Ovarian cancer: Diagnosis and treatment strategies (Review).

Ovarian cancer is a malignant tumor that seriously endangers health. Early ovarian cancer symptoms are frequently challenging to detect, resulting in a large proportion of patients reaching an advanced stage when diagnosed. Conventional diagnosis relies heavily on serum biomarkers and pathological examination, but their sensitivity and specificity require improvement. Targeted therapy inhibits tumor growth by targeting certain characteristics of tumor cells, such as signaling pathways and gene mutations. However, the effectiveness of targeted therapy varies among individuals due to differences in their unique biological characteristics and requires individualized strategies. Immunotherapy is a promising treatment for ovarian cancer due to its long-lasting antitumor effect. Nevertheless, issues such as variable efficacy, immune-associated adverse effects and drug resistance remain to be resolved. The present review discusses the diagnostic strategies, rationale, treatment strategies and prospects of targeted therapy and immunotherapy for ovarian cancer.

Research Progress on the Role of M6A in Regulating Economic Traits in Livestock.

Reversible regulation of N6-methyladenosine (m6A) methylation of eukaryotic RNA via methyltransferases is an important epigenetic event affecting RNA metabolism. As such, m6A methylation plays crucial roles in regulating animal growth, development, reproduction, and disease progression. Herein, we review the latest research advancements in m6A methylation modifications and discuss regulatory aspects in the context of growth, development, and reproductive traits of livestock. New insights are highlighted and perspectives for the study of m6A methylation modifications in shaping economically important traits are discussed.

Polycyclic aromatic hydrocarbon and its adducts in peripheral blood: Gene and environment interaction among Chinese population.

BACKGROUND: Benzo(a)pyrene (B[a]P) is the most widely concerned polycyclic aromatic hydrocarbons (PAHs), which metabolizes benzo(a)pyrene-7,8-dihydrodiol-9,10-epoxide (BPDE) in vivo to produce carcinogenic effect on the body. Currently, there is limited research on the role of the variation of metabolic enzymes in this process. METHODS: We carried out a study including 752 participants, measured the concentrations of 16 kinds PAHs in both particle and gaseous phases, urinary PAHs metabolites, leukocyte BPDE-DNA adduct and serum BPDE- Albumin (BPDE-Alb) adduct, and calculated daily intake dose (DID) to assess the cumulative exposure of PAHs. We conducted single nucleotide polymorphism sites (SNPs) of metabolic enzymes, explored the exposure-response relationship between the levels of exposure and BPDE adducts using multiple linear regression models. RESULT: Our results indicated that an interquartile range (IQR) increase in B[a]P, PAHs, BaPeq, 1-hydroxypyrene (1-OHP), 1-hydroxynaphthalene (1-OHNap) and 2-hydroxynaphthalene (2-OHNap) were associated with 26.53 %, 24.24 %, 28.15 %, 39.15 %, 12.85 % and 14.09 % increase in leukocyte BPDE-DNA adduct (all P < 0.05). However, there was no significant correlation between exposure with serum BPDE-Alb adduct (P > 0.05). Besides, we also found the polymorphism of CYP1A1(Gly45Asp), CYP2C9 (Ile359Leu), and UGT1A1(downstream) may affect BPDE adducts level. CONCLUSION: Our results indicated that leukocyte BPDE-DNA adduct could better reflect the exposure to PAHs. Furthermore, the polymorphism of CYP1A1, CYP2C9 and UGT1A1affected the content of BPDE adducts.

Linear diffusion noise boosted deep image prior for unsupervised sparse-view CT reconstruction.

Objective.Deep learning has markedly enhanced the performance of sparse-view computed tomography reconstruction. However, the dependence of these methods on supervised training using high-quality paired datasets, and the necessity for retraining under varied physical acquisition conditions, constrain their generalizability across new imaging contexts and settings.Approach.To overcome these limitations, we propose an unsupervised approach grounded in the deep image prior framework. Our approach advances beyond the conventional single noise level input by incorporating multi-level linear diffusion noise, significantly mitigating the risk of overfitting. Furthermore, we embed non-local self-similarity as a deep implicit prior within a self-attention network structure, improving the model's capability to identify and utilize repetitive patterns throughout the image. Additionally, leveraging imaging physics, gradient backpropagation is performed between the image domain and projection data space to optimize network weights.Main Results.Evaluations with both simulated and clinical cases demonstrate our method's effective zero-shot adaptability across various projection views, highlighting its robustness and flexibility. Additionally, our approach effectively eliminates noise and streak artifacts while significantly restoring intricate image details.Significance. Our method aims to overcome the limitations in current supervised deep learning-based sparse-view CT reconstruction, offering improved generalizability and adaptability without the need for extensive paired training data.

