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Background Accurate, scalable and sensitive diagnostic tools are crucial in determining prevalence of soil-transmitted helminths (STH), assessing infection intensities and monitoring treatment efficacy. However, assessments on treatment efficacy comparing traditional microscopic to newly emerging molecular approaches such as quantitative Polymerase Chain Reaction (qPCR) are scarce and hampered partly by lack of an established diagnostic gold standard. Methods We compared the performance of the copromicroscopic Kato-Katz method to qPCR in the framework of a randomized controlled trial on Pemba Island, Tanzania, evaluating treatment efficacy based on cure rates of albendazole monotherapy versus ivermectin-albendazole against Trichuris trichiura and concomitant STH infections. Day-to-day variability of both diagnostic methods was assessed to elucidate reproducibility of test results by analysing two stool samples before and two stool samples after treatment of 160 T. trichiura Kato-Katz positive participants, partially co-infected with Ascaris lumbricoides and hookworm, per treatment arm ( n  = 320). As negative controls, two faecal samples of 180 Kato-Katz helminth negative participants were analysed. Results Fair to moderate correlation between microscopic egg count and DNA copy number for the different STH species was observed at baseline and follow-up. Results indicated higher sensitivity of qPCR for all three STH species across all time points; however, we found lower test result reproducibility compared to Kato-Katz. When assessed with two samples from consecutive days by qPCR, cure rates were significantly lower for T. trichiura (23.2 vs 46.8%), A. lumbricoides (75.3 vs 100%) and hookworm (52.4 vs 78.3%) in the ivermectin-albendazole treatment arm, when compared to Kato-Katz. Conclusions qPCR diagnosis showed lower reproducibility of test results compared to Kato-Katz, hence multiple samples per participant should be analysed to achieve a reliable diagnosis of STH infection. Our study confirms that cure rates are overestimated using Kato-Katz alone. Our findings emphasize that standardized and accurate molecular diagnostic tools are urgently needed for future monitoring within STH control and/or elimination programmes.

Machine learning techniques have great potential to improve medical diagnostics, offering ways to improve accuracy, reproducibility and speed, and to ease workloads for clinicians. In the field of histopathology, deep learning algorithms have been developed that perform similarly to trained pathologists for tasks such as tumor detection and grading. However, despite these promising results, very few algorithms have reached clinical implementation, challenging the balance between hope and hype for these new techniques. This Review provides an overview of the current state of the field, as well as describing the challenges that still need to be addressed before artificial intelligence in histopathology can achieve clinical value. Recent advances in machine learning techniques have created opportunities to improve medical diagnostics, but implementing these advances in the clinic will not be without challenge.

- In 2013, an evidence-based guideline was published by the College of American Pathologists, the International Association for the Study of Lung Cancer, and the Association for Molecular Pathology to set standards for the molecular analysis of lung cancers to guide treatment decisions with targeted inhibitors. New evidence has prompted an evaluation of additional laboratory technologies, targetable genes, patient populations, and tumor types for testing. - To systematically review and update the 2013 guideline to affirm its validity; to assess the evidence of new genetic discoveries, technologies, and therapies; and to issue an evidence-based update. - The College of American Pathologists, the International Association for the Study of Lung Cancer, and the Association for Molecular Pathology convened an expert panel to develop an evidence-based guideline to help define the key questions and literature search terms, review abstracts and full articles, and draft recommendations. - Eighteen new recommendations were drafted. The panel also updated 3 recommendations from the 2013 guideline. - The 2013 guideline was largely reaffirmed with updated recommendations to allow testing of cytology samples, require improved assay sensitivity, and recommend against the use of immunohistochemistry for EGFR testing. Key new recommendations include ROS1 testing for all adenocarcinoma patients; the inclusion of additional genes ( ERBB2, MET, BRAF, KRAS, and RET) for laboratories that perform next-generation sequencing panels; immunohistochemistry as an alternative to fluorescence in situ hybridization for ALK and/or ROS1 testing; use of 5% sensitivity assays for EGFR T790M mutations in patients with secondary resistance to EGFR inhibitors; and the use of cell-free DNA to \"rule in\" targetable mutations when tissue is limited or hard to obtain.

