In Vivo Imaging MicroRNA with Bright Fluorescent RNA Aptamer Through Target-Mediated Entropy-Driven Toehold Exchange.

MicroRNAs (miRNAs) play vital roles in biological activities, but their in vivo imaging is still challenging due to the low abundance and the lack of efficient fluorescent tools. RNA aptamers with high affinity and low background emerge for bioimaging yet suffering from low brightness. We introduce a rational design based on target-mediated entropy-driven toehold exchange (EDTE) to induce the release of RNA aptamer and subsequently light up corresponding fluorophore, which achieves selective imaging of miRNAs with good stability in both living cells and tumor-bearing mouse. Through tailoring recognition unit of the EDTE probes, highly sensitive imaging of different miRNAs including miRNA-125b and miRNA-21 is achieved, confirming its universal bioimaging applications. In comparison with the reported "one-to-one" model, the EDTE strategy shows a remarkable 4.6-time improvement in signal/noise ratio for intracellular imaging of the same miRNA. Particularly, it realizes sensitive imaging of miRNA in vivo, providing a promising tool in investigating functions and interactions of disease-associated miRNAs.

GDF15 ameliorates sepsis-induced lung injury via AMPK-mediated inhibition of glycolysis in alveolar macrophage.

Growth differentiation factor 15 (GDF15) as a stress response cytokine is involved in the development and progression of several diseases associated with metabolic disorders. However, the regulatory role and the underlying mechanisms of GDF15 in sepsis remain poorly defined. Our study analyzed the levels of GDF15 and its correlations with the clinical prognosis of patients with sepsis. In vivo and in vitro models of sepsis were applied to elucidate the role and mechanisms of GDF15 in sepsis-associated lung injury. We observed strong correlations of plasma GDF15 levels with the levels of C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), and lactate as well as Sequential Organ Failure Assessment (SOFA) scores in patients with sepsis. In the mouse model of lipopolysaccharide-induced sepsis, recombinant GDF15 inhibited the proinflammatory responses and alleviated lung tissue injury. In addition, GDF15 decreased the levels of cytokines produced by alveolar macrophages (AMs). The anti-inflammatory effect of glycolysis inhibitor 2-DG on AMs during sepsis was mediated by GDF15 via inducing the phosphorylation of the α-subunit of eukaryotic initiation factor 2 (eIF2α) and the expression of activating transcription factor 4 (ATF4). Furthermore, we explored the mechanism underlying the beneficial effects of GDF15 and found that GDF15 inhibited glycolysis and mitogen-activated protein kinases (MAPK)/nuclear factor-κB (NF-κB) signaling via promoting AMPK phosphorylation. This study demonstrated that GDF15 inhibited glycolysis and NF-κB/MAPKs signaling via activating AMP-activated protein kinase (AMPK), thereby alleviating the inflammatory responses of AMs and sepsis-associated lung injury. Our findings provided new insights into novel therapeutic strategies for treating sepsis.

The enhancement of enzyme cascading via tetrahedral DNA framework modification.

Enzyme clustering is widely used in many organisms to increase the catalytic efficiency of cascade reactions. Inspired by nature, organizing enzymes within a cascade reaction also draws much attention in both basic research and industrial processes. An important step for organizing enzymes precisely in vitro is enzyme modification. However, modifying enzymes without sacrificing their activity remains challenging until now. For example, labeling enzymes with DNA, one of the well-established enzyme modification methods, has been shown to significantly reduce the enzymatic activity. Herein we report an enzyme conjugation method that can rescue the reduction of enzymatic activity caused by DNA labeling. We demonstrate that immobilizing DNA-modified enzymes on the vertex of TDNs (tetrahedral DNA nanostructures) enhances the enzymatic activity compared with their unmodified counterparts. Using this strategy, we have further developed an ultra-sensitive and high-throughput electrochemical biosensor for sarcosine detection, which holds great promise for prostate cancer screening.

