Diffusion-weighted imaging-based radiomics model using automatic machine learning to differentiate cerebral cystic metastases from brain abscesses.

OBJECTIVES: To develop a radiomics model based on diffusion-weighted imaging (DWI) utilizing automated machine learning method to differentiate cerebral cystic metastases from brain abscesses. MATERIALS AND METHODS: A total of 186 patients with cerebral cystic metastases (n = 98) and brain abscesses (n = 88) from two clinical institutions were retrospectively included. The datasets (129 from institution A) were randomly portioned into separate 75% training and 25% internal testing sets. Radiomics features were extracted from DWI images using two subregions of the lesion (cystic core and solid wall). A thorough image preprocessing method was applied to DWI images to ensure the robustness of radiomics features before feature extraction. Then the Tree-based Pipeline Optimization Tool (TPOT) was utilized to search for the best optimized machine learning pipeline, using a fivefold cross-validation in the training set. The external test set (57 from institution B) was used to evaluate the model's performance. RESULTS: Seven distinct TPOT models were optimized to distinguish between cerebral cystic metastases and abscesses either based on different features combination or using wavelet transform. The optimal model demonstrated an AUC of 1.00, an accuracy of 0.97, sensitivity of 1.00, and specificity of 0.93 in the internal test set, based on the combination of cystic core and solid wall radiomics signature using wavelet transform. In the external test set, this model reached 1.00 AUC, 0.96 accuracy, 1.00 sensitivity, and 0.93 specificity. CONCLUSION: The DWI-based radiomics model established by TPOT exhibits a promising predictive capacity in distinguishing cerebral cystic metastases from abscesses.

Machine learning unveils RNA polymerase II binding as a predictor for SMAD2-dependent transcription dynamics in response to Actvin signalling.

The transforming growth factor-ß (TGF-ß) superfamily, including Nodal and Activin, plays a critical role in various cellular processes. Understanding the intricate regulation and gene expression dynamics of TGF-ß signalling is of interest due to its diverse biological roles. A machine learning approach is used to predict gene expression patterns induced by Activin using features, such as histone modifications, RNA polymerase II binding, SMAD2-binding, and mRNA half-life. RNA sequencing and ChIP sequencing datasets were analysed and differentially expressed SMAD2-binding genes were identified. These genes were classified into activated and repressed categories based on their expression patterns. The predictive power of different features and combinations was evaluated using logistic regression models and their performances were assessed. Results showed that RNA polymerase II binding was the most informative feature for predicting the expression patterns of SMAD2-binding genes. The authors provide insights into the interplay between transcriptional regulation and Activin signalling and offers a computational framework for predicting gene expression patterns in response to cell signalling.

Sarcopenia and treatment failure in inflammatory bowel disease: a systematic review and meta-analysis.

BACKGROUND: The association between sarcopenia and treatment outcomes in inflammatory bowel disease (IBD) is currently a subject of controversy. METHODS: A systematic search was performed of PubMed, Embase, Web of Science, and the Cochrane Library for studies published until April 2023. The quality assessment of each included study was performed using the Newcastle-Ottawa Scale. RESULTS: Seventeen studies were included with 2,895 IBD patients. Sarcopenia exhibited an increased risk of treatment failure (OR=2.00, 95% CI: 1.43-2.79) and notably increased the need for surgery (OR=1.54,95%CI:1.06-2.23) as opposed to a pharmacologic treatment plan change (OR=1.19, 95% CI:0.71-2.01) among IBD patients. However, no significant association was found between sarcopenia and treatment failure in corticosteroid (OR=1.21, 95% CI: 0.55-2.64) or biologic agent (OR=1.65, 95% CI: 0.93-2.92) cohorts. Sarcopenia was also linked to elevated treatment failure risks in patients with Crohn's disease (OR=1.82, 95% CI: 1.15-2.90) and those diagnosed with ulcerative colitis (OR=2.55, 95% CI: 1.05-6.21), spanning both Asian (OR=1.88, 95% CI: 1.29-2.74) and non-Asian regions (OR=2.17, 95% CI: 1.48-3.18). CONCLUSIONS: Sarcopenia was considered a novel marker for use in clinical practice to predict treatment failure, specifically, the need for surgery in IBD patients. This distinct cohort necessitates clinical attention and tailored care strategies.

