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.

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.

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.

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.

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.

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.

The predictive value of T-cell chimerism for disease relapse after allogeneic hematopoietic stem cell transplantation.

Introduction: Chimerism is closely correlated with disease relapse after allogeneic hematopoietic stem cell transplantation (allo-HSCT). However, chimerism rate is dynamic changes, and the sensitivity of different chimerism requires further research. Methods: To investigate the predictive value of distinct chimerism for relapse, we measured bone marrow (BM), peripheral blood (PB), and T-cell (isolated from BM) chimerism in 178 patients after allo-HSCT. Results: Receiver operating characteristic (ROC) curve showed that T-cell chimerism was more suitable to predict relapse after allo-HSCT compared with PB and BM chimerism. The cutoff value of T-cell chimerism for predicting relapse was 99.45%. Leukemia and myelodysplastic syndrome (MDS) relapse patients' T-cell chimerism was a gradual decline from 2 months to 9 months after allo-HSCT. Higher risk of relapse and death within 1 year after allo-HSCT. The T-cell chimerism rates in remission and relapse patients were 99.43% and 94.28% at 3 months after allo-HSCT (P = 0.009), 99.31% and 95.27% at 6 months after allo-HSCT (P = 0.013), and 99.26% and 91.32% at 9 months after allo-HSCT (P = 0.024), respectively. There was a significant difference (P = 0.036) for T-cell chimerism between early relapse (relapse within 9 months after allo-HSCT) and late relapse (relapse after 9 months after allo-HSCT) at 2 months after allo-HSCT. Every 1% increase in T-cell chimerism, the hazard ratio for disease relapse was 0.967 (95% CI: 0.948-0.987, P<0.001). Discussion: We recommend constant monitoring T-cell chimerism at 2, 3, 6, and 9 months after allo-HSCT to predict relapse.

Effects of Cage Implantation Depth on Sagittal Parameters and Functional Outcomes in Posterior Lumbar Interbody Fusion for the Treatment of L4-L5 Lumbar Degenerative Spondylolisthesis.

OBJECTIVE: In the treatment of lumbar degenerative spondylolisthesis (LDS) with Posterior lumbar interbody fusion (PLIF) surgery, interbody fusion implants play a key role in supporting the vertebral body and facilitating fusion. The objective of this study was to assess the impact of implantation depth on sagittal parameters and functional outcomes in patients undergoing PLIF surgery. METHODS: This study reviewed 128 patients with L4-L5 LDS between January 2016 and August 2019. All patients underwent an open PLIF surgery that included intravertebral decompression, implantation of pedicle screws and cage. We grouped according to the position of the center of the cage relative to the L5 vertebral endplate. Patients with the center of the cage located at the anterior 1/2 of the upper end plate of the L5 vertebral body were divided into Anterior group, and located at the posterior 1/2 of the upper end plate of the L5 vertebral body were divided into Posterior group. The lumbar lordosis (LL), segmental lordosis (SL), sacral slope (SS), pelvic incidence (PI), pelvic tilt (PT) and slope degree (SD) was measured for radiographic outcomes. We used the visual analog scale (VAS) and the oswestry disability index (ODI) score to assess functional outcomes. Paired t-test was used to compare imaging and bedside data before and after surgery between the two groups, and independent sample t-test, χ2 test and Fisher exact test were used to compare the data between the two groups. RESULT: The mean follow-up of Anterior group was 44.13 ± 9.23 months, and Posterior group was 45.62 ± 10.29 months (P > 0.05). The LL, SL, PT, SS, SD and PI-LL after operation showed great improvements, relative to the corresponding preoperative values in both groups (P < 0.05). Compared to Posterior group, Anterior group exhibited far enhanced SL (15.49 ± 3.28 vs. 13.67 ± 2.53, P < 0.05), LL (53.47 ± 3.21 vs. 52.08 ± 3.15, P < 0.05) outcomes and showed depressed PI-LL (8.87 ± 5.05 vs. 10.73 ± 5.39, P < 0.05) outcomes at the final follow-up. Meanwhile, the SL in Anterior group (16.18 ± 3.99) 1 months after operation were also higher than in Posterior group (14.12 ± 3.57) (P < 0.05). We found that VAS and ODI at the final follow-up in Anterior group (3.62 ± 0.96, 25.19 ± 5.25) were significantly lower than those in Posterior group (4.12 ± 0.98, 27.68 ± 5.13) (P < 0.05). CONCLUSIONS: For patients with LDS, the anteriorly placed cage may provide better improvement of SL after PLIF surgery. Meanwhile, the anteriorly placed cage may achieve better sagittal parameters of LL and PI-LL and functional outcomes at the final follow-up.

