Cysteamine Nanoemulsion Delivery by Inhalation to Attenuate Adverse Effects of Exposure to Cigarette Smoke: A Metabolomics Study in Wistar Rats.

There is a pressing need for novel pharmacological interventions and drug delivery innovations to attenuate the cigarette smoke-associated oxidative stress and lung disease. We report here on the attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) and metabolomics of Wistar rats exposed to cigarette smoke for 28 days. The animals were treated for 15 days with plain cysteamine given orally or cysteamine as nanoemulsion given orally or via inhalation. The study design also included two control groups as follows: rats exposed to cigarette smoke but did not receive a treatment (diseased control group) and rats neither exposed to cigarette smoke nor a treatment (normal control group). The targeted metabolomics using Parallel Reaction Monitoring showed that in the diseased control group, ornithine, nicotinamide, xanthine, hypoxanthine, and caprolactam were increased compared with the normal control group. In addition, (±)8(9)-DiHET, which was initially downregulated in the diseased control group, exhibited a reversal of this trend with cysteamine nanoemulsion given via inhalation. The cysteamine nanoemulsion delivered by inhalation highlighted the importance of the route of drug administration for targeting the lungs. To the best of our knowledge, this is the first work to use ATR-FTIR and metabolomics in Wistar rat lung tissues, suggesting how cysteamine nanoemulsion can potentially reduce cigarette smoke-induced oxidative damage. The metabolites reported herein have potential implications for discovery of novel theranostics and, thus, to cultivate diagnostic and therapeutic innovation for early prevention and treatment of cigarette smoke-associated lung diseases.

Dataset of NIR, MIR and FIR spectroscopy of fuels in maritime cases and biodiesel-diesel blends B7 and B10 from Malaysia.

The dataset contains Fourier-transform infrared (FTIR) spectroscopic analysis of fuels in maritime cases and biodiesel-diesel blends B7 and B10 from Malaysia. Fuels in maritime cases were donated by Agensi Penguatkuasaan Maritim Malaysia (APMM) in March 2023. The crime-related oil samples originated from maritime crime scenes located within Terengganu and Johor, Malaysia. Meanwhile, B7(DE5) and B10(D0) samples were obtained from pump stations in 2021. They are fuels used in Malaysian transportation system. The FTIR analysis was acquired in the full regions of FTIR (6000-80 cm-1) which are near-infrared (NIR), mid-infrared (MIR), and far-infrared (FIR). The IR spectra were recorded using Bruker Invenio-R (Universiti Putra Malaysia) spectrometer equipped with attenuated total reflection (ATR) (2 mm) diamond with an accumulation of 64 scans at a spectral resolution of 4 cm-1. Spectral analysis was carried out by OPUS 8.7.41. The data highlights the potential of NIR, MIR, and FIR spectroscopy as a powerful tool for forensic analysis in maritime crime investigations. This includes the potential of utilizing the Hierarchical Clustering Analysis (HCA) to discriminate between type of fuels in forensic cases.

Risk assessment of cigarette butts and microplastic pollution in a drinking and irrigation water basin of West Anatolia, Türkiye.

Cigarette butts (CBs) and Microplastics (MPs) have serious harmful effects on the environment and living organisms despite their small size. This research aims to investigate the abundance and pollution status of CBs and MPs in Tahtali Dam Basin (West Anatolia, Türkiye) which is the most important drinking and irrigation water resources. Clean Environment Index (CEI) and Cigarette Butt Pollution Index (CBPI) were used to determine pollution degree of the basin. The total number of CBs were 1.478 items, the total number of MPs were 477 items/m2 in the basin. As a result of this study, MP particles weren't found in Balaban Stream. Highest number of MP particles observed in 100-250 µm (45%) size class. The most abundant MP type and colour were, fragment (54%) and white (42%), respectively. Polyethylene terephthalate (50%) was the most abundant type of polymer according to the ATR- FTIR analysis. As a result of the CEI and CBPI, the upstream stations of the stream were classified as "clean" status, while downstream sampling points of the stream and Balaban Lake coasts were classified as "extremely dirty" status. The calculated volumes of MP particulates from mining facility, agricultural and recreational activities indicate that anthropogenic factors are the most important MP source in the Tahtali Dam Basin. This study is the first study about MP and CB pollution of the freshwater ecosystems in the region.

