Multicopy Expression of the Marine Antimicrobial Peptide Spgillcin 177–189 in Pichia pastoris for High-Yield Production and Potent Activity Against Foodborne Pathogens

Bacterial foodborne contamination poses a dual challenge of chemical preservative risks and antibiotic resistance, drives the need for green production of natural antimicrobial alternatives. The reported cationic antimicrobial peptide (AMP) Spgillcin177−189 derived from the Scylla paramamosain, has strong antimicrobial activity against Staphylococcus aureus and clinical isolation strains. To meet industry demand in future, large-scale production of Spgillcin177− 189 is essential. In the study, Pichia pastoris expression system was established for production of the recombinant Spgillcin177− 189 (rSp­ gillcin177−189). Then, multicopy strategy was selectively designed by employing the Golden Gate assembly technology to efficiently construct multi-copy plasmid s, which significantly enhanced the expression level of Spgillcin177− 189. A yield of 126.1 mg/L was harvested with 2.75-fold higher that of the single-copy strain. In addition, the recombinant Spgill­ cin177 − 189 exhibited potent antibacterial activity against multiple foodborne pathogens within a MIC range of 5.25–84 µg/ mL. It also showed effective bactericidal activity and anti-biofilm activity against Staphylococcus aureus and Vibrio parahaemolyticus. rSpgillcin177 − 189 exhibited good thermostability, with no obvious cytotoxicity and hemolytic activity. rSpgillcin177 − 189 may interact with microbial surface components via hydrogen bonding, which were vital for peptide activity in combating bacteria. The rSpgillcin177 − 189 specifically targeting the cell membrane, disrupted bacterial mem­ brane integrity and leading to cell death. This study provided a very feasible genetic engineering strategy for large-scale production of rSpgillcin177 − 189, which will be applied at a lower cost in agricultural and food industries in future.

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A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods

Sperm morphology, as an indicator of fertility, is a critical tool in semen analysis. In this study, a smartphone-based hybrid system that fully automates the sperm morphological analysis is introduced with the aim of eliminating unwanted human factors. Proposed hybrid system consists of two progressive steps: automatic segmentation of possible sperm shapes and classification of normal/ab-normal sperms. In the segmentation step, clustering techniques with/without group sparsity approach were tested to extract region of interests from the images. Subsequently, a novel publicly available morphological sperm image data set, whose labels were identified by experts as non-sperm, normal and abnormal sperm, was created as the ground truths of classification step. In the c lassification step, conventional and ensemble machine learning methods were applied to domain-specific features that were extracted by using wavelet transform and descriptors. Additionally, as an alternative to conventional features, three deep neural network architectures, which can extract high-level features from raw images after using statistical learning, were employed to increase the proposed method’s performance. The results show that, for the conventional features, the highest classification accuracies were achieved as 80.5% and 83.8% by using the waveletand descriptor-based features that were fed to the Support Vector Machines respectively. On the other hand, the Mobile-Net, which is a very convenient network for smartphones, achieved 87% accuracy. In the light of obtained results, it is seen that a fully automatic hybrid system, which uses the group sparsity to enhance segmentation performance and the Mobile-Net to obtain high-level robust features, can be an effective m obile solution for the sperm morphology analysis problem.

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When the mindful ones experience flow: a moderated-mediation model of

Purpose – The emerging live streaming technology has provided a novel means for streamers to interact with viewers, allowing for synchronous and vivid demonstrations of products for sale. However, individual streamers as sellers still struggle to improve sales in their live stores. Drawing upon flow theory, our study proposes and tests a moderated-mediation model that explores (1) the indirect influences of telepresence and social presence as two important live streaming affordances on viewers’ purchase intentions through the immersive state of flow and (2) the dynamic contingency embedded in the indirect relationships between presence and purchases through flow as created by mindfulness. Design/methodology/approach – We collected survey data from 251 experienced consumers of a three-year Kuaishou store run by an Inner Mongolian singer in China. We applied a covariance-based structural equation modeling approach to examine the first-stage moderated-mediation model. Findings – Our results show that viewers’ flow state mediated the effects of telepresence and social presence on purchase intentions of both virtual gifts and physical products. Additionally, mindfulness toward live streaming strengthened the mediation effect of flow on the relationship between telepresence and purchase intentions but weakened its mediation effect between social presence and purchase intentions. Originality/value – Our study not only expands the existing knowledge on live commerce but also systematically addresses the theoretical tension between flow and mindfulness as two important user states that coexist in the live commerce context. Our findings also reveal practical implications for streamers, managers and designers of live commerce.

