[Clinical effect of acupuncture combined with medication on diabetic peripheral neuropathy and its influence on serum lipid metabolism].

OBJECTIVE: To observe the clinical effect on diabetic peripheral neuropathy (DPN) treated with acupuncture combined with medication and explore its effect mechanism. METHODS: Sixty-two patients of DPN were randomly divided into a combined therapy group (31 cases) and a medication group (31 cases, 2 cases dropped out); besides, 20 healthy subjects were recruited as a normal group. On the base of routine intervention, in the medication group, thioctic acid capsules were administrated orally, 0.2 g each time, 3 times a day. In the combined therapy group, besides the medication as the medication group, acupuncture was performed on bilateral Quchi (LI 11), Waiguan (TE 5), Hegu (LI 4), Tianshu (ST 25), Zusanli (ST 36), Sanyinjiao (SP 6) and Taichong (LR 3) and the needles were retained for 30 min, acupuncture was delivered once daily, 6 times a week. The duration of treatment was 4 weeks in the two groups. The score of Toronto clinical scoring system (TCSS), the nerve conduction velocity of median nerve (MN) and common peroneal nerve (CPN) were observed before and after treatment in the two intervention groups; and the serum lipid metabolism was detected before and after treatment in the two intervention groups and the normal group. RESULTS: Compared with that before treatment, the scores of TCSS were reduced in the combined therapy group and the medication group (P<0.05) after treatment, and the score decrease in the combined therapy group was larger than that of the medication group (P<0.001). The motor nerve conduction velocity and the sensory nerve conductive velocity of MN and CPN after treatment all increased in the combined therapy group and the medication group compared with those before treatment (P<0.05), and the improvements in the combined therapy group were larger than those of the medication group (P<0.001). Before treatment DPN patients had 365 differential lipid metabolites, including sphingosine (SPH, d18:0), involved in the inositol phosphate metabolism, compared with the subjects of the normal group. There were 103 differential lipid metabolites in the medication group before and after treatment, including lysophosphatidyl ethanolamine (LPE, 18:1/0:0), participated in glycerophospholipid metabolism. In the combined therapy group, before and after treatment, there were 99 differential lipid metabolites, including lysophosphatidylcholine (LPC, 18:0/0:0), participated in the neuroactive ligand-receptor interaction. Acupuncture greatly affected 50 lipid metabolites such as lysophosphatidic acid (LPA, 0:0/22:6), LPA(0:0/18:2) and LPC(O-18:0), which was mainly involved in glycerophospholipid metabolism. CONCLUSION: Acupuncture combined with medication ameliorates the symptoms and the nerve conduction velocity in DPN patients, which may be related to the regulation of serum lipid metabolism.

Comprehensive quantitative determination of aquifer confinement based on tidal response of well water level and its application in North China.

Aquifer confinement represents a pivotal property that significantly influences the vulnerability and contamination risk of groundwater resources. Several methods have been proposed for determining aquifer confinement by analyzing the response of well water level to Earth tides and atmospheric tides. In this study, we evaluated the performance of the existing single methods and put forward an optimized comprehensive approach. We compared the determination results of the three single methods with those of a comprehensive method using water-level data from 39 earthquake precursor monitoring wells in North China. The results demonstrate that the comprehensive method effectively determined aquifer confinement, significantly reducing the uncertainty associated with the three single methods. The application of the comprehensive method in North China reveals that aquifer confinement may undergo temporal variations during long-term continuous observation, especially in areas where the confining properties of aquifers may vary due to human activities and earthquakes. In such areas, the comprehensive method facilitates accurate assessment of groundwater vulnerability, as well as the potential dispersion of underground pollutants.

Electroacupuncture improves peripheral neuropathy by up-regulating Sirt1/PGC-1α/TFAM pathway in type 2 diabetes rats with peripheral neuropathy. / 基于Sirt1/PGC-1α/TFAM通路探讨电针治疗2åž‹ç³–å°¿ç— å‘¨å›´ç¥žç»ç— å˜çš„æœºåˆ¶.

