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 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.

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.

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.

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.

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.

To improve the stability of amino acid ester derivatives of DB02, a series of 24 amide derivatives of DB02 amino acids as non-nucleoside HIV-1 reverse transcriptase inhibitor were designed and synthesized based on bioisosterism by replacing amino acid ester scaffold with more stable amide bond. The anti-HIV-1 activity of these compounds was evaluated by MTT assay and counting the number of syncytia. Most of the target compounds showed a potential anti-HIV-1 activity, among which compounds 2d, 2i, 2l, 2s, and 2w had better antiviral effect than lead compound DB02, with a therapeutic index > 1 000.00. Finally, the structure-activity relationship of these compounds was discussed, which provided new ideas for the further development of DB02 derivatives.