Investigating the Impact of Varied Dietary Protein Levels on Litopenaeus vannamei: An Exploration of the Intestinal Microbiota and Transcriptome Responses.

This study explored the effects of dietary protein levels on Litopenaeus vannamei with its intestinal microbiota and transcriptome responses. Previous studies on the effects of dietary protein levels on L. vannamei have focused on growth performance, antioxidant indices, and digestive enzyme activity, but few studies have been conducted at the microbiological and molecular levels. In this study, five isolipid experimental diets with protein levels of 32% (P32), 36% (P36), 40% (P40), 44% (P44), and 48% (P48) were used in an L. vannamei (0.63 ± 0.02 g) feeding trial for 56 days. At the end of the feeding trial, the growth performance, immunity, intestinal health, and transcriptional responses of L. vannamei were determined. This study demonstrated that higher protein levels (P44) led to superior weight gain and growth rates for L. vannamei, with lower feed conversion ratios (FCR) observed in the P48 and P44 groups compared to the P32 and P36 groups (p ≤ 0.05). The P44 and P48 groups also showed a notably higher protein efficiency ratio (PER) compared to others (p ≤ 0.05), and there was no significant difference between them. Upon Vibrio parahaemolyticus infection, the P48 group exhibited a significantly lower survival rate (SR) within 48 h, while during 72 h of white spot syndrome virus (WSSV) infection, the P44 group had a notably higher survival rate than the P32 group (p ≤ 0.05). Digestive enzyme activity and antioxidant levels in L. vannamei initially increased and then decreased as protein levels increased, usually peaking in the P40 or P44 groups. Lower dietary protein levels significantly reduced the relative abundance of beneficial bacteria and increased the relative abundance of pathogenic bacteria in the intestines of L. vannamei. Transcriptome sequencing analysis revealed that most differentially expressed genes (DEGs) were up-regulated and then down-regulated as dietary protein levels increased. Furthermore, KEGG pathway enrichment analysis indicated that several immune and metabolic pathways, including metabolic pathways, glutathione metabolism, cytochrome P450, and lysosome and pancreatic secretion, were significantly enriched. In summary, the optimal feed protein level for L. vannamei shrimp was 40-44%. Inappropriate feed protein levels reduced antioxidant levels and digestive enzyme activity and promoted pathogen settlement, deceasing factors in various metabolic pathways that respond to microorganisms through transcriptional regulation. This could lead to stunted growth in L. vannamei and compromise their immune function.

Toxic effects of nanoplastics and microcystin-LR coexposure on the liver-gut axis of Hypophthalmichthys molitrix.

Plastic products and nutrients are widely used in aquaculture facilities, resulting in copresence of nanoplastics (NPs) released from plastics and microcystins (MCs) from toxic cyanobacteria. The potential effects of NPs-MCs coexposure on aquatic products require investigation. This study investigated the toxic effects of polystyrene (PS) NPs and MC-LR on the gut-liver axis of silver carp Hypophthalmichthys molitrix, a representative commercial fish, and explored the effects of the coexposure on intestinal microorganism structure and liver metabolic function using traditional toxicology and multi-omics association analysis. The results showed that the PS-NPs and MC-LR coexposure significantly shortened villi length, and the higher the concentration of PS-NPs, the more obvious the villi shortening. The coexposure of high concentrations of PS-NPs and MC-LR increased the hepatocyte space in fish, and caused obvious loss of gill filaments. The diversity and richness of the fish gut microbes significantly increased after the PS-NPs exposure, and this trend was amplified in the copresence of MC-LR. In the coexposure, MC-LR contributed more to the alteration of fish liver metabolism, which affected the enrichment pathway in glycerophospholipid metabolism and folic acid biosynthesis, and there was a correlation between the differential glycerophospholipid metabolites and affected bacteria. These results suggested that the toxic mechanism of PS-NPs and MC-LR coexposure may be pathological changes of the liver, gut, and gill tissues, intestinal microbiota disturbance, and glycerophospholipid metabolism imbalance. The findings not only improve the understanding of environmental risks of NPs combined with other pollutants, but also provide potential microbiota and glycerophospholipid biomarkers in silver carp.

Radiomics analysis using MR imaging of subchondral bone for identification of knee osteoarthritis.

