Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification.

Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.

Intraocular cetuximab: Safety and effect on axial elongation in young Guinea pigs with lens-induced myopization.

This study aimed to examine the intraocular tolerability of the epidermal growth factor receptor antibody cetuximab, when applied intravitreally, and its effect on axial elongation. Guinea pigs aged 2-3 weeks were subjected to bilateral plano glasses and bilateral lens-induced myopization (LIM) as a single procedure for group I (n = 8) and group II (n = 8), respectively. In the animals of group III (n = 8), group IV (n = 8), and group V (n = 8), the right eyes of the animals, in addition to LIM, received four weekly intravitreal injections of cetuximab (Erbitux®) in doses of 6.25 µg, 12.5 µg, and 25 µg, respectively. As controls, the left eyes, in addition to LIM, received corresponding intraocular injections of phosphate-buffered saline. The animals underwent regular ophthalmoscopic examinations and biometry for axial length measurements. With increasing doses of cetuximab, the inter-eye difference in axial elongation (at study end, left eyes minus right eyes) were significantly the smallest in group I (0.00 ± 0.02 mm) and group II (-0.01 ± 0.02 mm), they were larger in group III (0.04 ± 0.04 mm) and group IV (0.10 ± 0.03 mm), and they were the largest in group V (0.11 ± 0.01 mm). The inter-eye difference in axial elongation enlarged (P < 0.001) with the number of injections applied. Retinal thickness at the posterior pole (right eyes) was significantly thicker in group V than in group II (P < 0.01). The density of apoptotic cells (visualized by TUNEL-staining) did not vary significantly between any of the groups (all P > 0.05). The results suggest that intravitreal injections of cetuximab in young guinea pigs with LIM resulted in a reduction in axial elongation in a dose-dependent and number of treatment-dependent manner. Intraocular toxic effects, such as intraocular inflammation, retinal thinning, or an increased density of apoptotic cells in the retina, were not observed in association with the intravitreally applied cetuximab.

Injured sensory neurons-derived galectin-3 contributes to neuropathic pain via programming microglia in the spinal dorsal horn.

Emerging studies have demonstrated spinal microglia play a critical role in central sensitization and contribute to chronic pain. Although several mediators that contribute to microglia activation have been identified, the mechanism of microglia activation and its functionally diversified mechanisms in pathological pain are still unclear. Here we report that injured sensory neurons-derived Galectin-3 (Gal3) activates and reprograms microglia in the spinal dorsal horn (SDH) and contributes to neuropathic pain. Firstly, Gal3 is predominantly expressed in the isolectin B4 (IB4)-positive non-peptidergic sensory neurons and significantly up-regulated in dorsal root ganglion (DRG) neurons and primary afferent terminals in SDH in the partial sciatic nerve ligation (pSNL)-induced neuropathic pain model. Gal3 knockout (Gal3 KO) mice showed a significant decrease in mechanical allodynia and Gal3 inhibitor TD-139 produced a significant anti-allodynia effect in the pSNL model. Furthermore, pSNL-induced microgliosis was compromised in Gal3 KO mice. Additionally, intrathecal injection of Gal3 produces remarkable mechanical allodynia by direct activation of microglia, which have enhanced inflammatory responses with TNF-α and IL-1ß up-regulation. Thirdly, using single-nuclear RNA sequencing (snRNA-seq), we identified that Gal3 targets microglia and induces reprogramming of microglia, which may contribute to neuropathic pain establishment. Finally, Gal3 enhances excitatory synaptic transmission in excitatory neurons in the SDH via microglia activation. Our findings reveal that injured sensory neurons-derived Gal3 programs microglia in the SDH and contribute to neuropathic pain.

Dual Inhibition of Factor XIIa and Factor XIa Produces a Synergistic Anticoagulant Effect.

