An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genes.

BACKGROUND: Major depressive disorder (MDD) and interstitial cystitis (IC) are two highly debilitating conditions that often coexist with reciprocal effect, significantly exacerbating patients' suffering. However, the molecular underpinnings linking these disorders remain poorly understood. METHODS: Transcriptomic data from GEO datasets including those of MDD and IC patients was systematically analyzed to develop and validate our model. Following removal of batch effect, differentially expressed genes (DEGs) between respective disease and control groups were identified. Shared DEGs of the conditions then underwent functional enrichment analyses. Additionally, immune infiltration analysis was quantified through ssGSEA. A diagnostic model for MDD was constructed by exploring 113 combinations of 12 machine learning algorithms with 10-fold cross-validation on the training sets following by external validation on test sets. Finally, the "Enrichr" platform was utilized to identify potential drugs for MDD. RESULTS: Totally, 21 key genes closely associated with both MDD and IC were identified, predominantly involved in immune processes based on enrichment analyses. Immune infiltration analysis revealed distinct profiles of immune cell infiltration in MDD and IC compared to healthy controls. From these genes, a robust 11-gene (ABCD2, ATP8B4, TNNT1, AKR1C3, SLC26A8, S100A12, PTX3, FAM3B, ITGA2B, OLFM4, BCL7A) diagnostic signature was constructed, which exhibited superior performance over existing MDD diagnostic models both in training and testing cohorts. Additionally, epigallocatechin gallate and 10 other drugs emerged as potential targets for MDD. CONCLUSION: Our work developed a diagnostic model for MDD employing a combination of bioinformatic techniques and machine learning methods, focusing on shared genes between MDD and IC.

Bridging Visual and Textual Semantics: Towards Consistency for Unbiased Scene Graph Generation.

Scene Graph Generation (SGG) aims to detect visual relationships in an image. However, due to long-tailed bias, SGG is far from practical. Most methods depend heavily on the assistance of statistics co-occurrence to generate a balanced dataset, so they are dataset-specific and easily affected by noises. The fundamental cause is that SGG is simplified as a classification task instead of a reasoning task, thus the ability capturing the fine-grained details is limited and the difficulty in handling ambiguity is increased. By imitating the way of dual process in cognitive psychology, a Visual-Textual Semantics Consistency Network (VTSCN) is proposed to model the SGG task as a reasoning process, and relieve the long-tailed bias significantly. In VTSCN, as the rapid autonomous process (Type1 process), we design a Hybrid Union Representation (HUR) module, which is divided into two steps for spatial awareness and working memories modeling. In addition, as the higher order reasoning process (Type2 process), a Global Textual Semantics Modeling (GTS) module is designed to individually model the textual contexts with the word embeddings of pairwise objects. As the final associative process of cognition, a Heterogeneous Semantics Consistency (HSC) module is designed to balance the type1 process and the type2 process. Lastly, our VTSCN raises a new way for SGG model design by fully considering human cognitive process. Experiments on Visual Genome, GQA and PSG datasets show our method is superior to state-of-the-art methods, and ablation studies validate the effectiveness of our VTSCN. The source codes are released on GitHub: https://github.com/Nora-Zhang98/VTSCN.

Discovery of a Novel CSF-1R Inhibitor with Highly Improved Pharmacokinetic Profiles and Superior Efficacy in Colorectal Cancer Immunotherapy.

Blocking CSF-1/CSF-1R pathway has emerged as a promising strategy to remodel tumor immune microenvironment (TME) by reprogramming tumor-associated macrophages (TAMs). In this work, a novel CSF-1R inhibitor C19 with a highly improved pharmacokinetic profile and in vivo anticolorectal cancer (CRC) efficiency was successfully discovered. C19 could effectively reprogram M2-like TAMs to M1 phenotype and reshape the TME by inducing the recruitment of CD8+ T cells into tumors and reducing the infiltration of immunosuppressive Tregs/MDSCs. Deeper mechanistic studies revealed that C19 facilitated the infiltration of CD8+ T cells by enhancing the secretion of chemokine CXCL9, thus significantly potentiating the anti-CRC efficiency of PD-1 blockade. More importantly, C19 combined with PD-1 mAb could induce durable antitumor immune memory, effectively overcoming the recurrence of CRC. Taken together, our findings suggest that C19 is a promising therapeutic option for sensitizing CRC to anti-PD-1 therapy.

