Identify glomeruli in human kidney tissue images using a deep learning approach

Shubham, Shubham; Jain, Nikita; Gupta, Vishal; Ariffin, Mazeyanti Mohd; Ahmadian, Ali

doi:10.1007/s00500-021-06143-z

Cited by 21 publications

(13 citation statements)

References 33 publications

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“…Mathematical modelling of the whole problem discussed above suggests that the two systems (peripheral and core regions) of coupled equations ( 21)-( 27) are to be solved along with the coupled boundary limitations defined in equation (25). e system of the core region has been solved by an exact method, and the results are elaborated underneath:…”

Section: Solution Methodsmentioning

confidence: 99%

“…Mathematical simulations of many biologically inspired systems are developed and produced effect outputs in finding the cure or histories of many biologically inspired systems. Since decades, these models and simulation are vital in reducing animal experimentation [25][26][27][28][29]. So, major contributions of the current work can be overviewed as (i) is article endures some novel applications of the flow of two-layer fluid in a flexible tube.…”

Section: Introductionmentioning

confidence: 99%

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Flow Analysis of Two-Layer Nano/Johnson–Segalman Fluid in a Blood Vessel-like Tube with Complex Peristaltic Wave

Zeeshan

Riaz

Alzahrani

et al. 2022

Mathematical Problems in Engineering

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Biologically inspired micropumps using the phenomena of peristalsis are highly involved in targeted drugging in pharmacological engineering. This study analyzed theoretically the transport of two immiscible fluids in a long flexible tube. The core region contains Johnson–Segalman non-Newtonian fluid, while the peripheral region is saturated by nanofluid. It is assumed that Darcy’s porous medium is encountered close to the walls of the tube. A complex peristaltic wave is transmitted on the compliant wall which induces the flow. Equations of continuity, momentum, energy, and nanoparticle concentration are used in modelling the problem. The modelled problem for both the regions, i.e., core and peripheral regions are developed with the assumptions of long wavelength and creeping flow. Temperature, velocity, and shear stress at the interface are assumed to be equal. The system of equations is solved analytically. The graphical results for different involving parameters are displayed and thoroughly discussed. It is received that the heat transfer goes inverse with fluid viscosity in the peripheral region, but opposite measurements are obtained in the core region. This theoretical model may be considerable in some medical mechanisms such as targeted drug delivery, differential diagnosis, and hyperthermia. Moreover, no study on non-Newtonian nanofluid is reported yet for the two-layered flow system, so this study will give a good addition in the literature of biomedical research.

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Section: Solution Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flow Analysis of Two-Layer Nano/Johnson–Segalman Fluid in a Blood Vessel-like Tube with Complex Peristaltic Wave

Zeeshan

Riaz

Alzahrani

et al. 2022

Mathematical Problems in Engineering

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“…Future research in the healthcare field has virtually endless possibilities. These regard the use of Big Data Analytics to diagnose specific conditions [ 47 , 66 , 69 , 76 ], propose an approach that can be used in other healthcare applications and create mechanisms to identify “patients like me” [ 75 , 80 ]. Big Data Analytics could also be used for studies related to the spread of pandemics, the efficacy of covid treatment [ 18 , 79 ], or psychology and psychiatry studies, e.g.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

The use of Big Data Analytics in healthcare

2022

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The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities. The direct research was carried out based on research questionnaire and conducted on a sample of 217 medical facilities in Poland. Literature studies have shown that the use of Big Data Analytics can bring many benefits to medical facilities, while direct research has shown that medical facilities in Poland are moving towards data-based healthcare because they use structured and unstructured data, reach for analytics in the administrative, business and clinical area. The research positively confirmed that medical facilities are working on both structural data and unstructured data. The following kinds and sources of data can be distinguished: from databases, transaction data, unstructured content of emails and documents, data from devices and sensors. However, the use of data from social media is lower as in their activity they reach for analytics, not only in the administrative and business but also in the clinical area. It clearly shows that the decisions made in medical facilities are highly data-driven. The results of the study confirm what has been analyzed in the literature that medical facilities are moving towards data-based healthcare, together with its benefits.

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“…At present, artificial intelligence has been widely used in human health care. Shubham et al ( 14 ) used deep learning methods to identify glomerulus in human kidney tissue images. Movassagh et al ( 15 ) used a new method to train neural networks.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Nomogram to Predict Cancer-Specific Survival for Middle-Aged Patients With Early-Stage Hepatocellular Carcinoma

Wen

Tang

Luo³

2022

Front. Public Health

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BackgroundHepatocellular carcinoma is a common cause of death in middle-aged patients. We aimed to construct a new nomogram to predict cancer-specific survival (CSS) in middle-aged patients with hepatocellular carcinoma at an early stage.MethodWe collected clinicopathological information on early middle-aged patients with hepatocellular carcinoma from the SEER database. Univariate and multivariate Cox regression models were used to screen the independent risk factors for prognosis. These risk factors were used to construct predictions of CSS in patients with hepatocellular carcinoma. Consistency index (C- index), calibration curve, area under the receiver operating curve (AUC) were used. A decision analysis curve (DCA) was used to evaluate the clinical utility of the predictive model.ResultsA total of 6,286 patients with hepatocellular carcinoma in early middle age were enrolled. Univariate and multivariate Cox regression analysis showed that sex, marriage, race, histological tumor grade, T stage, surgery, chemotherapy, AFP, and tumor size were independent risk factors for prognosis. All independent risk factors were included in the nomogram to predict CSS at 1-, 3-, and 5-years in early middle age patients with hepatocellular carcinoma. In the training cohort and validation cohort, the C-index of the prediction model was 0.728 (95%CI: 0.716–0.740) and 0.733 (95%CI: 0.715–0.751), respectively. The calibration curve showed that the predicted value of the prediction model is highly consistent with the observed value. AUC also suggested that the model has good discrimination. DCA suggested that the nomogram had better predictive power than T staging.ConclusionWe constructed a new nomogram to predict CSS in middle-aged patients with early-stage hepatocellular carcinoma. This prediction model has good accuracy and reliability, which can help patients and doctors to judge prognosis and make clinical decisions.

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Identify glomeruli in human kidney tissue images using a deep learning approach

Cited by 21 publications

References 33 publications

Flow Analysis of Two-Layer Nano/Johnson–Segalman Fluid in a Blood Vessel-like Tube with Complex Peristaltic Wave

Flow Analysis of Two-Layer Nano/Johnson–Segalman Fluid in a Blood Vessel-like Tube with Complex Peristaltic Wave

The use of Big Data Analytics in healthcare

Development and Validation of a Nomogram to Predict Cancer-Specific Survival for Middle-Aged Patients With Early-Stage Hepatocellular Carcinoma

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