Search

Cultural heritage artefacts, such as stone works, books, manuscripts, and parchments, are subjected to severe biodeterioration processes over time caused by fungi, algae, lichens, and complex communities of microbial biofilms. Fungal contaminations are widespread as active or dormant spores which are present in the air and on objects and can lead to irreversible biodeterioration processes. Highly toxic and hardly biodegradable compounds are commonly used in cultural heritage conservation and restoration practices, which can be harmful even to the treated materials. Therefore, sector operators may be exposed to both biological and chemical risks, and often the failure to use adequate protective equipment increases the exposure risk. The interest of many researchers has been recently orienting, in this sector also, towards the use of natural products, as a valid alternative both for operators' health and environmental protection. This review aims to provide an overview of the biological and chemical risks associated with the treatment of fungal biodeterioration of artistic works and suggests the use of natural substances as a possible alternative to chemical synthetic products for the safety of restoration operators.

Abstract Background Neuroendocrine neoplasms (NENs) represent a heterogeneous class of rare tumors with increasing incidence. They are characterized by the ability to secrete peptide hormones and biogenic amines but other reliable biomarkers are lacking, making diagnosis and identification of the primary site very challenging. While in some NENs, such as the pancreatic ones, next generation sequencing technologies allowed the identification of new molecular hallmarks, our knowledge of the molecular profile of NENs from other anatomical sites is still poor. Methods Starting from the concept that NENs from different organs may be clinically and genetically correlated, we applied a multi-omics approach by combining multigene panel testing, CGH-array, transcriptome and miRNome profiling and computational analyses, with the aim to highlight common molecular and functional signatures of gastroenteropancreatic (GEP)-NENs and medullary thyroid carcinomas (MTCs) that could aid diagnosis, prognosis and therapy. Results By comparing genomic and transcriptional profiles, ATM-dependent signaling emerged among the most significant pathways at multiple levels, involving gene variations and miRNA-mediated regulation, thus representing a novel putative druggable pathway in these cancer types. Moreover, a set of circulating miRNAs was also selected as possible diagnostic/prognostic biomarkers useful for clinical management of NENs. Conclusions These findings depict a complex molecular and functional landscape of NENs, shedding light on novel therapeutic targets and disease biomarkers to be exploited.

This study monitored the anti-spike-receptor-binding domain (RBD) and neutralizing antibodies induced by the Pfizer/BioNTech mRNA BNT162b2 vaccine in a cohort of 163 healthcare workers aged ≤60 years. We have taken advantage of two study groups, both of whom received the first two doses in the same time window, but Group 1 (54 HCWs) received the third dose 2 months before Group 2 (68 HCWs) did. The cohorts were monitored from the 12th day after the first vaccine dose up to 1 month after the third vaccine dose for a total of eight time points and about 1 year of surveillance (T1 = 12 days after the first dose; T2 = 10 days after the second dose; T3 = 1 month after the second dose; T4 = 3 months after the second dose; T5 = 4 months after the second dose; T6 = 5 months after the second dose; T7 = 7 months after the second dose; T8 = 1 month after the third dose for Group 1; T8* = 9 months after the second dose for Group 2; T9 = 1 month after the third dose for Group 2). The mean value of anti-spike antibodies decreased faster over time, but at T7, its decline was significantly slowed (T7 vs. T8*). After the third dose, the anti-spike titer rose about 34-fold (T7 vs. T8 and T8* vs. T9) and the booster improved the anti-spike titer by about three times compared with that of the second dose (T3 vs. T8 and T3 vs. T9), and no difference was noted between the two groups. The neutralizing titer was evaluated at T3, T7, T8, and T9. Anti-spike and neutralizing antibodies were found to be strongly correlated (r2 = 0.980; p < 0.001). At T3, 70% of the participants had a neutralizing antibody titer >91% of total anti-spike antibodies that increased to 90% after the third dose (T8 and T9). However, when the anti-spike titer reached its lowest value (T7), the neutralizing antibody levels decreased even further, representing only 44% of total anti-spike antibodies (p < 0.0001). Our findings show that the third vaccine dose improves the humoral response, but the wane of the anti-spike and neutralizing antibody titers over time is more marked in the neutralizing antibodies.

