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Objective: It was elucidated the anti-inflammatory active components and their mechanisms of action in the fruits of Diospyros lotus. Methods: Ultra-performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was used to qualitatively analyze the chemical constituents of Diospyros lotus fruits, and combined with network pharmacology to explore the potential anti-inflammatory active components and their mechanisms of action, and validated the results by molecular docking. Results: A total of 26 flavonoid compounds, including 10 myricetin derivatives, 7 quercetin derivatives, 4 kaempferol derivatives, 2 apigenin derivatives, and 3 other flavonoid compounds, were identified from extract of D. lotus fruits based on molecular ion peaks and mass spectrometry cleavage characteristics in negative ion mode, among which 12 compounds were reported for the first time in the fruits. Network pharmacological analysis showed that compounds myricetin 3-sambubioside, myricetin 3-(2G-rhamnosylrutinoside), morin, myricetin 3-rhamnosyl-(1->2)-rhamnoside, isorhamnetin-3-O-glucoside, myricetin, quercetin, naringenin, apigenin and kaempferol had potential anti-inflammatory activities and exerted anti-inflammatory effects mainly related to PI3K-Akt, EGFR tyrosine kinase inhibitor resistance and cancer signaling pathway. Molecular docking showed that the active components stably docked with the key targets, and the results were consistent with the network pharmacological predictions. Conclusion: Flavonoids in D. lotus extracts have potential anti-inflammatory effects and are useful as adjunctive preventive agents in inflammatory diseases.

The relentless rise in heart disease incidence, a leading global cause of death, presents a significant public health challenge. Precise prediction of heart disease risk and early interventions are crucial. This study investigates the performance improvement of heart disease prediction models using machine learning and deep learning algorithms. Initially, we utilized the Heart Failure Prediction Dataset from Kaggle. After preprocessing to ensure data quality, three distinct feature engineering techniques were applied: PCA for dimensionality reduction, ET for feature selection, and Pearson's correlation coefficient for feature selection. We assessed their impact on model performance. The dataset was then partitioned into three different data split ratios—1:9, 2:8, and 3:7—to determine their specific effects on model performance. Twelve machine learning classifiers—LGBM, Adaboost, XGB, RF, DT, KNN, LR, GNB, ET, SVC, GB, and Bagging—were trained and evaluated based on five key metrics: accuracy, recall, precision, F1 score, and training time. The influence of different feature engineering methods and data partitioning ratios on model performance were systematically analyzed using paired-sample t-tests. Among the feature engineering methods compared, the Bagging classifier, when combined with feature selection via ET, exhibited superior performance. It achieved an accuracy of 97.48 % and an F1-Score of 97.48 % with a data split ratio of 1:9 between the test and training sets. With a 2:8 split, the accuracy was 94.96 % and the F1-Score was 94.95 %. For a 3:7 split, the accuracy was 94.12 % and the F1-Score was 94.11 %. Paired sample T-test results indicate that feature selection using Pearson correlation coefficient can shorten training duration, but this also leads to a decline in classifier performance. After applying PCA dimensionality reduction, compared with the control group, there was no significant difference in the training efficiency and efficacy of the classifier. However, feature selection through ET significantly reduced the training time for various classifiers while ensuring their performance.

Abstract The branching fraction of D + → K S 0 π 0 e + ν e $$ {D}^{+}\to {K}_S^0{\pi}^0{e}^{+}{\nu}_e $$ is measured for the first time using 7.93 fb −1 of e + e − annihilation data collected at the center-of-mass energy s $$ \sqrt{s} $$ = 3.773 GeV with the BESIII detector operating at the BEPCII collider, and is determined to be B D + → K S 0 π 0 e + ν e = 0.881 ± 0.017 stat . ± 0.016 syst . % $$ \mathcal{B}\left({D}^{+}\to {K}_S^0{\pi}^0{e}^{+}{\nu}_e\right)=\left(0.881\pm {0.017}_{\textrm{stat}.}\pm {0.016}_{\textrm{syst}.}\right)\% $$ . Based on an analysis of the D + → K S 0 π 0 e + ν e $$ {D}^{+}\to {K}_S^0{\pi}^0{e}^{+}{\nu}_e $$ decay dynamics, we observe the S-wave and P-wave components with fractions of f S-wave = (6.13 ± 0.27stat. ± 0.30syst. )% and f K ¯ ∗ 892 0 = 93.88 ± 0.27 stat . ± 0.29 syst . % $$ {f}_{{\overline{K}}^{\ast }{(892)}^0}=\left(93.88\pm {0.27}_{\textrm{stat}.}\pm {0.29}_{\textrm{syst}.}\right)\% $$ , respectively. From these results, we obtain the branching fractions B D + → K S 0 π 0 S − wave e + ν e = 5.41 ± 0.35 stat . ± 0.37 syst . × 10 − 4 $$ \mathcal{B}\left({D}^{+}\to {\left({K}_S^0{\pi}^0\right)}_{S-\textrm{wave}}{e}^{+}{\nu}_e\right)=\left(5.41\pm {0.35}_{\textrm{stat}.}\pm {0.37}_{\textrm{syst}.}\right)\times {10}^{-4} $$ and B D + → K ¯ ∗ 892 0 e + ν e = 4.97 ± 0.11 stat . ± 0.12 syst . % $$ \mathcal{B}\left({D}^{+}\to {\overline{K}}^{\ast }{(892)}^0{e}^{+}{\nu}_e\right)=\left(4.97\pm {0.11}_{\textrm{stat}.}\pm {0.12}_{\textrm{syst}.}\right)\% $$ . In addition, the hadronic form-factor ratios of D + → K ¯ ∗ 892 0 e + ν e $$ {D}^{+}\to {\overline{K}}^{\ast }{(892)}^0{e}^{+}{\nu}_e $$ at q 2 = 0, assuming a single-pole dominance parameterization, are determined to be r V = V 0 A 1 0 = 1.43 ± 0.07 stat . ± 0.03 syst . $$ {r}_V=\frac{V(0)}{A_1(0)}=1.43\pm {0.07}_{\textrm{stat}.}\pm {0.03}_{\textrm{syst}.} $$ and r 2 = A 2 0 A 1 0 = 0.72 ± 0.06 stat . ± 0.02 syst . $$ {r}_2=\frac{A_2(0)}{A_1(0)}=0.72\pm {0.06}_{\textrm{stat}.}\pm {0.02}_{\textrm{syst}.} $$ .

Abstract Based on the advantages of triply periodic minimal surface (TPMS) porous structures, extensive research on NiTi shape memory alloy TPMS scaffolds has been conducted. However, the current reports about TPMS porous structures highly rely on the implicit equation, which limited the design flexibility. In this work, novel shell-based TPMS structures were designed and fabricated by laser powder bed fusion. The comparisons of manufacturability, mechanical properties, and shape recovery responses between traditional solid-based and novel shell-based TPMS structures were evaluated. Results indicated that the shell-based TPMS porous structures possessed larger Young’s moduli and higher compressive strengths. Specifically, Diamond shell structure possessed the highest Young’s moduli of 605.8±24.5 MPa, while Gyroid shell structure possessed the highest compressive strength of 43.90±3.32 MPa. In addition, because of the larger specific surface area, higher critical stress to induce martensite transformation, and lower austenite finish temperature, the Diamond shell porous structure exhibited much higher shape recovery performance (only 0.1% residual strain left at pre-strains of 6%) than other porous structures. These results substantially uncover the effects of structural topology on the mechanical properties and shape recovery responses of NiTi shape memory alloy scaffolds, and confirm the effectiveness of this novel structural design method. This research can provide guidance for the structural design application of NiTi porous scaffolds in bone implants.

