Detection of bissap calyces and bissap juices adulteration with sorghum leaves using NIR spectroscopy and VIS/NIR spectroscopy

 

1. Introduction

Adulteration of food and beverages is a growing concern, as it can lead to reduced nutritional benefits and potential health risks for consumers. In this study, the adulteration of bissap calyces and juices (‘sobolo’) with sorghum leaves was investigated using near-infrared (NIR) and ultraviolet-visible (VIS/NIR) spectroscopy. These analytical techniques, combined with chemometric methods, offer a rapid and reliable approach for detecting adulteration in food products. This research aims to assess the effectiveness of these spectroscopic techniques in identifying adulterants and quantifying their presence, ensuring better quality control and consumer safety.

2. Physicochemical Impact of Adulteration on Bissap Calyces and Juices

The presence of sorghum leaves in bissap calyces and juices significantly alters their physicochemical properties. Unadulterated samples exhibited lower pH levels and higher brix, titratable acidity, and total phenolic content. The intensity of color changes varied depending on the sample form (cut, whole, or powdered). These changes indicate a loss of some essential bioactive compounds in adulterated samples, potentially reducing the nutritional and antioxidant benefits of bissap juice. Understanding these physicochemical changes provides insights into the impact of adulteration on product quality.

3. Application of Near-Infrared and VIS/NIR Spectroscopy in Adulteration Detection

NIR and VIS/NIR spectroscopy offer non-destructive and rapid analytical methods for detecting food adulteration. In this study, these techniques were used to differentiate between pure and adulterated bissap samples. Spectral data provided critical information on chemical composition variations, allowing for the identification of adulterants. The results demonstrated that spectroscopy could effectively detect subtle differences in adulterated samples, particularly in juices made from cut calyces. This highlights the potential of spectroscopic methods as valuable tools for food authentication and quality assurance.

4. Chemometric Techniques for Adulteration Classification and Quantification

To enhance the accuracy of adulteration detection, chemometric methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Partial Least Squares Regression (PLSR) were applied. PCA showed no clear distinction between adulterated and unadulterated samples based on forms and concentrations, while LDA achieved 100% classification accuracy for cut samples but showed misclassifications in whole and powdered samples. PLSR models successfully predicted adulterant concentrations, demonstrating the potential of these statistical tools in food quality control.

5. Challenges in Detecting Adulteration in Bissap Juices

Although spectroscopy and chemometrics proved useful in identifying adulteration, some challenges were observed. Bissap juices adulterated with sorghum leaves were not easily distinguishable, particularly in whole and powdered forms. This suggests that certain adulteration methods can evade detection using standard analytical approaches. Further optimization of spectroscopic techniques, including combining multiple wavelengths and refining chemometric models, may enhance detection sensitivity. Addressing these challenges is crucial for ensuring the reliability of food authentication methods.

6. Future Prospects for Spectroscopy-Based Food Authentication

The integration of NIR and VIS/NIR spectroscopy with advanced chemometric techniques presents a promising approach for food authentication and adulteration detection. Future research should explore the use of machine learning algorithms to improve classification accuracy and predictive capabilities. Additionally, expanding the study to include other potential adulterants in bissap and related beverages can help develop a comprehensive authentication framework. The adoption of these techniques in regulatory and industrial settings could significantly enhance food safety and consumer trust.

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Diagnosis of leaf chlorophyll content based on close-range multispectral fluorescence image correction

 

1. Introduction

Multispectral fluorescence imaging has emerged as a valuable tool for studying plant stress responses and diagnosing nutrient deficiencies, particularly in agricultural research. Chlorophyll content is a key indicator of plant health, and its accurate assessment can enhance precision farming and crop management strategies. However, fluorescence shadow errors caused by leaf structure and excitation light variation pose challenges to accurate diagnosis. This study explores the BLF-CLAHE (Butterworth Low Filter Contrast Limited Adaptive Histogram Equalization) method to correct fluorescence shadow effects and optimize chlorophyll content diagnostics in maize leaves. By integrating deep learning models for enhanced feature extraction, this research provides an advanced approach for improving plant fluorescence analysis and chlorophyll content assessment.

