Linalool: Therapeutic Indication And Their Multifaceted Biomedical Applications.

This comprehensive review endeavors to illuminate the nuanced facets of linalool, a prominent monoterpene found abundantly in essential oils, constituting a massive portion of their composition. The biomedical relevance of linalool is a key focus, highlighting its therapeutic attributes observed through anti-nociceptive effects, anxiolytic properties, and behavioral modulation in individuals affected by dementia. These findings underscore the compound's potential application in biomedical applications. This review further explores contemporary formulations, delineating the adaptability of linalool in nano-emulsions, microemulsions, bio-capsules, and various topical formulations, including topical gels and lotions. This review covers published and granted patents between 2018-2024 and sheds light on the evolving landscape of linalool applications, revealing advancements in dermatological, anti-inflammatory, and antimicrobial domains.

Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging.

Introduction: This study explores the use of the latest You Only Look Once (YOLO V7) object detection method to enhance kidney detection in medical imaging by training and testing a modified YOLO V7 on medical image formats. Methods: Study includes 878 patients with various subtypes of renal cell carcinoma (RCC) and 206 patients with normal kidneys. A total of 5657 MRI scans for 1084 patients were retrieved. 326 patients with 1034 tumors recruited from a retrospective maintained database, and bounding boxes were drawn around their tumors. A primary model was trained on 80% of annotated cases, with 20% saved for testing (primary test set). The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model. Ten benchmark training sets were created with generated coordinates on not-segmented patients. The final model used to predict the kidney in the primary test set. We reported the positive predictive value (PPV), sensitivity, and mean average precision (mAP). Results: The primary training set showed an average PPV of 0.94 ± 0.01, sensitivity of 0.87 ± 0.04, and mAP of 0.91 ± 0.02. The best primary model yielded a PPV of 0.97, sensitivity of 0.92, and mAP of 0.95. The final model demonstrated an average PPV of 0.95 ± 0.03, sensitivity of 0.98 ± 0.004, and mAP of 0.95 ± 0.01. Conclusion: Using a semi-supervised approach with a medical image library, we developed a high-performing model for kidney detection. Further external validation is required to assess the model's generalizability.

Exploring the Feasibility of GPT-4 as a Data Extraction Tool for Renal Surgery Operative Notes.

INTRODUCTION: GPT-4 is a large language model with potential for multiple applications in urology. Our study sought to evaluate GPT-4's performance in data extraction from renal surgery operative notes. METHODS: GPT-4 was queried to extract information on laterality, surgery, approach, estimated blood loss, and ischemia time from deidentified operative notes. Match rates were determined by the number of "matched" data points between GPT-4 and human-curated extraction. Accuracy rates were calculated after manually reviewing "not matched" data points. Cohen's kappa and the intraclass coefficient were used to evaluate interrater agreement/reliability. RESULTS: Our cohort consisted of 1498 renal surgeries from 2003 to 2023. Match rates were high for laterality (94.4%), surgery (92.5%), and approach (89.4%), but lower for estimated blood loss (77.1%) and ischemia time (25.6%). GPT-4 was more accurate for estimated blood loss (90.3% vs 85.5% human curated) and similarly accurate for laterality (95.2% vs 95.3% human curated). Human-curated accuracy rates were higher for surgery (99.3% vs 93% GPT-4), approach (97.9% vs 90.8% GPT-4), and ischemia time (95.6% vs 30.7% GPT-4). Cohen's kappa was 0.96 for laterality, 0.83 for approach, and 0.71 for surgery. The intraclass coefficient was 0.62 for estimated blood loss and 0.09 for ischemia time. CONCLUSIONS: Match and accuracy rates were higher for categorical variables. GPT-4 data extraction was particularly error prone for variables with heterogenous documentation styles. The role of a standard operative template to aid data extraction will be explored in the future. GPT-4 can be utilized as a helpful and efficient data extraction tool with manual feedback.

Investigating the clinical reasoning abilities of large language model GPT-4: an analysis of postoperative complications from renal surgeries.

