Trends of alkaline phosphatase to prealbumin ratio in patients with hepatitis B linked to hepatocellular carcinoma development.

Background: Chronic hepatitis B often progresses silently toward hepatocellular carcinoma (HCC), a leading cause of mortality worldwide. Early detection of HCC is crucial, yet challenging.

Aim: To investigate the role of dynamic changes in alkaline phosphatase to prealbumin ratio (APR) in hepatitis B progression to HCC.

Research progress on the correlation between intestinal flora and colorectal cancer.

Colorectal cancer (CRC) is one of the most common gastrointestinal malignancies in the world. With the rapid pace of life and changes in diet structure, the incidence and mortality of CRC increase year by year posing a serious threat to human health. As the most complex and largest microecosystem in the human body, intestinal microecology is closely related to CRC.

Enhancing the diagnostic accuracy of colorectal cancer through the integration of serum tumor markers and hematological indicators with machine learning algorithms.

Background: Colorectal cancer has a high incidence and mortality rate due to a low rate of early diagnosis. Therefore, efficient diagnostic methods are urgently needed.

Purpose: This study assesses the diagnostic effectiveness of Carbohydrate Antigen 19-9 (CA19-9), Carcinoembryonic Antigen (CEA), Alpha-fetoprotein (AFP), and Cancer Antigen 125 (CA125) serum tumor markers for colorectal cancer (CRC) and investigates a machine learning-based diagnostic model incorporating these markers with blood biochemical indices for improved CRC detection.

Application of machine learning algorithms to identify people with low bone density.

Background: Osteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a need to develop a method to identify those at high risk for osteoporosis.

Synaptic transistor with multiple biological functions based on metal-organic frameworks combined with the LIF model of a spiking neural network to recognize temporal information.

Spiking neural networks (SNNs) have immense potential due to their utilization of synaptic plasticity and ability to take advantage of temporal correlation and low power consumption. The leaky integration and firing (LIF) model and spike-timing-dependent plasticity (STDP) are the fundamental components of SNNs. Here, a neural device is first demonstrated by zeolitic imidazolate frameworks (ZIFs) as an essential part of the synaptic transistor to simulate SNNs.

Single-cell sequencing and bulk RNA data reveal the tumor microenvironment infiltration characteristics of disulfidptosis related genes in breast cancer.

Background: Immunotherapy, represented by immune checkpoint inhibitors, has made significant progress in the treatment of cancer. Numerous studies have demonstrated that antitumor therapies targeting cell death exhibit synergistic effects with immunotherapy. Disulfidptosis is a recently discovered form of cell death, and its potential influence on immunotherapy, similar to other regulated cell death processes, requires further investigation.