Among all the structural formations, fiber-like structure is one of the most common modalities in organisms that undertake essential functions. Alterations in spatial organization of fibrous structures can reflect information of physiological and pathological activities, which is of significance in both researches and clinical applications. Hence, the quantification of subtle changes in fiber-like structures is potentially meaningful in studying structure-function relationships, disease progression, carcinoma staging and engineered tissue remodeling. In this study, we examined a wide range of methodologies that quantify organizational and morphological features of fibrous structures, including orientation, alignment, waviness and thickness. Each method was demonstrated with specific applications. Finally, perspectives of future quantification analysis techniques were explored.

Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems, yet still faces many challenges. Building on convolutional neural networks (CNNs), medical image segmentation has achieved tremendous progress. However, owing to the locality of convolution operations, CNNs have the inherent limitation in learning global context. To address the limitation in building global context relationship from CNNs, we proposeLGNet, a semantic segmentation network aiming to learn local and global features for fast and accurate medical image segmentation in this paper. Specifically, we employ a two-branch architecture consisting of convolution layers in one branch to learn local features and transformer layers in the other branch to learn global features. LGNet has two key insights: (1) We bridge two-branch to learn local and global features in an interactive way; (2) we present a novel multi-feature fusion model (MSFFM) to leverage the global contexture information from transformer and the local representational features from convolutions. Our method achieves state-of-the-art trade-off in terms of accuracy and efficiency on several medical image segmentation benchmarks including Synapse, ACDC and MOST. Specifically, LGNet achieves the state-of-the-art performance with Dice’s indexes of 80.15% on Synapse, of 91.70% on ACDC, and of 95.56% on MOST. Meanwhile, the inference speed attains at 172 frames per second with 224×224 input resolution. The extensive experiments demonstrate the effectiveness of the proposed LGNet for fast and accurate for medical image segmentation.

Bioprobe based on fluorescence is widely used in biological and medical research due to its high sensitivity and selectivity. Yet, its quantification in vivo is complicated and often compromised by the interaction between the fluorophore with the environmental factors, as well as the optical scattering and absorption by the tissue. A high florescence quantum yield and minimal interference by the environment are key requirements for designing an effective bioprobe, and the pre-requisitions severely limit the available options. We propose that a comprehensive evaluation of potential bioprobe can be achieved by simultaneously measuring both radiative and non-radiative transitions, the two fundamental and complementary pathways for the energy de-excitation. This approach will not only improve the accuracy of the quantification by catching the information from a broader spectrum of the energy, but also provide additional information of the probe environment that often impacts the balance between the two forms of the energy transition. This work first analyzes the underlying mechanism of the hypothesis. The practical feasibility is then tested by means of simultaneous measurements of photoacoustic signal for the non-radiative and fluorescence for the radiative energy processes, respectively. It is demonstrated that the systematic evaluation of the probe energy de-excitation results in an improved quantitative tracing of a bioprobe in complex environment.

The tumor microenvironment (TME) is now recognized as an important participant of tumor progression. As the most abundant extracellular matrix component in TME, collagen plays an important role in tumor development. The imaging study of collagen morphological feature in TME is of great significance for understanding the state of tumor. Multiphoton microscopy (MPM), based on second harmonic generation (SHG) and two-photon excitation fluorescence (TPEF), can be used to monitor the morphological changes of biological tissues without labeling. In this study, we used MPM for large-scale imaging of early invasive breast cancer from the tumor center to normal tissues far from the tumor. We found that there were significant differences in collagen morphology between breast cancer tumor boundary, near tumor transition region and normal tissues far from the tumor. Furthermore, the morphological feature of eight collagen fibers was extracted to quantify the variation trend of collagen in three regions. These results may provide a new perspective for the optimal negative margin width of breast-conserving surgery and the understanding of tumor metastasis.

The skin is heterogeneous and exerts strong scattering and aberration onto excitation light in multiphoton microscopy (MPM). Shifting to longer excitation wavelengths may help reduce skin scattering and aberration, potentially enabling larger imaging depths. However, previous demonstrations of skin MPM employ excitation wavelengths only up to the 1700nm window, leaving an open question as to whether longer excitation wavelengths are suitable for deep-skin MPM. Here, in order to explore the longer-wavelength territory, first, we demonstrate characterization of the broadband transmittance of excised mouse skin, revealing a high transmittance window at 2200nm. Then, we demonstrate third-harmonic generation (THG) imaging in mouse skin in vivo excited at this window. With 9mW optical power on the skin surface operating at 1MHz repetition rate, we can get THG signals of 250μm below the skin surface. Comparative THG imaging excited at the 1700nm window shows that as imaging depth increases, THG signals decay even faster than those excited at 2200nm. Our results thus uncover the 2200nm window as a new, promising excitation window potential for deep-skin MPM.