Identifying the coupling coordination relationship between urbanization and forest ecological security and its impact mechanism: Case study of the Yangtze River Economic Belt, China.

Boosting the coordination and symbiosis of urbanization and forest ecological security is notably critical for promoting regional green and sustainable development and achieving emission peak and carbon neutrality goals. However, there was still a lack of in-depth analysis of the coupling coordination relationship between urbanization and forest ecological security and its impact mechanism. On the basis of the data from 844 counties in the Yangtze River Economic Belt, this paper explored the spatial differences and influencing factors of the coupling coordination degree of urbanization and forest ecological security. The results manifested that: i) There were apparent spatial disparities in the urbanization index, forest ecological security index, comprehensive index, coupling degree and coupling coordination degree of the Yangtze River Economic Belt. Among them, the spatial pattern of coupling coordination degree had a strong consistency with urbanization index, that is, areas with higher urbanization index also had higher coupling coordination degree. ii) Based on coupling feature identification, it was found that 249 'problem areas' were mainly located in Yunnan Province, southeastern Guizhou Province, central Anhui Province, and central and eastern Jiangsu Province. The main factor for the formation was due to the lag of urbanization in coordinated development. iii) Among the socioeconomic indicators, population structure (0.136), per capita year-end financial institutions loan balance (0.409) and per capita fixed asset investment (0.202) all had a positive impact on coupling coordination degree, while location conditions (-0.126) had a negative impact. Among the natural indicators, soil organic matter (-0.212) and temperature (-0.094) had a negative impact on coupling coordination degree. iv) During the process of coordinated development, it was necessary to increase financial investment and financial support, actively formulate policies to attract talents, enhance the education and publicity of ecological civilization, and develop a green circular economy. The above measures can promote the harmonious development of urbanization and forest ecological security in the Yangtze River Economic Belt.

The assessment of forest ecological security and its determining indicators: A case study of the Yangtze River Economic Belt in China.

This study put forward an evaluation index system for measuring forest ecological security index (FESI). Taking the 1086 counties located in the Yangtze River Economic Belt as a case study, we investigated the change and its spatial pattern of FESI, as well as the determining indicators (both natural and socio-economic), with the support of Arcmap and GeoDA software. The average FESI value of the study counties in 2010 and 2015 was found to be 0.4226 and 0.4990, increased by 18.08%. Spatially, an evident spatial gradient change was identified, with FESI values in the upstream areas of the Yangtze River being higher than those in midstream areas, and the values of midstream areas in turn being higher than those in downstream areas. The eight tributary basins within the economic belt witnessed significantly different FESI values. Based on the results of this evaluation of FESI and its sub-evaluation indexes, we identified 46.04% of the total counties as constituting "problem areas". These problem areas were mainly concentrated in Shanghai, Jiangsu and Anhui provinces, followed by counties around Dongting Lake, Poyang Lake and in Sichuan province. A regression analysis was conducted in order to identify the determining indicators behind forest ecological security, with results indicating that the ratio of secondary industry, the urbanization rate, the per capita financial institution loan balance, accumulated temperature and wind speed all negatively impacted on FESI values, while population structure, soil organic matter and rainfall were revealed to play a positive role; all of these indicators were highly significant. Given these findings, we also set out a series of policy measures intended to promote the sustainable forest development of the study region. These include the vigorous development of tertiary industry and moves to reduce the proportion of the secondary industry in the national economy, the development of a circular economy, slowing the pace of urbanization, and continued increases in forestry investment in central cities - particularly in problem areas.

Establishment of Logic Gates Based on Conformational Changes in a Multiple-Factor Biomolecule Interaction Process by Dual Polarization Interferometry.

