Geriatric oral health care encounters significant challenges with the increase in the proportion of older individuals. Age-related changes in the dentition, muscles, and joints result in a decline in objective masticatory function, subjective restoration requirements, and acceptability among the elderly population, with individual variations influenced by systemic health. Considering functional requirements, the adaptability of stomatognathic and systemic health conditions, health economics and other factors, the authors believe that it should not be limited to the conventional "one-to-one" strategy for replacing missing teeth in geriatric prosthodontics. There is an urgent need for a precise and adaptable restoration strategy that is more suitable for older individuals. The proposal of a new concept of functional tooth loss updates the minimal restoration standards for elderly patients and establishes the theory of age-friendly functional restoration. Based on the restoration strategy of functional tooth loss, this paper proposes a new concept termed "age-friendly functional restoration of the stomatognathic system", which integrates treatment considerations including endodontics, periodontology, mucosa, muscles, temporomandibular joint, and systemic health. Efforts should be made in four areas as follows. Firstly, the "assessment of accessible function" should be enhanced by considering the interrelationship between stomatognathic and systemic health. Secondly, the "evaluation of appropriate function" is supposed to be optimised in view of subjective needs and objective evaluation of the stomatognathic system. Moreover, the "formulation of treatment plans" needs to be accomplished with the aid of assistive technologies, such as artificial intelligence, to accurately exert appropriate functional restoration. Lastly, the "management and maintenance of health" is likely to be strengthened through follow-ups, propaganda and education, and preventive healthcare, so as to improve quality of life and ultimately achieve healthy ageing among older individuals.
Humans ; Tooth Loss/therapy* ; Aged ; Stomatognathic System ; Oral Health ; Dental Care for Aged ; Dental Restoration, Permanent/methods*

Abstract In recent years, the prevalence of overweight and obesity among children and adolescents has risen rapidly, posing a serious threat to their physical and mental health. To provide scientific, systematic, and standardized weight management guidance for overweight and obese children and adolescents, the study focuses on the core concept of healthy lifestyle intervention, integrates multidisciplinary expert opinions and research findings,and proposes a comprehensive multidisciplinary intervention framework covering scientific exercise intervention, precise nutrition and diet, optimized sleep management, and standardized psychological support. It calls for the establishment of a multi agent collaborative management mechanism led by the government, implemented by families, fostered by schools, initiated by individuals, optimized by communities, reinforced by healthcare, and coordinated by multiple stakeholders. Emphasizing a child and adolescent centered approach, the consensus advocates for comprehensive, multi level, and personalized guidance strategies to promote the internalization and maintenance of a healthy lifestyle. It serves as a reference and provides recommendations for the effective prevention and control of overweight and obesity, and enhancing the health level of children and adolescents.

Objective:To investigate the clinical efficacy and safety of the triplet regimen of flumatinib, venetoclax (VEN) and azacitidine (AZA) for Philadelphia chromosome-positive (Ph +) mixed-phenotype acute leukemia (MPAL). Methods:The clinical data of 2 Ph + MPAL patients treated with triplet regimen of flumatinib, VEN and AZA who were admitted to the Institute of Hematology & Blood Diseases Hospital of Chinese Academy of Medical Sciences & Peking Union Medical College in February and March 2023 were retrospectively analyzed, and the relevant literature was reviewed. Results:Patient 1 was a 56-year-old female, and patient 2 was a 59-year-old male. Both patients were diagnosed with Ph + B cell/myeloid (B/My) MPAL. After the first course of induction chemotherapy with the triplet regimen, patient 1 achieved hematological complete remission (HCR), complete cytogenetic remission (CCyR) and major molecular response (MMR), and patient 2 achieved HCR and CCyR. During the entire treatment process, the adverse reactions of two patients were mainly fever and ≥ grade 3 hematological adverse reactions, which were relieved after the use of antibiotics and intermittent infusion of blood products. When the patient achieved HCR and received consolidation treatment with the same regimen, the adverse reactions were mild. Conclusions:The triplet regimen of flumatinib, VEN and AZA is safe and effective for the treatment of Ph + MPAL, and is a new induction therapy option for such patients.

