Revealing the metabolomics and biometrics underlying phytotoxicity mechanisms for polystyrene nanoplastics and dibutyl phthalate in dandelion (Taraxacum officinale).

Micro/nanoplastics (M/NPs) and phthalates (PAEs) are emerging pollutants. Polystyrene (PS) MPs and dibutyl phthalate (DBP) are typical MPs and PAEs in the environment. However, how dandelion plants respond to the combined contamination of MPs and PAEs remains unclear. In this study, we evaluated the individual and combined effects of PS NPs (10 mg L-1) and DBP (50 mg L-1) on dandelion (Taraxacum officinale) seedlings. The results showed that compared to control and individual-treated plants, coexposure to PS NPs and DBP significantly affected plant growth, induced oxidative stress, and altered enzymatic and nonenzymatic antioxidant levels of dandelion. Similarly, photosynthetic attributes and chlorophyll fluorescence kinetic parameters were significantly affected by coexposure. Scanning electron microscopy (SEM) results showed that PS particles had accumulated in the root cortex of the dandelion. Metabolic analysis of dandelion showed that single and combined exposures caused the plant's metabolic pathways to be profoundly reprogrammed. As a consequence, the synthesis and energy metabolism of carbohydrates, amino acids, and organic acids were affected because galactose metabolism, the citric acid cycle, and alanine, aspartic acid and glutamic acid metabolism pathways were significantly altered. These results provide a new perspective on the phytotoxicity and environmental risk assessment of MPs and PAEs in individual or coexposures.

Centrifugal Extraction-Assisted Fiber-Enhanced Raman Spectroscopy for Online Detection of Trace Furfural in Oil Power Equipment.

Oil-paper insulated equipment is integral in power conversion and supports low-loss electricity transport. As a characteristic byproduct of the oil-paper insulation system, the realization of efficient detection of furfural in oil is crucial to the safe operation of the power grid. We proposed a novel approach using dual-enhanced Raman spectroscopy for sensing trace liquid components. This method employs a centrifugal extractor to separate and enrich the targeted components, achieving selective enhancement. The optimal phase ratio was determined to be 30:1. A liquid-core fiber was used to optimize the laser transmission efficiency and Raman signal collection efficiency, resulting in a nonselective signal enhancement of 44.86. It also investigated the impact of intermolecular interactions on the shift of Raman spectra, identifying the reasons for the differences in Raman signals between pure furfural, furfural in oil, and furfural in water. A batch of samples with furfural dissolved in insulation oil was measured using this system and achieved a limit of detection of 0.091 mg/L. The stability of the dual-enhanced Raman platform was experimentally verified with a spectral intensity fluctuation of 0.68%. This method is fast, stable, adaptable, and suitable for the detection of a wide range of liquid ingredients.

Piezoelectric enhanced sulfur doped graphdiyne nanozymes for synergistic ferroptosis-apoptosis anticancer therapy.

Graphdiyne has excellent potential due to its enzymatic properties. Metal-free sulfur-doped Graphdiyne (S-GDY) has piezoelectric characteristics, and ultrasonic excitation of S-GDY enhances peroxidase activity. It can turn hydrogen peroxide into toxic hydroxyl radicals and induce apoptosis in 4T1 cells. More importantly, the ultrasound (US) enhanced nanozyme induced 4T1 cell ferroptosis by promoting an imbalanced redox reaction due to glutathione depletion and glutathione peroxidase 4 inactivation. S-GDY exhibited enhanced nanozyme activity in vitro and in vivo that may directly trigger apoptosis-ferroptosis for effective tumor therapy. Altogether, this study was expected to provide new insights into the design of piezoelectric catalytic nanozyme and expand their application in the catalytic therapy of tumors.

Amelioration of colitis progression by ginseng-derived exosome-like nanoparticles through suppression of inflammatory cytokines.