OBJECTIVES: To observe the effect of electroacupuncture (EA) on activation of silent information regulator 1 (Sirt1)/peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α)/mitochondrial transcription factor A (TFAM) pathway in type 2 diabetes (T2DM) rats with peripheral neuropathy (DPN) , so as to explore its possible mechanisms underlying improvement of DPN. METHODS: Thirty male SD rats were randomly divided into blank control group (n=8) and DPN model group (n=22) which were further divided into model group (n=8) and EA group (n=8) after successful modeling. The model of T2DM was established by high-fat diet and low-dose intraperitoneal injection of streptozocin (35 mg/kg). For rats of the EA group (anesthetized with isoflurane), EA stimulation (2 Hz/15 Hz, 2 mA) was applied to "Tianshu"(ST25) for 20 min, once daily, 6 times a week for 6 weeks. The blood glucose level, body weight, area under curve (AUC) of glucose tolerance test, and hind-paw mechanical pain threshold and thermal pain threshold were observed. The intra-epidermal nerve fiber density (IENFD) of the hind-foot pad was observed by immunofluorescence staining. The motor nerve conduction velocity (MNCV) of the sciatic nerve was measured by using electrophysiological method. H.E. staining was used to observe the histopathological changes of the sciatic nerve after modeling. Transmission electron microscopy (TEM) was used to observe the ultrastructural changes of the sciatic nerve. The protein expressions of energy-related Sirt1, PGC-1α and TFAM in the sciatic nerve was detected by Western blot. RESULTS: Compared with the blank control group, the model group had a higher blood glucose contents and AUC (P<0.001), a slower MNCV (P<0.01), and a decrease in the body weight and in the mechanical and thermal pain thresholds (P<0.001) and IENFD (P<0.001), and in the expression levels of Sirt1, PGC-1α and TFAM (P<0.05, P<0.01). In contrast to the model group, the EA group had a decrease in the blood glucose contents and AUC (P<0.05, P<0.01), and an increase in mechanical and thermal pain thresholds, MNCV, IENFD, and expression levels of Sirt1, PGC-1α and TFAM proteins (P<0.01, P<0.05). In addition, results of histopathological and ultrastructural changes of the sciatic nerve showed more fragmented and disordered distribution of axons on the transverse section, and extensive separation of myelin and axons, uneven myelin thickness, axonal degeneration and irregular shape in the model group, whereas in the EA group, the axons on the transverse section were relatively more dense and more complete, the myelin sheath of the sciatic nerve was relatively uniform, and the axonal shape was relatively regular with relatively milder lesions. CONCLUSIONS: EA up-regulates the expressions of Sirt1, PGC-1α, TFAM in T2DM rats with DPN, which may be associated with its functions in improving and repairing the injured peripheral nerves in rats with DPN.

Study on risk factors of diabetic peripheral neuropathy and establishment of a prediction model by machine learning.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a common complication of diabetes. Predicting the risk of developing DPN is important for clinical decision-making and designing clinical trials. METHODS: We retrospectively reviewed the data of 1278 patients with diabetes treated in two central hospitals from 2020 to 2022. The data included medical history, physical examination, and biochemical index test results. After feature selection and data balancing, the cohort was divided into training and internal validation datasets at a 7:3 ratio. Training was made in logistic regression, k-nearest neighbor, decision tree, naive bayes, random forest, and extreme gradient boosting (XGBoost) based on machine learning. The k-fold cross-validation was used for model assessment, and the accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC) were adopted to validate the models' discrimination and clinical practicality. The SHapley Additive exPlanation (SHAP) was used to interpret the best-performing model. RESULTS: The XGBoost model outperformed other models, which had an accuracy of 0·746, precision of 0·765, recall of 0·711, F1-score of 0·736, and AUC of 0·813. The SHAP results indicated that age, disease duration, glycated hemoglobin, insulin resistance index, 24-h urine protein quantification, and urine protein concentration were risk factors for DPN, while the ratio between 2-h postprandial C-peptide and fasting C-peptide(C2/C0), total cholesterol, activated partial thromboplastin time, and creatinine were protective factors. CONCLUSIONS: The machine learning approach helped established a DPN risk prediction model with good performance. The model identified the factors most closely related to DPN.