1H NMR-metabolomics studies on acute toxicity effect of lead in adult zebrafish (Danio rerio) model.

Zebrafish (Danio rerio) is ideal for studying the effects of toxins like lead or plumbum (Pb) which persist in the environment and harm body systems when absorbed. Increasing Pb concentration could result in a higher mortality rate and alteration of behavior and metabolism. The present study evaluates the acute toxicity effect of Pb on metabolome and behavior in adult zebrafish. The zebrafish were exposed to various Pb concentrations ranging from 0 to 30 mg/L for different periods (24, 48, and 72 h) before the fish samples were subjected to Nuclear Magnetic Resonance (NMR)-multivariate data analysis (MVDA) with additional support from behavioral assessment. The behavior of zebrafish was significantly altered after Pb inducement and the differential metabolites increased in low (5 mg/L) while decreased in high (10 mg/L) Pb concentrations. An ideal Pb induction could be achieved by 5 mg/L concentration in 24 h, which induced significant metabolite changes without irreversible damage. Continuing research on the effects of lead toxicity is crucial to develop effective prevention and treatment strategies.

Multivariate data analysis of process parameters affecting the growth and productivity of stable Chinese hamster ovary cell pools expressing SARS-CoV-2 spike protein as vaccine antigen in early process development.

The recent COVID-19 pandemic revealed an urgent need to develop robust cell culture platforms which can react rapidly to respond to this kind of global health issue. Chinese hamster ovary (CHO) stable pools can be a vital alternative to quickly provide gram amounts of recombinant proteins required for early-phase clinical assays. In this study, we analyze early process development data of recombinant trimeric spike protein Cumate-inducible manufacturing platform utilizing CHO stable pool as a preferred production host across three different stirred-tank bioreactor scales (0.75, 1, and 10 L). The impact of cell passage number as an indicator of cell age, methionine sulfoximine (MSX) concentration as a selection pressure, and cell seeding density was investigated using stable pools expressing three variants of concern. Multivariate data analysis with principal component analysis and batch-wise unfolding technique was applied to evaluate the effect of critical process parameters on production variability and a random forest (RF) model was developed to forecast protein production. In order to further improve process understanding, the RF model was analyzed with Shapley value dependency plots so as to determine what ranges of variables were most associated with increased protein production. Increasing longevity, controlling lactate build-up, and altering pH deadband are considered promising approaches to improve overall culture outcomes. The results also demonstrated that these pools are in general stable expressing similar level of spike proteins up to cell passage 11 (~31 cell generations). This enables to expand enough cells required to seed large volume of 200-2000 L bioreactor.

Enhanced harvest performance predictability through advanced multivariate data analysis of mammalian cell culture particle size distribution.

The industry's pursuit for higher antibody production has led to increased cell density cultures that impact the performance of subsequent product recovery steps. This increase in cell concentration has highlighted the critical role of solids concentration in centrifugation yield, while recent product degradation cases have shed light on the impact of cell lysis on product quality. Current methods for measuring solids concentration and cell lysis are not suited for early-stage high-throughput experimentation, which means that these cell culture outputs are not well characterized in early process development. This article describes a novel approach that leveraged the data from a widely-used automated cell counter (Vi-CELL™ XR) to accurately predict solids concentration and a common cell lysis indicator represented as lactate dehydrogenase (LDH) release. For this purpose, partial least squares (PLS) models were derived with k-fold cross-validation from the particle size distribution data generated by the cell counter. The PLS models showed good predictive potential for both LDH release and solids concentration. This novel approach reduced the time required for evaluating the solids concentration and LDH for a typical high-throughput cell culture system (with 48 bioreactors in parallel) from around 7 h down to a few minutes.

A multivariate data analysis approach for the investigation of in vitro derived metabolites of ACP-105 in comparison with human in vivo metabolites.

