Three-dimensional adaptive optical nanoscopy for thick specimen imaging at sub-50-nm resolution.

Understanding cellular organization demands the best possible spatial resolution in all three dimensions. In fluorescence microscopy, this is achieved by 4Pi nanoscopy methods that combine the concepts of using two opposing objectives for optimal diffraction-limited 3D resolution with switching fluorescent molecules between bright and dark states to break the diffraction limit. However, optical aberrations have limited these nanoscopes to thin samples and prevented their application in thick specimens. Here we have developed an improved iso-stimulated emission depletion nanoscope, which uses an advanced adaptive optics strategy to achieve sub-50-nm isotropic resolution of structures such as neuronal synapses and ring canals previously inaccessible in tissue. The adaptive optics scheme presented in this work is generally applicable to any microscope with a similar beam path geometry involving two opposing objectives to optimize resolution when imaging deep in aberrating specimens.

Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set.

Aqueous solubility is one of the most important physicochemical properties of drug molecules and a major driving force for oral drug absorption. To date, the performance of in silico models for the estimation of solubility for novel chemical space is limited. To investigate possible reasons and remedies for this, the Johnson and Johnson in-house aqueous solubility data with over 40,000 compounds was leveraged. All data were generated through the same high-throughput assay, providing a unique opportunity to explore the relationship between data quality, quantity, and model estimations. Six intrinsic solubility data sets with different sizes and noise levels were generated by making use of three different approaches: (i) inclusion or exclusion of amorphous solid residue, (ii) measured or experimental log D to identify the intrinsic solubility, and (iii) adopting or omitting a quality check process in the data processing workflow. A random forest regressor was trained on the data sets with three different sets of descriptors calculated from RDKit, ADMET predictor, or Mordred, and the performances were evaluated with nested cross-validation as well as ten refined test sets. The models confirm, as expected, that with the same data set size, high-quality data leads to better model performance; however, also, models trained with larger data sets containing analytical variability can give equally accurate estimations compared to models trained with small, clean, and diverse data sets. However, noise introduced by including the presence of amorphous solid postsolubility measurement in the training data set cannot be overcome by increasing data size, as they are introducing a biased systematic positive error in the data set, confirming the importance of critical data review. Finally, two top-performing models were tested on the first test set from the second solubility challenge, achieving RMSE values of 0.74 and 0.72 and log S ± 0.5 of 46 and 48%, respectively. These results demonstrated improved performance compared to those reported in the findings of the competition, highlighting that a single-source curated data set can enhance the prediction of intrinsic solubility.

Synchronous Phase-Shifting Interference for High Precision Phase Imaging of Objects Using Common Optics.

Quantitative phase imaging and measurement of surface topography and fluid dynamics for objects, especially for moving objects, is critical in various fields. Although effective, existing synchronous phase-shifting methods may introduce additional phase changes in the light field due to differences in optical paths or need specific optics to implement synchronous phase-shifting, such as the beamsplitter with additional anti-reflective coating and a micro-polarizer array. Therefore, we propose a synchronous phase-shifting method based on the Mach-Zehnder interferometer to tackle these issues in existing methods. The proposed method uses common optics to simultaneously acquire four phase-shifted digital holograms with equal optical paths for object and reference waves. Therefore, it can be used to reconstruct the phase distribution of static and dynamic objects with high precision and high resolution. In the experiment, the theoretical resolution of the proposed system was 1.064 µm while the actual resolution could achieve 1.381 µm, which was confirmed by measuring a phase-only resolution chart. Besides, the dynamic phase imaging of a moving standard object was completed to verify the proposed system's effectiveness. The experimental results show that our proposed method is suitable and promising in dynamic phase imaging and measurement of moving objects using phase-shifting digital holography.

VDE-Net: a two-stage deep learning method for phase unwrapping.

Phase unwrapping is a critical step to obtaining a continuous phase distribution in optical phase measurements and coherent imaging techniques. Traditional phase-unwrapping methods are generally low performance due to significant noise or undersampling. This paper proposes a deep convolutional neural network (DCNN) with a weighted jump-edge attention mechanism, namely, VDE-Net, to realize effective and robust phase unwrapping. Experimental results revealed that the weighted jump-edge attention mechanism, which is first proposed and simple to calculate, is useful for phase unwrapping. The proposed algorithm outperformed other networks or common attention mechanisms. In addition, an unseen wrapped phase image of a living red blood cell (RBC) was successfully unwrapped by the trained VDE-Net, thereby demonstrating its strong generalization capability.

