Red Green Research Articles

Spectrophotometric determination of olanzapine, fluoxetine HCL and its impurity using univariate and chemometrics methods reinforced by latin hypercube sampling: Validation and eco-friendliness assessments

Novel univariate and chemometrics-aided UV spectrophotometric methods were tailored to undergo the fundamentals of green and white analytical chemistry for the simultaneous estimation of a ternary mixture of olanzapine (OLA), fluoxetine HCL (FLU), and its toxic impurity 4-(Trifluoromethyl) phenol (FMP) without any prior separation. The dual-wavelength ratio spectrum univariate method was used to determine OLA and FLU in the presence of FMP in the range of (4–20) and (5–50) μg/ml, respectively. In compliance with the International Conference on Harmonization (ICH) standards, the technique was validated and established Remarkable accuracy (98–102%) and precision (< 2%) with limits of quantification (LOQs) of 0.432 and 2.002 μg/ml, respectively. Partial least squares (PLS) and artificial neural networks (ANNs) are chemometric methodologies that have advantages over the univariate method and use significant innovations employing Latin hypercube sampling (LHS), allowing the generation of a reliable validation set to guarantee the effectiveness and sustainability of these models. The concentration ranges used were (2–20), (2–20), and (5–50) μg/ml; for PLS, the LOQs were 0.602, 0.508, and 1.429 μg/ml, and the root mean square errors of prediction (RMSEPs) were 0.087, 0.048, and 0.159 for OLA, FMP, and FLU, respectively; and for ANNs, the LOQs were 0.551, 0.465, and 0.965 μg/ml, with RMSEPs of 0.056, 0.047, and 0.087 for OLA, FMP, and FLU, respectively. The developed methods yield a greener National Environmental Methods Index (NEMI) with an eco-scale assessment (ESA) score of 90 and a complementary Green Analytical Procedure Index (complex GAPI) in quadrants with an analytical greenness metric (AGREE) score of 0.8. The Red‒Green–Blue 12 algorithm (RGB 12) scored 88.9, outperforming on reported methods and demonstrating widespread practical and environmental approval. Statistical analysis revealed no noteworthy differences (P > 0.05) among the proposed and published techniques. Both pure powders and pharmaceutical capsules were analyzed via these methods.

Multi-color emission composites of white carbon dots and dyes through inner filter effect

A series of multi-color carbon dot composites were obtained by transforming white carbon dots (WCDs) into three primary colors using the dyes' inner filter effect (IFE). WCDs were combined with three distinct dyes (methyl red, bromocresol green, and methylene blue), and three fluorescence colors (red, yellow, and blue) were obtained. The colors of the emitting light excited by the same UV light were similar to the colors of the respective dyes, and have a large Stokes shift. The presence of the IFE between WCDs and dyes was proved through a variety of methods, the analysis of the relationship between the UV of dyes and fluorescence spectra of WCDs, the excitation-emission matrix, as well as the comparison PL decay profiles of WCDs before and after dye addition. The Stern-Volmer quenching constant KSV exhibited no temperature dependence during the IFE process with increasing concentrations of dyes. The UV spectra of the mixture of WCDs and dyes were simply the sum of two individual components, which implies that there is no chemical interaction between WCDs and dyes. Since methyl red and bromocresol green are acid-base indicators, the fluorescence color of the carbon dot composite also changed continuously during the dilute NaOH solution titration process. In furtherment, distributing the binary system of “WCDs + dye” within poly (vinyl alcohol) (PVA), three colors fluorescent composite films of “WCDs / Dye / PVA” were produced successfully.

