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Background The diagnosis of Parkinson disease (PD) is mainly based on clinical symptoms, and there is a lack of objective methods for correct diagnosis. At present, there have been studies on retinal structural changes as a biomark for early diagnosis of PD, but machine learning based on retinal structural changes for predicting early PD has not yet been studied. Objective To construct a machine learning model based on the characteristics of retinal structural changes, explore its value in early PD diagnosis, and the accuracy of different machine learning algorithms for early PD diagnosis. Methods From October 2021 to September 2022, 49 PD patients aged 40 to 70 years old (PD group) who attended outpatient clinics and were hospitalized in the department of neurology of Henan Provincial People's Hospital (PD group) and 39 healthy people with matching age and sex (healthy control group) who came to the hospital for physical examination were collected. All study subjects underwent swept-source optical coherence tomography and swept-source optical coherence tomography angiography, the thickness and vessel density of the macular retina were also quantitatively analyzed. The 88 subjects were randomly divided into the 62 training sets and 26 validation set according to the ratio of 7∶3. Variables with significant differences between the PD group and healthy control group were selected as the characteristic variables for inclusion in the machine learning model, and Logistic regression (LR) , K-nearest neighbor algorithm (KNN) , decision tree (DT) , random forest (RF) and extreme gradient boosting (XGboost) models were constructed in the training set. The area under the curve (AUC) , accuracy, sensitivity and specificity of the receiver operating characteristic (ROC) curve were used to evaluate the predictive value of the machine learning model based on retinal structural changes for early PD. Results Compared with the healthy control group, the density of the upper outer ring (A6) , the outer temporal outer ring (A7) , the lower outer ring (A8) and the outer nasal ring (A9) of the superficial capillaries in the PD group were reduced, the thickness of the upper inner ring (A2) , the inner temporal inner ring (A3) , the inferior inner ring (A4) , the inner ring of the nasal side (A5) of the retinal layer, A6, A7, A8 and A9, the thickness of A6 of the ganglion cell complex layer, the thickness of A7 of the nerve fiber layer, A2 and A4, A5, A6, A7, A8, A9 became thinner (P

Anti-cancer therapy is crucial in cancer prevention and anti-cancer, and thus, highly sensitive methods for detecting cancer biomarkers are essential for cancer early diagnosis. Herein, an electrochemical aptamer biosensor based on the CRISPR-Cas12a system was constructed for the detection of cancer tumor biomarker MUC1 mucin. The sensitivity was significantly prompted by enzyme-catalyzed signal amplification, and the selectivity was improved by the dual recognition of the aptamer to MUC1 and crRNA-Cas12a system to the aptamer. Glucose oxidase (GOD) was loaded on the surface of magnetic Fe3O4@Au (MGNP) via probe single-stranded DNA (pDNA) with the terminal modification of mercapto (–SH) to form GOD-pDNA/MGNP. The corresponding aptamer of MUC1 (MUC1 Apt) binds to its complementary ssDNA (cDNA) to form the activator Apt/cDNA, which is specifically recognized by crRNA-Cas12a and excites the trans-cleavage function of Cas12a, thus in turn trans-cleaves pDNA and detaches GOD from the magnetic particles. The magnetic beads were separated and transferred into a glucose solution, and the oxidation current of H2O2 produced by the catalytic reaction of GOD was measured on a Pt-modified magnetically-controlled glassy carbon electrode, resulting in an indirect determination of MUC1. The current change was linear with the logarithm of MUC1 concentration in the range from 1.0 × 10−17 g mL−1 to 1.0 × 10−10 g mL−1. The detection limit was as low as 7.01 × 10−18 g mL−1. The method was applied for the detection of MUC1 in medical samples. [ABSTRACT FROM AUTHOR]

Belowground interactions are crucial in the over-yielding of intercropping systems. However, the effects of belowground interactions on the yields of intercrops in faba bean-maize relay intercropping system are still unclear. In this study, field and pot experiments were conducted, and the biomass, yield, nutrient accumulation and photosynthetic parameters of intercrops were determined to analyze the effects of belowground interactions and relay intercropping. In the field experiments, the yield and biomass of faba bean and maize in the relay intercropping system was significantly increased compared with the monocropping. The relay intercropping promoted the accumulation of nitrogen and phosphorus in faba bean and maize but showed no effect on potassium accumulation. In the pot experiments, no barrier treatment significantly increased the nutrient accumulation of faba bean and maize, nutrient content in soil, photosynthesis, maize, yield, biomass, plant height, 100-grain weight of faba bean and maize. In summary, relay intercropping increased nutrient accumulation, photosynthesis, biomass and yield of crops. The belowground interactions of intercrops could provide good soil fertility conditions for crop growth. The results can provide references for high-yield cultivation of crops and sustainable development of agriculture. [ABSTRACT FROM AUTHOR]