Automatic Annotation Diagnostic Framework for Nasopharyngeal Carcinoma via Pathology-Fidelity GAN and Prior-Driven Classification.

Non-keratinizing carcinoma is the most common subtype of nasopharyngeal carcinoma (NPC). Its poorly differentiated tumor cells and complex microenvironment present challenges to pathological diagnosis. AI-based pathological models have demonstrated potential in diagnosing NPC, but the reliance on costly manual annotation hinders development. To address the challenges, this paper proposes a deep learning-based framework for diagnosing NPC without manual annotation. The framework includes a novel unpaired generative network and a prior-driven image classification system. With pathology-fidelity constraints, the generative network achieves accurate digital staining from H&E to EBER images. The classification system leverages staining specificity and pathological prior knowledge to annotate training data automatically and to classify images for NPC diagnosis. This work used 232 cases for study. The experimental results show that the classification system reached a 99.59% accuracy in classifying EBER images, which closely matched the diagnostic results of pathologists. Utilizing PF-GAN as the backbone of the framework, the system attained a specificity of 0.8826 in generating EBER images, markedly outperforming that of other GANs (0.6137, 0.5815). Furthermore, the F1-Score of the framework for patch level diagnosis was 0.9143, exceeding those of fully supervised models (0.9103, 0.8777). To further validate its clinical efficacy, the framework was compared with experienced pathologists at the WSI level, showing comparable NPC diagnosis performance. This low-cost and precise diagnostic framework optimizes the early pathological diagnosis method for NPC and provides an innovative strategic direction for AI-based cancer diagnosis.

Multicolor Tuning of Perylene Diimides Dyes for Targeted Organelle Imaging In Vivo.

The adjustment of the emission wavelengths and cell permeability of the perylene diimides (PDI) for multicolor cell imaging is a great challenge. Herein, based on a bay-region substituent engineering strategy, multicolor perylene diimides (MCPDI) were rationally designed and synthesized by introducing azetidine substituents on the bay region of PDIs. With the fine-tuned electron-donating ability of the azetidine substituents, these MCPDI showed high brightness, orange, red, and near infrared (NIR) fluorescence along with Stokes shifts increasing from 35 to 110 nm. Interestingly, azetidine substituents distorted to the plane of the MCPDI dyes, and the twist angle of monosubstituted MCPDI was larger than that of disubstituted MCPDI, which might efficiently decrease their π-π stacking. Moreover, all of these MCPDI dyes were cell-permeable and selectively stained various organelles for multicolor imaging of multiple organelles in living cells. Two-color imaging of lipid droplets (LDs) and other organelles stained with MCPDI dyes was performed to reveal the interaction between the LDs and other organelles in living cells. Furthermore, a NIR-emitting MCPDI dye with a mitochondria-targeted characteristic was successfully applied for tumor-specific imaging. The facile synthesis, excellent stability, high brightness, tunable fluorescence emission, and Stokes shifts make these MCPDI promising fluorescent probes for biological applications.

Element Screening of High-Entropy Silicon Anodes for Superior Li-Storage Performance of Li-Ion Batteries.

The high-entropy silicon anodes are attractive for enhancing electronic and Li-ionic conductivity while mitigating volume effects for advanced Li-ion batteries (LIBs), but are plagued by the complicated elements screening process. Inspired by the resemblances in the structure between sphalerite and diamond, we have selected sphalerite-structured SiP with metallic conductivity as the parent phase for exploring the element screening of high-entropy silicon-based anodes. The inclusion of the Zn in the sphalerite structure is crucial for improving the structural stability and Li-storage capacity. Within the same group, Li-storage performance is significantly improved with increasing atomic number in the order of BZnSiP3 < AlZnSiP3 < GaZnSiP3 < InZnSiP3. Thus, InZnSiP3-based electrodes achieved a high capacity of 719 mA h g-1 even after 1,500 cycles at 2,000 mA g-1, and a high-rate capacity of 725 mA h g-1 at 10,000 mA g-1, owing to its superior lithium-ion affinity, faster electronic conduction and lithium-ion diffusion, higher Li-storage capacity and reversibility, and mechanical integrity than others. Additionally, the incorporation of elements with larger atomic sizes leads to greater lattice distortion and more defects, further facilitating mass and charge transport. Following these screening rules, high-entropy disordered-cation silicon-based compounds such as GaCuSnInZnSiP6, GaCu(or Sn)InZnSiP5, and CuSnInZnSiP5, as well as high-entropy compounds with mixed-cation and -anion compositions, such as InZnSiPSeTe and InZnSiP2Se(or Te), are synthesized, demonstrating improved Li-storage performance with metallic conductivity. The phase formation mechanism of these compounds is attributed to the negative formation energies arising from elevated entropy.