BACKGROUND: To develop a magnetic resonance imaging (MRI)-based radiomics predictive model for the identification of knee osteoarthritis (OA), based on the tibial and femoral subchondral bone, and compare with the trabecular structural parameter-based model. METHODS: Eighty-eight consecutive knees were scanned with 3T MRI and scored using MRI osteoarthritis Knee Scores (MOAKS), in which 56 knees were diagnosed to have OA. The modality of sagittal three-dimensional balanced fast-field echo sequence (3D BFFE) was used to image the subchondral bone. Four trabecular structural parameters (bone volume fraction [BV/TV], trabecular thickness [Tb.Th], trabecular separation [Tb.Sp], and trabecular number) and 93 radiomics features were extracted from four regions of the lateral and medial aspects of the femur condyle and tibial plateau. Least absolute shrinkage and selection operator (LASSO) was used for feature selection. Machine learning-based support vector machine models were constructed to identify knee OA. The performance of the models was assessed by area under the curve (AUC) of the receiver operator characteristic (ROC). The correlation between radiomics features and trabecular structural parameters was analyzed using Pearson's correlation coefficient. RESULTS: Our radiomics-based classification model achieved the AUC score of 0.961 (95% confidence interval [CI], 0.912-1.000) when distinguishing between normal and knee OA, which was higher than that of the trabecular parameter-based model (AUC, 0.873; 95% CI, 0.788-0.957). The first-order, texture, and Laplacian of Gaussian-based radiomics features correlated positively with Tb.Th and BV/TV, but negatively with Tb.Sp (P < 0.05). CONCLUSIONS: Our results suggested that our MRI-based radiomics models can be used as biomarkers for the classification of OA and are superior to the conventional structural parameter-based model.

Effects of UV/Fe(II)/sulfite pre-treatment on NOM-enhanced Ca2+ scaling during nanofiltration treatment: Fouling mitigation, mechanisms, and correlation analysis of membrane resistance.

This study was aimed to evaluate the effects of a pre-treatment involving sulfite (S(IV)) synergistically activated by ultraviolet (UV)/Fe(II) on natural organic matter (NOM)-enhanced Ca2+ scaling during nanofiltration treatment. Based on the variations in the physicochemical properties and correlation analyses of irreversible resistance, the intrinsic fouling mechanisms were revealed from two aspects: bulk crystallization (interaction between NOM and inorganic ions) and surface crystallization (morphology of surface crystallization and a change in the Ca2+ concentration in the scaling layer). Furthermore, the degradation contribution rates of different free radicals during the UV/Fe(II)/S(IV) (UFS) treatment process were evaluated. During the reactions in the UFS, three free radicals (SO·-4, OH·- and e- aq) were generated, and in-situ Fe(III) was formed in-situ. The carboxyl groups of the NOM were attacked by the free radicals, resulting in decreased of carboxyl concentration and density. In addition, the bond between Ca2+ and NOM weakened, and hydrophobic (HPO) substances were mineralized. However, the Fe(III) formed in-situ was active and electropositive, competing with Ca2+ for the complexation active sites on the NOM. The synergy effect of bulk crystallization and surface crystallization led to a significant decrease in the particle size of feed solution. The crystal size and roughness of membrane surface also decreased, which was conducive to reducing the membrane irreversible resistance. Correlation analysis revealed that the HPO ratio, carboxyl density and particle size (> 100 nm) ratio were effective characterization parameters for predicting irreversible resistance. This study not only provides guidance for alleviating membrane fouling caused by NOM-enhanced Ca2+ scaling during the nanofiltration process, but also presents the rationality of irreversible resistance during nanofiltration process and various indicators with strong linear correlation.

Using radiomic features of lumbar spine CT images to differentiate osteoporosis from normal bone density.

OBJECTIVE: This study aimed to develop a predictive model to detect osteoporosis using radiomic features from lumbar spine computed tomography (CT) images. METHODS: A total of 133 patients were included in this retrospective study, 41 men and 92 women, with a mean age of 65.45 ± 9.82 years (range: 31-94 years); 53 had normal bone mineral density, 32 osteopenia, and 48 osteoporosis. For each patient, the L1-L4 vertebrae on the CT images were automatically segmented using SenseCare and defined as regions of interest (ROIs). In total, 1,197 radiomic features were extracted from these ROIs using PyRadiomics. The most significant features were selected using logistic regression and Pearson correlation coefficient matrices. Using these features, we constructed three linear classification models based on the random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms, respectively. The training and test sets were repeatedly selected using fivefold cross-validation. The model performance was evaluated using the area under the receiver operator characteristic curve (AUC) and confusion matrix. RESULTS: The classification model based on RF had the highest performance, with an AUC of 0.994 (95% confidence interval [CI]: 0.979-1.00) for differentiating normal BMD and osteoporosis, 0.866 (95% CI: 0.779-0.954) for osteopenia versus osteoporosis, and 0.940 (95% CI: 0.891-0.989) for normal BMD versus osteopenia. CONCLUSIONS: The excellent performance of this radiomic model indicates that lumbar spine CT images can effectively be used to identify osteoporosis and as a tool for opportunistic osteoporosis screening.