Low-Density Lipoprotein Cholesterol Treatment Target Achievement in Patients with Myocardial Infarction, Percutaneous Coronary Intervention, or Stroke in Hong Kong.

Background: Elevated concentrations of low-density lipoprotein cholesterol (LDL-C) are an important cause of recurrent cardiovascular events. This study aimed to describe the distribution and achieved concentrations of LDL-C among patients with myocardial infarction (MI), percutaneous coronary intervention (PCI), stroke, or transient ischaemic attack (TIA) in Hong Kong. Methods: Patients with a lipid test from a public hospital were identified from the Clinical Database and Analysis Reporting System of the Hong Kong Hospital Authority. Among patients with an inpatient hospitalization for MI, PCI, stroke or TIA, between 2003 to 2016, the distribution of LDL-C levels and the number (%) of patients achieving an absolute concentration of LDL-C < 1.8 mmol/L at baseline (in-hospital) and during 12 months after hospital discharge were described. Results: A total of 18417 patients were included (mean [SD] age, 70.0 [12.9] years; male, 60.3%), of which 3637 had MI, 4096 had PCI, and 10684 had stroke or TIA. At hospital discharge 12082 (65.6%) patients were prescribed statins, 690 (3.7%) were prescribed nonstatins, and 1849 (10.0%) achieved an LDL-C < 1.8 mmol/L. Overall, 5654 (30.7%) patients did not have LDL-C result available within 12 months of discharge (MI, 605 [16.6%]; PCI, 432 [10.5%]; stroke or TIA, 4617 [43.2%]). Among the overall cohort, 4591 (24.9%) patients achieved an LDL-C < 1.8 mmol/L during 12 months of follow-up (MI, 1288 [35.4%]; PCI, 1542 [37.6%]; stroke or TIA, 1761 [16.5%]). Improvements in achieved LDL-C were observed over time with a mean LDL-C 2.64 (0.92) mmol/L and 20.0% of patients achieving an LDL-C < 1.8 mmol/L in 2003 as compared with a mean LDL-C 1.86 (0.70) mmol/L and 53.9% of patients achieving an LDL-C < 1.8 mmol/L in 2016. Conclusions: In this single centre cohort study from Hong Kong, nearly half of patients with MI, PCI, or stroke in 2016 appear to qualify for intensification of lipid-modifying drug treatment in order to achieve a treatment goal of LDL-C < 1.8 mmol/L. Further research is required in Hong Kong to assess contemporary management of LDL-C in a larger group of patients with established atherosclerotic cardiovascular disease.

LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design.

Because of the strong relationship between the desired molecular activity and its structural core, the screening of focused, core-sharing chemical libraries is a key step in lead optimization. Despite the plethora of current research focused on in silico methods for molecule generation, to our knowledge, no tool capable of designing such libraries has been proposed. In this work, we present a novel tool for de novo drug design called LibINVENT. It is capable of rapidly proposing chemical libraries of compounds sharing the same core while maximizing a range of desirable properties. To further help the process of designing focused libraries, the user can list specific chemical reactions that can be used for the library creation. LibINVENT is therefore a flexible tool for generating virtual chemical libraries for lead optimization in a broad range of scenarios. Additionally, the shared core ensures that the compounds in the library are similar, possess desirable properties, and can also be synthesized under the same or similar conditions. The LibINVENT code is freely available in our public repository at https://github.com/MolecularAI/Lib-INVENT. The code necessary for data preprocessing is further available at: https://github.com/MolecularAI/Lib-INVENT-dataset.

Importance of attributes and willingness to pay for oral anticoagulant therapy in patients with atrial fibrillation in China: A discrete choice experiment.

