Reliability Of Analytical Results Research Articles

A Review on Advancement in Analytical Quality by Design (AQbD)

Analytical Quality by Design (AQbD) represents a transformative methodology in pharmaceutical development, anchored in a systematic, risk-based, and data-driven framework. This approach optimizes analytical methods, fostering heightened product quality, efficient regulatory compliance, and informed decision-making. The industry's increasing acceptance of AQbD principles signifies a paradigm shift towards enhanced efficiency, sustainability, and global harmonization. This review comprehensively explores AQbD principles, regulatory perspectives, and its applications, particularly in analytical method development, including high-performance liquid chromatography (HPLC) and high-performance thin-layer chromatography (HPTLC). Emphasis is placed on the symbiotic relationship between AQbD and analytical method validation (AMV), elucidating their collective role in ensuring reliable and accurate analytical results. Integrating AQbD in method transfer, automation, and control strategies underscores its pivotal role in achieving robust, efficient, and compliant analytical processes. The review delves into lifecycle management and continuous improvement, coupled with AQbD principles, ensuring sustained method reliability throughout the pharmaceutical product lifecycle. AQbD's significant contribution to pharmaceutical lifecycle management, optimization, and change control is explored, emphasizing its systematic, data-driven, and risk-based approach to method development, validation, and ongoing enhancement. This review illuminates AQbD's transformative impact on pharmaceutical analysis, aligning with industry trends toward quality, efficiency, and regulatory compliance.

Assessment of living quality in Guangdong: A hybrid knowledge-based and data-driven approach

Air, water, and radiation in urban living environments are crucial factors affecting human health, societal well-being, and sustainable development. This study developed a novel hybrid knowledge-based and data-driven approach for analyzing air, water, and radiation monitoring data to assess the quality of urban living natural environments (ULNEQ) in Guangdong Province. Fuzzy set-pair analysis was employed for data preprocessing, effectively incorporating domain knowledge into the raw data. Then, a hierarchical clustering algorithm was utilized to evaluate the ULNEQ level. The proposed approach enhanced data interpretability and optimized the clustering process, yielding more robust and reliable analytical results. The results revealed a diverse environmental landscape across the province, with Heyuan and Meizhou consistently maintaining high standards, whereas Dongguan and Jieyang exhibited notably poor conditions, ranking at Level IV or V for over 72% of the observed months. Key factors such as monthly average concentration ozone (0.56), the city water quality index (0.49), and the proportion of days with standard air quality (PDSAQ) (−0.39) significantly influenced the ULNEQ, with PDSAQ showing a negative correlation. Notably, a province-wide observation in June 2022 showed all cities maintaining ULEQ gradings of Level II or better. This innovative approach can be adapted to enhance the management and control of ULNEQ.

Deep Learning-Based Segmentation of Lesions from Wide-Field Vitiligo Images

Evaluation of vitiligo relies on accurate segmentation of lesions, and traditional segmentation methods mainly focus on near-field images. This study proposes a deep learning-based model for accurately segmenting lesions in wide-field vitiligo images. In this study, a dataset of 1267 wide-field vitiligo images was established to train and evaluate segmentation models. A Swin R-CNN model, which combined a Swin Transformer tiny network with a watershed algorithm, was proposed for segmenting lesions. The performances of the Swin R-CNN model and five other models were evaluated and compared through visual and quantitative perspectives. Additionally, the Spearman rank correlation test was performed to analyze result consistency between the Swin R-CNN model and dermatologists in measuring lesion area. The Swin R-CNN model accurately segmented lesions in the wide-field vitiligo images, surpassing other models in both visual and quantitative performance, with an average precision of 84.72% and an average recall of 77.81%. The correlation coefficients between the evaluation results of the Swin R-CNN model and three dermatologists were 0.88, 0.94, and 0.91, respectively. The Swin R-CNN model accurately segments lesions in the wide-field vitiligo images and quantifies lesion area at the dermatologist level. The Swin R-CNN model can provide reliable analytical results for the vitiligo evaluation.

