Types Of Samples Research Articles

Vascular endothelial growth factor in inflammatory bowel disease: A systematic review and meta-analysis.

Vascular endothelial growth factor (VEGF) is linked to inflammation and angiogenesis, indicating a possible role in inflammatory bowel disease (IBD) and its main clinical manifestations, Crohn's disease (CD) and ulcerative colitis (UC). This systematic review and meta-analysis investigated studies assessing circulating VEGF concentrations in IBD patients and healthy controls, considering the effect of IBD type, sample type and geographical location. A systematic search identified 18 studies (28 group comparators) investigating 1741 IBD patients and 1291 controls. Data were extracted and analysed using standardized mean differences (SMD) with 95% confidence intervals (CI). VEGF concentrations were significantly higher in IBD patients (SMD = .71, 95% CI .38 to 1.04; p < .001). UC patients showed higher VEGF concentrations than CD patients. Serum samples indicated significant VEGF elevations, unlike plasma samples. Significant VEGF increases were observed in studies conducted in Western Europe and Asia, but not in Eastern Europe. No significant differences were found between active and inactive disease. VEGF concentrations are elevated in IBD patients, with variations by disease type, sample type and geography. However, VEGF is not a reliable marker of disease activity. Future research should standardize methods and explore regional influences to enhance VEGF's clinical utility as a biomarker of IBD.

Next step of molecular pathology: next-generation sequencing in cytology

The evolving landscape of precision oncology underscores the pivotal shift from morphological diagnosis to treatment decisions driven by molecular profiling. Recent guidelines from the European Society for Medical Oncology recomend the use of next-generation sequencing (NGS) across a broader range of cancers, reflecting its superior efficiency and clinical value. NGS not only updates oncology testing by offering quicker, sample-friendly, and sensitive analysis but also reduces the need for multiple individual tests. Cytology samples, often obtained through less invasive methods, can yield high-quality genetic material suitable for molecular analysis. This article focuses on optimizing the use of cytology samples in NGS, and outlines their potential benefits in identifying actionable molecular alterations for targeted therapies across various solid tumors. It also addresses the need for validation studies and the strategies to incorporate or combine different types of samples into routine clinical practice. Integrating cytological and liquid biopsies into routine clinical practice, alongside conventional tissue biopsies, offers a comprehensive approach to tumor genotyping, early disease detection, and monitoring of therapeutic responses across various solid tumor types. For comprehensive biomarker characterization, all patient specimens, although limited, is always valuable.

Oxidative Stress in Canine Diseases: A Comprehensive Review

Oxidative stress (OS), defined as a disruption in redox balance favoring oxidants, has emerged as a major contributor to numerous diseases in human and veterinary medicine. While several reviews have explored the implication of OS in human pathology, an exhaustive review of the canine species is lacking. This comprehensive review aims to summarize the existing literature on the role of OS in canine diseases, highlighting its potentially detrimental effect on various organs and systems. Some inconsistencies among studies exist, likely due to varying biomarkers and sample types. However, there is substantial evidence supporting the involvement of OS in the development or progression of numerous canine disorders, such as cardiovascular, oncologic, endocrine, gastrointestinal, hematologic, renal, neurologic, infectious, and parasitic diseases, among others. Additionally, this review discusses the efficacy of antioxidant and pro-oxidant therapeutic agents for these conditions. Dietary interventions to counteract OS in dogs have gained significant attention in recent years, although further research on the topic is needed. This review aims to serve as a foundational resource for future investigations in this promising field.

