Point-of-Care Pathogen Testing Using Photonic Crystals and Machine Vision for Diagnosis of Urinary Tract Infections

Photon Management Enabled by Opal and Inverse Opal Photonic Crystals: from Photocatalysis to Photoluminescence Regulation

Light is a promising renewable energy source and can be converted into heat, electricity, and chemical energy. However, the efficiency of light-energy conversion is largely hindered by limited light-absorption coefficients and the low quantum yield of current-generation materials. Photonic crystals (PCs) can adjust the propagation and distribution of photons because of their unique periodic structures, which offers a compelling platform for photon management. The periodicity of materials with an alternating refractive index can be used to manipulate the dispersion of photons to generate the photonic bandgap (PBG), in which light is reflected. The slow photon effect, i. e., photon propagation at a reduced group velocity near the edges of the PBG, is widely regarded as another valuable optical property for manipulating light. Furthermore, multiple light scattering can increase the optical path, which is a vital optical property for PCs. Recently, the light reflected by PBG, the slow photon effect, and multiple light scattering have been exploited to improve light utilization efficiency in photoelectrochemistry, materials chemistry, and biomedicine to enhance light-energy conversion efficiency. In this review, the fabrication of opal or inverse opal PCs and the theory for improving the light utilization efficiency of photocatalysis, solar cells, and photoluminescence regulation are discussed. We envision photon management of opal or inverse opal PCs may provide a promising avenue for light-assisted applications to improve light-energy-conversion efficiency.

Machine Learning-Assistant Colorimetric Sensor Arrays for Intelligent and Rapid Diagnosis of Urinary Tract Infection

Urinary tract infections (UTIs), which can lead to pyelonephritis, urosepsis, and even death, are among the most prevalent infectious diseases worldwide, with a notable increase in treatment costs due to the emergence of drug-resistant pathogens. Current diagnostic strategies for UTIs, such as urine culture and flow cytometry, require time-consuming protocols and expensive equipment. We present here a machine learning-assisted colorimetric sensor array based on recognition of ligand-functionalized Fe single-atom nanozymes (SANs) for the identification of microorganisms at the order, genus, and species levels. Colorimetric sensor arrays are built from the SAN Fe1-NC functionalized with four types of recognition ligands, generating unique microbial identification fingerprints. By integrating the colorimetric sensor arrays with a trained computational classification model, the platform can identify more than 10 microorganisms in UTI urine samples within 1 h. Diagnostic accuracy of up to 97% was achieved in 60 UTI clinical samples, holding great potential for translation into clinical practice applications.

Antimicrobial Agent Functional Gold Nanocluster-Mediated Multichannel Sensor Array for Bacteria Sensing

Urinary tract infections (UTIs) have greatly affected human health in recent years. Accurate and rapid diagnosis of UTIs can enable a more effective treatment. Herein, we developed a multichannel sensor array for efficient identification of bacteria based on three antimicrobial agents (vancomycin, lysozyme, and bacitracin) functional gold nanoclusters (AuNCs). In this sensor, the fluorescence intensity of the three AuNCs was quenched to varying degrees by the bacterial species, providing a unique fingerprint for different bacteria. With this sensing platform, seven pathogenic bacteria, different concentrations of the same bacteria, and even bacterial mixtures were successfully differentiated. Furthermore, UTIs can be accurately identified with our sensors in ∼30 min with 100% classification accuracy. The proposed sensing systems offer a rapid, high-throughput, and reliable sensing platform for the diagnosis of UTIs.

Transdermal Delivery of Photosensitizer-Catalase Conjugate by Fluorinated Polyethylenimine for Enhanced Topical Photodynamic Therapy of Bacterial Infections