Application of microwave digestion for complete dissolution of igneous silicate rock samples: A simple and quick sample preparation procedure

In the area of geochemical analyses of rock solutions, achieving a complete sample dissolution is a fundamental prerequisite for obtaining accurate, precise and reliable analytical results. The challenge posed by the presence of resistant minerals such as zircon, rutile, corundum, spinel, tourmaline, beryl, chromite, and cassiterite in different silicate rocks is a well-recognized challenge in geological studies. These minerals, due to their resilient nature, demand additional efforts to ensure complete dissolution during sample preparation. The prevailing conventional sample digestion methods require several days of laboratory work and the handling of large amounts of multiple types of acids, which also increase sample blanks. Until recently, there was a widely held belief that microwave-assisted digestion, where microwave radiation is transformed to heat, faced limitations in achieving complete dissolution of refractory minerals. This prevailing opinion led to skepticism about the applicability of microwave-assisted digestion for sample preparation of e.g. igneous rock samples containing these minerals. This study introduces a novel, universal and quick closed-vessel (pressurized) high-temperature microwave-assisted digestion method appropriate for dissolution of all major types of igneous silicate rock samples, including rocks containing refractory minerals. This streamlined and expeditious procedure, comprising three steps, requires only a total time of ∼9 h. The method proves its versatility by successfully dissolving both, mafic igneous samples (e.g., basalt) with low-content of resistant minerals, and felsic igneous samples (e.g., granite) with relatively high-content of resistant minerals. To validate the reliability of this procedure, 36 trace elements were analyzed: Li, Be, Sc, V, Cr, Co, Ni, Cu, Zn, Ga, Rb, Sr, Y, Zr, Nb, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, Pb, Th and U in several geological Certified Reference Materials (CRMs). The CRMs including basalts JB-3, BCR-2, BHVO-2; andesites JA-2, AGV-2; granodiorite GSP-2; granite JG-2 and alkaline granite MGL-OShBO, were digested and analyzed using triple quadrupole Inductively Coupled-Plasma-Mass Spectrometer (ICP-QQQ). The results of the analysis demonstrate remarkable consistency, closely aligning with both certified and literature values.

Facile synthesis of sub-10 nm Cu-MOF nanofibers as electrochemical sensor element for picomolar chloramphenicol detection

Precise detection of antibiotics in aquatic environments is significant for human health and ecological environment development due to the excessive use of antibiotics. However, the accurate detection of ultralow-level antibiotics, such as picomolar level, has always been an extremely challenging task. Herein, we synthesized the sub-10 nm copper-based metal–organic framework nanofibers (Cu-MOF NFs) with a large specific area and high conductivity for achieving the electrochemical detection of picomolar-level chloramphenicol (CAP) in real water samples. As a result, by optimization of synthesis time and detection conditions, Cu-MOF NFs with a diameter of about 5 nm exhibited the ultralow limit of detection with 0.03 pM and wide detection range with 0.1––1.0 × 106 pM for CAP detection, as well as the designed sensor electrode has the excellent specificity, reproducibility, and stability. Importantly, the prepared sensor electrode exhibits reliable analytical results for CAP in real water samples, such as tap water, bottled water, and milk, indicating that designed sensor electrode has great application potential for the precise detection of antibiotics in practical samples.

Photostability of sennosides and their aglycones in solution.

Sennosides are the main active constituents of the dried leaves and/or pods of Senna alexandrina Mill. that are used as laxatives. A hypothesis is that aglycones are formed during the degradation of sennosides. However, it is unknown, whether this happens under visible light exposure and how photosensitive sennosides behave in solution. Pure anthraquinone glycosides were tested on their behaviour during sample preparation in the lab under visible light exposure in dependence on the instability of the solvent. Samples before and after exposure were analysed using UHPLC with UV/Vis and MS detection. Under visible light protection, the solutions were stable for 14 days at room temperature whereas a loss of 20%-60% was measured after 1 day of light exposure. The loss of sennosides due to degradation can be as fast as up to 2%-2.5% per hour, which might have a tremendous impact on phytochemical analysis results during the course of an analysis. The formation of aglycones was not observed in the degradation of sennosides and rhein-8-O-glucoside. Aglycones could not be found as a result of the forced degradation. The solutions of sennosides clearly need to be protected from light to obtain reliable analytical results, and light protection is a major point for the stability of liquid preparations.

