Study on the correlation parameters of Class II malocclusion in child tooth dentition early stage by using 3D technique.

OBJECTIVE: To record the dentition, jaw and facial growth and development of children with class II malocclusion at the age of 7-8 years old in the early dental transitional stage with 3D technology and to provide the study basis for the growth and development parameters of normal children and children with class II malocclusion. METHODS: Twenty-four children who were suffering class-II malocclusion in the early dental transitional stage and received treatment between July 2016 and July 2017 in our hospital were selected as the study group, and 20 healthy children were selected as the control group in the same period. SIRONA CEREC dentition scanning, 3D reconstruction of the lower mandible and 3d MD face scanning were performed on the children. Relevant data were recorded and compared. RESULTS: The dentition scanning results suggested that the study group had significantly larger anterior overbite and anterior overjet and smaller width of the upper arch than the control group; there was a significant difference between the two groups (P<0.05). The 3D reconstruction of the lower mandible suggested that the study group had smaller Go angle and SNB angle and shorter ANS-Me distance, Go-Me distance and N-Me distance compared to the control group; the differences had statistical significance (P<0.05). The face scanning results demonstrated that the nasolabial angle and facial convexity angle of the study group were significantly larger than those in the control group, and the difference was statistically significant (P<0.05). CONCLUSION: The dentition scanning results suggested that the study group had significantly larger anterior overbite and anterior overjet and smaller width of the upper arch than the control group; there was a significant difference between the two groups (P<0.05). The 3D reconstruction of the lower mandible suggested that the study group had smaller Go angle and SNB angle and shorter ANS-Me distance, Go-Me distance and N-Me distance compared to the control group; the differences had statistical significance (P<0.05). The face scanning results demonstrated that the nasolabial angle and facial convexity angle of the study group were significantly larger than those in the control group, and the difference was statistically significant (P<0.05).

Transfer learning improves predictions in lignin content of Chinese fir based on Raman spectra.

Lignin in biomass plays significant role in substitution of synthetic polymer and reduction of energy expenditure, and the lignin content was usually determined by wet chemical methods. However, the methods' heavy workload, low efficiency, huge consumption of chemicals and use of toxic reagents render them unsuitable for sustainable development and environmental protection. Chinese fir, a prevalent angiosperm tree, holds immense importance for various industries. Since our previous work found that Raman spectroscopy could accurately predict the lignin content in poplar, we propose that the lignin content of Chinese fir can be estimated by similar strategy. The results suggested that the peak at 2895 cm-1 is the optimal choice of internal standard peak and algorithm of XGBoost demonstrates the highest accuracy among all algorithms. Furthermore, transfer learning was successfully introduced to enhance the accuracy and robustness of the model. Ultimately, we report that a machine learning algorithm, combining transfer learning with XGBoost or LightGBM, offers an accurate, high-efficiency and environmental friendly method for predicting the lignin content of Chinese fir using Raman spectra.

Case Report of Painless Obstructive Jaundice: A Rare First Presentation of Small-Cell Lung Cancer.

Small-cell lung cancer (SCLC) is a very aggressive type of lung cancer that is of neuroendocrine origin. Because of the high levels of circulating tumor cells, it has a very high rate of metastasis. Obstructive jaundice as the initial manifestation of small cell lung carcinoma is rare. Most of the cases are due to extrahepatic cholestasis by biliary duct obstruction. The biliary duct obstruction may be secondary to metastasis to lymph nodes or pancreatic head metastasis. Obstructive jaundice secondary to intrahepatic cholestasis is even rarer. A 75-year-old male presented to the emergency department (ED) with a complaint of new-onset painless jaundice that his dentist incidentally detected. Examination revealed a mass in the right upper quadrant (RUQ) of the abdomen. Computed tomography (CT) angiography of the abdomen, pancreas, and pelvis shows innumerable hepatic hypodensities highly suspicious for metastatic disease. However, there was no extrahepatic dilatation or pancreatic mass. He was diagnosed with diffuse metastasis of small cell lung carcinoma (SCLC) by needle biopsy of the liver. He developed acute kidney injury and liver damage and thus compromised chemotherapy for SCLC. Later, the patient chose comfort care and passed away the next day. To our knowledge, this is the second reported case of SCLC initially presenting as obstructive jaundice secondary intrahepatic cholestasis by diffuse liver metastases.

