Can Bayesian Network empower propensity score estimation from Real World Data?

A new method, based on Bayesian Networks, to estimate propensity scores is proposed with the purpose to draw causal inference from real world data on the average treatment effect in case of a binary outcome and discrete covariates. The proposed method ensures maximum likelihood properties to the estimated propensity score, i.e. asymptotic efficiency, thus outperforming other available approach. Two point estimators via inverse probability weighting are then proposed, and their main distributional properties are derived for constructing confidence interval and for testing the hypotheses of absence of the treatment effect. Empirical evidence of the substantial improvements offered by the proposed methodology versus standard logistic modelling of propensity score is provided in simulation settings that mimic the characteristics of a real dataset of prostate cancer patients from Milan San Raffaele Hospital

Assessment of neural networks training strategies for histomorphometric analysis of synchrotron radiation medical images

Abstract Micro-computed tomography (μCT) obtained by synchrotron radiation (SR) enables magnified images with a high space resolution that might be used as a non-invasive and non-destructive technique for the quantitative analysis of medical images, in particular the histomorphometry (HMM) of bony mass. In the preprocessing of such images, conventional operations such as binarization and morphological filtering are used before calculating the stereological parameters related, for example, to the trabecular bone microarchitecture. However, there is no standardization of methods for HMM based on μCT images, especially the ones obtained with SR X-ray. Notwithstanding the several uses of artificial neural networks (ANNs) in medical imaging, their application to the HMM of SR-μCT medical images is still incipient, despite the potential of both techniques. The contribution of this paper is the assessment and comparison of well-known training algorithms as well as the proposal of training strategies (combinations of training algorithms, sub-image kernel and symmetry information) for feed-forward ANNs in the task of bone pixels recognition in SR-μCT medical images. For a quantitative comparison, the results of a cross validation and a statistical analysis of the results for 36 training strategies are presented. The ANNs demonstrated both very low mean square errors in the validation, and good quality segmentation of the image of interest for application to HMM in SR-μCT medical images

a new clinicobiological scoring system for the prediction of infection related mortality and survival after allogeneic hematopoietic stem cell transplantation

Abstract Infection-related mortality (IRM) is a substantial component of nonrelapse mortality (NRM) after allogeneic hematopoietic stem cell transplantation (allo-HSCT). No scores have been developed to predict IRM before transplantation. Pretransplantation clinical and biochemical data were collected from a study cohort of 607 adult patients undergoing allo-HSCT between January 2009 and February 2017. In a training set of 273 patients, multivariate analysis revealed that age >60 years ( P  = .003), cytomegalovirus host/donor serostatus different from negative/negative ( P P  = .004), and pretransplantation IgM level P  = .028) were independent predictors of increased IRM. Based on these results, we developed and subsequently validated a 3-tiered weighted prognostic index for IRM in a retrospective set of patients (n = 219) and a prospective set of patients (n = 115). Patients were assigned to 3 different IRM risk classes based on this index score. The score significantly predicted IRM in the training set, retrospective validation set, and prospective validation set ( P P P

Abnormal neutrophil signature in the blood and pancreas of presymptomatic and symptomatic type 1 diabetes

BACKGROUND. Neutrophils and their inflammatory mediators are key pathogenic components in multiple autoimmune diseases, while their role in human type 1 diabetes (T1D), a disease that progresses sequentially through identifiable stages prior to the clinical onset, is not well understood. We previously reported that the number of circulating neutrophils is reduced in patients with T1D and in presymptomatic at-risk subjects. The aim of the present work was to identify possible changes in circulating and pancreas-residing neutrophils throughout the disease course to better elucidate neutrophil involvement in human T1D. METHODS. Data collected from 389 subjects at risk of developing T1D, and enrolled in 4 distinct studies performed by TrialNet, were analyzed with comprehensive statistical approaches to determine whether the number of circulating neutrophils correlates with pancreas function. To obtain a broad analysis of pancreas-infiltrating neutrophils throughout all disease stages, pancreas sections collected worldwide from 4 different cohorts (i.e., nPOD, DiViD, Siena, and Exeter) were analyzed by immunohistochemistry and immunofluorescence. Finally, circulating neutrophils were purified from unrelated nondiabetic subjects and donors at various T1D stages and their transcriptomic signature was determined by RNA sequencing. RESULTS. Here, we show that the decline in β cell function is greatest in individuals with the lowest peripheral neutrophil numbers. Neutrophils infiltrate the pancreas prior to the onset of symptoms and they continue to do so as the disease progresses. Of interest, a fraction of these pancreasinfiltrating neutrophils also extrudes neutrophil extracellular traps (NETs), suggesting a tissue-specific pathogenic role. Whole-transcriptome analysis of purified blood neutrophils revealed a unique molecular signature that is distinguished by an overabundance of IFN-associated genes; despite being healthy, said signature is already present in T1D-autoantibody-negative at-risk subjects. CONCLUSIONS. These results reveal an unexpected abnormality in neutrophil disposition both in the circulation and in the pancreas of presymptomatic and symptomatic T1D subjects, implying that targeting neutrophils might represent a previously unrecognized therapeutic modality

