A Colorectal Coordinate-Driven Method for Colorectum and Colorectal Cancer Segmentation in Conventional CT Scans.

Automated colorectal cancer (CRC) segmentation in medical imaging is the key to achieving automation of CRC detection, staging, and treatment response monitoring. Compared with magnetic resonance imaging (MRI) and computed tomography colonography (CTC), conventional computed tomography (CT) has enormous potential because of its broad implementation, superiority for the hollow viscera (colon), and convenience without needing bowel preparation. However, the segmentation of CRC in conventional CT is more challenging due to the difficulties presenting with the unprepared bowel, such as distinguishing the colorectum from other structures with similar appearance and distinguishing the CRC from the contents of the colorectum. To tackle these challenges, we introduce DeepCRC-SL, the first automated segmentation algorithm for CRC and colorectum in conventional contrast-enhanced CT scans. We propose a topology-aware deep learning-based approach, which builds a novel 1-D colorectal coordinate system and encodes each voxel of the colorectum with a relative position along the coordinate system. We then induce an auxiliary regression task to predict the colorectal coordinate value of each voxel, aiming to integrate global topology into the segmentation network and thus improve the colorectum's continuity. Self-attention layers are utilized to capture global contexts for the coordinate regression task and enhance the ability to differentiate CRC and colorectum tissues. Moreover, a coordinate-driven self-learning (SL) strategy is introduced to leverage a large amount of unlabeled data to improve segmentation performance. We validate the proposed approach on a dataset including 227 labeled and 585 unlabeled CRC cases by fivefold cross-validation. Experimental results demonstrate that our method outperforms some recent related segmentation methods and achieves the segmentation accuracy in DSC for CRC of 0.669 and colorectum of 0.892, reaching to the performance (at 0.639 and 0.890, respectively) of a medical resident with two years of specialized CRC imaging fellowship.

The microbiome dynamics and interaction of endosymbiotic Symbiodiniaceae and fungi are associated with thermal bleaching susceptibility of coral holobionts.

The thermal bleaching percentage of coral holobionts shows interspecific differences under heat-stress conditions, which are closely related to the coral-associated microbiome. However, the ecological effects of community dynamics and interactions between Symbiodiniaceae and fungi on coral thermal bleaching susceptibility remain unclear. In this study, we analyzed the diversity, community structure, functions, and potential interaction of Symbiodiniaceae and fungi among 18 coral species from a high thermal bleaching risk atoll using next-generation sequencing. The results showed that heat-tolerant C3u sub-clade and Durusdinium dominated the Symbiodiniaceae community of corals and that there were no core amplicon sequence variants in the coral-associated fungal community. Fungal richness and the abundance of confirmed functional animal-plant pathogens were significantly positively correlated with the coral thermal bleaching percentage. Fungal indicators, including Didymellaceae, Chaetomiaceae, Schizophyllum, and Colletotrichum, were identified in corals. Each coral species had a complex Symbiodiniaceae-fungi interaction network (SFIN), which was driven by the dominant Symbiodiniaceae sub-clades. The SFINs of coral holobionts with low thermal bleaching susceptibility exhibited low complexity and high betweenness centrality. These results indicate that the extra heat tolerance of coral in Huangyan Island may be linked to the high abundance of heat-tolerant Symbiodiniaceae. Fungal communities have high interspecific flexibility, and the increase of fungal diversity and pathogen abundance was correlated with higher thermal bleaching susceptibility of corals. Moreover, fungal indicators were associated with the degrees of coral thermal bleaching susceptibility, including both high and intermediate levels. The topological properties of SFINs suggest that heat-tolerant coral have limited fungal parasitism and strong microbial network resilience.IMPORTANCEGlobal warming and enhanced marine heatwaves have led to a rapid decline in coral reef ecosystems worldwide. Several studies have focused on the impact of coral-associated microbiomes on thermal bleaching susceptibility in corals; however, the ecological functions and interactions between Symbiodiniaceae and fungi remain unclear. We investigated the microbiome dynamics and potential interactions of Symbiodiniaceae and fungi among 18 coral species in Huangyan Island. Our study found that the Symbiodiniaceae community of corals was mainly composed of heat-tolerant C3u sub-clade and Durusdinium. The increase in fungal diversity and pathogen abundance has close associations with higher coral thermal bleaching susceptibility. We first constructed an interaction network between Symbiodiniaceae and fungi in corals, which indicated that restricting fungal parasitism and strong interaction network resilience would promote heat acclimatization of corals. Accordingly, this study provides insights into the role of microorganisms and their interaction as drivers of interspecific differences in coral thermal bleaching.

