Dosimetric impact of contour editing on CT and MRI deep-learning autosegmentation for brain OARs.

PURPOSE: To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-based models. The correlation between geometric and dosimetric measures was also investigated to establish whether dosimetric assessment is required for clinical validation. METHOD: CT and MRI-based deep learning autosegmentation models were trained using edited and unedited clinical contours. Autosegmentations were dosimetrically compared to gold standard contours for a test cohort. D1%, D5%, D50%, and maximum dose were used as clinically relevant dosimetric measures. The statistical significance of dosimetric differences between the gold standard and autocontours was established using paired Student's t-tests. Clinically significant cases were identified via dosimetric headroom to the OAR tolerance. Pearson's Correlations were used to investigate the relationship between geometric measures and absolute percentage dose changes for each autosegmentation model. RESULTS: Except for the right orbit, when delineated using MRI models, the dosimetric statistical analysis revealed no superior model in terms of the dosimetric accuracy between the CT DL-AC models or between the MRI DL-AC for any investigated brain OARs. The number of patients where the clinical significance threshold was exceeded was higher for the optic chiasm D1% than other OARs, for all autosegmentation models. A weak correlation was consistently observed between the outcomes of dosimetric and geometric evaluations. CONCLUSIONS: Editing contours before training the DL-AC model had no significant impact on dosimetry. The geometric test metrics were inadequate to estimate the impact of contour inaccuracies on dose. Accordingly, dosimetric analysis is needed to evaluate the clinical applicability of DL-AC models in the brain.

GL67 lipid-based liposomal formulation for efficient siRNA delivery into human lung cancer cells.

The efficient delivery of small interfering RNA (siRNA) to the targeted cells significantly affects the regulation of the overexpressed proteins involved in the progression of several genetic diseases. SiRNA molecules in naked form suffer from low internalization across the cell membrane, high susceptibility to degradation by nuclease enzyme and low stability, which hinder their efficacy. Therefore, there is an urge to develop a delivery system that can protect siRNA from degradation and facilitate their uptake across the cell membrane. In this study, the cationic lipid (GL67) was exploited, in addition to DC-Chol and DOPE lipids, to design an efficient liposomal nanocarrier for siRNA delivery. The physiochemical characterizations demonstrated that the molar ratio of 3:1 has proper particle size measurements from 144 nm to 332 nm and zeta potential of -9 mV to 47 mV that depends on the ratio of the GL67 in the liposomal formulation. Gel retardation assay exhibited that increasing the percentage of GL67 in the formulations has a good impact on the encapsulation efficiency compared to DC-Chol. The optimal formulations of the 3:1 M ratio also showed high metabolic activity against A549 cells following a 24 h cell exposure. Flow cytometry findings showed that the highest GL67 lipid ratio (100 % GL67 and 0 % DC-Chol) had the highest percentage of cellular uptake. The lipoplex nanocarriers based on GL67 lipid could potentially influence treating genetic diseases owing to the high internalization efficiency and safety profile.

Liposome-Encapsulated Tobramycin and IDR-1018 Peptide Mediated Biofilm Disruption and Enhanced Antimicrobial Activity against Pseudomonas aeruginosa.

The inadequate eradication of pulmonary infections and chronic inflammation are significant complications in cystic fibrosis (CF) patients, who usually suffer from persistent and frequent lung infections caused by several pathogens, particularly Pseudomonas aeruginosa (P. aeruginosa). The ability of pathogenic microbes to protect themselves from biofilms leads to the development of an innate immune response and antibiotic resistance. In the present work, a reference bacterial strain of P. aeruginosa (PA01) and a multidrug-resistant isolate (MDR 7067) were used to explore the microbial susceptibility to three antibiotics (ceftazidime, imipenem, and tobramycin) and an anti-biofilm peptide (IDR-1018 peptide) using the minimum inhibition concentration (MIC). The most effective antibiotic was then encapsulated into liposomal nanoparticles and the IDR-1018 peptide with antibacterial activity, and the ability to disrupt the produced biofilm against PA01 and MDR 7067 was assessed. The MIC evaluation of the tobramycin antibacterial activity showed an insignificant effect on the liposomes loaded with tobramycin and liposomes encapsulating tobramycin and IDR-1018 against both P. aeruginosa strains to free tobramycin. Nevertheless, the biofilm formation was significantly reduced (p < 0.05) at concentrations of ≥4 µg/mL and ≤32 µg/mL for PA01 and ≤32 µg/mL for MDR 7067 when loading tobramycin into liposomes, with or without the anti-biofilm peptide compared to the free antibiotic, empty liposomes, and IDR-1018-loaded liposomes. A tobramycin concentration of ≤256 µg/mL was safe when exposed to a lung carcinoma cell line upon its encapsulation into the liposomal formulation. Tobramycin-loaded liposomes could be a potential candidate for treating lung-infected animal models owing to the high therapeutic efficacy and safety profile of this system compared to the free administration of the antibiotic.

