Therapeutic implications of dapagliflozin on the metabolomics profile of diabetic rats: A GC-MS investigation coupled with multivariate analysis

BACKGROUND

Diabetes mellitus is a complex metabolic disorder with systemic implications, necessitating the search for reliable biomarkers and therapeutic strategies. This study investigates the metabolomics profile alterations in diabetic rats, with a focus on the therapeutic effects of Dapagliflozin, a drug known to inhibit renal glucose reabsorption, using Gas Chromatography-Mass Spectrometry analysis.

METHODS

A GC-MS based metabolomics approach combined with multivariate and univariate statistical analyses was utilized to study serum samples from a diabetic model of Wistar rats, treated with dapagliflozin. Metabolomics pathways analysis was also performed to identify the altered metabolic pathways associated with the disease and the intervention.

RESULTS

Dapagliflozin treatment in diabetic rats resulted in normalized levels of metabolites associated with insulin resistance, notably branched-chain and aromatic amino acids. Improvements in glycine metabolism were observed, suggesting a modulatory role of the drug. Additionally, reduced palmitic acid levels indicated an alleviation of lipotoxic effects. The metabolic changes indicate a restorative effect of dapagliflozin on diabetes-induced metabolic perturbations.

CONCLUSIONS

The comprehensive metabolomics analysis demonstrated the potential of GC-MS in revealing significant metabolic pathway alterations due to dapagliflozin treatment in diabetic model rats. The therapy induced normalization of key metabolic disturbances, providing insights that could advance personalized diabetes mellitus management and therapeutic monitoring, highlighting the utility of metabolomics in understanding drug mechanisms and effects.

Derivative spectroscopy and wavelet transform as green spectrophotometric methods for abacavir and lamivudine measurement

Herein, two different sustainable and green signal processing spectrophotometric approaches, namely, derivative spectroscopy and wavelet transform, have been utilized for effective measurement of the antiretroviral therapy abacavir and lamivudine in their pharmaceutical formulations. These methods were used to enhance the spectral data and differentiate between the absorption bands of abacavir and lamivudine in order to accurately measure their concentrations. For determining abacavir and lamivudine, the first derivative spectrophotometric method has been applied to the zero-order and ratio spectra of both drugs. The same approach has been tested using the continuous wavelet transform method where a second order 2.4 of rbio and bior wavelet families were found to be optimum for measuring both drugs. Validation of the proposed methods affirmed their reliability in terms of linearity over the concentration range 1.5-30 µg/mL and 1.5-36 µg/mL for abacavir and lamivudine, respectively, precision (RSD < 2 %), and accuracy with mean recoveries ranging between 98 % and 102 %. Additionally, these spectrophotometric methodologies were applied to real pharmaceutical preparations and yielded results congruent with a prior chromatographic method. Most prominently, the proposed methods stood out for their greenness and sustainability with 97 points as evaluated by the analytical eco-scale method and a score value of 0.79 as analyzed by AGREE method, thereby making them suitable for resource-limited settings and highlighting the potential for broader application of green analytical methods in pharmaceutical analysis.

Synchronous spectrofluorimetry and chemometric modeling: A synergistic approach for analyzing simeprevir and daclatasvir, with application to pharmacokinetics evaluation

Simeprevir and daclatasvir represent a cornerstone in the management of Hepatitis C Virus infection, a global health concern that affects millions of people worldwide. In this study, we propose a synergistic approach combining synchronous spectrofluorimetry and chemometric modeling i.e. Partial Least Squares (PLS-1) for the analysis of simeprevir and daclatasvir in different matrices. Moreover, the study employs firefly algorithms to further optimize the chemometric models via selecting the most informative features thus improving the accuracy and robustness of the calibration models. The firefly algorithm was able to reduce the number of selected wavelengths to 47-44% for simeprevir and daclatasvir, respectively offering a fast and sensitive technique for the determination of simeprevir and daclatasvir. Validation results underscore the models' effectiveness, as evidenced by recovery rates close to 100% with relative root mean square error of prediction (RRMSEP) of 2.253 and 2.1381 for simeprevir and daclatasvir, respectively. Moreover, the proposed models have been applied to determine the pharmacokinetics of simeprevir and daclatasvir, providing valuable insights into their distribution and elimination patterns. Overall, the study demonstrates the effectiveness of synchronous spectrofluorimetry coupled with multivariate calibration optimized by firefly algorithms in accurately determining and quantifying simeprevir and daclatasvir in HCV antiviral treatment, offering potential applications in pharmaceutical formulation analysis and pharmacokinetic studies for these drugs.

Augmented least squares, a powerful chemometric approach for the spectroscopic analysis of the antiretroviral therapy abacavir, lamivudine and dolutegravir in their ternary mixture

Augmented least squares models such as concentration residual augmented classical least squares (CRACLS) and spectral residual augmented classical least squares (SRACLS) are powerful chemometric approaches that can be applied for spectroscopic analysis of many pharmaceutical compounds. Herein, both CRACLS and SRACL have been employed for UV spectral analysis of three antiretroviral therapy namely abacavir (ACV), lamivudine (LMV) and dolutegravir (DTG) in their ternary mixture. A partial factorial design has been utilized for calibration set construction then both CRACLS and SRACLS models have been optimized regarding the number of iterations and principal components, respectively, using a leave-one-out cross-validation procedure. It was found that a higher number of iterations and principal components were required for modelling the minor component DTG indicating more augmentation procedures to improve the models' accuracy. Validation of the proposed models was performed using external validation set of 13 mixtures and different validation parameters have been evaluated regarding models' predictive abilities. Both models showed excellent performance for analyzing ACV and LMV with relative root mean square error of prediction (RRMSEP) below 2 %. However, higher RRMSEP values around 5 % were observed for the minor component DTG suggesting that these models should be utilized with caution when analyzing minor components in mixtures. Furthermore, the suggested models have been applied for analyzing ACV, LMV and DTG in their pharmaceutical formulation and excellent agreement was observed between the suggested models and the reported chromatographic method posing these models as powerful chemometric approaches for quality control analysis of many pharmaceutical compounds.

