Needle Insertion Force Modeling using Genetic Programming Polynomial Higher Order Neural Network

Robotics ◽

10.4018/978-1-4666-4607-0.ch031 ◽

2013 ◽

pp. 619-637

Author(s):

Mehdi Fallahnezhad ◽

Hashem Yousefi

Keyword(s):

Neural Network ◽

Biological Tissue ◽

Constant Velocity ◽

Phase 1 ◽

Needle Insertion ◽

Prostate Brachytherapy ◽

Force Modeling ◽

Needle Insertion Force ◽

Force Displacement

Precise insertion of a medical needle as an end-effecter of a robotic or computer-aided system into biological tissue is an important issue and should be considered in different operations, such as brain biopsy, prostate brachytherapy, and percutaneous therapies. Proper understanding of the whole procedure leads to a better performance by an operator or system. In this chapter, the authors use a 0.98 mm diameter needle with a real-time recording of force, displacement, and velocity of needle through biological tissue during in-vitro insertions. Using constant velocity experiments from 5 mm/min up to 300 mm/min, the data set for the force-displacement graph of insertion was gathered. Tissue deformation with a small puncture and a constant velocity penetration are the two first phases in the needle insertion process. Direct effects of different parameters and their correlations during the process is being modeled using a polynomial neural network. The authors develop different networks in 2nd and 3rd order to model the two first phases of insertion separately. Modeling accuracies were 98% and 86% in phase 1 and 2, respectively.

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Multi-Objective Higher Order Polynomial Networks to Model Insertion Force of Bevel-Tip Needles

Psychology and Mental Health ◽

10.4018/978-1-5225-0159-6.ch074 ◽

2016 ◽

pp. 1712-1729

Author(s):

Hashem Yousefi ◽

Mehdi Fallahnezhad

Keyword(s):

Soft Tissue ◽

Brain Biopsy ◽

Needle Insertion ◽

Minimal Invasive ◽

Higher Order ◽

Prostate Brachytherapy ◽

Insertion Force ◽

Tissue Properties ◽

Order Polynomial ◽

Force Displacement

Needle insertion has been a very popular minimal invasive surgery method in cancer detection, soft tissue properties recognition and many other surgical operations. Its applications were observed in brain biopsy, prostate brachytherapy and many percutaneous therapies. In this study the authors would like to provide a model of needle force in soft tissue insertion. This model has been developed using higher order polynomial networks. In order to provide a predictive model one-dimensional force sensed on enacting end of bevel-tip needles. The speeds of penetration for quasi-static processes have chosen to be in the range of between 5 mm/min and 300 mm/min. Second and third orders of polynomials employed in the network which contains displacement and speed as their main affecting parameters in the simplified model. Results of fitting functions showed a reliable accuracy in force-displacement graph.

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Multi-Objective Higher Order Polynomial Networks to Model Insertion Force of Bevel-Tip Needles

International Journal of Natural Computing Research ◽

10.4018/ijncr.2015070103 ◽

2015 ◽

Vol 5 (3) ◽

pp. 54-70

Author(s):

Hashem Yousefi ◽

Mehdi Fallahnezhad

Keyword(s):

Soft Tissue ◽

Brain Biopsy ◽

Needle Insertion ◽

Minimal Invasive ◽

Higher Order ◽

Prostate Brachytherapy ◽

Insertion Force ◽

Tissue Properties ◽

Order Polynomial ◽

Force Displacement

Needle insertion has been a very popular minimal invasive surgery method in cancer detection, soft tissue properties recognition and many other surgical operations. Its applications were observed in brain biopsy, prostate brachytherapy and many percutaneous therapies. In this study the authors would like to provide a model of needle force in soft tissue insertion. This model has been developed using higher order polynomial networks. In order to provide a predictive model one-dimensional force sensed on enacting end of bevel-tip needles. The speeds of penetration for quasi-static processes have chosen to be in the range of between 5 mm/min and 300 mm/min. Second and third orders of polynomials employed in the network which contains displacement and speed as their main affecting parameters in the simplified model. Results of fitting functions showed a reliable accuracy in force-displacement graph.