Facile synthesis of ATTM@ZIF-8 modified pullulan hydrogels for enhanced adsorption of Congo red and malachite green

Efficient capture of dyes from wastewater is of great importance for environmental remediation. Yet constructing adsorbents with satisfactory adsorption efficiency and low cost remains a major challenge. This work reports a simple and scalable method for the fabrication of functionalized porous pullulan hydrogel adsorbent decorated with ATTM@ZIF-8 for the adsorption of congo red (CR) and malachite green (MG). The embedding of ammonium tetrathiomolybdate (ATTM) into the ZIF-8 nanoclusters offered additional adsorption sites and enlarged the pore size of the resulting ATTM@ZIF-8. The homogeneous dispersion of the nanoparticles in the three-dimensional network of polysaccharide gels prevents their agglomeration and thus improves the affinity for dye molecules. The resulting adsorbent AZP-20 at optimized composite ratios exhibits high activity, selectivity, interference resistance, reusability and cytocompatibility in dye adsorption applications, and possesses high removal rate of dye in real water systems. Batch experiments demonstrated that the adsorption rate of AZP-20 for MG and CR was 1645.28 mg g−1 and 680.33 mg g−1, and would be influenced by pH conditions. Adsorption kinetics followed pseudo-second-order model. Adsorption isotherms followed Langmuir model for MG and Freundlich model for CR. The adsorption of dye molecules primarily relied on electrostatic interaction (MG) and π-π stacking interaction (CR). Conclusively, the prepared AZPs adsorbent illuminated good application prospects in the treatment of complex component dye wastewater.

Integration of Stacked Object Recognition and Robotic Arm Gripping System Utilizing Mask Region-based Convolutional Neural Network and Red–Green–Blue Depth Images

Integration of Stacked Object Recognition and Robotic Arm Gripping System Utilizing Mask Region-based Convolutional Neural Network and Red–Green–Blue Depth Images

Chimeric Cell Therapy Transfers Healthy Donor Mitochondria in Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a severe X-linked disorder characterized by dystrophin gene mutations and mitochondrial dysfunction, leading to progressive muscle weakness and premature death of DMD patients. We developed human Dystrophin Expressing Chimeric (DEC) cells, created by the fusion of myoblasts from normal donors and DMD patients, as a foundation for DT-DEC01 therapy for DMD. Our preclinical studies on mdx mouse models of DMD revealed enhanced dystrophin expression and functional improvements in cardiac, respiratory, and skeletal muscles after systemic intraosseous DEC administration. The current study explored the feasibility of mitochondrial transfer and fusion within the created DEC cells, which is crucial for developing new therapeutic strategies for DMD. Following mitochondrial staining with MitoTracker Deep Red and MitoTracker Green dyes, mitochondrial fusion and transfer was assessed by Flow cytometry (FACS) and confocal microscopy. The PEG-mediated fusion of myoblasts from normal healthy donors (MBN/MBN) and normal and DMD-affected donors (MBN/MBDMD), confirmed the feasibility of myoblast and mitochondrial fusion and transfer. The colocalization of the mitochondrial dyes MitoTracker Deep Red and MitoTracker Green confirmed the mitochondrial chimeric state and the creation of chimeric mitochondria, as well as the transfer of healthy donor mitochondria within the created DEC cells. These findings are unique and significant, introducing the potential of DT-DEC01 therapy to restore mitochondrial function in DMD patients and in other diseases where mitochondrial dysfunction plays a critical role.Graphical

New bacterial strains isolated from Tunisian biotopes: A sustainable enzymatic approach for decolorization and detoxification of Congo Red and Malachite Green.

Seven bacterial strains, isolated from various Tunisian biotopes, were investigated for Congo Red (CR) and Malachite Green (MG) decolorization. The isolated strains underwent morphological and biochemical tests, including assessments for antibiotic sensitivity as well as biofilm formation. One selected strain, ST11, was partially identified as Paenibacillus sp. strain ST11. The newly isolated crude bacterial filtrates (NICBFs) effectively decolorized CR and MG. Specifically, six and seven NICBFs were found to be effective for degrading CR (150mgl-1) and MG (50mgl-1), respectively. Under non-optimized conditions, CR and MG could be decolorized up to 80% within 6-12h. The degradation products of CR and MG, characterized by UV-visible and FT-IR techniques, demonstrated both decolorization and transformation, highlighting the role of enzymes in dye degradation. Phytotoxicity and cytotoxicity studies evaluated the impact of treated and untreated CR and MG. Some NICBFs showed promise as powerful biological tools, reducing and sometimes detoxifying CR and MG, commonly used as fertilizers. The potential applications of these NICBFs in decolorization and bioremediation of dye-rich textile effluents were explored. The screening also identified environmentally friendly, cost-effective bacterial strains adaptable to various conditions through phytotoxicity and cytotoxicity studies.

