Machine learning assisted designing of non-fullerene electron acceptors: A quest for lower exciton binding energy.

The designing of acceptors materials for the organic solar cells is a hot topic. The normal experimental methods are tedious and expensive for large screening. Machine learning guided exploration is more suitable solution. Bagging regression, random forest regression, gradient boosting regression, and linear regression are trained to predict exciton binding energy. Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) methodology has utilized for designing of new non-fullerene acceptors (NFAs). The predicted values were used to select the designed NFAs. On the selected NFAs, clustering and chemical similarity analyses are also performed. Chemical fingerprints are used for this purpose, and the synthetic accessibility score of the new NFAs is also investigated.30 NFAs have selected with low exciton binding energy values. This approach will allow for the rapid screening of NFAs for organic solar cells. Our proposed framework stands out as a valuable tool for strategically selecting the most effective NFAs for organic solar cells and offers a streamlined approach for material discovery.

[Deep Learning-Based Key Frame Recognition Algorithm for Adrenal Vascular in X-Ray Imaging].

Adrenal vein sampling is required for the staging diagnosis of primary aldosteronism, and the frames in which the adrenal veins are presented are called key frames. Currently, the selection of key frames relies on the doctor's visual judgement which is time-consuming and laborious. This study proposes a key frame recognition algorithm based on deep learning. Firstly, wavelet denoising and multi-scale vessel-enhanced filtering are used to preserve the morphological features of the adrenal veins. Furthermore, by incorporating the self-attention mechanism, an improved recognition model called ResNet50-SA is obtained. Compared with commonly used transfer learning, the new model achieves 97.11% in accuracy, precision, recall, F1, and AUC, which is superior to other models and can help clinicians quickly identify key frames in adrenal veins.

An efficient lightweight network for image denoising using progressive residual and convolutional attention feature fusion.

While deep learning has become the go-to method for image denoising due to its impressive noise removal capabilities, excessive network depth often plagues existing approaches, leading to significant computational burdens. To address this critical bottleneck, we propose a novel lightweight progressive residual and attention mechanism fusion network that effectively alleviates these limitations. This architecture tackles both Gaussian and real-world image noise with exceptional efficacy. Initiated through dense blocks (DB) tasked with discerning the noise distribution, this approach substantially reduces network parameters while comprehensively extracting local image features. The network then adopts a progressive strategy, whereby shallow convolutional features are incrementally integrated with deeper features, establishing a residual fusion framework adept at extracting encompassing global features relevant to noise characteristics. The process concludes by integrating the output feature maps from each DB and the robust edge features from the convolutional attention feature fusion module (CAFFM). These combined elements are then directed to the reconstruction layer, ultimately producing the final denoised image. Empirical analyses conducted in environments characterized by Gaussian white noise and natural noise, spanning noise levels 15-50, indicate a marked enhancement in performance. This assertion is quantitatively corroborated by increased average values in metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index for Color images (FSIMc), outperforming the outcomes of more than 20 existing methods across six varied datasets. Collectively, the network delineated in this research exhibits exceptional adeptness in image denoising. Simultaneously, it adeptly preserves essential image features such as edges and textures, thereby signifying a notable progression in the domain of image processing. The proposed model finds applicability in a range of image-centric domains, encompassing image processing, computer vision, video analysis, and pattern recognition.

Systematic analysis of MASP-1 serves as a novel immune-related biomarker in sepsis and trauma followed by preliminary experimental validation.