Analysis and Visualization of Single-Cell Sequencing Data with Scanpy and MetaCell: A Tutorial.

The emergence and development of single-cell RNA sequencing (scRNA-seq) techniques enable researchers to perform large-scale analysis of the transcriptomic profiling at cell-specific resolution. Unsupervised clustering of scRNA-seq data is central for most studies, which is essential to identify novel cell types and their gene expression logics. Although an increasing number of algorithms and tools are available for scRNA-seq analysis, a practical guide for users to navigate the landscape remains underrepresented. This chapter presents an overview of the scRNA-seq data analysis pipeline, quality control, batch effect correction, data standardization, cell clustering and visualization, cluster correlation analysis, and marker gene identification. Taking the two broadly used analysis packages, i.e., Scanpy and MetaCell, as examples, we provide a hands-on guideline and comparison regarding the best practices for the above essential analysis steps and data visualization. Additionally, we compare both packages and algorithms using a scRNA-seq dataset of the ctenophore Mnemiopsis leidyi, which is representative of one of the earliest animal lineages, critical to understanding the origin and evolution of animal novelties. This pipeline can also be helpful for analyses of other taxa, especially prebilaterian animals, where these tools are under development (e.g., placozoan and Porifera).

An Integrated Machine Learning Framework Identifies Prognostic Gene Pair Biomarkers Associated with Programmed Cell Death Modalities in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a common and lethal urological malignancy for which there are no effective personalized therapeutic strategies. Programmed cell death (PCD) patterns have emerged as critical determinants of clinical prognosis and immunotherapy responses. However, the actual clinical relevance of PCD processes in ccRCC is still poorly understood. METHODS: We screened for PCD-related gene pairs through single-sample gene set enrichment analysis (ssGSEA), consensus cluster analysis, and univariate Cox regression analysis. A novel machine learning framework incorporating 12 algorithms and 113 unique combinations were used to develop the cell death-related gene pair score (CDRGPS). Additionally, a radiomic score (Rad_Score) derived from computed tomography (CT) image features was used to classify the CDRGPS status as high or low. Finally, we conclusively verified the function of PRSS23 in ccRCC. RESULTS: The CDRGPS was developed through an integrated machine learning approach that leveraged 113 algorithm combinations. CDRGPS represents an independent prognostic biomarker for overall survival and demonstrated consistent performance between training and external validation cohorts. Moreover, CDRGPS showed better prognostic accuracy compared to seven previously published cell death-related signatures. In addition, patients classified as high-risk by CDRGPS exhibited increased responsiveness to tyrosine kinase inhibitors (TKIs), mammalian Target of Rapamycin (mTOR) inhibitors, and immunotherapy. The Rad_Score demonstrated excellent discrimination for predicting high versus low CDRGPS status, with an area under the curve (AUC) value of 0.813 in the Cancer Imaging Archive (TCIA) database. PRSS23 was identified as a significant factor in the metastasis and immune response of ccRCC, thereby validating experimental in vitro results. CONCLUSIONS: CDRGPS is a robust and non-invasive tool that has the potential to improve clinical outcomes and enable personalized medicine in ccRCC patients.

Magnetic polyimide nanocomposite for analysis of parabens in cooking wine by magnetic solid-phase extraction coupled with gas chromatography - Mass spectrometry.