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CirBiTree: Citrullination Site Inference Based on a Fuzzy Neural Network and Flexible Neural Tree

Scientific Programming ◽

10.1155/2020/8847694 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Chuandong Song ◽

Haifeng Wang

Keyword(s):

Neural Network ◽

Fuzzy Neural Network ◽

Classification Model ◽

Peptide Sequence ◽

Sequence Information ◽

Post Translational Modification ◽

Fuzzy Neural ◽

Human Complex ◽

Better Than

Emerging evidence demonstrates that post-translational modification plays an important role in several human complex diseases. Nevertheless, considering the inherent high cost and time consumption of classical and typical in vitro experiments, an increasing attention has been paid to the development of efficient and available computational tools to identify the potential modification sites in the level of protein. In this work, we propose a machine learning-based model called CirBiTree for identification the potential citrullination sites. More specifically, we initially utilize the biprofile Bayesian to extract peptide sequence information. Then, a flexible neural tree and fuzzy neural network are employed as the classification model. Finally, the most available length of identified peptides has been selected in this model. To evaluate the performance of the proposed methods, some state-of-the-art methods have been employed for comparison. The experimental results demonstrate that the proposed method is better than other methods. CirBiTree can achieve 83.07% in sn%, 80.50% in sp, 0.8201 in F1, and 0.6359 in MCC, respectively.

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627 The DLL3-targeted half-life extended bispecific T cell engager (HLE BiTE®) immune-oncology therapy AMG 757 has potent antitumor activity in neuroendocrine cancer

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0627 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A663-A663

Author(s):

Keegan Cooke ◽

Juan Estrada ◽

Jinghui Zhan ◽

Jonathan Werner ◽

Fei Lee ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Tumor Growth ◽

Mouse Models ◽

T Cell Activation ◽

Phase 1 ◽

Phase 1 Study ◽

Human T Cells

BackgroundNeuroendocrine tumors (NET), including small cell lung cancer (SCLC), have poor prognosis and limited therapeutic options. AMG 757 is an HLE BiTE® immune therapy designed to redirect T cell cytotoxicity to NET cells by binding to Delta-like ligand 3 (DLL3) expressed on the tumor cell surface and CD3 on T cells.MethodsWe evaluated activity of AMG 757 in NET cells in vitro and in mouse models of neuroendocrine cancer in vivo. In vitro, co-cultures of NET cells and human T cells were treated with AMG 757 in a concentration range and T cell activation, cytokine production, and tumor cell killing were assessed. In vivo, AMG 757 antitumor efficacy was evaluated in xenograft NET and in orthotopic models designed to mimic primary and metastatic SCLC lesions. NSG mice bearing established NET were administered human T cells and then treated once weekly with AMG 757 or control HLE BiTE molecule; tumor growth inhibition was assessed. Pharmacodynamic effects of AMG 757 in tumors were also evaluated in SCLC models following a single administration of human T cells and AMG 757 or control HLE BiTE molecule.ResultsAMG 757 induced T cell activation, cytokine production, and potent T cell redirected killing of DLL3-expressing SCLC, neuroendocrine prostate cancer, and other DLL3-expressing NET cell lines in vitro. AMG 757-mediated redirected lysis was specific for DLL3-expressing cells. In patient-derived xenograft and orthotopic models of SCLC, single-dose AMG 757 effectively engaged human T cells administered systemically, leading to a significant increase in the number of human CD4+ and CD8+ T cells in primary and metastatic tumor lesions. Weekly administration of AMG 757 induced significant tumor growth inhibition of SCLC (figure 1) and other NET, including complete regression of established tumors and clearance of metastatic lesions. These findings warranted evaluation of AMG 757 (NCT03319940); the phase 1 study includes dose exploration (monotherapy and in combination with pembrolizumab) and dose expansion (monotherapy) in patients with SCLC (figure 2). A study of AMG 757 in patients with neuroendocrine prostate cancer is under development based on emerging data from the ongoing phase 1 study.Abstract 627 Figure 1AMG 757 Significantly reduced tumor growth in orthotopic SCLC mouse modelsAbstract 627 Figure 2AMG 757 Phase 1 study designConclusionsAMG 757 engages and activates T cells to kill DLL3-expressing SCLC and other NET cells in vitro and induces significant antitumor activity against established xenograft tumors in mouse models. These preclinical data support evaluation of AMG 757 in clinical studies of patients with NET.Ethics ApprovalAll in vivo work was conducted under IACUC-approved protocol #2009-00046.