Broiler breeder putative lipid biomarkers associated with sperm mobility

IntroductionBiomarkers indicative of sperm mobility in broiler breeders would provide the ability to screen for fertility potential, with a positive correlation established between sperm mobility and fertilization potential. This study characterized the lipidome of seminal plasma (SP), sperm cell (SC), and whole semen (WS) isolated from broiler breeder roosters with different sperm mobility phenotypes across key timepoints of the semen production cycle.MethodsWS samples were collected from five high mobility roosters and five low mobility roosters during early, mid, and late semen production, with SP separated from SC by centrifugation. Using multiple reaction monitoring (MRM) profiling, a total of 3241 lipid species were identified in rooster semen across ten lipid classes. Metaboanalyst 6.0 was used to analyze the relative ion intensity for each lipid species due to sperm mobility phenotype through a t-test and due to timepoint through a one-way ANOVA, with lipid ontology enrichment analysis performed using LION. Metaboanalyst 6.0 was also used to perform biomarker analysis for the sperm mobility phenotype in WS samples.ResultsLipid class total abundance differed with sample type, sperm mobility phenotype, and timepoint. A total of 31, 99, and 112 lipid species were found to be different between low and high mobility males across timepoints in the SP, SC, and WS samples, respectively. Lipid ontology enrichment analysis revealed stark contrasts in lipid-based functions key to sperm survival, storage, and productivity between low and high sperm mobility phenotypes. Through biomarker analysis, 8 lipid species were identified as excellent sperm mobility biomarkers that could be detected in early and mid-semen production.DiscussionTimepoint based changes in lipid species were unique to each sperm mobility phenotype, with low sperm mobility roosters exhibiting a larger number of lipid species changes over the semen production cycle in the SP and SC when compared to high sperm mobility roosters. This is the first study to characterize poultry semen lipidome using MRM profiling. The lipid species identified between low and high sperm mobility roosters could be utilized in the poultry industry as potential biomarkers of fertility potential, with the ability to screen for the economical trait of fertility potential early in semen production.

Postoj studentů sociální pedagogiky k homoparentálním rodinám

The paper is focused on the research of the attitude of the students of the Bachelor's and Master's degree programmes in social pedagogy from the J. E. Purkyně University in Ústí nad Labem, University of Ostrava and Masaryk University in Brno (N = 107) towards the issue of homoparental families and in a broader context towards homosexuality as such. Furthermore, we also focus on the students' perspective on how the issue of homoparental families is reflected in the educational content of the subjects in their curriculum. An accessible type of research sampling was used. The author's questionnaire constructed on the principle of Likert scales was used as a research instrument. The questionnaire includes items aimed at identifying attitudes towards gay marriage, the causes of homosexuality, the upbringing of children by homoparental parents, and the space devoted to this topic in the university training of future social educators. The data obtained were processed using descriptive methods of statistical analysis and further analyzed. The findings show that a significant majority of the respondents are in favour of homosexual couples receiving the same rights as heterosexual couples, including the possibility of marriage. Furthermore, the majority of respondents appear to be in favour of the naturalness of homosexual couples raising children. From the answers we can conclude that the students interviewed consider homosexuality as natural as heterosexuality and their attitudes are mostly positive.

Enriching the future of public health microbiology with hybridization bait capture.

SUMMARYPublic health microbiology focuses on microorganisms and infectious agents that impact human health. For years, this field has relied on culture or molecular methods to investigate complex samples of public health importance. However, with the increase in accuracy and decrease in sequencing cost over the last decade, there has been a transition to the use of next-generation sequencing in public health microbiology. Nevertheless, many available sequencing methods (e.g., shotgun metagenomics and amplicon sequencing) do not work well in complex sample types, require deep sequencing, or have inherent biases associated with them. Hybridization bait capture, also known as target enrichment, brings in solutions for such limitations. It is an increasingly popular technique to simultaneously characterize many thousands of genetic elements while reducing the amount of sequencing needed (thereby reducing the sequencing costs). Here, we summarize the concept of hybridization bait capture for public health, reviewing a total of 35 bait sets designed in six key topic areas for public health microbiology [i.e., antimicrobial resistance (AMR), bacteria, fungi, parasites, vectors, and viruses], and compare hybridization bait capture to previously relied upon methods. Furthermore, we provide an in-depth comparison of the three most popular bait sets designed for AMR by evaluating each of them against three major AMR databases: Comprehensive Antibiotic Resistance Database, Microbial Ecology Group Antimicrobial Resistance Database, and Pathogenicity Island Database. Thus, this article provides a review of hybridization bait capture for public health microbiologists.