The ability to detect single photons is becoming an enabling key capability in an increasing number of fields. Indeed, its scope is not limited to applications that specifically rely on single photons, such as quantum imaging, but extends to applications where a low signal is overwhelmed by background light, such as laser ranging, or in which faint excitation light is required not to damage the sample or harm the patient. In the last decades, SPADs gained popularity with respect to other single-photon detectors thanks to their small size, possibility to be integrated in complementary metal-oxide semiconductor processes, room temperature operability, low power supply and, above all, the possibility to be fast gated (to time filter the incoming signal) and to precisely timestamp the detected photons. The development of large digital arrays that integrates the detectors and circuits has allowed the implementation of complex functionality on-chip, tailoring the detectors to suit the need of specific applications. This review proposes a complete overview of silicon SPADs characteristics and applications. In the previous Part I, starting with the working principle, simulation models and required frontend, the paper moves to the most common parameters adopted in literature for characterizing SPAD performance and describes single pixels applications and their performance. In this Part II, the focus is posed on the development of SPAD arrays, presenting some of the most notable examples found in literature. The actual exploitation of these designs in real applications (e.g., automotive, bioimaging and radiation detectors) is then discussed.

The ability to detect single photons is becoming an enabling key capability in an increasing number of fields. Indeed, its scope is not limited to applications that specifically rely on single photons, such as quantum imaging, but extends to applications where a low signal is overwhelmed by background light, such as laser ranging, or in which faint excitation light is required not to damage the sample or harm the patient. In the last decades, SPADs gained popularity with respect to other single-photon detectors thanks to their small size, possibility to be integrated in Complementary Metal-Oxide Semiconductor processes, room temperature operability, low power supply and, above all, the possibility to be fast gated (to time filter the incoming signal) and to precisely timestamp the detected photons. The development of large digital arrays that integrates the detectors and circuits has allowed the implementation of complex functionality on-chip, tailoring the detectors to suit the need of specific applications. This review proposes a complete overview of silicon SPADs characteristics and applications. In this Part I, starting with the working principle, simulation models and required frontend, the paper moves to the most common parameters adopted in literature for characterizing SPADs, and describes single pixels applications and their performance. In the next Part II, the focus is then posed on the development of SPAD arrays, presenting some of the most notable examples found in literature. The actual exploitation of these designs in real applications (e.g., automotive, bioimaging and radiation detectors) is then discussed.

Abstract This report presents the case of a 13-year-old female patient with history of acute myeloid leukemia, who, after a bone marrow transplant, began to vomit and experienced rapidly progressive deterioration of consciousness, in addition to disseminated erythematous-violaceous macules, and some blisters with hemorrhagic content inside. Skin biopsy evidenced intravascular filamentous structures. A blood culture confirmed the presence of Fusarium oxysporum. Intravenous treatment with voriconazole was initiated. The patient evolved unfavorably with multiple necrotic skin lesions, ischemic brain lesions, and death.

Background Triple-negative breast cancers (TNBCs) display poor prognosis, have a high risk of tumour recurrence, and exhibit high resistance to drug treatments. Based on their gene expression profiles, the majority of TNBCs are classified as basal-like breast cancers. Currently, there are not available widely-accepted prognostic markers to predict outcomes in basal-like subtype, so the selection of new prognostic indicators for this BC phenotype represents an unmet clinical challenge. Results Here, we attempted to address this challenging issue by exploiting a bioinformatics pipeline able to integrate transcriptomic, genomic, epigenomic, and clinical data freely accessible from public repositories. This pipeline starts from the application of the well-established network-based SWIM methodology on the transcriptomic data to unveil important (switch) genes in relation with a complex disease of interest. Then, survival and linear regression analyses are performed to associate the gene expression profiles of the switch genes with both the patients’ clinical outcome and the disease aggressiveness. This allows us to identify a prognostic gene signature that in turn is fed to the last step of the pipeline consisting of an analysis at DNA level, to investigate whether variations in the expression of identified prognostic switch genes could be related to genetic (copy number variations) or epigenetic (DNA methylation differences) alterations in their gene loci, or to the activities of transcription factors binding to their promoter regions. Finally, changes in the protein expression levels corresponding to the so far identified prognostic switch genes are evaluated by immunohistochemical staining results taking advantage of the Human Protein Atlas. Conclusion The application of the proposed pipeline on the dataset of The Cancer Genome Atlas (TCGA)-Breast Invasive Carcinoma (BRCA) patients affected by basal-like subtype led to an in silico recognition of a basal-like specific gene signature composed of 11 potential prognostic biomarkers to be further investigated.

