A Mask Detection Algorithm Based on Improved Yolov5s

Aiming at the severe form of new coronavirus epidemic prevention and control, a target detection algorithm is proposed to detect whether masks are worn in public places. The Ghostnet and SElayer modules with fewer design parameters replace the BottleneckCSP part in the original Yolov5s network, which reduces the computational complexity of the model and improves the detection accuracy. The bounding box regression loss function DIOU is optimized, the DGIOU loss function is used for bounding box regression, and the center coordinate distance between the two bounding boxes is considered to achieve a better convergence effect. In the feature pyramid, the depthwise separable convolution DW is used to replace the ordinary convolution, which further reduces the amount of parameters and reduces the loss of feature information caused by multiple convolutions. The experimental results show that compared with the yolov5s algorithm, the proposed method improves the mAP by 4.6% and the detection rate by 10.7 frame/s in the mask wearing detection. Compared with other mainstream algorithms, the improved yolov5s algorithm has better generalization ability and practicability. © 2022 ACM.

Design and Implementation of Intelligent Truck Based on Azure Kinect

In recent years, due to the impact of COVID-19, the market prospect of non-contact handling has improved and the development potential is huge. This paper designs an intelligent truck based on Azure Kinect, which can save manpower and improve efficiency, and greatly reduce the infection risk of medical staff and community workers. The target object is visually recognized by Azure Kinect to obtain the center of mass of the target, and the GPS and Kalman filter are used to achieve accurate positioning. The 4-DOF robot arm is selected to grasp and transport the target object, so as to complete the non-contact handling work. In this paper, different shapes of objects are tested. The experiment shows that the system can accurately complete the positioning function, and the accuracy rate is 95.56%. The target object recognition is combined with the depth information to determine the distance, and the spatial coordinates of the object centroid are obtained in real time. The accuracy rate can reach 94.48%, and the target objects of different shapes can be recognized. When the target object is grasped by the robot arm, it can be grasped accurately according to the depth information, and the grasping rate reaches 92.67%. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Criteria Clustering and Supplier Segmentation Based on Sustainable Shared Value Using BWM and PROMETHEE

With the advent of healthy visions, two of the trends that have become extremely important in the supply chain in recent decades are corporate social responsibility (CSR) and sustainability, which have affected the activities of buyers and suppliers. The next trend that is emerging is the vision of creating shared value (CSV), which wants to move the supply chain toward solving social problems in a completely strategic way. This research intends to develop a step-by-step framework for evaluating and segmenting suppliers based on CSV criteria in the supply chain. In the first stage, the criteria for creating sustainable shared value (CSSV) are obtained through existing activities in the field of CSR. The obtained criteria are then divided into two categories, strategic and critical, and then the weight of each criterion is obtained using the best–worst method (BWM). In the next step, based on the Kraljic model, the suppliers are divided into four clusters using the preference ranking organization method for enrichment evaluation (PROMETHEE) technique. This framework helps the buyer to conclude and select purchasing decisions and relationships with suppliers through the lenses of CSV and sustainability.

