Plant secondary metabolites (SMs) are not only a useful array of natural products but also an important part of plant defense system against pathogenic attacks and environmental stresses. With ...remarkable biological activities, plant SMs are increasingly used as medicine ingredients and food additives for therapeutic, aromatic and culinary purposes. Various genetic, ontogenic, morphogenetic and environmental factors can influence the biosynthesis and accumulation of SMs. According to the literature reports, for example, SMs accumulation is strongly dependent on a variety of environmental factors such as light, temperature, soil water, soil fertility and salinity, and for most plants, a change in an individual factor may alter the content of SMs even if other factors remain constant. Here, we review with emphasis how each of single factors to affect the accumulation of plant secondary metabolites, and conduct a comparative analysis of relevant natural products in the stressed and unstressed plants. Expectantly, this documentary review will outline a general picture of environmental factors responsible for fluctuation in plant SMs, provide a practical way to obtain consistent quality and high quantity of bioactive compounds in vegetation, and present some suggestions for future research and development.
The greatest challenge in the analysis of herbal components lies in their variety and complexity. Therefore, efficient analytical tools for the separation and qualitative and quantitative analysis of ...multi-components are essential. In recent years, various emerging analytical techniques have offered significant support for complicated component analysis, with breakthroughs in selectivity, sensitivity, and rapid analysis. Among these techniques, supercritical fluid chromatography (SFC) has attracted much attention because of its high column efficiency and environmental protection. SFC can be used to analyze a wide range of compounds, including non-polar and polar compounds, making it a prominent analytical platform. The applicability of SFC for the separation and determination of natural products in herbal medicines is overviewed in this article. The range of applications was expanded through the selection and optimization of stationary phases and mobile phases. We also focus on the two-dimensional SFC analysis. This paper provides new insight into SFC method development for herbal medicine analysis.
N6‐methyladenosine (m6A) modification regulatory proteins are involved in the development of many types of cancer. KIAA1429 serves as a scaffold in bridging the catalytic core components of the m6A ...methyltransferase complex. The role of KIAA1429 in gastric cancer and its related mechanism has not been reported upon. The expression of KIAA1429 was detected in human gastric cancer tissues and cell lines by quantitative real‐time polymerase chain reaction and western blot. The effects of KIAA1429 on gastric cancer proliferation were evaluated by cell counting kit assays, colony formation assays, flow cytometry assay, and in vivo experiments with nude mice. And messenger RNA (mRNA) high‐throughput sequencing, RNA immunoprecipitation assay (RIP), luciferase assay, and a rescue experiment were used to identify the relationship between KIAA1429 and its specific targeted gene, c‐Jun. We found that KIAA1429 was upregulated in gastric cancer tissues, and expressed lower in adjacent tissues. The upregulated KIAA1429 promoted proliferation and downregulated KIAA1429 was proved to inhibit proliferation of gastric cancer in vitro and in vivo. Then, we identified the potential KIAA1429 regulating gene as c‐Jun by mRNAs high‐throughput sequencing and RIP assay. By luciferase assay, we verified that KIAA1429 regulated the expression of c‐Jun in an m6A‐independent manner. Finally, the overexpression of c‐Jun rescued the inhibition of proliferation caused by KIAA1429 knockdown in gastric cancer cells. KIAA1429 could act as an oncogene in gastric cancer by stabilizing c‐Jun mRNA in an m6A‐independent manner. This highlights the functional role for KIAA1429 as a potential prognostic biomarker and therapeutic target in gastric cancer.
In this study, we provided in vitro and in vivo evidence that KIAA1429 played a key role in promoting gastric cancer by regulating c‐Jun expression in an m6A independent manner. This finding has the potential to develop a new therapeutic target for the treatment of gastric cancer.
