Revolution of Medical Review: The Application of Meta-Analysis and Convolutional Neural Network-Natural Language Processing in Classifying the Literature for Head and Neck Cancer Radiotherapy.

This study explored the application of meta-analysis and convolutional neural network-natural language processing (CNN-NLP) technologies in classifying literature concerning radiotherapy for head and neck cancer. It aims to enhance both the efficiency and accuracy of literature reviews. By integrating statistical analysis with deep learning, this research successfully identified key studies related to the probability of normal tissue complications (NTCP) from a vast corpus of literature. This demonstrates the advantages of these technologies in recognizing professional terminology and extracting relevant information. The findings not only improve the quality of literature reviews but also offer new insights for future research on optimizing medical studies through AI technologies. Despite the challenges related to data quality and model generalization, this work provides clear directions for future research.

This study examines how advanced technologies like meta-analysis and machine learning, specifically through Convolutional Neural Networks and Natural Language Processing (CNN-NLP), can revolutionize the way medical researchers review literature on radiotherapy for head and neck cancer. Typically, reviewing vast amounts of medical studies is time-consuming and complex. This paper showcases a method that combines statistical analysis and AI to streamline the process, enhancing the accuracy and efficiency of identifying crucial research. By applying these technologies, the researchers were able to sift through thousands of articles rapidly, pinpointing the most relevant ones without the extensive manual effort usually required. This approach not only speeds up the review process but also improves the quality of the information extracted, making it easier for medical professionals to keep up with the latest findings and apply them effectively in clinical settings. The findings of this study are promising, demonstrating that integrating AI with traditional review methods can significantly aid in managing the ever-growing body of medical literature, potentially leading to better treatment strategies and outcomes for patients suffering from head and neck cancer. Despite some challenges like data quality and the need for extensive computational resources, the study provides a forward path for using AI to enhance medical research and practice.

Utilizing radiomics and dosiomics with AI for precision prediction of radiation dermatitis in breast cancer patients.

PURPOSE: This study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT). MATERIALS AND METHODS: This study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information. Using a Treatment Planning System (TPS), we segmented CT images into Regions of Interest (ROIs) to extract radiomic and dosiomic features, focusing on intensity, shape, texture, and dose distribution characteristics. Features significantly associated with the development of RD were identified using ANOVA and LASSO regression (p-value < 0.05). These features were then employed to train and evaluate Logistic Regression (LR) and Random Forest (RF) models, using tenfold cross-validation to ensure robust assessment of model efficacy. RESULTS: In this study, 102 out of 120 VMAT-treated breast cancer patients were included in the detailed analysis. Thirty-two percent of these patients developed Grade 2+ RD. Age and BMI were identified as significant clinical predictors. Through feature selection, we narrowed down the vast pool of radiomic and dosiomic data to 689 features, distributed across 10 feature subsets for model construction. In the LR model, the J subset, comprising DVH, Radiomics, and Dosiomics features, demonstrated the highest predictive performance with an AUC of 0.82. The RF model showed that subset I, which includes clinical, radiomic, and dosiomic features, achieved the best predictive accuracy with an AUC of 0.83. These results emphasize that integrating radiomic and dosiomic features significantly enhances the prediction of Grade 2+ RD. CONCLUSION: Integrating clinical, radiomic, and dosiomic characteristics into AI models significantly improves the prediction of Grade 2+ RD risk in breast cancer patients post-VMAT. The RF model analysis demonstrates that a comprehensive feature set maximizes predictive efficacy, marking a promising step towards utilizing AI in radiation therapy risk assessment and enhancing patient care outcomes.

Establishment and risk factor assessment of the abnormal body temperature probability prediction model (ABTP) for dairy cattle.

