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Food quality detection is an important method for ensuring food safety. Efficient quality detection methods can improve the efficiency of food circulation and reduce storage and labor costs. Traditional methods use instrumentation, testing reagents, or manual labor. These methods take a long time to detect, are time-consuming and labor-intensive, and require professionals to operate. Fruit, as a high-value food that provides essential nutrition for human beings, is susceptible to spoilage during packaging, transportation, and sales, so the freshness and safety assurance of fruit are a hot and difficult area of current research. Therefore, for the detection of fruit freshness, this paper proposes an efficient and nondestructive way to detect fruit freshness by using the machine learning algorithm convolutional neural network (CNN). This paper shows that convolutional neural networks have good performance in identifying the freshness of fruits through extensive experimental results and discusses the overfitting of machine learning based on the experimental results. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the result of body impedance analysis (BIA) in type 1 diabetes mellitus patients with diabetic peripheral neuropathy (DPN). Methods: A total of 125 T1DM patients were selected and enrolled into the DPN group (N=30) or the control group (N=95). BIA parameters and clinical characteristics were compared to find possible risk factors. After that, they were screened by binary logistic regression. The receiver operating characteristic (ROC) curve of meaningful index was portrayed, and then, the best cutoff was founded. Results: The DNP group has elder age, longer diabetes duration, higher blood urea nitrogen (BUN) concentration, and lower estimated glomerular filtration rate (eGFR) level. The DNP group has larger odds of other diabetic complications including diabetic peripheral vascular disease (DPVD) and diabetic nephropathy (DN). In BIA parameters, the DPN group had higher ratio of extracellular water (ECW) and total body water (TBW) than the control group (0.3969±0.0097 vs 0.3886±0.0086, p<0.001). Binary logistic regression indicated that increase of ECW/TBW (ECW/TBW≥0.395 vs ECW/TBW<0.395, OR=4.589, 95%CI 1.609~13.091, p=0.004) may be one of risk factors of DPN. In the ROC curve, area under curve (AUC) was 0.752 (95%CI 0.646~0.857) and the best cutoff of ECW/TBW to diagnose DPN was 0.3965. Conclusion: The increase of ECW/TBW may be a new risk factor of DPN and could help doctors predict the risk of DPN in T1DM patients. [ABSTRACT FROM AUTHOR]

Background: Little is known about the association between abnormal metabolic obesity states and the outcomes of chronic myeloid leukemia (CML), especially in patients with obesity with different metabolic status. Here, we used the Nationwide Readmissions Database to assess the effects of metabolically defined obesity on adverse outcomes of CML. Methods: Of the 35 460 557 (weighted) patients, we included 7931 adults with discharge diagnoses of CML from January 1, 2018 to June 30, 2018. The study population was observed until December 31, 2018 and divided into four groups based on body mass index and metabolic status. The primary outcome was the adverse outcomes of CML, including nonremission (NR)/relapse and severe mortality risk. Multivariate logistic regression analysis was performed to analyze data. Results: Metabolically unhealthy normal weight and metabolically unhealthy obesity were all risk factors for adverse outcomes of CML compared with metabolically healthy normal weight (all p < 0.01), and a significant difference was not found in the metabolically healthy obese. Female patients with metabolically unhealthy normal weight and metabolically unhealthy obesity had 1.23‐fold and 1.40‐fold increased NR/relapse risk, while male patients did not have this risk. Moreover, patients with a higher number of metabolic risk factors or with dyslipidemia were at higher risk of adverse outcomes, regardless of obesity status. Conclusions: Metabolic abnormalities were associated with adverse outcomes in patients with CML, irrespective of obesity status. Future treatment of patients with CML should consider the effects of obesity on their adverse outcomes under different metabolic status, especially in female patients. [ABSTRACT FROM AUTHOR]

