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Machine learning models for volume and weight estimation in breast reconstruction planning
No full textBackgroundAccurate estimation of breast volume and weight is critical for post-mastectomy reconstruction. Existing methods are frequently costly or complex. We developed a machine learning framework that leverages demographic and anthropometric data to address these challenges.MethodsWe collected data from 199 patients between 2021 and 2023. The workflow comprised data collection, pre-processing, feature selection, model training, and performance evaluation. Three feature selection techniques were applied: domain expert knowledge, Spearman's rank correlation, and the Boruta algorithm. Each feature set was used to train linear regression, random forest regression, and support vector regression models. Model performance was evaluated using the coefficient of determination (R2) and Pearson's correlation coefficient. Significant correlations were identified between breast volume or weight and key patient characteristics, such as BMI, breast cup size, ptosis severity, and anthropometric measurements.ResultsThe optimal linear regression model, which incorporated both domain-expert and statistically selected features, achieved R2 values of 81.8% for breast volume and 72% for breast weight.ConclusionThe results indicate that integrating demographic and anthropometric data with machine learning yields an accurate, interpretable, and accessible method for preoperative breast assessment. In contrast to conventional imaging or mathematical models, this approach eliminates costs related to imaging equipment, relies on routinely collected clinical data, reduces the need for specialized equipment and training, and enables rapid integration into existing clinical workflows. By overcoming the limitations of traditional methods, the proposed model provides a practical, efficient, and cost-effective solution for clinical practice
Privacy Enhancing Technologies for Intelligent Healthcare: Research Challenges and Opportunities
No full textThe efficient and secure processing of confidential health data always remained an important challenge for healthcare professionals and policymakers as this information needs to be shared among several parties for both data analytics and improved health treatments. In this regard, Privacy Enhancing Technologies (PETs) have already shown great potential in deploying intelligent healthcare systems for improved prognosis and diagnosis. This article explains important privacy-preserving techniques by focusing on their security models and performance issues. It specifically discusses libraries and tools that can be used to implement a particular PET model. Moreover, a detailed comparison is provided to highlight the strengths and weaknesses of each of the privacy enhancing approaches. It further sheds light on the security requirements of the health sector and summarizes state-of-the-art homomorphic encryption, secure multi-party computation, differential privacy, and trusted execution environment approaches used in the healthcare setting. Finally, important parameters are discussed that must be kept in consideration while choosing an optimal PET. The survey is concluded by presenting some future directions to improve the performance of PETs and their usage in the healthcare domain. To the best of our knowledge, it is the first article that comprehensively discusses PETs in the context of healthcare
Combination theorems for Wise's power alternative
No full textWe show that Wise's power alternative is stable under certain group constructions, use this to prove the power alternative for new classes of groups and recover known results from a unified perspective. For groups acting on trees, we introduce a dynamical condition that allows us to deduce the power alternative for the group from the power alternative for its stabilisers of points. As an application, we reduce the power alternative for Artin groups to the power alternative for free-of-infinity Artin groups, under some conditions on their parabolic subgroups. We also introduce a uniform version of the power alternative and prove it, among other things, for a large family of two-dimensional Artin groups. As a corollary, we deduce that these Artin groups have uniform exponential growth. Finally, we prove that the power alternative is stable under taking relatively hyperbolic groups. We apply this to show that various examples, including all free-by groups and a natural subclass of hierarchically hyperbolic groups, satisfy the uniform power alternative
Discovery of globally rare CYP51 mutations associated with azole resistance in Iranian Zymoseptoria tritici isolates
No full textBackgroundSeptoria tritici blotch, caused by Zymoseptoria tritici, is a major wheat disease worldwide. Demethylation inhibitor (DMI) fungicides, which target the sterol 14α-demethylase enzyme encoded by the CYP51 gene, remain central to disease control. In Iran, propiconazole is extensively applied, raising concerns about the evolution of resistance. This study investigated potential CYP51-mediated resistance mechanisms in Iranian Z. tritici populations.ResultsTwenty-eight isolates collected from six major wheat-producing provinces were assessed for propiconazole sensitivity using a microdilution assay and were classified as sensitive, tolerant, or resistant based on IC₅₀ values. Sequencing of the CYP51 coding region in representative isolates revealed several amino acid substitutions. Two novel mutations (G450R and G516D) were