Human-influenced changes in pollution status and potential risk of sediment heavy metals in Xincun Bay, a typical lagoon of Hainan, China.

Pollution status and ecological risks associated with sediment heavy metals (Cu, Pb, Zn, Cd, and Cr) were investigated around Xincun Bay, assessing their spatial variations and relationship with sediment physiochemical factors. Higher concentrations and associated risks were observed in the central region, where mariculture activities were concentrated, compared to non-maricultured areas. Despite with overall low concentrations, Cd had a higher ecological risk. Correlation and principal component analyses revealed similar sources for all metals in Xincun Bay. Heavy metal concentrations varied with expansion of mariculture operations in terms of quantity and scale, confirming the influence of mariculture activities. Sediments around mariculture had higher contents of clay, silt, and total organic carbon (TOC), and finer particle sizes. Quantitative analyses through correlation and linear regression indicated that TOC significantly regulated heavy metal concentration and distribution (p < 0.05). Considering its significant association with TOC, the influence of mean grain size should not be overlooked.

Sarcopenia and treatment failure in inflammatory bowel disease: a systematic review and meta-analysis

Background: The association between sarcopenia and treatment outcomes in inflammatory bowel disease (IBD) is currently a subject of controversy. Methods: A systematic search was performed of PubMed, Embase, Web of Science, and the Cochrane Library for studies published until April 2023. The quality assessment of each included study was performed using the Newcastle-Ottawa Scale. Results: Seventeen studies were included with 2,895 IBD patients. Sarcopenia exhibited an increased risk of treatment failure (OR=2.00, 95% CI: 1.43-2.79) and notably increased the need for surgery (OR=1.54,95%CI:1.06-2.23) as opposed to a pharmacologic treatment plan change (OR=1.19, 95% CI:0.71-2.01) among IBD patients. However, no significant association was found between sarcopenia and treatment failure in corticosteroid (OR=1.21, 95% CI: 0.55-2.64) or biologic agent (OR=1.65, 95% CI: 0.93-2.92) cohorts. Sarcopenia was also linked to elevated treatment failure risks in patients with Crohn's disease (OR=1.82, 95% CI: 1.15-2.90) and those diagnosed with ulcerative colitis (OR=2.55, 95% CI: 1.05-6.21), spanning both Asian (OR=1.88, 95% CI: 1.29-2.74) and non-Asian regions (OR=2.17, 95% CI: 1.48-3.18). Conclusions: Sarcopenia was considered a novel marker for use in clinical practice to predict treatment failure, specifically, the need for surgery in IBD patients. This distinct cohort necessitates clinical attention and tailored care strategies (AU)

[Photo-Degradation Mechanism and Pathway for Tetracycline in Simulated Seawater Under Irradiation of Visible Light].

To explore the mechanism and pathway for pollutant degradation in seawater by heterogeneous photocatalysts, the degradation of tetracycline (TC) in pure water and simulated seawater with different mesoporous TiO2 under the excitation of visible light was first investigated; then the effect of different salt ions on the photocatalytic degradation process was clarified. Combined with radical trapping experiments, electron spin resonance (ESR) spectroscopy, and intermediate product analysis, the main active species for photodegrading pollutants and the pathway of TC degradation in simulated seawater were investigated. The results showed that the photodegradation for TC in simulated seawater was significantly inhibited. Compared with the TC photodegradation in pure water, the reaction rate of the chiral mesoporous TiO2 photocatalyst for TC was reduced by approximately 70%, whereas the achiral mesoporous TiO2 photocatalyst could hardly degrade TC in seawater. Anions in simulated seawater had little effect on photodegradation, but Mg2+ and Ca2+ ions significantly inhibited the TC photodegradation process. Whether in water or simulated seawater, the active species generated by the catalyst after excitation by visible light were mainly holes, and each salt ion did not inhibit the generation of active species; thus, the degradation pathway both in simulated seawater and in water was the same. However, Mg2+ and Ca2+ would be enriched around the highly electronegative atoms in TC molecules, hindering the attack of holes to highly electronegative atoms in TC molecules, thereby inhibiting the photocatalytic degradation efficiency.

