Self-Efficacy in the Virtual and Real World

Introduction

Technological advancements have changed the way people think and interact. The new virtual model for exchanging ideas, goods, and services has also impacted their inner life differently. This model appears more convenient and attractive, as it saves travel expenses and reduces fatigue. Nevertheless, in-person interactions provide valuable insights into each contributor’s role and value in relationships. Considering the financial aspect of a specific work as essential to an individual in fulfilling life’s necessities, the costs of interaction type remain at the forefront when selecting a collaborative approach with a partner.

A person’s preference for virtual or face-to-face interaction with associates marks a difference in their inner world, as Computer- Generated data simulates realities without physical interaction, and its exclusion in analytics does not yield the same echoes in internal life. The mental activity includes processing information received from the outside and responding appropriately. The omission of some aspects in the analysis modifies the nuance of the residual effects.

The nervous cells’ versatility to outside signal varieties exhibits a range of conduct. Suppose behaviour is filled with love and confidence. In that case, it enhances the individual’s well-being, enabling them to succeed both professionally and socially, which leads to increased self-esteem and improved work productivity.

By contrast, discouraging virtual or face-to-face interactions with collaborators reduces work efficiency, often leading to the termination of a project. Inappropriate words and thought energy can negatively impact the functioning of nervous cells and, consequently, primarily affect the functioning of blood vessels, leading to disorders in vulnerable areas of the human body, such as metabolic abnormalities, depression, arterial hypertension, and even brain haemorrhage.

Evidence from clinical practice has demonstrated that social interactions play a significant role in shaping public health. Gentle, skilful, trustworthy, and respectful interactions, along with humour, enhance personal well-being, reduce burnout, and stimulate creativity. Job accomplishments that bring satisfaction increase selfworth and appreciation from others, which quickly circulate online. Typically, collaborators are interested in an individual’s work history, efficiency, and social interactions. Spreading the word about a provider’s best achievements enhances their reputation, yielding increased revenues, production growth, and corporate stability and expansion. This leads to a better personal, professional, and social life, fostering a relaxing and peaceful inner life-a key to overall well-being.

Self-appreciation and others’ judgment of our value continually change, affecting our emotions: excitement, triumph, admiration, sadness, or fear prompt changes in individual well-being. In troubled times, finding encouragement from friends or loved ones and practicing Self-Care that includes self-compassion helps mitigate the negative impact of others’ misconduct in our virtual or in-person relationships. Artificial intelligence plays a significant role in this direction. Virtual assistants and specialized programs, available on demand, can bring enjoyment during challenging times and provide benefits to end users when needed.

Group acceptance or rejection of an individual validates the fairness of their self-evaluation before they wish to cooperate with them.

Self-imperfection, as well as others’ biases in interpreting facts, is a common trait among humans.

Combining collaborative work in person with virtual forms complements each other and helps to save time - our precious reward in this ephemeral world.

The dual facets - positive and negative - of each person’s verbal or nonverbal communication, virtual or in-person, must be harmonized to mitigate frustrations smoothly as they arise.

As “day” and “nights” continuously succeed in our lives, making nights even more attractive with our lights and tempering the intense “sunlight,” we can make our lives less stressful, preserving good memories in the Universe when we inevitably pass.

