Microvascular Decompression Plus Partial Resection of Trigeminal Nerve Motor Root for Hemimasticatory Spasm Combined With Hemifacial Atrophy: A Good Choice or Not?

Hemimasticatory spasm (HMS) combined with hemifacial atrophy is a rare clinical entity with an unclear etiology. The authors report a 58-year-old female suffering from HMS and hemifacial atrophy, which performed microvascular decompression plus partial resection of the trigeminal nerve motor root, which is the first report in worldwide. The vascular compression of the trigeminal nerve motor root was found in surgery, the authors completed the aforementioned surgical method. The symptoms of HMS disappeared after surgery but recurred after 8 months.

Predicting rectal cancer prognosis from histopathological images and clinical information using multi-modal deep learning.

Objective: The objective of this study was to provide a multi-modal deep learning framework for forecasting the survival of rectal cancer patients by utilizing both digital pathological images data and non-imaging clinical data. Materials and methods: The research included patients diagnosed with rectal cancer by pathological confirmation from January 2015 to December 2016. Patients were allocated to training and testing sets in a randomized manner, with a ratio of 4:1. The tissue microarrays (TMAs) and clinical indicators were obtained. Subsequently, we selected distinct deep learning models to individually forecast patient survival. We conducted a scanning procedure on the TMAs in order to transform them into digital pathology pictures. Additionally, we performed pre-processing on the clinical data of the patients. Subsequently, we selected distinct deep learning algorithms to conduct survival prediction analysis using patients' pathological images and clinical data, respectively. Results: A total of 292 patients with rectal cancer were randomly allocated into two groups: a training set consisting of 234 cases, and a testing set consisting of 58 instances. Initially, we make direct predictions about the survival status by using pre-processed Hematoxylin and Eosin (H&E) pathological images of rectal cancer. We utilized the ResNest model to extract data from histopathological images of patients, resulting in a survival status prediction with an AUC (Area Under the Curve) of 0.797. Furthermore, we employ a multi-head attention fusion (MHAF) model to combine image features and clinical features in order to accurately forecast the survival rate of rectal cancer patients. The findings of our experiment show that the multi-modal structure works better than directly predicting from histopathological images. It achieves an AUC of 0.837 in predicting overall survival (OS). Conclusions: Our study highlights the potential of multi-modal deep learning models in predicting survival status from histopathological images and clinical information, thus offering valuable insights for clinical applications.

Genome-Wide Analysis of the Xyloglucan Endotransglucosylase/Hydrolase (XTH) Gene Family: Expression Pattern during Magnesium Stress Treatment in the Mulberry Plant (Morus alba L.) Leaves.

Mulberry (Morus alba L.), a significant fruit tree crop, requires magnesium (Mg) for its optimal growth and productivity. Nonetheless, our understanding of the molecular basis underlying magnesium stress tolerance in mulberry plants remains unexplored. In our previous study, we identified several differential candidate genes associated with Mg homeostasis via transcriptome analysis, including the xyloglucan endotransglucosylase/hydrolase (XTH) gene family. The XTH gene family is crucial for plant cell wall reconstruction and stress responses. These genes have been identified and thoroughly investigated in various plant species. However, there is no research pertaining to XTH genes within the M. alba plant. This research systematically examined the M. alba XTH (MaXTH) gene family at the genomic level using a bioinformatic approach. In total, 22 MaXTH genes were discovered and contained the Glyco_hydro_16 and XET_C conserved domains. The MaXTHs were categorized into five distinct groups by their phylogenetic relationships. The gene structure possesses four exons and three introns. Furthermore, the MaXTH gene promoter analysis reveals a plethora of cis-regulatory elements, mainly stress responsiveness, phytohormone responsiveness, and growth and development. GO analysis indicated that MaXTHs encode proteins that exhibit xyloglucan xyloglucosyl transferase and hydrolase activities in addition to cell wall biogenesis as well as xyloglucan and carbohydrate metabolic processes. Moreover, a synteny analysis unveiled an evolutionary relationship between the XTH genes in M. alba and those in three other species: A. thaliana, P. trichocarpa, and Zea mays. Expression profiles from RNA-Seq data displayed distinct expression patterns of XTH genes in M. alba leaf tissue during Mg treatments. Real-time quantitative PCR analysis confirmed the expression of the MaXTH genes in Mg stress response. Overall, this research enhances our understanding of the characteristics of MaXTH gene family members and lays the foundation for future functional genomic study in M. alba.