PURPOSE: Large language models, a subset of artificial intelligence, have immense potential to support human tasks. The role of these models in science and medicine is unclear, requiring strong critical thinking and analysis skills. The objective of our study was to evaluate GPT-4's abilities to assess postoperative complications after renal surgeries. MATERIALS AND METHODS: Discharge summaries were compiled, and patient information was deidentified in a Python-based program. Prompts were engineered in GPT-4 to assess for the presence of postoperative complications. GPT-4 was further asked to interpret each complication's Clavien-Dindo classification and institutional-specific category. GPT-4's database was compared to a human-curated database. Discrepancies were manually reviewed to calculate match and accuracy rates. RESULTS: Approximately 944 renal surgeries were conducted from August 2005 to March 2022. There was a 79.6% match rate between GPT-4 and human-curated data in detecting postoperative complications. Accuracy rates were 86.7% for GPT-4 and 92.9% for human-curated. A subgroup of 139 patients had a complication detected by both GPT-4 and human with available Clavien-Dindo classification and category information. There was a 37.4% overall match rate for Clavien-Dindo grade and 55.4% match rate for category. CONCLUSIONS: GPT-4 was able to accurately detect if there were any postoperative complications. It struggled with the complex task of further analyzing complications, especially with Clavien-Dindo classification, which requires more critical thinking and interpretation. While GPT-4 is not yet ready for advanced postoperative complication analysis, it can still be used to support clinicians in this endeavor.

Harnessing the Flexibility of Lightweight Cellulose Nanofiber Composite Aerogels for Superior Thermal Insulation and Fire Protection.

Thermally insulating materials from renewable and readily available resources are in high demand for ecologically beneficial applications. Cellulose aerogels made from lignocellulosic waste have various advantages. However, they are fragile and breakable when bent or compressed. In addition, cellulose aerogels are flammable and weather-sensitive. Hence, to overcome these problems, this work included the preparation of polyurethane (PU)-based cellulose nanofiber (CNF) aerogels that had flexibility, flame retardancy, and thermal insulation. Methyl trimethoxysilane (MTMS) and water-soluble ammonium polyphosphate (APP) were added to improve the cross-linking, hydrophobicity, and flame-retardant properties of aerogels. The flexibility of chemically cross-linked CNF aerogels is enhanced through the incorporation of polyurethane via the wet coagulating process. The aerogels obtained during this study have exhibited low weight (density: 35.3-91.96 kg/m3) together with enhanced hydrophobic properties, flame retardancy, and decreased thermal conductivity (26.7-36.7 mW/m K at 25 °C). Additionally, the flame-retardant properties were comprehensively examined and the underlying mechanism was deduced. The aerogels prepared in this study are considered unique in the nanocellulose aerogel category due to their integrated structural and performance benefits. The invention is considered to substantially contribute to the large-scale manufacture and use of insulation in construction, automobiles, and aerospace.

Non-Invasive Tumor Grade Evaluation in Von Hippel-Lindau-Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging-Based Study.

BACKGROUND: Pathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC). PURPOSE: To investigate the utility of texture analysis in evaluating Fuhrman grades of renal tumors in patients with Von Hippel-Lindau (VHL)-associated ccRCC, aiming to improve non-invasive diagnosis and personalized treatment. STUDY TYPE: Retrospective analysis of a prospectively maintained cohort. POPULATION: One hundred and thirty-six patients, 84 (61%) males and 52 (39%) females with pathology-proven ccRCC with a mean age of 52.8 ± 12.7 from 2010 to 2023. FIELD STRENGTH AND SEQUENCES: 1.5 and 3 T MRIs. Segmentations were performed on the T1-weighted 3-minute delayed sequence and then registered on pre-contrast, T1-weighted arterial and venous sequences. ASSESSMENT: A total of 404 lesions, 345 low-grade tumors, and 59 high-grade tumors were segmented using ITK-SNAP on a T1-weighted 3-minute delayed sequence of MRI. Radiomics features were extracted from pre-contrast, T1-weighted arterial, venous, and delayed post-contrast sequences. Preprocessing techniques were employed to address class imbalances. Features were then rescaled to normalize the numeric values. We developed a stacked model combining random forest and XGBoost to assess tumor grades using radiomics signatures. STATISTICAL TESTS: The model's performance was evaluated using positive predictive value (PPV), sensitivity, F1 score, area under the curve of receiver operating characteristic curve, and Matthews correlation coefficient. Using Monte Carlo technique, the average performance of 100 benchmarks of 85% train and 15% test was reported. RESULTS: The best model displayed an accuracy of 0.79. For low-grade tumor detection, a sensitivity of 0.79, a PPV of 0.95, and an F1 score of 0.86 were obtained. For high-grade tumor detection, a sensitivity of 0.78, PPV of 0.39, and F1 score of 0.52 were reported. DATA CONCLUSION: Radiomics analysis shows promise in classifying pathology grades non-invasively for patients with VHL-associated ccRCC, potentially leading to better diagnosis and personalized treatment. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results.

