Background Lung cancer, one of the most common malignant tumors worldwide, has long ranked first in cancer incidence and mortality, posing a severe challenge to public health systems. Objective To analyze the trends in incidence, mortality, and disability-adjusted life years (DALYs) of lung cancer among residents in Jing'an District, Shanghai, from 2002 to 2021, explore the impacts of population aging, population growth, and age-specific prevalence on disease burden, and provide a scientific basis for optimizing regional lung cancer prevention and control strategies. Methods Based on the cancer registration and cause-of-death surveillance data of registered residents in Jing'an District, Shanghai, from 2002 to 2021, Joinpoint regression models were used to analyze the annual change trends (APC) and average annual change trends (AAPC) of lung cancer incidence, mortality, DALY rate, and their age-standardized rates. Decomposition analysis was applied to quantify the contribution of population aging, population growth, and age-specific prevalence to changes in the number of new cases, deaths, and DALYs. Results From 2002 to 2021, the crude incidence rate of lung cancer in Jing'an District increased from 68.00 per 100000 to 144.36 per 100000 (AAPC=4.86%, P<0.001), and the age-standardized incidence rate rose from 43.09 per 100000 to 61.46 per 100000 (AAPC=2.89%, P<0.001). The growth rate of incidence was significantly higher in females (AAPC=6.73%) than in males (AAPC=3.62%). The crude mortality rate increased from 58.08 per 100000 to 66.11 per 100000 (AAPC=1.10%, P<0.001), while the age-standardized mortality rate decreased (AAPC=−1.54%, P<0.001). The crude DALY rate showed an upward trend (AAPC=0.94%, P=0.002), whereas the age-standardized DALY rate declined (AAPC=−1.63%, P<0.001). Significant differences were observed across genders and age groups: the mortality and DALY rates increased in males aged 60-64 years, while the fastest incidence growth occurred in females aged 45-64 years. The results of decomposition analysis indicated that the increase in new cases was primarily attributed to age-specific incidence (38.15%) and aging (37.31%); the growth in deaths and DALYs was driven by aging, while age-specific mortality and population growth showed offsetting effects. From 2002 to 2021, the probability of premature death from lung cancer among males (2.04%-2.52%) in Jing'an District showed no statistically significant trend (AAPC=−0.28%, P=0.440), while it presented a decreasing trend in the total population (1.37%-1.89%) and females (0.72%-1.26%) (AAPC=−0.80% and −2.23%, respectively; P=0.029 and <0.001, respectively). Conclusion From 2002 to 2021, the risk of lung cancer incidence in Jing’an District, Shanghai, continue to increase, with a disease burden exhibiting significant heterogeneity by gender and age: key features include a rapid increase in new cases and a trend toward younger onset among females, alongside rising mortality risk among males aged 60–64 years. Population aging and increasing age-specific incidence rates are primary drivers of the growing number of cases, while declining age-specific mortality rates mitigate the rise in mortality burden.

BACKGROUND:In recent years,deep learning technologies have been increasingly applied in the field of oral medicine,enhancing the efficiency and accuracy of oral imaging analysis and promoting the rapid development of intelligent oral medicine. OBJECTIVE:To elaborate the current research status,advantages,and limitations of deep learning based on oral imaging in the diagnosis and treatment decision-making of oral diseases,as well as future prospects,exploring new directions for the transformation of oral medicine under the backdrop of deep learning technology. METHODS:PubMed was searched for literature related to deep learning in oral medical imaging published from January 2017 to January 2024 with the search terms"deep learning,artificial intelligence,stomatology,oral medical imaging."According to the inclusion criteria,80 papers were finally included for review. RESULTS AND CONCLUSION:(1)Classic deep learning models include artificial neural networks,convolutional neural networks,recurrent neural networks,and generative adversarial networks.Scholars have used these models in competitive or cooperative forms to achieve more efficient interpretation of oral medical images.(2)In the field of oral medicine,the diagnosis of diseases and the formulation of treatment plans largely depend on the interpretation of medical imaging data.Deep learning technology,with its strong image processing capabilities,aids in the diagnosis of diseases such as dental caries,periapical periodontitis,vertical root fractures,periodontal disease,and jaw cysts,as well as preoperative assessments for procedures such as third molar extraction and cervical lymph node dissection,helping clinicians improve the accuracy and efficiency of decision-making.(3)Although deep learning is promising as an important auxiliary tool for the diagnosis and treatment of oral diseases,it still has certain limitations in model technology,safety ethics,and legal regulation.Future research should focus on demonstrating the scalability,robustness,and clinical practicality of deep learning,and finding the best way to integrate automated deep learning decision support systems into routine clinical workflows.