SwinHR: Hemodynamic-powered hierarchical vision transformer for breast tumor segmentation.

Accurate and automated segmentation of breast tumors in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a critical role in computer-aided diagnosis and treatment of breast cancer. However, this task is challenging, due to random variation in tumor sizes, shapes, appearances, and blurred boundaries of tumors caused by inherent heterogeneity of breast cancer. Moreover, the presence of ill-posed artifacts in DCE-MRI further complicate the process of tumor region annotation. To address the challenges above, we propose a scheme (named SwinHR) integrating prior DCE-MRI knowledge and temporal-spatial information of breast tumors. The prior DCE-MRI knowledge refers to hemodynamic information extracted from multiple DCE-MRI phases, which can provide pharmacokinetics information to describe metabolic changes of the tumor cells over the scanning time. The Swin Transformer with hierarchical re-parameterization large kernel architecture (H-RLK) can capture long-range dependencies within DCE-MRI while maintaining computational efficiency by a shifted window-based self-attention mechanism. The use of H-RLK can extract high-level features with a wider receptive field, which can make the model capture contextual information at different levels of abstraction. Extensive experiments are conducted in large-scale datasets to validate the effectiveness of our proposed SwinHR scheme, demonstrating its superiority over recent state-of-the-art segmentation methods. Also, a subgroup analysis split by MRI scanners, field strength, and tumor size is conducted to verify its generalization. The source code is released on (https://github.com/GDPHMediaLab/SwinHR).

Predicting the pathological invasiveness in patients with a solitary pulmonary nodule via Shapley additive explanations interpretation of a tree-based machine learning radiomics model: a multicenter study.

Background: Radiomics models could help assess the benign and malignant invasiveness and prognosis of pulmonary nodules. However, the lack of interpretability limits application of these models. We thus aimed to construct and validate an interpretable and generalized computed tomography (CT) radiomics model to evaluate the pathological invasiveness in patients with a solitary pulmonary nodule in order to improve the management of these patients. Methods: We retrospectively enrolled 248 patients with CT-diagnosed solitary pulmonary nodules. Radiomic features were extracted from nodular region and perinodular regions of 3 and 5 mm. After coarse-to-fine feature selection, the radiomics score (radscore) was calculated using the least absolute shrinkage and selection operator logistic method. Univariate and multivariate logistic regression analyses were performed to determine the invasiveness-related clinicoradiological factors. The clinical-radiomics model was then constructed using the logistic and extreme gradient boosting (XGBoost) algorithms. The Shapley additive explanations (SHAP) method was then used to explain the contributions of the features. After removing batch effects with the ComBat algorithm, we assessed the generalization of the explainable clinical-radiomics model in two independent external validation cohorts (n=147 and n=149). Results: The clinical-radiomic XGBoost model integrating the radscore, CT value, nodule length, and crescent sign demonstrated better predictive performance than did the clinical-radiomics logistic model in assessing pulmonary nodule invasiveness, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.889 [95% confidence interval (CI), 0.848-0.927] in the training cohort. The SHAP algorithm illustrates the contribution of each feature in the final model. The specific model decision process was visualized using a tree-based decision heatmap. Satisfactory generalization performance was shown with AUCs of 0.889 (95% CI, 0.823-0.942) and 0.915 (95% CI, 0.851-0.963) in the two external validation cohorts. Conclusions: An interpretable and generalized clinical-radiomics model for predicting pulmonary nodule invasibility was constructed to help clinicians determine the invasiveness of pulmonary nodules and devise assessment strategies in an easily understandable manner.

Molecular characterization of MyD88 as a potential biomarker for pesticide-induced stress in Bombyx mori.