Background: Dysregulated immune response in trauma and sepsis leads to the abnormal activation of the complement and coagulation systems. Mannose-binding lectin (MBL)-associated serine protease-1 (MASP-1) activates the lectin pathway of the complement system and mediates proinflammatory and procoagulant reactions. However, the potential effects of MASP-1 in trauma and sepsis have not yet been explored. Methods: We obtained five sepsis, two trauma, and one sepsis and trauma RNA-sequencing dataset from the Gene Expression Omnibus (GEO) database and conducted a comprehensive evaluation of the expression pattern, biological functions, and diagnostic value of MASP-1 in trauma and sepsis. Additionally, we investigated the association between MASP-1 expression and clinicopathological characteristics of trauma and sepsis. Furthermore, we collected clinical specimens to preliminarily validate the expression level and diagnostic efficacy of MASP-1 as well as the correlation of MASP-1 with clinical features of trauma and sepsis. Subsequently, we conducted a correlation analysis among MASP-1, immune cell infiltration, and immune and molecular pathways. Finally, we mechanistically analyzed the relationship among MASP-1, specific immune cells, and pivotal molecular pathways. Results: MASP-1 expression was significantly upregulated in the trauma/sepsis samples compared to the control samples in the GEO datasets. MASP-1 exhibited excellent diagnostic values (AUC > 0.7) in multiple datasets and at multiple time points and could efficiently distinguish trauma/sepsis samples from the control samples. Moreover, MASP-1 expression was significantly positively correlated with the severity of the disease (APACHE-II, CRP, and neutrophil levels). These results were further validated by real-time quantitative polymerase chain reaction and enzyme-linked immunosorbent assay. Functional enrichment analysis revealed that MASP-1 primarily promotes trauma and sepsis via the immune-related signaling pathway. MASP-1 was significantly correlated with the infiltration of specific immune cells (such as B cells, CD8 T cells, neutrophils, macrophages, and infiltrating lymphocytes) and immune and molecular pathways (such as checkpoint, HLA, IL6/JAK/STAT3 signaling, necrosis, T-cell co-inhibition, and T-cell co-stimulation). Finally, analysis of the transcription and single-cell data revealed that MASP-1 was specifically expressed in T cells, and further correlation analysis revealed a close correlation between MASP-1 expression, proportion of CD8 T cells, and IL6/JAK/STAT3 signaling scores. Conclusion: Our results suggest that MASP-1 can serve as an immune-related biomarker for the diagnosis and disease severity of trauma and sepsis. It may activate the IL6 JAK-STAT3 signaling pathway and promote CD8 T-cell depletion to trigger traumatic sepsis.

Stretching effects on non-adiabatic electron dynamic behavior in poly(dG)-poly(dC) DNA upon the proton irradiation.

The electronic excitations caused by DNA when exposed to ion radiation is essential to DNA damage. In this paper, we investigated the energy deposition and electron excitation process of DNA with reasonable stretching range upon proton irradiation based on time-dependent density functional theory. Stretching changes the strength of hydrogen bonding between the DNA base pairs, which in turn affects the Coulomb interaction between the projectile and DNA. As a semi-flexible molecule, the way of energy deposition is weakly sensitive to the stretching rate of DNA. However, the increase of stretching rate causes the increase of charge density along the trajectory channel, sequentially resulting in an increase in proton resistance along the intruding channel. The Mulliken charge analysis indicates that the guanine base and guanine ribose are ionized, meanwhile the cytosine base and cytosine ribose are reduced at all stretching rates. In a few femtoseconds, there exists an electron flow passing through the guanine ribose, guanine, cytosine base and the cytosine ribose in turn. This electron flow increases electron transfer and DNA ionization, promoting the side chain damage of the DNA upon ion irradiation. Our results provide a theoretical insight for deciphering the physical mechanism of the early stage of the irradiation process, and are also of great significance for the study of particle beam cancer therapy in different biological tissues.

Insight into potent TLR2 inhibitors for the treatment of disease caused by Mycoplasma pneumoniae based on machine learning approaches.

