Utilizing immobilized recombinant serine alkaline protease from Bacillus safensis lab418 in wound healing: Gene cloning, heterologous expression, optimization, and characterization.

Microbial proteases have proven their efficiency in various industrial applications; however, their application in accelerating the wound healing process has been inconsistent in previous studies. In this study, heterologous expression was used to obtain an over-yielding of the serine alkaline protease. The serine protease-encoding gene aprE was isolated from Bacillus safensis lab 418 and expressed in E. coli BL21 (DE3) using the pET28a (+) expression vector. The gene sequence was assigned the accession number OP610065 in the NCBI GenBank. The open reading frame of the recombinant protease (aprEsaf) was 383 amino acids, with a molecular weight of 35 kDa. The yield of aprEsaf increased to 300 U/mL compared with the native serine protease (SAFWD), with a maximum yield of 77.43 U/mL after optimization conditions. aprEsaf was immobilized on modified amine-functionalized films (MAFs). By comparing the biochemical characteristics of immobilized and free recombinant enzymes, the former exhibited distinctive biochemical characteristics: improved thermostability, alkaline stability over a wider pH range, and efficient reusability. The immobilized serine protease was effectively utilized to expedite wound healing. In conclusion, our study demonstrates the suitability of the immobilized recombinant serine protease for wound healing, suggesting that it is a viable alternative therapeutic agent for wound management.

Using Mean Arterial Pressure in Hypertension Diagnosis versus Using Either Systolic or Diastolic Blood Pressure Measurements.

Hypertension is a severe and highly prevalent disease. It is considered a leading contributor to mortality worldwide. Diagnosis guidelines for hypertension use systolic and diastolic blood pressure (BP) together. Mean arterial pressure (MAP), which refers to the average of the arterial blood pressure through a single cardiac cycle, can be an alternative index that may capture the overall exposure of the person to a heightened pressure. A clinical hypothesis, however, suggests that in patients over 50 years old in age, systolic BP may be more predictive of adverse events, while in patients under 50 years old, diastolic BP may be slightly more predictive. In this study, we investigated the correlation between cerebrovascular changes, (impacted by hypertension), and MAP, systolic BP, and diastolic BP separately. Several experiments were conducted using real and synthetic magnetic resonance angiography (MRA) data, along with corresponding BP measurements. Each experiment employs the following methodology: First, MRA data were processed to remove noise, bias, or inhomogeneity. Second, the cerebrovasculature was delineated for MRA subjects using a 3D adaptive region growing connected components algorithm. Third, vascular features (changes in blood vessel's diameters and tortuosity) that describe cerebrovascular alterations that occur prior to and during the development of hypertension were extracted. Finally, feature vectors were constructed, and data were classified using different classifiers, such as SVM, KNN, linear discriminant, and logistic regression, into either normotensives or hypertensives according to the cerebral vascular alterations and the BP measurements. The initial results showed that MAP would be more beneficial and accurate in identifying the cerebrovascular impact of hypertension (accuracy up to 95.2%) than just using either systolic BP (accuracy up to 89.3%) or diastolic BP (accuracy up to 88.9%). This result emphasizes the pathophysiological significance of MAP and supports prior views that this simple measure may be a superior index for the definition of hypertension and research on hypertension.

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.

Cytocompatibility of MG-63 osteosarcoma cells on chitosan/hydroxyapatite/lignin hybrid composite scaffoldin vitro.

This study aims at fabricating promising cytocompatible hybrid biocomposite scaffolds from chitosan (CS), hydroxyapatite (HAP) and lignin (L) for bone tissue engineering by using freeze-drying technique. Different ratios of HAP to L (50:0, 37.5:12.5, 25:25 and 12.5:37.5) were taken to determine the optimum ratio for obtaining a composite with superior properties. The mechanical and biological properties of the resulting composites were investigated. The mechanical results showed that the prepared composite with a ratio of 25:25 of HAP/L exhibited a remarkable enhancement in the mechanical properties compared to the others. Additionally, it was found from thein vitroresults that the addition of L enhanced the water uptake value of the resulting scaffolds indicating their increased hydrophilicity. As a result, a significant increase in the attachment and proliferation of MG-63 cell line (osteoblast like cells) was observed in composite scaffolds with L over the scaffold without L (CS/HAP). From these results, it could be suggested that the prepared composite scaffold with 25:25 of HAP/L is very promising biomaterials in bone tissue-engineering as it exhibited a better mechanical and biological properties than the other prepared composites.

State of the Art: Lung Cancer Staging Using Updated Imaging Modalities.

Lung cancer is among the most common mortality causes worldwide. This scientific article is a comprehensive review of current knowledge regarding screening, subtyping, imaging, staging, and management of treatment response for lung cancer. The traditional imaging modality for screening and initial lung cancer diagnosis is computed tomography (CT). Recently, a dual-energy CT was proven to enhance the categorization of variable pulmonary lesions. The National Comprehensive Cancer Network (NCCN) recommends usage of fluorodeoxyglucose positron emission tomography (FDG PET) in concert with CT to properly stage lung cancer and to prevent fruitless thoracotomies. Diffusion MR is an alternative to FDG PET/CT that is radiation-free and has a comparable diagnostic performance. For response evaluation after treatment, FDG PET/CT is a potent modality which predicts survival better than CT. Updated knowledge of lung cancer genomic abnormalities and treatment regimens helps to improve the radiologists' skills. Incorporating the radiologic experience is crucial for precise diagnosis, therapy planning, and surveillance of lung cancer.