The widespread use of pesticides hampers the immune system of non-target organisms, however, there is a lack of common biomarkers to detect such effects. Myeloid differentiation primary response factor 88 (MyD88) is a crucial junction protein in the Toll-like receptor signaling pathway, which plays an important role in the inflammatory response. In this study, we investigated MyD88 as a potential biomarker for pesticide-induced stress. Phylogenetic analysis revealed that MyD88 was a conserved protein in the evolution of vertebrates and invertebrates. MyD88s usually have death domain (DD) and Toll/interleukin-1 receptor (TIR) domain. Bombyx mori (B. mori) is an important economic insect that is sensitive to toxic substances. We found microbial pesticides enhanced the expression level of MyD88 in B. mori. Transcriptome analysis demonstrated that MyD88 expression level was increased in the fatbody after dinotefuran exposure, a third-generation neonicotinoid pesticide. Moreover, the expression of MyD88 was upregulated in fatbody and midgut by imidacloprid, a first-generation neonicotinoid pesticide. Additionally, insect growth regulator (IGR) pesticides, such as methoprene and fenoxycarb, could induce MyD88 expression in the fatbody of B. mori. These results indicated that MyD88 is a potential biomarker for pesticide-induced stress in B. mori. This study provides novel insights into screening common biomarkers for multiple pesticide stresses and important implications for the development of more sustainable pest management strategies.

New insights into the mechanisms of diabetic kidney disease: Role of circadian rhythm and Bmal1.

It is common for diabetic kidney disease (DKD) to be complicated by abnormal blood glucose, blood lipids, and blood pressure rhythms. Thus, it is essential to examine diagnostic and treatment plans from the perspective of circadian disruption. This brief review discusses the clinical relevance of circadian rhythms in DKD and how the core clock gene encoding brain and muscle arnt-like protein 1 (BMAL1) functions owing to the importance of circadian rhythm disruption processes, including the excretion of urinary protein and irregular blood pressure, which occur in DKD. Exploring Bmal1 and its potential mechanisms and signaling pathways in DKD following contact with Sirt1 and NF-κB is novel and important. Finally, potential pharmacological and behavioral intervention strategies for DKD circadian rhythm disturbance are outlined. This review aids in unveiling novel, potential molecular targets for DKD based on circadian rhythms.

An MRI-based scoring system for pretreatment risk stratification in locally advanced rectal cancer.

BACKGROUND: Accurately assessing the risk of recurrence in patients with locally advanced rectal cancer (LARC) before treatment is important for the development of treatment strategies. The purpose of this study is to develop an MRI-based scoring system to predict the risk of recurrence in patients with LARC. METHODS: This was a multicenter observational study that enrolled participants who underwent neoadjuvant chemoradiotherapy. To evaluate the risk of recurrence in these patients, we developed the mrDEC scoring system and assessed inter-reader agreement. Additionally, we plotted Kaplan-Meier curves to compare the 3-year disease-free survival (DFS) and 5-year overall survival (OS) rates among patients with different mrDEC scores. RESULTS: A total of 1287 patients with LARC were included in this study. We observed substantial inter-reader agreement for mrDEC. Based on the mrDEC scores ranging from 0 to 3, the patients were categorized into four groups. The 3-year DFS rates for the groups were 91.0%, 79.5%, 65.5%, and 44.0% (P < 0.0001), respectively, and the 5-year OS rates were 92.9%, 87.1%, 74.8%, and 44.5%, respectively (P < 0.0001). CONCLUSIONS: The mrDEC scoring system proved to be an effective tool for predicting the prognosis of patients with LARC and can assist clinicians in clinical decision-making.

Trends in unhealthy lifestyle factors in US NHANES respondents with cardiovascular disease for the period between 1999 and 2018.

