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Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can be separately labeled and imaged for live-cell applications. In addition, the conventional sequential channel imaging procedure is quite time consuming, as it needs to switch either different lasers or filters. Here, we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six distinct subcellular structures from three raw images with only two separate fluorescent labels. DBSN combines the intensity-balance model to compensate for uneven fluorescent labels for different structures and the structure-separation model to extract multiple different structures with the same fluorescent labels. Therefore, DBSN breaks the bottleneck of the existing technologies and holds immense potential applications in the field of cell biology.

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Cancer metastasis starts from early local invasion, during which tumor cells detach from the primary tumor, penetrate the extracellular matrix (ECM), and then invade neighboring tissues. However, the cellular mechanics in the detaching and penetrating processes have not been fully understood, and the underlying mechanisms that influence cell polarization and migration in the 3D matrix during tumor invasion remain largely unknown. In this study, we employed a dual tumor-spheroid model to investigate the cellular mechanisms of the tumor invasion. Our results revealed that the tensional force field developed by the active contraction of cells and tissues played a pivotal role in tumor invasion, acting as the driving force for remodeling the collagen fibers during the invasion process. The remodeled collagen fibers promoted cell polarization and migration because of the stiffening of the fiber matrix. The aligned fibers facilitated tumor cell invasion and directed migration from one spheroid to the other. Inhibiting/shielding the cellular contractility abolished matrix remodeling and re-alignment and significantly decreased tumor cell invasion. By developing a coarse-grained cell model that considers the mutual interaction between cells and fibers, we predicted the tensional force field in the fiber network and the associated cell polarization and cell-matrix interaction during cell invasion, which revealed a mechano-chemical coupling mechanism at the cellular level of the tumor invasion process. Our study highlights the roles of cellular mechanics at the early stage of tumor metastasis and may provide new therapeutic strategies for cancer therapy.

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Current whole slide image (WSI) segmentation aims at extracting tumor regions from the background. Unlike this, segmenting distinct tumor areas (instances) within a WSI driven by limited annotated data remains under-explored. In this paper, we formally propose semisupervised instance segmentation (Semi-IS) in WSIs. We address a key challenge: learning intra-class similarity and inter-class dissimilarity driven by unlabeled data. Specifically, we generally perceive the patch as composed of tokens (together), not the patch alone. We employ contrastive learning to develop a segmentation framework. In the SemiIS, we find that the boundaries of segmented instances are usually disturbed by noise. We jointly eliminate and preserve noise features to address this problem. We conduct extensive experiments to evaluate the effectiveness and generalizability of Semi-IS, including histopathology and cellular pathology. The results show that in clinical multi instance segmentation tasks, Semi-IS achieves almost fullsupervised state-of-the-art results with only 30% annotated data. Semi-IS can improve segmentation accuracy by about 2% on public cell pathology datasets.

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High-performance biosensors play a crucial role in elucidating the intricate spatiotemporal regulatory roles and dynamics of membrane phospholipids. However, enhancing the sensitivity and imaging performance remains a significant challenge. Here, optogenetic-based strategies are presented to optimize phospholipid biosensors. These strategies involves presequestering unbound biosensors in the cell nucleus and regulating their cytosolic levels with blue light to minimize background signal interference in phospholipid detection, particularly under conditions of high expression levels of biosensor. Furthermore, optically controlled phase separation and the SunTag system are employed to generate punctate probes for substrate detection, thereby amplifying biosensor signals and enhancing visualization of the detection process. These improved phospholipid biosensors hold great potential for enhancing the understanding of the spatiotemporal dynamics and regulatory roles of membrane lipids in live cells and the methodological insights in this study might be valuable for developing other high-performance biosensors.

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Diabetes mellitus (DM) is a common chronic disease affecting humans globally. It is characterized by abnormally elevated blood glucose levels due to the failure of insulin production or reduction of insulin sensitivity and functionality. Insulin and glucagon-like peptide (GLP)-1 replenishment or improvement of insulin resistance are the two major strategies to treat diabetes. Recently, optogenetics that uses genetically encoded light-sensitive proteins to precisely control cell functions has been regarded as a novel therapeutic strategy for diabetes. Here, we summarize the latest development of optogenetics and its integration with synthetic biology approaches to produce light-responsive cells for insulin/GLP-1 production, amelioration of insulin resistance and neuromodulation of insulin secretion. In addition, we introduce the development of cell encapsulation and delivery methods and smart bioelectronic devices for the in vivo application of optogenetics-based cell therapy in diabetes. The remaining challenges for optogenetics-based cell therapy in the clinical translational study are also discussed.