Efficient and eco-friendly detection of gabapentin using nitrogen-doped carbon quantum dots: an analytical and green chemistry approach

This study presents the development of an eco-friendly and highly selective mitrogen-doped carbon quantum dot based sensor (N-CQDs) for the detection of gabapentin - a commonly misused drug. A detailed characterization of N-CQDs spectral features and their interaction with gabapentin is provided. The optimal conditions for sensing, including pH value, buffer volume, N-CQDs concentration, and incubation time, were established. The results showed excellent fluorescence quenching at 475 nm (λex = 380 nm) due to the dynamic quenching mechanism, and the sensor demonstrated excellent linearity in the 0.5-8.0 μg mL-1 concentration range with correlation coefficients of more than 0.999, a limit of detection (LOD) of 0.160 and limit of quantification (LOQ) of 0.480 μg mL-1. The accuracy of the proposed sensor was acceptable with a mean accuracy of 99.91 for gabapentin detection. In addition, precision values were within the acceptable range, with RSD% below 2% indicating good repeatability and reproducibility of the sensor. Selectivity was validated using common excipients and pooled plasma samples. The proposed sensor accurately estimated gabapentin concentration in commercial pharmaceutical formulations and spiked plasma samples, exhibiting excellent comparability with previously published methods. The 'greenness' of the sensing system was evaluated using the Analytical GREEnness calculator, revealing low environmental impact and strong alignment with green chemistry principles with a greenness score of 0.76. Thus, the developed N-CQDs-based sensor offers a promising, eco-friendly, and effective tool for gabapentin detection in various situations, ranging from clinical therapeutics to forensic science.

Environmentally sustainable DRS-FTIR probe assisted by chemometric tools for quality control analysis of cinnarizine and piracetam having diverged concentration ranges: Validation, greenness, and whiteness studies

A novel diffuse reflectance fourier transform infrared spectroscopic method accompanied by chemometrics was optimized to fulfill the white analytical chemistry and green analytical chemistry principles for the quantification of cinnarizine and piracetam for the first time without any prior separation in their challenging pharmaceutical preparation, which has a pretty substantial difference in the concentration of cinnarizine/piracetam (1:16). Furthermore, the suggested method was used for cinnarizine/piracetam dissolution testing as an effective alternative to traditional methods. For the cinnarizine/piracetam dissolution tests, we used a dissolution vessel with 900 mL of phosphate buffer pH 2.5 at 37 °C ± 0.5 °C, then the sampling was carried out by frequent withdrawal of 20 µl samples from the dissolution vessel at a one-minute interval, over one hour, then representative fourier transform infrared spectra were recorded. To create a partial-least-squares regression model, a fractional factorial design with 5 different levels and 2 factors was used. This led to the creation of 25 mixtures, 15 as a calibration set and 10 as a validation set, with varying concentration ranges: 1-75 and 16-1000 μg/mL for cinnarizine/piracetam, respectively. Upon optimization of the partial-least-squares regression model, in terms of latent variables and spectral region, root mean square error of cross-validation of 0.477 and 0.270, for cinnarizine/piracetam respectively, were obtained. The optimized partial-least-squares regression model was further validated, providing good results in terms of recovery% (around 98 to 102 %), root mean square error of prediction (0.436 and 3.329), relative root mean square error of prediction (1.210 and 1.245), bias-corrected mean square error of prediction (0.059 and 0.081), and limit of detection (0.125 and 2.786) for cinnarizine/piracetam respectively. Ultimately, the developed method was assessed for whiteness, greenness, and sustainability using five assessment tools. the developed method achieved a greener national environmental method index and complementary green analytical procedure index quadrants with higher eco-scale assessment scores (91), analytical greenness metric scores (0.87), and red-greenblue 12 algorithm scores (89.7) than the reported methods, showing high practical and environmental acceptance for quality control of cinnarizine/piracetam.

Metabolites Profiling and Bioassays Reveal Bassia indica Ethanol Extract Protective Effect against Stomach Ulcers Development via HMGB1/TLR-4/NF-κB Pathway

Clinical manifestation of gastric ulcers is frequent, in addition to their costly drug regimens, warranting the development of novel drugs at lower costs. Although Bassia indica is well characterized for its anti-inflammatory and antioxidant potential, capacity of its ethanol extract (BIEE) to prevent stomach ulcers' progression has not been reported. A nuclear protein termed high-mobility group box 1 (HMGB1) plays a key role in the formation of stomach ulcers by triggering a number of inflammatory responses. The main purpose of the current investigation was to evaluate the in vivo anti-inflammatory and anti-ulcerogenic capabilities of BIEE against ethanol-induced gastric ulcers in rats via the HMGB1/TLR-4/NF-B signaling pathway. HMGB1 and Nuclear factor kappa (NF-B) expression, IL-1β and Nrf2 contents showed an increase along with ulcer development, concurrent with an increase in immunohistochemical TLR-4 level. In contrast, pre-treatment with BIEE significantly reduced HMGB1 and Nuclear factor kappa (NF-B) expression levels, IL-1β and Nrf2 contents and ulcer index value. Such protective action was further confirmed based on histological and immunohistochemical TLR-4 assays. Untargeted analysis via UPLC-ESI-Qtof-MS has allowed for the comprehensive characterization of 40 metabolites in BIEE mostly belonged to two main chemical classes, viz., flavonoids and lipids. These key metabolites, particularly flavonoids, suggesting a mediation for the anti-inflammatory and anti-ulcerogenic properties of BIEE, pose it as a promising natural drug regimen for treatment of stomach ulcers.

Integrated comparative metabolite profiling via NMR and GC-MS analyses for tongkat ali (Eurycoma longifolia) fingerprinting and quality control analysis

Tongkat ali commonly known as Malaysian Ginseng (Eurycoma longifolia) is a herbal root worldwide available in nutraceuticals, either as a crude powder or capsules blended with other herbal products. Herein, a multiplexed metabolomics approach based on nuclear magnetic resonance (NMR) and solid-phase microextraction combined with gas chromatography-mass spectrometry (SPME-GC-MS) was applied for authentic tongkat ali extract vs some commercial products quality control analysis. NMR metabolite fingerprinting identified 15 major metabolites mostly ascribed to sugars, organic and fatty acids in addition to quassinoids and cinnamates. Following that, multivariate analysis as the non-supervised principal component analysis (PCA) and supervised orthogonal partial least squares-discriminant analysis (OPLS-DA) were applied revealing that differences were related to fatty acids and 13,21-dihydroeurycomanone being more enriched in authentic root. SPME-GC-MS aroma profiling led to the identification of 59 volatiles belonging mainly to alcohols, aldehydes/furans and sesquiterpene hydrocarbons. Results revealed that aroma of commercial products showed relatively different profiles being rich in vanillin, maltol, and methyl octanoate. Whereas E-cinnamaldehyde, endo-borneol, terpinen-4-ol, and benzaldehyde were more associated to the authentic product. The present study shed the light for the potential of metabolomics in authentication and standardization of tongkat ali and identification of its true flavor composition.