Mycoplasma pneumoniae (MP) is one of the most common pathogens that causes acute respiratory tract infections. Children experiencing MP infection often suffer severe complications, lung injury, and even death. Previous studies have demonstrated that Toll-like receptor 2 (TLR2) is a potential therapeutic target for treating the MP-induced inflammatory response. However, the screening of natural compounds has received more attention for the treatment of bacterial infections to reduce the likelihood of bacterial resistance. Herein, we screened compounds by combining molecular docking and machine learning approaches to find potential lead compounds for treating MP infection. First, all compounds were docked with the TLR2 receptor protein to screen for potential candidates. To predict drug bioactivity, a machine learning model (random forest) was trained for TLR2 inhibitors to obtain the predictive model. The model achieved significant squared correlation coefficient (R2) values for the training set (0.85) and validation set (0.84) of compounds. The developed machine learning model was then used to predict the pIC50 values of the top 50 candidates from the Traditional Chinese compounds and Discovery Diversity sets of compounds. As a result, these compounds are capable of inhibiting the inflammatory response induced by MP. However, prior to bringing these compounds to market, it is necessary to verify these results with additional biological testing, including preclinical and clinical studies. Moreover, the present study provides a theoretical basis for the use of natural compounds as potential candidates to treat pneumonia caused by MP.

Dynamic interplay between thionine and DNA under carbon ion irradiation: a real-time first-principles study.

Understanding the interactions between deoxyribonucleic acid (DNA) and photosensitizer under ion irradiation benefits the development of aptasensors, DNA biosensors and cancer diagnosis. Using real-time time-depended density functional theory, by simulating high-energy C ion passing through DNA with poly(dG)·poly(dC) sequence and that with embedded thionine (3,7-diamino-5-phenothiazinium, TH), we compared the electronic stopping power (ESP), evolution of the structure and charge, and absorption spectrum. TH inserting leads the increase in space charge density, a larger electron de-excitation and a larger ESP, but the speed corresponding to the maximum ESP is almost same. When C ion passes through TH-DNA, the structure of TH slightly changes and there still exists noncovalent interaction between TH and DNA, but the absorption coefficient depends on the electron occupied state of TH when the ion passes through. These results indicate that at low radiation doses, TH still can be a DNA detector, although its response wavelength and intensity have been slightly changed, and provide a theoretical reference to improve the possible application of phenothiazine dye in DNA biosensor under ion irradiation.

Construction of Mental Health Knowledge Service Model Based on Online Medical Community.

This paper discusses a common mental disorder prevention mode to improve residents' mental health quality and achieve the "comprehensive and fine-grained" mental health education knowledge service. The construction of mental health knowledge service model is conducive to accurately grasp the group cognition and psychological changes and take the initiative to make decisions. This paper analyzes the needs of mental health education knowledge service system and combs the research status of applying information technology and artificial intelligence to mental health education at home and abroad, the resource data of five major online medical communities at home and abroad were screened and mined, and the mental health feature tags of college students were extracted. Based on the existing mental health diagnosis experience database and multisource text, reuse and optimize ontology integration method to systematically construct mental health education ontology. Taking mental illness as an example, the rule base is constructed and the personalized recommendation service of mental health is realized. The service model can infer and output all kinds of diagnosis and treatment knowledge to provide users with intelligent mental health education knowledge services.

The Influence of Knowledge Management Capacities on Pharmaceutical Firms Competitive Advantage: The Mediating Role of Supply Chain Agility and Moderating Role of Inter Functional Integration.

This study investigates the factors such as knowledge management capacities and their positive influence on firm competitive advantage or the supply chain agility of the firm and the underlying mechanisms (supply chain agility) that facilitate the firm's performance and leads to firm competitive advantage. It also explores the moderating role of inter-functional integration. We have collected the data from the 308 supply chain managers of pharmaceutical firms in Pakistan and questionnaires were used for data collection with multi-item scales already developed and validated. The findings suggest that knowledge management capacities significantly influence a firm's competitive advantage or supply chain agility. The supply chain agility fully mediates between absorptive capacity, transformative capacity, and firm competitive advantage. Further, supply chain agility partially mediates between inventive capacity and firm competitive advantage. Meanwhile, inter-functional integration moderates the relationship between supply chain agility and firm competitive advantage, with their positive relationship strengthening when inter-functional integration is high. The study provides empirical evidence that knowledge management capacities (such as absorptive capacity, transformative capacity, and inventive capacity), supply chain agility, and inter-functional can be important factors in improving firm performance.

