Recent Advancements and Unexplored Biomedical Applications of Green Synthesized Ag and Au Nanoparticles: A Review.

Green synthesis of silver (Ag) and gold (Au) nanoparticles (NPs) has acquired huge popularity owing to their potential applications in various fields. A large number of research articles exist in the literature describing the green synthesis of Ag and Au NPs for biomedical applications. However, these findings are scattered, making it time-consuming for researchers to locate promising advancements in Ag and Au NPs synthesis and their unexplored biomedical applications. Unlike other review articles, this systematic study not only highlights recent advancements in the green synthesis of Ag and Au NPs but also explores their potential unexplored biomedical applications. The article discusses the various synthesis approaches for the green synthesis of Ag and Au NPs highlighting the emerging developments and novel strategies. Then, the article reviews the important biomedical applications of green synthesized Ag and Au NPs by critically evaluating the expected advantages. To expose future research direction in the field, the article describes the unexplored biomedical applications of the NPs. Finally, the articles discuss the challenges and limitations in the green synthesis of Ag and Au NPs and their biomedical applications. This article will serve as a valuable reference for researchers, working on green synthesis of Ag and Au NPs for biomedical applications.

Hyperspectral imaging combined with blood oxygen saturation for in vivo analysis of small intestinal necrosis tissue.

Acute mesenteric ischemia (AMI) is a clinically significant vascular and gastrointestinal condition, which is closely related to the blood supply of the small intestine. Unfortunately, it is still challenging to properly discriminate small intestinal tissues with different degrees of ischemia. In this study, hyperspectral imaging (HSI) was used to construct pseudo-color images of oxygen saturation about small intestinal tissues and to discriminate different degrees of ischemia. First, several small intestine tissue models of New Zealand white rabbits were prepared and collected their hyperspectral data. Then, a set of isosbestic points were used to linearly transform the measurement data twice to match the reference spectra of oxyhemoglobin and deoxyhemoglobin, respectively. The oxygen saturation was measured at the characteristic peak band of oxyhemoglobin (560 nm). Ultimately, using the oxygenated hemoglobin reflectance spectrum as the benchmark, we obtained the relative amount of median oxygen saturation in normal tissues was 70.0 %, the IQR was 10.1 %, the relative amount of median oxygen saturation in ischemic tissues was 49.6 %, and the IQR was 14.6 %. The results demonstrate that HSI combined with the oxygen saturation computation method can efficiently differentiate between normal and ischemic regions of the small intestinal tissues. This technique provides a powerful support for internist to discriminate small bowel tissues with different degrees of ischemia, and also provides a new way of thinking for the diagnosis of AMI.

Calibration transfer via filter learning.

BACKGROUND: Calibration transfer is an essential activity in analytical chemistry in order to avoid a complete recalibration. Currently, the most popular calibration transfer methods, such as piecewise direct standardization and dynamic orthogonal projection, require a certain amount of standard or reference samples to guarantee their effectiveness. To achieve higher efficiency, it is desirable to perform the transfer with as few reference samples as possible. RESULTS: To this end, we propose a new calibration transfer method by using a calibration database from a master instrument (source domain) and only one spectrum with known properties from a slave instrument (target domain). We first generate a counterpart of this spectrum in the source domain by a multivariate Gaussian kernel. Then, we train a filter to make the response function of the slave instrument equivalent to that of the master instrument. To avoid the need for labels from the target domain, we also propose an unsupervised way to implement our method. Compared with several state-of-the-art methods, the results on one simulated dataset and two real-world datasets demonstrate the effectiveness of our method. SIGNIFICANCE: Traditionally, the demand for certain amounts of reference samples during calibration transfer is cumbersome. Our approach, which requires only one reference sample, makes the transfer process simple and fast. In addition, we provide an alternative for performing unsupervised calibration transfer. As such, the proposed method is a promising tool for calibration transfer.

Identification of near-infrared characteristic bands of small bowel necrosis based on cellwise detection algorithm.