ABSTRACT: Clinical practice shows that a critical unmet need in the field of thrombosis prevention is the availability of anticoagulant therapy without bleeding risk. Inhibitors against FXIa or FXIIa have been extensively studied because of their low bleeding risk. However, whether these compounds produce synergistic effects has not yet been explored. In this study, analyses of activated partial thromboplastin time in combination with the FXIa inhibitor PN2KPI and the FXIIa inhibitor Infestin4 at different proportions were performed using the SynergyFinder tool identifying synergistic anticoagulation effects. Both an FeCl 3 -induced carotid artery thrombosis mouse model and a transient occlusion of the middle cerebral artery mouse model showed that the combination of PN2KPI and Infestin4, which are 28.57% and 6.25% of the effective dose, respectively, significantly prevents coagulation, and furthermore, dual inhibition does not cause bleeding risk.

Design, expression and biological evaluation of DX-88mut as a novel selective factor XIa inhibitor for antithrombosis.

Thrombotic diseases, such as myocardial infarction, stroke, and deep vein thrombosis, severely threaten human health, and anticoagulation is an effective way to prevent such illnesses. However, most anticoagulant drugs in the clinic have different bleeding risks. Previous studies have shown that coagulation factor XI is an ideal target for safe anticoagulant drug development. Here, we designed the FXIa inhibitory peptide DX-88mut by replacing Loop1 (DGPCRAAHPR) and Loop2 (IYGGC) in DX-88, which is a clinical drug targeting PKa for the treatment of hereditary angioedema, using Loop1 (TGPCRAMISR) and Loop2 (FYGGC) in the FXIa inhibitory peptide PN2KPI, respectively. DX-88mut selectively inhibited FXIa against a panel of serine proteases with an IC50 value of 14.840 ± 0.453 nM, dose-dependently prolonged APTT in mouse, rat and human plasma, and potently inhibited FeCl3-induced carotid artery thrombosis in mice at a dose of 1 µmol/kg. Additionally, DX-88mut did not show a significant bleeding risk at a dose of 5 µmol/kg. Taken together, these results show that DX-88mut is a potential candidate for the development of a novel antithrombotic agent.

Lens-induced myopization and body weight in young guinea pigs.

BACKGROUND: To investigate the relationship between body weight and Axial length in guinea pigs. METHODS: Forty pigmented guinea pigs were randomly divided into two groups, namely control group and negative lens-induced myopization (LIM) group. After measuring the baseline axial length and body weight (BW), guinea pigs of LIM group received bilateral negative lens-induced myopization using - 10.0 diopters lenses. One week later, the lenses were removed and biometric and ophthalmoscopic examinations were repeated. RESULTS: Two groups of guinea pigs showed no statistical difference in initial body weight and eye axis length. Compared to the control group, the lens-induced group had a lower weight (P = 0.02) and a longer axial length (P < 0.01) at the end of study Neither at baseline nor at week 1 did AL correlate with BW in both groups (Control Baseline: r = 0.306, P = 0.19; Control Week1: r = 0.333, P = 0.15; LIM Baseline: r=-0.142, P = 0.55; LIM Week 1: r = 0.189, P = 0.42). Lens-induction had a significant effect on axial elongation (P < 0.01) while body weight had no impact on such aspect (P > 0.05). CONCLUSION: In guinea pigs of the same age, axial length was not correlated with body weight. Also, baseline body weight had no impact on natural axial length growth or lens-induced myopia. Lens-induction caused a significant reduction in body weight gain.

METTL3 depletion contributes to tumour progression and drug resistance via N6 methyladenosine-dependent mechanism in HR+HER2-breast cancer.