Applying a risk assessment guided evaluation for verifying comprehensive two-dimensional gas chromatography to analyse complex pharmaceuticals

The reliability of analytical results is critical and indispensable when applied in regulated environments such as the pharmaceutical industry. Therefore, analytical workflows must be validated. However, validation guidelines are often designed for quantitative targeted analysis and rarely apply to qualitative untargeted approaches. In this study, we employ a risk assessment approach to identify critical parameters which might influence the qualitative results derived by online derivatisation — comprehensive two-dimensional gas chromatography coupled to a high-resolution time-of-flight mass spectrometer (GC × GC-HR-ToF–MS) for the analysis of the active pharmaceutical ingredient (API) sodium bituminosulfonate (SBS). To show the complexity and feasibility of such an approach, we focus on investigating three potential risk factors: sample preparation, vapourability, and the thermal stability of sulfonates. Through the individual evaluation of these potential risk factors due to the application of sample preparation approaches and thermal gravimetric analysis (TGA), we demonstrate the high derivatisation efficiency and repeatability of the online derivatisation method and confirm the absence of derivatisation-induced side reactions. In addition, we also show the potential thermal instability of an incompletely derivatised API. To address the limitation of these individual assessments, we applied a holistic evaluation step with negative electrospray ionisation Fourier transform ion cyclotron resonance mass spectrometry (ESI( −) FT-ICR MS) as an orthogonal technique. This confirms that most of the API is detected via the presented GC-based method. Thereby, we demonstrated the practical feasibility of the risk assessment-based approach to ensure the validity of the qualitative data for a complex untargeted method.Graphical

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics.

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

Adsorption on UiO-66 for preconcentration, matrix separation, slurry sampling and in situ hydride generation-atomic fluorescence spectrometric determination of trace bismuth in alloy and water

The determination of trace bismuth in environmental or alloy samples are rather challenging due to the low abundance and interferences arising from complicated sample matrix including heavy metals. In this work, Zr-based metal organic frameworks (UiO-66) was prepared and utilized to selectively extract Bi(III) from water samples or digests of iron alloy. Following simple centrifugation separation, Bi(III)-adsorbed UiO-66 was re-suspended in dilute hydrochloric acid for direct slurry sampling and in-situ hydride generation for atomic fluorescence spectrometric (HG-AFS) detection of bismuth. Experimental conditions affecting the adsorption of Bi(III) on UiO-66 as well as the slurry sampling HG-AFS process were carefully investigated. Under the experimental conditions, a limit of detection (LOD) of 0.1 ng/mL was achieved for bismuth. Inspiringly, the method could resist interferences from transition metals like Cu(II), Ni(II) and Fe(III), and this ensured reliable analytical results for the determination of trace bismuth in high-purity iron alloy, seawater, and river water samples.

A CRISPR-Cas12a powered electrochemical sensor based on gold nanoparticles and MXene composite for enhanced nucleic acid detection

Rapid and accurate detection of nucleic acids plays a critical role in public health, food safety and environmental management. Clustered regularly interspaced short palindromic repeats powered electrochemical sensor (E-CRISPR) is attractive as a point-of-care (POC) testing platform to fulfil this purpose. Yet, bulk noble metals (e.g., gold and platinum) based electrodes that have been widely adopted in E-CRISPR suffer from limited analytical performance and high manufacturing costs. Here, to address this limitation, we present a carbon-based E-CRISPR modified with gold nanoparticles and MXene Ti3C2 (a class of two-dimensional transition metal carbide nanomaterials) that provides highly stable and sensitive transduction of CRISPR-Cas12a trans-cleavage activity. Through systematic evaluation and optimization, our AuNPs/MXene Ti3C2 based E-CRISPR achieve the quantification of human papillomavirus 18 (HPV-18) DNA with a wide range of concentrations from 10 pM to 500 nM with a detection limit of 1.95 pM. We further evaluate the selectivity, degradation resistance and detection capability of the developed sensor during long-term storage. Notably, AuNPs/MXene based E-CRISPR retain more than 70% of initial current after 2 months and deliver reliable analytical results that are unaffected over 42-day storage. Owing to its excellent biofouling-resistant and analytical performance and robust shelf life, our E-CRISPR sensor offers a universal, scalable and low-cost strategy for POC nucleic acid testing.

Efficient Detection of 2,6-Dinitrophenol with Silver Nanoparticle-Decorated Chitosan/SrSnO3 Nanocomposites by Differential Pulse Voltammetry.