Pathogenic bacteria induce subcutaneous infections pose serious threats to global public health. Recently, photodynamic therapy (PDT) has been proposed as a non-invasive approach for anti-microbial treatment without the risk to induce drug resistance. However, due to the hypoxic environment of most anaerobiont-infected sites, the therapeutic efficacy of oxygen consuming PDT has been limited. Herein, a transdermal delivery system is reported to allow effective delivery of photosensitizers into infected skin for PDT treatment of skin infections by bacteria. Considering the overproduction of hydrogen peroxide (H2 O2 ) in the abscess area, catalase (CAT), an enzyme that triggers H2 O2 decomposition to generate O2 , is conjugated with chlorine e6 (Ce6) to form a photosensitizer conjugate (Ce6-CAT) as an enhanced PDT agent against Staphylococcus Aureus. After screening a series of fluorinated low molecular weight polyethylenimine (F-PEI) with different fluorination degrees, the optimized F-PEI formulation is identified with the best transdermal delivery ability system. Upon mixing, the formed Ce6-CAT@F-PEI nanocomplex shows effective transdermal penetration after being applied to the skin surface. With light exposure of the infected skin, highly effective in vivo anti-bacterial PDT therapeutic effect with Ce6-CAT@F-PEI is observed. This work proposes a transdermal PDT therapeutic nanomedicine particularly promising for the anti-bacterial treatment of skin infections.

Implementing comprehensive machine learning models of multispecies toxicity assessment to improve regulation of organic compounds

Machine learning has made significant progress in assessing the risk associated with hazardous chemicals. However, most models were constructed by randomly selecting one algorithm and one toxicity endpoint towards single species, which may cause biased regulation of chemicals. In the present study, we implemented comprehensive prediction models involving multiple advanced machine learning and end-to-end deep learning to assess the aquatic toxicity of chemicals. The generated optimal models accurately unravel the quantitative structure-toxicity relationships, with the correlation coefficients of all training sets from 0.59 to 0.81 and of the test sets from 0.56 to 0.83. For each chemical, its ecological risk was determined from the toxicity information towards multiple species. The results also revealed the toxicity mechanism of chemicals was species sensitivity, and the high-level organisms were faced with more serious side effects from hazardous substances. The proposed approach was finally applied to screen over 16,000 compounds and identify high-risk chemicals. We believe that the current approach can provide a useful tool for predicting the toxicity of diverse organic chemicals and help regulatory authorities make more reasonable decisions.

Thermoresponsive Hydrogel-Enabled Thermostatic Photothermal Therapy for Enhanced Healing of Bacteria-Infected Wounds

Photothermal therapy (PTT) has emerged as an attractive technique for the treatment of bacterial infections. However, the uncontrolled heat generation in conventional PTT inevitably causes thermal damages to healthy tissues and/or organs. It is thus essential to develop a smart and universal strategy to regulate the photothermal equilibrium temperature to a preset safe threshold. Herein, a thermoresponsive hydrogel-enabled thermostatic PTT system for enhanced healing of bacteria-infected wounds is reported. In this system, the near-infrared (NIR)-triggered heat generation by photothermal nanomaterials is spontaneously transferred to a thermoresponsive hydrogel with a lower critical solution temperature (LCST), leading to its rapid phase transition by forming considerable light-scattering centers to block NIR penetration. Such a dynamic and reversible process automatically regulates the photothermal equilibrium temperature to the phase-transition point of the LCST-type hydrogel. In contrast to temperature-uncontrolled conventional PTT with severe thermal damages, the thermoresponsive hydrogel-enabled thermostatic PTT provides effective protection on healthy tissues and/or organs, which remarkably accelerates wound healing by efficient bacterial eradication. This study establishes a smart, simple and universal PTT platform, holding great promise in the safe and efficient treatment of bacterial skin infections.

Ultrasensitive Urinary Diagnosis of Organ Injuries Using Time-Resolved Luminescent Lanthanide Nano-bioprobes

Urinary sensing of synthetic biomarkers that are released into urine after specific activation in an in vivo disease environment is an emerging diagnosis strategy to overcome the insensitivity of a previous biomarker assay. However, it remains a great challenge to achieve sensitive and a specific urinary photoluminescence (PL) diagnosis. Herein, we report a novel urinary time-resolved PL (TRPL) diagnosis strategy by exploiting europium complexes of diethylenetriaminepentaacetic acid (Eu-DTPA) as synthetic biomarkers and designing the activatable nanoprobes. Notably, TRPL of Eu-DTPA in the enhancer can eliminate the urinary background PL for ultrasensitive detection. We achieved sensitive urinary TRPL diagnosis of mice kidney and liver injuries by using simple Eu-DTPA and Eu-DTPA-integrated nanoprobes, respectively, which cannot be realized by traditional blood assays. This work demonstrates the exploration of lanthanide nanoprobes for in vivo disease-activated urinary TRPL diagnosis for the first time, which might advance the noninvasive diagnosis of diverse diseases via tailorable nanoprobe designs.

