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Influenza virus infection is a major worldwide public health problem. Influenza virus infections are associated with a high hospitalization rate in children between the ages of 5 and 14. The predominant reason for poor influenza prognosis is the lack of any effective means for early diagnosis. Early diagnosis of severe illness is critical to improving patient outcome, and could be especially useful in areas with limited medical resources. Accurate, inexpensive, and easy-to-use diagnostic tools could improve early diagnosis and patient outcome, and reduce overall healthcare costs. We developed an interleukin-6 paper-based test strip that used colloidal gold-conjugated antibodies to detect human interleukin-6 protein. These complexes were captured on a paper-based test strip patterned with perpendicular T lines that were pre-coated with anti-human interleukin-6 antibodies. Applied serum samples interacted with these antibodies and presented as colored bands that could be read using a spectrum-based optical reader. The full-spectrum of the reflected light interleukin-6 protein signal could be obtained from the spectral optics module, and the standard could be used to quantitatively analyze interleukin 6 level in serum. We retrospectively evaluated 10 children (23 serum samples) with severe influenza virus infections, 26 children (26 serum samples) with mild influenza virus infections, and 10 healthy children (10 serum samples). Our system, the combined use of a paper-based test strip and a spectrum-based optical reader, provided both qualitative and quantitative information. When used with the optical reader, the detection limit was improved from a qualitative, naked-eye level of 400 pg/ml to a quantitative, optical reader level of 76.85 pg/ml. After monitoring serum interleukin-6 level via our system, we found a high correlation between our system results and those obtainable using a conventional sandwich enzyme-linked immunosorbent assay method (Rho = 0.706, p < 0.001). The sensitivity and specificity for differentiating between severe and mild influenza using our combined method (test strip coupled with optical reader) were 78.3 and 50.0%, respectively. When interleukin-6 was combined with serum C-reaction protein, the sensitivity and specificity were 85.7 and 95.5%, and the receiver operating characteristic area-under-the-curve was quite high (AUC = 0.911, p < 0.001). The potential advantages of our system, i.e., a paper-based test strip coupled with a spectrum-based optical reader, are as follows: 1) simple user operation; 2) rapid turnaround times–within 20 min; 3) high detection performance; and, 4) low-cost fabrication.

With the current worldwide outbreak of COVID-19, developing rapid, effective, and convenient detection tools has become imperative [...]

Aromatic amines are prevalently used in a wide variety of industries and are ubiquitous in foods and environment. Many of this class of compounds are potentially mutagenic or even carcinogenic, and the assessment and prediction of their mutagenicity are of practical importance because mutagenicity and carcinogenicity are toxicological end points that play major roles in the genesis of cancer and tumor. Quantitative structure-activity relationship of a homogeneous set of mutagenicity data (TA98 + S9), which was comprehensively compiled from literature, was developed by four machine learning methods, namely hierarchical support vector regression (HSVR), support vector machine, radial basis function neural networks, and genetic function algorithm. The predictions by these models are in good agreement with the experimental observations for those molecules in the training set (n = 97, r(2) = 0.78-0.93, q(2) = 0.64-0.93, root mean square error [RMSE] = 0.51-0.90, SD = 0.34-0.56) and the test set (n = 25, r(2) = 0.73-0.85, RMSE = 0.65-0.85, SD = 0.33-0.51). In addition, several validation criteria were adopted to verify those generated models, and a set of outliers was deliberately selected to examine the robustness of these four predictive models (n = 14, r(2) = 0.35-0.84, RMSE = 0.55-1.21, SD = 0.25-0.72). Finally, various cross-comparison schemes, namely forward comparisons, backward comparisons, and most common molecule comparisons, with assorted published predictive models were carried out. Our results indicate that the HSVR model is the most accurate, robust, and consistent and can be employed as a tool for predicting mutagenicity of aromatic amines.

Because of 2 to 10 months of the lower limb recovery, many therapists in clinical medical centers prescribe with partial weight bearing (PWB). For preventing the permanent walking disability, the patients after the surgery will do with it. The most physical therapists usually do it with a bath-room scale to weight the weight distribution between the 2 legs to adjust the pressures and make patients rehabilitated safely. We hence proposed a novel system and algorithm called gait classifier of pressure centers (GCPC) to sense the pressure distribution in PWB. We use 6 sensing points and transmit the pressure signals by Bluetooth to the custom terminals. Therefore, the data of PWB can be saved and processed by this system. With the lower cost and high portability, it can reduce the error by the quantization, objective views, and high portability. We provided this system to make the traditional prescription more friendly, precise, and feasible for the patients and therapists.

Radiation therapy is a major modality of cancer treatment. To achieve good local control, high radiation dose must be precisely focused to the target region. A geometric missing during radiotherapy could result in tumor regrowth or severe damage of the neighboring normal tissues. To prevent geometric missing, a real-time monitoring the patient movement becomes very important. Using image or infrared technology to detect the displacement has been proposed. However, Promoting the current detection efficiency is too poor to fulfill clinical requirement due to the low resolution of 512 512× pixels and slow complex calculation. To overcome these problems, this study proposed ScanNet algorithm to monitor the patient breathing, cough, or other movement. ScanNet algorithm could detect 1 mm shift under the resolution of 1920 × 1080 pixels in realtime and to meet the quality requirement of modern radiotherapy for monitoring displacement.