INTRODUCTION: Accurate diagnosis and treatment of kidney tumors greatly benefit from automated solutions for detection and classification on MRI. In this study, we explore the application of a deep learning algorithm, YOLOv7, for detecting kidney tumors on contrast-enhanced MRI. MATERIAL AND METHODS: We assessed the performance of YOLOv7 tumor detection on excretory phase MRIs in a large institutional cohort of patients with RCC. Tumors were segmented on MRI using ITK-SNAP and converted to bounding boxes. The cohort was randomly divided into ten benchmarks for training and testing the YOLOv7 algorithm. The model was evaluated using both 2-dimensional and a novel in-house developed 2.5-dimensional approach. Performance measures included F1, Positive Predictive Value (PPV), Sensitivity, F1 curve, PPV-Sensitivity curve, Intersection over Union (IoU), and mean average PPV (mAP). RESULTS: A total of 326 patients with 1034 tumors with 7 different pathologies were analyzed across ten benchmarks. The average 2D evaluation results were as follows: Positive Predictive Value (PPV) of 0.69 ± 0.05, sensitivity of 0.39 ± 0.02, and F1 score of 0.43 ± 0.03. For the 2.5D evaluation, the average results included a PPV of 0.72 ± 0.06, sensitivity of 0.61 ± 0.06, and F1 score of 0.66 ± 0.04. The best model performance demonstrated a 2.5D PPV of 0.75, sensitivity of 0.69, and F1 score of 0.72. CONCLUSION: Using computer vision for tumor identification is a cutting-edge and rapidly expanding subject. In this work, we showed that YOLOv7 can be utilized in the detection of kidney cancers.

Simultaneous assessment of mitochondrial DNA copy number and nuclear epigenetic age towards predictive models of development and aging.

OBJECTIVE: Mitochondrial dysfunction and nuclear epigenetic alterations, two hallmarks of aging, are associated with aberrant development and complex disease risk. Here, we report a method for the simultaneous assessment of mitochondrial DNA copy number (mtDNA-CN) and DNA methylation age (DNAm age) from the same DNA extraction using quantitative polymerase chain reaction (qPCR) and array data, respectively. RESULT: We present methods for the concurrent estimation of mtDNA-CN and DNAm age from the same DNA samples. This includes qPCR to estimate mtDNA-CN, representing the number of circular mitochondrial genomes in a cell, and DNA methylation microarray data to estimate the epigenetic age of an individual. Further, we provide a method for the combination of these metrics into a shared metric termed 'mtEpiAge'. This approach provides a valuable tool for exploring the interplay between mitochondrial dysfunction and nuclear epigenetic alterations, and their associations with disease and aging.

Imaging Genomics and Multiomics: A Guide for Beginners Starting Radiomics-Based Research.

Radiomics uses advanced mathematical analysis of pixel-level information from radiologic images to extract existing information in traditional imaging algorithms. It is intended to find imaging biomarkers related to the genomics of tumors or disease patterns that improve medical care by advanced detection of tumor response patterns in tumors and to assess prognosis. Radiomics expands the paradigm of medical imaging to help with diagnosis, management of diseases and prognostication, leveraging image features by extracting information that can be used as imaging biomarkers to predict prognosis and response to treatment. Radiogenomics is an emerging area in radiomics that investigates the association between imaging characteristics and gene expression profiles. There are an increasing number of research publications using different radiomics approaches without a clear consensus on which method works best. We aim to describe the workflow of radiomics along with a guide of what to expect when starting a radiomics-based research project.