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.

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.

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.

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.

Phase III, multicenter, randomized trial of 45 Gy versus 30 Gy thoracic radiation for extensive-stage small cell lung cancer (ES-SCLC): Study protocol.

BACKGROUND: Consolidative thoracic radiotherapy (cTRT) has previously shown benefit to patients with extensive stage small cell lung cancer (ES-SCLC) who respond to chemotherapy. However, the optimum dose of cTRT is unknown. The purpose of this randomized trial is to compare the efficacy of 45 Gy in 15 fractions with 30 Gy in 10 fractions cTRT in ES-SCLC. METHODS: This phase III, multicenter, randomized trial is designed to evaluate the safety and efficacy of different cTRT dose in ES-SCLC. Eligible patients with pathologically confirmed ES-SCLC who responded to 4-6 cycles of etoposide plus cisplatin (EP) or carboplatin (EC) chemotherapy were randomized 1:1 to receive either 30 Gy in 10 fractions (standard dose) or 45 Gy in 15 fractions (high dose) cTRT. The primary endpoint is 2-year overall survival (OS). Secondary endpoints include 2-year progression-free survival (PFS), 2-year local control (LC) and treatment related toxicity as measured by adverse events according to the Common Terminology Criteria for Adverse Events (version 4.0). DISCUSSION: The present study is the first randomized phase III trial designed to evaluate the efficacy of higher versus lower dose cTRT in ES-SCLC, providing evidence for future clinical practice in prolonging survival of patients with ES-SCLC.

PinMyMetal: A hybrid learning system to accurately model metal binding sites in macromolecules.

Metal ions are vital components in many proteins for the inference and engineering of protein function, with coordination complexity linked to structural (4-residue predominate), catalytic (3-residue predominate), or regulatory (2-residue predominate) roles. Computational tools for modeling metal ions in protein structures, especially for transient, reversible, and concentration-dependent regulatory sites, remain immature. We present PinMyMetal (PMM), a sophisticated hybrid machine learning system for predicting zinc ion localization and environment in macromolecular structures. Compared to other predictors, PMM excels in predicting regulatory sites (median deviation of 0.34 Å), demonstrating superior accuracy in locating catalytic sites (median deviation of 0.27 Å) and structural sites (median deviation of 0.14 Å). PMM assigns a certainty score to each predicted site based on local structural and physicochemical features independent of homolog presence. Interactive validation through our server, CheckMyMetal, expands PMM's scope, enabling it to pinpoint and validates diverse functional zinc sites from different structure sources (predicted structures, cryo-EM and crystallography). This facilitates residue-wise assessment and robust metal binding site design. The lightweight PMM system demands minimal computing resources and is available at https://PMM.biocloud.top. While currently trained on zinc, the PMM workflow can easily adapt to other metals through expanded training data.

Adaptive Synaptic Scaling in Spiking Networks for Continual Learning and Enhanced Robustness.

Synaptic plasticity plays a critical role in the expression power of brain neural networks. Among diverse plasticity rules, synaptic scaling presents indispensable effects on homeostasis maintenance and synaptic strength regulation. In the current modeling of brain-inspired spiking neural networks (SNN), backpropagation through time is widely adopted because it can achieve high performance using a small number of time steps. Nevertheless, the synaptic scaling mechanism has not yet been well touched. In this work, we propose an experience-dependent adaptive synaptic scaling mechanism (AS-SNN) for spiking neural networks. The learning process has two stages: First, in the forward path, adaptive short-term potentiation or depression is triggered for each synapse according to afferent stimuli intensity accumulated by presynaptic historical neural activities. Second, in the backward path, long-term consolidation is executed through gradient signals regulated by the corresponding scaling factor. This mechanism shapes the pattern selectivity of synapses and the information transfer they mediate. We theoretically prove that the proposed adaptive synaptic scaling function follows a contraction map and finally converges to an expected fixed point, in accordance with state-of-the-art results in three tasks on perturbation resistance, continual learning, and graph learning. Specifically, for the perturbation resistance and continual learning tasks, our approach improves the accuracy on the N-MNIST benchmark over the baseline by 44% and 25%, respectively. An expected firing rate callback and sparse coding can be observed in graph learning. Extensive experiments on ablation study and cost evaluation evidence the effectiveness and efficiency of our nonparametric adaptive scaling method, which demonstrates the great potential of SNN in continual learning and robust learning.