QSRR modeling of the chromatographic retention behavior of some quinolone and sulfonamide antibacterial agents using firefly algorithm coupled to support vector machine

Quinolone and sulfonamide are two classes of antibacterial agents with an opulent history of medicinal chemistry features that contribute to their bacterial spectrum, efficacy, pharmacokinetics, and adverse effect profiles. The urgent need for their use, combined with the escalating rate of their resistance, necessitates the development of suitable analytical methods that accelerate and facilitate their analysis. In this study, the advanced firefly algorithm (FFA) coupled with support vector regression (SVR) was used to select the most significant descriptors and to construct two quantitative structure-retention relationship (QSRR) models using a series of 11 selected quinolone and 13 sulfonamide drugs, respectively, to predict their retention behavior in HPLC. Precisely, the effect of the pH value and acetonitrile composition in the mobile phase on the retention behavior of quinolones and sulfonamides, respectively, were studied. The obtained QSRR models performed well in both internal and external validations, demonstrating their robustness and predictive ability. Y-randomization validation demonstrated that the obtained models did not result by statistical chance. Moreover, the obtained results shed the light on the molecular features that influence the retention behavior of these two classes under the current chromatographic conditions.

A New Chemometrically Assisted UV Spectrophotometric Method for Simultaneous Determination of Tamsulosin and Dutasteride in Their Pharmaceutical Mixture

Background

Tamsulosin (TAM) and dutasteride (DUT) are ranked among the most frequently prescribed therapies in urology. Interestingly, studies have also been carried out on TAM/DUT in terms of their ability to protect against recent COVID-19. However, very few studies were reported for their simultaneous quantification in their combined dosage form and were mainly based on chromatographic analysis. Subsequently, it is very important to offer a simple, selective, sensitive, and rapid method for the quantification of TAM and DUT in their challenging dosage form.

Objective

In this study, a new chemometrically assisted ultraviolet (UV) spectrophotometric method has been presented for the quantification of TAM and DUT without any prior separation.

Method

For the calibration set, a partial factorial experimental design was used, resulting in 25 mixtures with central levels of 20 and 25 μg/mL for TAM and DUT, respectively. In addition, to assess the predictive ability of the developed approaches, another central composite design of 13 samples was used as a validation set. Post-processing by chemometric analysis of the recorded zero-order UV spectra of these sets has been applied. These chemometric approaches include partial least-squares (PLS) and genetic algorithm (GA), as an effective variable selection technique, coupled with PLS.

Results

The models’ validation criteria displayed excellent recoveries and lower errors of prediction.

Conclusions

The proposed models were effectively used to determine TAM/DUT in their combined dosage form, and statistical comparison with the reported method revealed satisfactory results.

Highlights

Overall, this work presents powerful simple, selective, sensitive, and precise methods for simultaneous quantification of TAM/DUT in their dosage form with satisfactory results. The predictive ability and accuracy of the developed methods offer the opportunity to be employed as a quality control technique for the routine analysis of TAM/DUT when chromatographic instruments are not available.

Innovative electrochemical electrode modified with Al2O3 nanoparticle decorated MWCNTs for ultra-trace determination of tamsulosin and solifenacin in human plasma and urine samples and their pharmaceutical dosage form

A simple, cheap, sensitive, and time-saving square wave voltammetric (SWV) procedure using a carbon paste electrode modified with aluminum oxide nanoparticle decorated multi-walled carbon nanoparticles (Al₂O₃-NPs/MWCNTs/CPE) is presented for the ultra-sensitive determination of tamsulosin (TAM) and solifenacin (SOL), one of the most prescribed pharmaceutical combinations in urology. Characterization of the developed electrode was performed using scanning electron microscopy (SEM), X-ray diffraction (XRD) patterns, energy dispersive X-ray analysis (EDX), transmission electron microscopy (TEM) and FT-IR spectrophotometry. The voltammetric behavior of TAM/SOL was evaluated using Al₂O₃-NPs in different content and electrode compositions. The use of Al₂O₃ functionalized MWCNTs as a CPE modifier increased the process of electron transfer as well as improved the electrode active surface area therefore, ultra-sensitive results were acquired with a linear range of 10–100 and 12–125 ng ml⁻¹ for TAM and SOL respectively, and a limit of the detection value of 2.69 and 3.25 ng ml⁻¹ for TAM and SOL, respectively. Interestingly, the proposed method succeeded in quantifying TAM and SOL with acceptable percentage recoveries in dosage forms having diverged concentration ranges and in the biological fluids with very low peak plasma concentration (C_max). Furthermore, the proposed method was validated, according to the ICH criteria, and shown to be accurate and reproducible.

A SWV method using a carbon paste electrode modified with aluminum oxide nanoparticle-decorated multi-walled carbon nanoparticles is presented for ultra-sensitive determination of tamsulosin and solifenacin, one of the most prescribed pharmaceutical combinations in urology.

Analysis of the ternary antiretroviral therapy dolutegravir, lamivudine and abacavir using UV spectrophotometry and chemometric tools

Herein, a simple spectrophotometric method coupled with chemometric techniques i.e. partial least square (PLS) and genetic algorithm (GA) were utilized for the simultaneous determination of the vital ternary antiretroviral therapy dolutegravir (DTG), lamivudine (LMV), and abacavir (ACV) in their combined dosage form. Calibration (25 samples) and validation (13 samples) sets were prepared for these drugs at different concentrations via implementing partial factorial experimental designs. The zero order UV spectra of calibration and validation sets were measured and then subjected for further chemometric analysis. Partial least squares with/without variable selection procedures i.e. genetic algorithm (GA) were utilized to untangle the UV spectral overlapping of these mixtures. Cross-validation and external validation methods were applied to compare the performance of these chemometric techniques in terms of accuracy and predictive abilities. It was found that six latent variables were optimum for modelling DTG, four latent variables for modelling LMV and three latent variables for modelling ACV. Although, good recoveries with prompt predictive ability were attained by these PLS, GA-PLS showed better analytical performance owing to its capability to remove redundant variables i.e. the number of absorbance variables have been reduced to about 21-29%. The proposed chemometric methods can be reliably applied for simultaneous determination of DTG, LMV, and ACV in their laboratory prepared mixtures and pharmaceutical preparation posing these chemometric methods as worthy and substantial analytical tools in in-process testing and quality control analysis of many antiretroviral pharmaceutical preparations.