identified, together with rare variants previously reported at low global frequencies (E454K and L4V) and well-established resistance-associated changes such as Y461S and ΔY459/G460. These mutations defined seven haplotypes with variable resistance phenotypes. To validate these findings, we also performed whole-genome sequencing (WGS) on representative Iranian isolates. The WGS results were fully consistent with targeted sequencing, confirming the robustness of CYP51 mutation detection. Gene expression analysis showed inducible CYP51 upregulation in the most resistant isolate. Structural modelling using both homology-based and AlphaFold2 predictions indicated that the novel substitutions may alter surface electrostatics or cavity properties of the enzyme, potentially affecting fungicide binding.ConclusionsThis study documents the emergence of novel resistance-associated mutations in Iranian Z. tritici populations, expanding the spectrum of known CYP51 variants. The findings highlight the interplay between coding mutations, regulatory changes, and structural flexibility in shaping fungicide resistance. An integrated approach combining phenotypic assays, sequencing, expression analysis, and structural modelling provides a robust framework for monitoring resistance and informing sustainable fungicide use in wheat disease management
Embodied carbon tracking in the construction supply chain: phenomenological insights into challenges and strategies
No full textPurposeEffective tracking of embodied carbon (EC) across construction supply chains is critical for decarbonisation but remains challenged by fragmented data, regulatory gaps and limited digital integration. This study aims to identify key EC tracking challenges and essential implementation strategies and evaluates the role of digital technologies in facilitating comprehensive EC tracking.Design/methodology/approachA phenomenological research design was used, involving semi-structured interviews with eight UK-based construction professionals experienced in EC management.FindingsFindings revealed that barriers like data inconsistency and low awareness can be overcome through strategies such as automation, collaboration and early planning. Digital technologies were revealed to be pivotal enablers, enhancing transparency and real-time monitoring.Originality/valueThis study advances EC scholarship by providing phenomenological evidence from UK practitioners on supply-chain EC tracking, yielding a consolidated typology of challenges, implementation strategies and adoption levers. It bridges the gap between theoretical frameworks and practical implementation by empirically identifying the specific strategies that practitioners perceive as essential for overcoming EC tracking challenges within the construction supply chain. Furthermore, it provides empirical evidence on the pivotal role of digital technologies in enhancing data transparency and real-time monitoring, thereby contributing actionable insights for industry stakeholders striving for a low-carbon built environment. This study offers an actionable framework for industry practitioners, policymakers and technology developers to advance EC tracking
Longitudinal analysis shows possible distinct patterns of associations between conspiracy beliefs and either institutional distrust or the sense of precarity
No full textThe paper reports longitudinal analyses examining the extent to which institutional trust mediates the relationship between individuals' sense of precarity and their adherence to conspiracy beliefs. Across three waves, 925 participants (50.2% female) between the ages of 18 and 85 (M = 49.53; SD = 15.81) reported subjective appraisals of their financial situation (precarity), trust in institutions and adherence to conspiracy beliefs. The current study extends the previous analyses by including three-wave longitudinal data. The preregistered autoregressive cross-lagged panel model supports the notion that a sense of precarity follows adherence to conspiracy beliefs rather than preceding them, while institutional (dis)trust and conspiracy beliefs show a bidirectional pattern. However, the random-intercept cross-lagged panel model does not corroborate this, suggesting that the effects may be driven by stable between-person differences rather than actual within-person changes. Additionally, the latter model reveals two separate temporal patterns linking conspiracy beliefs with either the sense of precarity or institutional trust, opening the possibility that our results were driven by two distinct underlying mechanisms. The paper discusses the importance of longitudinal studies for a more accurate understanding of social-psychological realities in which conspiracy beliefs and suspicions of institutions may flourish
AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications
No full textEnzyme engineering drives innovation in biotechnology, medicine, and industry, yet conventional approaches remain limited by labour-intensive workflows, high costs, and narrow sequence diversity. Artificial intelligence (AI) is revolutionising this field by enabling rapid, precise, and data-driven enzyme design. Machine learning and deep learning models such as AlphaFold2, RoseTTAFold, ProGen, and ESM-2 accurately predict enzyme structure, stability, and catalytic function, facilitating rational mutagenesis and optimisation. Generative models, including ProteinGAN and variational autoencoders, enable de novo sequence creation with customised activity, while reinforcement learning enhances mutation selection and functional prediction. Hybrid AI–experimental workflows combine predictive modelling with high-throughput screening, accelerating discovery and reducing experimental demand. These strategies have led to the development of synthetic “synzymes” capable of catalysing non-natural reactions, broadening applications in pharmaceuticals, biofuels, and environmental remediation. The integration of AI-based retrosynthesis and pathway modelling further advances metabolic and process optimisation. Together, these innovations signify a shift from empirical, trial-and-error methods to predictive, computationally guided design. The novelty of this work lies in presenting a unified synthesis of emerging AI methodologies that collectively define the next generation of enzyme engineering, enabling the creation of sustainable, efficient, and functionally versatile biocatalysts
Experimental and kinetic insights into enzymatic synthesis of phosphatidylglycerol in an oscillatory baffled reactor
No full textPhosphatidylglycerol (PG) is a valuable product across pharmaceuticals, cosmetics and food industries, the conventional phospholipase D (PLD) syntheses however require organic solvents and very long reaction times to reach 50-74% yield at millilitre scale. The novelty of this study is that we have developed a solvent-free, fully aqueous synthesis route of PG using PLD-catalysed transphosphatidylation of phosphatidylcholine (PC) with glycerol in a 250 mL oscillatory baffled reactor (OBR). By optimising temperature, PLD concentration, glycerol-to-PC ratio and mixing, we achieved 63.5% PG conversion within 20 minutes with no detectable byproduct. Time-resolved kinetic analysis has revealed a three-phase mechanism in this reaction: an initial Michaelis–Menten behaviour, followed by product inhibition and eventual enzyme deactivation. We have then developed a multi-parameter kinetic model integrating intrinsic enzyme kinetics with operational variables, enabling quantitative predictions of reaction concentration, conversion and selectivity at high confidence level (R2>0.95). Coupling the green, solvent-free process with reactor intensification and mechanistic modelling establishes a scalable framework for PG manufacture and offers regulatory and sustainability advantages by avoiding volatile organic solvents and simplifying downstream processing
Isofrequency spin-wave imaging using color center magnetometry for magnon spintronics
No full textMagnon spintronics aims to harness spin waves in magnetic films for information technologies. Color center magnetometry is a promising tool for imaging spin waves, using electronic spins associated with atomic defects in solid-state materials as sensors. However, two main limitations persist: the magnetic fields required for spin-wave control detune the sensor-spin detection frequency, and this frequency is further restricted by the color center nature. Here, we overcome these limitations by decoupling the sensor spins from the spin-wave control fields -selecting color centers with intrinsic anisotropy axes orthogonal to the film magnetization- and by using color centers in diamond and hexagonal boron nitride to operate at complementary frequencies. We demonstrate isofrequency imaging of field-controlled spin waves in a magnetic half-plane and show how intrinsic magnetic anisotropies trigger bistable spin textures that govern spin-wave transport at device edges. Our results establish color center magnetometry as a versatile tool for advancing spin-wave technologies. [Abstract copyright: © 2025. The Author(s).
Thoracic epidural insertion using extended reality hologram assistance: a randomised trial of regional anaesthesia skill performance on the soft embalmed Thiel cadaver
No full textBackgroundMedical extended reality projects a 3D digital image onto live surface anatomy via a wearable device while allowing operators to interact with the real world. We have developed a 3D hologram of the thoracic spine from CT images of a soft embalmed Thiel cadaver and visualised spinal anatomy through a Microsoft Hololens2 headset. The primary objective of this study was to test whether thoracic epidural training performance on the cadaver was improved using extended reality compared with the landmark insertion technique.MethodsThe study comprised psychometric evaluation of anaesthetists of all grades, a lecture, needling practice on both a plastic simulator, and a soft embalmed Thiel cadaver and familiarisation with the Hololens2. Testing of cadaveric thoracic epidural procedural performance occurred at 10 randomly allocated anatomical sites between T1/2 and T 10/11, randomised equally to the landmark paramedian technique or hologram guidance. Both groups used loss of resistance to fluid as the procedural endpoint. The primary outcome was the log-log slope of Global Rating Scale assessments.ResultsFor 58 anaesthetists who completed the study, Global Rating Scale slopes did not differ between groups or by grade of anaesthetist. Secondary endpoints showed differences: the mean log number of needle movements was reduced in the extended reality group: 0.94 (0.95) vs 1.49 (0.97), P<0.001; and ideal performance, defined as a single needle insertion through skin and two or fewer subsequent needle movements, was higher in the extended reality group, 52% vs 28%, Relative Risk (RR) 1.50 (0.91–2.53).ConclusionsExtended reality hologram-assisted thoracic epidural insertion did not alter Global Rating Scale scores for the holographic vs landmark techniques, but reduced needle movements
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