Abstract Background The aim of the present review is to discuss how the promising field of biobanking can support health care research strategies. As the concept has evolved over time, biobanks have grown from simple biological sample repositories to complex and dynamic units belonging to large infrastructure networks, such as the Pan-European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI). Biobanks were established to support scientific knowledge. Different professional figures with varied expertise collaborate to obtain and collect biological and clinical data from human subjects. At same time biobanks preserve the human and legal rights of each person that offers biomaterial for research. Methods A literature review was conducted in April 2019 from the online database PubMed, accessed through the Bibliosan platform. Four primary topics related to biobanking will be discussed: (i) evolution, (ii) bioethical issues, (iii) organization, and (iv) imaging. Results Most biobanks were founded as local units to support specific research projects, so they evolved in a decentralized manner. The consequence is an urgent needing for procedure harmonization regarding sample collection, processing, and storage. Considering the involvement of biomaterials obtained from human beings, different ethical issues such as the informed consent model, sample ownership, veto rights, and biobank sustainability are debated. In the face of these methodological and ethical challenges, international organizations such as BBMRI play a key role in supporting biobanking activities. Finally, a unique development is the creation of imaging biobanks that support the translation of imaging biomarkers (identified using a radiomic approach) into clinical practice by ensuring standardization of data acquisition and analysis, accredited technical validation, and transparent sharing of biological and clinical data. Conclusion Modern biobanks permit large-scale analysis for individuation of specific diseases biomarkers starting from biological or digital material (i.e., bioimages) with well-annotated clinical and biological data. These features are essential for improving personalized medical approaches, where effective biomarker identification is a critical step for disease diagnosis and prognosis.

Bladder cancer is the 10th most common cancer type worldwide. Cystoscopy represents the gold standard for bladder cancer diagnosis, but this procedure is invasive and painful, hence the need to identify new biomarkers through noninvasive procedures. microRNAs (miRNAs) are considered to be promising diagnostic molecules, because they are very stable in biological fluids (including urine) and easily detectable. This systematic review analyses the power of urine miRNAs as bladder cancer diagnostic markers. We conducted this systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A total of 293 records related to miRNAs and their diagnostic significance in BC were retrieved from the PubMed and Embase databases. A systematic search of the literature was performed, and a total of 25 articles (N = 4054 participants) were identified and reviewed. Although many of the selected studies were of high scientific quality, the results proved to be quite heterogeneous, because we did not identify a univocal consensus for a specific miRNA signature but only isolated the signatures. We did not identify a univocal consensus for a specific diagnostic miRNA signature but only isolated the signatures, some of them with better diagnostic power compared to the others.