Estimation of Time-Since-Deposition of bloodstains on different surfaces using ATR-FTIR Spectroscopy and Chemometrics.

Blood is commonly discovered at crime scenes in various forms, including stains, dried residue, pools, and fingerprints on assorted surfaces. Estimating the age of bloodstains is a crucial aspect of reconstructing crime scenes. This research aimed to investigate how the nature of different surfaces affects the estimation of bloodstain age, utilizing a reliable and non-destructive approach. The study employed ATR-FTIR spectroscopy in conjunction with Chemometric techniques such as PCA (Principal Component Analysis) and OPLSR (Orthogonal Signal Correction Partial Least Square Regression Analysis) to analyze spectral data and develop regression models for estimating bloodstain age on cement, metal, and wooden surfaces for up to eleven days. The chemometric models for bloodstains on all three substrates demonstrated strong performance, with predictive Root Mean Square Error (RMSE) values ranging from 1.1 to 1.43 and R2 values from 0.84 to 0.89. Notably, the model developed for metal surfaces was found to be the most accurate with minimal prediction error. The findings of the study showed that the porosity of the substrates upon which bloodstains were found had a discernible influence on the age-related transformations observed in bloodstains; the majority of which occured within the spectral range of 2800 cm- 1 to 3500 cm- 1.

Microplastic from beach sediment to tissue: a case study on burrowing crab Dotilla blanfordi.

Background: Microplastics (MPs) are pervasive pollutants in the marine environment, exhibiting persistence in coastal sediment over extended periods. However, the mechanism of their uptake by marine organisms and distribution in habitat is less understood. The objective of the present study was to investigate the presence of MP contamination in burrow sediment, feeding pellets, and tissue of Dotilla blanfordi in the Gulf of Kachchh, Gujarat State. Methods: A total of 500 g of burrow sediment, 100 g of feeding pellets, and body tissue of 10 resident D. blanfordi were pooled as one replica. Such seven replicas from each site were analyzed for MP extraction from three sites, including Asharmata, Mandvi, and Serena, located in the Gulf of Kachchh. The standard protocol was used during the analysis of the collected samples in order to isolate MPs. Results: The abundance of MP was found higher in burrow sediment, feeding pellets and tissue of D. blanfordi at study site Mandvi, followed by Serena and Asharmata. The abundance of MP was found higher in D. blanfordi tissue, followed by burrow sediment and feeding pellet. A significant variation was observed in MP abundance among burrow sediment, feeding pellets, and tissue. MPs with various shapes (fiber, film, and fragment), sizes (1-2, 2-3, 3-4, and 4-5 mm), and colors (blue, green, black, pink, purple, red transparent) were recorded from all the study sites. Polyurethane and polyvinyl chloride were recognized as the chemical profile of the extracted MPs. The current investigation revealed greater accumulation of MPs in D. blanfordi's tissues compared to sediment and pellets, suggesting a risk of MP contamination in marine benthic fauna with a greater rate of bioaccumulation. D. blanfordi plays a significant role as a structuring agent for MP distribution in the intertidal flat through burrowing activity.

Iron (hydr)oxides-induced activation of sulfite for contaminants degradation: The critical role of structural Fe(III).

Iron-based sulfite (S(IV)) activation has emerged as a novel strategy to generate sulfate radicals (SO4•-) for contaminants degradation. However, numerous studies focused on dissolved iron-induced homogeneous activation processes while the potential of structural Fe(III) remains unclear. In this study, five iron (hydr)oxide soil minerals (FeOx) including ferrihydrite, schwertmannite, lepidocrocite, goethite and hematite, were successfully employed as sources of structural Fe(III) for S(IV) activation. Results showed that the catalytical ability of structural Fe(III) primarily depended on the crystallinity of FeOx instead of their specific surface area and particle size, with ferrihydrite and schwertmannite being the most active. Furthermore, in-situ ATR-FTIR spectroscopy and 2D-COS analysis revealed that HSO3- was initially adsorbed on FeO6 octahedrons of FeOx via monodentate inner-sphere complexation, ultimately oxidized into SO42- which was then re-adsorbed via outer-sphere complexation. During this process, strong oxidizing SO4•- and •OH were formed for pollutants degradation, confirmed by radical quenching experiments and electron spin resonance. Moreover, FeOx/S(IV) system exhibited superior applicability with respect to recycling test, real waters and twenty-six pollutants degradation. Eventually, plausible degradation pathways of three typical pollutants were proposed. This study highlights the feasibility of structural Fe(III)-containing soil minerals for S(IV) activation in wastewater treatment.

