THE ROLE OF HOTEL MANAGERS IN ENHANCING SUSTAINABLE DEVELOPMENT IN LEBANON

Objective: This study addresses the issue of corporate social responsibility with a special focus on Lebanese 5 star hotels. Point that a sustainable development approach has implications on organizational actions and strategies, recognize the role of managers in Lebanese hotels in applying and implementing sustainable development principles, and indicate the changes that occur in each department of the organization after the adoption of the CSR foundation.   Theoretical Framework: This topic presents the main concepts on which the research is based. It highlights everything related to sustainable development, corporate social responsibility and the role of managers, providing a solid foundation for understanding the research context.   Method: The focus is on critical realist philosophy and positivism, with qualitative analysis and inductive reasoning through observation and semi-structured interviews. Secondary data come from books, published and unpublished personal sources, newspapers, websites, blogs, diaries, government documents, and podcasts. Results and Discussion: The data collected show that the concept of sustainable development exists in Lebanon and that Lebanese hotels have become socially responsible, albeit to varying degrees. Managers have a key role to play in realizing this concept.   Research Implications: The practical and theoretical implications of this research were discussed, providing an accurate snapshot of the level of implementation of sustainable development in Lebanese hotels. It provides an information base, the application of which helps to raise the level of implementation of the concept of sustainability in hotels.   Originality/Value: The importance of the study lies in providing information on all the study variables, clarifying the role of managers in applying the concept of CSR, and highlighting the basic and sequential steps for applying CSR in Lebanese hotels

Plasposon mutagenesis in Pseudomonas aeruginosa isolates illustrates the role of ABC transporter in intrinsic resistance to antibiotics

Pseudomonas aeruginosa is an opportunist pathogen most commonly related to nosocomial infections. P. aeruginosa infection therapy poses a significant challenge due to its ability to resist various antibiotics currently available. As a result, excessive use of antibiotics during therapy expedites the development of multidrug resistant P. aeruginosa. Hence, this study aimed to identify novel genes involved in multiple antibiotic resistance using plasposon mutagenesis technique. One hundred and ten P. aeruginosa isolates were collected from various clinical sources involving urine, burns and wound’s pus.  An antimicrobial susceptibility test was performed to detect their resistance to 18 antibiotics. Results showed that all isolates were resistant to ampicillin and tetracycline, and the highest resistance ras were detected for nitrofurantoin and sulfamethoxazole (99%), followed by amoxiclav, cefotaxime, cefoxitin, ceftriaxone, and kanamycin (98%). While the lowest resistance rate was towards imipenem (49%). Plasposon mutagenesis was used to detect the genes involved in multi-antibiotic resistance. The pTnMod-Gm was introduced to the recipient P. aeruginosa PA4 isolate via triparental mating using E. coli HB101/ pRK2013 as a helper strain. Mutants were screened for resistance defects by plating them on nutrient agar supplemented with different antibiotics. Two mutants were identified; one (M1) exhibited susceptibility to tetracycline, cefotaxime, and ceftazidime, and the other (M9) to ceftazidime and ceftriaxone. The analysis of these mutants revealed the insertion of the plasposon into an open reading frame for the ABC transporter in P. aeruginosa, which plays a distinctive role in extruding antibiotics out of cells.

Effect of antibiotics on the expression of pyocyanin synthetic genes in Pseudomonas aeruginosa isolated from different clinical sources of a few hospitals in Mosul, Iraq