A Lightweight convolutional neural network for nicotine prediction in tobacco by near-infrared spectroscopy.

The content of nicotine, a critical component of tobacco, significantly influences the quality of tobacco leaves. Near-infrared (NIR) spectroscopy is a widely used technique for rapid, non-destructive, and environmentally friendly analysis of nicotine levels in tobacco. In this paper, we propose a novel regression model, Lightweight one-dimensional convolutional neural network (1D-CNN), for predicting nicotine content in tobacco leaves using one-dimensional (1D) NIR spectral data and a deep learning approach with convolutional neural network (CNN). This study employed Savitzky-Golay (SG) smoothing to preprocess NIR spectra and randomly generate representative training and test datasets. Batch normalization was used in network regularization to reduce overfitting and improve the generalization performance of the Lightweight 1D-CNN model under a limited training dataset. The network structure of this CNN model consists of four convolutional layers to extract high-level features from the input data. The output of these layers is then fed into a fully connected layer, which uses a linear activation function to output the predicted numerical value of nicotine. After the comparison of the performance of multiple regression models, including support vector regression (SVR), partial least squares regression (PLSR), 1D-CNN, and Lightweight 1D-CNN, under the preprocessing method of SG smoothing, we found that the Lightweight 1D-CNN regression model with batch normalization achieved root mean square error (RMSE) of 0.14, coefficient of determination (R 2) of 0.95, and residual prediction deviation (RPD) of 5.09. These results demonstrate that the Lightweight 1D-CNN model is objective and robust and outperforms existing methods in terms of accuracy, which has the potential to significantly improve quality control processes in the tobacco industry by accurately and rapidly analyzing the nicotine content.

Contrasting effects of different light regimes on the photoreactivities of allochthonous and autochthonous chromophoric dissolved organic matter.

Chromophoric dissolved organic matter (CDOM) plays an important role in ultraviolet (UV) light absorption in the ocean. CDOM is known to originate from either an allochthonous or autochthonous source and has varying compositions and levels of reactivity; however, the effects of individual radiation treatments and the combined effects of UVA and UVB on allochthonous and autochthonous CDOM remain poorly understood. Thus, here, we measured changes in the common optical properties of CDOM collected from China's marginal seas and the Northwest Pacific, using full-spectrum, UVA (315-400 nm), and UVB (280-315 nm) irradiation to induce photodegradation over the same time period (60 h). Excitation-emission matrices (EEMs) combined with parallel factor analysis (PARAFAC) identified four components: marine humic-like C1, terrestrial humic-like C2, soil fulvic-like C3, and tryptophan-like C4. Although the behaviours of these components during full-spectrum irradiation exhibited similar decreasing tendencies, three components (C1, C3, and C4) underwent direct photodegradation under UVB exposure, whereas C2 was more susceptible to UVA degradation. The diverse photoreactivities of the source-dependent components to different light treatments led to differing photochemical behaviours of other optical indices [aCDOM(355), aCDOM(254), SR, HIX, and BIX]. The results indicate that irradiation preferentially reduced the high humification degree or humic substance content of allochthonous DOM, and promoted the transformation from the allochthonous humic DOM components to recently produced components. Although values for the samples from different sources overlapped frequently, principal component analysis (PCA) indicated that the overall optical signatures could be linked to the original CDOM source features. The degradation of CDOM humification, aromaticity, molecular weight, and autochthonous fractions under exposure can drive the CDOM biogeochemical cycle in marine environments. These findings can aid in a better understanding of the effects of different combinations of light treatments and CDOM characteristics on CDOM photochemical processes.