Application of metagenomic next-generation sequencing in the diagnosis of infectious diseases

Metagenomic next-generation sequencing (mNGS) is a transformative approach in the diagnosis of infectious diseases, utilizing unbiased high-throughput sequencing to directly detect and characterize microbial genomes from clinical samples. This review comprehensively outlines the fundamental principles, sequencing workflow, and platforms utilized in mNGS technology. The methodological backbone involves shotgun sequencing of total nucleic acids extracted from diverse sample types, enabling simultaneous detection of bacteria, viruses, fungi, and parasites without prior knowledge of the infectious agent. Key advantages of mNGS include its capability to identify rare, novel, or unculturable pathogens, providing a more comprehensive view of microbial communities compared to traditional culture-based methods. Despite these strengths, challenges such as data analysis complexity, high cost, and the need for optimized sample preparation protocols remain significant hurdles. The application of mNGS across various systemic infections highlights its clinical utility. Case studies discussed in this review illustrate its efficacy in diagnosing respiratory tract infections, bloodstream infections, central nervous system infections, gastrointestinal infections, and others. By rapidly identifying pathogens and their genomic characteristics, mNGS facilitates timely and targeted therapeutic interventions, thereby improving patient outcomes and infection control measures. Looking ahead, the future of mNGS in infectious disease diagnostics appears promising. Advances in bioinformatics tools and sequencing technologies are anticipated to streamline data analysis, enhance sensitivity and specificity, and reduce turnaround times. Integration with clinical decision support systems promises to further optimize mNGS utilization in routine clinical practice. In conclusion, mNGS represents a paradigm shift in the field of infectious disease diagnostics, offering unparalleled insights into microbial diversity and pathogenesis. While challenges persist, ongoing technological advancements hold immense potential to consolidate mNGS as a pivotal tool in the armamentarium of modern medicine, empowering clinicians with precise, rapid, and comprehensive pathogen detection capabilities.

Can acoustic measurements predict gender perception in the voice?

To determine if there is an association between vocal gender presentation and the gender and context of the listener. Quantitative and transversal study. 47 speakers of Brazilian Portuguese of different genders were recorded. Recordings included sustained vowel emission, connected speech, and the expressive recital of a poem. Subsequently, four scripts were used in Praat to extract 16 acoustic measurements related to prosody. Voices underwent Auditory-Perceptual Assessment (APA) of the gender presentation by 236 people [65 speech and language pathologist (SLP) with experience in the area of the voice (SLP), 101 cisgender people (CG), and 70 transgender and non-binary people (TNB)]. Gender presentation was evaluated by visual analogue scale. Agreement analyses were executed among quantitative variables and multiple linear regression models were generated to predict APA, taking the judge context/gender and speaker gender into consideration. Acoustic analysis revealed that cis and transgender women had higher median fundamental frequency (fo) values than other genders. Cisgender women exhibited greater breathiness, while cisgender men showed more vocal quality deviations. In terms of APA, significant differences were observed among judge groups: SLP judged vowel samples differently from other groups, and TNB judged speech samples differently (both p<0.001). The predictive measures for the APA varied based on the sample type, speaker gender, and judge group. For vowel samples, only SLP judges had predictive measures (fo and ABI Jitter) for cisgender speakers. In number counting samples, predictive measures for cisgender speakers included fomed and HNR for CG judges, and fomed for both SLP and TNB judges. For transgender and non-binary speakers, predictive measures were fomed for CG and SLP judges, and fomed, CPPs, and ABI for TNB judges. In the poem recital task, predictive measures for cisgender speakers were fomed and HNR for both SLP and CG judges, with additional measures of cvint and sr for CG judges, and fomed, HNR, cvint, and fopeakwidth for TNB judges. For transgender and non-binary speakers, the predictive measures included a wider range of acoustic features such as fomed, fosd, sr, fomin, emph, HNR, Shimmer, and fo peakwidth for SLP judges, and fomed, fosd, sr, fomax, emph, HNR, and Shimmer for CG judges, while TNB judges considered fomed, sr, emph, fosd, Shimmer, HNR, Jitter, and fomax. There is an association between the perception of gender presentation in the voice and the gender or context of the listener and the speaker. Transgender and non-binary judges diverged to a higher degree from cisgender and SLP judges. Compared to the evaluation of cisgender speakers, all judge groups used a greater number of acoustic measurements when analyzing the speech of transgender and non-binary individuals in the poem recital samples.

Sensitive and reliable detection of KIT p.D816V mutation in decalcified archival bone marrow trephines.

