CTSG is a prognostic marker involved in immune infiltration and inhibits tumor progression though the MAPK signaling pathway in non-small cell lung cancer.

Purpose: This study aims to investigate the biological roles and molecular mechanisms of Cathepsin G (CTSG) in the progression of non-small cell lung cancer (NSCLC).

Methods: Western blotting and immunohistochemistry analyses of clinical samples were performed to determine the expression levels of CTSG in patients with NSCLC. Bioinformatic analysis of clinical datasets was conducted to evaluate the correlation between CTSG and lymph node metastasis, tumor stage, and immune cell infiltration.

IDO1-mediated kynurenine production inhibits IGFBP5 signaling to promote 5-fluorouracil-induced senescence escape and chemoresistance in colorectal cancer.

Cellular senescence is an irreversible state of growth arrest, and induction of senescence is considered a potential therapeutic strategy against cancer. Indoleamine 2,3-dioxygenase 1 (IDO1), an enzyme catabolizing L-tryptophan into kynurenine, plays a key role in tumor immune tolerance. However, the roles of IDO1 in cellular senescence and chemoresistance remain elusive.

Brain age prediction via cross-stratified ensemble learning.

As an important biomarker of neural aging, the brain age reflects the integrity and health of the human brain. Accurate prediction of brain age could help to understand the underlying mechanism of neural aging. In this study, a cross-stratified ensemble learning algorithm with staking strategy was proposed to obtain brain age and the derived predicted age difference (PAD) using T1-weighted magnetic resonance imaging (MRI) data.

Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network.

This study aimed to address the issue of larger prediction errors existing in intelligent predictive tasks related to Alzheimer's disease (AD). A cohort of 487 enrolled participants was categorized into three groups: normal control (138 individuals), mild cognitive impairment (238 patients), and AD (111 patients) in this study. An improved multifeature squeeze-and-excitation-dilated residual network (MFSE-DRN) was proposed for two important AD predictions: clinical scores and conversion probability.

From bench to bedside: Overview of magnetoencephalography in basic principle, signal processing, source localization and clinical applications.

Magnetoencephalography (MEG) is a non-invasive technique that can precisely capture the dynamic spatiotemporal patterns of the brain by measuring the magnetic fields arising from neuronal activity along the order of milliseconds. Observations of brain dynamics have been used in cognitive neuroscience, the diagnosis of neurological diseases, and the brain-computer interface (BCI). In this study, we outline the basic principle, signal processing, and source localization of MEG, and describe its clinical applications for cognitive assessment, the diagnoses of neurological diseases and mental disorders, preoperative evaluation, and the BCI.

Mitochondria-specific near-infrared photoactivation of peroxynitrite upconversion luminescent nanogenerator for precision cancer gas therapy.

Gas therapy is emerging as a highly promising therapeutic strategy for cancer treatment. However, there are limitations, including the lack of targeted subcellular organelle accuracy and spatiotemporal release precision, associated with gas therapy. In this study, we developed a series of photoactivatable nitric oxide (NO) donors NRh-R-NO (R = Me, Et, Bn, Pr, and Ph) based on an -nitrosated upconversion luminescent rhodamine scaffold.

Global incidence and death estimates of chronic kidney disease due to hypertension from 1990 to 2019, an ecological analysis of the global burden of diseases 2019 study.

Background: The intricate relationship between hypertension and chronic kidney disease (CKD) presents a global challenge for prevention of hypertension-related CKD. This study's objective is to analyze age, gender, regional disparities, and evolving trends in the disease burden of hypertension-related CKD. We aim to estimate changing spatial and temporal trends in incidence and mortality rates, considering the socio-demographic index (SDI), to inform health strategies effectively.

Capecitabine-based chemotherapy in early-stage triple-negative breast cancer: a meta-analysis.

Introduction: The efficacy and safety of adjuvant capecitabine in early-stage triple-negative breast cancer remains undefined. A meta-analysis was conducted to elucidate whether capecitabine-based regimens could improve survival in early-stage triple-negative breast cancer (TNBC).