Rapid Identification and Monitoring of Multiple Bacterial Infections Using Printed Nanoarrays

Fast and accurate detection of microbial cells in clinical samples is highly valuable but remains a challenge. Here, a simple, culture-free diagnostic system is developed for direct detection of pathogenic bacteria in water, urine, and serum samples using an optical colorimetric biosensor. It consists of printed nanoarrays chemically conjugated with specific antibodies that exhibits distinct color changes after capturing target pathogens. By utilizing the internal capillarity inside an evaporating droplet, target preconcentration is achieved within a few minutes to enable rapid identification and more efficient detection of bacterial pathogens. More importantly, the scattering signals of bacteria are significantly amplified by the nanoarrays due to strong near-field localization, which supports a visualizable analysis of the growth, reproduction, and cell activity of bacteria at the single-cell level. Finally, in addition to high selectivity, this nanoarray-based biosensor is also capable of accurate quantification and continuous monitoring of bacterial load on food over a broad linear range, with a detection limit of 10 CFU mL^-1 . This work provides an accessible and user-friendly tool for point-of-care testing of pathogens in many clinical and environmental applications, and possibly enables a breakthrough in early prevention and treatment.

An in situ exosomal miRNA sensing biochip based on multi-branched localized catalytic hairpin assembly and photonic crystals

Exosomal microRNAs (miRNAs) are emerging as attractive non-invasive and reliable biomarkers for disease diagnosis. In situ exosomal miRNA detection can avoid laborious and time-consuming exosome lysis, RNA extraction and effectively improve the accuracy. However, in situ exosomal miRNA detection is hampered by the low abundance of the targets and low permeability of the probes. Herein, an in situ exosomal miRNA sensing biochip based on multi-branched localized catalytic hairpin assembly (MLCHA) and photonic crystals (PCs) was proposed. The MLCHA probes could penetrate into the exosomes nondestructively due to its rigidity and generate amplified fluorescence signal upon recognizing the target miRNA. And then, the fluorescence signal was further enhanced by PCs to improve the sensitivity. The developed biosensor can not only detect exosomal miRNA in a concentration-dependent manner but also distinguish samples from cancer state and healthy state, which is potential for non-invasive clinical diagnostics.

Perspective and Prospects on persistent luminescent nanoparticles for biological imaging and tumor therapy

Persistent luminescent nanoparticles (PLNPs) are photoluminescent materials that can still emit luminescence after the cessation of the excitation light source. In recent years, due to their unique optical properties, the PLNPs have attracted extensive attention in the biomedical field. Since the PLNPs effectively eliminate autofluorescence interference from biological tissues, many researchers have contributed a lot of work in the fields of biological imaging and tumor therapy. This article mainly introduces the synthesis methods of the PLNPs and their progress in the application of biological imaging and tumor therapy, as well as the challenges and development prospects.

Photonic Crystal-Integrated Optoelectronic Devices with Naked-Eye Visualization and Digital Readout for High-Resolution Detection of Ultratrace Analytes

The detection of ultratrace analytes is highly desirable for the non-invasive monitoring of human diseases. However, a major challenge is fast, naked-eye, high-resolution ultratrace detection. Herein, a rectangular 3D composite photonic crystal (PC)-based optoelectronic device is first designed that combines the sensitivity-enhancing effects of PCs and optoelectronic devices with fast and real-time digital monitoring. A crack-free, centimeter-scale, mechanically robust ellipsoidal composite PCs with sufficient hardness and modulus, even exceeding most plastics and aluminum alloys, are developed. The high mechanical strength of ellipsoidal composite PCs allows them to be hand-machined into rectangular geometries that can be conformally covered with the centimeter-scale flat light-detection area without interference from ambient light, easily integrating 3D composite PC-based optoelectronic devices. The PC-based device's signal-to-noise ratio increases dramatically from original 30-40 to ≈60-70 dB. Droplets of ultratrace analytes on the device are identified by fast digital readout within seconds, with detection limits down to 5 µL, enabling rapid identification of ultratrace glucose in artificial sweat and diabetes risk. The developed 3D PC-based sensor offers the advantages of small size, low cost, and high reliability, paving the way for wider implementation in other portable optoelectronic devices.