The majority of mastocytosis cases are characterized by an activating mutation in the KIT gene in codon 816. The detection of this alteration is of importance for proper diagnostic workup. Therefore, reliable and sensitive methods for the detection of KIT Codon 816 hotspot mutations in various types of patient samples are required. Since mutated cancer genes are often overexpressed, we evaluated the feasibility and sensitivity of KIT p.D816V detection by analysing mRNA/cDNA instead of genomic DNA. From 80 bone marrow trephines harboring a KIT p.D816 mutation, seven were only mutated by mRNA/cDNA pyrosequencing and 11 only by digital PCR analysis of genomic DNA. These results clearly demonstrate that detection of clinically relevant mutations in mRNA extracted from routinely processed decalcified archival bone marrow trephines is not only possible in a reliable fashion but under many circumstances advantageous. This enables the direct correlation of genomic data with high-quality morphological evaluation.

Melissopalynological study of Melipona (Eomelipona) torrida Friese, 1916: a native stingless bee from southern Brazil

Few data are on bee–plant interactions for the Melipona torrida bee in southern Brazil. This is a native bee vulnerable to extinction that pollinates native plants and produces honey that is especially appreciated by local populations. Aiming to evaluate the floral sources visited by Melipona torrida bee in southern Brazil a palynology investigation on honeys of this bee were carried out. Honeys were extracted each month from March 2020 to November 2021 of hives from two locations of Rio Grande do Sul (Cachoeirinha and Canoas). The 34 samples were analysed with a light microscope. In the honey samples of the Melipona torrida bee were identified 39 pollen types, principally tree plants. Melipona torrida bee visited frequently the plants of Myrcia type, Eucalyptus sp., Schinus terebenthifolia, Mimosa bimucronata, Parapiptadenia rigida, Morus nigra and Solanum sisymbrifolium. The pollen diagram show differences in pollen percentages throughout the year and CONISS grouped the samples in five groups (MT-1–MT-5). Richness of pollen types of samples was low in the months of winter and March and high in the honeys of months of Spring and Summer. The honeys were monofloral and heterofloral, and native plants were more visited than exotic species. The 33 pollen types from honey of Melipona torrida bee from southern Brazil can help in the knowledge of the bee–plant interaction of this native bee and propose appropriate plant management around the hives.

Comparison of commonly used software pipelines for analyzing fungal metabarcoding data.

Metabarcoding targeting the internal transcribed spacer (ITS) region is commonly used to characterize fungal communities of various environments. Given their size and complexity, raw ITS sequences are necessarily processed and quality-filtered with bioinformatic pipelines. However, such pipelines are not yet standardized, especially for fungal communities, and those available may produce contrasting results. While some pipelines cluster sequences based on a specified percentage of base pair similarity into operational taxonomic units (OTUs), others utilize denoising techniques to infer amplicon sequencing variants (ASVs). While ASVs are now considered a more accurate representation of taxonomic diversity for prokaryote communities based on 16S rRNA amplicon sequencing, the applicability of this method for fungal ITS sequences is still debated. Here we compared the performance of two commonly used pipelines DADA2 (inferring ASVs) and mothur (clustering OTUs) on fungal metabarcoding sequences originating from two different environmental sample types (fresh bovine feces and pasture soil). At a 99% OTU similarity threshold, mothur consistently identified a higher fungal richness compared to DADA2. In addition, mothur generated homogenous relative abundances across multiple technical replicates (n = 18), while DADA2 results for the same replicates were highly heterogeneous. Our study highlights a potential pipeline-associated bias in fungal metabarcoding data analysis of environmental samples. Based on the homogeneity of relative abundances across replicates and the capacity to detect OTUs/ASVs, we suggest using OTU clustering with a similarity of 97% as the most appropriate option for processing fungal metabarcoding data.

Designing a molecularly imprinted polymer-based electrochemical sensor for the sensitive and selective detection of the antimalarial chloroquine phosphate.