Background: Damage to the healthy intestinal epithelial layer and regulation of the intestinal immune system, closely interrelated, are considered pivotal parts of the curative treatment for inflammatory bowel disease (IBD). Plant-based diets and phytochemicals can support the immune microenvironment in the intestinal epithelial barrier for a balanced immune system by improving the intestinal microecological balance and may have therapeutic potential in colitis. However, there have been only a few reports on the therapeutic potential of plant-derived exosome-like nanoparticles (PENs) and the underlying mechanism in colitis. This study aimed to assess the therapeutic effect of PENs from Panax ginseng, ginseng-derived exosome-like nanoparticles (GENs), in a mouse model of IBD, with a focus on the intestinal immune microenvironment. Method: To evaluate the anti-inflammatory effect of GENs on acute colitis, we treated GENs in Caco2 and lipopolysaccharide (LPS) -induced RAW 264.7 macrophages and analyzed the gene expression of pro-inflammatory cytokines and anti-inflammatory cytokines such as TNF-α, IL-6, and IL-10 by real-time PCR (RT-PCR). Furthermore, we further examined bacterial DNA from feces and determined the alteration of gut microbiota composition in DSS-induced colitis mice after administration of GENs through 16S rRNA gene sequencing analysis. Result: GENs with low toxicity showed a long-lasting intestinal retention effect for 48 h, which could lead to effective suppression of pro-inflammatory cytokines such as TNF-α and IL-6 production through inhibition of NF-κB in DSS-induced colitis. As a result, it showed longer colon length and suppressed thickening of the colon wall in the mice treated with GENs. Due to the amelioration of the progression of DSS-induced colitis with GENs treatment, the prolonged survival rate was observed for 17 days compared to 9 days in the PBS-treated group. In the gut microbiota analysis, the ratio of Firmicutes/Bacteroidota was decreased, which means GENs have therapeutic effectiveness against IBD. Ingesting GENs would be expected to slow colitis progression, strengthen the gut microbiota, and maintain gut homeostasis by preventing bacterial dysbiosis. Conclusion: GENs have a therapeutic effect on colitis through modulation of the intestinal microbiota and immune microenvironment. GENs not only ameliorate the inflammation in the damaged intestine by downregulating pro-inflammatory cytokines but also help balance the microbiota on the intestinal barrier and thereby improve the digestive system.

SCN-MLTPP: A Multi-Label Classifier for Predicting Therapeutic Properties of Peptides Using the Stacked Capsule Network.

Identifying the function of therapeutic peptides is an important issue in the development of novel drugs. To reduce the time and labor costs required to identify therapeutic peptides, computational methods are increasingly required. However, most of the existing peptide therapeutic function prediction models are used for predicting a single therapeutic function, ignoring the fact that a bioactive peptide might simultaneously consist of multi-activities. Furthermore, in the few existing multi-label classification models, the feature extraction procedures are still rough. We propose a multi-label framework, called SCN-MLTPP, with a stacked capsule network for predicting the therapeutic properties of peptides. Instead of using peptide sequence vectors alone, SCN-MLTPP extracts different view representation vectors from the therapeutic peptides and learns the contributions of different views to the properties of therapeutic peptides based on the dynamic routing mechanism. Benchmarking results show that as compared with existing multi-label predictors, SCN-MLTPP achieves better and more robust performance for different peptides. In addition, some visual analyses and case studies also demonstrate the model can reliably capture features from multi-view data and predict different peptides. The codes of SCN-MLTPP are available at https://github.com/zhc940702/MLTPP.

Design of optical anapole modes of all-dielectric nanoantennas for SERS applications.

To obtain large electric field enhancement while mitigating material losses, an all-dielectric nanoantenna composed of a heptamer and nanocubes is designed and analyzed. A numerical simulation by the finite element method reveals that the nanoantenna achieves the optical electric anapole modes, thereby significantly enhancing the coupling between different dielectrics to further improve the near-field enhancement and spontaneous radiation. Field enhancement factors |E/E 0|2 of 3,563 and 5,395 (AM1 and AM2) and a Purcell factor of 3,872 are observed in the wavelength range between 350 and 800 nm. This nanoantenna has promising potential in applications involving surface-enhanced Raman scattering and nonlinearities due to its low cost and excellent compatibility.

Effects of different cladding materials on orbital angular momentum modes propagating in photonic crystal fibers.