The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis.

Background: Molecular diagnostics are considered the most promising route to achievement of rapid, universal drug susceptibility testing for Mycobacterium tuberculosis complex (MTBC). We aimed to generate a WHO-endorsed catalogue of mutations to serve as a global standard for interpreting molecular information for drug resistance prediction. Methods: In this systematic analysis, we used a candidate gene approach to identify mutations associated with resistance or consistent with susceptibility for 13 WHO-endorsed antituberculosis drugs. We collected existing worldwide MTBC whole-genome sequencing data and phenotypic data from academic groups and consortia, reference laboratories, public health organisations, and published literature. We categorised phenotypes as follows: methods and critical concentrations currently endorsed by WHO (category 1); critical concentrations previously endorsed by WHO for those methods (category 2); methods or critical concentrations not currently endorsed by WHO (category 3). For each mutation, we used a contingency table of binary phenotypes and presence or absence of the mutation to compute positive predictive value, and we used Fisher's exact tests to generate odds ratios and Benjamini-Hochberg corrected p values. Mutations were graded as associated with resistance if present in at least five isolates, if the odds ratio was more than 1 with a statistically significant corrected p value, and if the lower bound of the 95% CI on the positive predictive value for phenotypic resistance was greater than 25%. A series of expert rules were applied for final confidence grading of each mutation. Findings: We analysed 41 137 MTBC isolates with phenotypic and whole-genome sequencing data from 45 countries. 38 215 MTBC isolates passed quality control steps and were included in the final analysis. 15 667 associations were computed for 13 211 unique mutations linked to one or more drugs. 1149 (7·3%) of 15 667 mutations were classified as associated with phenotypic resistance and 107 (0·7%) were deemed consistent with susceptibility. For rifampicin, isoniazid, ethambutol, fluoroquinolones, and streptomycin, the mutations' pooled sensitivity was more than 80%. Specificity was over 95% for all drugs except ethionamide (91·4%), moxifloxacin (91·6%) and ethambutol (93·3%). Only two resistance mutations were identified for bedaquiline, delamanid, clofazimine, and linezolid as prevalence of phenotypic resistance was low for these drugs. Interpretation: We present the first WHO-endorsed catalogue of molecular targets for MTBC drug susceptibility testing, which is intended to provide a global standard for resistance interpretation. The existence of this catalogue should encourage the implementation of molecular diagnostics by national tuberculosis programmes. Funding: Unitaid, Wellcome Trust, UK Medical Research Council, and Bill and Melinda Gates Foundation

The size of well differentiated pancreatic neuroendocrine tumors correlates with Ki67 proliferative index and is not associated with age

Background: Concerns exist about a conservative management of well-differentiated nonfunctioning small pancreatic neuroendocrine tumors (NF-PanNET) in young patients and when preoperative Ki67 proliferative index is >= 3%.Aim: To evaluate an association between age, tumor size and grading in patients with sporadic NF-PanNET who underwent curative resection.Methods: Patients who underwent surgery for sporadic NF-PanNET (excluding G3) were retrospectively analyzed. Linear regression analysis was performed to evaluate a possible correlation between continuous variables, whereas multiple logistic regression analysis was performed for determining predictors of NF-PanNET-G2.Results: Overall, 235 patients with NF-PanNET-G1/G2 were included. The median largest radiological diameter was 25 mm. Age correlated neither with tumor size (P = 0.675) nor with Ki67 index (P = 0.376). On multivariate linear regression analysis, factors independently associated with Ki67 index were NF-PanNET size (P = 0.031), perineural invasion (P = 0.004), microvascular invasion (P = 0.001) and necrosis (P = 0.009). The most accurate NF-PanNET size for predicting NF-PanNET-G2 was 25 mm. On multivariate analysis, a NF-PanNET size >25 mm was independently associated with the risk of having a PanNET-G2 (P = 0.025).Conclusion: No correlations exist between age and NF-PanNET size or proliferative index. Therefore, an a priori aggressive attitude is not justified in young patients with small NF-PanNET, as a long-life expectancy is probably unlikely to increase the risk of malignant transformation. (C) 2019 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved

Ten Year Results of Extensive Nodal Radiotherapy and Moderately Hypofractionated Simultaneous Integrated Boost in Unfavorable Intermediate-, High-, and Very High-Risk Prostate Cancer

Aims: To report 10-year outcomes of WPRT and HD moderately hypofractionated SIB to the prostate in UIR, HR, and VHR PCa. Methods: From 11/2005 to 12/2015, 224 UIR, HR, and VHR PCa patients underwent WPRT at 51.8 Gy/28 fractions and SIB at 74.2 Gy (EQD2 88 Gy) to the prostate. Androgen deprivation therapy (ADT) was prescribed in up to 86.2% of patients. Results: Median follow-up was 96.3 months (IQR: 71–124.7). Median age was 75 years (IQR: 71.3–78.1). At last follow up, G3 GI–GU toxicity was 3.1% and 8%, respectively. Ten-year biochemical relapse-free survival (bRFS) was 79.8% (95% CI: 72.3–88.1%), disease-free survival (DFS) 87.8% (95% CI: 81.7–94.3%), overall survival (OS) 65.7% (95% CI: 58.2–74.1%), and prostate cancer-specific survival (PCSS) 94.9% (95% CI: 91.0–99.0%). Only two patients presented local relapse. At univariate analysis, VHR vs. UIR was found to be a significant risk factor for biochemical relapse (HR: 2.8, 95% CI: 1.17–6.67, p = 0.021). After model selection, only Gleason Score ≥ 8 emerged as a significant factor for biochemical relapse (HR = 2.3, 95% CI: 1.12–4.9, p = 0.023). Previous TURP (HR = 3.5, 95% CI: 1.62–7.54, p = 0.001) and acute toxicity ≥ G2 (HR = 3.1, 95% CI = 1.45–6.52, p = 0.003) were significant risk factors for GU toxicity ≥ G3. Hypertension was a significant factor for GI toxicity ≥ G3 (HR = 3.63, 95% CI: 1.06–12.46, p = 0.041). ADT (HR = 0.31, 95% CI: 0.12–0.8, p = 0.015) and iPsa (HR = 0.37, 95% CI: 0.16–0.83, p = 0.0164) played a protective role. Conclusions: WPRT and HD SIB to the prostate combined with long-term ADT, in HR PCa, determine good outcomes with acceptable toxicity

Modeling, optimization, and comparable efficacy of T cell and hematopoietic stem cell gene editing for treating hyper‐IgM syndrome

Abstract Precise correction of the CD40LG gene in T cells and hematopoietic stem/progenitor cells (HSPC) holds promise for treating X‐linked hyper‐IgM Syndrome (HIGM1), but its actual therapeutic potential remains elusive. Here, we developed a one‐size‐fits‐all editing strategy for effective T‐cell correction, selection, and depletion and investigated the therapeutic potential of T‐cell and HSPC therapies in the HIGM1 mouse model. Edited patients’ derived CD4 T cells restored physiologically regulated CD40L expression and contact‐dependent B‐cell helper function. Adoptive transfer of wild‐type T cells into conditioned HIGM1 mice rescued antigen‐specific IgG responses and protected mice from a disease‐relevant pathogen. We then obtained ~ 25% CD40LG editing in long‐term repopulating human HSPC. Transplanting such proportion of wild‐type HSPC in HIGM1 mice rescued immune functions similarly to T‐cell therapy. Overall, our findings suggest that autologous edited T cells can provide immediate and substantial benefits to HIGM1 patients and position T‐cell ahead of HSPC gene therapy because of easier translation, lower safety concerns and potentially comparable clinical benefits