Objective:To evaluate the accuracy of the oral healthcare information provided by different large language models (LLM) to explore their feasibility and limitations in the application of oral auxiliary, treatment and health consultation.Methods:This study designed eight items comprising 47 questions in total related to the diagnosis and treatment of oral diseases [to assess the performance of LLM as an artificial intelligence (AI) medical assistant], and five items comprising 35 questions in total about oral health consultations (to assess the performance of LLM as a simulated doctor). These questions were answered individually by the five LLM models (Erine Bot, HuatuoGPT, Tongyi Qianwen, iFlytek Spark, ChatGPT). Two attending physicians with more than 5 years of experience independently rated the responses using the 3C criteria (correct, clear, concise), and the consistency between the raters was assessed using the Spearman rank correlation coefficient, and the Kruskal-Wallis test and Dunn post hoc test were used to assess the statistical differences between the models. Additionally, this study used 600 questions from the 2023 dental licensing examination to evaluate the time taken to answer, scores, and accuracy of each model.Results:As an AI medical assistant, LLM can assist doctors in diagnosis and treatment decision-making, with an inter-evaluator Spearman coefficient of 0.505 ( P<0.01). As a simulated doctor, LLM can carry out patient popularization, with an inter-evaluator Spearman coefficient of 0.533 ( P<0.01). The 3C scores of each model as an AI medical assistant and a simulated doctor were respectively: 2.00 (1.00, 3.00) and 2.00 (2.00, 3.00) points of Erine Bot, 1.00 (1.00, 2.00) and 2.00 (1.00, 2.00) points of HuatuoGPT, 2.00 (1.00, 2.00) and 2.00 (1.00, 3.00) points of Tongyi Qianwen, 2.00 (1.00, 2.00) and 2.00 (1.75, 2.25) points of iFlytek Spark, 3.00 (2.00, 3.00) and 3.00 (2.00, 3.00) points of ChatGPT (full score of 4 points). The Kruskal-Wallis test results showed that, as an AI medical assistant or a simulated doctor, there were statistically differences in the 3C scores among the five large language models (all P<0.001). The average score of the 5 LLMs on the dental licensing examination was 370.2, with an accuracy rate of 61.7% (370.2/600) and a time consumption of 94.6 min. Specifically, Erine Bot took 115 min, scored 363 points with an accuracy rate of 60.5% (363/600), HuatuoGPT took 224 min and scored 305 points with an accuracy rate of 50.8% (305/600), Tongyi Qianwen took 43 min, scored 438 points with an accuracy rate of 73.0% (438/600), iFlytek Spark took 32 min, scored 364 points with an accuracy rate of 60.7% (364/600), and ChatGPT took 59 min, scored 381 points with an accuracy rate of 63.5% (381/600). Conclusions:Based on the evaluation of LLM′s dual roles as an AI medical assistant and a simulated doctor, ChatGPT performes the best, with basically correct, clear and concise answers, followed by Erine Bot, Tongyi Qianwen and iFlytek Spark, with HuatuoGPT lagging behind significantly. In the dental licensing examination, all the 4 LLM, except for HuatuoGPT, reach the passing level, and the time consumpution for answering is significantly reduced compared to the 8 h required for the exam regulations in all of the five models. LLM has the feasibility of application in oral auxiliary, treatment and health consultation, and it can help both doctors and patients obtain medical information quickly. Howere, their outputs carry a risk of errors (since the 3C scoring results do not reach the full marks), so prudent judgment should be exercised when using them.

