Plexin domain-containing 1 may be a biomarker of poor prognosis in hepatocellular carcinoma patients, may mediate immune evasion.

BACKGROUND: For the first time, we investigated the oncological role of plexin domain-containing 1 (PLXDC1), also known as tumor endothelial marker 7 (TEM7), in hepatocellular carcinoma (HCC). AIM: To investigate the oncological profile of PLXDC1 in HCC. METHODS: Based on The Cancer Genome Atlas database, we analyzed the expression of PLXDC1 in HCC. Using immunohistochemistry, quantitative real-time polymerase chain reaction (qRT-PCR), and Western blotting, we validated our results. The prognostic value of PLXDC1 in HCC was analyzed by assessing its correlation with clinicopathological features, such as patient survival, methylation level, tumor immune microenvironment features, and immune cell surface checkpoint expression. Finally, to assess the immune evasion potential of PLXDC1 in HCC, we used the tumor immune dysfunction and exclusion (TIDE) website and immunohistochemical staining assays. RESULTS: Based on immunohistochemistry, qRT-PCR, and Western blot assays, overexpression of PLXDC1 in HCC was associated with poor prognosis. Univariate and multivariate Cox analyses indicated that PLXDC1 might be an independent prognostic factor. In HCC patients with high methylation levels, the prognosis was worse than in patients with low methylation levels. Pathway enrichment analysis of HCC tissues indicated that genes upregulated in the high-PLXDC1 subgroup were enriched in mesenchymal and immune activation signaling, and TIDE assessment showed that the risk of immune evasion was significantly higher in the high-PLXDC1 subgroup compared to the low-PLXDC1 subgroup. The high-risk group had a significantly lower immune evasion rate as well as a poor prognosis, and PLXDC1-related risk scores were also associated with a poor prognosis. CONCLUSION: As a result of this study analyzing PLXDC1 from multiple biological perspectives, it was revealed that it is a biomarker of poor prognosis for HCC patients, and that it plays a role in determining immune evasion status.

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm.

Aboveground biomass (AGB) is regarded as a critical variable in monitoring crop growth and yield. The use of hyperspectral remote sensing has emerged as a viable method for the rapid and precise monitoring of AGB. Due to the extensive dimensionality and volume of hyperspectral data, it is crucial to effectively reduce data dimensionality and select sensitive spectral features to enhance the accuracy of rice AGB estimation models. At present, derivative transform and feature selection algorithms have become important means to solve this problem. However, few studies have systematically evaluated the impact of derivative spectrum combined with feature selection algorithm on rice AGB estimation. To this end, at the Xiaogang Village (Chuzhou City, China) Experimental Base in 2020, this study used an ASD FieldSpec handheld 2 ground spectrometer (Analytical Spectroscopy Devices, Boulder, Colorado, USA) to obtain canopy spectral data at the critical growth stage (tillering, jointing, booting, heading, and maturity stages) of rice, and evaluated the performance of the recursive feature elimination (RFE) and Boruta feature selection algorithm through partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM) and ridge regression (RR). Moreover, we analyzed the importance of the optimal derivative spectrum. The findings indicate that (1) as the growth stage progresses, the correlation between rice canopy spectrum and AGB shows a trend from high to low, among which the first derivative spectrum (FD) has the strongest correlation with AGB. (2) The number of feature bands selected by the Boruta algorithm is 19~35, which has a good dimensionality reduction effect. (3) The combination of FD-Boruta-PCR (FB-PCR) demonstrated the best performance in estimating rice AGB, with an increase in R² of approximately 10% ~ 20% and a decrease in RMSE of approximately 0.08% ~ 14%. (4) The best estimation stage is the booting stage, with R2 values between 0.60 and 0.74 and RMSE values between 1288.23 and 1554.82 kg/hm2. This study confirms the accuracy of hyperspectral remote sensing in estimating vegetation biomass and further explores the theoretical foundation and future direction for monitoring rice growth dynamics.

13C-Stable isotope resolved metabolomics uncovers dynamic biochemical landscape of gut microbiome-host organ communications in mice.