BACKGROUND: Adherence to oral anticoagulant therapy in patients with atrial fibrillation (AF) in China is low. Patient preference, one of the main reasons for discontinuation of oral anticoagulant therapy, is an unfamiliar concept in China. METHODS AND FINDINGS: A discrete choice experiment (DCE) was conducted to quantify patient preference on 7 attributes of oral anticoagulant therapy: antidote (yes/no), food-drug interaction (yes/no), frequency of blood monitoring (no need, every 6/3/1 month[s]), risk of nonfatal major bleeding (0.7/3.1/5.5/7.8[%]), risk of nonfatal stroke (ischemic/hemorrhagic) or systemic embolism (0.6/3.2/5.8/8.4[%]), risk of nonfatal acute myocardial infarction (AMI) (0.2/1.0/1.8/2.5[%]), and monthly out-of-pocket cost (0/120/240/360 RMB) (0 to 56 USD). A total of 16 scenarios were generated by using D-Efficient design and were randomly divided into 2 blocks. Eligible patients were recruited and interviewed from outpatient and inpatient settings of 2 public hospitals in Beijing and Shenzhen, respectively. Patients were presented with 8 scenarios and asked to select 1 of 3 options: 2 unlabeled hypothetical treatments and 1 opt-out option. Mixed logit regression model was used for estimating patients' preferences of attributes of oral anticoagulants and willingness to pay (WTP) with adjustments for age, sex, education level, income level, city, self-evaluated health score, histories of cardiovascular disease/other vascular disease/any stroke/any bleeding, and use of anticoagulant/antiplatelet therapy. A total of 506 patients were recruited between May 2018 and December 2019 (mean age 70.3 years, 42.1% women). Patients were mainly concerned about the risks of AMI (ß: -1.03; 95% CI: -1.31, -0.75; p < 0.001), stroke or systemic embolism (ß: -0.81; 95% CI: -0.90, -0.73; p < 0.001), and major bleeding (ß: -0.69; 95% CI: -0.78, -0.60; p < 0.001) and were willing to pay more, from up to 798 RMB to 536 RMB (124 to 83 USD) monthly. The least concerning attribute was frequency of blood monitoring (ß: -0.31; 95% CI: -0.39, -0.24; p < 0.001). Patients had more concerns about food-drug interactions even exceeding preferences on the 3 risks, if they had a history of stroke or bleeding (ß: -2.47; 95% CI: -3.92, -1.02; p < 0.001), recruited from Beijing (ß: -1.82; 95% CI: -2.56, -1.07; p < 0.001), or men (ß: -0.96; 95% CI: -1.36, -0.56; p < 0.001). Patients with lower educational attainment or lower income weighted all attributes lower, and their WTP for incremental efficacy and safety was minimal. Since the patients were recruited from 2 major hospitals from developed cities in China, further studies with better representative samples would be needed. CONCLUSIONS: Patients with AF in China were mainly concerned about the safety and effectiveness of oral anticoagulant therapy. The preference weighting on food-drug interaction varied widely. Patients with lower educational attainment or income levels and less experience of bleeding or stroke had more reservations about paying for oral anticoagulant therapies with superior efficacy, safety, and convenience of use.

Association Between Nonvitamin K Antagonist Oral Anticoagulants or Warfarin and Liver Injury: A Cohort Study.

INTRODUCTION: The risk of liver injury in patients with atrial fibrillation (AF) using nonvitamin K antagonist oral anticoagulants (NOACs) has not been previously examined using liver function tests as the primary outcome in the real-world setting. This study assessed the association between NOACs (dabigatran, rivaroxaban, and apixaban) and warfarin and the risk of liver injury, as defined by laboratory tests. METHODS: Patients newly diagnosed with AF and prescribed NOACs or warfarin between 2010 and 2016, identified using the Hong Kong Clinical Database and Reporting System, were matched on age, sex, health status scores, comorbidities, and medications by propensity score on a 1:1 ratio. Risk of liver injury, defined as laboratory test values >3 times the upper limit of normal of alanine aminotransferase or aspartate aminotransferase and >2 times the upper limit of normal of total bilirubin, was compared between NOAC and warfarin users using Cox proportional hazards regression. RESULTS: After propensity score matching, 13,698 patients were included, of which 141 (2.1%) NOAC users and 232 (3.4%) warfarin users developed liver injury. The hazard ratio (HR) for NOAC vs warfarin users was 0.71 (95% confidence interval: 0.58-0.89). When comparing individual NOACs, only dabigatran (hazard ratio: 0.63; 95% confidence interval: 0.48-0.82) was associated with a lower risk of liver injury. DISCUSSION: Among patients with AF, NOACs as a group, and dabigatran alone were associated with a significantly lower risk of laboratory-based liver injury when compared with warfarin. However, liver injury occurs more frequently in real-world practice than in NOAC randomized controlled trials.