Herein, an ultra-sonication technique followed by a photoreduction technique was implemented to prepare silver nanoparticle-decorated Chitosan/SrSnO3 nanocomposites (Ag-decorated Chitosan/SrSnO3 NCs), and they were successively used as electron-sensing substrates coated on a glassy carbon electrode (GCE) for the development of a 2,6-dinitrophenol (2,6-DNP) efficient electrochemical sensor. The synthesized NCs were characterized in terms of morphology, surface composition, and optical properties using FESEM, TEM, HRTEM, BET, XRD, XPS, FTIR, and UV-vis analysis. Ag-decorated Chitosan/SrSnO3 NC/GCE fabricated with the conducting binder (PEDOT:PSS) was found to analyze 2,6-DNP in a wide detection range (LDR) of 1.5~13.5 µM by applying the differential pulse voltammetry (DPV) approach. The 2,6-DNP sensor parameters, such as sensitivity (54.032 µA µM-1 cm-2), limit of detection (LOD; 0.18 ± 0.01 µM), limit of quantification (LOQ; 0.545 µM) reproducibility, and response time, were found excellent and good results. Additionally, various environmental samples were analyzed and obtained reliable analytical results. Thus, it is the simplest way to develop a sensor probe with newly developed nanocomposite materials for analyzing the carcinogenic contaminants from the environmental effluents by electrochemical approach for the safety of environmental and healthcare fields in a broad scale.

Recommendations for Choosing a Test Dose for Abnormal Toxicity Testing

Abnormal toxicity testing helps to detect unexpected toxic impurities in medicinal products for parenteral use and in the corresponding active pharmaceutical ingredients of natural origin. In accordance with the requirements of the State Pharmacopoeia of the Russian Federation and in order to obtain reliable results, all test parameters, the most important of which is the test dose, should be correct. In this paper, the experts of the Scientific Centre for Expert Evaluation of Medicinal Products offer their recommendations on range finding. A correct test dose will provide reliable analytical results and ensure the safety of the evaluated medicinal products.

Method Validation and Measurement Uncertainty Evaluation of the Radiochemical Procedure for the Determination of 231Pa in Siliceous Cake by Gamma Spectrometry

An in-house radiochemical procedure for the separation of 231Pa from siliceous cake - a waste stream of monazite processing plant, followed by its determination using gamma ray spectrometry, has been developed in our laboratory. Evaluation of reliability of analytical results of the radiochemical procedure is important for validating the fitness of purpose of an analytical method. The present paper describes the statistical evaluation of the analytical results obtained, by estimating the overall uncertainty in the measurement process. The bottom-up approach, also called as ISO approach (ISO/IEC guide 98-3:2008), was adopted to evaluate the overall uncertainty in which the individual sources were identified, evaluated and propagated. The major contribution to the combined uncertainty in measurement of 231Pa was from efficiency calibration. Besides this, counting statistics in the sample and chemical yield of the process incorporated significant contribution to combined uncertainty. The precision of the results obtained was found to be acceptable, as ascertained by the Horwitz Ratio, for the performance of a single laboratory.

Experimental and density functional theoretical modeling of triazole pesticides extraction by Ti2C nanosheets as a sorbent in dispersive solid phase extraction method before HPLC-MS/MS analysis

In this research, a convenient, fast, and green dispersive solid phase extraction procedure based on Ti2C nanosheets was developed for effectively enrichment of some triazole pesticides prior to high performance liquid chromatography-tandem mass spectrometry. Characterization of the etching sorbent was carried out in details. Adsorption and desorption steps have been optimized in detail to obtain efficient phase separation and reliable analytical results. Under optimized conditions, the presented method exhibited a wide working range (0.52–1000 ng mL−1), low detection limits (0.03–0.30 ng mL−1) and acceptable extraction recoveries (70–75%). The introduced method precision was checked by calculating intra and inter-day relative standard deviations for three different concentrations of triazole pesticides. The matrix effect was investigated by the recovery study. Following validation studies, the presented method was successfully applied to the extraction and determination of the studied triazole pesticides from fruit juice samples. The adsorption mechanism was supported by theoretical studies. The interactions between the studied pesticides and Ti2C nanosheets were investigated with the help of density functional theoretical calculations. The calculations supported the obtained experimental results.

Technical efficiency and firm heterogeneity in stochastic frontier models: application to smallholder maize farms in Ethiopia