NSVDNet: Normalized Spatial-Variant Diffusion Network for Robust Image-Guided Depth Completion

Depth images captured by low-cost three-dimensional (3D) cameras are subject to low spatial density, requiring depth completion to improve 3D imaging quality. Image-guided depth completion aims at predicting dense depth images from extremely sparse depth measurements captured by depth sensors with the guidance of aligned Red–Green–Blue (RGB) images. Recent approaches have achieved a remarkable improvement, but the performance will degrade severely due to the corruption in input sparse depth. To enhance robustness to input corruption, we propose a novel depth completion scheme based on a normalized spatial-variant diffusion network incorporating measurement uncertainty, which introduces the following contributions. First, we design a normalized spatial-variant diffusion (NSVD) scheme to apply spatially varying filters iteratively on the sparse depth conditioned on its certainty measure for excluding depth corruption in the diffusion. In addition, we integrate the NSVD module into the network design to enable end-to-end training of filter kernels and depth reliability, which further improves the structural detail preservation via the guidance of RGB semantic features. Furthermore, we apply the NSVD module hierarchically at multiple scales, which ensures global smoothness while preserving visually salient details. The experimental results validate the advantages of the proposed network over existing approaches with enhanced performance and noise robustness for depth completion in real-use scenarios.

A Contribution-Aware Noise Feature representation model for image manipulation localization

Noise feature modules play a prevalent role in image manipulation localization. However, existing approach involves utilizing a specific noise feature module tailored to address distinct categories of tampering procedures, limiting its adaptability across diverse scenarios. This limitation becomes particularly pronounced in image manipulation localization where a singular noise module struggles to comprehensively represent all potential tampering types, given the inherent uncertainty surrounding the nature of tampering. A novel Contribution-Aware Noise Feature (CANF) representation model, specifically designed to tackle the intricacies of image tamper noise, is introduced in this paper. The proposed model integrates existing noise feature modules, autonomously enhancing their capabilities. This enhancement not only contributes significantly to image manipulation localization during the inference phase but also enables a quantitative evaluation of the distinct contributions of various noise modules to the inference results. To optimize the utilization of image information, the Red–Green–Blue (RGB) and CANF data are fused, leading to the development of a standardized image manipulation localization architecture. This architecture exhibits heightened efficiency in harnessing diverse types of information, thereby enhancing the overall performance of image manipulation localization. Experimental results demonstrate that our approach, alongside established methods IF-OSN, MSFF, PCL, and NCL, surpasses the state-of-the-art NCL by 8.5% and 5.4% concerning average F1 and AUC metrics across four standard image manipulation localization datasets: NIST16, CASIAv1, Columbia, and Coverage. In addition, we comprehensively explore the impact of various noise characterization modules on different types of tampering and the fact that Late Fusion is more conducive to image tampering localization.

Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery

Forest structural parameters are crucial for assessing ecological functions and forest quality. To improve the accuracy of estimating these parameters, various approaches based on remote sensing platforms have been employed. Although remote sensing yields high prediction accuracy in uniform, even-aged, simply structured forests, it struggles in complex structures, where accurately predicting forest structural parameters remains a significant challenge. Recent advancements in unmanned aerial vehicle (UAV) photogrammetry have opened new avenues for the accurate estimation of forest structural parameters. However, many studies have relied on a limited set of remote sensing metrics, despite the fact that selecting appropriate metrics as powerful explanatory variables and applying diverse models are essential for achieving high estimation accuracy. In this study, high-resolution RGB imagery from DJI Matrice 300 real-time kinematics was utilized to estimate forest structural parameters in a mixed conifer–broadleaf forest at the University of Tokyo Hokkaido Forest (Hokkaido, Japan). Structural and textual metrics were extracted from canopy height models, and spectral metrics were extracted from orthomosaics. Using random forest and multiple linear regression models, we achieved relatively high estimation accuracy for dominant tree height, mean tree diameter at breast height, basal area, mean stand volume, stem density, and broadleaf ratio. Including a large number of explanatory variables proved advantageous in this complex forest, as its structure is influenced by numerous factors. Our results will aid foresters in predicting forest structural parameters using UAV photogrammetry, thereby contributing to sustainable forest management.

