Optimal probiotic combinations for treating nonalcoholic fatty liver disease: A systematic review and network meta-analysis.

BACKGROUND: Probiotic administration is a promising therapy for improving conditions in NAFLD patients. This network meta-analysis aimed to compare and estimate the relative effects of probiotic interventions and identify the optimal probiotic species for the treatment of NAFLD (Nonalcoholic fatty liver disease) patients. METHODS: The PubMed, Web of Science, Embase, and Cochrane databases were searched from inception to 29 January 2024 to identify RCTs that were published in English. The GRADE framework was used to assess the quality of evidence contributing to each network estimate. RESULTS: A total of 35 RCTs involving 2212 NAFLD patients were included in the analysis. For primary outcomes, Lactobacillus + Bifidobacterium + Streptococcus exhibited the highest probability of being the finest probiotic combination in terms of enhancing acceptability as well as reducing AST (SMD: -1.95 95% CI: -2.90, -0.99), ALT (SMD = -1.67, 95% CI: -2.48, -0.85), and GGT levels (SMD = -2.17, 95% CI: -3.27, -1.06). In terms of the secondary outcomes, Lactobacillus + Bifidobacterium + Streptococcus was also the best probiotic combination for reducing BMI (SMD = -0.45, 95% CI: -0.86, -0.04), LDL levels (SMD = -0.45, 95% CI: -0.87, -0.02), TC levels (SMD = -1.09, 95% CI: -1.89, -0.29), and TNF-α levels (SMD = -1.73, 95% CI: -2.72, -0.74). CONCLUSION: This network meta-analysis revealed that Lactobacillus + Bifidobacterium + Streptococcus may be the most effective probiotic combination for the treatment of liver enzymes, lipid profiles, and inflammation factors. These findings can be used to guide the development of a probiotics-based treatment guideline for NAFLD since there are few direct comparisons between different therapies.

Suppression of JAK2/STAT3 Pathway by Notoginsenoside R1 Reduces Epithelial-Mesenchymal Transition in Non-small Cell Lung Cancer.

It has bene reported that a novel saponin-notoginsenoside R1 (NGR1) possesses strong anti-tumor activities. This study aimed to investigate the role and mechanism of NGR1 in non-small cell lung cancer (NSCLC). NSCLC cell viability, proliferation, migration, and invasiveness were assessed using the ex vivo assays. NSCLC xenograft mouse models were constructed to confirm the role of NGR1 in vivo. Epithelial-mesenchymal transition (EMT)-related proteins and key markers in the JAK2/STAT3 pathway were examined using immunoblotting and immunohistochemistry analyses. NGR1 treatment suppressed NSCLC cell growth ex vivo and in vivo. It also decreased the migratory and invasive capacities of NSCLC cells. Additionally, NGR1 increased E-cadherin expression and reduced N-cadherin, vimentin, and snail expression in TGF-ß1-treated NSCLC cells and xenograft tumors. JAK2/STAT3 pathway was inhibited by NGR1. Moreover, a specific inhibitor of JAK2, AG490, or STAT3 silencing significantly enhanced the effects of NGR1 against the EMT process in NSCLC cells. NGR1 restrains EMT process in NSCLC by inactivating JAK2/STAT3 signaling, suggesting the potential of NGR1 in anti-NSCLC therapy.

Research on fault diagnosis of rolling bearing based on improved convolutional neural network with sparrow search algorithm.

