Background: Diagnostics for pulmonary tuberculosis (PTB) are usually inaccurate, expensive, or complicated. The breathomics-based method may be an attractive option for fast and noninvasive PTB detection. Method: Exhaled breath samples were collected from 518 PTB patients and 887 controls and tested on the real-time high-pressure photon ionization time-of-flight mass spectrometer. Machine learning algorithms were employed for breathomics analysis and PTB detection mode, whose performance was evaluated in 430 blinded clinical patients. Results: The breathomics-based PTB detection model achieved an accuracy of 92.6%, a sensitivity of 91.7%, a specificity of 93.0%, and an AUC of 0.975 in the blinded test set (n = 430). Age, sex, and anti-tuberculosis treatment does not significantly impact PTB detection performance. In distinguishing PTB from other pulmonary diseases (n = 182), the VOC modes also achieve good performance with an accuracy of 91.2%, a sensitivity of 91.7%, a specificity of 88.0%, and an AUC of 0.961. Conclusions: The simple and noninvasive breathomics-based PTB detection method was demonstrated with high sensitivity and specificity, potentially valuable for clinical PTB screening and diagnosis.

Presently, a variety of soft sensing technologies are available, but there is still an opportunity to research on flexible sensors that can withstand high loads. Here, we report a highly stretchable, wide measuring range, and easy-to-fabricate liquid metal sensor capable of detecting force and strain modes. The key enabling design feature of the proposed sensor is the spiral winding combination of two different types of stretchable liquid metal microfluidic fibers. Pressing or elongating the fibers changes the geometry and, thus, the electrical resistance in a predictable way. The proposed sensor can offer a simple mechanism to measure force up to about 400 N and strain up to about 200% under limited laboratory conditions. The sensor signal is repeatable in both cases. The ability of our sensor in wide measuring range in actual application is verified via detecting the loads of one human body and driving car wheel. We further show a proof of concept of the soft sensor by a soft robotic actuator, enabling the identification of bending angles and object surface features.

Selective laser melting (SLM) forms specimens that often exhibit anisotropic mechanical properties. Most existing research only explains that the mechanical properties of specimens perpendicular to the build direction are superior to those parallel to the build direction. In this paper, the mechanical properties of SLM 316L SS specimens with different surfaces and different directions are compared. Finally, it was found that the mechanical properties of specimens on Face 3 are stronger than those on Face 1 and Face 2, while the mechanical properties of specimens on Face 1 and Face 2 are similar. For specimens in different directions on the same surface, the mechanical properties of Face 1 and Face 2 exhibit clear anisotropy, while the mechanical properties of Face 3 tend to be isotropic. In this paper, the EBSD technique was used to analyze the specimens. It was found that the anisotropy of the mechanical properties of Face 1 and Face 2 are attributed to the presence of texture and columnar crystals in the sample. This paper can provide accurate and reliable material performance data for the practical application of SLM 316L SS, thereby guiding the optimization of engineering design and manufacturing processes.