Exosomes/EVs: BIOXOME: A NATURAL LIPOSOME MIMETIC AS DRUG DELIVERY SYSTEM

Background & Aim: Liposomes are spherical-shaped vesicles composed of one or more lipid bilayers. The ability of liposomes to encapsulate hydro- or lipophilic drugs allowed these vesicles to become a useful drug delivery system. Natural cell membranes, such as Bioxome, have newly emerged as new source of materials for molecular delivery systems. Bioxome are biocompatible and GMP-compliant liposome-like membrane that can be produced from more than 200 cell types. Bioxome self-assemble, with in-process self-loading capacity and can be loaded with a variety of therapeutic compounds. Once close to the target tissue, Bioxome naturally fuse with the cell membrane and release the inner compound. Orgenesis is interested in evaluating the potential of Bioxome as new drug delivery system for treatment of several diseases, including skin repair, local tumour or COVID19. Methods, Results & Conclusion(s): Bioxome were obtained from adipose- derived Mesenchymal Stem Cells, with a process of organic- solvent lipid extraction, followed by lyophilization and sonication assemblage. During the sonication process, Bioxome were charged or not with several cargos. Size distribution of empty Bioxome was detected by Particle Size Analyzer (NanoSight). Electron Microscopy (EM) was performed to assess Bioxome morphology. Lipid content was evaluated by electrospray ionization system. Dose response in vitro test on human lung fibroblasts treated or not with Bioxome encapsulating a specific cargo (API) against COVID19 were performed. NanoSight analysis showed that nanoparticle size in Bioxome samples ranged between 170+/-50 nm, with a concentration ranging between 109-1010+/-106 particles/mL. EM clearly showed the double phospholipid layers that composes the Bioxome. Stability study demonstrated that Bioxome are stable in size and concentration up to 90 days at +4Cdegree or even at RT. No change in size between encapsulated Bioxome with small size (~340 Da) cargo vs empty Bioxome was observed up to 30 days storage. Lipidomic analysis approach revealed that the yield of lipids and their composition are satisfactory for a therapeutic product using Bioxome. Lastly, in the in vitro model of COVID19, Bioxome encapsulating API effectively saved cells from death (20x vs untreated cells) and at lower doses of API than these of non-encapsulated cargo (0.005 microM vs 0.1 microM). Bioxome seems to be an excellent candidate for liposome mimetic tool as drug delivery system for targeting specific organs and diseases treatment.Copyright © 2023 International Society for Cell & Gene Therapy

The Pivotal Role of Galectin-3 in Viral Infection: A Multifaceted Player in Host-Pathogen Interactions.

Galectin-3 (Gal-3), a beta-galactoside-binding lectin, plays a pivotal role in various cellular processes, including immune responses, inflammation, and cancer progression. This comprehensive review aims to elucidate the multifaceted functions of Gal-3, starting with its crucial involvement in viral entry through facilitating viral attachment and catalyzing internalization. Furthermore, Gal-3 assumes significant roles in modulating immune responses, encompassing the activation and recruitment of immune cells, regulation of immune signaling pathways, and orchestration of cellular processes such as apoptosis and autophagy. The impact of Gal-3 extends to the viral life cycle, encompassing critical phases such as replication, assembly, and release. Notably, Gal-3 also contributes to viral pathogenesis, demonstrating involvement in tissue damage, inflammation, and viral persistence and latency elements. A detailed examination of specific viral diseases, including SARS-CoV-2, HIV, and influenza A, underscores the intricate role of Gal-3 in modulating immune responses and facilitating viral adherence and entry. Moreover, the potential of Gal-3 as a biomarker for disease severity, particularly in COVID-19, is considered. Gaining further insight into the mechanisms and roles of Gal-3 in these infections could pave the way for the development of innovative treatment and prevention options for a wide range of viral diseases.

Counterterrorism resource allocation during a pandemic: The effects of dynamic target valuations when facing a strategic terrorist.

The outbreak of pandemics such as COVID-19 can result in cascading effects for global systemic risk. To combat an ongoing pandemic, governmental resources are largely allocated toward supporting the health of the public and economy. This shift in attention can lead to security vulnerabilities which are exploited by terrorists. In view of this, counterterrorism during a pandemic is of critical interest to the safety and well-being of the global society. Most notably, the population flows among potential targets are likely to change in conjunction with the trend of the health crisis, which leads to fluctuations in target valuations. In this situation, a new challenge for the defender is to optimally allocate his/her resources among targets that have changing valuations, where his/her intention is to minimize the expected losses from potential terrorist attacks. In order to deal with this challenge, in this paper, we first develop a defender-attacker game in sequential form, where the target valuations can change as a result of the pandemic. Then we analyze the effects of a pandemic on counterterrorism resource allocation from the perspective of dynamic target valuations. Finally, we provide some examples to display the theoretical results, and present a case study to illustrate the usability of our proposed model during a pandemic.

Oral Delivery of Nucleic Acid Therapies for Local and Systemic Action.