For the first time anelectrochemical sensor based on nanomaterial-supported molecularly imprinted polymers (MIPs) is applied to the sensitive and specific determination of chloroquine phosphate(CHL). The sensor was produced using an electropolymerization (EP) approach, and it was formed on a glassy carbon electrode (GCE) using CHL as a template and 2-acrylamido-2-methyl-1-propane sulfonic acid (AMPS) and aniline (ANI) as functional monomers. Incorporating Prussian blue polyethyleneglycol-amine nanoparticles (PB@PEG-NH2) in the MIP-based electrochemical sensor increased the active surface area and porosity. The developed CHL/AMPS-PANI/PB@PEG-NH2/MIP-GCE sensor was characterized morphologically and electrochemically using electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and scanning electron microscopy (SEM). The indirect measurement of CHL was accomplished in 5.0mmol L-1 [Fe(CN)6]-3/-4 solutionusing differential pulse voltammetry, displaying linear response between 1.75 × 10-12 and 2.50 × 10-13M, and limits of detection (LOD) and quantitation (LOQ) of 6.68 × 10-14M and 2.23 × 10-13M, respectively, in standard solutions. CHL recoveries in spiked serum and tablet form ranged from 99.13 to 101.51%, while the relative standard deviations (RSD%) were below 2.41% in both types of samples. In addition, the sensor's excellent selectivity was successfully demonstrated in the presence of components with a chemical structure similar to CHL.

Abstract B056: Towards a generalized machine learning model for Raman spectroscopy-based liquid biopsy in cancer screening using self-supervised learning

Abstract Head and neck cancers, which encompass a range of malignancies affecting the oral cavity, pharynx, and larynx, are critical to address due to their high morbidity and impact on quality of life. This underscores the need for effective and non-invasive diagnostic tools. Raman spectroscopy-based liquid biopsy detects cancers by analyzing intrinsic Raman fingerprints in biofluids without the need for molecular labels, making it a promising point-of-care screening method. However, Raman scattering is weak, and interpreting spectra from biological samples is challenging due to overlapping peaks, fluorescence background, cosmic rays, and instrument noise, which obscure the signal. Extensive preprocessing and advanced analytical techniques are usually needed to isolate the true Raman signal. While machine learning models have shown promise in classifying cancer status from Raman spectra, variations in Raman systems, lack of standardized protocols, and small patient cohorts limit their clinical utility. Our goal is to develop a model for Raman spectroscopy-based liquid biopsy that generalizes across different measurement conditions and clinical indications. A key challenge is the high cost of acquiring extensive labeled training data. To overcome this, we use a Siamese-based, self-supervised model trained on unlabeled spectral data. The model generates synthetic spectra that mimic real variations across Raman systems and measurement protocols by preprocessing the input and adding simulated factors like fluorescence background, noise, and cosmic rays. By maximizing the cosine similarity between the embeddings of the generated spectra and their originals, the model learns meaningful features of the Raman spectra. This approach not only expands the training set but also enhances the model’s stability across different instruments. In our preliminary study, we analyzed thousands of Raman spectra measured on liquid biopsy specimens (blood plasma and saliva) collected from a cohort of 71 head and neck cancer patients (spread across early and late-stage disease) and 44 non-cancerous controls. Spectra were divided into training and test sets with an 80:20 ratio. Each specimen was drop cast onto a quartz substrate and 25 repeated Raman point scans were performed in a grid over the dried spot. We augmented the training set to reach 9,200 spectra, and t-SNE plots of the embeddings of the test set (1,150 spectra) showed clear clustering separated by sample type, with distinct clusters for healthy individuals and cancer patients. Despite the excellent performance for cancer vs control, separation among cancer patients at different stages remains limited, but will be the target of future work building on this study. These results suggest that our label-free approach can effectively distinguish between healthy and cancerous samples using unlabeled data, advancing the potential for the widespread clinical application of Raman-based liquid biopsy powered by machine learning. Citation Format: Yifei Gu, Kwan Lun Chiu, Andrés Muñoz-Jaramillo, Andrew C. Birkeland, Maria Navas-Moreno, Randy Carney. Towards a generalized machine learning model for Raman spectroscopy-based liquid biopsy in cancer screening using self-supervised learning [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B056.

Abstract A022: Novel technology affords real-time PCR the sensitivity required for minimal residual disease monitoring

