A smartphone-based centrifugal mHealth platform implementing hollow daisy-shaped quick response chip for hematocrit measurement.

Due to the ever-increasing challenge of emerging and reemerging infections on global health, the development of POCT tools has been propelled. However, conventional point-of-care testing methods suffer from several limitations, including cumbersome operation, long detection times, and low accuracy, which hamper their widespread application. Compared to traditional disease diagnostic equipment, mobile health platforms offer several advantages, including portability, ease of operation, and automated analysis of detection results through recognition algorithms. Consequently, they hold great promise for the future. Here, we developed a smartphone-based centrifugal mHealth platform implementing daisy-shaped quick response chip for hematocrit measurement. The centrifugal microfluidic chip is combined with a smartphone through a back-clip-on mobile phone adapter whose control circuit is designed with low power consumption to enable the platform to operate without requiring a high-power source that is inconvenient to carry, thereby achieving the goal of portability. Concurrently, we designed a quick response chip featuring a unique hollow daisy structure that is in line with the properties of hematocrit detection. The distinctive configuration of the chip enables adequate centrifugal force to be supplied for hematocrit detection. Additionally, our customized quick response code recognition algorithm is able to recognize this chip, facilitating non-experts in performing hematocrit intelligent recognition with their smartphones.

Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence.

The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms.

Handyfuge Microfluidic for On-Site Antibiotic Susceptibility Testing.

Low-cost, rapid, and accurate acquisition of minimum inhibitory concentrations (MICs) is key to limiting the development of antimicrobial resistance (AMR). Until now, conventional antibiotic susceptibility testing (AST) methods are typically time-consuming, high-cost, and labor-intensive, making them difficult to accomplish this task. Herein, an electricity-free, portable, and robust handyfuge microfluidic chip was developed for on-site AST, termed handyfuge-AST. With simply handheld centrifugation, the bacterial-antibiotic mixtures with accurate antibiotic concentration gradients could be generated in less than 5 min. The accurate MIC values of single antibiotics (including ampicillin, kanamycin, and chloramphenicol) or their combinations against Escherichia coli could be obtained within 5 h. To further meet the growing demands of point-of-care testing, we upgraded our handyfuge-AST with a pH-based colorimetric strategy, enabling naked eye recognition or intelligent recognition with a homemade mobile app. Through a comparative study of 60 clinical data (10 clinical samples corresponding to six commonly used antibiotics), the accurate MICs by handyfuge-AST with 100% categorical agreements were achieved compared to clinical standard methods (area under curves, AUCs = 1.00). The handyfuge-AST could be used as a low-cost, portable, and robust point-of-care device to rapidly obtain accurate MIC values, which significantly limit the progress of AMR.

A magnet-actuated microfluidic array chip for high-throughput pretreatment and amplification and detection of multiple pathogens.

The outbreak of global infectious diseases has posed a significant threat to public health, requiring the rapid and accurate diagnosis of pathogens promptly for the society to implement immediate control measures to prevent widespread pandemics. In this work, a magnet-actuated microfluidic array chip (MMAC) is developed with integrated sample processing and nucleic acid amplification for the rapid detection of multiple pathogens by loop-mediated isothermal amplification. In comparison to previous works, where fluid control was dependent on external equipment or finger-based manual pressing, the fluid control of the MMAC is realized by magnetically actuating a ferric oxide (Fe3O4) doped polydimethylsiloxane (PDMS) layer that separates the sample from the LAMP reagent in a high-throughput manner, which not only reduces the complexity of fluid control but also enhances the repeatability of detection by eliminating variations in operation by different users. Examination with a testing sample containing Salmonella typhimurium and Escherichia coli showed high specificity for pathogen detection without cross-contamination. The lowest detection concentration was 5.2 copies per µL for Salmonella typhimurium with a detection time of 60 min. The proposed method demonstrated the simultaneous detection of multiple pathogens, which is potentially helpful in applications of immediate diagnosis.

[Progressive damage monitoring of corrugated composite skins by the FBG spectral characteristics].

In the present paper, a method of monitoring progressive damage of composite structures by non-uniform fiber Bragg grating (FBG) reflection spectrum is proposed. Due to the finite element analysis of corrugated composite skins specimens, the failure process under tensile load and corresponding critical failure loads of corrugated composite skin was predicated. Then, the non-uniform reflection spectrum of FBG sensor could be reconstructed and the corresponding relationship between layer failure order sequence of corrugated composite skin and FBG sensor reflection spectrums was acquired. A monitoring system based on FBG non-uniform reflection spectrum, which can be used to monitor progressive damage of corrugated composite skins, was built. The corrugated composite skins were stretched under this FBG non-uniform reflection spectrum monitoring system. The results indicate that real-time spectrums acquired by FBG non-uniform reflection spectrum monitoring system show the same trend with the reconstruction reflection spectrums. The maximum error between the corresponding failure and the predictive value is 8.6%, which proves the feasibility of using FBG sensor to monitor progressive damage of corrugated composite skin. In this method, the real-time changes in the FBG non-uniform reflection spectrum within the scope of failure were acquired through the way of monitoring and predicating, and at the same time, the progressive damage extent and layer failure sequence of corru- gated composite skin was estimated, and without destroying the structure of the specimen, the method is easy and simple to operate. The measurement and transmission section of the system are completely composed of optical fiber, which provides new ideas and experimental reference for the field of dynamic monitoring of smart skin.

Wavelet packet energy characterization of low velocity impacts and load localization by optical fiber Bragg grating sensor technique.

The low velocity impacts (LVIs) monitoring based on optical fiber Bragg grating (FBG) sensors have attracted more attention in recent years. The center wavelength migrations of FBG sensors were determined by strain and residual strain during and after LVI on composite material structure. We presented a method to discriminate the energy characters of LVI response signals related to LVI locations. By analyzing the wavelet packet energy spectra of LVI response signals monitored by FBG sensors, the sixth node's energy was found to be sensitive to LVI location. Thus, the sixth node's energies as LVI feature values, were used to predict the LVI locations by the method of support vector regression (SVR). By optimization of the SVR models' free parameters, predicting accuracy was 4.62% in the work.