Generation of homogeneous midbrain organoids with in vivo-like cellular composition facilitates neurotoxin-based Parkinson\u27s disease modeling

Recent studies have demonstrated the generation of midbrain-like organoids (MOs) from human pluripotent stem cells. However, the low efficiency of MO generation and the relatively immature and heterogeneous structures of the MOs hinder the translation of these organoids from the bench to the clinic. Here we describe the robust generation of MOs with homogeneous distribution of midbrain dopaminergic (mDA) neurons. Our MOs contain not only mDA neurons but also other neuronal subtypes as well as functional glial cells including astrocytes and oligodendrocytes. Furthermore, our MOs exhibit mDA neuron-specific cell death upon treatment with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, indicating that MOs could be a proper human model system for studying the in vivo pathology of Parkinson\u27s disease (PD). Our optimized conditions for producing homogeneous and mature MOs might provide an advanced patient-specific platform for in vitro disease modeling as well as for drug screening for PD

Deep Learning Methods for Predicting Tap-Water Quality Time Series in South Korea

South Korea currently lacks a real-time monitoring and anomaly detection system for detecting continuous tap water quality changes from the water source to faucet and pre-diagnosing hazards that threaten tap water safety. In this study, we constructed an accurate water quality prediction model that could comprehensively cover all water treatment facilities supplying tap water nationwide and verified the model using an integrated approach. To address the uncertainty of continuously changing water quality, we collected five years (2017–2021) of hourly water quality data from 33 large water purification plants and applied various deep learning techniques to construct an optimal prediction model. We repeated water quality prediction and evaluation over the following 24 h through a time series cross-validation of an untrained dataset of the previous five months. The optimized deep learning model achieved average and maximum prediction accuracy of 98.78 and 99.98%, respectively, and showed excellent performance in terms of the root mean squared error (0.0006), mean absolute error (0.0003), and Nash–Sutcliffe efficiency (0.9894). Thus, deep learning technology greatly improved the accuracy and efficiency of water quality prediction. The proposed model could provide prompt and accurate water quality information for large-scale water supply facilities nationwide and improve public health through the early diagnosis of water quality anomalies

Deep Learning Methods for Predicting Tap-Water Quality Time Series in South Korea

South Korea currently lacks a real-time monitoring and anomaly detection system for detecting continuous tap water quality changes from the water source to faucet and pre-diagnosing hazards that threaten tap water safety. In this study, we constructed an accurate water quality prediction model that could comprehensively cover all water treatment facilities supplying tap water nationwide and verified the model using an integrated approach. To address the uncertainty of continuously changing water quality, we collected five years (2017–2021) of hourly water quality data from 33 large water purification plants and applied various deep learning techniques to construct an optimal prediction model. We repeated water quality prediction and evaluation over the following 24 h through a time series cross-validation of an untrained dataset of the previous five months. The optimized deep learning model achieved average and maximum prediction accuracy of 98.78 and 99.98%, respectively, and showed excellent performance in terms of the root mean squared error (0.0006), mean absolute error (0.0003), and Nash–Sutcliffe efficiency (0.9894). Thus, deep learning technology greatly improved the accuracy and efficiency of water quality prediction. The proposed model could provide prompt and accurate water quality information for large-scale water supply facilities nationwide and improve public health through the early diagnosis of water quality anomalies

A Novel Retractable Robotic Device for Colorectal Endoscopic Submucosal Dissection

Background/Aims: Appropriate tissue tension and clear visibility of the dissection area using traction are essential for effective and safe endoscopic submucosal dissection (ESD). In this study, we developed a retractable robot-assisted traction device and evaluated its performance in colorectal ESD. Methods: An experienced endoscopist performed ESD 18 times on an ex vivo porcine colon using the robot and 18 times using the conventional method. The outcome measures were procedure time, dissection speed, procedure-related adverse events, and blind dissection rate. Results: Thirty-six colonic lesions were resected from ex vivo porcine colon samples. The total procedure time was significantly shorter in robot-assisted ESD (RESD) than in conventional ESD (CESD) (20.1±4.1 minutes vs 34.3±8.3 minutes, p<0.05). The submucosal dissection speed was significantly faster in the RESD group than in the CESD group (36.8±9.2 mm2/min vs 18.1±4.7 mm2/min, p<0.05). The blind dissection rate was also significantly lower in the RESD group (12.8%±3.4% vs 35.1%±3.9%, p<0.05). In an in vivo porcine feasibility study, the robotic device was attached to a colonoscope and successfully inserted into the proximal colon without damaging the colonic wall, and ESD was successfully performed. Conclusion : s: The dissection speed and safety profile improved significantly with the retractable RESD. Thus, our robotic device has the potential to provide simple, effective, and safe multidirectional traction during colonic ESD