Quantitative Blood Serum IVDr NMR Spectroscopy in Clinical Metabolomics of Cancer, Neurodegeneration, and Internal Medicine.

Despite more than two decades of metabolomics having joined the "omics" scenery, to date only a few novel blood metabolite biomarkers have found their way into the clinic. This is changing now by massive large-scale population metabolic phenotyping for both healthy and disease cohorts. Here, nuclear magnetic resonance (NMR) spectroscopy is a method of choice, as typical blood serum markers can be easily quantified and by knowledge of precise reference concentrations, more and more NMR-amenable biomarkers are established, moving NMR from research to clinical application. Besides customized approaches, to date two major commercial platforms have evolved based on either 600 MHz (14.1 Tesla) or 500 MHz (11.7 Tesla) high-field NMR systems. This chapter provides an introduction into the field of quantitative in vitro diagnostics research (IVDr) NMR at 600 MHz and its application within clinical research of cancer, neurodegeneration, and internal medicine.

Application of a Computational Metabolomics Workflow for the Diagnosis of Inborn Errors of Metabolism in a Laboratory Setting.

Inborn errors of metabolism constitute a set of hereditary diseases that impose severe medical and physical challenges in the affected individual, in particular, for the pediatric patient population. Timely diagnosis is crucial for these patients, as any delay could result in irreversible health damage, underscoring the importance of early initiation of personalized treatment. Current routine diagnostic screening for inborn errors of metabolism relies on various targeted analyses of established biomarkers. However, this approach is time-consuming, focuses on a limited number of tests (based on clinical information) with a relatively small number of biomarkers, and does not facilitate the identification of new markers. In contrast, untargeted metabolomics-based screening offers a more efficient diagnostic solution, by assessing thousands of metabolites across multiple metabolic pathways in a single test. This not only saves time but also conserves resources for clinicians, the diagnostic laboratory, and for patients.This chapter describes the computational workflow of our "Next Generation Metabolic Screening" approach, which is a metabolomics-based method that is currently applied at the Translational Metabolic Laboratory of the Radboud University Medical Center (the Netherlands) for the diagnosis of inborn errors of metabolism.

Real-road NOx and CO2 emissions of city and highway China-6 heavy-duty diesel vehicles.

The emission of heavy-duty vehicles has raised great concerns worldwide. The complex working and loading conditions, which may differ a lot from PEMS tests, raised new challenges to the supervision and control of emissions, especially during real-world applications. On-board diagnostics (OBD) technology with data exchange enabled and strengthened the monitoring of emissions from a large number of heavy-duty diesel vehicles. This paper presents an analysis of the OBD data collected from more than 800 city and highway heavy-duty vehicles in China using remote OBD data terminals. Real-world NOx and CO2 emissions of China-6 heavy-duty vehicles have been examined. The results showed that city heavy-duty vehicles had higher NOx emission levels, which was mostly due to longer time of low SCR temperatures below 180°C. The application of novel methods based on 3B-MAW also found that heavy-duty diesel vehicles tended to have high NOx emissions at idle. Also, little difference had been found in work-based CO2 emissions, and this may be due to no major difference were found in occupancies of hot running.

Integrating MALDI-TOF Mass Spectrometry with Machine Learning Techniques for Rapid Antimicrobial Resistance Screening of Foodborne Bacterial Pathogens.

Although MALDI-TOF mass spectrometry (MS) is considered as the gold standard for rapid and cost-effective identification of microorganisms in routine laboratory practices, its capability for antimicrobial resistance (AMR) detection has received limited focus. Nevertheless, recent studies explored the predictive performance of MALDI-TOF MS for detecting AMR in clinical pathogens when machine learning techniques are applied. This chapter describes a routine MALDI-TOF MS workflow for the rapid screening of AMR in foodborne pathogens, with Campylobacter spp. as a study model.

Complex In Silico RNA Design with MoiRNAiFold.

In the advent of the RNA therapeutics and diagnostics era, it is of great relevance to introduce new and more efficient RNA technologies that prove to be effective tools in practical contexts. Moreover, it is of utmost importance to develop and provide access to computational tools capable of designing such RNA constructs. Here we introduce one such novel diagnostics technology (Apta-SMART) and show how to design (using MoiRNAiFold) and implement it, step by step. Moreover, we show how to combine this technique with well-known RNA amplification methods and briefly mention some encouraging results.

