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 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

Metal-organic frameworks (MOFs) are crystalline materials with a multidimensional porous network structure, formed through coordination bonds with metal ions as nodes and organic ligands as connecting bridges. Due to their excellent physicochemical properties, MOFs have extensive applications in the field of biomedicine, ranging from antibacterials, drug carriers, imaging to sensors. Nanoscale metal-organic frameworks (nMOFs), commonly utilized drug carriers, can gain enhanced safety, targeted delivery, and superior therapeutic effect through endocytosis. In this review, we comprehensively summarize the factors influencing the endocytosis of nMOFs, focusing on three key physicochemical properties, particle size, morphology and surface modification. Based on different illness models, the review succinctly summarizes the latest advancements in understanding the endocytosis pathways of nMOFs while critically reflecting on the inherent limitations of current research methods. Lastly, the review offers valuable insights into future research methodologies and objectives, aiming to lay the groundwork and provide meaningful guidance for the synthesis and development of nMOFs as promising versatile drug carriers.

Objective:To investigate effect of Mycobacterium tuberculosis BCG-infected macrophages on damage and repair of renal tubular epithelial cells during development of renal tuberculosis.Methods:A co-culture model of BCG-infected M0 macrophages(upper chamber)and HK-2 cells(lower chamber)was established by Transwell,and THP-1 human monocyte macrophages were induced by 100 ng/ml phorbol ester(PMA)24 h to become M0 macrophages,and BCG infection cell model was established.Total cell protein was collected at 12 h and 24 h of infection,respectively.Western blot was used to detect expressions of M1 macrophage marker CD86 and M2 macrophage marker CD206 protein.M1 macrophage polarization marker cytokines IL-6 and TNF-α and M2 macrophage polarization marker cytokine TGF-β expressions in cell culture supernatant were detected by ELISA;experiment was divided into HK-2 group,BCG+HK-2 group,BCG+M0+HK-2 group and M0+HK-2 group,CCK-8 was used to detect viability of HK-2 cells in each group,and Hoechst test was used to detect HK-2 cells apoptosis in each group.Epithelial cell marker E-cadhren and fibroblast markerα-SMA expressions in HK-2 cells of each group were detected by Western blot.Results:After BCG infection of M0 macrophages,M1 macrophage viability was higher than 24 h at 12 h(P<0.05),and M2 macrophage was higher than 12 h at 24 h(P<0.05).After two cells co-culture,HK-2 cell viability was higher than 12 h at 24 h(P<0.001),apoptosis level was higher than 24 h at 12 h,epithelial cell marker protein E-cadherin protein level was higher than 12 h at 24 h(P<0.001),fibroblast level of cell marker protein α-SMA protein at 12 h was higher than that at 24 h(P<0.01).Conclusion:During development of renal tuberculosis,early BCG-infected macrophages may promote inflammatory injury of renal tubular epithelial cells through M1-type polarization;with prolongation of infec-tion time,they may repair renal tubular epithelial cells through M2-type polarization and plays an important protective role.

Atrial fibrillation(AF)is the most prevalent sustained cardiac arrhythmia among humans,with its incidence increasing significantly with age.Despite the high frequency of AF in clinical practice,its etiology and management remain elusive.To develop effective treatment strategies,it is imperative to comprehend the underlying mechanisms of AF;therefore,the establishment of animal models of AF is vital to explore its pathogenesis.While spontaneous AF is rare in most animal species,several large animal models,particularly those of pigs,dogs,and horses,have proven as invaluable in recent years in advancing our knowledge of AF pathogenesis and developing novel therapeutic options.This review aims to provide a comprehensive discussion of various animal models of AF,with an emphasis on the unique features of each model and its utility in AF research and treatment.The data summarized in this review provide valuable insights into the mechanisms of AF and can be used to evaluate the efficacy and safety of novel therapeutic interventions.

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.