The cervix is a collagen-rich connective tissue that must remain closed during pregnancy while undergoing progressive remodeling in preparation for delivery, which begins before the onset of the preterm labor process. Therefore, it is important to resolve the changes of collagen fibers during cervical remodeling for the prevention of preterm labor. Herein, we assessed the spatial organization of collagen fibers in a three-dimensional (3D) context within cervical tissues of mice on day 3, 9, 12, 15 and 18 of gestation. We found that the 3D directional variance, a novel metric of alignment, was higher on day 9 than that on day 3 and then gradually decreased from day 9 to day 18. Compared with two-dimensional (2D) approach, a higher sensitivity was achieved from 3D analysis, highlighting the importance of truly 3D quantification. Moreover, the depth-dependent variation of 3D directional variance was investigated. By combining multiple 3D directional variance-derived metrics, a high level of classification accuracy was acquired in distinguishing different periods of pregnancy. These results demonstrate that 3D directional variance is sensitive to remodeling of collagen fibers within cervical tissues, shedding new light on highly-sensitive, early detection of preterm birth (PTB).

Lipid droplets (LDs) participate in many physiological processes, the abnormality of which will cause chronic diseases and pathologies such as diabetes and obesity. It is crucial to monitor the distribution of LDs at high spatial resolution and large depth. Herein, we carried three-photon imaging of LDs in fat liver. Owing to the large three-photon absorption cross-section of the luminogen named NAP-CF₃ (1.67×10 − 79cm⁶ s²), three-photon fluorescence fat liver imaging reached the largest depth of 80μm. Fat liver diagnosis was successfully carried out with excellent performance, providing great potential for LDs-associated pathologies research.

Peroxynitrite (ONOO− ) contributes to oxidative stress and neurodegeneration in Parkinson’s disease (PD). Developing a peroxynitrite probe would enable in situ visualization of the overwhelming ONOO ⁻ flux and understanding of the ONOO ⁻ stress-induced neuropathology of PD. Herein, a novel α-ketoamide-based fluorogenic probe (DFlu) was designed for ONOO ⁻ monitoring in multiple PD models. The results demonstrated thatDFlu exhibits a fluorescence turn-on response to ONOO ⁻ with high specificity and sensitivity. The efficacy ofDFlu for intracellular ONOO ⁻ imaging was demonstrated systematically. The results showed thatDFlu can successfully visualize endogenous and exogenous ONOO ⁻ in cells derived from chemical and biochemical routes. More importantly, the two-photon excitation ability ofDFlu has been well demonstrated by monitoring exogenous/endogenous ONOO ⁻ production and scavenging in live zebrafish PD models. This work provides a reliable and promising α-ketoamide-based optical tool for identifying variations of ONOO ⁻ in PD models.

Human serum albumin (HSA) is the most abundant protein in plasma and plays an essential physiological role in the human body. Ethanol precipitation is the most widely used way to obtain HSA, and pH and ethanol are crucial factors affecting the process. In this study, infrared (IR) spectroscopy and near-infrared (NIR) spectroscopy in combination with chemometrics were used to investigate the changes in the secondary structure and hydration of HSA at acidic pH (5.6–3.2) and isoelectric pH when ethanol concentration was varied from 0% to 40% as a perturbation. IR spectroscopy combined with the two-dimensional correlation spectroscopy (2DCOS) analysis for acid pH system proved that the secondary structure of HSA changed significantly when pH was around 4.5. What’s more, the IR spectroscopy and 2DCOS analysis showed different secondary structure forms under different ethanol concentrations at the isoelectric pH. For the hydration effect analysis, NIR spectroscopy combined with the McCabe–Fisher method and aquaphotomics showed that the free hydrogen-bonded water fluctuates dynamically, with ethanol at 0–20% enhancing the hydrogen-bonded water clusters, while weak hydrogen-bonded water clusters were formed when the ethanol concentration increased continuously from 20% to 30%. These measurements provide new insights into the structural changes and changes in the hydration behavior of HSA, revealing the dynamic process of protein purification, and providing a theoretical basis for the selection of HSA alcoholic precipitation process parameters, as well as for further studies of complex biological systems.