Abstract Study of the interaction between fault activation and mining stress evolution in the longwall working face is helpful to provide a targeted area for fault type heavy mine pressure disaster control. Combining theoretical analysis, physical and numerical simulation, the mechanical mechanism of fault activation is analyzed, the interaction law between mining stress and fault activation is studied, and the influence of fault dip angle on the evolution of fault activation and mining stress is discussed. The minimum critical dip angles α of normal and reverse fault activation are π/4 + φ/2 and π/4-φ/2, respectively. During the mining process, the activation position of the fault surface, the peak values of stress and displacement gradually increase and transfer from the high position of the fault to the low position, and the peak value of the advance abutment pressure reaches the maximum at the fault. The advancing distance of the working face required for fault activation gradually decreases with the decrease of the fault dip angle, and the peak elevation area of the working face gradually increases with the decrease of the fault dip angle. Combined with the on-site microseismic monitoring results, it can be seen that when the working face is about 20 m away from the fault, the stress and energy increase sharply, which is the main control area of the impact disaster.

Abstract To investigate the role and molecular mechanism of miR-126 in unilateral ureteral occlusion (UUO). We used bioinformatics to analyse miRNAs specifically expressed in UUO. The mouse model of UUO was established using RAW264.7 cells cultured in vitro and in vivo. The mice were divided into control group, miR-126-NC (negative control) group and miR-126-KD (knockdown) group. Then the relative expression of miR-126 was detected by quantitative reverse transcription-polymerase chain reaction (qRT-PCR), the renal fibrosis was detected by Masson staining, and the protein expression of CD68, collagen I and collagen III in the kidney was detected by immunofluorescence assay. Immunohistochemistry detects α-SMA expression. Moreover, Western blotting was performed to measure the expressions of p-PI3K, CD163, CD206, CD86, iNOS, IL-1β, p-FAK, p-Rac-1, p-IRS-1 and MMP9. The relative fluorescence intensity of F-actin and p-FAK was detected by immunofluorescence assay, and the phagocytosis ability of macrophages was determined by phagocytosis assay with fluorescent microspheres. Bioinformatics analysis reveals miR-126-specific overexpression in UUO. Successful transfection of miR-126-NC and miR-126-KD was confirmed by RT-PCR. The selective reduction of miR-126 was validated by Masson, immunohistochemistry and immunofluorescence staining to decrease the area of UUO-induced renal fibrosis and to lower the expression of CD68, α-SMA, collagen I, and collagen III. The reduction of iNOS expression may also be achieved with selective knockdown of miR-126, as verified by cell tests. enhances the phagocytic ability of macrophages and the expression of p-PI3K, CD206, p-FAK, F-actin, p-Rac-1, p-IRS-1 and MMP9. MiR-126 can inhibit the PI3K signaling pathway, promote M1 macrophage polarization, and suppress the activation of FAK and Rac-1, thus accelerating the progression of UUO.

Abstract Stimulator of interferon genes (STING) is vital in the cytosolic DNA-sensing process and critical for initiating the innate immune response, which has important functions in host defense and contributes to the pathogenesis of inflammatory diseases. Zinc finger CCCH-type antiviral protein 1 (ZC3HAV1) specifically binds the CpG dinucleotides in the viral RNAs of multiple viruses and promotes their degradation. ZAPS (ZC3HAV1 short isoform) is a potent stimulator of retinoid acid-inducible gene I (RIG-I) signaling during the antiviral response. However, how ZC3HAV1 controls STING signaling is unclear. Here, we show that ZC3HAV1 specifically potentiates STING activation by associating with STING to promote its oligomerization and translocation from the endoplasmic reticulum (ER) to the Golgi, which facilitates activation of IRF3 and NF-κB pathway. Accordingly, Zc3hav1 deficiency protects mice against herpes simplex virus-1 (HSV-1) infection- or 5,6-dimethylxanthenone-4-acetic acid (DMXAA)-induced inflammation in a STING-dependent manner. These results indicate that ZC3HAV1 is a key regulator of STING signaling, which suggests its possible use as a therapeutic target for STING-dependent inflammation.

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is one of the most universal liver diseases with complicated pathogenesis throughout the world. Insulin resistance is a leading risk factor that contributes to the development of NAFLD. Vascular endothelial growth factor B (VEGFB) was described by researchers as contributing to regulating lipid metabolic disorders. Here, we investigated VEGFB as a main target to regulate insulin resistance and metabolic syndrome. Methods In this study, bioinformatics, transcriptomics, morphological experiments, and molecular biology were used to explore the role of VEGFB in regulating insulin resistance in NAFLD and its molecular mechanism based on human samples, animal models, and cell models. RNA-seq was performed to analyze the signal pathways associated with VEGFB and NAFLD; Palmitic acid and High-fat diet were used to induce insulin-resistant HepG2 cells model and NAFLD animal model. Intracellular glucolipid contents, glucose uptake, hepatic and serum glucose and lipid levels were examined by Microassay and Elisa. Hematoxylin-eosin staining, Oil Red O staining, and Periodic acid-schiff staining were used to analyze the hepatic steatosis, lipid droplet, and glycogen content in the liver. Western blot and quantitative real-time fluorescent PCR were used to verify the expression levels of the VEGFB and insulin resistance-related signals PI3K/AKT pathway. Results We observed that VEGFB is genetically associated with NAFLD and the PI3K/AKT signal pathway. After VEGFB knockout, glucolipids levels were increased, and glucose uptake ability was decreased in insulin-resistant HepG2 cells. Meanwhile, body weight, blood glucose, blood lipids, and hepatic glucose of NAFLD mice were increased, and hepatic glycogen, glucose tolerance, and insulin sensitivity were decreased. Moreover, VEGFB overexpression reduced glucolipids and insulin resistance levels in HepG2 cells. Specifically, VEGFB/VEGFR1 activates the PI3K/AKT signals by activating p-IRS1Ser307 expression, inhibiting p-FOXO1pS256 and p-GSK3Ser9 expressions to reduce gluconeogenesis and glycogen synthesis in the liver. Moreover, VEGFB could also enhance the expression level of GLUT2 to accelerate glucose transport and reduce blood glucose levels, maintaining glucose homeostasis. Conclusions Our studies suggest that VEGFB could present a novel strategy for treating NAFLD as a positive factor. Graphical Abstract

Abstract Metabolic reprogramming is a pivotal biological process in which cellular metabolic patterns change to meet the energy demands of increased cell growth and proliferation. In this review, we explore metabolic reprogramming and its impact on fibrotic diseases, providing a detailed overview of the key processes involved in the metabolic reprogramming of renal fibrosis, including fatty acid decomposition and synthesis, glycolysis, and amino acid catabolism. In addition, we report that Chinese medicine ameliorates renal inflammation, oxidative stress, and apoptosis in chronic kidney disease by regulating metabolic processes, thereby inhibiting renal fibrosis. Furthermore, we reveal that multiple targets and signaling pathways contribute to the metabolic regulatory effects of Chinese medicine. In summary, this review aims to elucidate the mechanisms by which Chinese medicine inhibits renal fibrosis through the remodeling of renal cell metabolic processes, with the goal of discovering new therapeutic drugs for treating renal fibrosis.

Abstract The characteristics of gas seepage in broken coal and rock composed of different particle sizes and grades were investigated in this study. On the basis of Darcy's law and non‐Darcy seepage theory, equations of gas permeability in the nonlinear seepage of broken coal and rock, as well as the porosity of broken coal and rock, under triaxial compression were determined. The stress loading path of gas seepage in broken coal and rock was developed. The characteristics of gas seepage in broken coal and rock composed of different particle sizes and grades were analyzed, and the results showed that the gas permeability after compression was proportional to the particle size of the broken coal and rock. Under triaxial compression, the gas permeability of the broken coal and rock composed of graded‐particle sizes was lower than that of the broken coal and rock composed of different single‐particle sizes. The gas permeability of the broken coal was lower than that of the broken rock mass, and the gas permeability and porosity of the broken coal and rock can be described by the exponential decay function. At a constant porosity, the gas permeability of the broken coal and rock was proportional to the size grading index under triaxial compression. The coefficient of viscosity and gravity of the flow are key factors influencing the flow permeability in broken coal and rock. This study provides a reference for on‐site practice such as the efficient extraction of gas in goafs.

