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Abstract The emergence and rapid spread of multidrug-resistant bacterial strains is a growing concern of public health. Inspired by the natural bactericidal surfaces of lotus leaves and shark skin, increasing attention has been focused on the use of mechano-bactericidal methods to create surfaces with antibacterial and/or bactericidal effects. There have been several studies exploring the bactericidal effect of nanostructured surfaces under various combinations of parameters. However, the correlation and synergies between these factors still need to be clarified. Recently machine learning (ML), which enables prediction or decision-making based on data, has been used in the field of biomaterials with promising results. In this study, we explored ML in nanotechnology to investigate the antimicrobial potential of nanostructured surfaces. A dataset of nanostructured surfaces and their antimicrobial properties was built by extracting the published literature. Based on the literature review and the distribution of our dataset, 70% bactericidal efficiency was selected as a practical benchmark for our classification model that balances stringent bactericidal performance with achievable targets in diverse conditions. Subsequently, we developed an ML classification model, which demonstrated an 81% accuracy in its predictive capability. A regression model was further developed to predict the value of bactericidal efficiency for nanostructured surfaces. Feature importance analysis of the ML models suggested that nanotopographical features have a greater influence on bactericidal properties than material properties, thus providing insight into the principles of the mechano-bactericidal effect of nanostructured surfaces. Overall, this ML model tool could help researchers to effectively select and design the parameters of the surface structure prior to experimentation, thereby improving the timeliness and reducing the number of experiments and the associated costs. Graphical Abstract

Abstract This work explores the application of Allium sativum (Garlic) extract, in the creation of novel polymeric core‐sheath fibers for wound therapy applications. The core‐sheath pressurized gyration (CS PG) technology is utilized to mass‐produce fibers with a polycaprolactone (PCL) core and a polyethylene oxide (PEO) sheath, loaded with garlic extract. The produced fibers maintain structural integrity, long‐term stability and provide a cell‐friendly surface with rapid antibacterial activity. The physical properties, morphology, therapeutic delivery, cytotoxicity, thermal and chemical stability of PCL, PEO, PEO/Garlic, Core‐Sheath (CS) PEO/PCL and PEO/Garlic/PCL fibers are analyzed. Findings show that the addition of garlic extract greatly increases the fibers’ thermal durability, while decreasing their diameter, thus improving cell adhesion and proliferation. In‐vitro release tests reveal a rapid release of garlic extract, which has significant antibacterial action against both Gram‐negative Escherichia coli (E. coli) and Gram‐positive Staphylococcus aureus (S. aureus) bacteria species. Cell viability experiments validate the fiber samples' biocompatibility and nontoxicity, making them appropriate for integrative medicine applications. These core‐sheath structures emphasize the potential of combining natural therapeutic agents with advanced material technologies to develop cost‐effective, sustainable and highly effective wound dressings, offering a promising solution to the growing concerns associated with conventional synthetic antibacterial agents.

Machine learning (ML) has been harnessed as a promising modelling tool for materials research. However, small data, or data scarcity, is a bottleneck when incorporating ML in studies involving experimentation. Current experiment planning methods show several disadvantages: one‐factor‐at‐a‐time (OFAT) experimentation became impractical due to limited laboratory resources; conventional design of experiments (DoE) failed to incorporate high‐dimensional features in ML; Surrogate‐based or Bayesian optimization (BO) shifted the goal to optimize material properties rather than guiding training data accumulation. The present research proposes leveraging active learning (AL) to strategically select critical data for experimentation. Two AL strategies, query‐by‐Committee (QBC) algorithm and Greedy method, are benchmarked against random query baseline on various materials datasets. AL is shown to efficiently reduce model prediction errors with minimal additional experiment data. Investigation of hyperparameters revealed benefits of applying AL at an early stage of experimental dataset construction. Moreover, AL is implemented and validated for an in‐house materials development task ‐ electrospray modelling. AL exploration as a paradigm is highlighted to guide experiment design for efficient data accumulation purposes, and its potential for further ML modelling. In doing so, the power of ML is expected to be fully unleashed to experimental researchers.

Almost a century's dedicated research into micro‐ and nanomaterials has yielded fruitful development of preparation methods, achieving fine control over product properties among a broad spectrum of materials. One critical application of these materials lies within the healthcare sector for diagnostic, prophylactic, and therapeutic purposes. However, bench‐to‐bedside translations are still hindered by some unmet demands, especially the scaling‐up from lab‐scale preparation to industry‐level production. The current review recapitulates the strategies of micro‐ and nanomaterial preparation from a holistic viewpoint. The similarities in synthesis and processing methods for various types of materials are highlighted. Furthermore, patents of commercialized nanomedicines are revisited to reveal a solid progress of micro‐ and nanomanufacturing in the last decade. In conclusion, further interdisciplinary research between fields in materials manufacturing is beneficial for the clinical translation and eventually unleashing the power of materials at small dimensions.