A magnetic polyimide (PI) nanocomposite has been synthesized by phase inversion of PI and simultaneous encapsulation of Fe3O4 nanoparticles. The Fe3O4/PI nanocomposite was characterized by a variety of characterization techniques, including infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, nitrogen adsorption-desorption isotherms, and vibrating sample magnetometry. The results showed that the prepared nanocomposite had a homogeneous structure, adequate specific surface area (76.1 m2/g) and high saturation magnetization (42.9 emu/g). Using parabens as model analytes, the performance of the Fe3O4/PI nanocomposite as an adsorbent for magnetic solid-phase extraction (MSPE) was evaluated. The extracted parabens were desorbed and determined by gas chromatography-mass spectrometry (GC-MS). The parameters affecting the extraction and desorption efficiency of parabens were optimized. Under the optimal conditions, the developed MSPE/GC-MS method was successfully applied to the determination of parabens in cooking wine. The MSPE/GC-MS method exhibited broad linearity (0.2-100 µg/L), low detection limits (0.04-0.05 µg/L), and satisfactory extraction recoveries (79.2 %-113.3 %) with relative standard deviations (RSDs) ranging from 0.7 % to 10.4 %. For real cooking wine samples, the spiked recoveries ranged from 91.7 % to 118.7 % with RSDs of 1.0 %-11.2 %. The results demonstrated that the Fe3O4/PI nanocomposite was an effective adsorbent, and this work provides a novel reference for the easy preparation of magnetic adsorbent materials.

Electrocatalytic reduction of nitrate to ammonia by Pd/In modified Nickel foam electrode in aqueous solution.

Nitrate pollution in surface water and ground water has drawn wide attention, which has brought challenges to human health and natural ecology. Electroreduction of nitrate to NH3 in waste water was a way to turn waste into wealth, which has attracted interest of many researchers. Using Nickel foam as substrate, we prepared Pd/In bimetallic electrode (NF-Pd/In) according to a two-step electrodeposition method. There are many irregularly shaped particles in the size range of 10 nm-100 nm accumulated on the surface of prepared NF-Pd/In electrode, which could supply high specific area and more active sites for nitrate electroreduction. FESEM-EDS, XRD and XPS analysis confirmed the uniform distribution of Pd and In on the surface of prepared NF-Pd/In electrode, with a mass ratio of 4.5/1. Above 96% of 100 mg/L NO3--N was removed and 95% of NH3 selectivity was reached after 5 h of reaction under -1.6 V vs. Ag/AgCl sat. KCl when using 0.05 mol/L of Na2SO4 as electrolyte. High concentration of NaCl (0.05 mol/L) in the test solution dramatically decreased the NH3 selectivity because the produced NH3 could be further oxidized to N2 by the formed HClO from Cl-. EIS tests indicated that the prepared NF-Pd/In electrode showed much lower electrode resistance than NF due to the adsorptive property and electrocatalytic ability for nitrate removal. Density functional theory (DFT) calculations indicated that the presence of In could promote the conversion of NO3- to *NO3 during the process of nitrate electroreduction to NH3. Circulating tests demonstrated the stability of prepared NF-Pd/In electrode.

Molybdenum, tungsten doped cobalt phosphides as efficient catalysts for coproduction of hydrogen and formate by glycerol electrolysis.

H2 and formate are important energy carriers in fuel-cells and feedstocks in chemical industry. The hydrogen evolution reaction (HER) coupling with electro-oxidative cleavage of thermodynamically favorable polyols is a promising way to coproduce H2 and formate via electrochemical means, highly active catalysts for HER and electrooxidative cleavage of polycols are the key to achieve such a goal. Herein, molybdenum (Mo), tungsten (W) doped cobalt phosphides (Co2P) deposited onto nickel foam (NF) substrate, denoted as Mo-Co2P/NF and W-Co2P/NF, respectively, were investigated as catalytic electrodes for HER and electrochemical glycerol oxidation reaction (GOR) to yield H2 and formate. The W-Co2P/NF electrode exhibited low overpotential (η) of 113 mV to attain a current density (J) of -100 mA cm-2 for HER, while the Mo-Co2P/NF electrode demonstrated high GOR efficiency for selective production of formate. In situ Raman and infrared spectroscopic characterizations revealed that the evolved CoO2 from Co2P is the genuine catalytic sites for GOR. The asymmetric electrolyzer based on W-Co2P/NF cathode and Mo-Co2P/NF anode delivered a J = 100 mA cm-2 at 1.8 V voltage for glycerol electrolysis, which led to 18.2 % reduced electricity consumption relative to water electrolysis. This work highlights the potential of heteroelement doped phosphide in catalytic performances for HER and GOR, and opens up new avenue to coproduce more widespread commodity chemicals via gentle and sustainable electrocatalytic means.

