Low-Energy Femtosecond Laser Lens Fragmentation Techniques for Cataract.

Femtosecond laser has become an important tool in cataract surgery due to its precision and minimally invasive nature. However, the strong scattering properties of the lens limit surgical efficiency, especially when it comes to effectively cutting highly opaque and hard cataracts with femtosecond laser. This study proposes a method for achieving optical transparency in opaque lenses through tartrazine treatment.

Automatic and accurate reconstruction of long-range axonal projections of single-neuron in mouse brain.

Single-neuron axonal projections reveal the route map of neuron output and provide a key cue for understanding how information flows across the brain. Reconstruction of single-neuron axonal projections requires intensive manual operations in tens of terabytes of brain imaging data and is highly time-consuming and labor-intensive. The main issue lies in the need for precise reconstruction algorithms to avoid reconstruction errors, yet current methods struggle with densely distributed axons, focusing mainly on skeleton extraction.

Non-invasive measurement of in vivo corneal steady-state biomechanical properties via controllable negative pressure inflation.

Objective: To measure the steady-state corneal biomechanical properties related to postoperative corneal ectasia, keratoconus, glaucoma and other ophthalmic diseases, we propose a novel in vivo measurement method.

Methods: By precisely manipulating ambient negative pressure via a suction device to achieve controlled in vivo corneal inflation, we analyzed the coupling relationship between corneal deformation response and negative pressure loading. The displacement corresponding to the corneal initial configuration under 0 mmHg was extrapolated.

A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons.

Neuron reconstruction is a critical step in quantifying neuronal structures from imaging data. Advances in molecular labeling techniques and optical imaging technologies have spurred extensive research into the patterns of long-range neuronal projections. However, mapping these projections incurs significant costs, as large-scale reconstruction of individual axonal arbors remains time-consuming.

PW-BALFC, a clinical dataset for detection and instance segmentation of bronchoalveolar lavage fluid cell.

Bronchoalveolar lavage fluid (BALF) cytology provides an important basis for the diagnosis and treatment of lung diseases. Current cytological analysis of BALF relies on manual microscopic examination, which is time-consuming, laborious, and experience-dependent. Automated identification of BALF cytology helps increase the accuracy and speed of screening qualified samples and subsequent cytomorphology analysis.

Meta-analysis of Tuina combined with other treatments for obesity.

Background: To evaluate the efficacy of Tuina (a form of Chinese therapeutic massage) combined with complementary therapies (such as auricular plaster therapy, acupuncture, or herbal medicine) on improving weight, body mass index (BMI), and body composition in obese patients.

Methods: A comprehensive search of CNKI, Wanfang, PubMed, Cochrane Library, and Web of Science (from January 2004 to March 2024) was conducted for randomized controlled trials (RCTs). Heterogeneity among studies was quantified using the I² statistic, and fixed-effects or random-effects models were applied as appropriate.

Effectiveness and safety of acupuncture for melasma: A meta-analysis of randomized controlled trials.

Background: Treating melasma remains challenging. We conducted a meta-analysis to assess the effectiveness and safety of acupuncture as a treatment option.

Methods: We searched three English and four Chinese databases up to January 2, 2024.

The impact of 3 different dietary interventions on overweight or obese adults: A network meta-analysis.

Objective: This network meta-analysis aims to investigate and compare the effectiveness of 3 dietary interventions - Mediterranean, ketogenic, and low-fat diet - on overweight and obese adults, with a comparison to traditional low-calorie diet.

Methods: A systematic review was conducted in both Chinese and English databases, including the China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), SinoMed, PubMed, Web of Science, Cochrane Library and Embase to identify relevant randomized controlled trials (RCTs) up to January 31, 2024. Two researchers independently screened and extracted data from the identified literature.

High-precision angle error compensation method for a Dove prism scanning system based on galvanometers.

Dove prisms suffer from angle and shift errors due to inevitable errors in manufacturing and installation, limiting their applicability in tasks requiring high-precision scanning. These errors, particularly angle errors, can significantly deform and ruin the intended scanning trajectory. Here, we propose a method for compensating the angle errors in Dove prisms using galvanometers.

A Clinical Bacterial Dataset for Deep Learning in Microbiological Rapid On-Site Evaluation.

Microbiological Rapid On-Site Evaluation (M-ROSE) is based on smear staining and microscopic observation, providing critical references for the diagnosis and treatment of pulmonary infectious disease. Automatic identification of pathogens is the key to improving the quality and speed of M-ROSE. Recent advancements in deep learning have yielded numerous identification algorithms and datasets.

Network pharmacological analysis on the mechanism of Linggui Zhugan decoction for nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD), represents a chronic progressive disease that imposes a significant burden on patients and the healthcare system. Linggui Zhugan decoction (LGZGD) plays a substantial role in treating NAFLD, but its exact molecular mechanism is unknown. Using network pharmacology, this study aimed to investigate the mechanism of action of LGZGD in treating NAFLD.

Does acupuncture improve the metabolic outcomes of obese/overweight children and adolescents?: A systematic review and meta-analysis.

