Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid CNN-LSTM based transfer learning.

Background: Grading of cancer histopathology slides requires more pathologists and expert clinicians as well as it is time consuming to look manually into whole-slide images. Hence, an automated classification of histopathological breast cancer sub-type is useful for clinical diagnosis and therapeutic responses. Recent deep learning methods for medical image analysis suggest the utility of automated radiologic imaging classification for relating disease characteristics or diagnosis and patient stratification.

Identification of Novel Ribonucleotide Reductase Inhibitors for Therapeutic Application in Bile Tract Cancer: An Advanced Pharmacoinformatics Study.

Biliary tract cancer (BTC) is constituted by a heterogeneous group of malignant tumors that may develop in the biliary tract, and it is the second most common liver cancer. Human ribonucleotide reductase M1 (hRRM1) has already been proven to be a potential BTC target. In the current study, a de novo design approach was used to generate novel and effective chemical therapeutics for BTC.

Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies.

Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for further assessment. A negative image-based (NIB) model was used for a similarity search by considering the complementary shape and electrostatics of the target and small molecules.

Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches.

Cardiovascular diseases (CDs) are a major concern in the human race and one of the leading causes of death worldwide. β-Adrenergic receptors (β1-AR and β2-AR) play a crucial role in the overall regulation of cardiac function. In the present study, structure-based virtual screening, machine learning (ML), and a ligand-based similarity search were conducted for the PubChem database against both β1- and β2-AR.

Patterning of corpus callosum integrity in glioma observed by MRI: Effect of 2D bi-axial lamellar brain architecture.

Purpose: Corpus callosum (CC) is a main channel histologically for glioma spreading, downgrading the prognosis, the infiltration occurring through cellular reaction-diffusion process. Preliminary clinical trial indicates that CC's surgical interruption appreciably enhances clinical outcome. We aim to find how high-grade glioma phenomenology is reflected in CC parameters, including various 3D diffusion eigenvalues differentially, whereby this information may be utilized for planning radiotherapy and surgical intervention.

A Correlational Study between Microstructural White Matter Properties and Macrostructural Gray Matter Volume Across Normal Ageing: Conjoint DTI and VBM Analysis.

We investigate the relationship between Gray matter's volume vis-a-vis White matter's integrity indices, such Axial diffusivity, Radial diffusivity, Mean diffusivity, and Fractional anisotropy, in individuals undergoing healthy aging. We investigated MRI scans of 177 adults across 20 to 85 years. We used Voxel-based morphometry, and FDT-FSL analysis for estimation of Gray matter volume and White matter's diffusion indices respectively.

Age-Related Differences in White Matter Integrity in Healthy Human Brain: Evidence from Structural MRI and Diffusion Tensor Imaging.

The aim is to investigate the relationship between microstructural white matter (WM) diffusivity indices and macrostructural WM volume (WMV) among healthy individuals (20-85 years). Whole-brain diffusion measures were calculated from diffusion tensor imaging using FMRIB software library while WMV was estimated through voxel-based morphometry, and voxel-based analysis was carried out using tract-based spatial statistics. Our results revealed that mean diffusivity, axial diffusivity, and radial diffusivity had shown good correlation with WMV but not for fractional anisotropy (FA).

A patient-specific therapeutic approach for tumour cell population extinction and drug toxicity reduction using control systems-based dose-profile design.

Background: When anti-tumour therapy is administered to a tumour-host environment, an asymptotic tapering extremity of the tumour cell distribution is noticed. This extremity harbors a small number of residual tumour cells that later lead to secondary malignances. Thus, a method is needed that would enable the malignant population to be completely eliminated within a desired time-frame, negating the possibility of recurrence and drug-induced toxicity.

Magnetic resonance image enhancement using stochastic resonance in Fourier domain.

Objective: In general, low-field MRI scanners such as the 0.5- and 1-T ones produce images that are poor in quality. The motivation of this study was to lessen the noise and enhance the signal such that the image quality is improved.

Stochastic resonance-based tomographic transform for computed tomographic image enhancement of brain lesions.

Motivated by statistical thermodynamics, we develop a technique using stochastic resonance-based tomographic transform for enhancement of noisy or indistinct computer-assisted tomographic images of the brain lesions for radiological diagnosis. The proposed method makes the edges of the lesion prominent, delineates the edematous zones more clearly, enhances the active zone in tumors, and clarifies the latent structure of the lesions, the mean enhancement index being 165%. The advantages of this method are that it can simultaneously operate both as an enhancement process and as a noise-reduction operation, and that the method can also optimally enhance an image even if the noise level is considerable.

Enhancement of ultrasound images using stochastic resonance-based wavelet transform.

Ultrasound diagnostic imaging technique is used to visualize muscles and internal organs, their size, structures and possible pathologies or lesions. The limited soft tissue contrast of ultrasound may lead to problems in characterizing perivascular soft tissues. We develop a technique using stochastic resonance (SR)-based wavelet transform for the enhancement of unclear diagnostic ultrasound images.