Peptide REF1 is a local wound signal promoting plant regeneration.

Plants frequently encounter wounding and have evolved an extraordinary regenerative capacity to heal the wounds. However, the wound signal that triggers regenerative responses has not been identified. Here, through characterization of a tomato mutant defective in both wound-induced defense and regeneration, we demonstrate that in tomato, a plant elicitor peptide (Pep), REGENERATION FACTOR1 (REF1), acts as a systemin-independent local wound signal that primarily regulates local defense responses and regenerative responses in response to wounding. We further identified PEPR1/2 ORTHOLOG RECEPTOR-LIKE KINASE1 (PORK1) as the receptor perceiving REF1 signal for plant regeneration. REF1-PORK1-mediated signaling promotes regeneration via activating WOUND-INDUCED DEDIFFERENTIATION 1 (WIND1), a master regulator of wound-induced cellular reprogramming in plants. Thus, REF1-PORK1 signaling represents a conserved phytocytokine pathway to initiate, amplify, and stabilize a signaling cascade that orchestrates wound-triggered organ regeneration. Application of REF1 provides a simple method to boost the regeneration and transformation efficiency of recalcitrant crops.

The roles of epigenetic regulation in graft-versus-host disease.

Allogeneic hematopoietic stem cell transplantation (aHSCT) is utilized as a potential curative treatment for various hematologic malignancies. However, graft-versus-host disease (GVHD) post-aHSCT is a severe complication that significantly impacts patients' quality of life and overall survival, becoming a major cause of non-relapse mortality. In recent years, the association between epigenetics and GVHD has garnered increasing attention. Epigenetics focuses on studying mechanisms that affect gene expression without altering DNA sequences, primarily including DNA methylation, histone modifications, non-coding RNAs (ncRNAs) regulation, and RNA modifications. This review summarizes the role of epigenetic regulation in the pathogenesis of GVHD, with a focus on DNA methylation, histone modifications, ncRNA, RNA modifications and their involvement and applications in the occurrence and development of GVHD. It also highlights advancements in relevant diagnostic markers and drugs, aiming to provide new insights for the clinical diagnosis and treatment of GVHD.

Dehydrogenative synthesis of N-functionalized 2-aminophenols from cyclohexanones and amines: Molecular complexities via one-shot assembly.

Polyfunctionalized arenes are privileged structural motifs in both academic and industrial chemistry. Conventional methods for accessing this class of chemicals usually involve stepwise modification of phenyl rings, often necessitating expensive noble metal catalysts and suffering from low reactivity and selectivity when introducing multiple functionalities. We herein report dehydrogenative synthesis of N-functionalized 2-aminophenols from cyclohexanones and amines. The developed reaction system enables incorporating amino and hydroxyl groups into aromatic rings in a one-shot fashion, which simplifies polyfunctionalized 2-aminophenol synthesis by circumventing issues associated with traditional arene modifications. The wide substrate scope and excellent functional group tolerance are exemplified by late-stage modification of complex natural products and pharmaceuticals that are unattainable by existing methods. This dehydrogenative protocol benefits from using 2,2,6,6-tetramethylpiperidine 1-oxyl (TEMPO) as oxidant that offers interesting chemo- and regio-selective oxidation processes. More notably, the essential role of in situ generated water is disclosed, which protects aliphatic amine moieties from overoxidation via hydrogen bond-enabled interaction.

Different biomarker ratios in peripheral blood have limited value in diagnosing periprosthetic joint infection after total joint arthroplasty: a single-center, retrospective study.

