Population growth, habitat destruction, climate change, and new diseases are all threats to the world's fisheries and aquaculture. Traditional selective breeding has achieved success, but has some drawbacks such as long breeding cycles, polygenetic nature of traits, and limited genetic diversity in closed populations. The potential of CRISPR-Cas genome editing is a recent technology that has been used in zebrafish since 2013, and is now revolutionizing the genetic improvement of Atlantic salmon, Nile tilapia, rainbow trout, channel catfish, grass carp, and others. This precision has been further improved by advanced technologies such as base editors, prime editors, CRISPRa, and CRISPRi. Applications include growth enhancement through myostatin disruption; increased disease resistance, reproductive efficiency, flesh quality and thermal/osmotic stress tolerance. These tackle key production bottlenecks, while contributing to lower use of antibiotics, lower environmental footprint and climate-resilient systems based on the UN SDGs. Even with these challenges, there are significant issues off-target effects, mosaicism, regulatory issues, public acceptance and polygenetic traits. Now, new technologies like artificial intelligence-driven design, multi-omics, multiplex editing, and epigenome editing provide answers. To be fully commercialised, regulatory harmonisation and transparent communication are paramount.

Catheter associated urinary tract infections (CAUTIs) are a leading cause of healthcare-associated infection, particularly in intensive care units (ICUs), where prolonged catheterisation and patient complexity increase risk. Reducing unnecessary catheter use and improving adherence to evidence-based practices are key priorities for patient safety. This case report describes a nurse-led, multidisciplinary quality improvement programme to reduce CAUTI rates in a transplant ICU through optimisation of catheter use and the introduction of non-invasive urinary management alternatives. Interventions included standardisation of catheter indication criteria, implementation of insertion, maintenance and early removal protocols, introduction of external urinary management devices and a structured staff education and audit programme. Performance was monitored using CAUTI incidence per 1,000 catheter days and compliance with catheter-related practices, supported by continuous audit and feedback. Following implementation, CAUTI rates decreased from a pre-intervention rate of 1.41 per 1,000 catheter days to sustained zero incidence between Q4 2018 and Q4 2024. Compliance with catheter care protocols improved, alongside increased documentation of indication for catheter use and earlier catheter removal. The adoption of external urinary catheter management devices contributed to reduced indwelling catheter use. In parallel, the unit reported zero device-related pressure injuries and improved patient comfort, as reflected in staff-reported patient feedback. This multifaceted approach achieved sustained elimination of CAUTI in a high-risk ICU setting, highlighting the effectiveness of nurse-led interventions, standardised practice, and non-invasive catheter alternatives in reducing device-associated harm.

This study presents an explainable artificial intelligence framework for climate-responsive residential envelope design in Gulf coastal cities by integrating building performance simulation, multi-objective optimization, machine learning, and explainability analysis. While previous studies have largely focused on minimizing energy consumption, limited research has simultaneously considered energy performance, capital cost, and thermal comfort within a unified and interpretable decision-support framework. The objective of this research was to identify dominant envelope design variables and derive practical design recommendations for residential buildings located in Dubai, Doha, and Manama. A two-story detached villa prototype was developed and simulated under representative coastal hot-arid climate conditions. Six envelope and operational design variables, including window-to-wall ratio (WWR), shading depth, cooling setpoint, glazing type, wall construction, and roof construction, were evaluated through a simulation-based optimization framework. A total of 600 design alternatives were generated using NSGA-II optimization and subsequently used to train Random Forest predictive models for energy use intensity (EUI), capital cost, and ASHRAE 55 thermal discomfort hours. SHAP (Shapley Additive Explanations) analysis was then applied to quantify variable importance and extract interpretable design rules. The results demonstrated strong predictive capability, with Random Forest models achieving R² values of 0.933 for EUI, 0.982 for capital cost, and 0.955 for thermal discomfort. SHAP analysis revealed that WWR was the dominant driver of energy performance, accounting for 65.2% of total feature importance, while wall construction exerted the greatest influence on capital cost. Thermal comfort was primarily governed by cooling setpoint, followed by WWR and shading depth. Dependence analysis further identified clear threshold relationships between envelope variables and performance outcomes. The proposed framework transforms optimization datasets into actionable design knowledge and provides interpretable decision support for architects, consultants, and developers seeking cost-effective and climate-responsive residential envelope solutions in Gulf coastal environments.