BACKGROUND: Gut microbiome metabolites are important modulators of host health and disease. However, the overall metabolic potential of the gut microbiome and interactions with the host organs have been underexplored. RESULTS: Using stable isotope resolved metabolomics (SIRM) in mice orally gavaged with 13C-inulin (a tracer), we first observed dynamic enrichment of 13C-metabolites in cecum contents in the amino acids and short-chain fatty acid metabolism pathways. 13C labeled metabolites were subsequently profiled comparatively in plasma, liver, brain, and skeletal muscle collected at 6, 12, and 24 h after the tracer administration. Organ-specific and time-dependent 13C metabolite enrichments were observed. Carbons from the gut microbiome were preferably incorporated into choline metabolism and the glutamine-glutamate/GABA cycle in the liver and brain, respectively. A sex difference in 13C-lactate enrichment was observed in skeletal muscle, which highlights the sex effect on the interplay between gut microbiome and host organs. Choline was identified as an interorgan metabolite derived from the gut microbiome and fed the lipogenesis of phosphatidylcholine and lysophosphatidylcholine in host organs. In vitro and in silico studies revealed the de novo synthesis of choline in the human gut microbiome via the ethanolamine pathway, and Enterococcus faecalis was identified as a major choline synthesis species. These results revealed a previously underappreciated role for gut microorganisms in choline biosynthesis. CONCLUSIONS: Multicompartmental SIRM analyses provided new insights into the current understanding of dynamic interorgan metabolite transport between the gut microbiome and host at the whole-body level in mice. Moreover, this study singled out microbiota-derived metabolites that are potentially involved in the gut-liver, gut-brain, and gut-skeletal muscle axes. Video Abstract.

Epileptic seizure prediction based on EEG using pseudo-three-dimensional CNN.

Epileptic seizures are characterized by their sudden and unpredictable nature, posing significant risks to a patient's daily life. Accurate and reliable seizure prediction systems can provide alerts before a seizure occurs, as well as give the patient and caregivers provider enough time to take appropriate measure. This study presents an effective seizure prediction method based on deep learning that combine with handcrafted features. The handcrafted features were selected by Max-Relevance and Min-Redundancy (mRMR) to obtain the optimal set of features. To extract the epileptic features from the fused multidimensional structure, we designed a P3D-BiConvLstm3D model, which is a combination of pseudo-3D convolutional neural network (P3DCNN) and bidirectional convolutional long short-term memory 3D (BiConvLstm3D). We also converted EEG signals into a multidimensional structure that fused spatial, manual features, and temporal information. The multidimensional structure is then fed into a P3DCNN to extract spatial and manual features and feature-to-feature dependencies, followed by a BiConvLstm3D input to explore temporal dependencies while preserving the spatial features, and finally, a channel attention mechanism is implemented to emphasize the more representative information in the multichannel output. The proposed has an average accuracy of 98.13%, an average sensitivity of 98.03%, an average precision of 98.30% and an average specificity of 98.23% for the CHB-MIT scalp EEG database. A comparison of the proposed model with other baseline methods was done to confirm the better performance of features through time-space nonlinear feature fusion. The results show that the proposed P3DCNN-BiConvLstm3D-Attention3D method for epilepsy prediction by time-space nonlinear feature fusion is effective.

Optical coherence tomography for multicellular tumor spheroid category recognition and drug screening classification via multi-spatial-superficial-parameter and machine learning.

Optical coherence tomography (OCT) is an ideal imaging technique for noninvasive and longitudinal monitoring of multicellular tumor spheroids (MCTS). However, the internal structure features within MCTS from OCT images are still not fully utilized. In this study, we developed cross-statistical, cross-screening, and composite-hyperparameter feature processing methods in conjunction with 12 machine learning models to assess changes within the MCTS internal structure. Our results indicated that the effective features combined with supervised learning models successfully classify OVCAR-8 MCTS culturing with 5,000 and 50,000 cell numbers, MCTS with pancreatic tumor cells (Panc02-H7) culturing with the ratio of 0%, 33%, 50%, and 67% of fibroblasts, and OVCAR-4 MCTS treated by 2-methoxyestradiol, AZD1208, and R-ketorolac with concentrations of 1, 10, and 25 µM. This approach holds promise for obtaining multi-dimensional physiological and functional evaluations for using OCT and MCTS in anticancer studies.

