OBJECTIVE: To investigate possible associations between physical function assessment scales, such as Short Physical Performance Battery (SPPB) and Berg Balance Scale (BBS), with all-cause mortality in acute decompensated heart failure (ADHF) patients. METHODS: A total of 108 ADHF patients were analyzed from October 2020 to October 2022, and followed up to May 2023. The association between baseline clinical characteristics and all-cause mortality was analyzed by univariate Cox regression analysis, while for SPPB and BBS, univariate Cox regression analysis was followed by receiver operating characteristic curves, in which the area under the curve represented their predictive accuracy for all-cause mortality. Incremental predictive values for both physical function assessments were measured by calculating net reclassification index and integrated discrimination improvement scores. Optimal cut-off value for BBS was then identified using restricted cubic spline plots, and survival differences below and above that cut-off were compared using Kaplan-Meier survival curves and the log-rank test. The clinical utility of BBS was measured using decision curve analysis. RESULTS: For baseline characteristics, age, female, blood urea nitrogen, as well as statins, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, or angiotensin receptor-neprilysin inhibitors, were predictive for all-cause mortality for ADHF patients. With respect to SPPB and BBS, higher scores were associated with lower all-cause mortality rates for both assessments; similar area under the curves were measured for both (0.774 for SPPB and 0.776 for BBS). Furthermore, BBS ≤ 36.5 was associated with significantly higher mortality, which was still applicable even adjusting for confounding factors; BBS was also found to have great clinical utility under decision curve analysis. CONCLUSIONS BBS or SPPB could be used as tools to assess physical function in ageing ADHF patients, as well as prognosticate on all-cause mortality. Moreover, prioritizing the improvement of balance capabilities of ADHF patients in cardiac rehabilitation regimens could aid in lowering mortality risk.

OBJECTIVE: Circadian rhythm disruption (CRD) is a risk factor that correlates with poor prognosis across multiple tumor types, including hepatocellular carcinoma (HCC). However, its mechanism remains unclear. This study aimed to define HCC subtypes based on CRD and explore their individual heterogeneity. METHODS: To quantify CRD, the HCC CRD score (HCCcrds) was developed. Using machine learning algorithms, we identified CRD module genes and defined CRD-related HCC subtypes in The Cancer Genome Atlas liver HCC cohort (n = 369), and the robustness of this method was validated. Furthermore, we used bioinformatics tools to investigate the cellular heterogeneity across these CRD subtypes. RESULTS: We defined three distinct HCC subtypes that exhibit significant heterogeneity in prognosis. The CRD-related subtype with high HCCcrds was significantly correlated with worse prognosis, higher pathological grade, and advanced clinical stages, while the CRD-related subtype with low HCCcrds had better clinical outcomes. We also identified novel biomarkers for each subtype, such as nicotinamide n-methyltransferase and myristoylated alanine-rich protein kinase C substrate-like 1. CONCLUSION We classify the HCC patients into three distinct groups based on circadian rhythm and identify their specific biomarkers. Within these groups greater HCCcrds was associated with worse prognosis. This approach has the potential to improve prediction of an individual's prognosis, guide precision treatments, and assist clinical decision making for HCC patients. Please cite this article as: Li XJ, Chang L, Mi Y, Zhang G, Zhu SS, Zhang YX, et al. Integrated-omics analysis defines subtypes of hepatocellular carcinoma based on circadian rhythm. J Integr Med. 2025; 23(4): 445-456.
Humans ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; Circadian Rhythm/genetics* ; Prognosis ; Male ; Female ; Biomarkers, Tumor/genetics* ; Middle Aged ; Machine Learning ; Computational Biology

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.

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.

Objective To investigate the relationship between peripheral blood neutrophil-to-lymphocyte ratio(NLR)and lactate dehydrogenase(LDH)level with seizures and brain damage in children with febrile convulsions(FC).Methods A total of 134 children with FC in the hospital from April 2021 to August 2023 were selected as observation group.Another 134 children with respiratory tract infections but without FC from the same period in the hospital were selected as the control group.FC children were further classified in-to mild group(frequency＜2 times and duration＜10 min,79 cases)and severe group(frequency≥2 times or duration≥10 min,55 cases)according to the severity of seizures.NLR and LDH level between two groups were compared,Pearson correlation analysis was used to analyze the correlation between NLR,LDH level in FC children and seizures.FC children were re-divided into brain damage group(38 cases)and non-brain dam-age group(96 cases)according to the presence or absence of brain damage.Univariate and multivariate Logis-tic analysis were used to investigate the factors influencing the brain damage in children.Results NLR and LDH level were notably elevated in the observation group compared with the control group(P＜0.05),and the NLR and LDH level were higher in the severe group compared with the mild group(P＜0.05).NLR and LDH level in children with FC were positively correlated with the duration and frequency of seizures(P＜0.05).Compared with the non-brain damage group,the proportion of children who had seizures ≥2 times and lasted ≥10 min and NLR and LDH level in the brain damage group were higher(P＜0.05).Multivariate Lo-gistic regression analysis denoted that the frequency of seizures,duration of seizures,NLR and LDH level were independent risk factors affecting brain damage in children with FC(P＜0.05).Conclusion NLR and LDH level in peripheral blood of children with FC is positively correlated with the duration and frequency of sei-zures.Moreover,the frequency of seizures,duration of seizures,NLR and LDH level are all risk factors for brain damage in children with FC.

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.

Hypertension heightens the risk of cardiovascular and renal complications in individuals with type 2 diabetes mellitus. Optimal blood pressure (BP) management is crucial for preventing these complications. This review consolidates evidence from clinical trials and major BP management guidelines to shed light on key aspects of hypertension management in diabetes. It addresses BP thresholds to initiate antihypertensive treatment, optimal BP control targets, recommended first-line antihypertensive edications, and BP monitoring plan for the prevention of chronic complications in type 2 diabetes.

To improve the current situation of multiple preoperative visits and evaluations for elderly patients and other patients with complex conditions, in December 2022, Peking Union Medical College Hospital established preoperative multidisciplinary evaluation clinic (shorted as joint clinic). The joint clinic established a multidisciplinary team, clarified service targets, and developed standardized clinic workflows to provide patients with a " one-stop" preoperative assessment(physical fitness assessment, nutritional assessment, and frailty assessment, etc.), nutritional optimization intervention, and prerehabilitation education and guidance services. This practice strengthened preoperative risk management, improved preoperative assessment efficiency, and ensured the safety of patients during the perioperative period. As of September 2023, the joint clinic had received a total of 128 patients, of which 86 underwent surgery after preoperative evaluation and prehabilitation optimization. The obesity rate, smoking rate, and number of frailty cases of these patients had decreased from 13.96%, 11.63%, and 18 at the time of visit to 9.30%, 4.65%, and 14 on the day before surgery, respectively. They had recovered well after surgery. This practice had improved the preoperative status of patients and created conditions for high-risk patients to undergo surgery smoothly, so as to provide references for other hospitals to carry out multidisciplinary collaborative preoperative evaluation works.