With the development of orbital angular momentum (OAM) photonic crystal fibers (PCFs) for more efficient communication, fiber claddings are important to the performance. In this paper, the influence of S i O 2 and four new optical materials, which are amethyst, SSK2, SF11, and LaSF09, as cladding materials, on the OAM mode characteristics is studied based on a common PCF for OAM transmission. In addition, the effective index difference, dispersion, confinement loss, and other properties of OAM modes transmitted in the five materials are derived by the finite element method. After in-depth analysis, universal rules can be obtained as guidelines for optimization of PCF in the future for improving the efficiency of optical fiber communication. Through chart analysis, it can be concluded that when materials of high effective refractive indices are used as cladding materials for PCF, the dispersion, nonlinear coefficient, confinement loss, mode purity, and other properties are significantly improved. Lower dispersion and confinement loss are more conducive to long-distance communication transmission. The decrease in nonlinear coefficient represents a better effect in suppressing nonlinear effects, and the increase in numerical aperture and mode purity respectively improves the transmission efficiency and stability of OAM communication. These conclusions provide universal rules for high-quality communication in the future.

Double-ring-disk hybrid nanostructures with slits for electric field enhancement.

Although noble metal nanoantennas have distinctive optical properties and local electric field enhancement, considerable non-radiative ohmic losses occur at the optical frequencies, consequently creating significant absorption and unwanted heating. Combining the plasmon mode of metal nanoantennas with the anapole mode of high refractive index dielectric materials offers a promising alternative to increase the electric field strength with minimal loss. Herein, a silicon disk with slots and two Au rings with a coupling mechanism are described. To elucidate the field enhancement mechanism, the near-field enhancement features and near-field electric field distributions are explored by a numerical simulation and multipole decomposition analysis. By opening the slit to generate high-intensity hot spots inside the disk, the electric field can be enhanced significantly, and nearby molecules can directly contact these hot spots. The resulting large field enhancement suggests significant applications to strong photon-exciton coupling and nonlinear photonics.

Determinants of response to CDK4/6 inhibitors in the real-world setting.

Despite widespread use and a known mechanism of action for CDK4/6 inhibitors in combination with endocrine therapy, features of disease evolution and determinants of therapeutic response in the real-world setting remain unclear. Here, a cohort of patients treated with standard-of-care combination regimens was utilized to explore features of disease and determinants of progression-free survival (PFS) and overall survival (OS). In this cohort of 280 patients, >90% of patients were treated with palbociclib in combination with either an aromatase inhibitor (AI) or fulvestrant (FUL). Most of these patients had modified Scarff-Bloom-Richardson (SBR) scores, and ER, HER2, and PR immunohistochemistry. Both the SBR score and lack of PR expression were associated with shorter PFS in patients treated with AI combinations and remained significant in multivariate analyses (HR = 3.86, p = 0.008). Gene expression analyses indicated substantial changes in cell cycle and estrogen receptor signaling during the course of treatment. Furthermore, gene expression-based subtyping indicated that predominant subtypes changed with treatment and progression. The luminal B, HER2, and basal subtypes exhibited shorter PFS in CDK4/6 inhibitor combinations when assessed in the pretreatment biopsies; however, they were not associated with OS. Using unbiased approaches, cell cycle-associated gene sets were strongly associated with shorter PFS in pretreatment biopsies irrespective of endocrine therapy. Estrogen receptor signaling gene sets were associated with longer PFS particularly in the AI-treated cohort. Together, these data suggest that there are distinct pathological and biological features of HR+/HER2- breast cancer associated with response to CDK4/6 inhibitors. Clinical trial registration number: NCT04526587.

Guideline on treating community-acquired pneumonia with Chinese patent medicines.