BackgroundBreast cancer (BC) is the most common cancer in females and despite advances in treatment, it represents the leading cause of cancer mortality in women worldwide. Conventional therapeutic modalities have significantly improved the management of BC patients, but subtype heterogeneity, drug resistance, and tumor relapse remain the major factors to hamper the effectiveness of therapy for BC. In this scenario, miRNA(miR)-based therapeutics offer a very attractive area of study. However, the use of miR-based therapeutics for BC treatment still represents an underdeveloped topic. Therefore, this systematic review aims at summarizing current knowledge on promising miR-based therapeutics for BC exploring original articles focusing on in vivo experiments.MethodsThe current systematic review was performed according to PRISMA guidelines. PubMed and EMBASE databases were comprehensively explored to perform the article search.ResultsTwenty-one eligible studies were included and analyzed: twelve focused on antitumor miR-based therapeutics and nine on metastatic miR-based therapeutics. We found 18 different miRs tested as potential therapeutic molecules in animal model experiments. About 90% of the selected studies evaluate the efficiency and the safety of miRs as therapeutic agents in triple-negative (TN)-BC mouse models. Among all founded miR-based therapeutics, miR-21 emerged to be the most investigated and proposed as a potential antitumoral molecule for TNBC treatment. Besides, miR-34a and miR-205a appeared to be successful antitumoral and antimetastatic molecules.ConclusionsOur analysis provides a snapshot of the current scenario regarding the miRs as therapeutic molecules in BC. Nevertheless, despite many efforts, none of the selected studies goes beyond preclinical studies, and their translatability in the clinical practice seems quite premature.

Big data processing, using omics data integration and machine learning (ML) methods, drive efforts to discover diagnostic and prognostic biomarkers for clinical decision making. Previously, we used the TCGA database for gene expression profiling of breast, ovary, and endometrial cancers, and identified a top-scoring network centered on the ERBB2 gene, which plays a crucial role in carcinogenesis in the three estrogen-dependent tumors. Here, we focused on microRNA expression signature similarity, asking whether they could target the ERBB family. We applied an ML approach on integrated TCGA miRNA profiling of breast, endometrium, and ovarian cancer to identify common miRNA signatures differentiating tumor and normal conditions. Using the ML-based algorithm and the miRTarBase database, we found 205 features and 158 miRNAs targeting ERBB isoforms, respectively. By merging the results of both databases and ranking each feature according to the weighted Support Vector Machine model, we prioritized 42 features, with accuracy (0.98), AUC (0.93–95% CI 0.917–0.94), sensitivity (0.85), and specificity (0.99), indicating their diagnostic capability to discriminate between the two conditions. In vitro validations by qRT-PCR experiments, using model and parental cell lines for each tumor type showed that five miRNAs (hsa-mir-323a-3p, hsa-mir-323b-3p, hsa-mir-331-3p, hsa-mir-381-3p, and hsa-mir-1301-3p) had expressed trend concordance between breast, ovarian, and endometrium cancer cell lines compared with normal lines, confirming our in silico predictions. This shows that an integrated computational approach combined with biological knowledge, could identify expression signatures as potential diagnostic biomarkers common to multiple tumors.

Breast, ovarian, and endometrial cancers have a major impact on mortality in women. These tumors share hormone-dependent mechanisms involved in female-specific cancers which support tumor growth in a different manner. Integrated computational approaches may allow us to better detect genomic similarities between these different female-specific cancers, helping us to deliver more sophisticated diagnosis and precise treatments. Recently, several initiatives of The Cancer Genome Atlas (TCGA) have encouraged integrated analyses of multiple cancers rather than individual tumors. These studies revealed common genetic alterations (driver genes) even in clinically distinct entities such as breast, ovarian, and endometrial cancers. In this study, we aimed to identify expression similarity signatures by extracting common genes among TCGA breast (BRCA), ovarian (OV), and uterine corpus endometrial carcinoma (UCEC) cohorts and infer co-regulatory protein–protein interaction networks that might have a relationship with the estrogen signaling pathway. Thus, we carried out an unsupervised principal component analysis (PCA)-based computational approach, using RNA sequencing data of 2,015 female cancer and 148 normal samples, in order to simultaneously capture the data heterogeneity of intertumors. Firstly, we identified tumor-associated genes from gene expression profiles. Secondly, we investigated the signaling pathways and co-regulatory protein–protein interaction networks underlying these three cancers by leveraging the Ingenuity Pathway Analysis software. In detail, we discovered 1,643 expression similarity signatures (638 downregulated and 1,005 upregulated genes, with respect to normal phenotype), denoted as tumor-associated genes. Through functional genomic analyses, we assessed that these genes were involved in the regulation of cell-cycle-dependent mechanisms, including metaphase kinetochore formation and estrogen-dependent S-phase entry. Furthermore, we generated putative co-regulatory protein–protein interaction networks, based on upstream regulators such as the ERBB2/HER2 gene. Moreover, we provided an ad-hoc bioinformatics workflow with a manageable list of intertumor expression similarity signatures for the three female-specific cancers. The expression similarity signatures identified in this study might uncover potential estrogen-dependent molecular mechanisms promoting carcinogenesis.