BACKGROUND: Periprosthetic joint infection (PJI) is a severe complication that can occur after total joint arthroplasty (TJA). The timely and accurate diagnosis of PJI is the key to treatment. This study investigated the diagnostic value of platelet to lymphocyte ratio (PLR), platelet count to mean platelet volume ratio (PVR), neutrophil to lymphocyte ratio (NLR) and monocyte to lymphocyte ratio (MLR) in PJI after total knee arthroplasty (TKA) and total hip arthroplasty (THA). METHODS: We performed a retrospective analysis of the patients who underwent revision hip or knee arthroplasty at our Institute between June 2015 and June 2020. Of the 187 patients reviewed, 168 were included in the study. According to the diagnostic criteria of the Musculoskeletal Infection Society (MSIS), 58 patients were in the PJI group, and 110 patients were in the aseptic loosening (AL) group. We recorded and compared the preoperative peripheral blood white blood cell (WBC) count, platelet count (PLT), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), PLR, PVR, NLR, and MLR in both groups. The diagnostic performance of the WBC, PLT, PLR, PVR, NLR, and MLR individually and in combination with the ESR and CRP for PJI diagnosis was evaluated by receiver operating characteristic (ROC) curves, and the sensitivity, specificity, positive predictive value, and negative predictive value were calculated. RESULTS: Compared to those in the AL group, the mean WBC, PLT, ESR, CRP, PLR, PVR, NLR, and MLR in the peripheral blood of the PJI group were significantly greater (P < 0.05). The analysis of the ROC curve revealed that the ESR, CRP, PLR, PVR, NLR, and MLR in peripheral blood had moderate effectiveness in diagnosing PJI, with area under the curve (AUC) values of 0.760 (95% CI: 0.688-0.823), 0.758 (95% CI: 0.687-0.821), 0.714 (95% CI: 0.639-0.781), 0.709 (95% CI: 0.634-0.777), 0.723 (95% CI: 0.649-0.789), and 0.728 (95% CI: 0.654-0.793), respectively. Conversely, the WBC and PLT counts demonstrated poor diagnostic value for PJI, with AUC values of 0.578 (95% CI: 0.499-0.653) and 0.694 (95% CI: 0.619-0.763), respectively. The results of the prediction model calculations revealed that the combined AUC of the WBC, PLT, ESR, CRP, PLR, PVR, NLR, and MLR was the highest at 0.853 (95% CI, 0.790-0.909), indicating good value in the diagnosis of PJI, with a sensitivity of 82.8% and a specificity of 72.7%. Moreover, the novel composite of parameters improved the accuracy and reliability in diagnosing PJI compared to the traditional biomarkers ESR and CRP (P = 0.015). CONCLUSION: Our study suggested that the diagnostic value of the peripheral blood biomarkers PLR, PVR, NLR, and MLR for diagnosing PJI is limited and not superior to that of the ESR or CRP. However, when the WBC, PLT, ESR, CRP, PLR, PVR, NLR, and MLR are combined, the diagnostic performance of PJI in TJA patients can be improved.

Preoperative CT-based radiomic prognostic index to predict the benefit of postoperative radiotherapy in patients with non-small cell lung cancer: a multicenter study.

BACKGROUND: The value of postoperative radiotherapy (PORT) for patients with non-small cell lung cancer (NSCLC) remains controversial. A subset of patients may benefit from PORT. We aimed to identify patients with NSCLC who could benefit from PORT. METHODS: Patients from cohorts 1 and 2 with pathological Tany N2 M0 NSCLC were included, as well as patients with non-metastatic NSCLC from cohorts 3 to 6. The radiomic prognostic index (RPI) was developed using radiomic texture features extracted from the primary lung nodule in preoperative chest CT scans in cohort 1 and validated in other cohorts. We employed a least absolute shrinkage and selection operator-Cox regularisation model for data dimension reduction, feature selection, and the construction of the RPI. We created a lymph-radiomic prognostic index (LRPI) by combining RPI and positive lymph node number (PLN). We compared the outcomes of patients who received PORT against those who did not in the subgroups determined by the LRPI. RESULTS: In total, 228, 1003, 144, 422, 19, and 21 patients were eligible in cohorts 1-6. RPI predicted overall survival (OS) in all six cohorts: cohort 1 (HR = 2.31, 95% CI: 1.18-4.52), cohort 2 (HR = 1.64, 95% CI: 1.26-2.14), cohort 3 (HR = 2.53, 95% CI: 1.45-4.3), cohort 4 (HR = 1.24, 95% CI: 1.01-1.52), cohort 5 (HR = 2.56, 95% CI: 0.73-9.02), cohort 6 (HR = 2.30, 95% CI: 0.53-10.03). LRPI predicted OS (C-index: 0.68, 95% CI: 0.60-0.75) better than the pT stage (C-index: 0.57, 95% CI: 0.50-0.63), pT + PLN (C-index: 0.58, 95% CI: 0.46-0.70), and RPI (C-index: 0.65, 95% CI: 0.54-0.75). The LRPI was used to categorize individuals into three risk groups; patients in the moderate-risk group benefited from PORT (HR = 0.60, 95% CI: 0.40-0.91; p = 0.02), while patients in the low-risk and high-risk groups did not. CONCLUSIONS: We developed preoperative CT-based radiomic and lymph-radiomic prognostic indexes capable of predicting OS and the benefits of PORT for patients with NSCLC.

SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning.

Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug-gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph convolutional network (GCN)-based models' state-of-the-art performance in prediction, their reliance on supervised learning makes them vulnerable to data sparsity, a common challenge in drug discovery, further complicating model development. In this study, we propose SGCLDGA, a novel computational model leveraging graph neural networks and contrastive learning to predict unknown drug-gene associations. SGCLDGA employs GCNs to extract vector representations of drugs and genes from the original bipartite graph. Subsequently, singular value decomposition (SVD) is employed to enhance the graph and generate multiple views. The model performs contrastive learning across these views, optimizing vector representations through a contrastive loss function to better distinguish positive and negative samples. The final step involves utilizing inner product calculations to determine association scores between drugs and genes. Experimental results on the DGIdb4.0 dataset demonstrate SGCLDGA's superior performance compared with six state-of-the-art methods. Ablation studies and case analyses validate the significance of contrastive learning and SVD, highlighting SGCLDGA's potential in discovering new drug-gene associations. The code and dataset for SGCLDGA are freely available at https://github.com/one-melon/SGCLDGA.

Massively parallel CRISPR-assisted homologous recombination enables saturation editing of full-length endogenous genes in yeast.

Performing saturation editing of chromosomal genes will enable the study of genetic variants in situ and facilitate protein and cell engineering. However, current in vivo editing of endogenous genes either lacks flexibility or is limited to discrete codons and short gene fragments, preventing a comprehensive exploration of genotype-phenotype relationships. To enable facile saturation editing of full-length genes, we used a protospacer adjacent motif-relaxed Cas9 variant and homology-directed repair to achieve above 60% user-defined codon replacement efficiencies in Saccharomyces cerevisiae genome. Coupled with massively parallel DNA design and synthesis, we developed a saturation gene editing method termed CRISPR-Cas9- and homology-directed repair-assisted saturation editing (CHASE) and achieved highly saturated codon swapping of long genomic regions. By applying CHASE to massively edit a well-studied global transcription factor gene, we found known and unreported genetic variants affecting an industrially relevant microbial trait. The user-defined codon editing capability and wide targeting windows of CHASE substantially expand the scope of saturation gene editing.

Detection of alkaline phosphatase activity with a CsPbBr3/Y6 heterojunction-based photoelectrochemical sensor.

An ultra-sensitive photoelectrochemical (PEC) sensor based on perovskite composite was developed for the determination of alkaline phosphatase (ALP) in human serum. In contrast to CsPbBr3 or Y6 that generated anodic current, the heterojunction of CsPbBr3/Y6 promoted photocarriers to separate and generated cathodic photocurrent. Ascorbic acid (AA) was produced by ALP hydrolyzing L-ascorbic acid 2-phosphate trisodium salt (AAP), which can combine with the holes on the photoelectrode surface, accelerating the transmission of photogenerated carriers, leading to enhanced photocurrent intensity. Thus, the enhancement of PEC current was linked to ALP activity. The PEC sensor exhibits good sensitivity for detection of ALP owing to the unique photoelectric properties of the CsPbBr3/Y6 heterojunction. The detection limit of the sensor was 0.012 U·L-1 with a linear dynamic range of 0.02-2000 U·L-1. Therefore, this PEC sensing platform shows great potential for the development of different PEC sensors.