BACKGROUND: The available evidence regarding the benefits of percutaneous coronary intervention (PCI) on patients receiving dialysis with coronary artery disease (CAD) is limited and inconsistent. This study aimed to evaluate the association between PCI and clinical outcomes as compared with medical therapy alone in patients undergoing dialysis with CAD in China. METHODS: This multicenter, retrospective study was conducted in 30 tertiary medical centers across 12 provinces in China from January 2015 to June 2021 to include patients on dialysis with CAD. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke. Secondary outcomes included all-cause death, the individual components of MACE, and Bleeding Academic Research Consortium criteria types 2, 3, or 5 bleeding. Multivariable Cox proportional hazard models were used to assess the association between PCI and outcomes. Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) were performed to account for potential between-group differences. RESULTS: Of the 1146 patients on dialysis with significant CAD, 821 (71.6%) underwent PCI. After a median follow-up of 23.0 months, PCI was associated with a 43.0% significantly lower risk for MACE (33.9% [ n  = 278] vs . 43.7% [ n  = 142]; adjusted hazards ratio 0.57, 95% confidence interval 0.45-0.71), along with a slightly increased risk for bleeding outcomes that did not reach statistical significance (11.1% vs . 8.3%; adjusted hazards ratio 1.31, 95% confidence interval, 0.82-2.11). Furthermore, PCI was associated with a significant reduction in all-cause and cardiovascular mortalities. Subgroup analysis did not modify the association of PCI with patient outcomes. These primary findings were consistent across IPTW, PSM, and competing risk analyses. CONCLUSION This study indicated that PCI in patients on dialysis with CAD was significantly associated with lower MACE and mortality when comparing with those with medical therapy alone, albeit with a slightly increased risk for bleeding events that did not reach statistical significance.
Humans ; Percutaneous Coronary Intervention/methods* ; Male ; Female ; Coronary Artery Disease/drug therapy* ; Retrospective Studies ; Renal Dialysis/methods* ; Middle Aged ; Aged ; China ; Proportional Hazards Models ; Treatment Outcome

Objective To investigate the application of the Health Effective Data and Information System(HEDIS)-based phased health education in infertile patients treated with assisted reproductive technology(ART).Methods A total of 120 infertile patients who were admitted to The First Affiliated Hospital of Naval Medical University from March 2023 to September 2023 were consecutively selected and randomly assigned to observation group or control group at a ratio of 1:1 using a random number table.During the ART treatment period,the control group was given conventional nursing care,while the observation group was given HEDIS based phased health education for nursing intervention.The negative emotion score,shame score,self-management ability score,and nursing satisfaction were compared between the two groups.Results After intervention,the Hamilton Anxiety Scale(HAMA)score and Hamilton Depression Scale(HAMD)score,and self-rating scale(ISS)scores of the observation group were lower than those of the control group(P<0.05).The degree of nursing satisfaction of the observation group was higher than that of the control group(P<0.05).Conclusion The HEDIS-based phased health education can alleviate the negative emotions of anxiety and depression,reduce the sense of shame,and enhance nursing satisfaction in infertile patients during ART treatment.

Pleckstrin homology-like domain family A member 1(PHLDA1)is a pro-apoptotic factor as well as a key regulator of metabolic diseases.In obesity-related diseases,PHLDA1 can reduce liver triglyceride production through inhibition of the expression of sterol regulatory?element binding proteins?1(SREBP?1),and reduce fat syn?thesis through inhibition of peroxisome proliferator?activated receptor γ(PPARγ).However,in cardiovascular dis?eases,PHLDA1 increases vascular calcification,dysfunction,thereby aggravates ischemia?reperfusion injury in the heart and brain.The dual role of PHLDA1 has also been confirmed in tumors.In summary,PHLDA1,as a multi?functional factor,plays different functional roles through various mechanisms.

