Optogenetic therapeutic strategies for diabetes mellitus.

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.

Genome-wide polygenic risk score for rheumatoid arthritis prediction in postmenopausal women.

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.

Boosting microscopic object detection via feature activation map guided poisson blending.

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.

Augmented Super-Resolution Radial Fluctuations (aSRRF) Pushing the Limits of Structured Illumination Microscopy.

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.

Coordinated Approach: Comprehensive Policy and Action Planning.

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.

Racial and ethnic difference in the risk of fractures in the United States: a systematic review and meta-analysis.

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.

Local-to-global spatial learning for whole-slide image representation and classification.

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.

Sivelestat sodium attenuates acute lung injury by inhibiting JNK/NF-κB and activating Nrf2/HO-1 signaling pathways.

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.

Awareness, understanding, and interest in personalized medicine: A cross-sectional survey study of college students.

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.

A deep learning based method for automatic analysis of high-throughput droplet digital PCR images.

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.

Deep learning-enabled fast DNA-PAINT imaging in cells.

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.

Versatile Graph Neural Networks Toward Intuitive Human Activity Understanding.

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).

Imaging Single-Vesicle Exocytosis with Total Internal Reflection Fluorescence Microscopy (TIRFM).

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.

Smoking and fracture risk in men: a meta-analysis of cohort studies, using both frequentist and Bayesian approaches.

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.

DNA-PAINT Imaging Accelerated by Machine Learning.

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.

Omega-3 polyunsaturated fatty acids promote SNAREs mediated GLUT4 vesicle docking and fusion.

Glucose homeostasis imbalance and insulin resistance (IR) are major contributors to the incidence of type 2 diabetes. Omega-3 polyunsaturated fatty acids (PUFAs) are key ingredients for maintaining cellular functions and improving insulin sensitivity. However, how omega-3 PUFAs modulate the dynamic process of glucose transport at the cellular level remains unclear. Here we unraveled eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) may regulate the glucose transporter 4 (GLUT4) vesicle trafficking in both normal and IR adipocytes. Both omega-3 PUFAs significantly increase glucose consumption within a range of 10-32% in the basal state. Furthermore, both EPA (200 µM) and DHA (100 µM) may significantly promote the serine/threonine protein kinase (Akt) phosphorylation by 70% and 40% in the physiological state of adipocytes, respectively. Both omega-3 PUFAs significantly advanced the Akt phosphorylation in a dose-dependent way and showed a ∼2-fold increase at the dose of 200 µM in the IR pathological state. However, they could not up-regulate the expression of GLUT4 and insulin-regulated aminopeptidase protein. We further revealed that both omega-3 PUFAs dynamically promote insulin-stimulated GLUT4 vesicle translocation and soluble N-ethylmaleimide-sensitive factor attachment protein receptor mediated vesicle docking and fusion to the plasma membrane via specifically modulating the expression of vesicle-associated membrane protein 2. Understanding the mechanisms by which omega-3 PUFAs modulate cellular metabolism and IR in peripheral tissues may provide novel insights into the potential impact of omega-3 PUFAs on the metabolic function and the management of IR.

Trends and disparities in osteoarthritis prevalence among US adults, 2005-2018.

Studies reporting trends and disparities of osteoarthritis (OA) in the United States are limited. We aimed to examine trends and disparities of OA prevalence among US adults, from 2005 to 2018. Continuous National Health and Nutrition Examination Survey (NHANES) data from 2005-2006 to 2017-2018 were analyzed. Age-adjusted and self-reported OA prevalence, stratified by race/ethnicity and socioeconomic status (SES), was calculated separately for men and women. The linear trend and the association between the survey cycles and OA prevalence were assessed. Age-adjusted and self-reported OA prevalence linearly increased in the seven survey cycles (both Plinear trend ≤ 0.0002) in men and women. Non-Hispanic Caucasians (both Plinear trend ≤ 0.0001) in both genders and Non-Hispanic African Americans women (Plinear trend ≤ 0.0001) had significantly increasing linear trends in OA prevalence. In addition, people with lower SES had a lower age-adjusted prevalence of self-reported OA when compared to those with higher SES. The increasing linear trends still existed among both men and women after adjusting for multiple confounders (both Plinear trend ≤ 0.002). There were significant rising trends and disparities in self-reported OA prevalence among US men and women between 2005 and 2018.

