Nanocellulose has gained significant traction as a viable material for tissue engineering. We investigated 3 chemically distinct varieties of tunicate-derived nanocellulose: carboxymethylated (CTC), enzymatically pretreated (ETC), and TEMPO-oxidised (TTC), with the objective of determining a candidate scaffold material for tissue engineering. The physicochemical properties of each variant were characterised by SEM, AFM, Raman spectroscopy, and mechanical compression. Notably, ETC had the lowest aspect ratio, smallest pore diameter, greatest optical clarity, and highest ultimate compressive strength, indicating superiority as a candidate tissue engineering scaffold. Rheological analysis revealed a significantly higher loss tangent for ETC and reduced viscosity at higher temperature, compared with CTC and TTC. ETC also demonstrated superior line width resolution both before and after calcium chloride crosslinking, indicating superior print fidelity and post-printing shape retention. All materials exhibited excellent biological compatibility by live/dead staining of embedded primary chondrocytes. NC was nonimmunogenic and did not stimulate the secretion of proinflammatory mediators when combined with whole blood. The implantation of NC-based bioink into immunocompetent mice did not evoke an adverse immunological reaction, and explanted constructs retained a stable form for 14 days. Tunicate-derived NC, particularly ETC, demonstrates excellent structural, biological, and mechanical properties with great potential for tissue engineering applications.

BACKGROUND:The majority of colorectal cancers (CRCs) are detected after symptomatic presentation to primary care. Given the shared symptoms of CRC and benign disorders, it is challenging to manage the risk of missed diagnosis. Colonoscopy resources cannot keep pace with increasing demand. There is a pressing need for access to simple triage tools in primary care to help prioritise patients for referral.AIM:To evaluate the performance of a novel spectroscopy-based CRC blood test in primary care.DESIGN & SETTING:Mixed-methods pilot study of test performance and GP focus group discussions in South Wales.METHOD:Patients on the urgent suspected cancer (USC) pathway were recruited for the Raman spectroscopy (RS) test coupled to machine learning classification ('Raman-CRC') to identify CRC within the referred population. Qualitative focus group work evaluated the acceptability of the test in primary care by thematic analysis of focus group theorising.RESULTS:A total of 532 patients aged ≥50 years referred on the USC pathway were recruited from 27 GP practices. Twenty-nine patients (5.0%) were diagnosed with CRC. Raman-CRC identified CRC with sensitivity 95.7%, specificity 69.3% with area under curve (AUC) of 0.80 compared with colonoscopy as the reference test (248 patients). Stage I and II cancers were detected with 78.6% sensitivity. Focus group themes underlined the convenience of a blood test for the patient and the test's value as a risk assessment tool in primary care.CONCLUSION:The findings support this novel, non-invasive, blood-based method to prioritise those patients most likely to have CRC. Raman-CRC may accelerate access to diagnosis with potential to improve cancer outcomes.

Spectral pre-processing is an essential step in data analysis for biomedical diagnostic applications of Raman spectroscopy, allowing the removal of undesirable spectral contributions that could mask biological information used for diagnosis. However, due to the specificity of pre-processing for a given sample type and the vast number of potential pre-processing combinations, optimisation of pre-processing via a manual “trial and error” format is often time intensive with no guarantee that the chosen method is optimal for the sample type. Here we present the use of high-performance computing (HPC) to trial over 2.4 million pre-processing permutations to demonstrate the optimisation on the pre-processing of human serum Raman spectra for colorectal cancer detection. The effect of varying pre-processing order, using extended multiplicative scatter correction, spectral smoothing, baseline correction, binning and normalization was considered. Permutations were assessed on their ability to detect patients with disease using a random forest (RF) algorithm trained with 102 patients (510 spectra) and independently tested with a set of 439 patients (1317 spectra) in a primary care patient cohort. Optimising via HPC enables improved performance in diagnostic abilities, with sensitivity increasing by 14.6%, specificity increasing by 6.9%, positive predictive value increasing by 3.4%, and negative predictive value increasing by 2.4% when compared to a standard pre-processing optimisation. Ultimate values of these metrics are very important for diagnostic adoption, and once diagnostics demonstrate good accuracy these types of optimisations can make a significant difference to roll-out of a test and demonstrating advantages over existing tests. We also provide tips/recommendations for pre-processing optimisation without the use of HPC. From the HPC permutations, recommendations for appropriate parameter constraints for conducting a more basic pre-processing optimisation are also detailed, thus helping model development for researchers not having access to HPC. Graphical Abstract