Wnt signaling: Modulating tumor-associated macrophages and related immunotherapeutic insights.

Wnt signaling pathways are highly conserved cascades that mediate multiple biological processes through canonical or noncanonical pathways, from embryonic development to tissue maintenance, but they also contribute to the pathogenesis of numerous cancers. Recent studies have revealed that Wnt signaling pathways critically control the interplay between cancer cells and tumor-associated macrophages (TAMs) in the tumor microenvironment (TME) and potentially impact the efficacy of cancer immunotherapy. In this review, we summarize the evidence that Wnt signaling pathways boost the maturation and infiltration of macrophages for immune surveillance in the steady state but also polarize TAMs toward immunosuppressive M2-like phenotypes for immune escape in the TME. Both cancer cells and TAMs utilize Wnt signaling to transmit signals, and this interaction is crucial for the carcinogenesis and progression of common solid cancers, such as colorectal, gastric, hepatocellular, breast, thyroid, prostate, kidney, and lung cancers; osteosarcoma; and glioma. Specifically, compared with those in solid cancers, Wnt signaling pathways play a distinct role in the pathogenesis of leukemia. Efforts to develop Wnt-based drugs for cancer treatment are still ongoing, and some indeed enhance the anticancer immune response. We believe that the combination of Wnt signaling-based therapy with conventional or immune therapies is a promising therapeutic approach and can facilitate personalized treatment for most cancers.

CSF1R inhibition reprograms tumor-associated macrophages to potentiate anti-PD-1 therapy efficacy against colorectal cancer.

PD-1 blockade therapy has made great breakthroughs in treatment of multiple solid tumors. However, patients with microsatellite-stable (MSS) colorectal cancer (CRC) respond poorly to anti-PD-1 immunotherapy. Although CRC patients with microstatellite instability (MSI) or microsatellite instability-high (MSI-H) can benefit from PD-1 blockade therapy, there are still some problems such as tumor recurrence. Tumor-associated macrophages (TAMs), most abundant immune components in tumor microenvironment (TME), largely limit the therapeutic efficacy of anti-PD-1 against CRC. The CSF1/CSF1R pathway plays a key role in regulating macrophage polarization, and blocking CSF1R signaling transduction may be a potential strategy to effectively reprogram macrophages and remodel TME. Here, we found that increasing expression of CSF1R in macrophages predicted poor prognosis in CRC cohort. Furthermore, we discovered a novel potent CSF1R inhibitor, PXB17, which significantly reprogramed M2 macrophages to M1 phenotype. Mechanically, PXB17 significantly blocked activation of PI3K/AKT/mTORC1 signaling, resulting in inhibition of cholesterol biosynthesis. Results from 3D co-culture system suggested that PXB17-repolarized macrophages could induce infiltration of CD8+ T lymphocytes in tumors and improve the immunosuppressive microenvironment. In vivo, PXB17 significantly halted CRC growth, with a stronger effect than PLX3397. In particular, PXB17 potently enhanced therapeutic activity of PD-1 mAb in CT-26 (MSS) model and prevented tumor recurrence in MC-38 (MSI-H) model by promoting formation of long-term memory immunity. Our study opens a new avenue for CSF1R in tumor innate and adaptive anti-tumor immunomodulatory activity and suggests that PXB17 is a promising immunotherapy molecule for enhancing the efficacy of PD-1 mAb or reducing tumor recurrence of CRC.

"Floating Catalytic Foam" with prominent heat-induced convection for the effective photocatalytic removal of antibiotics.