Spectrophotometric Determination of Aspirin and Omeprazole in the presence of Salicylic Acid as a Degradation Product: A Comparative Evaluation of Different Univariate/Multivariate Post Processing Algorithms

BACKGROUND

A recent combination of aspirin (ASP) and omeprazole (OMP) has been presented in a fixed dosage form for treatment of many CVD, particularly in patients with gastric diseases. However, ASP is very sensitive to degradation into salicylic acid (SAL) as its main degradation product. Hence, it is very important to develop methods for the determination of ASP and OMP in the presence of SAL.

OBJECTIVE

In this study, UV spectrophotometry assisted by different univariate/multivariate post processing algorithms have been presented for quantitative determination of ASP, OMP and SAL without any prior separation.

METHODS

The univariate/multivariate algorithms include double divisor ratio difference and double divisor mean centering as the univariate approaches while the multivariate methods include principal component regression (PCR) and partial least squares (PLS) models. Validation of the univariate methods was done according to the ICH guidelines while the multivariate models were validated using external validation set.

RESULTS

The univariate algorithms displayed excellent regression and validation capabilities in terms of linearity, accuracy, precision, and selectivity. Regarding PCR and PLS, the number of latent variables were carefully optimized, and the model's validation criteria displayed excellent recoveries and lower errors of prediction.

CONCLUSION

Our findings indicate that the developed methods were comparable to the only reported chromatographic methods but with much shorter analysis time and simplicity.

HIGHLIGHTS

Overall, this report presents the first spectrophotometric methods applied for determination of possible combination of ASP, OMP, and SAL, and poses theses methods as valuable analytical tools in in-process testing and quality control analysis.

Antiproliferative activity of Syzygium coriaceum, an endemic plant of Mauritius, with its UPLC-MS metabolite fingerprint: A mechanistic study

Flowering plants from the Syzygium genus have long been used in different ethnomedicinal systems worldwide and have been under scrutiny for their biological activities. Syzygium coriaceum, an endemic plant of Mauritius has been poorly studied for its potential application against cancer. Herein, Syzygium coriaceum leaf extract has been investigated for its anticancer effect against hepatocellular carcinoma (HepG2) cells. The anticancer activity was assessed using cell proliferation assays, flow cytometry, JC-1 mitochondrial membrane potential assay, and the COMET assay. Un-targeted metabolite profiling via ultra-performance liquid chromatography coupled to high-resolution qTOF-MS (UPLC-MS) and aided by molecular networking was employed to identify the crude extract metabolites. S. coriaceum treatment induced a dose-dependent increase in lactate dehydrogenase leakage into the culture media, peaking up to 47% (p ≤ 0.0001), compared to untreated control. Moreover, at 40 μg/mL, S. coriaceum led to 88.1% (p ≤ 0.0001) drop in mitochondrial membrane potential and 5.7% (p ≤ 0.001) increased in the number of the cell population in G0/G1 phase as well as increased (p < 0.05) the proportion of cells undergoing apoptotic/necrotic cell death. More so, at 10 μg/mL, S. coriaceum induced DNA damage which was 19 folds (p < 0.001) higher than that of untreated control cells. Metabolite profiling indicated the presence of 65 metabolites, out of which 59 were identified. Tannins, flavonoids, nitrogenous compounds, and organic acids were the most predominant classes of compounds detected. Our findings showed that the presence of tannins and flavonoids in S. coriaceum leaf extract could account for the multiple mechanisms of actions underlying the antiproliferative effect against HepG2 cells.

An evaluation of different bio-inspired feature selection techniques on multivariate calibration models in spectroscopy

Herein, two new swarm intelligence based algorithms namely; grey wolf optimization (GWO) and antlion optimization (ALO) algorithms were presented, for the first time, as variable selection tools in spectroscopic data analysis. In order to assess the performance of these algorithms, they were applied along with the recently introduced firefly algorithm (FFA) and the well-established genetic algorithm (GA) and particle swarm optimization (PSO) algorithm on four different spectroscopic datasets of varying sizes and nature (UV and IR). Partial least squares (PLS) regression models were built using the selected variables by these algorithms along with the full spectral data as the reference models. The obtained results prove that the ALO and GWO optimization algorithms select variables in most cases less than GA and PSO while keeping the PLS performance almost the same. Accordingly, these algorithms can be successfully used for variable selection in spectroscopic data analysis.

Metabolomic profiling to reveal the therapeutic potency of Posidonia oceanica nanoparticles in diabetic rats

Posidonia oceanica is a sea grass belonging to the family Posidoniaceae, which stands out as a substantial reservoir of bioactive compounds. In this study, the secondary metabolites of the P. oceanica rhizome were annotated using UPLC-HRESI-MS/MS, revealing 86 compounds including simple phenolic acids, flavonoids, and their sulphated conjugates. Moreover, the P. oceanica butanol extract exhibited substantial antioxidant and antidiabetic effects in vitro. Thus, a reliable, robust drug delivery system was developed through the encapsulation of P. oceanica extract in gelatin nanoparticles to protect active constituents, control their release and enhance their therapeutic activity. To confirm these achievements, untargeted GC-MS metabolomics analysis together with biochemical evaluation was employed to investigate the in vivo anti-diabetic potential of the P. oceanica nano-extract. The results of this study demonstrated that the P. oceanica gelatin nanoparticle formulation reduced the serum fasting blood glucose level significantly (p < 0.05) in addition to improving the insulin level, together with the elevation of glucose transporter 4 levels. Besides, multivariate/univariate analyses of the GC-MS metabolomic dataset revealed several dysregulated metabolites in diabetic rats, which were restored to normalized levels after treatment with the P. oceanica gelatin nanoparticle formulation. These metabolites mainly originate from the metabolism of amino acids, fatty acids and carbohydrates, indicating that this type of delivery was more effective than the plain extract in regulating these altered metabolic processes. Overall, this study provides novel insight for the potential of P. oceanica butanol extract encapsulated in gelatin nanoparticles as a promising and effective antidiabetic therapy.

