A nomogram for risk stratification of central cervical lymph node metastasis in patients with papillary thyroid carcinoma.

Background: Whether to perform prophylactic central lymph node dissection for cN0 papillary thyroid carcinoma (PTC) patients is still controversial. This retrospective study aimed to develop and validate a nomogram based on ultrasound and dual-energy computed tomography (DECT) for the risk stratification of central lymph node metastasis (CLNM) in patients with PTC. Methods: A total of 525 patients from 2017 to 2019 [Tianjin First Central Hospital (Hospital A)] were retrospectively analyzed to form the training cohort and to conduct internal validation. Another group of 204 patients in 2020 (Hospital A) formed the temporal validation cohort. A total of 107 patients in 2020 [Binzhou Medical University Hospital (Hospital B)] formed the geographic validation cohort, which was a retrospective cohort study. The area under the curve (AUC), calibration curve, and decision curve were used to evaluate the performance of the nomogram. The locally weighted regression curve was used for risk stratification. Results: Diameter, taller-than-wide, calcification, capsular invasion, and iodine concentration in the arterial and venous phases were independent risk predictors of CLNM. The AUC of the nomogram was 0.922 (95% confidence interval: 0.895-0.943) in the training cohort. Two external validation cohorts demonstrated the good performance of the nomogram in predicting CLNM, with AUCs of 0.912 and 0.861. The significantly improved net reclassification index and integrated discriminatory improvement index indicated that DECT was a powerful supplement to ultrasound for predicting CLNM. The risk stratification system divided all patients into low-risk (0-50 points), intermediate-risk (51-100 points), and high-risk groups (>100 points). Conclusions: The nomogram and risk stratification system estimated the utility of CLNM to guide individualized treatment of patients with PTC.

[Antibiotic use in very low birth weight/extremely low birth weight infants in 15 hospitals in Jiangsu Province of China: a multicenter survey]. / 江苏省15家医院极低/è¶ ä½Žå‡ºç”Ÿä½“é‡å„¿æŠ—ç”Ÿç´ ä½¿ç”¨çŽ°çŠ¶è°ƒæŸ¥.

OBJECTIVES: To investigate the current status of antibiotic use in very low birth weight/extremely low birth weight infants in Jiangsu Province of China, and to provide a clinical basis for the quality and improvement of antibiotic management in the neonatal intensive care unit (NICU). METHODS: A retrospective analysis was performed on the data on general conditions and antibiotic use in the very low birth weight/extremely low birth weight infants who were admitted to 15 hospitals of Jiangsu Province from January 1, 2019 to December 31, 2020. A questionnaire containing 10 measures to reduce antibiotic use was designed to investigate the implementation of these intervention measures. RESULTS: A total of 1 920 very low birth weight/extremely low birth weight infants were enrolled, among whom 1 846 (96.15%) were treated with antibiotic, and the median antibiotic use rate (AUR) was 50/100 patient-days. The AUR ranged from 24/100 to 100/100 patient-days in the 15 hospitals. After adjustment for the confounding factors including gestational age, birth weight, and neonatal critical score, the Poisson regression analysis showed that there was a significant difference in the adjusted AUR (aAUR) among the hospitals (P<0.01). The investigation results showed that among the 10 measures to reduce antibiotic use, 8 measures were implemented in less than 50% of these hospitals, and the number of intervention measures implemented was negatively correlated with aAUR (rs=-0.564, P=0.029). CONCLUSIONS: There is a high AUR among the very low birth weight/extremely low birth weight infants in the 15 hospitals of Jiangsu Province, with a significant difference among hospitals. The hospitals implementing a relatively few measures to reduce antibiotic use tend to have a high AUR. It is expected to reduce AUR in very low birth weight/extremely low birth weight infants by promoting the quality improvement of antibiotic use management in the NICU.

Mesoscale simulation on patterned nanotube model for amphiphilic block copolymer.

Self-assembly of AB diblock copolymer confined in concentric-cylindrical nanopores was studied using MesoDyn simulation. Our calculation shows that in this confined geometry a zoo of exotic structures can be formed. These structures include bicontinuous phases like carbon nanotube, imperfect single helixes and double helixes. Moreover, the dependence of the chain conformation on the volume fraction, concentration, the interactions between blocks and the diameter of the cylindrical pore are investigated. The results of these simulations can be used to predict the diblock copolymer morphologies confined in concentric-cylindrical nanopores and should be helpful in designing polymeric nanomaterials in the future.

Mesoscopic simulation studies on micellar phases of Pluronic P103 solution.

The microphase separation dynamics of the triblock copolymer surfactant P103 [(ethylene oxide)17(propylene oxide)60(ethylene oxide)17] was investigated by a dynamic variant of mean-field density functional theory. Different self-assembled aggregates, spherical micelles, micellar clusters and disk-like micelles, are explored in the solution. The spherical micelle above critical micelle concentration (CMC) is a dense core consisting mainly of PPO and a hydrated PEO swollen corona, and is in good agreement with the experimental results concerning their structures. At a concentration of 10-15%, micellar clusters with a larger PPO core form as a result of coalescence among spherical micelles. At concentrations above 16% by volume, a series of disk-like micelles come into being. The order parameters show that spherical micelles are easily formed, while the micellar clusters or disk-like micelles need a longer time to reach steady equilibrium. The results show that mesoscopic simulation can augment experimental results on amphiphilic polymers, and provide some mesoscopic information at the mesoscale level.