This study estimates the technical efficiency measures of maize producing farm households in Ethiopia using stochastic frontier (SF) panel models that take different approaches to model firm heterogeneity. The efficiency measures are found to vary depending on how the estimation model treats both unobserved and observed firm heterogeneity. Estimates from the ‘true’ random effects (TRE) models that treat firm effects as heterogeneity are found to be identical to those from pooled SF models. Those results differ from the ones generated from the basic random effects (RE) models that treat firm effects as part of overall technical inefficiency. The more flexible generalised ‘true’ random effects (GTRE) model that splits the error term into firm effects, persistent inefficiency, transient inefficiency, and a random noise component indicates the presence of higher levels of persistent inefficiency than transient inefficiency. The basic truncated-normal RE model and heteroscedastic RE model yields similar efficiency estimates. The GTRE model predict persistent efficiency measures similar to those from the basic RE and flexible RE model with environmental variables incorporated in the variance function as well as in the deterministic production frontier. These results imply that the RE and GTRE panel models provide reliable efficiency estimates for our data compared to the TRE models. All the estimated SF models generate comparable production function parameters in terms of magnitude and sign. Overall, the results underscore the importance of scrutinising stochastic frontier models for their reliability of analytical results before drawing policy inferences.

Computational Analysis of Water/Cu Nano Fluid Dynamic Viscosity using Molecular Dynamic Simulations

To enhance thermal management for micro-electric devices, nanofluids become promising working fluids for many thermo-fluid applications. Thermal characteristics of the working fluids can be improved by nano particles additives dispersed in the working fluid such as Cu-nanoparticles in water. The nanoparticle additives manage to alter thermal and dynamic properties of the working fluids such as the dynamic viscosity which plays an important role in specifying thermal and dynamic behaviour of the working media. In order to understand the effect of modifying the dynamic viscosity of the working media, the effective value of this property must be determined. The molecular dynamic (MD) simulation has been used to estimate the Cu/water nanofluid dynamic viscosity at partial volume fractions of φ=0.0125 % and φ= 0.02478 %, and at working temperatures 293 K, 303 K, 313 K, 323 K and 333 K. The used spherical shape nanoparticles are made up of numbers of 0.3-nm-diameter Cu-atoms. The MD simulation results have been compared to reliable experimental and analytical results. The estimated values of the dynamic viscosity using MD simulations converge very well to the experimental and analytical values of the dynamic viscosity, which reveals the advantages of using MD simulations to determine physical properties of nanofluid working medias and hence to design more efficient working fluids. The RDF shows good results for the SPCE model

Development of sodium hydroxide-induced homogenous liquid-liquid extraction-effervescent assisted dispersive liquid-liquid microextraction based on deep eutectic solvents; Application in the extraction of phytosterols from cow cream samples

In this study, sodium hydroxide-induced homogenous liquid-liquid extraction was combined with effervescent assisted-dispersive liquid-liquid microextraction and used for the extraction of phytosterols (lupeol, β-sitosterol, stigmasterol, campesterol, and brassicasterol) from cream samples. Two types of deep eutectic solvents (water-miscible and water-immiscible solvents) were prepared and used in the extraction procedure. In this approach, firstly, the sample was dissolved in a mixture of n-hexane and tetrabutylammonium bromide: ethylene glycol deep eutectic solvent (as extraction/disperser solvent) and a homogenous solution was obtained. After that, a few microliters of sodium hydroxide solution (as fatty acid saponification and phase separation agent) was quickly injected into the mixture. By doing so, a three-phase system was formed. Then, the supernatant phase (tetrabutylammonium bromide: ethylene glycol deep eutectic solvent) was removed and mixed with tetrabutylammonium bromide: dichloroacetic acid: octanoic acid deep eutectic solvent. The obtained solution was applied in the microextraction step. To obtain effective, reliable, robust, accurate, and precise analytical results, the effective parameters for the presented method were investigated and optimized in detail. Detection limits, extraction recoveries, and enrichment factors were in the ranges of 0.06–0.26 μg kg–1, 86–93 %, and 215–232, respectively. Intra- and inter-day repeatabilities were investigated at a concentration of 1.5 μg kg–1 (each analyte) and they were in the ranges of 5.3−7.5 and 7.2–8.8 %, respectively. Finally, the presented method was easily used in determination of the phytosterols in cow milk cream samples.

