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Acute kidney injury (AKI) is a significant health challenge associated with adverse patient outcomes and substantial economic burdens. Many authors have sought to prevent and predict AKI. Here, we comprehensively review recent advances in the use of artificial intelligence (AI) to predict AKI, and the associated challenges. Although AI may detect AKI early and predict prognosis, integration of AI-based systems into clinical practice remains challenging. It is difficult to identify AKI patients using retrospective data; information preprocessing and the limitations of existing models pose problems. It is essential to embrace standardized labeling criteria and to form international multi-institutional collaborations that foster high-quality data collection. Additionally, existing constraints on the deployment of evolving AI technologies in real-world healthcare settings and enhancement of the reliabilities of AI outputs are crucial. Such efforts will improve the clinical applicability, performance, and reliability of AKI Clinical Support Systems, ultimately enhancing patient prognoses.

Pseudoperonospora cubensis is a significant phytopathogen causing downy mildew disease in cucurbit crops. Understanding the molecular mechanisms underlying the interaction between P. cubensis and its host is essential for developing effective disease management strategies. RxLR effectors, secreted by pathogens, play a crucial role in modulating host immunity. In this study, we sequenced the genome of the P. cubensis isolate CNU-OTH and identified RxLR effector genes using bioinformatics tools. A total of 45 RxLR effector genes were identified from the genome of P. cubensis. Cloning and functional characterization of these effectors were performed through transient expression assays in Nicotiana benthamiana leaves. Subcellular localization of selected effectors was determined using GFP-tagged constructs. Functional characterization revealed that while most effectors did not induce a hypersensitive response (HR), a subset showed either weak or strong necrosis. Furthermore, several effectors demonstrated the ability to suppress cell death induced by BAX and INF1. Subcellular localization analysis indicated that RxLR effectors exhibited fluorescence in the nucleus and plasma membrane of N. benthamiana cells, suggesting diverse roles in host-pathogen interactions. This study provides insights into the genetic diversity and functional characterization of RxLR effectors in P. cubensis. Understanding the role of these effectors in manipulating host immunity is critical for developing strategies to combat downy mildew disease in cucurbit crops. The findings contribute to the broader understanding of plant-pathogen interactions and may facilitate the development of disease-resistant crop varieties.

Background Acute kidney injury (AKI) is a significant challenge in healthcare. While there are considerable researches dedicated to AKI patients, a crucial factor in their renal function recovery, is often overlooked. Thus, our study aims to address this issue through the development of a machine learning model to predict restoration of kidney function in patients with AKI. Methods Our study encompassed data from 350,345 cases, derived from three hospitals. AKI was classified in accordance with the Kidney Disease: Improving Global Outcomes. Criteria for recovery were established as either a 33% decrease in serum creatinine levels at AKI onset, which was initially employed for the diagnosis of AKI. We employed various machine learning models, selecting 43 pertinent features for analysis. Results Our analysis contained 7,041 and 2,929 patients’ data from internal cohort and external cohort respectively. The Categorical Boosting Model demonstrated significant predictive accuracy, as evidenced by an internal area under the receiver operating characteristic (AUROC) of 0.7860, and an external AUROC score of 0.7316, thereby confirming its robustness in predictive performance. SHapley Additive exPlanations (SHAP) values were employed to explain key factors impacting recovery of renal function in AKI patients. Conclusion This study presented a machine learning approach for predicting renal function recovery in patients with AKI. The model performance was assessed across distinct hospital settings, which revealed its efficacy. Although the model exhibited favorable outcomes, the necessity for further enhancements and the incorporation of more diverse datasets is imperative for its application in real-world.

Applying deep learning to medical research with limited data is challenging. This study focuses on addressing this difficulty through a case study, predicting acute respiratory failure (ARF) in patients with acute pesticide poisoning. Commonly, out-of-distribution (OOD) data are overlooked during model training in the medical field. Our approach integrates OOD data and transfer learning (TL) to enhance model performance with limited data. We fine-tuned a pre-trained multi-layer perceptron model using OOD data, outperforming baseline models. Shapley additive explanation (SHAP) values were employed for model interpretation, revealing the key factors associated with ARF. Our study is pioneering in applying OOD and TL techniques to electronic health records to achieve better model performance in scenarios with limited data. Our research highlights the potential benefits of using OOD data for initializing weights and demonstrates that TL can significantly improve model performance, even in medical data with limited samples. Our findings emphasize the significance of utilizing context-specific information in TL to achieve better results. Our work has practical implications for addressing challenges in rare diseases and other scenarios with limited data, thereby contributing to the development of machine-learning techniques within the medical field, especially regarding health inequities.

Numerous studies have evaluated the acoustic characteristics of soniferous snapping shrimp, but a few are based on long-term mooring measurements. In this study, underwater ambient noise signals were collected from a hydrophone moored 10 m from the sea bed in the coastal sea of western Jeju, South Korea, from mid-September 2019 for 90 days to analyze the variation in the sound of snapping shrimp. The kernel signal and a threshold value were utilized to identify the snapping shrimp, and the snap rate per minute was computed for quantitative analysis. The results show that the mean and standard deviation of the snap rate in the western sea of Jeju was 2132 ± 432 per minute during the whole measurement period. The surface water temperature and tidal level decreased by 7 °C from 25 °C and 50 cm from 190 cm, respectively, over 90 days. The snap rate decreased from September mainly due to the decrease in water temperature by 71 times per minute for every 1 °C decrease. It showed a circadian cycle, increasing by 17~24% at sunrise and sunset compared to the daytime minimum. The snap rate at night was the highest in late summer but the rate dropped like the one during the day in late fall. The snap rate at high tide was 13% higher on average than at low tide. The circadian and tidal changes of the snapping shrimp sound from long-term mooring measurements may be used as primary data for underwater ambient noise and the ecological behavior of snapping shrimp.

Forward Head Posture (FHP) refers to a condition where the head protrudes forward, significantly contributing to neck pain and being associated with decreased productivity and psychological distress. This study investigates the nuanced classification of FHP and proposes a universally applicable methodology for its identification and analysis using deep learning. Leveraging the Korean Facial Image (K-FACE) dataset, rigorous image preprocessing with the Yolo-v8 model was conducted to facilitate accurate measurement of the CranioVertebral Angle (CVA) from various perspectives. The study meticulously evaluated the classification effectiveness of three advanced deep learning models: EfficientNet-B7, NFNet-F7, and ResNet-152. Among these, EfficientNet-B7 demonstrated superior performance with an accuracy of 0.69 and a recall score of 0.69 compared to other models. Additionally, comparisons based on camera angles within the EfficientNet-B7 model highlighted its excellence, particularly at the ±75° angle. The importance of image regions in EfficientNet-B7 was confirmed through Grad-CAM analysis, emphasizing the critical role of the neck region in accurately classifying FHP images. This comprehensive performance comparison and the proposed detailed classification methodology underscore the potential for generalization in FHP classification. Furthermore, by leveraging a unique dataset and employing state-of-the-art classification techniques, this research offers a novel perspective on the discourse surrounding FHP. Future research could integrate expanded facial image datasets and apply transfer learning techniques to further enhance the precision of FHP classification, thereby improving diagnostic accuracy and offering targeted interventions for individuals experiencing neck pain associated with FHP. [ABSTRACT FROM AUTHOR]