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BACKGROUND: Rheumatoid arthritis (RA), a common autoimmune disease, exhibits a vital genetic component. Polygenic risk scores (PRS) derived from genome-wide association studies (GWAS) offer potential utility in predicting disease susceptibility. The present study aimed to develop and validate a PRS for predicting RA risk in postmenopausal women. METHODS: The study developed a novel PRS using 225,000 genetic variants from a GWAS dataset. The PRS was developed in a cohort of 8967 postmenopausal women and validated in an independent cohort of 6269 postmenopausal women. Among the development cohort, approximately 70% were Hispanic and approximately 30% were African American. The testing cohort comprised approximately 50% Hispanic and 50% Caucasian individuals. Stratification according to PRS quintiles revealed a pronounced gradient in RA prevalence and odds ratios. RESULTS: High PRS was significantly associated with increased RA risk in individuals aged 60-70 years, ≥ 70 years, and overweight and obese participants. Furthermore, at age 65 years, individuals in the bottom 5% of the PRS distribution have an absolute risk of RA at 30.6% (95% confidence interval = 18.5%-42.6%). The risk increased to 53.8% (95% confidence interval = 42.8%-64.9%) for those in the top 5% of the PRS distribution. CONCLUSIONS: The PRS developed in the present study is significantly associated with RA risk, showing the potential for early screening of RA in postmenopausal women. This work demonstrates the feasibility of personalized medicine in identifying high-risk individuals for RA, indicating the need for further studies to test the utility of PRS in other populations.

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Microscopic examination of visible components based on micrographs is the gold standard for testing in biomedical research and clinical diagnosis. The application of object detection technology in bioimages not only improves the efficiency of the analyst but also provides decision support to ensure the objectivity and consistency of diagnosis. However, the lack of large annotated datasets is a significant impediment in rapidly deploying object detection models for microscopic formed elements detection. Standard augmentation methods used in object detection are not appropriate because they are prone to destroy the original micro-morphological information to produce counterintuitive micrographs, which is not conducive to build the trust of analysts in the intelligent system. Here, we propose a feature activation map-guided boosting mechanism dedicated to microscopic object detection to improve data efficiency. Our results show that the boosting mechanism provides solid gains in the object detection model deployed for microscopic formed elements detection. After image augmentation, the mean Average Precision (mAP) of baseline and strong baseline of the Chinese herbal medicine micrograph dataset are increased by 16.3% and 5.8% respectively. Similarly, on the urine sediment dataset, the boosting mechanism resulted in an improvement of 8.0% and 2.6% in mAP of the baseline and strong baseline maps respectively. Moreover, the method shows strong generalizability and can be easily integrated into any main-stream object detection model. The performance enhancement is interpretable, making it more suitable for microscopic biomedical applications.

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Structured illumination microscopy (SIM) is a versatile super-resolution technique known for its compatibility with a wide range of probes and fast implementation. While 3D SIM is capable of achieving a spatial resolution of ∼120 nm laterally and ∼300 nm axially, attempting to further enhance the resolution through methods such as nonlinear SIM or 4-beam SIM introduces complexities in optical configurations, increased phototoxicity, and reduced temporal resolution. Here, we have developed a novel method that combines SIM with augmented super-resolution radial fluctuations (aSRRF) utilizing a single image through image augmentation. By applying aSRRF reconstruction to SIM images, we can enhance the SIM resolution to ∼50 nm isotopically, without requiring any modifications to the optical system or sample acquisition process. Additionaly, we have incorporated the aSRRF approach into an ImageJ plugin and demonstrated its versatility across various fluorescence microscopy images, showcasing a remarkable two-fold resolution increase.