Nucleic acid (NA) therapy has gained importance over the past decade due to its high degree of selectivity and minimal toxic effects over conventional drugs. Currently, intravenous (IV) or intramuscular (IM) formulations constitute majority of the marketed formulations containing nucleic acids. However, oral administration is traditionally preferred due to ease of administration as well as higher patient compliance. To leverage the benefits of oral delivery for NA therapy, the NA of interest must be delivered to the target site avoiding all degrading and inhibiting factors during its transition through the gastrointestinal tract. The oral route presents myriad of challenges to NA delivery, making formulation development challenging. Researchers in the last few decades have formulated various delivery systems to overcome such challenges and several reviews summarize and discuss these strategies in detail. However, there is a need to differentiate between the approaches based on target so that in future, delivery strategies can be developed according to the goal of the study and for efficient delivery to the desired site. The goal of this review is to summarize the mechanisms for target specific delivery, list and discuss the formulation strategies used for oral delivery of NA therapies and delineate the similarities and differences between local and systemic targeting oral delivery systems and current challenges.

In Silico Structure-Based Vaccine Design.

Structure-based vaccine design (SBVD) is an important technique in computational vaccine design that uses structural information on a targeted protein to design novel vaccine candidates. This increasing ability to rapidly model structural information on proteins and antibodies has provided the scientific community with many new vaccine targets and novel opportunities for future vaccine discovery. This chapter provides a comprehensive overview of the status of in silico SBVD and discusses the current challenges and limitations. Key strategies in the field of SBVD are exemplified by a case study on design of COVID-19 vaccines targeting SARS-CoV-2 spike protein.

Cellular Metabolism: A Fundamental Component of Degeneration in the Nervous System.

It is estimated that, at minimum, 500 million individuals suffer from cellular metabolic dysfunction, such as diabetes mellitus (DM), throughout the world. Even more concerning is the knowledge that metabolic disease is intimately tied to neurodegenerative disorders, affecting both the central and peripheral nervous systems as well as leading to dementia, the seventh leading cause of death. New and innovative therapeutic strategies that address cellular metabolism, apoptosis, autophagy, and pyroptosis, the mechanistic target of rapamycin (mTOR), AMP activated protein kinase (AMPK), growth factor signaling with erythropoietin (EPO), and risk factors such as the apolipoprotein E (APOE-ε4) gene and coronavirus disease 2019 (COVID-19) can offer valuable insights for the clinical care and treatment of neurodegenerative disorders impacted by cellular metabolic disease. Critical insight into and modulation of these complex pathways are required since mTOR signaling pathways, such as AMPK activation, can improve memory retention in Alzheimer's disease (AD) and DM, promote healthy aging, facilitate clearance of ß-amyloid (Aß) and tau in the brain, and control inflammation, but also may lead to cognitive loss and long-COVID syndrome through mechanisms that can include oxidative stress, mitochondrial dysfunction, cytokine release, and APOE-ε4 if pathways such as autophagy and other mechanisms of programmed cell death are left unchecked.

Signaling pathways and intervention for therapy of type 2 diabetes mellitus.

Type 2 diabetes mellitus (T2DM) represents one of the fastest growing epidemic metabolic disorders worldwide and is a strong contributor for a broad range of comorbidities, including vascular, visual, neurological, kidney, and liver diseases. Moreover, recent data suggest a mutual interplay between T2DM and Corona Virus Disease 2019 (COVID-19). T2DM is characterized by insulin resistance (IR) and pancreatic ß cell dysfunction. Pioneering discoveries throughout the past few decades have established notable links between signaling pathways and T2DM pathogenesis and therapy. Importantly, a number of signaling pathways substantially control the advancement of core pathological changes in T2DM, including IR and ß cell dysfunction, as well as additional pathogenic disturbances. Accordingly, an improved understanding of these signaling pathways sheds light on tractable targets and strategies for developing and repurposing critical therapies to treat T2DM and its complications. In this review, we provide a brief overview of the history of T2DM and signaling pathways, and offer a systematic update on the role and mechanism of key signaling pathways underlying the onset, development, and progression of T2DM. In this content, we also summarize current therapeutic drugs/agents associated with signaling pathways for the treatment of T2DM and its complications, and discuss some implications and directions to the future of this field.

The challenges of the genome-based identification of antifungal resistance in the clinical routine.