Decoding the effects of varied peer victimization forms on depression and anxiety among Chinese adolescents: An exploration through latent transition analysis.

This study aims to examine co-occurrence patterns of depression and anxiety among Chinese adolescents and their associations with various forms of peer victimization. We collected longitudinal data from 1005 middle school students using the Multidimensional Peer Victimization Scale, Center for Epidemiological Studies Depression Scale, and State-Trait Anxiety Inventory. Then we conducted latent profile analysis, latent transition analysis, and logistic regression analysis. The results reveal the presence of three depression-anxiety profiles among participants: low depression-anxiety group, moderate depression-anxiety group, and high depression-anxiety group. As verbal and relational victimization increase, adolescents are more likely to transition to a higher level of depression-anxiety profile. However, an increase in physical and property victimization predicts a transition to a lower level of depression-anxiety profile. The diverse effects resulting from different forms of victimization exhibit gender differences. For boys, an increase in relational victimization made participants in the moderate depression-anxiety group more likely to transition to the high depression-anxiety group, whereas this effect was not significant among girls. This study is theoretically significant for understanding the link between depression, anxiety, and their influencing factors. It suggests that educators, while addressing verbal and relational harm in adolescents, should reconsider the potential impact of physical and property harm. Opportunities to transform negative events into positive ones should be explored. Educators should tailor their focus based on gender, with a particular emphasis on addressing relational harm among male students. This underscores the need for differentiated approaches to effectively support students.

Associations of nirmatrelvir-ritonavir treatment with death and clinical improvement in hospitalized patients with COVID-19 during the Omicron wave in Beijing, China: a multicentre, retrospective cohort study.

BACKGROUND: The effectiveness of nirmatrelvir-ritonavir has mainly been shown in non-hospitalized patients with mild-to-moderate coronavirus disease 2019 (COVID-19). The real-world effectiveness of nirmatrelvir-ritonavir urgently needs to be determined using representative in-hospital patients with COVID-19 during the Omicron wave of the pandemic. METHODS: We performed a multicentre, retrospective study in five Chinese PLA General Hospital medical centers in Beijing, China. Patients hospitalized with COVID-19 from 10 December 2022 to 20 February 2023 were eligible for inclusion. A 1:1 propensity score matching was performed between the nirmatrelvir-ritonavir group and the control group. RESULTS: 1010 recipients of nirmatrelvir-ritonavir and 1010 matched controls were finally analyzed after matching. Compared with matched controls, the nirmatrelvir-ritonavir group had a lower incidence rate of all-cause death (4.6/1000 vs. 6.3/1000 person-days, p = 0.013) and a higher incidence rate of clinical improvement (47.6/1000 vs. 45.8/1000 person-days, p = 0.012). Nirmatrelvir-ritonavir was associated with a 22% lower all-cause mortality and a 14% higher incidence of clinical improvement. Initiation of nirmatrelvir-ritonavir within 5 days after symptom onset was associated with a 50% lower mortality and a 26% higher clinical improvement rate. By contrast, no significant associations were identified among patients receiving nirmatrelvir-ritonavir treatment more than 5 days after symptom onset. Nirmatrelvir-ritonavir was also associated with a 50% increase in survival days and a 12% decrease in days to clinical improvement. CONCLUSION: Among hospitalized patients with COVID-19 during the Omicron wave in Beijing, China, the early initiation of nirmatrelvir-ritonavir was associated with clinical benefits of lowering mortality and improving clinical recovery.

Alternations of Blood Pressure Following Surgical or Drug Therapy for Prolactinomas.

Several subtypes of pituitary neuroendocrine tumors (PitNETs), such as acromegaly and Cushing's disease, can result in hypertension. However, whether prolactinoma is associated with this complication remains unknown. Moreover, the effect of treatment with surgery or drugs on blood pressure (BP) is unknown. Herein, a retrospective study reviewed 162 patients with prolactinoma who underwent transsphenoidal surgery between January 2005 and December 2022. BP measurements were performed 1 day before and 5 days after surgery. Accordingly, patients' medical characteristics were recorded. In addition, in situ rat and xenograft nude-mice prolactinoma models have been used to mimic prolactinoma. In vivo BP and serum prolactin (PRL) levels were measured after cabergoline (CAB) administration in both rats and mice. Our data suggest that surgery can effectively decrease BP in prolactinoma patients with or without hypertension. The BP-lowering effect was significantly associated with several variables, including age, sex, disease duration, tumor size, invasion, dopamine agonists (DAs)-resistance, recurrence, and preoperative PRL levels. Moreover, in situ and xenograft prolactinomas induced BP elevation, which was alleviated by CAB treatment without and with a statistical difference in rats and mice, respectively. Thus, surgery or CAB can decrease BP in prolactinoma, indicating that pre- and postoperative BP management becomes essential.

