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Recent technological advancements led to the development of various plasma-based technologies for post-packaging modifications. The purpose of the present preclinical in vivo study was to assess the safety and efficacy of a novel chairside nonthermal gas plasma treatment for enhancing osseointegration of titanium implants. Six male mixed foxhounds underwent extraction of mandibular premolars and first molars, and the sockets healed for 42 days. Canine mandibles were randomized to receive either plasma-treated (test) or non-plasma-treated (control) dental implants. A total of 36 implants were placed in six animals, and they were sacrificed at 2 weeks (two animals), 4 weeks (two animals), and 6 weeks (two animals) after the implant surgery. When the radiographic analysis was performed, the changes in bone level were not statistically significant between the two groups at 2 weeks and 4 weeks. The difference became significant at 6 weeks (p = 0.016), indicating more bone loss from baseline to 6 weeks for the control group. The bone-to-implant contact (BIC) appeared to be higher for the test groups at all time points, and the BIC was significantly higher for the test group at 4 weeks (p = 0.046). In conclusion, this study underscored the potential of nonthermal plasma treatment in enhancing implant osseointegration. [ABSTRACT FROM AUTHOR]

Soft tissue sealing around zirconia (ZrO2) abutment is critical for the long-term stability of dental implants. The goal of the study is to develop a strong basal lamina (BL)-mediated epithelial attachment to ZrO2 via a novel physicochemical immobilization method. An electrophoretic fusion (EPF) method was applied to fuse a phosphonic acid (PA) linker to ZrO2 discs. Bindings of the PA linker and the following protease activated receptor 4 (PAR4) were verified by Fourier-transform infrared spectroscopy (FITR). Then, ZrO2 discs were doped in platelet-rich plasma (PRP). Platelet-derived growth factor (PDGF) was measured to assess platelet activation. PRP-doped discs were subsequently co-cultured with human gingival epithelial cells (OBA9) to evaluate establishment of basal lamina-mediated epithelial attachment. The EPF method achieved robust immobilization of the PA linker and PAR4 onto the ZrO2 surface. The resultant PAR4-coupled ZrO2 successfully induced platelet aggregation and activation. Furthermore, a BL-mediated epithelial attachment was established. The results are significant for clinical application to minimize the risk of developing peri-implant diseases. [ABSTRACT FROM AUTHOR]

Evaluating several vital signs and chest X-ray (CXR) reports regularly to determine the recovery of the pneumonia patients at general wards is a challenge for doctors. A recent study shows the identification of pneumonia by the history of symptoms and signs including vital signs, CXR, and other clinical parameters, but they lack predicting the recovery status after starting treatment. The goal of this paper is to provide a pneumonia status prediction system for the early affected patient's discharge from the hospital within 7 days or late discharge more than 7 days. This paper aims to design a multimodal data analysis for pneumonia status prediction using deep learning classification (MDA-PSP). We have developed a system that takes an input of vital signs and CXR images of the affected patient with pneumonia from admission day 1 to day 3. The deep learning then classifies the health status improvement or deterioration for predicting the possible discharge state. Therefore, the scope is to provide a highly accurate prediction of the pneumonia recovery on the 7th day after 3-day treatment by the SHAP (SHapley Additive exPlanation), imputation, adaptive imputation-based preprocessing of the vital signs, and CXR image feature extraction using deep learning based on dense layers-batch normalization (BN) with class weights for the first 7 days' general ward patient in MDA-PSP. A total of 3972 patients with pneumonia were enrolled by de-identification with an adult age of 71 mean ± 17 sd and 64% of them were male. After analyzing the data behavior, appropriate improvement measures are taken by data preprocessing and feature vectorization algorithm. The deep learning method of Dense-BN with SHAP features has an accuracy of 0.77 for vital signs, 0.92 for CXR, and 0.75 for the combined model with class weights. The MDA-PSP hybrid method-based experiments are proven to demonstrate higher prediction accuracy of 0.75 for pneumonia patient status. Henceforth, the hybrid methods of machine and deep learning for pneumonia patient discharge are concluded to be a better approach. [ABSTRACT FROM AUTHOR]

Background: Antinuclear antibody pattern recognition is vital for autoimmune disease diagnosis but labor-intensive for manual interpretation. To develop an automated pattern recognition system, we established machine learning models based on the International Consensus on Antinuclear Antibody Patterns (ICAP) at a competent level, mixed patterns recognition, and evaluated their consistency with human reading. Methods: 51,694 human epithelial cells (HEp-2) cell images with patterns assigned by experienced medical technologists collected in a medical center were used to train six machine learning algorithms and were compared by their performance. Next, we choose the best performing model to test the consistency with five experienced readers and two beginners. Results: The mean F1 score in each classification of the best performing model was 0.86 evaluated by Testing Data 1. For the inter-observer agreement test on Testing Data 2, the average agreement was 0.849 (κ) among five experienced readers, 0.844 between the best performing model and experienced readers, 0.528 between experienced readers and beginners. The results indicate that the proposed model outperformed beginners and achieved an excellent agreement with experienced readers. Conclusions: This study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods. [ABSTRACT FROM AUTHOR]