A Proline-Based Artificial Enzyme That Favors Aldol Condensation Enables Facile Synthesis of Aliphatic Ketones via Tandem Catalysis.

A proline-based artificial enzyme is prepared by grafting the l-proline moieties onto the surface of bovine serum albumin (BSA) protein through atom transfer radical polymerization (ATRP). The artificial enzyme, the BSA-PolyProline conjugate, prefers to catalyze the formation of unsaturated ketones rather than ß-hydroxy ketones in the reaction between acetone and aldehydes, which is difficult to achieve in free-proline catalysis. The altered reaction selectivity is ascribed to the locally concentrated l-proline moieties surrounding the BSA molecule, indicating a microenvironmental effect-induced switching of the reaction mechanism. Taking advantage of this selectivity, we used this artificial enzyme in conjunction with a natural enzyme, old yellow enzyme 1 (OYE1), to demonstrate a simple synthesis of different aliphatic ketones from acetone and aldehydes via tandem catalysis.

Body mass index, waist circumference, and mortality in subjects older than 80 years: a Mendelian randomization study.

BACKGROUND AND AIMS: Emerging evidence has raised an obesity paradox in observational studies of body mass index (BMI) and health among the oldest-old (aged ≥80 years), as an inverse relationship of BMI with mortality was reported. This study was to investigate the causal associations of BMI, waist circumference (WC), or both with mortality in the oldest-old people in China. METHODS: A total of 5306 community-based oldest-old (mean age 90.6 years) were enrolled in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) between 1998 and 2018. Genetic risk scores were constructed from 58 single-nucleotide polymorphisms (SNPs) associated with BMI and 49 SNPs associated with WC to subsequently derive causal estimates for Mendelian randomization (MR) models. One-sample linear MR along with non-linear MR analyses were performed to explore the associations of genetically predicted BMI, WC, and their joint effect with all-cause mortality, cardiovascular disease (CVD) mortality, and non-CVD mortality. RESULTS: During 24 337 person-years of follow-up, 3766 deaths were documented. In observational analyses, higher BMI and WC were both associated with decreased mortality risk [hazard ratio (HR) 0.963, 95% confidence interval (CI) 0.955-0.971 for a 1-kg/m2 increment of BMI and HR 0.971 (95% CI 0.950-0.993) for each 5 cm increase of WC]. Linear MR models indicated that each 1 kg/m2 increase in genetically predicted BMI was monotonically associated with a 4.5% decrease in all-cause mortality risk [HR 0.955 (95% CI 0.928-0.983)]. Non-linear curves showed the lowest mortality risk at the BMI of around 28.0 kg/m2, suggesting that optimal BMI for the oldest-old may be around overweight or mild obesity. Positive monotonic causal associations were observed between WC and all-cause mortality [HR 1.108 (95% CI 1.036-1.185) per 5 cm increase], CVD mortality [HR 1.193 (95% CI 1.064-1.337)], and non-CVD mortality [HR 1.110 (95% CI 1.016-1.212)]. The joint effect analyses indicated that the lowest risk was observed among those with higher BMI and lower WC. CONCLUSIONS: Among the oldest-old, opposite causal associations of BMI and WC with mortality were observed, and a body figure with higher BMI and lower WC could substantially decrease the mortality risk. Guidelines for the weight management should be cautiously designed and implemented among the oldest-old people, considering distinct roles of BMI and WC.

Lactobacillus rhamnosus HN001 facilitates the efficacy of dual PI3K/mTOR inhibition prolonging cardiac transplant survival and enhancing antitumor effect.