Background: The present definition of obesity based on body mass index (BMI) is not accurate and effective enough to identify hospitalized patients with a heavier burden, especially for postmenopausal hospitalized patients concomitant with osteoporosis. The link between common concomitant disorders of major chronic diseases such as osteoporosis, obesity, and metabolic syndrome (MS) remains unclear. Here, we aim to evaluate the impact of different metabolic obesity phenotypes on the burden of postmenopausal hospitalized patients concomitant with osteoporosis in view of unplanned readmissions. Methods: Data was acquired from the National Readmission Database 2018. The study population was classified into metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) patients. We estimated the associations between metabolic obesity phenotypes and 30- and 90-day unplanned readmissions. A multivariate Cox Proportional Hazards (PH) model was used to assess the effect of factors on endpoints, with results expressed as HR and 95% CI. Results: The 30-day and 90-day readmission rates for the MUNO and MUO phenotypes were higher than that of the MHNO group (all p < 0.05), whereas no significant difference was found between the MHNO and MHO groups. For 30-day readmissions, MUNO raised the risk mildly (hazard ratio [HR] = 1.110, p < 0.001), MHO had a higher risk (HR = 1.145, p = 0.002), and MUO further elevated this risk (HR = 1.238, p < 0.001). As for 90-day readmissions, both MUNO and MHO raised the risk slightly (HR = 1.134, p < 0.001; HR = 1.093, p = 0.014, respectively), and MUO had the highest risk (HR = 1.263, p < 0.001). Conclusions: Metabolic abnormalities were associated with elevated rates and risks of 30- or 90-day readmission among postmenopausal hospitalized women complicated with osteoporosis, whereas obesity did not seem to be innocent, and the combination of these factors led to an additional burden on healthcare systems and individuals. These findings indicate that clinicians and researchers should focus not only on weight management but also metabolism intervention among patients with postmenopausal osteoporosis. [ABSTRACT FROM AUTHOR]

(1) Background: As the introduction of "positive" diagnostic criteria for metabolic dysfunction-associated fatty liver disease (MAFLD) does not exclude alcohol consumption, some patients originally diagnosed with alcoholic fatty liver disease (AFLD) may be diagnosed with dual- etiology fatty liver disease (AFLD&MAFLD), which requires us to urgently explore the impact of the changes in this classification of AFLD on clinical manifestations. (2) Methods: Utilizing data from the Nationwide Inpatient Sample database 2016–2018, a total of 9269 participants with AFLD were selected. With the definition of MAFLD, these patients were further categorized into two groups: single AFLD and AFLD&MAFLD. The primary outcome was the risk of comorbidities and organ failures. The secondary outcomes were the length of stay, total charges, and in-hospital all-cause mortality. (3) Results: The patients with AFLD&MAFLD were older, were predominantly male, and had more comorbidities and organ failures compared to the patients with AFLD. These comorbidities included coronary atherosclerosis, myocardial infarction, cerebrovascular disease, arrhythmia, asthma, chronic obstructive pulmonary disease, and chronic kidney disease (all p values < 0.05). The patients with AFLD&MAFLD were more likely to develop acute and chronic heart and/or kidney failures than those with single AFLD (all p < 0.05). The length of stay and total charges of the patients in the AFLD&MAFLD group were greater than the single AFLD group (p = 0.029 and p < 0.001, respectively). No significant difference in all-cause mortality was observed. (4) Conclusions: The patients with AFLD&MAFLD have more comorbidities and organ failures, longer hospital stays, and higher hospitalization costs than the patients with single AFLD. Hence, patients with dual-etiology fatty liver disease deserve more attention from clinical staff during treatment. [ABSTRACT FROM AUTHOR]

Objectives. Peripheral artery disease (PAD) in diabetic populations is a vital chronic disease all over the world due to its high morbidity and mortality. It is important to find early simple screening biomarkers and find residual risk factors that may provide a new target for prevention and treatment of PAD in diabetic patients besides traditional cardiometabolic risk factors. Methods. We performed a cross-sectional retrospective study, and a total of 1671 T2DM participants were recruited. Receiver operating characteristic analysis, stepwise logistic regression analysis, points score system, and decision curve analysis were performed to assess the risk factors for PAD. Results. The prevalence of PAD in the study was 7.18% (n = 120). Compared to the participants with the lowest quartile of cystatin C (CysC), the risk of developing PAD in participants with the highest quartile of CysC increased 6.339-fold. The CysC was the superior indicators to distinguish participants with PAD from those without PAD, with an AUC of 0.716. Stepwise logistic regression analysis showed that CysC was independent risk factor for PAD besides traditional risk factors. Combined exposure to these traditional risk factors and CysC was associated with a stepwise increase in the risk of developing PAD and even increased 11.976-fold in participants with the highest quintiles of combined exposure score (CES) based on traditional risk factors and CysC compared to the participants with the lowest quintiles of CES. Conclusions. CysC was associated with PAD independent of potential risk factors in diabetic populations. The CysC was a reliable marker for the early screening of PAD in diabetic patients besides traditional cardiometabolic risk factors. [ABSTRACT FROM AUTHOR]