Neuroimaging signatures and a deep learning modeling for early diagnosing and predicting non-pharmacological therapy success for subclinical depression comorbid sleep disorders in college students.

Objective: College students with subclinical depression often experience sleep disturbances and are at high risk of developing major depressive disorder without early intervention. Clinical guidelines recommend non-pharmacotherapy as the primary option for subclinical depression with comorbid sleep disorders (sDSDs). However, the neuroimaging mechanisms and therapeutic responses associated with these treatments are poorly understood.

Comparative effectiveness of four techniques for identifying vertebral fragility fractures among elderly patients.

Objective: The incidence of vertebral fragile fractures peaked among the elderly population, and identifying individuals at high risk of vertebral fractures and promptly instituting preventions are of critical importance. This study aims to determine the efficacy and values of Hounsfield unit (HU) values, vertebral bone quality (VBQ) scores, bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA), and quantitative computed tomography (QCT) to discriminate between patients with and without vertebral fractures.

Methods: A thorough search was conducted across PubMed, EMBASE, Cochrane Library, Web of Science, CNKI, and Wan Fang Database to identify potential studies that met the eligibility criteria.

Glucocorticoids induce a maladaptive epithelial stress response to aggravate acute kidney injury.

Acute kidney injury (AKI) is a frequent and challenging clinical condition associated with high morbidity and mortality and represents a common complication in critically ill patients with COVID-19. In AKI, renal tubular epithelial cells (TECs) are a primary site of damage, and recovery from AKI depends on TEC plasticity. However, the molecular mechanisms underlying adaptation and maladaptation of TECs in AKI remain largely unclear.

A novel x-Ray and γ-Ray combination strategy for radiotherapy after breast-conserving surgery in patients with right breast cancer.

Background And Purpose: Radiotherapy is a primary therapeutic approach for breast cancer following breast-conserving surgery. The TaiChiB dual-modality radiotherapy system combining X-ray and focused γ-ray, offers a new approach to reduce the radiation dose of organs at risk (OARs) and has the potential to mitigate the adverse effects of radiotherapy. Currently, there are few studies on the dosimetric characteristics of the TaiChiB dual-modality system for actual treatment plans for specific diseases.

A deep neural network for positioning and inter-crystal scatter identification in multiplexed PET detectors: a simulation study.

High-resolution positron emission tomography (PET) relies on the accurate positioning of annihilation photons impinging the crystal array. However, conventional positioning algorithms in light-sharing PET detectors are often limited due to edge effects and/or the absence of additional information for identifying and correcting scattering within the crystal array (known as inter-crystal scattering). This study explores the feasibility of deep neural network (DNN) techniques for more precise event positioning in finely segmented and highly multiplexed PET detectors with light-sharing.

Double promoter and tandem gene strategy for efficiently expressing recombinant FGF21.

Background: Fibroblast growth factor 21 (FGF21) is a promising candidate for treating metabolic disorder diseases and has been used in phase II clinical trials. Currently, metabolic diseases are prevalent worldwide, underscoring the significant market potential of FGF21. Therefore, the production of FGF21 must be effectively improved to meet market demand.

Exploring the biological basis of CT imaging features in pancreatic neuroendocrine tumors: a two-center study.

Medical imaging offered a non-invasive window to visualize tumors, with radiomics transforming these images into quantitative data for tumor phenotyping. However, the intricate web linking imaging features, clinical endpoints, and tumor biology was mostly uncharted. This study aimed to unravel the connections between CT imaging features and clinical characteristics, including tumor histopathological grading, clinical stage, and endocrine symptoms, alongside immunohistochemical markers of tumor cell growth, such as the Ki-67 index and nuclear mitosis rate.

A Survey on Efficient Vision Transformers: Algorithms, Techniques, and Performance Benchmarking.

Vision Transformer (ViT) architectures are becoming increasingly popular and widely employed to tackle computer vision applications. Their main feature is the capacity to extract global information through the self-attention mechanism, outperforming earlier convolutional neural networks. However, ViT deployment and performance have grown steadily with their size, number of trainable parameters, and operations.

Constructing hierarchical attentive functional brain networks for early AD diagnosis.

Analyzing functional brain networks (FBN) with deep learning has demonstrated great potential for brain disorder diagnosis. The conventional construction of FBN is typically conducted at a single scale with a predefined brain region atlas. However, numerous studies have identified that the structure and function of the brain are hierarchically organized in nature.

Efficacy and safety analysis of hypofractionated and conventional fractionated radiotherapy in postoperative breast cancer patients.

Objectives: In this meta-analysis, we conducted a comparative analysis of the safety and efficacy of hypofractionated and conventional fractionated radiotherapy in individuals who had undergone surgery for breast cancer.

Methods: This study involved a systematic and independent review of relevant research articles published in reputable databases such as PubMed, Embase, Cochrane Library, and Web of Science. Two investigators conducted the review, which included studies published up to January 3, 2023.

Biomechanical Study of 3 Osteoconductive Materials Applied in Pedicle Augmentation and Revision for Osteoporotic Vertebrae: Allograft Bone Particles, Calcium Phosphate Cement, Demineralized Bone Matrix.

Objective: This study assessed biomechanical properties of pedicle screws enhanced or revised with 3 materials. We aimed to compare the efficacy of these materials in pedicle augmentation and revision.

Methods: One hundred twenty human cadaveric vertebrae were utilized for in vitro testing.

Akkermansia muciniphila MucT attenuates sodium valproate-induced hepatotoxicity and upregulation of Akkermansia muciniphila in rats.

In the previous study, we found that the oral sodium valproate (SVP) increased the relative abundance of Akkermansia muciniphila (A. muciniphila) in rats, and plasma aspartate transaminase (AST) and alanine aminotransferase (ALT) activities were positively correlated with A. muciniphila levels.

3D multi-modality Transformer-GAN for high-quality PET reconstruction.

Positron emission tomography (PET) scans can reveal abnormal metabolic activities of cells and provide favorable information for clinical patient diagnosis. Generally, standard-dose PET (SPET) images contain more diagnostic information than low-dose PET (LPET) images but higher-dose scans can also bring higher potential radiation risks. To reduce the radiation risk while acquiring high-quality PET images, in this paper, we propose a 3D multi-modality edge-aware Transformer-GAN for high-quality SPET reconstruction using the corresponding LPET images and T1 acquisitions from magnetic resonance imaging (T1-MRI).

Construction of a Collagen-like Protein Based on Elastin-like Polypeptide Fusion and Evaluation of Its Performance in Promoting Wound Healing.

In the healing of wounds, human-like collagen (hCol) is essential. However, collagen-based composite dressings have poor stability in vivo, which severely limits their current therapeutic potential. Based on the above, we have developed a recombinant fusion protein named hCol-ELP, which consists of hCol and an elastin-like peptide (ELP).

Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning.

Background Parenchymal Enhancement (BPE) quantification in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast cancer diagnosis and prognosis. However, the emerging deep learning-based breast fibroglandular tissue segmentation, a crucial step in automated BPE quantification, often suffers from limited training samples with accurate annotations. To address this challenge, we propose a novel iterative cycle-consistent semi-supervised framework to leverage segmentation performance by using a large amount of paired pre-/post-contrast images without annotations.