A unified consensus statement on medication-related osteonecrosis of the jaw (MRONJ) has not yet been established among the Asian member countries or regions of the Asian Federation of Osteoporosis Societies (AFOS). This study aimed to develop a consensus on MRONJ in patients with osteoporosis across these countries and regions. In this study, the term “Asia-Pacific” refers specifically to the Asian member countries and regions of AFOS. A structured survey consisting of nine MRONJ-related questions was distributed across 10 countries and regions to assess the level of agreement and summarize regional perspectives. In addition, a manual literature review and voting were conducted to evaluate the current evidence on MRONJ. The key aspects of MRONJ, including definition, staging, diagnosis, pathogenesis, risk factors, management, and prevention, were generally consistent among the AFOS countries and regions. The annual incidence and incidence rate of MRONJ associated with low-dose antiresorptive therapy in patients with osteoporosis ranged from 0.025% to 0.136% and 21 to 283 cases per 100,000 person-years, respectively. However, evidence regarding the benefits of drug discontinuation before dental surgery, such as tooth extraction, remains insufficient. Large-scale, multinational studies across AFOS countries and regions are warranted to determine the incidence of MRONJ better and evaluate the impact of antiresorptive drug discontinuation before dental procedures. These findings may contribute to the devel opment of effective evidence-based strategies for preventing MRONJ in patients with osteoporosis.

Objective To investigate the association between the weight-adjusted waist index (WWI) and prostate cancer (PCa) in order to provide reference for the screening and diagnosis of PCa.Methods Data of 18732 men aged ≥20 years were obtained from the US National Health and Nutrition Examination Survey (NHANES) conducted between 1999 and 2018. The relationship between WWI and PCa was assessed using a multivariate logistic regression model.A stratified analysis was performed across four time periods:1999—2006, 2007—2010, 2011—2014, and 2015—2018.Restricted cubic splines (RCS) and subgroup analyses were employed to evaluate the potential nonlinear associations and interactions between WWI and PCa risk. The receiver operating characteristic (ROC) curve was utilized to compare the predictive performance of WWI with other obesity-related indicators, including waist-to-height ratio (WtHR), body mass index (BMI), waist circumference, and body weight.Results In the fully adjusted model, each 1-unit increase in WWI was associated with a 74% higher risk of PCa (OR=1.74, 95%CI:1.44-2.11, P＜0.001).Individuals in the highest quartile of WWI had nearly five times greater odds of PCa compared to those in the lowest quartile (OR=4.96, 95%CI:2.69-9.15, P＜0.001).The observed association remained consistent across all four survey cycles.RCS analysis revealed a nonlinear positive association between WWI and PCa risk. Threshold analysis indicated that when WWI ≥9.97, there was a significant positive correlation with PCa risk (OR=1.39, 95% CI:1.18-1.64, P＜0.001).Subgroup analyses confirmed the robustness of this association across different demographic and clinical subpopulations.Furthermore, WWI demonstrated superior discriminatory ability for predicting PCa compared to WtHR, BMI, waist circumference, and body weight (AUC=0.706, 0.608, 0.509, 0.594, 0.488, P＜0.001).Conclusion For the general male population in the US, WWI serves as an independent risk factor for PCa.This association has remained stable over 1999—2018, and WWI outperforms conventional obesity indicators in terms of predictive accuracy.Therefore, individuals with WWI ≥9.97 should be prioritized for prostate-specific antigen (PSA) screening, which may enhance current screening and diagnostic strategies.

The clinical burden of colorectal cancer (CRC) is high. Population-based screening and early detection are essential to improve the long-term clinical outcome. Nonetheless, a significant proportion of patients still present at an advanced stage, including with acute large bowel obstruction. Image-enhanced endoscopy and artificial intelligence can improve the detection and diagnosis of colonic adenomas and early cancer. Endoscopic resection is regarded as the preferred curative treatment option for colonic adenoma and T0 and T1 CRC limited to the superficial submucosa. Emergency colonic stenting as bridge to interval curative surgery is increasingly accepted as a first-line option when technically feasible. Minimally invasive resection techniques such as laparoscopic colectomy and robot-assisted colorectal surgery have also come of age. These techniques reduce post-treatment morbidity, shorten the recovery process and can be cost-effective while maintaining long-term oncological cure. These outcome measures are relevant to our patients; therefore, minimally invasive approaches to curative resection should be embraced.
Humans ; Colorectal Neoplasms/surgery* ; Minimally Invasive Surgical Procedures/methods* ; Laparoscopy/methods* ; Colectomy/methods* ; Robotic Surgical Procedures/methods* ; Treatment Outcome ; Colonoscopy/methods*

Environmental damage affects many aspects of healthcare, from extreme weather events to evolving population disease. Singapore's healthcare sector has the world's second highest healthcare emissions per capita, hampering the nation's pledge to reduce emissions by 2030 and achieve net zero emissions by 2050. In this review, we provide an overview of the impact environmental damage has on healthcare, including facilities, supply chain and human health, and examine measures to address healthcare's impact on the environment. Utilising the 'R's of sustainability - rethinking, reducing/refusing, reusing/repurposing/reprocessing, repairing, recycling and research - we have summarised the opportunities and challenges across medical disciplines. Awareness and advocacy to adopt strategies at institutional and individual levels is needed to revolutionise our environmental footprint and improve healthcare sustainability. By leveraging evidence from ongoing trials and integrating sustainable practices, our healthcare system can remain resilient against environment-driven challenges and evolving healthcare demands while minimising further impacts of environmental destruction.
Humans ; Climate Change ; Delivery of Health Care ; Singapore ; Conservation of Natural Resources ; Sustainable Development ; Environment

Comfortable supragingival scaling uses ultrasonic cleaning with sedation and minimally invasive techniques to minimize the negative emotions of patients and improve patient compliance. At present, there is still much room for optimization of the environment, equipment, and operation steps for the development of comfortable supragingival scaling. On this basis, the Department of Periodontology, College of Stomatology, Xi'an Jiaotong University proposed a 16-step new model of comfortable supragingival scaling. The new model incorporates adjustments to the previous model concerning the environment, equipment, operational procedures, process links and services, comprising 16 steps for optimization and innovation. Clinical practice has confirmed that the 16-step new model of comfortable supra-gingival scaling can significantly improve patient satisfaction and adherence to medical treatment, and it has good promotional value.
Humans ; Dental Scaling/instrumentation* ; Patient Satisfaction ; Patient Compliance ; Gingiva

We reported a case with huge mass in the anterior neck region, which pathological diagnosis was thyroid metastasis from hepatocellular carcinoma(HCC). A tracheotomy was performed, and a tracheostomy tube was placed because of postoperative dyspnea. Thyroid metastasis from HCC is very rare. The causes include hematogenous metastasis, lymphatic metastasis and other factors. Furthermore, Hepatitis B virus (HBV) infection also plays an important role in tumor progression. Combined with the literature review, surgical resection of thyroid in the treatment of metastatic thyroid malignant tumors can prolong the survival. In clinical work, it is still necessary to develop a reasonable, standardized and comprehensive diagnosis and treatment strategy to improve the quality of life of patients and the therapeutic efficacy.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.