OBJECTIVES: To evaluate the strategies and outcomes of assisted reproductive technology (ART) for fertility preservation and promotion in women with malignant tumors, and to analyze ART outcomes across different tumor types. METHODS: We conducted a retrospective analysis of female patients who underwent ART for fertility preservation or treatment at the Reproductive Center of the Women's Hospital, Zhejiang University School of Medicine, between January 1, 2018, and December 31, 2023. A total of 163 ART-aided pregnancy patients with malignant tumors were included in the case group, among which 6 patients underwent embryo cryopreservation for fertility preservation before radiotherapy or chemotherapy. Additionally, 11 unmarried women underwent oocyte cryopreservation due to borderline ovarian tumors, ovarian cancer, breast cancer, or hematological malignancies. The control group was selected from women without a history of malignant tumors who received ART treatment during the same period, using propensity score matching at a ratio of 1∶2, resulting in 326 cases. Data were collected through the reproductive medical record system and telephone follow-up (as of October 31, 2024). Baseline characteristics, controlled ovarian hyperstimulation parameters, laboratory indicators, and pregnancy outcomes were compared between case and control groups and among patients with different tumor types, and the tumor recurrence of the patients was followed up. RESULTS: Patients in the case group had significantly lower ovarian reserve (AMH, AFC) and a higher proportion of diminished ovarian reserve compared to the control group (all P<0.01). Regarding the ovulation induction protocol, the proportion of patients using the minimal stimulation protocol in the case group was significantly higher than that in the control group (29.45% vs. 12.88%, P<0.01), and the total dosage of gonadotropins used was lower (P<0.01). In terms of assisted reproductive outcomes, there were no statistically significant differences between the two groups in the number of retrieved oocytes, number of high-quality embryos, fertilization rate, cumulative pregnancy rate, cumulative live birth rate, or miscarriage rate (all P>0.05). However, the number of oocyte retrieval cycles and embryo transfer cycles required to achieve a live birth outcome in the case group were significantly higher than those in the control group (both P<0.05). Subgroup analysis showed that there were no significant differences in cumulative pregnancy rate and live birth rate among patients with different tumor types (thyroid cancer, reproductive system tumors, breast cancer, lung cancer). Nevertheless, lung cancer patients had the lowest ovarian reserve and required the most oocyte retrieval cycles due to their older age; breast cancer patients had a relatively lower fertilization rate partially because some of them were complicated with male factors. A follow-up of 154 tumor patients (with a follow-up rate of 88.5%) revealed that 6 patients (4.20%) had tumor recurrence, and 1 breast cancer patient died due to tumor recurrence. None of the 11 unmarried patients who had undergone oocyte cryopreservation had used the cryopreserved oocytes for assisted pregnancy yet, and 1 patient who had undergone fertility preservation died due to tumor recurrence. CONCLUSIONS Women of reproductive age with malignant tumors are at risk of diminished fertility. ART can effectively preserve and promote fertility, enabling favorable pregnancy and live birth outcomes. It is recommended to initiate a multidisciplinary assessment promptly prior to radiotherapy/chemotherapy and formulate an individualized ART regimen for fertility preservation or promotion, so as to achieve reproductive goals or safeguard future fertility potential.

Dry eye disease (DED) is a prevalent and intractable ocular disease induced by a variety of causes. Elevated sphingomyelin (SM) levels and pro-inflammatory cytokines were detected on the ocular surface of DED patients, particularly in the meibomian glands. Sphingomyelin synthase 2 (SMS2), one of the proteins involved in SM synthesis, would light a novel way of developing a DED therapy strategy. Herein, we report the design and optimization of a series of novel thiophene carboxamide derivatives to afford 14l with an improved highly potent inhibitory activity on SM synthesis (IC_{50, SMS2} = 28 nmol/L). Moreover, 14l exhibited a notable protective effect of anti-inflammation and anti-apoptosis on human corneal epithelial cells (HCEC) under TNF-α-hyperosmotic stress conditions in vitro, with an acceptable ocular specific distribution (corneas and meibomian glands) and pharmacokinetics (PK) profiles (t _{1/2, cornea} = 1.11 h; t _{1/2, meibomian glands} = 4.32 h) in rats. Furthermore, 14l alleviated the dry eye symptoms including corneal fluorescein staining scores and tear secretion in a dose-dependent manner in mice. Mechanically, 14l reduced the mRNA expression of Tnf-α, Il-1β and Mmp-9 in corneas, as well as the proportion of very long chain SM in meibomian glands. Our findings provide a new strategy for DED therapy based on selective SMS2 inhibitors.

