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Sclerotinia sclerotiorum (Lib.) de Bary is the causative agent of stem white mold disease which severely reduces major crop productivity including soybean and rapeseed worldwide. The current study aimed to explore plant growth-promoting traits and biocontrol of new isolated Bacillus subtilis BS-2301 to suppress S. sclerotiorum through various mechanisms. The results indicated that the BS-2301 exhibited strong biocontrol potential against S. sclerotiorum up to 74% both in dual culture and partition plate experiments. The BS-2301 and its crude extract significantly suppressed S. sclerotiorum growth involving excessive reactive oxygen species (ROS) production in mycelia for rapid death. Furthermore, the treated hyphae produced low oxalic acid (OA), a crucial pathogenicity factor of S. sclerotiorum. The SEM and TEM microscopy of S. sclerotiorum showed severe damage in terms of cell wall, cell membrane breakage, cytoplasm displacement, and organelles disintegration compared to control. The pathogenicity of S. sclerotiorum exposed to BS-2301 had less disease progression potential on soybean leaves in the detached leaf assay experiment. Remarkably, the strain also demonstrated broad-range antagonistic activity with 70%, and 68% inhibition rates against Phytophthora sojae and Fusarium oxysporum, respectively. Furthermore, the strain exhibits multiple plant growth-promoting and disease-prevention traits, including the production of indole-3-acetic acid (IAA), siderophores, amylases, cellulases and proteases as well as harboring calcium phosphate decomposition activity. In comparison to the control, the BS-2301 also showed great potential for enhancing soybean seedlings growth for different parameters, including shoot length 31.23%, root length 29.87%, total fresh weight 33.45%, and total dry weight 27.56%. The antioxidant enzymes like CAT, POD, SOD and APX under BS-2301 treatment were up-regulated in S. sclerotiorum infected plants along with the positive regulation of defense-related genes (PR1-2, PR10, PAL1, AOS, CHS, and PDF1.2). These findings demonstrate that the BS-2301 strain possesses a notable broad-spectrum biocontrol potential against different phytopathogens and provides new insight in suppressing S. sclerotiorum through various mechanisms. Therefore, BS-2301 will be helpful in the development of biofertilizers for sustainable agricultural practices.

Plant parasitic nematodes (PPNs) pose a significant threat to global crop productivity, causing an estimated annual loss of US $157 billion in the agriculture industry. While synthetic chemical nematicides can effectively control PPNs, their overuse has detrimental effects on human health and the environment. Biocontrol agents (BCAs), such as bacteria and fungi in the rhizosphere, are safe and promising alternatives for PPNs control. These BCAs interact with plant roots and produce extracellular enzymes, secondary metabolites, toxins, and volatile organic compounds (VOCs) to suppress nematodes. Plant root exudates also play a crucial role in attracting beneficial microbes toward infested roots. The complex interaction between plants and microbes in the rhizosphere against PPNs is mostly untapped which opens new avenues for discovering novel nematicides through multi-omics techniques. Advanced omics approaches, including metagenomics, transcriptomics, proteomics, and metabolomics, have led to the discovery of nematicidal compounds. This review summarizes the status of bacterial and fungal biocontrol strategies and their mechanisms for PPNs control. The importance of omics-based approaches for the exploration of novel nematicides and future directions in the biocontrol of PPNs are also addressed. The review highlighted the potential significance of multi-omics techniques in biocontrol of PPNs to ensure sustainable agriculture.

In the contemporary landscape of healthcare, the early and accurate prediction of diabetes has garnered paramount importance, especially in the wake of the COVID-19 pandemic where individuals with diabetes exhibit increased vulnerability. This research embarked on a mission to enhance diabetes prediction by employing state-of-the-art machine learning techniques. Initial evaluations highlighted the Support Vector Machines (SVM) classifier as a promising candidate with an accuracy of 76.62%. To further optimize predictions, the study delved into advanced feature engineering techniques, generating interaction and polynomial features that unearthed hidden patterns in the data. Subsequent correlation analyses, visualized through heatmaps, revealed significant correlations, especially with attributes like Glucose. By integrating the strengths of Decision Trees, Gradient Boosting, and SVM in an ensemble model, we achieved an accuracy of 93.2%, showcasing the potential of harmonizing diverse algorithms. This research offers a robust blueprint for diabetes prediction, holding profound implications for early diagnosis, personalized treatments, and preventive care in the context of global health challenges and with the goal of increasing life expectancy.