Pyocyanin is a blue-green phenazine pigment and one of the most virulent factors produced by the opportunistic pathogen Pseudomonas aeruginosa. It has a redox activity and a toxic impact on living cells, as it interacts with oxygen to produce reactive oxygen species (ROS). Using antibiotics at a sub-lethal dose has an unexpected influence on the expression of pyocyanin-producing genes. In this study, qPCR technique was performed to identify the effect of eight antibiotics (cefotaxime, ampicillin, amoxiclav, ceftazidime, ceftriaxone, chloramphenicol, kanamycin and tetracycline) on the gene expression level of pyocyanin synthetic genes in P. aeruginosa isolated from different clinical sources of a few hospitals in Mosul, Iraq using qPCR technique. It was found that when P. aeruginosa was grown in media containing cefotaxime (CTX 30 µg/mL), ampicillin (AM 25 µg/mL) or amoxiclav (AMC 30 µg/mL), up-regulated the expression of pyocyanin producing genes belonging to different operons thereby increased pyocyanin production. Overexpression occurred in (CTX) treatment in PhzA1 operon with 235.56 fold change and phzM and phzS genes with 340.14, 280.13 fold change, respectively. Lower expression levels showed in tetracycline (TE 30 µg/mL) treatment, which was a (1.44) fold change for phzA1 and a (1.64, 1.08) fold change for phzM and phzS genes. More caution should be considered when delivering antibiotics to treat P. aeruginosa infections, as using drugs that the bacteria resists or at sub-lethal concentrations may trigger up-regulation of virulence factors, aiding in the spread of the disease.

LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes

The semantic segmentation of surgical scenes is a prerequisite for task automation in robot assisted interventions. We propose LapSeg3D, a novel DNN-based approach for the voxel-wise annotation of point clouds representing surgical scenes. As the manual annotation of training data is highly time consuming, we introduce a semi-autonomous clustering-based pipeline for the annotation of the gallbladder, which is used to generate segmented labels for the DNN. When evaluated against manually annotated data, LapSeg3D achieves an F1 score of 0.94 for gallbladder segmentation on various datasets of ex-vivo porcine livers. We show LapSeg3D to generalize accurately across different gallbladders and datasets recorded with different RGB-D camera systems

LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes

The semantic segmentation of surgical scenes is a prerequisite for task automation in robot assisted interventions. We propose LapSeg3D, a novel DNN-based approach for the voxel-wise annotation of point clouds representing surgical scenes. As the manual annotation of training data is highly time consuming, we introduce a semi-autonomous clustering-based pipeline for the annotation of the gallbladder, which is used to generate segmented labels for the DNN. When evaluated against manually annotated data, LapSeg3D achieves an F1 score of 0.94 for gallbladder segmentation on various datasets of ex-vivo porcine livers. We show LapSeg3D to generalize accurately across different gallbladders and datasets recorded with different RGB-D camera systems.Comment: 6 pages, 5 figures, accepted at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japa

Augmented Reality-based Robot Control for Laparoscopic Surgery

Minimally invasive surgery is the standard formany abdominal interventions, with an increasing use of tele-manipulated robots. As collaborative robots enter the field ofmedical interventions, their intuitive control needs to be ad-dressed. Augmented reality can thereby support a surgeonby representing the surgical scene in a natural way. In thiswork, an augmented reality based robot control for laparo-scopic cholecystectomy is presented. A user can interact withthe virtual scene to clip the cystic duct and artery as well asto manipulate the deformable gallbladder. An evaluation wasperformed based on the SurgTLX and system usability scale

The importance of machine learning in autonomous actions for surgical decision making

Surgery faces a paradigm shift since it has developed rapidly in recent decades, becoming a high-tech discipline. Increasingly powerful technological developments such as modern operating rooms, featuring digital and interconnected equipment and novel imaging as well as robotic procedures, provide several data sources resulting in a huge potential to improve patient therapy and surgical outcome by means of Surgical Data Science. The emerging field of Surgical Data Science aims to improve the quality of surgery through acquisition, organization, analysis, and modeling of data, in particular using machine learning (ML). An integral part of surgical data science is to analyze the available data along the surgical treatment path and provide a context-aware autonomous action by means of ML methods. Autonomous actions related to surgical decision-making include preoperative decision support, intraoperative assistance functions, as well as robot-assisted actions. The goal is to democratize surgical skills and enhance the collaboration between surgeons and cyber-physical systems by quantifying surgical experience and making it accessible to machines, thereby improving patient therapy and outcome. The article introduces basic ML concepts as enablers for autonomous actions in surgery, highlighting examples for such actions along the surgical treatment path