Selective androgen receptor modulators (SARMs) such as ACP-105 are prohibited in sports due to their anabolic properties. ACP-105 has in previous equine studies shown to undergo extensive metabolism, which makes its metabolite profile important to investigate in humans, since the metabolism is unknown in this species. The aims of the study were to systematically optimize in vitro microsome incubations for improved metabolite yield and to utilize a multivariate data analysis (MVDA) approach to aid the metabolite discovery. Microsomes together with S9 fractions were used at optimal conditions, both with and without phase II additives. Furthermore, the relevance of the in vitro derived metabolites was evaluated as analytical targets in doping control by comparison with results from a human post-administration urine sample collected after a single dose of 100 µg ACP-105. All samples were analyzed with liquid chromatography - Orbitrap mass spectrometry. The use of the systematical optimization and MVDA greatly simplified the search and a total of 18 in vitro metabolites were tentatively identified. The yield of the two main monohydroxylated isomers increased by 24 and 10 times, respectively. In the human urine sample, a total of seven metabolites of ACP-105, formed by a combination of hydroxylations and glucuronic acid conjugations, were tentatively identified. The main metabolites were two monohydroxylated forms that are suggested as analytical targets for human doping control after hydrolysis. All the in vivo metabolites could be detected with the MVDA approach on the in vitro models, demonstrating its usefulness for prediction of the in vivo metabolite profile.

1 H NMR-based process understanding and biochemical marker identification methodology for monitoring CHO cell culture process during commercial-scale manufacturing.

Controlling the process of CHO cell fed-batch culture is critical for biologics quality control. However, the biological complexity of cells has hampered the reliable process understanding for industrial manufacturing. In this study, a workflow was developed for the consistency monitoring and biochemical marker identification of the commercial-scale CHO cell culture process through 1 H NMR assisted with multivariate data analysis (MVDA). Firstly, a total of 63 metabolites were identified in this study object in 1 H NMR spectra of the CHO cell-free supernatants. Secondly, multivariate statistical process control (MSPC) charts were used to evaluate process consistency. According to MSPC charts, the batch-to-batch quality consistency was high, indicating the CHO cell culture process at the commercial scale was well-controlled and stable. Then, the biochemical marker identification was provided through orthogonal partial least square discriminant analysis (OPLS-DA) based S-line plots during the cell logarithmic expansion, stable growth, and decline phases. Identified biochemical markers of the three cell growth phases were as follows: L-glutamine, pyroglutamic acid, 4-hydroxyproline, choline, glucose, lactate, alanine, and proline were of the logarithmic growth phase; isoleucine, leucine, valine, acetate, and alanine were of the stable growth phase; acetate, glycine, glycerin, and gluconic acid were of the cell decline phase. Additional potential metabolic pathways that might influence the cell culture phase transitions were demonstrated. The workflow proposed in this study demonstrates that the combination of MVDA tools and 1 H NMR technology is highly appealing to the research of the biomanufacturing process, and applies well to provide guidance in future work on consistency evaluation and biochemical marker monitoring of the production of other biologics.

Non-invasive real-time monitoring of cell concentration and viability using Doppler ultrasound.

Bioprocess optimization towards higher productivity and better quality control relies on real-time process monitoring tools to measure process and culture parameters. Cell concentration and viability are among the most important parameters to be monitored during bioreactor operations that are typically determined using optical methods on an extracted sample. In this paper, we have developed an online non-invasive sensor to measure cell concentration and viability based on Doppler ultrasound. An ultrasound transducer is mounted outside the bioreactor vessel and emits a high frequency tone burst (15 MHz) through the vessel wall. Acoustic backscatter from cells in the bioreactor depends on cell concentration and viability. The backscattered signal is collected through the same transducer and analyzed using multivariate data analysis (MVDA) to characterize and predict the cell culture properties. We have developed accurate MVDA models to predict the Chinese hamster ovary (CHO) cell concentration in a broad range from 0.1 × 106 cells/mL to 100 × 106 cells/mL, and cell viability from 3% to 99%. The non-invasive monitoring is ideal for single use bioreactor and the in-situ measurements removes the burden for offline sampling and dilution steps. This method can be similarly applied to other suspension cell culture modalities.

Profiling of Volatile Organic Compounds from Four Plant Growth-Promoting Rhizobacteria by SPME-GC-MS: A Metabolomics Study.