Objective: To investigate the epidemiological characteristics of varicella outbreaks in primary and middle schools, and to establish a risk predictive model, so as to provide scientific guidance for the prevention of varicella outbreaks in schools. Methods: Based on a nested case-control study, primary and middle schools in 4 districts of Shanghai (Yangpu District and Jingan District) and Hangzhou (Xiaoshan District and Linping District) from January to December 2023 were selected to observe the status of varicella outbreaks. Associated factors of varicella outbreaks were investigated and used for establishing the predictive model, which was evaluated by the Hosmer-Lemeshow(H-L) goodness of fit test, receiver operating characteristic (ROC) curve, Calibration curve, decision curve analysis (DCA). Results: A total of 98 varicella outbreaks were included, with 195 schools without varicella outbreaks during the same period as controls. Eight factors, including the availability of warm water in restroom, availability of hand soap in restroom, average class size, duration of student attendance at school per day, presence of a fulltime school doctor, hesitancy of the school principal towards varicella vaccination, and rates of first and second doses of varicella vaccination, were identified as potential factors for school varicella outbreaks, with statistically significant differences (χ2/Z=10.01, 20.49, 17.43, 9.74, 32.17, 6.60, 2.20, 3.39, P<0.05). The 8 variables above were employed to construct a risk predictive model, and Hosmer-Lemeshow goodness of fit test yielded a χ2 value of 5.863 (P>0.05); the area under the ROC curve (AUC) was 0.846 (95%CI=0.799-0.893); Calibration curve analysis indicated good consistency between predicted and actual values of the model. DCA demonstrated favorable predictive performance of the model over a wide range. Conclusions The predictive model for school varicella outbreaks demonstrates satisfactory accuracy and efficacy. It suggested to make good use of this prediction model and take relevant measures to reduce the risk of varicella transmission in schools.

OBJECTIVE To construct a risk prediction model for non-compliance with inhaled medication in patients with chronic obstructive pulmonary disease （COPD）. METHODS A retrospective analysis was conducted on 365 COPD patients admitted to the cough and wheeze pharmaceutical care clinic of the First Hospital of Qinhuangdao from October 2021 to October 2023. The patients admitted from October 2021 to June 2023 were selected as the model group （n＝303）， and the patients admitted from July to October 2023 were selected as the validation group （n＝62）. The model group was divided into compliance subgroup （n＝126） and non-compliance subgroup （n＝177）. Univariate analysis combined with multivariate Logistic regression analysis were used to analyze the risk factors for non-compliance with inhaled formulations in patients； the risk prediction model was established through regression analysis， and the accuracy of the model prediction was evaluated based on the validation group of patients. RESULTS Multivariate Logistic regression analysis showed that simultaneous use of 2 inhaled formulations （OR＝3.730， 95%CI 1.996-6.971， P＜0.001）， the number of acute exacerbations within one year ≥2 （OR＝2.509， 95%CI 1.509-4.173， P＜0.001）， smoking （OR＝2.167， 95%CI 1.309-3.588， P＝0.003）， complicated with anxiety/depression （OR＝2.112， 95%CI 1.257-3.499， P＝0.004） and mMRC grading≥2 levels （OR＝1.701， 95%CI 1.014-2.853， P＝0.044） were risk factors for non-compliance with inhaled preparations. Based on this， a risk prediction model was established and the ROC curve was drawn. The areas under the curve of the model group and validation group were 0.836 and 0.928， and the overall accuracy of the model’s prediction was 88.71%. CONCLUSIONS The predictive model based on the simultaneous use of 2 inhaled formulations， the number of acute exacerbations within one year ≥2， smoking， complicated with anxiety/depression， mMRC grading ≥2 levels has certain predictive value for the risk of non-compliance with inhaled formulations for COPD patients.

