Vietnam boasts a rich and diverse flora, with many endemic species. Among them, Ngoc Linh ginseng (Vietnamese ginseng; scientific name: Panax vietnamensis Ha et Grushv.), a high-value endemic ginseng species, has been recognized as a national treasure. While numerous studies have been conducted on its rhizomes and roots, research on its leaves remains limited. In this study, six compounds (1–6) were isolated from the methanol extract of the leaves of P. vietnamensis. Their structures were elucidated using ESI-MS, 1D and 2D NMR spectroscopic methods, and comparisons with known literature data. The identified compounds are: 12β,20(R),25-β trihydroxydammara-3-O-β-D-glucopyranoside (1); 12β,20(R),25-trihydroxydammara-3-O-β-D-glucopyranosyl- (1→2)-β-D-glucopyranoside (2); notoginsenoside SFt1 (3); ginsenoside Rh2 (4); ginsenoside Rg3 (5) and notoginsenoside L1 (6). Except for compound 3, which was isolated from the leaves for the first time, the other five compounds are reported from this species for the first time. The α-glucosidase inhibition assay of the pure isolated compounds revealed that compounds 1, 4, and 6 exhibited significant activities, with IC50 values of 133.5, 105.5, and 14.9, respectively. For comparison, the positive control, acarbose, had an IC50 value of 138.2 µM.

Vietnam boasts a rich and diverse flora, with many endemic species. Among them, Ngoc Linh ginseng (Vietnamese ginseng; scientific name: Panax vietnamensis Ha et Grushv.), a high-value endemic ginseng species, has been recognized as a national treasure. While numerous studies have been conducted on its rhizomes and roots, research on its leaves remains limited. In this study, six compounds (1–6) were isolated from the methanol extract of the leaves of P. vietnamensis. Their structures were elucidated using ESI-MS, 1D and 2D NMR spectroscopic methods, and comparisons with known literature data. The identified compounds are: 12β,20(R),25-β trihydroxydammara-3-O-β-D-glucopyranoside (1); 12β,20(R),25-trihydroxydammara-3-O-β-D-glucopyranosyl- (1→2)-β-D-glucopyranoside (2); notoginsenoside SFt1 (3); ginsenoside Rh2 (4); ginsenoside Rg3 (5) and notoginsenoside L1 (6). Except for compound 3, which was isolated from the leaves for the first time, the other five compounds are reported from this species for the first time. The α-glucosidase inhibition assay of the pure isolated compounds revealed that compounds 1, 4, and 6 exhibited significant activities, with IC50 values of 133.5, 105.5, and 14.9, respectively. For comparison, the positive control, acarbose, had an IC50 value of 138.2 µM.

Background: Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promise for detecting and quantifying protein biomarkers. We investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin- fixed paraffin-embedded samples. Methods: We developed a supervised machine-learning model and tested its performance using proteomics data from 46 thyroid tissue samples. Results: We applied a random forest classifier utilizing five protein biomarkers (ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3). This classifier achieved areas under the curve (AUCs) of 1.00 and accuracy rates of 1.00 in training samples for distinguishing the invasive encapsulated follicular variant of papillary thyroid carcinoma from non-malignant samples. Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC >0.5. Conclusions We demonstrated that integration of high-throughput proteomics with machine learning can effectively differentiate the invasive encapsulated follicular variant of papillary thyroid carcinoma from other follicular pattern thyroid tumors.

Vietnam boasts a rich and diverse flora, with many endemic species. Among them, Ngoc Linh ginseng (Vietnamese ginseng; scientific name: Panax vietnamensis Ha et Grushv.), a high-value endemic ginseng species, has been recognized as a national treasure. While numerous studies have been conducted on its rhizomes and roots, research on its leaves remains limited. In this study, six compounds (1–6) were isolated from the methanol extract of the leaves of P. vietnamensis. Their structures were elucidated using ESI-MS, 1D and 2D NMR spectroscopic methods, and comparisons with known literature data. The identified compounds are: 12β,20(R),25-β trihydroxydammara-3-O-β-D-glucopyranoside (1); 12β,20(R),25-trihydroxydammara-3-O-β-D-glucopyranosyl- (1→2)-β-D-glucopyranoside (2); notoginsenoside SFt1 (3); ginsenoside Rh2 (4); ginsenoside Rg3 (5) and notoginsenoside L1 (6). Except for compound 3, which was isolated from the leaves for the first time, the other five compounds are reported from this species for the first time. The α-glucosidase inhibition assay of the pure isolated compounds revealed that compounds 1, 4, and 6 exhibited significant activities, with IC50 values of 133.5, 105.5, and 14.9, respectively. For comparison, the positive control, acarbose, had an IC50 value of 138.2 µM.