2. Impact of Maize Leaf Structure on Fluorescence Imaging

Leaf structure plays a critical role in influencing fluorescence imaging accuracy. The natural curvature and venation of maize leaves create variations in fluorescence intensity, leading to uneven light distribution and shadowing effects. These structural differences contribute to directional variability, with fluorescence intensity coefficients of variation (CV) being higher in the horizontal direction than in the vertical direction. Understanding the interplay between leaf morphology and fluorescence signal distribution is crucial for developing correction techniques that enhance imaging precision and minimize diagnostic errors in chlorophyll content analysis.

3. Frequency Domain Analysis of Fluorescence Information

Fluorescence information is distributed across different frequency domains, with the low-frequency domain containing a significantly higher proportion (over 65%) of relevant fluorescence data. This study's frequency domain analysis reveals that fluorescence yield varies with excitation light sources, with blue light inducing stronger signals compared to UV and red light. By leveraging this spectral insight, researchers can design more effective imaging corrections and diagnostic models. Filtering techniques such as Butterworth Low Filter (BLF) enhance critical fluorescence information, reducing noise interference and improving the reliability of plant health assessments.

4. BLF-CLAHE: A Novel Image Enhancement Method for Fluorescence Imaging

The BLF-CLAHE technique combines the advantages of Butterworth Low Filtering and Contrast Limited Adaptive Histogram Equalization to improve fluorescence image quality. Traditional CLAHE methods, while effective in some applications, struggle with uneven fluorescence intensity distribution caused by plant structures. BLF-CLAHE enhances fluorescence signals by selectively amplifying relevant image features while reducing distortions. Comparative analysis before and after correction demonstrates that BLF-CLAHE significantly improves fluorescence intensity distribution, leading to more accurate chlorophyll content assessments in maize leaves.

5. Deep Learning-Based Optimization of Chlorophyll Content Diagnosis

Deep learning techniques provide a powerful approach for extracting fluorescence information and refining chlorophyll content diagnostic models. By integrating deep learning algorithms, the study demonstrates an increase in diagnostic accuracy from 0.665 using conventional methods to 0.874, highlighting the potential of AI-driven analysis in agricultural imaging. Deep learning models enable automated feature extraction, reducing the need for manual corrections and improving the robustness of plant health assessments. This research underscores the growing role of artificial intelligence in precision agriculture and plant physiology studies.

6. Future Perspectives and Applications in Precision Agriculture

The findings of this study have significant implications for the future of precision agriculture and plant monitoring. The integration of advanced imaging techniques with deep learning models can revolutionize real-time crop health monitoring, enabling early detection of nutrient deficiencies and stress conditions. Future research should focus on expanding the application of BLF-CLAHE and deep learning methods to other crops, optimizing imaging parameters, and integrating field-based remote sensing technologies. Further development of AI-driven chlorophyll assessment models could lead to fully automated diagnostic systems, improving agricultural productivity and sustainability.

New insights into corrosion initiation and propagation in a hot-dip Al-Zn-Mg-Si alloy coating via multiscale analytical microscopy.

1. Introduction

Corrosion of coated steel in coastal environments is a significant challenge in materials engineering, affecting structural integrity and longevity. Pre-painted hot-dip Zn-55Al-2Mg-1.5Si coated steel has been widely used due to its superior corrosion resistance; however, the underlying mechanisms governing its degradation remain inadequately understood. This study utilizes multiscale analytical microscopy to provide novel mechanistic insights into the corrosion initiation and propagation processes in this alloy system. By integrating electrochemical analysis with microstructural observations, this research elucidates how both chemical composition and phase morphology influence corrosion behavior.