Microplastic ingestion by sea turtles around Tokyo Bay: Level of water pollution influences ingestion amounts.

We investigated the contents inside the esophagus and stomach of turtles inside and outside of the Tokyo Bay area, which face high and low risks of microplastic (MP) exposure, respectively. 65 synthetic particles were recovered from 8 out of 22 turtles, using ATR-FTIR followed by density separation with calcium chloride solution. Statistical analysis indicated that turtles in high-risk areas ingested significantly more MPs than those in low-risk areas. As the inflow of MPs from major rivers influences pollution levels in the ocean, the results of this study highlight the importance of major rivers for MP ingestion by turtles. Additionally, we discussed the current methodology's shortcomings and addressed scope for subsequent research, along with suggestions on future conservation.

Combining spectrum and machine learning algorithms to predict the weathering time of empty puparia of Sarcophaga peregrine (Diptera: Sarcophagidae).

The weathering time of empty puparia could be important in predicting the minimum postmortem interval (PMImin). As corpse decomposition progresses to the skeletal stage, empty puparia often remain the sole evidence of fly activity at the scene. In this study, we used empty puparia of Sarcophaga peregrina (Diptera: Sarcophagidae) collected at ten different time points between January 2019 and February 2023 as our samples. Initially, we used the scanning electron microscope (SEM) to observe the surface of the empty puparia, but it was challenging to identify significant markers to estimate weathering time. We then utilized attenuated total internal reflectance Fourier transform infrared spectroscopy (ATR-FTIR) to detect the puparia spectrogram. Absorption peaks were observed at 1064 cm-1, 1236 cm-1, 1381 cm-1, 1538 cm-1, 1636 cm-1, 2852 cm-1, 2920 cm-1. Three machine learning models were used to regress the spectral data after dimensionality reduction using principal component analysis (PCA). Among them, eXtreme Gradient Boosting regression (XGBR) showed the best performance in the wavenumber range of 1800-600 cm-1, with a mean absolute error (MAE) of 1.20. This study highlights the value of refining these techniques for forensic applications involving entomological specimens and underscores the considerable potential of combining FTIR and machine learning in forensic practice.

Optimization of mid-infrared noninvasive blood-glucose prediction model by support vector regression coupled with different spectral features.

Mid-infrared spectral analysis of glucose in subcutaneous interstitial fluid has been widely employed as a noninvasive alternative to the standard blood-glucose detection requiring blood-sampling via skin-puncturing, but improving the confidence level of such a replacement remains highly desirable. Here, we show that with an innovative metric of attributes in measurements and data-management, a high accuracy in correlating the test results of our improved spectral analysis to those of the standard detection is accomplished. First, our comparative laser speckle contrast imaging of subcutaneous interstitial fluid in fingertips, thenar and hypothenar reveal that spectral measurements from hypothenar, with an attenuated total reflection Fourier transform infrared spectrometer, give much stronger signals than the stereotype measurements from fingertips. Second, we demonstrate that discriminative selection of the spectral locations and ranges, to minimize spectral interference and maximize signal-to-noise, are critically important. The optimal band is pinned at that between 1000 ± 3 cm-1 and1040 ± 3 cm-1. Third, we propose an individual exclusive prediction model by adopting the support vector regression analysis of the spectral data from four subjects. The average predicted coefficient of determination, root mean square error and mean absolute error of four subjects are 0.97, 0.21 mmol/L, 0.17 mmol/L, respectively, and the average probability of being in Zone A of the Clark error grid is 100.00 %. Additionally, we demonstrate with the Bland and Altman plot that our proposed model has the highest consistency with portable blood glucose meter detection method.

Comparative profiling of serum biomarkers and ATR-FTIR spectroscopy for differential diagnosis of patients with rheumatoid and psoriatic arthritis - a pilot study.