Free fatty acids induce bile acids overproduction and oxidative damage of bovine hepatocytes via inhibiting FXR/SHP signaling.

Hepatic oxidative injury induced by free fatty acids (FFA) and metabolic disorders of bile acids (BA) increase the risk of metabolic diseases in dairy cows during perinatal period. However, the effects of FFA on BA metabolism remained poorly understood. In present study, high concentrations of FFA caused cell impairment, oxidative stress and BA overproduction. FFA treatment increased the expression of BA synthesis-related genes [cholesterol 7a-hydroxylase (CYP7A1), hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 7, sterol 12α-hydroxylase, sterol 27-hydroxylase and oxysterol 7α-hydroxylase], whereas reduced BA exportation gene (ATP binding cassette subfamily C member 1) and inhibited farnesoid X receptor/small heterodimer partner (FXR/SHP) pathway in bovine hepatocytes. Knockdown of nuclear receptor subfamily 1 group H member 4 (NR1H4) worsened FFA-caused oxidative damage and BA production, whereas overexpression NR1H4 ameliorated FFA-induced BA production and cell oxidative damage. Besides, reducing BA synthesis through knockdown of CYP7A1 can alleviate oxidative stress and hepatocytes impairment caused by FFA. In summary, these data demonstrated that regulation of FXR/SHP-mediated BA metabolism may be a promising target in improving hepatic oxidative injury of dairy cows during high levels of FFA challenges.

Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning.

Autism spectrum disorder (ASD) is a heterogeneous, early-onset neurodevelopmental condition characterized by persistent impairments in social interaction and communication. This study aims to delineate ASD subtypes based on individual gray matter brain networks and provide new insights from a graph theory perspective. In this study, we extracted and normalized single-subject gray matter networks and calculated each network's topological properties. The heterogeneity through discriminative analysis (HYDRA) method was utilized to subtype all patients based on network properties. Next, we explored the differences among ASD subtypes in terms of network properties and clinical measures. Our investigation identified three distinct ASD subtypes. In the case-control study, these subtypes exhibited significant differences, particularly in the precentral gyrus, lingual gyrus, and middle frontal gyrus. In the case analysis, significant differences in global and nodal properties were observed between any two subtypes. Clinically, subtype 1 showed lower VIQ and PIQ compared to subtype 3, but exhibited higher scores in ADOS-Communication and ADOS-Total compared to subtype 2. The results highlight the distinct brain network properties and behaviors among different subtypes of male patients with ASD, providing valuable insights into the neural mechanisms underlying ASD heterogeneity.

Ammonium Tetrakis(pentafluorophenyl)borate: Preparation and Application in Olefin Coordination Polymerization as the Cocatalyst Compound.

Metallocene catalysts have attracted much attention from academia and industry for their excellent catalytic activity in the field of olefin polymerization. Cocatalysts play a key role in metallocene catalytic systems, which can not only affect the overall catalytic activity, but also have an obvious influence on the structure and properties of the polymer. Although methylaluminoxane (MAO) is currently the most widely used cocatalyst, its price increases the production cost of polyolefin materials. Ammonium tetrakis(pentafluorophenyl)borate has shown excellent performance in polymerization, being one of the best substitutes for the traditional cocatalyst MAO. Compared with the main catalyst, whose composition and structure are relatively complex, the research on cocatalyst is very limited. This review mainly introduces the research history, preparation methods, and application progress in polymerization of ammonium tetrakis(pentafluorophenyl)borate, deepening our understanding of the role of cocatalyst in polymerization, with the hope of inspiring brand-new thinking on improving and enhancing the overall performance of catalyst systems.

Expression of hepatic genes involved in bile acid metabolism in dairy cows with fatty liver.