2. The Role of Phase Morphology in Corrosion Initiation

Traditional corrosion studies primarily focus on the electrochemical properties of alloy phases, but this research highlights the critical role of phase morphology in corrosion initiation. Sub-micron Zn particles embedded within the Zn-Al binary eutectic structure are identified as the initial sites of corrosion due to their high dissolution propensity. The morphology and distribution of these Zn particles determine the localized electrochemical environment, influencing the onset of material degradation. Understanding this interplay between microstructure and electrochemical activity provides valuable insights into designing corrosion-resistant coatings.

3. Corrosion Propagation Mechanisms in Zn-55Al-2Mg-1.5Si Coatings

Following the initial dissolution of Zn particles, corrosion propagates through multiple pathways, including the dissolution of larger Zn-rich regions and the selective leaching of Mg from MgZn₂ and Mg₂Si intermetallic phases. This stage represents a transition from localized to more widespread material degradation. The study’s findings emphasize that Mg dealloying accelerates corrosion by disrupting the phase equilibrium, thereby creating localized anodic sites that promote further dissolution. These insights are crucial for predicting long-term performance and improving alloy formulations for enhanced durability.

4. Degradation of Al-Rich Dendrites and Corrosion Product Formation

In the later stages of corrosion, primary Al-rich dendrites undergo dissolution, leading to structural instability and volumetric expansion in the corrosion products. The transformation of Al-rich phases into corrosion byproducts affects the mechanical properties of the coating, contributing to crack formation and delamination. This phase-specific degradation mechanism highlights the limitations of Al-rich coatings in prolonged coastal exposure and suggests avenues for modifying the microstructure to enhance corrosion resistance.

5. Multiscale Analytical Microscopy for Corrosion Analysis

The study employs an advanced multiscale analytical microscopy approach, integrating scanning electron microscopy (SEM), transmission electron microscopy (TEM), and energy-dispersive X-ray spectroscopy (EDS) to characterize corrosion progression at multiple length scales. These techniques provide high-resolution imaging and elemental mapping, allowing researchers to correlate phase-specific corrosion behaviors with microstructural features. This methodology establishes a comprehensive framework for studying complex corrosion systems and developing more resilient coatings.

6. Implications for Alloy Design and Future Research Directions

The findings from this research have significant implications for the design of next-generation corrosion-resistant coatings. By understanding the role of phase morphology in corrosion initiation and propagation, material scientists can develop alloy compositions with optimized microstructures to minimize early-stage degradation. Future research should focus on tailoring phase distributions, introducing nano-scale reinforcements, and exploring advanced surface treatments to enhance durability in aggressive environments. Additionally, in-situ electrochemical microscopy could provide real-time insights into dynamic corrosion processes, further advancing the field.

Machine learning-driven Raman spectroscopy: A novel approach to lipid profiling in diabetic kidney disease

 

1. Introduction

Diabetes mellitus is a chronic metabolic disorder that continues to impact a growing number of individuals worldwide. Poor management of Type 2 Diabetes Mellitus (T2DM) can lead to severe metabolic disturbances, often resulting in organ dysfunction, particularly affecting the kidneys. The early detection and monitoring of diabetic nephropathy are crucial for effective intervention and disease management. This study explores the combined use of Raman spectroscopy, biochemical lipid profiling, and machine learning (ML) techniques to detect and analyze kidney alterations associated with T2DM, providing a novel approach to diagnosing diabetes-related kidney damage.

2. Raman Spectroscopy for Molecular Analysis in Diabetic Nephropathy

Raman spectroscopy is a powerful analytical tool for detecting molecular changes at the biochemical level. In the context of diabetic nephropathy, this technique has identified significant differences in lipid content and molecular vibrations within kidney tissues. A particularly notable finding is the identification of the 1777 cm⁻¹ Raman band as a potential spectroscopic marker for diabetic kidney damage. These spectral differences provide valuable insights into the molecular impact of diabetes on renal tissues, offering a non-invasive and precise method for disease monitoring and diagnosis.

