Achondroplasia (ACH) is a common inherited skeletal dysplasia (inherited dwarfism) that compromises quality of life across the lifespan. In 2021, vosoritide became the first approved precision therapy for ACH and is now available in more than 40 countries. Compared with prior symptomatic measures, vosoritide has demonstrated favorable efficacy and a reassuring safety profile. Nevertheless, existing international ACH guidelines largely emphasize complication management and symptomatic care, and there is no unified consensus on pharmacologic therapy. To address this gap, an international expert group developed the International Consensus Guidelines for the Implementation and Monitoring of Vosoritide Therapy in Patients with Achondroplasia providing systematic recommendations that span the continuum of care - from initial patient contact and pre-treatment assessment to medication counseling, injection training, and long-term outcome monitoring. These recommendations complement and refine current management and nursing protocols for individuals with ACH and offer practical guidance for clinicians across diverse regions. This article highlights key elements of the guideline to provide evidence-based support and clinical direction for healthcare professionals in China treating children with ACH using vosoritide.
Humans ; Achondroplasia/drug therapy* ; Consensus ; Practice Guidelines as Topic ; Child

Drug-induced sleep endoscopy (DISE) is used for directly visualizing sites of obstruction among patients with obstructive sleep apnea (OSA). Owing to the scarcity of data, there is still no consensus on the anesthetic regimen for conducting pediatric DISE.

This paper presents a 5-year-old patient who underwent DISE using an opioid-sparing regimen with dexmedetomidine and propofol infusion.

Simultaneous dexmedetomidine and propofol infusion is a promising opioid-sparing regimen for pediatric DISE.

Human ; Male ; Child Preschool: 2-5 Yrs Old ; Endoscopy ; Propofol ; Dexmedetomidine ; Sleep Apnea, Obstructive ; Anesthetics ; Apnea ; Consensus ; Paper ; Patients ; Pharmaceutical Preparations ; Research Report ; Sleep ; Sleep Apnea Syndromes ; World Health Organization

Background: A certain percentage of the vaccinated population initially did not want to get vaccinated but changed their minds (from 30% to 70%). By October 2022, World Bank reported that the Philippines had 77.8% COVID-19 vaccination rate. Knowing the factors that changed their decision can help improve the vaccination rate. Objective: This survey aimed to identify the factors that influence positive change in vaccination decisions. Methods: This survey was conducted in the Philippines among Filipinos aged 18-80 years old between March to April 2022. The dependent variable in the study was decision change, a binary variable coded as 1 for a vaccinated person who changed their decision from no to yes and 0 for an unvaccinated person who did not change their decision from yes to no. Results: Age (adjusted odds ratio [aOR] = 0.92, 95% CI = 0.89-0.96) and having a college degree (aOR=11.707, 95% CI=3.23-42.41) are related to changing decisions. Young and college degree holders are likely to change their decisions positively about getting vaccinated. Employer requirement also influences decision change because it affects a person's livelihood. High scores on vaccine confidence (aOR = 1.181, 95% CI = 1.12-1.25) and awareness (aOR = 1.318, 95% CI = 1.08-1.61) are associated with decision change. Conclusion Being young, educated, employed with a requirement to vaccinate, and having high vaccine awareness and confidence are strongly associated with a positive change in the decision to get vaccinated.
Vaccines ; Vaccination ; Philippines ; Awareness ; Covid-19

