Amplify antimicrobial photo dynamic therapy efficacy with poly-beta-amino esters (PBAEs)

AbstractLight-activated antimicrobial agents (photosensitisers) are promising alternatives to antibiotics for the treatment of skin infections and wounds through antimicrobial photo dynamic therapy (aPDT); utilisation of this technique is still restricted by general low efficacy requiring long exposure time (in the order of tens of minutes) that make the treatment very resource intensive. We report for the first time the possibility of harvesting the cell penetrating properties of poly-beta-amino esters (PBAEs) in combination with toluidine blue O (TBO) to shorten aPDT exposure time. Candidates capable of inactivation rates 30 times quicker than pure TBO were discovered and further improvements through PBAE backbone optimisation could be foreseen. Efficacy of the complexes was PBAE-dependent on a combination of TBO uptake and a newly discovered and unexpected role of PBAEs on reactive species production. Chemometric approach of partial least square regression was employed to assess the critical PBAE properties involved in this newly observed phenomenon in order to elicit a possible mechanism. The superior antimicrobial performance of this new approach benefits from the use of well established, low-cost and safe dye (TBO) coupled with inexpensive, widely tested and biodegradable polymers also known to be safe. Moreover, no adverse cytotoxic effects of the PBAEs adjuvated TBO delivery have been observed on a skin cells in vitro model demonstrating the safety profile of this new technology.

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Amplify Antimicrobial Photo Dynamic Therapy Efficacy With Poly-beta-amino Esters (PBAEs)

10.21203/rs.3.rs-147055/v1 ◽

2021 ◽

Author(s):

Stefano Perni ◽

Emily Preedy ◽

Polina Prokopovich

Keyword(s):

Exposure Time ◽

Antimicrobial Agents ◽

Low Cost ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Amino Esters ◽

Dynamic Therapy ◽

Photo Dynamic Therapy

Abstract Light-activated antimicrobial agents (photosensitisers) are promising alternatives to antibiotics for the treatment of skin infections and wounds through antimicrobial Photo Dynamic Therapy (aPDT); utilisation of this technique is still restricted by general low efficacy requiring long exposure time (in the order of tens of minutes) that make the treatment very resource intensive. We report for the first time the possibility of harvesting the cell penetrating properties of poly-beta-amino esters (PBAEs) in combination with toluidine blue O (TBO) to shorten aPDT exposure time. Candidates capable of inactivation rates 30 times quicker than pure TBO were discovered and further improvements through PBAE backbone optimisation could be foreseen. Efficacy of the complexes was PBAE-dependent on a combination of TBO uptake and a newly discovered and unexpected role of PBAEs on reactive species production. Chemometric approach of partial least square regression was employed to assess the critical PBAE properties involved in this newly observed phenomenon in order to elicit a possible mechanism.The superior antimicrobial performance of this new approach benefits from the use of well established, low-cost and safe dye (TBO) coupled with inexpensive, widely tested and biodegradable polymers also known to be safe. Moreover, no adverse cytotoxic effects of the PBAEs adjuvated TBO delivery have been observed on a skin cells in vitro model demonstrating the safety profile of this new technology.

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Discrimination of the Essential Oils Obtained from Four Apiaceae Species Using Multivariate Analysis Based on the Chemical Compositions and Their Biological Activity

Plants ◽

10.3390/plants10081529 ◽

2021 ◽

Vol 10 (8) ◽

pp. 1529

Author(s):

Dilafruz N. Jamalova ◽

Haidy A. Gad ◽

Davlat K. Akramov ◽

Komiljon S. Tojibaev ◽

Nawal M. Al Musayeib ◽

...

Keyword(s):

Essential Oils ◽

Reducing Power ◽

Maximum Activity ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Diffusion Method ◽