Solid organ transplantation is a crucial treatment for patients who have reached the end stage of heart, lung, kidney, or liver failure. However, the likelihood of developing cancer post-transplantation increases. Additionally, primary malignant tumors remain a major obstacle to the long-term survival of transplanted organs. Therefore, it is essential to investigate effective therapies that can boost the immune system's ability to combat cancer and prevent allograft rejection. We established a mouse orthotopic liver tumor model and conducted allogeneic heterotopic heart transplantation. Various treatments were administered, and survival curves were generated using the Kaplan-Meier method. We also collected graft samples and measured inflammatory cytokine levels in the serum using an inflammatory array. The specificity of the histochemical techniques was tested by staining sections. We administered a combination therapy of phosphoinositide 3-kinase/mammalian target of rapamycin (PI3K/mTOR) dual inhibitor BEZ235 and Lactobacillus rhamnosus HN001 to primary liver cancer model mice with cardiac allografts. Consistent with our prior findings, L. rhamnosus HN001 alleviated the intestinal flora imbalance caused by BEZ235. Our previous research confirmed that the combination of BEZ235 and L. rhamnosus HN001 significantly prolonged cardiac transplant survival. IMPORTANCE: We observed that the combination of phosphoinositide 3-kinase/mammalian target of rapamycin (PI3K/mTOR) dual inhibitor BEZ235 and Lactobacillus rhamnosus HN001 notably prolonged cardiac transplant survival while also inhibiting the progression of primary liver cancer. The combination therapy was efficacious in treating antitumor immunity and allograft rejection, as demonstrated by the efficacy results. We also found that this phenomenon was accompanied by the regulation of inflammatory IL-6 expression. Our study presents a novel and effective therapeutic approach to address antitumor immunity and prevent allograft rejection.

OsLESV and OsESV1 promote transitory and storage starch biosynthesis to determine rice grain quality and yield.

Transitory starch is an important carbon source in leaves, and its biosynthesis and metabolism are closely related to grain quality and yield. The molecular mechanisms controlling leaf transitory starch biosynthesis and degradation and their effects on rice (Oryza sativa) quality and yield remain unclear. Here, we show that OsLESV and OsESV1, the rice orthologs of AtLESV and AtESV1, are associated with transitory starch biosynthesis in rice. The total starch and amylose contents in leaves and endosperms are significantly reduced, and the final grain quality and yield are compromised in oslesv and osesv1 single and oslesv esv1 double mutants. Furthermore, we found that OsLESV and OsESV1 bind to starch, and this binding depends on a highly conserved C-terminal tryptophan-rich region that acts as a starch-binding domain. Importantly, OsLESV and OsESV1 also interact with the key enzymes of starch biosynthesis, granule-bound starch synthase I (GBSSI), GBSSII, and pyruvate orthophosphote dikiase (PPDKB), to maintain their protein stability and activity. OsLESV and OsESV1 also facilitate the targeting of GBSSI and GBSSII from plastid stroma to starch granules. Overexpression of GBSSI, GBSSII, and PPDKB can partly rescue the phenotypic defects of the oslesv and osesv1 mutants. Thus, we demonstrate that OsLESV and OsESV1 play a key role in regulating the biosynthesis of both leaf transitory starch and endosperm storage starch in rice. These findings deepen our understanding of the molecular mechanisms underlying transitory starch biosynthesis in rice leaves and reveal how the transitory starch metabolism affects rice grain quality and yield, providing useful information for the genetic improvement of rice grain quality and yield.

Circulating miR-30e-3p induces disruption of neurite development in SH-SY5Y cells by targeting ABI1, a novel biomarker for schizophrenia.

Schizophrenia (SCZ) represents a set of enduring mental illnesses whose underlying etiology remains elusive, posing a significant challenge to public health. Previous studies have shown that the neurodevelopmental process involving small molecules such as miRNA and mRNA is one of the etiological hypotheses of SCZ. We identified and verified that miR-30e-3p and ABI1 can be used as biomarkers in peripheral blood transcriptome sequencing data of patients with SCZ, and confirmed the regulatory relationship between them. To further explore their involvement, we employed retinoic acid (RA)-treated SH-SY5Y differentiated cells as a model system. Our findings indicate that in RA-induced SH-SY5Y cells, ABI1 expression is up-regulated, while miR-30e-3p expression is down-regulated. Functionally, both miR-30e-3p down-regulation and ABI1 up-regulation promote apoptosis and inhibit the proliferation of SH-SY5Y cells. Subsequently, the immunofluorescence assay detected the expression location and abundance of the neuron-specific protein ß-tubulinIII. The expression levels of neuronal marker genes MAPT, TUBB3 and SYP were detected by RT-qPCR. We observed that these changes of miR-30e-3p and ABI1 inhibit the neurite growth of SH-SY5Y cells. Rescue experiments further support that ABI1 silencing can correct miR-30e-3p down-regulation-induced SH-SY5Y neurodevelopmental defects. Collectively, our results establish that miR-30e-3p's regulation of neurite development in SH-SY5Y cells is mediated through ABI1, highlighting a potential mechanism in SCZ pathogenesis.