Traditional approaches to the intelligent fault diagnosis of rolling bearings have predominantly relied on manual expertise for feature extraction, a practice that compromises robustness. In addition, the existing convolutional neural network (CNN) is characterized by an overabundance of parameters and a substantial requirement for training samples. To address these limitations, this study introduces a novel fault diagnosis algorithm for rolling bearings, integrating a one-dimensional convolutional neural network (1DCNN) with a support vector machine (SVM) to form an enhanced 1DCNN-SVM model. This model is further refined using the sparrow search algorithm (SSA) for the optimal adjustment of the parameters of 1DCNN-SVM. Specifically, by substituting the CNN's final softmax layer with an SVM, the model becomes better suited for processing limited data volumes. In addition, the incorporation of batch normalization and dropout layers within the CNN framework significantly augments its fault classification accuracy for rolling bearings, concurrently mitigating the risk of overfitting. The SSA is subsequently applied to refine three principal hyper-parameters: batch size, initial learning rate, and the L2 regularization coefficient, thereby overcoming the challenges associated with manually adjusting parameters, such as extended processing times and unpredictable outcomes. Empirical tests on Case Western Reserve University (CWRU) datasets revealed the model's superior performance, with the SSA-optimized 1DCNN-SVM showcasing diagnostic accuracies over 98%, marked improvements over conventional models, and a significant reduction in processing times. This method not only marks a significant advancement in intelligent fault diagnosis for rolling bearings but also demonstrates the potential of integrating machine learning for more precise and efficient diagnostics. The SSA-1DCNN-SVM model, optimized for accuracy and minimal data use, sets a new standard in fault diagnosis, relevant for machinery health monitoring and maintenance strategies across various industries.

Development of an on-site diagnostic LAMP assay for rapid differentiation of the invasive pest Phthorimaea absoluta (Meyrick) using insect tissues.

BACKGROUND: The tomato leafminer, Phthorimaea absoluta (Meyrick) (Lepidoptera: Gelechiidae), is a destructive invasive pest that originated in South America and has spread within China since 2017. A rapid method for on-site identification of P. absoluta is urgently needed for interception of this pest across China. RESULTS: We developed a loop-mediated isothermal amplification (LAMP) technique to differentiate P. absoluta from Liriomyza sativae, Chromatomyia horticola, and Phthorimaea operculella using extracted genomic DNA, which was then refined to create an on-site LAMP diagnostic method that can be performed under field conditions without the need for laboratory equipment. CONCLUSION: In the present research, we developed an on-site diagnostic method for rapid differentiation of P. absoluta from other insects with similar morphology or damage characteristics in China. © 2024 Society of Chemical Industry.

Assessment of optimal combinations of therapeutic probiotics for depression, anxiety, and stress.

BACKGROUND: Accumulating data show that probiotics may be beneficial for reducing depressive, anxiety, and stress symptoms. However, the best combinations and species of probiotics have not been identified. The objective of our study was to assess the most effective combinations and components of different probiotics through network meta-analysis. METHOD: A systematic search of four databases, PubMed, Web of Science, Cochrane, and Embase, was conducted from inception to 11 January 2024. The GRADE framework was used to assess the quality of evidence contributing to each network estimate. RESULTS: We deemed 45 trials eligible, these included 4053 participants and 10 types of interventions. The quality of evidence was rated as high or moderate. The NMA revealed that Bifidobacterium exhibited a greater probability of being the optimal probiotic species for improving anxiety symptoms (SMD = -0.80; 95% CI -1.49 to -0.11), followed by Lactobacillus (SMD = -0.49; 95% CI -0.85 to -0.12). In addition, for multiple strains, compared with the other interventions, Lactobacillus + Bifidobacterium (SMD = -0.41; 95% CI -0.73 to -0.10) had a positive effect on depression. CONCLUSION: The NMA revealed that Lactobacillus and Bifidobacterium had prominent efficacy in the treatment of individuals with anxiety, depression, and combination of Lactobacillus + Bifidobacterium had a similar effect. With few direct comparisons available between probiotic species, this NMA may be instrumental in shaping the guidelines for probiotic treatment of psychological disorders.

Appropriate Prescribing for older adults with Multimorbidity (Pro-M): protocol for a feasibility study.