Objectives: To examine national trends in unhealthy lifestyle factors among adults with cardiovascular disease (CVD) in the United States (US) between 1999 and 2018. Methods: We analyzed data from National Health and Nutrition Examination Survey (NHANES), a nationally representative survey of participants with CVD who were aged ≥20 years, which was conducted between 1999 and 2000 and 2017-2018. CVD was defined as a self-report of congestive heart failure, coronary heart disease, angina, heart attack, or stroke. The prevalence rate of each unhealthy lifestyle factor was calculated among adults with CVD for each of the 2-year cycle surveys. Regression analyses were used to assess the impact of sociodemographic characteristics (age, sex, race/ethnicity, family income, education level, marital status, and employment status). Results: The final sample included 5610 NHANES respondents with CVD. The prevalence rate of their current smoking status remained stable among respondents with CVD between 1999 and 2000 and 2017-2018. During the same period, there was a decreasing trend in the age-adjusted prevalence rate of poor diet [primary American Heart Association (AHA) score <20; 47.5% (37.9%-57.0%) to 37.5% (25.7%-49.3%), p < 0.01]. Physical inactivity marginally increased before decreasing, with no statistical significance. The prevalence rate of sedentary behavior increased from 2007 to 2014 but subsequently returned to its original level in 2018 with no statistical significance. The age-adjusted prevalence rate of obesity increased from 32% (27.2%-36.8%) in 1999-2000 to 47.9% (39.9%-55.8%) in 2017-2018 (p < 0.001). The age-adjusted prevalence rate of depression increased from 7% (4.2%-9.9%) in 1999-2000 to 13.9% (10.2%-17.6%) in 2017-2018 (p = 0.056). Trends in mean for each unhealthy lifestyle factor were similar after adjustment for age. We found that respondents who had low education and income levels were at a higher risk of being exposed to unhealthy lifestyle factors (i.e., smoking, poor diet, and physical inactivity) than those who had high education and income levels. Conclusions: There is a significant reduction in the prevalence rate of poor diet among US adults with CVD between 1999 and 2018, while the prevalence rate of obesity showed increasing trends over this period. The prevalence rate of current smoking status, sedentary behavior, and depression was either stable or showed an insignificant increase. These findings suggest that there is an urgent need for health policy interventions targeting unhealthy lifestyles among adults with CVD.

[Effects of three sequential occlusal adjustment methods by articulating papers on the characteristics of implant delayed occlusion for single molar].

PURPOSE: To compare the effects of three occlusal adjustment methods in different sequences by articulating paper on the delayed occlusal characteristics of single molars. METHODS: Thirty-two implants of first molars were divided into group A(n=12), group B (n=12) and group C (n=12) by sequential adjustment according to random number means, and (100+40), (100+50+30) and (100+40+20) µm sequence occlusal papers were used for occlusal adjustment, respectively. TeeTester was used to measure the delay time and force ratio between prosthesis and adjacent teeth at restoration day, 3 and 6 months after restoration, and to record the number of cases readjusting in each group during follow-up. SPSS 25.0 software package was used for data analysis. RESULTS: There were significant differences in delay time between groups at restoration day (P＜0.05), and 3 and 6 months after restoration, delay time of group C was still smaller than that of group A and B (P＜0.05). During follow-up, the time of each group showed a trend of shortening (P＜0.05), but there was still delayed occlusion. Compared with group B and C, the force ratio in group A was lower at each time(P＜0.05). The ratio of each group showed an increasing trend during follow-up (P＜0.05), and group C showed the largest increase (P＜0.001). The number of cases readjusting was relatively small in group A, and the most was in group C(P＜0.05). There was positive correlation between delay time and force difference of prosthesis and adjacent teeth(P＜0.001). CONCLUSIONS: The (100+40) µm sequence group had higher occlusal stability and better clinical applicability. The smaller the occlusal contact space realized by the sequential method, the greater the change might be, which requires close follow-up in clinical practice.

Theoretical study on the influence of different solvents on the growth morphology of 3,4-bis(3-nitrofurazan-4-yl)furoxan (DNTF).