Background: Although increasing evidence has revealed the efficacy of acupuncture in obesity/overweight, actual improvement in metabolism in children and adolescents is unclear. Therefore, we conducted a meta-analysis to evaluate this correlation.

Methods: A comprehensive search was conducted using multiple databases, including Medline, Cochrane, Embase, Web of Science, Chinese Biomedical Literature Database, China National Knowledge Infrastructure, Chinese Scientific Journal Database, and Wan-fang Data, to identify relevant randomized controlled trials published before February 1, 2023.

Dual supervised sampling networks for real-time segmentation of cervical cell nucleus.

The morphology of the cervical cell nucleus is the most important consideration for pathological cell identification. And a precise segmentation of the cervical cell nucleus determines the performance of the final classification for most traditional algorithms and even some deep learning-based algorithms. Many deep learning-based methods can accurately segment cervical cell nuclei but will cost lots of time, especially when dealing with the whole-slide image (WSI) of tens of thousands of cells.

Super-resolution Segmentation Network for Reconstruction of Packed Neurites.

Neuron reconstruction can provide the quantitative data required for measuring the neuronal morphology and is crucial in brain research. However, the difficulty in reconstructing dense neurites, wherein massive labor is required for accurate reconstruction in most cases, has not been well resolved. In this work, we provide a new pathway for solving this challenge by proposing the super-resolution segmentation network (SRSNet), which builds the mapping of the neurites in the original neuronal images and their segmentation in a higher-resolution (HR) space.

Minimizing Probability Graph Connectivity Cost for Discontinuous Filamentary Structures Tracing in Neuron Image.

Neuron tracing from optical image is critical in understanding brain function in diseases. A key problem is to trace discontinuous filamentary structures from noisy background, which is commonly encountered in neuronal and some medical images. Broken traces lead to cumulative topological errors, and current methods were hard to assemble various fragmentary traces for correct connection.

StainNet: A Fast and Robust Stain Normalization Network.

Stain normalization often refers to transferring the color distribution to the target image and has been widely used in biomedical image analysis. The conventional stain normalization usually achieves through a pixel-by-pixel color mapping model, which depends on one reference image, and it is hard to achieve accurately the style transformation between image datasets. In principle, this difficulty can be well-solved by deep learning-based methods, whereas, its complicated structure results in low computational efficiency and artifacts in the style transformation, which has restricted the practical application.

Foreground Estimation in Neuronal Images With a Sparse-Smooth Model for Robust Quantification.

3D volume imaging has been regarded as a basic tool to explore the organization and function of the neuronal system. Foreground estimation from neuronal image is essential in the quantification and analysis of neuronal image such as soma counting, neurite tracing and neuron reconstruction. However, the complexity of neuronal structure itself and differences in the imaging procedure, including different optical systems and biological labeling methods, result in various and complex neuronal images, which greatly challenge foreground estimation from neuronal image.

Automated Neuron Tracing Using Content-Aware Adaptive Voxel Scooping on CNN Predicted Probability Map.

Neuron tracing, as the essential step for neural circuit building and brain information flow analyzing, plays an important role in the understanding of brain organization and function. Though lots of methods have been proposed, automatic and accurate neuron tracing from optical images remains challenging. Current methods often had trouble in tracing the complex tree-like distorted structures and broken parts of neurite from a noisy background.

GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population.

Recent technological advancements have facilitated the imaging of specific neuronal populations at the single-axon level across the mouse brain. However, the digital reconstruction of neurons from a large dataset requires months of manual effort using the currently available software. In this study, we develop an open-source software called GTree (global tree reconstruction system) to overcome the above-mentioned problem.

3D high resolution generative deep-learning network for fluorescence microscopy imaging.

Microscopic fluorescence imaging serves as a basic tool in many research areas including biology, medicine, and chemistry. With the help of optical clearing, large volume imaging of a mouse brain and even a whole body has been enabled. However, constrained by the physical principles of optical imaging, volume imaging has to balance imaging resolution and speed.

Brain-Wide Shape Reconstruction of a Traced Neuron Using the Convex Image Segmentation Method.

Neuronal shape reconstruction is a helpful technique for establishing neuron identity, inferring neuronal connections, mapping neuronal circuits, and so on. Advances in optical imaging techniques have enabled data collection that includes the shape of a neuron across the whole brain, considerably extending the scope of neuronal anatomy. However, such datasets often include many fuzzy neurites and many crossover regions that neurites are closely attached, which make neuronal shape reconstruction more challenging.

DeepBouton: Automated Identification of Single-Neuron Axonal Boutons at the Brain-Wide Scale.

Fine morphological reconstruction of individual neurons across the entire brain is essential for mapping brain circuits. Inference of presynaptic axonal boutons, as a key part of single-neuron fine reconstruction, is critical for interpreting the patterns of neural circuit wiring schemes. However, automated bouton identification remains challenging for current neuron reconstruction tools, as they focus mainly on neurite skeleton drawing and have difficulties accurately quantifying bouton morphology.