Decoding mitochondrial-nuclear (epi)genome interactions: the emerging role of ncRNAs.

Bidirectional communication between the mitochondria and the nucleus is required for several physiological processes, and the nuclear epigenome is a key mediator of this relationship. ncRNAs are an emerging area of discussion for their roles in cellular function and regulation. In this review, we highlight the role of mitochondrial-encoded ncRNAs as mediators of communication between the mitochondria and the nuclear genome. We focus primarily on retrograde signaling, a process in which the mitochondrion relays ncRNAs to translate environmental stress signals to changes in nuclear gene expression, with implications on stress responses that may include disease(s). Other biological roles of mitochondrial-encoded ncRNAs, such as mitochondrial import of proteins and regulation of cell signaling, will also be discussed.

Communication between the nucleus (the cell control center) and the mitochondria (the energy-producing factories of the cell) is important for keeping cells working properly. Though communication goes both ways, signals sent from the mitochondria to the nucleus have become a big topic of discussion because they have been found to affect disease. ncRNAs are another topic that has been gaining traction. These are RNA transcripts that, instead of coding for proteins, have other roles in controlling our cells. Here we discuss ncRNAs that come from the mitochondria, called mt-ncRNAs. By sending mt-ncRNAs to the nucleus, mitochondria can send messages to the nucleus to help cells adapt to stress or changes in the environment. These mt-ncRNAs demonstrate the importance of mitochondria in controlling our cells. By studying this process, we gain information that helps in treating diseases.

Evaluating the performance of Generative Pre-trained Transformer-4 (GPT-4) in standardizing radiology reports.

OBJECTIVE: Radiology reporting is an essential component of clinical diagnosis and decision-making. With the advent of advanced artificial intelligence (AI) models like GPT-4 (Generative Pre-trained Transformer 4), there is growing interest in evaluating their potential for optimizing or generating radiology reports. This study aimed to compare the quality and content of radiologist-generated and GPT-4 AI-generated radiology reports. METHODS: A comparative study design was employed in the study, where a total of 100 anonymized radiology reports were randomly selected and analyzed. Each report was processed by GPT-4, resulting in the generation of a corresponding AI-generated report. Quantitative and qualitative analysis techniques were utilized to assess similarities and differences between the two sets of reports. RESULTS: The AI-generated reports showed comparable quality to radiologist-generated reports in most categories. Significant differences were observed in clarity (p = 0.027), ease of understanding (p = 0.023), and structure (p = 0.050), favoring the AI-generated reports. AI-generated reports were more concise, with 34.53 fewer words and 174.22 fewer characters on average, but had greater variability in sentence length. Content similarity was high, with an average Cosine Similarity of 0.85, Sequence Matcher Similarity of 0.52, BLEU Score of 0.5008, and BERTScore F1 of 0.8775. CONCLUSION: The results of this proof-of-concept study suggest that GPT-4 can be a reliable tool for generating standardized radiology reports, offering potential benefits such as improved efficiency, better communication, and simplified data extraction and analysis. However, limitations and ethical implications must be addressed to ensure the safe and effective implementation of this technology in clinical practice. CLINICAL RELEVANCE STATEMENT: The findings of this study suggest that GPT-4 (Generative Pre-trained Transformer 4), an advanced AI model, has the potential to significantly contribute to the standardization and optimization of radiology reporting, offering improved efficiency and communication in clinical practice. KEY POINTS: • Large language model-generated radiology reports exhibited high content similarity and moderate structural resemblance to radiologist-generated reports. • Performance metrics highlighted the strong matching of word selection and order, as well as high semantic similarity between AI and radiologist-generated reports. • Large language model demonstrated potential for generating standardized radiology reports, improving efficiency and communication in clinical settings.

Mitochondrial DNA Copy Number and Heteroplasmy in Monozygotic Twins Discordant for Schizophrenia.