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org). Malignant tumors do not only contain cancer cells. Normal cells from the body also infiltrate tumors. These often include a variety of immune cells that can help detect and kill cancer cells. Many evidences suggest that the proportion of different immune cell types in a tumor can affect tumor growth and which treatments are effective. Researchers often study tumors by measuring the expression of genes, i.e., which genes are active in tumors. However, the proportion of different cell types in the tumor is often not measured for tumors studied at the gene expression level. Racle et al. have now demonstrated that a new computer-based tool can accurately detect all the main cell types in a tumor directly from the expression of genes in this tumor. The tool is called “Estimating the Proportion of Immune and Cancer cells” – or EPIC for short. It compares the level of expression of genes in a tumor with a library of the gene expression profiles from specific cell types that can be found in tumors and uses this information to predict how many of each type of cell are present. Experimental measurements of several human tumors confirmed that EPIC’s predictions are accurate. EPIC is freely available online. Since the active genes in tumors from many patients have already been documented together with clinical data, researchers could use EPIC to investigate whether the cell types in a tumor affect how harmful it is or how well a particular treatment works on it. In the future, this information could help to identify the best treatment for a particular patient and may reveal new genes that cause malignant tumors to develop and grow.

CRISPR-based nucleic acid detection assays offer a potent and dependable tool in modern nucleic acid detection, owing to their high specificity, rapidity, sensitivity, ease of use, and broad applicability.The combination of the CRISPR system and nucleic acid amplification technology has transformed the modern biomedical landscape, markedly augmenting the specificity of nucleic acid amplification, thereby advancing the field of precision medicine.The one-pot nucleic acid detection assay based on the CRISPR system realizes the integration of nucleic acid amplification and CRISPR detection into a single reaction tube, which not only simplifies the experimental operations but also significantly reduces the risk of aerosol contamination. The integration of nucleic acid amplification (NAA) with the CRISPR detection system has led to significant advancements and opportunities for development in molecular diagnostics. Nevertheless, the incompatibility between CRISPR cleavage and NAA has significantly impeded the commercialization of this technology. Currently, several one-pot detection strategies based on CRISPR systems have been devised to address concerns regarding aerosol contamination risk and operational complexity associated with step-by-step detection as well as the sensitivity limitation of conventional one-pot methods. In this review, we provide a comprehensive introduction and outlook of the various solutions of the one-pot CRISPR assay for practitioners who are committed to developing better CRISPR nucleic acid detection technologies to promote the progress of molecular diagnostics. The integration of nucleic acid amplification (NAA) with the CRISPR detection system has led to significant advancements and opportunities for development in molecular diagnostics. Nevertheless, the incompatibility between CRISPR cleavage and NAA has significantly impeded the commercialization of this technology. Currently, several one-pot detection strategies based on CRISPR systems have been devised to address concerns regarding aerosol contamination risk and operational complexity associated with step-by-step detection as well as the sensitivity limitation of conventional one-pot methods. In this review, we provide a comprehensive introduction and outlook of the various solutions of the one-pot CRISPR assay for practitioners who are committed to developing better CRISPR nucleic acid detection technologies to promote the progress of molecular diagnostics.

Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution. Cell- and tissue-type model outputs are combined into 607 HIFs that quantify specific and biologically-relevant characteristics across five cancer types. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment and can predict diverse molecular signatures (AUROC 0.601–0.864), including expression of four immune checkpoint proteins and homologous recombination deficiency, with performance comparable to ‘black-box’ methods. Our HIF-based approach provides a comprehensive, quantitative, and interpretable window into the composition and spatial architecture of the tumor microenvironment. Computational methods have made progress in improving classification accuracy and throughput of pathology workflows, but lack of interpretability remains a barrier to clinical integration. Here, the authors present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features.