BACKGROUND: Chemotherapy is an important strategy for the treatment of hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+HER2-) breast cancer (BC), but this subtype has a low response rate to chemotherapy. Growing evidence indicates that N6-methyladenosine (m6A) is the most common RNA modification in eukaryotic cells and that methyltransferase-like 3 (METTL3) participates in tumour progression in several cancer types. Therefore, exploring the function of METTL3 in HR+HER2- BC initiation and development is still important. METHODS: mRNA and protein expression levels were analysed by quantitative real-time polymerase chain reaction and western blotting, respectively. Cell proliferation was detected by CCK-8 and colony formation assays. Cell cycle progression was assessed by flow cytometry. Cell migration and invasion were analysed by wound healing assays and transwell assays, respectively, and apoptosis was analysed by TUNEL assays. Finally, m6A modification was analysed by methylated RNA immunoprecipitation. RESULTS: Chemotherapy-induced downregulation of the m6A modification is regulated by METTL3 depletion in HR+HER2- BC. METTL3 knockdown in MCF-7/T47D cells decreased the drug sensitivity of HR+HER2- BC cells by promoting tumour proliferation and migration and inhibiting apoptosis. Mechanistically, CDKN1A is a downstream target of METTL3 that activates the AKT pathway and promotes epithelial-mesenchymal transformation (EMT). Moreover, a decrease in BAX expression was observed when m6A modification was inhibited with METTL3 knockdown, and apoptosis was inhibited by the reduction of caspase-3/-9/-8. CONCLUSION: METTL3 depletion promotes the proliferation and migration and decreases the drug sensitivity of HR+HER2- BC via regulation of the CDKN1A/EMT and m6A-BAX/caspase-9/-3/-8 signalling pathways, which suggests METTL3 played a tumour-suppressor role and it could be a potential biomarker for predicting the prognosis of patients with HR+HER2- BC.

Automatic retinoblastoma screening and surveillance using deep learning.

BACKGROUND: Retinoblastoma is the most common intraocular malignancy in childhood. With the advanced management strategy, the globe salvage and overall survival have significantly improved, which proposes subsequent challenges regarding long-term surveillance and offspring screening. This study aimed to apply a deep learning algorithm to reduce the burden of follow-up and offspring screening. METHODS: This cohort study includes retinoblastoma patients who visited Beijing Tongren Hospital from March 2018 to January 2022 for deep learning algorism development. Clinical-suspected and treated retinoblastoma patients from February 2022 to June 2022 were prospectively collected for prospective validation. Images from the posterior pole and peripheral retina were collected, and reference standards were made according to the consensus of the multidisciplinary management team. A deep learning algorithm was trained to identify "normal fundus", "stable retinoblastoma" in which specific treatment is not required, and "active retinoblastoma" in which specific treatment is required. The performance of each classifier included sensitivity, specificity, accuracy, and cost-utility. RESULTS: A total of 36,623 images were included for developing the Deep Learning Assistant for Retinoblastoma Monitoring (DLA-RB) algorithm. In internal fivefold cross-validation, DLA-RB achieved an area under curve (AUC) of 0.998 (95% confidence interval [CI] 0.986-1.000) in distinguishing normal fundus and active retinoblastoma, and 0.940 (95% CI 0.851-0.996) in distinguishing stable and active retinoblastoma. From February 2022 to June 2022, 139 eyes of 103 patients were prospectively collected. In identifying active retinoblastoma tumours from all clinical-suspected patients and active retinoblastoma from all treated retinoblastoma patients, the AUC of DLA-RB reached 0.991 (95% CI 0.970-1.000), and 0.962 (95% CI 0.915-1.000), respectively. The combination between ophthalmologists and DLA-RB significantly improved the accuracy of competent ophthalmologists and residents regarding both binary tasks. Cost-utility analysis revealed DLA-RB-based diagnosis mode is cost-effective in both retinoblastoma diagnosis and active retinoblastoma identification. CONCLUSIONS: DLA-RB achieved high accuracy and sensitivity in identifying active retinoblastoma from the normal and stable retinoblastoma fundus. It can be used to surveil the activity of retinoblastoma during follow-up and screen high-risk offspring. Compared with referral procedures to ophthalmologic centres, DLA-RB-based screening and surveillance is cost-effective and can be incorporated within telemedicine programs. CLINICAL TRIAL REGISTRATION: This study was registered on ClinicalTrials.gov (NCT05308043).