The near-infrared spectroscopy is often used to distinguish small bowel necrosis due to necrotizing enterocolitis (NEC). The characteristic bands of small bowel necrosis, as an important basis for evaluating the confidence of the differentiation results, are challenging to identify quickly. In this study, we proposed to identify characteristic bands of lesion samples based on hyperspectral imaging (HSI) and cellwise outlier detection. Rabbits were used as an animal model to simulate the clinical symptoms of NEC. The rabbits were detected at intervals of 10, 30, 60, and 90 min. The characteristic bands were identified within the same rabbit, between different rabbits and at different times. The result showed the bands near 763 nm, corresponding to the absorption peak of deoxyhemoglobin, were the characteristic bands separating samples with NEC. The identification result was plausible because hypoxia was the main cause of NEC. The method was easy to perform.

Ag@Au core-shell nanoparticle-based surface-enhanced Raman scattering coupled with chemometrics for rapid determination of chloramphenicol residue in fish.

The alarming increase in drug-resistant bacteria in fish resulting from the misuse of antibiotics poses a significant threat to ecosystems and human health. Therefore, the development of a reliable approach for detecting antibiotic residues in fish is crucial. In this study, a rapid and simple method for detecting chloramphenicol (CAP) residue in tilapia was developed using surface-enhanced Raman scattering (SERS) combined with chemometric algorithms. Silver and gold core-shell nanoparticles (Ag@Au CSNPs) were used as SERS nanosensors to achieve strong signal amplification with an enhancement factor of 2.67 × 106. The results demonstrated that the variable combination population analysis-partial least square (VCPA-PLS) model combined with the standard normal variable transformation pretreatment method exhibited the best predictive performance with a detection limit of 1 × 10-5 µg/mL. Thus, an SERS technique was established based on Ag@Au CSNPs combined with VCPA-PLS to rapidly detect CAP in tilapia.

Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning.

Acquiring large amounts of hyperspectral data of small intestinal tissue with real labels in the clinic is difficult, and the data shows inter-patient variability. Building an automatic identification model using a small dataset presents a crucial challenge in obtaining a strong generalization of the model. This study aimed to explore the performance of hyperspectral imaging and transfer learning techniques in the automatic identification of normal and ischemic necrotic sites in small intestinal tissue. Hyperspectral data of small intestinal tissues were collected from eight white rabbit samples. The transfer component analysis (TCA) method was performed to transfer learning on hyperspectral data between different samples and the variability of data distribution between samples was reduced. The results showed that the TCA transfer learning method improved the accuracy of the classification model with less training data. This study provided a reliable method for single-sample modelling to detect necrotic sites in small intestinal tissue .

ZnO@Ag-Functionalized Paper-Based Microarray Chip for SERS Detection of Bacteria and Antibacterial and Photocatalytic Inactivation.

Bacterial infections caused by pathogenic microorganisms have become a serious, widespread health concern. Thus, it is essential and required to develop a multifunctional platform that can rapidly and accurately determine bacteria and effectively inhibit or inactivate pathogens. Herein, a microarray SERS chip was successfully synthesized using novel metal/semiconductor composites (ZnO@Ag)-ZnO nanoflowers (ZnO NFs) decorated with Ag nanoparticles (Ag NPs) arrayed on a paper-based chip as a supporting substrate for in situ monitoring and photocatalytic inactivation of pathogenic bacteria. Typical Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli and Vibrio parahemolyticus were selected as models. Partial least-squares discriminant analysis (PLS-DA) was performed to minimize the dimensionality of SERS spectra data sets and to develop a cost-effective identification model. The classification accuracy was 100, 97.2, and 100% for S. aureus, E. coli, and V. parahemolyticus, respectively. The antimicrobial activity of ZnO@Ag was proved by the microbroth dilution method, and the minimum inhibitory concentrations (MICs) of S. aureus, E. coli, and V. parahemolyticus were 40, 50, and 55 µg/mL, respectively. Meanwhile, it demonstrated remarkable photocatalytic performance under natural sunlight for the inactivation of pathogenic bacteria, and the inactivation rates for S. aureus, E. coli, and V. parahemolyticus were 100, 97.03 and 97.56%, respectively. As a result, the microarray chip not only detected the bacteria with high sensitivity but also confirmed the antibacterial and photocatalytic sterilization properties. Consequently, it offers highly prospective strategies for foodborne diseases caused by pathogenic bacteria.

A robust method to improve the regression accuracy of LIBS data: determination of heavy metal Cu in Tegillarca granosa.