Accurate transfer of implant positions is critical for achieving passive fit in full-arch implant-supported prostheses. Photogrammetry has emerged as a promising digital impression technique, offering superior accuracy by eliminating cumulative stitching errors inherent to intraoral scanning. This narrative review synthesizes current evidence on photogrammetry technologies for full-arch implant rehabilitation, comparing accuracy, workflow efficiency, clinical outcomes, and limitations against conventional and intraoral scanning methods. Electronic searches of PubMed, Scopus, and Web of Science were conducted (2015-2026). Photogrammetry systems demonstrate significantly superior trueness (10-50 µm) and precision (4-18 µm) compared to intraoral scanning (trueness up to 731.7 µm in full-arch applications). A 2025 meta-analysis confirmed photogrammetry's superior trueness in distance deviation (p = .001) and angular deviation (p = .02). Intraoral photogrammetry achieves comparable accuracy to extraoral systems while capturing soft tissue in a unified scan. Navigated photogrammetry enables conversion-less provisional fabrication. While equipment costs and learning curves remain barriers, emerging smartphone-based systems promise broader accessibility. Photogrammetry represents the most accurate digital method for full-arch implant position capture, with emerging intraoral and navigated systems addressing workflow limitations.

Quality indicators (QIs) are essential tools for evaluating laboratory performance across the preanalytical, analytical, and postanalytical phases of the total testing process. Recent accreditation standards, including ISO 15189:2022 and NABL 112A, emphasize risk-based thinking, performance evaluation, and continuous improvement through systematic monitoring of quality indicators. Despite their widespread adoption, quality management in many laboratories remains largely reactive, relying on the retrospective review of performance data and corrective actions after deviations have occurred. Such approaches may fail to identify emerging risks in complex and data-intensive laboratory environments. Artificial intelligence (AI) has emerged as a promising technology capable of transforming quality indicator monitoring through continuous data analysis, pattern recognition, anomaly detection, and predictive analysis. By leveraging data generated from laboratory information systems, automated analyzers, quality control programs, and operational workflows, AI can identify hidden trends and forecast quality failures before they affect the patient care. Potential applications include the prediction of specimen rejection, hemolysis, quality control instability, instrument downtime, turnaround time delays, and communication errors. This review examines the role of AI in laboratory quality management and discusses its potential to shift quality monitoring from a reactive to predictive paradigm. A novel Continuous Quality Intelligence Framework (CQIF) is proposed to illustrate how quality indicators, integrated data systems, predictive analytics, and continuous improvement processes can be combined to support proactive risk management. This framework aligns with the principles of ISO 15189:2022 and NABL 112A and provides a conceptual roadmap for future AI-enabled quality systems. The adoption of predictive quality management approaches has the potential to improve patient safety, operational efficiency, accreditation readiness, and overall laboratory performance.

Cameroon's bijural judicial system, combining civil law in Francophone regions and common law in Anglophone areas, creates unique training demands that its judicial institutions have struggled to meet. Using doctrinal analysis and comparative review of three unreported primary judgments, this article examines the decade-long controversy over the Originating Summons procedure in Anglophone Cameroonian courts as a lens through which to analyse the structural training deficit at the heart of the bijural judiciary. The article demonstrates that the same statutory provision, Section 10 of the Southern Cameroons High Court Law 1955, produces opposed judicial outcomes across different courts and different time periods. This divergence is shown to be structurally produced by the absence of systematic continuous professional development and failure to issue harmonising procedural guidance. The omission of a dedicated English-speaking judicial intake in the 2025/2026 recruitment cycle appears to signal the effective discontinuation of the Common Law Section, although no formal decree abolishing the Section has been identified, which has intensified this deficit. The article proposes targeted reforms anchored in a comparative analysis of successful bijural judicial training models.

It is clear that when we love, we feel happy, and when we hate, we feel miserable. Many wise Sufi poets and thinkers believed that living a good life means being happy, and the best way to achieve this happiness is through love, faith, and kindness. They thought that the highest form of life is a life filled with love and spiritual devotion. Love, profoundly simple yet infinitely complex, has been a subject of contemplation for philosophers, poets, and mystics throughout the ages. In the philosophy of, love or Ishq transcends the mundane and attains a sublime, ethereal essence that permeates the universe. He believes that through love and faith, people are able to live together and accept each other in a system of co-existence. Relationships are formed and maintained through interpersonal interaction. The system of life develops when people interact with one another, form bonds of love, friendship, affection, faith, and concern for one another, and go about their everyday lives. This article delves into Rumi’s profound insights on love, faith, interrelationships between man and God, Divine love, and transformative power. Rumi’s poetry is replete with metaphors and allegories that convey the multifaceted nature of love. He portrays love as a journey, a longing, and a union, an ever-evolving dance between lover and beloved, self and other, human and Divine. Through his mystical imagery and lyrical expression, Rumi invites readers to transcend the limitations of the ego and experience the boundless expanses of the heart. His philosophy of love extends beyond the realm of human relationships to encompass love for all creation. He celebrates the beauty of nature, the harmony of the cosmos, and the inherent goodness of existence as manifestations of Divine love and faith. In doing so, Rumi invites us to cultivate a deep reverence for life and to recognize the sacred presence that permeates every aspect of reality.