Tab-Cox: An Interpretable Deep Survival Analysis Model for Patients with Nasopharyngeal Carcinoma based on TabNet.

The nutritional status of cancer patients is closely associated with the clinical progression of the disease. A survival analysis model combined with a neural network can predict future disease trends in patients, facilitating early prevention and assisting physicians in making diagnoses. However, the complexity of neural networks and their incompatibility with medical tabular data can reduce the interpretability of the model. To address this issue, thr paper propose a novel survival analysis model called Tab-Cox, which combines TabNet and Cox models. This model is specifically designed to predict the survival outcomes of patients with nasopharyngeal carcinoma. The model utilizes TabNet's sequential attention mechanism to extract more interpretable features, providing an interpretable method for identifying disease risk factors. Consequently, the model ensures accurate survival prediction while also making the results more comprehensible for both patients and doctors. The paper tested the efficacy of the model by conducting experiments on various diverse datasets in comparison with other commonly used survival models. The results showed that the proposed model delivered the highest or second-highest accuracy across all datasets. Furthermore, the paper conducted a comparative interpretability analysis against the classical Cox model. In addition and compare the interpretability of the Tab-Cox model with the classical Cox model and discuss the advantages and disadvantages of its interpretability. This demonstrates that Tab-Cox can assist doctors in identifying risk factors that are challenging to capture using artificial methods.

Enhancing Predictions of Drug Solubility through Multidimensional Structural Characterization Exploitation.

Solubility is not only a significant physical property of molecules but also a vital factor in smallmolecule drug development. Determining drug solubility demands stringent equipment, controlled environments, and substantial human and material resources. The accurate prediction of drug solubility using computational methods has long been a goal for researchers. In this study, we introduce MSCSol, a solubility prediction model that integrates multidimensional molecular structure information. We incorporate a graph neural network with geometric vector perceptrons (GVP-GNN) to encode 3D molecular structures, representing spatial arrangement and orientation of atoms, as well as atomic sequences and interactions. We also employ Selective Kernel Convolution combined with Global and Local attention mechanisms to capture molecular features context at different scales. Additionally, various descriptors are calculated to enrich the molecular representation. For the 2D and 3D structural data of molecules, we design different data augmentation strategies to enhance generalization ability and prevent the model from learning irrelevant information. Extensive experiments on benchmark and independent datasets demonstrate MSCSol's superior performance. Ablation studies further confirm the effectiveness of different modules. Interpretability analysis highlights the importance of various atomic groups and substructures for solubility and verifies that our model effectively captures functional molecular structures and higher-order knowledge. The source code and datasets are freely available at https://github.com/ZiyuFanCSU/MSCSol.

DPMGCDA: Deciphering circRNA-Drug Sensitivity Associations with Dual Perspective Learning and Path-Masked Graph Autoencoder.

Accumulating evidence has indicated that the expression of circular RNAs (circRNAs) can affect the cellular sensitivity to drugs and significantly influence drug efficacy. However, traditional experimental approaches for validating these associations are resource-intensive and time-consuming. To address this challenge, we propose a computational framework termed DPMGCDA leveraging dual perspective learning and path-masked graph autoencoder to predict circRNA-drug sensitivity associations. Initially, we construct circRNA-circRNA fusion similarity networks and drug-drug fusion similarity networks using similarity network fusion, ensuring a comprehensive integration of information. Based on the above, we built the circRNA homogeneous graph, the drug homogeneous graph, and the circRNA-drug heterogeneous graph. Next, we form the initial node features in the circRNA-drug heterogeneous graph from the homogeneous graph-level perspective and the combined feature-level perspective and complete the prediction of potential associations using the path-masked graph autoencoder in both perspectives. The predictions under both perspectives are finally combined to obtain the final prediction score. Transductive setting experiments and inductive setting experiments all demonstrate that our method, DPMGCDA, outperforms state-of-the-art approaches. Additionally, we verify the necessity of employing dual perspective learning through ablation tests and analyze the effective encoding capability of the path-masked graph autoencoder for features through embedding visualization. Moreover, case studies on four drugs corroborate DPMGCDA's ability to identify potential circRNAs associated with new drugs.

Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip.

By mimicking the neurons and synapses of the human brain and employing spiking neural networks on neuromorphic chips, neuromorphic computing offers a promising energy-efficient machine intelligence. How to borrow high-level brain dynamic mechanisms to help neuromorphic computing achieve energy advantages is a fundamental issue. This work presents an application-oriented algorithm-software-hardware co-designed neuromorphic system for this issue. First, we design and fabricate an asynchronous chip called "Speck", a sensing-computing neuromorphic system on chip. With the low processor resting power of 0.42mW, Speck can satisfy the hardware requirements of dynamic computing: no-input consumes no energy. Second, we uncover the "dynamic imbalance" in spiking neural networks and develop an attention-based framework for achieving the algorithmic requirements of dynamic computing: varied inputs consume energy with large variance. Together, we demonstrate a neuromorphic system with real-time power as low as 0.70mW. This work exhibits the promising potentials of neuromorphic computing with its asynchronous event-driven, sparse, and dynamic nature.

A sponge-like nanofiber melatonin-loaded scaffold accelerates vascularized bone regeneration via improving mitochondrial energy metabolism.

Electrospun nanofibers have been widely employed in bone tissue engineering for their ability to mimic the micro to nanometer scale network of the native bone extracellular matrix. However, the dense fibrous structure and limited mechanical support of these nanofibers pose challenges for the treatment of critical size bone defects. In this study, we propose a facile approach for creating a three-dimensional scaffold using interconnected electrospun nanofibers containing melatonin (Scaffold@MT). The hypothesis posited that the sponge-like Scaffold@MT could potentially enhance bone regeneration and angiogenesis by modulating mitochondrial energy metabolism. Melatonin-loaded gelatin and poly-lactic-acid nanofibers were fabricated using electrospinning, then fragmented into shorter fibers. The sponge-like Scaffold@MT was created through a process involving homogenization, low-temperature lyophilization, and chemical cross-linking, while maintaining the microstructure of the continuous nanofibers. The incorporation of short nanofibers led to a low release of melatonin and increased Young's modulus of the scaffold. Scaffold@MT demonstrated positive biocompatibility by promoting a 14.2 % increase in cell proliferation. In comparison to the control group, Scaffold@MT significantly enhanced matrix mineralization by 3.2-fold and upregulated the gene expression of osteoblast-specific markers, thereby facilitating osteogenic differentiation of bone marrow mesenchymal stem cells (BMMSCs). Significantly, Scaffold@MT led to a marked enhancement in the mitochondrial energy function of BMMSCs, evidenced by elevated adenosine triphosphate (ATP) production, mitochondrial membrane potential, and protein expression of respiratory chain factors. Furthermore, Scaffold@MT promoted the migration of human umbilical vein endothelial cells (HUVECs) and increased tube formation by 1.3 times compared to the control group, accompanied by an increase in vascular endothelial growth factor (VEGFA) expression. The results of in vivo experiments indicate that the implantation of Scaffold@MT significantly improved vascularized bone regeneration in a distal femur defect in rats. Micro-computed tomography analysis conducted 8 weeks post-surgery revealed that Scaffold@MT led to optimal development of new bone microarchitecture. Histological and immunohistochemical analyses demonstrated that Scaffold@MT facilitated bone matrix deposition and new blood vessel formation at the defect site. Overall, the utilization of melatonin-loaded nanofiber sponges exhibits significant promise as a scaffold that promotes bone growth and angiogenesis, making it a viable option for the repair of critical-sized bone defects.