Abstract Monitoring minimal residual disease (MRD) provides invaluable information for disease progression and therapeutic decision making. However, there are many challenges to reliably detect key mutations or biomarkers, such as the accessibility, turnaround time, and sensitivity of current methods. High sensitivity is crucial due to the limited DNA available in certain sample types such as liquid biopsy. Next generation sequencing (NGS) is capable of highly sensitive detection but is dependent on high sample input and read depth, which is not always feasible due to the variable nature of circulating tumor DNA (ctDNA) in blood or plasma. In addition, NGS is not an economical nor rapid option for recurrent longitudinal monitoring. Digital PCR (dPCR) offers the sensitivity required for MRD monitoring but has yet to be widely adopted for this application. While real-time and qPCR are commonly used in oncology research, RT-PCR does not have the sensitivity required for early detection of key MRD mutations. Here, we present novel patent-pending technology that enhances the sensitivity of RT-PCR, allowing this common platform to be used to reliably detect low abundant mutations. We performed comparisons between GT Molecular’s patent-pending technology paired with RT-PCR and commercially available research use only dPCR and NGS assays to assess the sensitivity this new technology provides for RT-PCR. Cell-free DNA (cfDNA) was extracted from real patient or contrived plasma samples using the QIAamp Circulating Nucleic Acid Kit and analyzed using RT-PCR and dPCR assays targeting KRAS or ESR1 mutations. Samples with KRAS or ESR1 mutations were tested at variant allele frequencies (VAFs) varying between 1%-0.01% to evaluate the ability of GT Molecular’s new technology to detect low abundant mutations using RT-PCR. KRAS and ESR1 mutations were reliably detected with RT-PCR at VAFs as low as 0.01%, demonstrating sensitivity comparable to dPCR. Samples demonstrating positivity for KRAS or ESR1 mutations via dPCR were also positive via RT-PCR. With this patent-pending technology, RT-PCR is now capable of achieving the sensitivity required for MRD monitoring, providing critical information from liquid biopsy samples at a fraction of the cost. Citation Format: Mary Stischer, Nick Allen, Stephanie Barbari, Sarah Kane. Novel technology affords real-time PCR the sensitivity required for minimal residual disease monitoring [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A022.

Determination of Vitamin C Concentration in Various Fresh Orange and Lemon Samples from Janzour Region Using Volumetric Titration

Vitamin C is an essential nutrient crucial for human health, playing a vital role in numerous physiological processes, including immune system support and collagen synthesis. Citrus fruits, such as oranges and lemons, are recognized as rich sources of this vitamin. The primary purpose of this study is to determine the vitamin C concentration in various fresh orange and lemon juices. The samples were randomly collected from the local market in Janzour. The study included 18 samples representing 6 different citrus varieties (3 samples per variety). Vitamin C concentration was statistically estimated using a titrimetric method with a standardized solution of 2,6-dichlorophenolindophenol (DCPIP) as the titrant. The obtained results revealed some kind of variations in vitamin C content among different citrus varieties and even within samples of the same variety. Abu Surah orange samples exhibited the highest vitamin C concentration (mean range: 38.9-41.6 mg/dl), while sweet orange samples had the lowest (mean range: 5.1- 8.5 mg/dl). Vitamin C concentrations in other varieties ranged as follows: Sour orange: 9.22-24.9 mg/dl, Mandarin: 9.0 -16.2 mg/dl, Tarocco: 28.2-33.3 mg/dl, and Lemon: 14.5-15.8 mg/dl. These results showed evidently that vitamin C levels were varied even within the same sample type. Such variations would draw attention to the importance of choosing varieties when searching for food sources of this vital nutrient.

Uji Analisis Nikel Ore/Ni (Sampel Cek) Menggunakan Metode Press Pellet Berdasarkan Variasi Suhu Pengeringan, Waktu Pengeringan, dan Berat Sampel Timbangan Press

The purpose of the study was to determine the changes in nickel levels as a result of the analysis on the variation in temperature and drying time as well as the weight of press pellet scale samples from one of the same sample types. This research method is in the form of an experiment with a factorial research design from analysis parameters in the form of MC percentage and nickel ore content of Ni element. The results of the analysis showed that drying at 105°C and 125°C achieved a constant MC during a heating time of 5 to 6 hours. Meanwhile, at a temperature of 145-185 degrees Celsius with an interval of 2-6 hours, the MC level has experienced a constant weight of 44.44%. At the same temperature treatment, which is 105oC, different constant times are obtained. This is influenced by the difference in weight between the two samples, namely in the AP/5 and AP/6 range of 0.360 grams, while AP ORI weighs 1.8 kg. The results of the Epsilon 4 reading showed that the variation in drying temperature at 105°C to 185°C and drying time of 3-6 hours affected the nickel ore content in the range of 1.50% to 1.53%, close to the standard value of nickel 1.50%. The lower the temperature and drying time, the humidity level of the sample increases, causing the nickel ore content to decrease, while the higher the temperature and drying time increases the risk of nickel decomposition. The optimum temperature and drying time is 165°C for 2-4 hours. The press sample weight is between 8-14 grams resulting in a stable nickel content at 1.47%-1.50%, with an optimum weight of 10-14 grams for optimal sample thickness and density.

