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.

Background: This research’s aims are to determine the prevalence of anxiety among students at Hanoi Medical University in the academic year 2020-2021 and some related factors. Methods: This study used a descriptive cross-sectional study on 1325 students at Hanoi Medical University, using DASS-21 scale with questionnaire of anxiety subscale and a self-designed questionnaire. Students were chosen to the study conveniently during the period of data collection. Results: The result of this study showed that the prevalence of anxiety among students was 42.3%. In this population, the moderate subgroup accounted for the biggest prevalence, at 19.1%. The figures for other subgroups, including mild, severe and extremely severe, were 8%, 6.6% and 8.6% respectively. Relevant factors to students’ anxiety were financial difficulties, online learning difficulties and a factor related to Covid 19, which was “afraid of coming home due to the chance of infecting family members with the disease”. Conclusion: Nearly half of students were likely to have anxiety disorders, requiring support measures. Students needed to practice physical exercises, manage schedules to improve their health. Besides, universities should provide financial and academic support to struggling students.

STRESS IN RELATIONSHIP WITH BURNOUT DUE TO COVID-19: THE MODERATING ROLE OF COPING STRATEGIES This study investigates the relationship between stress and burnout related to the COVID-19 pandemic, focusing on the moderating influence of positive and negative coping strategies. In a cross-sectional survey involving 3,664 Vietnamese citizens aged 18 and above, the research explores the dynamics of stress and burnout symptoms associated with the ongoing pandemic. The results reveal a negative impact of stress symptoms on COVID-19-related burnout among the population. Notably, both positive and negative coping strategies are identified as significant moderators in the interplay between stress and burnout. This suggests that individuals’ coping mechanisms influence the severity of stress and burnout experienced in dealing with COVID-19 challenges. These findings contribute to the foundational understanding of the stress-burnout relationship during the pandemic and provide essential insights for interventions. By recognizing the moderating role of coping strategies, this study lays the groundwork for strategies aimed at enhancing individuals’ coping abilities, ultimately reducing stress and burnout levels. These insights hold the potential to assist individuals in adapting more effectively to the challenges posed by the COVID-19 pandemic, fostering improved mental well-being in the face of ongoing uncertainties. ASEAN Journal of Psychiatry, Vol. 25 (3) March, 2024; 1-10.

Objectives: Vibrio parahaemolyticus is a major foodborne pathogen in aquatic animals and a threat to human health worldwide. This study investigated the prevalence, antimicrobial resistance, antimicrobial resistance genes (ARGs), and biofilm formation of V. parahaemolyticus strains isolated from fish mariculture environments in Cat Ba Island, Vietnam. Methods: In total, 150 rearing water samples were collected from 10 fish mariculture farms in winter and summer. A polymerase chain reaction assay was used to identify V. parahaemolyticus, its virulence factors, and ARGs. The antimicrobial resistance patterns and biofilm formation ability of V. parahaemolyticus strains were investigated using the disk diffusion test and a microtiter plate-based crystal violet method, respectively. Results: Thirty-seven V. parahaemolyticus isolates were recovered from 150 samples. The frequencies of the tdh and trh genes among V. parahaemolyticus isolates were 8.1% and 21.6%, respectively. More than 90% of isolates were susceptible to ceftazidime, cefotaxime, and chloramphenicol, but over 72% were resistant to ampicillin, tetracycline, and erythromycin. Furthermore, 67.57% of isolates exhibited multidrug resistance. The presence of ARGs related to gentamicin (aac(3)-IV), tetracycline (tetA) and ciprofloxacin (qnrA) in V. parahaemolyticus isolates was identified. Conversely, no ARGs related to ampicillin or erythromycin resistance were detected. Biofilm formation capacity was detected in significantly more multidrug-resistant isolates (64.9%) than non-multidrug-resistant isolates (18.9%). Conclusion Mariculture environments are a potential source of antibiotic-resistant V.parahaemolyticus and a hotspot for virulence genes and ARGs diffusing to aquatic environments. Thus, the prevention of antibiotic-resistant foodborne vibriosis in aquatic animals and humans requires continuous monitoring.

