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.

Methods: This study included 41 patients with single-level grades 1 or 2 LS from February 2023 to February 2024. Clinical outcomes were assessed using the Visual Analog Scale (VAS) for back and leg pain and the Oswestry Disability Index (ODI). Bridwell fusion grades were evaluated via lumbar spine computed tomography performed 6 months postoperatively. Results: Over a mean follow-up period of 10.6 months (range, 7–18 months), significant improvements were observed in VAS scores for low back pain (from 7.8±0.8 to 2.1±1.4) and leg pain (from 8.1±1.3 to 1.9±1.5) as well as ODI scores (from 50.4±15.4 to 14.8±10.5). The cohort consisted of patients with grades 1 (73.2%) and 2 LS (26.8%) at L4–L5 (58.6%), L5–S1 (34.1%), and L3–L4 (7.3%) levels. The mean operation time was 182.8±36.4 minutes, with a mean intraoperative blood loss of 190.5±81.3 mL and a mean hospital stay of 7.2±3.6 days. Successful fusion (Bridwell grades I/II) was achieved in 82.9% of the cases, with a 4.9% incidence of cage subsidence. Minor complications included durotomies in two patients (4.9%), whereas no major complications, such as nerve root injury, hardware-related issues, or postoperative infections, were reported. Conclusions The described BE-LIF technique, using HA bone grafts, which are an autologous bone from the preserved IAP, and a TLIF cage, is a viable, safe, and effective option for treating low-grade LS. This approach achieves favorable clinical outcomes and high fusion rates, which provides a cost-effective alternative to advanced surgical implants.

Methods: This study included 41 patients with single-level grades 1 or 2 LS from February 2023 to February 2024. Clinical outcomes were assessed using the Visual Analog Scale (VAS) for back and leg pain and the Oswestry Disability Index (ODI). Bridwell fusion grades were evaluated via lumbar spine computed tomography performed 6 months postoperatively. Results: Over a mean follow-up period of 10.6 months (range, 7–18 months), significant improvements were observed in VAS scores for low back pain (from 7.8±0.8 to 2.1±1.4) and leg pain (from 8.1±1.3 to 1.9±1.5) as well as ODI scores (from 50.4±15.4 to 14.8±10.5). The cohort consisted of patients with grades 1 (73.2%) and 2 LS (26.8%) at L4–L5 (58.6%), L5–S1 (34.1%), and L3–L4 (7.3%) levels. The mean operation time was 182.8±36.4 minutes, with a mean intraoperative blood loss of 190.5±81.3 mL and a mean hospital stay of 7.2±3.6 days. Successful fusion (Bridwell grades I/II) was achieved in 82.9% of the cases, with a 4.9% incidence of cage subsidence. Minor complications included durotomies in two patients (4.9%), whereas no major complications, such as nerve root injury, hardware-related issues, or postoperative infections, were reported. Conclusions The described BE-LIF technique, using HA bone grafts, which are an autologous bone from the preserved IAP, and a TLIF cage, is a viable, safe, and effective option for treating low-grade LS. This approach achieves favorable clinical outcomes and high fusion rates, which provides a cost-effective alternative to advanced surgical implants.

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.

Methods: This study included 41 patients with single-level grades 1 or 2 LS from February 2023 to February 2024. Clinical outcomes were assessed using the Visual Analog Scale (VAS) for back and leg pain and the Oswestry Disability Index (ODI). Bridwell fusion grades were evaluated via lumbar spine computed tomography performed 6 months postoperatively. Results: Over a mean follow-up period of 10.6 months (range, 7–18 months), significant improvements were observed in VAS scores for low back pain (from 7.8±0.8 to 2.1±1.4) and leg pain (from 8.1±1.3 to 1.9±1.5) as well as ODI scores (from 50.4±15.4 to 14.8±10.5). The cohort consisted of patients with grades 1 (73.2%) and 2 LS (26.8%) at L4–L5 (58.6%), L5–S1 (34.1%), and L3–L4 (7.3%) levels. The mean operation time was 182.8±36.4 minutes, with a mean intraoperative blood loss of 190.5±81.3 mL and a mean hospital stay of 7.2±3.6 days. Successful fusion (Bridwell grades I/II) was achieved in 82.9% of the cases, with a 4.9% incidence of cage subsidence. Minor complications included durotomies in two patients (4.9%), whereas no major complications, such as nerve root injury, hardware-related issues, or postoperative infections, were reported. Conclusions The described BE-LIF technique, using HA bone grafts, which are an autologous bone from the preserved IAP, and a TLIF cage, is a viable, safe, and effective option for treating low-grade LS. This approach achieves favorable clinical outcomes and high fusion rates, which provides a cost-effective alternative to advanced surgical implants.

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.

Objective: To investigate the cause of a foodborne outbreak that occurred in Dong Nai province, Viet Nam, in 2024, and implement control measures. Methods: An initial investigation was conducted to confirm the outbreak, which was followed by epidemiological and environmental investigations to find the plausible causative food item. Clinical specimens and food samples were tested to identify the pathogen. Results: A total of 547 symptomatic cases were recorded, of whom two were in severe condition requiring extracorporeal membrane oxygenation and ventilation, one of whom died. Among 99 interviewed cases, the mean incubation time was 9 hours (range 2–24 hours), with the main symptoms being fever, abdominal pain, diarrhoea and vomiting. All patients had eaten banh mi from a local bakery. Salmonella spp. were identified in food samples and clinical specimens. The bakery halted production, and the outbreak ended after 1 week. Discussion: All the patients were exposed to only one food in common, which facilitated the investigation process. This outbreak is a reminder to small retailers and take-away shops of the importance of food safety management in preventing similar future outbreaks. All food handlers must comply with food hygiene principles, especially in hot temperatures, which boosts bacterial growth.

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.

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.