Leaf litter chemistry and its effects on soil microorganisms in different ages of Zanthoxylum planispinum var. Dintanensis.

BACKGROUND: Leaf litter is the products of metabolism during the growth and development of plantation, and it is also an important component of nutrient cycling in plantation ecosystems. However, leaf litter chemistry and its effects on soil microorganisms in different ages, as well as the interactions between chemical components in leaf litter have been rarely reported. Based on this, this paper took Zanthoxylum planispinum var. dintanensis (hereafter Z. planispinum) plantations of 5-7, 10-12, 20-22, and 28-32 years old as the objects. By using one-way ANOVA, Pearson correlation analysis and redundancy analysis, we investigated leaf litter chemistry and its effects on soil microorganisms in different ages, and to reveal internal correlation of various chemical components in leaf litter, which can provide a scientific basis for the regulation of soil microbial activity in plantations. RESULTS: The variation of organic carbon with plantation age was more stable compared to total nitrogen and phosphorus of leaf litter. Nitrogen resorption was stronger than phosphorus resorption efficiency in Z. planispinum, and resorption efficiencies of leaf nitrogen and phosphorus for different ages were lower than the global average. Total nitrogen was highly significantly positively correlated with lignin, and total potassium was significantly positively correlated with tannin, suggesting the increase of inorganic substances in leaf litter would promote the accumulation of secondary metabolites. The leaf litter chemical traits explained up to 72% of soil microorganisms, where lignin was positively correlated with fungi and negatively correlated with bacteria, indicating that fungi are able to decompose lower quality litter and can break down complex and stable organic compounds more rapidly than bacteria. The nutrient elements carbon and nitrogen in the leaf litter and their interrelationship also have a great impact on soil microorganisms, because carbon is not only the element that provides energy, but also the element with the largest content in the microbiota. CONCLUSIONS: The sustained increase in inorganic nutrients of leaf litter did not favor the decomposition of secondary metabolites, but rather inhibited the degradation of leaf litter. The significant positive effect of the leaf litter chemistry on soil microorganisms indicates the important role of leaf litter in promoting nutrient cycling in Z. planispinum plantations.

Observation of Acoustic Non-Hermitian Bloch Braids and Associated Topological Phase Transitions.

Topological features embedded in ancient braiding and knotting arts endow significant impacts on our daily life and even cutting-edge science. Recently, fast growing efforts are invested to the braiding topology of complex Bloch bands in non-Hermitian systems. This new classification of band topology goes far beyond those established in Hermitian counterparts. Here, we present the first acoustic realization of the topological non-Hermitian Bloch braids, based on a two-band model easily accessible for realizing any desired knot structure. The non-Hermitian bands are synthesized by a simple binary cavity-tube system, where the long-range, complex-valued, and momentum-resolved couplings are accomplished by a well-controlled unidirectional coupler. In addition to directly visualizing various two-band braiding patterns, we unambiguously observe the highly elusive topological phase transitions between them. Not only do our results provide a direct demonstration for the non-Hermitian band topology, but also the experimental techniques open new avenues for designing unconventional acoustic metamaterials.

RNA-Seq-based transcriptomics analysis during the photodynamic therapy of primary cells in secondary hyperparathyroidism.