An Efficient Framework for Securing the Smart City Communication Networks.

Recently, smart cities have increasingly been experiencing an evolution to improve the lifestyle of citizens and society. These emerge from the innovation of information and communication technologies (ICT) which are able to create a new economic and social opportunities. However, there are several challenges regarding our security and expectation of privacy. People are already involved and interconnected by using smart phones and other appliances. In many cities, smart energy meters, smart devices, and security appliances have recently been standardized. Full connectivity between public venues, homes, cares, and some other social systems are on their way to be applied, which are known as Internet of Things. In this paper, we aim to enhance the performance of security in smart city communication networks by using a new framework and scheme that provide an authentication and high confidentiality of data. The smart city system can achieve mutual authentication and establish the shared session key schemes between smart meters and the control center in order to secure a two-way communication channel. In our extensive simulation, we investigated and evaluated the security performance of the smart city communication network with and without our proposed scheme in terms of throughput, latency, load, and traffic received packet per seconds. Furthermore, we implemented and applied a man-in-the-middle (MITM) attack and network intrusion detection system (NIDS) in our proposed technique to validate and measure the security requirements maintaining the constrained resources.

Augmented Reality (AR) and Cyber-Security for Smart Cities-A Systematic Literature Review.

Augmented Reality (AR) and cyber-security technologies have existed for several decades, but their growth and progress in recent years have increased exponentially. The areas of application for these technologies are clearly heterogeneous, most especially in purchase and sales, production, tourism, education, as well as social interaction (games, entertainment, communication). Essentially, these technologies are recognized worldwide as some of the pillars of the new industrial revolution envisaged by the industry 4.0 international program, and are some of the leading technologies of the 21st century. The ability to provide users with required information about processes or procedures directly into the virtual environment is archetypally the fundamental factor in considering AR as an effective tool for different fields. However, the advancement in ICT has also brought about a variety of cybersecurity challenges, with a depth of evidence anticipating policy, architectural, design, and technical solutions in this very domain. The specific applications of AR and cybersecurity technologies have been described in detail in a variety of papers, which demonstrate their potential in diverse fields. In the context of smart cities, however, there is a dearth of sources describing their varied uses. Notably, a scholarly paper that consolidates research on AR and cybersecurity application in this context is markedly lacking. Therefore, this systematic review was designed to identify, describe, and synthesize research findings on the application of AR and cybersecurity for smart cities. The review study involves filtering information of their application in this setting from three key databases to answer the predefined research question. The keynote part of this paper provides an in-depth review of some of the most recent AR and cybersecurity applications for smart cities, emphasizing potential benefits, limitations, as well as open issues which could represent new challenges for the future. The main finding that we found is that there are five main categories of these applications for smart cities, which can be classified according to the main articles, such as tourism, monitoring, system management, education, and mobility. Compared with the general literature on smart cities, tourism, monitoring, and maintenance AR applications appear to attract more scholarly attention.

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques.

Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.

Phytosomes as an Emerging Nanotechnology Platform for the Topical Delivery of Bioactive Phytochemicals.

The emergence of phytosome nanotechnology has a potential impact in the field of drug delivery and could revolutionize the current state of topical bioactive phytochemicals delivery. The main challenge facing the translation of the therapeutic activity of phytochemicals to a clinical setting is the extremely low absorption rate and poor penetration across biological barriers (i.e., the skin). Phytosomes as lipid-based nanocarriers play a crucial function in the enhancement of pharmacokinetic and pharmacodynamic properties of herbal-originated polyphenolic compounds, and make this nanotechnology a promising tool for the development of new topical formulations. The implementation of this nanosized delivery system could enhance the penetration of phytochemicals across biological barriers due to their unique physiochemical characteristics, improving their bioavailability. In this review, we provide an outlook on the current knowledge of the biological barriers of phytoconstituents topical applications. The great potential of the emerging nanotechnology in the delivery of bioactive phytochemicals is reviewed, with particular focus on phytosomes as an innovative lipid-based nanocarrier. Additionally, we compared phytosomes with liposomes as the gold standard of lipid-based nanocarriers for the topical delivery of phytochemicals. Finally, the advantages of phytosomes in topical applications are discussed.