scholarly journals Using mathematical models to estimate drug resistance and treatment efficacy via CT scan measurements of tumour volume

1990 ◽  
Vol 62 (4) ◽  
pp. 671-675 ◽  
Author(s):  
WM Gregory ◽  
RH Reznek ◽  
M Hallett ◽  
ML Slevin
Keyword(s):  
Drug Resistance ◽  
Mathematical Models ◽  
Ct Scan ◽  
Treatment Efficacy ◽  
Tumour Volume

scholarly journals Certain Investigations on the Application of Machine learning Algorithms and Deep Learning Architectures for Covid -19 Diagnosis

2021 ◽  
Author(s):  
Soundariya R.S. ◽  
◽  
Tharsanee R.M. ◽  
Vishnupriya B ◽  
Ashwathi R ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mathematical Models ◽  
Ct Scan ◽  
Virus Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
X Ray ◽  
Laboratory Test Results ◽  
Learning Architectures

Corona virus disease (Covid - 19) has started to promptly spread worldwide from April 2020 till date, leading to massive death and loss of lives of people across various countries. In accordance to the advices of WHO, presently the diagnosis is implemented by Reverse Transcription Polymerase Chain Reaction (RT- PCR) testing, that incurs four to eight hours’ time to process test samples and adds 48 hours to categorize whether the samples are positive or negative. It is obvious that laboratory tests are time consuming and hence a speedy and prompt diagnosis of the disease is extremely needed. This can be attained through several Artificial Intelligence methodologies for prior diagnosis and tracing of corona diagnosis. Those methodologies are summarized into three categories: (i) Predicting the pandemic spread using mathematical models (ii) Empirical analysis using machine learning models to forecast the global corona transition by considering susceptible, infected and recovered rate. (iii) Utilizing deep learning architectures for corona diagnosis using the input data in the form of X-ray images and CT scan images. When X-ray and CT scan images are taken into account, supplementary data like medical signs, patient history and laboratory test results can also be considered while training the learning model and to advance the testing efficacy. Thus the proposed investigation summaries the several mathematical models, machine learning algorithms and deep learning frameworks that can be executed on the datasets to forecast the traces of COVID-19 and detect the risk factors of coronavirus.

scholarly journals Cell line-specific network models of ER+ breast cancer identify PI3Kα inhibitor sensitivity factors and drug combinations

2020 ◽  
Author(s):  
Jorge Gómez Tejeda Zañudo ◽  
Pingping Mao ◽  
Clara Alcon ◽  
Kailey J. Kowalski ◽  
Gabriela N. Johnson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Resistance ◽  
Mathematical Models ◽  
Cell Line ◽  
Network Models ◽  
Drug Combinations ◽  
Cancer Drug ◽  
Bh3 Mimetics ◽  
Cancer Drug Resistance ◽  
Sensitivity Factors

Durable control of invasive solid tumors necessitates identifying therapeutic resistance mechanisms and effective drug combinations. A promising approach to tackle the cancer drug resistance problem is to build mechanistic mathematical models of the signaling network of cancer cells, and explicitly model the dynamics of information flow through this network under distinct genetic conditions and in response to perturbations. In this work, we used a network-based mathematical model to identify sensitivity factors and drug combinations for the PI3K&alpha inhibitor alpelisib, which was recently approved for ER+ PIK3CA mutant breast cancer. We experimentally validated the model-predicted efficacious combination of alpelisib and BH3 mimetics (e.g. MCL1 inhibitors) in ER+ breast cancer cell lines. We also experimentally validated the reduced sensitivity to alpelisib caused by FOXO3 knockdown, which is a novel potential resistance mechanism. Our experimental results showed cell line-specific sensitivity to the combination of alpelisib and BH3 mimetics, which was driven by the choice of BH3 mimetics. We find that cell lines were sensitive to the addition of either MCL1 inhibitor s63845 alone or in combination with BCL-XL/BCL-2 inhibitor navitoclax, and that the need for the combination of both BH3 mimetics was predicted by the expression of BCL-XL. Based on these results, we developed cell line-specific network models that are able to recapitulate the observed differential response to alpelisib and BH3 mimetics, and also incorporate the most recent knowledge on resistance and response to PI3K&alpha inhibitors. Overall, we present an approach for the development, experimental testing, and refining of mathematical models, which we apply to the context of PI3K&alpha inhibitor drug resistance in breast cancer. Our approach predicted and validated PI3K&alpha inhibitor sensitivity factors (FOXO3 knockdown) and drug combinations (BH3 mimetics), and illustrates that network-based mathematical models can contribute to overcoming the challenge of cancer drug resistance.

scholarly journals Superinfection and the evolution of resistance to antimalarial drugs

2012 ◽  
Vol 279 (1743) ◽  
pp. 3834-3842 ◽  
Author(s):  
Eili Y. Klein ◽  
David L. Smith ◽  
Ramanan Laxminarayan ◽  
Simon Levin
Keyword(s):  
Drug Resistance ◽  
Plasmodium Falciparum ◽  
General Theory ◽  
Mathematical Models ◽  
Random Sampling ◽  
Transmission Rate ◽  
Drug Resistant ◽  
Antimalarial Drug Resistance ◽  
Resistance Evolution ◽  
Evolution Of Resistance

