A prospective study comparing quantitative Cytomegalovirus (CMV) polymerase chain reaction in plasma and pp65 antigenemia assay in monitoring patients after allogeneic stem cell transplantation

BACKGROUND: Low levels of Cytomegalovirus (CMV) viral load are frequently detected following allogeneic stem cell transplantation (SCT) and CMV disease may still develop in some allogeneic SCT patients who have negative pp65-antigenemia (pp65-Ag) or undetectable DNA. Pp65Ag is a sensitive method to diagnose CMV infection. Quantitative CMV-DNA PCR assay in plasma has been proposed to monitor CMV infection in SCT patients. We evaluated the clinical utility of pp65Ag and PCR assay in plasma of SCT recipients. METHODS: In a prospective longitudinal study, 38 consecutive patients at risk of CMV infection (donor and/or recipient CMV seropositive) were weekly monitored for CMV infection by both quantitative CMV-PCR in plasma (COBAS AMPLICOR CMV MONITOR) and pp65 Ag, during the first 100 days after SCT. RESULTS: A total of 534 blood samples were simultaneously analysed for pp65Ag and PCR. Overall, 28/38 patients (74%) had active CMV infection within 100 days from SCT. In 16 patients, CMV was first detected by pp65 Ag alone; in 5 patients by both methods and in 6 by PCR assay alone; one patient had CMV biopsy-proven intestinal disease without pp65Ag and PCR assays positivity before CMV disease. Overall, three patients developed intestinal CMV disease (7.9%): one had negative both pp65Ag and PCR assays before CMV disease, one had disease and concomitant positivity of both methods, while in the remaining patient, only pp65Ag was positive before CMV disease. CONCLUSION: Plasma PCR(COBAS AMPLICOR CMV MONITOR) and pp65Ag assays were effective in detecting CMV infection, however, discordance between both methods were frequently observed. Plasma PCR and pp65Ag assays may be complementary for diagnosis and management of CMV infection

[Membranous glomerulonephritis secondary to allogeneic stem cell transplant: review of the literature]

: Renal injury associated with hematopoietic stem cell transplant (HSCT) may be related to a combination of factors. Chronic graft-versus-host disease (cGVHD) is the most common complication of allogeneic HSCT. Although the kidneys are not considered the primary target organs for GVHD, chronic impairment of renal function may occur in 20% to 60% of HSCT patients. Membranous glomerulonephritis (MG) is the most frequent renal complication observed in patients who develop nephrotic syndrome after allogeneic HSCT. In this setting, the pathogenesis of MG is not clearly understood and the most appropriate treatment approach has not been established. In order to summarize the current knowledge on this issue, a review of the pertinent literature has been performed. The available data on MG diagnosed in patients submitted to allogeneic HSCT were identified using the MEDLINE database (last accessed: Jan 30, 2012). Fifty-nine patients with allogeneic HSCT-related MG with a median age of 43 years were identified. MG occurred at a median time of 17 months after allogeneic HSCT. A history of acute or concomitant clinically apparent cGVHD was present in 69% and 31% of cases, respectively. cGVHD, nonmyeloablative conditioning regimens, immunosuppression withdrawal, and the use of peripheral blood stem cell grafts were identified as risk factors. Among the 53 patients with available outcome data, complete remission, partial response, and inefficacy of treatment were recorded in 65%, 22% and 13% of cases, respectively. MG after allogeneic HSCT seems to be etiologically related to subclinical or overt cGVHD, which flares up after discontinuation of immunosuppression. The available measures can induce sustained long-term remission in about two-thirds of affected patients

Radiomics Analysis on Contrast-Enhanced Spectral Mammography Images for Breast Cancer Diagnosis: A Pilot Study

Contrast-enhanced spectral mammography is one of the latest diagnostic tool for breast care; therefore, the literature is poor in radiomics image analysis useful to drive the development of automatic diagnostic support systems. In this work, we propose a preliminary exploratory analysis to evaluate the impact of different sets of textural features in the discrimination of benign and malignant breast lesions. The analysis is performed on 55 ROIs extracted from 51 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) from the breast cancer screening phase between March 2017 and June 2018. We extracted feature sets by calculating statistical measures on original ROIs, gradiented images, Haar decompositions of the same original ROIs, and on gray-level co-occurrence matrices of the each sub-ROI obtained by Haar transform. First, we evaluated the overall impact of each feature set on the diagnosis through a principal component analysis by training a support vector machine classifier. Then, in order to identify a sub-set for each set of features with higher diagnostic power, we developed a feature importance analysis by means of wrapper and embedded methods. Finally, we trained an SVM classifier on each sub-set of previously selected features to compare their classification performances with respect to those of the overall set. We found a sub-set of significant features extracted from the original ROIs with a diagnostic accuracy greater than 80 % . The features extracted from each sub-ROI decomposed by two levels of Haar transform were predictive only when they were all used without any selection, reaching the best mean accuracy of about 80 % . Moreover, most of the significant features calculated by HAAR decompositions and their GLCMs were extracted from recombined CESM images. Our pilot study suggested that textural features could provide complementary information about the characterization of breast lesions. In particular, we found a sub-set of significant features extracted from the original ROIs, gradiented ROI images, and GLCMs calculated from each sub-ROI previously decomposed by the Haar transform

BESIDE \u2013BEhavioral integrated System for diagnosis, support and monItoring of neuro-Degenerative diseasEs

Il progetto di ricerca industriale e sviluppo sperimentale denominato BESIDE intende applicare tecniche di intelligenza artificiale per l\u2019analisi di pattern motori (Gait Analysis) e comportamentali di individui affetti da malattie neurodegenerative. In particolare, il progetto intende individuare i sensori pi\uf9 idonei e sviluppare un complesso sistema di sorveglianza IoT in grado di fornire evidenze specifiche agli staff medici inerenti i comportamenti e le evoluzioni del decadimento fisico del paziente anche quando lo stesso non \ue8 sotto diretta osservazione