Community-acquired pneumonia (CAP) is one of the most common infectious diseases, and its morbidity and mortality increase with age. Resistance and mutations development make the use of anti-infective therapy challenging. Chinese patent medicines (CPMs) are often used to treat CAP in China and well tolerable. However, currently there are no evidence-based guideline for the treatment of CAP with CPMs, and the misuse of CPMs is common. Therefore, we established a guideline panel to develop this guideline. We identified six clinical questions through two rounds of survey, and we then systematically searched relevant evidence and performed meta-analyses, evidence summaries and GRADE decision tables to draft recommendations, which were then voted on by a consensus panel using the Delphi method. Finally, we developed ten recommendations based on evidence synthesis and expert consensus. For the treatment of severe CAP in adults, we recommend Tanreqing injection, Reduning injection, Xuebijing injection, Shenfu injection, and Shenmai injection respectively. For the treatment of non-severe CAP in adults, we recommend Tanreqing injection, Reduning injection, Lianhua Qingwen capsule/granule, Qingfei Xiaoyan Pill and Shufeng Jiedu capsule respectively. CPMs have great potential to help in the fight against CAP worldwide, but more high-quality studies are still needed to strengthen the evidence.

Discovery of a photosensitizing PI3K inhibitor for tumor therapy: Design, synthesis and in vitro biological evaluation.

In drug development, optical triggering of cancer therapy is increasingly used. Herein, we report a novel photosensitive PI3K inhibitor FD2157, which bears a photoprotecting moiety and can be efficiently cleaved with enhanced anticancer activity upon short-term light irradiation. In biological assessment, FD2157 exhibited remarkably enhanced anticancer activity in inhibition of PI3K pathway against melanoma cell lines upon light irradiation (4 min). Hence, this photosensitive PI3K inhibitor FD2157 may represent a valuable tool compound for studying the PI3K pathway and further optimization toward light-triggered cancer treatment.

BEA-Net: Body and Edge Aware Network with Multi-Scale Short-Term Concatenation for Medical Image Segmentation.

Medical image segmentation is indispensable for diagnosis and prognosis of many diseases. To improve the segmentation performance, this study proposes a new 2D body and edge aware network with multi-scale short-term concatenation for medical image segmentation. Multi-scale short-term concatenation modules which concatenate successive convolution layers with different receptive fields, are proposed for capturing multi-scale representations with fewer parameters. Body generation modules with feature adjustment based on weight map computing via enlarging the receptive fields, and edge generation modules with multi-scale convolutions using Sobel kernels for edge detection, are proposed to separately learn body and edge features from convolutional features in decoders, making the proposed network be body and edge aware. Based on the body and edge modules, we design parallel body and edge decoders whose outputs are fused to achieve the final segmentation. Besides, deep supervision from the body and edge decoders is applied to ensure the effectiveness of the generated body and edge features and further improve the final segmentation. The proposed method is trained and evaluated on six public medical image segmentation datasets to show its effectiveness and generality. Experimental results show that the proposed method achieves better average Dice similarity coefficient and 95% Hausdorff distance than several benchmarks on all used datasets. Ablation studies validate the effectiveness of the proposed multi-scale representation learning modules, body and edge generation modules and deep supervision. The code is available at https://github.com/hulinkuang/BEA-Net.

Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework.

Adverse Drug Reactions (ADRs) have a direct impact on human health. As continuous pharmacovigilance and drug monitoring prove to be costly and time-consuming, computational methods have emerged as promising alternatives. However, most existing computational methods primarily focus on predicting whether or not the drug is associated with an adverse reaction and do not consider the core issue of drug benefit-risk assessment-whether the treatment outcome is serious when adverse drug reactions occur. To this end, we categorize serious clinical outcomes caused by adverse reactions to drugs into seven distinct classes and present a deep learning framework, so-called GCAP, for predicting the seriousness of clinical outcomes of adverse reactions to drugs. GCAP has two tasks: one is to predict whether adverse reactions to drugs cause serious clinical outcomes, and the other is to infer the corresponding classes of serious clinical outcomes. Experimental results demonstrate that our method is a powerful and robust framework with high extendibility. GCAP can serve as a useful tool to successfully address the challenge of predicting the seriousness of clinical outcomes stemming from adverse reactions to drugs.

Facile Synthesis of Basic Copper Carbonate Nanosheets for Photoacoustic Imaging-Guided Tumor Apoptosis and Ferroptosis and the Extension Exploration of the Synthesis Method.