A major issue in the control of malaria is the evolution of drug resistance. Ecological theory has demonstrated that pathogen superinfection and the resulting within-host competition influences the evolution of specific traits. Individuals infected with Plasmodium falciparum are consistently infected by multiple parasites; however, while this probably alters the dynamics of resistance evolution, there are few robust mathematical models examining this issue. We developed a general theory for modelling the evolution of resistance with host superinfection and examine: (i) the effect of transmission intensity on the rate of resistance evolution; (ii) the importance of different biological costs of resistance; and (iii) the best measure of the frequency of resistance. We find that within-host competition retards the ability and slows the rate at which drug-resistant parasites invade, particularly as the transmission rate increases. We also find that biological costs of resistance that reduce transmission are less important than reductions in the duration of drug-resistant infections. Lastly, we find that random sampling of the population for resistant parasites is likely to significantly underestimate the frequency of resistance. Considering superinfection in mathematical models of antimalarial drug resistance may thus be important for generating accurate predictions of interventions to contain resistance.

Inter-fraction variation in interstitial high-dose-rate brachytherapy

2015 ◽  
Vol 14 (2) ◽  
pp. 143-151 ◽  
Author(s):  
Saravanan Kandasamy ◽  
K. S. Reddy ◽  
Vivekanandan Nagarajan ◽  
Parthasarathy Vedasoundaram ◽  
Gunaseelan Karunanidhi
Keyword(s):  
Ct Scan ◽  
Dose Rate ◽  
Organs At Risk ◽  
Breast Implant ◽  
Quality Care ◽  
Significant Risk ◽  
Tumour Volume ◽  
High Dose Rate ◽  
High Dose ◽  
Hdr Brachytherapy

AbstractAimTo evaluate the inter-fraction variation in interstitial high-dose-rate (HDR) brachytherapy. To assess the positional displacement of catheters during the fractions and the resultant impact on dosimetry.BackgroundAlthough brachytherapy continues to be a key cornerstone of cancer care, it is clear that treatment innovations are needed to build on this success and ensure that brachytherapy continues to provide quality care for patients. The dosimetric advantages offered by HDR brachytherapy to the tumour volume rely on catheter positions being accurately reproduced for all fractions of treatment.Materials and methodsA total of 66 patients treated over a period of 22 months were considered for this study. All the patients underwent computer tomography (CT) scan and three-dimensional treatment planning was carried out. Brachytherapy treatment was delivered by the HDR afterloading system. On completing the last fraction, CT scan was repeated and treatment re-planning was done. The variation in position of the implanted applicators and their impact on dosimetric parameters were analysed using both the plans.ResultsFor all breast-implant patients, the catheter displacement and D90dose to clinical target volume were <3 mm and 3%, respectively. The displacement for carcinoma of the tongue, carcinoma of the buccal mucosa, carcinoma of the floor of mouth, carcinoma of the cervix, soft-tissue sarcoma and carcinoma of the lip were comparatively high.ConclusionInter-fraction errors occur frequently in interstitial HDR brachytherapy. If no action is taken, it will result in a significant risk of geometrical miss and overdose to the organs at risk. It is not recommended to use a single plan to deliver all the fractions. Imaging is recommended before each fraction and decision on re-planning must be taken.

scholarly journals Treatment efficacy and tuberculosis relapses in HIV infected patients with severe immunosuppression who started antiretroviral therapy

2020 ◽  
Vol 98 (10) ◽  
pp. 11-18
Author(s):  
E. V. Korzh ◽  
N. A. Podchos ◽  
L. V. Striga ◽  
T. S. Izvekova ◽  
N. A. Malyavko
Keyword(s):  
Drug Resistance ◽  
Antiretroviral Therapy ◽  
Cause Of Death ◽  
Treatment Efficacy ◽  
Multiple Drug Resistance ◽  
Cd4 Count ◽  
Multiple Drug ◽  
Severe Immunosuppression ◽  
Associated Diseases ◽  
Discharge From Hospital

The objective: to analyze treatment efficacy and causes of tuberculosis relapses in HIV-infected patients with severe immunosuppression who have started antiretroviral therapy (ART). Subjects and methods. 139 case histories were studied, those case history belonged to the patients with TB/HIV co-infection and CD4 count below 100 cells/μl, a median of 33.2 cells/μl – 4.2%, who started ART in the in-patient unit. The efficacy of inpatient treatment was assessed; 89 patients were followed up after discharge from hospital. The follow-up period lasted from January 2011 to May 2019. Results. ART did not increase the efficacy of the in-patient stage of TB/HIV treatment due to the development of immune reconstitution inflammatory syndrome, which occurred in 34.5% of patients and accounted for 70.0% of hospital lethality cases. After discharge from hospital, 69.7% of patients successfully completed anti-tuberculosis chemotherapy, 25.8% died before completing treatment, the main cause of death was tuberculosis (56.5%), including multiple drug resistance in 30.8% of cases. At the outpatient stage, 29.1% of patients interrupted ART, their death rate was higher (p = 0.007), and tuberculosis and HIV-associated diseases became the cause of death more often (p = 0.042) versus the compliant patients. Tuberculosis relapses developed in 17.7% after 16.7 ± 1.7 months after completion of treatment; 63.6% had multiple drug resistance, patients with tuberculosis relapses interrupted ART more often (p = 0.002), had a lower CD4 count (p = 0.030) versus patients without relapses. As of May 2019, 46.1% of patients survived and had no signs of active tuberculosis; 42.7% died, tuberculosis dominated among the causes of death – 50.0% (in 52.6% – multiple drug resistance) as well as HIV-associated diseases (21.1%).

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