Prediction of microvascular invasion in hepatocellular carcinoma based on preoperative Gd-EOB-DTPA-enhanced MRI: Comparison of predictive performance among 2D, 2D-expansion and 3D deep learning models.

Purpose: To evaluate and compare the predictive performance of different deep learning models using gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma. Methods: The data of 233 patients with pathologically confirmed hepatocellular carcinoma (HCC) treated at our hospital from June 2016 to June 2021 were retrospectively analyzed. Three deep learning models were constructed based on three different delineate methods of the region of interest (ROI) using the Darwin Scientific Research Platform (Beijing Yizhun Intelligent Technology Co., Ltd., China). Manual segmentation of ROI was performed on the T1-weighted axial Hepatobiliary phase images. According to the ratio of 7:3, the samples were divided into a training set (N=163) and a validation set (N=70). The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of three models, and their sensitivity, specificity and accuracy were assessed. Results: Among 233 HCC patients, 109 were pathologically MVI positive, including 91 men and 18 women, with an average age of 58.20 ± 10.17 years; 124 patients were MVI negative, including 93 men and 31 women, with an average age of 58.26 ± 10.20 years. Among three deep learning models, 2D-expansion-DL model and 3D-DL model showed relatively good performance, the AUC value were 0.70 (P=0.003) (95% CI 0.57-0.82) and 0.72 (P<0.001) (95% CI 0.60-0.84), respectively. In the 2D-expansion-DL model, the accuracy, sensitivity and specificity were 0.7143, 0.739 and 0.688. In the 3D-DL model, the accuracy, sensitivity and specificity were 0.6714, 0.800 and 0.575, respectively. Compared with the 3D-DL model (based on 3D-ResNet), the 2D-DL model is smaller in scale and runs faster. The frames per second (FPS) for the 2D-DL model is 244.7566, which is much larger than that of the 3D-DL model (73.3374). Conclusion: The deep learning model based on Gd-EOB-DTPA-enhanced MRI could preoperatively evaluate MVI in HCC. Considering that the predictive performance of 2D-expansion-DL model was almost the same as the 3D-DL model and the former was relatively easy to implement, we prefer the 2D-expansion-DL model in practical research.

Bioaccumulation and potential human health risks of metals in commercially important fishes and shellfishes from Hangzhou Bay, China.

Hangzhou Bay is facing severe anthropogenic perturbation because of its geographic position. We studied species-specific bioaccumulation of metals in commercially important fishes and shellfishes, and calculated the potential human health hazards through their consumption, which has not been reported earlier from this area. The hierarchy of metal concentration in organisms was in the decreasing order of Zn (10.32 ± 7.13) > Cu (2.40 ± 2.66) > As (0.42 ± 0.26) > Cr (0.11 ± 0.08) > Cd (0.07 ± 0.07) > Pb (0.05 ± 0.02) > Hg (0.012 ± 0.009). Except for Cd and As concentrations in fishes, metal concentrations have not exceeded the national and international guideline values. P. laevis and P. trituberculatus were the most bioaccumulative of the species studied. According to the non-carcinogenic risk assessment, children were more susceptible to metal contamination than adults. The carcinogenic risk (CR) values indicated that children were likely to experience carcinogenic threats for taking cancer-causing agents As and Cd through fish consumption. In terms of organisms, intake of two crab species, P. trituberculatus and E. sinensis, as well as the oyster species P. laevis, could be detrimental to consumers.

Skeletal muscle index and muscle attenuation with liver cirrhosis as survival prognosticators by sex.

