To develop an intelligent diagnostic model for osteoporosis screening based on low-dose chest computed tomography (LDCT). The model incorporates automatic deep-learning thoracic vertebrae of cancellous bone (TVCB) segmentation model and radiomics analysis. A total of 442 participants who underwent both LDCT and quantitative computed tomography (QCT) examinations were enrolled and were randomly allocated to the training, internal testing, and external testing cohorts. The TVCB automatic segmentation model was trained using VB-Net. The accuracy of the segmentation was evaluated using the Dice coefficient. Predictive models for assessing bone mineral density (BMD) were constructed utilizing radiomics analysis based on automatic segmentation (ASeg model) and manual segmentation (MSeg model), respectively. The BMD predictive model based on ASeg and MSeg included the identification of normal and abnormal BMD (first-level model), and osteopenia and osteoporosis (second-level model). The diagnostic performance of the radiomics models were evaluated using the area under the curve (AUC), sensitivity and specificity. The Dice coefficients of the TVCB segmentation model in the internal and external testing cohorts were found to be 0.988 ± 0.014 and 0.939 ± 0.034, respectively. In the first-level model, the AUC of the ASeg model exhibited comparable performance to that of the MSeg model for both the internal (0.985 vs. 0.946, P = 0.080) and external (0.965 vs. 0.955, P = 0.724) testing cohorts. Similarly, in the second-level model, the AUC of the ASeg model was found to be comparable to that of the MSeg model for both the internal (0.933 vs. 0.920, P = 0.794) and external (0.907 vs. 0.892, P = 0.805) testing cohorts. A fully automated pipeline for TVCB segmentation and BMD assessment with radiomics analysis can be used for opportunistic BMD screening in chest LDCT.

Avocado oil is excellent functional oil. Effects of three extraction methods (squeezing extraction, supercritical carbon dioxide extraction, and aqueous extraction) on the species, composition, and contents of lipids in avocado oil were analyzed via ultra-performance liquid chromatography–time-of-flight tandem mass spectrometry (UPLC-TOF-MS/MS), and the differential components of lipids were revealed by OrthogonalPartialLeast Squares-DiscriminantAnalysis (OPLS-DA), S-plot combined with variable importance in the projection (VIP). The results showed that the fatty acid composition of avocado oil mainly consisted of oleic acid (36–42%), palmitic acid (25–26%), linoleic acid (14–18%), and palmitoleic acid (10–12%). A total of 134 lipids were identified first from avocado oil, including 122 glycerides and 12 phospholipids, and the total number of carbon atoms contained in the fatty acid side chains of the lipids was 32–68, and the number of double bonds was 0–9. Forty-eight differential lipid compounds with significant effects of the three extraction methods on the lipid composition of avocado oil were excavated, among which the differences in triglycerides (TG), phosphatidylethanol (PEtOH), and phosphatidylmethanol (PMeOH) contents were highly significant, which provided basic data to support the subsequent guidance of avocado oil processing, quality evaluation, and functional studies.

Integration of thermoelectric generators with phase change materials (PCMs) is expected to be a promising technology for harvesting and converting waste heat to electricity, thus alleviating energy shortage and ecological damage. However, the low output electricity and inherent hardness of PCMs have limited their wide application. Herein, a multifunctional phase-change organohydrogel filled with paraffin wax (PW) microspheres is prepared using Pickering emulsion technology and UV-initiated polymerization. The PW was encapsulated in cellulose nanofibril (CNF)–MXene–Fe₃O₄ hybrid shells and then inlaid into a flexible polyacrylamide (PAAm) matrix. This unique structural feature provides enhanced thermoelectric conversion, superior shape stability, and excellent mechanical properties. The resultant organohydrogel can continuously harvest waste heat, transfer it into a thermoelectric generation system, and output electrical energy. The generated output voltage and power can reach 627.0 mV and 65.7 mW, respectively. The thermoelectric generator also exhibited good thermal cycling stability (for more than 50 cycles). Besides, the composite organohydrogel possesses satisfactory thermosensing capacity to monitor real-time temperature variation and hot steam behavior. This work opens up opportunities for highly efficient conversion of waste heat to electricity.

This paper analyzed the effects of four drying methods (heat pump drying, hot air drying, sun drying, and freeze drying) on the volatile organic compounds (VOCs) in fresh ‘Guiyan1’ Amomum villosum Lour.

Volatile organic compounds (VOCs) in Citrus sinensis ‘hongjiang’ oranges significantly vary depending on the fruit variety, cultivation mode, and cultivation location. The effect of these three factors on VOCs was experimentally determined in this study. In total, 102 VOCs were separated via headspace solid-phase microextraction and identified via gas chromatography-mass spectrometry, and the differential components were analyzed by partial least-squares-discriminant analysis (OPLS-DA). The VOCs of ‘hongjiang’ mainly comprised alkenes, alcohols, aldehydes, and ketones. They were well clustered in OPLS-DA and principal component analysis (PCA), and the seven groups were distinctly differentiated. The results of the S-plot, variable importance in projection (VIP), and heatmap analyses showed that these factors had a significant impact on VOCs in ‘hongjiang’. The characteristic VOCs between the two pairs were significant, while the net room cultivation mode had the most substantial effect on VOCs.