The incidence of opportunistic yeast infections in humans has been increasing over recent years. These infections are difficult to treat and diagnose, in part due to the large number and broad diversity of species that can underlie the infection. In addition, resistance to one or several antifungal drugs in infecting strains is increasingly being reported, severely limiting therapeutic options and showcasing the need for rapid detection of the infecting agent and its drug susceptibility profile. Current methods for species and resistance identification lack satisfactory sensitivity and specificity, and often require prior culturing of the infecting agent, which delays diagnosis. Recently developed high-throughput technologies such as next generation sequencing or proteomics are opening completely new avenues for more sensitive, accurate and fast diagnosis of yeast pathogens. These approaches are the focus of intensive research, but translation into the clinics requires overcoming important challenges. In this review, we provide an overview of existing and recently emerged approaches that can be used in the identification of yeast pathogens and their drug resistance profiles. Throughout the text we highlight the advantages and disadvantages of each methodology and discuss the most promising developments in their path from bench to bedside.

Aptamers are short single stranded DNA or RNA oligonucleotides that can recognize analytes with extraordinary target selectivity and affinity. Despite their promising properties and diagnostic potential, the number of commercial applications remains scarce. In order to endow them with novel recognition motifs and enhanced properties, chemical modification of aptamers has been pursued. This review focuses on chemical modifications, aimed at increasing the binding affinity for the aptamer’s target either in a non-covalent or covalent fashion, hereby improving their application potential in a diagnostic context. An overview of current methodologies will be given, thereby distinguishing between pre- and post-SELEX (Systematic Evolution of Ligands by Exponential Enrichment) modifications.

Background. Blood culture-negative endocarditis (BCNE) may account for up to 31% of all cases of endocarditis. Methods. We used a prospective, multimodal strategy incorporating serological, molecular, and histopathological assays to investigate specimens from 819 patients suspected of having BCNE. Results. Diagnosis of endocarditis was first ruled out for 60 patients. Among 759 patients with BCNE, a causative microorganism was identified in 62.7%, and a noninfective etiology in 2.5%. Blood was the most useful specimen, providing a diagnosis for 47.7% of patients by serological analysis (mainly Q fever and Bartonella infections). Broad-range polymerase chain reaction (PCR) of blood and Bartonella-specific Western blot methods diagnosed 7 additional cases. PCR of valvular biopsies identified 109 more etiologies, mostly streptococci, Tropheryma whipplei, Bartonella species, and fungi. Primer extension enrichment reaction and autoimmunohistochemistry identified a microorganism in 5 additional patients. No virus or Chlamydia species were detected. A noninfective cause of endocarditis, particularly neoplasic or autoimmune disease, was determined by histological analysis or by searching for antinuclear antibodies in 19 (2.5%) of the patients. Our diagnostic strategy proved useful and sensitive for BCNE workup. Conclusions. We highlight the major role of zoonotic agents and the underestimated role of noninfective diseases in BCNE. We propose serological analysis for Coxiella burnetii and Bartonella species, detection of antinuclear antibodies and rheumatoid factor as first-line tests, followed by specific PCR assays for T. whipplei, Bartonella species, and fungi in blood. Broad-spectrum 16S and 18S ribosomal RNA PCR may be performed on valvular biopsies, when available.

Parkinson’s disease (PD), Parkinson’s disease with dementia (PDD) and dementia with Lewy bodies (DLB) are three clinically, genetically and neuropathologically overlapping neurodegenerative diseases collectively known as the Lewy body diseases (LBDs). A variety of molecular mechanisms have been implicated in PD pathogenesis, but the mechanisms underlying PDD and DLB remain largely unknown, a knowledge gap that presents an impediment to the discovery of disease-modifying therapies. Transcriptomic profiling can contribute to addressing this gap, but remains limited in the LBDs. Here, we applied paired bulk-tissue and single-nucleus RNA-sequencing to anterior cingulate cortex samples derived from 28 individuals, including healthy controls, PD, PDD and DLB cases (n = 7 per group), to transcriptomically profile the LBDs. Using this approach, we (i) found transcriptional alterations in multiple cell types across the LBDs; (ii) discovered evidence for widespread dysregulation of RNA splicing, particularly in PDD and DLB; (iii) identified potential splicing factors, with links to other dementia-related neurodegenerative diseases, coordinating this dysregulation; and (iv) identified transcriptomic commonalities and distinctions between the LBDs that inform understanding of the relationships between these three clinical disorders. Together, these findings have important implications for the design of RNA-targeted therapies for these diseases and highlight a potential molecular “window” of therapeutic opportunity between the initial onset of PD and subsequent development of Lewy body dementia.