A novel immune­related lncRNA as a prognostic biomarker in HER2+ breast cancer.

Human epidermal growth factor receptor 2 (HER2)+ breast cancer is characterized by high malignancy and poor prognosis. Long non-coding (lnc)RNAs are crucial in breast cancer progression and prognosis, especially in tumor-associated immune processes. The present study aimed to elucidate novel lncRNAs related to immune function that could serve as biomarkers for both diagnosis and prognosis of this cancer subtype. Using data from The Cancer Genome Atlas and The Immunology Database and Analysis Portal, correlation analysis was performed to identify differentially expressed lncRNAs and immune-related genes. Through receiver operating characteristic analysis, the diagnostic value of specific lncRNAs was identified and evaluated, with a focus on their capacity to distinguish between cancerous and non-cancerous states. The present research revealed 22 differentially expressed lncRNAs and 23 differentially expressed immune-related genes, with 19 immune-related lncRNAs. A total of 13 of these lncRNAs demonstrated diagnostic relevance. In particular, it was demonstrated that the expression of lncRNA CTC-537E7.2 was significantly correlated with patient survival, suggesting its potential as a prognostic marker. Additionally, the expression of lncRNA CTC-537E7.2 was significantly correlated with clinical parameters, such as hormone receptor status and patient demographics. Moreover, it exhibited associations with four distinct immune cell types and demonstrated involvement in the Janus kinase-signal transducer and activator of transcription pathway. Further assessment by in situ hybridization confirmed the increased expression of lncRNA CTC-537E7.2 in samples from HER2+ patients, reinforcing its significance. In summary, the present study uncovered a novel prognostic biomarker for HER2+ breast cancer, thereby laying the groundwork for investigating the underlying molecular mechanisms driving the development of this subtype of breast cancer.

Tracking the effects of PLGA-based nanoparticles on protein expression in living cells through quantitative proteomics.

Mass spectrometry (MS)-based proteomics can identify and quantify the differential abundance of expressed proteins in parallel, and bottom-up proteomic approaches are even approaching comprehensive coverage of the complex eukaryotic proteome. Protein-nanoparticle (NP) interactions have been extensively studied owing to their importance in biological applications and nanotoxicology. However, the proteome-level effects of NPs on cells have received little attention, although changes in protein abundance can reflect the direct effects of nanocarriers on protein expression. Herein, we investigated the effect of PLGA-based NPs on protein expression in HepG2 cells using a label-free quantitative proteomics approach with data independent acquisition (DIA). The percentage of two-fold change in the protein expression of cells treated with PLGA-based NPs was less than 10.15% during a 6 hour observation period. Among the changed proteins, we found that dynamic proteins involved in cell division, localization, and transport are more likely to be more susceptible to PLGA-based NPs.

Corrigendum: Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery.

[This corrects the article DOI: 10.3389/fpls.2023.1284235.].

Morphological entropy encodes cellular migration strategies on multiple length scales.

Cell migration is crucial for numerous physiological and pathological processes. A cell adapts its morphology, including the overall and nuclear morphology, in response to various cues in complex microenvironments, such as topotaxis and chemotaxis during migration. Thus, the dynamics of cellular morphology can encode migration strategies, from which diverse migration mechanisms can be inferred. However, deciphering the mechanisms behind cell migration encoded in morphology dynamics remains a challenging problem. Here, we present a powerful universal metric, the Cell Morphological Entropy (CME), developed by combining parametric morphological analysis with Shannon entropy. The utility of CME, which accurately quantifies the complex cellular morphology at multiple length scales through the deviation from a perfectly circular shape, is illustrated using a variety of normal and tumor cell lines in different in vitro microenvironments. Our results show how geometric constraints affect the MDA-MB-231 cell nucleus, the emerging interactions of MCF-10A cells migrating on collagen gel, and the critical transition from proliferation to invasion in tumor spheroids. The analysis demonstrates that the CME-based approach provides an effective and physically interpretable tool to measure morphology in real-time across multiple length scales. It provides deeper insight into cell migration and contributes to the understanding of different behavioral modes and collective cell motility in more complex microenvironments.