Aetiologies of bacterial tick-borne febrile illnesses in humans in Africa: diagnostic limitations and the need for improvement.

Tick-borne febrile illnesses caused by pathogens like Anaplasma spp., Bartonella spp., Borrelia spp., Ehrlichia spp., Coxiella burnetii, Francisella tularensis, and Rickettsia spp., are significant health concerns in Africa. The epidemiological occurrence of these pathogens is closely linked to the habitats of their vectors, prevalent in rural and semi-urban areas where humans and livestock coexist. The overlapping clinical presentations, non-specific symptoms, and limited access to commercially available in vitro diagnostics in resource-limited settings exacerbate the complexity of accurate diagnoses. This review aimed to systematically extract and analyze existing literature on tick-borne febrile illnesses in Africa, highlighting the diagnostic challenges and presenting an up-to-date overview of the most relevant pathogens affecting human populations. A comprehensive literature search from January 1990 to June 2024 using databases like PubMed, Cochrane Library, Science Direct, EMBASE, and Google Scholar yielded 13,420 articles, of which 70 met the inclusion criteria. Anaplasma spp. were reported in Morocco, Egypt, and South Africa; Francisella spp. in Kenya and Ethiopia; Ehrlichia spp. in Cameroon; Bartonella spp. in Senegal, Namibia, South Africa, and Ethiopia; Borrelia spp. in Senegal, Gabon, Tanzania, and Ethiopia; Coxiella burnetii in 10 countries including Senegal, Mali, and South Africa; and Rickettsia spp. in 14 countries including Senegal, Algeria, and Uganda. Data were analyzed using a fixed-effect model in R version 4.0.1 and visualized on an African map using Tableau version 2022.2. This review highlights the urgent need for improved diagnostics to better manage and control tick-borne febrile illnesses in Africa.

MRI-based deep learning for differentiating between bipolar and major depressive disorders.

Mood disorders, particularly bipolar disorder (BD) and major depressive disorder (MDD), manifest changes in brain structure that can be detected using structural magnetic resonance imaging (MRI). Although structural MRI is a promising diagnostic tool, prevailing diagnostic criteria for BD and MDD are predominantly subjective, sometimes leading to misdiagnosis. This challenge is compounded by a limited understanding of the underlying causes of these disorders. In response, we present SE-ResNet, a Residual Network (ResNet)-based framework designed to discriminate between BD, MDD, and healthy controls (HC) using structural MRI data. Our approach extends the traditional Squeeze-and-Excitation (SE) layer by incorporating a dedicated branch for spatial attention map generation, equipped with soft-pooling, a 7 × 7 convolution, and a sigmoid function, intended to detect complex spatial patterns. The fusion of channel and spatial attention maps through element-wise addition aims to enhance the model's ability to discriminate features. Unlike conventional methods that use max-pooling for downsampling, our methodology employs soft-pooling, which aims to preserve a richer representation of input features and reduce data loss. When evaluated on a proprietary dataset comprising 303 subjects, the SE-ResNet achieved an accuracy of 85.8 %, a recall of 85.7 %, a precision of 85.9 %, and an F1 score of 85.8 %. These performance metrics suggest that the SE-ResNet framework has potential as a tool for detecting psychiatric disorders using structural MRI data.

One-Pot Assay for Rapid Detection of Stenotrophomonas maltophilia by RPA-CRISPR/Cas12a.

Stenotrophomonas maltophilia (S. maltophilia, SMA) is a common opportunistic pathogen that poses a serious threat to the food industry and human health. Traditional detection methods for SMA are time-consuming, have low detection rates, require complex and expensive equipment and professional technical personnel for operation, and are unsuitable for on-site detection. Therefore, establishing an efficient on-site detection method has great significance in formulating appropriate treatment strategies and ensuring food safety. In the present study, a rapid one-pot detection method was established for SMA using a combination of Recombinase Polymerase Amplification (RPA) and CRISPR/Cas12a, referred to as ORCas12a-SMA (one-pot RPA-CRISPR/Cas12a platform). In the ORCas12a-SMA detection method, all components were added into a single tube simultaneously to achieve one-pot detection and address the problems of nucleic acid cross-contamination and reduced sensitivity caused by frequent cap opening during stepwise detection. The ORCas12a-SMA method could detect at least 3 × 10° copies·µL-1 of SMA genomic DNA within 30 min at 37 °C. Additionally, this method exhibited sensitivity compared to the typical two-step RPA-CRISPR/Cas12a method. Overall, the ORCas12a-SMA detection offered the advantages of rapidity, simplicity, high sensitivity and specificity, and decreased need for complex large-scale instrumentation. This assay is the first application of the one-pot platform based on the combination of RPA and CRISPR/Cas12a in SMA detection and is highly suitable for point-of-care testing. It helps reduce losses in the food industry and provides assistance in formulating timely and appropriate antimicrobial treatment plans.