Cancer presenting with non-specific vague symptoms remains a clinical challenge. The purpose of this study was to assess the feasibility of serum Raman spectroscopy for cancer detection in a rapid diagnosis center (RDC) setting. The primary aim was to identify significant spectral peaks of change in sera from cancer patients and the secondary aim was to assign molecular species at Raman peaks. In this prospective observation study of a secondary care RDC, patients referred with vague cancer-related symptoms were recruited. Raman spectra of blood sera of 54 patients was obtained. Of these, 10 patients were diagnosed with cancer, and 44 no significant pathology (control). Common spectral increase/decrease between control and cancer was seen in spectral peaks 830 cm−1, 878 cm−1, 1031 cm−1, 1174 cm−1, 1397 cm−1 tentatively attributed to amino acids, carbohydrates, fatty acids, and proteins. Individual differences between cancer and control via statistical analysis identifies 3 peaks with significance for all 10 of the cancer patients. The peaks are 878 cm−1, 1449 cm−1 and 1519 cm−1, tentatively attributed to proteins, amino acids, lipids, fatty acids, glycoproteins, carbohydrates, and carotenoids. Differences are also seen for at least 9 of the cancers in the peaks at 830 cm−1, 851 cm−1, 1127 cm−1, 1174 cm−1, 1270 cm−1, and 1656 cm−1, tentatively attributed to amino acids, lactate, lipids, triglycerides, carbohydrates, and proteins. Raman spectroscopy has the potential to enhance RDC referral criteria through the detection of peak differences seen commonly with different cancer types. Development of Artificial Intelligence (AI) based models could enable rapid detection and discrimination of different cancer types with more data availability.

Spectral pre-processing is an essential step in data analysis for biomedical diagnostic applications of Raman spectroscopy, allowing the removal of undesirable spectral contributions that could mask biological information used for diagnosis. However, due to the specificity of pre-processing for a given sample type and the vast number of potential pre-processing combinations, optimisation of pre-processing via a manual "trial and error" format is often time intensive with no guarantee that the chosen method is optimal for the sample type. Here we present the use of highperformance computing (HPC) to trial over 2.4 million pre-processing permutations to demonstrate the optimisation on the pre-processing of human serum Raman spectra for colorectal cancer detection. The effect of varying preprocessing order, using extended multiplicative scatter correction, spectral smoothing, baseline correction, binning and normalization was considered. Permutations were assessed on their ability to detect patients with disease using a random forest (RF) algorithm trained with 102 patients (510 spectra) and independently tested with a set of 439 patients (1317 spectra) in a primary care patient cohort. Optimising via HPC enables improved performance in diagnostic abilities, with sensitivity increasing by 14.6%, specificity increasing by 6.9%, positive predictive value increasing by 3.4%, and negative predictive value increasing by 2.4% when compared to a standard pre-processing optimisation. Ultimate values of these metrics are very important for diagnostic adoption, and once diagnostics demonstrate good accuracy these types of optimisations can make a significant difference to roll-out of a test and demonstrating advantages over existing tests. We also provide tips/recommendations for pre-processing optimisation without the use of HPC. From the HPC permutations, recommendations for appropriate parameter constraints for conducting a more basic pre-processing optimisation are also detailed, thus helping model development for researchers not having access to HPC.

Preeclampsia (PE) is a common obstetric disorder typically affecting 2-8% of all pregnancies and can lead to several adverse obstetric outcomes for both mother and fetus with the greatest burden of severe outcomes in low middle-income countries (LMICs), therefore, screening for PE is vital. Globally, screening is based on maternal characteristics and medical history which are nonspecific for the disorder. In 2004, the World Health Organization acknowledged that no clinically useful test was able to predict the onset of PE, which prompted a universal search for alternative means of screening. Over the past decade or so, emphasis has been placed on the use of maternal characteristics in conjunction with biomarkers of disease combined into predictive algorithms, however these are yet to transition into the clinic and are cost prohibitive in LMICs. As a result, the screening paradigm for PE remains unchanged. It is evident that novel approaches are needed. Vibrational spectroscopy, specifically Raman spectroscopy and Fourier-transform infrared spectroscopy (FTIR), could provide better alternatives suited for implementation in low resource settings as no specialized reagents are required for conventional approaches and there is a drive to portable platforms usable in both urban and rual community settings. These techniques are based on light scattering and absorption, respectively, allowing detailed molecular analysis of samples to produce a unique molecular fingerprint of diseased states. The specificity of vibrational spectroscopy might well make it suited for application in other obstetric disorders such as gestational diabetes mellitus and obstetric cholestasis. In this review, we summarize current approaches sought as alternatives to current screening methodologies and introduce how vibrational spectroscopy could offer superior screening and diagnostic paradigms in obstetric care. Additionally, we propose a real benefit of such tools in LMICs where limited resources battle the higher prevalence of obstetric disorders.