Optimisation of the sampling method for skin microbiome studies in healthy children: a pilot cohort study

IntroductionRecent interest in the diverse ecosystem of bacteria, fungi and viruses that make up the skin microbiome has led to numerous studies investigating the skin microbiome in healthy skin and in dermatological conditions. However, skin microbiome analysis is challenging due to relatively low numbers of skin microorganisms compared to mucosal sites, such as the respiratory or gastrointestinal tracts. Microbiome results are heavily influenced by sampling methods. Previous sampling methods include that of cotton swabs, tape stripping, patch sampling and punch biopsies. It is essential to have a standardised sampling method for microbiome studies to have comparable results between studies. Two non-invasive methods of sampling the skin microbiome; a skin scraping versus a flocked swab were chosen as methodologies likely to be efficient, effective, and easy to access for future skin microbiome studies in children. Here we compare the two sampling methods to describe the composition of the skin microbiome in healthy children.MethodSamples were collected from six healthy children aged three to nine years from the skin overlying the cubital fossa, cheek and axilla using (i) flocked swabs and (ii) skin scrapings with a glass slide. Samples were collected from the left and right sides of the body at two separate time points, one week apart. Quantitative PCR of the gene encoding 16S ribosomal ribonucleic acid (rRNA) was performed to compare the bacterial load collected by each sampling method. Full-length 16S rRNA gene amplicon sequencing was performed to compare the relationship of sampling method and time with the diversity and ecology of bacteria between different body sites.ResultsFrom six children, 78 flocked swabs and 78 skin scraping samples were collected, along with details of their overall health and skin care practices. qPCR results indicated higher total bacterial load from flocked swabs compared with skin scrapings. Flocked swabs and skin scraping methods had very similar bacterial compositional profiles. The skin microbiome was diverse between individuals and remained relatively stable within individuals over time.DiscussionOverall, results were similar between sample types, however bacterial DNA yield was higher for flocked swab samples (compared to skin scraping methods) and with a simpler protocol is the preferred sampling method for future studies.

Feature genes identification and immune infiltration assessment in abdominal aortic aneurysm using WGCNA and machine learning algorithms

ObjectiveAbdominal aortic aneurysm (AAA) is a life-threatening vascular condition. This study aimed to discover new indicators for the early detection of AAA and explore the possible involvement of immune cell activity in its development.MethodsSourced from the Gene Expression Omnibus, the AAA microarray datasets GSE47472 and GSE57691 were combined to generate the training set. Additionally, a separate dataset (GSE7084) was designated as the validation set. Enrichment analyses were carried out to explore the underlying biological mechanisms using Disease Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology. We then utilized weighted gene co-expression network analysis (WGCNA) along with 3 machine learning techniques: least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, to identify feature genes for AAA. Moreover, data were validated using the receiver operating characteristic (ROC) curve, with feature genes defined as those having an area under the curve above 85% and a p-value below 0.05. Finally, the single sample gene set enrichment analysis algorithm was applied to probe the immune landscape in AAA and its connection to the selected feature genes.ResultsWe discovered 72 differentially expressed genes (DEGs) when comparing healthy and AAA samples, including 36 upregulated and 36 downregulated genes. Functional enrichment analysis revealed that the DEGs associated with AAA are primarily involved in inflammatory regulation and immune response. By intersecting the result of 3 machine learning algorithms and WGCNA, 3 feature genes were identified, including MRAP2, PPP1R14A, and PLN genes. The diagnostic performance of all these genes was strong, as revealed by the ROC analysis. A significant increase in 15 immune cell types in AAA samples was observed, based on the analysis of immune cell infiltration. In addition, the 3 feature genes show a strong linkage with different types of immune cells.ConclusionThree feature genes (MRAP2, PPP1R14A, and PLN) related to the development of AAA were identified. These genes are linked to immune cell activity and the inflammatory microenvironment, providing potential biomarkers for early detection and a basis for further research into AAA progression.