Optimizing the Probabilistic Neural Network Model with the Improved Manta Ray Foraging Optimization Algorithm to Identify Pressure Fluctuation Signal Features.

To improve the identification accuracy of pressure fluctuation signals in the draft tube of hydraulic turbines, this study proposes an improved manta ray foraging optimization (ITMRFO) algorithm to optimize the identification method of a probabilistic neural network (PNN). Specifically, first, discrete wavelet transform was used to extract features from vibration signals, and then, fuzzy c-means algorithm (FCM) clustering was used to automatically classify the collected information. In order to solve the local optimization problem of the manta ray foraging optimization (MRFO) algorithm, four optimization strategies were proposed. These included optimizing the initial population of the MRFO algorithm based on the elite opposition learning algorithm and using adaptive t distribution to replace its chain factor to optimize individual update strategies and other improvement strategies. The ITMRFO algorithm was compared with three algorithms on 23 test functions to verify its superiority. In order to improve the classification accuracy of the probabilistic neural network (PNN) affected by smoothing factors, an improved manta ray foraging optimization (ITMRFO) algorithm was used to optimize them. An ITMRFO-PNN model was established and compared with the PNN and MRFO-PNN models to evaluate their performance in identifying pressure fluctuation signals in turbine draft tubes. The evaluation indicators include confusion matrix, accuracy, precision, recall rate, F1-score, and accuracy and error rate. The experimental results confirm the correctness and effectiveness of the ITMRFO-PNN model, providing a solid theoretical foundation for identifying pressure fluctuation signals in hydraulic turbine draft tubes.

Real-world effectiveness of nirmatrelvir-ritonavir versus azvudine in hospitalized patients with COVID-19 during the omicron wave in Beijing: a multicenter retrospective cohort study.

BACKGROUND AND AIM: Two oral antivirals (Nirmatrelvir- ritonavir and Azvudine) are widely used in China practice during the Omicron wave of the pandemic. However, little evidence regarding the real-world effectiveness of these two oral antivirals in in-hospital patients. We aimed to evaluate the clinical effectiveness of nirmatrelvir-ritonavir versus azvudine among adult hospitalized patients with COVID-19. METHODS: This retrospective cohort study used data from three Chinese PLA General Hospital medical centres. Hospitalized patients with COVID-19 treated with azvudine or nirmatrelvir-ritonavir from Dec 10, 2022, to February 20, 2023, and did not require invasive ventilation support on admission were eligible for inclusion. RESULTS: After exclusions and propensity-score matching, the final analysis included 486 azvudine recipients and 486 nirmatrelvir-ritonavir recipients. By 28 days of initiation of the antivirus treatment, the crude incidence rate of all-cause death was similar in both types of antivirus treatment (nirmatrelvir-ritonavir group 2.8 events 1000 person-days [95% CI, 2.1-3.6] vs azvudine group 3.4 events/1000 person-days [95% CI, 2.6-4.3], P = 0.38). Landmark analysis showed that all-cause death was lower in the nirmatrelvir-ritonavir (3.5%) group than the azvudine (6.8%, P = 0.029) within the initial 10-day admission period, while no significant difference was observed for results between 10 and 28 days follow-up. There was no significant difference between the nirmatrelvir-ritonavir group and the azvudine group in cumulative incidence of the composite disease progression event (8.6% with nirmatrelvir-ritonavir vs. 10.1% with azvudine, HR, 1.22; 95% CI 0.80-1.86, P = 0.43). CONCLUSION: Among patients hospitalized with COVID-19 during the omicron wave in Beijing, similar in-hospital clinical outcomes on 28 days were observed between patients receiving nirmatrelvir-ritonavir and azvudine. However, it is worth noticing that nirmatrelvir-ritonavir appears to hold an advantage over azvudine in reducing early mortality. Further randomized controlled trials are needed to verify the efficacy of those two antivirus medications especially in early treatment.

