Multi-scale feature aggregation and fusion network with self-supervised multi-level perceptual loss for textures preserving low-dose CT denoising.

OBJECTIVE: The textures and detailed structures in computed tomography (CT) images are highly desirable for clinical diagnosis. This study aims to expand the current body of work on textures and details preserving convolutional neural networks for low-dose CT (LDCT) image denoising task. APPROACH: This study proposed a novel Multi-scale Feature Aggregation and Fusion network (MFAF-net) for LDCT image denoising. Specifically, we proposed a Multi-scale Residual Feature Aggregation Module (MRFAM) to characterize multi-scale structural information in CT images, which captures regional-specific inter-scale variations using learned weights. We further proposed a Cross-level Feature Fusion Module (CFFM) to integrate cross-level features, which adaptively weights the contributions of features from encoder to decoder by using a Spatial Pyramid Attention (SPA) mechanism. Moreover, we proposed a Self-supervised Multi-level Perceptual Loss Module (SMPLM) to generate multi-level auxiliary perceptual supervision for recovery of salient textures and structures of tissues and lesions in CT images, which takes advantage of abundant semantic information at various levels. We introduced parameters for the perceptual loss to adaptively weight the contributions of auxiliary features of different levels and we also introduced an automatic parameter tuning strategy for these parameters. MAIN RESULTS: Extensive experimental studies were performed to validate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method can achieve better performance on both fine textures preservation and noise suppression for CT image denoising task compared with other competitive CNN based methods. SIGNIFICANCE: The proposed MFAF-net takes advantage of multi-scale receptive fields, cross-level features integration and self-supervised multi-level perceptual loss, enabling more effective recovering of fine textures and detailed structures of tissues and lesions in CT images.

Comparative metabolomics reveals serum metabolites changes in goats during different developmental stages.

Goats can provide meat, milk and skins for humans and are livestock with high economic benefits. However, despite their economic significance, the comprehensive analysis of goats' serum metabolic profile and its intricate alterations throughout their developmental journey remains conspicuously absent. To investigate the stage-specificity and dynamic change characteristics of metabolites during the growth and development of goats, this study compared the alterations in serum hormone levels and serum biochemical markers across different developmental stages of female goats (1, 60, 120 and 180 days old; n = 5). Additionally, a serum untargeted LC-MS metabolomics analysis was conducted. A total of 504 DAMs were identified with age. The results indicated that PE, PC, Lyso-PE, Lyso-PC and FAFHA may play important roles in lipid metabolism in goats after birth. Weighted gene co-expression network analysis (WGCNA) identified two metabolite modules (Turquoise and Yellow) and key metabolites within these modules that were significantly associated with phenotypic features. L-carnitine may be a metabolite related to muscle development in goats. The findings of this study demonstrate notable variations in serum metabolites across distinct developmental phases in goats. Lipids and organic acids play important roles in different developmental stages of goats.

Dynamic Assembly of DNA Nanostructures in Cancer Cells Enables the Coupling of Autophagy Activating and Real-Time Tracking.

Developing dynamic nanostructures for in situ regulation of biological processes inside living cells is of great importance in biomedical research. Herein we report the cascaded assembly of Y-shaped branched DNA nanostructure (YDN) during intracellular autophagy. YDN contains one arm with semi-i-motif sequence and Cy3-BHQ2, and another arm with an apurinic/apyrimidinic (AP) site and Cy5-BHQ3. Upon uptake by cancer cells, intermolecular i-motif structures are formed in response to lysosomal H+, causing the formation of YDN-dimer and the recovery of Cy3 fluorescence; when escapes occur from the lysosome to the cytoplasm, the YDN-dimer responds to the overexpressed APE1, leading to the assembly of YDN into the DNA network and the fluorescence recovery of Cy5. Simultaneously, the cascaded assembly activates autophagy, and thus the process of assembly of YDN and autophagy flux can be spatiotemporally coupled. This work illustrates the potential of DNA nanostructures for the in situ regulation of intracellular dynamic events with spatiotemporal control.