Theoretical research on betavoltaic batteries require calculating characteristic parameters of the radiation-voltage conversion device，in order to predict the output characteristics under irradiation. Through the calculated output characteristics，important theoretical references for the design of betavoltaic batteries are provided. Our algorithm calculates the characteristic parameters of the radiation-voltage conversion device based on the tested dark current-voltage curve and evaluates the quality of the characteristic parameters by statistical methods. To prove the effectiveness of the algorithm，the current-voltage curves under irradiation were predicted，and compared with those obtained by exposing to a real radioactive source. Calculation results achieved good results in both curve fitting and output characteristic prediction of the radiation-voltage conversion device. The results show our algorithm can predict the output of betavoltaic battery through the dark current-voltage curve without irradiation and provide guidance for the design of batavoltaic batteries.

Abstract Objective To analyze the genotypes of VZV in Jiangsu province to identify vaccine strains and wild strains, providing a molecular biological background for the effective prevention and control of varicella. Method Stratified sampling was used to collect herpes fluid or throat swab from patients diagnosed with varicella. ORF22 was carried out, and the restriction enzyme site of ORF38, ORF54 and ORF62 were detected. Results All 207 virus strains were Clade 2 type by sequencing the PCR products of ORF22. The sequencing results showed that five SNP sites changed compared to the Dumas reference strain(Clade 1). From A to G at 37,902, from T to c at 38,055, from A to C at 38,081, and from G to A at 38,177, from G to A at 39,394. The prevalent VZV genotypes in Jiangsu is consistent with the P-Oka. The restriction enzyme site analysis of PCR amplification products from ORF38 (PstI), ORF54 (BglI), ORF62 (SmaI) showed that all 207 virus strains were wild-type. There were two different types of the wild strains, and 183 strains (88.4%) were PstI (+), BglI (+), SmaI (-). The wild strains between different regions showed no significant differences (χ2 = 0.05, P = 0.982). Conclusions The prevalent VZV genotypes are Clade 2 and the prevalent virus strains are wild strains in Jiangsu Province, the primary wild strain observed is mainly PstI (+), BglI (+), SmaI (-).

Abstract Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, fibrotic lung disease with limited treatment options and efficacy. Evidence suggests that IPF arises from genetic, environmental, and aging-related factors. The pathogenic mechanisms of IPF primarily involve dysregulated repeated microinjuries to epithelial cells, abnormal fibroblast/myofibroblast activation, and extracellular matrix (ECM) deposition, but thus far, the exact etiology remains unclear. Noncoding RNAs (ncRNAs) play regulatory roles in various biological processes and have been implicated in the pathophysiology of multiple fibrotic diseases, including IPF. This review summarizes the roles of ncRNAs in the pathogenesis of IPF and their potential as diagnostic and therapeutic targets.

Abstract Background Wild jujube trees in Ningxia, China, demonstrate exceptional drought tolerance. The identification of quantitative trait loci (QTLs) associated with drought resistance and linked genes could significantly enhance molecular breeding efforts for this species. This study involved the measurement of nine drought resistance indicators were measured in 150 wild jujube trees from five regions in Ningxia. Genome-wide association studies (GWAS) were carried out using a range of mixed linear models to pinpoint SNP markers linked to drought resistance. Results The coefficients of variation for the nine leaf traits in wild jujube trees ranged from 14.76 to 62.17%, with broad-sense heritability estimates falling between 0.84 and 0.99. Through GWAS analysis, a total of 12 significant SNPs and 162 potential genes associated with drought resistance were detected. This SNPs explained phenotypic variance ranging from 20.74 to 50.37%. Gene Ontology (GO) functional annotation highlighted five crucial candidate genes‒ZjMYB44, ZjUCLOC, ZjDnaJ50, ZjUCHL22 and ZjHSFB‒linked to drought tolerance in wild jujube. These genes demonstrated a positive correlation with drought tolerance within the wild jujube population. Conclusions Our findings indicate that these five genes likely play a pivotal role in conferring drought tolerance in wild jujubes. This study offers new insights to support the development of drought-resistant jujube varieties, thereby contributing to sustainable agricultural practices and bolstering food security in arid regions.

Abstract Plant height is a critical agronomic trait closely linked to yield, primarily regulated by Gibberellins (GA) and auxins, which interact in complex ways. However, the mechanism underlying their interactions remain incompletely understood. In this study, we identified a tomato mutant exhibiting significantly reduced plant height. Through gene cloning and bulked segregant analysis (BSA) sequencing, we found that the mutant gene corresponds to the tomato auxin response factor gene SlARF5/MP. Here, we show that overexpression of SlARF5/MP significantly enhances plant height. Additionally, treatment with GA3 restored the plant height of the mutant to wild-type (WT) levels, indicating that GA content is a key factor influencing plant height. We also observed significant upregulation of GA-biosynthesis genes, including GA2-oxidases GA20ox3 and GA20ox4, as well as the GA3 biosynthesis gene GA3ox1, in SlARF5-overexpressing plants. Furthermore, we demonstrated that SlARF5 directly binds to SlGA2ox3, which mediates the conversion of GA3 to inactive GA, therebyregulating its expression. Our findings suggest that SlARF5 modulates GA3 metabolism by regulating GA synthesis genes, ultimately leading to alterations in plant height.

Abstract Presenilin-associated protein (PSAP) is a mitochondrial proapoptotic protein as established in cell biology studies. It remains unknown whether it involves in neurodegenerative diseases. Here, we explored PASP expression in adult and aged human brains and its alteration relative to Alzheimer-disease (AD)-type neuropathology. In pathology-free brains, light PASP immunoreactivity (IR) occurred among largely principal neurons in the cerebrum and subcortical structures. In the brains with AD pathology, enhanced PSAP IR occurred in neuronal and neuritic profiles with a tangle-like appearance, with PSAP and pTau protein levels elevated in neocortical lysates relative to control. Neuronal/neuritic profiles with enhanced PSAP IR partially colocalized with pTau, but invariably with Amylo-Glo labelled tangles. The neuronal somata with enhanced PASP IR also showed diminished IR for casein kinase 1 delta (Ck1δ), a marker of granulovacuolar degeneration; and diminished IR for sortilin, which is normally expressed in membrane and intracellular protein sorting/trafficking organelles. In old 3xTg-AD mice with β-amyloid and pTau pathologies developed in the brain, PSAP IR in the cerebral sections exhibited no difference relative to wildtype mice. These findings indicate that PSAP upregulation is involved in the course of tangle formation especially in the human brain during aging and in AD pathogenesis.

Abstract Introduction Intestinal obstruction is a common complication in patients with advanced malignancies, often attributed to the disease itself or as a side effect of opioid analgesics used for pain management. However, the occurrence of intestinal obstruction following antituberculosis therapy is rare. Case presentation We report a unique case of a 58-year-old Asian male diagnosed with stage IV pancreatic carcinoma and pulmonary tuberculosis. The patient was initiated on a regimen of ethambutol hydrochloride, pyrazinamide, rifampicin, and isoniazid tablets (II) for tuberculosis, alongside morphine for the management of severe cancer-related pain. Subsequently, he developed symptoms indicative of intestinal obstruction. Despite discontinuation of morphine, the patient’s symptoms persisted until he autonomously ceased all medications, leading to a rapid improvement in his condition. This unexpected resolution highlighted the antituberculosis drugs as the probable cause of his intestinal obstruction. Conclusions This case underscores the importance of considering antituberculosis drugs as a potential cause of intestinal obstruction, especially in patients who do not respond to conventional management strategies for drug-induced gastrointestinal side effects. It also emphasizes the need for heightened vigilance and monitoring when prescribing these medications to patients with advanced malignancies, to promptly identify and address rare but significant side effects.

