Inhibition of 3-D tumor spheroids by timed-released hydrophilic and hydrophobic drugs from multilayered polymeric microparticles.

First-line cancer chemotherapy necessitates high parenteral dosage and repeated dosing of a combination of drugs over a prolonged period. Current commercially available chemotherapeutic agents, such as Doxil and Taxol, are only capable of delivering single drug in a bolus dose. The aim of this study is to develop dual-drug-loaded, multilayered microparticles and to investigate their antitumor efficacy compared with single-drug-loaded particles. Results show hydrophilic doxorubicin HCl (DOX) and hydrophobic paclitaxel (PTX) localized in the poly(dl-lactic-co-glycolic acid, 50:50) (PLGA) shell and in the poly(l-lactic acid) (PLLA) core, respectively. The introduction of poly[(1,6-bis-carboxyphenoxy) hexane] (PCPH) into PLGA/PLLA microparticles causes PTX to be localized in the PLLA and PCPH mid-layers, whereas DOX is found in both the PLGA shell and core. PLGA/PLLA/PCPH microparticles with denser shells allow better control of DOX release. A delayed release of PTX is observed with the addition of PCPH. Three-dimensional MCF-7 spheroid studies demonstrate that controlled co-delivery of DOX and PTX from multilayered microparticles produces a greater reduction in spheroid growth rate compared with single-drug-loaded particles. This study provides mechanistic insights into how distinctive structure of multilayered microparticles can be designed to modulate the release profiles of anticancer drugs, and how co-delivery can potentially provide better antitumor response.

Collagen-cellulose composite thin films that mimic soft-tissue and allow stem-cell orientation.

Mechanical properties of collagen films are less than ideal for biomaterial development towards musculoskeletal repair or cardiovascular applications. Herein, we present a collagen-cellulose composite film (CCCF) compared against swine small intestine submucosa in regards to mechanical properties, cell growth, and histological analysis. CCCF was additionally characterized by FE-SEM, NMR, mass spectrometry, and Raman Microscopy to elucidate its physical structure, collagen-cellulose composition, and structure activity relationships. Mechanical properties of the CCCF were tested in both wet and dry environments, with anisotropic stress-strain curves that mimicked soft-tissue. Mesenchymal stem cells, human umbilical vein endothelial cells, and human coronary artery smooth muscle cells were able to proliferate on the collagen films with specific cell orientation. Mesenchymal stem cells had a higher proliferation index and were able to infiltrate CCCF to a higher degree than small intestine submucosa. With the underlying biological properties, we present a collagen-cellulose composite film towards forthcoming biomaterial-related applications.

Designing drug-loaded multi-layered polymeric microparticles.

This work reports how novel multi-layered (from double-layered to quadruple-layered) microparticles comprising immiscible polymers can be fabricated through a simple, economical, reliable and versatile one-step solvent evaporation method. These multi-layered microparticles would be excellent candidates to overcome problems inherent in single-layered microparticles for drug delivery. Particle morphologies, layer configurations, and drug distribution were determined by scanning electron microscopy and Raman mapping. Key process parameters achieving the formation of the multi-layered structure were identified. Encapsulation of multiple drugs and layer localization of these drugs within these multi-layered microparticles have also shown to be possible, which were driven by drug-polymer affinity. This one-step fabrication technique can therefore be used for tailoring particle designs, thus facilitating the development of multiparticulate drug delivery devices.

A new insight for an old system: protein-PEG colocalization in relation to protein release from PCL/PEG blends.

Quantification of protein-polymer colocalization in a phase-separated polymer blend gives important insights into the protein release mechanism. Here, we report on the first visualization of protein-poly(ethylene glycol) (protein-PEG) colocalization in poly(ε-caprolactone)/poly(ethylene glycol) (PCL/PEG) blend films using a combined application of confocal Raman mapping and confocal laser scanning microscopy (CLSM) imaging. The degree of protein-PEG colocalization was further quantified via a novel image processing technique. This technique also allowed us to characterize the 3-D protein distribution within the films. Our results showed that the proteins were homogeneously distributed within the film matrix, independent of PEG content. However, the degree of protein-PEG colocalization was inversely proportional to PEG content, ranging from 65 to 94%. This quantitative data on protein-PEG colocalization was used along with in vitro PEG leaching profile to construct a predictive model for overall protein release. Our prediction matched well with the experimental protein release profile, which is characterized by an initial burst release and a subsequent slower diffusional release. More importantly, the success of this predictive model has highlighted the influence of protein-PEG colocalization on the protein release mechanism.