Flexible Full-Inorganic Ultrathin Films with Stable Circularly Polarized Luminescence Covering the Visible to Near-Infrared Region.

Circularly polarized luminescence (CPL) materials hold significant value in various fields, including information storage, secure communication, three-dimensional displays, biological detection, and optoelectronic devices. Using the Langmuir-Schaeffer (LS) assembly technique, we successfully construct a series of large-area flexible optical ultrathin films. Impressively, the inorganic assembled ultrathin films exhibit excellent CPL optical activity covering the visible to near-infrared (NIR) region, with the luminescence asymmetry factor glum ranging from 0.59 to 0.72. Moreover, such ultrathin films also display outstanding mechanical flexibility, the optical activity of which even after 240 bending cycles shows almost no difference compared to the unbent samples. Owing to the ultra-broadband optical activity and ultra-stable optical activity of such full-inorganic assembled materials on flexible substrates, coupled with their excellent processability and outstanding mechanical flexibility, we anticipate they will find use in many fields such as communication technology and flexible optoelectronics.

HMGA1 regulates the mitochondrial apoptosis pathway in sepsis-induced cardiomyopathy.

High mobility group protein AT-hook 1 (HMGA1), an architectural transcription factor, has previously been reportedto play an essential role in architectural remodeling processes. However, its effects on cardiovascular diseases, particularly sepsis-induced cardiomyopathy, have remained unclear. The study aimed to investigate the role of HMGA1 in lipopolysaccharide-induced cardiomyopathy. Mice subjected to lipopolysaccharide for 12 h resulted in cardiac dysfunction. We used an adeno-associated virus 9 delivery system to achieve cardiac-specific expression of the HMGA1 gene in the mice. H9c2 cardiomyocytes were infected with Ad-HMGA1 to overexpress HMGA1 or transfected with si-HMGA1 to knock down HMGA1. Echocardiography was applied to measure cardiac function. RT-PCR was used to detect the transcriptional level of inflammatory cytokines. CD45 and CD68 immunohistochemical staining were used to detect inflammatory cell infiltration and TUNEL staining to evaluate the cardiomyocyte apoptosis, MitoSox was used to detect mitochondrial reactive oxygen species, JC-1 was used todetect Mitochondrial membrane potential. Our findings revealed that the overexpression of HMGA1 exacerbated myocardial inflammation and apoptosis in response to lipopolysaccharide treatment. Additionally, we also observed that H9c2 cardiomyocytes with HMGA1 overexpression exhibited enhanced inflammation and apoptosis upon stimulation with lipopolysaccharide for 12 h. Conversely, HMGA1 knockdown in H9c2 cardiomyocytes attenuated lipopolysaccharide-induced cardiomyocyte inflammation and apoptosis. Further investigations into the molecular mechanisms underlying these effects showed that HMGA1 promoted lipopolysaccharide-induced mitochondrial-dependent cardiomyocyte apoptosis. The study reveals that HMGA1 worsens myocardial inflammation and apoptosis in response to lipopolysaccharide treatment. Mechanically, HMGA1 exerts its effects by regulating the mitochondria-dependent apoptotic pathway.

Risk factors, prognostic factors, and nomograms for distant metastases in patients with gastroenteropancreatic neuroendocrine tumors: a population-based study.