BACKGROUND: Rheumatoid arthritis (RA) and psoriatic arthritis (PsA) are chronic inflammatory diseases in which innate and adaptive responses of the immune system are induced. RA and PsA have complex signaling pathways. Despite the differences in their clinical presentation, there is a great demand for fast and accurate diagnosis of diseases to implement treatment and plan an individual therapeutic strategy quickly. In this report, we present the results of differential diagnosis of patients with RA and PsA and healthy subjects (C, control group), allowing for reliable differentiation of groups of rheumatoid patients based on biochemical parameters, attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectra, and combined data sets. MATERIALS AND METHODS: Biochemical analyses, ELISA (enzyme-linked immunosorbent assays), and multiplex assays were conducted for blood sera from patients with RA (n = 32), patients with PsA (n = 28), and the control group (n = 18). ATR-FTIR spectra were collected for lyophilized sera. RESULTS: The combination of six biochemical parameters (WBC, ESR, RF, CRP, HCC-4/CCL16, and HMGB1/HMGB) allowed the development of the partial least squares discriminant analysis (PLS-DA) model with an overall accuracy (OA) of 80% for test samples. The best separation between RA, PsA, and the control group was obtained utilizing spectral data. Using the interval PLS algorithm (iPLS) specific spectral ranges were selected and a classifier characterized by OA value for test set equal to 88% was obtained. This parameter, for the hybrid PLS-DA model constructed using selected biochemical parameters and a significantly reduced number of spectral variables, reached the level of 84%. CONCLUSIONS: PLS-DA models developed on the basis of spectral data enable effective differentiation of patients with RA, patients with PsA, and healthy subjects. They appeared to be insensitive to existing inflammation processes which opens interesting perspectives for new diagnostic tests and algorithms for identification of patients with RA and PsA.

Identification of geographical origins of Gastrodia elata Blume based on multisource data fusion.

INTRODUCTION: Identifying the geographical origin of Gastrodia elata Blume contributes to the scientific and rational utilization of medicinal materials. In this study, infrared spectroscopy was combined with machine learning algorithms to distinguish the origin of G. elata BI. OBJECTIVE: Realization of rapid and accurate identification of the origin of G. elata BI. MATERIALS AND METHODS: Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectra and Fourier transform near-infrared (FT-NIR) spectra were collected for 306 samples of G. elata BI. SAMPLES: Firstly, a support vector machine (SVM) model was established based on the single-spectrum and the full-spectrum fusion data. To investigate whether feature-level fusion strategy can enhance the model's performance, the sequential and orthogonalized partial least squares discriminant analysis (SO-PLS-DA) model was established to extract and combine two types of spectral features. Next, six algorithms were employed to extract feature variables, SVM model was established based on the feature-level fusion data. To avoid complicated preprocessing and feature extraction processes, a residual convolutional neural network (ResNet) model was established after converting the raw spectral data into spectral images. RESULTS: The accuracy of the feature-level fusion model is better as compared to the single-spectrum model and the fusion model with full-spectrum, and SO-PLS-DA is simpler than feature-level fusion based on the SVM model. The ResNet model performs well in classification but requires more data to enhance its generalization capability and training effectiveness. CONCLUSION: Sequential and orthogonalized data fusion approaches and ResNet models are powerful solutions for identifying the geographic origin of G. elata BI.

Promoting longevity in aged liver through NLRP3 inflammasome inhibition using tauroursodeoxycholic acid (TUDCA) and SCD probiotics.