Genome-wide transcriptome profiling of mulberry (Morus alba) response to boron deficiency and toxicity reveal candidate genes associated with boron tolerance in leaves.

Mulberry (Morus alba) is an essential plant with countless economic benefits; however, its growth and metabolic processes are hampered by boron (B) stresses. Very little research has been performed to elucidate boron tolerance and detoxification mechanisms in this species. The M. alba cultivar, Yu-711, was exposed to five different concentrations of boric acid (H3BO3), including deficient (T1; 0 mM) moderate B deficiency (T2; 0.02 mM), sufficient (CK; 0.1 mM) and toxic (T3 and T4; 0.5 and 1 mM) levels for 18 days of growth in pots experiment. Transcriptome analysis of B deficiency and toxicity treatments was performed on mulberry leaves. The transcriptome data reveal that a total of 6114 genes were differentially expressed (DEGs), of which 3830 were up-regulated and 2284 were down-regulated. A comparative analysis between treatment groups CK-vs-T1 (deficiency) and CK-vs-T4 (toxicity) indicates that 590 and 1383 genes were down-regulated in both deficiency and B toxicity, respectively. The results show that 206 genes were differentially expressed in all treatments. B deficiency and toxicity significantly altered the expression of the key aquaporins (PIP2-1, PIP2-7, PIP2-4 and NIP3-1) and high-affinity boron transporter genes (BOR1 and BOR7). In addition, boron stress also altered the expression of antioxidants and photosynthesis-related genes. B stresses were found to alter several transcription factors including ERF1B, which is associated with the regulation of boron uptake and the synthesis and signaling of phytohormones. Unravelling the mechanisms of B tolerance and detoxification is important and would give us further insight into how B stresses affect mulberry plants.

3D genome structural variations play important roles in regulating seed oil content of Brassica napus.

Dissecting the complex regulatory mechanism of seed oil content (SOC) is one of the main research goals in Brassica napus. Increasing evidence suggests that genome architecture is linked to multiple biological functions. However, the effect of genome architecture on SOC regulation remains unclear. Here, we used high-throughput chromatin conformation capture to characterize differences in the three-dimensional (3D) landscape of genome architecture of seeds from two B. napus lines, N53-2 (with high SOC) and Ken-C8 (with low SOC). Bioinformatics analysis demonstrated that differentially accessible regions and differentially expressed genes between N53-2 and Ken-C8 were preferentially enriched in regions with quantitative trait loci (QTLs)/associated genomic regions (AGRs) for SOC. A multi-omics analysis demonstrated that expression of SOC-related genes was tightly correlated with genome structural variations in QTLs/AGRs of B. napus. The candidate gene BnaA09g48250D, which showed structural variation in a QTL/AGR on chrA09, was identified by fine-mapping of a KN double-haploid population derived from hybridization of N53-2 and Ken-C8. Overexpression and knockout of BnaA09g48250D led to significant increases and decreases in SOC, respectively, in the transgenic lines. Taken together, our results reveal the 3D genome architecture of B. napus seeds and the roles of genome structural variations in SOC regulation, enriching our understanding of the molecular mechanisms of SOC regulation from the perspective of spatial chromatin structure.

The Competing Endogenous RNAs Regulatory Genes Network Mediates Leaf Shape Variation and Main Effector Gene Function in Mulberry Plant (Morus alba).

Mulberry plants (Morus alba) have leaf shapes, ranging from unlobed to lobed, which are crucial for yield, growth, and adaptability, indicating their ability to adapt to their environment. Competing endogenous RNAs (ceRNAs) constitute a web of RNAs within the organism's transcriptional regulatory system, including protein-coding genes (mRNAs), microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and others. In this study, samples for ceRNA sequencing were categorized into two groups: whole leaves and lobed leaves, each group with three replicates. In addition, we isolated, cloned, and characterized the precursor miRNA (miR156x) from the leaves of M. alba. miR156x precursor had a length of 107 base pairs and a minimum folding free energy of 50.27 kcal/mol. We constructed a pCAMBIA-35S-GUS-miR156x dual overexpression vector and established a transient transformation system for mulberry. At an optimal transformation solution (OD600 = 0.7), the GUS gene showed a higher expression in the leaves of transiently transformed mulberry with miR156x overexpression, four days after transformation, while the target genes of miR156x had decreased expression in the same leaves. Investigations into the transgenic mulberry plants uncovered various modifications to physio-chemical parameters including POD, SOD, PRO, MDA, soluble proteins and sugars, and chlorophyl content. miRNAs in the plants were found to act as negative regulators of gene expression in response to changes in leaf shape regulation, which was confirmed in vitro using dual-luciferase reporter assays. Subsequently, we cloned Maspl3 in vitro and conducted GST-Pull down assays, obtaining multiple proteins that interacted with the Maspl3 gene. This indicates that the miR156x/Maspl3/MSTRG.25812.1 regulatory module contributes to the differences in mulberry leaf shape.