BACKGROUND AND OBJECTIVES: It has been proven that skeletal muscle index (SMI) and muscle attenuation (MA) are correlated with outcomes in liver cirrhosis. However, whether there are sex differences in these factors re-mains unknown. We aimed to analyze the predictive ability of SMI and MA for the prognosis of cirrhotic patients of different sexes and promote computed tomography (CT) use in body composition assessment. METHODS AND STUDY DESIGN: CT images taken at the 3rd lumbar vertebra from 223 patients were quantified for body composi-tion. A Cox regression model was used to assess associations between mortality and body composition. Time-dependent receiver operating characteristic curves were calculated to evaluate the predictive ability of SMI and MA for the 1-, 3- and 5- year mortality of cirrhotic patients. RESULTS: The majority of patients with liver cirrhosis were male (64.6%), and there was a weak linear correlation between SMI and MA in males (r=0.33, p<0.001). In the sex stratified multivariate Cox regression analysis, SMI in males (HR=0.95; 95% CI, 0.91-0.98; p=0.002) and MA in females (HR=0.91; 95% CI, 0.87-0.96; p<0.001) were independently associated with mortality. The areas under the curve (AUCs) of SMI (AUC=0.718) and MA (AUC=0.705) were similar in the 5-year mortality predic-tion of males, while in females, MA (AUC=0.797) had a stronger predictive ability than SMI (AUC=0.541). CONCLUSIONS: SMI in males and MA in females are independent prognostic factors for liver cirrhosis. For females, MA may be a more sensitive indicator of mortality prediction than SMI, while in males, they are equivalent.

False positive results: a challenge for laboratory physicians and hematologists in treating multiple myeloma with daratumumab.

Daratumumab injection was approved by China in 2019 for the treatment of recurrent or refractory multiple myeloma. However, the molecular weight of daratumumab, an immunoglobin G1 kappa human monoclonal antibody, was similar to that of M protein and could not be distinguished from IgG κ M protein in SPEP and SIFE. It might lead to false-positive detection resulting in misdiagnose and confusing evaluation of therapeutic response, especially for patients with IgG κ M proteins. Herein, we reported two cases encountered in our daily clinical work. These two case reports could serve as a reminder to global hematologists who have not yet started or just begun to use the drug of daratumumab.

Reference ranges of T lymphocyte subsets by single-platform among healthy population in southwest China.

BACKGROUND: Appropriate reference ranges of T lymphocyte subsets are essential for immune status evaluation of patients with immunological diseases. We aim to establish the age- and sex-related reference intervals of T lymphocyte subsets by single-platform for the southwest China population using the indirect method with the data resulting from 53,822 cases of periodic health examination individuals in the Laboratory Information System (LIS) of West China Hospital from 2018 to 2020. METHODS: We used the Box-Cox conversion combined with the Tukey method to normalize the data and eliminate the outliers, and the nonparametric method to estimate the 95% distribution reference intervals. RESULTS: We initially established the reference ranges of T lymphocyte subsets by single-platform among healthy population in southwest China by indirect method (See text for details). Using the standard normal deviate test (z-test) suggested by Harris and Boyd according to CLSI EP28-A3C, which is more scientific, we found the reference ranges of T lymphocyte subsets should be differentiated by ages and genders since the reference ranges of T lymphocyte subsets by single-platform in different ages and genders are significantly different. CONCLUSIONS: We further demonstrated the absolute count of CD3 + T cell, CD3 + CD4 + T cell, CD3 + CD8 + T cell decreased with aging, which is more marked in men and CD3 + CD8 + T cell count, and the obtained reference intervals were superior to the reference intervals derived from the reagent specification currently in use.

StainNet: A Fast and Robust Stain Normalization Network.

Stain normalization often refers to transferring the color distribution to the target image and has been widely used in biomedical image analysis. The conventional stain normalization usually achieves through a pixel-by-pixel color mapping model, which depends on one reference image, and it is hard to achieve accurately the style transformation between image datasets. In principle, this difficulty can be well-solved by deep learning-based methods, whereas, its complicated structure results in low computational efficiency and artifacts in the style transformation, which has restricted the practical application. Here, we use distillation learning to reduce the complexity of deep learning methods and a fast and robust network called StainNet to learn the color mapping between the source image and the target image. StainNet can learn the color mapping relationship from a whole dataset and adjust the color value in a pixel-to-pixel manner. The pixel-to-pixel manner restricts the network size and avoids artifacts in the style transformation. The results on the cytopathology and histopathology datasets show that StainNet can achieve comparable performance to the deep learning-based methods. Computation results demonstrate StainNet is more than 40 times faster than StainGAN and can normalize a 100,000 × 100,000 whole slide image in 40 s.

