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.
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.
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.
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.
To explore the application of Quality by Design (QbD) tools in assessing geographical variations of Phyllanthus emblica (P. emblica) from five distinct Indian states.
In the current experiment, the Box-Behnken design with a reduced quartic model and 105 runs was employed with the use of the Design Expert software for randomized response surface mapping. Three different extraction methods (Soxhlet, maceration, and sonication) along with three solventst [distilled water, methanol, and water-methanol mixture (50 : 50 v/v)] were considered in the present study. The anti-oxidant activities, total flavonoid content (TFC), and total phenolic content (TPC) in the P. emblica were determined and analysed by gas chromatography-mass spectrometry (GC-MS) to identify the major components.
The QbD overlay plot showed that the extractive value of the P. emblica was no less than 30% w/w, 2,2-diphenyl-1-picrylhydrazyl (DPPH) no less than 60% mcg/mL (micrograms per millilitre), TFC no less than 75 mg QE/g (milligrams of quercetin equivalents per gram), and TPC no less than 80 mg GAE/g (milligrams of gallic acid equivalents per gram). Moreover, the GC-MS data confirmed the presence of variation in the bioactives of P. emblica extracts.
The model was significant in describing the variation in extractive value, DPPH, TFC, and TPC. The QbD approach may tend to prioritize thoroughness in the extraction process, ultimately resulting in improved quality in the extracted products.
To investigate the underlying mechanism of the compound Bugansan Decoction (补肝散, BGSD) in intervening learning and memory in D-galactose (D-gal)-induced aging rats.
A total of 40 rats were randomly assigned to four groups: control, model, BGSD [14.06 g/(kg·d)], and piracetam [0.4 g/(kg·d)] groups, with 10 rats in each group. D-gal [400 mg/(kg·d)] was injected intraperitoneally to establish the aging rat model. The rats' body weight, water intake, food intake, and gripping strength were recorded each week. The eight-arm maze and step-down test were used to measure the rats' capacity for learning and memory. Liver, thymus, spleen, and brain tissues were weighed to calculate the corresponding organ indices; serum malondialdehyde (MDA) content and superoxide dismutase (SOD) activity were measured. Hematoxylin and eosin (HE) staining was adopted to observe the pathological changes of the hippocampus; enzyme-linked immunosorbent assay (ELISA) was used to detect the levels of tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-1β in the hippocampus. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of receptors for advanced glycation end products (RAGE), nuclear factor-κB (NF-κB), TNF-α, IL-6, and IL-1β mRNA in the hippocampus. Western blot (WB) was employed to detect the expression levels of advanced glycation end products (AGEs), RAGE, and NF-κB protein in the hippocampus.
In D-gal-induced aging rats, BGSD significantly increased food intake, water intake, body weight, gripping strength, and organ indices (P < 0.05), and significantly decreased working memory error (WME), reference memory error (RME), and total memory errors (TE) in an eight-arm maze (P < 0.05). In the step-down test, step-down latency was prolonged and the frequency of errors dropped (P < 0.05). Additionally, BGSD could lessen the harm done to hippocampus neurons, increase serum SOD activity, lower MDA levels, and down-regulate the expression levels of the pro-inflammatory molecules TNF-α, IL-6, and IL-1β (P < 0.05). Further findings showed that BGSD significantly decreased hippocampal AGEs, RAGE, and NF-κB expression (P < 0.05).
By blocking the AGEs/RAGE/NF-κB signaling pathway, BGSD may regulate the neuroinflammatory damage in D-gal-induced aging rats, and thus improve learning and memory.