BACKGROUND: The aim of this study was to identify changes in gene expression before and after 5-aminolevulinic acid-mediated photodynamic therapy (5-ALA-PDT) and to investigate the potential mechanism of 5-ALA-PDT based on ribonucleic acid sequencing (RNA-Seq) analysis. METHODS: Secondary hyperparathyroidism (SHPT) primary cells were isolated from surgically excised specimens and exposed to laser light. The transcription profiles of SHPT primary cells were identified through RNA-Seq. Differentially expressed genes (DEGs) were identified. Enrichment of functions and signaling pathway analysis were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Quantitative real-time polymerase chain reaction (RT-qPCR) and western blot analysis were used to validate genes based on RNA-Seq results. RESULTS: In total, 1320 DEGs were identified, of which 1019 genes were upregulated and 301 genes were downregulated. GO and KEGG pathway analyses identified significantly enriched pathways in DEGs, including TGF beta in extracellular matrix (ECM), negative regulation of triglyceride biosynthetic process, protein heterodimerization activity, systemic lupus erythematosus, ECM-receptor interaction, focal adhesion and protein digestion and absorption. Protein-protein interaction (PPI) network analyses identified potential heat shock protein (HSP) interactions among the DEGs. Eight HSP genes were also identified that were most likely involved in 5-ALA-PDT, which were further validated by RT-qPCR and western blotting. CONCLUSIONS: The findings of this descriptive study reveal changes in the transcriptome profile during 5-ALA-PDT, suggesting that gene expression and mutation, signaling pathways, and the molecular network are altered in SHPT primary cells. The above findings provide new insight for further studies on the mechanisms underlying 5-ALA-PDT in SHPT.

From Black Boxes to Actionable Insights: A Perspective on Explainable Artificial Intelligence for Scientific Discovery.

The application of Explainable Artificial Intelligence (XAI) in the field of chemistry has garnered growing interest for its potential to justify the prediction of black-box machine learning models and provide actionable insights. We first survey a range of XAI techniques adapted for chemical applications and categorize them based on the technical details of each methodology. We then present a few case studies to illustrate the practical utility of XAI, such as identifying carcinogenic molecules and guiding molecular optimizations, in order to provide chemists with concrete examples of ways to take full advantage of XAI-augmented machine learning for chemistry. Despite the initial success of XAI in chemistry, we still face the challenges of developing more reliable explanations, assuring robustness against adversarial actions, and customizing the explanation for different applications and needs of the diverse scientific community. Finally, we discuss the emerging role of large language models like GPT in generating natural language explanations and discusses the specific challenges associated with them. We advocate that addressing the aforementioned challenges and actively embracing new techniques may contribute to establishing machine learning as an indispensable technique for chemistry in this digital era.

The prognostic significance of clinicopathological characteristics in early-onset versus late-onset colorectal cancer liver metastases.

PURPOSE: This study aimed to explore the prognostic significance of clinicopathological characteristics in early-onset versus late-onset colorectal liver metastases (CRLM). METHODS: The data of CRLM patients who underwent hepatectomy from September 2010 to September 2020 were retrospectively analyzed. According to the age of primary cancer diagnosis, patients were divided into early-onset CRLM (EOCRLM) and late-onset CRLM (LOCRLM) groups. Clinicopathological parameters were compared between the two groups. Cox regression model and Kaplan-Meier method were used to analyze the effect of clinicopathological parameters on overall survival (OS) and recurrence-free survival (RFS). RESULTS: In total, 431 CRLM patients were identified, 130 with EOCRLM and 301 with LOCRLM. Compared with LOCRLM patients, EOCRLM patients had lower American Society of Anesthesia (ASA) grade and longer operation time (204 vs. 179 min). More aggressive features were presented in EOCRLM patients including synchronous liver metastases (76.9% vs. 61.1%) and bilobar involvement (43.8% vs. 33.2%). No significant difference in OS or RFS was found between the two groups. Multivariate analysis of EOCRLM group showed that preoperative CA19-9 level and RAS/BRAF status were predictive of OS, while bilobar involvement and preoperative CEA level were associated with RFS. In LOCRLM group, the number of CRLM, preoperative CA19-9 level, and BRAF status were associated with OS, while the number of CRLM was associated with RFS. CONCLUSIONS: The preoperative CA19-9 level, RAS/BRAF status, bilobar involvement, and preoperative CEA level were predictive of EOCRLM patient prognosis, while the number of CRLM, preoperative CA19-9 level, and BRAF status were predictive of LOCRLM patient prognosis.