Investigating the effect of moisture protection on solid-state stability and dissolution of fenofibrate and ketoconazole solid dispersions using PXRD, HSDSC and Raman microscopy.

Enhanced dissolution of poorly soluble active pharmaceutical ingredients (APIs) in amorphous solid dispersions often diminishes during storage due to moisture-induced re-crystallization. This study aims to investigate the influence of moisture protection on solid-state stability and dissolution profiles of melt-extruded fenofibrate (FF) and ketoconazole (KC) solid dispersions. Samples were kept in open, closed and Activ-vials(®) to control the moisture uptake under accelerated conditions. During 13-week storage, changes in API crystallinity were quantified using powder X-ray diffraction (PXRD) (Rietveld analysis) and high sensitivity differential scanning calorimetry (HSDSC) and compared with any change in dissolution profiles. Trace crystallinity was observed by Raman microscopy, which otherwise was undetected by PXRD and HSDSC. Results showed that while moisture protection was ineffective in preventing the re-crystallization of amorphous FF, KC remained X-ray amorphous despite 5% moisture uptake. Regardless of the degree of crystallinity increase in FF, the enhanced dissolution properties were similarly diminished. Moisture uptake above 10% in KC samples also led to re-crystallization and significant decrease in dissolution rates. In conclusion, eliminating moisture sorption may not be sufficient in ensuring the stability of solid dispersions. Analytical quantification of API crystallinity is crucial in detecting subtle increase in crystallinity that can diminish the enhanced dissolution properties of solid dispersions.

Effect of excipient particle size distribution variability on compact tensile strength; and its in-line prediction by force-displacement and force-time profiling.

BACKGROUND: Direct compression is potentially sensitive to particle size distribution (PSD) variability in pharmaceutical grade excipients. Yet, the impact is insufficiently studied. Furthermore, the use of force sensor as a process analytical technology (PAT) platform, to monitor the effect of PSD variations on compact tensile strength, is a readily available but underutilized strategy. METHODS: To address these shortfalls, the effect of PSD variability on compaction was investigated. Low (4% w/w drug) and high (15% w/w drug) dose blends comprising chlorpheniramine, microcrystalline cellulose and spray-agglomerated lactose were tableted. The PSD of spray-agglomerated lactose was varied by adding ungranulated fines to simulate commercially-relevant variability. Tensile strength and disintegration time were determined. The use of force sensor, to generate force-displacement and force-time profiles, for in-line tensile strength prediction was evaluated. RESULTS: Increasing proportion of ungranulated fines (≥ 16%) reduced tensile strength by 10% and 4% in low and high dose formulations (p < 0.02). Increased friction during compaction hindered particle movement and reduced the energy available for bonding. Nevertheless, disintegration performances were equally acceptable for immediate drug release (≈ 30 s). Modelling of tensile strength with force-displacement and force-time profiles yielded ≥ 98% accuracy for in-line prediction (relative root mean square error of prediction = 3.7% and 4.8%). CONCLUSION: A better understanding of the relationship between PSD variability and direct compression was attained; and force-displacement and force-time profiling are pragmatic and elegant PAT strategies. Significantly, with further refinements, the force sensor in the rotary tablet press can be repurposed for process monitoring and quality inspection. This unlocks opportunities for process understanding and control, without additional investments in PAT platforms.

Application of Raman microscopy to biodegradable double-walled microspheres.

Raman mapping measurements were performed on the cross section of the ternary-phase biodegradable double-walled microsphere (DWMS) of poly(D,L-lactide-co-glycolide) (50:50) (PLGA), poly(L-lactide) (PLLA), and poly(epsilon-caprolactone) (PCL), which was fabricated by a one-step solvent evaporation method. The collected Raman spectra were subjected to a band-target entropy minimization (BTEM) algorithm in order to reconstruct the pure component spectra of the species observed in this sample. Seven pure component spectral estimates were recovered, and their spatial distributions within DWMS were determined. The first three spectral estimates were identified as PLLA, PLGA 50:50, and PCL, which were the main components in DWMS. The last four spectral estimates were identified as semicrystalline polyglycolic acid (PGA), dichloromethane (DCM), copper-phthalocyanine blue, and calcite, which were the minor components in DWMS. PGA was the decomposition product of PLGA. DCM was the solvent used in DWMS fabrication. Copper-phthalocyanine blue and calcite were the unexpected contaminants. The current result showed that combined Raman microscopy and BTEM analysis can provide a sensitive characterization tool to DWMS, as it can give more specific information on the chemical species present as well as the spatial distributions. This novel analytical method for microsphere characterization can serve as a complementary tool to other more established analytical techniques, such as scanning electron microscopy and optical microscopy.