An overview of pharmacological activities of baicalin and its aglycone baicalein: New insights into molecular mechanisms and signaling pathways.

The flavonoids, baicalin, and its aglycone baicalein possess multi-fold therapeutic properties and are mainly found in the roots of Oroxylum indicum (L.) Kurz and Scutellaria baicalensis Georgi. These flavonoids have been reported to possess various pharmacological properties, including antibacterial, antiviral, anticancer, anticonvulsant, anti-oxidant, hepatoprotective, and neuroprotective effects. The pharmacological properties of baicalin and baicalein are due to their abilities to scavenge reactive oxygen species (ROS) and interaction with various signaling molecules associated with apoptosis, inflammation, autophagy, cell cycle, mitochondrial dynamics, and cytoprotection. In this review, we summarized the molecular mechanisms underlying the chemopreventive and chemotherapeutic applications of baicalin and baicalein in the treatment of cancer and inflammatory diseases. In addition, the preventive effects of baicalin and baicalein on mitochondrial dynamics and functions were highlighted with a particular emphasis on their anti-oxidative and cytoprotective properties. The current review highlights could be useful for future prospective studies to further improve the pharmacological applications of baicalein and baicalin. These studies should define the threshold for optimal drug exposure, dose optimization and focus on therapeutic drug monitoring, objective disease markers, and baicalin/baicalein drug levels.

Association Between Variants of the Mannose-Binding Lectin 2 Gene and Susceptibility to Sepsis in the Hainan Island.

BACKGROUND Sepsis has emerged as a leading cause of death in the intensive care unit. A growing number of studies have shown that genetic variants, especially single nucleotide polymorphisms, are key determinants of inter-individual variation in sepsis response. Therefore, early prediction of the onset and progression of sepsis, along with early intervention in high-risk patients, should be performed to effectively reduce the morbidity and mortality of the disease. MATERIAL AND METHODS A total of 581 Chinese patients were enrolled in this study, including 271 patients with sepsis and 310 patients without. We measured gene polymorphisms of MBL2 and serum levels of MBL2, tumor necrosis factor (TNF-alpha), interleukin (IL)-6, IL-4, and IL-10 in all patients. The effects of site mutations on the binding of MBL2 to mannose-associated serine protease 1 (MASP1) and MASP2 were also analyzed. RESULTS Of 3 site mutations in the MBL2 gene (rs5030737, rs1800450, and rs1800451), only rs1800450 had a mutant (G/A) genotype. The frequency of the GA genotype and A allele in the sepsis group was higher than that in the non-sepsis group. Furthermore, rs1800450G/A was associated with decreased serum MBL2 and IL-10 levels and decreased MBL2-MASP1 and MBL2-MASP2 interactions. Bioinformatics analysis showed that rs1800450G/A reduced the structural stability of the MBL2 protein and affected its function. CONCLUSIONS MBL2 rs1800450G/A was associated with a higher risk of sepsis, which possibly involved a decreased level of serum MBL2 that broke the balance of inflammation and weakened the binding of MBL2 to MASP1 and MASP2.

A novel compact broadband and radiation efficient antenna design for medical IoT healthcare system.

