Early identification and prevention of depressive symptoms in breast cancer patients.

Breast cancer ranks among the most prevalent cancers worldwide, particularly affecting young and middle-aged women, who face higher recurrence rates, lower survival rates, and substantial psychological health challenges. Depressive symptoms, frequently observed in breast cancer patients, significantly influence treatment outcomes and quality of life. Recent years have seen a growing focus on these symptoms, emphasizing early identification and intervention.

Kinesin family member 14 in digestive tract malignancies: Oncogenic mechanisms, clinical implications, and therapeutic prospects.

In this editorial, we comment on the article by Qin , recently published in the . Malignant tumors of the digestive tract represent a significant health threat. Kinesin family member 14 (KIF14), a critical kinesin, is pivotal in the proliferation, migration, and invasion of tumor cells.

Iron-Catalyzed Tunable Alkene Migratory Silylation and Transposition.

The example of iron-catalyzed alkenes migratory silylation and transposition has been demonstrated, affording a tunable approach to synthesize thermodynamically stable allylsilanes and internal alkenes with high efficiency and regioselectivity. These reactions showcase several advantageous features, including good functional group tolerance, excellent regioselectivity, a broad substrate scope, scalability to gram-scale synthesis, and late-stage functionalization of bio-relevant molecules. Furthermore, the relay catalytic mechanism of the migratory silylation, involving both iron-silyl and iron-hydride intermediates, provides valuable insights into iron-catalyzed coupling reactions, opening new avenues for the development of novel transformations under iron catalysis.

Fast-adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging.

Lattice light-sheet microscopy provides a crucial observation window into intra- and intercellular physiology of living specimens but at the diffraction-limited resolution or anisotropic super-resolution with structured illumination. Here we present meta-learning-empowered reflective lattice light-sheet virtual structured illumination microscopy (Meta-rLLS-VSIM), which upgrades lattice light-sheet microscopy to a near-isotropic super resolution of ~120 nm laterally and ~160 nm axially without modifications of the core optical system or loss of other live-cell imaging metrics. Moreover, we devised an adaptive online training approach by synergizing the front-end imaging system and back-end meta-learning framework, which alleviated the demand for training data by tenfold and reduced the total time for data acquisition and model training down to tens of seconds.

Rising of natural therapies: Potential and challenges of traditional Chinese medicine in the management of gastrointestinal diseases.

In the contemporary medical landscape, the burgeoning interest in natural therapies, particularly for managing gastrointestinal disorders, has brought traditional Chinese medicine (TCM) to the forefront. This article explains the core principles and clinical applications of TCM in treating these conditions, furthering the discourse through an examination of integrated TCM strategies, as demonstrated in the study by Zhou . While TCM has shown promising clinical outcomes, it encounters significant hurdles in standardization, mechanistic research, and clinical validation.

Harnessing multi-omics approaches to elucidate the role of Chinese herbal compounds in chemotherapy-induced gastrointestinal damage.

In this editorial, we discuss the findings reported by Wang in the latest issue of the . Various research methodologies, including microbiome analysis, assert that the Tzu-Chi Cancer-Antagonizing and Life-Protecting II Decoction of Chinese herbal compounds mitigates inflammatory responses by inhibiting the NF-κB signaling pathway. This action helps maintain the dynamic equilibrium of the intestinal microecology and lessens chemotherapy-induced gastrointestinal damage.

Development of a diagnostic classification model for lateral cephalograms based on multitask learning.

Objectives: This study aimed to develop a cephalometric classification method based on multitask learning for eight diagnostic classifications.

Methods: This study was retrospective. A total of 3,310 lateral cephalograms were collected to construct a dataset.

Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms.

Background: Malocclusion, characterized by dental misalignment and improper occlusal relationships, significantly impacts oral health and daily functioning, with a global prevalence of 56%. Lateral cephalogram is a crucial diagnostic tool in orthodontic treatment, providing insights into various structural characteristics.

Methods: This study introduces a pre-training approach using multi-center lateral cephalograms for self-supervised learning, aimed at improving model generalization across diverse clinical data domains.

A neural network for long-term super-resolution imaging of live cells with reliable confidence quantification.

Super-resolution (SR) neural networks transform low-resolution optical microscopy images into SR images. Application of single-image SR (SISR) methods to long-term imaging has not exploited the temporal dependencies between neighboring frames and has been subject to inference uncertainty that is difficult to quantify. Here, by building a large-scale fluorescence microscopy dataset and evaluating the propagation and alignment components of neural network models, we devise a deformable phase-space alignment (DPA) time-lapse image SR (TISR) neural network.

Research of orthodontic soft tissue profile prediction based on conditional generative adversarial networks.

Objective: This study constructed a new conditional generative adversarial network (CGAN) model to predict changes in lateral appearance following orthodontic treatment.

Methods: Lateral cephalometric radiographs of adult patients were obtained before (T1) and after (T2) orthodontic treatment. The expanded dataset was divided into training, validation, and test sets by random sampling in a ratio of 8:1:1.

Use of traditional Chinese medicine bezoars and bezoar-containing preparations in hepatocarcinoma.

This manuscript used network pharmacology and experimental verification to analyze the anti-hepatocarcinoma mechanism of action of bezoars in traditional Chinese medicine (TCM), discovering that it can affect the immune cells within the tumor microenvironment and related pathways to produce inhibitory effects in liver cancer. In TCM, bezoars have a unique therapeutic advantage in the prevention and treatment of tumors. They play an anti-tumorigenic role by regulating the immune microenvironment through multi-component, multi-target and multi-pathway mechanisms.