CONTEXT: 3,4-bis(3-nitrofurazan-4-yl)furoxan (DNTF) is a new energetic compound with high energy and high density, and it is an important component of propellant and melt cast explosive. In order to study the effect of solvent on the growth morphology of DNTF, the growth plane of DNTF in vacuum is predicted by attachment energy (AE) model, and then the modified attachment energy of each growth plane in different solvents is calculated by molecular dynamics simulation. The morphology of crystal in solvent is predicted by modified attachment energy (MAE) model. The factors affecting crystal growth in solvent environment are analyzed by mass density distribution, radial distribution function and diffusion coefficient. The results show that the growth morphology of crystal in solvent is not only related to the adsorption strength of solvent to crystal plane, but affected by the attraction of crystal plane to solute. The hydrogen bond plays an important role in the adsorption strength between solvent and crystal plane. The polarity of solvent has a great influence on the crystal morphology, and the interaction between the solvent with stronger polarity and the crystal plane is stronger. The morphology of DNTF in n-butanol solvent is closer to spherical, which can effectively reduce the sensitivity of DNTF. METHODS: The molecular dynamics simulation is carried out under the COMPASS force field of Materials Studio software. Gaussian software is used to calculate the electrostatic potential of DNTF at B3LYP-D3/6-311 + G (d, p) theoretical level.

CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer.

OBJECTIVES: The aim of this study is to evaluate the feasibility of clinicopathological characteristics and computed tomography (CT) morphological features in predicting lymph node metastasis (LNM) for patients with T1 colorectal cancer (CRC). METHODS: A total of 144 patients with T1 CRC who underwent CT scans and surgical resection were retrospectively included in our study. The clinicopathological characteristics and CT morphological features were assessed by two observers. Univariate and multiple logistic regression analyses were used to identify significant LNM predictive variables. Then a model was developed using the independent predictive factors. The predictive model was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate the calibration curve and relative C-index. RESULTS: LNM were found in 30/144 patients (20.83%). Four independent risk factors were determined in the multiple logistic regression analysis, including presence of necrosis (adjusted odds ratio [OR] = 10.32, 95% confidence interval [CI] 1.96-54.3, p = 0.004), irregular outer border (adjusted OR = 5.94, 95% CI 1.39-25.45, p = 0.035), and heterogeneity enhancement (adjusted OR = 7.35, 95% CI 3.11-17.38, p = 0.007), as well as tumor location (adjusted ORright-sided colon = 0.05 [0.01-0.60], p = 0.018; adjusted ORrectum = 0.22 [0.06-0.83], p = 0.026). In the internal validation cohort, the model showed good calibration and good discrimination with a C-index of 0.89. CONCLUSIONS: There are significant associations between lymphatic metastasis status and tumor location as well as CT morphologic features in T1 CRC, which could help the doctor make decisions for additional surgery after endoscopic resection. KEY POINTS: • LNM more frequently occurs in left-sided T1 colon cancer than in right-sided T1 colon and rectal cancer. • CT morphologic features are risk factors for LNM of T1 CRC, which may be related to fundamental biological behaviors. • The combination of tumor location and CT morphologic features can more effectively assist in predicting LNM in patients with T1 CRC, and decrease the rate of unnecessary extra surgeries after endoscopic resection.

Use of Pretreatment Multiparametric MRI to Predict Tumor Regression Pattern to Neoadjuvant Chemotherapy in Breast Cancer.

RATIONALE AND OBJECTIVES: To develop an easy-to-use model by combining pretreatment MRI and clinicopathologic features for early prediction of tumor regression pattern to neoadjuvant chemotherapy (NAC) in breast cancer. MATERIALS AND METHODS: We retrospectively analyzed 420 patients who received NAC and underwent definitive surgery in our hospital from February 2012 to August 2020. Pathologic findings of surgical specimens were used as the gold standard to classify tumor regression patterns into concentric and non-concentric shrinkage. Morphologic and kinetic MRI features were both analyzed. Univariable and multivariable analyses were performed to select the key clinicopathologic and MRI features for pretreatment prediction of regression pattern. Logistic regression and six machine learning methods were used to construct prediction models, and their performance were evaluated with receiver operating characteristic curve. RESULTS: Two clinicopathologic variables and three MRI features were selected as independent predictors to construct prediction models. The apparent area under the curve (AUC) of seven prediction models were in the range of 0.669-0.740. The logistic regression model yielded an AUC of 0.708 (95% confidence interval [CI]: 0.658-0.759), and the decision tree model achieved the highest AUC of 0.740 (95% CI: 0.691-0.787). For internal validation, the optimism-corrected AUCs of seven models were in the range of 0.592-0.684. There was no significant difference between the AUCs of the logistic regression model and that of each machine learning model. CONCLUSION: Prediction models combining pretreatment MRI and clinicopathologic features are useful for predicting tumor regression pattern in breast cancer, which can assist to select patients who can benefit from NAC for de-escalation of breast surgery and modify treatment strategy.