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A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture

Plant Methods ◽

10.1186/s13007-021-00714-9 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Mina Salehi ◽

Siamak Farhadi ◽

Ahmad Moieni ◽

Naser Safaie ◽

Mohsen Hesami

Keyword(s):

Neural Network ◽

Fruit Fly ◽

Corylus Avellana ◽

General Regression Neural Network ◽

Fruit Fly Optimization Algorithm ◽

Harvesting Time ◽

Fruit Fly Optimization ◽

General Regression ◽

Concentration Levels

Abstract Background Paclitaxel is a well-known chemotherapeutic agent widely applied as a therapy for various types of cancers. In vitro culture of Corylus avellana has been named as a promising and low-cost strategy for paclitaxel production. Fungal elicitors have been reported as an impressive strategy for improving paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. The objectives of this research were to forecast and optimize growth and paclitaxel biosynthesis based on four input variables including cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day and CSC harvesting time in C. avellana cell culture, as a case study, using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) via data mining approach for the first time. Results GRNN-FOA models (0.88–0.97) showed the superior prediction performances as compared to regression models (0.57–0.86). Comparative analysis of multilayer perceptron-genetic algorithm (MLP-GA) and GRNN-FOA showed very slight difference between two models for dry weight (DW), intracellular and extracellular paclitaxel in testing subset, the unseen data. However, MLP-GA was slightly more accurate as compared to GRNN-FOA for total paclitaxel and extracellular paclitaxel portion in testing subset. The slight difference was observed in maximum growth and paclitaxel biosynthesis optimized by FOA and GA. The optimization analysis using FOA on developed GRNN-FOA models showed that optimal CE [4.29% (v/v)] and CF [5.38% (v/v)] concentration levels, elicitor adding day (17) and harvesting time (88 h and 19 min) can lead to highest paclitaxel biosynthesis (372.89 µg l−1). Conclusions Great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion support excellent performance of developed GRNN-FOA models. Overall, GRNN-FOA as new mathematical tool may pave the way for forecasting and optimizing secondary metabolite production in plant in vitro culture.

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Nonclinical Development of SRK-181: An Anti-Latent TGFβ1 Monoclonal Antibody for the Treatment of Locally Advanced or Metastatic Solid Tumors

International Journal of Toxicology ◽

10.1177/1091581821998945 ◽

2021 ◽

pp. 109158182199894

Author(s):

Brian T. Welsh ◽

Ryan Faucette ◽

Sanela Bilic ◽

Constance J. Martin ◽

Thomas Schürpf ◽

...

Keyword(s):

Transforming Growth Factor ◽

Checkpoint Inhibitors ◽

Human Peripheral Blood ◽

Phase 1 ◽

Locally Advanced ◽

Acquired Resistance ◽

Transforming Growth Factor Β ◽

Human Antibody ◽

Primary Resistance

Checkpoint inhibitors offer a promising immunotherapy strategy for cancer treatment; however, due to primary or acquired resistance, many patients do not achieve lasting clinical responses. Recently, the transforming growth factor-β (TGFβ) signaling pathway has been identified as a potential target to overcome primary resistance, although the nonselective inhibition of multiple TGFβ isoforms has led to dose-limiting cardiotoxicities. SRK-181 is a high-affinity, fully human antibody that selectively binds to latent TGFβ1 and inhibits its activation. To support SRK-181 clinical development, we present here a comprehensive preclinical assessment of its pharmacology, pharmacokinetics, and safety across multiple species. In vitro studies showed that SRK-181 has no effect on human platelet function and does not induce cytokine release in human peripheral blood. Four-week toxicology studies with SRK-181 showed that weekly intravenous administration achieved sustained serum exposure and was well tolerated in rats and monkeys, with no treatment-related adverse findings. The no-observed-adverse-effect levels levels were 200 mg/kg in rats and 300 mg/kg in monkeys, the highest doses tested, and provide a nonclinical safety factor of up to 813-fold (based on Cmax) above the phase 1 starting dose of 80 mg every 3 weeks. In summary, the nonclinical pharmacology, pharmacokinetic, and toxicology data demonstrate that SRK-181 is a selective inhibitor of latent TGFβ1 that does not produce the nonclinical toxicities associated with nonselective TGFβ inhibition. These data support the initiation and safe conduct of a phase 1 trial with SRK-181 in patients with advanced cancer.

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CTNI-18. FINAL RESULTS OF A PHASE 2 STUDY OF EFFICACY, SAFETY AND INTRATUMORAL PHARMACOKINETICS (PK) OF SELINEXOR MONOTHERAPY IN RECURRENT GLIOBLASTOMA (rGBM)

Neuro-Oncology ◽

10.1093/neuonc/noaa215.185 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii46-ii46

Author(s):

Andrew Lassman ◽

Patrick Wen ◽

Martin van den Bent ◽

Scott Plotkin ◽

Annemiek Walenkamp ◽

...