This paper investigates and develops a novel compact broadband and radiation efficient antenna design for the medical internet of things (M-IoT) healthcare system. The proposed antenna comprises of an umbrella-shaped metallic ground plane (UsMGP) and an improved radiator. A hybrid approach is employed to obtain the optimal results of antenna. The proposed solution is primarily based on the utilization of etching slots and a loaded stub on the ground plane and rectangular patch. The antenna consists of a simple rectangular patch, a 50 Ƹ microstrip feed line, and a portion of the ground plane printed on a relatively inexpensive flame retardant material (FR4) thick substrate with an overall compact dimension of 22 × 28 × 1.5 mm3. The proposed antenna offers compact, broadband and radiation efficient features. The antenna is carefully designed by employing the approximate calculation formulae extracted from the transmission line model. Besides, the parameters study of important variables involved in the antenna design and its influence on impedance matching performance are analyzed. The antenna shows high performance, including impedance bandwidth of 7.76 GHz with a range of 3.65Ƀ11.41 GHz results in 103% wider relative bandwidth at 10 dB return loss, 82% optimal radiation efficiency in the operating band, reasonable gain performance, stable monopole-shaped radiation patterns and strong current distribution across the antenna lattice. The suggested antenna is manufactured, and simulation experiments evaluate its performance. The findings indicate that the antenna is well suited for medical IoT healthcare systems applications.

Signifying the Effect of Relational and Experiential Cognitive Styles on Entrepreneurial Behavior: A Mediated Moderated Model.

This study investigates the influence of rational and experiential cognitive styles on entrepreneurial behavior. Specifically, the moderating role of entrepreneurial intention and informal learning has been contemplated. Data has been accumulated from 320 undergraduate students of universities situated in Pakistan. Statistical Package for the Social Sciences (SPSS) and AMOS have been executed to examine the data and conduct statistical techniques. After confirming the validity and reliability of data and scale, results have signified that both cognitive styles significantly positively impact entrepreneurial behavior. Moreover, informal learning moderates the relationship between entrepreneurial intention and cognitive styles (rational and experiential). Meanwhile, entrepreneurial intention mediates the relationship between cognitive styles (rational and experiential) and entrepreneurial behavior emphatically.

A framework for efficient brain tumor classification using MRI images.

A brain tumor is an abnormal growth of brain cells inside the head, which reduces the patient's survival chance if it is not diagnosed at an earlier stage. Brain tumors vary in size, different in type, irregular in shapes and require distinct therapies for different patients. Manual diagnosis of brain tumors is less efficient, prone to error and time-consuming. Besides, it is a strenuous task, which counts on radiologist experience and proficiency. Therefore, a modern and efficient automated computer-assisted diagnosis (CAD) system is required which may appropriately address the aforementioned problems at high accuracy is presently in need. Aiming to enhance performance and minimise human efforts, in this manuscript, the first brain MRI image is pre-processed to improve its visual quality and increase sample images to avoid over-fitting in the network. Second, the tumor proposals or locations are obtained based on the agglomerative clustering-based method. Third, image proposals and enhanced input image are transferred to backbone architecture for features extraction. Fourth, high-quality image proposals or locations are obtained based on a refinement network, and others are discarded. Next, these refined proposals are aligned to the same size, and finally, transferred to the head network to achieve the desired classification task. The proposed method is a potent tumor grading tool assessed on a publicly available brain tumor dataset. Extensive experiment results show that the proposed method outperformed the existing approaches evaluated on the same dataset and achieved an optimal performance with an overall classification accuracy of 98.04%. Besides, the model yielded the accuracy of 98.17, 98.66, 99.24%, sensitivity (recall) of 96.89, 97.82, 99.24%, and specificity of 98.55, 99.38, 99.25% for Meningioma, Glioma, and Pituitary classes, respectively.

A weighted region-based level set method for image segmentation with intensity inhomogeneity.

Image segmentation is a fundamental task in image processing and is still a challenging problem when processing images with high noise, low resolution and intensity inhomogeneity. In this paper, a weighted region-based level set method, which is based on the techniques of local statistical theory, level set theory and curve evolution, is proposed. Specifically, a new weighted pressure force function (WPF) is first presented to flexibly drive the closed contour to shrink or expand outside and inside of the object. Second, a faster and smoother regularization term is added to ensure the stability of the curve evolution and that there is no need for initialization in curve evolution. Third, the WPF is integrated into the region-based level set framework to accelerate the speed of the curve evolution and improve the accuracy of image segmentation. Experimental results on medical and natural images demonstrate that the proposed segmentation model is more efficient and robust to noise than other state-of-the-art models.