Improving Tamoxifen Performance in Inducing Apoptosis and Hepatoprotection by Loading on a Dual Nanomagnetic Targeting System.

BACKGROUND: Although tamoxifen (TMX) belongs to selective estrogen receptor modulators (SERMs) and selectively binds to estrogen receptors, it affects other estrogen-producing tissues due to passive diffusion and non-differentiation of normal and cancerous cells and leads to side effects. METHODS: The problems expressed about tamoxifen (TMX) encouraged us to design a new drug delivery system based on magnetic nanoparticles (MNPs) to simultaneously target two receptors on cancer cells through folic acid (FA) and hyaluronic acid (HA) groups. The mediator of binding of two targeting agents to MNPs is a polymer linker, including dopamine, polyethylene glycol, and terminal amine (DPN). RESULTS: Zeta potential, dynamic light scattering (DLS), and Field emission scanning electron microscopy (FESEM) methods confirmed that MNPs-DPN-HA-FA has a suitable size of ~105 nm and a surface charge of -41 mV, and therefore, it can be a suitable option for carrying TMX and increasing its solubility. The cytotoxic test showed that the highest concentration of MNPs-DPN-HA-FA-TMX decreased cell viability to about 11% after 72 h of exposure compared to the control. While the protective effect of modified MNPs on normal cells was evident, unlike tamoxifen, the survival rate of liver cells, even after 180 min of treatment, was not significantly different from the control group. The protective effect of MNPs was also confirmed by examining the amount of malondialdehyde, and no significant difference was observed in the amount of lipid peroxidation caused by modified MNPs compared to the control. Flow cytometry proved that TMX can induce apoptosis by targeting MNPs. Real-time PCR showed that the modified MNPs activated the intrinsic and extrinsic mitochondrial pathways of apoptosis, so the Bak1/Bclx ratio for MNPs-DPN-HA-FA-TMX and free TMX was 70.82 and 0.38, respectively. Also, the expression of the caspase-3 gene increased 430 times compared to the control. On the other hand, only TNF gene expression, which is responsible for metastasis in some tumors, was decreased by both free TMX and MNPs-DPN-HA-FA-TMX. Finally, molecular docking proved that MNPs-DPN-HA-FA-TMX could provide a very stable interaction with both CD44 and folate receptors, induce apoptosis in cancer cells, and reduce hepatotoxicity. CONCLUSION: All the results showed that MNPs-DPN-HA-FA-TMX can show good affinity to cancer cells using targeting agents and induce apoptosis in metastatic breast ductal carcinoma T-47D cell lines. Also, the protective effects of MNPs on hepatocytes are quite evident, and they can reduce the side effects of TMX.

IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

Evaluation of models to predict prognosis in patients with advanced hepatocellular carcinoma treated with TACE combined with apatinib.

BACKGROUND: The HAP, Six-and-Twelve, Up to Seven, and ALBI scores have been substantiated as reliable prognostic markers in patients presenting with intermediate and advanced hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE) treatment. Given this premise, our research aims to assess the predictive efficacy of these models in patients with intermediate and advanced HCC receiving a combination of TACE and Apatinib. Additionally, we have conducted a meticulous comparative analysis of these four scoring systems to discern their respective predictive capacities and efficacies in combined therapy. METHODS: Performing a retrospective analysis on the clinical data from 200 patients with intermediate and advanced HCC, we studied those who received TACE combined with Apatinib at the First Affiliated Hospital of the University of Science and Technology of China between June 2018 and December 2022. To identify the factors affecting survival, the study performed univariate and multivariate Cox regression analyses, with calculations of four different scores: HAP, Six-and-Twelve, Up to Seven, and ALBI. Lastly, Harrell's C-index was employed to compare the prognostic abilities of these scores. RESULTS: Cox proportional hazards model results revealed that the ALBI score, presence of portal vein tumor thrombus (PVTT, )and tumor size are independent determinants of prognostic survival. The Kaplan-Meier analyses showed significant differences in survival rates among patients classified by the HAP, Six-and-Twelve, Up to Seven, and ALBI scoring methods. Of the evaluated systems, the HAP scoring demonstrated greater prognostic precision, with a Harrell's C-index of 0.742, surpassing the alternative models (P < 0.05). In addition, an analysis of the area under the AU-ROC curve confirms the remarkable superiority of the HAP score in predicting short-term survival outcomes. CONCLUSION: Our study confirms the predictive value of HAP, Six-and-Twelve, Up to Seven, and ALBI scores in intermediate to advanced Hepatocellular Carcinoma (HCC) patients receiving combined Transarterial Chemoembolization (TACE) and Apatinib therapy. Notably, the HAP model excels in predicting outcomes for this specific HCC subgroup.