Tegillarca granosa (T. granosa) is susceptible to contamination by heavy metals, which poses potential health risks for consumers. Laser-induced breakdown spectroscopy (LIBS) combined with the classical partial least squares (PLS) model has shown promise in determining heavy metal concentrations in T. granosa. However, the presence of outliers during calibration can compromise the model's integrity and diminish its predictive capabilities. To address this issue, we propose using a robust method for partial least squares, RSIMPLS, to improve the accuracy of Cu prediction in T. granosa. The RSIMPLS algorithm was employed to analyze and process the high-dimensional LIBS data and utilized diagnostic plots to identify various types of outliers. By selectively eliminating certain outliers, a robust calibration method was achieved. The results showed that LIBS spectroscopy has the potential to predict Cu in T. granosa, with a coefficient of determination (Rp2) of 0.79 and a root mean square error of prediction (RMSEP) of 11.28. RSIMPLS significantly improved the prediction accuracy of Cu concentrations with a 43% decrease in RMSEP compared to the PLS. These findings validated the effectiveness of combining LIBS data with the RSIMPLS algorithm for the prediction of Cu concentrations in T. granosa.

Antimicrobial efficacy of Mentha piperata-derived biogenic zinc oxide nanoparticles against UTI-resistant pathogens.

Misuse of antibiotics leads to the worldwide spread of antibiotic resistance, which motivates scientists to create new antibiotics. The recurring UTI due to antibiotics-resistant microorganism's challenges scientists globally. The biogenic nanoparticles have the potential to meet the escalating requirements of novel antimicrobial agents. The green synthesis of nanoparticles (NPs) gained more attention due to their reliable applications against resistant microbes. The current study evaluates the biogenic ZnO NPs of Mentha piperata extract against resistant pathogens of urinary tract infections by agar well diffusion assay. The biogenic ZnO NPs revealed comparatively maximum inhibition in comparison to synthetic antibiotics against two bacterial strains (Proteus mirabilis, Pseudomonas aeruginosa) and a fungal strain (Candida albicans).The synthesized biogenic ZnO NPs alone revealed maximum activities than the combination of plant extract (PE) and ZnO NPs, and PE alone. The physiochemical features of ZnO NPs characterized through UV-Vis spectroscopy, FTIR, XRD, SEM, and EDX. The UV-Vis spectroscopy revealed 281.85 nm wavelengths; the XRD pattern revealed the crystalline structure of ZnO NPs. The FTIR analysis revealed the presence of carboxylic and nitro groups, which could be attributed to plant extract. SEM analysis revealed spherical hollow symmetry due to electrostatic forces. The analysis via EDX confirmed the presence of Zn and oxygen in the sample. The physiochemical features of synthesized ZnO NPs provide pivotal information such as quality and effectiveness. The current study revealed excellent dose-dependent antimicrobial activity against the pathogenic isolates from UTI-resistant patients. The higher concentration of ZnONPs interacts with the cell membrane which triggers oxidative burst. They may bind with the enzymes and proteins and brings epigenetic alteration which leads to membrane disruption or cell death.

Facile synthesis of fluorescence-SERS dual-probe nanocomposites for ultrasensitive detection of sulfur-containing gases in water and beer samples.

A highly structured fluorescent-SERS dual-probe nanocomposites were synthesized for the determination of sulfur-containing gases in water and beer samples. Initially, Au@Ag NPs were prepared by growing the Ag shell on the Au core in situ, modified with surfactant and fabricated with Zn2+. Then, MOF-5-NH2 assembled Au@Ag NPs were obtained through coordination between Zn sites and 2-aminoterephthalic acid. The principle was based on redox reaction between H2S and Au@Ag NPs, and the fluorescence turn-on effects were due to the charge transfer between SO2 and amino groups. The SERS intensity was related to the concentration of H2S (5 âˆ¼ 60 nM), and an ultra-low detection limit of 2.26 nM was achieved. Importantly, the fluorescence performance was applied for SO2 analysis and exhibited good linear response. Moreover, the platform for H2S and SO2 in real samples revealed satisfactory results (95.6 âˆ¼ 101.6% and 99.0 âˆ¼ 104.4%). Therefore, the proposed system offered a precise detection of H2S/SO2 in food/environmental settings.

Advances in surface-enhanced Raman spectroscopy technology for detection of foodborne pathogens.

