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The prodrug approach has emerged as a promising solution to combat bacterial resistance and enhance treatment efficacy against bacterial infections. Here, we report an adenosine triphosphate (ATP)-activated prodrug system for on-demand treatment of bacterial infection. The prodrug system benefits from the synergistic action of zeolitic imidazolate framework-8 and polyacrylamide hydrogel microsphere, which simultaneously transports indole-3-acetic acid and horseradish peroxidase in a single carrier while preventing the premature activation of indole-3-acetic acid. The ATP-responsive characteristic of zeolitic imidazolate framework-8 allows the prodrug system to be activated by the ATP secreted by bacteria to generate reactive oxygen species (ROS), displaying exceptional broad-spectrum antimicrobial ability. Upon disruption of the bacterial membrane by ROS, the leaked intracellular ATP from dead bacteria can accelerate the activation of the prodrug system to further enhance antibacterial efficiency. In vivo experiments in a mouse model demonstrates the applicability of the prodrug system for wound disinfection with minimal side effects. Prodrugs are increasingly promising in tackling bacterial resistance and efficacy of treatment. Here, the authors encapsulated horseradish peroxidase and zeolitic imidazolate framework-8 loaded with indole-3-acetic acid in polyacrylamide hydrogel microspheres for ATP-activated wound disinfection.

In nature, individual cells contain multiple isolated compartments in which cascade enzymatic reactions occur to form essential biological products with high efficiency. Here, we report a cell-inspired design of functional hydrogel particles with multiple compartments, in which different enzymes are spatially immobilized in distinct domains that enable engineered, one-pot, tandem reactions. The dense packing of different compartments in the hydrogel particle enables effective transportation of reactants to ensure that the products are generated with high efficiency. To demonstrate the advantages of micro-environmental modifications, we employ the copolymerization of acrylic acid, which leads to the formation of heterogeneous multi-compartmental hydrogel particles with different pH microenvironments. Upon the positional assembly of glucose oxidase and magnetic nanoparticles, these hydrogel particles are able to process a glucose-triggered, incompatible, multistep tandem reaction in one pot. Furthermore, based on the high cytotoxicity of hydroxyl radicals, a glucose-powered therapeutic strategy to kill cancer cells was approached. Cells contain isolated compartments where cascade enzymatic biochemical reactions occur to form essential biological products with high efficiency. Here the authors produce functional hydrogel particles with multiple compartments via microfluidics that contain spatially immobilized natural enzymes in distinct domains for one-pot, tandem reactions.

Precise control of energy migration between sensitizer ions and activator ions in lanthanide-doped upconversion nanoparticles (UCNPs) nowadays has been extensively investigated to achieve efficient photon upconversion. However, these UCNPs generally emit blue, green or red light only under fixed excitation conditions. In this work, regulation of the photon transition process between different energy levels of a single activator ion to obtain tunable upconversion fluorescence under different excitation conditions is achieved by introducing a modulator ion. The cross-relaxation process between modulator ion and activator ion can be controlled to generate tunable luminescence from the same lanthanide activator ion under excitation at different wavelengths or with different laser power density and pulse frequency. This strategy has been tested and proven effective in two different nanocrystal systems and its usefulness has been demonstrated for high-level optical encryption. Here, the authors report tunable luminescence from a single lanthanide ion upon changing excitation conditions through co-doping an energy-modulator ion, thus adjusting the photon transition process of the lanthanide activator ion. Optical encryption has also been demonstrated as an application of this universal strategy.