Suspected colorectal cancer (CRC) referrals based on non-specific symptoms currently lead to large numbers of patients being referred for invasive investigations and poor yield in cancer detection. Secondary care diagnostics, particularly endoscopy, struggle to meet the ever-increasing demand and patients face lengthy waits from the point of referral. Here we propose a blood test utilising high-throughput Raman spectroscopy and machine learning as an accurate triage tool. We present results from the first mixed methods clinical validation study of its kind, evaluating the ability of the test to perform in its target population of primary care patients, and its acceptability to those administering and receiving the test. The test was able to accurately rule out cancer with a negative predictive value of 98.0%. This performance could reduce the number of invasive diagnostic procedures in the cohort by at least 47%. Collectively, our findings promote a novel, non-invasive solution to triage CRC referrals with potential to reduce patient anxiety, accelerate access to treatment and improve outcomes.

Herein, we present an experimental/computational approach for probing the interaction between metal contacts and carbon nanotubes (CNTs) with regard to creating the most efficient, low resistance junction. Tungsten probes have been coated with copper or chromium and the efficiency of nanocontact transport into multiwalled carbon nanotubes (MWCNTs) has been investigated experimentally, using scanning tunneling spectroscopy and nanoscale two-point probe I-V measurements, and in silico, employing DFT calculations. Experimental I-V measurements suggest the relative conductivity of the metal-CNT interaction to be Cu > W > Cr. It has been found that copper when in contact with MWCNTs results in a high density of states at the Fermi level, which contributes states to the conduction band. It was observed that the density of states also increased when chromium and tungsten probes were in contact with CNTs; however, in these cases the density of states increase would only occur under high voltage/high temperature situations. This is demonstrated by an increase in the experimental electrical resistance when compared to the copper probe. These results suggest that in future copper tips should be used when carrying out all intrinsic conduction measurements on CNTs, and they also provide a rationale for the ultraconductivity of Cu-CNT and Cu-graphene composites.

Measurement of the angular and overlap dependence of the conduction between two identical carbon nanotubes (CNTs), with the same diameter and chirality, has only been possible through theoretical calculations; however, our observation of increased resistance adjacent to the junction between two CNTs facilitates such measurements. Since electrical resistance was found to increase with increased diameter ratio, applying 10 V to one of dissimilar diameter CNTs results in cleavage at the junction. Manipulation of the resulting identical CNTs (created by cutting a single CNT) allows for the direct measurement of the angular and parallel overlap conduction. Angular (13° < θ < 63°) dependence shows two minima (22° and 44°) and a maximum at 30°, and conduction between parallel CNTs increases with overall tip separation but shows a sinusoidal relationship with contact length, consistent with the concept of atomic scale registry.

ZnO nanosheets are polycrystalline nanostructures that are used in devices including solar cells and gas sensors. However, for efficient and reproducible device operation and contact behaviour the conductivity characteristics must be controlled and surface contaminants removed. Here we use low doses of argon bombardment to remove surface contamination and make reproducible lower resistance contacts. Higher doses strip the surface of the nanosheets altering the contact type from near-ohmic to rectifying by removing the donor-type defects, which photoluminescence shows to be concentrated in the near-surface. Controlled doses of argon treatments allow nanosheets to be customised for device formation.

Vibrational spectroscopic techniques such as Raman spectroscopy and Fourier transform infrared spectroscopy (FTIR) have huge potential for the analysis of biological specimens. The techniques allow the user to gain label-free, non-destructive biochemical information about a given sample. Previous studies using vibrational spectroscopy with the specific application of diagnosing colorectal diseases such as cancer have mainly focused on in vivo or in vitro studies of tissue specimens using microscopy or probe based techniques. There have been few studies of vibrational spectroscopic techniques based on the analysis of blood serum for the advancement of colorectal cancer diagnostics. With growing interest in the field of liquid biopsies, this study presents the development of a high-throughput (HT) serum Raman spectroscopy platform and methodology and compares dry and liquid data acquisition of serum samples. This work considers factors contributing to translatability of the methodologies such as HT design, inter-user variability and sample handling effects on diagnostic capability. The HT Raman methods were tested on a pilot dataset of serum from 30 cancer patients and 30 matched control patients using statistical analysis via cross-validated PLS-DA with a maximum achieved a sensitivity of 83% and specificity of 83% for detecting colorectal cancer.