DNA-based logic gates stimulate the development of molecular scale computers and show enormous potential in nanotechnology, biotechnology, and medicine. However, the reported detectors to date usually require one to label appropriate signal probes, resulting in not only a high cost but also potentially tedious manipulation. For the first time, we established a label-free logic gate by regarding the structure-related signal as output. Dual polarization interferometry (DPI) was employed to reveal the detailed conformational transitions occurring in the multiple-factor biomolecule interactions and then was utilized as a detection tool of logic gate. As a vital merit of this system, the dependence of the density output signal on the interaction with multiple-factor input can mimic the function of signal communication in OR, INHIBIT, and IDENTITY logic gates and the INHIBIT-OR cascade circuit. Additionally, the DPI signal with logic stringency can unambiguously distinguish conformational polymorphisms and compare structural stability. This study provides a new way for the construction of a label-free logic gate, supplements information deficiency of reaction details, and extends the application of DPI in logic operation.

Fluorometric and Colorimetric Dual-Readout Immunoassay Based on an Alkaline Phosphatase-Triggered Reaction.

Alkaline phosphatase (ALP) usually acts as a signal transmitter in enzyme-linked immunosorbent assay (ELISA); therefore, developing an attractive ALP activity assay, especially using a preferable substrate, would help improve the efficiency and convenience of ELISA in practical applications. Herein we have first prepared an original and creative substrate, named m-hydroxyphenyl phosphate sodium salt ( m-HPP), with a desirable dephosphorylation site for ALP. On the basis of the ALP-catalyzed hydrolysis of m-HPP to resorcinol and its subsequent specific nucleophilic reaction with dopamine, we have exploited a fluorometric and colorimetric dual-readout ALP activity assay and ALP-based ELISA system. Under the employed experimental conditions, highly sensitive and specific assay of ALP and cardiac troponin I (cTnI) has been accomplished in a straightforward way. Furthermore, the commendable sensing performance of our proposed ELISA in the determination of the cTnI level in diluted human serum unambiguously illustrates great potential in the early diagnosis of acute myocardial infarction.

Photo-Induced Electron Transfer-Based Versatile Platform with G-Quadruplex/Hemin Complex as Quencher for Construction of DNA Logic Circuits.

G-quadruplex has been developed as an innovator for analytical chemistry and biomedicine due to its vibrant binding activity, structural polymorphism, and critical roles in biological regulation. Herein, a simple but versatile platform was obtained by integrating split G-quadruplex and fluorophore into a molecular beacon, where the photoinduced electron transfer could occur when the fluorophore approached the preformed G-quadruplex/hemin complexes. Such design subtly combined the G4 disruption-induced fluorescent turn-on strategy and the photoinduced electron transfer property into one platform for constructing the logic circuits. On the basis of such a universal platform, a series of binary logic gates (OR, INHIBIT, AND, and XOR), a combinatorial gate (INHIBIT-OR), and even a complex logic operation for discrimination of multiples of three from natural numbers less than ten have been successfully achieved only by employing such platform as work unit and single-strand DNAs as inputs. The set-reset function of this platform could be realized by alternatively introducing blocking and releasing strands. In addition, this platform could operate in a biological matrix stably and precisely. Therefore, such a universal platform lays the foundation for complicating the logic systems, realizing the biocomputing and also points out a new direction for target detection.

An Enzyme Cascade-Triggered Fluorogenic and Chromogenic Reaction Applied in Enzyme Activity Assay and Immunoassay.

An enzyme cascade-triggered reaction with novel signal generation mechanism is beneficial for the development and insight of the enzyme cascade, which is extensively used for signal transduction in potential applications. Inspired by the fluorogenic and chromogenic reaction between dopamine and resorcinol, and the specific catalytic properties of alkaline phosphatase (ALP) and tyrosinase, we designed and synthesized an unconventional substrate of ALP, named p-aminoethyl-phenyl phosphate disodium salt (PAPP). As expected, the ALP and tyrosinase-incubated PAPP solution exhibited pale yellow with intense blue fluorescence upon addition of resorcinol, owing to the ALP-catalyzed transformation of PAPP into an intermediate tyramine, and the tyrosinase-catalyzed hydroxylation of tyramine to dopamine, as well as the specific reaction between dopamine and resorcinol. Therefore, an enzyme cascade system has been developed herein based on the ALP and tyrosinase coupled enzymes-triggered fluorogenic and chromogenic reaction. According to the direct relationship between the activity of ALP/tyrosinase and absorbance/fluorescence intensity of the resultant solution, the proposed enzyme cascade-triggered reaction was utilized for assaying ALP and tyrosinase activity with fluorometric and colorimetric dual-readout signals. Furthermore, such enzyme cascade catalysis process was integrated into the ALP-based cascade enzyme-linked immunosorbent assay with dual-readout signals, resulting in the sensitive detection of cardiac troponin I in diluted serum.