BACKGROUND: Potentially inappropriate prescribing is common among older adults with multimorbidity due to various reasons, from concurrent application of multiple single-disease clinical guidelines to fragmentation of care. Interventions such as medication review have been implemented worldwide to reduce inappropriate prescribing for older adults. However, the implementability of such interventions are underexplored in the outpatient clinics in Singapore's public hospitals. Hence, the Pro-M study aims to assess the feasibility of implementing a physician-pharmacist collaborative care intervention in geriatric medicine outpatient clinics to facilitate appropriate prescribing for older adults in Singapore. METHODS: This is a single-arm, non-randomised feasibility study using a pre-post evaluation design. This study consists of two parts: (1) implementation phase of the intervention (6 months) and an (2) evaluation phase (3 months). Eligible patients will be recruited from geriatric medicine outpatient clinics at two public hospitals in Singapore through convenience sampling. The main components of the Pro-M intervention are: (1) pharmacist-facilitated medication reviews with feedback on any medication issues and potential recommendations to physicians, and (2) physicians communicating changes to other relevant prescribers. The evaluation phase will involve surveying and interviewing physicians and pharmacists involved in the implementation of the intervention. A mixed-method approach will be employed for data collection and analysis. The quantitative and qualitative findings will be triangulated and reported using Proctor's implementation outcomes: appropriateness, penetration, acceptability, fidelity, feasibility, and sustainability. A basic cost analysis will be conducted alongside the study. DISCUSSION: This is a phase 2 study to test the feasibility of implementing an intervention that was co-created with stakeholders during phase 1 development of an intervention to optimise prescribing for older adults with multimorbidity. The implementation will be assessed using Proctor's implementation outcomes to provide insights on the process and the feasibility of implementing medication reviews for older adults with multimorbidity as a routine practice in outpatient clinics. Data collected from this study will inform a subsequent scale-up study. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05756478. Registered on 06 March 2023.

Imperatorin inhibits oxidative stress injury and neuroinflammation via the PI3K/AKT signaling pathway in the MPTP-induced Parkinson's disease mouse.

Parkinson's disease (PD) is a disorder of neurodegeneration. Imperatorin is an active natural furocoumarin characterized by antioxidant, anti-inflammatory, and potent vasodilatory properties. Therefore, we aimed to investigate the biological functions of imperatorin and its mechanisms against PD progression. C57BL/6 mice were intraperitoneally injected with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP; 30 mg/kg) daily for 5 consecutive days to mimic PD conditions in vivo. The MPTP-induced PD model mice were intraperitoneally injected with imperatorin (5 mg/kg) for 25 consecutive days after MPTP administration. The motor and cognitive functions of mice were examined by rotarod test, hanging test, narrow beam test and Morris water maze test. After analysis of MWM test, the expression levels of tyrosine hydroxylase and Iba-1 in the substantia nigra pars compacta were measured by immunohistochemistry staining, immunofluorescence staining and western blotting. The expression levels of striatal dopamine and its metabolite 3,4-dihydroxyphenylacetic acid were also measured. The protein levels of inducible nitric-oxide synthase, cyclooxygenase-2, phosphorylated phosphatidylinositol 3-kinase (PI3K) and phosphorylated protein kinase B (Akt) in the mouse striatum were estimated by western blotting. The expression levels of proinflammatory cytokines including tumor necrosis factor, interleukin (IL)-1ß and IL-6 in the mouse striatum were measured by ELISA kits. The expression levels of superoxide dismutase, malondialdehyde and glutathione in the mouse midbrains were measured with commercially available kits. TUNEL staining was performed to identify the apoptosis of midbrain cells. Histopathologic changes in the mouse striata were assessed by hematoxylin-eosin staining. Imperatorin treatment markedly improved spatial learning and memory abilities of MPTP-induced PD mice. The MPTP-induced dopaminergic neuron loss in the mouse striata was inhibited by imperatorin. Imperatorin also suppressed neuroinflammation and neuronal oxidative stress in the midbrains of MPTP-induced PD mice. Mechanistically, imperatorin treatment inhibited the MPTP-induced reduction in the PI3K/Akt pathway. Imperatorin treatment can prevent dopaminergic neuron degeneration and improve cognitive functions via its potent antioxidant and anti-inflammatory properties in an MPTP-induced PD model in mice by regulating the PI3K/Akt pathway.

Vitamin D promotes the folate transport and metabolism in zebrafish (Danio rerio).