Mediating Effect of Tobacco Dependence on the Association Between Maternal Smoking During Pregnancy and Chronic Obstructive Pulmonary Disease: Case-Control Study.

BACKGROUND: Maternal smoking during pregnancy (MSDP) is a known risk factor for offspring developing chronic obstructive pulmonary disease (COPD), but the underlying mechanism remains unclear. OBJECTIVE: This study aimed to explore whether the increased COPD risk associated with MSDP could be attributed to tobacco dependence (TD). METHODS: This case-control study used data from the nationwide cross-sectional China Pulmonary Health study, with controls matched for age, sex, and smoking status. TD was defined as smoking within 30 minutes of waking, and the severity of TD was assessed using the Fagerstrom Test for Nicotine Dependence. COPD was diagnosed when the ratio of forced expiratory volume in 1 second to forced vital capacity was <0.7 in a postbronchodilator pulmonary function test according to the 2017 Global Initiative for Chronic Obstructive Lung Disease criteria. Logistic regression was used to examine the correlation between MSDP and COPD, adjusting for age, sex, BMI, educational attainment, place of residence, ethnic background, occupation, childhood passive smoking, residential fine particulate matter, history of childhood pneumonia or bronchitis, average annual household income, and medical history (coronary heart disease, hypertension, and diabetes). Mediation analysis examined TD as a potential mediator in the link between MSDP and COPD risk. The significance of the indirect effect was assessed through 1000 iterations of the "bootstrap" method. RESULTS: The study included 5943 participants (2991 with COPD and 2952 controls). Mothers of the COPD group had higher pregnancy smoking rates (COPD: n=305, 10.20%; controls: n=211, 7.10%; P<.001). TD was more prevalent in the COPD group (COPD: n=582, 40.40%; controls: n=478, 33.90%; P<.001). After adjusting for covariates, MSDP had a significant effect on COPD (ß=.097; P<.001). There was an association between MSDP and TD (ß=.074; P<.001) as well as between TD and COPD (ß=.048; P=.007). Mediation analysis of TD in the MSDP-COPD association showed significant direct and indirect effects (direct: ß=.094; P<.001 and indirect: ß=.004; P=.03). The indirect effect remains present in the smoking population (direct: ß=.120; P<.001 and indirect: ß=.002; P=.03). CONCLUSIONS: This study highlighted the potential association between MSDP and the risk of COPD in offspring, revealing the mediating role of TD in this association. These findings contribute to a deeper understanding of the impact of prenatal tobacco exposure on lung health, laying the groundwork for the development of relevant prevention and treatment strategies.

Effects of long-term exposure to ambient fine particulate matter and its specific components on blood pressure and hypertension incidence.

BACKGROUND: Epidemiological evidence on the association of PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 µm) and its specific components with hypertension and blood pressure is limited. METHODS: We applied information of participants from the World Health Organization's (WHO) Study on Global Ageing and Adult Health (SAGE) to estimate the associations of long-term PM2.5 mass and its chemical components exposure with blood pressure (BP) and hypertension incidence in Chinese adults ≥ 50 years during 2007-2018. Generalized linear mixed model and Cox proportional hazard model were applied to investigate the effects of PM2.5 mass and its chemical components on the incidence of hypertension and BP, respectively. RESULTS: Each interquartile range (IQR = 16.80 µg/m3) increase in the one-year average of PM2.5 mass concentration was associated with a 17 % increase in the risk of hypertension (HR = 1.17, 95 % CI: 1.10, 1.24), and the population attributable fraction (PAF) was 23.44 % (95 % CI: 14.69 %, 31.55 %). Each IQR µg/m3 increase in PM2.5 exposure was also related to increases of systolic blood pressure (SBP) by 2.54 mmHg (95 % CI:1.99, 3.10), and of diastolic blood pressure (DBP) by 1.36 mmHg (95 % CI: 1.04, 1.68). Additionally, the chemical components of SO42-, NO3-, NH4+, OM, and BC were also positively associated with an increased risk of hypertension incidence and elevated blood pressure. CONCLUSIONS: These results indicate that long-term exposure to PM2.5 mass and its specific components may be major drivers of escalation in hypertension diseases.