Rapid control and prevention of diseases caused by foodborne pathogens is one of the existing food safety regulatory issues faced by various countries and has received wide attention from all sectors of society. The development of rapid and reliable detection methods for foodborne pathogens remains a hot research area for food safety and public health because of the limitations of complex steps, time-consuming, low sensitivity, or poor selectivity of commonly used methods. Surface-enhanced Raman spectroscopy (SERS), as a novel spectroscopic technique, has the advantages of high sensitivity, selectivity, rapid and nondestructive detection and has exhibited broad application prospects in the determination of pathogenic bacteria. In this study, the enhancement mechanisms of SERS are briefly introduced, then the characteristics and properties of liquid-phase, rigid solid-phase, and flexible solid-phase are categorized. Furthermore, a comprehensive review of the advances in label-free or label-based SERS strategies and SERS-compatible techniques for the detection of foodborne pathogens is provided, and the advantages and disadvantages of these methods are reviewed. Finally, the current challenges of SERS technology applied in practical applications are listed, and the possible development trends of SERS in the field of foodborne pathogens detection in the future are discussed.

Simultaneous quantitation of free fatty acid in rice by synergetic data fusion of colorimetric sensor arrays, NIR, and MIR spectroscopy.

This study evaluated the feasibility of colorimetric sensor array (CSA), near-infrared (NIR) and mid-infrared (MIR) spectroscopy for quantitation of free fatty acids in rice using data fusion. Purposely, different data sets of low-level (CSA-NIRLL, CSA-MIRLL, and NIR-MIRLL) and mid-level (CSA-NIRML, CSA-MIRML, and NIR-MIRML) fusion were adopted to enhance the statistical parameters. The model performance was evaluated using coefficient of determination for prediction, (R2p), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). Synergetic low-level and mid-level fusion model yielded 0.7707 ≤ R2p ≤ 0.8275, 14.4 ≤ RMSEP ≤ 16.3 and 2.19 ≤ RPD ≤ 2.48; and 0.7788 ≤ R2p ≤ 0.8571, 12.4 ≤ RMSEP ≤ 16.8 and 2.12 ≤ RPD ≤ 2.88, respectively. The CSA-NIRML model delivered an optimal performance for prediction of free fatty acid. The integration of CSA, NIR and MIR was feasible and could improve the prediction accuracy of free fatty acids in rice.

Recent Advances in Silver and Gold Nanoparticles-Based Colorimetric Sensors for Heavy Metal Ions Detection: A Review.

Silvetr and gold nanoparticles-based colorimetric sensors (Ag/Au-NPs-CSns) allow potential prospects for the development of efficient sensors owing to their unique shape- and size-dependent optical properties. In this review, recent (2020) advances in morphology-controllable synthesis, shape/size-dependent performance, sensing mechanism, challenges and prospects of Ag/Au-NPs-CSns for the detection of heavy metals are discussed. The size/shape-controlled synthesis of innovative Ag/Au-NPs-CSns is reviewed critically and the possible role of different parameters like temperature, time, pH, stabilizing/capping agents, reducing agents and concentration/nature of precursors are discussed. Then, we highlighted how the shape, size, optimum composition, functionalization, stabilizing/capping agents, surface structure and synergism influence the optical properties and sensing efficiency of Ag/Au-NPs-CSns. This review attempted to accentuate the efficiency of novel multimetallic Ag/AuNPs-CSns as compare to their monometallic counterparts and explained how the incorporation of multi-metals affects their performance. Besides, the sensing mechanisms of Ag/Au-NPs-CSns with special reference to recently published studies are discussed. Finally, challenges and prospects in the controllable-synthesis and practical applications of these sensors are studied. This mechanistic approach and timely review can be promisingly considered as a valuable reference and will help fuel new ideas for the development of novel colorimetric sensors. HighlightsA review of recent advances in Ag/Au-NPs-CSns for heavy metal ions detectionMorphology of Ag/Au-NPs-CSns regulate their optical properties/sensing efficiencyPromising Ag/Au-NPs-CSns can be synthesized by adjusting experimental parametersHybrid and functionalized Ag/Au-NPs-CSns have superior sensing performanceSize/shape transformation, aggregation/anti- and oxidation are sensing mechanisms.

Dispersive micro solid phase extraction based ionic liquid functionalized ZnO nanoflowers couple with chromatographic methods for rapid determination of aflatoxins in wheat and peanut samples.