The increasing number of chronic and life-threatening infections caused by antimicrobial resistant fungal isolates is of critical concern. Low DNA sequencing cost may facilitate the identification of the genomic profile leading to resistance, the resistome, to rationally optimize the design of antifungal therapies. However, compared to bacteria, initiatives for resistome detection in eukaryotic pathogens are underdeveloped. Firstly, reported mutations in antifungal targets leading to reduced susceptibility must be extensively collected from the literature to generate comprehensive databases. This information should be complemented with specific laboratory screenings to detect the highest number possible of relevant genetic changes in primary targets and associations between resistance and other genomic markers. Strikingly, some drug resistant strains experience high-level genetic changes such as ploidy variation as much as duplications and reorganizations of specific chromosomes. Such variations involve allelic dominance, gene dosage increments and target expression regime effects that should be explicitly parameterized in antifungal resistome prediction algorithms. Clinical data indicate that predictors need to consider the precise pathogen species and drug levels of detail, instead of just genus and drug class. The concomitant needs for mutation accuracy and assembly quality assurance suggest hybrid sequencing approaches involving third-generation methods will be utilized. Moreover, fatal fast infections, like fungemia and meningitis, will further require both sequencing and analysis facilities are available in-house. Altogether, the complex nature of antifungal resistance demands extensive sequencing, data acquisition and processing, bioinformatic analysis pipelines, and standard protocols to be accomplished prior to genome-based protocols are applied in the clinical setting.

Valorization of hazardous plastic wastes into value-added resources by catalytic pyrolysis-gasification: A review of techno-economic analysis

Plastic waste pollution has grown exponentially since the 1950s. This situation was exacerbated when the volume of personal protective equipment (PPE)-based plastic waste surged after the COVID-19 pandemic. Plastic waste management such as landfills and incineration have adverse effects on the environment and human health due to the leaching of hazardous chemicals and the emission of toxic gases. Modern solutions such as biodegradable plastics and green brick technology are expensive and not well developed to valorize the current accumulation of plastic waste. This has led to the emergence of thermal degradation processes, which is faster and more realistic to solve the PPE-based plastic waste buildup. Pyrolysis and gasification systems to valorize plastic waste into hydrocarbons and fuels are discussed and compared with examples respectively. Scoping review approach is employed to conduct this study. To further increase the value of the final product of plastic waste management, the integrated pyrolysis system to upcycle plastic waste to carbon nanomaterials (CNMs) and the factors affecting the production of non-condensable gases are critically reviewed. The importance of feedstock composition, catalyst type, pyrolysis operating condition (including gas condition and temperature profiles) based on various studies is discussed. The potential and limitation of an integrated pyrolysis system are assessed from kinetic analysis, economic analysis and life-cycle assessment. This review is expected to contribute to the industrial-scale development of sustainable upcycling of plastic waste and enhance the production of desirable gas components for CNM synthesis for environmental sustainability.

Mechanisms of olfactory impairment in COVID-19: a systematic review

One of the symptoms of a new coronavirus infection (COVID-19) is a complete or partial violation of the sense of smell. The aim of the work is to analyze the published results of scientific research on the mechanisms of olfactory impairment in COVID-19. Material and methods. Research was conducted for publications in Pubmed on the problem of olfactory impairment in COVID-19 using terms indexed by MeSH. The systematic review was compiled in accordance with the checklist Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement (PRISMA). Results. Publication's analysis has shown that the existing ideas about conductive anosmia are insufficient to explain the causes of olfactory impairment caused by SARS-CoV-2. It has been established that ACE2 and TMPRSS2 receptors located on the surface of target cells are necessary for the penetration of a new coronavirus. It is known that these receptors are mainly located on the cells of the olfactory epithelium. The main hypothesis of olfactory impairment in COVID-19 is that anosmia/hyposmia is caused by damage not to neuronal cells (as previously assumed), but to the olfactory epithelium. There is no confirmation of the point of view about the damage of SARS-CoV-2 olfactory bulbs and olfactory neurons, since they do not express receptor proteins for the virus on their surface.Copyright © 2022 by the authors.