Frailty Trajectories in Chinese Older Adults: Evidence From the China Health and Retirement Longitudinal Study.

Background and Objectives: The individual heterogeneity in the progression of frailty has not been fully disclosed. Studies on frailty trajectories in Chinese older adults are rare and lack evidence suggesting that the frailty trajectories follow similar patterns to those in other countries. This study aims to identify distinct frailty trajectories in a nationwide cohort of community-dwelling older adults in China and explore the relationship between demographic, socioeconomic, and behavioral factors, and frailty trajectories. Research Design and Methods: We included an analytical sample of 8,993 individuals aged 50 and older from the China Health and Retirement Longitudinal Study. We used group-based trajectory models to identify patterns of frailty trajectories over time. Multinomial logistic regression was used to estimate the relationship between demographic, socioeconomic, and behavioral factors, and group membership. Results: Three frailty trajectories were identified: "Low and stable trajectory" (56.8% of the respondents), "Moderate and increasing trajectory" (34.4%), and "High and increasing trajectory" (8.8%). Older age (odds ratio [OR] = 7.37, 95% confidence interval [CI]: 5.90-9.20), being female (OR = 1.79, 95% CI: 1.42-2.27), no formal education (OR = 4.91, 95% CI: 2.33-10.36), living in rural areas (OR = 1.22, 95% CI: 1.01-1.47), low level of physical activity (OR = 2.65, 95% CI: 1.94-3.62), and residing in Northeast China (OR = 3.53, 95% CI: 2.56-4.88) were associated with the rapid progression of frailty, whereas moderate alcohol consumption appears to be associated with low and stable frailty trajectory (OR = 0.45, 95% CI: 0.35-0.58). Discussion and Implications: The findings of the study emphasize a significant number of older adults with moderate and increasing as well as high and increasing frailty trajectories in China, which is cause for concern.

Early microbial intervention reshapes phenotypes of newborn Bos taurus through metabolic regulations.

BACKGROUND: The rumen of neonatal calves has limited functionality, and establishing intestinal microbiota may play a crucial role in their health and performance. Thus, we aim to explore the temporal colonization of the gut microbiome and the benefits of early microbial transplantation (MT) in newborn calves. RESULTS: We followed 36 newborn calves for 2 months and found that the composition and ecological interactions of their gut microbiomes likely reached maturity 1 month after birth. Temporal changes in the gut microbiome of newborn calves are widely associated with changes in their physiological statuses, such as growth and fiber digestion. Importantly, we observed that MT reshapes the gut microbiome of newborns by altering the abundance and interaction of Bacteroides species, as well as amino acid pathways, such as arginine biosynthesis. Two-year follow-up of those calves further showed that MT improves their later milk production. Notably, MT improves fiber digestion and antioxidant capacity of newborns while reducing diarrhea. MT also contributes to significant changes in the metabolomic landscape, and with putative causal mediation analysis, we suggest that altered gut microbial composition in newborns may influence physiological status through microbial-derived metabolites. CONCLUSIONS: Our study provides a metagenomic and metabolomic atlas of the temporal development of the gut microbiome in newborn calves. MT can alter the gut microbiome of newborns, leading to improved physiological status and later milk production. The data may help develop strategies to manipulate the gut microbiota during early life, which may be relevant to the health and production of newborn calves.

Image segmentation of mouse eye in vivo with optical coherence tomography based on Bayesian classification.

OBJECTIVES: Optical coherence tomography (OCT) is a new imaging technology that uses an optical analog of ultrasound imaging for biological tissues. Image segmentation plays an important role in dealing with quantitative analysis of medical images. METHODS: We have proposed a novel framework to deal with the low intensity problem, based on the labeled patches and Bayesian classification (LPBC) model. The proposed method includes training and testing phases. During the training phase, firstly, we manually select the sub-images of background and Region of Interest (ROI) from the training image, and then extract features by patches. Finally, we train the Bayesian model with the features. The segmentation threshold of each patch is computed by the learned Bayesian model. RESULTS: In addition, we have collected a new dataset of mouse eyes in vivo with OCT, named MEVOCT, which can be found at URL https://17861318579.github.io/LPBC. MEVOCT consists of 20 high-resolution images. The resolution of every image is 2048 × 2048 pixels. CONCLUSIONS: The experimental results demonstrate the effectiveness of the LPBC method on the new MEVOCT dataset. The ROI segmentation is of great importance for the distortion correction.