Vitamin D (VD) is a fat-soluble sterol that possesses a wide range of physiological functions. The present study aimed to evaluate the effects of VD on folate metabolism in zebrafish and further investigated the underlying mechanism. Wild-type (WT) zebrafish were fed with a diet containing 0 IU/kg VD3 or 800 IU/kg VD3 for 3 wk. Meanwhile, cyp2r1 mutant zebrafish with impaired VD metabolism was used as another model of VD deficiency. Our results showed that VD deficiency in zebrafish suppressed the gene expression of folate transporters, including reduced folate carrier (RFC) and proton-coupled folate transporter (PCFT) in the intestine. Moreover, VD influenced the gene expression of several enzymes related to cellular folate metabolism in the intestine and liver of zebrafish. Importantly, VD-deficient zebrafish contained a remarkably lower level of folate content in the liver. Notably, VD was incapable of altering folate metabolism in zebrafish when gut microbiota was depleted by antibiotic treatment. Further studies proved that gut commensals from VD-deficient fish displayed a lower capacity to produce folate than those from WT fish. Our study revealed the potential correlation between VD and folate metabolism in zebrafish, and gut microbiota played a key role in VD-regulated folate metabolism in zebrafish.NEW & NOTEWORTHY Our study has identified that VD influences intestinal uptake and transport of folate in zebrafish while also altering hepatic folate metabolism and storage. Interestingly, the regulatory effects of VD on folate transport and metabolism diminished after the gut flora was interrupted by antibiotic treatment, suggesting that the regulatory effects of VD on folate metabolism in zebrafish are most likely dependent on the intestinal flora.

[Research Status of Nanomaterial Medical Device and Discussion on Biological Evaluation].

In recent years, China has made great progress in basic nanomedicine, nanotoxicology and nanobiology research. Nanotechnology has been continuously applied in biomaterial and medical device, more and more medical devices applying nanomaterials are developed and manufactured. In order to gain more comprehension and accurate understanding of the research and industrial development in nanobiomaterial medical devices, this study reviewed the common nanomaterial in medical devices and the regulatory situation of nanomaterial medical devices at home and abroad, and discussed the current challenges in biological evaluation of nanomaterial medical devices, with a view to providing ideas for the safety evaluation and research of related products.

Research on the classification of early-stage brain edema by combining intrinsic optical signal imaging and laser speckle contrast imaging.

The early detection and pathological classification of brain edema are very important for symptomatic treatment. The dual-optical imaging system (DOIS) consists of intrinsic optical signal imaging (IOSI) and laser speckle contrast imaging (LSCI), which can acquire cerebral hemodynamic parameters of mice in real-time, including changes of oxygenated hemoglobin concentration ( Δ C HbO 2 ), deoxyhemoglobin concentration (ΔCHbR) and relative cerebral blood flow (rCBF) within the field of view. The slope sum of Δ C HbO 2 , ΔCHbR and rCBF was proposed to classify vasogenic edema (VE) and cytotoxic edema (CE). The slope sum values in the VE and CE group remain statistically different and the classification results provide higher accuracy of more than 93% for early brain edema detection. In conclusion, the differences of hemodynamic parameters between VE and CE in the early stage were revealed and the method helps in the classification of early brain edema.

Effects of riboflavin deficiency and high dietary fat on hepatic lipid accumulation: a synergetic action in the development of non-alcoholic fatty liver disease.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is characterized by excessive lipid accumulation in the liver. Riboflavin, one of water soluble vitamins, plays a role in lipid metabolism and antioxidant function. However, the effects of riboflavin deficiency on NAFLD development have not yet to be fully explored. METHODS: In the present study, an animal model of NAFLD was induced by high fat diet feeding in mice and a cellular model of NAFLD was developed in HepG2 cells by palmitic acid (PA) exposure. The effects of riboflavin deficiency on lipid metabolism and antioxidant function were investigated both in vivo and in vitro. In addition, the possible role of peroxisome proliferator-activated receptor gamma (PPARγ) was studied in HepG2 cells using gene silencing technique. RESULTS: The results showed that riboflavin deficiency led to hepatic lipid accumulation in mice fed high fat diet. The expressions of fatty acid synthase (FAS) and carnitine palmitoyltransferase 1 (CPT1) were up-regulated, whereas that of adipose triglyceride lipase (ATGL) down-regulated. Similar changes in response to riboflavin deficiency were demonstrated in HepG2 cells treated with PA. Factorial analysis revealed a significant interaction between riboflavin deficiency and high dietary fat or PA load in the development of NAFLD. Hepatic PPARγ expression was significantly upregulated in mice fed riboflavin deficient and high fat diet or in HepG2 cells treated with riboflavin deficiency and PA load. Knockdown of PPARγ gene resulted in a significant reduction of lipid accumulation in HepG2 cells exposed to riboflavin deficiency and PA load. CONCLUSIONS: There is a synergetic action between riboflavin deficiency and high dietary fat on the development of NAFLD, in which PPARγ may play an important role.