One-step fabrication of triple-layered polymeric microparticles with layer localization of drugs as a novel drug-delivery system.

Particulate systems have tremendous potential to achieve controlled release and targeted delivery of drugs. However, conventional single-layered particles have several inherent limitations, including initial burst release, the inability to provide zero-order release, and a lack of time-delayed or pulsatile release of therapeutic agents. Multilayered particles have the potential to overcome these disadvantages. Herein, it is shown how triple-layered polymeric microparticles can be fabricated through a simple, economical, reliable, and versatile one-step solvent evaporation technique. Particle morphologies and layer configurations are determined by scanning electron microscopy, polymer dissolution tests, and Raman mapping. Key fabrication parameters that affect the formation of triple-layered polymeric microparticles comprising poly(DL-lactide-co-glycolide) (50:50), poly(L-lactide), and poly(ethylene-co-vinyl acetate) (40 wt% vinyl acetate) are discussed, along with their formation mechanisms. Layer thickness and the configurations of these microparticles are altered by changing the polymer mass ratios. Finally, it is shown that drugs can be localized in specific layers of the microparticles. This fabrication process can therefore be used to tailor microparticle designs, thus allowing such "designer" particulate drug-delivery systems to function across a wide range of applications.

Formation and degradation of biodegradable triple-layered microparticles.

In this work, we report how biodegradable triple-layered microparticles can be fabricated through a simple, reliable, and economical one-step solvent evaporation technique. Characterization of triple-layered PLGA (shell)/PLLA (middle layer)/EVA (core) microparticles was conducted and their formation mechanism was described. Subsequently, in vitro hydrolytic degradation of these microparticles was investigated. It was found that the PLGA shell degraded rapidly leaving behind double-walled microparticles of PLLA/EVA after 40 days. The middle PLLA layer degraded more rapidly than expected because of the migration of PLGA oligomers that created a hydrophilic and acidic microenvironment in the PLLA layer. These degradation results therefore provide important insights into how triple-layered microparticles degrade, and how their degradation characteristics affect the drug releasing properties of these novel microparticles.

De-risking excipient particle size distribution variability with automated robust mixing: Integrating quality by design and process analytical technology.

BACKGROUND: Particle size distribution (PSD) variability in excipients affects mixing. In response, manufacturers rely on raw material control and rigidly defined process parameters to achieve quality. However, this status quo is costly; and diverges from regulatory exceptions for process robustness. Although robustness improves cost and material usage efficiency, it remains under-adopted. METHOD: To address this gap, a robust batch mixing operation that mitigated the impact of PSD variability was evaluated, with blends comprising chlorpheniramine, microcrystalline cellulose and lactose. PSD of lactose was varied to simulate commercially-relevant variability. Due to PSD-induced rheological variations, the blends had different optimal mixing speeds. For the automation study, near infrared (NIR) spectroscopy; process optimization and endpoint detection algorithms; and control hardware were integrated within a cluster of software environments. NIR spectroscopy was employed for in-line PSD characterization and blend monitoring, to modulate mixing speed and detect endpoint (feedforward and feedback control). RESULTS: NIR spectroscopy rapidly detected PSD variations by the 6th-9th rotations, to activate feedforward control, which mitigated the effect of PSD variability and reduced the mixing time by 13-34%. Endpoints were correctly detected. PSD variations and blend homogeneity were accurately predicted (relative standard error of prediction ≤ 2%). CONCLUSION: The automated robust mixing operation was successful. Pertinently, NIR spectrometer can be adopted for multimodal sensing. Its applicability for production-driven characterization of raw materials in batch and continuous pharmaceutical processing should be further explored. Lastly, this study laid the groundwork for end-to-end implementation of process analytical technology in robust batch processing.

3D projection and multivariate image analysis for quantitative visual modelling of mixability and rheological behavior of lactose powder.

