Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis.

Images of laser scattering patterns generated by bacteria in urine are promising resources for deep learning. However, floating bacteria in urine produce dynamic scattering patterns and require deep learning of spatial and temporal features. We hypothesized that bacteria with variable bacterial densities and different Gram staining reactions would generate different speckle images. After deep learning of speckle patterns generated by various densities of bacteria in artificial urine, we validated the model in an independent set of clinical urine samples in a tertiary hospital. Even at a low bacterial density cutoff (1,000 CFU/mL), the model achieved a predictive accuracy of 90.9% for positive urine culture. At a cutoff of 50,000 CFU/mL, it showed a better accuracy of 98.5%. The model achieved satisfactory accuracy at both cutoff levels for predicting the Gram staining reaction. Considering only 30 min of analysis, our method appears as a new screening tool for predicting the presence of bacteria before urine culture. IMPORTANCE This study performed deep learning of multiple laser scattering patterns by the bacteria in urine to predict positive urine culture. Conventional urine analyzers have limited performance in identifying bacteria in urine. This novel method showed a satisfactory accuracy taking only 30 min of analysis without conventional urine culture. It was also developed to predict the Gram staining reaction of the bacteria. It can be used as a standalone screening tool for urinary tract infection.

One-step immunoassay without washing steps for influenza A virus detection using ISFET.

A one-step immunoassay for influenza A virus detection was developed using two different microbeads and a filter-inserted bottle. Two bead types with diameters of 15 (capture bead) and 3 (detection bead) µm were prepared to specifically detect influenza A virus. Anti-influenza A virus antibodies were coated on both bead types, whereas urease was immobilized only on the detection bead. An influenza A-positive sample could form a sandwich complex with the capture and detection beads; this complex would not pass through the filter, which had a controlled pore size. As the detection bead was used at a limiting concentration, it would be prevented from crossing the filter; thus, it would further react with the substrate urea and consequently increase the pH. An influenza A-negative sample would fail to form the sandwich complex in the presence of the capture and detection beads. Accordingly, the detection bead would pass through the filter into the urea buffer and increase the pH. The pH change in the urease reaction could be quantitatively measured by an indicator such as phenol red or using ion-selective field-effect transistor (ISFET). This one-step immunoassay was used for the detection of influenza A virus in real samples. The receiver operating characteristic (ROC) plot analysis showed an area under the curve (AUC) value of 0.931; the sensitivity and specificity of the assay was 80% and 90%, respectively, at a cutoff value of 0.9986. These results demonstrate that the one-step immunoassay could increase the sensitivity of influenza A virus detection in real samples.

Electrical Characteristics and pH Response of a Parylene-H Sensing Membrane in a Si-Nanonet Ion-Sensitive Field-Effect Transistor.

We report the electrical characteristics and pH responses of a Si-nanonet ion-sensitive field-effect transistor with ultra-thin parylene-H as a gate sensing membrane. The fabricated device shows excellent DC characteristics: a low subthreshold swing of 85 mV/dec, a high current on/off ratio of ~107 and a low gate leakage current of ~10-10 A. The low interface trap density of 1.04 × 1012 cm-2 and high field-effect mobility of 510 cm²V-1s-1 were obtained. The pH responses of the devices were evaluated in various pH buffer solutions. A high pH sensitivity of 48.1 ± 0.5 mV/pH with a device-to-device variation of ~6.1% was achieved. From the low-frequency noise characterization, the signal-to-noise ratio was extracted as high as ~3400 A/A with the lowest noise equivalent pH value of ~0.002 pH. These excellent intrinsic electrical and pH sensing performances suggest that parylene-H can be promising as a sensing membrane in an ISFET-based biosensor platform.

Tunable threshold resistive switching characteristics of Pt-Fe2O3 core-shell nanoparticle assembly by space charge effect.

Tunable threshold resistive switching characteristics of Pt-Fe(2)O(3) core-shell nanoparticle (NP) assembly were investigated. The colloidal Pt-Fe(2)O(3) core-shell NPs with a Pt core diameter of ∼3 nm and a total diameter of ∼15 nm were chemically synthesized by a one-step process. These NPs were assembled as a layer with a thickness of ∼80 nm by repeated dip-coating between Ti and Pt electrodes on a flexible polyethersulfone (PES) substrate. The Ti/NPs/Pt/PES structure exhibited the threshold switching, i.e. volatile transition from high to low resistance state at a high voltage and vice versa at a low voltage. The current-voltage measurements after charging and discharging NPs revealed that the resistance state and threshold switching voltage of the assembly could be tuned by the space charges stored in high density trap sites of Pt cores in Pt-Fe(2)O(3) core-shell NP assembly. These results demonstrated the possible tuning of threshold switching of core-shell NP assembly by the space charge effect, which can be potentially utilized for the tunable selection device element in nonvolatile memory circuits.