Methods: The current study searched Medline, Embase, Cochrane Library, Web of Science, and ClinicalTrials.

Quantization via Distillation and Contrastive Learning.

Quantization is a critical technique employed across various research fields for compressing deep neural networks (DNNs) to facilitate deployment within resource-limited environments. This process necessitates a delicate balance between model size and performance. In this work, we explore knowledge distillation (KD) as a promising approach for improving quantization performance by transferring knowledge from high-precision networks to low-precision counterparts.

Automatic recognition of cephalometric landmarks via multi-scale sampling strategy.

The identification of head landmarks in cephalometric analysis significantly contributes in the anatomical localization of maxillofacial tissues for orthodontic and orthognathic surgery. However, the existing methods face the limitations of low accuracy and cumbersome identification process. In this pursuit, the present study proposed an automatic target recognition algorithm called Multi-Scale YOLOV3 (MS-YOLOV3) for the detection of cephalometric landmarks.

Classification of Alzheimer's disease using robust TabNet neural networks on genetic data.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and its onset is significantly associated with genetic factors. Being the capabilities of high specificity and accuracy, genetic testing has been considered as an important technique for AD diagnosis. In this paper, we presented an improved deep learning (DL) algorithm, namely differential genes screening TabNet (DGS-TabNet) for AD binary and multi-class classifications.

Revealing the correlations between brain cortical characteristics and susceptibility genes for Alzheimer disease: a cross-sectional study.

Background: Alzheimer disease (AD) is a progressive neurodegenerative disease closely related to genes and characterized by the atrophy of the cerebral cortex. Correlations between imaging phenotypes and the susceptibility genes for AD, as demonstrated in the findings of genome-wide association studies (GWASs), still need to be addressed due to the complicated structure of the human cortex.

Methods: In our study, an improved GWAS method, whole cortex characteristics GWAS (WCC-GWAS), was proposed.

TNFα-induced IDH1 hyperacetylation reprograms redox homeostasis and promotes the chemotherapeutic sensitivity.

The heterogeneity and drug resistance of colorectal cancer (CRC) often lead to treatment failure. Isocitrate dehydrogenase 1 (IDH1), a rate-limiting enzyme in the tricarboxylic acid cycle, regulates the intracellular redox environment and mediates tumor cell resistance to chemotherapeutic drugs. The aim of this study was to elucidate the mechanism underlying the involvement of IDH1 acetylation in the development of CRC drug resistance under induction of TNFα.

A combined deformable model and medical transformer algorithm for medical image segmentation.

Deep learning-based segmentation models usually require substantial data, and the model usually suffers from poor generalization due to the lack of training data and inefficient network structure. We proposed to combine the deformable model and medical transformer neural network on the image segmentation task to alleviate the aforementioned problems. The proposed method first employs a statistical shape model to generate simulated contours of the target object, and then the thin plate spline is applied to create a realistic texture.

Coupling analysis between functional and structural brain networks in Alzheimer's disease.

The coupling between functional and structural brain networks is difficult to clarify due to the complicated alterations in gray matter and white matter for the development of Alzheimer's disease (AD). A cohort of 112 participants [normal control group (NC, 62 cases), mild cognitive impairment group (MCI, 31 cases) and AD group (19 cases)], was recruited in our study. The brain networks of rsfMRI functional connectivity (rsfMRI-FC) and diffusion tensor imaging structural connectivity (DTI-SC) across the three groups were constructed, and their correlations were evaluated by Pearson's correlation analyses and multiple comparison with Bonferroni correction.

Small is Beautiful: Compressing Deep Neural Networks for Partial Domain Adaptation.

Domain adaptation is a promising way to ease the costly data labeling process in the era of deep learning (DL). A practical situation is partial domain adaptation (PDA), where the label space of the target domain is a subset of that in the source domain. Although existing methods yield appealing performance in PDA tasks, it is highly presumable that computation overhead exists in deep PDA models since the target is only a subtask of the original problem.