A strategy combining collision cross section(CCS) prediction and quantitative structure-retention relationship(QSRR) model for quinoline and isoquinoline alkaloids was established based on ...UHPLC-IM-Q-TOF-MS and applied to Phellodendri Chinensis Cortex and Phellodendri Amurensis Cortex. The strategy included the following three steps.(1) The molecular features were extracted by the "find features" algorithm.(2) The potential quinoline and isoquinoline alkaloids were screened by filtering the original characteristic ions extracted from Phellodendri Chinensis Cortex and Phellodendri Amurensis Cortex by the established CCS vs m/z prediction interval.(3) According to the retention time of candidate compounds predicted by QSRR model, the chemical constituents were identified in combination with the characteristic fragment ions and pyrolysis law of secondary mass spectrometry. With the strategy, a total of 80 compounds were predicted, and 15 were identified accurately. The strategy is effective for the identificatio
The novel coronavirus disease (COVID-19) pandemic remains a global public health crisis, presenting a broad range of challenges. To help address some of the main problems, the scientific community ...has designed vaccines, diagnostic tools and therapeutics for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The rapid pace of technology development, especially with regard to vaccines, represents a stunning and historic scientific achievement. Nevertheless, many challenges remain to be overcome, such as improving vaccine and drug treatment efficacies for emergent mutant strains of SARS-CoV-2. Outbreaks of more infectious variants continue to diminish the utility of available vaccines and drugs. Thus, the effectiveness of vaccines and drugs against the most current variants is a primary consideration in the continual analyses of clinical data that supports updated regulatory decisions. The first two vaccines granted Emergency Use Authorizations (EUAs), BNT162b2 and mRNA-1273, still show more than 60% protection efficacy against the most widespread current SARS-CoV-2 variant, Omicron. This variant carries more than 30 mutations in the spike protein, which has largely abrogated the neutralizing effects of therapeutic antibodies. Fortunately, some neutralizing antibodies and antiviral COVID-19 drugs treatments have shown continued clinical benefits. In this review, we provide a framework for understanding the ongoing development efforts for different types of vaccines and therapeutics, including small molecule and antibody drugs. The ripple effects of newly emergent variants, including updates to vaccines and drug repurposing efforts, are summarized. In addition, we summarize the clinical trials supporting the development and distribution of vaccines, small molecule drugs, and therapeutic antibodies with broad-spectrum activity against SARS-CoV-2 strains.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Ferroptosis is one of the critical pathological events in spinal cord injury. Erythropoietin has been reported to improve the recovery of spinal cord injury. However, whether ferroptosis is involved ...in the neuroprotective effects of erythropoietin on spinal cord injury has not been examined. In this study, we established rat models of spinal cord injury by modified Allen's method and intraperitoneally administered 1000 and 5000 IU/kg erythropoietin once a week for 2 successive weeks. Both low and high doses of erythropoietin promoted recovery of hindlimb function, and the high dose of erythropoietin led to better outcome. High dose of erythropoietin exhibited a stronger suppressive effect on ferroptosis relative to the low dose of erythropoietin. The effects of erythropoietin on inhibiting ferroptosis-related protein expression and restoring mitochondrial morphology were similar to those of Fer-1 (a ferroptosis suppressor), and the effects of erythropoietin were largely diminished by RSL3 (ferroptosis activator). In vitro experiments showed that erythropoietin inhibited RSL3-induced ferroptosis in PC12 cells and increased the expression of xCT and Gpx4. This suggests that xCT and Gpx4 are involved in the neuroprotective effects of erythropoietin on spinal cord injury. Our findings reveal the underlying anti-ferroptosis role of erythropoietin and provide a potential therapeutic strategy for treating spinal cord injury.
Imagine having a knowledge graph that can extract medical health knowledge related to patient diagnosis solutions and treatments from thousands of research papers, distilled using machine learning ...techniques in healthcare applications. Medical doctors can quickly determine treatments and medications for urgent patients, while researchers can discover innovative treatments for existing and unknown diseases. This would be incredible! Our approach serves as an all-in-one solution, enabling users to employ a unified design methodology for creating their own knowledge graphs. Our rigorous validation process involves multiple stages of refinement, ensuring that the resulting answers are of the utmost professionalism and solidity, surpassing the capabilities of other solutions. However, building a high-quality knowledge graph from scratch, with complete triplets consisting of subject entities, relations, and object entities, is a complex and important task that requires a systematic approach. To address this, we have developed a comprehensive design flow for knowledge graph development and a high-quality entities database. We also developed knowledge distillation schemes that allow you to input a keyword (entity) and display all related entities and relations. Our proprietary methodology, multiple levels refinement (MLR), is a novel approach to constructing knowledge graphs and refining entities level-by-level. This ensures the generation of high-quality triplets and a readable knowledge graph through keyword searching. We have generated multiple knowledge graphs and developed a scheme to find the corresponding inputs and outputs of entity linking. Entities with multiple inputs and outputs are referred to as joints, and we have created a joint-version knowledge graph based on this. Additionally, we developed an interactive knowledge graph, providing a user-friendly environment for medical professionals to explore entities related to existing or unknown treatments/diseases. Finally, we have advanced knowledge distillation techniques.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•Diabetic retinopathy (DR) is one of the leading causes of blindness globally. Detecting and treating DR at an earlier stage is desirable to reduce the incidence and progression of visual loss.•A ...systematic review with a meta-analysis of relevant studies was performed to quantify the performance of DL algorithms to detect DR.•The pooled area under the receiving operating curve (AUROC) of DR was 0.97 (95%CI: 0.95–0.98), sensitivity was 0.83 (95%CI: 0.83–0.83), and specificity was 0.92 (95%CI: 0.92–0.92).•The findings of our study showed that DL-algorithms had high sensitivity and specificity for detecting referable DR from retinal fundus photographs.
Diabetic retinopathy (DR) is one of the leading causes of blindness globally. Earlier detection and timely treatment of DR are desirable to reduce the incidence and progression of vision loss. Currently, deep learning (DL) approaches have offered better performance in detecting DR from retinal fundus images. We, therefore, performed a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms for detecting DR.