Cognitive Profiles Stratified by Education Using Montreal Cognitive Assessment in Parkinson's Disease Patients with Freezing of Gait.

Purpose: Parkinson's disease (PD) patients with freezing of gait (FOG) may present with complex and heterogeneous cognitive profiles. Owing to limited access to comprehensive neuropsychological battery in ordinary clinical practice, the Montreal Cognitive Assessment (MoCA) is likely to be easily available cognitive data for comparisons among studies. This study aims to explore the cognitive profiles stratified by education using MoCA in PD patients with FOG. Patients and Methods: PD patients with FOG (FOG+, n = 52) and without FOG (FOG-, n = 71) were included in our study. MoCA items were categorized into five subsections (attention/working memory, executive function, episodic memory, language, and visuospatial function) referring to previously published criteria. Cognitive assessments were compared based on five subsections between groups stratified by three education levels (0-6 years, 7-12 years, and >12 years). The association of cognitive measurements with FOG were analyzed using binary logistic regression models with adjustment for variables. Results: The total scores and subscores of each subsection of MoCA were similar between two groups of each education level. Further detailed analysis showed that a composite measure labeled "Attention/working memory-Composite" (abbreviated to Attention-C), consisting of the scores of four items (target detection task, serial sevens, digit forward and backward, and sentence repetition), were lower significantly in FOG+ group compared with FOG- group in patients with education year ≤6 years. The significant association of Attention-C with FOG held true when controlling for disease duration, but not for H-Y stage, MDS-UPDRS III, HAMA, and HAMD. Conclusion: Overall, our findings gave a hint that Attention-C derived from MoCA might be a potential factor associated with FOG in PD patients with lower education level (education year ≤ 6 years), which will need to be validated in future studies.

Radiomics of Preoperative Multi-Sequence Magnetic Resonance Imaging Can Improve the Predictive Performance of Microvascular Invasion in Hepatocellular Carcinoma.

Background: The aim of the study is to demonstrate that radiomics of preoperative multi-sequence magnetic resonance imaging (MRI) can indeed improve the predictive performance of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods: A total of 206 patients with pathologically confirmed HCC who underwent preoperative enhanced MRI were retrospectively recruited. Univariate and multivariate logistic regression analysis identified the independent clinicoradiologic predictors of MVI present and constituted the clinicoradiologic model. Recursive feature elimination (RFE) was applied to select radiomics features (extracted from six sequence images) and constructed the radiomics model. Clinicoradiologic model plus radiomics model formed the clinicoradiomics model. Five-fold cross-validation was used to validate the three models. Discrimination, calibration, and clinical utility were used to evaluate the performance. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to compare the prediction accuracy between models. Results: The clinicoradiologic model contained alpha-fetoprotein (AFP)_lg10, radiological capsule enhancement, enhancement pattern and arterial peritumoral enhancement, which were independent risk factors of MVI. There were 18 radiomics features related to MVI constructed the radiomics model. The mean area under the receiver operating curve (AUC) of clinicoradiologic, radiomics and clinicoradiomics model were 0.849, 0.925 and 0.950 in the training cohort and 0.846, 0.907 and 0.933 in the validation cohort, respectively. The three models' calibration curves fitted well, and decision curve analysis (DCA) confirmed the clinical usefulness. Compared with the clinicoradiologic model, the NRI of radiomics and clinicoradiomics model increased significantly by 0.575 and 0.825, respectively, and the IDI increased significantly by 0.280 and 0.398, respectively. Conclusions: Radiomics of preoperative multi-sequence MRI can improve the predictive performance of MVI in HCC.

GPAT1 Deficiency in Mice Modulates NASH Progression in a Model-Dependent Manner.

