DOGMA-seq and multimodal, single-cell analysis in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a complex cancer, yet advances in recent years from integrated genomics methods have helped improve diagnosis, treatment, and means of patient stratification. A recent example of a powerful, multimodal method is DOGMA-seq, which can measure chromatin accessibility, gene expression, and cell-surface protein levels from the same individual cell simultaneously. Previous bimodal single-cell techniques, such as CITE-seq (Cellular indexing of transcriptomes and epitopes), have only permitted the transcriptome and cell-surface protein expression measurement.

Trichoptysis: a pathognomonic presentation of anterior mediastinal teratoma.

Germ cell tumours are usually found in the gonads, while the most common extragonadal site is the anterior mediastinum. When these tumours involve the tracheobronchial tree, patients present with trichoptysis or coughing up of hair. We present a rare case of a woman who presented with trichoptysis and was evaluated and diagnosed with benign mature teratoma of the anterior mediastinum with bronchopulmonary involvement.

ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs.

Pneumonia, a leading cause of mortality in children under five, is usually diagnosed through chest X-ray (CXR) images due to its efficiency and cost-effectiveness. However, the shortage of radiologists in the Least Developed Countries (LDCs) emphasizes the need for automated pneumonia diagnostic systems. This article presents a Deep Learning model, Zero-Order Optimized Convolutional Neural Network (ZooCNN), a Zero-Order Optimization (Zoo)-based CNN model for classifying CXR images into three classes, Normal Lungs (NL), Bacterial Pneumonia (BP), and Viral Pneumonia (VP); this model utilizes the Adaptive Synthetic Sampling (ADASYN) approach to ensure class balance in the Kaggle CXR Images (Pneumonia) dataset.

Multi-view domain adaption based multi-scale convolutional conditional invertible discriminator for cross-subject electroencephalogram emotion recognition.

Cross subject Electroencephalogram (EEG) emotion recognition refers to the process of utilizing electroencephalogram signals to recognize and classify emotions across different individuals. It tracks neural electrical patterns, and by analyzing these signals, it's possible to infer a person's emotional state. The objective of cross-subject recognition is to create models or algorithms that can reliably detect emotions in both the same person and several other people.

Occult collision tumor of the gastroesophageal junction comprising adenocarcinomas with distinct molecular profiles.

Collision tumors, characterized by the coexistence of two unique neoplasms in close approximation, are rare and pose diagnostic challenges. This is particularly true when the unique neoplasms are of the same histologic type. Here we report such a case where comprehensive tumor profiling by next generation sequencing (NGS) as well as immunohistochemistry revealed two independent adenocarcinomas comprising what was initially diagnosed as a single adenocarcinoma of the gastroesophageal (GEJ) junction.