Maternal quercetin administration during gestation and lactation decrease endoplasmic reticulum stress and related inflammation in the adult offspring of obese female rats.

PURPOSE: Maternal obesity is a risk factor for metabolic diseases in offspring. The aim of this study was to investigate whether quercetin administration during gestation and lactation could have any protective effect against the impact of maternal obesity on increased sensitivity to obesity and metabolic disorders in offspring. METHODS: Female Sprague-Dawley rats were fed a high-fat diet to induce obesity. Obese dams were administered 0, 50, 100 or 200 mg/kg body weight (BW) quercetin intragastrically during gestation and lactation. Normal weight dams were used as controls. The F1 generation was fed with a standard diet after weaning, and blood glucose, lipids and inflammatory factors were assessed. Expression of biomarkers involved endoplasmic reticulum (ER) stress, and related inflammatory pathways in liver and adipose tissues were analyzed at postnatal day 100. RESULTS: Maternal obesity resulted in increased birth weight, postnatal BW gain, hyperglycemia, hyperlipemia, hyperinsulinemia, increased serum levels of inflammatory factors, and up-regulated biomarkers involved in ER stress and related inflammatory pathways in the offspring. Maternal quercetin intervention (QI) had significant ameliorating effects on maternal blood lipids, especially cholesterol, which resulted in improved glucose metabolism and insulin sensitivity and alleviated ER stress and related inflammation in the grown offspring of obese dams. CONCLUSIONS: Maternal QI in obese dams during gestation and lactation reduced birth weight and postnatal BW gain in the offspring, and helped to improve insulin sensitivity and lipid metabolism of the mature offspring via reducing ER stress and related inflammation in the liver and adipose tissue.

Inflammo-immune perspective on the association of eight migraine risk factors with migraine: a multi-omics Mendelian randomization study.

Background: Migraine risk factors are associated with migraine susceptibility, yet their mechanisms are unclear. Evidence suggests a role for inflammatory proteins and immune cells in migraine pathogenesis. This study aimed to examine the inflammo-immune association between eight migraine risk factors and the disorder. Methods: This study utilized inverse variance weighted (IVW) method and colocalization analysis to explore potential causal relationships between eight migraine risk factors, migraine, 731 immune cells, and 91 circulating inflammatory proteins. Mediation Mendelian randomization (MR) was further used to confirm the mediating role of circulating inflammatory proteins and immune cells between the eight migraine risk factors and migraine. Results: Migraine risk factors are linked to 276 immune cells and inflammatory proteins, with cigarettes smoked per day strongly co-localized with CD33-HLA DR+ cells. Despite no co-localization, 23 immune cells/inflammatory proteins relate to migraine. Depression, all anxiety disorders, and sleep apnea are correlated with migraine, and all anxiety disorders are supported by strong co-localization evidence. However, the mediating effect of inflammatory proteins and immune cells between eight migraine risk factors and migraine has not been confirmed. Conclusion: We elucidate the potential causal relationships between eight migraine risk factors, migraine, immune cells, and inflammatory proteins, enhancing our understanding of the molecular etiology of migraine pathogenesis from an inflammatory-immune perspective.

Lipid-lowering drugs and inflammatory bowel disease's risk: a drug-target Mendelian randomization study.