Immobilized photocatalysts represent a promising candidate for the wastewater treatments due to their good reusability, high stability and low eco-risk. Mass transfer within the immobilized catalytic bed is a crucial process that determines the contacting, adsorption, and degradation kinetics in the photodegradation. In this study, a floating catalytic foam (FCF) with a prominent pumping effect was designed to promote mass transfer. The polyurethane foam immobilized with rGO/TiO2/ultrathin-g-C3N4 photocatalyst (PRTCN) was prepared by a simple dip-coating and Uv-light aging process. It was found that the hydrophilic-hydrophobic interfaces could not only contribute to the floating of the catalyst but also establish a temperature gradient across the floating immobilized catalyst. In addition, the temperature gradient induced convection could serve as a built-in pump to effectively promote the diffusion and adsorption of target antibiotic molecules during the photocatalytic process. Therefore, the PRTCN demonstrated a high photodegradation and mineralization efficiency with excellent reusability and anti-interference capability. Moreover, the photodegradation mechanism and the intermediates' toxicity of norfloxacin were detailly investigated by ultra-high resolution electrospray time-of-flight mass spectrometry, density functional theory simulation and ECOSAR estimation. This work proposed a facile and sustainable strategy to enhance the mass transfer problem on immobilized photocatalysts, which could promote the application of the immobilized photocatalysts in the real water-treatment scenarios.

Amorphous-crystalline heterostructures enable energy-level matching of cobalt sulfide/nickel iron layered double hydroxide for efficient oxygen evolution reaction.

Interface engineering of heterostructures has emerged as a promising approach to enhance the catalytic activity of nonprecious electrocatalysts. Herein, a novel amorphous cobalt sulfide-crystalline nickel iron layered double hydroxide (a-CoS@NiFe-LDH) hybrid material is presented for application as an electrocatalyst for oxygen evolution reaction (OER). Benefitting from the well-matched energy level structures, the a-CoS@NiFe-LDH catalyst delivers a low overpotential of 221 ± 14 mV at an OER current density of 20 mA cm-2 and a small Tafel slope of 83.1 mV dec-1, showing good OER properties. First-principle computations reveal that the electronic interaction between amorphous cobalt sulfide (a-CoS) and crystalline nickel iron layered double hydroxide (NiFe-LDH) components within a-CoS@NiFe-LDH promotes the adsorbate evolution mechanism and reduces the adsorption energies for oxygen intermediates, thereby enhancing the activity and stability for OER. This work opens up a new avenue to enhance the OER catalytic efficiency via the construction of amorphous-crystalline heterostructures.

Stepwise Synthesis of Cl-Decorated Trinuclear-Cu Cluster-Based Frameworks for C2H2/C2H4 and C2H2/CO2 Separation.

A novel Cl-decorated trinuclear-Cu cluster-based MOF (NbU-7-Cl, NbU denotes Ningbo University) was synthesized by a stepwise synthesis strategy. Compared to one-step reactions, the strategy of combining cationic templates with single-crystal-to-single-crystal transformation provides more possibilities for the design and postsynthetic modification of multifunctional materials. Note that the chloride ions are attached to the copper ions of the planar trinuclear cluster nodes in a fully symmetric or partially asymmetric manner. The insertion of the chloride ion can alter the overall symmetry and adsorption energy in addition to occupying the appropriate asymmetric orbit and reducing the effective active sites of metal. The activated NbU-7-Cl displays improved C2H2 uptake capacity and C2H2/C2H4 and C2H2/CO2 separation performance, which is proved by breakthrough experiments.

Low-Latency Federated Learning via Dynamic Model Partitioning for Healthcare IoT.

Federated learning (FL) is receiving much attention in the Healthcare Internet of Things (H-IoT) to support various instantaneous E-health services. Today, the deployment of FL suffers from several challenges, such as high training latency and data privacy leakage risks, especially for resource-constrained medical devices. In this article, we develop a three-layer FL architecture to decrease training latency by introducing split learning into FL. We formulate a long-term optimization problem to minimize the local model training latency while preserving the privacy of the original medical data in H-IoT. Specially, a Privacy-ware Model Partitioning Algorithm (PMPA) is proposed to solve the formulated problem based on the Lyapunov optimization theory. In PMPA, the local model is partitioned properly between a resource-constrained medical end device and an edge server, which meets privacy requirements and energy consumption constraints. The proposed PMPA is separated into two phases. In the first phase, a partition point set is obtained using Kullback-Leibler (KL) divergence to meet the privacy requirement. In the second phase, we employ the model partitioning function, derived through Lyapunov optimization, to select the partition point from the partition point set that that satisfies the energy consumption constraints. Simulation results show that compared with traditional FL, the proposed algorithm can significantly reduce the local training latency. Moreover, the proposed algorithm improves the efficiency of medical image classification while ensuring medical data security.

