Complex Impedance and Modulus Analysis on Porous and Non-Porous Scaffold Composites Due to Effect of Hydroxyapatite/Starch Proportion.

This study aims to investigate the electric responses (complex modulus and complex impedance analysis) of hydroxyapatite/starch bone scaffold as a function of hydroxyapatite/starch proportion and the microstructural features. Hence, the non-porous and porous hydroxyapatite/starch composites were fabricated with various hydroxyapatite/starch proportions (70/30, 60/40, 50/50, 40/60, 30/70, 20/80, and 10/90 wt/wt%). Microstructural analysis of the porous hydroxyapatite/starch composites was carried out by using scanning electron microscopy. It shows that the formation of hierarchical porous microstructures with high porosity is more significant at a high starch proportion. The complex modulus and complex impedance analysis were conducted to investigate the electrical conduction mechanism of the hydroxyapatite/starch composites via dielectric spectroscopy within a frequency range from 5 MHz to 12 GHz. The electrical responses of the hydroxyapatite/starch composites are highly dependent on the frequency, material proportion, and microstructures. High starch proportion and highly porous hierarchical microstructures enhance the electrical responses of the hydroxyapatite/starch composite. The material proportion and microstructure features of the hydroxyapatite/starch composites can be indirectly reflected by the simulated electrical parameters of the equivalent electrical circuit models.

Lumped-Element Circuit Modeling for Composite Scaffold with Nano-Hydroxyapatite and Wangi Rice Starch.

Mechanistic studies of the interaction of electromagnetic (EM) fields with biomaterials has motivated a growing need for accurate models to describe the EM behavior of biomaterials exposed to these fields. In this paper, biodegradable bone scaffolds were fabricated using Wangi rice starch and nano-hydroxyapatite (nHA). The effects of porosity and composition on the fabricated scaffold were discussed via electrical impedance spectroscopy analysis. The fabricated scaffold was subjected to an electromagnetic field within the X-band and Ku-band (microwave spectrum) during impedance/dielectric measurement. The impedance spectra were analyzed with lumped-element models. The impedance spectra of the scaffold can be embodied in equivalent circuit models composed of passive components of the circuit, i.e., resistors, inductors and capacitors. It represents the morphological, structural and chemical characteristics of the bone scaffold. The developed models describe the impedance characteristics of plant tissue. In this study, it was found that the ε' and ε″ of scaffold composites exhibited up and down trends over frequencies for both X-band and Ku-band. The circuit models presented the lowest mean percentage errors of Z' and Z″, i.e., 3.60% and 13.80%, respectively.

Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review.

Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.

Automated knowledge-assisted mitosis cells detection framework in breast histopathology images.

Based on the Nottingham Histopathology Grading (NHG) system, mitosis cells detection is one of the important criteria to determine the grade of breast carcinoma. Mitosis cells detection is a challenging task due to the heterogeneous microenvironment of breast histopathology images. Recognition of complex and inconsistent objects in the medical images could be achieved by incorporating domain knowledge in the field of interest. In this study, the strategies of the histopathologist and domain knowledge approach were used to guide the development of the image processing framework for automated mitosis cells detection in breast histopathology images. The detection framework starts with color normalization and hyperchromatic nucleus segmentation. Then, a knowledge-assisted false positive reduction method is proposed to eliminate the false positive (i.e., non-mitosis cells). This stage aims to minimize the percentage of false positive and thus increase the F1-score. Next, features extraction was performed. The mitosis candidates were classified using a Support Vector Machine (SVM) classifier. For evaluation purposes, the knowledge-assisted detection framework was tested using two datasets: a custom dataset and a publicly available dataset (i.e., MITOS dataset). The proposed knowledge-assisted false positive reduction method was found promising by eliminating at least 87.1% of false positive in both the dataset producing promising results in the F1-score. Experimental results demonstrate that the knowledge-assisted detection framework can achieve promising results in F1-score (custom dataset: 89.1%; MITOS dataset: 88.9%) and outperforms the recent works.

Docking studies and ligand recognition in folylpolyglutamate synthetase.

Folylpolyglutamate synthetase (FPGS) catalyzes the sequential addition of several glutamates to folate, forming gamma-linked polyglutamate folates of varying lengths. To understand how this protein is capable of accommodating ligands of different length and net charge, we have performed docking studies for folate substrates and glutamate based on the ternary crystal structure of Lactobacillus casei FPGS. Our results suggest two locations for folate binding, the one seen in the crystal structure and another distinct cavity. According to our model and experimental data, it is highly probable that folate can bind in both sites, and we suggest that the new pocket is especially important for the initial addition of the first glutamate residue. Docking longer substrates, di- and triglutamylated folates, showed how these molecules bind in the same sites. The longer folates also adopted transition-state-like conformations that may help us to understand the ligation reaction in FPGS and influence the design of mechanism-based inhibitors for anticancer or antimicrobial therapy.