Mechanism of Photodegradation of Organic Pollutants in Seawater by TiO2-Based Photocatalysts and Improvement in Their Performance.

The mechanism of photodegradation of organic pollutants in seawater by TiO2-based catalysts irradiated by visible light was first explored by adding holes and free radical traps. The results showed that the photogenerated holes formed by the catalyst played a key role in the degradation of organic pollutants, regardless of whether the photodegradation occurred in seawater or pure water. Considering that the Yb-TiO2-rGO catalyst has a strong adsorption for organics, the salt ion almost did not interfere with the adsorption of pollutants by Yb-TiO2-rGO. Therefore, the degradation performance of Yb-TiO2-rGO did not remarkably change in the two water systems. For P25-ZN with a weak adsorption capacity for organics, several salt ions in the seawater hindered the contact of pollutants with the catalyst surface. Thus, the degradation rate of P25-ZN for phenol was significantly reduced. After the solvothermal reduction treatment for catalysts using ethylene glycol (EG) as the solvent, the increase in the Ti3+ content in the catalyst improved the visible-light response and activity of the catalyst. In addition, a small amount of EG grafted on the catalyst surface promoted the photocatalytic reaction process on the catalyst surface, thereby effectively resisting the interference of salt ions.

Transcriptomic study of the mechanism by which the Kai Yu Zhong Yu recipe improves oocyte quality in a stressed mouse model.

ETHNOPHARMACOLOGICAL RELEVANCE: The Kai Yu Zhong Yu recipe (KYZY) is a classic herbal formula in traditional Chinese medicine (TCM) that has been used to treat infertility associated with psychological stress for more than three hundred years. AIM OF THE STUDY: Psychological stress has major impacts on fertility, with variable outcomes depending on the nature, strength, and duration of the stress. Stress can directly disturb ovulation, oocyte quality, maturation, and embryo development. The aim of this study is to investigate the molecular mechanism by which KYZY improves oocyte developmental potential under psychological stress. MATERIALS AND METHODS: ICR female mice aged 4-5 weeks were randomly divided into five groups: control, stressed in the chronic unpredictable stress model (CUSM), and stressed plus KYZY treatment at 38.2 g/kg (KYZYH), 19.1 g/kg (KYZYM), or 9.6 g/kg (KYZYL). Ovary function was assessed by measuring serum levels of estradiol (E2), luteinizing hormone (LH), follicle-stimulating hormone (FSH), and anti-Müllerian hormone (AMH). Oocyte quality was evaluated in terms of reactive oxygen species (ROS) levels, apoptotic DNA fragmentation, and mitochondria distribution. We used RNA sequencing (RNAseq) to identify differentially expressed genes (DEGs) between groups and then further analyzed the DEGs for gene ontology (GO) term enrichment and protein-protein interactions. RESULTS: Mice in the stressed group had reduced serum E2, LH, and FSH as well as increased ROS levels, increased apoptosis, and disturbed mitochondria distribution in oocytes. Treatment with KYZY at all three doses reversed or ameliorated these negative effects of stress. DEG analysis identified 187 common genes between the two comparisons (stressed vs. control and KYZYM vs. stressed), 33 of which were annotated with six gene ontology (GO)'s biological process (BP) terms: cell differentiation, apoptosis, ATP synthesis, protein homo-oligomerization, neuron migration, and negative regulation of peptidase activity. Protein-protein interaction network analysis of DEGs identified key hub genes. Notably, the genes Atp5o and Cyc1 were both involved in the ATP synthesis and among the top three hub genes, suggesting that regulation of oocyte mitochondrial electron transport and ATP synthesis is important in the response to stress and also is a possible mechanism of action for KYZY. CONCLUSIONS: KYZY was effective in ameliorating the adverse effects of stress on oocyte competence, possibly by targeting the mitochondrial respiratory chain and ATP synthase.