Differences in survival of prostate cancer Gleason 8-10 disease and the establishment of a new Gleason survival grading system.

BACKGROUND: Although the latest Gleason grading system in 2014 has distinguished between Gleason 3 + 4 and 4 + 3, Gleason 8 and Gleason 9-10 are remained systemically classified. METHODS: A total of 261,125 patients diagnosed with prostate cancer (PCa) were selected between 2005 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. We used propensity score matching to balance clinical variables and then compared overall survival (OS) and cancer-specific survival (CSS) between Gleason score subgroups. We further establish a new Gleason survival grading system based on the hazard ratio (HR) values of each Gleason subgroup. Cox proportional hazards models and Kaplan-Meier curves were used to compare patient survival. RESULTS: Among PCa patients with Gleason score 8 disease, patients with Gleason 5 + 3 had significantly worse OS and CSS than those with Gleason 3 + 5 (OS: HR = 1.26, p = 0.042; CSS: HR = 1.42, p = 0.005) and 4 + 4 (HR = 1.50 for OS and HR = 1.69 for CSS, p < 0.001 for all). PCa patients with Gleason 5 + 3 and Gleason 4 + 5 may have the similar OS and CSS (reference Gleason score <=6, 5 + 3: OS HR = 2.44, CSS HR = 7.63; 4 + 5: OS HR = 2.40, CSS HR = 8.92; p < 0.001 for all). The new Gleason survival grading system reclassified the grades 4 and 5 of the 2014 updated Gleason grading system into three hierarchical grades, which makes the classification of grades more detailed and accurate. CONCLUSION: PCa patients with Gleason 8-10 may have three different survival subgroups, Gleason 3 + 5 and 4 + 4, Gleason 5 + 3 and 4 + 5, and Gleason 5 + 4 and 5 + 5. Our results maximize risk stratification for PCa patients, provide guidance for clinicians to assess their survival and clinical management, and make a recommendation for the next Gleason grading system update.

Enhancing the stability of perovskites by constructing heterojunctions of graphene/MASnI3.

In this paper, the geometric and electronic properties of heterojunctions constructed using a graphene sheet and an MASnI3 surface were investigated by performing first-principles calculations based on the density functional theory. Our results show that the interaction between graphene and the MASnI3 surface is in the scope of van der Waals interactions. In the heterojunctions, electrons transfer from graphene to the MASnI3 surface, resulting in the formation of a built-in electric field in the interface, which is favorable for the separation of electrons and holes. The absorption spectra showed that the absorption intensity of the heterojunction in the visible region is slightly smaller than that of the pristine MASnI3 surface. The energy barriers of water molecules diffusing through MASnI3 surfaces are relatively low, but when a water molecule penetrates the graphene sheet into the interior of the MASnI3 it has to overcome an energy barrier of as high as 9 eV. It is found that the water diffusions through the surfaces cause very severe damage to the structures of the graphene sheet and MASnI3 surface. So, the graphene can block the penetration of water into the inside of the material and retard the degradation of the perovskites. Coating a graphene sheet onto the MASnI3 surface to form a heterojunction is an effective strategy of enhancing the stability and performance of perovskite solar cells. This study could provide an in-depth understanding of the properties of graphene/MASnI3 heterojunctions and contribute to the design strategy of perovskite-based solar cells.

GPx1-mediated DNMT1 expression is involved in the blocking effects of selenium on OTA-induced cytotoxicity and DNA damage.