The role of aspartic acid 143 in E. coli tRNA-guanine transglycosylase: insights from mutagenesis studies and computational modeling.

tRNA guanine transglycosylase (TGT) is a tRNA-modifying enzyme which catalyzes the posttranscriptional exchange of guanine in position 34 of tRNA(Y,H,N,D) with the modified base queuine in eukaryotes or its precursor, preQ(1) base, in eubacteria. Thus, TGT must recognize the guanine in tRNA and the free base queuine or preQ(1) to catalyze this exchange. The crystal structure of Zymomonas mobilis TGT with preQ(1) bound suggests that a key aspartate is critically involved in substrate recognition. To explore this, a series of site-directed mutants of D143 in Escherichia coli TGT were made and characterized to investigate heterocyclic substrate recognition. Our data confirm that D143 has significant impact on K(M) of guanine; however, the trend in the K(M) data (D143A < D143N < D143S < D143T) is unexpected. Computational studies were used to further elucidate the interactions between guanine and the D143 mutants. A homology model of E. coli TGT was created, and the role of D143 was investigated by molecular dynamic simulations of guanine bound to the wild-type and D143-mutant TGTs. To validate the model systems against our kinetic data, free energies of binding were fit using the linear interaction energy (LIE) method. This is a unique application of the LIE method because the same ligand is bound to several mutant proteins rather than one protein binding several ligands. The atomic detail gained from the simulations provided a better understanding of the binding affinities of guanine with the mutant TGTs, revealing that water molecules enter the active site and hydrogen bond to the ligand and compensate for lost protein-ligand interactions. The trend of binding affinity for wild-type > D143A > D143N > D143S > D143T appears to be directly related to the degree of hydrogen bonding available to guanine in the binding site.

Structural biology and structure-based inhibitor design of cholera toxin and heat-labile enterotoxin.

Structural biology studies on cholera toxin and the closely related heat-labile enterotoxin from enterotoxigenic Escherichia coli over the past decade have shed light on the mechanism of toxin action at molecular and atomic levels. Also, components of the extracellular protein secretion apparatus that translocate the toxins across the outer membrane are being investigated. At the same time, structure-based design has led to various classes of compounds targeting different toxin sites, including highly potent multivalent inhibitors that block the toxin receptor-binding process.

Missense mutations in the regulatory domain of PKC gamma: a new mechanism for dominant nonepisodic cerebellar ataxia.

We report a nonepisodic autosomal dominant (AD) spinocerebellar ataxia (SCA) not caused by a nucleotide repeat expansion that is, to our knowledge, the first such SCA. The AD SCAs currently comprise a group of > or =16 genetically distinct neurodegenerative conditions, all characterized by progressive incoordination of gait and limbs and by speech and eye-movement disturbances. Six of the nine SCAs for which the genes are known result from CAG expansions that encode polyglutamine tracts. Noncoding CAG, CTG, and ATTCT expansions are responsible for three other SCAs. Approximately 30% of families with SCA do not have linkage to the known loci. We recently mapped the locus for an AD SCA in a family (AT08) to chromosome 19q13.4-qter. A particularly compelling candidate gene, PRKCG, encodes protein kinase C gamma (PKC gamma), a member of a family of serine/threonine kinases. The entire coding region of PRKCG was sequenced in an affected member of family AT08 and in a group of 39 unrelated patients with ataxia not attributable to trinucleotide expansions. Three different nonconservative missense mutations in highly conserved residues in C1, the cysteine-rich region of the protein, were found in family AT08, another familial case, and a sporadic case. The mutations cosegregated with disease in both families. Structural modeling predicts that two of these amino acid substitutions would severely abrogate the zinc-binding or phorbol ester-binding capabilities of the protein. Immunohistochemical studies on cerebellar tissue from an affected member of family AT08 demonstrated reduced staining for both PKC gamma and ataxin 1 in Purkinje cells, whereas staining for calbindin was preserved. These results strongly support a new mechanism for neuronal cell dysfunction and death in hereditary ataxias and suggest that there may be a common pathway for PKC gamma-related and polyglutamine-related neurodegeneration.

A density-functional study of the mechanism for the diastereoselective epoxidation of chiral allylic alcohols by the titanium peroxy complexes.

The epoxidation of three stereolabeled methyl-substituted chiral allylic alcohols with (1,2)A and/or (1,3)A allylic strain, namely 3-methylbut-3-en-2-ol (1a), pent-3-en-2-ol (1b), and 3-methylpent-3-en-2-ol (1c), have been studied by the density-functional theory method, B3LYP/6-31+G(d,p). For each substrate we calculated the two prereaction complexes with Ti(OH)(4)/MeOOH (the oxidant model for Ti(O-i-Pr)(4)/t-BuOOH), their threo and erythro transition states for oxygen transfer, and the corresponding product complexes. For substrate 1a, the erythro transition state is 0.91 kcal/mol of lower energy than the threo one; for substrates 1b and 1c, the threo compared to the erythro transition states are by 1.05 and 0.21 kcal/mol more favorable, respectively. The threo/erythro product ratios have been estimated from the computed free energies for the competing threo and erythro transition states 3a-c in CH(2)Cl(2) solution to be 12:88 (1a), 92:8 (1b), and 77:23 (1c), which are in good accordance with the experimental values 22:78 (1a), 91:9 (1b), and 83:17 (1c). The diastereoselectivity of this diastereoselective oxyfunctionalization is rationalized in terms of the competition between (1,3)A and (1,2)A strain and the electronic advantage for the spiro transition state. In addition, solvent effects are also play a role for the diastereoselectivity at the same time.