Algicidal Activity of Novel Marine Bacterium Paracoccus sp. Strain Y42 against a Harmful Algal-Bloom-Causing Dinoflagellate, Prorocentrum donghaiense.

Prorocentrum donghaiense blooms occur frequently in the Yangtze River estuary and the adjacent East China Sea. These blooms have damaged marine ecosystems and caused enormous economic losses over the past 2 decades. Thus, highly efficient, low-cost, ecofriendly approaches must be developed to control P. donghaiense blooms. In this study, a bacterial strain (strain Y42) was identified as Paracoccus sp. and was used to lyse P. donghaiense The supernatant of the strain Y42 culture was able to lyse P. donghaiense, and the algicidal activity of this Y42 supernatant was stable with different temperatures and durations of light exposure and over a wide pH range. In addition to P. donghaiense, Y42 showed high algicidal activity against Alexandrium minutum, Scrippsiella trochoidea, and Skeletonema costatum, suggesting that it targets primarily Pyrrophyta. To clarify the algicidal effects of Y42, we assessed algal lysis and determined the chlorophyll a contents, photosynthetic activity, and malondialdehyde contents of P. donghaiense after exposure to the Y42 supernatant. Scanning electron microscopy and transmission electron microscopy analyses showed that the Y42 supernatant disrupted membrane integrity and caused algal cell breakage at the megacytic zone. Photosynthetic pigment loss and significant declines in both photosynthetic efficiency and the electron transport rate indicated that the Y42 supernatant damaged the photosynthetic system of P. donghaiense Malondialdehyde overproduction indicated that the Y42 supernatant caused lipid peroxidation and oxidative damage to membrane systems in the algal cell, ultimately leading to death. The findings of this study reveal the potential of Y42 to remove algal cells from P. donghaiense blooms.IMPORTANCEP. donghaiense is one of the most common dinoflagellate species that form harmful algal blooms, which frequently cause serious ecological pollution and pose health hazards to humans and other animals. Screening for bacteria with high algicidal activity against P. donghaiense and studying their algicidal processes and characteristics will contribute to an understanding of their algicidal effects and provide a theoretical basis for preventing algal blooms and reducing their harm to the environment. This study reports the algicidal activity and characteristics of Paracoccus against P. donghaiense The stability of the algicidal activity of Paracoccus in different environments (including different temperature, pH, and sunlight conditions) indicates its potential for use in the control of P. donghaiense blooms.

Fluorescence Light-Up Biosensor for MicroRNA Based on the Distance-Dependent Photoinduced Electron Transfer.

It is demonstrated that miRNAs exhibit significant regulatory roles in a series of biological processes and associated with diverse human diseases. Herein, we report a convenient fluorescent biosensor for the quantitative determination of miR-21, a key miRNA related to cardio-cerebrovascular diseases. Our proposal involves not only the rational design of single stranded DNA as the probe, successively including a C-rich sequence as the synthetic template of DNA/Ag nanoclusters (DNA/AgNCs), a complementary (Com) sequence to hybridize with the miR-21, and a G-rich sequence to form a complex of G-quadruplex/hemin but also the distance-dependent property of photoinduced electron transfer (PET) between the preformed DNA/AgNCs (electron donor) and G-quadruplex/hemin complex (electron acceptor). In the presence of the target miR-21, the initial flexible single strand Com in the probe turns to the rigid Com/RNA heteroduplexes, and then the PET could be interrupted owing to the extended distance between the electron donor and acceptor, accompanying with the fluorescence quenching and recovery of DNA/AgNCs. Therefore, a fluorescence light-up biosensor for miR-21 could be developed through the monitoring of the degree of fluorescence recovery of DNA/AgNCs. Preferential to other previous PET-based detection methods, we construct the biosensor by utilizing the distance dependent property for the first time and only need to adjust the sequences of Com in different miRNAs assays.

