Small molecules targeting immune checkpoint proteins for cancer immunotherapy: a patent and literature review (2020-2024).

Introduction: Targeting immune checkpoint proteins (ICPs) via small molecules open a new window for cancer immunotherapy. Herein, we summarize recent advances of small molecules with novel chemical structures targeting ICPs, discusses their anti-tumor efficacies, which are important for the development of novel small molecules for cancer immunotherapy.

Areas Covered: In this review, the latest patents and literature were gathered through the comprehensive searches in the databases of European Patent Office (EPO), Cortellis Drug Discovery Intelligence (CDDI), PubMed and Web of Science using ICPs and compounds as key words.

Whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma.

Existing prognostic models are useful for estimating the prognosis of lung adenocarcinoma patients, but there remains room for improvement. In the current study, we developed a deep learning model based on histopathological images to predict the recurrence risk of lung adenocarcinoma patients. The efficiency of the model was then evaluated in independent multicenter cohorts.

Foundation model of ECG diagnosis: Diagnostics and explanations of any form and rhythm on ECG.

We propose a knowledge-enhanced electrocardiogram (ECG) diagnosis foundation model (KED) that utilizes large language models to incorporate domain-specific knowledge of ECG signals. This model is trained on 800,000 ECGs from nearly 160,000 unique patients. Despite being trained on single-center data, KED demonstrates exceptional zero-shot diagnosis performance across various regions, including different locales in China, the United States, and other regions.

A novel diagnosis method combined dual-channel SE-ResNet with expert features for inter-patient heartbeat classification.

As the number of patients with cardiovascular diseases (CVDs) increases annually, a reliable and automated system for detecting electrocardiogram (ECG) abnormalities is becoming increasingly essential. Scholars have developed numerous methods of arrhythmia classification using machine learning or deep learning. However, the issue of low classification rates of individual classes in inter-patient heartbeat classification remains a challenge.

Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis.

Background: Long-term monitoring of Electrocardiogram (ECG) recordings is crucial to diagnose arrhythmias. Clinicians can find it challenging to diagnose arrhythmias, and this is a particular issue in more remote and underdeveloped areas. The development of digital ECG and AI methods could assist clinicians who need to diagnose arrhythmias outside of the hospital setting.

TAG: Teacher-Advice Mechanism With Gaussian Process for Reinforcement Learning.

Reinforcement learning (RL) still suffers from the problem of sample inefficiency and struggles with the exploration issue, particularly in situations with long-delayed rewards, sparse rewards, and deep local optimum. Recently, learning from demonstration (LfD) paradigm was proposed to tackle this problem. However, these methods usually require a large number of demonstrations.

Weighted Heterogeneous Graph-Based Incremental Automatic Disease Diagnosis Method.

The objective of this study is to construct a multi-department symptom-based automatic diagnosis model. However, it is difficult to establish a model to classify plenty of diseases and collect thousands of disease-symptom datasets simultaneously. Inspired by the thought of "knowledge graph is model", this study proposes to build an experience-infused knowledge model by continuously learning the experiential knowledge from data, and incrementally injecting it into the knowledge graph.

Multiple high-regional-incidence cardiac disease diagnosis with deep learning and its potential to elevate cardiologist performance.

Currently, due to lack of large-scale datasets containing multiple arrhythmias and acute coronary syndrome-related diseases, AI-aided diagnosis for cardiac diseases is limited in clinical scenarios. Whether AI-based ECG diagnosis can assist cardiologists to improve performance has not been reported. We constructed a large-scale dataset containing multiple high-regional-incidence arrhythmias and ACS-related diseases, including 162,622 12-lead ECGs collected between January 2018 and March 2021.

A novel P-QRS-T wave localization method in ECG signals based on hybrid neural networks.

As the number of people suffering from cardiovascular diseases increases every year, it becomes essential to have an accurate automatic electrocardiogram (ECG) diagnosis system. Researchers have adopted different methods, such as deep learning, to investigate arrhythmias classification. However, the importance of ECG waveform features is generally ignored when deep learning approaches are applied to classification tasks.

A review of arrhythmia detection based on electrocardiogram with artificial intelligence.

Introduction: With the widespread availability of portable electrocardiogram (ECG) devices, there will be a surge in ECG diagnoses. Traditional computer-aided diagnosis of arrhythmia mainly relies on the rules of medical knowledge, which are insufficient due to the limitations of data quality and human expert knowledge. The research of arrhythmia detection methods based on artificial intelligence (AI) techniques can assist physicians in high-precision arrhythmia diagnosis.

The Ecological Consciousness of Natural Writing in British and American Romantic Literature.

The ecological crisis made the British and American ecological literature develop rapidly in the 20th century ecological thought. As a unique literary style that expresses the relationship between nature and man, British and American ecological literature has a far-reaching romantic tradition, and returning to nature is its eternal theme and dream. The study of the romantic tradition of British and American ecological literature has important implications for the development of ecological literature and ecological criticism.

[A heart sound classification method based on joint decision of extreme gradient boosting and deep neural network].

Heart sound is one of the common medical signals for diagnosing cardiovascular diseases. This paper studies the binary classification between normal or abnormal heart sounds, and proposes a heart sound classification algorithm based on the joint decision of extreme gradient boosting (XGBoost) and deep neural network, achieving a further improvement in feature extraction and model accuracy. First, the preprocessed heart sound recordings are segmented into four status, and five categories of features are extracted from the signals based on segmentation.