Nickel-doped magnetic carbon aerogel derived from xanthan gum: a competent catalyst for the degradation of single and binary dye-based water pollutants.

Toxic organic dyes (colorants) are one of the main causes of water pollution that releases destructive effluents in the environment. To overcome this issue, a fundamental need to produce a novel, efficient catalyst for the degradation and mineralization of dye mixtures has arisen. The objective of this research is to develop an eminent Ni-doped magnetic carbon aerogel (Ni-MCA) catalyst using graft co-polymerization method having xanthan gum as backbone doped with Ni-magnetic nanoparticles (Ni-MNPs), that do not show agglomeration and easy to separate. The examination revealed that Ni-MCA provided exceptional magnetic characteristics (Ms = 52.75emu/g) and potent catalytic activity for the degradation of mono- as well as binary-dye solutions of Congo red (CR) and methyl green (MG) dyes. The formation was verified by various characterization techniques such as FTIR, VSM, XRD, XPS, SEM, TEM, and EDX mapping. Interestingly, Ni-MCA shows faster result on anionic dye CR up to 97% with degradation rate of 5.647 × 10-1min-1, and MG dye shows degradation of 95.7% with the degradation rate of 2.169 × 10-1min-1, while dye mixture is showing 90% degradation with rate of 2.159 × 10-1min-1.

Impact of dichromatic lighted incubation on hatching result and post-hatch performance of broiler chickens.

This study was planned to evaluate the impact of dichromatic lights during incubation on the hatching and post-hatch performance of broiler chickens. A total of 500 eggs of broiler breeder (Ross 308; Age 44 weeks) were evenly divided according to a completely randomized design into 4 treatments having 5 replicates and 25 eggs each. Treatments consisted of dichromatic lights Blue + Red (BR), Green + Red (GR) and Green + Blue (GB) provided at an intensity of 250lx for 12h a day along with a Dark (D) environment. After hatching 200 chicks (50 from each respective light group) were divided into 4 treatments with 5 replicates each having 10 chicks. Results indicated a higher embryo index (13.12%) in the GR group on the 12th day of incubation; while an ideal hatch window was observed in GR and GB (98.18% and 96.00% hatched chicks) lighting groups. In hatching traits, higher hatchability (86.15) and hatch of fertile (93.85) percentages were observed in GR lighting followed by GB, BR and Dark treatment groups; while dead-in shell embryos were lowest in the GR group. In growth performance, higher feed intake (513.20g) and body weight (479.20g) were observed in the GB group followed by GR, BR and dark group; and feed conversion ratio (FCR) was better in the GR group (1.06). In welfare parameters, improved physical asymmetry (0.90mm) and tonic immobility (54.40s) were measured in the GR group followed by GB, BR and the dark group. It was concluded that under experimental conditions when broiler breeder eggs are provided with GR lighting during incubation, it can help to improve hatchability, growth performance and welfare traits in chicks.

Meta-learning with hypernetworks: Cost-effective fault detection in Parabolic Trough plants

Detecting receivers with broken glass envelopes in Parabolic Trough plants presents a crucial challenge in Concentrated Solar Power systems. While existing research has addressed this problem, some limitations persist, particularly in the context of data collection and the accuracy of the proposed methods. To overcome these challenges, we propose a fully automated and cost-efficient solution. Leveraging thermal camera data, we develop a novel cropping algorithm that achieves a 91% success rate in detecting the location of the receiver tubes without the need for labeled samples. Additionally, we introduce a luminescence-based filter for color images in Red–Green–Blue format to assess biases in the training and test sets. To solve our target problem we rely on Convolutional Neural Network architectures augmented with Hypernetworks as meta-learners. Our proposal is inspired by traditional voting methods but it is trainable end-to-end. In our test set, the image-based meta-learners obtain the best performance over all evaluation metrics. Furthermore, we provide experimentally driven insights to the weaknesses and strengths of our approach. Combining the experimental results with the luminescence-based filter, we achieve a robust method with a 95% F1-Score for detecting broken glass samples, including edge cases. Our approach surpasses the best individual learner by 2.1%–5.8%, depending on brightness conditions.