BACKGROUND & AIMS: Nonalcoholic fatty liver disease (NAFLD), and its more severe form, nonalcoholic steatohepatitis (NASH), is the leading cause for liver failure and liver cancer. Although the etiology is likely multifactorial, genes involved in regulating lipid metabolism are enriched in human NAFLD genome-wide association studies (GWAS), pointing to dysregulated lipid metabolism as a major pathogenic factor. Glycerol-3-phosphate acyltransferase 1 (GPAT1), encoded by GPAM, converts acyl-CoAs and glycerol-3-phosphate into lysophosphatidic acid and has been shown to regulate lipid accumulation in the liver. However, its role in mediating the progression from NAFLD to NASH has not been explored. METHODS: GPAT1-deficient mice were generated and challenged with diets inducing hepatic steatosis and NASH. Effects of GPAT1 deficiency on lipid and systemic metabolic end points were evaluated. RESULTS: Ablating GPAT1 globally or specifically in mouse hepatocytes reduced hepatic steatosis in the context of diet-induced or genetic obesity. Interestingly, blunting of progression from NAFLD to NASH in global GPAT1 knockout (KO) mice was model dependent. GPAT1 KO mice were protected from choline deficient, amino acid defined high-fat diet-induced NASH development, but not from the high fat, high carbohydrate, and high cholesterol diet-induced NASH. CONCLUSIONS: Our preclinical data support the notion that lipid metabolism pathways regulated by GPAT1 in hepatocytes play an essential role in NASH progression, albeit in a model-dependent manner.

Vitamin D regulates glucose metabolism in zebrafish (Danio rerio) by maintaining intestinal homeostasis.

Vitamin D (VD) is a steroid hormone that is widely known to play an important role in maintaining mineral homeostasis, and regulating various physiological functions. Our previous results demonstrated that the interruption of VD metabolism caused hyperglycemia in zebrafish. In the present study we further explored the mechanism that VD regulates glucose metabolism by maintaining intestinal homeostasis in zebrafish. Our results showed that the expression of several peptide hormones including gastric inhibitory peptide, peptide YY, and fibroblast growth factor 19 in the intestine decreased, while the expression of sodium glucose cotransporter-1 and gcg was increased in the intestine of the zebrafish fed with the VD3-deficient diet. Consistently, similar results were obtained in cyp2r1-/- zebrafish, in which endogenous VD metabolism is blocked. Furthermore, the results obtained from germ-free zebrafish exhibited that VD-regulated glucose metabolism was partly dependent on the microbiota in zebrafish. Importantly, the transplantation of gut microbiota collected from cyp2r1-/- zebrafish to germ-free zebrafish led to hyperglycemic symptoms in the fish, which were associated with the altered structure and functions of the microbiota in cyp2r1-/- zebrafish. Interestingly, the treatments with acetate or Cetobacterium somerae, a potent acetate producer, lowered the glucose contents whereas augmented insulin expression in zebrafish larvae. Notably, acetate supplementation alleviated hyperglycemia in cyp2r1-/- zebrafish and other diabetic zebrafish. In conclusion, our study has demonstrated that VD modulates the gut microbiota-SCFAs-gastrointestinal hormone axis, contributing to the maintenance of glucose homeostasis.

Atmospheric Turbulence Aberration Correction Based on Deep Learning Wavefront Sensing.

In this paper, research was conducted on Deep Learning Wavefront Sensing (DLWS) neural networks using simulated atmospheric turbulence datasets, and a novel DLWS was proposed based on attention mechanisms and Convolutional Neural Networks (CNNs). The study encompassed both indoor experiments and kilometer-range laser transmission experiments employing DLWS. In terms of indoor experiments, data were collected and training was performed on the platform built by us. Subsequent comparative experiments with the Shack-Hartmann Wavefront Sensing (SHWS) method revealed that our DLWS model achieved accuracy on par with SHWS. For the kilometer-scale experiments, we directly applied the DLWS model obtained from the indoor platform, eliminating the need for new data collection or additional training. The DLWS predicts the wavefront from the beacon light PSF in real time and then uses it for aberration correction of the emitted laser. The results demonstrate a substantial improvement in the average peak intensity of the light spot at the target position after closed-loop correction, with a remarkable increase of 5.35 times compared to the open-loop configuration.

Butyrate induces STAT3/HIF-1α/IL-22 signaling via GPCR and HDAC3 inhibition to activate autophagy in head kidney macrophages from turbot (Scophthalmus maximus L.).