To produce clothes made with engineered fabrics to monitor the physiological parameters of workers, strain sensors were produced by depositing two different types of water-based inks (P1 and P2) suitably mixed with graphene nanoplatelets (GNPs) on a fabric. We evaluated the biocompatibility of fabrics with GNPs (GNP fabric) through in vitro and in vivo assays. We investigated the effects induced on human keratinocytes by the eluates extracted from GNP fabrics by the contact of GNP fabrics with cells and by seeding keratinocytes directly onto the GNP fabrics using a cell viability test and morphological analysis. Moreover, we evaluated in vivo possible adverse effects of the GNPs using the model system Caenorhabditis elegans. Cell viability assay, morphological analysis and Caenorhabditis elegans tests performed on smart fabric treated with P2 (P2GNP fabric) did not show significant differences when compared with their respective control samples. Instead, a reduction in cell viability and changes in the membrane microvilli structure were found in cells incubated with smart fabric treated with P1. The results were helpful in determining the non-toxic properties of the P2GNP fabric. In the future, therefore, graphene-based ink integrated into elastic fabric will be developed for piezoresistive sensors.

Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the radiogenomic phenotypes in breast cancer. Thirty-six female patients with breast cancer were enrolled in this study. Radiomic features were extracted from MRI and PET imaging techniques for malignant and healthy lesions in each patient. To reduce within-subject bias, the ratio of radiomic features extracted from both lesions was calculated for each patient. Radiomic features were further normalized, comparing the z-score, quantile, and whitening normalization methods to reduce between-subjects bias. After feature reduction by Spearman’s correlation, a methodological approach based on a principal component analysis (PCA) was applied. The results were compared and validated on twenty-seven patients to investigate the tumor grade, Ki-67 index, and molecular cancer subtypes using classification methods (LogitBoost, random forest, and linear discriminant analysis). The classification techniques achieved high area-under-the-curve values with one PC that was calculated by normalizing the radiomic features via the quantile method. This pilot study helped us to establish a robust framework of analysis to generate a combined radiomic signature, which may lead to more precise breast cancer prognosis.

This study aimed to assess the potential of pomegranate peel powder as a natural preservative. Its effects were tested on fruit salad quality decay during refrigerated storage. Nectarine and pineapple, equally portioned in polypropylene containers and covered with fructose syrup, were closed using a screw cap in air, with and without the addition of a by-product peel powder. Specifically, amounts of 2.5% and 5% (w/v) of pomegranate peel powder were put into each container. Both the microbiological and sensory qualities of the fruit salad were monitored during storage at 5 °C for 28 days. The results demonstrated that the fruit salad with the by-products showed lower counts of total mesophilic bacteria, total psychrotrophic microorganisms, yeasts, and lactic acid bacteria compared to the control, thus confirming the recognized antimicrobial properties of pomegranate peel. The other interesting finding of this study is that the addition of the investigated by-product in fruit salad did not worsen the main sensory attributes of fresh-cut fruit. Therefore, these preliminary results suggest that pomegranate peel powder has potential applications as a natural preservative in the fresh-cut food sector.