Red–Green–Blue Light Emission from Composition Tunable Semiconductor Micro‐Tripods

AbstractMicro/nanoscale lasers that span the entire visible spectrum, especially those in red, green, and blue colors, are not only essential for a variety of optical devices but also have important applications in visible color communication, multi‐color fluorescence sensing, and wavelength division multiplexing. Despite the great efforts made to achieve multi‐color lasing using a variety of approaches, on‐chip white light emission and even red, green, and blue multi‐color lasers still suffer from considerable challenges in micro‐nano structures. Here, CdSxSe1−x, CdS, and ZnS micro‐tripod structures are successfully prepared using chemical vapor deposition approaches. The micro‐photoluminescence (µ‐PL) spectra and PL‐mapping of these micro‐tripods reveal various emissions at 630, 508, and 460 nm, respectively. Additionally, white‐light emissions based on these composition‐tunable tripods are realized through an end‐coupling structure system. Moreover, room‐temperature modes tunable lasers are observed clearly from three legs of these micro‐tripods, with a low threshold of ≈48.39 µJ cm−2 and a high quality factor of 1227.3. The realization of micro‐tripods‐based lasers may provide an innovative way for highly integrated photonic circuits and communications.

Four chemometric models enhanced by Latin hypercube sampling design for quantification of anti-COVID drugs: sustainability profiling through multiple greenness, carbon footprint, blueness, and whiteness metrics

Montelukast sodium (MLK) and Levocetirizine dihydrochloride (LCZ) are widely prescribed medications with promising therapeutic potential against COVID-19. However, existing analytical methods for their quantification are unsustainable, relying on toxic solvents and expensive instrumentation. Herein, we pioneer a green, cost-effective chemometrics approach for MLK and LCZ analysis using UV spectroscopy and intelligent multivariate calibration. Following a multilevel multifactor experimental design, UV spectral data was acquired for 25 synthetic mixtures and modeled via classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), and genetic algorithm-PLS (GA-PLS) techniques. Latin hypercube sampling (LHS) strategically constructed an optimal validation set of 13 mixtures for unbiased predictive performance assessment. Following optimization of the models regarding latent variables (LVs) and wavelength region, the optimum root mean square error of cross-validation (RMSECV) was attained at 2 LVs for the 210–400 nm spectral range (191 data points). The GA-PLS model demonstrated superb accuracy, with recovery percentages (R%) from 98 to 102% for both analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.0943, 0.1872) and (0.1926, 0.1779) for MLK and LCZ, respectively, as well bias-corrected mean square error of prediction (BCMSEP) of -0.0029 and 0.0176, relative root mean square error of prediction (RRMSEP) reaching 0.7516 and 0.6585, and limits of detection (LOD) reaching 0.0813 and 0.2273 for MLK and LCZ respectively. Practical pharmaceutical sample analysis was successfully confirmed via standard additions. We further conducted pioneering multidimensional sustainability evaluations using state-of-the-art greenness, blueness, and whiteness tools. The method demonstrated favorable environmental metrics across all assessment tools. The obtained Green National Environmental Method Index (NEMI), and Complementary Green Analytical Procedure Index (ComplexGAPI) quadrants affirmed green analytical principles. Additionally, the method had a high Analytical Greenness Metric (AGREE) score (0.90) and a low carbon footprint (0.021), indicating environmental friendliness. We also applied blueness and whiteness assessments using the high Blue Applicability Grade Index (BAGI) and Red–Green–Blue 12 (RGB 12) algorithms. The high BAGI (90) and RGB 12 (90.8) scores confirmed the method's strong applicability, cost-effectiveness, and sustainability. This work puts forward an optimal, economically viable green chemistry paradigm for pharmaceutical quality control aligned with sustainable development goals.