Recent advances in ctDNA detection using electrochemical biosensor for cancer.

In the quest for early cancer diagnosis, early identification and treatment are paramount. Recently, ctDNA detection has emerged as a viable avenue for early screening of cancer. The examination of ctDNA in fluid biopsies has gained substantial attention in tumor diagnosis and therapy. Both the scientific community and industry are actively exploring this field. However, developing cost-effective, portable, and real-time ctDNA measurement methods using conventional gene detection equipment poses a significant challenge. This challenge has led to the exploration of alternative approaches. Electrochemical biosensors, distinguished by their heightened sensitivity, remarkable specificity, affordability, and excellent portability, have emerged as a promising avenue for ctDNA detection. This review is dedicated to the specific focus on ctDNA detection, highlighting recent advancements in this evolving detection technology. We aimed to reference previous studies related to ctDNA-targeted cancer detection using electrochemical biosensors to advocate the utilization of electrochemical biosensors in healthcare diagnostics. Further research is imperative for the effective integration of ctDNA analysis into point-of-care cancer testing. Innovative approaches utilizing multiple markers need to be explored to advance this technology and make substantial contributions to societal well-being.

Is there an association between salivary immune and microbial profile with dental health in systematically healthy children?

OBJECTIVE: This study aimed to characterize the inflammatory profile of systemically healthy children's saliva and its association with clinical diagnoses of caries and gingival inflammation. MATERIALS AND METHODS: Unstimulated saliva was collected from 100 children before clinical dental examinations. The saliva samples were analyzed for total protein and specific inflammatory cytokines (IL-10, IL-8, IL-6, and TNFα) with Bradford and ELISA assays, respectively. Salivary bacteria were quantified using a quantitative real-time polymerase chain assay. The salivary values were then correlated with age, DMFT index, plaque index (PI), and gingival index (GI). RESULTS: The mean age of the cohort was 8.08 ± 0.23 years with 49% females, the mean DMF of the cohort was 2.64 ± 0.31, the mean GI was 0.51 ± 0.06, and the mean PI was 1.33 ± 0.07. Significant correlations were found between PI with DMFT and GI. Children with DMFT > 2 had significantly higher levels of IL-8 compared with children with DMFT ≤ 2. IL-6 and TNFα were significantly higher among children with PI > 1 than among children with PI ≤ 1. CONCLUSIONS: Salivary cytokine were found to be associate with clinical parameters as DMFT and PI, thus may be a potential tool that reflects dental health status. CLINICAL RELEVANCE: The presence of salivary cytokines in children may reflect evaluation of dental caries and oral inflammation.

Natural outbreak of Mycobacterium caprae infection in imported laboratory cynomolgus macaques (Macaca fascicularis): diagnostic pitfalls and management of safety precautions.