Bile acids are cholesterol-derived molecules that are primarily produced in the liver. In nonruminants with fatty liver, overproduction of bile acids is associated with liver injury. During the transition period, fatty liver is a metabolic disorder that can affect up to 50% of high-producing dairy cows. The purpose of this study was to provide a comprehensive evaluation on hepatic bile acid metabolism in dairy cows with fatty liver by assessing expression changes of genes involved in bile acid synthesis, export and uptake. The serum activities of aspartate aminotransferase, alanine aminotransferase and glutamate dehydrogenase and concentration of total bile acids were all greater, whereas serum concentration of total cholesterol was lower in cows with fatty liver than in healthy cows. Content of total bile acids was higher but total cholesterol was slightly lower in liver tissues from fatty liver cows than from healthy cows. The hepatic mRNA abundance of cholesterol 7a-hydroxylase (CYP7A1), hydroxy-delta-5-steroid dehydrogenase, 3 ß- and steroid delta-isomerase 7 (HSD3B7) and sterol 12α-hydroxylase (CYP8B1), enzymes involved in the classic pathway of bile acid synthesis, was higher in fatty liver cows than in healthy cows. Compared with healthy cows, the hepatic mRNA abundance of alternative bile acid synthesis pathway-related genes sterol 27-hydroxylase (CYP27A1) and oxysterol 7α-hydroxylase (CYP7B1) did not differ in cows with fatty liver. The protein and mRNA abundance of bile acid transporter bile salt efflux pump (BSEP) were lower in the liver of dairy cow with fatty liver. Compared with healthy cows, the hepatic mRNA abundance of bile acid transporters solute carrier family 51 subunit α (SLC51A), ATP binding cassette subfamily C member 1 (ABCC1) and 3 (ABCC3) was greater in cows with fatty liver, whereas the solute carrier family 51 subunit ß (SLC51B) did not differ. The expression of genes involved in bile acid uptake, including solute carrier family 10 member 1 (NTCP), solute carrier organic anion transporter family member 1A2 (SLCO1A2) and 2B1 (SLCO2B1) was upregulated in dairy cows with fatty liver. Furthermore, the hepatic protein and mRNA abundance of bile acid metabolism regulators farnesoid X receptor (FXR) and small heterodimer partner (SHP) were lower in cows with fatty liver than in healthy cows. Overall, these data suggest that inhibition of FXR signaling pathway may lead to the increased bile acid synthesis and uptake and decreased secretion of bile acids from hepatocytes to the bile, which elevates hepatic bile acids content in dairy cows with fatty liver. As the hepatotoxicity of bile acids has been demonstrated on nonruminant hepatocytes, it is likely that the liver injury is induced by increased hepatic bile acids content in dairy cows with fatty liver.

Electrifying oxidation of ethylene and propylene.

Ethylene and propylene, as essential precursors in the chemical industry, have been playing a pivotal role in the production of various value-added chemicals that find wide applications in diverse sectors, such as polymer synthesis, lithium-ion battery electrolytes, antifreeze agents and pharmaceuticals. Nevertheless, traditional methods for olefin functionalization including chlorohydrination and epoxidation involve energy-intensive steps and environment-detrimental by-products. In contrast, electrocatalysis is emerging as a promising and sustainable approach for olefin oxidation via utilizing renewable electricity. Recent advancements in energy storage and conversion technologies have intensified the research efforts toward designing efficient electrocatalysts for the selective oxidation of ethylene and propylene, highlighting the shift towards more sustainable production methods. Herein, we summarize recent progress in the electrocatalytic oxidation of ethylene and propylene, focusing on achievement in catalyst design, reaction system selection and mechanism exploration. We figure out the advantages of different oxidation methods for improved performance and discuss the various types of catalysts like noble metals, non-noble metals, metal oxides and carbon-based materials, in facilitating the electrochemical oxidation of ethylene and propylene. Finally, we also provide an overview of current challenges and problems requiring further works.

Anions Regulation of 1D Perovskite Intrusion-Behavior for Efficient and Stable Perovskite Solar Cells.

Constructing a 1D/3D perovskite heterojunction has recently emerged as a prevalent approach for elevating the efficiency and stability of perovskite solar cells (PSCs), due to the excellent defect-passivation capacity and enhanced resistance to water and oxygen of 1D perovskite. However, the 1D perovskite commonly exhibits much poorer charge carrier transport ability when compared with its 3D counterpart. Tailoring the intrusion depth of a 1D perovskite into the 1D/3D heterojunction is thus of key importance for PSCs but remains a great challenge. We introduce herein a novel anion-regulation strategy that can effectively tune the intrusion behavior of 1D perovskite into 3D perovskite to form a 1D/3D heterojunction with gradual structure and gradient energy-level alignment. This gradual 1D/3D-perovskite interface leads to outstanding defect passivation performance, together with a desired balance between charge transport and moisture/oxygen blocking. Consequently, the PSCs with a 1D/3D perovskite heterojunction resulting from tetra-n-butylammonium acetate (TBAAc) treatment yield a remarkable enhancement in power conversion efficiency (PCE) from 18.4 to 20.1%. The unencapsulated device also demonstrates excellent stability and retains 90% of its initial PCE after 2400 h of storage in the air atmosphere with 30 ± 5% humidity at 25 ± 5 °C.