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning.

Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI, these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus, the diagnosis of LTBI faces many challenges, such as the lack of effective biomarkers from Mycobacterium tuberculosis (MTB) for distinguishing LTBI, the low diagnostic efficacy of biomarkers derived from the human host, and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture, as the gold standard for diagnosing tuberculosis, is time-consuming and cannot distinguish between ATB and LTBI. In this article, we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI, including the innate and adaptive immune responses, multiple immune evasion mechanisms of MTB, and epigenetic regulation. Based on this knowledge, we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis, as well as the advantages and limitations of ML in this context. Finally, we discuss the future development directions of ML applied to LTBI diagnosis.

Does early adversity predict aggression among Chinese male violent juvenile offenders? The mediating role of life history strategy and the moderating role of meaning in life.

BACKGROUND: Adolescent aggression has long been of interest to researchers. However, few studies have examined the influencing factors and mechanisms of aggression among violent juvenile offenders. This study tests a moderated mediation model with Chinese male violent juvenile offenders as subjects. Specifically, it explores the relationship between early adversity and aggression, as well as the mechanisms of life history strategy and meaning in life in this relationship. METHODS: A total of 537 Chinese male violent juvenile offenders completed the Childhood Environment Scale, the Life History Strategy Short Form Scale, the Aggression Questionnaire, and the Meaning in Life Questionnaire. After controlling for socioeconomic status (SES), the current cross-sectional study used structural equation modeling (SEM) to examine a moderated mediation model. RESULTS: The results showed that life history strategy mediated the relationship between early adversity and aggression, and early adversity affected individuals' aggression by accelerating their life history strategies. The results also showed that meaning in life moderated the relationship between early adversity and life history strategy. For individuals with high meaning in life scores, the negative predictive effect of early adversity on life history strategy was stronger than that for individuals with low meaning in life scores. CONCLUSION: The results of this study can advance the understanding of how early adversity affects aggression among violent juvenile offenders and provide theoretical support for prison staff to develop educational strategies and subsequent interventions.

The Effects of 1-Deoxynojirimycin from Mulberry on Oxidative Stress and Inflammation in Laying Hens and the Direct Effects on Intestine Epithelium Cells In Vitro.

The intestine is highly vulnerable to various factors and has been proposed as a promising determinant for poultry health. Phytogenic or plant-derived feed additives can be used to help improve intestinal health. In this study, we aimed to investigate the effects of DNJ on the antioxidative parameters, including malondialdehyde (MDA), total superoxide dismutase (T-SOD), catalase (CAT), glutathione peroxidase (GSH-Px), and inflammatory cytokines (IL-6, IL-1ß, and TNF-α), in plasma and intestinal tissues using layers supplemented with or without the DNJ extract of mulberry leaves (DNJ-E) via the ELISA method. A total of 192 healthy Hy-Line Brown layers, aged 47 weeks old, were used to conduct a 56-day study. All hens were randomly separated into four groups as follows: a basal diet containing 0 mg/kg DNJ-E(CON), 50 mg/kg, 100 mg/kg, and 150 mg/kg DNJ-E. Furthermore, the potential mechanism by which DNJ influences intestinal function was also investigated in in vitro cultured intestinal epithelium cells (IEC) with quantification methods including the use of a cell counting kit-8 (CCK8), ELISA, qRT-PCR, and ROS detection. The results showed that CAT in plasma significantly increased following 50 mg/kg DNJ-E supplementation. Moreover, 50 mg/kg DNJ-E supplementation was associated with increases in T-SOD in the jejunum and ileum. However, there was no significant difference in inflammatory cytokines between groups in in vivo experiments. Subsequent in vitro IEC studies revealed that cell viability increased significantly following 5 µM and 10 µM DNJ treatments while decreasing significantly following 20 µM DNJ treatment. Antioxidative parameters improved at 5 µM and 10 µM DNJ concentrations. However, there were no ameliorative effects on antioxidant parameters observed under 20 µM DNJ treatment. The expression levels of Nrf2 mRNA increased significantly under DNJ treatment. DNJ treatment was associated with significant changes in the expression of genes of inflammatory cytokines. In conclusion, our study revealed that DNJ could improve oxidative stress and inflammation responses in the chicken intestine. These findings provide a theoretical reference for the development of functional feed additives that regulate intestinal health and lay the foundation for systematically revealing the mechanism of DNJ.

