scholarly journals Pancreatic Cancer: Molecular, Biochemical, Chemopreventive, and Therapeutic Aspects

2010 ◽  
Vol 10 ◽  
pp. 1967-1970 ◽  
Author(s):  
Juan Iovanna ◽  
José Luis Neira
Keyword(s):  
Pancreatic Cancer ◽  
Research Effort ◽  
Treatment Strategies ◽  
Diagnostic Tools ◽  
Signal Pathways ◽  
Major Pathway ◽  
Treatment Regimens ◽  
Oncogenic Pathways ◽  
Clinically Significant ◽  
Molecular Aspects

Pancreatic cancer (PC) is the fourth leading cause of cancer death, with a median survival of 6 months and a dismal 5-year survival rate of 3–5%, a figure which has remained relatively unchanged over the past 25 years. PC is one of the most difficult diseases to treat due to late initial diagnosis and to resistance to the usual treatments. The presence of occult or clinical metastases at the time of diagnosis, together with the lack of effective chemotherapies, contributes to the high mortality in patients with PC. Its lethal nature stems from its propensity to disseminate rapidly to the lymphatic system and distant organs. Yet, understanding and stopping metastasis may prove to be one of the great potential strategies of treating PC. There is a dire need for the design of new and targeted therapeutic strategies that can overcome the drug resistance and improve the clinical outcome for patients diagnosed with the illness. The knowledge of the molecular aspects of PC is very important, and it is likely to be helpful in the design of newer drugs and the molecular selection of existing agents for targeted therapy. The inhibition of signal pathways can be carried out not only by small molecules, able to bind to selected regions of the target protein, but also by using large molecules as antibodies. The pathway to successful new therapies has been inhibited because of the rapidity with which agents tend to move into randomized, controlled trials without the extensive early testing necessary to optimize treatment regimens. However, lessons have been learned and our collective research effort has generated a substantial platform of knowledge from which further work will spring. The bioavailability of compounds such as antisense oligonucleotides and siRNAs in humans remains a big hurdle, which will require further improvement of gene-delivery strategies. Finally, the long-term goal of the therapy individualization for patients is possible if factors that predict treatment response, such as biological markers, could be determined accurately. These approaches are likely to comprise a mixture of targeted agents in combination with conventional chemotherapy and radiotherapy. For a clinically significant effect to be achieved, treatment strategies should either be in the form of (1) a “horizontal” approach, in which several oncogenic pathways (as those described in this series of reviews) are inhibited; or (2) a “vertical” approach, whereby multiple levels of a major pathway are targeted. Combination therapies, together with improved diagnostic tools and predictive markers, are ultimately desired in order to improve the bleak outlook for patients diagnosed with PC.

scholarly journals Stochastic Evolution of Pancreatic Cancer Metastases During Logistic Clonal Expansion

2019 ◽  
pp. 1-11 ◽  
Author(s):  
Kimiyo N. Yamamoto ◽  
Lin L. Liu ◽  
Akira Nakamura ◽  
Hiroshi Haeno ◽  
Franziska Michor
Keyword(s):  
Pancreatic Cancer ◽  
Cancer Progression ◽  
Growth Models ◽  
Treatment Strategies ◽  
Evolutionary Model ◽  
Growth Patterns ◽  
Logistic Growth ◽  
Metastatic Progression ◽  
Treatment Regimens ◽  
Analytical Approximations

Despite recent progress in diagnostic and multimodal treatment approaches, most cancer deaths are still caused by metastatic spread and the subsequent growth of tumor cells in sites distant from the primary organ. So far, few quantitative studies are available that allow for the estimation of metastatic parameters and the evaluation of alternative treatment strategies. Most computational studies have focused on situations in which the tumor cell population expands exponentially over time; however, tumors may eventually be subject to resource and space limitations so that their growth patterns deviate from exponential growth to adhere to density-dependent growth models. In this study, we developed a stochastic evolutionary model of cancer progression that considers alterations in metastasis-related genes and intercellular growth competition leading to density effects described by logistic growth. Using this stochastic model, we derived analytical approximations for the time between the initiation of tumorigenesis and diagnosis, the expected number of metastatic sites, the total number of metastatic cells, the size of the primary tumor, and survival. Furthermore, we investigated the effects of drug administration and surgical resection on these quantities and predicted outcomes for different treatment regimens. Parameter values used in the analysis were estimated from data obtained from a pancreatic cancer rapid autopsy program. Our theoretical approach allows for flexible modeling of metastatic progression dynamics.

