Purpose:
To create a model that will better predict the survival time for patients that were treated with stereotactic radiosurgery for brain metastases using a support vector machine regression ...model.
Methods:
This study utilized data from 481 patients, which were equally divided into training and validation datasets randomly. The predictor variables for the SVM model consisted of the actual survival time of the patient, the number of brain metastases, the GPA and KPS scores, prescription dose, and the size of the largest PTV. The resulting survival time predictions were analyzed against the actual survival times by single parameter classification and two-parameter classification. The predicted mean survival times within each classification are compared with the actual values to obtain the confidence interval associated with the model’s predictions.
Results:
The number of metastases and KPS scores, were consistently shown to be the strongest predictors after single parameter classification, and were thus chosen for as first classifiers for the two-parameter classification. When the survival times were analyzed with the number of metastases as the first classifier, the best correlation was obtained for patients with 3 metastases, while patients with 4 or 5 metastases had significantly worse results. When the KPS score was used as the first classifier, patients with a KPS score of 60 and 70 had similar strong correlation results.
Conclusion:
The number of metastases and the KPS score both showed to be good predictors of patient survival time. The model was less accurate for patients with more metastases and certain KPS score ranges due to the lack of training data.
•A novel compartmental epidemic model SIPHERD is employed for COVID-19 prediction for India where it has reached at alarming level.•Impact of lockdown and the number of tests conducted per day on ...predictions of containment is studied.•Purely Asymptomatic cases and spread from them as well as Exposed in incubation period considered.•Increasing the tests per day by 10k every day, stringent measures on social-distancing and strict lockdown in July have significant impact on the disease spread.
Originating from Wuhan, China, in late 2019, and with a gradual spread in the last few months, COVID-19 has become a pandemic crossing 9 million confirmed positive cases and 450 thousand deaths. India is not only an overpopulated country but has a high population density as well, and at present, a high-risk nation where COVID-19 infection can go out of control. In this paper, we employ a compartmental epidemic model SIPHERD for COVID-19 and predict the total number of confirmed, active and death cases, and daily new cases. We analyze the impact of lockdown and the number of tests conducted per day on the prediction and bring out the scenarios in which the infection can be controlled faster. Our findings indicate that increasing the tests per day at a rapid pace (10k per day increase), stringent measures on social-distancing for the coming months and strict lockdown in the month of July all have a significant impact on the disease spread.
Medicinal plants have historically proven their value as a source of molecules with therapeutic potential, and nowadays still represent an important pool for the identification of novel drug leads. ...In the past decades, pharmaceutical industry focused mainly on libraries of synthetic compounds as drug discovery source. They are comparably easy to produce and resupply, and demonstrate good compatibility with established high throughput screening (HTS) platforms. However, at the same time there has been a declining trend in the number of new drugs reaching the market, raising renewed scientific interest in drug discovery from natural sources, despite of its known challenges. In this survey, a brief outline of historical development is provided together with a comprehensive overview of used approaches and recent developments relevant to plant-derived natural product drug discovery. Associated challenges and major strengths of natural product-based drug discovery are critically discussed. A snapshot of the advanced plant-derived natural products that are currently in actively recruiting clinical trials is also presented. Importantly, the transition of a natural compound from a “screening hit” through a “drug lead” to a “marketed drug” is associated with increasingly challenging demands for compound amount, which often cannot be met by re-isolation from the respective plant sources. In this regard, existing alternatives for resupply are also discussed, including different biotechnology approaches and total organic synthesis.
While the intrinsic complexity of natural product-based drug discovery necessitates highly integrated interdisciplinary approaches, the reviewed scientific developments, recent technological advances, and research trends clearly indicate that natural products will be among the most important sources of new drugs also in the future.
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The problem solution is considered for an airstream flow around a rectangular parallelepiped railway car model. The results of computer modeling in ANSYS CFX software system of air flow aerodynamics ...at its deviation from the longitudinal axis of the car are given. The k-ε turbulence model is used to close the Navier–Stokes equations averaged by Reynolds. The distribution diagrams of the flow velocities and pressures on the frontal and side surfaces of the vehicle are obtained. The values of aerodynamic drag coefficients of the car depending on the attack angle are determined. It is shown that when the attack angle increases from 0 to 10°, the aerodynamic coefficient changes nonlinearly, and this increase corresponds to the experimental values. The developed numerical modeling technique makes it possible to analyze the airflow around both railways rolling stock and automobiles.
In the cellulose scientific community, hydrogen bonding is often used as the explanation for a large variety of phenomena and properties related to cellulose and cellulose based materials. Yet, ...hydrogen bonding is just one of several molecular interactions and furthermore is both relatively weak and sensitive to the environment. In this review we present a comprehensive examination of the scientific literature in the area, with focus on theory and molecular simulation, and conclude that the relative importance of hydrogen bonding has been, and still is, frequently exaggerated.
Our aim was to compare the different methods of modeling the effect of circulating blood flow on the thermal lesion dimensions created by radio frequency (RF) cardiac ablation and on the maximum ...blood temperature. Computational models were built to study the temperature distributions and lesion dimensions created by a nonirrigated electrode by two RF energy delivery protocols (constant voltage and constant temperature) under high and low blood flow conditions. Four methods of modeling the effect of circulating blood flow on lesion dimensions and temperature distribution were compared. Three of them considered convective coefficients at the electrode-blood and tissue-blood interfaces to model blood flow: 1) without including blood as a part of the domain; 2) constant electrical conductivity of blood; and 3) temperature-dependent electrical conductivity of blood (+2%/°C). Method 4) included blood motion and was considered to be a reference method for comparison purposes. Only Method 4 provided a realistic blood temperature distribution. The other three methods predicted lesion depth values similar to those of the reference method (differences smaller than 1 mm), regardless of ablation mode and blood flow conditions. Considering the aspects of lesion size and maximum temperature reached in blood and tissue, Method 2 seems to be the most suitable alternative to Method 4 in order to reduce the computational complexity. Our findings could have an important implication in future studies of RF cardiac ablation, in particular, in choosing the most suitable method to model the thermal effect of circulating blood.
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