The rhizosphere microbiome is a major determinant of plant health. Plant-beneficial or plant growth-promoting rhizobacteria (PGPR) influence plant growth, plant development and adaptive responses, such as induced resistance/priming. These new eco-friendly choices have highlighted volatile organic compounds (biogenic VOCs) as a potentially inexpensive, effective and efficient substitute for the use of agrochemicals. Secreted bacterial VOCs are low molecular weight lipophilic compounds with a low boiling point and high vapor pressures. As such, they can act as short- or long-distance signals in the rhizosphere, affecting competing microorganisms and impacting plant health. In this study, secreted VOCs from four PGPR strains (Pseudomonas koreensis (N19), Ps. fluorescens (N04), Lysinibacillus sphaericus (T19) and Paenibacillus alvei (T22)) were profiled by solid-phase micro-extraction gas chromatography mass spectrometry (SPME-GC-MS) combined with a multivariate data analysis. Metabolomic profiling with chemometric analyses revealed novel data on the composition of the secreted VOC blends of the four PGPR strains. Of the 121 annotated metabolites, most are known as bioactives which are able to affect metabolism in plant hosts. These VOCs belong to the following classes: alcohols, aldehydes, ketones, alkanes, alkenes, acids, amines, salicylic acid derivatives, pyrazines, furans, sulfides and terpenoids. The results further demonstrated the presence of species-specific and strain-specific VOCs, characterized by either the absence or presence of specific VOCs in the different strains. These molecules could be further investigated as biomarkers for the classification of an organism as a PGPR and selection for agricultural use.

Advancing Raman model calibration for perfusion bioprocesses using spiked harvest libraries.

BACKGROUND: Raman spectroscopy has gained popularity to monitor multiple process indicators simultaneously in biopharmaceutical processes. However, robust and specific model calibration remains a challenge due to insufficient analyte variability to train the models and high cross-correlation of various media components and artifacts throughout the process. MAIN METHODS: A systematic Raman calibration workflow for perfusion processes enabling highly specific and fast model calibration was developed. Harvest libraries consisting of frozen harvest samples from multiple CHO cell culture bioreactors collected at different process times were established. Model calibration was subsequently performed in an offline setup using a flow cell by spiking process harvest with glucose, raffinose, galactose, mannose, and fructose. MAJOR RESULTS: In a screening phase, Raman spectroscopy was proven capable not only to distinguish sugars with similar chemical structures in perfusion harvest but also to quantify them independently in process-relevant concentrations. In a second phase, a robust and highly specific calibration model for simultaneous glucose (root mean square error prediction [RMSEP] = 0.32 g L-1 ) and raffinose (RMSEP = 0.17 g L-1 ) real-time monitoring was generated and verified in a third phase during a perfusion process. IMPLICATION: The proposed novel offline calibration workflow allowed proper Raman peak decoupling, reduced calibration time from months down to days, and can be applied to other analytes of interest including lactate, ammonia, amino acids, or product titer.

Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing.

Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a 'hybrid ML' or 'white box ML' to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.

Mvda is required for zebrafish early development.

BACKGROUND: The MVD gene mutations are identified in porokeratosis, which is considered a skin-specific autoinflammatory keratinization disease. However, the biological function of MVD gene remains largely unknown. Therefore, we analyzed the function of mvda gene, orthologous to the human MVD gene, in developing zebrafish. METHODS: Morpholino antisense oligonucleotide technique was used to generate mvda loss-of-function phenotypes. Knockdown of mvda was confirmed by RT-PCR and Sanger sequencing. Scanning and transmission electron microscopy were performed to analyze the morphology of the epidermis. Angiogenesis study was presented using the Tg(fli1a:EGFP)y1 transgenic strain. In addition, acridine orange staining was used to examine the apoptotic cells in vivo. RESULTS: As expected, the mvda morphants showed abnormal morphology of the epidermis. Moreover, we observed ectopic sprouts in trunk angiogenesis and impaired formation of the caudal vein plexus in the mvda-deficient zebrafish. Besides, increased apoptosis was found throughout the tail, heart, and eyes in mvda zebrafish morphants. CONCLUSIONS: These findings indicated the essential role of mvda in the early development of zebrafish. This was the first in vivo knockdown study of the zebrafish mvda gene, which might offer insight into the biological function of the human MVD gene.

Identification of α-Amylase and α-Glucosidase Inhibitors and Ligularoside A, a New Triterpenoid Saponin from Passiflora ligularis Juss (Sweet Granadilla) Leaves, by a Nuclear Magnetic Resonance-Based Metabolomic Study.