Editorial for Special Issue: "Synthesis and Application of Biomass-Derived Carbon-Based Nanomaterial".

Biomass-derived carbon-based nanomaterials represent a group of green and high-quality materials which can be potentially employed in the fields of environmental protection, energy conversion and clean energy storage [...].

A Novel No-Reference Quality Assessment Metric for Stereoscopic Images with Consideration of Comprehensive 3D Quality Information.

Recently, stereoscopic image quality assessment has attracted a lot attention. However, compared with 2D image quality assessment, it is much more difficult to assess the quality of stereoscopic images due to the lack of understanding of 3D visual perception. This paper proposes a novel no-reference quality assessment metric for stereoscopic images using natural scene statistics with consideration of both the quality of the cyclopean image and 3D visual perceptual information (binocular fusion and binocular rivalry). In the proposed method, not only is the quality of the cyclopean image considered, but binocular rivalry and other 3D visual intrinsic properties are also exploited. Specifically, in order to improve the objective quality of the cyclopean image, features of the cyclopean images in both the spatial domain and transformed domain are extracted based on the natural scene statistics (NSS) model. Furthermore, to better comprehend intrinsic properties of the stereoscopic image, in our method, the binocular rivalry effect and other 3D visual properties are also considered in the process of feature extraction. Following adaptive feature pruning using principle component analysis, improved metric accuracy can be found in our proposed method. The experimental results show that the proposed metric can achieve a good and consistent alignment with subjective assessment of stereoscopic images in comparison with existing methods, with the highest SROCC (0.952) and PLCC (0.962) scores being acquired on the LIVE 3D database Phase I.

Self-supported amorphous phosphide catalytic electrodes for electrochemical hydrogen production coupling with methanol upgrading.

Efficient catalytic electrodes for cathodic hydrogen evolution reaction (HER) and anodic oxygen evolution reaction (OER) are pivotal for massive production of green hydrogen from water electrolysis, and the further replacement of kinetically sluggish OER by tailored elecrooxidation of certain organics is a promising way to co-produce hydrogen and value-added chemicals via a more energy-saving and safer manner. Herein, amorphous Ni-Co-Fe ternary phosphides (NixCoyFez-Ps) with different Ni:Co:Fe ratios electrodeposited onto Ni foam (NF) substrate were served as self-supported catalytic electrodes for alkaline HER and OER. The Ni4Co4Fe1-P electrode deposited in solution at Ni:Co:Fe ratio of 4:4:1 displayed low overpotential (61 mV at -20 mA cm-2) and acceptable durability for HER, while the Ni2Co2Fe1-P electrode fabricated in deposition solution at Ni:Co:Fe ratio of 2:2:1 showed good OER efficiency (overpotential of 275 mV at 20 mA cm-2) and robust durability, the further replacement of OER by anodic methanol oxidation reaction (MOR) enabled selective production of formate with 110 mV lower anodic potential at 20 mA cm-2. The HER-MOR co-electrolysis system based on Ni4Co4Fe1-P cathode and Ni2Co2Fe1-P anode could save 1.4 kWh of electric energy per cubic meter of H2 relative to mere water electrolysis. The current work offers a feasible approach to co-produce H2 and value-upgraded formate via an energy-saving manner by rational design of catalytic electrodes and construction of co-electrolysis system, and paves the way for cost-effective co-preparation of more value-added organics and green hydrogen via electrolysis.

A three-dimensional renal tumor anatomy and intrarenal relationship nephrometry (ADDD) for robot-assisted partial nephrectomy : 3D-CT based nephrometry for RAPN.