3. Lipid Metabolic Profiling and Biochemical Alterations in T2DM

Lipid metabolism plays a crucial role in diabetes progression, and disruptions in lipid homeostasis contribute to kidney damage. The study’s biochemical lipid profiling revealed distinct alterations in phosphatidylcholines and glycerophospholipids, which are essential components of cellular membranes and metabolic pathways. Specifically, variations in 11 phosphatidylcholines and 9 acyl-alkylphosphatidylcholines were observed between control and T2DM groups. These findings highlight the importance of lipidomics in understanding diabetic nephropathy and support the integration of lipid biomarkers for early diagnosis and targeted therapeutic strategies.

4. Machine Learning for Enhanced Spectroscopic Data Interpretation

The incorporation of machine learning (ML) techniques significantly improved the accuracy, selectivity, and specificity of Raman spectroscopy analysis. ML algorithms were employed to detect spectral changes associated with diabetic kidney damage, allowing for automated classification of control and T2DM groups. By enhancing data interpretation and pattern recognition, ML provides a robust framework for developing predictive models in diabetic nephropathy diagnostics. This study demonstrates how artificial intelligence can refine spectral data analysis, making Raman-based diagnostics more reliable and accessible for clinical applications.

5. Correlation Between Raman Spectroscopy and Lipid Profiling

An important aspect of this study is the correlation between Raman spectroscopic data and lipid metabolic profiling in both control and T2DM groups. The study found distinct differences in how these two datasets interact, reinforcing the importance of multi-modal approaches in disease diagnostics. While Raman spectroscopy detects molecular vibrations indicative of biochemical changes, lipidomics provides complementary metabolic insights. Understanding these correlations enhances the diagnostic potential of both techniques and supports their integration into comprehensive disease-monitoring frameworks.

6. Future Perspectives and Clinical Implications

Despite the promising findings, this study was conducted on a limited sample size due to ethical committee constraints, emphasizing the need for further validation with larger cohorts. Future research should focus on expanding the dataset, integrating additional biomarkers, and refining ML models for even higher accuracy. Additionally, clinical trials are necessary to establish Raman spectroscopy and lipid profiling as standard tools for diabetic nephropathy diagnosis. With further advancements, this approach has the potential to revolutionize early detection strategies, enabling personalized medicine and improving patient outcomes in diabetes-related kidney disease.

Clinical validation of RNA sequencing for Mendelian disorder diagnostics:

                                

1. Introduction

Despite significant progress in clinical sequencing, a considerable proportion of diagnostic cases remain unresolved due to the limitations of DNA-based testing alone. RNA sequencing (RNA-seq) has emerged as a promising tool to enhance genetic diagnostics by providing crucial functional insights into gene expression and splicing. While RNA-seq has been widely used in research, its clinical implementation remains a challenge. This study focuses on the development and validation of a clinical diagnostic RNA-seq test aimed at improving diagnostic outcomes for individuals with suspected genetic disorders, particularly in cases where comprehensive DNA testing has failed to provide conclusive results.

2. The Role of RNA Sequencing in Clinical Diagnostics

RNA sequencing offers a complementary approach to DNA-based testing by analyzing gene expression and alternative splicing, which are critical for understanding disease mechanisms. Unlike DNA sequencing, which detects genomic variants, RNA-seq provides dynamic insights into gene activity, allowing the identification of aberrant expression and splicing defects. This capability is particularly useful for diagnosing conditions caused by regulatory mutations, splice-site alterations, or deep intronic variants. The integration of RNA-seq into clinical workflows has the potential to significantly enhance diagnostic yield by uncovering disease-associated molecular signatures that would otherwise remain undetected.

3. Development of a Clinical Diagnostic RNA-Seq Test

To bridge the gap between research and clinical application, a robust diagnostic RNA-seq test was developed and validated for individuals undergoing genetic evaluation. The test processes RNA samples derived from fibroblasts or blood, utilizing advanced bioinformatics pipelines to detect expression outliers and splicing abnormalities. By establishing gene expression and splicing reference ranges, the test can systematically identify deviations associated with pathogenic variants. This development represents a significant step toward translating RNA-seq from an experimental tool into a standardized clinical diagnostic assay.

