Robust and sensitive colorimetric detection of glutathione with double-triggering MOF-Fe(DTNB).

Glutathione (GSH) levels have been well validated to correlate with a variety of physiological and pathological conditions, such as malignancy, cardiovascular disease and aging, making the development of accurate, robust and sensitive GSH detection methods highly desirable. In this study, a novel metal-organic framework (MOF-Fe(DTNB))-based colorimetric method with a favorable dual-triggering function was proposed. MOF-Fe(DTNB) exhibits high peroxidase activity, which can catalyze the oxidation of colorless 3,3',5,5'-tetramethylbenzidine (TMB) to blue ox-TMB by hydrogen peroxide (HO).

Pain Characteristics of the Posterior Longitudinal Ligament in Percutaneous Endoscopic Lumbar Discectomy and its Significance: A Retrospective Study.

Introduction: In percutaneous endoscopic lumbar discectomy (PELD), pain occurs when the posterior longitudinal ligament (PLL) is exposed, removed, and decompressed. However, pain characteristics of the PLL stimulated in PELD have not been reported.

Methods: A total of 932 patients underwent PELD under local anesthesia.

NEPE Propellant Mesoscopic Modeling and Damage Mechanism Study Based on Inversion Algorithm.

To accurately characterize the mesoscopic properties of NEPE (Nitrate Ester Plasticized Polyether) propellant, the mechanical contraction method was used to construct a representative volume element (RVE) model. Based on this model, the macroscopic mechanical response of NEPE propellant at a strain rate of 0.0047575 s was simulated and calculated, and the parameters of the cohesive zone model (CZM) were inversely optimized using the Hooke-Jeeves algorithm by comparing the simulation results with the results of the uniaxial tensile test of NEPE propellants.

A neural mechanism for conserved value computations integrating information and rewards.

Behavioral and economic theory dictate that we decide between options based on their values. However, humans and animals eagerly seek information about uncertain future rewards, even when this does not provide any objective value. This implies that decisions are made by endowing information with subjective value and integrating it with the value of extrinsic rewards, but the mechanism is unknown.

Discovering covalent inhibitors of protein-protein interactions from trillions of sulfur(VI) fluoride exchange-modified oligonucleotides.

Molecules that covalently engage target proteins are widely used as activity-based probes and covalent drugs. The performance of these covalent inhibitors is, however, often compromised by the paradox of efficacy and risk, which demands a balance between reactivity and selectivity. The challenge is more evident when targeting protein-protein interactions owing to their low ligandability and undefined reactivity.

Quantum Imitation Learning.

Despite remarkable successes in solving various complex decision-making tasks, training an imitation learning (IL) algorithm with deep neural networks (DNNs) suffers from the high-computational burden. In this work, we propose quantum IL (QIL) with a hope to utilize quantum advantage to speed up IL. Concretely, we develop two QIL algorithms: quantum behavioral cloning (Q-BC) and quantum generative adversarial IL (Q-GAIL).

Recent Advances for Quantum Neural Networks in Generative Learning.

Quantum computers are next-generation devices that hold promise to perform calculations beyond the reach of classical computers. A leading method towards achieving this goal is through quantum machine learning, especially quantum generative learning. Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts.

Enhanced Recovery after Total Joint Arthroplasty (TJA): A Contemporary Systematic Review of Clinical Outcomes and Usage of Key Elements.

Background: Enhanced recovery after surgery (ERAS) is a pathway designed to improve the care of surgical patients and achieve early recovery. The clinical outcomes and usage of key elements of ERAS pathways in total joint arthroplasty (TJA) need further reanalysis. This article aims to provide an overview of the latest clinical outcomes and current usage of key elements of ERAS pathways in TJA.

Preoperative ultrasound identification and localization of the inferior parathyroid glands in thyroid surgery.

Introduction: The parathyroid glands are important endocrine glands for maintaining calcium and phosphorus metabolism, and they are vulnerable to accidental injuries during thyroid cancer surgery. The aim of this retrospective study was to investigate the application of high-frequency ultrasound imaging for preoperative anatomical localization of the parathyroid glands in patients with thyroid cancer and to analyze the protective effect of this technique on the parathyroid glands and its effect on reducing postoperative complications.