Co-exposure to urban particulate matter and aircraft noise adversely impacts the cerebro-pulmonary-cardiovascular axis in mice

Worldwide, up to 8.8 million excess deaths/year have been attributed to air pollution, mainly due to the exposure to fine particulate matter (PM). Traffic-related noise is an additional contributor to global mortality and morbidity. Both health risk factors substantially contribute to cardiovascular, metabolic and neuropsychiatric sequelae. Studies on the combined exposure are rare and urgently needed because of frequent co-occurrence of both risk factors in urban and industrial settings. To study the synergistic effects of PM and noise, we used an exposure system equipped with aerosol generator and loud-speakers, where C57BL/6 mice were acutely exposed for 3d to either ambient PM (NIST particles) and/or noise (aircraft landing and take-off events). The combination of both stressors caused endothelial dysfunction, increased blood pressure, oxidative stress and inflammation. An additive impairment of endothelial function was observed in isolated aortic rings and even more pronounced in cerebral and retinal arterioles. The increase in oxidative stress and inflammation markers together with RNA sequencing data indicate that noise particularly affects the brain and PM particularly affects the lungs. Noise also increased levels of circulating stress hormones adrenaline and noradrenaline, while PM increased levels of circulating cytokines CD68 and MCP-1. The combination of both stressors has additive adverse effects on the cardiovascular system that are based on PM-induced systemic inflammation and noise-triggered stress hormone signaling. We demonstrate an additive upregulation of ACE-2 in the lung, suggesting that there may be an increased vulnerability to COVID-19 infection. The data warrant further mechanistic studies to characterize the propagation of primary target tissue damage (lung, brain) to remote organs such as aorta and heart by combined noise and PM exposure.Copyright © 2023

Stereotactic Radiotherapy for the Treatment of Recurrent Refractory Vt in Chagas Disease Patients

Background: Stereotactic radiotherapy (SBRT) is a new therapeutic option for patients with scar related ventricular tachycardia (VT). Objective(s): To describe our experience with the use of SBRT for the treatment of recurrent VT in patients with Chagas Cardiomyopathy (CCM) in whom catheter ablation is not an option. Method(s): We selected patients with Chagas Disease that underwent SBRT for recurrent VT treatment. The target sites of SBRT were planned based on CMR and CT reconstruction on ADAS software, bipolar voltage maps from previous CA procedures and VT morphology induced on a electrophysiologic study performed SBRT planning. Target sites were decided together by electrophysiology and radiation oncology group. Result(s): We performed SBRT in six CCM patients July 2021 to July 2022. Most patients were male (66.7%), mean age 62.3+/-5.7 years-old and EF 28.5% (Q1: 20 Q3:42.7). One patient (16.7%) had two prior catheter ablation, four (66.7%) had one and one patient had no prior ablation, but had severe pulmonary fibrosis after COVID and was O2 dependent. The mean PTV (planning target volume) was 85+/-14 mL and the ITV (internal target volume) was 29+/-4 ml, with safe constraints regarding the esophagus and stomach. In a mean FU of 244+/-173days, 3 (50%) patients presented VT recurrence after blanking period. Two patients died 86 and 50 days after SBRT. The median number of VT episodes reduced from 13 (6.25;44.75) to 7.5 (3;7.5) (P = 0.093). All alive patients stop presenting VT in a median period of 174 (Q1: 44.75: Q3: 199) days, being at the end of the follow-up in a median of 196 (Q1: 137;Q3: 246) days without new VT episodes. Conclusion(s): SBRT presents a high rate of early recurrence in Chagas disease patients that improves during timeCopyright © 2023

Observational cohort study of the clinical outcomes associated with B.1.1.7/SGTF among hospitalized COVID-19 patients in Lebanon.

Background: The B.1.1.7 SARS-CoV-2 variant results in spike gene target failure (SGTF) in reverse transcription-quantitative polymerase chain reaction (RT-PCR) assays. Few studies have been published on the clinical impact of B.1.1.7/SGTF. Aims: To assess the incidence of B.1.1.7/SGTF and its associated clinical characteristics among hospitalized COVID-19 patients. Methods: This observational, single-centre, cohort study was conducted between December 2020 and February 2021 and included 387 hospitalized COVID-19 patients. The Kaplan-Meier method was used for survival analysis, and logistic regression to identify risk factors associated with B.1.1.7/SGTF. Results: By February 2021, B.1.1.7/SGTF (88%) dominated the SARS-CoV-2 PCR results in a Lebanese hospital. Of the 387 eligible COVID-19 patients confirmed by SARS-CoV-2 RT-PCR, 154 (40%) were non-SGTF and 233 (60%) were B.1.1.1.7/SGTF; this was associated with a higher mortality rate among female patients [22/51 (43%) vs 7/37 (19%); P = 0.0170]. Among patients in the B.1.1.7/SGTF group, most were aged ≥ 65 years [162/233 (70%) vs 74/154 (48%); P < 0.0001]. Independent predictors of B.1.1.7/SGTF infection were hypertension (OR = 0.415; CI: 0.242-0.711; P = 0.0010), age ≥ 65 years (OR = 0.379; CI: 0.231-0.622; P < 0.0001), smoking (OR = 1.698; CI: 1.023-2.819; P = 0.0410), and cardiovascular disease (OR = 3.812; CI: 2.215-6.389; P < 0.0001). Only non-SGTF patients experienced multi-organ failure [5/154 (4%) vs 0/233 (0%); P = 0.0096]. Conclusion: There was a clear difference between the clinical features associated with B.1.1.7/SGTF and non-SGTF lineages. Tracking viral evolution and its clinical impact is crucial for proper understanding and management of the COVID-19 pandemic.