Targeting PHB2 mediated mitophagy alleviates non-esterified fatty acid-induced mitochondrial dysfunction in bovine mammary epithelial cells.

Mitochondrial dysfunction has been reported to occur in the mammary gland of dairy cows suffering from ketosis. Prohibitin 2 (PHB2) plays a crucial role in regulating mitophagy, which clears impaired mitochondria to maintain normal mitochondrial function. Therefore, the current study aimed to investigate how PHB2 mediates mitophagy, thereby influencing mitochondrial function in the bovine mammary epithelial cell MAC-T. First, mammary gland tissue and blood samples were collected from healthy cows (control; n = 15, BHB <0.6 mM) and cows with clinical ketosis (CK; n = 15, BHB >3.0 mM). Compared with the control group, the CK group exhibited lower dry matter intake (DMI), milk production, milk protein, milk lactose, and serum glucose. In contrast, milk fat, serum nonesterified fatty acids (NEFA) and BHB were greater in CK group. The protein abundance of PHB2, peroxisome proliferator activated receptor-γ coactivator-1α (PGC-1α), mitofusin 2 (MFN2) in whole cell lysates (WCL), as well as PHB2, sequestosome-1 (SQSTM1, also called p62), microtubule-associated protein 1 light chain 3-II (LC3-II), and ubiquitinated proteins in mitochondrial fraction were significantly lower in the CK group. ATP content of mammary gland tissue in CK group was lower than that of healthy cows. Second, MAC-T were cultured and treated with NEFA (0, 0.3, 0.6, 1.2 mM). MAC-T treated with 1.2 mM NEFA displayed decreased protein abundance of PHB2, PGC-1α, MFN2 in WCL, as well as protein abundance of PHB2, p62, LC3-II, and ubiquitinated proteins in mitochondrial fraction. The content of ATP and JC-1 aggregates in 1.2 mM NEFA group were lower than in the 0 mM NEFA group. Additionally, 1.2 mM NEFA disrupted the fusion between mitochondria and lysosomes. MAC-T were then pretreated with 100 nM rapamycin, followed by treatment with or without NEFA. Rapamycin alleviated impaired mitophagy and mitochondria dysfunction induced by 1.2 mM NEFA. Third, MAC-T were transfected with small interfering RNA to silence PHB2 or a plasmid for overexpression of PHB2, followed by treatment with or without NEFA. The silencing of PHB2 aggravated 1.2 mM NEFA induced impaired mitophagy and mitochondrial dysfunction, whereas the overexpression of PHB2 alleviated these effects. Overall, this study provides evidence that PHB2, in regulation of mitophagy, is a mechanism for bovine mammary epithelial cells to counteract NEFA-induced mitochondrial dysfunction.

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

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

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

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

Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery.

The Soil Plant Analysis Development (SPAD) is a vital index for evaluating crop nutritional status and serves as an essential parameter characterizing the reproductive growth status of winter wheat. Non-destructive and accurate monitorin3g of winter wheat SPAD plays a crucial role in guiding precise management of crop nutrition. In recent years, the spectral saturation problem occurring in the later stage of crop growth has become a major factor restricting the accuracy of SPAD estimation. Therefore, the purpose of this study is to use features selection strategy to optimize sensitive remote sensing information, combined with features fusion strategy to integrate multiple characteristic features, in order to improve the accuracy of estimating wheat SPAD. This study conducted field experiments of winter wheat with different varieties and nitrogen treatments, utilized UAV multispectral sensors to obtain canopy images of winter wheat during the heading, flowering, and late filling stages, extracted spectral features and texture features from multispectral images, and employed features selection strategy (Boruta and Recursive Feature Elimination) to prioritize sensitive remote sensing features. The features fusion strategy and the Support Vector Machine Regression algorithm are applied to construct the SPAD estimation model for winter wheat. The results showed that the spectral features of NIR band combined with other bands can fully capture the spectral differences of winter wheat SPAD during the reproductive growth stage, and texture features of the red and NIR band are more sensitive to SPAD. During the heading, flowering, and late filling stages, the stability and estimation accuracy of the SPAD model constructed using both features selection strategy and features fusion strategy are superior to models using only a single feature strategy or no strategy. The enhancement of model accuracy by this method becomes more significant, with the greatest improvement observed during the late filling stage, with R2 increasing by 0.092-0.202, root mean squared error (RMSE) decreasing by 0.076-4.916, and ratio of performance to deviation (RPD) increasing by 0.237-0.960. In conclusion, this method has excellent application potential in estimating SPAD during the later stages of crop growth, providing theoretical basis and technical support for precision nutrient management of field crops.

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

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

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

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

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

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