Analysing complex datasets while maintaining the interpretability and explainability of outcomes for clinicians and patients is challenging, not only in viral infections. These datasets often include a variety of heterogeneous clinical, demographic, laboratory, and personal data, and it is not a single factor but a combination of multiple factors that contribute to patient characterisation and host response. Therefore, multivariate approaches are needed to analyse these complex patient datasets, which are impossible to analyse with univariate comparisons (e.g., one immune cell subset versus one clinical factor). Using a SARS-CoV-2 infection as an example, we employed a patient similarity network (PSN) approach to assess the relationship between host immune factors and the clinical course of infection and performed visualisation and data interpretation. A PSN analysis of ~85 immunological (cellular and humoral) and ~70 clinical factors in 250 recruited patients with coronavirus disease (COVID-19) who were sampled four to eight weeks after a PCR-confirmed SARS-CoV-2 infection identified a minimal immune signature, as well as clinical and laboratory factors strongly associated with disease severity. Our study demonstrates the benefits of implementing multivariate network approaches to identify relevant factors and visualise their relationships in a SARS-CoV-2 infection, but the model is generally applicable to any complex dataset.

• Keywords
• COVID-19 severity, IgM and IgG levels, data visualisation, minimal immune signature, multivariate data analysis, patient similarity network,
• MeSH
• COVID-19 * MeSH
• Humans MeSH
• Antibodies, Viral MeSH
• SARS-CoV-2 * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Antibodies, Viral MeSH

Despite the shared pattern of surface antigens, neoplastic cells in chronic lymphocytic leukemia (CLL) are highly heterogeneous in CD5 expression, a marker linked to a proliferative pool of neoplastic cells. To further characterize CD5high and CD5low neoplastic cells, we assessed the chemokine receptors (CCR5, CCR7, CCR10, CXCR3, CXCR4, CXCR5) and adhesion molecules (CD54, CD62L, CD49d) on the CD5high and CD5low subpopulations, defined by CD5/CD19 coexpression, in peripheral blood of CLL patients (n = 60) subgrouped according to the IgHV mutational status (IgHV mut, n = 24; IgHV unmut, n = 36). CD5high subpopulation showed a high percentage of CXCR3 (P < 0.001), CCR10 (P = 0.001), and CD62L (P = 0.031) and high levels of CXCR5 (P = 0.005), CCR7 (P = 0.013) compared to CD5low cells expressing high CXCR4 (P < 0.001). Comparing IgHV mut and IgHV unmut patients, high levels of CXCR3 on CD5high and CD5low subpopulations were detected in the IgHV mut patients, with better discrimination in CD5low subpopulation. Levels of CXCR3 on CD5low subpopulation were associated with time to the next treatment, thus further confirming its prognostic value. Taken together, our analysis revealed higher CXCR3 expression on both CD5high and CD5low neoplastic cells in IgHV mut with a better prognosis compared to IgHV unmut patients. Contribution of CXCR3 to CLL pathophysiology and its suitability for prognostication and therapeutic exploitation deserves future investigations.

• MeSH
• CD5 Antigens metabolism   MeSH
• Biomarkers MeSH
• Chemotaxis immunology   MeSH
• Leukemia, Lymphocytic, Chronic, B-Cell diagnosis   genetics   metabolism   therapy   MeSH
• Immunophenotyping MeSH
• Kaplan-Meier Estimate MeSH
• Middle Aged MeSH
• Humans MeSH
• Cell Adhesion Molecules metabolism   MeSH
• Mutation * MeSH
• Cell Movement MeSH
• Prognosis MeSH
• Receptors, Chemokine metabolism   MeSH
• Receptors, CXCR3 genetics   metabolism   MeSH
• ROC Curve MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Immunoglobulin Heavy Chains genetics   MeSH
• Immunoglobulin Variable Region genetics   MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• CD5 Antigens MeSH
• Biomarkers MeSH
• CXCR3 protein, human MeSH Browser
• Cell Adhesion Molecules MeSH
• Receptors, Chemokine MeSH
• Receptors, CXCR3 MeSH
• Immunoglobulin Heavy Chains MeSH
• Immunoglobulin Variable Region MeSH