Amines-mediated ß-glucose pentaacetate to generate photoluminescent polymer-carbon nanodots for visual monitoring the freshness of shrimp.

In this paper, a portable fluorescence-based functional hydrogel loaded with ß-d-glucose pentaacetate (ß-D-GP) is designed for high-sensitive quantification of amine vapor and visual monitoring of freshness of shrimp. We found for the first time that amine vapor can mediate ß-D-GP to generate photoluminescent polymer-carbon nanodots (PCNDs) with good optical properties. On this basis, a functional hydrogel sensing platform is simply formed by solidifying ß-D-GP in agarose hydrogels. When exposure to the volatile amines released from the spoilage of shrimp, ß-D-GP in hydrogel is immediately mediated by amines to generate PCNDs, resulting in obvious fluorescence-based color variation of functional hydrogel. Notably, a smartphone is used to obtain digital photographs and RGB (Red/Green/Blue) information of hydrogels for on-site quantitative analysis. The gray value of G/(R + B) of hydrogel shows good linearity with trimethylamine (TMA) vapor concentration in the range of 0-59.49 × 10-9 mol dm-3. More importantly, the G/(R + B) value of functional hydrogel is successfully used to assess the freshness of shrimp. Consequently, this strategy provides a low-cost, portable fluorescence analysis device with promising applications in achieving high-sensitive, nondestructive, and on-site food safety evaluation of animal-derived aquatic products.

A fluorescent and scattering dual-mode probe based on a carbon dot@cerium-guanosine monophosphate coordination polymer network for tetracycline detection.

Dual-mode sensing with a two-signal read-out is conducive to the improvement of detection accuracy. Herein, a fluorescent and scattering dual-mode chemosensor for tetracycline (TC) is proposed based on a carbon dot@cerium-guanosine monophosphate (CD@GMP-Ce) coordination polymer network. The inexpensive CD@GMP-Ce was prepared by exploiting the adaptive inclusion capability of coordination polymers and possessed remarkable fluorescence and strong Rayleigh scattering. The functional CD@GMP-Ce demonstrated fluorescence and scattering, the two optical-signal responses to TC simultaneously. Based on TC-specific fluorescence and scattering decline, the dual-mode detection of TC was established and the probe's detection limits were 43 nM in the fluorescence mode and 77 nM in the scattering mode, respectively. Furthermore, the potential application of the dual-mode sensor was verified by measuring TC in milk and tap-water samples. The study not only provides a new perspective for the development of assay methods for TC but also expands the applications of cerium coordination polymers.

Machine learning prediction of lignin content in poplar with Raman spectroscopy.

Based on features extracted from Raman spectra, regularization algorithms, SVR, DT, RF, LightGBM, CatBoost, and XGBoost were used to develop prediction models for lignin content in poplar. Firstly, Raman features extracted from FT-Raman spectra after data processing were used as input of models and determined lignin contents were output. Secondly, grid-search combined with cross-validation was used to adjust the hyper-parameters of models. Finally, the predictive models were built by aforementioned algorithms. The results indicated regularization algorithms, SVR, DT held test R2 were >0.80 which means the predictive values from model still deviate from measured ones. Meanwhile, RF, LightGBM, CatBoost, and XGBoost were better than above algorithms, and their test R2 were >0.91 which suggesting the predictive values was nearly close to measured ones. Therefore, fast and accurate methods for predicting lignin content were obtained and will be useful for screening suitable lignocellulosic resource with expected lignin content.