This investigation explores the combined influence of SCD Probiotics and tauroursodeoxycholic acid (TUDCA) on liver health in elderly male Sprague-Dawley rats. Through the administration of intravenous TUDCA (300 mg/kg) and oral SCD Probiotics (3 mL at 1 × 10^8 CFU) daily for one week, this study evaluates the biomolecular composition, histopathological alterations, and inflammasome activity in the liver. Analytical methods encompassed ATR-FTIR spectroscopy integrated with machine learning for the assessment of biomolecular structures, RT-qPCR for quantifying inflammasome markers (NLRP3, ASC, Caspase-1, IL18, IL1ß), and histological examinations to assess liver pathology. The findings reveal that TUDCA prominently enhanced lipid metabolism by reducing cholesterol esters, while SCD Probiotics modulated both lipid and protein profiles, notably affecting fatty acid chain lengths and protein configurations. Histological analysis showed significant reductions in cellular degeneration, lymphatic infiltration, and hepatic fibrosis. Furthermore, the study noted a decrease in the immunoreactivity for NLRP3 and ASC, suggesting suppressed inflammasome activity. While SCD Probiotics reduced the expression of certain inflammasome-related genes, they also paradoxically increased AST and LDH levels. Conversely, an exclusive elevation in albumin levels was observed in the group treated with SCD Probiotics, implying a protective role against liver damage. These results underscore the therapeutic potential of TUDCA and SCD Probiotics for managing age-associated liver disorders, illustrating their individual and synergistic effects on liver health and pathology. This study provides insights into the complex interactions of these agents, advocating for customized therapeutic approaches to combat liver fibrosis, enhance liver functionality, and decrease inflammation in aging populations.

Assessment of microplastics in coastal ecosystem of Bangladesh.

Microplastics (MPs) pose one of the major environmental threats to marine organisms and ecosystems on a global scale. The present study investigated MPs in surface water, beach sediments, and fish in two coastal areas of Bangladesh namely Cox's Bazar and Kuakata. The MPs were identified and characterized using three different techniques, including the binocular microscope, the ATR-FTIR (Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy), and SEM-EDS (Scanning Electron Microscopy- Energy Dispersive Spectroscopy). The number of MPs in seawater was 10.1 ± 3.10 and 8.52 ± 3.92 items/100 L and in beach sediment, 13.2 ± 3.68 and 9.48 ± 3.63 items/100 g in Cox's Bazar and Kuakata, respectively. In fish samples, the abundance of MPs was 7.82 ± 1.28 and 6.82 ± 1.87 items/individual species of Cox's Bazar and Kuakata, respectively, where the highest quantities of MP were found in Euthynnus affinisand Sillago sihama and the lowest in Terapon jarbua and Pampus chinensisin Cox's Bazar and Kuakata, respectively. The number of MPs in GITs (Gastrointestinal tracts) was 1.63 ± 0.991 and 1.25 ± 0.546 items/g GIT and in BW (Body Weight) were 0.042 ± 0.014 and 0.037 ± 0.014 items/g BW in Cox's Bazar and Kuakata, respectively. There revealed a positive correlation between MP abundance and GIT weight and body weight in fish species. MPs were predominantly fiber-shaped, white/transparent, and small size. The most common MP polymers were polyethylene and polypropylene. SEM images of MPs demonstrate surface roughness, cracks, mechanical weathering and oxidative weathering, demonstrating their ongoing environmental exposure. The EDS spectrum unearthed that the MPs contained several elements (C, N, O, Na, Al, Fe, and Si). Findings from this study might be useful in coastal plastic particle management and to mitigate the potential risks associated with them.

Hybrid wavelength selection strategy combined with ATR-FTIR spectroscopy for preliminary exploration of vintage labeling traceability of sauce-flavor baijiu.

The allure of substantial profits has perpetuated the illicit trade of counterfeit vintage labels for baijiu. While various approaches have been employed to intelligently ascertain the vintage of baijiu, many of them are both cost-intensive and time-consuming. This work pioneered the use of Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric analysis, offering a non-destructive and economically viable method for discriminating sauce-flavor baijiu across different aging periods (1-, 2-, and 3-year). In this research, principal component analysis (PCA) was first conducted to explore clustering trends among distinct vintage groups. Subsequently, the effect of spectral pre-processing on modeling performance was explored. For wavelength selection, four wavelength selection methods (ReliefF, random forest variable importance (RFVI), variable importance in projection (VIP), and Venn) were first used to identify the subset of candidate features that potentially best mapped the vintage labels. Immediately following this, to explore the possibility of further improving the identification capabilities of the model as well as to reduce the redundant data that may still be present, sequential backward selection (SBS) was utilized for secondary feature reduction within the subset of candidates. The amalgamation of these two techniques is termed a "hybrid wavelength selection strategy." Additionally, the dimensionality reduction effects of PCA and kernel principal component analysis (KPCA) were compared to demonstrate the robustness of the proposed method. Finally, classification models such as partial least squares discriminant analysis (PLS-DA), random forest (RF), and grasshopper optimization algorithm-based support vector machine (GOA-SVM) were developed. The results show that the spectral data need not be pre-processed, and the proposed hybrid wavelength selection strategy can further improve the identification ability of the model. Among the many models developed, ReliefF-SBS-GOA-SVM emerged as the most proficient classification model, yielding accuracy, sensitivity, and specificity rates of 94.44%, 95.23%, and 94.44%, respectively. This method not only holds promise for the discrimination of baijiu class attributes such as brand, origin, flavor, and vintage but also exhibits potential applicability in other non-targeted identification studies involving spectroscopy methodologies.

