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Screening for prostate cancer relies on the serum prostate-specific antigen test, which provides a high rate of false positives (80%). This results in a large number of unnecessary biopsies and subsequent overtreatment. Considering the frequency of the test, there is a critical unmet need of precision screening for prostate cancer. Here, we introduced a urinary multimarker biosensor with a capacity to learn to achieve this goal. The correlation of clinical state with the sensing signals from urinary multimarkers was analyzed by two common machine learning algorithms. As the number of biomarkers was increased, both algorithms provided a monotonic increase in screening performance. Under the best combination of biomarkers, the machine learning algorithms screened prostate cancer patients with more than 99% accuracy using 76 urine specimens. Urinary multimarker biosensor leveraged by machine learning analysis can be an important strategy of precision screening for cancers using a drop of bodily fluid.

An extraction technique for subgap density of states (DOS) in an n-channel amorphous InGaZnO thin-film transistor (TFT) by using multifrequency capacitance-voltage (C-V ) characteristics is proposed and verified by comparing the measured I-V characteristics with the technology computeraided design simulation results incorporating the extracted DOS as parameters. It takes on the superposition of exponential tail states and exponential deep states with characteristic parameters for N TA = 1.1×10 17 cm −3 ·eV −1 , N DA = 4×10 15 cm −3 ·eV −1 , kT TA = 0.09 eV, and kT DA = 0.4 eV. The proposed technique allows obtaining the frequency-independent C-V curve, which is very useful for oxide semiconductor TFT modeling and characterization, and considers the nonlinear relation between the energy level of DOS and the gate voltage V GS . In addition, it is a simple, fast, and accurate extraction method for DOS in amorphous InGaZnO TFTs without optical illumination, temperature dependence, and numerical iteration.Index Terms-Amorphous, density of states (DOS), indiumgallium-zinc-oxide (InGaZnO), multifrequency capacitancevoltage (C-V ) characteristics, technology computer-aided design (TCAD), thin-film transistors (TFTs).

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Current methods to detect avian influenza viruses (AIV) are time consuming and lo inw sensitivity, necessitating a faster and more sensitive sensor for on-site epidemic detection in poultry farms and urban population centers. This study reports a field effect transistor (FET) based AIV sensor that detects nucleoproteins (NP) within 30 minutes, down to an LOD of 10 EID mL from a live animal cloacal swab. Previously reported FET sensors for AIV detection have not targeted NPs, an internal protein shared across multiple strains, due to the difficulty of field-effect sensing in a highly ionic lysis buffer. The AIV sensor overcomes the sensitivity limit with an FET-based platform enhanced with a disposable well gate (DWG) that is readily replaceable after each measurement. In a single procedure, the virus-containing sample is immersed in a lysis buffer mixture to expose NPs to the DWG surface. In comparison with commercial AIV rapid kits, the AIV sensor is proved to be highly sensitive, fast, and compact, proving its potential effectiveness as a portable biosensor.

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