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BACKGROUND: Schools play a vital role in student health, and a collaborative approach may affect health factors such as physical activity (PA) and nutrition. There is a lack of recent literature synthesizing collaborative approaches in K-12 settings. We present updated evidence about interventions that used a coordinated school health approach to support K-12 student PA and nutrition in the United States. METHODS: A 2-phase literature review search included a search of systematic reviews for individual qualifying studies (2010-2018), followed by a search for individual articles (2010-2020) that evaluated a coordinated approach or use of school wellness councils, committees, or teams to address PA and/or nutrition. RESULTS: We identified 35 articles describing 30 studies and grouped them by intervention type. Interventions demonstrated promising findings for environmental changes and student dietary and PA behaviors. IMPLICATIONS: Coordinated and multicomponent interventions demonstrated significant improvements or null results, indicating that implementation of programs and/or policies to promote healthier eating and PA practices may support and do not appear to hinder environmental or behavioral outcomes. CONCLUSIONS: Schools can use a coordinated approach to implement opportunities for PA and nutrition; this may influence students' PA and dietary behaviors.

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This systematic review and meta-analysis examined the association between race and ethnicity and fracture risk in the United States. We identified relevant studies by searching PubMed and EMBASE for studies published from the databases' inception date to December 23, 2022. Only observational studies conducted in the US population that reported the effect size of racial-ethnic minority groups versus white people were included. Two investigators independently conducted literature searches, study selection, risk of bias assessment, and data abstraction; discrepancies were resolved by consensus or consultation of a third investigator. Twenty-five studies met the inclusion criteria, and the random-effects model was used to calculate the pooled effect size due to heterogeneity between the studies. Using white people as the reference group, we found that people of other races and ethnic groups had a significantly lower fracture risk. In Black people, the pooled relative risk (RR) was 0.46 (95% confidence interval (CI), 0.43-0.48, p < 0.0001). In Hispanics, the pooled RR was 0.66 (95% CI, 0.55-0.79, p < 0.0001). In Asian Americans, the pooled RR was 0.55 (95% CI, 0.45-0.66, p < 0.0001). In American Indians, the pooled RR was 0.80 (95% CI, 0.41-1.58, p = 0.3436). Subgroup analysis by sex in Black people revealed the strength of association was greater in men (RR = 0.57, 95% CI = 0.51-0.63, p < 0.0001) than in women (RR = 0.43, 95% CI = 0.39-0.47, p < 0.0001). Our findings suggest that people of other races and ethnic groups have a lower fracture risk than white people.

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Whole-slide image (WSI) provides an important reference for clinical diagnosis. Classification with only WSI-level labels can be recognized for multi-instance learning (MIL) tasks. However, most existing MIL-based WSI classification methods have moderate performance on correlation mining between instances limited by their instance- level classification strategy. Herein, we propose a novel local-to-global spatial learning method to mine global position and local morphological information by redefining the MIL-based WSI classification strategy, better at learning WSI-level representation, called Global-Local Attentional Multi-Instance Learning (GLAMIL). GLAMIL can focus on regional relationships rather than single instances. It first learns relationships between patches in the local pool to aggregate region correlation (tissue types of a WSI). These correlations then can be further mined to fulfill WSI-level representation, where position correlation between different regions can be modeled. Furthermore, Transformer layers are employed to model global and local spatial information rather than being simply used as feature extractors, and the corresponding structure improvements are present. In addition, we evaluate GIAMIL on three benchmarks considering various challenging factors and achieve satisfactory results. GLAMIL outperforms state-of-the-art methods and baselines by about 1 % and 10 %, respectively.