Colorectal cancer (CRC) is the fourth most common cancer in the United Kingdom and is the second largest cause of cancer related death in the United Kingdom after lung cancer. Currently in the United Kingdom there is not a diagnostic test that has sufficient differentiation between patients with cancer and those without cancer so the current referral system relies on symptomatic presentation in a primary care setting. Raman spectroscopy and surface enhanced Raman spectroscopy (SERS) are forms of vibrational spectroscopy that offer a non-destructive method to gain molecular information about biological samples. The techniques offer a wide range of applications from in vivo or in vitro diagnostics using endoscopic probes, to the use of micro-spectrometers for analysis of biofluids. The techniques have the potential to detect molecular changes prior to any morphological changes occurring in the tissue and therefore could offer many possibilities to aid the detection of CRC. The purpose of this review is to look at the current state of diagnostic technology in the United Kingdom. The development of Raman spectroscopy and SERS in clinical applications relation for CRC will then be discussed. Finally, future areas of research of Raman/SERS as a clinical tool for the diagnosis of CRC are also discussed.

Adsorption of functional groups to the surface of plasmonic nanoparticles provides a platform for localised optical sensing. Over the past decade, nanoscale sensors for intracellular pH measurement based on surface‐enhanced Raman spectroscopy (SERS) have been developed. However, the approaches by which pH‐SERS measurements are made and analysed can greatly impact the precision and accuracy of pH calibration. To improve pH nanosensors, the sources of experimental variation must be determined and the data must be optimally analysed. Here we report the plasmon‐induced decarboxylation of para‐mercaptobenzoic acid (pMBA) pH reporters attached to gold nanoparticles and conclude a strong association with laser power. The detrimental decarboxylation of pMBA has profound implications on the sensitivity and reliability of the pH sensor. Decarboxylation spectral signatures map directly onto those that are typically used to record pH changes, and, hence, the greatest implication of decarboxylation of pH sensors is inaccurate or false pH reporting. Here a more robust spectral analysis for pH sensing based upon an optimal spectral region for pH calibration is presented together with a unique application of the multivariate statistical technique, principal component analysis (PCA). PCA interprets complex spectral dynamics, and by direct comparisons with the typically employed ratiometric analysis, a significant improvement in generating accurate pH sensing is demonstrated. An application of these methods in determining the pH of internalised nanosensors in macrophage cells further promotes these step changes in pH measurement methodology via the avoidance of disruptive spectral signatures that arise in real applications. Copyright © 2016 John Wiley & Sons, Ltd.

Electrodeposited copper thiocyanate (CuSCN) thin films and nanowires have been investigated by X-ray photoelectron spectroscopy (XPS), Raman, and optical spectroscopy. In addition, atomic force microscopy (AFM) together with scanning and transmission electron microscopy (SEM and TEM) have been employed for structural characterization. The multiple technique approach allows the correlation between structural, chemical, and electrical properties that are unique to the structure of this material. It has been found that CuSCN thin films and nanowires exhibit high crystalline quality with a close to stoichiometric composition. The XPS and Raman spectra suggest that the thiocyanate ion is bound to copper mainly through its S-end, with approximately 12-14% bound via the N-end. The applied absorption spectroscopy (Tauc and Urbach plots) points toward the possible coexistence of two large band gaps for the electrodeposited CuSCN. While its interpretation may be problematic from a purely physical perspective, we believe that this is a direct consequence of the occurrence of two CuSCN domains identified by XPS and Raman. A prominent absorption tail is observed that is assigned as either being due to the high concentration of the traps or a result of coexisting CuSCN domains. This absorption tail should not be an obstacle for the use of the copper thiocyanate in electronic devices, as the trap density could be reduced by annealing. In addition, nonannealed electrodeposited CuSCN thin films and nanowires of this type have recently been integrated into polymer solar cells and high efficiency has been obtained.