The potential of P. oceanica butanol extract encapsulated in gelatin nanoparticles as a promising and effective antidiabetic therapy has been investigated via metabolomics.

Emerging analytical tools for the detection of the third gasotransmitter H2S, a comprehensive review

BACKGROUND

Hydrogen sulfide (H2S) is currently considered among the endogenously produced gaseous molecules that exert various signaling effects in mammalian species. It is the third physiological gasotransmitter discovered so far after NO and CO. H2S was originally ranked among the toxic gases at elevated levels to humans. Currently, it is well-known that, in the cardiovascular system, H2S exerts several cardioprotective effects including vasodilation, antioxidant regulation, inhibition of inflammation, and activation of anti-apoptosis. With an increasing interest in monitoring H2S, the development of analysis methods should now follow.

AIM OF REVIEW

This review stages special emphasis on the several analytical technologies used for its determination including spectroscopic, chromatographic, and electrochemical methods. Advantages and limitations with regards to the application of each technique are highlighted with special emphasis on its employment for H2S in vivo measurement i.e., biofluids, tissues.

KEY SCIENTIFIC CONCEPTS AND IMPORTANT FINDINGS OF REVIEW

Fluorescence methods applied for H2S measurement offer an attractive non-invasive and promising approach in addition to its selectivity, however they cannot be considered as H2S-specific probes. On the other hand, colorimetric assays are among the most common methods used for in vitro H2S detection, albeit their employment in vivo H2S measurement has not yet been possible . Separation techniques such as gas or liquid chromatography offer higher selectivity compared to direct spectrophotometric or fluorescence methods especially for suitable for endpoint H2S measurements i.e. plasma or tissue samples. Despite all the developed analytical procedures used for H2S determination, the need for highly selective, much work should be devoted to resolve all the pitfalls of the current methods.

Analysis of quinary therapy targeting multiple cardiovascular diseases using UV spectrophotometry and chemometric tools

Herein, UV spectrophotometry assisted by multivariate chemometric analysis have been presented for quantitative determination of complex quinary therapy containing atenolol, ramipril, hydrochlorothiazide, simvastatin and aspirin without any prior separation. Such combination is very useful for treating various cardiovascular diseases (CVD) including high blood pressure, hypercholesterolemia in addition to its antiplatelet aggregating activity. Calibration (15 samples) and validation (10 samples) sets were prepared of different concentrations for these drugs via implementing partial factorial experimental design. The zero order UV spectra of these sets were recorded and then subjected for further chemometric analysis. Partial least square (PLS) with/without variable selection procedure i.e. genetic algorithm (GA) were employed to untangle the UV spectral overlapping of these mixtures. The performance of these chemometric techniques were compared in terms of accuracy and predictive abilities using cross-validation and external validation methods. It was found that PLS provides good recoveries with prompt predictive ability albeit GA-PLS exhibited better analytical performance owing to its capability to remove redundant variables i.e. the number of absorbance variables had been reduced to about 19-28%. The developed methods allowed reliable determination of such complex therapy in its laboratory prepared mixtures and pharmaceutical preparation within comparable results to those reported by HPLC method, posing these chemometric methods as valuable and indispensable analytical tools in in-process testing and quality control analysis of many pharmaceutical compounds targeting CVD.

Metabolomics in the Context of Plant Natural Products Research: From Sample Preparation to Metabolite Analysis

Plant-derived natural products have long been considered a valuable source of lead compounds for drug development. Natural extracts are usually composed of hundreds to thousands of metabolites, whereby the bioactivity of natural extracts can be represented by synergism between several metabolites. However, isolating every single compound from a natural extract is not always possible due to the complex chemistry and presence of most secondary metabolites at very low levels. Metabolomics has emerged in recent years as an indispensable tool for the analysis of thousands of metabolites from crude natural extracts, leading to a paradigm shift in natural products drug research. Analytical methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) are used to comprehensively annotate the constituents of plant natural products for screening, drug discovery as well as for quality control purposes such as those required for phytomedicine. In this review, the current advancements in plant sample preparation, sample measurements, and data analysis are presented alongside a few case studies of the successful applications of these processes in plant natural product drug discovery.

UHPLC-MS metabolome based classification of umbelliferous fruit taxa: a prospect for phyto-equivalency of its different accessions and in response to roasting

Herbs of the Umbelliferae family are popular spices valued worldwide for their many nutritional and health benefits. Herein, five chief umbelliferous fruits viz., cumin, fennel, anise, coriander and caraway were assessed for its secondary metabolites diversity along with compositional changes incurring upon roasting as analyzed via ultra-high performance liquid chromatography coupled to photodiode array and electrospray ionization mass detectors UHPLC-qToF/MS. A total of 186 metabolites were annotated, according to metabolomics society guidelines, belonging mainly to flavonoids, fatty acids and phenolic acids. Multivariate models viz., PCA, HCA and OPLS-DA were further employed to assess fruits' heterogeneity in an untargeted manner and determine mechanistic changes in bioactive makeup post roasting viz., glycosidic cleavage, lipid degradation and Maillard reaction. Finally, the fruits' antioxidant activity showed decline upon roasting and in correlation with its total phenolic content. This study presents the first complete map of umbelliferous fruit metabolome, compositional differences and its roasting effect.

First metabolome map for umbelliferous fruits secondary metabolites profiling via UHPLC-QToF/MS with scrutinizing its metabolome compositional changes incurred upon roasting.

Translational AI and Deep Learning in Diagnostic Pathology

There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise, and safe.

Early breast milk exposure modifies brain connectivity in preterm infants

Preterm infants are at increased risk of alterations in brain structure and connectivity, and subsequent neurocognitive impairment. Breast milk may be more advantageous than formula feed for promoting brain development in infants born at term, but uncertainties remain about its effect on preterm brain development and the optimal nutritional regimen for preterm infants. We test the hypothesis that breast milk exposure is associated with improved markers of brain development and connectivity in preterm infants at term equivalent age. We collected information about neonatal breast milk exposure and brain MRI at term equivalent age from 47 preterm infants (mean postmenstrual age [PMA] 29.43 weeks, range 23.28-33.0). Network-Based Statistics (NBS), Tract-based Spatial Statistics (TBSS) and volumetric analysis were used to investigate the effect of breast milk exposure on white matter water diffusion parameters, tissue volumes, and the structural connectome. Twenty-seven infants received exclusive breast milk feeds for ≥75% of days of in-patient care and this was associated with higher connectivity in the fractional anisotropy (FA)-weighted connectome compared with the group who had < 75% of days receiving exclusive breast milk feeds (NBS, p = 0.04). Within the TBSS white matter skeleton, the group that received ≥75% exclusive breast milk days exhibited higher FA within the corpus callosum, cingulum cingulate gyri, centrum semiovale, corticospinal tracts, arcuate fasciculi and posterior limbs of the internal capsule compared with the low exposure group after adjustment for PMA at birth, PMA at image acquisition, bronchopulmonary dysplasia, and chorioamnionitis (p < 0.05). The effect on structural connectivity and tract water diffusion parameters was greater with ≥90% exposure, suggesting a dose effect. There were no significant groupwise differences in brain volumes. Breast milk feeding in the weeks after preterm birth is associated with improved structural connectivity of developing networks and greater FA in major white matter fasciculi.