Materials And Methods: A total of 165 patients who were operated for thyroid cancer in our hospital were included.

Machine learning quantitatively characterizes the deformation and destruction of explosive molecules.

Although explosives have been widely used in mines, road development, old building demolishing, and munition explosions; currently, how chemical bonds between atoms break and recombine, how the molecular structure is deformed and destroyed, how the reaction product molecules are formed, and the details for this rapid change process in explosive reactions are not yet fully understood, which limits the full use of explosive energy and safer use of explosives. This paper presents a quantitative model of molecular structure deformation using machine learning algorithms as well as a qualitative model of its relationship with molecular structure destruction, based on a molecular dynamics simulation and detailed analysis of the shock-loaded ε-CL-20, providing new perspectives for explosive community research. Specifically, the quantitative model of molecular structure deformation establishes the quantitative relationship between the molecular volume change and molecular position change, and between molecular distance change and molecular volume change using the machine learning algorithms such as Delaunay triangulation, clustering, and gradient descent.

Bioinformatic Analysis of PTTG Family and Prognosis and Immune Cell Infiltration in Gastric Cancer.

Gastric cancer is the sixth highest incidence rate in the world. Although treatment has made progress, the prospect of gastric cancer patients is bleak. Difficulties and future prospects of immunotherapy in cancer treatment.

Bioinformatics analysis: relationship between adrenocortical carcinoma and KIFs.

Adrenal cortical cancer has a relatively low incidence, but a dismal 5-year survival rate. Surgical intervention is the gold standard of care today. In spite of this progress, patients continue to have a dismal outlook.

The targets of aspirin in bladder cancer: bioinformatics analysis.

Background: The anti-carcinogenic properties of aspirin have been observed in some solid tumors. However, the molecular mechanism of therapeutic effects of aspirin on bladder cancer is still indistinct. We introduced a bioinformatics analysis approach, to explore the targets of aspirin in bladder cancer (BC).

Case report: Unique ultrasound feature of thyroid metastases in occult breast cancer.

Occult breast cancer is an uncommon type of breast cancer. Metastases of occult breast cancer to other tissues are rather rare. We present a rare case of thyroid metastases in a 46-year-old woman who underwent occult breast cancer.

A Nonlinear Viscoelastic Constitutive Model for Solid Propellant with Rate-Dependent Cumulative Damage.

Solid propellant is a composite material exhibiting classic nonlinear viscoelastic mechanical characteristic, which is due in a large part to a cumulative damage process caused by the formation and growth of microflaws inside. The standard relaxation tests and uniaxial tension tests under different velocities of hydroxyl-terminated polybutadiene (HTPB) propellant are carried out in this paper, where Digital Image Correlation (DIC) technique is applied to record deformation. The experimental results show that the material mechanical behavior is rate-dependent.

The TAS Test project: a prospective longitudinal validation of new online motor-cognitive tests to detect preclinical Alzheimer's disease and estimate 5-year risks of cognitive decline and dementia.

Background: The worldwide prevalence of dementia is rapidly rising. Alzheimer's disease (AD), accounts for 70% of cases and has a 10-20-year preclinical period, when brain pathology covertly progresses before cognitive symptoms appear. The 2020 Lancet Commission estimates that 40% of dementia cases could be prevented by modifying lifestyle/medical risk factors.

Bacteriophage protein PEIP is a potent Bacillus subtilis enolase inhibitor.

Enolase is a highly conserved enzyme that presents in all organisms capable of glycolysis or fermentation. Its immediate product phosphoenolpyruvate is essential for other important processes like peptidoglycan synthesis and the phosphotransferase system in bacteria. Therefore, enolase inhibitors are of great interest.

Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network.

In this study, we present a systematic scheme to identify the material parameters in constitutive model of hyperelastic materials such as rubber. This approach is proposed based on the combined use of general regression neural network, experimental data and finite element analysis. In detail, the finite element analysis is carried out to provide the learning samples of GRNN model, while the results observed from the uniaxial tensile test is set as the target value of GRNN model.