Neuroprotective effects of polyphyllin VI against rotenone-induced toxicity in SH-SY5Y cells

BackgroundA substantial body of evidence is drawing connections between Parkinson's disease (PD) and the phenomena of oxidative stress and mitochondrial dysfunction. Polyphyllin VI (PPVI), an active compound found in Rhizoma Paridis-commonly known as Chonglou (CL) in China, has been identified for its various pharmacological properties, including anti-tumor and anti-inflammatory effects. ObjectiveIn the present study, an in vitro model of PD was established by treating SH-SY5Y cells with rotenone (ROT), to evaluate the potential neuroprotective effects of polyphyllin VI and its underlying mechanism. MethodsSH-SY5Y cells were treated with ROT to establish an in vitro model of PD. The effects of polyphyllin VI on cell viability were assessed using the resazurin assay. Cell morphology was examined using a microscope. The YO-PRO-1/PI was used to detect apoptosis. Mito-Tracker Red CMXRos, Mito-Tracker Green, and JC-1 were used to detect the effects of polyphyllin Ⅵ on mitochondrial viability, morphology, and function. Oxidative stress-related marker detection kits were used to identify the effects of polyphyllin VI on oxidative stress. Western blot analysis was employed to investigate the signaling pathways associated with neuroprotection. ResultsPPVI increased ROT-induced SH-SY5Y cell viability and improved ROT-induced cellular morphological changes. PPVI ameliorated ROT-induced oxidative stress status, and attenuated mitochondrial function and morphological changes. PPVI may exert neuroprotective effects through FOXO3α/CREB1/DJ-1-related signaling pathways. ConclusionThese preliminary findings suggested that PPVI possesses neuroprotective attributes in vitro, and it may be a potential candidate for PD treatment. However, extensive research is necessary to fully understand the mechanisms of PPVI and its effectiveness both in vitro and in vivo.

Automating Ground Control Point Detection in Drone Imagery: From Computer Vision to Deep Learning

Drone-based photogrammetry typically requires the task of georeferencing aerial images by detecting the center of Ground Control Points (GCPs) placed in the field. Since this is a very labor-intensive task, it could benefit greatly from automation. In this study, we explore the extent to which traditional computer vision approaches can be generalized to deal with variability in real-world drone data sets and focus on training different residual neural networks (ResNet) to improve generalization. The models were trained to detect single keypoints of fixed-sized image tiles with a historic collection of drone-based Red–Green–Blue (RGB) images with black and white GCP markers in which the center was manually labeled by experienced photogrammetry operators. Different depths of ResNets and various hyperparameters (learning rate, batch size) were tested. The best results reached sub-pixel accuracy with a mean absolute error of 0.586. The paper demonstrates that this approach to drone-based mapping is a promising and effective way to reduce the human workload required for georeferencing aerial images.

Ultra-strong adsorption of organic dyes and antibiotic onto the alk-MXene/ZIF adsorbents with a specific intercalation structure

By in-situ growing tiny ZIF (ZIF-67/ZIF-8) particles on the interlayer and surface of alkalized MXene (alk-MXene), alk-MXene/ZIF adsorbents with specific intercalation structure and ultra-strong adsorption capacity for antibiotics and dyes are designed. The introduction of ZIF particles offers a larger specific surface area and more adsorption functional groups in alk-MXene/ZIF composites. Changing the additive amount of alk-MXene (1, 0.5 and 0.25 g) can effectively tune the adsorption properties and morphology of the composites. Herein, when the additive amount of alk-MXene is 0.5 g (sample S2) exhibits optimal adsorption performance with maximum adsorption capacities qm of 539.7, 1053.3 and 7111.3 mg g−1 for Congo Red (CR), Tetracycline (TC) and Malachite Green (MG). That is, too much (sample S1) or too little (sample S3) addition of the alk-MXene into the composites will suffer the performance degradation. Impressively, the composites show fast adsorption kinetics, strong recycling ability and powerful anti-interference capacity to multiple ions in contaminants. Benefiting from the intercalation structure, ZIF particles protect alk-MXene from being oxidized in air, making the composites exhibit excellent chemical stability. Typically, Sample S2 retains its stable microstructure and outstanding adsorption properties in air for 45 days. Also, the actual application of sample S2 is analyzed by filtering the adsorbates multiple times. Furthermore, it is demonstrated that the synergistic effects of electrostatic interaction, hydrogen bonding and π-π stacking lead to the excellent adsorption capacity of alk-MXene/ZIF composites. This study suggests that alk-MXene/ZIF can be regarded as a high-efficiency and promising adsorbent to remove organic dyes and antibiotics.