Social media is a powerful communication tool and a reflection of our digital environment. Social media acted as an augmenter and influencer during and after COVID-19. Many of the people sharing social media posts were not actually aware of their mental health status. This situation warrants to automate the detection of mental disorders. This paper presents a methodology for the detection of mental disorders using micro facial expressions. Micro-expressions are momentary, involuntary facial expressions that can be indicative of deeper feelings and mental states. Nevertheless, manually detecting and interpreting micro-expressions can be rather challenging. A deep learning HybridMicroNet model, based on convolution neural networks, is proposed for emotion recognition from micro-expressions. Further, a case study for the detection of mental health has been undertaken. The findings demonstrated that the proposed model achieved a high accuracy when attempting to diagnose mental health disorders based on micro-expressions. The attained accuracy on the CASME dataset was 99.08%, whereas the accuracy that was achieved on SAMM dataset was 97.62%. Based on these findings, deep learning may prove to be an effective method for diagnosing mental health conditions by analyzing micro-expressions.

The COVID-19 pandemic has greatly affected human behavior, creating a need for individuals to be more cautious about health and safety protocols. People are becoming more aware of their surroundings and the importance of minimizing the risk of exposure to potential sources of infection. This shift in mindset is particularly important in indoor environments, especially hospitals, where there is a greater risk of virus transmission. The implementation of route planning in these areas, aimed at minimizing interaction and exposure, is crucial for positively influencing individual behavior. Accurate maps of buildings help provide location-based services, prepare for emergencies, and manage infrastructural facilities. There aren’t any maps available for most installations, and there are no proven techniques to categorize features within indoor areas to provide location-based services. During a pandemic like COVID-19, the direct connection between the masses is one of the significant preventive steps. Hospitals are the main stakeholders in managing such situations. This study presents a novel method to create an adaptive 3D model of an indoor space to be used for localization and routing purposes. The proposed method infuses LiDAR-based data-driven methodology with a Quantum Geographic Information System (QGIS) model-driven process using game theory. The game theory determines the object localization and optimal path for COVID-19 patients in a real-time scenario using Nash equilibrium. Using the proposed method, comprehensive simulations and model experiments were done using QGIS to identify an optimized route. Dijkstra algorithm is used to determine the path assessment score after obtaining several path plans using dynamic programming. Additionally, Game theory generates path ordering based on the custom scenarios and user preference in the input path. In comparison to other approaches, the suggested way can minimize time and avoid congestion. It is demonstrated that the suggested technique satisfies the actual technical requirements in real-time. As we look forward to the post-COVID era, the tactics and insights gained during the pandemic hold significant value. The techniques used to improve indoor navigation and reduce interpersonal contact within healthcare facilities can be applied to maintain a continued emphasis on safety, hygiene, and effective space management in the long term. The use of three-dimensional (3D) modeling and optimization methodologies in the long-term planning and design of indoor spaces promotes resilience and flexibility, encouraging the adoption of sustainable and safe practices that extend beyond the current pandemic.

COVID-19 is an epidemic disease that results in death and significantly affects the older adult and those afflicted with chronic medical conditions. Diabetes medication and high blood glucose levels are significant predictors of COVID-19-related death or disease severity. Diabetic individuals, particularly those with preexisting comorbidities or geriatric patients, are at a higher risk of COVID-19 infection, including hospitalization, ICU admission, and death, than those without Diabetes. Everyone’s lives have been significantly changed due to the COVID-19 outbreak. Identifying patients infected with COVID-19 in a timely manner is critical to overcoming this challenge. The Real-Time Polymerase Chain Reaction (RT-PCR) diagnostic assay is currently the gold standard for COVID-19 detection. However, RT-PCR is a time-consuming and costly technique requiring a lab kit that is difficult to get in crises and epidemics. This work suggests the CIDICXR-Net50 model, a ResNet-50-based Transfer Learning (TL) method for COVID-19 detection via Chest X-ray (CXR) image classification. The presented model is developed by substituting the final ResNet-50 classifier layer with a new classification head. The model is trained on 3,923 chest X-ray images comprising a substantial dataset of 1,360 viral pneumonia, 1,363 normal, and 1,200 COVID-19 CXR images. The proposed model’s performance is evaluated in contrast to the results of six other innovative pre-trained models. The proposed CIDICXR-Net50 model attained 99.11% accuracy on the provided dataset while maintaining 99.15% precision and recall. This study also explores potential relationships between COVID-19 and Diabetes.

