Protective effect of unsaturated fatty acids on cognitive impairment in CKD patients: Results from the National Health and Nutrition Examination Survey (2011-2014).

BACKGROUND: It is still unknown whether unsaturated fatty acids (UFA) have the same effect on preventing cognitive impairment in chronic kidney disease (CKD) patients as in healthy people. OBJECTIVE: To investigate the protective effect of dietary UFA intake and proportion on cognitive impairment in patients with CKD. METHODS: We extracted data from the National Health and Nutrition Examination Survey (NHANES, 2011-2014) on participants with a previous diagnosis of CKD and at least one complete cognitive assessment (Consortium to Establish a Registry for Alzheimer's Disease test, Animal Fluency Test and Digit Symbol Substitution Test). We used the lower quartile of the total scores of these three tests as the cut-off point, and divided the participants into two groups of normal cognitive performance and low cognitive performance to extract participants' intake of various UFA from the NHANES dietary module.

Artificial intelligence can accurately distinguish IgA nephropathy from diabetic nephropathy under Masson staining and becomes an important assistant for renal pathologists.

Introduction: Hyperplasia of the mesangial area is common in IgA nephropathy (IgAN) and diabetic nephropathy (DN), and it is often difficult to distinguish them by light microscopy alone, especially in the absence of clinical data. At present, artificial intelligence (AI) is widely used in pathological diagnosis, but mainly in tumor pathology. The application of AI in renal pathological is still in its infancy. Methods: Patients diagnosed as IgAN or DN by renal biopsy in First Affiliated Hospital of Zhejiang Chinese Medicine University from September 1, 2020 to April 30, 2022 were selected as the training set, and patients who diagnosed from May 1, 2022 to June 30, 2022 were selected as the test set. We focused on the glomerulus and captured the field of the glomerulus in Masson staining WSI at 200x magnification, all in 1,000 × 1,000 pixels JPEG format. We augmented the data from training set through minor affine transformation, and then randomly split the training set into training and adjustment data according to 8:2. The training data and the Yolov5 6.1 algorithm were used to train the AI model with constant adjustment of parameters according to the adjusted data. Finally, we obtained the optimal model, tested this model with test set and compared it with renal pathologists. Results: AI can accurately detect the glomeruli. The overall accuracy of AI glomerulus detection was 98.67% and the omission rate was only 1.30%. No Intact glomerulus was missed. The overall accuracy of AI reached 73.24%, among which the accuracy of IgAN reached 77.27% and DN reached 69.59%. The AUC of IgAN was 0.733 and that of DN was 0.627. In addition, compared with renal pathologists, AI can distinguish IgAN from DN more quickly and accurately, and has higher consistency. Discussion: We constructed an AI model based on Masson staining images of renal tissue to distinguish IgAN from DN. This model has also been successfully deployed in the work of renal pathologists to assist them in their daily diagnosis and teaching work.

Construct a classification decision tree model to select the optimal equation for estimating glomerular filtration rate and estimate it more accurately.

Chronic kidney disease (CKD) has become a worldwide public health problem and accurate assessment of renal function in CKD patients is important for the treatment. Although the glomerular filtration rate (GFR) can accurately evaluate the renal function, the procedure of measurement is complicated. Therefore, endogenous markers are often chosen to estimate GFR indirectly. However, the accuracy of the equations for estimating GFR is not optimistic. To estimate GFR more precisely, we constructed a classification decision tree model to select the most befitting GFR estimation equation for CKD patients. By searching the HIS system of the First Affiliated Hospital of Zhejiang Chinese Medicine University for all CKD patients who visited the hospital from December 1, 2018 to December 1, 2021 and underwent Gate's method of 99mTc-DTPA renal dynamic imaging to detect GFR, we eventually collected 518 eligible subjects, who were randomly divided into a training set (70%, 362) and a test set (30%, 156). Then, we used the training set data to build a classification decision tree model that would choose the most accurate equation from the four equations of BIS-2, CKD-EPI(CysC), CKD-EPI(Cr-CysC) and Ruijin, and the equation was selected by the model to estimate GFR. Next, we utilized the test set data to verify our tree model, and compared the GFR estimated by the tree model with other 13 equations. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Bland-Altman plot were used to evaluate the accuracy of the estimates by different methods. A classification decision tree model, including BSA, BMI, 24-hour Urine protein quantity, diabetic nephropathy, age and RASi, was eventually retrieved. In the test set, the RMSE and MAE of GFR estimated by the classification decision tree model were 12.2 and 8.5 respectively, which were lower than other GFR estimation equations. According to Bland-Altman plot of patients in the test set, the eGFR was calculated based on this model and had the smallest degree of variation. We applied the classification decision tree model to select an appropriate GFR estimation equation for CKD patients, and the final GFR estimation was based on the model selection results, which provided us with greater accuracy in GFR estimation.

[Exploration on the acupoint selection rule for the treatment of postpartum hypogalactia with acupuncture and moxibustion based on the set visualization analysis system].

Based on the set visualization analysis system and Apriori algorithm, the acupoint selection rule for the treatment of postpartum hypogalactia with acupuncture and moxibustion was explored. The clinical research articles relevant with postpartum hypogalactia treated with acupuncture and moxibustion were retrieved from CNKI, Wanfang and VIP databases till July 15, 2019 since the database establishment. According to the inclusion and exclusion criteria, the articles were screened and the acupoint prescriptions were extracted. On the base of the set visualization analysis system and Apriori algorithm, the characteristics of acupoints and the common acupoint combination were analyzed for the treatment of postpartum hypogalactia with acupuncture and moxibustion. Totally, 120 articles are included and 72 acupoints are extracted for the treatment of postpartum hypogalactia with acupuncture and moxibustion, of which, the conception vessel and the stomach meridian of foot-yangming are the most commonly used and the combination of Danzhong (CV 17), Rugen (ST 18), Zusanli (ST 36), Shaoze (SI 1) and Taichong (LR 3) is of the high frequency. The analyses of set visualization, acupoint association rule and acupoint combination network consistently indicate that the above mentioned 5 acupoints are the basic combination for the treatment of postpartum hypogalactia with acupuncture and moxibustion, co-acting on tonifying and nourishing qi and blood, smoothing liver qi and relieving stagnation, activating circulation and promoting lactation. This combination effectively treats postpartum hypogalactia caused by the source insufficiency of transformation of qi and blood and qi stagnation.