Deep-learning segmentation method for optical coherence tomography angiography in ophthalmology.

PURPOSE: The optic disc and the macular are two major anatomical structures in the human eye. Optic discs are associated with the optic nerve. Macular mainly involves degeneration and impaired function of the macular region. Reliable optic disc and macular segmentation are necessary for the automated screening of retinal diseases. METHODS: A swept-source OCTA system was designed to capture OCTA images of human eyes. To address these segmentation tasks, first, we constructed a new Optic Disc and Macula in fundus Image with optical coherence tomography angiography (OCTA) dataset (ODMI). Second, we proposed a Coarse and Fine Attention-Based Network (CFANet). RESULTS: The five metrics of our methods on ODMI are 98.91 % , 98.47 % , 89.77 % , 98.49 % , and 89.77 % , respectively. CONCLUSIONS: Experimental results show that our CFANet has achieved good performance on segmentation for the optic disc and macula in OCTA.

Characterization of MicroRNA expression profiles in the ovarian tissue of goats during the sexual maturity period.

BACKGROUND: The ovary is an important reproductive organ in mammals, and its development directly affects the sexual maturity and reproductive capacity of individuals. MicroRNAs (miRNAs) are recognized as regulators of reproductive physiological processes in various animals and have been shown to regulate ovarian development through typical targeting and translational repression. However, little is known about the regulatory role of miRNAs in ovarian tissue development during sexual maturity in goats. To comprehensively profile the different physiological stages of sexual maturation in goats, we performed small-RNA sequencing of ovarian tissue samples collected at four specific time points (1 day after birth (D1), 2 months old (M2), 4 months old (M4), and 6 months old (M6)). In addition, we used ELISAs to measure serum levels of reproductive hormones to study their temporal changes. RESULTS: The results showed that serum levels of gonadotropin-releasing hormone, follicle-stimulating hormone, luteinizing hormone, oestradiol, progesterone, oxytocin, and prolactin were lower in goats at the D1 stage than at the other three developmental stages (P < 0.05). The secretion patterns of these seven hormones show a similar trend, with hormone levels reaching their peaks at 4 months of age. A total of 667 miRNAs were detected in 20 libraries, and 254 differentially expressed miRNAs and 3 groups of miRNA clusters that had unique expression patterns were identified (|log2-fold change|> 1, FDR < 0.05) in the 6 comparison groups. RT‒qPCR was employed to confirm that the expression pattern of the 15 selected miRNAs was consistent with the Illumina sequencing results. Gene ontology analyses revealed significant enrichment of GO terms such as cell proliferation regulation, epithelial cell development, and amino acid transport, as well as important signaling pathways including the MAPK signaling pathway, the PI3K-Akt signaling pathway, and the oestrogen signaling pathway. Further miRNA‒mRNA regulation network analysis revealed that 8 differentially expressed miRNAs (chi-miR-1343, chi-miR-328-3p, chi-miR-877-3p, chi-miR-296-3p, chi-miR-128-5p, chi-miR-331-3p, chi-miR-342-5p and chi-miR-34a) have important regulatory roles in ovarian cell proliferation, hormone secretion and metabolism-related biological processes. CONCLUSIONS: Overall, our study investigated the changes in serum hormone and miRNA levels in the ovaries. These data provide a valuable resource for understanding the molecular regulatory mechanisms of miRNAs in ovarian tissue during the sexual maturity period in goats.

Structure-preserving low-dose computed tomography image denoising using a deep residual adaptive global context attention network.