In-lab synthesized turn-off fluorescence sensor for estimation of Gemigliptin and Rosuvastatin polypill appraised by Spider diagram, AGREE and whiteness metrics

Gemigliptin-Rosuvastatin single-pill combination is a promising therapeutic tool in the effective control of hyperglycemia and hypercholesterolemia. Organic sensors with high quantum yields have profoundly significant applications in the pharmaceutical industry, such as routine quality control of marketed formulations. Herein, the fluorescence sensor, 2-Morpholino-4,6-dimethyl nicotinonitrile 3, (λex; 226 nm, λem; 406 nm), was synthesized with a fluorescence quantum yield of 56.86% and fully characterized in our laboratory. This sensor showed high efficiency for the determination of Gemigliptin (GEM) and Rosuvastatin (RSV) traces through their stoichiometric interactions and simultaneously fractionated by selective solvation. The interaction between the stated analytes and sensor 3 was a quenching effect. Various experimental parameters and the turn-off mechanism were addressed. The adopted approach fulfilled the ICH validation criteria and showed linear satisfactory ranges, 0.2–2 and 0.1–1 μg/mL for GEM and RSV, respectively with nano-limits of detection less than 30 ng/mL for both analytes. The synthesized sensor has been successfully applied for GEM and RSV co-assessment in their synthetic polypill with excellent % recoveries of 98.83 ± 0.86 and 100.19 ± 0.64, respectively. No statistically significant difference between the results of the proposed and reported spectrophotometric methods in terms of the F- and t-tests. Ecological and whiteness appraisals of the proposed study were conducted via three novel approaches: the Greenness Index via Spider Diagram, the Analytical Greenness Metric, and the Red–Green–Blue 12 model. The aforementioned metrics proved the superiority of the adopted approach over the previously published one regarding eco-friendliness and sustainability. Our devised fluorimetric turn-off sensing method showed high sensitivity, selectivity, feasibility, and rapidity with minimal cost and environmental burden over other sophisticated techniques, making it reliable in quality control labs.

Color vision of people with headaches

The aim of the study is to obtain new data on possible changes in color vision of people with headaches. The object of the study is people who periodically experience primary headaches of different nature. The subject of the study is the specificity of perception of chromatic parameters of the environment and color sensitivity of people with headaches of different nature, quality, intensity and frequency. The data were collected using the Farnsworth-Munsell standard clinical color vision test (FM-100). The study involved 65 people aged 17 to 66 years. The results of the assessment of color vision in the experimental group were compared with the normative indicators and data obtained in the age-matched control group (N=40). Special attention was paid to discussing the possible causes of the identified disorders and analyzing the correlation between various aspects of headache with individual indicators of color sensitivity. The data analysis included calculating the total error (TES), as well as partial errors (PES) along the blue-yellow (B-Y) and red-green (R-G) axes. We also identified partial errors for individual tones. The study revealed a noticeable specificity in the perception of blue-green and blue shades of patients with severe, chronic and non-localized headaches. Since the detected color vision disorders appear to be temporary and are most likely typical of the premonitory phase of headaches, the results of this study may contribute to understanding of headache predictors. Since the identified changes relate to only one group of (blue) shades, the findings of the study can be used in the development of alternative treatment methods. In particular, in choosing the optimal color of tinted lenses, which are offered to patients to relieve headaches and reduce their frequency.

Gnuastro: Visualizing the Full Dynamic Range in Color Images

Color plays a crucial role in the visualization, interpretation, and analysis of multi-wavelength astronomical images. However, generating color images that accurately represent the full dynamic range of astronomical sources is challenging. In response, Gnuastro v0.22 introduces the program astscript-color-faint-gray, which is extensively documented in the Gnuastro manual. It employs a nonlinear transformation to assign an 8-bit RGB (Red–Green–Blue) value to brighter pixels, while the fainter ones are shown in an inverse grayscale. This approach enables the simultaneous visualization of low surface brightness features within the same image. This research note is reproducible with Maneage, on the Git commit c206945.