In recent years,with the constant rise in the number of infertility patients,the rapid and accurate assessment of sperm quality has become a key challenge in reproductive medicine.Currently,traditional methods for sperm classification suffer from limitations in efficiency,subjectivity,and cost-effectiveness,and hence fail to meet clinical needs.With the introduction of artificial intelligence(AI),innovative approaches have been offered to address these issues.Herein,we systematically reviewed the latest progress in AI applications in the field of male infertility,focusing on the role of AI in the assessment of sperm concentration,motility,morphology,DNA fragmentation index,and the treatment of non-obstructive azoospermia.It has been reported that AI technologies,such as convolutional neural networks,demonstrate high accuracy and efficiency in assessing sperm concentration and motility.Particularly in morphological analysis,the performance of AI has been validated in multiple studies,significantly enhancing objectivity and clinical utility.However,the assessment of DNA fragmentation index remains underexplored due to the lack of support in advanced imaging technology,with only a few models showing promise.Additionally,AI significantly improves sperm detection rates in modified testicular sperm extraction through AI-driven image recognition,offering a breakthrough in the treatment of patients with non-obstructive azoospermia.We also discussed the application of natural language processing technology in patient pre-consultation and follow-up,such as automated data collection and intelligent tracking systems,which demonstrate AI's potential to optimize medical workflows.In the future,with the accumulation of high-quality datasets,algorithm optimization,and advances in imaging technology,the application of artificial intelligence is expected to enable multi-dimensional comprehensive screening and play a greater role in the diagnosis and treatment of male infertility.

Objective:To investigate the efficacy and safety of oral maintenance therapy with tegafur for residual lesions in patients with locally advanced esophageal squamous cell carcinoma (ESCC) after chemoradiotherapy.Methods:A retrospective cohort study was conducted. A total of 38 patients with locally advanced ESCC who had residual lesions after receiving albumin bound paclitaxel combined with platinum chemoradiotherapy from March 2019 to September 2021 in Jiangsu Province Suqian Hospital were selected. All patients were divided into the maintenance treatment group (20 cases) and the non-maintenance treatment group (18 cases) based on whether they received oral maintenance therapy with tegafur after chemoradiotherapy. The progression-free survival (PFS) of both groups was compared and the adverse reactions of the maintenance treatment group were analyzed.Results:There were no statistically significant differences in baseline data between the 2 groups (both P > 0.05). The 1, 2, 3-year PFS rates for the maintenance treatment group were 95.0%, 78.1%, and 58.3%, respectively, with a median PFS time of 33.65 months (95% CI: 29.04-38.26 months); the 1, 2, 3-year PFS rates in the non-maintenance treatment group were 88.9%, 54.5%, and 12.1%, respectively, with a median PFS time of 25.08 months (95% CI: 20.97-29.18 months); there was a statistically significant difference in PFS between the 2 groups ( χ2 = 5.36, P = 0.021). The common adverse reactions in the maintenance treatment group included hematological adverse reactions, hand foot syndrome, decreased appetite, and fatigue. The more common adverse reactions were neutropenia [85.0% (17/20)] and leukopenia [65.0% (13/20)]; 9 cases experienced grade 3-4 adverse reactions, which were relieved by adjusting the dosage or discontinuing the medication. Conclusions:Oral maintenance therapy with tegafur may improve the survival of ESCC patients with residual lesions after chemoradiotherapy and the adverse reactions are controllable.