Background: Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promise for detecting and quantifying protein biomarkers. We investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin- fixed paraffin-embedded samples. Methods: We developed a supervised machine-learning model and tested its performance using proteomics data from 46 thyroid tissue samples. Results: We applied a random forest classifier utilizing five protein biomarkers (ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3). This classifier achieved areas under the curve (AUCs) of 1.00 and accuracy rates of 1.00 in training samples for distinguishing the invasive encapsulated follicular variant of papillary thyroid carcinoma from non-malignant samples. Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC >0.5. Conclusions We demonstrated that integration of high-throughput proteomics with machine learning can effectively differentiate the invasive encapsulated follicular variant of papillary thyroid carcinoma from other follicular pattern thyroid tumors.

Vietnam boasts a rich and diverse flora, with many endemic species. Among them, Ngoc Linh ginseng (Vietnamese ginseng; scientific name: Panax vietnamensis Ha et Grushv.), a high-value endemic ginseng species, has been recognized as a national treasure. While numerous studies have been conducted on its rhizomes and roots, research on its leaves remains limited. In this study, six compounds (1–6) were isolated from the methanol extract of the leaves of P. vietnamensis. Their structures were elucidated using ESI-MS, 1D and 2D NMR spectroscopic methods, and comparisons with known literature data. The identified compounds are: 12β,20(R),25-β trihydroxydammara-3-O-β-D-glucopyranoside (1); 12β,20(R),25-trihydroxydammara-3-O-β-D-glucopyranosyl- (1→2)-β-D-glucopyranoside (2); notoginsenoside SFt1 (3); ginsenoside Rh2 (4); ginsenoside Rg3 (5) and notoginsenoside L1 (6). Except for compound 3, which was isolated from the leaves for the first time, the other five compounds are reported from this species for the first time. The α-glucosidase inhibition assay of the pure isolated compounds revealed that compounds 1, 4, and 6 exhibited significant activities, with IC50 values of 133.5, 105.5, and 14.9, respectively. For comparison, the positive control, acarbose, had an IC50 value of 138.2 µM.

Background: Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promise for detecting and quantifying protein biomarkers. We investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin- fixed paraffin-embedded samples. Methods: We developed a supervised machine-learning model and tested its performance using proteomics data from 46 thyroid tissue samples. Results: We applied a random forest classifier utilizing five protein biomarkers (ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3). This classifier achieved areas under the curve (AUCs) of 1.00 and accuracy rates of 1.00 in training samples for distinguishing the invasive encapsulated follicular variant of papillary thyroid carcinoma from non-malignant samples. Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC >0.5. Conclusions We demonstrated that integration of high-throughput proteomics with machine learning can effectively differentiate the invasive encapsulated follicular variant of papillary thyroid carcinoma from other follicular pattern thyroid tumors.

Vietnam boasts a rich and diverse flora, with many endemic species. Among them, Ngoc Linh ginseng (Vietnamese ginseng; scientific name: Panax vietnamensis Ha et Grushv.), a high-value endemic ginseng species, has been recognized as a national treasure. While numerous studies have been conducted on its rhizomes and roots, research on its leaves remains limited. In this study, six compounds (1–6) were isolated from the methanol extract of the leaves of P. vietnamensis. Their structures were elucidated using ESI-MS, 1D and 2D NMR spectroscopic methods, and comparisons with known literature data. The identified compounds are: 12β,20(R),25-β trihydroxydammara-3-O-β-D-glucopyranoside (1); 12β,20(R),25-trihydroxydammara-3-O-β-D-glucopyranosyl- (1→2)-β-D-glucopyranoside (2); notoginsenoside SFt1 (3); ginsenoside Rh2 (4); ginsenoside Rg3 (5) and notoginsenoside L1 (6). Except for compound 3, which was isolated from the leaves for the first time, the other five compounds are reported from this species for the first time. The α-glucosidase inhibition assay of the pure isolated compounds revealed that compounds 1, 4, and 6 exhibited significant activities, with IC50 values of 133.5, 105.5, and 14.9, respectively. For comparison, the positive control, acarbose, had an IC50 value of 138.2 µM.