As one of short-chain fatty acids, butyrate is an important metabolite of dietary fiber by the fermentation of gut commensals. Our recent study uncovered that butyrate promoted IL-22 production in fish macrophages to augment the host defense. In the current study, we further explored the underlying signaling pathways in butyrate-induced IL-22 production in fish macrophages. Our results showed that butyrate augmented the IL-22 expression in head kidney macrophages (HKMs) of turbot through binding to G-protein receptor 41 (GPR41) and GPR43. Moreover, histone deacetylase 3 (HDAC3) inhibition apparently up-regulated the butyrate-enhanced IL-22 generation, indicating HDACs were engaged in butyrate-regulated IL-22 secretion. In addition, butyrate triggered the STAT3/HIF-1α signaling to elevate the IL-22 expression in HKMs. Importantly, the evidence in vitro and in vivo was provided that butyrate activated autophagy in fish macrophages via IL-22 signaling, which contributing to the elimination of invading bacteria. In conclusion, we clarified in the current study that butyrate induced STAT3/HIF-1α/IL-22 signaling pathway via GPCR binding and HDAC3 inhibition in fish macrophages to activate autophagy that was involved in pathogen clearance in fish macrophages.

Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images.

Sentinel lymph node metastasis (SLNM) is a crucial predictor for breast cancer treatment and survival. This study was designed to propose deep learning (DL) models based on grayscale ultrasound, color Doppler flow imaging (CDFI), and elastography images, and to evaluate how DL radiomics can be used to classify SLNM in breast cancer. Clinical and ultrasound data of 317 patients diagnosed with breast cancer at the Second Affiliated Hospital of Nanchang University were collected from January 2018 to December 2021 and randomly divided into training and internal validation cohorts at a ratio of 7:3. An external validation cohort comprising data from Nanchang Third Hospital with 42 patients collected. Three DL models, namely DL-grayscale, DL-CDFI, and DL-elastography, were proposed to predict SLNM by analyzing grayscale ultrasound, CDFI, and elastography images. Three DL models were compared and evaluated to assess diagnostic performance based on the area under the curve (AUC). The AUCs of the DL-grayscale were 0.855 and 0.788 in the internal and external validation cohorts, respectively. For the DL-CDFI model, the AUCs were 0.761 and 0.728, respectively. The diagnostic performance of DL-elastography was superior to that of the DL-grayscale and DL-CDFI. The AUC of the DL-elastography model was 0.879 in the internal validation cohort, with a classification accuracy of 86.13%, sensitivity of 91.60%, and specificity of 82.79%. The generalization capability of DL-elastography remained high in the external cohort, with an AUC of 0.876, and an accuracy of 85.00%. DL radiomics can be used to classify SLNM in breast cancer using ultrasound images. The proposed DL-elastography model based on elastography images achieved the best diagnostic performance and holds good potential for the management of patients with SLNM.

Research on fault diagnosis of electric submersible pump based on improved convolutional neural network with Bayesian optimization.

A conventional intelligent fault diagnosis approach for electric submersible pumps (ESPs) is heavily dependent on manual expertise for feature extraction. Meanwhile, a conventional convolutional neural network (CNN) exhibits an excess of parameters and requires a substantial volume of training samples. In this paper, a fault diagnosis algorithm of ESPs based on a Bayesian optimization-one-dimensional convolutional neural network-support vector machine (BO-1DCNN-SVM) is proposed by combining a 1DCNN with a SVM and using the algorithm BO to tune the improved 1DCNN. First, the method uses the self-extracting feature capability of the 1DCNN to solve the problem that traditional diagnosis methods over-rely on manual experience extraction. Meanwhile, the last layer of the convolutional neural network Softmax layer is replaced by the SVM to effectively process a few sample data. The accuracy and generalization ability of the fault classification of the electric submersible pump are improved. Then, the Bayesian optimization algorithm is used to find the optimal combination of hyperparameters for the improved 1DCNN-SVM model to further improve the prediction performance of the fault diagnosis model. Finally, the experimental results achieved a classification accuracy of 96.64%, which is 5% higher than existing CNN approaches with data samples of similar scale in this paper. The proposed method also proved to be highly accurate and robust in fault diagnosis.