Quantification and polymeric characterization of microplastics in composts and their accumulation in lettuce.

Organic fertilizers have become a vector for the transport of microplastics (MPs), which pose human health concerns through the food chain. This study aimed to quantify and characterize MPs in eight different compost samples of various raw materials and their subsequent translocation to lettuce (Lacuta sativa) grown on contaminated composts. The results revealed that the MP abundance ranged from 3810 to 16530 MP/kg. Municipal solid waste compost (MSWC) had highest abundance (16082 ± 632 MP/kg), followed by leaf compost (LC) and organic compost (OC) (6299 ± 1011 and 3680 ± 419 MP/kg, respectively). MPs of <100 µm in size were most dominant in MSWC and LC. Fragments and fibers were the prevalent shape types, with white/transparent colored MPs being more abundant. Polyethylene (PE), polypropylene (PP) and polyethylene terephthalate (PET) were the dominant polymers. MPs accumulation in the lettuce leaves was greatest in the lettuce plants grown on MSWC, followed by those grown on LC and OC, indicating that MSWC grown lettuce is not suitable for human consumption. The decrease in the growth (leaf length, number of leaves, leaf fresh and weights) and physiological (membrane stability index, relative water contents) parameters of lettuce was in line with the trend of MP accumulations. Hence, it is highly important to regulate the plastic contents in compost because it is a threat to ecosystems and human health.

Gaussian process regression coupled with mRMR to predict adulterant concentration in cocaine.

Street cocaine is often mixed with various substances that intensify its harmful effects. This paper proposes a framework to identify attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) intervals that best predict the concentration of adulterants in cocaine samples. Wavelengths are ranked according to their relevance through ReliefF and mRMR feature selection approaches, and an iterative process removes less relevant wavelengths based on the ranking suggested by each approach. Gaussian Process (GP) regression models are constructed after each wavelength removal and the prediction performance is evaluated using RMSE. The subset balancing a low RMSE value and a small percentage of retained wavelengths is chosen. The proposed framework was validated using a dataset consisting of 345 samples of cocaine with different amounts of levamisole, caffeine, phenacetin, and lidocaine. Averaged over the four adulterants, the GP regression coupled with the mRMR retained 1.07 % of the 662 original wavelengths, outperforming PLS and SVR regarding prediction performance.

Enhancing Icephobic Coatings: Exploring the Potential of Dopamine-Modified Epoxy Resin Inspired by Mussel Catechol Groups.

A nature-inspired approach was employed through the development of dopamine-modified epoxy coating for anti-icing applications. The strong affinity of dopamine's catechol groups for hydrogen bonding with water molecules at the ice/coating interface was utilized to induce an aqueous quasi-liquid layer (QLL) on the surface of the icephobic coatings, thereby reducing their ice adhesion strength. Epoxy resin modification was studied by attenuated total reflectance infrared spectroscopy (ATR-FTIR) and nuclear magnetic resonance spectroscopy (NMR). The surface and mechanical properties of the prepared coatings were studied by different characterization techniques. Low-temperature ATR-FTIR was employed to study the presence of QLL on the coating's surface. Moreover, the freezing delay time and temperature of water droplets on the coatings were evaluated along with push-off and centrifuge ice adhesion strength to evaluate their icephobic properties. The surface of dopamine-modified epoxy coating presented enhanced hydrophilicity and QLL formation, addressed as the main reason for its remarkable icephobicity. The results demonstrated the potential of dopamine-modified epoxy resin as an effective binder for icephobic coatings, offering notable ice nucleation delay time (1316 s) and temperature (-19.7 °C), reduced ice adhesion strength (less than 40 kPa), and an ice adhesion reduction factor of 7.2 compared to the unmodified coating.

