Lymphoma

An AI-driven strategy for active compounds discovery and non-destructive quality control in traditional Chinese medicine: A case of Xuefu Zhuyu Oral Liquid.

The modernization and globalization of traditional Chinese medicine (TCM) face challenges such as unclear active compounds and inadequate quality control. Taking Xuefu Zhuyu Oral Liquid (XZOL) as an example, this study proposed an artificial intelligence (AI) -driven strategy for active compounds discovery and non-destructive quality control. Firstly, the multi-wavelength fusion high-performance l...

Machine learning workflows beyond linear models in low-data regimes.

Data-driven methodologies are transforming chemical research by providing chemists with digital tools that accelerate discovery and promote sustainability. In this context, non-linear machine learning algorithms are among the most disruptive technologies in the field and have proven effective for handling large datasets. However, in data-limited scenarios, linear regression has traditionally preva...

Using machine learning methods to predict the diabatic bond dissociation energy of non-heme iron complexes.

Bond dissociation energy (BDE) is an important property in chemical research. In the process of non-heme iron complex catalytic reactions, diabatic BD...

[Radiosurgery of benign intracranial lesions. Indications, results , and perspectives].

Stereotactic radiosurgery (SRS) is a non-invasive technique that is transforming the management of benign intracranial lesions through its precision a...

Learning-Based Classification of B- and T-Cell Lymphoma on Histopathological Images: A Multicenter Study.

Lymphoma, a clonal malignancy from lymphocytes, includes diverse subtypes requiring distinct immunohistochemical stains for accurate diagnosis. Limite...

Prediction of Glomerular Filtration Rate Following Partial Nephrectomy for Localized Renal Cell Carcinoma with Different Machine Learning Techniques.

: Partial nephrectomy (PN) is the preferred option for treating localized cT1 renal cell carcinoma (RCC), as it preserves renal function in most patie...

Predicting growth parameters of biofertilizer inoculated pepper, using root capacitance assessments and artificial neural networks in two soils.

Monitoring the root system plays an important role in understanding plant physiological processes; however, its assessment using non-destructive metho...

Blood-brain barrier crossing biopolymer targeting c-Myc and anti-PD-1 activate primary brain lymphoma immunity: Artificial intelligence analysis.

Primary Central Nervous System Lymphoma is an aggressive central nervous system neoplasm with poor response to pharmacological treatment, partially du...

Identification of Phase Transformations in Alloy and Non-Alloy Steel During Austempering Using Acoustic Emission and Neural Network.

This research was carried out for selected alloy (bearing) and non-alloy (tool) steel. The steels were subjected to austempering. The hardening temper...

Clinical Validation of Artificial Intelligence Algorithms for the Diagnosis of Adult Obstructive Sleep Apnea and Sleep Staging From Oximetry and Photoplethysmography-SleepAI.

Home sleep apnea tests (HSATs) have emerged as alternatives to in-laboratory polysomnography (PSG), but Type IV HSATs often show limited diagnostic pe...

Integration of sentinel node mapping and molecular classification in endometrial cancer staging.

Sentinel node mapping has gained popularity in surgical staging of endometrial cancer, providing a less invasive alternative to lymphadenectomy for st...

Wearable sleep recording augmented by artificial intelligence for Alzheimer's disease screening.

The recent emergence of wearable devices will enable large scale remote brain monitoring. This study investigated whether multimodal wearable sleep re...

[Use of Machine Learning Methods to Identify Soil Parent Materials in a High-cadmium Geological Background Area].

Recently, the characteristics of high Cd content and low Cd mobility in karstic soil of a high geological background area in south China have received...

Machine learning algorithms integrating positron emission tomography/computed tomography features to predict pathological complete response after neoadjuvant chemoimmunotherapy in lung cancer.

OBJECTIVES: Reliable methods for predicting pathological complete response (pCR) in non-small cell lung cancer (NSCLC) patients undergoing neoadjuvant...

In-situ and Non-contact Etch Depth Prediction in Plasma Etching via Machine Learning (ANN & BNN) and Digital Image Colorimetry

Precise monitoring of etch depth and the thickness of insulating materials, such as Silicon dioxide and silicon nitride, is critical to ensuring dev...

A Comprehensive Literature Review Discussing Diagnostic Challenges of Prinzmetal or Vasospastic Angina.

This narrative review addresses the diagnostic complexities of vasospastic angina (VSA), also known as Prinzmetal angina, by analyzing findings from p...

Pixels2Points: Fusing 2D and 3D Features for Facial Skin Segmentation

Face registration deforms a template mesh to closely fit a 3D face scan, the quality of which commonly degrades in non-skin regions (e.g., hair, bea...

Machine-learning approach facilitates prediction of whitefly spatiotemporal dynamics in a plant canopy.

Plant-specific insect scouting and prediction are still challenging in most crop systems. In this article, a machine-learning algorithm is proposed to...

Physiological neural representation for personalised tracer kinetic parameter estimation from dynamic PET

Dynamic positron emission tomography (PET) with [$^{18}$F]FDG enables non-invasive quantification of glucose metabolism through kinetic analysis, of...

Representation Learning via Non-Contrastive Mutual Information

Labeling data is often very time consuming and expensive, leaving us with a majority of unlabeled data. Self-supervised representation learning meth...