracranial hemorrhage [11.1% (4/36) vs. 2.4% (5/205), χ2=4.22], and periventricular echogenicity [33.3% (12/36) vs. 7.8% (16/205), χ2=17.03] (all P<0.05). Multivariate analysis identified vaginal delivery ( OR=0.190, 95% CI: 0.068-0.527), lower corrected GA ( OR=0.678, 95% CI: 0.488-0.941), and periventricular echogenicity ( OR=11.339, 95% CI: 3.250-39.559) as independent factors affecting accuracy (all P<0.05). Conclusion:The random forest-based model shows optimal accuracy for predicting brain maturity in preterm infants. Vaginal delivery, lower corrected GA, and periventricular echogenicity reduce its predictive accuracy.

With the trend of population aging, the number of patients with comorbidities is increasing, who are the main subjects of general practice service in general hospitals. Tongji Hospital created a new service model and opened a multidisciplinary outpatient clinic based on general practice department (MDT general practic clinic) for patients with comorbidities in December 2021, which has improved the clinical outcomes, and the medical experience and satisfaction of patients. This article elaborates on the organizational structure, team building, and operational process of the MDT general practice clinic for comorbidities; analyzes the characteristics of patients and the implementation effects, to provide a reference for comorbidity patient service in general hospitals.

Objective:To develop machine learning models for interpreting brain maturity in preterm infants based on electroencephalographic (EEG) features and analyze factors affecting interpretation accuracy.Methods:This prospective study enrolled preterm infants requiring bedside EEG monitoring in the Department of Neonatology at the First Hospital of Jilin University from January 2023 to March 2024. Data from each integer-corrected gestational age (GA) group were randomly split into training and testing sets (7∶3 ratio) using Python's sklearn.model_selection.train_test_split function. Three machine learning models, including support vector regression (SVR), random forest, and decision tree, were employed to analyze EEG signals. Model performance was evaluated against manually interpreted GA as the gold standard using prediction deviation, mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient ( r). Accuracy was defined based on the difference between predicted and manually interpreted GA (categorized into accurate and inaccurate groups), with a difference less than one week considered accurate. Statistical analyses included Chi-square test (or Fisher's exact test), t-test, Mann-Whitney U test, and multivariate logistic regression. Results:Among 241 preterm infants (training set: n=168; testing set: n=73), the random forest model demonstrated optimal performance: concordance rate 90.4% (66/73) with MAE 0.378 weeks, RMSE 0.577 weeks, and r=0.932 ( P<0.001). The decision tree model achieved 87.7% concordance (64/73) with MAE 0.316 weeks, while SVR showed 64.2% concordance (47/73) and MAE 0.840 weeks. Stratified by GA, random forest performed best in the 34 weeks group [concordance 100.0% (52/52), MAE 0.269 weeks], followed by the 32-34 weeks group [89.0% (81/91), MAE 0.448 weeks] and <32 weeks group [88.8% (87/98), MAE 0.561 weeks]. Compared to the accurate group ( n=205), the inaccurate group ( n=36) had higher rates of vaginal delivery [41.7% (15/36) vs. 19.5% (40/205), χ2=8.53], grade ≥Ⅱ intracranial hemorrhage [11.1% (4/36) vs. 2.4% (5/205), χ2=4.22], and periventricular echogenicity [33.3% (12/36) vs. 7.8% (16/205), χ2=17.03] (all P<0.05). Multivariate analysis identified vaginal delivery ( OR=0.190, 95% CI: 0.068-0.527), lower corrected GA ( OR=0.678, 95% CI: 0.488-0.941), and periventricular echogenicity ( OR=11.339, 95% CI: 3.250-39.559) as independent factors affecting accuracy (all P<0.05). Conclusion:The random forest-based model shows optimal accuracy for predicting brain maturity in preterm infants. Vaginal delivery, lower corrected GA, and periventricular echogenicity reduce its predictive accuracy.

