Kimura disease (KD) is a rare, chronic inflammatory disease of unknown cause and is characterized by painless s.c. swellings and lymphadenopathy commonly affecting the head and neck region. Much therapeutics has been used to treat KD, but is not satisfactory because of frequent relapse. Imatinib has been reported previously to be useful for treatment of hypereosinophilic syndrome and may work by selectively blocking protein-tyrosine kinases, such as platelet-derived growth factor receptor, and c-Kit. We carried out immunohistochemical examination of platelet-derived growth factor receptor-alpha and c-Kit in tissues from patients with KD. The results were positive and suggested that Imatinib might be an effective drug for the treatment of the disease. We have also briefly reviewed the epidemiology, aetiology, clinical manifestations, laboratory and pathological examinations, differential diagnoses, treatment and prognosis of KD in this manuscript.

Most women in the perinatal period face sleep issues, which can affect their mental health. Only a few studies have focused on sleep trajectories and depressive symptoms of women during the perinatal period in China. This study aims to explore the development trajectory of sleep quality by classifying pregnant women according to the changes in their sleep quality during pregnancy and postpartum and investigate the correlation between different sleep quality trajectory groups and depressive symptoms. The Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep quality, and the Edinburgh Postnatal Depression Scale (EPDS) was used to assess the symptoms of depression. Participants (n = 412) completed the assessment of sleep quality, depressive symptoms, and some sociodemographic and obstetric data at 36 weeks of gestation, 1 week after delivery, and 6 weeks after delivery. The group-based trajectory model (GBTM) was used to complete the trajectory classification, and logistic regression was used to analyze the predictive factors of postpartum depressive symptoms. Four different sleep quality trajectories were determined: “stable-good,” “worsening,” “improving,” and “stable-poor” groups. The results demonstrate that poor sleep trajectories, social support and parenting experience during the perinatal period are related to postpartum depression. Screening for prenatal sleep problems is crucial for identifying the onset of perinatal depressive symptoms.

Background Optimal ω-6/ω-3 polyunsaturated fatty acids ratio (PUFAR) is reported to exert protective effects against chronic diseases. However, data on PUFAR and diabetic retinopathy (DR) remains scarce. We aimed to thoroughly quantify whether and how PUFAR was related to DR as well as its role in DR detection. Methods This two-centre case-control study was conducted from August 2017 to June 2018 in China, participants were matched using a propensity score matching algorithm. We adopted multivariable logistic regression models and restricted cubic spline analyses to estimate the independent association of PUFAR with DR, adjusting for confounders identified using a directed acyclic graph. The value of PUFAR as a biomarker for DR identification was further evaluated by receiver operating characteristic analyses and Hosmer-Lemeshow tests. Findings An apparent negative relationship between PUFAR and DR was observed. Adjusted odds of DR decreased by 79% (OR: 0·21, 95% CI: 0·10–0·40) with an interquartile range increase in PUFAR. Similar results were also obtained in tertile analysis. As compared to those in the 1st tertile of PUFAR, the adjusted odds of DR decreased by 76% (OR: 0·24, 95% CI: 0·08–0·66) and 93% (OR: 0·07, 95% CI: 0·03–0·22) for subjects in the 2nd and 3rd tertiles, respectively. Good calibration and discrimination of the PUFAR associated predictive model were detected and PUFAR = 35 would be an ideal cut-off value for DR identification. Interpretation Our results suggest that serum PUAFR is inversely associated with DR. Although PUFAR-alteration is not observed amongst different stages of DR, it can serve as an ideal biomarker in distinguishing patients with DR from those without DR. Funding This study was funded by Natural Science Foundation of Zhejiang Province, Zhejiang Basic Public Welfare Research Project, the Major Project of the Eye Hospital of Wenzhou Medical University, and the Academician's Science and Technology Innovation Program in Zhejiang province. Part of this work was also funded by the National Nature Science Foundation of China, and Research Project for College Students in Wenzhou Medical University.

Objective Early identification of diabetic retinopathy (DR) is key to prioritizing therapy and preventing permanent blindness. This study aims to propose a machine learning model for DR early diagnosis using metabolomics and clinical indicators. Methods From 2017 to 2018, 950 participants were enrolled from two affiliated hospitals of Wenzhou Medical University and Anhui Medical University. A total of 69 matched blocks including healthy volunteers, type 2 diabetes, and DR patients were obtained from a propensity score matching-based metabolomics study. UPLC-ESI-MS/MS system was utilized for serum metabolic fingerprint data. CART decision trees (DT) were used to identify the potential biomarkers. Finally, the nomogram model was developed using the multivariable conditional logistic regression models. The calibration curve, Hosmer–Lemeshow test, receiver operating characteristic curve, and decision curve analysis were applied to evaluate the performance of this predictive model. Results The mean age of enrolled subjects was 56.7 years with a standard deviation of 9.2, and 61.4% were males. Based on the DT model, 2-pyrrolidone completely separated healthy controls from diabetic patients, and thiamine triphosphate (ThTP) might be a principal metabolite for DR detection. The developed nomogram model (including diabetes duration, systolic blood pressure and ThTP) shows an excellent quality of classification, with AUCs (95% CI) of 0.99 (0.97–1.00) and 0.99 (0.95–1.00) in training and testing sets, respectively. Furthermore, the predictive model also has a reasonable degree of calibration. Conclusions The nomogram presents an accurate and favorable prediction for DR detection. Further research with larger study populations is needed to confirm our findings.