Recent Advances in Colorimetric Sensors Based on Gold Nanoparticles for Pathogen Detection

Infectious pathogens cause severe threats to public health due to their frightening infectivity and lethal capacity. Rapid and accurate detection of pathogens is of great significance for preventing their infection. Gold nanoparticles have drawn considerable attention in colorimetric biosensing during the past decades due to their unique physicochemical properties. Colorimetric diagnosis platforms based on functionalized AuNPs are emerging as a promising pathogen-analysis technique with the merits of high sensitivity, low-cost, and easy operation. This review summarizes the recent development in this field. We first introduce the significance of detecting pathogens and the characteristics of gold nanoparticles. Four types of colorimetric strategies, including the application of indirect target-mediated aggregation, chromogenic substrate-mediated catalytic activity, point-of-care testing (POCT) devices, and machine learning-assisted colorimetric sensor arrays, are systematically introduced. In particular, three biomolecule-functionalized AuNP-based colorimetric sensors are described in detail. Finally, we conclude by presenting our subjective views on the present challenges and some appropriate suggestions for future research directions of colorimetric sensors.

A Dual‐Encoded Bead‐Based Immunoassay with Tunable Detection Range for COVID‐19 Serum Evaluation

Serological assay for coronavirus 2019 (COVID‐19) patients including asymptomatic cases can inform on disease progression and prognosis. A detection method taking into account multiplex, high sensitivity, and a wider detection range will help to identify and treat COVID‐19. Here we integrated color‐size dual‐encoded beads and rolling circle amplification (RCA) into a bead‐based fluorescence immunoassay implemented in a size sorting chip to achieve high‐throughput and sensitive detection. We used the assay for quantifying COVID‐19 antibodies against spike S1, nucleocapsid, the receptor binding domain antigens. It also detected inflammatory biomarkers including interleukin‐6, interleukin‐1β, procalcitonin, C‐reactive protein whose concentrations range from pg mL⁻¹ to μg mL⁻¹. Use of different size beads integrating with RCA results in a tunable detection range. The assay can be readily modified to simultaneously measure more COVID‐19 serological molecules differing by orders of magnitude.

Rapid Detection of Urinary Tract Infection in 10 min by Tracking Multiple Phenotypic Features in a 30 s Large-Volume Scattering Video of Urine Microscopy

Rapid point-of-care (POC) diagnosis of bacterial infection diseases provides clinical benefits of prompt initiation of antimicrobial therapy and reduction of the overuse/misuse of unnecessary antibiotics for nonbacterial infections. We present here a POC compatible method for rapid bacterial infection detection in 10 min. We use a large-volume solution scattering imaging (LVSi) system with low magnifications (1-2×) to visualize bacteria in clinical samples, thus eliminating the need for culture-based isolation and enrichment. We tracked multiple intrinsic phenotypic features of individual cells in a short video. By clustering these features with a simple machine learning algorithm, we can differentiate Escherichia coli from similar-sized polystyrene beads, distinguish bacteria with different shapes, and distinguish E. coli from urine particles. We applied the method to detect urinary tract infections in 104 patient urine samples with a 30 s LVSi video, and the results showed 92.3% accuracy compared with the clinical culture results. This technology provides opportunities for rapid bacterial infection diagnosis at POC settings.

Novel polymer-based thermoresponsive photonic crystal sensors with broad wavelength shifts

Novel broad wavelength-shifted thermoresponsive sensors were fabricated by introducing ferrocene groups into polymeric photonic crystals. They are more suitable thermosensors due to their advantages, such as simple preparation, broad wavelength shifts (up to 162 nm), visible color change, and strong anti-interference ability.