4. Validation Process and Benchmarking Strategies

The clinical validation of the RNA-seq test involved analyzing 130 samples, including 90 negative and 40 positive cases, with known diagnostic findings. To ensure reliability, benchmarking was performed using data from the GM24385 lymphoblastoid sample produced by the Genome in a Bottle Consortium. Short-read and long-read sequencing techniques were employed to define provisional expression and splicing standards. Reference ranges for each gene and splicing junction were established using a control dataset, allowing the identification of disease-associated outliers with high specificity and sensitivity. This rigorous validation process underscores the potential of RNA-seq as a reliable diagnostic tool.

5. Performance Evaluation and Clinical Implications

To assess the effectiveness of the RNA-seq test, its diagnostic pipeline was applied to 40 positive samples from the Undiagnosed Diseases Network (UDN), all of which had prior confirmed genetic diagnoses. The test successfully detected known pathogenic expression and splicing outliers, demonstrating its clinical utility. By integrating RNA-seq into routine diagnostic workflows, clinicians can improve the detection of previously elusive genetic disorders, refine variant interpretation, and enhance patient care. Additionally, RNA-seq provides valuable insights for functional validation of variants of uncertain significance (VUS), facilitating more accurate genetic counseling and disease management.

6. Future Perspectives and Broader Adoption of Clinical RNA-Seq

The successful validation of this diagnostic RNA-seq test paves the way for broader clinical adoption, but challenges remain in standardizing protocols and ensuring widespread accessibility. Future research should focus on expanding reference datasets to cover diverse populations, integrating RNA-seq with emerging multi-omics approaches, and improving computational methods for automated interpretation. The incorporation of long-read sequencing and machine learning-driven analysis will further enhance the precision and scalability of RNA-based diagnostics.

 

Biophotonics and nanorobotics for biomedical imaging, biosensing, drug delivery, and therapy.

 

1. Introduction

Biophotonics-based nanorobotics is a groundbreaking advancement in biomedical engineering that integrates light-based technologies with nanorobotic systems to enhance disease diagnosis and treatment. These nanoscale robots can navigate biological environments, enabling targeted drug delivery, improved imaging, and precise therapeutic interventions. This field is rapidly evolving, with innovations that leverage synthetic intelligence, novel nanomaterials, and bioluminescence-assisted mechanisms. As biophotonic nanorobotics continue to advance, they promise to revolutionize modern medicine by offering minimally invasive, highly accurate, and real-time solutions for complex medical conditions.

2. Biophotonics and Nanorobotics: Fundamental Concepts and Biomedical Applications

Biophotonics refers to the study and application of light interactions with biological tissues, while nanorobotics involves the design of nanoscale robotic systems for medical use. The fusion of these fields allows for the precise manipulation of biological structures at the cellular and molecular levels. Applications range from high-resolution imaging techniques, such as fluorescence and Raman-based methods, to targeted photothermal therapies for cancer treatment. In addition, nanorobots with biophotonic capabilities improve drug delivery by responding to external light stimuli, ensuring site-specific treatment with minimal side effects.

3. Categorization of Biophotonic Nanorobots Based on Nanomaterials and Functional Mechanisms

Biophotonic nanorobots are classified according to the nanomaterials used in their construction, their functional mechanisms, and their intended biomedical applications. Common nanomaterials include gold and silver nanoparticles, quantum dots, and carbon-based nanostructures, which exhibit unique optical properties suitable for imaging and therapeutic applications. Functional mechanisms vary from optically controlled motion and light-triggered drug release to hybrid propulsion strategies that combine magnetic and optical actuation. This categorization helps optimize nanorobot design for specific clinical uses, improving their efficacy and biocompatibility.

4. Challenges in Biophotonic Nanorobotics: Biocompatibility, Motion Control, and Navigation

Despite significant progress, several challenges hinder the clinical translation of biophotonic nanorobotics. Biocompatibility remains a primary concern, as synthetic nanomaterials must be non-toxic and safely degradable within the human body. Ensuring persistent and controlled motion within biological environments is another challenge, as nanorobots must overcome fluidic resistance and immune responses. Self-sufficient navigation structures, which rely on light-sensitive components and artificial intelligence, are being developed to enable autonomous operation. Addressing these challenges is critical for advancing the practical use of nanorobots in real-world medical applications.