Elimination of tumor cells using carbonate nanomaterials with tumor microenvironment-responsive capacity has been explored as an effective strategy. However, their therapeutic outcomes are always compromised by the relatively low intratumoral accumulation and limited synthesis method. Herein, a novel kind of basic copper carbonate nanosheets was designed and prepared using a green synthesis method for photoacoustic imaging-guided tumor apoptosis and ferroptosis therapy. These nanosheets were synthesized with the assistance of dopamine and ammonium bicarbonate (NH4HCO3) and the loading of glucose oxidase (GOx). NH4HCO3 could not only provide an alkaline environment for the polymerization of dopamine but also supply carbonates for the growth of nanosheets. The formed nanosheets displayed good acid and near-infrared light responsiveness. After intercellular uptake, they could be degraded to release Cu2+ and GOx, generating hydroxyl radicals through a Cu+-mediated Fenton-like reaction, consuming glucose, up-regulating H2O2 levels, and down-regulating GSH levels. Tumor elimination could be achieved by hydroxyl radical-induced apoptosis and ferroptosis. More amusingly, this synthesis method can be extended to several kinds of mono-element and multi-element carbonate nanomaterials (e.g., Fe, Mn, and Co), showing great potential for further tumor theranostics.

Discovery and identification of a novel PI3K inhibitor with enhanced CDK2 inhibition for the treatment of triple negative breast cancer.

Blocking the PI3K pathway has been recognized as a promising strategy for cancer therapy. Herein, we report the discovery of novel PI3K inhibitors utilizing 7-azaindole-based fragment-oriented growth. Among them, compound FD2056 stands out as the most promising candidate, maintaining potent inhibitory activity against PI3K and enhanced CDK2 inhibition, and showing moderate selectivity among 108 kinases. In cellular assays, the inhibitor FD2056 demonstrated superior anti-proliferative profiles over reference compounds against TNBC cells and significantly increased apoptosis of MDA-MB-231 cells in a dose-dependent manner. Moreover, FD2056 showed more efficacious anti-TNBC activity than the corresponding drugs BKM120 and CYC202 at an oral dose of 15 mg/kg in the MDA-MB-231 xenograft model, inhibiting tumor growth by 43% with no observable toxic effects. All these results suggest that FD2056 has potential for further development as a promising anticancr compound, and co-targeting PI3K and CDK2 pathways may provide an alternative therapeutic strategy for the treatment of TNBC.

MSDRP: a deep learning model based on multisource data for predicting drug response.

MOTIVATION: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell lines. RESULTS: In this paper, we propose a deep learning framework, named MSDRP for drug response prediction. MSDRP uses an interaction module to capture interactions between drugs and cell lines, and integrates multiple associations/interactions between drugs and biological entities through similarity network fusion algorithms, outperforming some state-of-the-art models in all performance measures for all experiments. The experimental results of de novo test and independent test demonstrate the excellent performance of our model for new drugs. Furthermore, several case studies illustrate the rationality for using feature vectors derived from drug similarity matrices from multisource data to represent drugs and the interpretability of our model. AVAILABILITY AND IMPLEMENTATION: The codes of MSDRP are available at https://github.com/xyzhang-10/MSDRP.

IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions.

MOTIVATION: A single gene may yield several isoforms with different functions through alternative splicing. Continuous efforts are devoted to developing machine-learning methods to predict isoform functions. However, existing methods do not consider the relevance of each feature to specific functions and ignore the noise caused by the irrelevant features. In this case, we hypothesize that constructing a feature selection framework to extract the function-relevant features might help improve the model accuracy in isoform function prediction. RESULTS: In this article, we present a feature selection-based approach named IsoFrog to predict isoform functions. First, IsoFrog adopts a reversible jump Markov Chain Monte Carlo (RJMCMC)-based feature selection framework to assess the feature importance to gene functions. Second, a sequential feature selection procedure is applied to select a subset of function-relevant features. This strategy screens the relevant features for the specific function while eliminating irrelevant ones, improving the effectiveness of the input features. Then, the selected features are input into our proposed method modified domain-invariant partial least squares, which prioritizes the most likely positive isoform for each positive MIG and utilizes diPLS for isoform function prediction. Tested on three datasets, our method achieves superior performance over six state-of-the-art methods, and the RJMCMC-based feature selection framework outperforms three classic feature selection methods. We expect this proposed methodology will promote the identification of isoform functions and further inspire the development of new methods. AVAILABILITY AND IMPLEMENTATION: IsoFrog is freely available at https://github.com/genemine/IsoFrog.