Simultaneous Optical Detection of Multiple Bacterial Species Using Nanometer-Scaled Metal-Organic Hybrids

This paper describes a simple strategy to identify bacteria using the optical properties of the nanohybrid structures (NHs) of polymer-coated metal nanoparticles (NPs). NHs, in which many small NPs are encapsulated in polyaniline particles, are useful optical labels because they produce strong scattered light. The light-scattering characteristics of NHs are strongly dependent on the constituent metal elements of NPs. Gold NHs (AuNHs), silver NHs (AgNHs), and copper NHs (CuNHs) produce white, reddish, and bluish scattered light, respectively. Moreover, unlike NPs, the color of the scattered light does not change even when NHs are aggregated. Introducing an antibody into NHs induces antigen-specific binding to cells, enabling the identification of bacteria based on light scattering. Multiple bacterial species adsorbed on the slide can be identified within a single field of view under a dark field microscope based on the color of the scattered light. Therefore, it is a useful development for safety risk assessments at manufacturing sites, such as those for foods, beverages, and drugs, and environmental surveys that require rapid detection of multiple bacteria.

Emerging Bioanalytical Devices and Platforms for Rapid Detection of Pathogens in Environmental Samples

The development of robust bioanalytical devices and biosensors for infectious pathogens is progressing well with the advent of new materials, concepts, and technology. The progress is also stepping towards developing high throughput screening technologies that can quickly identify, differentiate, and determine the concentration of harmful pathogens, facilitating the decision-making process for their elimination and therapeutic interventions in large-scale operations. Recently, much effort has been focused on upgrading these analytical devices to an intelligent technological platform by integrating them with modern communication systems, such as the internet of things (IoT) and machine learning (ML), to expand their application horizon. This review outlines the recent development and applications of bioanalytical devices and biosensors to detect pathogenic microbes in environmental samples. First, the nature of the recent outbreaks of pathogenic microbes such as foodborne, waterborne, and airborne pathogens and microbial toxins are discussed to understand the severity of the problems. Next, the discussion focuses on the detection systems chronologically, starting with the conventional methods, advanced techniques, and emerging technologies, such as biosensors and other portable devices and detection platforms for pathogens. Finally, the progress on multiplex assays, wearable devices, and integration of smartphone technologies to facilitate pathogen detection systems for wider applications are highlighted.

Cyclic swelling enabled, electrically conductive 3D porous structures for microfluidic urinalysis devices

Urinalysis is a simple and non-invasive approach for the diagnosis and monitoring of organ health and also is often used as a facile technique in assessment of substance abuse. However, quantitative urinalysis is predominantly limited to clinical laboratories. Here, we present an electrical sensing based, reusable, cellular microfluidic device that offers a fast urinalysis through quantitative reading of the electrical signals. The spatial soft porous scaffolds decorated with electrically conductive multiwalled carbon nanotubes that are capable of physically interacting with biomarkers in urine are developed through a cyclic swelling/absorption process of soft materials and are utilized to manufacture the cellular microfluidic device. The sensing capability, sensitivity and reusability (via sunlight exposure) of the device to monitor red blood cells, Escherichia coli, and albumin are systemically demonstrated by programming mechanical deformation of porous scaffolds. Ex vivo experiments in disease mouse models confirm the diagnosis robustness of the device in comparable results with existing biochemical tests. The full integration of electrically conductive nanomaterials into soft scaffolds provides a foundation for devising bioelectronic devices with mechanically programmable microfluidic features in a low-cost manner, with broad applications for rapid disease diagnoses through body fluid.

Rapid identification of bacterial mixtures in urine using MALDI-TOF MS-based algorithm profiling coupled with magnetic enrichment

Urinary tract infections (UTIs) are a severe public health problem caused by mono- or poly-bacteria. Culture-based methods are routinely used for the diagnosis of UTIs in clinical practice, but those are time consuming. Rapid and unambiguous identification of each pathogen in UTIs can have a significant impact on timely diagnoses and precise treatment. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is an alternative method for the identification of pathogens in clinical laboratories. However, a certain number of pure bacteria are required for MALDI-TOF MS analysis. Here, we explored a strategy combining magnetic enrichment and MALDI-TOF MS for the rapid identification of pathogenic bacterial mixtures in urine. Fragment crystallizable mannose-binding lectin-modified Fe₃O₄ (Fc-MBL@Fe₃O₄) was used for rapid enrichment and the individual-peak-based similarity model as the analytical tool. Within 30 min, a mixture of the four most prevalent UTI-causing bacteria, Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, and Pseudomonas aeruginosa, was successfully identified using this method. This rapid MALDI-TOF MS-based strategy has potential applications in the clinical identification of UTI pathogens.