In sports events, the rapid recovery after high-intensity training or sport competition performance is very important for athletes’ performance and health. The aim of this study is to evaluate the effect of laser acupuncture and electrical stimulation on the recovery from exercise fatigue, using mice with swimming fatigue as experimental model and the electromyography (EMG) and the Raman spectroscopy of blood as evaluation indicators. Root mean square (RMS) and mean power frequency (MPF) of EMG were analyzed after laser acupuncture and electrical stimulation. The amplitude frequency combined analysis (JASA) showed that the proportion of muscles in the fatigue recovery area of the control group, the laser acupuncture group, the multi-channel laser acupuncture group and the laser combined with electrical stimulation group were 34.78%, 39.13%, 39.13% and 43.48%, respectively. Raman spectroscopy of the mice blood during fatigue recovery showed there is a significant difference between the multi-channel laser acupuncture group and the laser combined with electric stimulation group compared with the recovery period and fatigue period (P<0.05) at the peak of 997cm− 1 and the laser combined electrical stimulation group had a statistical difference in the recovery period compared with the fatigue period (P<0.05) at the peak of 1561cm− 1. The results showed that laser acupuncture combined with electrical stimulation was beneficial to fatigue recovery in mice, and had the potential value in sports fatigue recovery.

Vascular segmentation is a crucial task in biomedical image processing, which is significant for analyzing and modeling vascular networks under physiological and pathological states. With advances in fluorescent labeling and mesoscopic optical techniques, it has become possible to map the whole-mouse-brain vascular networks at capillary resolution. However, segmenting vessels from mesoscopic optical images is a challenging task. The problems, such as vascular signal discontinuities, vessel lumens, and background fluorescence signals in mesoscopic optical images, belong to global semantic information during vascular segmentation. Traditional vascular segmentation methods based on convolutional neural networks (CNNs) have been limited by their insufficient receptive fields, making it challenging to capture global semantic information of vessels and resulting in inaccurate segmentation results. Here, we propose SegVesseler, a vascular segmentation method based on Swin Transformer. SegVesseler adopts 3D Swin Transformer blocks to extract global contextual information in 3D images. This approach is able to maintain the connectivity and topology of blood vessels during segmentation. We evaluated the performance of our method on mouse cerebrovascular datasets generated from three different labeling and imaging modalities. The experimental results demonstrate that the segmentation effect of our method is significantly better than traditional CNNs and achieves state-of-the-art performance.

Structured illumination microscopy (SIM) is suitable for biological samples because of its relatively low-peak illumination intensity requirement and high imaging speed. The system resolution is affected by two typical detection modes: Point detection and area detection. However, a systematic analysis of the imaging performance of the different detection modes of the system has rarely been conducted. In this study, we compared laser point scanning point detection (PS-PD) and point scanning area detection (PS-AD) imaging in nonconfocal microscopy through theoretical analysis and simulated imaging. The results revealed that the imaging resolutions of PS-PD and PS-AD depend on excitation and emission point spread functions (PSFs), respectively. Especially, we combined the second harmonic generation (SHG) of point detection (P-SHG) and area detection (A-SHG) with SIM to realize a nonlinear SIM-imaging technique that improves the imaging resolution. Moreover, we analytically and experimentally compared the nonlinear SIM performance of P-SHG with that of A-SHG.

The volumetric imaging of two-photon microscopy expands the focal depth and improves the throughput, which has unparalleled superiority for three-dimension samples, especially in neuroscience. However, emerging in volumetric imaging is still largely customized, which limits the integration with commercial two-photon systems. Here, we analyzed the key parameters that modulate the focal depth and lateral resolution of polarized annular imaging and proposed a volumetric imaging module that can be directly integrated into commercial two-photon systems using conventional optical elements. This design incorporates the beam diameter adjustment settings of commercial two-photon systems, allowing flexibility to adjust the depth of focus while maintaining the same lateral resolution. Further, the depth range and lateral resolution of the design were verified, and the imaging throughput was demonstrated by an increase in the number of imaging neurons in the awake mouse cerebral cortex.