Evidence for Peripheral Immune Activation in Parkinson's Disease.

BACKGROUND: Accumulating evidence has revealed that peripheral immunity is involved in Parkinson's disease (PD). However, the results regarding the percentage of T-cell subsets are inconsistent, and the changes of immunoglobins levels have been seldom studied in PD patients. METHODS: Serum levels of the percentage of T-cell subsets and immunoglobulins were measured in 761 PD patients and 761 age- and gender-matched healthy controls. The correlations between the variables of peripheral immune activation (PIA) and the clinical characteristics of PD were analyzed using correlation analysis. RESULTS: The pooled results showed that PD patients had higher proportional levels of CD3+ T and CD4+ T lymphocytes than healthy controls. CD8+ T cell percentages were similar in PD patients and controls, and the CD4/CD8 ratio was significantly higher in the PD population. No significant differences in IgG, IgA, or IgM levels between these two groups were found. CD4+ T cell percentage was inversely correlated with the H&Y stage, and IgG level was positively correlated with disease duration and UPDRS part III. Subgroup analyses showed that these associations existed in female patients, but not in male patients. CONCLUSION: The enhanced immune activation in the peripheral system is indicated in PD, and dynamic alterations in CD4+ T cell percentage and IgG level suggest an active role for peripheral immunity in the disease progression, especially in female PD patients.

Trace metal contamination and species-specific bioaccumulation in the Zhoushan Fishery, northwestern East China Sea.

Metal contamination in fishery water may pose a serious threat to aquatic products and human health. In this study, the contents of seven trace metals were assessed in water, sediment, and ten commercially important species (seven fish and three crustaceans) with different trophic guilds, habitat preferences, and motility, collected from the Zhoushan Fishery, northwest East China Sea. In general, the results showed that the concentrations of trace metals in water and sediment were lower than the safety thresholds set by the National Seawater Quality Standard of China and the sediment quality guidelines, except for Cu, As, and Cr in sediment. The high metal concentrations were spatially distributed in the west of the Zhoushan Fishery, which is probably due to the chemical pollution generated from many large international ports and chemical industries in Hangzhou Bay. The metal concentrations in the species were lower than the legislation thresholds established by the Commission Regulation and China National Standard, except for Cd in two crustacean species. However, a health risk assessment indicated that the consumption of the analyzed seafood is safe. However, there is a potential risk to local consumers who prefer crustaceans. From a species-specific bioaccumulation point of view, species in high trophic guilds, benthivores, species with low motility, or those living near the sediment have been found to be most likely to accumulate metals. Our findings could contribute to the understanding of the accumulation tendencies of metals in species of different trophic guilds with varying habitat preferences and motility and provide valuable data to environmental and seafood safety managers.

Reduced growth capacity of preimplantation mouse embryos in chronic unpredictable stress model.

Psychological stress can affect female reproduction by deteriorating oocyte quality, but the molecular mechanism is unclear. In this study, we used the chronic unpredictable stress model to study the effect of psychological stress on mouse oocyte competence during preimplantation stage, and RNA sequencing in single oocytes to analyze differential gene expression at the transcription level. Stress changed the serum levels of glucocorticoids and reduced oocyte developmental potential, depending on the strength of the stress. Strong stress (two stressors per day) reduced the fertilization rate and induced significant apoptosis in blastocysts. Moderate stress (one stressor per day) reduced the cleavage rate and blastocyst formation rate. Weak stress (one stressor every 2 days) did not have any significant negative effect on the fertilization, cleavage, and blastocyst formation. Hatching rate was not affected by stress, but stress retarded the development of the expanded blastocysts and inhibited the embryo development at early stages. Transcriptome analysis revealed that stress disturbed the expression of cell cycle regulators and apoptotic genes. The hub genes identified through protein-protein interaction analysis include Msln, Ceacam12, Psg16, Psg17, and Psg23, which are all carcinoembryonic or related genes involved in cell adhesion, proliferation, and migration. Thus, stress was inhibitory on fertilization and early embryo development in mice.