The leaves of Passiflora ligularis Juss (known as sweet granadilla for its edible fruits) are a crop byproduct that is discarded. With the aim of contributing to give value-added products from these crop by-side products to farmers of Colombian Andes, we carried out a 1H-NMR-metabolomics analysis of polar extracts from leaves collected in three locations and stored in two conditions in order to identify glucosyl-hydrolase inhibitors. Variations in the metabolic profile and the bioactivity among samples were analyzed by orthogonal partial least square discriminant analysis. Thus, 1H-NMR signals related to polyphenolic compounds, saponins, and amino acids were correlated with higher inhibitory activities. Moreover, a targeted NMR and HPLC-MS/MS analysis allowed the identification of 14 polyphenolic compounds and the structural characterization of a new triterpenoid saponin, ligularoside A. The measurements of IC50 values for α-amylase and α-glycosidase inhibitors allowed the identification of quercetin-3-O-ß-glucoside, kaempferol-3-O-ß-glucoside, and ligularoside A as the most active compounds. These results suggest that P. ligularis leaves are a source of glucosyl-hydrolase inhibitors and lay the foundation for exploring additional applications.

Spectroscopy integration to miniature bioreactors and large scale production bioreactors-Increasing current capabilities and model transfer.

Spectroscopy techniques are being implemented within the biopharmaceutical industry due to their non-destructive ability to measure multiple analytes simultaneously, however, minimal work has been applied focussing on their application at small scale. Miniature bioreactor systems are being applied across the industry for cell line development as they offer a high-throughput solution for screening and process optimization. The application of small volume, high-throughput, automated analyses to miniature bioreactors has the potential to significantly augment the type and quality of data from these systems and enhance alignment with large-scale bioreactors. Here, we present an evaluation of 1. a prototype that fully integrates spectroscopy to a miniature bioreactor system (ambr®15, Sartorius Stedim Biotech) enabling automated Raman spectra acquisition, 2. In 50 L single-use bioreactor bag (SUB) prototype with an integrated spectral window. OPLS models were developed demonstrating good accuracy for multiple analytes at both scales. Furthermore, the 50 L SUB prototype enabled on-line monitoring without the need for sterilization of the probe prior to use and minimal light interference was observed. We also demonstrate the ability to build robust models due to induced changes that are hard and costly to perform at large scale and the potential of transferring these models across the scales. The implementation of this technology enables integration of spectroscopy at the small scale for better process understanding and generation of robust models over a large design space while facilitating model transfer throughout the scales enabling continuity throughout process development and utilization and transfer of ever-increasing data generation from development to manufacturing.

Mvda is required for zebrafish early development

BACKGROUND: The MVD gene mutations are identified in porokeratosis, which is considered a skin-specific autoin- flammatory keratinization disease. However, the biological function of MVD gene remains largely unknown. Therefore, we analyzed the function of mvda gene, orthologous to the human MVD gene, in developing zebrafish. METHODS: Morpholino antisense oligonucleotide technique was used to generate mvda loss-of-function phenotypes. Knockdown of mvda was confirmed by RT-PCR and Sanger sequencing. Scanning and transmission electron microscopy were performed to analyze the morphology of the epidermis. Angiogenesis study was presented using the Tg(fli1a:EGFP)yl transgenic strain. In addition, acridine orange staining was used to examine the apoptotic cells in vivo. RESULTS: As expected, the mvda morphants showed abnormal morphology of the epidermis. Moreover, we observed ectopic sprouts in trunk angiogenesis and impaired formation of the caudal vein plexus in the mvda-deficient zebrafish. Besides, increased apoptosis was found throughout the tail, heart, and eyes in mvda zebrafish morphants. CONCLUSIONS: These findings indicated the essential role of mvda in the early development of zebrafish. This was the first in vivo knockdown study of the zebrafish mvda gene, which might offer insight into the biological function of the human MVD gene.

NMR-based metabolic profiling to follow the production of anti-phytopathogenic compounds in the culture of the marine strain Streptomyces sp. PNM-9.

Actinobacteria are the major source of bioactive secondary metabolites and are featured in the search for antimicrobial compounds. We used nuclear magnetic resonance (RMN)-metabolic profiling and multivariate data analysis (MVDA) to correlate the metabolites' production of Streptomyces sp. PNM-9 from the algae Dictyota sp. and their biological activity against the rice phytopathogenic bacteria Burkholderia spp. The compounds 2-methyl-N-(2'-phenylethyl)-butanamide (1) and 3-methyl-N-(2'-phenylethyl)-butanamide (2) were identified through MVDA and 2D NMR experiments in the organic extract of a 15-days LB media culture of Streptomyces sp. PNM-9. Compounds 1 and 2 were isolated and their structures confirmed by one- and two-dimensional NMR and mass spectrometry (MS) data. Compounds 1 and 2 were active against the rice pathogenic bacteria Burkholderia glumae (ATCC 33,617) displaying minimal inhibitory concentration (MIC) values of 2.43 mM and 1.21 mM, respectively. The metabolomics-guided approach employing NMR-metabolic profiling was useful for marine microbial bioprospecting and suggested Streptomyces sp. PNM-9 strain and its compounds as a potential control against phytopathogenic bacteria.