Background: Low-dose computed tomography (LDCT) scans can effectively reduce the radiation damage to patients, but this is highly detrimental to CT image quality. Deep convolutional neural networks (CNNs) have shown their potential in improving LDCT image quality. However, the conventional CNN-based approaches rely fundamentally on the convolution operations, which are ineffective for modeling the correlations among nonlocal similar structures and the regionally distinct statistical properties in CT images. This modeling deficiency hampers the denoising performance for CT images derived in this manner. Methods: In this paper, we propose an adaptive global context (AGC) modeling scheme to describe the nonlocal correlations and the regionally distinct statistics in CT images with negligible computation load. We further propose an AGC-based long-short residual encoder-decoder (AGC-LSRED) network for efficient LDCT image noise artifact-suppression tasks. Specifically, stacks of residual AGC attention blocks (RAGCBs) with long and short skip connections are constructed in the AGC-LSRED network, which allows valuable structural and positional information to be bypassed through these identity-based skip connections and thus eases the training of the deep denoising network. For training the AGC-LSRED network, we propose a compound loss that combines the L1 loss, adversarial loss, and self-supervised multi-scale perceptual loss. Results: Quantitative and qualitative experimental studies were performed to verify and validate the effectiveness of the proposed method. The simulation experiments demonstrated the proposed method exhibits the best result in terms of noise suppression [root-mean-square error (RMSE) =9.02; peak signal-to-noise ratio (PSNR) =33.17] and fine structure preservation [structural similarity index (SSIM) =0.925] compared with other competitive CNN-based methods. The experiments on real data illustrated that the proposed method has advantages over other methods in terms of radiologists' subjective assessment scores (averaged scores =4.34). Conclusions: With the use of the AGC modeling scheme to characterize the structural information in CT images and of residual AGC-attention blocks with long and short skip connections to ease the network training, the proposed AGC-LSRED method achieves satisfactory results in preserving fine anatomical structures and suppressing noise in LDCT images.

Combined effect of ozone and household air pollution on COPD in people aged less than 50 years old.

OBJECTIVES: Air pollution has been suggested as an important risk factor for chronic obstructive pulmonary disease (COPD); however, evidence of interactive effects on COPD between different factors was sparse, especially for young adults. We aimed to assess the combined effects of ambient ozone (O3) and household air pollution on COPD in young individuals. METHODS: We conducted a population-based study of residents aged 15-50 years in the low-income and middle-income regions of western China. We used multivariable logistic regression models to examine the associations between long-term ozone exposure and COPD in young individuals. RESULTS: A total of 6537 young cases were identified among the participants, with a COPD prevalence rate of 7.8 (95% CI 7.2% to 8.5%), and most young COPD individuals were asymptomatic. Exposure to household air pollution was associated with COPD in young patients after adjustment for other confounding factors (OR 1.82, 95% CI 1.41 to 2.37). We also found positive associations of COPD with O3 per IQR increase of 20 ppb (OR 1.92, 95% CI 1.59 to 2.32). The individual effects of household air pollution and O3 were 1.68 (95% CI 1.18 to 2.46) and 1.55 (95% CI 0.99 to 2.43), respectively, while their joint effect was 3.28 (95% CI 2.35 to 4.69) with the relative excess risk due to interaction of 1.05 (95% CI 0.33 to 1.78). CONCLUSIONS: This study concludes that exposure to ambient O3 and household air pollution might be important risk factors for COPD among young adults, and simultaneous exposure to high levels of the two pollutants may intensify their individual effects.

Telomerase-Mediated Self-Assembly of DNA Network in Cancer Cells Enabling Mitochondrial Interference.