Molecular Pathology and Therapeutic Strategies of Type 2 Diabetes.

Diabetes Mellitus (DM) is a metabolic disorder characterized by hyperglycemia. Over the years, scientists have identified many factors that may have causal relationships with DM development. Identified factors are either genetic or environmental, and they may promote or prevent DM development. This review discusses various factors that are involved in the molecular pathogenesis, development, and therapeutic strategies of type 2 diabetes. DM is caused by interactions between multiple factors and triggers. Altered metabolic pathways and cellular functions, primarily in organs involved in glucose metabolisms, such as the pancreas and liver, often result in metabolic dysfunction, leading to DM. Additionally, abnormal levels of some factors, the presence of some pathogens, or the use of some types of medicine, such as immuno-inflammatory mediators, glucagon, apolipoprotein E4, chromogranin-A, exosomes, vitamin D, viruses, glucocorticoid medication, and antipsychotic drugs, may play roles in the development of DM. Some of these factors and mechanisms are well-studied, while others are more controversial and have contradicting experimental results. Further research is needed to confirm the roles of these factors in DM and fully understand how they contribute to DM development. Numerous medications for diabetics have been developed to help alleviate the symptoms of hyperglycemia and its complications. Several types of small compounds or peptide drugs with anti-diabetic effects can decrease blood glucose levels, improve insulin resistance, and inhibit key enzymes involved in the development and progression of diabetes. Here, we review the commonly used effective antidiabetic drugs, including the most recent innovative ones, such as GLP- 1R/GIPR and GLP-1R/GCGR agonists, and Chinese medicine.

Trends in Unhealthy Lifestyle Factors among Adults with Stroke in the United States between 1999 and 2018.

BACKGROUND: Unhealthy lifestyle factors are risk factors for stroke, and they play a key role in stroke secondary prevention. A better understanding of these factors may aid with improvements in public health policy. OBJECTIVE: Our objective was to comprehensively understand the trends in unhealthy lifestyle factors in people who have previously had a stroke in the US. METHODS: Utilizing data from the biannual United States National Health and Nutrition Examination Surveys (NHANESs) between 1999 and 2018, we collated data on unhealthy lifestyle factors (smoking, alcohol drinking, depression, unhealthy diet, high BMI, physical inactivity, and sedentary behavior) in adults with a history of stroke. The Joinpoint Regression model was used to calculate the annual percentage change (APC) and average annual percentage change (AAPC) to identify trends. Logistic regression modeling was used to identify the influence of sociodemographic factors (age, sex, race/ethnicity, marital status, employment status, family income, and highest education level). RESULTS: The analysis included 2017 respondents with a history of stroke. Current alcohol drinking (39.3% (95% confidence interval: 29.8, 48.7) to 57.4% (45.7, 69.0) p = 0.008) and obesity (39.2% (28.3, 50.2) to 49.4% (38.9, 59.8) p = 0.029) increased significantly from 1999 to 2018. The prevalence of smoking and depression remained generally stable. The proportion of respondents with an unhealthy diet decreased from 1999 (44.5% (32.4, 56.5)) to 2011 (29.0% (17.5, 40.4) p = 0.019), but then returned to its original prevalence in 2018 (42.0% (31.4, 52.7)). From 2007 to 2018, the proportion of respondents who were physically inactive decreased significantly, from 70.4% (64.4, 76.3) to 55.1% (46.1, 64.2; p = 0.017). After a gradual increase in sedentary activity from 2007 to 2012, this declined from 2013 to 2018, with no statistical significance. We found stroke survivors who were widowed, divorced, separated, or unemployed were at a higher risk of having unhealthy lifestyles than those who were employed or had other marital statuses. CONCLUSIONS: A modest reduction in the prevalence of physical inactivity was observed in Americans with a history of stroke between 1999 and 2018. The prevalences of smoking, drinking, depression, poor diet, obesity, and sedentary behavior were stable or increasing.