5. Artificial Intelligence and Machine Learning for Enhanced Nanorobotic Functionality

The integration of artificial intelligence (AI) and machine learning (ML) is revolutionizing the field of biophotonic nanorobotics. AI-driven algorithms improve the precision of nanorobot navigation, enabling real-time decision-making and adaptive responses to dynamic biological conditions. ML models facilitate predictive analysis, optimizing drug delivery and therapeutic strategies based on patient-specific data. Furthermore, AI enhances image processing techniques, allowing for better visualization and analysis of nanorobotic interactions with tissues. The fusion of AI with nanorobotics opens new avenues for personalized and efficient medical treatments.

6. Future Perspectives: Bioluminescence-Assisted Nanorobotics and Hybrid Actuation Strategies

The future of biophotonic nanorobotics lies in the integration of advanced nanomaterials, enzyme-based actuation, and bioluminescence-driven mechanisms. Bioluminescence-assisted nanorobots utilize light generated from biological reactions to enhance imaging and therapeutic functionalities without external light sources. Hybrid actuation methods, combining optical, magnetic, and biochemical cues, promise superior control over nanorobot movement and function. Manufacturing innovations, including 3D nanoprinting and biomimetic fabrication, will further enhance nanorobot efficiency. These emerging trends position biophotonic nanorobotics as a transformative force in biomedical research, with vast potential for minimally invasive diagnostics and therapeutics.

Peroxisome Proliferator Activator α Agonist Clofibrate Induces Pexophagy in Coconut Oil-Based High-Fat Diet-Fed Rats

Introduction

In the realm of nutritional science and metabolic research, understanding the mechanisms of cellular processes such as pexophagy is crucial. Pexophagy, the selective degradation of peroxisomes through autophagy, plays a vital role in maintaining cellular homeostasis. This blog explores the latest findings on how clofibrate, a well-known PPARα agonist, influences pexophagy in rats subjected to a high-fat diet (HFD) based on coconut oil. These insights potentially pave the way for novel therapeutic strategies targeting metabolic disorders.

Methodology

The study meticulously divided rats into distinct groups to evaluate the impact of a coconut oil-based HFD. One group received clofibrate, allowing for a comparative analysis of pexophagy markers between treated and untreated subjects. The selected markers, including NBR1, p62, and LAMP2, provided a comprehensive assessment of the pexophagy process.

Effects of High-Fat Diet and Clofibrate

Feeding rats a high-fat diet resulted in noticeable hepatic steatosis, characterized by an accumulation of fat in liver cells. This condition served as a foundation for assessing clofibrate's efficacy. When administered clofibrate, a remarkable alteration in the expression of pexophagy-related genes was observed, suggesting a potent induction of peroxisome proliferation.

Role of Pexophagy Markers

The expression levels of pexophagy markers such as NBR1 and p62 were significantly modulated following clofibrate treatment, indicating enhanced degradation of peroxisomes. Additionally, LAMP2, a crucial lysosomal membrane protein, showed increased activity, further corroborating the upregulation of pexophagic pathways.

Implications on Mammalian Pexophagy

Ultrastructural observations provided deeper insights into clofibrate's impact on cellular architecture, revealing increased prevalence of autophagic vesicles engulfing peroxisomes. Such findings suggest that PPARα agonists like clofibrate could regulate peroxisome turnover, offering potential applications in addressing metabolic conditions linked to impaired peroxisome function.

Conclusion

The research underscores the pivotal role clofibrate plays in modulating pexophagy within the context of a high-fat diet. By elucidating the molecular pathways influenced by PPARα agonists, this study lays the groundwork for developing targeted therapeutic approaches for metabolic diseases. Clofibrate's ability to stimulate peroxisome turnover presents promising avenues for future exploration in metabolic and cellular health research.