Browsing by Author "Zhao J"

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    Effect of Berberine on Cardiovascular Disease Risk Factors: A Mechanistic Randomized Controlled Trial.
    (MDPI (Basel, Switzerland), 2021-07-26) Zhao JV; Yeung W-F; Chan Y-H; Vackova D; Leung JYY; Ip DKM; Zhao J; Ho W-K; Tse H-F; Schooling CM
    Cardiovascular disease (CVD) is a major contributor to the global burden of disease. Berberine, a long-standing, widely used, traditional Chinese medicine, is thought to have beneficial effects on CVD risk factors and in women with polycystic ovary syndrome. The mechanisms and effects, specifically in men, possibly via testosterone, have not been examined previously. To assess the effect of berberine on CVD risk factors and any potential pathway via testosterone in men, we conducted a randomized, double-blind, placebo-controlled, parallel trial in Hong Kong. In total, 84 eligible Chinese men with hyperlipidemia were randomized to berberine (500 mg orally, twice a day) or placebo for 12 weeks. CVD risk factors (lipids, thromboxane A2, blood pressure, body mass index and waist-hip ratio) and testosterone were assessed at baseline, and 8 and 12 weeks after intervention. We compared changes in CVD risk factors and testosterone after 12 weeks of intervention using analysis of variance, and after 8 and 12 weeks using generalized estimating equations (GEE). Of the 84 men randomized, 80 men completed the trial. Men randomized to berberine had larger reductions in total cholesterol (-0.39 mmol/L, 95% confidence interval (CI) -0.70 to -0.08) and high-density lipoprotein cholesterol (-0.07 mmol/L, 95% CI -0.13 to -0.01) after 12 weeks. Considering changes after 8 and 12 weeks together, berberine lowered total cholesterol and possibly low-density lipoprotein-cholesterol (LDL-c), and possibly increased testosterone. Changes in triglycerides, thromboxane A2, blood pressure, body mass index and waist-hip ratio after the intervention did not differ between the berberine and placebo groups. No serious adverse event was reported. Berberine is a promising treatment for lowering cholesterol. Berberine did not lower testosterone but instead may increase testosterone in men, suggesting sex-specific effects of berberine. Exploring other pathways and assessing sex differences would be worthwhile, with relevance to drug repositioning and healthcare.
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    Potential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning: A prospective cohort study in breast cancer patients
    (Elsevier B V on behalf of the Science China Press, 2024-06-15) Zhang S; Yang B; Yang H; Zhao J; Zhang Y; Gao Y; Monteiro O; Zhang K; Liu B; Wang S
    An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin (H&E) histology, such as frozen section, are time-, resource-, and labor-intensive, and involve specimen-consuming concerns. Here, we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography (D-FFOCT), a label-free optical imaging method, and deep learning for bedside tumor diagnosis during surgery. To classify the benign and malignant breast tissues, we conducted a prospective cohort trial. In the modeling group (n = 182), D-FFOCT images were captured from April 26 to June 20, 2018, encompassing 48 benign lesions, 114 invasive ductal carcinoma (IDC), 10 invasive lobular carcinoma, 4 ductal carcinoma in situ (DCIS), and 6 rare tumors. Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches. Subsequently, from June 22 to August 17, 2018, independent tests (n = 42) were conducted on 10 benign lesions, 29 IDC, 1 DCIS, and 2 rare tumors. The model yielded excellent performance, with an accuracy of 97.62%, sensitivity of 96.88% and specificity of 100%; only one IDC was misclassified. Meanwhile, the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures. In the simulated intraoperative margin evaluation procedure, the time required for our novel workflow (approximately 3 min) was significantly shorter than that required for traditional procedures (approximately 30 min). These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraoperative cancer diagnosis independently of traditional pathology laboratory procedures.