Dynamics Playing a Key Role in the Covalent Binding of Inhibitors to Focal Adhesion Kinase.

Covalent kinase inhibitors (CKIs) have recently garnered considerable attention, yet the rational design of CKIs continues to pose a great challenge. In the discovery of CKIs targeting focal adhesion kinase (FAK), it has been observed that the chemical structure of the linkers plays a key role in achieving covalent targeting of FAK. However, the mechanism behind the observation remains elusive.

Automatic soft-tissue analysis on orthodontic frontal and lateral facial photographs based on deep learning.

Background: To establish the automatic soft-tissue analysis model based on deep learning that performs landmark detection and measurement calculations on orthodontic facial photographs to achieve a more comprehensive quantitative evaluation of soft tissues.

Methods: A total of 578 frontal photographs and 450 lateral photographs of orthodontic patients were collected to construct datasets. All images were manually annotated by two orthodontists with 43 frontal-image landmarks and 17 lateral-image landmarks.

Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy.

Computational super-resolution methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1.

Three-dimensional finite element analysis on the effects of maxillary protraction with an individual titanium plate at multiple directions and locations.

Objective: : A three-dimensional-printed individual titanium plate was applied for maxillary protraction to eliminate side effects and obtain the maximum skeletal effect. This study aimed to explore the stress distribution characteristics of sutures during maxillary protraction using individual titanium plates in various directions and locations.

Methods: : A protraction force of 500 g per side was applied at forward and downward angles between 0° and 60° with respect to the Frankfort horizontal plane, after which the titanium plate was moved 2 and 4 mm upward and downward, respectively.

STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning.

Accurate tooth segmentation and numbering are the cornerstones of efficient automatic dental diagnosis and treatment. In this paper, a multitask learning architecture has been proposed for accurate tooth segmentation and numbering in panoramic X-ray images. A graph convolution network was applied for the automatic annotation of the target region, a modified convolutional neural network-based detection subnetwork (DSN) was used for tooth recognition and boundary regression, and an effective region segmentation subnetwork (RSSN) was used for region segmentation.

Topological spin-orbit-coupled fermions beyond rotating wave approximation.

The realization of spin-orbit-coupled ultracold gases has driven a wide range of research and is typically based on the rotating wave approximation (RWA). By neglecting the counter-rotating terms, RWA characterizes a single near-resonant spin-orbit (SO) coupling in a two-level system. Here, we propose and experimentally realize a new scheme for achieving a pair of two-dimensional (2D) SO couplings for ultracold fermions beyond RWA.

Catalytic Domino Three-Component Synthesis of Functionalized Heterocycles from Carbon Dioxide.

A catalytic domino, three-component reaction has been developed for the transformation of carbon dioxide into functionalized six-membered cyclic carbonates. The catalytic process combines an initial carboxylative cyclization of β-epoxy alcohols followed by an oxa-Michael reaction affording an unparalleled scope of heterocyclic structures. The wide range of functional groups, including free-alcohols, empowers the access to a range of products including C-oxo-based bicyclic heterocycles.

Preoperative simulated surgery on 3D model assists osteotomy feasibility verification and surgical guidance for patients with cubitus valgus/varus deformity: a retrospective observational study.

Background: As the common delayed complication of supracondylar fractures in children, cubitus valgus/varus deformity might lead to pain and loss of motion of the elbow. The current corrective treatment might not be accurate enough and even contribute to postoperative deformity. This study retrospectively analyzed the clinical value of preoperative simulated surgery on 3D model-assisted osteotomy feasibility verification and surgical guidance for cubitus valgus/varus deformity.

Study on Modelling Method of Resilient Mat Used under Floating Slab Track.

Kelvin's model is widely used to simulate the dynamic characteristic of a resilient mat under a slab track. To develop an effective calculation model for a resilient mat using a solid element, a three-parameter viscoelasticity model (3PVM) was employed. With the help of the user-defined material mechanical behavior, the proposed model was implemented in software ABAQUS.

Automatic analysis of lateral cephalograms based on high-resolution net.

Introduction: Cephalometric analysis is essential in orthodontic treatment, and it is progressing toward automatic cephalometric analysis. This study aimed to establish a cephalometric landmark detection model on the basis of a high-resolution net and improve the accuracy with high resolution.

Methods: A total of 2000 lateral cephalograms were collected to construct a dataset, and the number of target landmarks was 51.

Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes.

The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restoration, but still have substantial potential for artifacts. In this study, we developed rationalized deep learning (rDL) for structured illumination microscopy and lattice light sheet microscopy (LLSM) by incorporating prior knowledge of illumination patterns and, thereby, rationally guiding the network to denoise raw images.

A Novel Catalytic Route to Polymerizable Bicyclic Cyclic Carbonate Monomers from Carbon Dioxide.

A new catalytic route has been developed for the coupling of epoxides and CO affording polymerizable six-membered bicyclic carbonates. Cyclic epoxides equipped with a β-positioned OH group can be transformed into structurally diverse bicyclic cyclic carbonates in good yields and with high selectivity. Key to the chemo-selectivity is the difference between the reactivity of syn- and anti-configured epoxy alcohols, with the latter leading to six-membered ring carbonate formation in the presence of a binary Al aminotriphenolate complex/DIPEA catalyst.