INTRODUCTION: Obstructive sleep apnoea (OSA) is common in Singapore, with moderate to severe OSA affecting around 30% of residents. These consensus statements aim to provide scientifically grounded recommendations for the management of OSA, standar-dise the management of OSA in Singapore and promote multidisciplinary collaboration. METHOD: An expert panel, which was convened in 2024, identified several areas of OSA management that require guidance. The expert panel reviewed the current literature and developed consensus statements, which were later independently voted on using a 3-point Likert scale (agree, neutral or disagree). Consensus (total ratings of agree and neutral) was set a priori at ≥80% agreement. Any statement not reaching consensus was excluded. RESULTS: The final consensus included 49 statements that provide guidance on the screening, diagnosis and management of adults with OSA. Additionally, 23 statements on the screening, diagnosis and management of paediatric OSA achieved consensus. These 72 consensus statements considered not only the latest clinical evidence but also the benefits and harms, resource implications, feasibility, acceptability and equity impact of the recommendations. CONCLUSION The statements presented in this paper aim to guide clinicians based on the most updated evidence and collective expert opinion from sleep specialists in Singapore. These recommendations should augment clinical judgement rather than replace it. Management decisions should be individualised, taking into account the patient's clinical characteristics, as well as patient and caregiver concerns and preferences.
Humans ; Sleep Apnea, Obstructive/diagnosis* ; Singapore ; Consensus ; Adult

T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

OBJECTIVE: Jiedu Recipe (JR), a Chinese herbal remedy, has been shown to prolong overall survival time and decrease recurrence and metastasis rates in patients with hepatocellular carcinoma (HCC). This work investigated the mechanism of JR in HCC treatment. METHODS: The chemical constituents of JR were detected using liquid chromatography-mass spectrometry. The potential anti-HCC mechanism of JR was screened using network pharmacology and messenger ribonucleic acid (mRNA) microarray chip assay, followed by experimental validation in human HCC cells (SMMC-7721 and Huh7) in vitro and a nude mouse subcutaneous transplantation model of HCC in vivo. HCC cell characteristics of proliferation, migration and invasion under hypoxic setting were investigated using thiazolyl blue tetrazolium bromide, wound healing and Transwell assays, respectively. Image-iT™ Hypoxia Reagent was added to reveal hypoxic conditions. Stem cell sphere formation assay was used to detect the stemness. Epithelial-mesenchymal transition (EMT) markers like E-cadherin, vimentin and α-smooth muscle actin, and pluripotent transcription factors including nanog homeobox, octamer-binding transcription factor 4, and sex-determining region Y box protein 2 were analyzed using Western blotting and real-time polymerase chain reaction. Western blot was performed to ascertain the anti-HCC effect of JR under hypoxia involving the Wnt/β-catenin pathway. RESULTS: According to network pharmacology and mRNA microarray chip analysis, JR may potentially act on hypoxia and inhibit the Wnt/β-catenin pathway. In vitro and in vivo experiments showed that JR significantly decreased hypoxia, and suppressed HCC cell features of proliferation, migration and invasion; furthermore, the hypoxia-induced increases in EMT and stemness marker expression in HCC cells were inhibited by JR. Results based on the co-administration of JR and an agonist (LiCl) or inhibitor (IWR-1-endo) verified that JR suppressed HCC cancer stem-like properties under hypoxia by blocking the Wnt/β-catenin pathway. CONCLUSION JR exerts potent anti-HCC effects by inhibiting cancer stemness via abating the Wnt/β-catenin pathway under hypoxic conditions. Please cite this article as: Guo BJ, Ruan Y, Wang YJ, Xiao CL, Zhong ZP, Cheng BB, Du J, Li B, Gu W, Yin ZF. Jiedu Recipe, a compound Chinese herbal medicine, inhibits cancer stemness in hepatocellular carcinoma via Wnt/β-catenin pathway under hypoxia. J Integr Med. 2023; 21(5): 474-486.
Animals ; Mice ; Humans ; Carcinoma, Hepatocellular/genetics* ; beta Catenin/pharmacology* ; Liver Neoplasms/genetics* ; Drugs, Chinese Herbal/therapeutic use* ; RNA, Messenger/therapeutic use* ; Cell Line, Tumor ; Cell Proliferation ; Cell Movement ; Gene Expression Regulation, Neoplastic

Objectives: To investigate the current application status and implementation difficulties of extracorporeal cardiopulmonary resuscitation (ECPR) in children with sudden cardiac arrest. Methods: This cross-sectional survey was conducted in 35 hospitals. A Children's ECPR Information Questionnaire on the implementation status of ECPR technology (abbreviated as the questionnaire) was designed, to collect the data of 385 children treated with ECPR in the 35 hospitals. The survey extracted the information about development of ECPR, the maintenance of extracorporeal membrane oxygenation (ECMO) machine, the indication of ECPR, and the difficulties of implementation in China. These ECPR patients were grouped based on their age, the hospital location and level, to compare the survival rates after weaning and discharge. The statistical analysis used Chi-square test and one-way analysis of variance for the comparison between the groups, LSD method for post hoc testing, and Bonferroni method for pairwise comparison. Results: Of the 385 ECPR cases, 224 were males and 161 females. There were 185 (48.1%) survival cases after weaning and 157 (40.8%) after discharge. There were 324 children (84.2%) receiving ECPR for cardiac disease and 27 children (7.0%) for respiratory failure. The primary cause of death in ECPR patients was circulatory failure (82 cases, 35.9%), followed by brain failure (80 cases, 35.0%). The most common place of ECPR was intensive care unit (ICU) (278 cases, 72.2%); ECPR catheters were mostly inserted through incision (327 cases, 84.9%). There were 32 hospitals (91.4%) had established ECMO emergency teams, holding 125 ECMO machines in total. ECMO machines mainly located in ICU (89 pieces, 71.2%), and the majority of hospitals (32 units, 91.4%) did not have pre-charged loops. There were no statistically significant differences in the post-withdrawal and post-discharge survival rates of ECPR patients among different age groups, regions, and hospitals (all P>0.05). The top 5 difficulties in implementing ECPR in non-ICU environments were lack of ECMO machines (16 times), difficulty in placing CPR pipes (15 times), long time intervals between CPR and ECMO transfer (13 times), lack of conventional backup ECMO loops (10 times), and inability of ECMO emergency teams to quickly arrive at the site (5 times). Conclusion: ECPR has been gradually developed in the field of pediatric critical care in China, and needs to be further standardized. ECPR in non-ICU environment remains a challenge.
Child ; Female ; Humans ; Male ; Aftercare ; Cardiopulmonary Resuscitation/methods* ; Cross-Sectional Studies ; Death, Sudden, Cardiac/prevention & control* ; East Asian People ; Heart Arrest/therapy* ; Patient Discharge ; Retrospective Studies ; Surveys and Questionnaires