Background: Patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) have a poor prognosis for distant metastasis. Currently, there are no studies on predictive models for the risk of distant metastasis in GEP-NETs. Methods: In this study, risk factors associated with metastasis in patients with GEP-NETs in the Surveillance, Epidemiology, and End Results (SEER) database were analyzed by univariate and multivariate logistic regression, and a nomogram model for metastasis risk prediction was constructed. Prognostic factors associated with distant metastasis in patients with GEP-NETs were analyzed by univariate and multivariate Cox, and a nomogram model for prognostic prediction was constructed. Finally, the performance of the nomogram model predictions is validated by internal validation set and external validation set. Results: A total of 9145 patients with GEP-NETs were enrolled in this study. Univariate and multivariate logistic analysis demonstrated that T stage, N stage, tumor size, primary site, and histologic types independent risk factors associated with distant metastasis in GEP-NETs patients (p value < 0.05). Univariate and multivariate Cox analyses demonstrated that age, histologic type, tumor size, N stage, and primary site surgery were independent factors associated with the prognosis of patients with GEP-NETs (p value < 0.05). The nomogram model constructed based on metastasis risk factors and prognostic factors can predict the occurrence of metastasis and patient prognosis of GEP-NETs very effectively in the internal training and validation sets as well as in the external validation set. Conclusion: In conclusion, we constructed a new distant metastasis risk nomogram model and a new prognostic nomogram model for GEP-NETs patients, which provides a decision-making reference for individualized treatment of clinical patients.

An entropy-based approach for assessing the directional persistence of cell migration.

Cell migration, which is primarily characterized by directional persistence, is essential for the development of normal tissues and organs, as well as for numerous pathological processes. However, there is a lack of simple and efficient tools to analyze the systematic properties of persistence based on cellular trajectory data. Here, we present a novel approach, the entropy of angular distribution , which combines cellular turning dynamics and Shannon entropy to explore the statistical and time-varying properties of persistence that strongly correlate with cellular migration modes. Our results reveal the changes in the persistence of multiple cell lines that are tightly regulated by both intra- and extracellular cues, including Arpin protein, collagen gel/substrate, and physical constraints. Significantly, some previously unreported distinctive details of persistence have also been captured, helping to elucidate how directional persistence is distributed and evolves in different cell populations. The analysis suggests that the entropy of angular distribution-based approach provides a powerful metric for evaluating directional persistence and enables us to better understand the relationships between cellular behaviors and multiscale cues, which also provides some insights into the migration dynamics of cell populations, such as collective cell invasion.

Robust and sensitive conductive nanocomposite hydrogel with bridge cross-linking-dominated hierarchical structural design.

Conductive hydrogels have a remarkable potential for applications in soft electronics and robotics, owing to their noteworthy attributes, including electrical conductivity, stretchability, biocompatibility, etc. However, the limited strength and toughness of these hydrogels have traditionally impeded their practical implementation. Inspired by the hierarchical architecture of high-performance biological composites found in nature, we successfully fabricate a robust and sensitive conductive nanocomposite hydrogel through self-assembly-induced bridge cross-linking of MgB2 nanosheets and polyvinyl alcohol hydrogels. By combining the hierarchical lamellar microstructure with robust molecular B─O─C covalent bonds, the resulting conductive hydrogel exhibits an exceptional strength and toughness. Moreover, the hydrogel demonstrates exceptional sensitivity (response/relaxation time, 20 milliseconds; detection lower limit, ~1 Pascal) under external deformation. Such characteristics enable the conductive hydrogel to exhibit superior performance in soft sensing applications. This study introduces a high-performance conductive hydrogel and opens up exciting possibilities for the development of soft electronics.

Homogeneous Non-Metallocene Group 4 Metals Ligated with [N,N] Bidentate Ligand(s) for Olefin Polymerization.

The development of catalysts has significantly advanced the progress of polyolefin materials. In particular, group 4 (Ti, Zr, Hf) non-metallocene catalysts ligated with [N,N] bidentate ligand(s) have garnered increasing attention in the field of olefin polymerization due to their structurally stability and exceptional polymerization behaviors. Ligands containing nitrogen donors are diverse and at the core of many highly active catalysts. They mainly include amidine, guanidinato, diamine, and various N-heterocyclic ligands, which can be used to obtain a series of new polyolefin materials, such as ultrahigh molecular weight polyethylene (UHWMPE), olefin copolymers (ethylene/norbornene and ethylene/α-olefin) with high incorporations, and high isotactic or syndiotactic polypropylene after coordination with group 4 metals and activation by cocatalysts. Herein, we focus on the advancements and applications of this field over the past two decades, and introduce the catalyst precursors with [N,N] ligand(s), involving the effects of ligand structure, cocatalyst selection, and polymerization conditions on the catalytic activity and polymer properties.