BACKGROUND: This study reports the use of multivariate time and image analysis of avalanche videographic data for quantitative visual modelling of mixability. Its usefulness, in mechanistically modelling a powder's rheological behavior in relation to mixing, was evaluated. METHODS: Particle size distribution (PSD) of a pharmaceutical grade lactose powder was modified to reflect commercially encountered variability. The PSD variants were rheologically distinct and had different mixability. Avalanche testing was performed on the modified lactose powders. Avalanche rheological properties (ARP) profiles and videos were collected for numerical and quantitative visual modelling, respectively. In quantitative visual modelling, videos captured were transformed into serial projected images. Important features of the projected images were extracted as eigen-images, to derive the avalanche rheological visual metric (ARVM). Mixability was modelled as a function of ARP or ARVM and the rotation speed. RESULTS: Relative to the ARP model, the ARVM models were highly interpretable. As a univariate expression of ARP, ARVM also possessed construct validity (r2 greater than 0.99, slope ≥ 0.96). Important rheological features of the lactose powders were holistically visualized within a single eigen-image which enabled the generation of simpler models (5 versus 34 variables for ARP model). The ARVM models predicted mixability of lactose powders with greater accuracy than the ARP model (relative root mean square error of external validation ≤ 3.30% versus 4.96%). CONCLUSIONS: Quantitative visual modelling is a viable alternative to purely numerical approaches. Most significantly, the model's interpretability and concreteness enable manufacturers to readily understand the risk posed by PSD variability on manufacturing processes and swiftly take pre-emptive actions, without being mired in multivariate data complexity. In addition, the use of quantitative visual approach in time series imaging, for studying and monitoring industrial processes, could also be explored.

The effect of rotation speed and particle size distribution variability on mixability: An avalanche rheological and multivariate image analytical approach.

The utility of modulating rotation speed in tumble mixing and its mechanistic interplay with particle size distribution (PSD) variability in excipients remain underexplored. They were investigated in this study. For the present purpose, PSD of a commercial grade lactose was modified to reflect commercially relevant variations; and mixed with microcrystalline cellulose and chlorpheniramine in a double-cone blender, at various rotation speeds. Model of mixing was constructed using avalanche rheological properties and was also rendered as quantifiable visual models using avalanche rheological visual metric (ARVM), to uncover mechanistic relationships. ARVM was derived through multivariate image analysis of avalanche flow. It was observed that increasing rotation speed reduced the number of rotations needed to achieve blend homogeneity by 30-33% for PSD variants with 16-20% fines, while increasing the number of rotations by 134% in PSD variants with less than 15% fines (p ≈ 0.00). ARVM successfully modelled (root mean square error of external validation = 2.46%) and revealed the mechanistic interplay. With increased proportion of fines, lactose exhibited quasi-parabolic motion with disaggregation of soft agglomerates and improved mixing. With decreased proportion of fines, lactose flowed as coherent wave-like masses with imperceptible dispersive tendency and increased dilation, which impeded mixing. In conclusion, this study contributes to process understanding and ideas for designing robust mixing operations. It showcases the usefulness of a quantitative visual approach, exemplified by the ARVM, to evaluate material variability and uncover its mechanistic impact on processing.

Use of Raman microscopy and multivariate data analysis to observe the biomimetic growth of carbonated hydroxyapatite on bioactive glass.

In the present contribution, the biomimetic growth of carbonated hydroxyapatite (HA) on bioactive glass were investigated by Raman microscopy. Bioactive glass samples were immersed in simulated body fluid (SBF) buffered solution at pH 7.40 up to 17 days at 37 degrees C. Raman microscopy mapping was performed on the bioglass samples immersed in SBF solution for different periods of time. The collected data was then analyzed using the band-target entropy minimization technique to extract the observable pure component Raman spectral information. In this study, the pure component Raman spectra of the precursor amorphous calcium phosphate, transient octacalcium phosphate, and matured HA were all recovered. In addition, pure component Raman spectra of calcite, silica glass, and some organic impurities were also recovered. The resolved pure component spectra were fit to the normalized measured Raman data to provide the spatial distribution of these species on the sample surfaces. The current results show that Raman microscopy and multivariate data analysis provide a sensitive and accurate tool to characterize the surface morphology, as well as to give more specific information on the chemical species present and the phase transformation of phosphate species during the formation of HA on bioactive glass.

Combined online spectroscopic, calorimetric, and chemometric analysis: reaction enthalpy determinations in single and parallel reactions.