An essential Noc3p dimerization cycle mediates ORC double-hexamer formation in replication licensing.

Replication licensing, a prerequisite of DNA replication, helps to ensure once-per-cell-cycle genome duplication. Some DNA replication-initiation proteins are sequentially loaded onto replication origins to form pre-replicative complexes (pre-RCs). ORC and Noc3p bind replication origins throughout the cell cycle, providing a platform for pre-RC assembly. We previously reported that cell cycle-dependent ORC dimerization is essential for the chromatin loading of the symmetric MCM double-hexamers. Here, we used Saccharomyces cerevisiae separation-of-function NOC3 mutants to confirm the separable roles of Noc3p in DNA replication and ribosome biogenesis. We also show that an essential and cell cycle-dependent Noc3p dimerization cycle regulates the ORC dimerization cycle. Noc3p dimerizes at the M-to-G1 transition and de-dimerizes in S-phase. The Noc3p dimerization cycle coupled with the ORC dimerization cycle enables replication licensing, protects nascent sister replication origins after replication initiation, and prevents re-replication. This study has revealed a new mechanism of replication licensing and elucidated the molecular mechanism of Noc3p as a mediator of ORC dimerization in pre-RC formation.

Midgut membrane protein BmSUH facilitates Bombyx mori nucleopolyhedrovirus oral infection.

Baculoviruses are virulent pathogens that infect a wide range of insects. They initiate infections via specific interactions between the structural proteins on the envelopes of occlusion-derived virions (ODVs) and the midgut cell surface receptors in hosts. However, host factors that are hijacked by baculoviruses for efficient infection remain largely unknown. In this study, we identified a membrane-associated protein sucrose hydrolase (BmSUH) as an ODV binding factor during Bombyx mori nucleopolyhedrovirus (BmNPV) primary infection. BmSUH was specifically expressed in the midgut microvilli where the ODV-midgut fusion happened. Knockout of BmSUH by CRISPR/Cas9 resulted in a significantly higher survival rate after BmNPV orally infection. Liquid chromatography-tandem mass spectrometry analysis and co-immunoprecipitation analysis demonstrated that PIF protein complex required for ODV binding could interact with BmSUH. Furthermore, fluorescence dequenching assay showed that the amount of ODV binding and fusion to the midgut decreased in BmSUH mutants compared to wild-type silkworm, suggesting the role of BmSUH as an ODV binding factor that mediates the ODV entry process. Based on a multilevel survey, the data showed that BmSUH acted as a host factor that facilitates BmNPV oral infection. More generally, this study indicated that disrupting essential protein-protein interactions required for baculovirus efficient entry may be broadly applicable to against viral infection.

Environmental-related variation of stoichiometric traits in body and organs of non-native sailfin catfishes Pterygoplichthys spp.

Intraspecific variation in stoichiometric traits was thought to be an adaptive response to reduce the elemental imbalance between organism and diet in the habitat. Studying the spatial variation of stoichiometric traits of non-native species and the factors contributing to the variation could help to better understand the invasion mechanism of non-native fish. In this study, stoichiometric traits (i.e. carbon [C], phosphorus [P], calcium [Ca] and their ratios) variation in the body and organs of non-native sailfin catfishes Pterygoplichthys spp. were investigated across 13 river sections in the main river basins of Guangdong province. The relationships between environmental factors and stoichiometric traits were analyzed using a general linear model and an information-theoretic approach. A manipulated feeding experiment was conducted to investigate the impact of food quality on the stoichiometry of sailfin catfishes in a greenhouse. Sailfin catfishes exhibited considerable variability in body and organ elemental composition. Site identity was the main factor contributing to the variation, which could be explained by a combination of environmental factors including climate, diet quality, fish species richness and trophic status in the invaded rivers. Water chemistry (i.e. total nitrogen and phosphorus, ammonia nitrogen and soluble reactive phosphorus) contributed to the most variation of stoichiometric traits. Imbalances of P and Ca between sailfin catfishes and food resources varied among sampling sites, reflecting the spatial heterogeneity of nutrients limitation. Juvenile sailfin catfishes exhibited stoichiometric homeostasis (0 < 1/H < 0.25) for all elemental contents and ratios in the feeding experiment. These findings suggested variation in stoichiometric traits of sailfin catfishes might be attributed to the changes in elemental metabolism to cope with context-specific environments. This study provided heuristic knowledge about environmental-related variation in stoichiometric traits, which could enhance the understanding of the non-native species' adaptation to resource fluctuation in the invaded ecosystems.