Complex Trajectories of Brain Development in the Healthy Human Fetus

This study characterizes global and hemispheric brain growth in healthy human fetuses during the second half of pregnancy using three-dimensional MRI techniques. We studied 166 healthy fetuses that underwent MRI between 18 and 39 completed weeks gestation. We created three-dimensional high-resolution reconstructions of the brain and calculated volumes for left and right cortical gray matter (CGM), fetal white matter (FWM), deep subcortical structures (DSS), and the cerebellum. We calculated the rate of growth for each tissue class according to gestational age and described patterns of hemispheric growth. Each brain region demonstrated major increases in volume during the second half of gestation, the most pronounced being the cerebellum (34-fold), followed by FWM (22-fold), CGM (21-fold), and DSS (10-fold). The left cerebellar hemisphere, CGM, and DSS had larger volumes early in gestation, but these equalized by term. It has been increasingly recognized that brain asymmetry evolves throughout the human life span. Advanced quantitative MRI provides noninvasive measurements of early structural asymmetry between the left and right fetal brain that may inform functional and behavioral laterality differences seen in children and young adulthood.

Different spectrophotometric methods applied for the analysis of simeprevir in the presence of its oxidative degradation product: Acomparative study

Five simple spectrophotometric methods were developed for the determination of simeprevir in the presence of its oxidative degradation product namely, ratio difference, mean centering, derivative ratio using the Savitsky-Golay filters, second derivative and continuous wavelet transform. These methods are linear in the range of 2.5-40μg/mL and validated according to the ICH guidelines. The obtained results of accuracy, repeatability and precision were found to be within the acceptable limits. The specificity of the proposed methods was tested using laboratory prepared mixtures and assessed by applying the standard addition technique. Furthermore, these methods were statistically comparable to RP-HPLC method and good results were obtained. So, they can be used for the routine analysis of simeprevir in quality-control laboratories.

Brain Development in Fetuses of Mothers with Diabetes: A Case-Control MR Imaging Study

BACKGROUND AND PURPOSE

Offspring exposed to maternal diabetes are at increased risk of neurocognitive impairment, but its origins are unknown. With MR imaging, we investigated the feasibility of comprehensive assessment of brain metabolism (¹H-MRS), microstructure (DWI), and macrostructure (structural MRI) in third-trimester fetuses in women with diabetes and determined normal ranges for the MR imaging parameters measured.

MATERIALS AND METHODS

Women with singleton pregnancies with diabetes (n = 26) and healthy controls (n = 26) were recruited prospectively for MR imaging studies between 34 and 38 weeks' gestation.

RESULTS

Data suitable for postprocessing were obtained from 79%, 71%, and 46% of women for ¹H-MRS, DWI, and structural MRI, respectively. There was no difference in the NAA/Cho and NAA/Cr ratios (mean [SD]) in the fetal brain in women with diabetes compared with controls (1.74 [0.79] versus 1.79 [0.64], P = .81; and 0.78 [0.28] versus 0.94 [0.36], P = .12, respectively), but the Cho/Cr ratio was marginally lower (0.46 [0.11] versus 0.53 [0.10], P = .04). There was no difference in mean [SD] anterior white, posterior white, and deep gray matter ADC between patients and controls (1.16 [0.12] versus 1.16 [0.08], P = .96; 1.54 [0.16] versus 1.59 [0.20], P = .56; and 1.49 [0.23] versus 1.52 [0.23], P = .89, respectively) or volume of the cerebrum (243.0 mL [22.7 mL] versus 253.8 mL [31.6 mL], P = .38).

CONCLUSIONS

Acquiring multimodal MR imaging of the fetal brain at 3T from pregnant women with diabetes is feasible. Further study of fetal brain metabolism in maternal diabetes is warranted.

Molecular and Quantum Mechanical Study for the Separation of Cefprozil in the Presence of Its Alkaline Degradation Product Using RP-HPLC with UV Detection

A reversed-phase HPLC method (RP-HPLC) with UV detection was developed and validated for the quantitative determination of cefprozil, a second-generation cephalosporin. Due to β-lactam ring instability under alkaline conditions, this RP-HPLC method was applied for the determination of cefprozil in the presence of its possible degradation product. The interactions that govern the separation process with stationary phase were investigated at both molecular and quantum mechanical levels. Moreover, electrostatic potential maps were generated to determine the sites of interaction with mobile phase. The suggested method was validated in compliance with International Conference on Harmonization guidelines and successfully applied for the determination of cefprozil in its commercial pharmaceutical formulation.

SEGMA: An Automatic SEGMentation Approach for Human Brain MRI Using Sliding Window and Random Forests

Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore, there is an increasing need for automated segmentation tools that can be applied to images acquired at different life stages. We developed an automatic segmentation method for human brain MRI, where a sliding window approach and a multi-class random forest classifier were applied to high-dimensional feature vectors for accurate segmentation. The method performed well on brain MRI data acquired from 179 individuals, analyzed in three age groups: newborns (38–42 weeks gestational age), children and adolescents (4–17 years) and adults (35–71 years). As the method can learn from partially labeled datasets, it can be used to segment large-scale datasets efficiently. It could also be applied to different populations and imaging modalities across the life course.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.