References

• Global Market Insights (2024) Artificial Intelligence in Drug Discovery Market Size Report.
• Chavda VP, Vihol D, Patel A, Redwan EM, Uversky VN, et al. (2023) Bioinformatics Tools for Pharmaceutical Drug Product Development. John Wiley Sons Ltd Hoboken NJ USA Introduction to Bioinformatics AI and ML for Pharmaceuticals: 1-18.
• Debleena Paul, Gaurav Sanap, Snehal Shenoy, Dnyaneshwar Kalyane, Kiran Kalia, et al. (2020) Artifcial intelligence in drug discovery and development. Drug Discov Today 26(1): 80-93.
• Patil RS, Kulkarni SB, Gaikwad VL (2023) Artificial intelligence in pharmaceutical regulatory affairs. Drug Discov Today 28(9): 103700.
• Kazi Asraf Ali, SK Mohin, Puja Mondal, Susmita Goswami, Soumya Ghosh, et al. (2024) Influence of artificial intelligence in modern pharmaceutical formulation and drug development. Futur J Pharm Sci: 10(53).
• Ahmed Al Kuwaiti, Khalid Nazer, Abdullah Al Reedy, Shaher Al Shehri, Afnan Al Muhanna, et al. (2023) a review of the role of artificial intelligence in healthcare. J Pers Med 13(6): 951.
• Landin M, Rowe RC (2013) Artificial neural networks technology to model, understand, and optimize drug formulations. In Formulation tools for pharmaceutical development: 7-37.
• Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT (2016) Computational approaches in target identification and drug discovery. Comput Struct Biotechnol J 14: 177-184.
• Dorothea Emig, Alexander Ivliev, Olga Pustovalova, Lee Lancashire, Svetlana Bureeva, et al. (2013) Drug target prediction and repositioning using an integrated network-based approach. PLoS One 8(4): e60618.
• Solans C, Izquierdo P, Nolla (2005) Nano-emulsions. Curr Opin Colloid Interface Sci 10:102-110.
• Gursoy RN, Benita S (2004) Self-emulsifying drug delivery systems (SEDDS) for improved oral delivery of lipophilic drugs. Biomed Pharmacother Biomedecine Pharmacother 58(3): 173-182.
• Bhupinder Singh, Shantanu Bandopadhyay, Rishi Kapil, Ramandeep Singh, O Katare, et al. (2009) Self-emulsifying drug delivery systems (SEDDS): formulation development, characterization, and applications. Crit Rev Ther Drug Carrier Syst 26(5): 427-521.
• Dimitrios G Fatouros, Flemming Seier Nielsen, Dionysios Douroumis, Leontios J Hadjileontiadis, Anette Mullertz, et al. (2008) In vitro-in vivo correlations of self-emulsifying drug delivery systems combining the dynamic lipolysis model and neuro-fuzzy networks. Eur J Pharm Biopharm Off J Arbeitsgemeinschaft Pharm Verfahrenstechnik EV 69(3): 887-898.
• Parikh KJ, Sawant KK (2018) Comparative study for optimization of pharmaceutical self-emulsifying pre-concentrate by design of experiment and artificial neural network. AAPS PharmSciTech 19(7): 3311-3321.
• Chen Wen Li, Sheng Yong Yang, Rui He, Wan Jun Tao, Zong Ning Yin, et al. (2011) Development of quantitative structure-property relationship models for self-emulsifying drug delivery system of 2-aryl propionic acid NSAIDs. J Nanomater :1-12.
• Giang Thi Thu Vu, Nghia Thi Phan, Huyen Thi Nguyen, Hung Canh Nguyen, Yen Thi Hai Tran, et al. (2020) Application of the artificial neural network to optimize the formulation of self-nanoemulsifying drug delivery system containing rosuvastatin. J Appl Pharm Sci: 10(9).
• Ying Huang, Qinghe Yao, Chune Zhu, Xuan Zhang, Lingzhen Qin, et al. (2015) Comparison of novel granulated pellet-containing tablets and traditional pellet-containing tablets by artificial neural networks. Pharm Dev Technol 20(6): 670-675.
• Aleksander Mendyk, Peter Kleinebudde, Markus Thommes, Angelina Yoo, Jakub Szlęk, et al. (2010) Analysis of pellet properties with use of artificial neural networks. Eur J Pharm Sci Off J Eur Fed Pharm Sci 41(3-4): 421-429.