The intricate relationship between COVID-19 and diabetes has garnered increasing attention within the medical community. Emerging evidence suggests that individuals with diabetes may experience heightened vulnerability to COVID-19 and, in some cases, develop diabetes as a post-complication following the viral infection. Additionally, it has been observed that patients taking cough medicine containing steroids may face an elevated risk of developing diabetes, further underscoring the complex interplay between these health factors. Based on previous research, we implemented deep-learning models to diagnose the infection via chest x-ray images in coronavirus patients. Three Thousand (3000) x-rays of the chest are collected through freely available resources. A council-certified radiologist discovered images demonstrating the presence of COVID-19 disease. Inception-v3, ShuffleNet, Inception-ResNet-v2, and NASNet-Large, four standard convoluted neural networks, were trained by applying transfer learning on 2,440 chest x-rays from the dataset for examining COVID-19 disease in the pulmonary radiographic images examined. The results depicted a sensitivity rate of 98 % (98%) and a specificity rate of almost nightly percent (90%) while testing those models with the remaining 2080 images. In addition to the ratios of model sensitivity and specificity, in the receptor operating characteristics (ROC) graph, we have visually shown the precision vs. recall curve, the confusion metrics of each classification model, and a detailed quantitative analysis for COVID-19 detection. An automatic approach is also implemented to reconstruct the thermal maps and overlay them on the lung areas that might be affected by COVID-19. The same was proven true when interpreted by our accredited radiologist. Although the findings are encouraging, more research on a broader range of COVID-19 images must be carried out to achieve higher accuracy values. The data collection, concept implementations (in MATLAB 2021a), and assessments are accessible to the testing group.

Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model’s resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications.

IntroductionRecently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. MethodThis research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications.ResultsThe proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively.DiscussionThe experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.

Background: Gastrodin has been widely used clinically in China as an antihypertensive drug. However, its effect on hypertensive renal injury is yet to be elucidated. The current study aimed to investigate the effects of gastrodin on hypertensive renal injury and its underlying mechanisms by network pharmacology analysis and validation in vivo and in vitro.Methods: A total of 10 spontaneously hypertensive rats (SHRs) were randomly categorized into the following two groups: SHR and SHR + Gastrodin groups. Wistar Kyoto (WKY) rats were used as the control group (n = 5). The SHR + Gastrodin group was intragastrically administered gastrodin (3.5 mg/kg/day), and the rats in both WKY and SHR groups were intragastrically administered an equal amount of double-distilled water for 10 weeks. Hematoxylin-eosin, Masson’s trichrome, and Sirius red staining were used to detect the pathological changes and collagen content in the renal tissues. Network pharmacology analysis was performed to explore its potential targets and related pathways. In vitro, the CCK-8 assay was used to determine the cell viability. Immunohistochemistry and western-blotting analyses were employed to assess the protein expression associated with renal fibrosis and transforming growth factor-β1 (TGF-β1) pathway-related proteins in the renal tissues or in TGF-β1-stimulated rat kidney fibroblast cell lines (NRK-49F).Results: Gastrodin treatment attenuates renal injury and pathological alterations in SHRs, including glomerular sclerosis and atrophy, epithelial cell atrophy, and tubular dilation. Gastrodin also reduced the accumulation of collagen in the renal tissues of SHRs, which were confirmed by downregulation of α-SMA, collagen I, collagen III protein expression. Network pharmacology analysis identified TGFB1 and SMAD2 as two of lead candidate targets of gastrodin on against hypertensive renal injury. Consistently, gastrodin treatment downregulated the increase of the protein expression of TGF-β1, and ratios of both p-Smad2/Smad2 and p-Samd3/Smad3 in renal tissues of SHRs. In vitro, gastrodin (25–100 μM) treatment significantly reversed the upregulation of α-SMA, fibronectin, collagen I, as well as p-Smad2 and p-Smad3 protein expressions without affecting the cell viability of TGF-β1 stimulated NRK-49F cells.Conclusion: Gastrodin treatment significantly attenuates hypertensive renal injury and renal fibrosis and suppresses TGF-β1/Smad2/3 signaling in vivo and in vitro.