Multi-echo in steady-state acquisition improves MRI image quality and lumbosacral radiculopathy diagnosis efficacy compared with T2 fast spin-echo sequence.

PURPOSE: This study compares the performance of a 4-min multi-echo in steady-state acquisition (MENSA) with a 6-min fast spin echo with variable flip angle (CUBE) protocol for the assessment of lumbosacral plexus nerve root lesions. METHODS: Seventy-two subjects underwent MENSA and CUBE sequences on a 3.0-T MRI scanner. Two musculoskeletal radiologists independently assessed the images for quality and diagnostic capability. A qualitative assessment scoring system for image quality and quantitative nerve signal-to-noise ratio (SNR) and iliac vein and muscle contrast-to-noise ratios (CNR) was applied. Using surgical reports as the reference, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curves (AUC) were evaluated. Intraclass correlation coefficients (ICC) and weighted kappa were used to calculate reliability. RESULTS: MENSA image quality rating (3.679 ± 0.47) was higher than for CUBE images (3.038 ± 0.68), and MENSA showed higher mean nerve root SNR (36.935 ± 8.33 vs. 27.777 ± 7.41), iliac vein CNR (24.678 ± 6.63 vs. 5.210 ± 3.93), and muscle CNR (19.414 ± 6.07 vs. 13.531 ± 0.65) than CUBE (P < 0.05). Weighted kappa and ICC values indicated good reliability. Sensitivity, specificity, and accuracy of diagnosis based on MENSA images were 96.23%, 89.47%, and 94.44%, respectively, and AUC was 0.929, compared with 92.45%, 84.21%, 90.28%, and 0.883 for CUBE images. The two correlated ROC curves were not significantly different. Weighted kappa values for intraobserver (0.758) and interobserver (0.768-0.818) reliability were substantial to perfect. CONCLUSION: A time-efficient 4-min MENSA protocol exhibits superior image quality and high vascular contrast with the potential to produce high-resolution lumbosacral nerve root images.

Development and validation of medical record-based logistic regression and machine learning models to diagnose diabetic retinopathy.

PURPOSES: Many factors were reported to be associated with diabetic retinopathy (DR); however, their contributions remained unclear. We aimed to evaluate the prognostic and diagnostic accuracy of logistic regression and three machine learning models based on various medical records. METHODS: This was a cross-sectional study. We investigated the prevalence and associations of DR among 757 participants aged 40 years or older in the 2005-2006 National Health and Nutrition Examination Survey (NHANES). We trained the models to predict if the participants had DR with 15 predictor variables. Area under the receiver operating characteristic (AUROC) and mean squared error (MSE) of each algorithm were compared in the external validation dataset using a replicate cohort from NHANES 2007-2008. RESULTS: Among the 757 participants, 53 (7.00%) subjects had DR, the mean (standard deviation, SD) age was 57.7 (13.04), and 78.0% were male (n = 42). Logistic regression revealed that female gender (OR = 4.130, 95% CI: 1.820-9.380; P < 0.05), HbA1c (OR = 1.665, 95% CI: 1.197-2.317; P < 0.05), serum creatine level (OR = 2.952, 95% CI: 1.274-6.851; P < 0.05), and eGFR level (OR = 1.009, 95% CI: 1.000-1.014, P < 0.05) increased the risk of DR. The average performance obtained from internal validation was similar in all models (AUROC ≥ 0.945), and k-nearest neighbors (KNN) had the highest value with an AUROC of 0.984. In external validation, they remained robust or with modest reductions in discrimination with AUROC still ≥ 0.902, and KNN also performed the best with an AUROC of 0.982. Both logistic regression and machine learning models had good performance in the clinical diagnosis of DR. CONCLUSIONS: This study highlights the utility of comparing traditional logistic regression to machine learning models. We found that logistic regression performed as well as optimized machine learning methods when classifying DR patients.