Calorimetry and signal processing: Vibrational spectroscopies, heat-flow microcalorimetry, and multivariate analysis are combined to decouple the reaction enthalpies of parallel reactions [picture: see text]. This methodology allows the evaluation of reaction enthalpy from complex systems without recourse to conventional kinetic modeling. Simultaneous in situ/online spectroscopy and heat-flow measurements as well as multivariate analyses are performed, apparently for the first time, to determine heats of reaction for single and parallel reactions. Two different vibrational spectroscopy techniques, namely Raman and FTIR spectroscopy, are used in conjunction with flow-through TAM III microcalorimetry. With respect to the spectroscopic analysis, the reaction spectra are first analyzed to determine the pure-component spectra and the corresponding concentrations without recourse to external calibration. With respect to the calorimetric analysis, a soft modeling approach is employed to determine the heats of reaction without recourse to any conventional kinetic models. This combined approach is implemented to determine the extents of reaction as well as the corresponding heats of reaction at 298.15 K and 0.1 MPa for a) the hydrolysis of acetic anhydride (single reaction) and b) the hydrolysis of methyl paraben and ethyl paraben in alkaline solution (both single and parallel reactions). In the latter case, the heat-flow contributions from the two simultaneous reactions are successfully decoupled. Taken together, these results demonstrate proof of concept for the present approach. The newly developed methodology appears to be quite general and particularly useful for investigating complex reaction systems. This is particularly true for multiple simultaneous reactions and reactions where the detailed kinetic expressions are not available, or cannot be easily determined. The use of extents of reaction is also very helpful where there is high variability in reaction rates, that is, due to the presence of impurities, changes in catalyst activity, or concentrations, temperature, and pH.

Latent fingerprints analysis using tape-lift, Raman microscopy, and multivariate data analysis methods.

This paper describes the use of combined techniques, i.e. Raman spectral mapping, tape-lift, and multivariate data analysis, to extract chemical information of latent fingerprint and/or trace amounts of materials deposited in fingerprints. The tape-lift method was employed to lift trace particles, extrinsic materials, or sebum deposited on the finger of an individual after recent handling of such materials. The analysis of the tape-lifted materials was performed by Raman spectral mapping at a specific area. The collected mixture Raman spectra containing signals from lifting media and lifted materials was then deconvoluted using a powerful multivariate technique, namely band-target entropy minimization (BTEM). Three cases, i.e. a sebum-rich fingerprint after touching the forehead, a drug model comprising ibuprofen, L-arginine, and sodium bicarbonate, and an additive model comprising sucrose and aspartame were investigated. BTEM could recover all pure component spectra of both lifting media and tape-lifted materials. As such, all these test substances can be correctly identified using their unique pure Raman spectral signatures. In addition, the spatial distributions of all these identified components could also be determined. These combined three techniques hold promise as a new tool in forensic applications.

Combination of Raman microscopy, multiwell plate experimental designs, and BTEM analysis for high-throughput experimentation.

Both nonreactive and reactive multiwell plate experiments were combined with Raman microscopy and band-target entropy minimization (BTEM) analysis. The multicomponent nonreactive experiments showed that accurate pure component spectral estimation is possible without recourse to any spectral libraries. The multicomponent reactive experiments showed that, in addition to accurate pure component spectral estimation, concentration profiles can be obtained for quantitative purposes. In the present case, the solvent and time dependence of a cycloaddition reaction was addressed as the high-throughput experimentation issue. A total of 1152 experimental spectra were collected and analyzed. Two methods were used, namely, (A) each solvent set was individually analyzed and (B) the entire set of spectra, from 4 different solvents, were analyzed all together. Method B provided very satisfactory results. The present study with combined Raman-multiwell plate-BTEM analysis establishes proof of concept. The new approach appears to be applicable to other frequently conducted combinatorial/high-throughput experimentations. These include, but are not restricted to, chemo- and regioselective studies, solid-phase syntheses, etc.

Investigating the effect and mechanism of particle size distribution variability on mixing using avalanche testing and multivariate modelling.

Particle size distribution (PSD) variability in excipients is widely thought to affect the mixing process and the achievement of blend homogeneity. Yet, few studies have addressed this issue by attempting to ascertain the relationship and elucidate its mechanism. To address this, the model material, lactose, was modified to reflect commercially relevant PSD variations and mixed with microcrystalline cellulose and chlorpheniramine in a double-cone blender. Multivariate modelling and avalanche testing were applied to elucidate the relationship and mechanism. PSD variability can cause significant change in mixing time, by 8 times and 3 times (p ≈ 0.00) for high and low dose drug formulations, respectively. Achievement of blend homogeneity depended on the dispersive mixing mechanism (r2 = 0.99). Dispersive mixing was adversely affected by powder cohesiveness and powder dilation, which increased as the proportion of fine particles in lactose powder increased. This study yielded three conclusions. Firstly, PSD variability in pharmaceutical grade excipients can cause unacceptable prolongation in mixing time. Secondly, the impact of PSD variability on continuous mixing and other batch mixing of various scales, requires investigation. Lastly, the current findings can contribute to the development of robust mixing operations in the form of offline pre-emptive measures and inline process control strategies.