Background:. This study was undertaken to characterize the effects of upper eyelid blepharoplasty on blink dynamics and to evaluate the hypothesis that changes in blink dynamics following blepharoplasty are associated with postoperative dry eye. Methods:. The voluntary blink of 14 eyes of 7 patients with dermatochalasis undergoing upper eyelid blepharoplasty was recorded with a high-speed camera preoperatively and 6–8 months postoperatively, alongside a group of 11 controls. The images were analyzed for palpebral aperture, blink duration, and maximum velocity during opening and closing phases. Patients undergoing blepharoplasty were assessed for dry eye symptoms pre- and postoperatively at 6–8 months using the ocular surface disease index score. Results:. Despite intraoperative orbicularis oculi resection, there was no significant compromise of blink duration or maximum velocity of eyelid opening or closure post-blepharoplasty. Postoperatively, patients had an increase in palpebral aperture compared with both preoperatively (8.71 versus 7.85 mm; P = 0.013) and control groups (8.71 versus 7.87 mm; P = 0.04). Postoperatively at 6–8 months, there was an increase in dry eye symptoms in 6 of 7 patients compared with preoperatively (ocular surface disease index, 16.6 versus 12.5; P < 0.05). There was no positive correlation between the increase in palpebral aperture and the increase in dry eye symptoms (r = –0.4; P = 0.30). Conclusions:. Using modern videographic technology, this study demonstrates that upper eyelid blepharoplasty results in an increase in resting palpebral aperture but has no effect on dynamic blink parameters. Changes in palpebral aperture or blink dynamics are unlikely to be the cause of dry eye syndrome following blepharoplasty.

The use of combination therapies for the treatment of a range of conditions is now well established, with the component drugs usually being delivered either as distinct medicaments or combination products that contain physical mixes of the two active ingredients. There is, however, a compelling argument for the development of compartmentalised systems whereby the release, stability and incorporation environment of the different drugs may be tailored. Here we outline the development of polymeric fine fiber systems whereby two drugs used for the treatment of wounds may be separately incorporated. Fibers were delivered using a newly developed handheld electrospinning device that allows treatment at the site of need. Crucially, the delivery system is portable and may be used for the administration of drug-loaded fibers directly into the wound in situ, thereby potentially allowing domiciliary or site-of-trauma administration. The three-layered fiber developed in this study has polyethylene glycol as the outermost layer, serving as a structural support for the inner layers. The inner layers comprised iodine complexed with polyvinylpyrrolidone (PVP) and metronidazole dispersed in polycaprolactone (PCL) as a slow release core. The systems were characterized in terms of structure and architecture using scanning electron microscopy, transmission electron microscopy, attenuated total reflection Fourier transform infrared spectroscopy and diffractometry. As antibacterial creams are still used for managing infected wounds, the performance of our trilayered fiber was studied in comparison with creams containing similar active drugs. Drug release was measured by UV analysis, while antimicrobial efficiency was measured using agar diffusion and suspension methods. It was found that the trilayered systems, averaging 3.16 µm in diameter, released more drug over the study period and were confirmed by the microbacterial studies to be more effective against P. aeruginosa, a bacterium commonly implicated in infected wounds. Overall, the portable system has been shown to be capable of not only incorporating the two drugs in distinct layers but also of delivering adequate amounts of drugs for a more effective antibacterial activity. The portability of the device and its ability to generate distinct layers of multiple active ingredients make it promising for further development for wound healing applications in terms of both practical applicability and antimicrobial efficacy.

Microbubbles generated by microfluidic techniques have gained substantial interest in various industries such as cosmetics, food engineering, and the biomedical field. The microfluidic T-junction provides exquisite control over processing parameters, however, it relies on pressure driven flows only; therefore, bubble size variation is limited especially for viscous solutions. A novel set-up to superimpose an alternating current (AC) oscillation onto a direct current (DC) field is invented in this work, capitalising on the possibility to excite bubble resonance phenomenon and properties, and introducing relevant parameters such as frequency, AC voltage, and waveform to further control bubble size. A capillary embedded T-junction microfluidic device fitted with a stainless-steel capillary was utilised for microbubble formation. Furthermore, a numerical model of the T-junction was developed by integrating the volume of fluid (VOF) method with the electric module; simulation results were attained for the formation of the microbubbles with a particular focus on the flow fields along the detachment of the emerging bubble. Two main types of experiments were conducted in this framework: the first was to test the effect of applied AC voltage magnitude and the second was to vary the applied frequency. Experimental results indicated that higher frequencies have a pronounced effect on the bubble diameter within the 100 Hz and 2.2 kHz range, whereas elevated AC voltages tend to promote bubble elongation and growth. Computational results suggest there is a uniform velocity field distribution along the bubble upon application of a superimposed field and that microbubble detachment is facilitated by the recirculation of the dispersed phase. Furthermore, an ideal range of parameters exists to tailor monodisperse bubble size for specific applications.