Schizophrenia (SZ) is a severe, complex, and common mental disorder with high heritability (80%), an adult age of onset, and high discordance (∼50%) in monozygotic twins (MZ). Extensive studies on familial and non-familial cases have implicated a number of segregating mutations and de novo changes in SZ that may include changes to the mitochondrial genome. Yet, no single universally causal variant has been identified, highlighting its extensive genetic heterogeneity. This report specifically focuses on the assessment of changes in the mitochondrial genome in a unique set of monozygotic twins discordant (MZD) for SZ using blood. Genomic DNA from six pairs of MZD twins and two sets of parents (N = 16) was hybridized to the Affymetrix Human SNP Array 6.0 to assess mitochondrial DNA copy number (mtDNA-CN). Whole genome sequencing (WGS) and quantitative polymerase chain reaction (qPCR) was performed for a subset of MZD pairs and their parents and was also used to derive mtDNA-CN estimates. The WGS data were further analyzed to generate heteroplasmy (HP) estimates. Our results show that mtDNA-CN estimates for within-pair and mother-child differences were smaller than comparisons involving unrelated individuals, as expected. MZD twins showed discordance in mtDNA-CN estimates and displayed concordance in directionality of differences for mtDNA-CN across all technologies. Further, qPCR performed better than Affymetrix in estimating mtDNA-CN based on relatedness. No reliable differences in HP were detected between MZD twins. The within-MZD differences in mtDNA-CN observed represent postzygotic somatic changes that may contribute to discordance of MZ twins for diseases, including SZ.

Different nanocellulose morphologies (cellulose nanofibers, nanocrystals and nanospheres) extracted from Sunn hemp (Crotalaria Juncea).

Nanocellulose of different morphologies was extracted from Sunn Hemp (Crotalaria Juncea) using acid hydrolysis. The work focused on two objectives: first, to valorize the Sunn Hemp fibers for nanocellulose (NC) production, and second, to study the effects of acid concentration on different morphologies of NC and their properties. The study extracted nanocellulose at five different concentrations of H2SO4: 16 %, 32 %, 48 %, 64 %, and 72 %. Obtained nanocellulose was characterized by Scanning Electron Microscopy (FE-SEM), Atomic Force Microscopy (AFM), Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD) and Thermogravimetric Analysis (TGA). AFM and FE-SEM confirmed the production of three different morphologies of nanocellulose. The NC-32 had a web-like structure typically observed for cellulose nanofibrils (CNF), whereas NC-48 and NC-64 were observed as cellulose nanocrystals (CNC) with rod-like and needle-like shapes, respectively, and NC-72 displayed spherical particles termed cellulose nanospheres (CNS). The total crystallinity index of NC was calculated using FTIR, and a similar trend of crystallinity was also observed from XRD analysis. NC-32 was obtained with the highest yield of 94.83 %, followed by 91.40 % and 81.70 % for NC-48 and NC-64, respectively, whereas NC-72 yielded the lowest yield of 12.03 %. NC-72 had the highest thermal stability among other NC morphologies.

Cell culture media dependent in vitro dynamics and culture characteristics of adult caprine dermal fibroblast cells.

The enhanced availability of functional fibroblasts from precious tissue samples requires an ideal cell-culture system. Therefore, this study was designed to investigate the performance of caprine adult fibroblast cells (cadFibroblast) when cultivated in different culture media. The cadFibroblast cell lines from adult Barbari (Capra hircus) bucks were established and the effect of different media viz. DMEM/F-12 [with low-glucose (5.5 mM; DL) and high-glucose (30 mM; DH)], α-MEM [with low-glucose (5.5 mM; ML) and with high-glucose (30 mM; MH)], and fibroblast growth medium (FGM) were evaluated. Cells were then compared for growth characteristics and in-vitro dynamics through cellular morphology, proliferation, population-doubling time, double-immunocytochemistry, colony-forming units, wound healing, transwell migration, and differential expression of fibroblast-specific markers (FSP-1 and vimentin). The results of immunocytochemistry, transwell migration/invasion, and wound healing assays showed the superiority of DH over DL and other media tested. Whereas, similar effects of glucose supplementation and expression of FSP-1 were not observed in α-MEM. Transwell migration was significantly (p < 0.05) lower in FGM compared with other media tested. Overall, our results illustrate the media-dependent deviation in in-vitro dynamics and culture characteristics of cadFibroblasts that may be useful to develop strategies to cultivate these cells efficiently for research and downstream applications.