Multiplexed all-solid-state ion-sensitive light-addressable potentiometric sensor (ISLAPS) system based on silicone-rubber for physiological ions detection.

Light-addressable potentiometric sensor (LAPS) has been widely used in biomedical applications since its advent. As a member of the potentiometric sensors, ion-sensitive LAPS (ISLAPS) can be obtained by modifying ion selective sensing membrane on the sensor surface. Compared with the conventional ion-selective electrodes (ISEs) with liquid contact, the all-solid-state ISEs have more advantages such as easy maintenance, more convenient for miniaturization and practical applications. However, the commonly used ion-sensitive membrane (ISM) matrix like PVC has many limitations such as poor adhesion to silicone-based sensor and easy overflow of the plasticizer from the membrane. In this work, LAPS was combined with a variety of ionophore-doped all-solid-state silicone-rubber ISMs for the first time, to establish a program-controlled multiplexed ISLAPS system for physiological ions (Na+, K+, Ca2+ and H+) detection. The silicone-rubber ISMs have better adhesion to silicon-based sensors without containing plasticizers, which can avoid the plasticizer pollution and improve the long-term stability. A layer of poly(3-octylthiophene-2,5-diyl) (P3OT) was pre-modified on the sensor surface to inhibit the formation of an aqueous layer and improve the sensor lifetime. With the aid of a translation stage, the light spot automatically illuminated the detection sites in sequence, and the response of the four ions could be obtained in one measurement within 1 min. The proposed multiplexed ISLAPS has good sensitivity with micromolar limit of detection (LOD), good selectivity and long-term stability (more than 3 months). The results of the real Dulbecco's Modified Eagle Medium (DMEM) sample detection proved that the ISLAPS system can be used for the physiological ions detection, and is promising to realize a multi-parameter microphysiometer.

Applications of upconversion nanoparticles in cellular optogenetics.

Upconversion-mediated optogenetics is an emerging powerful technique to remotely control and manipulate the deep-tissue protein functions and signaling pathway activation. This technique uses lanthanide upconversion nanoparticles (UCNPs) as light transducers and through near-infrared light to indirectly activate the traditional optogenetic proteins. With the merits of high spatiotemporal resolution and minimal invasiveness, this technique enables cell-type specific manipulation of cellular activities in deep tissues as well as in living animals. In this review, we introduce the latest development of optogenetic modules and UCNPs, with emphasis on the integration of UCNPs with cellular optogenetics and their biomedical applications on the control of neural/brain activity, cancer therapy and cardiac optogenetics in vivo. Furthermore, we analyze the current developed strategies to optimize and advance the upconversion-mediated optogenetics and discuss the remaining challenges of its further applications in biomedical study and clinical translational research. STATEMENT OF SIGNIFICANCE: Optogenetics harnesses photoactivatable proteins to optically stimulate and control intracellular activities. UCNPs-mediated NIR-activatable optogenetics uses lanthanide upconversion nanoparticles (UCNPs) as light transducers and utilizes near-infrared (NIR) light to indirectly activate the traditional optogenetic proteins. The integration of UCNPs with cellular optogenetics has showed great promise in biomedical applications in regulating neural/brain activity, cancer therapy and cardiac optogenetics in vivo. The evolution and optimization of functional UCNPs and the discovery and engineering of novel optogenetic modules would both contribute to the advance of such unique hybrid technology, which may lead to discoveries in biomedical research and provide new treatments for human diseases.