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[Abstract]: [Objective]: To construct a Nomogram model for the prediction of essential hypertension (EH) risks with the use of traditional Chinese medicine (TCM) syndrome elements principles in conjunction with cutting-edge biochemical detection technologies. [Methods]: A case-control study was conducted, involving 301 patients with essential hypertension in the hypertensive group and 314 without in the control group. Comprehensive data, including the information on the four TCM diagnoses, general data, and blood biochemical indicators of participants in both groups, were collected separately for analysis. The differentiation principles of syndrome elements were used to discern the location and nature of hypertension. One-way analysis was carried out to screen for potential risk factors of the disease. Least absolute shrinkage and selection operator (LASSO) regression was used to identify factors that contribute significantly to the model, and eliminate possible collinearity problems. At last, multivariate logistic regression analysis was used to both screen and quantify independent risk factors essential for the prediction model. The “rms” package in the R Studio was used to construct the Nomogram model, creating line segments of varying lengths based on the contribution of each risk factor to aid in the prediction of risks of hypertension. For internal model validation, the Bootstrap program package was utilized to perform 1000 repetitions of sampling and generate calibration curves. [Results]: The results of the multivariate logistic regression analysis revealed that the risk factors of EH included age, heart rate (HR), waist-to-hip ratio (WHR), uric acid (UA) levels, family medical history, sleep patterns (early awakening and light sleep), water intake, and psychological traits (depression and anger). Additionally, TCM syndrome elements such as phlegm, Yin deficiency, and Yang hyperactivity contributed to the risk of EH onset as well. TCM syndrome elements liver, spleen, and kidney were also considered the risk factors of EH. Next, the Nomogram model was constructed using the aforementioned 14 risk predictors, with an area under the curve (AUC) of 0.868 and a 95% confidence interval (CI) ranging from 0.840 to 0.895. The diagnostic sensitivity and specificity were found to be 80.7% and 85.0%, respectively. Internal validation confirmed the model’s robust predictive performance, with a consistency index (C-index) of 0.879, underscoring the model’s strong predictive ability. [Conclusion]: By integrating TCM syndrome elements, the Nomogram model has realized the objective, qualitative, and quantitative selection of early warning factors for developing EH, resulting in the creation of a more comprehensive and precise prediction model for EH risks.

A filamentous fungus producing polygalacturonase (29.2 U/mL) was isolated from orchard soil in northern Guangxi, and identified as Trichoderma koningiopsis. Using polygalacturonic acid as the substrate, the optimum reaction temperature of the enzyme was 50 ℃, and it maintained more than 70% of the maximum activity at 50 ℃ for 1 h, which was identified as a medium-high temperature tolerant enzyme. The enzyme had unique weakly acidic catalytic characteristics. The optimum reaction pH was 5.0, and it retained 73% of its maximum activity at pH 6.0, and was tolerant to pH 2.5–8.0. Zn2+ partially activated the enzyme activity, while Mn2+ inhibited the enzyme. It could depectinize five weakly acid fruit pulps (papaya, banana, banana, and white- and red-fleshed pitaya) but to different extents. The yield of juice of banana pulp increased by 24.4%, the viscosity of papaya pulp decreased by 82.5%, and the transmittance of red-fleshed pitaya pulp increased by 31.9%, indicating good depectinization efficiency for the three fruits. The polygalacturonase-producing strain has good prospects for its application in juice production from tropical and subtropical perishable fruits.

Frailty, representing the physiological reserve and tolerance of the body, serves as a crucial evaluation index of the overall status of the older adults. This study aimed to investigate the prevalence of preoperative frailty and its impact on postoperative outcomes among older adults with lumbar degenerative disease in China. Patients and Methods: In this prospective study, a total of 280 patients aged 60 and above, diagnosed with lumbar degenerative disease and scheduled for surgical intervention were enrolled. The prevalence of frailty pre-surgery was evaluated using the Tilburg Frailty Indicator (TFI) and the modified Frailty Index 11 (mFI-11). The primary outcome was postoperative complication within 30 days post-surgery. The secondary outcomes were the length of hospital stay, hospital costs, reoperation within 30 days post-surgery and unplanned readmission within 30 days post-discharge. Both univariable and multivariable logistic regression were employed to screen and identify the risk factors predisposing patients to postoperative complications. Results: A total of 272 older adults were included in the study ultimately. The frailty detection rates of TFI and mFI-11 were 15.8% (43/272) and 10.7% (29/272) respectively. Thirty-four patients (12.5%) encountered complications. Significantly elevated rates of complications, prolonged hospital stays, increased hospital costs, and heightened readmission rates were observed in the frail group compared to the non-frail group (P< 0.05). Univariable analysis showed that the potential factors related to complications are TFI, mFI-11 and albumin. Multivariable logistic regression revealed that TFI was an independent risk factor for postoperative complications (OR=5.371, 95% CI: 2.338– 12.341, P < 0.001). Conclusion: Frailty was an independent predictor of postoperative complications in older adults undergoing lumbar fusion surgery. Frailty assessment should be performed in such patients to improve preoperative risk stratification and optimize perioperative management strategies. [ABSTRACT FROM AUTHOR]