Exploring ncRNA-Drug Sensitivity Associations Via Graph Contrastive Learning.

Increasing evidence has shown that noncoding RNAs (ncRNAs) can affect drug efficiency by modulating drug sensitivity genes. Exploring the association between ncRNAs and drug sensitivity is essential for drug discovery and disease prevention. However, traditional biological experiments for identifying ncRNA-drug sensitivity associations are time-consuming and laborious. In this study, we develop a novel graph contrastive learning approach named NDSGCL to predict ncRNA-drug sensitivity. NDSGCL uses graph convolutional networks to learn feature representations of ncRNAs and drugs in ncRNA-drug bipartite graphs. It integrates local structural neighbours and global semantic neighbours to learn a more comprehensive representation by contrastive learning. Specifically, the local structural neighbours aim to capture the higher-order relationship in the ncRNA-drug graph, while the global semantic neighbours are defined based on semantic clusters of the graph that can alleviate the impact of data sparsity. The experimental results show that NDSGCL outperforms basic graph convolutional network methods, existing contrastive learning methods, and state-of-the-art prediction methods. Visualization experiments show that the contrastive objectives of local structural neighbours and global semantic neighbours play a significant role in contrastive learning. Case studies on two drugs show that NDSGCL is an effective tool for predicting ncRNA-drug sensitivity associations. Source code and datasets can be available on https://github.com/altriavin/NDSGCL.

Vectorized dataset of silted land formed by check dams on the Chinese Loess Plateau.

Check dams on the Chinese Loess Plateau (CLP) have captured billions of tons of eroded sediment, substantially reducing sediment load in the Yellow River. However, uncertainties persist regarding the precise sediment capture and the role of these dams in Yellow River flow and sediment dynamics due to the lack of available spatial distribution datasets. We produced the first vectorized dataset of silted land formed by check dams on the CLP, combining high-resolution and easily accessible Google Earth images with object-based classification methods. The accuracy of the dataset was verified by 1947 collected test samples, and the producer's accuracy and user's accuracy of the dam lands were 88.9% and 99.5%, respectively. Our dataset not only provides fundamental information for accurately assessing the ecosystem service functions of check dams, but also helps to interpret current changes in sediment delivery of the Yellow River and plan future soil and water conservation projects.

Experimental demonstration of 17.5 Gb/s physical layer key distribution over 100†km fiber link based on channel physical intrinsic property and polarization reciprocity.

Secure key distribution (SKD) schemes based on fiber channel reciprocity provide information-theoretic security as well as a simple symmetric structure. However, the nonlinear effects and backscattering effects introduced during the bidirectional transmission process degrade the channel reciprocity. Recent unidirectional SKD schemes avoid non-reciprocal factors but require additional negotiation mechanisms to aggregate the transmitter and receiver data. Here, we propose a unidirectional SKD scheme based on channel physical intrinsic property and polarization reciprocity. The designed loopback structure constructs asymmetry between legitimate and illegitimate parties while aggregating data. The deployment of a broadband chaotic entropy source significantly improves the key generation rate (KGR). In the experiment, the KGR reaches 17.5 Gb/s, and the distribution distance reaches 100 km.