Azo-Based Hypoxia-Responsive Self-Assembly Near-Infrared Fluorescent Nanoprobe for In Vivo Real-Time Bioimaging of Tumors.

Hypoxia is an obvious characteristic of cancer, especially solid tumors. which may give rise to the expansion of invasion and metastasis. Exploring near-infrared (NIR) nanoprobes that could accurately evaluate the degree of hypoxia will contribute to the assessment of the degree of malignant neoplasms, so as to adopt more accurate and individualized treatment options Here, we have developed a self-assembled NIR organic nanoprobe to specifically and authoritatively detect the oxygen concentration and to evaluate the level of hypoxia.

White matter microstructural impairments in amyotrophic lateral sclerosis: A mean apparent propagator MRI study.

Background: White matter (WM) impairment is a hallmark of amyotrophic lateral sclerosis (ALS). This study evaluated the capacity of mean apparent propagator magnetic resonance imaging (MAP-MRI) for detecting ALS-related WM alterations.

Methods: Diffusion images were obtained from 52 ALS patients and 51 controls.

Brain network analyses of diffusion tensor imaging for brain aging.

The approach of graph-based diffusion tensor imaging (DTI) networks has been used to explore the complicated structural connectivity of brain aging. In this study, the changes of DTI networks of brain aging were quantitatively and qualitatively investigated by comparing the characteristics of brain network. A cohort of 60 volunteers was enrolled and equally divided into young adults (YA) and older adults (OA) groups.

γ-Glutamyl transpeptidase-activatable near-infrared nanoassembly for tumor fluorescence imaging-guided photothermal therapy.

Precise treatment of tumors is attracting increasing attention. Molecular probes simultaneously demonstrating the diagnostic signal and pharmacological effect in response to tumor microenvironment are highly desired. γ-glutamyl transpeptidase (GGT) is a biomarker with significantly up-regulated expression in the tumor area.

Classification of white blood cells using weighted optimized deformable convolutional neural networks.

Background: Machine learning (ML) algorithms have been widely used in the classification of white blood cells (WBCs). However, the performance of ML algorithms still needs to be addressed for being short of gold standard data sets, and even the implementation of the proposed algorithms.

Methods: In this study, the method of two-module weighted optimized deformable convolutional neural networks (TWO-DCNN) was proposed for WBC classification.

Near-infrared fluorescent chemodosimeter for real-time in vivo evaluation of HS-release efficiency of prodrug.

Hydrogen sulfide (H2S) is a significant gasotransmitter. A deficiency in H2S might contribute to some serious diseases. The development of H2S drugs has received a great deal of attention.

Functional connectivity changes of nucleus Accumbens Shell portion in left mesial temporal lobe epilepsy patients.

Growing evidence has supported that the nucleus accumbens (NAc), especially its shell portion, has been involved in epileptogenesis. However, relevant studies on vivo human brain are quite limited. In this study, we investigated left mesial temporal lobe epilepsy (MTLE) related function connectivity (FC) changes of NAc subregions using resting-state functional magnetic resonance imaging.

A robust automated surface-matching registration method for neuronavigation.

Purpose: The surface-matching registration method in the current neuronavigation completes the coarse registration mainly by manually selecting anatomical landmarks, which increases the registration time, makes the automatic registration impossible and sometimes results in mismatch. It may be more practical to use a fast, accurate, and automatic spatial registration method for the patient-to-image registration.

Methods: A coarse-to-fine spatial registration method to automatically register the patient space to the image space without placing any markers on the head of the patient was proposed.

An Automatic Spatial Registration Method for Image-Guided Neurosurgery System.

Objective: This study aimed to investigate the feasibility of an automatic marker-free patient-to-image spatial registration method based on the 4-points congruent sets (4PCS) and iterative closest point (ICP) algorithm for the image-guided neurosurgery system (IGNS).

Methods: A portable scanner was used to obtain the point cloud of the patient's entire head. The 4PCS algorithm, which is resilient to noise and outliers, automatically registered the point cloud in the patient space to the surface reconstructed from the patient's preoperative images in the image space without any assumptions about initial alignment.