LDHzyme-assisted high-performance on-site tracking of levodopa pharmacokinetics for Parkinson's disease management.

Parkinson's disease (PD) is a progressive neurodegenerative disorder marked by the loss of dopaminergic neurons and the consequent decline in motor and cognitive functions. The primary therapeutic agent levodopa necessitates precise dosing due to its narrow therapeutic window and complex pharmacokinetics. This study presents the development of a novel CuCoFe-LDHzyme-based sweat sensor for real-time monitoring of levodopa concentration in PD patients.

Class-Incremental Learning: A Survey.

Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world.

mRNA therapeutics for disease therapy: principles, delivery, and clinical translation.

Messenger RNA (mRNA) has become a key focus in the development of therapeutic agents, showing significant potential in preventing and treating a wide range of diseases. The COVID-19 pandemic in 2020 has accelerated the development of mRNA nucleic therapeutics and attracted significant investment from global biopharmaceutical companies. These therapeutics deliver genetic information into cells without altering the host genome, making them a promising treatment option.

Adversarial training for prostate cancer classification using magnetic resonance imaging.

Background: To use adversarial training to increase the generalizability and diagnostic accuracy of deep learning models for prostate cancer diagnosis.

Methods: This multicenter study retrospectively included 396 prostate cancer patients who underwent magnetic resonance imaging (development set, 297 patients from Shanghai Jiao Tong University Affiliated Sixth People's Hospital and Eighth People's Hospital; test set, 99 patients from Renmin Hospital of Wuhan University). Two binary classification deep learning models for clinically significant prostate cancer classification [PM1, pretraining Visual Geometry Group network (VGGNet)-16-based model 1; PM2, pretraining residual network (ResNet)-50-based model 2] and two multiclass classification deep learning models for prostate cancer grading (PM3, pretraining VGGNet-16-based model 3; PM4: pretraining ResNet-50-based model 4) were built using apparent diffusion coefficient and T2-weighted images.

Effect of increased positive end-expiratory pressure on intracranial pressure and cerebral oxygenation: impact of respiratory mechanics and hypovolemia.

Background: To evaluate the impact of positive end-expiratory pressure (PEEP) on intracranial pressure (ICP) in animals with different respiratory mechanics, baseline ICP and volume status.

Methods: A total of 50 male adult Bama miniature pigs were involved in four different protocols (n = 20, 12, 12, and 6, respectively). Under the monitoring of ICP, brain tissue oxygen tension and hemodynamical parameters, PEEP was applied in increments of 5 cm HO from 5 to 25 cm HO.

Synthesizing High--Value Diffusion-weighted Imaging of the Prostate Using Generative Adversarial Networks.

Purpose: To develop and evaluate a diffusion-weighted imaging (DWI) deep learning framework based on the generative adversarial network (GAN) to generate synthetic high--value ( =1500 sec/mm) DWI (SYN) sets from acquired standard--value ( = 800 sec/mm) DWI (ACQ) and acquired standard--value ( = 1000 sec/mm) DWI (ACQ) sets.

Materials And Methods: This retrospective multicenter study included 395 patients who underwent prostate multiparametric MRI. This cohort was split into internal training (96 patients) and external testing (299 patients) datasets.

Efficacy and safety of sirolimus early conversion protocol in liver transplant patients with hepatocellular carcinoma: A single-arm, multicenter, prospective study.

Mammalian target of rapamycin (mTOR) inhibitor as an attractive drug target with promising antitumor effects has been widely investigated. High quality clinical trial has been conducted in liver transplant (LT) recipients in Western countries. However, the pertinent studies in Eastern world are paucity.

Calculation of Apparent Diffusion Coefficients in Prostate Cancer Using Deep Learning Algorithms: A Pilot Study.

Background: Apparent diffusion coefficients (ADCs) obtained with diffusion-weighted imaging (DWI) are highly valuable for the detection and staging of prostate cancer and for assessing the response to treatment. However, DWI suffers from significant anatomic distortions and susceptibility artifacts, resulting in reduced accuracy and reproducibility of the ADC calculations. The current methods for improving the DWI quality are heavily dependent on software, hardware, and additional scan time.

Learning to Classify With Incremental New Class.

New class detection and effective model expansion are of great importance in incremental data mining. In open incremental data environments, data often come with novel classes, e.g.

Advanced zoomed diffusion-weighted imaging vs. full-field-of-view diffusion-weighted imaging in prostate cancer detection: a radiomic features study.

Objectives: We aimed to compare the efficiency of prostate cancer (PCa) detection using a radiomics signature based on advanced zoomed diffusion-weighted imaging and conventional full-field-of-view DWI.