Objective:To evaluate the accuracy of the oral healthcare information provided by different large language models (LLM) to explore their feasibility and limitations in the application of oral auxiliary, treatment and health consultation.Methods:This study designed eight items comprising 47 questions in total related to the diagnosis and treatment of oral diseases [to assess the performance of LLM as an artificial intelligence (AI) medical assistant], and five items comprising 35 questions in total about oral health consultations (to assess the performance of LLM as a simulated doctor). These questions were answered individually by the five LLM models (Erine Bot, HuatuoGPT, Tongyi Qianwen, iFlytek Spark, ChatGPT). Two attending physicians with more than 5 years of experience independently rated the responses using the 3C criteria (correct, clear, concise), and the consistency between the raters was assessed using the Spearman rank correlation coefficient, and the Kruskal-Wallis test and Dunn post hoc test were used to assess the statistical differences between the models. Additionally, this study used 600 questions from the 2023 dental licensing examination to evaluate the time taken to answer, scores, and accuracy of each model.Results:As an AI medical assistant, LLM can assist doctors in diagnosis and treatment decision-making, with an inter-evaluator Spearman coefficient of 0.505 ( P<0.01). As a simulated doctor, LLM can carry out patient popularization, with an inter-evaluator Spearman coefficient of 0.533 ( P<0.01). The 3C scores of each model as an AI medical assistant and a simulated doctor were respectively: 2.00 (1.00, 3.00) and 2.00 (2.00, 3.00) points of Erine Bot, 1.00 (1.00, 2.00) and 2.00 (1.00, 2.00) points of HuatuoGPT, 2.00 (1.00, 2.00) and 2.00 (1.00, 3.00) points of Tongyi Qianwen, 2.00 (1.00, 2.00) and 2.00 (1.75, 2.25) points of iFlytek Spark, 3.00 (2.00, 3.00) and 3.00 (2.00, 3.00) points of ChatGPT (full score of 4 points). The Kruskal-Wallis test results showed that, as an AI medical assistant or a simulated doctor, there were statistically differences in the 3C scores among the five large language models (all P<0.001). The average score of the 5 LLMs on the dental licensing examination was 370.2, with an accuracy rate of 61.7% (370.2/600) and a time consumption of 94.6 min. Specifically, Erine Bot took 115 min, scored 363 points with an accuracy rate of 60.5% (363/600), HuatuoGPT took 224 min and scored 305 points with an accuracy rate of 50.8% (305/600), Tongyi Qianwen took 43 min, scored 438 points with an accuracy rate of 73.0% (438/600), iFlytek Spark took 32 min, scored 364 points with an accuracy rate of 60.7% (364/600), and ChatGPT took 59 min, scored 381 points with an accuracy rate of 63.5% (381/600). Conclusions:Based on the evaluation of LLM′s dual roles as an AI medical assistant and a simulated doctor, ChatGPT performes the best, with basically correct, clear and concise answers, followed by Erine Bot, Tongyi Qianwen and iFlytek Spark, with HuatuoGPT lagging behind significantly. In the dental licensing examination, all the 4 LLM, except for HuatuoGPT, reach the passing level, and the time consumpution for answering is significantly reduced compared to the 8 h required for the exam regulations in all of the five models. LLM has the feasibility of application in oral auxiliary, treatment and health consultation, and it can help both doctors and patients obtain medical information quickly. Howere, their outputs carry a risk of errors (since the 3C scoring results do not reach the full marks), so prudent judgment should be exercised when using them.

Objective:To investigate the efficacy and safety of oral maintenance therapy with tegafur for residual lesions in patients with locally advanced esophageal squamous cell carcinoma (ESCC) after chemoradiotherapy.Methods:A retrospective cohort study was conducted. A total of 38 patients with locally advanced ESCC who had residual lesions after receiving albumin bound paclitaxel combined with platinum chemoradiotherapy from March 2019 to September 2021 in Jiangsu Province Suqian Hospital were selected. All patients were divided into the maintenance treatment group (20 cases) and the non-maintenance treatment group (18 cases) based on whether they received oral maintenance therapy with tegafur after chemoradiotherapy. The progression-free survival (PFS) of both groups was compared and the adverse reactions of the maintenance treatment group were analyzed.Results:There were no statistically significant differences in baseline data between the 2 groups (both P > 0.05). The 1, 2, 3-year PFS rates for the maintenance treatment group were 95.0%, 78.1%, and 58.3%, respectively, with a median PFS time of 33.65 months (95% CI: 29.04-38.26 months); the 1, 2, 3-year PFS rates in the non-maintenance treatment group were 88.9%, 54.5%, and 12.1%, respectively, with a median PFS time of 25.08 months (95% CI: 20.97-29.18 months); there was a statistically significant difference in PFS between the 2 groups ( χ2 = 5.36, P = 0.021). The common adverse reactions in the maintenance treatment group included hematological adverse reactions, hand foot syndrome, decreased appetite, and fatigue. The more common adverse reactions were neutropenia [85.0% (17/20)] and leukopenia [65.0% (13/20)]; 9 cases experienced grade 3-4 adverse reactions, which were relieved by adjusting the dosage or discontinuing the medication. Conclusions:Oral maintenance therapy with tegafur may improve the survival of ESCC patients with residual lesions after chemoradiotherapy and the adverse reactions are controllable.