Ochratoxin A (OTA) is a potent nephrotoxin. Selenium (Se) is an essential micronutrient for humans and animals, and plays a key role in antioxidant defense. To date, little is known about the effect of Se on OTA-induced DNA damage. In this study, the protective effects of Se (from selenomethionine) against OTA-induced cytotoxicity and DNA damage were investigated by using PK15 cells as a model. The results showed that OTA at 4.0 µg/mL induced cytotoxicity and DNA damage. Se at 0.5, 1, 2 and 4 µM significantly blocked OTA-induced cytotoxicity and DNA damage. Furthermore, Se blocked the increases of DNMT1, DNMT3a and HDAC1 mRNA and protein expression, reversed the decreases of glutathione peroxidase 1 (GPx1) mRNA and protein expression, and promoted the increases of SOCS3 mRNA and protein expression induced by OTA. Overexpression of GPx1 by pcDNA3.1-GPx1 inhibited the OTA-induced DNMT1 expression, promoted OTA-induced SOCS3 expression, and prevented the OTA-induced cytotoxicity and DNA damage. In contrast, knock-down of GPx1 by using a GPx1-specific siRNA had the opposite effects. The results suggest that GPx1-mediated DNMT1 expression is involved in the blocking effects of selenium on OTA-induced cytotoxicity and DNA damage.

Evaluation of grain yield based on digital images of rice canopy.

BACKGROUND: Rice canopy changes are associated with changes in the red light (R), green light (G), and blue light (B) value parameters of digital images. To rapidly diagnose the responses of rice to nitrogen (N) fertilizer application and planting density, a simple model based on digital images was developed for predicting and evaluating rice yield. RESULTS: N application rate and planting density had significant effects on rice yield. Rice yield first increased and then decreased with increasing of N rates, while the rice yield always increased significantly with increasing planting density. The normalized redness intensity (NRI), normalized greenness intensity (NGI), and normalized blueness intensity (NBI) values of the rice canopy varied among stages; however, they were primarily affected by N fertilizer rates, while planting density had no significant effects. Furthermore, the significant relationships of grain yield with NRI and NBI at the late filling stage could be fitted by quadratic equations, but there was no significant relationship observed between grain yield and NGI across all stages. In addition, a field validation experiment showed that the predicted yield based on the fitted quadratic equations was consistent with the measured yield. CONCLUSION: The NRI, NGI, and NBI values of rice canopy were mainly affected by N fertilizer rates, while the planting density had no significant effect. The significant relationships between grain yield with NRI and NBI at the late filling stage could be fitted by quadratic equations. Therefore, the canopy NRI and NBI at the late filling stage as measured by digital photography could be used to predict grain yield in southern China.

Ribavirin sensitizes nasopharyngeal carcinoma to 5-fluorouracil through suppressing 5-fluorouracil-induced ERK-dependent-eIF4E activation.

Although overexpression of eukaryotic translation initiation factor 4E (eIF4E) is associated with enhanced growth and poor prognosis in nasopharyngeal carcinoma (NPC), the function of eIF4E in NPC response to chemotherapy has not been revealed. In this work, we demonstrate that eIF4E inhibition using both ribavirin and siRNA targets NPC cells and enhances the efficacy of 5-fluorouracil (5-FU). Mechanism studies indicate that 5-FU treatment increases phosphorylation of eIF4E in NPC cells, and this is dependent on ERK activation. eIF4E inhibition thus significantly sensitizes NPC cell response to 5-FU. Of note, ribavirin is a clinically available anti-viral drug. We show that ribavirin exhibits preferential toxicity to NPC with normal nasopharyngeal epithelial cells largely unaffected. Ribavirin acts on NPC cells via inhibiting eIF4E/Akt signaling, and the suppression of eIF4E by ribavirin are not the consequence of inhibition of eIF4E upstream signaling: Mnk and mTOR. In two independent NPC xenograft mouse models, ribavirin at well-tolerated dose that significantly inhibited NPC growth as single drug alone and its combination with 5-FU completely arrests tumor growth throughout the whole duration of treatment, without causing toxicity in mice. Our findings provide the better understanding on the role of eIF4E in NPC in response to 5-FU and preclinical rationale to explore ribavirin as a sensitizing strategy to treat NPC, particularly in those who develop 5-FU resistance.