Objective:To discuss the predictive value of lung ultrasound score(LUS)for the use of mechanical ventilation(MV)and pulmonary surfactant(PS)in the preterm infants with late-onset respiratory distress syndrome(RDS).Methods:The prospective analysis was conducted on the late-onset preterm infants(gestational age 340/7-366/7 weeks)complicated with RDS;in total,67 late-onset infants complicated with RDS were included.The infants were divided into MV group(n=36),non-MV group(n=31),PS group(n=30),and non-PS group(n=37)based on the necessity to use MV and PS within 48 h after birth.Lung ultrasound examination was performed on all the infants 2 h after admission,and before the application of PS,and the LUS for 6-zone,10-zone,and 12-zone partitions were calculated.Receiver operating characteristic(ROC)curve for the prediction of MV and PS application in the infants with late-onset RDS were drawn by LUS with different partitions,and the predictive values of different partition methods were compared by DeLong method.Results:Compared with non-PS group,the birth weight,LUS,positive end expiratory pressure(PEEP),mean airway pressure(MAP),MAP×fraction of inspired oxygen(FiO2)/PaO2 value,duration of mechanical ventilation,and hospital stay of the infants in PS group were increased(P<0.05),and the ratio of PaO2/FiO2 was decreased(P<0.01).Compared with non-MV group,the birth weight,LUS,PEEP,MAP,MAP×FiO2/PaO2 value,duration of mechanical ventilation and hospital stay of the infants in MV group were increased(P<0.05),and the ratio of PaO2/FiO2 was decreased(P<0.01).PEEP,MAP,and LUS were identified as the influencing factors for application of PS in the late-onset preterm infants complicated with RDS when employing 6-zone LUS to predict the application of PS[odds ratio(OR)>1,P<0.05].When employing 10-zone and 12-zone LUS for the use of PS,MAP×FiO2/PaO2 and LUS were the influencing factors(OR>1,P<0.05).The area under curve(AUC)for predicting the application of PS in the late-onset infants complicated with RDS by 6-zone,10-zone,and 12-zone LUS were 0.909,0.904,and 0.915,respectively,all showing good predictive values;the AUCs for predicting the application of MV by 6-zone,10-zone,and 12-zone LUS were 0.868,0.872,and 0.887,respectively,all showing good predictive values as well.Conclusion:LUS can effectively predict the necessity for whether or not applying MV and PS in the late-onset infants complicated with RDS,and MAP combined with LUS can enhance the capability to predict the application of MV.

Objective:To study the clinical value of lung ultrasound score (LUSsc) within 2 h after birth for pulmonary surfactant (PS) use in preterm infants with respiratory distress syndrome (RDS).Methods:From July 2019 to May 2021, preterm infants with RDS hospitalized in our hospital and received pulmonary ultrasound within 2 h after birth were prospectively enrolled. 12-area LUSsc was calculated. The infants were assigned into <32 weeks group and 32-36 weeks group according to gestational age (GA). Simple random sampling was carried out in each group with 1/5 as the validation set and the other 4/5 as the training set. The infants were also assigned into PS group and non-PS group according to PS usage within 24 h after birth. Receiver operator characteristic (ROC) curve of LUSsc predicting PS usage was drawn and validated.Results:A total of 857 RDS infants were enrolled, including 313 in <32 weeks group and 544 in 32-36 weeks group. For <32 weeks group, area under curve (AUC) of LUSsc>8.5 predicting PS use was 0.779 (95% CI 0.722-0.837), with 76.4% sensitivity and 81.4% specificity. The accuracy of using LUSsc>8.5 as cut-off predicting actual clinical PS application was 82.3% (Kappa value 0.692, P<0.05, McNemar's test P>0.05).For 32-36 weeks group, AUC of LUSsc>9.5 predicting PS use was 0.785 (95% CI 0.723-0.848), with 71.1% sensitivity and 81.7% specificity. The accuracy of using LUSsc>9.5 as cut-off predicting actual clinical PS application was 92.6% (Kappa value 0.772, P<0.05, McNemar's test P>0.05). Conclusions:LUSsc within 2 h after birth is independent predictor of PS use in preterm infants with RDS. For <32 weeks group, LUSsc>8.5 suggests PS application and for 32-36 weeks group the cut-off is LUSsc>9.5.

Although the function of tRNAs in the translational process is well established, it remains controversial whether tRNA abundance is tightly associated with translational efficiency (TE) in mammals. Moreover, how critically the expression of tRNAs contributes to the establishment of tissue-specific proteomes in mammals has not been well addressed. Here, we measured both tRNA expression using demethylase-tRNA sequencing (DM-tRNA-seq) and TE of mRNAs using ribosome-tagging sequencing (RiboTag-seq) in the brain, heart, and testis of mice. Remarkable variation in the expression of tRNA isodecoders was observed among different tissues. When the statistical effect of isodecoder-grouping on reducing variations is considered through permutating the anticodons, we observed an expected reduction in the variation of anticodon expression across all samples, an unexpected smaller variation of anticodon usage bias, and an unexpected larger variation of tRNA isotype expression at amino acid level. Regardless of whether or not they share the same anticodons, the isodecoders encoding the same amino acids are co-expressed across different tissues. Based on the expression of tRNAs and the TE of mRNAs, we find that the tRNA adaptation index (tAI) and TE are significantly correlated in the same tissues but not between tissues; and tRNA expression and the amino acid composition of translating peptides are positively correlated in the same tissues but not between tissues. We therefore hypothesize that the tissue-specific expression of tRNAs might be due to post-transcriptional mechanisms. This study provides a resource for tRNA and translation studies, as well as novel insights into the dynamics of tRNAs and their roles in translational regulation.
Animals ; Mice ; Anticodon/genetics* ; Transcriptome ; Protein Biosynthesis ; RNA, Transfer/chemistry* ; Amino Acids/metabolism* ; Mammals/metabolism*