BACKGROUND: Inflammatory bowel disease (IBD) has been associated with lipid-lowering drugs in observational studies. Drug-target Mendelian randomization (MR) was utilized in this study to examine the causal relationship between lipid-lowering drugs and incidence of IBD, aiming to identify new preventive uses for the drugs. METHODS: We identified instrumental variables for three classes of lipid-lowering drugs: HMGCR inhibitors, PCSK9 inhibitors, and NPC1L1 inhibitors, using data from the Global Lipids Genetics Consortium. Summary statistics of IBD were obtained from UK Inflammatory Bowel Disease Genetics. The summary-data-based MR (SMR) and the inverse-variance weighted (IVW) MR were used for analysis. Sensitivity analyses were performed by conventional MR methods. RESULTS: The SMR analysis showed no significant genetic association between increased gene expression of HMGCR, PCSK9, and NPC1L1 and IBD, Crohn's disease (CD) and ulcerative colitis (UC). According to IVW-MR analysis, increased HMGCR expression is associated with a reduced risk of IBD (OR = 0.73, 95% confidence interval (CI) 0.59-0.90, P = 0.003) and CD (OR = 0.75, 95% CI 0.57-0.97, P = 0.03), but not with UC. Additionally, increased NPC1L1 gene expression was associated with elevated risk of IBD (OR = 1.60, 95% CI 1.07-2.40, P = 0.023), but not with CD and UC. However, no significant causal relationships were found between PCSK9 gene expression and IBD, CD, and UC. The sensitivity analysis demonstrated no evidence of heterogeneity or pleiotropy among the reported results. CONCLUSIONS: The heightened expression of genetic variations in HMGCR inhibitor targets could potentially reduce the risk of IBD and CD, while genetic variation in the expression of NPC1L1 targets was positively associated with IBD.

Advancements in strategies for overcoming the blood-brain barrier to deliver brain-targeted drugs.

The blood-brain barrier is known to consist of a variety of cells and complex inter-cellular junctions that protect the vulnerable brain from neurotoxic compounds; however, it also complicates the pharmacological treatment of central nervous system disorders as most drugs are unable to penetrate the blood-brain barrier on the basis of their own structural properties. This dramatically diminished the therapeutic effect of the drug and compromised its biosafety. In response, a number of drugs are often delivered to brain lesions in invasive ways that bypass the obstruction of the blood-brain barrier, such as subdural administration, intrathecal administration, and convection-enhanced delivery. Nevertheless, these intrusive strategies introduce the risk of brain injury, limiting their clinical application. In recent years, the intensive development of nanomaterials science and the interdisciplinary convergence of medical engineering have brought light to the penetration of the blood-brain barrier for brain-targeted drugs. In this paper, we extensively discuss the limitations of the blood-brain barrier on drug delivery and non-invasive brain-targeted strategies such as nanomedicine and blood-brain barrier disruption. In the meantime, we analyze their strengths and limitations and provide outlooks on the further development of brain-targeted drug delivery systems.

MACTFusion: Lightweight Cross Transformer for Adaptive Multimodal Medical Image Fusion.

Multimodal medical image fusion aims to integrate complementary information from different modalities of medical images. Deep learning methods, especially recent vision Transformers, have effectively improved image fusion performance. However, there are limitations for Transformers in image fusion, such as lacks of local feature extraction and cross-modal feature interaction, resulting in insufficient multimodal feature extraction and integration. In addition, the computational cost of Transformers is higher. To address these challenges, in this work, we develop an adaptive cross-modal fusion strategy for unsupervised multimodal medical image fusion. Specifically, we propose a novel lightweight cross Transformer based on cross multi-axis attention mechanism. It includes cross-window attention and cross-grid attention to mine and integrate both local and global interactions of multimodal features. The cross Transformer is further guided by a spatial adaptation fusion module, which allows the model to focus on the most relevant information. Moreover, we design a special feature extraction module that combines multiple gradient residual dense convolutional and Transformer layers to obtain local features from coarse to fine and capture global features. The proposed strategy significantly boosts the fusion performance while minimizing computational costs. Extensive experiments, including clinical brain tumor image fusion, have shown that our model can achieve clearer texture details and better visual quality than other state-of-the-art fusion methods.

Working status and risk of Alzheimer's disease: A Mendelian randomization study.

