Statistical pattern recognition classification with computer vision images for assessing the furan content of fried dough pieces

Integrative analysis of crosstalk genes and diagnostic biomarkers in lupus-associated osteoporosis

Systemic lupus erythematosus (SLE) patients are at greater risk of developing osteoporosis (OP) than the general population. This study aimed to identify crosstalk genes between SLE and OP and to validate their diagnostic accuracy as biomarkers. Data analysis based on Gene Expression Omnibus (GEO) datasets was conducted. We utilized Weighted Gene Co-Expression Network Analysis (WGCNA) and differential expression analysis to identify crosstalk genes (CGs). Machine learning algorithms and consensus clustering were applied to screen shared diagnostic biomarkers and construct two predictive models featuring key genes. We also investigated potential subgroups, immune infiltration across different subtypes, and validated hub mRNAs using quantitative real-time PCR (qPCR). Molecular docking was performed to simulate the interaction of a small molecule compound with its target. We identified 19 CGs and developed two predictive models: the IL1R2-GADD45B and CHI3L1-IL1R2-SPTLC2 diagnostic score thresholds. The CHI3L1-IL1R2-SPTLC2 model showed improved predictive accuracy for lupus-associated osteoporosis. The C2 subtype was found to potentially regulate bone metabolism in SLE patients. Immune infiltration analysis indicated a strong association between CGs and multiple immunocytes, with IL1R2 being a common element in both models. Molecular docking suggests that Anakinra's therapeutic effect may involve IL1R2. Our study introduces novel diagnostic biomarkers and predictive models for lupus-associated osteoporosis, with a particular focus on IL1R2 as an innovative biomarker and therapeutic target. These are anticipated to aid early screening and risk assessment in SLE patients, pending large-scale clinical validation.

Detecting cooking state of grilled chicken by electronic nose and computer vision techniques

Determination of food doneness remains a challenge for automation in the cooking industry. The complex physicochemical processes that occur during cooking require a combination of several methods for their control. Herein, we utilized an electronic nose and computer vision to check the cooking state of grilled chicken. Thermogravimetry, differential mobility analysis, and mass spectrometry were employed to deepen the fundamental insights towards the grilling process. The results indicated that an electronic nose could distinguish the odor profile of the grilled chicken, whereas computer vision could identify discoloration of the chicken. The integration of these two methods yields greater selectivity towards the qualitative determination of chicken doneness. The odor profile is matched with detected water loss, and the release of aromatic and sulfur-containing compounds during cooking. This work demonstrates the practicability of the developed technique, which we compared with a sensory evaluation, for better deconvolution of food state during cooking.