Association between preterm brain injury and exposure to chorioamnionitis during fetal life

Preterm infants are susceptible to inflammation-induced white matter injury but the exposures that lead to this are uncertain. Histologic chorioamnionitis (HCA) reflects intrauterine inflammation, can trigger a fetal inflammatory response, and is closely associated with premature birth. In a cohort of 90 preterm infants with detailed placental histology and neonatal brain magnetic resonance imaging (MRI) data at term equivalent age, we used Tract-based Spatial Statistics (TBSS) to perform voxel-wise statistical comparison of fractional anisotropy (FA) data and computational morphometry analysis to compute the volumes of whole brain, tissue compartments and cerebrospinal fluid, to test the hypothesis that HCA is an independent antenatal risk factor for preterm brain injury. Twenty-six (29%) infants had HCA and this was associated with decreased FA in the genu, cingulum cingulate gyri, centrum semiovale, inferior longitudinal fasciculi, limbs of the internal capsule, external capsule and cerebellum (p < 0.05, corrected), independent of degree of prematurity, bronchopulmonary dysplasia and postnatal sepsis. This suggests that diffuse white matter injury begins in utero for a significant proportion of preterm infants, which focuses attention on the development of methods for detecting fetuses and placentas at risk as a means of reducing preterm brain injury.

Parcellation of the Healthy Neonatal Brain into 107 Regions Using Atlas Propagation through Intermediate Time Points in Childhood

Neuroimage analysis pipelines rely on parcellated atlases generated from healthy individuals to provide anatomic context to structural and diffusion MRI data. Atlases constructed using adult data introduce bias into studies of early brain development. We aimed to create a neonatal brain atlas of healthy subjects that can be applied to multi-modal MRI data. Structural and diffusion 3T MRI scans were acquired soon after birth from 33 typically developing neonates born at term (mean postmenstrual age at birth 39⁺⁵ weeks, range 37⁺²–41⁺⁶). An adult brain atlas (SRI24/TZO) was propagated to the neonatal data using temporal registration via childhood templates with dense temporal samples (NIH Pediatric Database), with the final atlas (Edinburgh Neonatal Atlas, ENA33) constructed using the Symmetric Group Normalization (SyGN) method. After this step, the computed final transformations were applied to T2-weighted data, and fractional anisotropy, mean diffusivity, and tissue segmentations to provide a multi-modal atlas with 107 anatomical regions; a symmetric version was also created to facilitate studies of laterality. Volumes of each region of interest were measured to provide reference data from normal subjects. Because this atlas is generated from step-wise propagation of adult labels through intermediate time points in childhood, it may serve as a useful starting point for modeling brain growth during development.

Different spectrophotometric methods applied for the analysis of binary mixture of flucloxacillin and amoxicillin: A comparative study

Three different spectrophotometric methods were applied for the quantitative analysis of flucloxacillin and amoxicillin in their binary mixture, namely, ratio subtraction, absorbance subtraction and amplitude modulation. A comparative study was done listing the advantages and the disadvantages of each method. All the methods were validated according to the ICH guidelines and the obtained accuracy, precision and repeatability were found to be within the acceptable limits. The selectivity of the proposed methods was tested using laboratory prepared mixtures and assessed by applying the standard addition technique. So, they can be used for the routine analysis of flucloxacillin and amoxicillin in their binary mixtures.

Stability-indicating methods for the analysis of ciprofloxacin in the presence of its acid induced degradation product: A comparative study

Four rapid, simple, accurate and precise spectrophotometric methods were used for the determination of ciprofloxacin in the presence of its acidic degradation product. The methods under study are ratio derivative, ratio difference, mean centering and dual wavelength. All the methods were validated according to the ICH guidelines and the obtained accuracy, precision and repeatability were found to be within the acceptable limits. The selectivity of the proposed methods was tested using laboratory prepared mixtures and assessed by applying the standard addition technique. So, they can be used for the routine analysis of ciprofloxacin in quality-control laboratories.

Stability indicating methods for the analysis of cefprozil in the presence of its alkaline induced degradation product

Three simple, specific, accurate and precise spectrophotometric methods were developed for the determination of cefprozil (CZ) in the presence of its alkaline induced degradation product (DCZ). The first method was the bivariate method, while the two other multivariate methods were partial least squares (PLS) and spectral residual augmented classical least squares (SRACLS). The multivariate methods were applied with and without variable selection procedure (genetic algorithm GA). These methods were tested by analyzing laboratory prepared mixtures of the above drug with its alkaline induced degradation product and they were applied to its commercial pharmaceutical products.

Effect of genetic algorithm as a variable selection method on different chemometric models applied for the analysis of binary mixture of amoxicillin and flucloxacillin: A comparative study

Different chemometric models were applied for the quantitative analysis of amoxicillin (AMX), and flucloxacillin (FLX) in their binary mixtures, namely, partial least squares (PLS), spectral residual augmented classical least squares (SRACLS), concentration residual augmented classical least squares (CRACLS) and artificial neural networks (ANNs). All methods were applied with and without variable selection procedure (genetic algorithm GA). The methods were used for the quantitative analysis of the drugs in laboratory prepared mixtures and real market sample via handling the UV spectral data. Robust and simpler models were obtained by applying GA. The proposed methods were found to be rapid, simple and required no preliminary separation steps.

Zero order and signal processing spectrophotometric techniques applied for resolving interference of metronidazole with ciprofloxacin in their pharmaceutical dosage form

Four rapid, simple, accurate and precise spectrophotometric methods were used for the determination of ciprofloxacin in the presence of metronidazole as interference. The methods under study are area under the curve, simultaneous equation in addition to smart signal processing techniques of manipulating ratio spectra namely Savitsky-Golay filters and continuous wavelet transform. All the methods were validated according to the ICH guidelines where accuracy, precision and repeatability were found to be within the acceptable limits. The selectivity of the proposed methods was tested using laboratory prepared mixtures and assessed by applying the standard addition technique. So, they can therefore be used for the routine analysis of ciprofloxacin in quality-control laboratories.

Optimized methodology for neonatal diffusion tensor imaging processing and study-specific template construction

Diffusion tensor imaging (DTI) has been widely used to study cerebral white matter microstructure in vivo. There is a plethora of open source tools available to perform pre-processing, analysis and template or atlas construction, however very few have been optimized for use with neonatal DTI data. Here we present a fully automated modular pipeline optimized for neonatal DTI data and the construction of study-specific tensor templates. We compare our methodology to an existing one. It is anticipated that the construction of population or study-specific templates will facilitate better group comparisons of neonatal populations both in health and disease.