Near infrared spectroscopy for rapid and in-line detection of particle size distribution variability in lactose during mixing.

Particle size distribution (PSD) variability in excipients may cause unacceptable prolongation of mixing time needed to achieve blend homogeneity. Therefore, it is vital to modulate mixing through real-time monitoring of PSD variability. Notwithstanding the criticality of PSD variability, real-time measurement of PSD during mixing is relatively unexplored; and this is the focus of the present study. The model excipient was commercial grade lactose with modified PSD that conformed to the manufacturer's specifications. It was mixed with microcrystalline cellulose and chlorpheniramine in a double-cone blender. High and low dose blends were prepared and near infrared spectroscopy (NIRS) was used to collect spectral data, during mixing, for chemometric modelling of PSD. Four modelling approaches based on partial least squares regression (PLSR) were applied. The models were highly interpretable and rapidly measured PSD near the beginning of mixing (5th to 6th rotation), with accuracy (relative standard error of prediction <5.0%, r2 ≈ 1.00, slope ≈ 1.00). Therefore, NIR chemometric modelling is a viable strategy to detect variability in PSD of excipients during blending and could enable real-time control of mixing. Most significantly, this strategy is potentially transferable to the monitoring and controlling of batch and continuous processes, where PSD is either a source of process variability or a critical quality attribute.

Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines.

The ability of combining near-infrared (NIR) Raman spectroscopy with support vector machines (SVM) for improving multi-class classification between different histopathological groups in tissues was evaluated in this study. A total of 105 colonic tissue specimens from 59 patients including 41 normal, 18 hyperplastic polyps and 46 adenocarcinomas were used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was utilized for tissue Raman spectroscopic measurements at 785-nm laser excitation. A total of 817 tissue Raman spectra were acquired and subjected to principal components analysis (PCA) for SVM-based multi-class classification, in which 324 Raman spectra were from normal, 184 from polyps and 309 from adenocarcinomatous colonic tissue. Two types of SVM (i.e., C-SVM and nu-SVM) with three different kernel functions (linear, polynomial and Gaussian radial basis function (RBF) in combination with PCA were used to develop effective diagnostic algorithms for classification of Raman spectra of different colonic tissues. The performance of various SVM-based algorithms was evaluated and compared using a leave-one-out, cross-validation method. The results showed that in the C-SVM classification, the maximum overall diagnostic accuracy of 99.3, 99.4 and 99.9% can be achieved using the linear, polynomial and RBF kernels, respectively; while in the nu-SVM classification, the maximum overall diagnostic accuracy of 98.4, 98.5 and 99.6% can be obtained using the linear, polynomial and RBF kernels, respectively. All the polyps can be identified from normal and adenocarcinomatous tissue using the C-SVM algorithms. The RBF C-SVM algorithm was proven to be the best classifier for providing the highest diagnostic accuracy (99.9%) for multi-class classification. This study demonstrates that NIR Raman spectroscopy in combination with a powerful SVM technique has great potential for providing an effective and accurate diagnostic schema for cancer diagnosis in the colon.

DNA detection using nanostructured SERS substrates with Rhodamine B as Raman label.

A technique is demonstrated to detect DNA hybridization at low concentrations, based on Surface-Enhanced Raman Scattering (SERS) using silicon nanostructures coated with gold-silver as substrate. Standard silicon process technologies were employed to fabricate the SERS substrates featuring nanogaps with a characteristic distance of 15+/-10nm. Target DNA was hybridized with cysteine-modified Peptide Nucleic Acids (PNA), which was previously fixed into the nanogaps as the capture sites. After hybridization, the introduced phosphate groups from the backbone of the target DNA showed strong affinity to an inorganic linker, Zr(4+), so that resulting in the assembly substrate-PNA-DNA-Zr. Since PNA does not possess phosphate groups, the linker is avoided when there is no hybridization from the complimentary DNA. Subsequently, the assembly of substrate-PNA-DNA-Zr was incubated with a Raman label, Rhodamine B (RB). The carboxylic acid group in RB reacted with the linker Zr(4+) allowing this Raman Label to be attached to the assembly substrate-PNA-DNA-Zr. The Raman peaks corresponding to RB were selected to detect the target DNA, with a detection limit of 1 x 10(-12)M.