A novel deep learning model for glioma epilepsy associated with the identification of human cytomegalovirus infection injuries based on head MR.

Purpose: In this study, a deep learning model was established based on head MRI to predict a crucial evaluation parameter in the assessment of injuries resulting from human cytomegalovirus infection: the occurrence of glioma-related epilepsy. The relationship between glioma and epilepsy was investigated, which serves as a significant indicator of labor force impairment. Methods: This study enrolled 142 glioma patients, including 127 from Shengjing Hospital of China Medical University, and 15 from the Second Affiliated Hospital of Dalian Medical University. T1 and T2 sequence images of patients' head MRIs were utilized to predict the occurrence of glioma-associated epilepsy. To validate the model's performance, the results of machine learning and deep learning models were compared. The machine learning model employed manually annotated texture features from tumor regions for modeling. On the other hand, the deep learning model utilized fused data consisting of tumor-containing T1 and T2 sequence images for modeling. Results: The neural network based on MobileNet_v3 performed the best, achieving an accuracy of 86.96% on the validation set and 75.89% on the test set. The performance of this neural network model significantly surpassed all the machine learning models, both on the validation and test sets. Conclusion: In this study, we have developed a neural network utilizing head MRI, which can predict the likelihood of glioma-associated epilepsy in untreated glioma patients based on T1 and T2 sequence images. This advancement provides forensic support for the assessment of injuries related to human cytomegalovirus infection.

A nickel(ii)-based one-dimensional organic-inorganic halide perovskite ferroelectric with the highest Curie temperature.

Organic-inorganic halide perovskites (OIHPs) are very eye-catching due to their chemical tunability and rich physical properties such as ferroelectricity, magnetism, photovoltaic properties and photoluminescence. However, no nickel-based OIHP ferroelectrics have been reported so far. Here, we designed an ABX3 OIHP ferroelectric (3-pyrrolinium)NiCl3, where the 3-pyrrolinium cations are located on the voids surrounded by one-dimensional chains composed of NiCl6-face-sharing octahedra via hydrogen bonding interactions. Such a unique structure enables the (3-pyrrolinium)NiCl3 with a high spontaneous polarization (P s) of 5.8 µC cm-2 and a high Curie temperature (T c) of 428 K, realizing dramatic enhancement of 112 and 52 K compared to its isostructural (3-pyrrolinium)MCl3 (M = Cd, Mn). To our knowledge, remarkably, (3-pyrrolinium)NiCl3 should be the first case of nickel(ii)-based OIHP ferroelectric to date, and its T c of 428 K (35 K above that of BaTiO3) is the highest among all reported one-dimensional OIHP ferroelectrics. This work offers a new structural building block for enriching the family of OIHP structures and will inspire the further exploration of new nickel(ii)-based OIHP ferroelectrics.