Multivariate data analysis of growth medium trends affecting antibody glycosylation.

Use of multivariate data analysis for the manufacturing of biologics has been increasing due to more widespread use of data-generating process analytical technologies (PAT) promoted by the US FDA. To generate a large dataset on which to apply these principles, we used an in-house model CHO DG44 cell line cultured in automated micro bioreactors alongside PAT with four commercial growth media focusing on antibody quality through N-glycosylation profiles. Using univariate analyses, we determined that different media resulted in diverse amounts of terminal galactosylation, high mannose glycoforms, and aglycosylation. Due to the amount of in-process data generated by PAT instrumentation, multivariate data analysis was necessary to ascertain which variables best modeled our glycan profile findings. Our principal component analysis revealed components that represent the development of glycoforms into terminally galacotosylated forms (G1F and G2F), and another that encompasses maturation out of high mannose glycoforms. The partial least squares model additionally incorporated metabolic values to link these processes to glycan outcomes, especially involving the consumption of glutamine. Overall, these approaches indicated a tradeoff between cellular productivity and product quality in terms of the glycosylation. This work illustrates the use of multivariate analytical approaches that can be applied to complex bioprocessing problems for identifying potential solutions.

Multivariate data analysis of capacitance frequency scanning for online monitoring of viable cell concentrations in small-scale bioreactors.

Viable cell concentration (VCC) is one of the most important process attributes during mammalian cell cultivations. Current state-of-the-art measurements of VCC comprise offline methods which do not allow for continuous process data. According to the FDA's process analytical technology initiative, process monitoring and control should be applied to gain process understanding and to ensure high product quality. In this work, the use of an inline capacitance probe to monitor online VCCs of a mammalian CHO cell culture process in small-scale bioreactors (250 mL) was investigated. Capacitance sensors using single frequency are increasingly common for biomass monitoring. However, the single-frequency signal corresponds to the cell polarization that represents the viable cell volume. Therefore single-frequency measurements are dependent on cell diameter changes. Measuring the capacitance across various frequencies (frequency scanning) can provide information about the VCC and cope with changing cell diameter. Applying multivariate data analysis on the frequency scanning data successfully enabled direct online monitoring of VCCs in this study. The multivariate model was trained with data from 5 standard cultivations. The model provided a prediction of VCCs with relative errors from 5.5 to 11%, which is a good agreement with the acceptance criterion based on the offline reference method accuracy (approximately 10% relative error) and strongly improved compared with single-frequency results (16 to 23% relative error). Furthermore, robustness trials were conducted to demonstrate the model's predictive ability under challenging conditions. The process deviations in regard to dilution steps and feed variations were detected immediately in the online prediction of the VCC with relative errors between 6.7 and 13.2%. Thus in summary, the presented method on capacitance frequency scanning demonstrates its suitability for process monitoring and control that can save batches, time, and cost. Graphical abstract.

Real-time quantification and supplementation of bioreactor amino acids to prolong culture time and maintain antibody product quality.

Real-time monitoring of cell cultures in bioreactors can enable expedited responses necessary to correct potential batch failure perturbations which may normally go undiscovered until the completion of the batch and result in failure. Currently, analytical technologies are dedicated to real-time monitoring of bioreactor parameters such as pH, dissolved oxygen, and temperature, nutrients such as glucose and glutamine, or metabolites such as lactate. Despite the importance of amino acids as the building blocks of therapeutic protein products, other than glutamine their concentrations are not commonly measured. Here, we present a study into amino acid monitoring, supplementation strategies, and how these techniques may impact the cell growth profiles and product quality. We used preliminary bioreactor runs to establish baselines by determining initial amino acid consumption patterns, the results of which were used to select a pool of amino acids which gets depleted in the bioreactor. These amino acids were combined into blends which were supplemented into bioreactors during a subsequent run, the concentrations of which were monitored using a mass spectrometry based at-line method we developed to quickly assess amino acid concentrations from crude bioreactor media. We found that these blends could prolong culture life, reversing a viable cell density decrease that was leading to batch death. Additionally, we assessed how these strategies might impact protein product quality, such as the glycan profile. The amino acid consumption data were aligned with the final glycan profiles in principal component analysis to identify which amino acids are most closely associated with glycan outcomes.

