ORCID Profile
0000-0002-3436-035X
Current Organisations
University of Genoa
,
Laurentian University
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Publisher: Informa UK Limited
Date: 10-01-2018
DOI: 10.1080/17425247.2018.1424825
Abstract: Tumor is a heterogeneous mass of malignant cells co-existing with non-malignant cells. This co-existence evolves from the initial developmental stages of the tumor and is one of the hallmarks of cancer providing a protumorigenic niche known as tumor microenvironment (TME). Proliferation, invasiveness, metastatic potential and maintenance of stemness through cross-talk between tumors and its stroma forms the basis of TME. The article highlights the developmental phases of a tumor from dysplasia to the formation of clinically detectable tumors. The authors discuss the mechanistic stages involved in the formation of TME and its contribution in tumor outgrowth and chemoresistance. The authors have reviewed various approaches for targeting TME and its hallmarks along with their advantages and pitfalls. The authors also highlight cancer stem cells (CSCs) that are resistant to chemotherapeutics and thus a primary reason for tumor recurrence thereby, posing a challenge for the oncologists. Recent understanding of the cellular and molecular mechanisms involved in acquired chemoresistance has enabled scientists to target the tumor niche and TME and modulate and/or disrupt this communication leading to the transformation from a tumor-supportive niche environment to a tumor-non-supporting environment and give synergistic results towards an effective management of cancer.
Publisher: Wiley
Date: 24-07-2006
DOI: 10.1002/JSFA.2576
Publisher: IEEE
Date: 2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2007
Abstract: We connect in a grid-enabled pipeline an ontology-based environment for proteomics spectra management with a machine learning platform for unbiased predictive analysis. We exploit two existing software platforms (MS-Analyzer and BioDCV), the emerging proteomics standards, and the middleware and computing resources of the EGEE Biomed VO grid infrastructure. In the setup, BioDCV is accessed by the MS-Analyzer workflow as a Web service, thus providing a complete grid environment for proteomics data analysis. Predictive classification studies on MALDI-TOF data based on this environment are presented.
Publisher: Public Library of Science (PLoS)
Date: 05-03-2012
Publisher: Informa UK Limited
Date: 04-05-2019
DOI: 10.1080/17425247.2019.1609937
Abstract: The emergence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) is a major health issue and continues to be a global health concern. Despite significant advancements in treatment modalities, ~1.6 million deaths worldwide occur due to TB infection. This is because of tuberculosis reservoirs in the alveoli making it a challenge for the formulation scientist to target this. This review recent investigations on the forefront of pulmonary drug delivery for managing MDR-TB and XDR-TB. Novel delivery systems like liposomes, niosomes, employing carbohydrate, and -coated molecules via conjugation to selectively deliver the drugs to the lung TB reservoir via pulmonary administration are discussed. Poor patient adherence to treatment due to side effects and extended therapeutic regimen leads to drug-resistant TB. Thus, it is essential to design novel strategies this issue by developing new chemical entities and/or new delivery systems for delivery to the lungs, consequently reducing the side effects, the frequency and the duration of treatment. Delivery of drugs to enhance the efficacy of new/existing anti-TB drugs to overcome the resistance and enhance patient compliance is underway.
Publisher: Elsevier BV
Date: 12-2007
DOI: 10.1016/J.MICRON.2007.06.013
Abstract: Nanoencapsulation may improve activity of protein or polypeptide antimicrobials against a variety of microorganisms. In this study, nanoliposomes prepared from different lipids (Phospholipon 90H, Phospholipon 100H, dipalmitoylphosphatidylcholine (DPPC), stearylamine (SA), dicetyl phosphate (DCP) and cholesterol) by a new, non-toxic and scalable method, were tested for their capacity to encapsulate nisin Z and target bacteria (Bacillus subtilis and Pseudomonasaeruginosa). Factors affecting the entrapment efficiency (charge and cholesterol concentration in the vesicles) and stability of nanoliposomes were assessed. The nanoliposomes and their bacterial targeting were visualised, using different microscopes under air and liquid environments. Nisin was entrapped in different nanoliposomes with encapsulation efficiencies (EE) ranging from 12% to 54%. Anionic vesicles possessed the highest EE for nisin while increase in cholesterol content in lipid membranes up to 20% molar ratio resulted in a reduction in EE. Stability of nanoliposome-encapsulated nisin was demonstrated for at least 14 months at 4 degrees C (DPPC:DCP:CHOL vesicles) and for 12 months at 25 degrees C (DPPC:SA:CHOL vesicles).
Publisher: Oxford University Press (OUP)
Date: 28-09-2007
DOI: 10.1093/BIB/BBN008
Abstract: The search for predictive biomarkers of disease from high-throughput mass spectrometry (MS) data requires a complex analysis path. Preprocessing and machine-learning modules are pipelined, starting from raw spectra, to set up a predictive classifier based on a shortlist of candidate features. As a machine-learning problem, proteomic profiling on MS data needs caution like the microarray case. The risk of overfitting and of selection bias effects is pervasive: not only potential features easily outnumber s les by 10(3) times, but it is easy to neglect information-leakage effects during preprocessing from spectra to peaks. The aim of this review is to explain how to build a general purpose design analysis protocol (DAP) for predictive proteomic profiling: we show how to limit leakage due to parameter tuning and how to organize classification and ranking on large numbers of replicate versions of the original data to avoid selection bias. The DAP can be used with alternative components, i.e. with different preprocessing methods (peak clustering or wavelet based), classifiers e.g. Support Vector Machine (SVM) or feature ranking methods (recursive feature elimination or I-Relief). A procedure for assessing stability and predictive value of the resulting biomarkers' list is also provided. The approach is exemplified with experiments on synthetic datasets (from the Cromwell MS simulator) and with publicly available datasets from cancer studies.