Bifunctional three-way DNA junction-based strand displacement recycling for amplifiable electrochemical bivariate biosensing

It's intriguing to utilize the branched arms of three-way DNA junction (3WDJ) for modifying specific recognizing and/or sensing elements of multivariate analytes. Herein, by using two targeting DNA segments (T and T*) specific to SARS-CoV-2 as analyte models, an electrochemical bivariate biosensor was created based on a functional 3WDJ including two -NH2-labeled recognizable probes (RP and RP*) and an assistant probe (AP), while its two branched arms hybridized with four helping DNA blockers. In the electrode surface electrodeposited in HAuCl4, the 3WDJ was stably immobilized via Au-N bonds to specifically recognize and bind T and T*, with which two modified signaling probes by electroactive methylene blue (SP-MB) and ferrocene (SP*-Fc) were introduced to initiate two strand displacement reactions. Resultantly, SP-MB and SP*-Fc were guided to be complementarily hybridized in two arms of 3WDJ, replacing T and T* to execute two individual repeatable recycling for signal amplification. Thus, MB and Fc were oriented proximal to the modified electrode surface for significantly increased electrochemical current signals, respectively dependent on T and T*. With the branched arms of rapidly assembled 3WDJ, the discernible detection of bivariate targets was achievable, showing superb simplification, high sensitivity, and potentially more accurate electrochemical assay of multivariate targets. Data Availability Data will be made available on request.

Pre-Avatar: An Automatic Presentation Generation Framework Leveraging Talking Avatar

Since the beginning of the COVID-19 pandemic, remote conferencing and school-teaching have become important tools. The previous applications aim to save the commuting cost with real-time interactions. However, our application is going to lower the production and reproduction costs when preparing the communication materials. This paper proposes a system called Pre-Avatar, generating a presentation video with a talking face of a target speaker with 1 front-face photo and a 3-minute voice recording. Technically, the system consists of three main modules, user experience interface (UEI), talking face module and few-shot text-to-speech (TTS) module. The system firstly clones the target speaker's voice, and then generates the speech, and finally generate an avatar with appropriate lip and head movements. Under any scenario, users only need to replace slides with different notes to generate another new video. The demo has been released here11https://pre-avatar.github.io/ and will be published as free software for use. © 2022 IEEE.

Drug Design and Disease Diagnosis: The Potential of Deep Learning Models in Biology

Early prediction and detection enable reduced transmission of human diseases and provide healthcare professionals ample time to make subsequent diagnoses and treatment strategies. This, in turn, aids in saving more lives and results in lower medical costs. Designing small chemical molecules to treat fatal disorders is also urgently needed to address the high death rate of these diseases worldwide. A recent analysis of published literature suggested that deep learning (DL) based models apply more potential algorithms to hybrid databases of chemical data. Considering the above, we first discussed the concept of DL architectures and their applications in drug development and diagnostics in this review. Although DL-based approaches have applications in several fields, in the following sections of the arti-cle, we focus on recent developments of DL-based techniques in biology, notably in structure predic-tion, cancer drug development, COVID infection diagnostics, and drug repurposing strategies. Each review section summarizes several cutting-edge, recently developed DL-based techniques. Additionally, we introduced the approaches presented in our group, whose prediction accuracy is relatively compara-ble with current computational models. We concluded the review by discussing the benefits and draw-backs of DL techniques and outlining the future paths for data collecting and developing efficient computational models.Copyright © 2023 Bentham Science Publishers.