The study aims to develop an abnormal body temperature probability (ABTP) model for dairy cattle, utilizing environmental and physiological data. This model is designed to enhance the management of heat stress impacts, providing an early warning system for farm managers to improve dairy cattle welfare and farm productivity in response to climate change. The study employs the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to analyze environmental and physiological data from 320 dairy cattle, identifying key factors influencing body temperature anomalies. This method supports the development of various models, including the Lyman Kutcher-Burman (LKB), Logistic, Schultheiss, and Poisson models, which are evaluated for their ability to predict abnormal body temperatures in dairy cattle effectively. The study successfully validated multiple models to predict abnormal body temperatures in dairy cattle, with a focus on the temperature-humidity index (THI) as a critical determinant. These models, including LKB, Logistic, Schultheiss, and Poisson, demonstrated high accuracy, as measured by the AUC and other performance metrics such as the Brier score and Hosmer-Lemeshow (HL) test. The results highlight the robustness of the models in capturing the nuances of heat stress impacts on dairy cattle. The research develops innovative models for managing heat stress in dairy cattle, effectively enhancing detection and intervention strategies. By integrating advanced technologies and novel predictive models, the study offers effective measures for early detection and management of abnormal body temperatures, improving cattle welfare and farm productivity in changing climatic conditions. This approach highlights the importance of using multiple models to accurately predict and address heat stress in livestock, making significant contributions to enhancing farm management practices.

Development of a risk prediction model for radiation dermatitis following proton radiotherapy in head and neck cancer using ensemble machine learning.

PURPOSE: This study aims to develop an ensemble machine learning-based (EML-based) risk prediction model for radiation dermatitis (RD) in patients with head and neck cancer undergoing proton radiotherapy, with the goal of achieving superior predictive performance compared to traditional models. MATERIALS AND METHODS: Data from 57 head and neck cancer patients treated with intensity-modulated proton therapy at Kaohsiung Chang Gung Memorial Hospital were analyzed. The study incorporated 11 clinical and 9 dosimetric parameters. Pearson's correlation was used to eliminate highly correlated variables, followed by feature selection via LASSO to focus on potential RD predictors. Model training involved traditional logistic regression (LR) and advanced ensemble methods such as Random Forest and XGBoost, which were optimized through hyperparameter tuning. RESULTS: Feature selection identified six key predictors, including smoking history and specific dosimetric parameters. Ensemble machine learning models, particularly XGBoost, demonstrated superior performance, achieving the highest AUC of 0.890. Feature importance was assessed using SHAP (SHapley Additive exPlanations) values, which underscored the relevance of various clinical and dosimetric factors in predicting RD. CONCLUSION: The study confirms that EML methods, especially XGBoost with its boosting algorithm, provide superior predictive accuracy, enhanced feature selection, and improved data handling compared to traditional LR. While LR offers greater interpretability, the precision and broader applicability of EML make it more suitable for complex medical prediction tasks, such as predicting radiation dermatitis. Given these advantages, EML is highly recommended for further research and application in clinical settings.

The price continuity, return and volatility spillover effects of regular and after-hours trading.

This study employs a bivariate EGARCH model to examine the Taiwan Futures Exchange's regular and after-hours trading, focusing on the critical aspects of spillover and expiration effects, as well as volatility clustering and asymmetry. The objective of this study is to observe the impact on the trading sessions in Taiwan by the influences of the European and American markets, focusing on the essential roles of the price discovery function and risk disclosure effectiveness of the regular hours trading. This research is imperative considering the increasing interconnectedness of global financial markets and the need for comprehensive risk assessment for investment strategies. It also examines the hedging behavior of after-hours traders, thereby aiming to contribute to pre-investment analysis by future investors. This examination is vital for understanding the dynamics of after-hours trading and its influence on market stability. Results indicate price continuity between both trading sessions, with regular trading often determining after-hours price ranges. Consequently, after-hours price changes can inform regular trading decisions. This finding highlights the importance of after-hours trading for shaping market expectations. Significant profit potential exists in after-hours trading open interest, which serves speculative and hedging purposes. While regular trading volatility influences after-hours trading, the reverse is not true. This suggests Taiwan market information poses a higher risk impact than European and American market data, emphasizing the unique position of the Taiwan market in the global financial ecosystem. After-hours trading volatility reflects the absorption of international market information and plays a crucial role in advance revelation of risks. This underscores the importance of after-hours trading in global risk management and strategy formulation.

Hermetic Seal of Organic Light Emitting Diode with Glass Frit.