A generic electrochemical method of 'bioreceptor' antibody attachment to phenyl amine functionalized graphitic surfaces is demonstrated. Micro-channels of chemically modified multi-layer epitaxial graphene (MLEG) have been used to provide a repeatable and reliable response to nano-molar (nM) concentrations of the cancer risk (oxidative stress) biomarker 8-hydroxydeoxyguanosine (8-OHdG). X-ray photoelectron spectroscopy, Raman spectroscopy are used to characterize the functionalized MLEG. Confocal fluorescence microscopy using fluorescent-labelled antibodies indicates that the anti-8-OHdG antibody selectively binds to the phenyl amine-functionalized MLEG's channel. Current-voltage measurements on functionalized channels showed repeatable current responses from antibody-biomarker binding events. This technique is scalable, reliable, and capable of providing a rapid, quantitative, label-free assessment of biomarkers at nano-molar (<20 nM) concentrations in analyte solutions. The sensitivity of the sensor device was investigated using varying concentrations of 8-OHdG, with changes in the sensor's channel resistance observed upon exposure to 8-OHdG. Detection of 8-OHdG concentrations as low as 0.1 ng ml(-1) (0.35 nM) has been demonstrated. This is five times more sensitive than reported enzyme linked immunosorbent assay tests (0.5 ng ml(-1)).

Understanding the role of defects in graphene is the key to tailoring the properties of graphene and promoting the development of graphene‐based devices. Defects can affect the electronic properties of a device while also offering a means by which to functionalize the local properties. Using tip‐enhanced Raman spectroscopy (TERS), heightened defect sensitivity was demonstrated on graphene edges, folds, and overlapping regions. Measurements confirm that TERS can provide simultaneous structural and spectral information on a localized scale, hence offering defect characterization on a scale that is not obtainable using conventional Raman spectroscopy. This study observed preferential enhancement of the D band signal on multilayered graphene and ultrathin graphite; in addition, other key defect signatures were also enhanced and detected. We present our findings in relation to theoretical predictions of graphene defect signatures and an analysis of the sensitivity of TERS in measuring two‐dimensional structures. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract Single-walled carbon nanotubes (SWCNTs) have recently attracted great attention because of their fibrous structure and high aspect ratio. Here the genotoxic potential of 400–800 nm, 1–3 μm and 5–30 μm SWCNT with respect to their geometry and surface characteristics was studied. Following thorough physico-chemical characterisation, human bronchial epithelial (BEAS−2B) and lymphoblastoid (MCL-5) cells were treated with SWCNT for 24 or 48 h. This showed significant increases in micronucleus frequency in a time- and dose-dependent manner in both cell types in the absence of cytotoxicity. Over the same dose range, only 1–3 μm SWCNT gave rise to significant increases in hprt point mutations at doses ≥25 μg/ml. Cellular 2,7-dichlorodihydrofluoresceindiacetate (DCFH-DA) fluorescence assay and RT-PCR for oxidative pathway gene profiling revealed a possible oxidative mechanism for the genotoxicity observed in the 1–3 μm SWCNT. Consequently, this study has demonstrated that SWCNT genotoxicity is dependent on its secondary structure under experimental conditions and oxidative stress alone cannot account for the observed damage.

In this study scanning near-field optical microscopy (SNOM) has been utilised in conjunction with quantum dot labelling to interrogate the biomolecular composition of cell membranes. The technique overcomes the limits of optical diffraction found in standard fluorescence microscopy and also yields vital topographic information. The technique has been applied to investigate cell-cell adhesion in human epithelial cells. This has been realised through immunofluorescence labelling of the cell-cell adhesion protein E-cadherin. Moreover, a dual labelling protocol has been optimised to facilitate a comparative study of the adhesion mechanisms and the effect of aberrant adhesion protein expression in both healthy and cancerous epithelial cells. This study reports clear differences in the morphology and phenotype of healthy and cancerous cells. In healthy prostate epithelial cells (PNT2), E-cadherin was predominantly located around the cell periphery and within filopodial extensions. The presence of E-cadherin appeared to be enhanced when cell-cell contact was established. In contrast, examination of metastatic prostate adenocarcinoma cells (PC-3) revealed no E-cadherin labelling around the periphery of the cells. This lack of functional E-cadherin in PC-3 cells coincided with a markedly different morphology and PC-3 cells were not found to form close cell-cell associations with their neighbours. We have demonstrated that with a fully optimised sample preparation methodology, multiplexed quantum dot labelling in conjunction with SNOM imaging can be successfully applied to interrogate biomolecular localisation within delicate cellular membranes.