In Situ Fluorogenic and Chromogenic Reactions for the Sensitive Dual-Readout Assay of Tyrosinase Activity.

As a well-known copper-containing oxidase, tyrosinase has been anticipated to serve as the biomarker of skin diseases. We describe here an exquisite label-free fluorescent and colorimetric dual-readout assay of its activity, inspired by the specific oxidation ability of monophenolamine substrates to catecholamines and a unique fluorogenic reaction between resorcinol and catecholamines. By employing commercially available tyramine as the model substrate (dopamine as the product), it is found that the tyrosinase-incubated tyramine solution exhibits obvious pale yellow with intense blue fluorescence in the presence of resorcinol and O2, where the absorbance and fluorescence intensity are directly related to the concentration of added tyrosinase (i.e., the amount of conversion of tyramine to dopamine). The overall process of sensing tyrosinase activity takes less than 100 min at ambient temperature and pressure conditions with exceedingly simple operation procedure, explicit response mechanism, and formation of fluorophore with high quantum yield from scratch. Furthermore, such a convenient, rapid, cost-effective, and highly sensitive dual-readout assay exhibits promising prospect for the tyrosinase activity in extensive bioassays and clinic research as well as in screening potential tyrosinase inhibitors.

An interval chance-constrained fuzzy modeling approach for supporting land-use planning and eco-environment planning at a watershed level.

An interval chance-constrained fuzzy land-use allocation (ICCF-LUA) model is proposed in this study to support solving land resource management problem associated with various environmental and ecological constraints at a watershed level. The ICCF-LUA model is based on the ICCF (interval chance-constrained fuzzy) model which is coupled with interval mathematical model, chance-constrained programming model and fuzzy linear programming model and can be used to deal with uncertainties expressed as intervals, probabilities and fuzzy sets. Therefore, the ICCF-LUA model can reflect the tradeoff between decision makers and land stakeholders, the tradeoff between the economical benefits and eco-environmental demands. The ICCF-LUA model has been applied to the land-use allocation of Wujiang watershed, Guizhou Province, China. The results indicate that under highly land suitable conditions, optimized area of cultivated land, forest land, grass land, construction land, water land, unused land and landfill in Wujiang watershed will be [5015, 5648] hm2, [7841, 7965] hm2, [1980, 2056] hm2, [914, 1423] hm2, [70, 90] hm2, [50, 70] hm2 and [3.2, 4.3] hm2, the corresponding system economic benefit will be between 6831 and 7219 billion yuan. Consequently, the ICCF-LUA model can effectively support optimized land-use allocation problem in various complicated conditions which include uncertainties, risks, economic objective and eco-environmental constraints.

High IDH1 expression is associated with a poor prognosis in cytogenetically normal acute myeloid leukemia.

The prognostic value of IDH1 mutations has been systematically evaluated in acute myeloid leukemia (AML) patients recently. However, the role of IDH1 expression in AML is still under exploration. To investigate the clinical significance, we analyzed the IDH1/2 expression in 320 patients with cytogenetically normal AML (CN-AML) by quantitative real-time reverse-transcription polymerase chain reaction. High expression of IDH1 was predominant in patients with FLT3-ITD and DNMT3A mutations and less prevalent in cases with CEBPA double allele mutations. Strong association was observed between high IDH1 expression and low expression of microRNA 181 family. Prognosis was adversely affected by high IDH1 expression, with shorter overall survival and event-free survival in the context of clinical characteristics, including age, WBC count, and gene mutations of NPM1, FLT3-ITD, CEBPA, IDH1, IDH2 and DNMT3A in CN-AML. Moreover, the clinical outcome of IDH1 expression in terms of overall survival, event-free survival and complete remission rate still remained in multivariate models in CN-AML. Importantly, the prognostic value was validated using the published microarray data from 79 adult patients treated according to the German AMLCG-1999 protocol. Our results demonstrated that high IDH1 expression is associated with a poor prognosis of CN-AML.