Engineered TCR T-cell therapy targeting mass spectrometry-identified natural epitope in PDAC.

Tumor antigens are crucial targets for T-cell-based therapy to induce tumor-specific rejection. However, identifying pancreatic ductal adenocarcinoma (PDAC)-specific T-cell epitopes has been challenging. Using advanced mass spectrometry (MS) analysis, we previously identified cancer-associated, class I MHC-bound epitopes shared by multiple PDAC patients with different HLA-A types. Here, we investigated one of these epitopes, LAMC2203-211, a naturally occurring nonmutated epitope on the LAMC2 protein. Following stimulation with the LAMC2203-211 peptide, we cloned T-cell receptors (TCRs) and transduced them into the Jurkat human T-cell line using a lentiviral vector. We found that Jurkat cells expressing LAMC2203-211-specific TCRs resulted in potent, LAMC2 specific, in vitro cytotoxic effects on PDAC cells. Furthermore, in mice that harbored either subcutaneously or orthotopically implanted tumors originating from both HLA-A allele-matched and unmatched PDAC patients, tumor growth was suppressed in a LAMC2-dependent manner following the infusion of LAMC2-targeting T cells. We have therefore developed a LAMC2-specific TCR-based T-cell therapy strategy likely suitable for many PDAC patients. This is the first study to adopt MS analysis to identify natural CD8+ T-cell epitopes in PDAC that could potentially serve as targets for PDAC immunotherapy.

Feedstock optimization with rice husk chicken manure and mature compost during chicken manure composting: Quality and gaseous emissions.

This study investigated the impact of mature compost input on compost quality, greenhouse gases (GHGs, i.e. methane and nitrous oxide) and ammonia emissions during chicken manure and rice husk chicken manure co-composting. The experiment used different volumes of mature compost: 10% (T1), 20% (T2), and 30% (T3) to replace rice husk chicken manure. Results showed that mature compost enhanced compost maturity by promoting the activities of Bacillus, Caldicoprobacter, Thermobifida, Pseudogracilibacillus, Brachybacterium, and Sinibacillus. Compared to CK, T1, T2, and T3 reduced NH3 emission by 32.07%, 33.64%, and 56.12%, and mitigated 14.97%, 16.57%, and 26.18% of total nitrogen loss, respectively. Additionally, T2 and T3 reduced CH4 emission by 40.98% and 62.24%, respectively. The N2O emissions were positive correlation with Lactobacillus, Pseudogracilibacillus and ammonium nitrogen (p < 0.05), while T2 reducing total greenhouse effects. Therefore, replacing rice husk chicken manure with 20% mature compost is an efficient and promising approach for composting.

Anti-glioma effect of ginseng-derived exosomes-like nanoparticles by active blood-brain-barrier penetration and tumor microenvironment modulation.

Inhibition of tumor growth and normalization of immune responses in the tumor microenvironment (TME) are critical issues for improving cancer therapy. However, in the treatment of glioma, effective nanomedicine has limited access to the brain because of the blood-brain barrier (BBB). Previously, we demonstrated nano-sized ginseng-derived exosome-like nanoparticles (GENs) consisting of phospholipids including various bioactive components, and evaluated anti-tumor immune responses in T cells and Tregs to inhibit tumor progression. It was found that the enhanced targeting ability of GENs to the BBB and glioma induced a significant therapeutic effect and exhibited strong efficacy in recruiting M1 macrophage expression in the TME. GENs were demonstrated to be successful candidates in glioma therapeutics both in vitro and in vivo, suggesting excellent potential for inhibiting glioma progression and regulating tumor-associated macrophages (TAMs).