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Risk of Revision and Reoperation After ACL Reconstruction: Comparison of Quadriceps Tendon, Patellar Tendon, and Hamstring Autografts Stratified by Patient Sex and Age

(Abstract not found)

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Left versus right ventricular pacing during TAVR and balloon aortic valvuloplasty: A systematic review and meta-analysis

Introduction: While right ventricular pacing (RVP) is the conventional temporary pacing modality used for transcatheter aortic valve replacement (TAVR), this approach possesses inherent risks and procedural challenges. We aim to assess and compare the safety and efficacy of left ventricular pacing (LVP) and RVP during TAVR and balloon aortic valvuloplasty (BAV). Methods: Following PRISMA guidelines, a comprehensive literature search was conducted in four databases from inception to December 15th, 2023. We included observational studies and clinical trials comparing LVP with RVP during TAVR and BAV procedures. Primary outcomes included short-term mortality, mortality due to cardiac tamponade, and procedural complications including bleeding, vascular complications, and cardiac tamponade. Secondary outcomes comprised procedure duration and length of hospital stay. Results: Five studies involving 830 patients with RVP and 1577 with LVP were included. Short-term mortality was significantly higher in the RVP group (RR 2.32, 95% CI: [1.37–3.93], P = .002), as was the incidence of cardiac tamponade (RR 2.19, 95% CI: [1.11–4.32], P = .02). LVP demonstrated shorter hospital stays (MD = 1.34 d, 95% CI: [0.90, 1.78], P < .001) and reduced procedure duration (MD = 7.75 min, 95% CI: [5.08, 10.41], P < .00001) compared to RVP. New pacemaker implantation was higher in the RVP group (RR 2.23, 95% CI: [1.14, 4.39], P = .02). Conclusion: LVP during TAVR and BAV emerges a safer alternativ

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Minimum 10-Year Outcomes of Adolescents Undergoing Contemporary Hip Arthroscopic Surgery for Femoroacetabular Impingement Syndrome

(Abstract not found)

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Hybrid cognitive authority and algorithmic subjectivity: rethinking knowledge management in AI-driven communication

Purpose – This paper aims to explore the transformative impact of generative artificial intelligence (AI) and human-computer interaction (HCI) on knowledge management in business, focusing on how AIdriven communication reshapes organizational practices. It examines the role of HCI in designing usercentric AI tools and introduces Hybrid Cognitive Authority (the co-construction of knowledge between human and AI agents) and Algorithmic Subjectivity (AI-generated communication simulating intent without cognition) to evaluate their effects on decision-making, knowledge flows and ethical governance in commercial settings. Design/methodology/approach – A mixed-methods approach integrates interdisciplinary genealogical analysis, qualitative case studies and critical discourse analysis. The study traces the evolution of commercial communication from mid-20th-century pragmatics to AI-mediated paradigms, synthesizing insights from cognitive science, information systems and digital epistemology. Five case studies of AI and HCI applications in business (e.g. customer service, recruitment) and discourse analysis of AI-generated artifacts provide empirical evidence for assessing knowledge management outcomes. Findings – Generative AI, supported by HCI, enhances knowledge management by improving efficiency and scalability, but raises challenges related to transparency, algorithmic bias and accountability. Empirical cases demonstrate how hybrid human-AI systems optimize knowledge processes while highlighting ethical risks, such as biased outputs. The proposed framework of Hybrid Cognitive Authority and Algorithmic Subjectivity necessitates governance models that balance AI automation with human oversight to maintain trust and interpretive agency. Orig inality/value – Unlike prior studies that view AI as a passive tool, this research positions AI as an active knowledge co-constructor, advancing knowledge management scholarship through the novel concepts of Hybrid Cognitive Authority and Algorithmic Subjectivity. It bridges theory and practice by offering actionable strategies for businesses to leverage AI and HCI responsibly, contributing to economic efficiency, ethical governance and societal trust in AI-driven communication.

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Humble leadership in development volunteer management in China: its

Purpose – This study aims to explore the impact of humble leadership on job crafting and job satisfaction among development volunteers, who contribute time and skills to support disadvantaged communities. Specifically, it examines how humble leaders motivate volunteers to actively adjust their work, leading to increased job satisfaction. This research fills a gap in understanding the role of humble leadership in development volunteer management. Design/methodology/approach – This study utilized a quantitative research methodology with snowball sampling to collect data from 334 volunteers who participated in a development volunteering program in rural China. Four variables – humble leadership, job crafting, job satisfaction and volunteer role identity – were measured using translated scales . The data were analyzed using descriptive statistics, confirmatory factor analysis, correlation analysis, structural equation modeling and bootstrapping. Findings – Humble leaders encouraged volunteers to shape their work, enhancing engagement and satisfaction. A stronger role identity strengthened the influence of humble leaders on job crafting, further increasing satisfaction. The findings suggest that humble leaders promote volunteers’ long-term efficacy by recognizing their individual contributions and creating an open and supportive work environment. Research limitations/implications – The study provides practical implications for volunteer management. Organizations may benefit from encouraging humble leadership and enhancing volunteers’ role identity through training and selection. However, focusing on a single volunteering program in China may limit the generalizability of the findings. Future studies should explore contextual differences across regions. Originality/ value – This study highlights the importance of humble leadership in volunteer management, particularly in enhancing leadership’s role in long-term mission-driven projects.

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixedlanguage content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks.

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Optimising vaccine immunogenicity in ageing populations: key strategies

(Abstract not found)

Download PDF: https://soala.eu.org/rh2ijc