Methods: A total of 136 patients, including 73 patients with PCa and 63 without PCa, underwent multi-parametric magnetic resonance imaging (mp-MRI). Radiomic features were extracted from prostate lesion areas segmented on full-field-of-view DWI with b-value = 1500 s/mm (f-DWI), advanced zoomed DWI images with b-value = 1500 s/mm (z-DWI), calculated zoomed DWI with b-value = 2000 s/mm (z-calDWI), and apparent diffusion coefficient (ADC) maps derived from both sequences (f-ADC and z-ADC).

[The X-Ray Fluorescence Spectrometer Based on Pyroelectric Effect].

Pyroelectric X-ray generator is implemented, and an X-ray fluorescence spectrometer is accomplished by combining the pyroelectric X-ray generator with a high energy resolution silicon drift detector. Firstly, the parameters of the X-ray generator are decided by analyzing and calculating the influence of the thickness of the pyroelectriccrystal and the thickness of the target on emitted X-ray. Secondly, the emitted X-ray is measured.

Structural characterization of enzymatic products in the dTDP-d-Qui4NFo biosynthetic pathway using electrospray ionization tandem mass spectrometry.

Rationale: Structural characterization of biosynthetic precursors is very important in assigning enzymatic function to proteins that have been identified as functional homologs on the basis of sequence homology alone. The objective of this study is to demonstrate the use of electrospray ionization tandem mass spectrometry (ESI-MS/MS) as a powerful technique for the characterization of enzymatic products in the biosynthetic pathway of deoxythymidine 5'-diphosphate-4-formamido-4,6-dideoxy-D-glucose (dTDP-D-Qui4NFo) in Providencia alcalifaciens O30.

Methods: The glucose-1-phosphate thymidyltransferase (RmlA), dTDP-d-glucose 4,6-dehydratase (RmlB), dTDP-4-keto-6-deoxy-d-glucose aminotransferase (VioA), and formyltransferase (VioF) catalyzed reactions were directly monitored by ESI-MS, followed by a detailed structural characterization of the final enzymatic products using ESI-MS/MS in the negative-ion mode after minimal cleanup.

Random forests-based differential analysis of gene sets for gene expression data.

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes.

[ESI fragmentation studies of six unusual nucleotide sugars].

Unusual dTDP-sugars are key intermediate in many pathogenic bacteria. In this study, negative-ion electrospray tandem mass spectrometry (ESI-MS-MS) with collision-induced dissociation (CID) was used to study the fragmentation characteristics of six unusual nucleotide diphosphate sugars. The results indicated the major fragment of the six unusual nucleoside sugars observed in the ESI-MS-MS spectra resulted from cleavage of diphosphate moiety and their characteristic fragment ions at m/z 401, 383, and 321, correspond to [TDP-H] together with fragment ions resulting from the loss of water and phosphate moiety, respectively.

[Estradiol stimulates proliferation of prostatic smooth muscle cells via estrogen receptor alpha and IGF1].

Objective: To investigate the effects of estrogen receptor alpha (ERa) and insulin-like growth factor 1 (IGF1) on the proliferation of prostatic smooth muscle cells (PSMCs) in vitro.

Methods: The ERalpha shRNA expression frame was subcloned to the pGSadeno adenovirus vector by homologous recombination technology to construct the pGSaaeno-ERalpha vector. After the mouse PSMCs were transfected in vitro by pGSaaeno-ERalpha, the mRNA and protein expression levels of ERalpha were detected by RT-PCR and Western blot respectively.

Study of phase coherence degradation induced by a tapered semiconductor amplifier with frequency-modulated continuous-wave and pulsed seed lasers.

We investigate the phase coherence between a seed laser and a laser amplified by a tapered semiconductor amplifier (TSA) when the seed laser is either continuous wave (CW) or pulsed. The phase fluctuations in the time domain are employed to describe the degradation of phase coherence induced by a TSA. The amplified laser is measured to be approximately 99.

The investigation on sEMG of lower extremity when a slip occurs in level walking.

The purpose of the present study was to determine the relationship between the surface electromyography (sEMG) variables and slip events using principal component analysis (PCA). Ten healthy young adults were required to walk on the oily surface on a self-selected comfortable pace. The sEMG signals of lower extremity muscles were recorded and analyzed, while kinematics data was recorded to assist slip definitions.

[Liquid chromatography frontal analysis of the protein binding of glimepiride].

Aim: To study the protein binding of glimepiride.

Methods: An HPLC-FA method is performed by using Pinkerton GFF II-S5-80 internal-surface reversed-phase silica support (150 mm x 4.6 mm ID, 5 microm) at pH 7.