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Sivelestat sodium (SIV), a neutrophil elastase inhibitor, is mainly used for the clinical treatment of acute respiratory distress syndrome (ARDS) or acute lung injury (ALI). However, studies investigating the effects of SIV treatment of ALI are limited. Therefore, this study investigated the potential molecular mechanism of the protective effects of SIV against ALI. Human pulmonary microvascular endothelial cells (HPMECs) were stimulated with tumor necrosis factor α (TNF-α), and male Sprague-Dawley rats were intratracheally injected with Klebsiella pneumoniae (KP) and treated with SIV, ML385, and anisomycin (ANI) to mimic the pathogenetic process of ALI in vitro and in vivo, respectively. The levels of inflammatory cytokines and indicators of oxidative stress were assessed in vitro and in vivo. The wet/dry (W/D) ratio of lung tissues, histopathological changes, inflammatory cells levels in bronchoalveolar lavage fluid (BALF), and survival rates of rats were analyzed. The JNK/NF-κB (p65) and Nrf2/HO-1 levels in the HPMECs and lung tissues were analyzed by western blot and immunofluorescence analyses. Administration of SIV reduced the inflammatory factors levels, intracellular reactive oxygen species (ROS) production, and malondialdehyde (MDA) levels and increased the levels of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) in lung tissues. Meanwhile, SIV alleviated pathological injuries, decreased the W/D ratio, and inflammatory cell infiltration in lung tissue. In addition, SIV also inhibited the activation of JNK/NF-κB signaling pathway, promoted nuclear translocation of Nrf2, and upregulated the expression of heme oxygenase 1 (HO-1). However, ANI or ML385 significantly reversed these changes. SIV effectively attenuated the inflammatory response and oxidative stress. Its potential molecular mechanism was related to the JNK/NF-κB activation and Nrf2/HO-1 signaling pathway inhibition. This further deepened the understanding of the protective effects of SIV against ALI.

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INTRODUCTION: Personalized Medicine (PM) holds great potential in healthcare. A few existing surveys have investigated awareness, understanding, and interest regarding PM in the general public; however, studies investigating college students' opinions about PM are lacking. This study aimed to evaluate the college student's awareness, understanding, and interest in PM, and their opinion was also analyzed by their gender and major. METHODS: The study samples were undergraduate students enrolled at the University of Nevada, Las Vegas (UNLV). A web-based survey with 42 questions was emailed to all UNLV undergraduate students. Overall survey results were analyzed by gender and each student's major. A chi-square test evaluated the significant association between responses to questions with regard to gender or major. RESULTS: Among the participants, 1225 students completed the survey. This survey found that most college students had a neutral attitude to PM and were not entirely familiar with this field. For example, most students (57.6%) had a "neutral" attitude toward PM. In addition, 77.6% of students never received any personal genetic testing. More than 80% of students thought "interests" was the most important factor in using PM, and 50% of respondents chose "somewhat likely" to the recommendation about PM from the doctor. Also of importance was the finding that a significant association between the most important factor of using PM and gender was observed (p = 0.04), and the associations between a student's major affected his or her reaction to PM, how well informed she or he was about PM, his or her attitude toward a doctor's recommendation about using PM were all significant (all participant's p<0.004). CONCLUSION: UNLV undergraduate students had a neutral attitude to PM and were not entirely familiar with this field.

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Droplet digital PCR (ddPCR) is a technique for absolute quantification of nucleic acid molecules and is widely used in biomedical research and clinical diagnosis. ddPCR partitions the reaction solution containing target molecules into a large number of independent microdroplets for amplification and performs quantitative analysis of target molecules by calculating the proportion of positive droplets by the principle of Poisson distribution. Accurate recognition of positive droplets in ddPCR images is of great importance to guarantee the accuracy of target nucleic acid quantitative analysis. However, hand-designed operators are sensitive to interference and have disadvantages such as low contrast, uneven illumination, low sample copy number, and noise, and their accuracy and robustness still need to be improved. Herein, we developed a deep learning-based high-throughput ddPCR droplet detection framework for robust and accurate ddPCR image analysis, and the experimental results show that our method achieves excellent performance in the recognition of positive droplets (99.71%) within a limited time. By combining the Hough transform and a convolutional neural network (CNN), our novel method can automatically filter out invalid droplets that are difficult to be identified by local or global encoding methods and realize high-precision localization and classification of droplets in ddPCR images under variable exposure, contrast, and uneven illumination conditions without the need for image pre-processing and normalization processes.

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DNA-based point accumulation in nanoscale topography (DNA-PAINT) is a well-established technique for single-molecule localization microscopy (SMLM), enabling resolution of up to a few nanometers. Traditionally, DNA-PAINT involves the utilization of tens of thousands of single-molecule fluorescent images to generate a single super-resolution image. This process can be time-consuming, which makes it unfeasible for many researchers. Here, we propose a simplified DNA-PAINT labeling method and a deep learning-enabled fast DNA-PAINT imaging strategy for subcellular structures, such as microtubules. By employing our method, super-resolution reconstruction can be achieved with only one-tenth of the raw data previously needed, along with the option of acquiring the widefield image. As a result, DNA-PAINT imaging is significantly accelerated, making it more accessible to a wider range of biological researchers.

