Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

OBJECTIVE: Identifying novel strategies to prevent particulate matter (PM)-induced lung injury is crucial for the reduction of the morbidity of chronic respiratory diseases. The combined intervention represented by herbal formulae for simultaneously targeting multiple pathological processes can provide a more beneficial effect than the single intervention. The aim of this paper is therefore to design a safe and effective medicinal and edible Chinese herbs (MECHs) formula against PM-induced lung injury. METHODS: PM-induced oxidative stress, inflammatory response and apoptosis A549 cell model were used to screen anti-oxidant, anti-inflammatory and anti-apoptotic MECHs, respectively. A network pharmacology method was utilized to rationally design a novel herbal formula. Ultra performance liquid chromatography-mass spectrometer was utilized to assess the quality control of MECHs formula. The excretion of magnetic iron oxide nanospheres of the MECHs formula was estimated in zebrafish. The MECH formula against PM-induced lung injury was investigated with mice experiments. RESULTS: Five selected herbs were rationally designed to form a new MECH formula, including Citri Exocarpium Rubrum (Juhong), Lablab Semen Album (Baibiandou), Atractylodis Macrocephalae Rhizoma (Baizhu), Mori Folium (Sangye) and Polygonati Odorati Rhizoma (Yuzhu). The formula effectively promoted the magnetic iron oxide nanospheres excretion in zebrafish. The mid/high dose formula significantly prevented PM-induced lung damage in mice by enhancing the activity of SOD and GSH-Px, reducing the MDA and ROS level and attenuating the upregulation of pro-inflammatory cytokine (IL-6, IL-8, IL-1β and TNF-α), down regulating the protein expression of NF-κB, STAT3 and Caspase-3. CONCLUSION Our findings suggest that the effective MECHs formula will become a novel strategy for preventing PM-induced lung injury and provide a paradigm for the development of functional foods using MECHs.

Humans ; Female ; Genes, erbB-2 ; Breast Neoplasms ; Gene Expression ; Adenocarcinoma

Objective: To investigate the clinicopathological characteristics of plurihormonal PIT1-lineage pituitary neuroendocrine tumors. Methods: Forty-eight plurihormonal PIT1-lineage tumors were collected between January 2018 and April 2022 from the pathological database of Sanbo Brain Hospital, Capital Medical University. The related clinical and imaging data were retrieved. H&E, immunohistochemical and special stains were performed. Results: Out of the 48 plurihormonal PIT1-lineage tumors included, 13 cases were mature PIT1-lineage tumors and 35 cases were immature PIT1-lineage tumors. There were some obvious clinicopathological differences between the two groups. Clinically, the mature plurihormonal PIT1-lineage tumor mostly had endocrine symptoms due to increased hormone production, while a small number of immature PIT1-lineage tumors had endocrine symptoms accompanied by low-level increased serum pituitary hormone; patients with the immature PIT1-lineage tumors were younger than the mature PIT1-lineage tumors; the immature PIT1-lineage tumors were larger in size and more likely invasive in imaging. Histopathologically, the mature PIT1-lineage tumors were composed of large eosinophilic cells with high proportion of growth hormone expression, while the immature PIT1-lineage tumors consisted of chromophobe cells with a relatively higher expression of prolactin; the mature PIT1-lineage tumors had consistently diffuse cytoplasmic positive staining for keratin, while the immature PIT1-lineage tumors had various expression for keratin; the immature PIT1-lineage tumors showed more mitotic figures and higher Ki-67 proliferation index; in addition, 25.0% (12/48) of PIT1-positive plurihormonal tumors showed abnormal positive staining for gonadotropin hormones. There was no significant difference in the progression-free survival between the two groups (P=0.648) by Kaplan-Meier analysis. Conclusions: Plurihormonal PIT1-lineage tumor belongs to a rare type of PIT1-lineage pituitary neuroendocrine tumors, most of which are of immature lineage. Clinically increased symptoms owing to pituitary hormone secretion, histopathologically increased number of eosinophilic tumor cells with high proportion of growth hormone expression, diffusely cytoplasmic keratin staining and low proliferative activity can help differentiate the mature plurihormonal PIT1-lineage tumors from the immature PIT1-lineage tumors. The immature PIT1-lineage tumors have more complicated clinicopathological characteristics.
Humans ; Neuroendocrine Tumors ; Pituitary Neoplasms/pathology* ; Pituitary Hormones ; Growth Hormone/metabolism* ; Keratins

Humans ; Anaplastic Lymphoma Kinase ; Histiocytosis, Langerhans-Cell ; Receptor Protein-Tyrosine Kinases ; Lymphoma, Large-Cell, Anaplastic/pathology*

Child ; Humans ; Singapore ; Health Personnel ; Delivery of Health Care

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

To evaluate the inhibitory effects of drugs on the growth of Babesia gibsoni, relative quantification real-time PCR method was developed in this study. The 18S rRNA gene was used as a target gene for the 2–ΔΔCt method analysis. Additionally, chicken RNA was added to the parasitized blood before total RNA extraction. The chicken β-actin gene was selected as an internal control gene for the 2–ΔΔCt method analysis. The 100 µL parasitized blood samples with different percentages of parasitized erythrocytes (PPEs) (3%, 1.5%, 0.75%, 0.375% and 0.1875%) were prepared for relative quantification of B. gibsoni. Regression analysis results revealed significant linear relationships between the relative quantification value and parasitemia. 18S rRNA gene expression was significantly decreased after treatment with diminazene aceturate and artesunate in vitro drug sensitivity test. This result suggested that this relative quantification real-time PCR method can be used to evaluate the effects of drug inhibition.

INTRODUCTION: In this study, we described paediatric sports injuries seen in the paediatric emergency department of a large, tertiary paediatric hospital in Singapore and evaluated risk factors for severe sports injuries. METHODS: This is a retrospective review of a paediatric trauma surveillance registry from February 2012 to October 2017, including patient demographics, type of sports, circumstances, type of injuries, and clinical management in the hospital. Patients 5 to 17 years old with a sports-related injury were included. We performed logistic regression to identify predictors of severe sports injuries (defined by Injury Severity Score of ≥9), injuries requiring hospitalisation, trauma team activation, resuscitation, or those that resulted in death. RESULTS: Among 10,951 patients analysed, the most common injuries sustained were fractures (4,819, 44.0%), sprains and contusions (3,334, 30.4%). For patients with severe injuries, the median length of hospital stay was 2 days (IQR 1-3 days), and time away from sports was 162 days (IQR 104-182 days). Predictors for severe injuries include transportation by emergency medical service (aOR 6.346, 95% CI 5.147-7.823), involvement in rugby (aOR 2.067, 95% CI 1.446-2.957), neurological injuries (aOR 4.585, 95% CI 2.393-4.365), dislocations (aOR 2.779, 95% CI 1.744-4.427), fractures (aOR 1.438, 95% CI 1.039-1.990), injuries to the head and neck (aOR 2.274, 95% CI 1.184-4.365), and injuries to the abdomen and pelvis (aOR 5.273, 95% CI 3.225-8.623). CONCLUSION Predictors for severe sports injuries identified may aid in risk stratification and resource allocation.