Achieving efficient photon upconversion under low irradiance is not only a fundamental challenge but also central to numerous advanced applications spanning from photovoltaics to biophotonics. However, to date, almost all approaches for upconversion luminescence intensification require stringent controls over numerous factors such as composition and size of nanophosphors. Here, we report the utilization of dielectric microbeads to significantly enhance the photon upconversion processes in lanthanide-doped nanocrystals. By modulating the wavefront of both excitation and emission fields through dielectric superlensing effects, luminescence amplification up to 5 orders of magnitude can be achieved. This design delineates a general strategy to converge a low-power incident light beam into a photonic hotspot of high field intensity, while simultaneously enabling collimation of highly divergent emission for far-field accumulation. The dielectric superlensing-mediated strategy may provide a major step forward in facilitating photon upconversion processes toward practical applications in the fields of photobiology, energy conversion, and optogenetics. Emission levels useful for applications from upconversion nanoparticles require high laser irradiance. Here, Liang et al. exploit the superlensing effect from dielectric microbeads to enhance the luminescence efficiency of upconversion nanoparticles and show its application for optogenetics.

Deterministic lateral displacement (DLD) method for particle separation in microfluidic devices has been extensively used for particle separation in recent years due to its high resolution and robust separation. DLD has shown versatility for a wide spectrum of applications for sorting of micro particles such as parasites, blood cells to bacteria and DNA. DLD model is designed for spherical particles and efficient separation of blood cells is challenging due to non-uniform shape and size. Moreover, separation in sub-micron regime requires the gap size of DLD systems to be reduced which exponentially increases the device resistance, resulting in greatly reduced throughput. This paper shows how simple application of asymmetrical DLD gap-size by changing the ratio of lateral-gap (G L ) to downstream-gap (G D ) enables efficient separation of RBCs without greatly restricting throughput. This method reduces the need for challenging fabrication of DLD pillars and provides new insight to the current DLD model. The separation shows an increase in DLD critical diameter resolution (separate smaller particles) and increase selectivity for non-spherical RBCs. The RBCs separate better as compared to standard DLD model with symmetrical gap sizes. This method can be applied to separate non-spherical bacteria or sub-micron particles to enhance throughput and DLD resolution.

Radiomics, which extract large amount of quantification image features from diagnostic medical images had been widely used for prognostication, treatment response prediction and cancer detection. The treatment options for lung nodules depend on their diagnosis, benign or malignant. Conventionally, lung nodule diagnosis is based on invasive biopsy. Recently, radiomics features, a non-invasive method based on clinical images, have shown high potential in lesion classification, treatment outcome prediction. Lung nodule classification using radiomics based on Computed Tomography (CT) image data was investigated and a 4-feature signature was introduced for lung nodule classification. Retrospectively, 72 patients with 75 pulmonary nodules were collected. Radiomics feature extraction was performed on non-enhanced CT images with contours which were delineated by an experienced radiation oncologist. Among the 750 image features in each case, 76 features were found to have significant differences between benign and malignant lesions. A radiomics signature was composed of the best 4 features which included Laws_LSL_min, Laws_SLL_energy, Laws_SSL_skewness and Laws_EEL_uniformity. The accuracy using the signature in benign or malignant classification was 84% with the sensitivity of 92.85% and the specificity of 72.73%. The classification signature based on radiomics features demonstrated very good accuracy and high potential in clinical application.

Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide, with approximately 70% to 80% of adults with COPD being undiagnosed. Patients with undiagnosed COPD are at increased risk of poor outcomes and a worsened quality of life, making early detection a crucial strategy to mitigate the impact of COPD and reduce the burden on healthcare systems. In the past decade, increased interest has been focused on the development of effective strategies and instrument for COPD early detection. However, identifying undiagnosed cases of COPD is still challenging. Both screening and case-finding approaches have been adopted to identify undiagnosed COPD, with case-finding being recommended by the 2023 Global Initiative for Chronic Obstructive Lung Disease (GOLD) guideline and the updated United States Preventive Services Task Force (USPTF) recommendation. Nonetheless, the approaches, criteria, and instruments used for early detection of COPD are varied. However, advances in the taxonomy and risk factors of COPD are continuously being investigated. It is important to continuously assess the current state of knowledge on COPD early detection, given the challenges associated with identifying undiagnosed COPD. This review aims to highlight recent advances in early detection of COPD. To discuss the current challenge and opportunity in COPD early detection, providing an overview of existing literature on COPD case-finding strategies, including the approaches, criteria for subjects, and instruments. The review also summarizes the current progress in COPD case-findings and proposes a COPD case-finding flowchart as an efficient method for identifying at risk COPD patients.