Land resources allocation strategies in an urban area involving uncertainty: a case study of Suzhou, in the Yangtze River Delta of China.

A large number of mathematical models have been developed to support land resource allocation decisions and land management needs; however, few of them can address various uncertainties that exist in relation to many factors presented in such decisions (e.g., land resource availabilities, land demands, land-use patterns, and social demands, as well as ecological requirements). In this study, a multi-objective interval-stochastic land resource allocation model (MOISLAM) was developed for tackling uncertainty that presents as discrete intervals and/or probability distributions. The developed model improves upon the existing multi-objective programming and inexact optimization approaches. The MOISLAM not only considers economic factors, but also involves food security and eco-environmental constraints; it can, therefore, effectively reflect various interrelations among different aspects in a land resource management system. Moreover, the model can also help examine the reliability of satisfying (or the risk of violating) system constraints under uncertainty. In this study, the MOISLAM was applied to a real case of long-term urban land resource allocation planning in Suzhou, in the Yangtze River Delta of China. Interval solutions associated with different risk levels of constraint violation were obtained. The results are considered useful for generating a range of decision alternatives under various system conditions, and thus helping decision makers to identify a desirable land resource allocation strategy under uncertainty.

Use of a three-dimensional model to predict heavy metal (copper) fluxes in the Oujiang estuary.

Measuring pollutant concentrations in major tributaries is the standard method for establishing pollutant fluxes to the sea. However, this method is costly and difficult, and may be subject to a great deal of uncertainty due to the presence of unknown sources. This uncertainty presents challenges to managers and scientists in reducing contaminant discharges to water bodies. As one less costly method, a three-dimensional model was developed and used to predict pollutant fluxes to the sea. The sorptive contaminant model was incorporated into hydrodynamic and sediment models. Adsorption-desorption of copper by sediments in the Oujiang estuary were described using Henry's law. The model was validated using measured data for water surface elevations, flow velocity/direction, suspended sediment concentrations, and the proportion of copper sorbed to sediment. The validated model was then applied to predict fluxes of copper. Combined with the measured data, the copper concentration in the Oujiang River discharge was calculated as 13.0 µg/L and copper fluxes were calculated as 52 t in 2010. This copper flux prediction was verified using measured dissolved copper concentrations. Comparisons between the modeled and measured results showed good agreement at most stations, demonstrating that copper flux prediction in the Oujiang estuary was reasonably accurate.

A sensitive "off-on" electrochemiluminescence DNA sensor based on signal cascade amplification circuit and distance-dependent energy transfer.

A sensitive "off-on" electrochemiluminescence (ECL) DNA sensor was constructed based on Exo III-assisted cascade amplification system. In the cascade amplification circuit, target DNA and Exo III cutting substrate were designed into an inverted T-shaped binding mode to form a stable DNA junction, thus effectively triggering Exo III digestion cycle. During the biosensor assembly process, ferrocene (Fc) and distance-dependent ECL resonance energy transfer (ECL-RET) and surface plasmon resonance (SPR) effects were introduced to regulate the ECL of semiconductor quantum dots (QDs). Carboxylated ZnCdSe/ZnS QDs were used as ECL signal probes and K2S2O8 was coreactant, and the initial cathodic ECL signal of QDs was efficiently quenched through electron and energy transfer with Fc and ECL-RET with Au NPs, leaving the system in "off" state. After the products of cascade amplification were introduced into the electrode surface, the single-stranded DNA modified with Fc was displaced, and the distance between Au NPs and QDs became farther, resulting in a transition from ECL-RET to SPR, and then a significant ECL signal boost was achieved, turning the system into "on" state. The combination of efficient cascade amplification system and sensitive "off-on" ECL signal change mode enabled the biosensing platform to detect target DNA with high selectivity (able to distinguish single-base mutated DNA) and ultra-high sensitivity (limit of detection was 31.67 aM, S/N = 3), providing a new perspective for designing highly sensitive and programmable ECL biosensors.