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Benefiting from the advanced human visual system, humans naturally classify activities and predict motions in a short time. However, most existing computer vision studies consider those two tasks separately, resulting in an insufficient understanding of human actions. Moreover, the effects of view variations remain challenging for most existing skeleton-based methods, and the existing graph operators cannot fully explore multiscale relationship. In this article, a versatile graph-based model (Vers-GNN) is proposed to deal with those two tasks simultaneously. First, a skeleton representation self-regulated scheme is proposed. It is among the first trials that successfully integrate the idea of view adaptation into a graph-based human activity analysis system. Next, several novel graph operators are proposed to model the positional relationships and learn the abstract dynamics between different human joints and parts. Finally, a practical multitask learning framework and a multiobjective self-supervised learning scheme are proposed to promote both the tasks. The comparative experimental results show that Vers-GNN outperforms the recent state-of-the-art methods for both the tasks, with the to date highest recognition accuracies on the datasets of NTU RGB + D (CV: 97.2%), UWA3D (88.7%), and CMU (1000 ms: 1.13).

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Total internal reflection fluorescence microscopy (TIRFM) provides extremely thin optical sectioning with excellent signal-to-noise ratios, which allows for visualization of membrane dynamics at the cell surface with superb spatiotemporal resolution. In this chapter, TIRFM is used to record and analyze exocytosis of single glucose transporter-4 (GLUT4) containing vesicles in 3T3-L1 adipocytes.

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Past studies indicate that men are more likely to smoke and be at higher risk of smoking-related conditions than women. Our research aimed, through meta-analysis, to assess the association between smoking and fracture risk in men. The following databases were searched, including MEDLINE, EMBASE, Scopus, PsycINFO, ISI Web of Science, Google Scholar, WorldCat, and Open Grey, for identifying related studies. A random-effects model was used to pool the confounder-adjusted relative risk (R.R.). Frequentist and Bayesian hierarchical random-effects models were used for the analysis. The heterogeneity and publication bias were evaluated in this study. Twenty-seven studies met the inclusion criteria. Overall, smoking is associated with a significantly increased risk of fracture in both the frequentist approach (R.R., 1.37; 95% confidence interval: 1.22, 1.53) and the Bayesian approach (R.R., 1.36; 95% credible interval: 1.22, 1.54). Significant heterogeneity was observed in the meta-analysis (Higgin's I2 = 83%) and Cochran's Q statistic (p < 0.01). A significant association was also observed in multiple pre-specified sensitivity and subgroup analyses. Similar results were observed in the group containing a large sample size (≥ 10,000 participants), and the group has a small sample size (< 10,000 participants); the pooled R.R was 1.23 (95% confidence interval, 1.07-1.41) and 1.56 (95% confidence interval, 1.37-1.78), respectively. With the Bayesian method, the effect size was 1.23 (95% credible interval, 1.05, 1.45) for the large sample size group and 1.57 (95% credible interval, 1.35, 1.82) for the small sample size group. Smoking is associated with a significant increase in fracture risk for men. Thus, smoking cessation would also greatly reduce fracture risk in all smokers, particularly in men.

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DNA point accumulation in nanoscale topography (DNA-PAINT) is an easy-to-implement approach for localization-based super-resolution imaging. Conventional DNA-PAINT imaging typically requires tens of thousands of frames of raw data to reconstruct one super-resolution image, which prevents its potential application for live imaging. Here, we introduce a new DNA-PAINT labeling method that allows for imaging of microtubules with both DNA-PAINT and widefield illumination. We develop a U-Net-based neural network, namely, U-PAINT to accelerate DNA-PAINT imaging from a widefield fluorescent image and a sparse single-molecule localization image. Compared with the conventional method, U-PAINT only requires one-tenth of the original raw data, which permits fast imaging and reconstruction of super-resolution microtubules and can be adopted to analyze other SMLM datasets. We anticipate that this machine learning method enables faster and even live-cell DNA-PAINT imaging in the future.