How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules.

Pulmonary nodules are the precursors of bronchogenic carcinoma, its early detection facilitates early treatment which save a lot of lives. Unfortunately, pulmonary nodule detection and classification are liable to subjective variations with high rate of missing small cancerous lesions which opens the way for implementation of artificial intelligence (AI) and computer aided diagnosis (CAD) systems. The field of deep learning and neural networks is expanding every day with new models designed to overcome diagnostic problems and provide more applicable and simply used models. We aim in this review to briefly discuss the current applications of AI in lung segmentation, pulmonary nodule detection and classification.

Impact of stress and hypertension on the cerebrovasculature.

OBJECTIVES: Both stress and hypertension (HTN) are considered major health problems that negatively impact the cerebral vasculature. In this article we summarize the possible relationship between stress and HTN. METHODS: We conducted a systematic review of the literature using a database search of MEDLINE, PubMed, Scopus, and Web of Science. RESULTS: Psychological stress is known to be an important risk factor for essential hypertension. Acute stress can induce transient elevations of blood pressure in the context of the fight-or-flight response. With increased intensity and duration of a perceived harmful event, the normal physiological response is altered, resulting in a failure to return to the resting levels. These changes are responsible for the development of HTN. Genetic and behavioral factors are also very important for the pathogenesis of hypertension under chronic stress situation. In addition, HTN and chronic stress may lead to impaired auto-regulation, regional vascular remodeling, and breakdown of the blood brain barrier (BBB). The effects of both HTN and chronic stress on the cerebral blood vessels shows that both have common structural and functional effects including endothelial damage with subsequent increased wall thickness, vessel resistance, stiffness, arterial atherosclerosis, and altered hemodynamics. CONCLUSION: Most of the above mentioned vascular effects of stress were primarily reported in animal models. Further in-vivo standardization of pathological vascular indices and imaging modalities is warranted. Radiological quantification of these cerebrovascular changes is therefore essential for in depth understanding of the healthy and diseased cerebral arteries functions, identification and stratification of patients at risk of cardiovascular and neurological adverse events, enactment of preventive measures prior to the onset of systemic HTN, and the initiation of personalized medical management.

A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations.

Hypertension is a leading cause of mortality in the USA. While simple tools such as the sphygmomanometer are widely used to diagnose hypertension, they could not predict the disease before its onset. Clinical studies suggest that alterations in the structure of human brains' cerebrovasculature start to develop years before the onset of hypertension. In this research, we present a novel computer-aided diagnosis (CAD) system for the early detection of hypertension. The proposed CAD system analyzes magnetic resonance angiography (MRA) data of human brains to detect and track the cerebral vascular alterations and this is achieved using the following steps: i) MRA data are preprocessed to eliminate noise effects, correct the bias field effect, reduce the contrast inhomogeneity using the generalized Gauss-Markov random field (GGMRF) model, and normalize the MRA data, ii) the cerebral vascular tree of each MRA volume is segmented using a 3-D convolutional neural network (3D-CNN), iii) cerebral features in terms of diameters and tortuosity of blood vessels are estimated and used to construct feature vectors, iv) feature vectors are then used to train and test various artificial neural networks to classify data into two classes; normal and hypertensive. A balanced data set of 66 subjects were used to test the CAD system. Experimental results reported a classification accuracy of 90.9% which supports the efficacy of the CAD system components to accurately model and discriminate between normal and hypertensive subjects. Clinicians would benefit from the proposed CAD system to detect and track cerebral vascular alterations over time for people with high potential of developing hypertension and to prepare appropriate treatment plans to mitigate adverse events.

A Novel Framework for Early Detection of Hypertension using Magnetic Resonance Angiography.

Hypertension is a leading mortality cause of 410,000 patients in USA. Cerebrovascular structural changes that occur as a result of chronically elevated cerebral perfusion pressure are hypothesized to precede the onset of systemic hypertension. A novel framework is presented in this manuscript to detect and quantify cerebrovascular changes (i.e. blood vessel diameters and tortuosity changes) using magnetic resonance angiography (MRA) data. The proposed framework consists of: 1) A novel adaptive segmentation algorithm to delineate large as well as small blood vessels locally using 3-D spatial information and appearance features of the cerebrovascular system; 2) Estimating the cumulative distribution function (CDF) of the 3-D distance map of the cerebrovascular system to quantify alterations in cerebral blood vessels' diameters; 3) Calculation of mean and Gaussian curvatures to quantify cerebrovascular tortuosity; and 4) Statistical and correlation analyses to identify the relationship between mean arterial pressure (MAP) and cerebral blood vessels' diameters and tortuosity alterations. The proposed framework was validated using MAP and MRA data collected from 15 patients over a 700-days period. The novel adaptive segmentation algorithm recorded a 92.23% Dice similarity coefficient (DSC), a 94.82% sensitivity, a 99.00% specificity, and a 10.00% absolute vessels volume difference (AVVD) in delineating cerebral blood vessels from surrounding tissues compared to the ground truth. Experiments demonstrated that MAP is inversely related to cerebral blood vessel diameters (p-value < 0.05) globally (over the whole brain) and locally (at circle of Willis and below). A statistically significant direct correlation (p-value < 0.05) was found between MAP and tortuosity (medians of Gaussian and mean curvatures, and average of mean curvature) globally and locally (at circle of Willis and below). Quantification of the cerebrovascular diameter and tortuosity changes may enable clinicians to predict elevated blood pressure before its onset and optimize medical treatment plans of pre-hypertension and hypertension.