Tuberculosis (TB) is a major health threat for humans and for non-human primates used for toxicology or research purposes. Emerging mycobacterial species represent a major challenge for diagnosis and surveillance programs. Here, we report a natural outbreak of Mycobacterium caprae in imported cynomolgus macaques (Macaca fascicularis) that occurred at AnaPath Research S.A.U. (APR). The macaques underwent repeated negative intradermal tuberculin tests (IDT) before importation and at the European quarantine station. Exhaustive TB screening was started at APR after confirmation of one positive case at another facility. The animal in question belonged to the same colony received at APR. Diagnostic approaches included clinical examination, PCR, culture, spoligotyping, IDT testing, interferon-γ release assay (IGRA), and thoracoabdominal ultrasound (US). Three regulatory toxicity studies and stock animals were affected. The macaques lacked clinical signs, except for one showing a fistulizing nodule in the right inguinal area, which tested positive for the Mycobacterium tuberculosis complex by PCR. All animals were necropsied and 10 macaques (n=114) showed gross and histologic findings compatible with TB confirmed by PCR and culture. M. caprae was identified as the etiological agent by Direct Variable Repeat spacer oligonucleotide typing (DVR spoligotyping). The infection was traced to Asia via the SB1622 spoligotype involved, confirming that the animals were infected prior to their import into Europe. Tuberculin skin test (TST), IGRA, and US were only sensitive in detecting advanced cases of M. caprae infection. One staff member showed a positive TST reaction, which was handled in accordance with the Spanish government's health regulations. All the sanitary measures implemented were effective in eradicating the disease.

Whole-genome sequencing resolves biochemical misidentification of Neisseria species from urogenital specimens.

Neisseria meningitidis (Nm) and Neisseria gonorrhoeae (Ng) are human pathogens that sometimes occupy the same anatomical niche. Ng, the causative agent of gonorrhea, infects 87 million individuals annually worldwide and is an urgent threat due to increasing drug resistance. Ng is a pathogen of the urogenital tract and may infect the oropharyngeal or rectal site, often asymptomatically. Conversely, Nm is an opportunistic pathogen. While often a commensal in the oropharyngeal tract, it is also the leading cause of bacterial meningitis with 1.2 million cases globally, causing significant morbidity and mortality. Horizontal gene transfer (HGT) is likely to occur between Ng and Nm due to their shared anatomical niches and genetic similarity, which poses challenges for accurate detection and treatment. Routine surveillance through the Gonococcal Isolate Surveillance Project and Strengthening the U.S. Response to Resistant Gonorrhea detected six concerning urogenital Neisseria isolates with contradicting species identification in Milwaukee (MIL). While all six isolates were positive for Ng using nucleic acid amplification testing (NAAT) and matrix-assisted laser desorption/ionization time of flight identified the isolates as Ng, two biochemical tests, Gonochek-II and API NH, classified them as Nm. To address this discrepancy, we performed whole-genome sequencing (WGS) using Illumina MiSeq on all isolates and employed various bioinformatics tools. Species detection analysis using BMScan, which uses WGS data, identified all isolates as Ng. Furthermore, Kraken revealed over 98% of WGS reads mapped to the Ng genome and <1% to Nm. Recombination analysis identified putative HGT in all MIL isolates within the γ-glutamyl transpeptidase (ggt) gene, a key component in the biochemical tests used to differentiate between Nm and Ng. Further analysis identified Nm as the source of HGT event. Specifically, the active Nm ggt gene replaced the Ng pseudogenes, ggt1 and ggt2. Together, this study demonstrates that closely related Neisseria species sharing a niche underwent HGT, which led to the misidentification of species following biochemical testing. Importantly, NAAT accurately detected Ng. The misidentification highlights the importance of using WGS to continually evaluate diagnostic or bacterial identification tests.

Advancements in leukemia management: Bridging diagnosis, prognosis and nanotechnology (Review).

Leukemia is a cancer that starts in blood stem cells in the bone marrow. Today, the proper diagnosis and prognosis of leukemia are essential in mitigating the morbidity and mortality associated with this malignancy. The advent of novel biomarkers, particularly those related to minimal residual disease, has paved the way for personalized therapeutic strategies and enables the quantitative assessment of patient responses to treatment regimens. Novel diagnostic and targeted drug delivery may be helpful for the improved management of leukemia. Genetic clinical parameters, such as chromosomal abnormalities, are crucial in diagnosing and guiding treatment decisions. These genetic markers also provide valuable prognostic information, helping to predict patient outcomes and tailor personalized treatment plans. In the present review, the studies on the diagnostic and prognostic parameters of leukemia were analyzed. The prognosis of leukemia was investigated in most of the studies, and the remaining were performed on diagnosis. The clinical and laboratory prognostic parameters were the most common, followed by diagnostic hematological parameters, diagnostic blood parameter studies, and diagnostic immunological parameters. Clinical and laboratory prognostic and hematologic parameters were the most extensively studied. The methods used to diagnose and prognose the leukemia cases in these studies were predominantly clinical hematology. Numerous surface proteins and receptors, including CD45, CD27, CD29, CD38, CD27, CD123, CD56 and CD25, react similarly in various kinds of leukemia, which are ideal for targeted drug delivery. Drug delivery to leukemia cells encounters several significant obstacles, including heterogeneity, that hinder the effectiveness of treatment. Nanocarriers play a critical role in targeted drug delivery for leukemia by enhancing the precision of treatments directed at surface proteins and receptors. Additionally, they can be functionalized with targeting drugs and antibodies to target specific tissues and cells.