The chicken industry, in which broiler chickens are bred, is the largest poultry industry in Taiwan. In a traditional poultry house, breeders must usually observe the health of the broilers in person on the basis of their breeding experience at regular times every day. When a breeder finds unhealthy broilers, they are removed manually from the poultry house to prevent viruses from spreading in the poultry house. Therefore, in this study, we designed and constructed a novel small removal system for dead chickens for Taiwanese poultry houses. In the mechanical design, this system mainly contains walking, removal, and storage parts. It comprises robotic arms with a fixed end and sweep-in devices for sweeping dead chickens, a conveyor belt for transporting chickens, a storage cache for storing chickens, and a tracked vehicle. The designed system has dimensions of approximately 1.038 × 0.36 × 0.5 m3, and two dead chickens can be removed in a single operation. The walking speed of the chicken removal system is 3.3 cm/s. In order to enhance the automation and artificial intelligence in the poultry industry, the identification system was used in a novel small removal system. The conditions of the chickens in a poultry house can be monitored remotely by using a camera, and dead chickens can be identified through deep learning based on the YOLO v4 algorithm. The precision of the designed system reached 95.24% in this study, and dead chickens were successfully moved to the storage cache. Finally, the designed system can reduce the contact between humans and poultry to effectively improve the overall biological safety.

Due to its genetic similarity to humans, yeast serves as a vital model organism in life sciences and medicine, allowing for the study of crucial biological processes such as cell division and metabolism for drug development. However, current tools for measuring yeast extracellular secretion lack the sensitivity, throughput, and speed required for large-scale metabolic analysis. Here, we present an ultrasensitive, large-scale analysis of yeast extracellular secretion using molecular sensors on the membrane surface of mother yeast cells. These sensors remain selectively confined to mother yeast cells during cell division, enabling high-sensitivity detection, high-throughput screening and rapid single-yeast assays. Their detection limit is 100 nM, and they can screen over 10 7 single cells per run. We achieve a > 30-fold speed boost compared to conventional droplet-based screening, allowing us to identify the top 0.05% of secretory strains from 2.2 × 10 6 variants within just 12 minutes. The platform offers potential for large-scale single-yeast metabolic analysis and bio-fabrication. Current yeast extracellular secretion measuring tools lack the sensitivity, throughput, and speed required for large-scale metabolic analysis. Here, the authors introduce molecular sensors on mother yeast cell membranes to screen for single-cell secretions and rapidly isolate productive strains.

An earthquake of magnitude 9.0 occurred near the east coast of Honshu (Tohoku area), Japan, producing overwhelming Earth surface motions and inducing devastating tsunamis, which then traveled into the ionosphere and significantly disturbed the electron density within it (hereafter referred to as seismotraveling ionospheric disturbances (STIDs)). The total electron content (TEC) derived from nationwide GPS receiving networks in Japan and Taiwan is employed to monitor STIDs triggered by seismic and tsunami waves of the Tohoku earthquake. The STIDs first appear as a disk‐shaped TEC increase about 7 min after the earthquake occurrence centered at about 200 km east of the epicenter, near the west edge of the Japan Trench. Fast propagating disturbances related to Rayleigh waves quickly travel away from the epicenter along the main island of Japan with a speed of 2.3–3.3 km/s, accompanied by sequences of concentric circular TEC wavefronts and followed by circular ripples (close to a tsunami speed of about 720–800 km/h) that travel away from the STID center. These are the most remarkable STIDs ever observed where signatures of Rayleigh waves, tsunami waves, etc., simultaneously appear in the ionosphere. Key Points Ionospheric disturbances generated by earthquake and tsunami Greatest disturbances ever seen containing signatures of following waves Rayleigh, acoustic, and tsunami‐generated waves