Objectives: The prevalence of posttraumatic stress disorder (PTSD) has increased, particularly among individuals who have recovered from coronavirus disease 2019 (COVID-19) infection. Health literacy is considered a “social vaccine” that helps people respond effectively to the pandemic. We aimed to investigate the association between long COVID-19 and PTSD, and to examine the modifying role of health literacy in this association. Methods: A cross-sectional study was conducted at 18 hospitals and health centers in Vietnamfrom December 2021 to October 2022. We recruited 4,463 individuals who had recovered from COVID-19 infection for at least 4 weeks. Participants provided information about their sociodemographics, clinical parameters, health-related behaviors, health literacy (usingthe 12-item short-form health literacy scale), long COVID-19 symptoms and PTSD (Impact Event Scale-Revised score of 33 or higher). Logistic regression models were used to examine associations and interactions. Results: Out of the study sample, 55.9% had long COVID-19 symptoms, and 49.6% had PTSD.Individuals with long COVID-19 symptoms had a higher likelihood of PTSD (odds ratio [OR], 1.86; 95% confidence interval [CI], 1.63–2.12; p < 0.001). Higher health literacy was associated with a lower likelihood of PTSD (OR, 0.98; 95% CI, 0.97–0.99; p = 0.001). Compared to those without long COVID-19 symptoms and the lowest health literacy score, those with long COVID-19 symptoms and a 1-point health literacy increment had a 3% lower likelihood of PTSD (OR, 0.97; 95% CI, 0.96–0.99; p = 0.001). Conclusion Health literacy was found to be a protective factor against PTSD and modified the negative impact of long COVID-19 symptoms on PTSD.

Objective: Academic stress is associated with mental health disorders, notably depression and anxiety among students. Mitigating stress can decrease the incidence of mental health disorders and improve student well-being. This study explored factors influencing academic stress among secondary school students in Vietnam.Materials and Methods: A three-year longitudinal study was conducted using a self-reported questionnaire with 611 students from four secondary schools in Hue City, Vietnam. Academic stress was evaluated using the Educational Stress Scale for Adolescents (ESSA). Family factors, including the number of siblings and parental educational levels; lifestyle factors, including physical activity and sleep; and academic factors, including grade point average and attending extra classes were evaluated. Linear regression models were used to analyze the associations between the ESSA scores at follow-up and family, lifestyle, and academic factors at baseline.Results: A total of 341 students completed both the baseline and follow-up surveys and answered the questions required for this analysis. The mean ESSA score of 341 students increased from 46.4 ± 7.6 (mean ± SD) to 53.5 ± 10.8, from 2018 to 2021. The multivariate model revealed that the number of siblings, higher father’s educational attainment level, female gender, lower academic scores, and attending extra classes were associated with overall academic stress. In contrast, no associations were observed between the variables of lifestyle, parental concentration, and parental acceptance and overall academic stress.Conclusion: The findings highlight the impact of family factors and study workload on academic stress, emphasizing the need for proper care from family and school to reduce or prevent student academic stress and provide them with a comfortable and healthy learning environment.

FACTORS ASSOCIATED WITH VIETNAMESE STUDENTS’ SELF-PERCEIVED MENTAL HEALTH PROBLEMS DURING THE COVID-19 LOCKDOWN: A CROSS-SECTIONAL STUDY Abstract The COVID-19 pandemic has stated the critical importance of addressing the mental health care needs of students. With the abrupt and extensive changes brought about by the pandemic, such as lockdowns and shifts to online learning, students worldwide faced significant psychological challenges. This context led to a comprehensive study focusing on Vietnamese students, aiming to understand their perceptions of mental health amidst these unprecedented times. The research specifically targeted the initial lockdown phase, seeking to describe the prevalence of mental health issues and identify the factors contributing to these problems among students in Vietnam. By examining these aspects, the study aimed to provide insights into the mental well-being of students during a major global crisis. A cross-sectional study was conducted among 1889 students in North and South Vietnam from 30 August, 2021 to 5 September, 2021. Socio-demographic information was collected via an online survey and the SF-12 Mental Composite Score (MCS) was used to assess self-perceived mental health problems. The study results show that the prevalence of selfperceived mental health problems, defined by an MCS<1st quartile, was 21.9% (95%CI: 20%-23.8%) among students during the COVID-19 lockdown. Regression models indicated that the following factors were significantly related to self-perceived mental health problems: Female sex, not living with family, financial difficulty, decreased health status, decreased academic performance and decreased social relationships during the COVID-19 lockdown. The study showed that many factors related to student’s self-perceived mental health problems during COVID-19, affecting a significant number of students. Suggestions for teachers and university administrators were also discussed. ASEAN Journal of Psychiatry, Vol. 25 (8) October, 2024;1-9.

Salmonella spp. are pathogens involved in most salmonellosis in rabbits. This study examined Salmonella disease in rabbits raised in Thua Thien Hue, Vietnam. Two hundred and 56 rectal swabs of rabbits were taken, and a carrier rate of 33.98% was found. In addition, all the isolated Salmonella spp. strains were 100% motile; positive for H2S, catalase, Voges Proskauer, coagulase, citrate, maltose, and dextrose; and negative for indole, methyl red, urease, oxidase, sucrose, and lactose. The Kirby-Bauer method showed that these Salmonella strains were susceptible to doxycycline (93.2%), tetracycline (84.1%), and levofloxacin (65.9%). On the other hand, they were highly resistant to streptomycin (95.5%), ampicillin (93.2%), colistin (40.9%), and gentamicin (34.1%). Furthermore, polymerase chain reaction used to screen for virulence and specific genes of Salmonella strains showed that all Salmonella strains isolated carried InvA, fimA, and Stn.