Optimization and application of ultrasound-assisted sugar based deep eutectic solvent dispersive liquid–liquid microextraction for the determination and extraction of aflatoxin M1 in milk samples

This study presents an ultrasound-assisted sugar based deep eutectic solvent dispersive liquid–liquid microextraction procedure (UA sugar-DES DLLME) for the extraction of aflatoxin M1 (AFM1) in milk samples prior to its determination by UV–VIS spectrophotometer. Also, in this approach, different sugar based deep eutectic solvents were prepared and their ability in extraction and separation of the AFM1 from the samples were investigated. The UA sugar-DES DLLME procedure consists of three steps. In the first step, pyrogallol and sugar based-DES solutions were added to the sample solution at pH 3.8. In the second step, ultrasound was applied to the solution obtained and the AFM1 were extracted into the UA sugar based-DES phase. In the final step, the resulting mixture was centrifuged and the sediment phase was separated from the mixture. After the microextraction, the sediment phase was analyzed at 279 nm by UV–VIS spectrophotometer. In order to obtain reliable, accurate and precise analytical results, the effective microextraction parameters were investigated and optimized using response surface methodology. Under optimum conditions, detection limit, extraction recovery, and enrichment factor were 6.1 ng L–1, 91 ± 3–107 ± 6%, and 328, respectively. Intraday and inter-day studies were performed at concentrations of 50, 100 and 200 ng L–1 of the AFM1 for the precision of the UA sugar-DES DLLME procedure. The intraday and inter-day precision results ranged from 3.3 to 5.1% and 4.1–6.2%, respectively. Also, it was successfully used for determination of the selected milks.

Nanozyme catalysis-assisted ratiometric multicolor sensing of heparin based on target-specific electrostatic-induced aggregation

Monitoring the level of heparin in clinical matrices is significant because of its pivotal role in preventing thrombosis. Compared to traditional single-signal sensors, multi-signal ratiometric detection can provide anti-interference results especially in complicated environments. However, fabricating an easy-to-operation, low-cost and robust sensor for the ratiometric detection of heparin still remains challenging. Here we propose a novel nanosensor for the ratiometric multicolor sensing of heparin with high performance. The sensor is based on the specific electrostatic interaction between the target and a positively charged species generated from nanozyme catalysis. FeMoO4 nanorods are explored as an oxidase mimic for the first time, showing a high activity at neutral pH to catalyze the colorless 3,3′,5,5′-tetramethylbenzidine (TMB) oxidation to blue TMBox with an absorbance at 652 nm. Heparin can induce the rapid aggregation of the produced TMBox intermediate with rich positive charges due to their strong electrostatic interaction, leading to the formation of a purple Heparin-TMBox complex providing a signal at 565 nm. With the increase of heparin, the color changes from blue to indigo and further purple, enabling the multicolor sensing of the target. As a result, ultrasensitive determination of heparin was obtained with a very low detection limit. The fabricated nanosensor could differentiate heparin from complex species with no interferences, and it provided reliable analytical results for heparin in both serum and plasma. With robust performance, low cost and facile fabrication, the sensor holds great potential in monitoring heparin for clinical applications.

Biopharmaceutical Study of Triamcinolone Acetonide Semisolid Formulations for Sublingual and Buccal Administration.

The mouth can be affected by important inflammatory processes resulting from localized or systemic diseases such as diabetes, AIDS and leukemia, among others, and are manifested in various types of buccal sores typically presenting pain. This work focuses on the design, formulation, and characterization of four semisolid formulations for oral mucosa in order to symptomatically treat these painful processes. The formulations have two active pharmaceutical ingredients, triamcinolone acetonide (TA) and lidocaine hydrochloride (LIDO). The formula also contains, as an excipient, Orabase®, which is a protective, hydrophobic, and anhydrous adhesive vehicle, used to retain or facilitate the application of active pharmaceutical ingredients to the oral mucosa. After designing the formulations, an analytical method for TA was validated using HPLC so as to achieve reliable analytical results. Franz-type diffusion cells were used to perform drug release studies using synthetic membrane, and permeation studies using buccal mucosa, estimating the amount and rate of TA permeated across the tissue. Additionally, sublingual permeation studies were carried out to evaluate a scenario of a continuous contact of the tongue with the applied formulation. Permeation fluxes and the amount of TA retained within sublingual mucosa were similar to those in buccal mucosa, also implying anti-inflammatory activity in the part of the tongue that is in direct contact with the formulation. In addition, the dynamic conditions of the mouth were recreated in terms of the presence of phosphate buffered saline, constant movement of the tongue, pH, and temperature, using dissolution equipment. The amount of TA released into the phosphate buffered saline in dynamic conditions (subject to being ingested) is well below the normal oral doses of TA, for which the formulation can be considered safe. The formulations applied to buccal or sublingual mucosas under dynamic conditions permit the successful retention of TA within either tissue, where it exerts anti-inflammatory activity. The four formulations studied show a pseudoplastic and thixotropic behavior, ideal for topical application. These results evidence the potential of these topical formulations in the treatment of inflammatory processes in the buccal mucosa.