A systematic literature search on EMBASE, PubMed, Google Scholar, Scopus was performed between January 1, 2000, and March 31, 2019. The search strategy was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, and DL-based study design was mandatory for articles inclusion. Two independent authors screened abstracts and titles against inclusion and exclusion criteria. Data were extracted by two authors independently using a standard form and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used for the risk of bias and applicability assessment.
Twenty-three studies were included in the systematic review; 20 studies met inclusion criteria for the meta-analysis. The pooled area under the receiving operating curve (AUROC) of DR was 0.97 (95%CI: 0.95–0.98), sensitivity was 0.83 (95%CI: 0.83–0.83), and specificity was 0.92 (95%CI: 0.92–0.92). The positive- and negative-likelihood ratio were 14.11 (95%CI: 9.91–20.07), and 0.10 (95%CI: 0.07–0.16), respectively. Moreover, the diagnostic odds ratio for DL models was 136.83 (95%CI: 79.03–236.93). All the studies provided a DR-grading scale, a human grader (e.g. trained caregivers, ophthalmologists) as a reference standard.
The findings of our study showed that DL algorithms had high sensitivity and specificity for detecting referable DR from retinal fundus photographs. Applying a DL-based automated tool of assessing DR from color fundus images could provide an alternative solution to reduce misdiagnosis and improve workflow. A DL-based automated tool offers substantial benefits to reduce screening costs, accessibility to healthcare and ameliorate earlier treatments.
Normalized difference vegetation index (NDVI) is one of the most important vegetation indices in crop remote sensing. It features a simple, fast, and non-destructive method and has been widely used ...in remote monitoring of crop growing status. Beer-Lambert law is widely used in calculating crop leaf area index (LAI), however, it is time-consuming detection and low in output. Our objective was to improve the accuracy of monitoring LAI through remote sensing by integrating NDVI and Beer-Lambert law. In this study, the Beer-Lambert law was firstly modified to construct a monitoring model with NDVI as the independent variable. Secondly, experimental data of wheat from different years and various plant types (erectophile, planophile and middle types) was used to validate the modified model. The results showed that at 130 DAS (days after sowing), the differences in NDVI, leaf area index (LAI) and extinction coefficient (k) of the three plant types with significantly different leaf orientation values (LOVs) reached the maximum. The NDVI of the planophile-type wheat reached saturation earlier than that of the middle and erectophile types. The undetermined parameters of the model (LAI = -ln (a
× NDVI + b
)/(a
× NDVI + b
)) were related to the plant type of wheat. For the erectophile-type cultivars (LOV ≥ 60°), the parameters for the modified model were, a
= 0.306, a
= -0.534, b
= -0.065, and b
= 0.541. For the middle-type cultivars (30° < LOV < 60°), the parameters were, a
= 0.392, a
= -0.88
, b
= 0.028, and b
= 0.845. And for the planophile-type cultivars (LOV ≤ 30°), those parameters were, a
= 0.596, a
= -1.306, b
= 0.014, and b
= 1.130. Verification proved that the modified model based on integrating NDVI and Beer-Lambert law was better than Beer-Lambert law model only or NDVI-LAI direct model only. It was feasible to quantitatively monitor the LAI of different plant-type wheat by integrating NDVI and Beer-Lambert law, especially for erectophile-type wheat (R
= 0.905, RMSE = 0.36, RE = 0.10). The monitoring model proposed in this study can accurately reflect the dynamic changes of plant canopy structure parameters, and provides a novel method for determining plant LAI.
Remote sensing has been used as an important means of estimating crop production, especially for the estimation of crop yield in the middle and late growth period. In order to further improve the ...accuracy of estimating winter wheat yield through remote sensing, this study analyzed the quantitative relationship between satellite remote sensing variables obtained from HJ-CCD images and the winter wheat yield, and used the partial least square (PLS) algorithm to construct and validate the multivariate remote sensing models of estimating the yield. The research showed a close relationship between yield and most remote sensing variables. Significant multiple correlations were also recorded between most remote sensing variables. The optimal principal components numbers of PLS models used to estimate yield were 4. Green normalized difference vegetation index (GNDVI), optimized soil-adjusted vegetation index (OSAVI), normalized difference vegetation index (NDVI) and plant senescence reflectance index (PSRI) were sensitive variables for yield remote sensing estimation. Through model development and model validation evaluation, the yield estimation model's coefficients of determination (R
) were 0.81 and 0.74 respectively. The root mean square error (RMSE) were 693.9 kg ha
and 786.5 kg ha
. It showed that the PLS algorithm model estimates the yield better than the linear regression (LR) and principal components analysis (PCA) algorithms. The estimation accuracy was improved by more than 20% than the LR algorithm, and was 13% higher than the PCA algorithm. The results could provide an effective way to improve the estimation accuracy of winter wheat yield by remote sensing, and was conducive to large-area application and promotion.
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