Different approaches in manipulating ratio spectra applied for the analysis of Cefprozil in presence of its alkaline-induced degradation product: a comparative study

Four simple, accurate and precise stability-indicating spectrophotometric methods manipulating ratio spectra were developed and validated for simultaneous determination of Cefprozil (CZ) and its alkaline-induced degradation product (DCZ) without prior separation namely; ratio difference, mean centering, derivative ratio using Savitsky-Golay filter and continuous wavelet transform. The accuracy, precision and linearity ranges of the proposed methods were determined. The methods were validated and the specificity was assessed by analyzing synthetic mixtures containing the drug and its degradate. The four methods were applied for the determination of the cited drug in tablets and the obtained results were statistically compared with those of a reported method. The comparison showed that there are no significant differences between the proposed methods and the reported method regarding both accuracy and precision.

Tract shape modeling detects changes associated with preterm birth and neuroprotective treatment effects

Preterm birth is associated with altered connectivity of neural circuits. We developed a tract segmentation method that provides measures of tract shape and integrity (probabilistic neighborhood tractography, PNT) from diffusion MRI (dMRI) data to test the hypotheses: 1) preterm birth is associated with alterations in tract topology (R), and tract-averaged mean diffusivity (〈D〉) and fractional anisotropy (FA); 2) neural systems are separable based on tract-averaged dMRI parameters; and 3) PNT can detect neuroprotective treatment effects.

dMRI data were collected from 87 preterm infants (mean gestational age 29⁺¹ weeks, range 23⁺² –34⁺⁶) at term equivalent age and 24 controls (mean gestational age 39⁺⁶ weeks). PNT was used to segment eight major fasciculi, characterize topology, and extract tract-averaged 〈D〉 and FA.

Tract topology was altered by preterm birth in all tracts except the splenium (p < 0.05, false discovery rate [FDR] corrected). After adjustment for age at scan, tract-averaged 〈D〉 was increased in the genu and splenium, right corticospinal tract (CST) and the left and right inferior longitudinal fasciculi (ILF) in preterm infants compared with controls (p < 0.05, FDR), while tract-averaged FA was decreased in the splenium and left ILF (p < 0.05, FDR). Specific fasciculi were separable based on tract-averaged 〈D〉 and FA values. There was a modest decrease in tract-averaged 〈D〉 in the splenium of preterm infants who had been exposed to antenatal MgSO₄ for neuroprotection (p = 0.002).

Tract topology is a biomarker of preterm brain injury. The data provide proof of concept that tract-averaged dMRI parameters have utility for evaluating tissue effects of perinatal neuroprotective strategies.

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Probabilistic neighborhood tractography models tract shape from neonatal dMRI.

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The shape of major fasciculi is altered in association with preterm birth.

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Developing neural systems are separable based on dMRI parameters.

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Tract-averaged dMRI parameters are sensitive to neuroprotective treatment effects.

Robust motion correction and outlier rejection of in vivo functional MR images of the fetal brain and placenta during maternal hyperoxia

Subject motion is a major challenge in functional magnetic resonance imaging studies (fMRI) of the fetal brain and placenta during maternal hyperoxia. We propose a motion correction and volume outlier rejection method for the correction of severe motion artifacts in both fetal brain and placenta. The method is optimized to the experimental design by processing different phases of acquisition separately. It also automatically excludes high-motion volumes and all the missing data are regressed from ROI-averaged signals. The results demonstrate that the proposed method is effective in enhancing motion correction in fetal fMRI without large data loss, compared to traditional motion correction methods.

Automatic whole brain MRI segmentation of the developing neonatal brain

Magnetic resonance (MR) imaging is increasingly being used to assess brain growth and development in infants. Such studies are often based on quantitative analysis of anatomical segmentations of brain MR images. However, the large changes in brain shape and appearance associated with development, the lower signal to noise ratio and partial volume effects in the neonatal brain present challenges for automatic segmentation of neonatal MR imaging data. In this study, we propose a framework for accurate intensity-based segmentation of the developing neonatal brain, from the early preterm period to term-equivalent age, into 50 brain regions. We present a novel segmentation algorithm that models the intensities across the whole brain by introducing a structural hierarchy and anatomical constraints. The proposed method is compared to standard atlas-based techniques and improves label overlaps with respect to manual reference segmentations. We demonstrate that the proposed technique achieves highly accurate results and is very robust across a wide range of gestational ages, from 24 weeks gestational age to term-equivalent age.

Common genetic variants and risk of brain injury after preterm birth

BACKGROUND

The role of heritable factors in determining the common neurologic deficits seen after preterm birth is unknown, but the characteristic phenotype of neurocognitive, neuroanatomical, and growth abnormalities allows principled selection of candidate genes to test the hypothesis that common genetic variation modulates the risk for brain injury.

METHODS

We collected an MRI-linked genomic DNA library from 83 preterm infants and genotyped tag single nucleotide polymorphisms in 13 relevant candidate genes. We used tract-based spatial statistics and deformation-based morphometry to examine the risks conferred by carriage of particular alleles at tag single nucleotide polymorphisms in a restricted number of genes and related these to the preterm cerebral endophenotype.

RESULTS

Carriage of the minor allele at rs2518824 in the armadillo repeat gene deleted in velocardiofacial syndrome (ARVCF) gene, which has been linked to neuronal migration and schizophrenia, and rs174576 in the fatty acid desaturase 2 gene, which encodes a rate-limiting enzyme for endogenous long chain polyunsaturated fatty acid synthesis and has been linked to intelligence, was associated with white matter abnormality measured in vivo using diffusion tensor imaging (P = .0009 and P = .0019, respectively).

CONCLUSIONS

These results suggest that genetic variants modulate white matter injury after preterm birth, and known susceptibilities to neurologic status in later life may be exposed by the stress of premature exposure to the extrauterine environment.

Retinal blood vessels extraction using probabilistic modelling

The analysis of retinal blood vessels plays an important role in detecting and treating retinal diseases. In this review, we present an automated method to segment blood vessels of fundus retinal image. The proposed method could be used to support a non-intrusive diagnosis in modern ophthalmology for early detection of retinal diseases, treatment evaluation or clinical study. This study combines the bias correction and an adaptive histogram equalisation to enhance the appearance of the blood vessels. Then the blood vessels are extracted using probabilistic modelling that is optimised by the expectation maximisation algorithm. The method is evaluated on fundus retinal images of STARE and DRIVE datasets. The experimental results are compared with some recently published methods of retinal blood vessels segmentation. The experimental results show that our method achieved the best overall performance and it is comparable to the performance of human experts.