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.

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.

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.

Background: Anaplastic lymphoma kinase (ALK) mutations have been identified as a prominent cause of some familial and sporadic neuroblastoma (NB). ALK expression in NB and its relationship with clinical and histopathological features remains controversial. This study investigated ALK expression and its potential relations with these features in NB. Methods: Ninety cases of NB at the Department of Pathology, University of Medicine and Pharmacy at Ho Chi Minh City, Viet Nam from 01/01/2018 to 12/31/2021, were immunohistochemically stained with ALK (D5F3) antibody. The ALK expression and its relations with some clinical and histopathological features were investigated. Results: The rate of ALK expression in NB was 91.1%. High ALK expression (over 50% of tumor cells were positive with moderate-strong intensity) accounted for 65.6%, and low ALK expression accounted for 34.4%. All the MYCN-amplified NB patients had ALK immunohistochemistry positivity, most cases had high ALK protein expression. The undifferentiated subtype of NB had a lower ALK-positive rate than the poorly differentiated and differentiated subtype. The percentages of ALK positivity were significantly higher in more differentiated histological types of NB (p = .024). There was no relation between ALK expression and: age group, sex, primary tumor location, tumor stage, MYCN status, clinical risk, Mitotic-Karyorrhectic Index, prognostic group, necrosis, and calcification. Conclusions ALK was highly expressed in NB. ALK expression was not related to several clinical and histopathological features. More studies are needed to elucidate the association between ALK expression and ALK gene status and to investigate disease progression, especially the oncogenesis of ALK-positive NB.

Background: The identification of idiopathic inflammatory myopathies (IIMs) requires a comprehensive analysis involving clinical manifestations and histological findings. This study aims to provide insights into the histopathological and immunohistochemical aspects of IIMs. Methods: This retrospective case series involved 56 patients diagnosed with IIMs at the Department of Pathology, University of Medicine and Pharmacy at Ho Chi Minh City, from 2019 to 2023. The histology and immunohistochemical expression of HLA-ABC, HLA-DR, C5b-9, Mx1/2/3, and p62 were detected. Results: We examined six categories of inflammatory myopathy, including immunemediated necrotizing myopathy (58.9%), dermatomyositis (DM; 23.2%), overlap myositis (8.9%), antisynthetase syndrome (5.4%), inclusion body myositis (IBM; 1.8%), and polymyositis (1.8%). The average age of the patients was 49.7 ± 16.1 years, with a female-to-male ratio of 3:1. Inflammatory cell infiltration in the endomysium was present in 62.5% of cases, perifascicular atrophy was found in 17.8%, and fiber necrosis was observed in 42 cases (75.0%). Rimmed vacuoles were present in 100% of cases in the IBM group. Immunohistochemistry showed the following positivity rates: HLA-ABC (89.2%), HLA-DR (19.6%), C5b-9 (57.1%), and Mx1/2/3 (10.7%). Mx1/2/3 expression was high in DM cases. p62 vacuole deposits were noted in the IBM case. The combination of membrane attack complex and major histocompatibility complex I helped detect IIMs in 96% of cases. Conclusions The diagnosis of IIMs and their subtypes should be based on clinical features and histopathological characteristics. Immunohistochemistry plays a crucial role in the diagnosis and differentiation of these subgroups.

Objectives: The objective of this study was to characterize mental health issues among Vietnamese healthcare workers (HCWs) and to identify related factors. Methods: A cross-sectional study was conducted with 990 HCWs in 2021. Their mental health status was measured using the Depression, Anxiety, and Stress Scale. Results: In total, 49.9%, 52.3%, and 29.8% of respondents were found to have depression, anxiety, and stress, respectively. The multivariable linear regression model revealed that factors associated with increased anxiety scores included depression scores (β, 0.45; 95% confidence interval [CI], 0.39 to 0.51) and stress scores (β, 0.46; 95% CI, 0.41 to 0.52). Factors associated with increased depression scores included being frontline HCWs (β, 0.57; 95% CI, 0.10 to 1.10), stress scores (β, 0.50; 95% CI, 0.45 to 0.56), and anxiety scores (β, 0.41; 95% CI, 0.36 to 0.47), while working experience was associated with reduced depression scores (β, -0.08; 95% CI, -0.16 to -0.01). Factors associated with increased stress scores included working experience (β, 0.08; 95% CI, 0.00 to 0.16), personal protective equipment interference with daily activities (β, 0.55; 95% CI, 0.07 to 1.00), depression scores (β, 0.54; 95% CI, 0.48 to 0.59), and anxiety scores (β, 0.45; 95% CI, 0.39 to 0.50), while age was associated with reduced stress scores (β, -0.12; 95% CI, -0.20 to -0.05). Conclusions Specific interventions are necessary to enhance and promote the mental health of HCWs so they can successfully cope with the circumstances of the pandemic.

Background: Periostin (PN) concentration increases in the blood of patients after acute myocardial infarction (AMI) and affects the process of cardiac remodelling leading to myocardial fibrosis. This study aimed to evaluate the correlation between serum PN levels with cardiac function and short-term prognosis (after 3 months of AMI) in patients with non-ST-elevation AMI. Methods: Case-control study, 3-month follow-up. 35 patients with AMI and 37 healthy people were chosen as the control group. In the group of patients, serum PN was obtained from day 5 - 7 of the disease. The correlation between PN and TIMI, GRACE scores, body mass index (BMI), laboratory findings, and 3-month post-MI data including pro B-type natriuretic peptide (pro-BNP) and echocardiographic parameters. Results: Serum PN levels increased significantly when patients had AMI, negatively correlated with ejection fraction (EF) (r = - 0.462, p = 0.005), positively correlated with left ventricular end-diastolic diameter (LVDd) (r = 0.413, p = 0.014). Conclusions: AMI increases serum PN levels, and PN can be used to predict cardiac function 3 months after MI in patients with non-ST elevation AMI.