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:To observe the expression changes of nuclear factor erythroid 2 related factor 2 (Nrf2) and glutathione peroxidase (GPX4) in human pulmonary microvascular endothelial cells (HPMEC) under different experimental conditions, and to explore the role of Nrf2 in inhibiting ferroptosis in the process of alleviating hyperoxic lung injury(HLI).Methods:Hyperoxic model was established by hyperoxia exposure.HPMEC were treated with blank control (control group), oxygen exposure at the concentration of 950 mL/L (hyperoxia group), oxygen exposure at the concentration of 950 mL/L+ 10 μmol/L Ferrostatin (ferroptosis inhibitor group) and oxygen exposure at the concentration of 950 mL/L + 10 μmol/L ML385 (Nrf2 inhibitor group). Cell viability at 24 h and 48 h was tested by the Cell Counting Kit-8 assay, and reactive oxygen species (ROS) levels were detected by a commercial ROS kit.The mRNA and protein levels of Nrf2 and GPX4 were detected by real-time quantitative polymerase chain reaction and Western blot, respectively.Differences were analyzed using the Student′s t-test for a two-group comparison or one-way ANOVA test among groups. Results:(1)Compared with the control group, significantly decreased viability and increased ROS levels were detected in hyperoxia group.Meanwhile, the mRNA (24 h: 0.750±0.010 vs.1.010±0.160, 48 h: 0.690±0.050 vs.1.000±0.070) and protein levels of GPX4 (24 h: 0.160±0.010 vs.0.290±0.010, 48 h: 0.190±0.010 vs.0.250±0.010) at 24 h and 48 h were significantly downregulated, while the mRNA (24 h: 1.740±0.050 vs.1.000±0.050, 48 h: 2.130±0.020 vs.1.000±0.030) and protein levels of Nrf2 (24 h: 0.840±0.010 vs.0.480±0.010, 48 h: 0.840±0.010 vs.0.550±0.030) at 24 h and 48 h were significantly upregulated in hyperoxia group than those of control group (all P<0.05). (2)Compared with the hyperoxia group, significantly increased viability and decreased ROS levels were detected in ferroptosis inhibitor group.Meanwhile, the mRNA (24 h: 1.520±0.110, 48 h: 1.880±0.050) and protein levels of GPX4 (24 h: 0.290±0.010, 48 h: 0.250±0.004) at 24 h and 48 h were significantly upregulated, while the mRNA (24 h: 0.780±0.040, 48 h: 0.760±0.030) and protein levels of Nrf2 (24 h: 0.480±0.010, 48 h: 0.540±0.020) at 24 h and 48 h were significantly downregulated in ferroptosis inhibitor group than those of hyperoxia group (all P<0.05). (3)Compared with the hyperoxia group, significantly decreased viability and increased ROS levels were detected in Nrf2 inhibitor group.Meanwhile, the mRNA (24 h: 0.600±0.030, 48 h: 0.590±0.003) and protein levels of GPX4 (24 h: 0.150±0.001, 48 h: 0.180±0.001) at 24 h and 48 h were significantly downregulated, while the mRNA level of Nrf2 was significantly upregulated at 24 h (3.360±0.130), but downregulated at 48 h (1.430±0.130) (all P<0.05). No significant difference was detected in the protein level of Nrf2 at 24 h and 48 h between hyperoxia group and Nrf2 inhibitor group ( P>0.05). Conclusions:Ferroptosis is involved in the development of HLI, and Nrf2 is able to alleviate hyperoxic lung injury by inhibiting ferroptosis.Therefore, inhibition of ferroptosis by Nrf2 may provide a new therapeutic target for HLI.

To design a treatment plan for patients with epididymal obstruction, we explored the potential impact of factors such as body mass index (BMI) and age on the surgical outcomes of vasoepididymostomy (VE). In this retrospective study, 181 patients diagnosed with obstructive azoospermia (OA) due to epididymal obstruction between September 2014 and September 2017 were reviewed. All patients underwent single-armed microsurgical intussusception VEs with longitudinal two-suture placement performed by a single surgeon (KH) in a single hospital (Peking University Third Hospital, Beijing, China). Six factors that could possibly influence the patency rates were analyzed, including BMI, age, mode of anastomosis, site of anastomosis, and sperm motility and quantity in the intraoperative epididymal fluid. Single-factor outcome analysis was performed via Chi-square test and multivariable analysis was performed using logistic regression. A total of 159 (87.8%, 159/181) patients were followed up. The follow-up time (mean ± standard deviation [s.d.]) was 27.7 ± 9.3 months, ranging from 12 months to 48 months. The overall patency rate was 73.0% (116/159). The multivariable analysis revealed that BMI and age significantly influenced the patency rate (P = 0.008 and 0.028, respectively). Younger age (≤28 years; odds ratio [OR] = 3.531, 95% confidence interval [95% CI]: 1.397-8.924) and lower BMI score (<26.0 kg m^-2; OR = 2.352, 95% CI: 1.095-5.054) appeared to be associated with a higher patency rate. BMI and age were independent factors affecting the outcomes of microsurgical VEs depending on surgical expertise and the use of advanced technology.
Humans ; Male ; Adult ; Retrospective Studies ; Body Mass Index ; Epididymis/surgery* ; Vas Deferens/surgery* ; Treatment Outcome ; Sperm Motility ; Microsurgery ; Surgeons ; Vasovasostomy