New Trends and Developments in Patients with Severe Aortic Stenosis – Towards Optimal Treatment?

2009 ◽  
Vol 5 (2) ◽  
pp. 81 ◽  
Author(s):  
Martijn WA van Geldorp ◽  
Johanna JM Takkenberg ◽  
Ad JJC Bogers ◽  
A Pieter Kappetein ◽  
◽  
...  
Keyword(s):  
Aortic Stenosis ◽  
Severe Aortic Stenosis ◽  
Surgical Techniques ◽  
Treatment Strategies ◽  
Tissue Doppler ◽  
Doppler Imaging ◽  
Treatment Modalities ◽  
Diagnostic Tools ◽  
Number Of Patients ◽  
Transcatheter Valve

Over the next few decades the number of patients diagnosed with aortic stenosis is expected to rise as the population ages and the use of several diagnostic tools expands. This will result in a growing need for both medical and surgical treatment and stimulate the development of new diagnostic and surgical techniques. This article briefly describes the prevalence, pathogenesis and clinical presentation of patients with aortic stenosis and focuses on developments in diagnostic tools, treatment strategies and treatment modalities: the use of echocardiography, tissue Doppler imaging, stress testing and biomarkers is discussed, as well as timing of surgery and the role microsimulation can play in prosthesis selection. Furthermore, newly developed transcatheter valve implantation techniques and their possible role in treating ‘inoperable’ or ‘elderly’ patients are discussed.

A Systematic Study of Novel Drug Delivery Mechanisms and Treatment Strategies for Pancreatic Cancer

2021 ◽  
pp. 102539
Author(s):  
Umme Hani ◽  
Riyaz Ali M. Osmani ◽  
Ayesha Siddiqua ◽  
Shadma Wahab ◽  
Sadia Batool ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Pancreatic Cancer ◽  
Systematic Study ◽  
Treatment Strategies ◽  
Novel Drug

scholarly journals Novel Treatments and Technologies Applied to the Cure of Neuroblastoma

Children ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 482
Author(s):  
Irene Paraboschi ◽  
Laura Privitera ◽  
Gabriela Kramer-Marek ◽  
John Anderson ◽  
Stefano Giuliani
Keyword(s):  
Research Effort ◽  
Treatment Strategies ◽  
Adverse Outcomes ◽  
Treatment Protocols ◽  
Ongoing Research ◽  
Hematopoietic Stem ◽  
Novel Treatments ◽  
Therapeutic Modalities ◽  
High Risk Disease ◽  
Novel Treatment Strategies

Neuroblastoma (NB) is the most common extracranial solid tumour in childhood, accounting for approximately 15% of all cancer-related deaths in the paediatric population1. It is characterised by heterogeneous clinical behaviour in neonates and often adverse outcomes in toddlers. The overall survival of children with high-risk disease is around 40–50% despite the aggressive treatment protocols consisting of intensive chemotherapy, surgery, radiation therapy and hematopoietic stem cell transplantation2,3. There is an ongoing research effort to increase NB’s cellular and molecular biology knowledge to translate essential findings into novel treatment strategies. This review aims to address new therapeutic modalities emerging from preclinical studies offering a unique translational opportunity for NB treatment.

scholarly journals Immune Cell Modulation of the Extracellular Matrix Contributes to the Pathogenesis of Pancreatic Cancer

Biomolecules ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 901
Author(s):  
Ramiz S. Ahmad ◽  
Timothy D. Eubank ◽  
Slawomir Lukomski ◽  
Brian A. Boone
Keyword(s):  
Pancreatic Cancer ◽  
Extracellular Matrix ◽  
Immune Cells ◽  
Immune Cell ◽  
Treatment Strategies ◽  
Stellate Cells ◽  
Pancreatic Stellate Cells ◽  
Ductal Adenocarcinoma ◽  
Cellular Mechanisms ◽  
Novel Treatment Strategies