A novel LED-based 2D-fluorescence spectroscopy system for in-line monitoring of Chinese hamster ovary cell cultivations - Part I.

A new two-dimensional fluorescence sensor system was developed for in-line monitoring of mammalian cell cultures. Fluorescence spectroscopy allows for the detection and quantification of naturally occurring intra- and extracellular fluorophores in the cell broth. The fluorescence signals correlate to the cells' current redox state and other relevant process parameters. Cell culture pretests with twelve different excitation wavelengths showed that only three wavelengths account for a vast majority of spectral variation. Accordingly, the newly developed device utilizes three high-power LEDs as excitation sources in combination with a back-thinned CCD-spectrometer for fluorescence detection. This setup was first tested in a lab design of experiments study with process relevant fluorophores proving its suitability for cell culture monitoring with LOD in the µg/L range. The sensor was then integrated into a CHO-K1 cell culture process. The acquired fluorescence spectra of several batches were evaluated using multivariate methods. The resulting batch evolution models were challenged in deviating and "golden batch" validation runs. These first tests showed that the new sensor can trace the cells' metabolic state in a fast and reliable manner. Cellular distress is quickly detected as a deviation from the "golden batch".

Towards malaria elimination in Savannakhet, Lao PDR: mathematical modelling driven strategy design.

BACKGROUND: The number of Plasmodium falciparum malaria cases around the world has decreased substantially over the last 15 years, but with the spread of resistance against anti-malarial drugs and insecticides, this decline may not continue. There is an urgent need to consider alternative, accelerated strategies to eliminate malaria in countries like Lao PDR, where there are a few remaining endemic areas. A deterministic compartmental modelling tool was used to develop an integrated strategy for P. falciparum elimination in the Savannakhet province of Lao PDR. The model was designed to include key aspects of malaria transmission and integrated control measures, along with a user-friendly interface. RESULTS: Universal coverage was the foundation of the integrated strategy, which took the form of the deployment of community health workers who provided universal access to early diagnosis, treatment and long-lasting insecticidal nets. Acceleration was included as the deployment of three monthly rounds of mass drug administration targeted towards high prevalence villages, with the addition of three monthly doses of the RTS,S vaccine delivered en masse to the same high prevalence sub-population. A booster dose of vaccine was added 1 year later. The surveillance-as-intervention component of the package involved the screening and treatment of individuals entering the simulated population. CONCLUSIONS: In this modelling approach, the sequential introduction of a series of five available interventions in an integrated strategy was predicted to be sufficient to stop malaria transmission within a 3-year period. These interventions comprised universal access to early diagnosis and adequate treatment, improved access to long-lasting insecticidal nets, three monthly rounds of mass drug administration together with RTS,S vaccination followed by a booster dose of vaccine, and screening and treatment of imported cases.

Staging research of human lung cancer tissues by high-resolution magic angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS 1 H NMR) and multivariate data analysis.

AIM: High-resolution magic-angle spinning proton nuclear magnetic resonance (HRMAS 1 H NMR) spectroscopy technique was employed to analyze the metabonomic characterizations of lung cancer tissues in hope to identify potential diagnostic biomarkers for malignancy detection and staging research of lung tissues. METHODS: HRMAS 1 H NMR spectroscopy technique can rapidly provide important information for accurate diagnosis and staging of cancer tissues owing to its noninvasive nature and limited requirement for the samples, and thus has been acknowledged as an excellent tool to investigate tissue metabolism and provide a more realistic insight into the metabonomics of tissues when combined with multivariate data analysis (MVDA) such as component analysis and orthogonal partial least squares-discriminant analysis in particular. RESULTS: HRMAS 1 H NMR spectra displayed the metabonomic differences of 32 lung cancer tissues at the different stages from 32 patients. The significant changes (P < 0.05) of some important metabolites such as lipids, aspartate and choline-containing compounds in cancer tissues at the different stages had been identified. Furthermore, the combination of HRMAS 1 H NMR spectroscopy and MVDA might potentially and precisely provided for a high sensitivity, specificity, prediction accuracy in the positive identification of the staging for the cancer tissues in contrast with the pathological data in clinic. CONCLUSION: This study highlighted the potential of metabonomics in clinical settings so that the techniques might be further exploited for the diagnosis and staging prediction of lung cancer in future.