Detection of Filariid Infections in Mexican Primate Populations Through qPCR.

Filariae are parasitic nematodes of high veterinary and medical importance, responsible for some acute tropical diseases. They are transmitted through the bite of hematophagous vectors such as biting midges and blackflies. Filariae are among the most prevalent vector-borne parasitoses in Neotropical primates in which severe infections can cause inflammatory reactions and tissue damage. Given the location inside the host (peritoneal cavity, bloodstream, and lymphatics), the detection of filariid nematodes is challenging and is mostly postmortem; hence the scarcity of studies on the prevalence of filariae in wild primate populations. Here, we report the prevalence of filariid infections in free-ranging populations of Geoffroy's spider (Ateles geoffroyi) and black howler (Alouatta pigra) monkeys across southern Mexico, using a combination of noninvasive sampling and molecular diagnostic techniques. Fecal samples were screened for filariid DNA by qPCR protocols. A total of 88 samples were examined with an overall prevalence of 26%. Filariae were slightly more common in spider monkeys compared to howler monkeys. This study constitutes the first report of the prevalence of infection of filariid nematodes in populations of wild spider monkey across southern Mexico, and the first reporting of filariae in black howler monkeys, as part of a new era of primate parasitology and the diagnostics of parasite infections in light of the everyday more affordable molecular tools.

A Double Lysis Method for Animal Rotavirus RNA Extraction From Stool Samples.

Globally, porcine rotavirus is a leading cause of gastroenteritis in nursing and post-weaning piglets, as well as adult pigs. Between February 2015 and June 2016, 156 fecal samples were collected from pigs in the Northeastern part of Accra, Ghana, and screened for Group A rotavirus using the ProflowTM Kit. Here, we describe different extraction methods that were employed to recover high-quality RNA for downstream analysis, with emphasis on a novel hybrid extraction method. The hybrid approach with a kit and manual extraction method led to a 10-fold greater RNA yield versus the kit-based method alone. The new extraction method gave an average purity ratio (A260/A280) of 1.8, which was also significantly higher than that obtained solely from the manual or kit-based extraction methods. Our novel hybrid approach will be useful in the extraction of rotavirus from animal fecal samples, thus improving the yield of RNA for downstream analysis. © 2024 Wiley Periodicals LLC. Basic Protocol: Hybrid 2: A double lysis method for RNA extraction from animal stool samples Support Protocol 1: The GenElute extraction method Support Protocol 2: Hybrid 1 extraction method.

Antibiotic tolerance among clinical isolates: mechanisms, detection, prevalence, and significance.

SUMMARYAntibiotic treatment failures in the absence of resistance are not uncommon. Recently, attention has grown around the phenomenon of antibiotic tolerance, an underappreciated contributor to recalcitrant infections first detected in the 1970s. Tolerance describes the ability of a bacterial population to survive transient exposure to an otherwise lethal concentration of antibiotic without exhibiting resistance. With advances in genomics, we are gaining a better understanding of the molecular mechanisms behind tolerance, and several studies have sought to examine the clinical prevalence of tolerance. Attempts have also been made to assess the clinical significance of tolerance through in vivo infection models and prospective/retrospective clinical studies. Here, we review the data available on the molecular mechanisms, detection, prevalence, and clinical significance of genotypic tolerance that span ~50 years. We discuss the need for standardized methodology and interpretation criteria for tolerance detection and the impact that methodological inconsistencies have on our ability to accurately assess the scale of the problem. In terms of the clinical significance of tolerance, studies suggest that tolerance contributes to worse outcomes for patients (e.g., higher mortality, prolonged hospitalization), but historical data from animal models are varied. Furthermore, we lack the necessary information to effectively treat tolerant infections. Overall, while the tolerance field is gaining much-needed traction, the underlying clinical significance of tolerance that underpins all tolerance research is still far from clear and requires attention.