The precise control of the artificially induced reactions inside living cells is emerging as an effective strategy for the regulation of cell functions. Nevertheless, the manipulation of the assembly of exogenous molecules into artificial architectures in response to intracellular-specific signals remains a grand challenge. Herein, we achieve the precise self-assembly of deoxyribonucleic acid (DNA) network inside cancer cells, specifically responding to telomerase, and realize effective mitochondrial interference and the consequent regulation of cellular behaviors. Two functional DNA modules were designed: a mitochondria-targeting branched DNA and a telomerase-responsive linear DNA. Upon uptake by cancer cells, the telomerase primer in linear DNA responded to telomerase, and a strand displacement reaction was triggered by the reverse transcription of telomerase, thus releasing a linker DNA from the linear DNA. The linker DNA afterward hybridized with the branched DNA to form a DNA network on mitochondria. The DNA network interfered with the function of mitochondria, realizing the apoptosis of cancer cells. This system was further administered in a nude mouse tumor model, showing remarkable suppression of tumor growth. We envision that the telomerase-mediated intracellular self-assembly of the DNA network provides a promising route for cancer therapy.

A task-unified network with transformer and spatial-temporal convolution for left ventricular quantification.

Quantification of the cardiac function is vital for diagnosing and curing the cardiovascular diseases. Left ventricular function measurement is the most commonly used measure to evaluate the function of cardiac in clinical practice, how to improve the accuracy of left ventricular quantitative assessment results has always been the subject of research by medical researchers. Although considerable efforts have been put forward to measure the left ventricle (LV) automatically using deep learning methods, the accurate quantification is yet a challenge work as a result of the changeable anatomy structure of heart in the systolic diastolic cycle. Besides, most methods used direct regression method which lacks of visual based analysis. In this work, a deep learning segmentation and regression task-unified network with transformer and spatial-temporal convolution is proposed to segment and quantify the LV simultaneously. The segmentation module leverages a U-Net like 3D Transformer model to predict the contour of three anatomy structures, while the regression module learns spatial-temporal representations from the original images and the reconstruct feature map from segmentation path to estimate the finally desired quantification metrics. Furthermore, we employ a joint task loss function to train the two module networks. Our framework is evaluated on the MICCAI 2017 Left Ventricle Full Quantification Challenge dataset. The results of experiments demonstrate the effectiveness of our framework, which achieves competitive cardiac quantification metric results and at the same time produces visualized segmentation results that are conducive to later analysis.

Joint Effects of Long-Term Exposure to Ambient Fine Particulate Matter and Ozone on Asthmatic Symptoms: Prospective Cohort Study.

BACKGROUND: The associations of long-term exposure to air pollutants in the presence of asthmatic symptoms remain inconclusive and the joint effects of air pollutants as a mixture are unclear. OBJECTIVE: We aimed to investigate the individual and joint associations of long-term exposure to ambient fine particulate matter (PM2.5) and daily 8-hour maximum ozone concentrations (MDA8 O3) in the presence of asthmatic symptoms in Chinese adults. METHODS: Data were derived from the World Health Organization Study on Global Ageing and Adult Health (WHO SAGE) cohort study among adults aged 50 years or older, which was implemented in 1 municipality and 7 provinces across China during 2007-2018. Annual average MDA8 O3 and PM2.5 at individual residential addresses were estimated by an iterative random forest model and a satellite-based spatiotemporal model, respectively. Participants who were diagnosed with asthma by a doctor or taking asthma-related therapies or experiencing related conditions within the past 12 months were recorded as having asthmatic symptoms. The individual associations of PM2.5 and MDA8 O3 with asthmatic symptoms were estimated by a Cox proportional hazards regression model, and the joint association was estimated by a quantile g-computation model. A series of subgroup analyses was applied to examine the potential modifications of some characteristics. We also calculated the population-attributable fraction (PAF) of asthmatic symptoms attributed to PM2.5 and MDA8 O3. RESULTS: A total of 8490 adults older than 50 years were included, and the average follow-up duration was 6.9 years. During the follow-up periods, 586 (6.9%) participants reported asthmatic symptoms. Individual effect analyses showed that the risk of asthmatic symptoms was positively associated with MDA8 O3 (hazard ratio [HR] 1.12, 95% CI 1.01-1.24, for per quantile) and PM2.5 (HR 1.18, 95% CI 1.05-1.31, for per quantile). Joint effect analyses showed that per equal quantile increment of MDA8 O3 and PM2.5 was associated with an 18% (HR 1.18, 95% CI 1.05-1.33) increase in the risk of asthmatic symptoms, and PM2.5 contributed more (68%) in the joint effects. The individual PAFs of asthmatic symptoms attributable to PM2.5 and MDA8 O3 were 2.86% (95% CI 0.17%-5.50%) and 4.83% (95% CI 1.42%-7.25%), respectively, while the joint PAF of asthmatic symptoms attributable to exposure mixture was 4.32% (95% CI 1.10%-7.46%). The joint associations were greater in participants with obesity, in urban areas, with lower family income, and who used unclean household cooking fuel. CONCLUSIONS: Long-term exposure to PM2.5 and MDA8 O3 may individually and jointly increase the risk of asthmatic symptoms, and the joint effects were smaller than the sum of individual effects. These findings informed the importance of joint associations of long-term exposure to air pollutants with asthma.