Analysis of the Dissolution Behavior of Theophylline and Its Cocrystal Using ATR-FTIR Spectroscopic Imaging.

Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopic imaging is a powerful tool to visualize the distribution of components, and it has been used to analyze drug release from tablets. In this work, ATR-FTIR spectroscopic imaging was applied for observing the dissolution of molecular crystals from tablet compacts. The IR spectra provided chemically specific information about the transformation of crystal structures during the dissolution experiments. Theophylline (TPL) anhydrate and its cocrystals were used as model systems of molecular crystals. The IR spectra during the dissolution of TPL revealed information about the crystal structure of TPL, which transformed from anhydrate to monohydrate in water. During a dissolution test of a model cocrystal system, it was suggested that an active pharmaceutical ingredient (API) and a coformer were dissolved in water simultaneously. The IR spectra that were acquired during the dissolution of a cocrystal tablet showed new spectral bands attributed to the API after 5 min. This suggested that the precipitation of API was observed during the dissolution experiment. Measurements from ATR-FTIR spectroscopic imaging can visualize the drug release from the tablet and determine the transformation of molecular crystals during their dissolution. These results will have an impact on clarifying the dissolution mechanism of molecular crystals.

Structural investigation of interactions between halogenated flavonoids and the lipid membrane along with their role as cytotoxic agents.

This study focuses on understanding the structural and molecular changes in lipid membranes under the influence of six halogenated flavonoid derivatives differing in the number and position of substitution of chlorine and bromine atoms (D1-D6). Utilizing various analytical techniques, including fluorometric methods, dynamic light scattering (DLS), attenuated Fourier transform infrared spectroscopy (ATR- FTIR), and FT-Raman spectroscopy, the research aims to elucidate the mechanisms underlying the interaction of flavonoids with cell membranes. Additionally, the study includes in silico analyses to explore the physicochemical properties of these compounds and their potential pharmaceutical applications, along with toxicity studies to assess their effects on cancer, normal, and red blood cells. Our study showed the ability of halogenated derivatives to interact mostly with the outer part of the membrane, especially in the lipid heads region however, some of them were able to penetrate deeper into the membrane and affect the fluidity of hydrocarbon chains. The potential to reduce cancer cell viability, the lack of toxicity towards erythrocytes, and the favourable physicochemical and pharmacokinetic properties suggest these halogenated flavonoids potential candidates for exploring their potential for medical use.

Thermal effects and drugs competition on the palmitate binding capacity of human serum albumin.

Human serum albumin (HSA) is the most abundant plasma protein of the circulatory system. It is a multidomain, multifunctional protein that, combining diverse affinities and wide specificity, binds, stores, and transports a variety of biological compounds, pharmacores, and fatty acids. HSA is finding increasing uses in drug-delivery due to its ability to carry functionalized ligands and prodrugs. All this raises the question of competition for binding sites occupancy in case of multiple ligands, which in turn influences the protein structure/dynamic/function relationship and also has an impact on the biomedical applications. In this work, the effects of interactive binding of palmitic acid (PA), warfarin (War) and ibuprofen (Ibu) on the thermal stability of HSA were studied using DSC, ATR-FTIR, and EPR. PA is a high-affinity physiological ligand, while the two drugs are widely used for their anticoagulant (War) and anti-inflammatory (Ibu) efficacy, and are exogenous compounds that accommodate in the deputed drug site DS1 and DS2, respectively overlapping with some of the fatty acid binding sites. The results indicate that HSA acquires the highest thermal stability when it is fully saturated with PA. The binding of this physiological ligand does not hamper the binding of War or Ibu to the native state of the protein. In addition, the three ligands bind simultaneously, suggesting a synergic cooperative influence due to allosteric effects. The increased thermal stability subsequent to binary and multiple ligands binding moderates protein aggregation propensity and restricts protein dynamics. The biophysics findings provide interesting features about protein stability, aggregation, and dynamics in interaction with multiple ligands and are relevant in drug-delivery.