Abstract Background It was reported that more than 96% of autism spectrum disorder (ASD) children are accompanied with different degrees of sensory processing abnormalities, and up to 50% of ASD children exhibit abnormal auditory response. Studies have confirmed that some ASD children’s abnormal auditory response may be related to their abnormal auditory processing. Prior research demonstrated that ASD children’s auditory processing has high heterogeneity, thus, ASD children’s auditory processing may have different developmental trajectories. However, no study has concentrated on the developmental trajectories of ASD children’s auditory processing. In addition, auditory processing plays a crucial role in ASD children’s language development, thus, ASD children’s different language development outcomes may be related to different auditory processing development tracks. Therefore, this study aims to explore the developmental trajectory of auditory processing in ASD children and analyze the relationship between different developmental trajectories of auditory processing and language impairment. Methods/Design In this study, 220 ASD children aging 3 years and 0 months to 4 years and 11 months are recruited as the research objects, and their demographic characteristics are collected. The subjects are tested for peripheral hearing, intelligence, and autism symptoms. Furthermore, ASD children’s auditory processing and language development are evaluated at baseline, 1 year, and 2 years later. In addition, ASD children’s auditory processing is evaluated by electrophysiological test and the Preschool Auditory Processing Assessment Scale. Moreover, ASD children’s language skills are assessed using the Language Development Assessment Scale for Children Aged 1–6. The various categories of the developmental trajectory of ASD children’s auditory processing are examined through the latent category growth model. Additionally, a hierarchical regression model is developed to analyze the predictive impact of different auditory processing development trajectories on language impairment in ASD children. Discussion This longitudinal study will explore the categories of auditory processing developmental trajectories in ASD children, and analyze the relationship between different categories of auditory processing developmental trajectories and language development, providing new ideas and targeted targets for the rehabilitation training of language impairment in ASD children, as well as promoting early and accurate interventions for ASD children.

Abstract Elderly individuals display metabolite alterations that may contribute to development of cognitive impairment following surgery and anesthesia. However, these relationships remain largely unexplored. The study aims to assess the S-methyl-5-thioadenosine (MTA) is associated with postoperative delayed neurocognitive recovery (dNCR). We assess altered metabolites following anesthesia/surgery in both mice and patients to identify blood biomarkers of dNCR. Preoperative and postoperative plasma metabolites are determined by widely targeted metabolomics. The brains of mice with anesthesia/surgery show decreased MTA and activated MTA phosphorylase. Mice also show that preoperative administration of MTA can prevent inflammation and cognitive decline. In clinical patients, we detect lower preoperative serum MTA levels in those who developed dNCR. Both low preoperative and postoperative blood MTA levels are associated with increased risk of postoperative dNCR. These results suggest that anesthesia/surgery induces cognitive decline through methionine synthesis pathways and that MTA could be a perioperative predictor of dNCR.

Abstract Background Cesarean scar pregnancy (CSP), a distinct form of ectopic pregnancy, presents challenges in effective management. It is categorized into three subtypes according to the location of placental implantation and the thickness of the myometrium at the uterine scar. Nevertheless, the optimal choice of treatment modalities for these subtypes remains largely unexamined. Methods In this retrospective analysis, we investigated the cases of 130 patients diagnosed with CSP who underwent diverse treatment approaches, namely ultrasound-guided dilation and curettage (D&C), hysteroscopic surgery alone or in combination with laparoscopic surgery (HCoLC), or uterine artery embolization (UAE) followed by curettage. Clinical data were meticulously retrieved from medical records and follow-up data, and a comparative analysis of relevant indicators was carried out across the different CSP subtypes. Results From January 2017 to December 2021, 35 patients underwent D&C, 85 underwent HCoLC, and 10 received UAE as a pretreatment. In the D&C group, the success rates for Type I and Type II CSP were 64.29% (18/28) and 14.28% (1/7), respectively. Significant differences were observed between the success and failure groups in terms of gestational sac size and clinical classification. Compared to Type I CSP, Type II CSP exhibited significantly longer surgical durations and higher hospitalization costs (P

Background: Existing methods for MRI brain tumor segmentation often suffer from excessive model parameters and suboptimal performance in delineating tumor boundaries. Methods: For this issue, a lightweight MRI brain tumor segmentation method, enhanced by hierarchical feature fusion (EHFF), is proposed. This method reduces model parameters while improving segmentation performance by integrating hierarchical features. Initially, a fine-grained feature adjustment network is crafted and guided by global contextual information, leading to the establishment of an adaptive feature learning (AFL) module. This module captures the global features of MRI brain tumor images through macro perception and micro focus, adjusting spatial granularity to enhance feature details and reduce computational complexity. Subsequently, a hierarchical feature weighting (HFW) module is constructed. This module extracts multi-scale refined features through multi-level weighting, enhancing the detailed features of spatial positions and alleviating the lack of attention to local position details in macro perception. Finally, a hierarchical feature retention (HFR) module is designed as a supplementary decoder. This module retains, up-samples, and fuses feature maps from each layer, thereby achieving better detail preservation and reconstruction. Results: Experimental results on the BraTS 2021 dataset demonstrate that the proposed method surpasses existing methods. Dice similarity coefficients (DSC) for the three semantic categories ET, TC, and WT are 88.57%, 91.53%, and 93.09%, respectively.

Terahertz (THz) computed tomography (THz CT) exhibits the potential to provide a wealth of data, surpassing that of THz tomographic imaging in applications such as detecting embedded defects, particularly defect evolution within a glass fiber-reinforced polymer. To realize high-resolution THz CT, a systematic approach guided by wave propagation simulation was employed. First, the front wave of the THz beam was fine-tuned to realize a beam diameter of

Uranium-zirconium alloy is an important nuclear fuel in Integral Fast Reactor, which is of great significance to study its basic physical properties at high temperature by using advanced calculation methods. This work used deep potential molecular dynamics method, which combines the high accuracy of the first principles with the high efficiency of the classical molecular dynamics, to perform an evaluation of the static and thermophysical properties of body-centered cubic phase zirconium, uranium, and uranium-zirconium alloy. Firstly, the deep potential(DP) models of body-centered cubic zirconium(Zr-BCC), body-centered cubic uranium(U-BCC), and body-centered cubic uranium-zirconium alloy(U-Zr（BCC）) were trained by using deep neural network machine learning. Secondly, the DP models were used to predict equilibrium state equation, lattice constant, elastic properties, and phonon spectrum of the three systems, and the predicted results can reach the accuracy of the first principles. Then, the variation of heat capacity and density at constant pressure of Zr-BCC, U-BCC, and U-Zr（BCC） with temperature were predicted by using DP models, and the results are in good agreement with the experimental values. The research results show that the machine learning method provides an important path for successfully exploring more complex nuclear fuel properties.

Erodium cicutarium is an annual herbaceous plant valued for its applications in traditional medicine. However, the chloroplast genome of E. cicutarium has yet to be reported. In this study, we assembled chloroplast genomes of Erodium cicutarium using Illumina sequencing reads. The chloroplast genome was 114,652 bp long, harbored 111 complete genes, and its overall GC content was 39.1%. In Maximum Likelihood (ML) and Bayesian inference (BI) trees, the 13 Erodium species divided into three main clades, with E. cicutarium and E. carvifolium forming a monophyletic group, suggesting a close relationship between the two species. The E. cicutarium cp genome presented in this study lays a good foundation for the Erodium.