Postmortem-Derived Exosomal MicroRNA 486-5p as Potential Biomarkers for Ischemic Heart Disease Diagnosis.

Exosomes are nanovesicles 30-150 nm in diameter released extracellularly. Those isolated from human body fluids reflect the characteristics of their cells or tissues of origin. Exosomes carry extensive biological information from their parent cells and have significant potential as biomarkers for disease diagnosis and prognosis. However, there are limited studies utilizing exosomes in postmortem diagnostics. In this study, we extended our initial research which identified the presence and established detection methodologies for exosomes in postmortem fluids. We analyzed exosomal miRNA extracted from plasma and pericardial fluid samples of a control group (n = 13) and subjects with acute myocardial infarction (AMI; n = 24). We employed next-generation sequencing (NGS) to investigate whether this miRNA could serve as biomarkers for coronary atherosclerosis leading to acute myocardial infarction. Our analysis revealed 29 miRNAs that were differentially expressed in the AMI group compared to the control group. Among these, five miRNAs exhibited more than a twofold increase in expression across all samples from the AMI group. Specifically, miR-486-5p levels were significantly elevated in patients with high-grade (type VI or above) atherosclerotic plaques, as per the American Heart Association criteria, highlighting its potential as a predictive biomarker for coronary atherosclerosis progression. Our results indicate that postmortem-derived exosomal microRNAs can serve as potential biomarkers for various human diseases, including cardiovascular disorders. This finding has profound implications for forensic diagnostics, a field critically lacking diagnostic markers.

Assessing Alzheimer's disease via plasma extracellular vesicle-derived mRNA.

INTRODUCTION: Alzheimer's disease (AD), the most prevalent neurodegenerative disorder globally, has emerged as a significant health concern. Recently it has been revealed that extracellular vesicles (EVs) play a critical role in AD pathogenesis and progression. Their stability and presence in various biofluids, such as blood, offer a minimally invasive window for monitoring AD-related changes. METHODS: We analyzed plasma EV-derived messenger RNA (mRNA) from 82 human subjects, including individuals with AD, mild cognitive impairment (MCI), and healthy controls. With next-generation sequencing, we profiled differentially expressed genes (DEGs), identifying those associated with AD. RESULTS: Based on DEGs identified in both the MCI and AD groups, a diagnostic model was established based on machine learning, demonstrating an average diagnostic accuracy of over 98% and showed a strong correlation with different AD stages. DISCUSSION: mRNA derived from plasma EVs shows significant promise as a non-invasive biomarker for the early detection and continuous monitoring of AD. Highlights: The study conducted next-generation sequencing (NGS) of mRNA derived from human plasma extracellular vesicles (EVs) to assess Alzheimer's disease (AD).Profiling of plasma EV-derived mRNA shows a significantly enriched AD pathway, indicating its potential for AD-related studies.The AD-prediction model achieved a receiver-operating characteristic area under the curve (ROC-AUC) of more than 0.98, with strong correlation to the established Clinical Dementia Rating (CDR).

Plasma-derived extracellular vesicles (EVs) as biomarkers of sepsis in burn patients via label-free Raman spectroscopy.

Sepsis following burn trauma is a global complication with high mortality, with ∼60% of burn patient deaths resulting from infectious complications. Diagnosing sepsis is complicated by confounding clinical manifestations of the burn injury, and current biomarkers lack the sensitivity and specificity required for prompt treatment. There is a strong rationale to assess circulating extracellular vesicles (EVs) from patient liquid biopsy as sepsis biomarkers due to their release by pathogens from bacterial biofilms and roles in the subsequent immune response. This study applies Raman spectroscopy to patient plasma-derived EVs for rapid, sensitive, and specific detection of sepsis in burn patients, achieving 97.5% sensitivity and 90.0% specificity. Furthermore, spectral differences between septic and non-septic burn patient EVs could be traced to specific glycoconjugates of bacterial strains associated with sepsis morbidity. This work illustrates the potential application of EVs as biomarkers in clinical burn trauma care and establishes Raman analysis as a fast, label-free method to specifically identify features of bacterial EVs relevant to infection amongst the host background.