Associations Among Depression, Hemoglobin A1c Level, and Prognosis in Patients With Coronary Artery Disease: A Prospective Study.

Background: Depression is ubiquitous in patients with coronary artery disease (CAD). The relationship between depression and hemoglobin A1c (HbA1c) is controversial. The combined effect of high HbA1c and depression on prognosis is unclear, especially in non-diabetic CAD patients. We sought to explore these associations. Methods: 558 CAD patients were included in this prospective study. Patients were grouped by HbA1c levels and the status of clinical depression. The average follow-up period was about 2.2 years, and Cox proportional hazards models were used to compare the differences of prognosis in all the groups. Results: Clinical depression had no associations with HbA1c in all CAD patients (P for Pearson correlation = 0.74). In the all four groups, compared to group 1 (patients without clinical depression and low HbA1c), group 3 (without clinical depression and high HbA1c) had a higher risk of MACE (adjusted hazard ratio [aHR], 1.97; 95% confidence interval [CI], 1.2-3.25) and composite events (aHR, 1.67; 95% CI, 1.09-2.053). Group 4 (patients with clinical depression and high HbA1c) had higher HRs for MACE (aHR, 2.9; 95%CI, 1.32-6.38) and composite events (aHR, 2.12; 95% CI, 1.06-4.25). In CAD patients without diabetes, patients with clinical depression and high HbA1c had a higher risk of MACE (HR, 2.71; 95% CI, 1.02-7.19), non-cardiac readmission (HR,3.48; 95% CI, 1.26-9.57) and composite events (HR,2.44; 95% CI, 1.08-5.53) than those with no clinical depression and low HbA1c. In patients with comorbidities of depression and diabetes, patients with depression and high HbA1c more likely to experienced non-cardiac readmissions (HR, 4.49; 95% CI, 1.31-15.38) than patients with no depression and low HbA1c only. In all the above analysis, p-values for interaction between clinical depression and HbA1c were not statistically significant. Conclusions: The presence of both depression and high HbA1c lead to a worse prognosis in CAD patients than one risk factor alone, no matter with or without the comorbidity of diabetes in these CAD patients. For patients with CAD and depression, lower HbA1c may be required.

Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification.

BACKGROUND: High immune infiltration is associated with favourable prognosis in patients with non-small-cell lung cancer (NSCLC), but an automated workflow for characterizing immune infiltration, with high validity and reliability, remains to be developed. METHODS: We performed a multicentre retrospective study of patients with completely resected NSCLC. We developed an image analysis workflow for automatically evaluating the density of CD3+ and CD8+ T-cells in the tumour regions on immunohistochemistry (IHC)-stained whole-slide images (WSIs), and proposed an immune scoring system "I-score" based on the automated assessed cell density. RESULTS: A discovery cohort (n = 145) and a validation cohort (n = 180) were used to assess the prognostic value of the I-score for disease-free survival (DFS). The I-score (two-category) was an independent prognostic factor after adjusting for other clinicopathologic factors. Compared with a low I-score (two-category), a high I-score was associated with significantly superior DFS in the discovery cohort (adjusted hazard ratio [HR], 0.54; 95% confidence interval [CI] 0.33-0.86; P = 0.010) and validation cohort (adjusted HR, 0.57; 95% CI 0.36-0.92; P = 0.022). The I-score improved the prognostic stratification when integrating it into the Cox proportional hazard regression models with other risk factors (discovery cohort, C-index 0.742 vs. 0.728; validation cohort, C-index 0.695 vs. 0.685). CONCLUSION: This automated workflow and immune scoring system would advance the clinical application of immune microenvironment evaluation and support the clinical decision making for patients with resected NSCLC.