Abstract Background Metabolic regulation of various amino acids have been proven to be effective in preventing cardiovascular disease (CVD). The impact of tryptophan, an essential amino acid, on the risk of developing CVD has not been fully elucidated. Aims The aim of this meta-analysis was to systematically review evidence of the effects of tryptophan on CVD risk. Methods The PubMed, Embase, Web of Science, Cochrane Library, and China National Knowledge Infrastructure (CNKI) databases were searched to collect relevant trials from inception to August 2024. The means and hazard ratios (HRs) were extracted and pooled. Subgroup analysis was performed to identify pooled effect estimates, and sensitivity analysis was conducted to assess the robustness of the pooled estimates. Results Data were collected from 34,370 people under follow-up for CVD events in 13 studies, including cohort studies and case-control studies. They were categorized into three groups on the basis of sample type and indicators: the plasma tryptophan level group, the plasma tryptophan CVD hazard group, and the urinary tryptophan CVD hazard group. The CVD included in this study were coronary artery disease, heart failure, and peripheral artery disease. Twelve studies on plasma tryptophan were meta-analyzed. The plasma tryptophan levels in CVD patients were generally lower than those in individuals without CVD (SMD = -8.57, 95%CI (-15.77, -1.37), P = 0.02). Decreased circulating tryptophan levels are associated with cardiovascular disease risk (HR = 0.85, 95%CI (0.78, 0.92), P

Abstract It is currently unclear whether there is a connection between Life’s Essential 8 (LE8) and cardiovascular disease (CVD), as well as mortality in people with nonalcoholic fatty liver disease (NAFLD). Our goal was to explore these relationships by examining data collected in the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. We identified eligible participants with NAFLD based on NHANES 2005–2018 data. CVD status was acquired through self-reported information, and using the National Death Index, mortality data were prospectively matched. The diagnosis of NAFLD relied on noninvasive biomarkers. The research involved 9094 individuals who were identified as having NAFLD, with a mean age of 52.05 years. Each incremental LE8 score exhibited a significant association, leading to a 3%, 3%, 4%, 3%, 3%, 4%, and 4% reduction in the odds of experiencing CVD, ischemic heart disease, congestive heart failure, coronary heart disease, heart attack, angina, and stroke in individuals with NAFLD. A strong correlation was found between maintaining a superior level of Cardiovascular Health (CVH), as shown by a LE8 score ranging from 80 to 100, and a reduced occurrence of CVD and its various forms in NAFLD (all p for trend

Abstract Immunodeficiency 11B with atopic dermatitis (IMD11B, OMIM:617638) is rare primary immunodeficiency disease caused by germline dominant negative (DN) mutations in the CARD11 gene. Affected patients present with immune dysfunction, recurrent infections and atopic dermatitis. In this study, we sought to identify and characterize the genetic variant in one patient with periodic fever, recurrent infections, and eczema. Trio whole-exome sequencing (WES) was employed in this patient and her parents, and Sanger sequencing validated the potential pathogenic variant. In vitro functional study was performed to evaluate the pathogenicity of genetic variant identified. A very rare missense mutation (c.2324C > T, p.S775L) in CARD11 gene (NM_032415) was identified by WES in the patient but not her parents. Luciferase reporter assays and co-immunoprecipitation demonstrated mutation exerts a dominant-interfering effect on wild-type CARD11, inhibiting the activity of NF-κB. RNA sequencing analysis also confirmed that mutant CARD11 inhibited down-stream transcriptional activity of NF-κB. A review of literature doesn’t found significant genotype–phenotype correlation. We identified a vary rare DN CARD11 mutation, expanding the genetic and phenotypic spectrum of CARD11.

Abstract Background Selecting the optimal non-invasive diagnostic model for MASLD (Metabolic Dysfunction-Associated Steatosis Liver Disease) and steatosis progression is a critical issue given the variety of available models. We aimed to compare the performance of eight clinical prediction models for diagnosing and predicting the progression of hepatic steatosis using MRI-PDFF (Magnetic Resonance Imaging-Derived Proton Density Fat Fraction), and validate the findings with FibroScan and histopathological results. Methods In this study, 846 participants were initially enrolled, with 108 undergoing liver biopsy and 706 completing one-year follow-up, including 26 who underwent repeat biopsy. We calculated scores for eight clinical prediction models (FAST, KNAFLD, HSI, FLI, Liver Fat Score, Liver Fat Equation, BAAT, LAP) using collected clinical data and defined steatosis progression as a 30% relative increase in liver fat content (LFC) measured by MRI-PDFF. CAP(Controlled Attenuation Parameter) and LSM (Liver Stiffness Measurement) were obtained by Fibroscan. MRI-PDFF served as the reference standard for evaluating model accuracy, and sensitivity analyses were performed using liver biopsy and Fibroscan results. Results Among the eight clinical models, NAS (nonalcoholic fatty liver disease activity score) showed higher correlation with the FAST and KNAFLD models (r: 0.62 and 0.52, respectively). Among the whole cohort (N = 846), KNAFLD was the best model for predicting different degrees of hepatic steatosis (AUC = 0.84). When the KNAFLD score was above 2.935, LFC was significantly higher (4.4% vs. 19.7%, P -0.02, LFC increased (8.6–10.9%, P

Safely and effectively increasing the permeability of coal seams and improving the efficiency of coalbed methane extraction is the key to promoting efficient energy utilization. With the increase of coal mining depth in China, the difficulty of coalbed methane mining is increasing, and the risk of gas outbursts is becoming more and more serious. The expansion fracturing technology has the unique advantages of no noise, no vibration, and no dust, which makes it have broad application prospect in the field of coal seam permeability enhancement. Through the investigation and analysis of the existing research results, the reaction mechanism of static expansion materials and the mechanism of expansion cracking and permeability improvement were analyzed, and the influence of parameters such as borehole diameter, spacing, and shape on the cracking effect of the coal body was combed, and the mechanical models of coal damage (coal damage fracture model and crack propagation model) in the process of expansion cracking were summarized, and the application status of expansion cracking technology in the field of coal seam permeability enhancement was expounded, and the research direction and development suggestions of expansion fracking technology in the field of coal seam permeability enhancement were proposed.

Abstract Recently, the scalarization of the Schwarzschild black hole has been extensively studied. In this work, we explore the scalarization of the Taub-NUT black hole within the context of the extended scalar-tensor-Gauss-Bonnet theory, which admits a Ricci-flat Taub-NUT black hole as a solution. We carried out an analysis of the probe scalar field to identify the mass parameter and NUT parameter (m, n) where hairy black holes begin to emerge. Subsequently, we used the shooting method to construct the scalarized Taub-NUT black hole numerically. Unlike the Schwarzschild case, there are two branches of new hairy black holes that are smoothly connected. We calculated the entropy of the scalarized black holes and compared these entropies with those of scalar-free Taub-NUT black holes, finding that the entropies of the new hairy black holes are larger. A novel phenomenon emerges in this system: the entropy of the black holes at the bifurcation point is constant for a positive mass parameter. We then conjecture a maximal entropy bound for all scalarized black holes whose mass parameter at the bifurcation point is greater than zero.

Abstract Alzheimer's disease (AD) has emerged as the most prevalent and complex neurodegenerative disorder among the elderly population. However, the genetic comorbidity etiology for AD remains poorly understood. In this study, we conducted pleiotropic analysis for 41 AD phenotypic comorbidities, identifying ten genetic comorbidities with 16 pleiotropy genes associated with AD. Through biological functional and network analysis, we elucidated the molecular and functional landscape of AD genetic comorbidities. Furthermore, leveraging the pleiotropic genes and reported biomarkers for AD genetic comorbidities, we identified 50 potential biomarkers for AD diagnosis. Our findings deepen the understanding of the occurrence of AD genetic comorbidities and provide new insights for the search for AD diagnostic markers. Graphical Abstract Study pipeline.