Cascade dynamic assembly/disassembly of DNA nanoframework enabling the controlled delivery of CRISPR-Cas9 system.

CRISPR-Cas9 has been explored as a therapeutic agent for down-regulating target genes; the controlled delivery of Cas9 ribonucleoprotein (RNP) is essential for therapeutic efficacy and remains a challenge. Here, we report cascade dynamic assembly/disassembly of DNA nanoframework (NF) that enables the controlled delivery of Cas9 RNP. NF was prepared with acrylamide-modified DNA that initiated cascade hybridization chain reaction (HCR). Through an HCR, single-guide RNA was incorporated to NF; simultaneously, the internal space of NF was expanded, facilitating the loading of Cas9 protein. NF was designed with hydrophilic acylamino and hydrophobic isopropyl, allowing dynamic swelling and aggregation. The responsive release of Cas9 RNP was realized by introducing disulfide bond-containing N,N-bis(acryloyl)cystamine that was specifically in response to glutathione of cancer cells, triggering the complete disassembly of NF. In vitro and in vivo investigations demonstrated the high gene editing efficiency in cancer cells, the hypotoxicity in normal cells, and notable antitumor efficacy in a breast cancer mouse model.

Unsupervised deep consistency learning adaptation network for cardiac cross-modality structural segmentation.

Deep neural networks have recently been succeessful in the field of medical image segmentation; however, they are typically subject to performance degradation problems when well-trained models are tested in another new domain with different data distributions. Given that annotated cross-domain images may inaccessible, unsupervised domain adaptation methods that transfer learnable information from annotated source domains to unannotated target domains with different distributions have attracted substantial attention. Many methods leverage image-level or pixel-level translation networks to align domain-invariant information and mitigate domain shift issues. However, These methods rarely perform well when there is a large domain gap. A new unsupervised deep consistency learning adaptation network, which adopts input space consistency learning and output space consistency learning to realize unsupervised domain adaptation and cardiac structural segmentation, is introduced in this paper The framework mainly includes a domain translation path and a cross-modality segmentation path. In domain translation path, a symmetric alignment generator network with attention to cross-modality features and anatomy is introduced to align bidirectional domain features. In the segmentation path, entropy map minimization, output probability map minimization and segmentation prediction minimization are leveraged to align the output space features. The model conducts supervised learning to extract source domain features and conducts unsupervised deep consistency learning to extract target domain features. Through experimental testing on two challenging cross-modality segmentation tasks, our method has robust performance compared to that of previous methods. Furthermore, ablation experiments are conducted to confirm the effectiveness of our framework.

Comparison of the lower limit of normal to the fixed ratio method for the diagnosis of airflow obstruction at high altitudes: a large cross-sectional survey of subjects living between 3000-4700 m above sea level.

