AIMS Biophysics最新文献

Photographic investigations of rising bubbles in seawater revealed that each bubble may conduct a single or bi-spiraling motion, which resemble architecture of RNA or DNA respectively. The rotational motion results from acceleration of ionic hydrates, which are separated to anionic and cationic domains at the upper and bottom curvatures of the bubble. Afterwards, rotational motion undergoes further acceleration in the bubble upper vortex, followed by deceleration at the vortex tip. During that phase, the spiraling motion cause significant friction that result in polarization of electronegative atoms of H, C, N, O and P. These may be simultaneously arranged around a whirling cationic strands and form phosphate groups, ribose and nitrogen bases equipped with H₂ and H₃ rotors. It is hypothesized that such hydrogen rotors may operate as generators of electrons, which may be detached from valence shells of electropositive atoms. Then, electrons may flow via nitrogen bases and deoxyribose or ribose to phosphate groups. Next, the negatively charged edges of phosphate groups may attract cationic hydrates and energize their rotational motion in the grooves, then causing also its spiraling projection outward. That may be responsible for replication of nucleotides and its arrangement along the cationic flow into RNA or DNA polymers, in the same manner as originally produced by rising bubbles. Moreover, it points that hydrogen rotors may generate energy needed for viability as well as interact with all physical and chemical fields.

The metal ion is ubiquitous in the human body and is essential to biochemical reactions. The study of the metal ion complexes and their charge transfer nature will be fruitful for drug design and may be beneficial for the extension of the field. In this regard, investigations into charge transport properties from ligands to metal ion complexes and their stability are crucial in the medical field. In this work, the DFT technique has been applied to analyze the delocalization of electrons from the water ligands to a core metal ion. At the B3LYP level of approximation, natural bond orbital (NBO) analysis was performed for the first five distinct complexes [Mg(H₂O)₆]²⁺ and [[Mg(H₂O)₆](H₂O)_n]²⁺; n = 1-4. All these complexes were optimized and examined with the higher basis set 6-311++G(d, p). In the complex [Mg(H₂O)₆]²⁺, the amount of natural charge transport from ligands towards the metal ion was 0.179e, and the greatest stabilization energy was observed to be 22.67 kcal/mol. The donation of the p orbitals in the hybrid orbitals was increased while approaching the oxygen atoms of H₂O ligands in the 1^st coordination sphere with the magnesium ions. The presence of water ligands within the 2^nd coordination sphere increased natural charge transfer and decreased the stabilizing energy of the complexes. This may be due to the ligand-metal interactions.

Electroencephalography (EEG) is a widely used medical procedure that helps to identify abnormalities in brain wave patterns and measures the electrical activity of the brain. The EEG signal comprises different features that need to be distinguished based on a specified property to exhibit recognizable measures and functional components that are then used to evaluate the pattern in the EEG signal. Through extraction, feature loss is minimized with the embedded signal information. Additionally, resources are minimized to compute the vast range of data accurately. It is necessary to minimize the information processing cost and implementation complexity to improve the information compression. Currently, different methods are being implemented for feature extraction in the EEG signal. The existing methods are subjected to different detection schemes that effectively stimulate the brain signal with the interface for medical rehabilitation and diagnosis. Schizophrenia is a mental disorder that affects the individual's reality abnormally. This paper proposes a statistical local binary pattern (SLBP) technique for feature extraction in EEG signals. The proposed SLBP model uses statistical features to compute EEG signal characteristics. Using Local Binary Pattern with proposed SLBP model texture based on a labeling signal with an estimation of the neighborhood in signal with binary search operation. The classification is performed for the earlier-prediction shizophrenia stage, either mild or severe. The analysis is performed considering three classes, i.e., normal, mild, and severe. The simulation results show that the proposed SLBP model achieved a classification accuracy of 98%, which is ~12% higher than the state-of-the-art methods.

The impact of denaturing and stabilizing osmolytes on protein conformational dynamics has been extensively explored due to the significant contribution of protein solvation to the stability, function, malfunction and regulation of globular proteins. We studied the effect of two nonspecific organic molecules, urea, which is a conventional denaturant, and dimethyl sulfoxide (DMSO), which is a multilateral organic solvent, on the stability and conformational dynamics of a non-inhibitory serpin, ovalbumin (OVA). A differential scanning microcalorimetry (DSC) experimental series conducted in the phosphate buffer solutions containing 0–30% of additives revealed the destabilizing impact of both urea and DMSO in a mild acidic media, manifested in the gradual decrease of thermal unfolding enthalpy and transition temperature. These findings differ from the results observed in our study of the mild alkaline DMSO buffered solutions of OVA, where the moderate stabilization of OVA was observed in presence of 5–10% of DMSO. However, the overall OVA interaction patterns with urea and DMSO are consistent with our previous findings on the stability and conformational flexibility of another model globular protein, α-chymotrypsin, in similar medium conditions. The obtained results could be explained by preferential solvation patterns. Positive preferential solvation of protein by urea in urea/water mixtures mainly weakens the hydrophobic interactions of the protein globule and eventually leads to the disruption of the tertiary structure within the whole range of urea concentrations. Alternatively, under certain experimental conditions in DMSO/water mixtures, positive preferential solvation by water molecules can be observed. We assume that the switch to the positive preferential solvation by DMSO, which is shown to have a soft maximum around 20–30% DMSO, could be shifted towards lower additive concentrations due to the intrinsic capability of ovalbumin OVA to convert into a heat-stable, yet flexible set of conformations that have increased the surface hydrophobicity, characteristic to molten-globule-like states.

Accurate detection of non-alcoholic fatty liver disease (NAFLD) through biopsies is challenging. Manual detection of the disease is not only prone to human error but is also time-consuming. Using artificial intelligence and deep learning, we have successfully demonstrated the issues of the manual detection of liver diseases with a high degree of precision. This article uses various neural network-based techniques to assess non-alcoholic fatty liver disease. In this investigation, more than five thousand biopsy images were employed alongside the latest versions of the algorithms. To detect prominent characteristics in the liver from a collection of Biopsy pictures, we employed the YOLOv3, Faster R-CNN, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and SSD models. A highlighting point of this paper is comparing the state-of-the-art Instance Segmentation models, including Mask R-CNN, U-Net, YOLOv5 Instance Segmentation, YOLOv7 Instance Segmentation, and YOLOv8 Instance Segmentation. The extent of severity of NAFLD and non-alcoholic steatohepatitis was examined for liver cell ballooning, steatosis, lobular, and periportal inflammation, and fibrosis. Metrics used to evaluate the algorithms' effectiveness include accuracy, precision, specificity, and recall. Improved metrics are achieved by optimizing the hyperparameters of the associated models. Additionally, the liver is scored in order to analyse the information gleaned from biopsy images. Statistical analyses are performed to establish the statistical relevance in evaluating the score for different zones.

Magnetic tweezers have emerged as a vital force spectroscopy tool for characterizing the mechanical properties of nucleic acids and their interactions with proteins. Harnessing the principles of magnetic theory, magnetic tweezers allow for the precise manipulation of biological compounds at the single-molecule level through the imposition of a magnetic field. This review focuses on the application of magnetic tweezers in the context of DNA studies, with a particular emphasis on the mechanical properties of DNA and its dynamic interactions with proteins and enzymes. These interactions are essential to genomic transactions such as DNA replication, repair, and transcription. Over the last few decades, magnetic tweezer technology has experienced significant advancements, leading to the development of different types of magnetic tweezers. These technological breakthroughs have opened up new avenues of scientific research, including studies related to DNA elasticity, supercoiling, replication, and repair.