Abstract According to the World Health Organization (WHO), pneumonia kills about 2 million children under the age of 5 every year. Traditional machine learning methods can be used to diagnose chest X-rays of pneumonia in children, but there is a privacy and security issue in centralizing the data for training. Federated learning prevents data privacy leakage by sharing only the model and not the data, and it has a wide range of application in the medical field. We use federated learning method for classification, which effectively protects data security. And for the data heterogeneity phenomenon existing in the actual scenario, which will seriously affect the classification effect, we propose a method based on two-end control variables. Specifically, based on the classical federated learning FedAvg algorithm, we modify the loss function on the client side by adding a regular term or a penalty term, and add momentum after the average aggregation on the server side. The federated learning approach prevents the data privacy leakage problem compared to the traditional machine learning approach. In order to solve the problem of low classification accuracy due to data heterogeneity, our proposed method based on two-end control variables achieves an average improvement of 2% and an accuracy of 98% on average, and 99% individually, compared to the previous federated learning algorithms and the latest diffusion model-based method. The classification results and methodology of this study can be utilized by clinicians worldwide to improve the overall detection of pediatric pneumonia.

Abstract Eczema is common in children, and its onset is affected by both genetic and environmental factors. We investigated the effects of genetic and environmental factors on the incidence of eczema in preschool children. 515 preschool children with eczema and 515 children participating in the physical examination were enrolled. The study included the incidence of childhood eczema, the child’s birth and feeding conditions, the history of eczema in the parents, and relevant environmental risk factors, and to comprehensively analyze the genetic and environmental factors influencing childhood eczema. Among 1030 children, 173 parents (8.4%) had eczema, with a heritability of 73.59% for boys’ parents and 58.59% for girls’ parents. Multivariate logistic regression results showed that premature infants, low birth weight, children who had used antibiotics before the age of 1 year the living environment between the first year of mother pregnancy and the first year of the child is humid, a father with a history of eczema, a mother with a history of eczema are risk factors for eczema in children. Actively preventing environmental factors related to eczema may be an effective means to reduce the risk of eczema in children.

Abstract Coronary artery disease (CAD) is one of the most common cardiovascular disorders affecting millions of individuals globally. It is the leading cause of mortality in both the wealthy and impoverished nations. CAD patients exhibit a wide range of symptoms, some of which are not evident until a major incident occurs. The development of techniques for early detection and precise diagnosis is heavily dependent on research. The proposed system introduces a novel approach, Generative Adversarial Networks Augmented Naïve Bayes (GAN-ANB), to classify high-risk CAD patients using Coronary Computed Tomography Angiography (CCTA) imaging data. The database included images from Coronary Computed Tomography Angiography (CCTA) records of 5,000 individuals. The developed GAN framework consists of a generator to generate synthetic patient profiles, and a discriminator to distinguish between genuine and synthetic profiles to improve the identification of high-risk CAD patients. Adding synthetic data to the training process allowed the discriminator to be utilized further to improve predictive modeling. The performance of the GAN-enhanced prediction model was assessed using accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve (ROC). The model exhibited an outstanding Dice Similarity Coefficient (0.91), Mean Intersection Over Union (0.90), recall (0.96), and precision (0.98) in differentiating between high-risk and low-risk individuals. The identification of high-risk patients with CAD is greatly enhanced by the integration of GANs with clinical and imaging data. ROC of 0.99 was achieved by the GAN-ANB model, which outperformed conventional machine learning models, was achieved using the GAN-ANB model. High cholesterol level, diabetes, and some CCTA-derived imaging characteristics, including plaque load and luminal stenosis, were among the major predictors. This method offers a powerful tool for early diagnosis and intervention, potentially leading to improved patient outcomes and lower healthcare expenditure.

Abstract Community detection is a critical component of network analysis and a hot topic in social computing. Detecting community structure in dynamic networks has important theoretical and practical implications for understanding the intrinsic function of networks and predicting network behavior. However, the majority of existing dynamic community detection methods adopt shallow models, which have limited ability to excavate complex non-linear structures and tend to generate undesirable community structures. In order to obtain an accurate and robust community structure in dynamic networks, we are inspired by network representation learning and utilize the deep learning to detect evolving communities in dynamic networks. In this paper, we propose a novel dynamic community detection method by fusing Deep Learning and Evolutionary Clustering (DLEC). This work attempts to combine deep learning and evolutionary clustering into a unified framework. First, we propose a matrix construction strategy to fully reveal the inherent community structures via the underlying community memberships. Then, we develop a novel multi-layer deep autoencoder framework that consists of multiple non-linear functions to extract the latent deep representation of the dynamic network. Based on the evolutionary clustering framework, a graph regularization term is introduced to ensure the smoothness of the community evolution. Finally, we employ the K-means clustering algorithm on the low-dimensional network space to obtain the community structure. Extensive experimental results on synthetic and real-world networks show that the proposed DLEC algorithm can effectively detect high-quality communities in dynamic networks.

Abstract Aiming at the problem of limited transmission energy of liquid crystal tunable filter (LCTF), a dual-wavelength transmission system with high signal-to-noise ratio (SNR) is proposed in this paper. The proposed transmission factor Qp is the main influence on the number and location of transmission wavelengths as well as the bandwidth of each transmission wavelength for dual-wavelength systems. Dual-wavelength LCTF can improve the effective transmission energy of the system by increasing the number of filtering channels, and the transmission energy can be increased by about 1.8 times and 70% at short and long wavelengths, respectively, which improves the signal-to-noise ratio (SNR) of the system. Moreover, the dual-wavelength LCTF system is even possible to increase the transmission energy by about 7% at a 33% increase in spectral resolution. Therefore, the dual-wavelength LCTF transmission method not only can improve the SNR of target detection with dual-wavelength response features, but also can effectively solve the problem of contradiction between spectral resolution and spectral transmission energy.

Abstract Background Prostate cancer (PCa) usually manifests atypical symptoms in the early stage, and once symptoms appear, most PCa patients have developed to the advanced stage, failing to undergo radical surgery. In this study, PCa occurrence-related biomarkers were explored based on single-cell RNA sequencing (scRNA-seq) data. Methods scRNA-seq data of prostate normal (Normal), benign prostatic hyperplasia (BPH), and PCa (Tumor) samples were acquired from the Gene Expression Omnibus (GEO). Cellular subsets associated with PCa occurrence were obtained using cell annotation. Additionally, the mRNA expression of nuclear enriched abundant transcript 1 (NEAT1) was detected by quantitative real-time PCR (qRT-PCR). The effects of NEAT1 on cell proliferation and apoptosis were analyzed by 5-ethynyl-2-deoxyuridine (EdU) and flow cytometry. Subsequently, cell-derived xenograft (CDX) models were constructed and divided into the LV-NC and LV-shNEAT1 groups. After the tumor tissues of CDX model mice in each group were extracted, the cell growth and Ki67 expression were observed separately using hematoxylin-eosin (H&E) staining and immunohistochemistry (IHC). Results Ten cellular subsets were obtained via cell annotation, and significantly differential changes were observed between Basal intermediate and Luminal during the course of BPH to PCa. NEAT1−Luminal was highly recruited in the Tumor group with low stemness and high malignancy scores. Matrix metallopeptidase 7 (MMP7)− keratin 17 (KRT17)−Basal intermediate had high ratios in the Tumor group with low stemness and high malignancy scores. The results of pseudotime analysis revealed that NEAT1−Luminal in the Tumor group were consistently distributed with tumor stage cells. In vitro assays showed that NEAT1 expression was elevated in PCa cells, and NEAT1 knockdown could inhibit cell proliferation and induce apoptosis. CDX assays indicated that silencing NEAT1 could reduce the growth rate of PCa tumor volume in CDX model mice. H&E staining results showed that nuclei of tumor cells were reduced and exhibited lighter color in the LV-shNEAT1 group compared with the LV-NC group. IHC results showed that Ki67 positivity was significantly lower in the LV-shNEAT1 group than in the LV-NC group. Conclusion NEAT1 expression is increased in PCa, and NEAT1 can be a potential biomarker and therapeutic target for PCa.