Publisher: Informa UK Limited
Date: 05-10-2017
DOI: 10.1080/17425247.2017.1241230
Abstract: Chitosan is the second most abundant natural polysaccharide. It belongs a family of polycationic polymers comprised of repetitive units of glucosamine and N-acetylglucosamine. Its biodegradability, nontoxicity, non-immunogenicity and biocompatibility along with properties like mucoadhesion, fungistatic and bacteriogenic have made chitosan an appreciated polymer with numerous applications in the pharmaceutical, comestics and food industry. However, the limited solubility of chitosan at alkaline and neutral pH limits its widespread commercial use. This can be circumvented by fabrication of chitosan by graft copolymerization with acyl, alkyl, monomeric and polymeric moieties. Areas covered: Modifications like quarterization, thiolation, acylation and grafting result in copolymers with higher mucoadhesion strength, increased hydrophobic interactions (advantageous in hydrophobic drug entrapment), and increased solubility in alkaline pH, the ability for adsorption of metal ions, protein and peptide delivery and nutrient delivery. Insights on methods of polymerization, including atomic transfer radical polymerization and click chemistry are discussed. Applications of such modified chitosan copolymers in medical and surgical, and drug delivery, including nasal, oral and buccal delivery have also been covered. Expert opinion: Despite a number of successful investigations, commercialization of chitosan copolymers still remains a challenge. Further advancements in polymerization techniques may address the unmet needs of the healthcare industry.
Publisher: Public Library of Science (PLoS)
Date: 18-10-2019
Publisher: Elsevier BV
Date: 08-2007
DOI: 10.1002/JPS.20902
Publisher: Informa UK Limited
Date: 03-06-2019
DOI: 10.1080/17425247.2019.1621287
Abstract: Coinfection with This review focuses on the pathogenesis of HIV-TB coinfection and the current management approaches of this coinfection. It presents a detailed discussion of current investigations in novel drug delivery systems for effective targeting of HIV-TB lung reservoirs, especially via pulmonary drug delivery. Additionally, emphasis is given to the need of HIV-TB cotargeting, an unmet need in management of HIV-TB coinfection. To achieve the goal of complete eradication of HIV-TB reservoirs in lungs requires focused research strategies to be undertaken in the area of pulmonary delivery systems. These endeavors could eventually lead to better patient compliance and improved treatment outcomes. The treatment regimen of HIV-TB coinfection is associated with a major drawback of low therapeutic concentration of drugs in lungs. Nanotechnology provides an excellent platform for delivery of anti-TB and anti-HIV drugs via the pulmonary route thereby serving as a viable and effective means of managing the mycobacterial and HIV reservoirs in the lungs.
Publisher: Oxford University Press (OUP)
Date: 16-11-2007
DOI: 10.1093/BIOINFORMATICS/BTM550
Abstract: Motivation: We propose a method for studying the stability of biomarker lists obtained from functional genomics studies. It is common to adopt res ling methods to tune and evaluate marker-based diagnostic and prognostic systems in order to prevent selection bias. Such caution promotes honest estimation of class prediction, but leads to alternative sets of solutions. In microarray studies, the difference in lists may be bewildering, also due to the presence of modules of functionally related genes. Methods for assessing stability understand the dependency of the markers on the data or on the predictor's type and help selecting solutions. Results: A computational framework for comparing sets of ranked biomarker lists is presented. Notions and algorithms are based on concepts from permutation group theory. We introduce several algebraic indicators and metric methods for symmetric groups, including the Canberra distance, a weighted version of Spearman's footrule. We also consider distances between partial lists and an aggregation of sets of lists into an optimal list based on voting theory (Borda count). The stability indicators are applied in practical situations to several synthetic, cancer microarray and proteomics datasets. The addressed issues are predictive classification, presence of modules, comparison of alternative biomarker lists, outlier removal, control of selection bias by randomization techniques and enrichment analysis. Availability: Supplementary Material and software are available at the address biodcv.fbk.eu/listspy.html Contact: furlan@fbk.eu Supplementary information: Supplementary data are available at Bioinformatics online.
Publisher: Informa UK Limited
Date: 28-10-2009
DOI: 10.3109/08982100902913204
Abstract: Cancer continues to be a major cause of morbidity and mortality worldwide. While discovery of new drugs and cancer chemotherapy opened a new era for the treatment of tumors, optimized concentration of drug at the target site is only possible at the expense of severe side effects. Nanoscale carrier systems have the potential to limit drug toxicity and achieve tumor localization. When linked with tumor-targeting moieties, such as tumor-specific ligands or monoclonal antibodies, the nanocarriers can be used to target cancer-specific receptors, tumor antigens, and tumor vasculatures with high affinity and precision. This article is an overview of advances and prospects in the applications of nanocarrier technology in cancer therapy. Applications of nanoliposomes, dendrimers, and nanoparticles in cancer therapy are explained, along with their preparation methods and targeting strategies.
No related grants have been discovered for Annalisa Barla.