The insufficient standardization of diagnostic next-generation sequencing (NGS) still limits its implementation in clinical practice, with the correct detection of mutations at low variant allele frequencies (VAF) facing particular challenges. We address here the standardization of sequencing coverage depth in order to minimize the probability of false positive and false negative results, the latter being underestimated in clinical NGS. There is currently no consensus on the minimum coverage depth, and so each laboratory has to set its own parameters. To assist laboratories with the determination of the minimum coverage parameters, we provide here a user-friendly coverage calculator. Using the sequencing error only, we recommend a minimum depth of coverage of 1,650 together with a threshold of at least 30 mutated reads for a targeted NGS mutation analysis of ≥3% VAF, based on the binomial probability distribution. Moreover, our calculator also allows adding assay-specific errors occurring during DNA processing and library preparation, thus calculating with an overall error of a specific NGS assay. The estimation of correct coverage depth is recommended as a starting point when assessing thresholds of NGS assay. Our study also points to the need for guidance regarding the minimum technical requirements, which based on our experience should include the limit of detection (LOD), overall NGS assay error, input, source and quality of DNA, coverage depth, number of variant supporting reads, and total number of target reads covering variant region. Further studies are needed to define the minimum technical requirements and its reporting in diagnostic NGS.

• Keywords
• TP53 gene, coverage depth calculator, next-generation sequencing, sequencing error, small subclones, variant allele frequency (VAF),
• Publication type
• Journal Article MeSH

OBJECTIVE: Aseptic loosening (AL) is the most frequent long-term reason for revision of total knee arthroplasty (TKA) affecting about 15-20% patients within 20 years after the surgery. Although there is a solid body of evidence about the crucial role of inflammation in the AL pathogenesis, scared information on inflammation signature and its time-axis in tissues around TKA exists. DESIGN: The inflammation protein signatures in pseudosynovial tissues collected at revision surgery from patients with AL (AL, n = 12) and those with no clinical/radiographic signs of AL (non-AL, n = 9) were investigated by Proximity Extension Assay (PEA)-Immunoassay and immunohistochemistry. RESULTS: AL tissues had elevated levels of TNF-family members sTNFR2, TNFSF14, sFasL, sBAFF, cytokines/chemokines IL8, CCL2, IL1RA/IL36, sIL6R, and growth factors sAREG, CSF1, comparing to non-AL. High interindividual variability in protein levels was evident particularly in non-AL. Levels of sTNFR2, sBAFF, IL8, sIL6R, and MPO discriminated between AL and non-AL and were associated with the time from index surgery, suggesting the cumulative character of inflammatory osteolytic response to prosthetic byproducts. The source of elevated inflammatory molecules was macrophages and multinucleated osteoclast-like cells in AL and histiocytes and osteoclast-like cells in non-AL tissues, respectively. All proteins were present in higher levels in osteoclast-like cells than in macrophages. CONCLUSIONS: Our study revealed a differential inflammation signature between AL and non-AL stages of TKA. It also highlighted the unique patient's response to TKA in non-AL stages. Further confirmation of our preliminary results on a larger cohort is needed. Analysis of the time-axis of processes ongoing around TKA implantation may help to understand the mechanisms driving periprosthetic bone resorption needed for diagnostic/preventative strategies.

• MeSH
• Cytokines metabolism   MeSH
• Histiocytes metabolism   pathology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Macrophages metabolism   pathology   MeSH
• Osteoclasts metabolism   pathology   MeSH
• Reoperation MeSH
• Bone Resorption complications   metabolism   physiopathology   surgery   MeSH
• Prosthesis Failure adverse effects   MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Arthroplasty, Replacement, Knee adverse effects   MeSH
• Inflammation complications   metabolism   physiopathology   surgery   MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Cytokines MeSH