Keyword(s):

Nuclear Export ◽

Phase 1 ◽

Progression Free Survival ◽

Median Number ◽

Recurrent Glioblastoma ◽

Phase 2 ◽

Nuclear Retention ◽

Phase 2 Trial ◽

Human Gbm

Abstract BACKGROUND Selinexor is an FDA-approved first-in-class, oral selective nuclear export inhibitor which forces nuclear retention of many tumor suppressor proteins. METHODS We conducted a phase 2 trial of selinexor monotherapy for adults with recurrent GBM including a surgical arm to explore intratumoral PK and 3 medical arms to optimize dosing. Prior treatment with radiotherapy and temozolomide was required; prior bevacizumab was exclusionary. The primary endpoint was 6-month progression-free survival (6mPFS) rate. RESULTS Selinexor administered ~2 hours pre-operatively yieleded average intratumoral concentration (136 nM, n=6) comparable to the in vitro IC50 (130 nM) from 7 primary human GBM cell lines. Among all 68 patients accrued to 3 medical arms (~85 mg BIW, n=24; 60 mg BIW, n=14; 80 mg QW, n=30), median age was 56 years (21–78). Median number of prior lines of therapies was 2 (1–7). At 80 mg QW, 28% patients were progression-free at the end of cycle 6; the 6mPFS was 17%; disese control rate by RANO was 37% (1 CR, 2 PRs, 7 SD) among 27 evaluable patients; responses were durable (median 11.1 months), and treatment lasted for 442, 547 and 1282 days in 3 responders, as of data lock, with one responder remaining on treatment off study; median overall survival was 10.2 months with 95% CI (7.0, 15.4). The ~85 mg BIW-schedule was abandoned due to poor tolerability. The related adverse events (all grades) in patients on ~85 mg BIW/60 mg BIW/80 mg QW were nausea (41.7%/64.3%/66.7%), fatigue (70.8%/71.4%/50.0%), neutropenia (29.2%/14.3%/33.3%), decreased appetite (45.8%/71.4%/26.7%), thrombocytopenia (66.7%/28.6%/23.3%) and weight loss (16.7%,/42.9%/6.7%). CONCLUSION Selinexor monotherapy demonstrated encouraging intratumoral penetration and efficacy, with durable disease control in rGBM. Monotherapy dose at 80 mg QW is recommended for further development in rGBM. A phase 1/2 study of combination therapy for newly diagnosed or rGBM has been initiated (NCT04421378).

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ABL001, a Bispecific Antibody Targeting VEGF and DLL4, with Chemotherapy, Synergistically Inhibits Tumor Progression in Xenograft Models

International Journal of Molecular Sciences ◽

10.3390/ijms22010241 ◽

2020 ◽

Vol 22 (1) ◽

pp. 241

Author(s):

Dong-Hoon Yeom ◽

Yo-Seob Lee ◽

Ilhwan Ryu ◽

Sunju Lee ◽

Byungje Sung ◽

...

Keyword(s):

Combination Therapy ◽

Tumor Progression ◽

Cancer Patients ◽

Bispecific Antibody ◽

Phase 1 ◽

Tumor Vessel ◽

Vegf Inhibitors ◽

Xenograft Models ◽

Antibody Targeting

Delta-like-ligand 4 (DLL4) is a promising target to augment the effects of VEGF inhibitors. A simultaneous blockade of VEGF/VEGFR and DLL4/Notch signaling pathways leads to more potent anti-cancer effects by synergistic anti-angiogenic mechanisms in xenograft models. A bispecific antibody targeting VEGF and DLL4 (ABL001/NOV1501/TR009) demonstrates more potent in vitro and in vivo biological activity compared to VEGF or DLL4 targeting monoclonal antibodies alone and is currently being evaluated in a phase 1 clinical study of heavy chemotherapy or targeted therapy pre-treated cancer patients (ClinicalTrials.gov Identifier: NCT03292783). However, the effects of a combination of ABL001 and chemotherapy on tumor vessels and tumors are not known. Hence, the effects of ABL001, with or without paclitaxel and irinotecan were evaluated in human gastric or colon cancer xenograft models. The combination treatment synergistically inhibited tumor progression compared to each monotherapy. More tumor vessel regression and apoptotic tumor cell induction were observed in tumors treated with the combination therapy, which might be due to tumor vessel normalization. Overall, these findings suggest that the combination therapy of ABL001 with paclitaxel or irinotecan would be a better clinical strategy for the treatment of cancer patients.

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