Updates on the Treatment of Pancreatic Diseases: Focus on Surgery, Electrochemotherapy and Rituximab

Pellat, Anna

doi:10.3390/jcm11010239

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…As depicted, three genes (Eqs. ( 8), (9), and (10), respectively) are included in the individual F , represented by Equation (7). Finally, the class of the cancer is obtained by comparing F with the threshold 0.01, as shown in Equation (11).…”

Section: Modelingmentioning

confidence: 99%

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A Genetic Programming-based Approach for Classifying Pancreatic Adenocarcinoma: the SICED Experience

D’Angelo

Scoppettuolo

Cammarota

et al. 2022

Preprint

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Ductal adenocarcinoma of the pancreas is a cancer with a high mortality rate. Among the main reasons for this baleful prognosis is that, in most patients, this neoplasm is diagnosed at a too advanced stage. Clinical oncology research is now particularly focused on decoding the cancer molecular onset by understanding the complex biological architecture of tumor cell proliferation. In this direction, Machine-Learning has proved to be a valid solution in many sectors of the biomedical field, thanks to its ability to mine useful knowledge by biological and genetic data. Since the major risk factor is represented by genetic predisposition, the aim of this study is to find a mathematical model describing the complex relationship existing between genetic mutations of the involved genes and the onset of the disease. To this end, an approach based on evolutionary algorithms (Evolutionary Algorithm) is proposed. In particular, Genetic Programming is used, which allows solving a Symbolic Regression problem through the use of Genetic Algorithms. The identification of these correlations is a typical objective of the diagnostic approach, and is one of the most critical and complex activities in the presence of large amounts of data that are difficult to correlate through traditional statistical techniques. The formula obtained highlights the importance of the complex relationship existing between the different gene's mutations present in the tumor tissue of the group of patients considered.

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Section: Modelingmentioning

confidence: 99%

“…MCN is a cysticforming, mucin-producing neoplasm and a correct clinical approach is often hampered by the difficult differential diagnosis in respect to intraductal papillary mucinous neoplasms (IPMN) or oligocystic serous adenomas before surgery [8,9]. Of note, both IPMNs and MCNs can shift towards a more aggressive form [10].…”

Section: Introductionmentioning

confidence: 99%

A Genetic Programming-based Approach for Classifying Pancreatic Adenocarcinoma: the SICED Experience

D’Angelo

Scoppettuolo

Cammarota

et al. 2022

Preprint

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“…Electrochemotherapy functions through a variety of mechanisms, including: the cytotoxic effect of the chemotherapeutic which is enhanced by electroporation; vasoconstriction of tumor blood vessel for several hours due to sympathetic nerve stimulation which reduces drug washout from the tumor; vascular disruption due to cytotoxicity towards the endothelial cells of tumor blood vessels; the immune response to immunogenic cell death and enhanced tumor antigen expression [61][62][63]. Electrochemotherapy has been used to enhance the delivery of a variety of drugs [59,60,[64][65][66][67][68], and has been observed to be a swift, safe and effective treatment method for cancers that tends to enhance the quality of life of patients [69][70][71][72][73].…”

Section: Introductionmentioning

confidence: 99%

Electrochemically Enhanced Delivery of Pemetrexed from Electroactive Hydrogels

et al. 2022

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Electroactive hydrogels based on derivatives of polyethyleneglycol (PEG), chitosan and polypyrrole were prepared via a combination of photopolymerization and oxidative chemical polymerization, and optionally doped with anions (e.g., lignin, drugs, etc.). The products were analyzed with a variety of techniques, including: FT-IR, UV-Vis, 1H NMR (solution state), 13C NMR (solid state), XRD, TGA, SEM, swelling ratios and rheology. The conductive gels swell ca. 8 times less than the non-conductive gels due to the presence of the interpenetrating network (IPN) of polypyrrole and lignin. A rheological study showed that the non-conductive gels are soft (G′ 0.35 kPa, G″ 0.02 kPa) with properties analogous to brain tissue, whereas the conductive gels are significantly stronger (G′ 30 kPa, G″ 19 kPa) analogous to breast tissue due to the presence of the IPN of polypyrrole and lignin. The potential of these biomaterials to be used for biomedical applications was validated in vitro by cell culture studies (assessing adhesion and proliferation of fibroblasts) and drug delivery studies (electrochemically loading the FDA-approved chemotherapeutic pemetrexed and measuring passive and stimulated release); indeed, the application of electrical stimulus enhanced the release of PEM from gels by ca. 10–15% relative to the passive release control experiment for each application of electrical stimulation over a short period analogous to the duration of stimulation applied for electrochemotherapy. It is foreseeable that such materials could be integrated in electrochemotherapeutic medical devices, e.g., electrode arrays or plates currently used in the clinic.

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“…MCN is a cystic-forming, mucinproducing neoplasm and a correct clinical approach is often hampered by the difficult differential diagnosis in respect to intraductal papillary mucinous neoplasms (IPMN) or oligocystic serous adenomas before surgery (Scholten et al 2018;Xie et al 2021). Of note, both IPMNs and MCNs can shift toward a more aggressive form (Coriat and Pellat 2022).…”

Section: Introductionmentioning

confidence: 99%

A genetic programming-based approach for classifying pancreatic adenocarcinoma: the SICED experience

et al. 2022

View full text Add to dashboard Cite

Ductal adenocarcinoma of the pancreas is a cancer with a high mortality rate. Among the main reasons for this baleful prognosis is that, in most patients, this neoplasm is diagnosed at a too advanced stage. Clinical oncology research is now particularly focused on decoding the cancer molecular onset by understanding the complex biological architecture of tumor cell proliferation. In this direction, machine learning has proved to be a valid solution in many sectors of the biomedical field, thanks to its ability to mine useful knowledge by biological and genetic data. Since the major risk factor is represented by genetic predisposition, the aim of this study is to find a mathematical model describing the complex relationship existing between genetic mutations of the involved genes and the onset of the disease. To this end, an approach based on evolutionary algorithms is proposed. In particular, genetic programming is used, which allows solving a symbolic regression problem through the use of genetic algorithms. The identification of these correlations is a typical objective of the diagnostic approach and is one of the most critical and complex activities in the presence of large amounts of data that are difficult to correlate through traditional statistical techniques. The mathematical model obtained highlights the importance of the complex relationship existing between the different gene’s mutations present in the tumor tissue of the group of patients considered.

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Updates on the Treatment of Pancreatic Diseases: Focus on Surgery, Electrochemotherapy and Rituximab

Abstract: The pancreas plays an important role in the human body with its two main endocrine and exocrine functions [...]

Cited by 3 publications

References 13 publications

A Genetic Programming-based Approach for Classifying Pancreatic Adenocarcinoma: the SICED Experience

A Genetic Programming-based Approach for Classifying Pancreatic Adenocarcinoma: the SICED Experience

Electrochemically Enhanced Delivery of Pemetrexed from Electroactive Hydrogels

A genetic programming-based approach for classifying pancreatic adenocarcinoma: the SICED experience

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