Time-of-Flight (TOF) based Light Detection and Ranging (LiDAR) is a widespread technique for distance measurements in both single-spot depth ranging and 3D mapping. Single Photon Avalanche Diode (SPAD) detectors provide single-photon sensitivity and allow in-pixel integration of a Time-to-Digital Converter (TDC) to measure the TOF of single-photons. From the repetitive acquisition of photons returning from multiple laser shots, it is possible to accumulate a TOF histogram, so as to identify the laser pulse return from unwelcome ambient light and compute the desired distance information. In order to properly predict the TOF histogram distribution and design each component of the LiDAR system, from SPAD to TDC and histogram processing, we present a detailed statistical modelling of the acquisition chain and we show the perfect matching with Monte Carlo simulations in very different operating conditions and very high background levels. We take into consideration SPAD non-idealities such as hold-off time, afterpulsing, and crosstalk, and we show the heavy pile-up distortion in case of high background. Moreover, we also model non-idealities of timing electronics chain, namely, TDC dead-time, limited number of storage cells for TOF data, and TDC sharing. Eventually, we show how the exploit the modelling to reversely extract the original LiDAR return signal from the distorted measured TOF data in different operating conditions.

Leukocytes are major cellular components of the inflammatory and immune response systems. After their generation in the bone marrow from hematopoietic stem cells, they maturate as granulocytes (neutrophils, eosinophils, and basophils), monocytes, and lymphocytes. The abnormal accumulation and proliferation of immature blood cells (blasts) lead to severe and widespread diseases such as leukemia. We have recently shown that KCTD15, a member of the potassium channel tetramerization domain containing protein family (KCTD), is remarkably upregulated in leukemic B-cells. Here, we extend our investigation by monitoring the KCTD15 expression levels in circulating lymphocytes, monocytes, and granulocytes, as well as in leukemia cells. Significant differences in the expression level of KCTD15 were detected in normal lymphocytes, monocytes, and granulocytes. Interestingly, we also found overexpression of the protein following leukemic transformation in the case of myeloid cell lineage. Indeed, KCTD15 was found to be upregulated in K562 and NB4 cells, as well as in HL-60 cell lines. This in vitro finding was corroborated by the analysis of KCTD15 mRNA of acute myeloid leukemia (AML) patients reported in the Microarray Innovations in Leukemia (MILE) dataset. Collectively, the present data open interesting perspectives for understanding the maturation process of leukocytes and for the diagnosis/therapy of acute leukemias.

Introdução: O neuroblastoma é o tumor sólido extracraniano mais frequente em crianças. A proximadamente 50% dos pacientes são classificados como de alto risco considerando as características clínicas, biológicas e histológicas. Objetivo: Descrever a população atendida no Centro Hemato-Oncológíco Pediátrico (CHOP) do Centro Hospitalario Pereira Rossell (CHPR) com diagnóstico de neuroblastoma de alto risco, seu tratamento e sobrevida. Método: Estudo descritivo, retrospectivo, de todos os pacientes com neuroblastoma de alto risco diagnosticados no CHOP, no período entre 2001 e 2015. O Registro Nacional de Câncer Pediátrico está localizado no CHOP, bem como o Arquivo de Histórias Clínicas de todos os pacientes. Os dados são coletados e analisados pelo setor estatístico. Resultados: Foram diagnosticados 35 pacientes. Vinte (57%) eram do sexo masculino. Mediana de idade: 36,6 meses (5-93). Localização suprarrenal: 23 (66%). estádio IV 100%. Metástases, medula óssea e osso: 27 (71%). Trinta e três pacientes receberam transplante de células-tronco hematopoiéticas (TCTH) (94%). Status prévio a TCTH, remissão completa: 19 (58%), remissão parcial: 14 (42%). incidência de mortalidade relacionada ao tratamento: 15% e de recaídas: 68%. Mediana do tempo de recaída: 15 meses (3-52). a probabilidade de sobrevida global e sobrevida livre de eventos aos cinco anos foi de 37,8% ± 8,4 e 23,8% ± 7,3 (mediana de seguimento 40 meses). Conclusão: Apesar do tratamento intensivo e medidas de suporte adequadas, o prognóstico em crianças com neuroblastoma de alto continua sendo pobre no Uruguai. é necessário incorporar novas estratégias terapêuticas que ainda não estão disponíveis em nosso meio.