Nacre-inspired, strong, tough silk fibroin hydrogels based on biomineralization and the layer-by-layer assembly of ordered silk fabric.

Hydrogels are attractive materials with structures and functional properties similar to biological tissues and widely used in biomedical engineering. However, traditional synthetic hydrogels possess poor mechanical strength, and their applications are limited. Herein, a multidimensional material design method is developed; it includes the in situ gelation of silk fabric and nacre-inspired layer-by-layer assembly, which is used to prepare silk fibroin (SF) hydrogels. The in situ gelation method of silk fabric introduces a directionally ordered fabric network in a silk substrate, considerably enhancing the strength of hydrogels. Based on the nacre structure, the layer-by-layer assembly method enables silk hydrogels to break through the size limit and increase the thickness, realizing the longitudinal extension of the hydrogels. The application of the combined biomineralization and hot pressing method can effectively reduce interface defects and improve the interaction between organic and inorganic interfaces. The multidimensional material design method helps increase the strength (287.78 MPa), toughness (18.43 MJ m-3), and fracture energy (50.58 kJ m-2) of SF hydrogels; these hydrogels can weigh 2000 times their own weight. Therefore, SF hydrogels designed using the aforementioned combined method can realize the combination of strength and toughness and be used in biological tissue engineering and structural materials.

LEARN algorithm: a novel option for predicting non-alcoholic steatohepatitis.

Background: There is an unmet need for accurate non-invasive methods to diagnose non-alcoholic steatohepatitis (NASH). Since impedance-based measurements of body composition are simple, repeatable and have a strong association with non-alcoholic fatty liver disease (NAFLD) severity, we aimed to develop a novel and fully automatic machine learning algorithm, consisting of a deep neural network based on impedance-based measurements of body composition to identify NASH [the bioeLectrical impEdance Analysis foR Nash (LEARN) algorithm]. Methods: A total of 1,259 consecutive subjects with suspected NAFLD were screened from six medical centers across China, of which 766 patients with biopsy-proven NAFLD were included in final analysis. These patients were randomly subdivided into the training and validation groups, in a ratio of 4:1. The LEARN algorithm was developed in the training group to identify NASH, and subsequently, tested in the validation group. Results: The LEARN algorithm utilizing impedance-based measurements of body composition along with age, sex, pre-existing hypertension and diabetes, was able to predict the likelihood of having NASH. This algorithm showed good discriminatory ability for identifying NASH in both the training and validation groups [area under the receiver operating characteristics (AUROC): 0.81, 95% CI: 0.77-0.84 and AUROC: 0.80, 95% CI: 0.73-0.87, respectively]. This algorithm also performed better than serum cytokeratin-18 neoepitope M30 (CK-18 M30) level or other non-invasive NASH scores (including HAIR, ION, NICE) for identifying NASH (P value <0.001). Additionally, the LEARN algorithm performed well in identifying NASH in different patient subgroups, as well as in subjects with partial missing body composition data. Conclusions: The LEARN algorithm, utilizing simple easily obtained measures, provides a fully automated, simple, non-invasive method for identifying NASH.

Magnesium Nutrient Application Induces Metabolomics and Physiological Responses in Mulberry (Morus alba) Plants.