OBJECTIVE: To assess the short-term lag effects of climate and air pollution on hospital admissions for cardiovascular and respiratory diseases, and to develop deep learning-based models for daily hospital admission prediction. METHODS: A multi-city study was conducted in Tokyo's 23 wards, Osaka City, and Nagoya City. Random forest models were employed to assess the synergistic short-term lag effects (lag0, lag3, and lag7) of climate and air pollutants on hospitalization for five cardiovascular diseases (CVDs) and two respiratory diseases (RDs). Furthermore, we developed hybrid deep learning models that integrated an autoencoder (AE) with a Long Short-Term Memory network (AE+LSTM) to predict daily hospital admissions. RESULTS: On the day of exposure (lag0), air pollutants, particularly nitrogen oxides (NO x), exhibited the strongest influence on hospital admissions for CVD and RD, with pronounced effects observed for hypertension (I10-I15), ischemic heart disease (I20), arterial and capillary diseases (I70-I79), and lower respiratory infections (J20-J22 and J40-J47). At longer lags (lag3 and lag7), temperature and precipitation were more influential predictors. The AE+LSTM model outperformed the standard LSTM, improving the prediction accuracy by 32.4% for RD in Osaka and 20.94% for CVD in Nagoya. CONCLUSION Our findings reveal the dynamic, time-varying health risks associated with environmental exposure and demonstrate the utility of deep learnings in predicting short-term hospital admissions. This framework can inform early warning systems, enhance healthcare resource allocation, and support climate-adaptive public health strategies.
Humans ; Hospitalization/statistics & numerical data* ; Cardiovascular Diseases/epidemiology* ; Japan/epidemiology* ; Air Pollutants/analysis* ; Air Pollution/adverse effects* ; Cities/epidemiology* ; Climate ; Respiratory Tract Diseases/epidemiology* ; Deep Learning ; Male

Congenital orofacial cleft, the most common birth defect in the maxillofacial region, exhibits a wide range of prognosis depending on the severity of deformity and underlying etiology. Non-syndromic congenital orofacial clefts typically present with milder deformities and more favorable treatment outcomes, whereas syndromic congenital orofacial clefts often manifest with concomitant organ abnormalities, which pose greater challenges for treatment and result in poorer prognosis. This consensus provides an elaborate classification system for varying degrees of orofacial clefts along with corresponding diagnostic and therapeutic guidelines. Results serve as a crucial resource for families to navigate prenatal screening results or make informed decisions regarding treatment options while also contributing significantly to preventing serious birth defects within the development of population.
Humans ; Cleft Lip/diagnosis* ; Cleft Palate/diagnosis* ; Consensus ; Prenatal Diagnosis ; Female

OBJECTIVES: This study aimed to construct a risk prediction model for the occurrence of the demora-lization syndrome in patients with oral cancer and provide a scientific basis for the prevention of this syndrome in patients with oral cancer and the development of personalized care programs. METHODS: A total of 486 patients with oral cancer in West China Hospital of Stomatology of Sichuan University and Sun Yat-sen Memorial Hospital of Sun Yat-sen University from 2024 March to July were selected by convenience sampling. We integrated clinical data and evidence from previous studies to identify the key variables affecting the demoralization syndrome in patients with oral cancer. The 486 patients were divided into a training set and a validation set in an 8∶2 ratio. A clinical risk prediction model was established based on the individual data of 365 patients in the development cohort. Through least absolute shrinkage and selection operator (LASSO) regression, a moderate to severe risk prediction model of demoralization syndrome in oral cancer was constructed, and a clinical machine-learning nomogram was constructed. Bootstrap resampling was used for internal validation. The data of 121 patients in the validation cohort were externally validated. RESULTS: The incidence of the demoralization syndrome in patients with oral cancer was 405 cases (83.3%), of which 279 cases (57.4%) were mild, 176 cases (36.2%) were moderate, and 31 cases (6.4%) were severe. The core model, including patient education level, disease understanding, and MDASI-HN score, was used to predict the risk of outcome. Internal validation of the model yielded C statistic of 0.783 6 (95% CI: 0.78-0.87), beta of 0.843 4, and calibration intercept of -0.040 6. Through external validation, the validation set C statistic was 0.80 (95%CI: 0.71-0.87), beta was 0.80, and calibration intercept was -0.08. CONCLUSIONS Our risk prediction mo-del of the demoralization syndrome in patients with oral cancer performed robustly in validation cohorts of different nur-sing environments. The model has good correction and good discrimination and can be used as an evaluation and prediction item at admission.
Humans ; Mouth Neoplasms/complications* ; Male ; Female ; Nomograms ; Middle Aged ; Syndrome ; Aged ; Adult ; Risk Factors ; Risk Assessment ; Machine Learning