The Importance of Knowledge of Study Techniques and Their Impact on Academic Performance

Objective: The main objective of the research is to identify the role of knowledge about study techniques and its impact on the academic performance of students of the Academic Unit of Accounting and Administration (UACYA) at the Autonomous University of Nayarit (UAN). With the results obtained, it will be possible to demonstrate the importance of training them in this subject and make proposals for improvement. Achieving benefits for the student, being a conscious and responsible subject of their cognitive processes and the construction of their knowledge. Theoretical Framework: The conceptualization of meaningful learning is presented, from its precursor David Ausubel (1969), in which the individual is the constructor of his own knowledge, relating the concepts to be learned and with the information he already possesses. Achieving this, the guideline is given to obtain self-regulated learning, where learners are able to plan, control and direct their own learning, through metacognitive, motivational and behavioral processes. Supported by the use of strategies of study habits and techniques that allow them to meet their objectives and impacting their academic performance. Method: A non-experimental design was used, since there was no control with the variables under study, and it was also of a transectional correlational type, since the correlations between the factors under study were analyzed. The students analyzed were those who were found in the facilities of the Academic Unit of Accounting and Administration, so the type of sampling was of convenience, achieving a sample of 328 students. To analyze the correlations, the exploratory and confirmatory factor analysis was used, where first a divergent and convergent analysis was performed, then the reliability analysis, and once the instrument was validated, the exploratory factor analysis was performed, which used the AMOS (Analysis of Moment Structures) software for the confirmatory analysis and adequacy of the model. Results and Discussion: When applying the factor analysis, it was found that the variables were correctly grouped in three components, with a result of 56.478% of the total variance, being acceptable for the social studies, subsequently the KMO test was 0.799 and Bartlett's sphericity was less than .001, confirming the analysis. After elimination of items (p3 and p4) in the Confirmatory Factor Analysis, the fit indices complied with the acceptable values of CFI and RMSEA, obtaining a final adjusted and valid model, i.e. three consistent and relevant dimensions for future research. Research Implications: The practical and theoretical implications of this study highlight the importance of seeking strategies to generate in higher level learners the knowledge about study habits and techniques, which will lead them to be able to manage their own knowledge, and therefore fulfill their academic objectives. At the same time to seek to promote further educational research on issues related to meaningful and self-regulated learning. Originality/Value: This study contributes to the literature by demonstrating the importance of understanding the study habits and techniques of higher education students, and how their application in the learning process impacts academic performance. It sets the guideline for higher education institutions to develop in their students the ability to manage their own knowledge.

The Effect of Sampling Schedule on Assessment of Dietary Measures: Evidence From Blue Monkeys (Cercopithecus mitis stuhlmanni).

Accurately assessing primate diets is important in studies of behavioral ecology and evolution. While previous research has compared sampling methods (scan, focal), we examined how sampling schedule influences accuracy of dietary measures. We define sampling schedule as the combined distribution (random vs. consecutive) and frequency of sampling days within a given month. Under field conditions, time may be required to locate a study group, and we therefore also subtracted 1, 2, or 3 h from the beginning of all non-consecutive days in each sampling schedule to mimic observation time lost to search. From a dense (near daily) 5-year record of feeding behavior derived from focal animal follows of adult females in five wild blue monkey (Cercopithecus mitis) groups, we created data subsets matching various sampling schedules, and compared monthly dietary measures calculated from each subset to those based on the full data set. These measures included (1) the proportion of observation time feeding on fruit, (2) diet composition (three top-ranked food items), (3) species richness of plant diet, (4) Shannon-Wiener diversity index based on plant species, and (5) Holmes-Pitelka index expressing dietary overlap with the previous month. We used generalized linear mixed models to assess how frequency and sampling type (a combination of distribution and hours lost) relate to a subset's deviation from the full data set, where a smaller deviation (or higher chance of matching, for diet composition) implies greater accuracy. For all dietary measures, increasing observation frequency increased accuracy. The response to distribution varied among measures, but sampling types generally differed more at lower frequencies. Deviation varied widely within and between dietary measures, and some sampling schedules resulted in values with large percentage differences from the "full" data. Accordingly, when designing and comparing studies, researchers should consider how sampling schedules may influence the accuracy of the dietary measures of interest.