Transcriptome dataset of two Pistacia species: Pistacia chinensis and Pistacia weinmannifolia.

Pistacia chinensis and Pistacia weinmannifolia are small trees and are distributed in East Asia, in particular China. The data on P. chinensis presented in this article is associated with the research article, "DOI: 10.5010/JPB.2019.46.4.274" [1]. Both P. chinensis and P. weinmannifolia have long been used as ethnobotanical plants to treat various illnesses, including dysentery, inflammatory swelling, rheumatism, liver diseases, influenza, lung cancer, etc. Many studies have been carried out to delve into the pharmaceutical properties of these Pistacia species using plant extracts, but genomic studies are very rarely performed to date. To enrich the genetic information of these two species, RNA sequencing was conducted using a pair-end Illumina HiSeq2500 sequencing system, resulting in 2.6 G of raw data from P. chinensis (Accession no: SRR10136265) and 2.7 G bases from P. weinmannifolia (Accession no: SRR10136264). Transcriptome shotgun assembly using three different assembly tools generated a total of 18,524 non-redundant contigs (N50, 1104 bp) from P. chinensis and 18,956 from P. weinmannifolia (N50, 1137 bp). The data is accessible at NCBI BioProject: PRJNA566127. These data would be crucial for the identification of genes associated with the compounds exerting pharmaceutical properties and also for molecular marker development.

Early-onset stroke among people with disabilities: a national database study in South Korea from 2008 to 2017.

Background: This study investigated 10-year trend in the incidence and prevalence of ischemic, hemorrhagic, and overall strokes according to the severity and type of disability between people with and without disabilities. Methods: This serial cross-sectional analysis was conducted using national health information data during a 10-year period from 2008 to 2017. Age-standardized incidence and prevalence were analyzed for each year, according to the presence, severity, and type of disability. The odds ratio (OR) of stroke was examined using multivariable logistic regression after adjusting for socio-demographic and clinical variables collected in 2017. Findings: In total, 413,398,084 people were enrolled between 2008 and 2017. In 2017, 43,552,192 people aged 19 or older were included and 5.8% was disabled. For 10 years, age-standardized incidence of ischemic and hemorrhagic stroke decreased significantly regardless of the presence of disability. However, age-standardized incidence of stroke in disabled were almost 2.5 times higher than the non-disabled in 2017. Stroke occurs 20 years earlier in people with disabilities than in those without disabilities. In 2017, people with disabilities had higher odds of stroke compared to those without disability (OR = 4.11, 95% confidence interval [CI]: 4.06-4.16), particularly among those with severe disabilities (OR = 4.75, 95% CI: 4.67-4.84). People with major internal organ impairment showed the highest incidence of stroke (OR = 5.95, 95% CI: 5.73-6.17). The main risk factors for stroke presented in this study were disability factors, chronic diseases, and advanced age. Interpretation: People with disabilities are at a greater risk of developing stroke incidence. Developing a public health policy and identifying the risk factors for stroke in people with disabilities would be beneficial. Funding: This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (No. 2022R1I1A3070074).

Accurate detection for dental implant and peri-implant tissue by transfer learning of faster R-CNN: a diagnostic accuracy study.

BACKGROUND: The diagnosis of dental implants and the periapical tissues using periapical radiographs is crucial. Recently, artificial intelligence has shown a rapid advancement in the field of radiographic imaging. PURPOSE: This study attempted to detect dental implants and peri-implant tissues by using a deep learning method known as object detection on the implant image of periapical radiographs. METHODS: After implant treatment, the periapical images were collected and data were processed by labeling the dental implant and peri-implant tissue together in the images. Next, 300 images of the periapical radiographs were split into 80:20 ratio (i.e. 80% of the data were used for training the model while 20% were used for testing the model). These were evaluated using an object detection model known as Faster R-CNN, which simultaneously performs classification and localization. This model was evaluated on the classification performance using metrics, including precision, recall, and F1 score. Additionally, in order to assess the localization performance, an evaluation through intersection over union (IoU) was utilized, and, Average Precision (AP) was used to assess both the classification and localization performance. RESULTS: Considering the classification performance, precision = 0.977, recall = 0.992, and F1 score = 0.984 were derived. The indicator of localization was derived as mean IoU = 0.907. On the other hand, considering the indicators of both classification and localization performance, AP showed an object detection level of AP@0.5 = 0.996 and AP@0.75 = 0.967. CONCLUSION: Thus, the implementation of Faster R-CNN model for object detection on 300 periapical radiographic images including dental implants, resulted in high-quality object detection for dental implants and peri-implant tissues.

Fabrication of non-enzymatic biosensor based on metallic catalyst-TiO2 hollow sphere nanocomposite for determining biomolecules.

A PtRu@TiO2-hollow nanocomposite for the detection of biomolecules was synthesized by chemical reduction. First, poly(styrene-co-vinylphenylboronic acid), PSB, was prepared as a template (approximately 250 nm) by surfactant-free emulsion polymerization. Second, PSB/TiO2 core-shell spheres were prepared by sol-gel reaction. Finally, TiO2 hollow spheres (TiO2-H) were then formed after removing the PSB template by calcination at 450 degrees C under air atmosphere. To prepare the electrocatalyst, PtRu nanoparticles (NPs) were deposited onto the TiO2-H surface by chemical reduction. The prepared PtRu@TiO2-H nanocomposite was characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD), and elemental analysis. A non-enzymatic sensor was fabricated by depositing the as-prepared PtRu@TiO2-H nanocomposite on the surface of a glassy carbon electrode (GCE), which was prepared by a hand casting method with Nafion solution as a binder. The sensor was tested as a biomolecule sensor, especially for the detection of glucose and dopamine. The cyclic voltammograms (CV) obtained during the oxidation studies revealed that the PtRu@TiO2-H nanocomposite showed better catalytic function toward the oxidation of dopamine. The sensing range of the non-enzymatic sensor for glucose was 5.0-100 mM in a phosphate buffer. The results demonstrated the potential usefulness of this bimetallic@TiO2-H bifunctional catalyst for biosensor applications.