Dental treatments for children and adolescents have unique clinical characteristics that differ from dental care for adults in terms of children's physiology, psychology, and behavior. These differences impose specific requirements on the application of local anesthesia in pediatric dental procedures. This article presents expert consensus on the principles of local anesthesia techniques in pediatric dental therapies, including the use of common anesthetic drugs and dosage control, safety and efficacy evaluation, and prevention and management of complications. The aim is to improve the safety and quality of pediatric dental treatments and offer guidance for clinical application by dentists.
Humans ; Child ; Anesthesia, Local/methods* ; Consensus ; Anesthesia, Dental/methods* ; Adolescent ; Anesthetics, Local/administration & dosage* ; Dental Care for Children

Bone grafting is a primary method for treating bone defects. Among various graft materials, xenogeneic bone substitutes are widely used in clinical practice due to their abundant sources, convenient processing and storage, and avoidance of secondary surgeries. With the advancement of domestic production and the limitations of imported products, an increasing number of bone filling or grafting substitute materials isentering clinical trials. Relevant experts have drafted this consensus to enhance the management of medical device clinical trials, protect the rights of participants, and ensure the scientific and effective execution of trials. It summarizes clinical experience in aspects, such as design principles, participant inclusion/exclusion criteria, observation periods, efficacy evaluation metrics, safety assessment indicators, and quality control, to provide guidance for professionals in the field.
Humans ; Bone Substitutes/therapeutic use* ; Randomized Controlled Trials as Topic/methods* ; Consensus ; Bone Transplantation ; Research Design

Artificial intelligence (AI) is rapidly advancing in periodontology, bringing new opportunities to clinical diagnosis, risk assessment, personalized treatment planning, and remote patient care. Leveraging core technologies such as deep learning, machine learning, and natural language processing, AI significantly enhances the sensitivity of early periodontal disease detection and provides precise quantification of alveolar bone loss and soft tissue damage. AI facilitates multimodal data integration by synthesizing medical history, lifestyle factors, and imaging data, thereby offering enhanced accurate risk prediction and personalized therapeutic recommendations. By integrating remote monitoring with tailored health counseling, AI helps patients maintain adherence to self-care protocols, significantly improving their oral health-related quality of life and treatment satisfaction. Moreover, AI demonstrates considerable potential in periodontal research and education, particularly in large-scale data mining, virtual clinical case simulations, and natural language processing-assisted literature management. Nevertheless, challenges remain concerning model generalizability, data quality, ethical concerns, and interpretability. The advancement of multi-center big-data platforms is expected to foster a profound integration of AI and periodontology, propelling precision medicine and digital healthcare, enabling holistic management from prevention to long-term care, and enhancing diagnostic efficiency and patient health outcomes.
Humans ; Artificial Intelligence ; Periodontics/methods* ; Periodontal Diseases/therapy* ; Deep Learning ; Precision Medicine ; Quality of Life

Immediate implant-supported fixed restoration in edentulous jaws demonstrates a success rate comparable to that of conventional implant restoration. However, this approach still presents a certain degree of technique sensitivity. In the field of immediate implant-supported fixed restoration in dentistry, a repeatable and stable jaw relation is the prerequisite for the design and fabrication of prostheses. It also reduces chairside denture placement and occlusal adjustment time and lowers the risk of occlusion-related complications. For patients with terminal dentition, the precise transfer of jaw relation following full-arch implantation serves as the fundamental basis for implant-supported occlusal reconstruction and functional restoration. This process is also a key research focus and challenge in the area of implant-supported occlusal rehabilitation. This review summarizes the procedures and methods for determining and transferring jaw relation in immediate implant-supported fixed restoration. It aims to serve as a basis for clinical decision making in implant-supported fixed restorations for terminal dentition patients.
Humans ; Dental Prosthesis, Implant-Supported ; Clinical Decision-Making ; Immediate Dental Implant Loading ; Jaw, Edentulous/surgery*