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a five-year survival rate of only 9%. PDAC is characterized by a dense, fibrotic stroma composed of extracellular matrix (ECM) proteins. This desmoplastic stroma is a hallmark of PDAC, representing a significant physical barrier that is immunosuppressive and obstructs penetration of cytotoxic chemotherapy agents into the tumor microenvironment (TME). Additionally, dense ECM promotes hypoxia, making tumor cells refractive to radiation therapy and alters their metabolism, thereby supporting proliferation and survival. In this review, we outline the significant contribution of fibrosis to the pathogenesis of pancreatic cancer, with a focus on the cross talk between immune cells and pancreatic stellate cells that contribute to ECM deposition. We emphasize the cellular mechanisms by which neutrophils and macrophages, specifically, modulate the ECM in favor of PDAC-progression. Furthermore, we investigate how activated stellate cells and ECM influence immune cells and promote immunosuppression in PDAC. Finally, we summarize therapeutic strategies that target the stroma and hinder immune cell promotion of fibrogenesis, which have unfortunately led to mixed results. An enhanced understanding of the complex interactions between the pancreatic tumor ECM and immune cells may uncover novel treatment strategies that are desperately needed for this devastating disease.

scholarly journals Can mean ADC value and ADC ratio of benign prostate tissue to prostate cancer assist in the prediction of clinically significant prostate cancer within the PI-RADSv2 scoring system?

2020 ◽  
Vol 51 (1) ◽  
Author(s):  
Samar Ramzy Ragheb ◽  
Reem Hassan Bassiouny
Keyword(s):  
Prostate Cancer ◽  
Sensitivity And Specificity ◽  
Gleason Score ◽  
Treatment Strategies ◽  
Serum Levels ◽  
Prostatic Tissue ◽  
Histopathological Analysis ◽  
Adc Value ◽  
The Mean ◽  
Clinically Significant

Abstract Background The aim of this study is to investigate whether quantitative DW metrics can provide additive value to the reliable categorization of lesions within existing PI-RADSv2 guidelines. Fifty-eight patients with clinically suspicious prostate cancer who underwent PR examination, PSA serum levels, sextant TRUS-guided biopsies, and bi-parametric MR imaging were included in the study. Results Sixty-six lesions were detected by histopathological analysis of surgical specimens. The mean ADC values were significantly lower in tumor than non-tumor tissue. The mean ADC value inversely correlated with Gleason score of tumors with a significant p value < 0.001.Conversely, a positive relationship was found between the ADC ratio (ADC of benign prostatic tissue to prostate cancer) and the pathologic Gleason score with a significant elevation of the ADC ratio along with an increase of the pathologic Gleason score (p < 0.001). ROC curves constructed for the tumor ADC and ADC ratio helped to distinguish pathologically aggressive (Gleason score ≥ 7) from non-aggressive (Gleason score ≤ 6) tumors and to correlate it with PIRADSv2 scoring to predict the presence of clinically significant PCA (PIRADSv2 DW ≥ 4). The ability of the tumor ADC and ADC ratio to predict highly aggressive tumors (GS> 7) was high (AUC for ADC and ADC ratio, 0.946 and 0.897; p = 0.014 and 0.039, respectively). The ADC cut-off value for GS ≥ 7 was < 0.7725 and for GS ≤ 6 was > 0.8620 with sensitivity and specificity 97 and 94%. The cutoff ADC ratio for predicting (GS > 7) was 1.42 and for GS ≤ 6 was > 1.320 with sensitivity and specificity 97 and 92%. By applying this ADC ratio cut-off value the sensitivity and specificity of reader 1 for correct categorization of PIRADSv2 DW > 4 increased from 90 and 68% to 95 and 90% and that of reader 2 increased from 94 and 88% to 97 and 92%, respectively. Conclusion Estimation of DW metrics (ADC and ADC ratio between benign prostatic tissue and prostate cancer) allow the non-invasive assessment of biological aggressiveness of prostate cancer and allow reliable application of the PIRADSv2 scoring to determine clinically significant cancer (DW score > 4) which may contribute in planning initial treatment strategies.

scholarly journals Bacterial Membrane Vesicles in Pneumonia: From Mediators of Virulence to Innovative Vaccine Candidates