Sufficiently activated mature natural killer cells derived from peripheral blood mononuclear cells substantially enhance antitumor activity.

BACKGROUND: Peripheral blood-derived natural killer (NK) cells spontaneously lyse tumor cells without prior sensitization. However, NK cells in peripheral blood (PBNK cells) are in a resting state and exhibit inhibitory phenotypes and impaired cytotoxicity. Thus, strengthening the cytotoxic effector function of PBNK cells and improving NK cell expansion in vitro for a convenient allogeneic therapy are essential. MATERIALS AND METHODS: Pure cytokine activation and expansion of NK cells (super NK [SNK]) from peripheral blood mononuclear cells were studied. Markers of activated and inhibited NK cells and cytokine secretion by NK cells were examined using flow cytometry. NK cell antitumor activity in vitro was assessed using lactate dehydrogenase (LDH) cytotoxicity assay and an Incucyte real-time imaging system. Additionally, the function of SNK cells against ascites caused by ovarian cancer in NOD-Prkdc(em26Cd52)il2rg(em26Cd22)/Nju (NCG) mice was determined. In a further investigation of the differences between PBNK and SNK, the mRNA of both cells was sequenced and analyzed. RESULTS: Human peripheral blood mononuclear cells showed selective NK cell expansion upon cytokine activation and culture. Both SNK and PBNK cells expressed activation markers, but at different levels, and SNK cells secreted more cytokines related to cytotoxicity than PBNK cells did. Accordingly, SNK cells exhibited strong antitumor activity ex vivo and improved NCG mice survival after intraperitoneal ovarian cancer transplantation. Mechanistically, SNK cells expressed more genes associated with nucleotide metabolism, fatty acid, and ATP metabolism than PBNK cells. CONCLUSION: SNK cells derived from peripheral blood mononuclear cells have sufficiently activated mature characteristics and high antitumor activity, rendering them a highly promising and essential therapeutic approach for cancer treatment.

In silico bioactivity prediction of proteins interacting with graphene-based nanomaterials guides rational design of biosensor.

Graphene-based nanomaterials have attracted significant attention for their potentials in biomedical and biotechnology applications in recent years, owing to the outstanding physical and chemical properties. However, the interaction mechanism and impact on biological activity of macro/micro biomolecules still require more concerns and further research in order to enhance their applicability in biosensors, etc. Herein, an integrated method has been developed to predict the protein bioactivity performance when interacting with nanomaterials for protein-based biosensor. Molecular dynamics simulation and molecular docking technique were consolidated to investigate several nanomaterials: C60 fullerene, single-walled carbon nanotube, pristine graphene and graphene oxide, and their effect when interacting with protein. The adsorption behavior, secondary structure changes and protein bioactivity changes were simulated, and the results of protein activity simulation were verified in combination with atomic force spectrum, circular dichroism spectrum fluorescence and electrochemical experiments. The best quantification alignment between bioactivity obtained by simulation and experiment measurements was further explored. The two proteins, RNase A and Exonuclease III, were regarded as analysis model for the proof of concept, and the prediction accuracy of protein bioactivity could reach up to 0.98. The study shows an easy-to-operate and systematic approach to predict the effects of graphene-based nanomaterials on protein bioactivity, which holds guiding significance for the design of protein-related biosensors. In addition, the proposed prediction model is not limited to carbon-based nanomaterials and can be extended to other types of nanomaterials. This facilitates the rapid, simple, and low-cost selection of efficient and biosafe nanomaterials candidates for protein-related applications in biosensing and biomedical systems.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors.

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.