A Bibliometric Analysis for Low-Intensity Ultrasound Study Over the Past Three Decades.

Low-intensity ultrasound (LI-US) is a non-invasive stimulation technique that has emerged in recent years and has been shown to have positive effects on neuromodulation, fracture healing, inflammation improvement, and metabolic regulation. This study reports the conclusions of a bibliometric analysis of LI-US. Input data for the period between 1995 and 2022, including 7209 related articles in the field of LI-US, were collected from the core library of the Web of Science (WOS) database. Using these data, a set of bibliometric indicators was obtained to gain knowledge on different aspects: global production, research areas, and sources analysis, contributions of countries and institutions, author analysis, citation analysis, and keyword analysis. This study combined the data analysis capabilities provided by the WOS database, making use of two bibliometric software tools: R software and VOS viewer to achieve analysis and data exploration visualization, and predicted the further development trends of LI-US. It turns out that the United States and China are co-leaders while Zhang ZG is the most significant author in LI-US. In the future, the hot spots of LI-US will continue to focus on parameter research, mechanism discussion, safety regulations, and neuromodulation applications.

SREBP2 inhibitor betulin sensitizes hepatocellular carcinoma to lenvatinib by inhibiting the mTOR/IL-1ß pathway.

Lenvatinib has become the first-line therapy in advanced hepatocellular carcinoma (HCC), but its efficacy is still limited because of the inevitable development of resistance. It has been reported that cellular cholesterol levels are associated with tyrosine kinase inhibitor (TKI) efficacy. Here, we show that betulin, a sterol regulatory element-binding protein 2 (SREBP2) inhibitor, markedly enhances the anti-tumor effect of lenvatinib in HCC both in vitro and in vivo. Our results also show that the combination treatment of lenvatinib and betulin synergistically inhibits the proliferation and clonogenicity of HCC cells. The mRNA and protein expressions of IL-1ß are markedly decreased in HCC cells treated with betulin, while the sensitivity of HCC cells to lenvatinib is enhanced. Moreover, we find that the knockdown of IL-1ß also enhances the efficacy of lenvatinib, and recombinant IL-1ß protein rescues cell viability, which is reduced by lenvatinib in HCC cells. Further mechanistic studies indicate that betulin decreases the level of IL-1ß in HCC cells by inhibiting the mTOR signaling pathway. Finally, the growth of the tumors in xenograft mouse models subjected to combination treatment is significantly suppressed. In summary, our study reveals that the SREBP2 inhibitor betulin sensitizes hepatocellular carcinoma to lenvatinib by inhibiting the mTOR/IL-1ß pathway, which may be a promising therapeutic strategy for patients with HCC.

A Novel TGF-ß-Related Signature for Predicting Prognosis, Tumor Microenvironment, and Therapeutic Response in Colorectal Cancer.