Plants are the primary source of food for world’s population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. The research focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we propose a context-aware 3D Convolutional Neural Network (CNN) model based on CANet architecture that considers the ambiguity of plant lesion placement in the plant leaf image subregions. A Deep CNN is employed to recognize the subtype of leaf lesion using the segmented lesion area. Finally, the plant’s survival is predicted using a hybrid method combining CNN and Linear Regression. To evaluate the efficacy and effectiveness of our proposed plant disease detection scheme and survival prediction, we utilized the Plant Village Benchmark Dataset, which is composed of several photos of plant leaves affected by a certain disease. Using the DICE and IoU matrices, the segmentation model performance for plant leaf lesion segmentation is evaluated. The proposed lesion segmentation model achieved an average accuracy of 92% with an IoU of 90%. In comparison, the lesion subtype recognition model achieves accuracies of 91.11%, 93.01 and 99.04 for pepper, potato and tomato plants. The higher accuracy of the proposed model indicates that it can be utilized for real-time disease detection in unmanned aerial vehicles and offline to offer crop health updates and reduce the risk of low yield.

Bacteriosis is one of the most prevalent and deadly infections that affect peach crops globally. Timely detection of Bacteriosis disease is essential for lowering pesticide use and preventing crop loss. It takes time and effort to distinguish and detect Bacteriosis or a short hole in a peach leaf. In this paper, we proposed a novel LightWeight (WLNet) Convolutional Neural Network (CNN) model based on Visual Geometry Group (VGG-19) for detecting and classifying images into Bacteriosis and healthy images. Profound knowledge of the proposed model is utilized to detect Bacteriosis in peach leaf images. First, a dataset is developed which consists of 10000 images: 4500 are Bacteriosis and 5500 are healthy images. Second, images are preprocessed using different steps to prepare them for the identification of Bacteriosis and healthy leaves. These preprocessing steps include image resizing, noise removal, image enhancement, background removal, and augmentation techniques, which enhance the performance of leaves classification and help to achieve a decent result. Finally, the proposed LWNet model is trained for leaf classification. The proposed model is compared with four different CNN models: LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The proposed model obtains an accuracy of 99%, which is higher than LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The achieved results indicate that the proposed model is more effective for the detection of Bacteriosis in peach leaf images, in comparison with the existing models.

Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming, challenging and error-prone. To reduce the risk of disease severity, machine vision technology (i.e., artificial intelligence) can play a significant role. In the alternative method, the severity of the disease can be diminished through computer technologies and the cooperation of humans. These methods can also eliminate the disadvantages of manual observation. In this work, we proposed a solution to detect tomato plant disease using a deep leaning-based system utilizing the plant leaves image data. We utilized an architecture for deep learning based on a recently developed convolutional neural network that is trained over 18,161 segmented and non-segmented tomato leaf images—using a supervised learning approach to detect and recognize various tomato diseases using the Inception Net model in the research work. For the detection and segmentation of disease-affected regions, two state-of-the-art semantic segmentation models, i.e., U-Net and Modified U-Net, are utilized in this work. The plant leaf pixels are binary and classified by the model as Region of Interest (ROI) and background. There is also an examination of the presentation of binary arrangement (healthy and diseased leaves), six-level classification (healthy and other ailing leaf groups), and ten-level classification (healthy and other types of ailing leaves) models. The Modified U-net segmentation model outperforms the simple U-net segmentation model by 98.66 percent, 98.5 IoU score, and 98.73 percent on the dice. InceptionNet1 achieves 99.95% accuracy for binary classification problems and 99.12% for classifying six segmented class images; InceptionNet outperformed the Modified U-net model to achieve higher accuracy. The experimental results of our proposed method for classifying plant diseases demonstrate that it outperforms the methods currently available in the literature.