An experimental and numerical approach for thermal performance investigation of solar flat plate collector.

The present work aims to investigate thermal performance of a solar flat plate collector using water and Cu-MWCNTs nanoparticle-based hybrid nanofluid both experimentally and numerically. X-ray diffraction and FESEM with EDAX mapping were performed to characterize nanoparticles. The experimental setup was developed for thermal performance of FPC varying flow rates (0.5, 1.0, 1.5 LPM), inclination angle (25°, 30°, 35°, 40°, 45°), volume concentration (0%, 0.1%, 0.2%, 0.3%, 0.4%), and intensity (400 W/m2). The 3D numerical model having similar geometry as of actual flat plate collector was modeled using Fluents 15.0. The SST turbulence model was used to capture the chaotic changes in the velocity, temperature, and pressure fields. The experimental findings revealed 79.74% improvement in instantaneous efficiency at 0.4% vol., 1.5 LPM, 45° inclination angle, and 400 W/m2 intensity. The maximum deviation between the experimental and numerically calculated outlet and inlet temperature difference (ΔT) was 3.5% using a hybrid nanofluid. When numerical data are compared, instantaneous efficiency and heat gain both deviate by 2.8% and 2.9% from experimental values. Because of the numerical simulation analysis, it is possible to observe the temperature and flow pattern in flat plate collectors using nanofluids under a set of operating conditions, which would not be possible without the simulation.

Complications after Nephron-sparing Interventions for Renal Tumors: Imaging Findings and Management.

The two primary nephron-sparing interventions for treating renal masses such as renal cell carcinoma are surgical partial nephrectomy (PN) and image-guided percutaneous thermal ablation. Nephron-sparing surgery, such as PN, has been the standard of care for treating many localized renal masses. Although uncommon, complications resulting from PN can range from asymptomatic and mild to symptomatic and life-threatening. These complications include vascular injuries such as hematoma, pseudoaneurysm, arteriovenous fistula, and/or renal ischemia; injury to the collecting system causing urinary leak; infection; and tumor recurrence. The incidence of complications after any nephron-sparing surgery depends on many factors, such as the proximity of the tumor to blood vessels or the collecting system, the skill or experience of the surgeon, and patient-specific factors. More recently, image-guided percutaneous renal ablation has emerged as a safe and effective treatment option for small renal tumors, with comparable oncologic outcomes to those of PN and a low incidence of major complications. Radiologists must be familiar with the imaging findings encountered after these surgical and image-guided procedures, especially those indicative of complications. The authors review cross-sectional imaging characteristics of complications after PN and image-guided thermal ablation of kidney tumors and highlight the respective management strategies, ranging from clinical observation to interventions such as angioembolization or repeat surgery. Work of the U.S. Government published under an exclusive license with the RSNA. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Quiz questions for this article are available in the Online Learning Center. See the invited commentary by Chung and Raman in this issue.

Influence of graphene oxide on rheology, mechanical, dielectric, and triboelectric properties of poly(vinyl alcohol) nanocomposite hydrogels prepared via a facile one step process.