2021 ◽  
Vol 22 (8) ◽  
pp. 3858
Author(s):  
Felix Behrens ◽  
Teresa C. Funk-Hilsdorf ◽  
Wolfgang M. Kuebler ◽  
Szandor Simmons
Keyword(s):  
Extracellular Vesicles ◽  
Inflammatory Responses ◽  
Significant Proportion ◽  
Treatment Strategies ◽  
Membrane Vesicles ◽  
Multidrug Resistant ◽  
Biochemical Properties ◽  
Outer Membrane Vesicles ◽  
Vaccine Candidates ◽  
Treatment Regimens

Pneumonia due to respiratory infection with most prominently bacteria, but also viruses, fungi, or parasites is the leading cause of death worldwide among all infectious disease in both adults and infants. The introduction of modern antibiotic treatment regimens and vaccine strategies has helped to lower the burden of bacterial pneumonia, yet due to the unavailability or refusal of vaccines and antimicrobials in parts of the global population, the rise of multidrug resistant pathogens, and high fatality rates even in patients treated with appropriate antibiotics pneumonia remains a global threat. As such, a better understanding of pathogen virulence on the one, and the development of innovative vaccine strategies on the other hand are once again in dire need in the perennial fight of men against microbes. Recent data show that the secretome of bacteria consists not only of soluble mediators of virulence but also to a significant proportion of extracellular vesicles—lipid bilayer-delimited particles that form integral mediators of intercellular communication. Extracellular vesicles are released from cells of all kinds of organisms, including both Gram-negative and Gram-positive bacteria in which case they are commonly termed outer membrane vesicles (OMVs) and membrane vesicles (MVs), respectively. (O)MVs can trigger inflammatory responses to specific pathogens including S. pneumonia, P. aeruginosa, and L. pneumophila and as such, mediate bacterial virulence in pneumonia by challenging the host respiratory epithelium and cellular and humoral immunity. In parallel, however, (O)MVs have recently emerged as auspicious vaccine candidates due to their natural antigenicity and favorable biochemical properties. First studies highlight the efficacy of such vaccines in animal models exposed to (O)MVs from B. pertussis, S. pneumoniae, A. baumannii, and K. pneumoniae. An advanced and balanced recognition of both the detrimental effects of (O)MVs and their immunogenic potential could pave the way to novel treatment strategies in pneumonia and effective preventive approaches.

Emerging Treatment Strategies in Pancreatic Cancer

Pancreas ◽  
2021 ◽  
Vol 50 (6) ◽  
pp. 773-787
Author(s):  
Andrew Trunk ◽  
Laura Miotke ◽  
Christopher Nevala-Plagemann ◽  
Helena Verdaguer ◽  
Teresa Macarulla ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Treatment Strategies

scholarly journals Risk Scores and Machine Learning to Identify Patients With Acute Periprosthetic Joints Infections That Will Likely Fail Classical Irrigation and Debridement

2021 ◽  
Vol 8 ◽  
Author(s):  
Marjan Wouthuyzen-Bakker ◽  
Noam Shohat ◽  
Javad Parvizi ◽  
Alex Soriano
Keyword(s):  
Machine Learning ◽  
Joint Infection ◽  
Treatment Strategies ◽  
Risk Scores ◽  
Related Factors ◽  
Duration Of Symptoms ◽  
Treatment Regimens ◽  
Irrigation And Debridement ◽  
Individualize Treatment ◽  
Independent Risk Factors

The most preferred treatment for acute periprosthetic joint infection (PJI) is surgical debridement, antibiotics and retention of the implant (DAIR). The reported success of DAIR varies greatly and depends on a complex interplay of several host-related factors, duration of symptoms, the microorganism(s) causing the infection, its susceptibility to antibiotics and many others. Thus, there is a great clinical need to predict failure of the “classical” DAIR procedure so that this surgical option is offered to those most likely to succeed, but also to identify those patients who may benefit from more intensified antibiotic treatment regimens or new and innovative treatment strategies. In this review article, the current recommendations for DAIR will be discussed, a summary of independent risk factors for DAIR failure will be provided and the advantages and limitations of the clinical use of preoperative risk scores in early acute (post-surgical) and late acute (hematogenous) PJIs will be presented. In addition, the potential of implementing machine learning (artificial intelligence) in identifying patients who are at highest risk for failure of DAIR will be addressed. The ultimate goal is to maximally tailor and individualize treatment strategies and to avoid treatment generalization.

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