The rapid growth of vascular smooth muscle cells (VSMCs) represents crucial pathological changes during the development of hypertensive vascular remodeling. Although quercetin exhibits significantly therapeutic effects on antihypertension, the systematic role of quercetin and its exact mode of action in relation to the VSMCs growth and its hypertension-related networking pharmacology is not well-documented. Therefore, the effect of quercetin was investigated using networking pharmacology followed by in vitro strategies to explore its efficacy against angiotensin II (Ang II)-induced cell proliferation. Putative genes of hypertension and quercetin were collected using database mining, and their correlation was investigated. Subsequently, a network of protein-protein interactions was constructed and gene ontology (GO) analysis was performed to identify the role of important genes (including CCND1) and key signaling pathways [including cell proliferation and Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) pathway]. We therefore further investigated the effects of quercetin in Ang II-stimulated VSMCs. This current research revealed that quercetin significantly reduced the cell confluency, cell number, and cell viability, as well as expression of proliferating cell nuclear antigen (PCNA) in Ang II-stimulated VSMCs. Mechanistic study by western blotting confirmed that quercetin treatment attenuated the activation of JAK2 and STAT3 by reducing its phosphorylation in Ang II stimulated VSMCs. Collectively, the current study revealed the inhibitory effects of quercetin on proliferation of Ang II stimulated VSMCs, by inhibiting the activation of JAK2/STAT3 signaling might be one of underlying mechanisms.

In plants senescence is the final stage of plant growth and development that ultimately leads to death. Plants experience age-related as well as stress-induced developmental ageing. Senescence involves significant changes at the transcriptional, post-translational and metabolomic levels. Furthermore, phytohormones also play a critical role in the programmed senescence of plants. Nitric oxide (NO) is a gaseous signalling molecule that regulates a plethora of physiological processes in plants. Its role in the control of ageing and senescence has just started to be elucidated. Here, we review the role of NO in the regulation of programmed cell death, seed ageing, fruit ripening and senescence. We also discuss the role of NO in the modulation of phytohormones during senescence and the significance of NO-ROS cross-talk during programmed cell death and senescence.

Paclitaxel resistance is a challenging factor in chemotherapy resulting in poor prognosis and cancer recurrence. Signal transducer and activator of transcription factor 3 (STAT3), a key transcription factor, performs a critical role in cancer development, cell survival and chemoresistance, while its inactivation overwhelms drug resistance in numerous cancer types including lung cancer. Additionally, the fucosyltransferase 4 (FUT4) is a crucial enzyme in post-translational modification of cell-surface proteins involved in various pathological conditions such as tumor multidrug resistance (MDR). The P-glycoprotein (P-GP) is the well-known ABC transporter member that imparts drug resistance in different cancer types, most notably paclitaxel resistance in lung cancer cells. LncRNA-MALAT1 exerts a functional role in the cancer development as well as the drug resistance and is linked with STAT3 activation and activity of FUT4. Moreover, STAT3-mediated induction of P-GP is well-documented. Natural compounds of Sesquiterpene Lactone (SL) family are well-known for their anticancer properties with particular emphasis over STAT3 inhibitory capabilities. In this study, we explored the positive correlation of MALAT1 with STAT3 and FUT4 activity in paclitaxel resistant A549 (A549/T) lung cancer cells. Additionally, we investigated the anticancer activity of two well-known members of SLs, alantolactone (ALT) and Brevilin A (Brv-A), in A549/T lung cancer cells. ALT and Brv-A induced apoptosis in A549/T cells. Furthermore, these two natural SLs suppressed MALAT1 expression, STAT3 activation, and FUT4 and P-GP expression which are the hallmarks for paclitaxel resistance in A549 lung cancer cells. The inhibition of MALAT1 enhanced the competence of these SLs members significantly, which accounted for the growth inhibition as well as anti-migratory and anti-invasive effects of ALT and Brv-A. These findings suggest SLs to be the promising agents for overcoming paclitaxel resistance in A549 lung cancer cells.

