The development of a clinical nomogram to predict medication nonadherence in patients with knee osteoarthritis.

Knee osteoarthritis (KOA) is a common bone disease in older patients. Medication adherence is of great significance in the prognosis of this disease. Therefore, this study analyzed the high-risk factors that lead to medication nonadherence in patients with KOA and constructed a nomogram risk prediction model. The basic information and clinical characteristics of inpatients diagnosed with KOA at the Department of Orthopedics, The Affiliated Hospital of Chengde Medical University, were collected from January 2020 to January 2022. The Chinese version of the eight-item Morisky scale was used to evaluate medication adherence. The Kellgren-Lawrence (KL) classification was performed in combination with the imaging data of patients. Least absolute shrinkage and selection operator regression analysis and logistic multivariate regression analysis were used to analyze high-risk factors leading to medication nonadherence, and a prediction model of the nomogram was constructed. The model was internally verified using bootstrap self-sampling. The index of concordance (C-index), area under the operating characteristic curve (AUC), decision curve, correction curve, and clinical impact curve were used to evaluate the model. A total of 236 patients with KOA were included in this study, and the non-adherence rate to medication was 55.08%. Seven influencing factors were included in the nomogram prediction: age, underlying diseases, diabetes, age-adjusted Charlson comorbidity index (aCCI), payment method, painkillers, and use of traditional Chinese medicine. The C-index and AUC was 0.935. The threshold probability of the decision curve analysis was 0.02-0.98. The nomogram model can be effectively applied to predict the risk of medication adherence in patients with KOA, which is helpful for medical workers to identify and predict the risk of individualized medication adherence in patients with KOA at an early stage of treatment, and then carry out early intervention.

Rapid identification of flaxseed oil based on portable fiber optic Raman spectroscopy combined with an oil microscopy method.

To explore a fast, simple, and accurate method to identify adulteration in flaxseed oil, the Raman spectral data of 130 samples containing flaxseed, canola, cottonseed, and adulterated oils were obtained using a portable fiber optic Raman spectrometer. The Raman spectral results showed that the Raman spectra of the flaxseed and canola oils had noticeable peak shifts, whereas the peak positions of the flaxseed and cottonseed oils were relatively similar. Clear peak intensity differences were observed in the flaxseed, cottonseed, and canola oils, mainly at 868 cm-1 , 1022 cm-1 , 1265 cm-1 , and 1655 cm-1 , with Raman shift intensities in the following order: Iflaxseed oil  > Icottonseed oil  > Icanola oil . Similarly, the peak intensity of the flaxseed and adulterated oils also exhibited certain differences (at 868 cm-1 , 1022 cm-1 , 1265 cm-1 , and 1655 cm-1 ), and the Raman shift intensity tended to decrease gradually with the increasing content of canola and cottonseed oils in the flaxseed oil. Additionally, the results of Raman spectroscopy combined with the "oil microscopy" method exhibited large variations in the radar patterns of the flaxseed, canola, and cottonseed oils, whereas the radar patterns of the flaxseed and adulterated oils closely resembled each other. The results indicated that Raman spectroscopy in combination with oil microscopy more effectively revealed the subtle differences in the Raman shift intensity, serving as a more visual and comprehensive approach for differentiating the quality variations between pure flaxseed oil and other oil species and adulterated oil. PRACTICAL APPLICATION: This study analyzed the Raman spectra of flaxseed, canola, cottonseed, and adulterated oils using fiber optic Raman spectroscopy. Combined with the oil microscopy method for comprehensive evaluation and analysis, it is feasible to effectively identify the quality differences among flaxseed, canola, cottonseed, and adulterated oils.