The transforming growth factor beta (TGF-ß) signaling plays a critical role in immune evasion and tumor progression. However, its modulatory influences on prognosis, tumor microenvironment (TME), and therapeutic efficacy remain unknown in colorectal cancer (CRC). We summarized TGF-ß-related genes and comprehensively estimated their expression pattern in 2142 CRC samples from 9 datasets. Two distinct cluster patterns were divided and biological characteristics of each pattern were further analyzed. Then, to quantify the TGF-ß cluster pattern of individual CRC patient, we generated the TGF-ß score (TGFBscore) model based on TGF-ß cluster pattern-relevant differentially expressed genes (DEGs). Subsequently, we conducted correlation analysis for TGFBscore and clinical prognosis, consensus molecular subtypes (CMSs), TME characteristics, liver metastasis, drug response, and immunotherapeutic efficacy in CRC. We illustrated transcriptional and genetic alterations of TGF-ß-relevant genes, which were closely linked with carcinogenic pathways. We identified two different TGF-ß cluster patterns, characterized by a high and a low TGFBscore. The TGFBscore-high group was significantly linked with worse patient survival, epithelial-mesenchymal transition (EMT) activation, liver metastasis tendency, and the infiltration of immunosuppressive cells (regulatory T cells [Tregs], M2 macrophages, cancer-associated fibroblasts [CAFs], and myeloid-derived suppressor cells [MDSCs]), while the TGFBscore-low group was linked with a survival advantage, epithelial phenotype, early CRC staging, and the infiltration of immune-activated cells (B cell, CD4 T cell, natural killer T [NKT] cell, and T helper 1 [Th1] cell). In terms of predicting drug response, TGFBscore negatively correlated (sensitive to TGFBscore-high group) with drugs targeting PI3K/mTOR, JNK and p38, RTK signaling pathways, and positively correlated (sensitive to TGFBscore-low group) with drugs targeting EGFR signaling pathway. Also, TGFBscore could predict the efficacy of different anti-tumor therapies. TGFBscore-low patients might benefit more from anti-PDL1 immunotherapy, adjuvant chemotherapy (ACT), and ERBB targeted therapy, whereas TGFBscore-high patients might benefit more from antiangiogenic targeted therapy. Our study constructed a novel TGF-ß scoring model that could predict prognosis, liver metastasis tendency, and TME characteristics for CRC patients. More importantly, this work emphasizes the potential clinical utility of TGFBscore in evaluating the efficacy of chemotherapy, targeted therapy, and immunotherapy, guiding individualized precision treatment in CRC.

A Novel Deep Convolutional Neural Network Combining Global Feature Extraction and Detailed Feature Extraction for Bearing Compound Fault Diagnosis.

This study researched the application of a convolutional neural network (CNN) to a bearing compound fault diagnosis. The proposed idea lies in the ability of CNN to automatically extract fault features from complex raw signals. In our approach, to extract more effective features from a raw signal, a novel deep convolutional neural network combining global feature extraction with detailed feature extraction (GDDCNN) is proposed. First, wide and small kernel sizes are separately adopted in shallow and deep convolutional layers to extract global and detailed features. Then, the modified activation layer with a concatenated rectified linear unit (CReLU) is added following the shallow convolution layer to improve the utilization of shallow global features of the network. Finally, to acquire more robust features, another strategy involving the GMP layer is utilized, which replaces the traditional fully connected layer. The performance of the obtained diagnosis was validated on two bearing datasets. The results show that the accuracy of the compound fault diagnosis is over 98%. Compared with three other CNN-based methods, the proposed model demonstrates better stability.

Involvement of Serotonergic Projections from the Dorsal Raphe to the Medial Preoptic Area in the Regulation of the Pup-Directed Paternal Response of Male Mandarin Voles.

Male mammals display different paternal responses to pups, either attacking or killing the young offspring, or contrastingly, caring for them. The neural circuit mechanism underlying the between-individual variation in the pup-directed responsiveness of male mammals remains unclear. Monogamous mandarin voles were used to complete the present study. The male individuals were identified as paternal and infanticidal voles, according their behavioral responses to pups. It was found that the serotonin release in the medial preoptic area (MPOA), as well as the serotonergic neuron activity, significantly increased upon licking the pups, but showed no changes after attacking the pups, as revealed by the in vivo fiber photometry of the fluorescence signal from the 5-HT 1.0 sensor and the calcium imaging indicator, respectively. It was verified that the 5-HTergic neural projections to the MPOA originated mainly from the ventral part of the dorsal raphe (vDR). Furthermore, the chemogenetic inhibition of serotonergic projections from the vDR to the MPOA decreased the paternal behaviors and shortened the latency to attack the pups. In contrast, the activation of serotonergic neurons via optogenetics extended the licking duration and inhibited infanticide. Collectively, these results elucidate that the serotonergic projections from the vDR to the MPOA, a previously unrecognized pathway, regulate the paternal responses of virgin male mandarin voles to pups.