BACKGROUND: Alzheimer's disease (AD) has become a common illness affecting the elderly, adding to society's social and financial burden. We used two-sample Mendelian randomization (MR) in this study to determine the association between working status and AD. METHODS: We performed a two-sample MR analysis. The genetic associations were derived from the UK Biobank (n = 263,615) and the International Genomics of Alzheimer's Project (n = 63,926). Inverse variance weighted (IVW), MR-Egger, and weighted median were used in the MR analysis. The funnel plot, Cochran's Q test, MR-Egger intercept test, and leave-one-out analysis were used in sensitivity analyses. Further risk factor analyses were carried out to look into the potential mediators. RESULTS: Jobs involve heavy manual or physical work (OR = 2.13, 95%CI 1.36-3.36; p = .0011), job involves mainly walking or standing (OR = 1.74, 95%CI 1.19-2.54; p = .004), and job involves shift work (OR = 2.78, 95%CI 1.14-6.80; p = .02) increased the risk of AD in the IVW analysis. There was no heterogeneity and no horizontal pleiotropy in the sensitivity analysis. Risk factor analysis suggested that each of the above association may be mediated by different risk factors. CONCLUSION: Our study adds to the evidence that the development of AD is associated with the working status (job involves heavy manual or physical work, job involves mainly walking or standing, and job involves shift work) by using extensive human genetic data.

Sleep traits and risk of end-stage renal disease: a mendelian randomization study.

BACKGROUND: Epidemiological evidence relating sleep disorders to end-stage renal disease (ESRD) has been obscure. The present study is sought to examine the association between sleep traits and ESRD. METHODS: For this analysis, we selected genetic instruments for sleep traits from published genome-wide association studies (GWAS). As instrumental variables, independent genetic variations linked with seven sleep-related features (sleep duration, getting up in the morning, daytime napping, chronotype of morning/evening person, sleeplessness/insomnia, non-snoring, and daytime dozing) were chosen. A two-sample Mendelian randomization (TSMR) study was conducted to assess the causal relationship between sleep traits and ESRD (N = 33,061). The reverse MR analysis subsequently determined the causal relationship between ESRD and sleep traits. The causal effects were estimated using inverse variance weighted, MR-Egger, weighted median. To conduct sensitivity studies, Cochran's Q test, MR-Egger intercept test, MR-PRESSO, leave-one-out analysis, and funnel plot were used. To study the potential mediators, multivariable mendelian randomization analyses were undertaken further. RESULTS: Genetically predicted sleeplessness/ insomnia (OR = 6.11, 95%CI 1.00-37.3, P = 0.049, FDR = 0.105), getting up in the morning easily(OR = 0.23, 95%CI 0.063-0.85; P = 0.0278, FDR = 0.105), non-snoring (OR = 4.76E-02, 95%CI 2.29E-03-0.985, P = 0.0488, FDR = 0.105) was suggestively associated with the risk of ESRD. However, we found no evidence favoring a causal association between other sleep traits and ESRD through the IVW method. CONCLUSION: The present TSMR found no strong evidence of a bidirectional causal association between genetically predicted sleep traits and ESRD.

Causal associations between inflammatory bowel disease and primary biliary cholangitis: a two-sample bidirectional Mendelian randomization study.

Inflammatory bowel disease (IBD) has been reported to be associated with hepatobiliary diseases. Previous observational and Mendelian randomization (MR) studies have suggested a causal association between IBD and primary sclerosing cholangitis (PSC). However, it is unclear whether IBD has a causal association with primary biliary cholangitis (PBC): another autoimmune liver disease. We obtained genome-wide association study (GWAS) statistics from published GWASs for PBC, UC, and CD. We screened qualified instrumental variables (IVs) based on the three major assumptions of MR. To determine the causal relationships between UC or CD and PBC, two-sample MR analyses were performed using inverse variance-weighted (IVW), MR-Egger, and weighted median (WM) methods, and sensitivity analyses were conducted to validate the robustness of the results. We also conducted reverse MR analysis to reveal the causal association between PBC and UC or CD. UC was associated with a higher risk of PBC (OR 1.35, 95% CI 1.05-1.73, P = 0.02) in the IVW method, and CD was associated with an increased risk of PBC (OR 1.18, 95% CI 1.03-1.36, P = 0.02) in IVW. The weighted median and MR-Egger regression of both diseases showed a consistent direction but were not statistically significant. Results of the reverse MR analysis did not suggest genetic susceptibility that PBC was associated with an increased risk of UC (OR 1.05, 95% CI 0.95-1.17, P = 0.34) or CD (OR 1.1, 95% CI 0.99-1.20, P = 0.06). The present study revealed that IBD subtypes could increase the incidence of PBC, but in turn, PBC did not increase the incidence of IBD subtypes. Understanding that IBD and PBC constitute mutual risk factors can help with the clinical management of both diseases.