The components of OLED encapsulation with hermetic sealing and a 1026-day lifetime were measured by PXI-1033. The optimal characteristics were obtained when the thickness of the TPBi layer was 20 nm. This OLED obtained a maximum luminance (Lmax) of 25,849 cd/m2 at a current density of 1242 mA/cm2, an external quantum efficiency (EQE) of 2.28%, a current efficiency (CE) of 7.20 cd/A, and a power efficiency (PE) of 5.28 lm/W. The efficiency was enhanced by Lmax 17.2%/EQE 0.89%/CE 42.1%/PE 41.9%. The CIE coordinates of 0.32, 0.54 were all green OLED elements with wavelengths of 532 nm. The shear strain and leakage test gave results of 16 kgf and 8.92 × 10-9 mbar/s, respectively. The reliability test showed that the standard of MIL-STD-883 was obtained.

Synthesis of vibroarthrographic signals in knee osteoarthritis diagnosis training.

BACKGROUND: Vibroarthrographic (VAG) signals are used as useful indicators of knee osteoarthritis (OA) status. The objective was to build a template database of knee crepitus sounds. Internships can practice in the template database to shorten the time of training for diagnosis of OA. METHODS: A knee sound signal was obtained using an innovative stethoscope device with a goniometer. Each knee sound signal was recorded with a Kellgren-Lawrence (KL) grade. The sound signal was segmented according to the goniometer data. The signal was Fourier transformed on the correlated frequency segment. An inverse Fourier transform was performed to obtain the time-domain signal. Haar wavelet transform was then done. The median and mean of the wavelet coefficients were chosen to inverse transform the synthesized signal in each KL category. The quality of the synthesized signal was assessed by a clinician. RESULTS: The sample signals were evaluated using different algorithms (median and mean). The accuracy rate of the median coefficient algorithm (93 %) was better than the mean coefficient algorithm (88 %) for cross-validation by a clinician using synthesis of VAG. CONCLUSIONS: The artificial signal we synthesized has the potential to build a learning system for medical students, internships and para-medical personnel for the diagnosis of OA. Therefore, our method provides a feasible way to evaluate crepitus sounds that may assist in the diagnosis of knee OA.

Normal tissue complication probability modeling for cochlea constraints to avoid causing tinnitus after head-and-neck intensity-modulated radiation therapy.

BACKGROUND: Radiation-induced tinnitus is a side effect of radiotherapy in the inner ear for cancers of the head and neck. Effective dose constraints for protecting the cochlea are under-reported. The aim of this study is to determine the cochlea dose limitation to avoid causing tinnitus after head-and-neck cancer (HNC) intensity-modulated radiation therapy (IMRT). METHODS: In total 211 patients with HNC were included; the side effects of radiotherapy were investigated for 422 inner ears in the cohort. Forty-nine of the four hundred and twenty-two samples (11.6%) developed grade 2+ tinnitus symptoms after IMRT, as diagnosed by a clinician. The Late Effects of Normal Tissues-Subjective, Objective, Management, Analytic (LENT-SOMA) criteria were used for tinnitus evaluation. The logistic and Lyman-Kutcher-Burman (LKB) normal tissue complication probability (NTCP) models were used for the analyses. RESULTS: The NTCP-fitted parameters were TD 50 = 46.31 Gy (95% CI, 41.46-52.50), γ 50 = 1.27 (95% CI, 1.02-1.55), and TD 50 = 46.52 Gy (95% CI, 41.91-53.43), m = 0.35 (95% CI, 0.30-0.42) for the logistic and LKB models, respectively. The suggested guideline TD 20 for the tolerance dose to produce a 20% complication rate within a specific period of time was TD 20 = 33.62 Gy (95% CI, 30.15-38.27) (logistic) and TD 20 = 32.82 Gy (95% CI, 29.58-37.69) (LKB). CONCLUSIONS: To maintain the incidence of grade 2+ tinnitus toxicity <20% in IMRT, we suggest that the mean dose to the cochlea should be <32 Gy. However, models should not be extrapolated to other patient populations without further verification and should first be confirmed before clinical implementation.

Structural changes in foreign investors' trading behavior and the corresponding impact on Taiwan's stock market.

This study investigates the impact of the expected and unexpected trading behavior of foreign investors on return volatilities during structural change periods. And the jump intensity model pinpoints crucial events that have influenced the stock market. The empirical results find that there has been a stabilizing effect of foreign investment on Taiwan's stock market as restrictions on foreign trading have been gradually relaxed, as opposed to there being a complete relaxation of the restrictions imposed on Qualified Foreign Institutional Investors (QFIIs).