The present investigation aims to develop hydrogels with higher mechanical stability for triboelectric applications by adopting a simple method to fabricate a graphene oxide (GO) incorporated poly(vinyl alcohol) (PVA) nanocomposite hydrogel. Instead of the traditional repeated freeze-thaw method, high-shear solution mixing followed by solvent exchange with deionized water was adopted. Morphological observations showed dense and undulated microstructures in the nanocomposite hydrogel with increased GO concentration. Attenuated Total Reflection Fourier Transform Infrared spectroscopy confirmed a higher degree of intermolecular H-bonding between the hydroxyl group of PVA and oxygenated groups of GO, which leads to a robust gel formation. The formation of a robust PVA/GO nanocomposite hydrogel was examined through rheological investigations at room temperature. Nanoindentation analysis estimated a significant increase in hardness and Young's modulus of the nanocomposite hydrogels. Broadband dielectric spectroscopy showed the variation of the dielectric properties of the PVA/GO nanocomposite hydrogels with increased GO concentration. The PVA/GO nanocomposite hydrogels exhibited a maximum output voltage of 3.65 V at 0.075 wt% GO content during finger tapping experiment suggesting the potential for triboelectric applications. The extensive analysis demonstrates the influence of a very low concentration of GO on the variation of the morphology, rheology, mechanical, dielectric, and triboelectric properties of PVA/GO nanocomposite hydrogels.

Insights into thermal degradation kinetics and liquid crystalline behavior of cellulose nanocrystals from the waste of Cajanus cajan (pigeon pea).

Cellulose nanocrystals (CNCs) are essential for advancing nanotechnology and modern science. This work used the Cajanus cajan stem, an agricultural waste, as a lignocellulosic mass, which can serve as a supply of CNCs. After extraction from the Cajanus cajan stem, CNCs have been thoroughly characterized. FTIR (Infrared Spectroscopy) and ssNMR (solid-state Nuclear Magnetic Resonance) successfully validated eliminating additional components from the waste stem. The ssNMR and XRD (X-ray diffraction) were utilized to compare the crystallinity index. For structural analysis, the XRD of cellulose Iß was simulated to compare with the extracted CNCs. Various mathematical models inferred thermal stability and its degradation kinetics to ensure its high-end applications. Surface analysis established the rod-like shape of the CNCs. Rheological measurements were performed to gauge the liquid crystalline properties of CNC. The anisotropic liquid crystalline CNCs' birefringence proves that the Cajanus cajan stem is a promising resource for making CNCs for cutting-edge applications.

Different Effects of Sugars and Methods to Preserve Post-Thaw Functional Properties of Cryopreserved Caprine Spermatogonial Stem Cells.

The present study aimed to identify the effects of sugar and methods (slow freezing [SF] vs. fast freezing [FF]) on post-thaw in vitro functional characteristics of cryopreserved caprine spermatogonial stem cells (cSSCs) and the cells obtained from cryopreserved testis tissue of prepubertal Barbari bucks. For this, in experiment 1, cSSCs were isolated and cryopreserved by either SF or FF method with different non-permeable (sugars; trehalose [140 mm; 140T or 400 mm; 400T] and sucrose [140 mm; 140S or 400 mm; 400S]) or/and permeable (5% ethylene glycol [EG] and dimethyl sulfoxide) cryoprotectants. After 1 week of cryopreservation, the cSSCs were thawed and cultured for evaluation of their characteristics. Further, in experiment 2, the effectiveness of sugars (trehalose [140 mm] or sucrose [140 mm]) for cryopreservation of testicular tissues of prepubertal Barbari bucks using the SF or FF method was evaluated. After 1 week of cryopreservation, the tissues were thawed and cSSCs were isolated and cultured for 3 weeks. In both experiments, cSSCs were evaluated for recovery rate, proliferation, metabolic viability, senescence, and stemness markers' expression. The recovery rate was 1.3-, 1.3-, and 1.1-fold higher in the 140T group compared with EG, 140S, and 400S groups, respectively. Similarly, the expression of stemness markers (protein gene product 9.5 and octamer-binding transcription factor-4) was relatively higher in 140T group compared with the other groups. In experiment 2, the recovery rate of cells per unit tissue weight was significantly (p < 0.05) higher when cryopreserved using 140 mm trehalose compared with other groups. The results of immunocytochemical analyses imply the expression of pluripotent stem cell markers in cSSCs following cryopreservation. Overall, the outcome of the study demonstrates different effects of sugars and methods on post-thaw functional properties of cSSCs with superiority of 140 mm trehalose using SF method over other treatment groups. These results are important for ex vivo expansion and differentiation of cSSCs for fertility preservation and their other downstream applications.