DiffRaman: A conditional latent denoising diffusion probabilistic model for enhancing bacterial identification via Raman spectra generation under limited data.

Raman spectroscopy has attracted significant attention in various biochemical detection fields, especially in the rapid identification of pathogenic bacteria. The integration of this technology with deep learning to facilitate automated bacterial Raman spectroscopy diagnosis has emerged as a key focus in recent research. However, the diagnostic performance of existing deep learning methods largely depends on a sufficient dataset, and in scenarios where there is a limited availability of Raman spectroscopy data, it is inadequate to fully optimize the numerous parameters of deep neural networks.

Effects of Mytilus edulis derived plasmalogens against atherosclerosis via lipid metabolism and MAPK signaling pathway.

Mytilus edulis-derived plasmalogens (Pls) are rich in polyunsaturated fatty acids, which are reportedly effective in ameliorating cardiovascular disease. The purpose of this study was to clarify the underlying mechanisms of Pls against atherosclerosis (AS) in ApoE mice induced by a high-fat diet (HFD), through a comprehensive analysis of hepatic metabolomics and aortic transcriptomics data. The results demonstrated a significant reduction in pathological indicators associated with AS following Pls treatment.

Mining Global and Local Semantics from Unlabeled Spectra for Spectral Classification.

Non-destructive detection methods based on molecular vibrational spectroscopy are pivotal in fields such as analytical chemistry and medical diagnostics. Recent advances have integrated deep learning with vibrational spectroscopy, significantly enhancing spectral recognition accuracy. However, these methods often rely on large annotated spectral datasets, limiting their general applicability.

Enzymatic Preparation, In-Depth Molecular Analysis, and Digestion Simulation of Palmitoleic Acid (ω-7)-Enriched Fish Oil Triacylglycerols.

In this study, an enzymatic reaction was developed for synthesizing pure triacylglycerols (TAG) with a high content of palmitoleic acid (POA) using fish byproduct oil. The characteristics of synthesized structural TAGs rich in POA (POA-TAG) were analyzed in detail through ultrahigh-performance liquid chromatography Q Exactive orbitrap mass spectrometry. Optimal conditions were thoroughly investigated and determined for reaction systems, including the use of Lipozyme TL IM and Novozym 435, 15 wt % lipase loading, substrate mass ratio of 1:3, and water content of 2.

DeepS: Accelerating 3D Mass Spectrometry Imaging via a Deep Neural Network.

Mass spectrometry imaging (MSI) is a powerful methodology that enables the visualization of the spatial distribution of biomolecules, including lipids, peptides, and proteins, from biological tissue sections. While two-dimensional (2D) MSI has been widely reported in various applications, three-dimensional (3D) MSI can enable the mapping of biomolecule distribution in complex biological structures (e.g.

Clinical significance of the serum miR-455-5p expression in patients with neonatal sepsis.

Sepsis is a systemic inflammatory response caused by infection and is a major cause of neonatal death. This study explored the miR-455-5p in neonatal sepsis, and further investigated the diagnostic and prognostic value of miR-455-5p in neonatal sepsis (NS). The levels of serum miR-455-5p in 88 healthy controls and 90 NS patients were examined by quantitative real-time polymerase chain reaction (qRT-PCR).

GLAGC: Adaptive Dual-Gamma Function for Image Illumination Perception and Correction in the Wavelet Domain.

Low-contrast or uneven illumination in real-world images will cause a loss of details and increase the difficulty of pattern recognition. An automatic image illumination perception and adaptive correction algorithm, termed as GLAGC, is proposed in this paper. Based on Retinex theory, the illumination of an image is extracted through the discrete wavelet transform.