Cytotoxic Activities ◽

Chemical Compositions

The chemical composition of the essential oils obtained from the aerial parts of four Apiaceae species, namely Elaeosticta allioides (EA), E. polycarpa (EP), Ferula clematidifolia (FC), and Hyalolaena intermedia (HI), were determined using gas chromatography. Altogether, 100 volatile metabolites representing 78.97, 81.03, 85.78, and 84.49% of the total components present in EA, EP, FC, and HI oils, respectively, were reported. allo-Ocimene (14.55%) was the major component in FC, followed by D-limonene (9.42%). However, in EA, germacrene D (16.09%) was present in a high amount, while heptanal (36.89%) was the predominant compound in HI. The gas chromatographic data were subjected to principal component analysis (PCA) to explore the correlations between these species. Fortunately, the PCA score plot could differentiate between the species and correlate Ferula to Elaeosticta species. Additionally, the antioxidant activity was evaluated in vitro using the 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH), 2,2-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), and the ferric reducing power (FRAP) assays. In addition, the antimicrobial activity using the agar diffusion method was assessed, and the minimum inhibitory concentrations (MICs) were determined. Furthermore, the cell viability MTT assay was performed to evaluate the cytotoxicity of the essential oils against hepatic (HepG-2) and cervical (HeLa) cancer cell lines. In the DPPH assay, FC exhibited the maximum activity against all the antioxidant assays with IC50 values of 19.8 and 23.0 μg/mL for the DPPH and ABTS assays, respectively. Ferula showed superior antimicrobial and cytotoxic activities as well. Finally, a partial least square regression model was constructed to predict the antioxidant capacity by utilizing the metabolite profiling data. The model showed excellent predictive ability by applying the ABTS assay.

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Taste Masking Study Based on an Electronic Tongue: the Formulation Design of 3D Printed Levetiracetam Instant-Dissolving Tablets

Pharmaceutical Research ◽

10.1007/s11095-021-03041-9 ◽

2021 ◽

Author(s):

Zengming Wang ◽

Jingru Li ◽

Xiaoxuan Hong ◽

Xiaolu Han ◽

Boshi Liu ◽

...

Keyword(s):

Drug Release ◽

Electronic Tongue ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Taste Masking ◽

Formulation Design ◽

3D Printed

Abstract Purpose Proper taste-masking formulation design is a critical issue for instant-dissolving tablets (IDTs). The purpose of this study is to use the electronic tongue to design the additives of the 3D printed IDTs to improve palatability. Methods A binder jet 3D printer was used to prepare IDTs of levetiracetam. A texture analyzer and dissolution apparatus were used to predict the oral dispersion time and in vitro drug release of IDTs, respectively. The palatability of different formulations was investigated using the ASTREE electronic tongue in combination with the design of experiment and a model for masking bitter taste. Human gustatory sensation tests were conducted to further evaluate the credibility of the results. Results The 3D printed tablets exhibited rapid dispersion (<30 s) and drug release (2.5 min > 90%). The electronic tongue had an excellent ability of taste discrimination, and levetiracetam had a good linear sensing performance based on a partial least square regression analysis. The principal component analysis was used to analyze the signal intensities of different formulations and showed that 2% sucralose and 0.5% spearmint flavoring masked the bitterness well and resembled the taste of corresponding placebo. The results of human gustatory sensation test were consistent with the trend of the electronic tongue evaluation. Conclusions Owing to its objectivity and reproducibility, this technique is suitable for the design and evaluation of palatability in 3D printed IDT development.

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Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Remote Sensing ◽

10.3390/rs13040641 ◽

2021 ◽

Vol 13 (4) ◽

pp. 641

Author(s):

Gopal Ramdas Mahajan ◽

Bappa Das ◽

Dayesh Murgaokar ◽

Ittai Herrmann ◽

Katja Berger ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Spectral Indices ◽

Learning Models ◽

Leaf Nutrients ◽

Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

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Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

Applied Sciences ◽

10.3390/app11020618 ◽

2021 ◽

Vol 11 (2) ◽

pp. 618

Author(s):

Tanvir Tazul Islam ◽

Md Sajid Ahmed ◽

Md Hassanuzzaman ◽

Syed Athar Bin Amir ◽

Tanzilur Rahman

Keyword(s):

Blood Glucose ◽

Blood Glucose Level ◽

Glucose Level ◽

Quantitative Estimation ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Frequency Noise ◽

Optical Noise

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

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A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽

10.3390/molecules26061546 ◽

2021 ◽

Vol 26 (6) ◽

pp. 1546

Author(s):

Ioanna Dagla ◽

Anthony Tsarbopoulos ◽

Evagelos Gikas

Keyword(s):

Design Of Experiments ◽

High Performance ◽

Similarity Index ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Distance Measures ◽