In our previous study, several iron-related outer membrane proteins in Aeromonas hydrophila, a serious pathogen of farmed fish, conferred high immunoprotectivity to fish, and were proposed as potential vaccine candidates. However, the protective efficacy of these extracellular proteins against A. hydrophila remains largely unknown. Here, we identified secreted proteins that were differentially expressed in A. hydrophila LP-2 in response to iron starvation using an iTRAQ-based quantitative proteomics method. We identified 341 proteins, of which 9 were upregulated in response to iron starvation and 24 were downregulated. Many of the differently expressed proteins were associated with protease activity. We confirmed our proteomics results with Western blotting and qPCR. We constructed three mutants by knocking out three genes encoding differentially expressed proteins (Δorf01830, Δorf01609, and Δorf03641). The physiological characteristics of these mutants were investigated. In all these mutant strains, protease activity decreased, and Δorf01609, and Δorf01830 were less virulent in zebrafish. This indicated that the proteins encoded by these genes may play important roles in bacterial infection. We next evaluated the immune response provoked by the six iron-related recombinant proteins (ORF01609, ORF01830, ORF01839, ORF02943, ORF03355, and ORF03641) in zebrafish as well as the immunization efficacy of these proteins. Immunization with these proteins significantly increased the zebrafish immune response. In addition, the relative percent survival (RPS) of the immunized zebrafish was 50–80% when challenged with three virulent A. hydrophila strains, respectively. Thus, these extracellular secreted proteins might be effective vaccine candidates against A. hydrophila infection in fish.

Metabolite-enabled killing of antibiotic-resistant pathogens by antibiotics is an attractive strategy to manage antibiotic resistance. Our previous study demonstrated that alanine or/and glucose increased the killing efficacy of kanamycin on antibiotic-resistant bacteria, whose action is through up-regulating TCA cycle, increasing proton motive force and enhancing antibiotic uptake. Despite the fact that alanine altered several metabolic pathways, other mechanisms could be potentially involved in alanine-mediated kanamycin killing of bacteria which remains to be explored. In the present study, we adopted proteomic approach to analyze the proteome changes induced by exogenous alanine. Our results revealed that the expression of three outer membrane proteins was altered and the deletion of nagE and fadL decreased the intracellular kanamycin concentration, implying their possible roles in mediating kanamycin transport. More importantly, the integrated analysis of proteomic and metabolomic data pointed out that alanine metabolism could connect to riboflavin metabolism that provides the source for reactive oxygen species (ROS) production. Functional studies confirmed that alanine treatment together with kanamycin could promote ROS production that in turn potentiates the killing of antibiotic-resistant bacteria. Further investigation showed that alanine repressed the transcription of antioxidant-encoding genes, and alanine metabolism to riboflavin metabolism connected with riboflavin metabolism through TCA cycle, glucogenesis pathway and pentose phosphate pathway. Our results suggest a novel mechanism by which alanine facilitates kanamycin killing of antibiotic-resistant bacteria via promoting ROS production.

The rapid growth of vascular smooth muscle cells (VSMCs) represents crucial pathological changes during the development of hypertensive vascular remodeling. Although quercetin exhibits significantly therapeutic effects on antihypertension, the systematic role of quercetin and its exact mode of action in relation to the VSMCs growth and its hypertension-related networking pharmacology is not well-documented. Therefore, the effect of quercetin was investigated using networking pharmacology followed by in vitro strategies to explore its efficacy against angiotensin II (Ang II)-induced cell proliferation. Putative genes of hypertension and quercetin were collected using database mining, and their correlation was investigated. Subsequently, a network of protein-protein interactions was constructed and gene ontology (GO) analysis was performed to identify the role of important genes (including CCND1) and key signaling pathways [including cell proliferation and Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) pathway]. We therefore further investigated the effects of quercetin in Ang II-stimulated VSMCs. This current research revealed that quercetin significantly reduced the cell confluency, cell number, and cell viability, as well as expression of proliferating cell nuclear antigen (PCNA) in Ang II-stimulated VSMCs. Mechanistic study by western blotting confirmed that quercetin treatment attenuated the activation of JAK2 and STAT3 by reducing its phosphorylation in Ang II stimulated VSMCs. Collectively, the current study revealed the inhibitory effects of quercetin on proliferation of Ang II stimulated VSMCs,by inhibiting the activation of JAK2/STAT3 signaling might be one of underlying mechanisms. [ABSTRACT FROM AUTHOR]