BACKGROUND: There is no general agreement on the preferential use of a fixed ratio (FR) of forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) < 0.7 vs. the lower limit of normal (LLN) of FEV1/FVC to define airflow obstruction. Determining the impact of these different cut-off levels in people living at high altitudes has not been studied. We assessed the prevalence of airflow obstruction and its clinical characteristics in residents living at high altitude using a fixed ratio and the LLN of FEV1/FVC according to Global Lung Initiative 2012 (GLI) reference values. METHODS: Using a multistage stratified sampling method, 3702 participants (aged ≥ 15 years) living at an altitude of 3000-4700 m in Tibet were included. RESULTS: 11.4% and 7.7% of participants had airflow obstruction according to GLI-LLN and a fixed FEV1/FVC cut-off value, respectively. The participants in the FR-/LLN+ group were younger, predominantly female, more frequently exposed to household air pollution, and had a higher proportion of chronic obstructive pulmonary disease assessment test scores ≥ 10 than those in the FR-/LLN- group. They also had a significantly lower FEV1 and a higher frequency of small airway dysfunction. Compared with the participants of the FR+/LLN+ group, those in the FR-/LLN+ group showed no significant difference in the risk factors for airflow obstruction and respiratory symptoms, but had a lower prevalence of small airway dysfunction. CONCLUSIONS: Defining airflow obstruction according to LLN, instead of using an FR, identified younger individuals with more frequent clinical symptoms of airflow obstruction and small airway dysfunction.

DMCT-Net: dual modules convolution transformer network for head and neck tumor segmentation in PET/CT.

Objective.Accurate segmentation of head and neck (H&N) tumors is critical in radiotherapy. However, the existing methods lack effective strategies to integrate local and global information, strong semantic information and context information, and spatial and channel features, which are effective clues to improve the accuracy of tumor segmentation. In this paper, we propose a novel method called dual modules convolution transformer network (DMCT-Net) for H&N tumor segmentation in the fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) images.Approach.The DMCT-Net consists of the convolution transformer block (CTB), the squeeze and excitation (SE) pool module, and the multi-attention fusion (MAF) module. First, the CTB is designed to capture the remote dependency and local multi-scale receptive field information by using the standard convolution, the dilated convolution, and the transformer operation. Second, to extract feature information from different angles, we construct the SE pool module, which not only extracts strong semantic features and context features simultaneously but also uses the SE normalization to adaptively fuse features and adjust feature distribution. Third, the MAF module is proposed to combine the global context information, channel information, and voxel-wise local spatial information. Besides, we adopt the up-sampling auxiliary paths to supplement the multi-scale information.Main results.The experimental results show that the method has better or more competitive segmentation performance than several advanced methods on three datasets. The best segmentation metric scores are as follows: DSC of 0.781, HD95 of 3.044, precision of 0.798, and sensitivity of 0.857. Comparative experiments based on bimodal and single modal indicate that bimodal input provides more sufficient and effective information for improving tumor segmentation performance. Ablation experiments verify the effectiveness and significance of each module.Significance.We propose a new network for 3D H&N tumor segmentation in FDG-PET/CT images, which achieves high accuracy.

Identification and Characterization of circRNAs in Non-Lactating Dairy Goat Mammary Glands Reveal Their Regulatory Role in Mammary Cell Involution and Remodeling.

This study conducted transcriptome sequencing of goat-mammary-gland tissue at the late lactation (LL), dry period (DP), and late gestation (LG) stages to reveal the expression characteristics and molecular functions of circRNAs during mammary involution. A total of 11,756 circRNAs were identified in this study, of which 2528 circRNAs were expressed in all three stages. The number of exonic circRNAs was the largest, and the least identified circRNAs were antisense circRNAs. circRNA source gene analysis found that 9282 circRNAs were derived from 3889 genes, and 127 circRNAs' source genes were unknown. Gene Ontology (GO) terms, such as histone modification, regulation of GTPase activity, and establishment or maintenance of cell polarity, were significantly enriched (FDR < 0.05), which indicates the functional diversity of circRNAs' source genes. A total of 218 differentially expressed circRNAs were identified during the non-lactation period. The number of specifically expressed circRNAs was the highest in the DP and the lowest in LL stages. These indicated temporal specificity of circRNA expression in mammary gland tissues at different developmental stages. In addition, this study also constructed circRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) regulatory networks related to mammary development, immunity, substance metabolism, and apoptosis. These findings help understand the regulatory role of circRNAs in mammary cell involution and remodeling.