Linear Regression Models ◽

Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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The inflammatory profile of cerebrospinal fluid, plasma, and saliva from patients with severe neuropathic pain and healthy controls-a pilot study

BMC Neuroscience ◽

10.1186/s12868-021-00608-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Mika Jönsson ◽

Björn Gerdle ◽

Bijar Ghafouri ◽

Emmanuel Bäckryd

Keyword(s):

Cerebrospinal Fluid ◽

Neuropathic Pain ◽

Pain Intensity ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Therapeutic Interventions ◽

Healthy Controls ◽

Inflammatory Profile

Abstract Background Neuropathic pain (NeuP) is a complex, debilitating condition of the somatosensory system, where dysregulation between pro- and anti-inflammatory cytokines and chemokines are believed to play a pivotal role. As of date, there is no ubiquitously accepted diagnostic test for NeuP and current therapeutic interventions are lacking in efficacy. The aim of this study was to investigate the ability of three biofluids - saliva, plasma, and cerebrospinal fluid (CSF), to discriminate an inflammatory profile at a central, systemic, and peripheral level in NeuP patients compared to healthy controls. Methods The concentrations of 71 cytokines, chemokines and growth factors in saliva, plasma, and CSF samples from 13 patients with peripheral NeuP and 13 healthy controls were analyzed using a multiplex-immunoassay based on an electrochemiluminescent detection method. The NeuP patients were recruited from a clinical trial of intrathecal bolus injection of ziconotide (ClinicalTrials.gov identifier NCT01373983). Multivariate data analysis (principal component analysis and orthogonal partial least square regression) was used to identify proteins significant for group discrimination and protein correlation to pain intensity. Proteins with variable influence of projection (VIP) value higher than 1 (combined with the jack-knifed confidence intervals in the coefficients plot not including zero) were considered significant. Results We found 17 cytokines/chemokines that were significantly up- or down-regulated in NeuP patients compared to healthy controls. Of these 17 proteins, 8 were from saliva, 7 from plasma, and 2 from CSF samples. The correlation analysis showed that the most important proteins that correlated to pain intensity were found in plasma (VIP > 1). Conclusions Investigation of the inflammatory profile of NeuP showed that most of the significant proteins for group separation were found in the less invasive biofluids of saliva and plasma. Within the NeuP patient group it was also seen that proteins in plasma had the highest correlation to pain intensity. These preliminary results indicate a potential for further biomarker research in the more easily accessible biofluids of saliva and plasma for chronic peripheral neuropathic pain where a combination of YKL-40 and MIP-1α in saliva might be of special interest for future studies that also include other non-neuropathic pain states.

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Suggestions on the Contribution of Methyl Eugenol and Eugenol to Bay Laurel (Laurus nobilis L.) Essential Oil Preservative Activity through Radical Scavenging

Molecules ◽

10.3390/molecules26082342 ◽

2021 ◽

Vol 26 (8) ◽

pp. 2342

Author(s):

Nikolaos Nenadis ◽

Maria Papapostolou ◽

Maria Z. Tsimidou

Keyword(s):

Essential Oil ◽

Radical Scavenging Activity ◽

Pearson Correlation ◽

Radical Scavenging ◽

Methyl Eugenol ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Positive Contribution ◽

Scavenging Activity

The present study examined the radical scavenging potential of the two benzene derivatives found in the bay laurel essential oil (EO), namely methyl eugenol (MEug) and eugenol (Eug), theoretically and experimentally to make suggestions on their contribution to the EO preservative activity through such a mechanism. Calculation of appropriate molecular indices widely used to characterize chain-breaking antioxidants was carried out in the gas and liquid phases (n-hexane, n-octanol, methanol, water). Experimental evidence was based on the DPPH• scavenging assay applied to pure compounds and a set of bay laurel EOs chemically characterized with GC-MS/FID. Theoretical calculations suggested that the preservative properties of both compounds could be exerted through a radical scavenging mechanism via hydrogen atom donation. Eug was predicted to be of superior efficiency in line with experimental findings. Pearson correlation and partial least square regression analyses of the EO antioxidant activity values vs. % composition of individual volatiles indicated the positive contribution of both compounds to the radical scavenging activity of bay laurel EOs. Eug, despite its low content in bay laurel EOs, was found to influence the most the radical scavenging activity of the latter.

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