Abstract Background This research aimed to develop and validate a dynamic nomogram for predicting the risk of high care dependency during the hospital-family transition periods in older stroke patients. Methods 309 older stroke patients in the hospital-family transition periods who were treated in the Department of Neurology outpatient clinics of three general hospitals in Jinzhou, Liaoning Province from June to December 2023 were selected as the training set. The patients were investigated with the General Patient Information Questionnaire, the Care Dependency Scale (CDS), the Tilburg Frailty Inventory (TFI), the Hamilton Anxiety Rating Scale (HAMA), the Hamilton Depression Rating Scale-17 (HAMD-17), and the Mini Nutrition Assessment Short Form (MNA-SF). Lasso-logistic regression analysis was used to screen the risk factors for high care dependency in older stroke patients during the hospital-family transition period, and a dynamic nomogram model was constructed. The model was uploaded in the form of a web page based on Shiny apps. The Bootstrap method was employed to repeat the process 1000 times for internal validation. The model’s predictive efficacy was assessed using the calibration plot, decision curve analysis curve (DCA), and area under the curve (AUC) of the receiver operator characteristic (ROC) curve. A total of 133 older stroke patients during the hospital-family transition periods who visited the outpatient department of Neurology of three general hospitals in Jinzhou from January to March 2024 were selected as the validation set for external validation of the model. Results Based on the history of stroke, chronic disease, falls in the past 6 months, depression, malnutrition, and frailty, build a dynamic nomogram. The AUC under the ROC curves of the training set was 0.830 (95% CI: 0.784–0.875), and that of the validation set was 0.833 (95% CI: 0.766-0.900). The calibration curve was close to the ideal curve, and DCA results confirmed that the nomogram performed well in terms of clinical applicability. Conclusion The online dynamic nomogram constructed in this study has good specificity, sensitivity, and clinical practicability, which can be applied to senior stroke patients as a prediction and assessment tool for high care dependency. It is of great significance to guide the development of early intervention strategies, optimize resource allocation, and reduce the care burden on families and society.

ABSTRACT: The nutritional components and quality of milk are influenced by the rumen microbiota and its metabolites at different lactation stages. Hence, rumen fluid and milk samples from 6 dairy cows fed the same diet were collected during peak lactation, early mid-lactation, and later mid-lactation. Untargeted metabolomics and 16S rRNA sequencing were applied for analyzing milk and rumen metabolites, as well as rumen microbial composition, respectively. The levels of lipid-related metabolites, l-glutamate, glucose-1-phosphate, and acetylphosphate in milk exhibited lactation-dependent attenuation. Maltol, N-acetyl-d-glucosamine, and choline, which are associated with milk flavor or coagulation properties, as well as l-valine, lansioside A, clitocine, and ginsenoside La, increased significantly in early mid-lactation and later mid-lactation, especially in later mid-lactation. The obvious increase in rumen microbial diversities (ACE and Shannon indices) were observed in early mid-lactation compared with peak lactation. Twenty-one differential bacterial genera of the rumen were identified, with Succinivibrionaceae_UCG-001, Candidatus Saccharimonas, Fibrobacter, and SP3-e08 being significantly enriched in peak lactation. Rikenellaceae_RC9_gut_group, Eubacterium_ruminantium_group, Lachnospira, Butyrivibrio, Eubacterium_hallii_group, and Schwartzia were most significantly enriched in early mid-lactation. In comparison, only 2 bacteria (unclassified_f__Prevotellaceae and Prevotellaceae_UCG-001) were enriched in later mid-lactation. For rumen metabolites, LysoPE(16:0), l-glutamate, and l-tyrosine had higher levels in peak lactation, whereas PE(17:0/0:0), PE(16:0/0:0), PS(18:1(9Z)/0:0), l-phenylalanine, dulcitol, 2-(methoxymethyl)furan, and 3-phenylpropyl acetate showed higher levels in early mid-lactation and later mid-lactation. Multiomics-integrated analysis revealed that a greater abundance of Fibrobacter contributed to phospholipid content in milk by increasing ruminal acetate, l-glutamate, and LysoPE(16:0). Prevotellaceae_UCG-001 and unclassified_f_Prevotellaceae provide substrates for milk metabolites of the same category by increasing ruminal l-phenylalanine and dulcitol contents. These results demonstrated that milk metabolomic fingerprints and critical functional metabolites during lactation, and the key bacteria in rumen related to them. These findings provide new insights into the development of functional dairy products.

Abstract The pathophysiology of immune checkpoint inhibitor-related pneumonitis remains incompletely understood. We conducted single-cell and T-cell receptor transcriptomic sequencing on the bronchoalveolar lavage fluid from five patients with grade ≥2 immune checkpoint inhibitor-related pneumonitis. Our analyses revealed a prominent enrichment of T cells in the bronchoalveolar lavage fluid of patients with immune checkpoint inhibitor-related pneumonitis. Within the CD4 + T cell subset, Tfh-like T cells were highly enriched and exhibited signatures associated with inflammation and clonal expansion. Regulatory T cells were also enriched and displayed enhanced inhibitory functions. Within the CD8 + T-cell subset, effector memory/tissue-resident memory T cells with an elevated cytotoxic phenotype were highly infiltrated. Among myeloid cells, alveolar macrophages were depleted, while pro-inflammatory intermediate monocytes were elevated. Dendritic cells demonstrated enhanced antigen presentation capabilities. Cytokines CXCR4, CXCL13, TNF-α, IFN-α, IFN-γ, and TWEAK were elevated. Through a comprehensive single-cell analysis, we depicted the landscape of immune checkpoint inhibitor-related pneumonitis.

Abstract Examining tumor-associated macrophages in the immune microenvironment of non-small cell lung cancer (NSCLC) is essential for gaining an understanding of the genesis and development of NSCLC as well as for identifying key clinical therapeutic targets. Although previous studies have reported the diverse phenotypes and functions of macrophages in tumor tissues, thereby highlighting their significant role in the tumor microenvironment, the characteristic differences and correlations between tumor and peritumor tissue-derived macrophages that are necessary for an understanding of NSCLC progression remain unclear. Based on single-cell RNA sequencing, we generated a comprehensive dataset of transcriptomes from NSCLC tumor and peritumor tissues, thereby facilitating comprehensive analysis and providing significant insights. In summary, our dataset will serve as a valuable transcriptomic resource for further studies investigating NSCLC development.

Abstract The common pochard (Aythya ferina) is a freshwater diving duck found in the Palearctic region that has been classified as vulnerable by the IUCN due to continuous and rapid population declines across their distribution. To gain a better understanding of its genetic mechanism of adaptive evolution, we successfully sequenced and assembled the first high-quality chromosome-level genome of A. ferina using Illumina, Nanopore and Hi-C sequencing technologies. A total assembly length of 1,130.78 Mbp was obtained, with over 98.81% (1,117.37Mbp) of sequence anchored to 35 pseudo-chromosomes. We predicted 17,232 protein-coding genes, 95.9% of which were functionally annotated. We identified 339 expanded and 937 contracted gene families in the genome of A. ferina, and detected 95 genes that have been positively selected. The significantly enriched Gene Ontology and enriched pathways were related to energy metabolism, immune, nervous, and sensory systems, suggests that these factors likely played an important role in its evolution. Importantly, we recovered signatures of positive selection on genes related to vasoconstriction that may be associated with thermoregulatory adaptations of A. ferina for underwater diving. Overall, the high-quality genome assembly and annotation in this study provides valuable genomic resources for ecological and evolutionary studies, as well as toward the conservation of A. ferina.

