Correlation between Combined Urinary Metal Exposure and Grip Strength under Three Statistical Models: A Cross-sectional Study in Rural Guangxi.

Objective: This study aimed to investigate the potential relationship between urinary metals copper (Cu), arsenic (As), strontium (Sr), barium (Ba), iron (Fe), lead (Pb) and manganese (Mn) and grip strength.

Methods: We used linear regression models, quantile g-computation and Bayesian kernel machine regression (BKMR) to assess the relationship between metals and grip strength.

Results: In the multimetal linear regression, Cu (β = -2.

[Optimization of ethanol precipitation process of Nauclea officinalis extract based on concept of quality by design(QbD)].

The ethanol precipitation process of Nauclea officinalis extract was optimized based on the concept of quality by design(QbD). Single factor tests were carried out to determine the levels of test factors. The ethanol volume fraction, pre-ethanol precipitation drug concentration, and ethanol precipitation time were taken as critical process parameters(CPPs).

Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock.

A debate is currently ongoing as to whether intensive livestock farms may constitute reservoirs of clinically relevant antimicrobial resistance (AMR), thus posing a threat to surrounding communities. Here, combining shotgun metagenome sequencing, machine learning (ML), and culture-based methods, we focused on a poultry farm and connected slaughterhouse in China, investigating the gut microbiome of livestock, workers and their households, and microbial communities in carcasses and soil. For both the microbiome and resistomes in this study, differences are observed across environments and hosts.

A multi-channel deep convolutional neural network for multi-classifying thyroid diseases.

Background And Objective: Thyroid disease instances have been continuously increasing since the 1990s, and thyroid cancer has become the most rapidly rising disease among all the malignancies in recent years. Most existing studies focused on applying deep convolutional neural networks for detecting thyroid cancer. Despite their satisfactory performance on binary classification tasks, limited studies have explored multi-class classification of thyroid disease types; much less is known of the diagnosis of co-existence situation for different types of thyroid diseases.

Deep convolutional neural networks in thyroid disease detection: A multi-classification comparison by ultrasonography and computed tomography.

Background And Objective: As one of the largest endocrine organs in the human body, the thyroid gland regulates daily metabolism. Early detection of thyroid disease leads to reduced mortality rates. The diagnosis of thyroid disease is usually made by radiologists and pathologists, which heavily relies on their experience and expertise.

Multi-channel convolutional neural network architectures for thyroid cancer detection.

Early detection of malignant thyroid nodules leading to patient-specific treatments can reduce morbidity and mortality rates. Currently, thyroid specialists use medical images to diagnose then follow the treatment protocols, which have limitations due to unreliable human false-positive diagnostic rates. With the emergence of deep learning, advances in computer-aided diagnosis techniques have yielded promising earlier detection and prediction accuracy; however, clinicians' adoption is far lacking.