• Peh KK, Lim CP, Quek SS, Khoh KH (2000) Use of artificial neural networks to predict drug dissolution profiles and evaluation of network performance using similarity factor. Pharm Res 17(11): 1384-1388.
• Sankalia MG, Mashru RC, Sankalia JM, Sutariya VB (2005) Papain entrapment in alginate beads for stability improvement and site-specific delivery: physicochemical characterization and factorial optimization using neural network modeling. AAPS PharmSciTech 6(2): E209-222.
• Vaithiyalingam S, Khan MA (2002) Optimization and characterization of controlled release multi- particulate beads formulated with a customized cellulose acetate butyrate dispersion. Int J Pharm 234(1-2): 179-193.
• Asadi H, Rostamizadeh K, Salari D, Hamidi M (2011) Preparation of biodegradable nanoparticles of tri-block PLA-PEG-PLA copolymer and determination of factors controlling the particle size using artificial neural network. J Microencapsul 28(5): 406-416.
• Baharifar H, Amani A (2017) Size, loading efficiency, and cytotoxicity of albumin-loaded chitosan nanoparticles: an artificial neural networks study. J Pharm Sci 106(1): 411-417.
• Rania A Hashad, Rania A H Ishak, Sherif Fahmy, Samar Mansour, Ahmed S Geneidi, et al. (2016) Chitosan-tripolyphosphate nanoparticles: optimization of formulation parameters for improving process yield at a novel pH using artificial neural networks. Int J Biol Macromol 86: 50-58.
• Shahsavari S, Rezaie Shirmard L, Amini M, Abedin Dokoosh F (2017) Application of artificial neural networks in the design and optimization of a nanoparticulate fingolimod delivery system based on biodegradable Poly(3-Hydroxybutyrate-Co-3-Hydroxyvalerate). J Pharm Sci 106(1): 176-182.
• Karim S Shalaby, Mahmoud E Soliman, Luca Casettari, Giulia Bonacucina, Marco Cespi, et al. (2014) Determination of factors controlling the particle size and entrapment efficiency of noscapine in PEG/PLA nanoparticles using artificial neural networks. Int J Nanomed 9: 4953-4964.
• Leonardi D, Salomón CJ, Lamas MC, Olivieri AC (2009) Development of novel formulations for Chagas’ disease: optimization of benznidazole chitosan microparticles based on artificial neural networks. Int J Pharm 367(1-2): 140-147.
• Yongqiang Li, Mohammadreza R Abbaspour, Paul V Grootendorst, Andrew M Rauth, Xiao Yu Wu, et al. (2015) Optimization of controlled release nanoparticle formulation of verapamil hydrochloride using artificial neural networks with genetic algorithm and response surface methodology. Eur J Pharm Biopharm Off J Arbeitsgemeinschaft Pharm Verfahrenstechnik EV 94: 170-179.
• Metwally AA, Hathout RM (2015) Computer-assisted drug formulation design: novel approach in drug delivery. Mol Pharm 12(8): 2800-2810.
• Hassanzadeh P, Atyabi F, Dinarvand R (2017) Application of modelling and nanotechnology-based approaches: the emergence of breakthroughs in theranostics of central nervous system disorders. Life Sci 182: 93-103.
• Youshia J, Ali ME, Lamprecht A (2017) Artificial neural network based particle size prediction of polymeric nanoparticles. Eur J Pharm Biopharm Off J Arbeitsgemeinschaft Pharm Verfahrenstechnik EV 119: 333-342.
• Rizkalla N, Hildgen P (2005) Artificial neural networks: comparison of two programs for modeling a process of nanoparticle preparation. Drug Dev Ind Pharm 31(10): 1019-1033.
• Rosalia Rodríguez Dorado, Mariana Landín, Ayça Altai, Paola Russo, Rita P Aquino, et al. (2018) A novel method for the production of core-shell microparticles by inverse gelation optimized with artificial intelligent tools. Int J Pharm 538(1-2): 97-104.
• Ruchika S Patil, Samruddhi B Kulkarni, Vinod L Gaikwad (2023) Artificial intelligence in pharmaceutical regulatory affairs. Drug Discovery Today 28(9): 103700.