Mulberry (Morus alba) is a significant plant with numerous economic benefits; however, its growth and development are affected by nutrient levels. A high level of magnesium (Mg) or magnesium nutrient starvation are two of the significant Mg factors affecting plant growth and development. Nevertheless, M. alba's metabolic response to different Mg concentrations is unclear. In this study, different Mg concentrations, optimal (3 mmol/L), high (6 mmol/L and 9 mmol/L), or low (1 and 2 mmol/L) and deficient (0 mmol/L), were applied to M. alba for three weeks to evaluate their effects via physiological and metabolomics (untargeted; liquid chromatography-mass spectrometry (LC-MS)) studies. Several measured physiological traits revealed that Mg deficiency and excess Mg altered net photosynthesis, chlorophyll content, leaf Mg content and fresh weight, leading to remarkable reductions in the photosynthetic efficiency and biomass of mulberry plants. Our study reveals that an adequate supply of the nutrient Mg promoted the mulberry's physiological response parameters (net photosynthesis, chlorophyll content, leaf and root Mg content and biomass). The metabolomics data show that different Mg concentrations affect several differential metabolite expressions (DEMs), particularly fatty acyls, flavonoids, amino acids, organic acid, organooxygen compounds, prenol lipids, coumarins, steroids and steroid derivatives, cinnamic acids and derivatives. An excessive supply of Mg produced more DEMs, but negatively affected biomass production compared to low and optimum supplies of Mg. The significant DEMs correlated positively with mulberry's net photosynthesis, chlorophyll content, leaf Mg content and fresh weight. The mulberry plant's response to the application of Mg used metabolites, mainly amino acids, organic acids, fatty acyls, flavonoids and prenol lipids, in the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. These classes of compounds were mainly involved in lipid metabolism, amino acid metabolism, energy metabolism, the biosynthesis of other secondary metabolites, the biosynthesis of other amino acids, the metabolism of cofactors and vitamin pathways, indicating that mulberry plants respond to Mg concentrations by producing a divergent metabolism. The supply of Mg nutrition was an important factor influencing the induction of DEMs, and these metabolites were critical in several metabolic pathways related to magnesium nutrition. This study provides a fundamental understanding of DEMs in M. alba's response to Mg nutrition and the metabolic mechanisms involved, which may be critical to the mulberry genetic breeding program.

The impact of boron nutrient supply in mulberry (Morus alba) response to metabolomics, enzyme activities, and physiological parameters.

Boron (B) is essential for normal and healthy plant growth. Therefore, Boron stress is a common abiotic stress that limits plant growth and productivity. However, how mulberry copes with boron stress remains unclear. In this study, seedlings of the Morus alba cultivar, Yu-711, were treated with five different concentrations of boric acid (H3BO3), including deficient (0 and 0.02 mM), sufficient (0.1 mM) and toxic (0.5 and 1 mM) levels. Physiological parameters, enzymatic activities and non-targeted liquid chromatography-mass spectrometry (LC-MS) technique were employed to evaluate the effects of boron stress on the net photosynthetic rate (Pn), chlorophyll content, stomatal conductance (Gs), transpiration rate (Tr), intercellular CO2 concentration (Ci) and metabolome signatures. Physiological analysis revealed that Boron deficiency and toxicity induced a decline in Pn, Ci, Gs, Tr, and chlorophyll content. Also, enzymatic activities, including catalase (CAT) and superoxide dismutase (SOD), decreased, while POD activity increased in response to Boron stress. Osmotic substances such as soluble sugars, soluble proteins, and proline (PRO) presented elevated levels under all Boron concentrations. Metabolome analysis indicated that differential metabolites, including amino acids, secondary metabolites, carbohydrates, and lipids, played a key role in Yu-711's response to Boron stress. These metabolites were mainly involved in amino acid metabolism, biosynthesis of other secondary metabolites, lipid metabolism, metabolism of cofactors and vitamins, and metabolism of other amino acids pathways. Our findings reveal the various metabolites pathways in mulberry response to boron nutrient supply and may serve as fundamental knowledge in breeding resistance mulberry plants, so that it can cope with climate changes.

Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm.

To improve the identification accuracy of the vibration states of hydraulic units, an improved artificial rabbits optimization algorithm (IARO) adopting an adaptive weight adjustment strategy is developed for optimizing the support vector machine (SVM) to obtain an identification model, and the vibration signals with different states are classified and identified. The variational mode decomposition (VMD) method is used to decompose the vibration signals, and the multi-dimensional time-domain feature vectors of the signals are extracted. The IARO algorithm is used to optimize the parameters of the SVM multi-classifier. The multi-dimensional time-domain feature vectors are input into the IARO-SVM model to realize the classification and identification of vibration signal states, and the results are compared with those of the ARO-SVM model, ASO-SVM model, PSO-SVM model and WOA-SVM model. The comparative results show that the average identification accuracy of the IARO-SVM model is higher at 97.78% than its competitors, which is 3.34% higher than the closest ARO-SVM model. Therefore, the IARO-SVM model has higher identification accuracy and better stability, and can accurately identify the vibration states of hydraulic units. The research can provide a theoretical basis for the vibration identification of hydraulic units.