Aflatoxins (AFs) contaminate agricultural products in a wide range of ways during their harvesting, storage and transport. Therefore, the detection of AFs has certain practical significance. Herein, a dispersive micro solid phase extraction (D-µSPE) technology was constructed based on 1-hexyl-3-methylimidazolium hexafluorophosphate ([HMIM][PF6]) fabricated ZnO nanoflowers for AFs extraction from food matrix before HPLC procedure. The key parameters affecting the extraction efficiency were studied. Under optimal experimental conditions, the method showed excellent linearity with high correlation coefficients (≥0.994). LOD and LOQ were 0.034 and 0.114 µg/kg for AFB1, 0.024 and 0.082 µg/kg for AFB2, 0.067 and 0.226 µg/kg for AFG1 and 0.025 and 0.084 µg/kg for AFG2. The recovery of actual samples spiked with analytes (at 5, 15 and 20 µg/kg) were from 93.8 to 105.1%. Overall, an accurate AFs analysis method was developed and could be applied to the determination of AFs in various food and agricultural products.

Catalytic hairpin activated gold-magnetic/gold-core-silver-shell rapid self-assembly for ultrasensitive Staphylococcus aureus sensing via PDMS-based SERS platform.

Staphylococcus aureus (S. aureus) has been identified as a marker of food contamination, closely associated with human health. This work designs a sensitive and rapid bio-detection strategy for S. aureus based on hybridization chain reaction-assisted surface enhanced Raman scattering (HCR-assisted-SERS) signal amplification. In this approach, the interaction between the aptamer (Apt) and its partial complementary DNA strands (cDNA) fabricated on the surface of gold-assisted magnetic nanoparticles (Au-MNPs) and the subsequent detachment of the cDNA results in the activation of the HCR process. In the HCR, a pair of hairpin structured DNA probes (H1 and H2) with sticky ends self-assembles to form a long DNA polymer. Subsequently, the output and amplification of the SERS signal were performed by conjugating 4-ATP modified Au@Ag NPs with the obtained DNA polymer via a specific Ag-S bond, and further collected through a self-administered polydimethylsiloxane (PDMS) cone-shaped support array. The precise quantification of S. aureus was performed in the concentration range of 28 to 2.8 × 106 cfu/mL, achieving a detection limit of 0.25 cfu/mL. This strategy was further applied to S. aureus detection in spiked milk samples with good recoveries (91-102%) and the relative standard deviation (4.35-8.41%). The sensing platform also showed satisfactory validation results (p > 0.05) using the traditional plate counting method. The proposed HCR-assisted SERS probe can be extended to other foodborne pathogenic bacteria types via engineering appropriate Apt and DNA initiators, thus, inspiring widespread applications in food safety and biomedical research.

Recent Progress in Nanoparticles Based Sensors for the Detection of Mercury (II) Ions in Environmental and Biological Samples.

To maintain a green and sustainable environment for human beings, rapid detection of potentially toxic heavy metals like mercury (Hg(II)) has attracted great attention. Recently, sensors have been designed which can selectively detect Hg(II) over other common available cations and give a naked eye or fluorometric response. In the last two decades, the trend is shifting from bulky organic chemosensors toward nanoparticles due to their rapid response, low cost, eco-friendly and easy synthesis. In this review, promising nanoparticles-based sensors for Hg(II) detection are discussed. The nano-sensors are functionalized with nucleotide or other suitable materials which coordinate with Hg(II) ions and give clear color or fluorescence change. The operational mechanisms are discussed focusing on its four basic types. The nanoparticles-based sensors are even able to detect Hg in three different oxidation states (Hg(II), Hg(I) and Hg(0)). Recently, the trend has been shifted from ordinary nanoparticles to magnetic nanoparticles to simultaneously detect and remove Hg(II) ions from environmental samples. Furthermore, the nano-sensors for Hg(II) are compared with each other and with the reported organic chemosensors.

An Up-conversion signal probe-MnO2 nanosheet sensor for rapid and sensitive detection of tetracycline in food.