Abstract Background N4-Acetylcytidine (ac4C), a highly conserved post-transcriptional mechanism, plays a pivotal role in RNA modification and tumor progression. However, the molecular mechanism by which ac4C modification mediates tumor immunosuppression remains elusive in triple-negative breast cancer (TNBC). Methods NAT10 expression was analyzed in TNBC samples in the level of mRNA and protein, and compared with the corresponding normal tissues. ac4C modification levels also measured in the TNBC samples. The effects of NAT10 on immune microenvironment and tumor metabolism were investigated. NAT10-mediated ac4C and its downstream regulatory mechanisms were determined in vitro and in vivo. The combination therapy of targeting NAT10 in TNBC was further explored. Results The results revealed that the loss of NAT10 inhibited TNBC development and promoted T cell activation. Mechanistically, NAT10 upregulated JunB expression by increasing ac4C modification levels on its mRNA. Moreover, JunB further up-regulated LDHA expression and facilitated glycolysis. By deeply digging, remodelin, a NAT10 inhibitor, elevated the surface expression of CTLA-4 on T cells. The combination of remodelin and CTLA-4 mAb can further activate T cells and inhibite tumor progression. Conclusion Taken together, our study demonstrated that the NAT10-ac4C-JunB-LDHA pathway increases glycolysis levels and creates an immunosuppressive tumor microenvironment (TME). Consequently, targeting this pathway may assist in the identification of novel therapeutic strategies to improve the efficacy of cancer immunotherapy.

Achieving robust joint utilization of multidomain characteristics and deep-network features while maintaining a high jamming-recognition accuracy with limited samples is challenging. To address this issue, this paper proposes a multidomain characteristic-guided multimodal contrastive recognition method for active radar jamming. This method involves first thoroughly extracting the multidomain characteristics of active jamming and then designing an optimization unit to automatically select effective characteristics and generate a text modality imbued with implicit expert knowledge. The text modality and involved time-frequency transformation image are separately fed into text and image encoders to construct multimodal-feature pairs and map them to a high-dimensional space for modal alignment. The text features are used as anchors and a guide to time-frequency image features for aggregation around the anchors through contrastive learning, optimizing the image encoder’s representation capability, achieving tight intraclass and separated interclass distributions of active jamming. Experiments show that compared to existing methods, which involve directly combining multidomain characteristics and deep-network features, the proposed guided-joint method can achieve differential feature processing, thereby enhancing the discriminative and generalization capabilities of recognition features. Moreover, under extremely small-sample conditions (2～3 training samples for each type of jamming), the accuracy of our method is 9.84% higher than those of comparative methods, proving the effectiveness and robustness of the proposed method.

Traditional physical methods for measuring solubility were often affected by viscosity, density, and surface tension. In this study, we selected appropriate electrolytes to determine the solubility of impurities in the lead-bismuth circuit at reactor operating temperatures using electrochemical methods. Taking the measurement of the solubility of impurity Ce in liquid Bi as an example, we demonstrated that electrochemical methods can accurately determine its solubility in the LiCl-KCl molten salt. Furthermore, the successful measurement of other impurities such as La, Ce, U, and Mn in lead-bismuth eutectic (LBE) using the designed system has confirmed the universality of this method.

Background/purpose: 2016 American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) primary Sjögren's syndrome (SS) diagnostic criteria did not incorporate radiographic examination while staging SS according to salivary gland imaging and serological autoantibody tests was not discussed. The aim is to study the value of parotid sialography for diagnosing SS, and to initially explore the method of staging SS based on the results of imaging and serological autoantibody tests. Materials and methods: 287 patients’ clinical records were included. The sensitivity and specificity of parotid sialography in the diagnosis of SS were investigated. SS patients were categorized into early stage (autoantibody positive, imaging does not support SS), active stage (autoantibody positive, imaging supports SS), and quiescent stage (autoantibody negative, imaging supports SS), clinical characteristics of different stages were compared. Results: The sensitivity of parotid sialography for the diagnosis of SS was 82.6%, the specificity was 71.5%. 10-minute USFR of the patients in the active stage (0.18 ± 0.38 ml/10min) was significantly lower than that of early stage (0.34 ± 0.47 ml/10min) and quiescent stage (0.54 ± 0.52 ml/10min), P = 0.010, and the rate of confirmed SS was significantly higher in the active stage (82.9%) than that in the early stage (44.4%) and the quiescent stages (14.8%), P

Effective management of malignant tumor-induced bone defects remains challenging due to severe systemic side effects, substantial tumor recurrence, and long-lasting bone reconstruction post tumor resection. Magnesium and its alloys have recently emerged in clinics as orthopedics implantable metals but mostly restricted to mechanical devices. Here, by deposition of calcium-based bilayer coating on the surface, a Mg-based composite implant platform is developed with tailored degradation characteristics, simultaneously integrated with chemotherapeutic (Taxol) loading capacity. The delicate modulation of Mg degradation occurring in aqueous environment is observed to play dual roles, not only in eliciting desirable osteoinductivity, but allows for modification of tumor microenvironment (TME) owing to the continuous release of degradation products. Specifically, the sustainable H2 evolution and Ca2+ from the implant is distinguished to cooperate with local Taxol delivery to achieve superior antineoplastic activity through activating Cyt-c pathway to induce mitochondrial dysfunction, which in turn leads to significant tumor-growth inhibition in vivo. In addition, the local chemotherapeutic delivery of the implant minimizes toxicity and side effects, but markedly fosters osteogenesis and bone repair with appropriate structure degradation in rat femoral defect model. Taken together, a promising intraosseous administration strategy with biodegradable Mg-based implants to facilitate tumor-associated bone defect is proposed.

To clarify the adsorption behavior of fluorocarbon surfactants on PMMA surfaces, the contact angles of two nonionic fluorocarbon surfactants (FNS-1 and FNS-2) and an anionic fluorocarbon surfactant (FAS) on polymethylmethacrylate (PMMA) surface were determined using the sessile drop method. Moreover, the effects of molecular structures on the surface tension, adhesion tension, solid–liquid interfacial tension, and adhesion work of the three fluorocarbon surfactants were investigated. The results demonstrate that the adsorption amounts for three fluorocarbon surfactants at the air–water interface are 4~5 times higher than those at the PMMA–solution interface. The three fluorocarbon surfactants adsorb on the PMMA surface by polar groups before CMC and by hydrophobic chains after CMC. Before CMC, FNS-2 with the smallest molecular size owns the highest adsorption amount, while FAS with large-branched chains and electrostatic repulsion has the smallest adsorption amount. After CMC, the three fluorocarbon surfactants form aggregates at the PMMA-liquid interface. FAS possesses the smallest adsorption amount after CMC. Besides, FNS-1 possesses a higher adsorption amount than FNS-2 due to the longer fluorocarbon chain and the lower CMC value of FNS-1. The adsorption behaviors of nonionic and anionic fluorocarbon surfactants on the PMMA surface are different. FAS forms interfacial aggregates before CMC, which may be attributed to the electrostatic interaction between the anionic head of FAS and the PMMA surface.

Abstract To assess the safety of varicella vaccine (VarV) by conducting post-marketing surveillance on adverse events following immunization (AEFI) in Jiangsu Province, China. Methods We utilized the AEFI Information System of mainland China to monitor and categorize adverse reactions associated with VarV. Results The incidence rate of AEFI was significantly higher after the first dose (48.79/100,000 doses) compared to the second dose (45.18/100,000 doses) (χ2 = 4.63, P = 0.031). Regional variations in AEFI incidence were observed within Jiangsu Province. Common reactions comprised 90.96% of AEFIs, while rare reactions and coincidental events accounted for 6.59% and 0.51%, respectively. Notably, there were no adverse events linked to vaccine quality, program errors, psychogenic reactions, or fatalities. Over 96% of AEFIs occurred within three days of VarV administration, with redness at the injection site (2.6 cm to 5 cm in diameter) being the most frequently observed symptom. Conclusion VarV demonstrates a commendable safety profile. Although there was a slight increase in AEFI incidence between 2022 and 2023, common vaccine reactions were predominantly observed, and the rates of rare reactions remained very low.