Investigating association between gut microbiota and sarcopenia-related traits: a Mendelian randomization study.

Background: Observational studies have indicated a potential link between gut microbiota and sarcopenia. However, the underlying mechanisms and a causal relationship have not been established. Thus, the objective of this study is to examine the possible causal association between gut microbiota and sarcopenia-related traits, including low hand-grip strength and appendicular lean mass (ALM), to shed light on the gut-muscle axis. Methods: To investigate the potential impact of gut microbiota on low hand-grip strength and ALM, we utilized a two-sample Mendelian randomization (MR) approach. Summary statistics were obtained from genome-wide association studies of gut microbiota, low hand-grip strength, and ALM. The primary MR analysis employed the random-effects inverse-variance weighted (IVW) method. To assess the robustness, we conducted sensitivity analyses using the MR pleiotropy residual sum and outlier (MR-PRESSO) test to detect and correct for horizontal pleiotropy, as well as the MR-Egger intercept test and leave-one-out analysis. Results: Alcaligenaceae, Family XIII, and Paraprevotella were positively associated with the risk of low hand-grip strength (P-values < 0.05). Streptococcaceae were negatively associated with low hand-grip strength (P-values < 0.05). Eight bacterial taxa (Actinomycetales, Actinomycetaceae, Bacteroidaceae, Porphyromonadaceae, Prevotellaceae, Bacteroides, Marvinbryantia, and Phascolarctobacterium) were associated with a higher risk of ALM (P-values < 0.05). Eubacterium fissicatena group was negatively associated with ALM (P-values < 0.05). Conclusion: We found several gut microbiota components causally associated with sarcopenia-related traits. Our findings provided insights into novel strategies for the prevention and treatment of sarcopenia through the regulation of the gut microbiota, contributing to a better understanding of the gut-muscle axis.

A bistable autoregulatory module in the developing embryo commits cells to binary expression fates.

Bistable autoactivation has been proposed as a mechanism for cells to adopt binary fates during embryonic development. However, it is unclear whether the autoactivating modules found within developmental gene regulatory networks are bistable, unless their parameters are quantitatively determined. Here, we combine in vivo live imaging with mathematical modeling to dissect the binary cell fate dynamics of the fruit fly pair-rule gene fushi tarazu (ftz), which is regulated by two known enhancers: the early (non-autoregulating) element and the autoregulatory element. Live imaging of transcription and protein concentration in the blastoderm revealed that binary Ftz fates are achieved as Ftz expression rapidly transitions from being dictated by the early element to the autoregulatory element. Moreover, we discovered that Ftz concentration alone is insufficient to activate the autoregulatory element, and that this element only becomes responsive to Ftz at a prescribed developmental time. Based on these observations, we developed a dynamical systems model and quantitated its kinetic parameters directly from experimental measurements. Our model demonstrated that the ftz autoregulatory module is indeed bistable and that the early element transiently establishes the content of the binary cell fate decision to which the autoregulatory module then commits. Further in silico analysis revealed that the autoregulatory element locks the Ftz fate quickly, within 35 min of exposure to the transient signal of the early element. Overall, our work confirms the widely held hypothesis that autoregulation can establish developmental fates through bistability and, most importantly, provides a framework for the quantitative dissection of cellular decision-making.

Quantitative phase imaging of living red blood cells combining digital holographic microscopy and deep learning.

Digital holographic microscopy as a non-contacting, non-invasive, and highly accurate measurement technology, is becoming a valuable method for quantitatively investigating cells and tissues. Reconstruction of phases from a digital hologram is a key step in quantitative phase imaging for biological and biomedical research. This study proposes a two-stage deep convolutional neural network named VY-Net, to realize the effective and robust phase reconstruction of living red blood cells. The VY-Net can obtain the phase information of an object directly from a single-shot off-axis digital hologram. We also propose two new indices to evaluate the reconstructed phases. In experiments, the mean of the structural similarity index of reconstructed phases can reach 0.9309, and the mean of the accuracy of reconstructions of reconstructed phases is as high as 91.54%. An unseen phase map of a living human white blood cell is successfully reconstructed by the trained VY-Net, demonstrating its strong generality.