The irrational use of tetracycline (TC) poses a serious threat to human health, which calls for the development of efficient and reliable detection methods. Herein, an ideal sensor based on luminescence resonance energy transfer (LRET) between aptamer modified up-conversion nanoparticles as signal probes (donors) and manganese dioxide (MnO2) nanosheets (acceptors) was developed for TC detection in food samples. As a result of van der Waals forces between the nucleobases of the aptamer and the basal plane of MnO2 nanosheets, the distance of the donors and acceptors was shortened. The emission spectrum of the signal probes and the absorption spectrum of MnO2 nanosheets overlapped, resulting in LRET, and quenching of up-conversion luminescence. The TC-specific aptamer could fold into a complex conformational structure to provide recognition sites for TC. In the presence of TC, the aptamer was found to preferentially combine with TC due to the stacking of planar moieties, hydrogen bonding interactions and molecular shape complementarity, causing the separation of signal probes and nanosheets, and luminescence recovery. Consequently, a low detection limit of 0.0085 ng/mL was achieved with a wide detection range of 0.01-100 ng/mL. Moreover, the ability of the sensor to detect TC was confirmed in actual food samples and compared with the traditional ELISA with satisfactory results (p > 0.05).

SERS-based Au@Ag NPs Solid-phase substrate combined with chemometrics for rapid discrimination of multiple foodborne pathogens.

In this study, a surface enhanced Raman scattering (SERS) sensor based on Au@Ag NPs solid-phase substrate combined with chemometrics was constructed for the discrimination of three pathogenic bacteria (Staphylococcus aureus, Escherichia coli and Listeria monocytogenes). The Au@Ag NPs were synthesized and self-assembled on filter paper using the dip-coating method. The good absorbency of the filter paper immobilized the bacteria on the substrate, increased the interaction between the bacteria and the substrate, and enhanced the SERS signal of the bacteria. The main peaks of the bacterial spectra were close to each other, but the relative intensities of the vibrational peaks were significantly different, and each strain exhibited unique Raman peaks. The combination of partial least squared discriminant analysis (PLS-DA) method with bacterial SERS allowed the effective identification of the three bacteria. Moreover, the method was applied for the quantitative detection of Staphylococcus aureus with a minimum detection concentration of 104 cfu/mL.

Characterization and source analysis of heavy metals contamination in microplastics by Laser-Induced Breakdown Spectroscopy.

The increasing presence of microplastics in marine environment is a critical issue and the plastic-metal contamination has received much attention. However, conventional methods for heavy metal determination are time-consuming, need sample pretreatments, require a strict operation environment, or have high limits of detection. In this study, heavy metals contaminated microplastics samples collected from a remote coral island were quantified and analyzed by using Laser-Induced Breakdown Spectroscopy (LIBS). The characters of the trace metals in microplastics were used to determine the sources of the contaminants, and the potential origins of the metals were demonstrated from the statistical analysis. LIBS is a facile and non-destructive trace analysis technique and the strategy led to rapid and multi-metals detection of individual samples. Heavy metals such as copper (Cu), lead (Pb), iron (Fe), cadmium (Cd), zinc (Zn), manganese (Mn), chromium (Cr) were detected and quantified in the individual microplastics samples. The findings showed that LIBS is a promising strategy for the characterization of microplastics and for the analysis of the source of heavy metals contaminants present in the microplastics particles.

Radioprotective Potential of Nutraceuticals and their Underlying Mechanism of Action.

Radiations are an efficient treatment modality in cancer therapy. Besides the treatment effects of radiations, the ionizing radiations interact with biological systems and generate reactive oxygen species that interfere with the normal cellular process. Previous investigations have been conducted only on few synthetic radioprotectors, mainly owing to some limiting effects. The nutraceuticals act as efficient radioprotectors to protect the tissues from the deleterious effects of radiation. The main radioprotection mechanism of nutraceuticals is the scavenging of free radicals while other strategies involve modulation of signaling transduction pathways like MAPK (JNK, ERK1/2, ERK5, and P38), NF-kB, cytokines, and their protein regulatory gene expression. The current review is focused on the radioprotective effects of nutraceuticals including vitamin E, -C, organosulphur compounds, phenylpropanoids, and polysaccharides. These natural entities protect against radiation-induced DNA damage. The review mainly entails the antioxidant perspective and radioprotective molecular mechanism of nutraceuticals, DNA repair pathway, anti-inflammation, immunomodulatory effects and regeneration of hematopoietic cells.