OBJECTIVE: To develop a 3D scoring system of tumor anatomy and intrarenal relationship for assessing surgical complexity and outcomes of robot-assisted partial nephrectomy (RAPN). METHODS: We prospectively enrolled patients with a renal tumor who had a 3D model and underwent RAPN between Mar 2019 and Mar 2022. The ADDD nephrometry consisted of the contact surface area between tumor and parenchyma (A), the depth of tumor invasion into the renal parenchyma (D1), the distance from tumor to the main intrarenal artery (D2), and to the collecting system (D3). The primary outcomes included perioperative complication rate and trifecta outcome (WIT ≤ 25 min, negative surgical margins, and no major complications). RESULTS: We enrolled a total of 301 patients. The mean tumor size was 2.93 ± 1.44 cm. There were 104 (34.6%) patients, 119 (39.5%) patients, and 78 (25.9%) patients in the low-, intermediate-, and high-risk groups, respectively. Each point increase in the ADDD score increased the risk of complications [hazard ratio (HR) 1.501]. A lower grade indicated a lower risk of failed trifecta (HR low group 15.103, intermediate group 9.258) and renal function damage (HR low risk 8.320, intermediate risk 3.165) compared to the high-risk group. The AUC of ADDD score and grade were 0.738 and 0.645 for predicting major complications, 0.766 and 0.714 for predicting trifecta outcome, and 0.746 and 0.730 for predicting postoperative renal function reservation. CONCLUSION: The 3D-ADDD scoring system shows the tumor anatomy and its intraparenchymal relationships and has better efficacy in predicting surgical outcomes of RAPN.

Modulating multidrug resistance to drug-based antitumor therapies through NF-κB signaling pathway: mechanisms and perspectives.

INTRODUCTION: Despite the advances made in cancer treatment in the past decades, therapeutic efficacy is still quite challenging, partially due to the emergence of multidrug resistance (MDR). It is crucial to decipher the underlying mechanisms of resistance in order to develop new therapeutic strategies for cancer patients. Previous studies have shown that activation of nuclear factor-κB (NF-κB) plays key roles in various cellular processes including proliferation, anti-apoptosis, metastasis, invasion, and chemoresistance. AREAS COVERED: In this review, we conduct an integrated analysis of the evidence suggesting the vital roles of the NF-κB signaling pathway in MDR during chemotherapy, immunotherapy, endocrine, and targeted therapy. A literature search was performed on NF-κB and drug resistance in PubMed up to February 2023. EXPERT OPINION: This review summarizes that the NF-κB signaling pathway exhibits a crucial role in enhancing drug resistance in chemotherapy, immunotherapy, endocrine, and targeted therapy. The application of combination therapy with existing antineoplastic drugs and a safe NF-κB inhibitor could become a promising strategy in cancer treatment. A better understanding of the pathway and mechanisms of drug resistance may help exploit safer and more effective NF-κB-targeting agents for clinical use in the future.

Green Production of Biomass-Derived Carbon Materials for High-Performance Lithium-Sulfur Batteries.

Lithium-sulfur batteries (LSBs) with a high energy density have been regarded as a promising energy storage device to harness unstable but clean energy from wind, tide, solar cells, and so on. However, LSBs still suffer from the disadvantages of the notorious shuttle effect of polysulfides and low sulfur utilization, which greatly hider their final commercialization. Biomasses represent green, abundant and renewable resources for the production of carbon materials to address the aforementioned issues by taking advantages of their intrinsic hierarchical porous structures and heteroatom-doping sites, which could attribute to the strong physical and chemical adsorptions as well as excellent catalytic performances of LSBs. Therefore, many efforts have been devoted to improving the performances of biomass-derived carbons from the aspects of exploring new biomass resources, optimizing the pyrolysis method, developing effective modification strategies, or achieving further understanding about their working principles in LSBs. This review firstly introduces the structures and working principles of LSBs and then summarizes recent developments in research on carbon materials employed in LSBs. Particularly, this review focuses on recent progresses in the design, preparation and application of biomass-derived carbons as host or interlayer materials in LSBs. Moreover, outlooks on the future research of LSBs based on biomass-derived carbons are discussed.