OBJECTIVE To construct a risk prediction model for non-compliance with inhaled medication in patients with chronic obstructive pulmonary disease （COPD）. METHODS A retrospective analysis was conducted on 365 COPD patients admitted to the cough and wheeze pharmaceutical care clinic of the First Hospital of Qinhuangdao from October 2021 to October 2023. The patients admitted from October 2021 to June 2023 were selected as the model group （n＝303）， and the patients admitted from July to October 2023 were selected as the validation group （n＝62）. The model group was divided into compliance subgroup （n＝126） and non-compliance subgroup （n＝177）. Univariate analysis combined with multivariate Logistic regression analysis were used to analyze the risk factors for non-compliance with inhaled formulations in patients； the risk prediction model was established through regression analysis， and the accuracy of the model prediction was evaluated based on the validation group of patients. RESULTS Multivariate Logistic regression analysis showed that simultaneous use of 2 inhaled formulations （OR＝3.730， 95%CI 1.996-6.971， P＜0.001）， the number of acute exacerbations within one year ≥2 （OR＝2.509， 95%CI 1.509-4.173， P＜0.001）， smoking （OR＝2.167， 95%CI 1.309-3.588， P＝0.003）， complicated with anxiety/depression （OR＝2.112， 95%CI 1.257-3.499， P＝0.004） and mMRC grading≥2 levels （OR＝1.701， 95%CI 1.014-2.853， P＝0.044） were risk factors for non-compliance with inhaled preparations. Based on this， a risk prediction model was established and the ROC curve was drawn. The areas under the curve of the model group and validation group were 0.836 and 0.928， and the overall accuracy of the model’s prediction was 88.71%. CONCLUSIONS The predictive model based on the simultaneous use of 2 inhaled formulations， the number of acute exacerbations within one year ≥2， smoking， complicated with anxiety/depression， mMRC grading ≥2 levels has certain predictive value for the risk of non-compliance with inhaled formulations for COPD patients.

Objective To analyze the clinical outcomes of early invasive fungal disease(IFD)in patients after allogenetic hematopoietic stem cell transplantation(allo-HCST)with metagenomic next-generation sequencing(mNGS).Methods A retrospective analysis was conducted on patients undergoing allo-HCST in our Bone Marrow Transplantation Center between July 2021 and October 2022.These patients experienced one of the following conditions within 100 d after transplantation:① Patients with persistent fever and negative blood culture after empiric antimicrobial therapy for 72 h or longer;② Hyperpyrexia of unknown origin occurred again after effective anti-infection in the past;③ Symptoms in lower respiratory tract associated with lung lesions on CT scan,and empiric anti-infective therapy was ineffective.Peripheral blood or bronchoscopic alveolar lavage fluid were tested with mNGS,and overall survival(OS)and non-relapse mortality(NRM)were analyzed.Results There were 60 patients enrolled in this study.For the peripheral blood samples of 47 cases and bronchoalveolar lavage fluid samples of 13 cases,mNGS found that 19 cases were negative to pathogens,30 cases were non-fungal positive,and 11 case were fungal positive,including 3 cases of aspergillus,5 cases of mucor,2 cases of Candida tropicalis,and 1 case of Trichosporon asahii.Of the 11 patients with fungal positive,8 achieved complete remission after antifungal therapy according to the mNGS results.The 1-year OS and NRM of the 60 patients were 70.0％(95％CI:64.1％～75.9％)and 20.0％(95％CI:11.9％～32.5％),respectively,while those of the fungal infection patients were 54.5％(95％CI:49.5％～69.5％)and 36.4％(95％ CI:15.5％～70.3％),respectively.No significant differences were seen in 1-year OS(P=0.487)and 1-year NRM(P=0.358)among the negative,fungal infection and non-fungal infection patients,neither OS(P=0.238)and NRM(P=0.154)between the fungal infection and the non-fungal infection patients.Conclusion mNGS can rapidly diagnose the early IFD after allo-HSCT,which is helpful for timely and effective treatment and improves the prognosis of patients.

Objective:To explore the clinical, biochemical and genetic characteristics of three children with Isoleucine metabolic disorders due to variants of HSD17B10 and ACAT1 genes. Methods:Two children with 17β hydroxysteroid dehydrogenase 10 (HSD17B10) deficiency and a child with β-ketothiolase deficiency (BKD) diagnosed at Shanghai Children′s Hospital between 2014 and 2021 were selected as the study subjects. Clinical data of the children were collected. The children were subjected to blood acylcarnitine, urinary organic acid and genetic testing, and candidate variants were analyzed with bioinformatic tools.Results:The main symptoms of the three children had included epilepsy, developmental delay, hypotonia and acidosis. Their blood acylcarnitine methylcrotonyl carnitine (C5: 1), 3-hydroxyisovalerylcarnitine (C5-OH) and 3-hydroxybutylcarnitine (C4OH) were increased to various extents, and urine organic acids including methyl crotonylglycine and 2-methyl-3-hydroxybutyric acid were significantly increased. Child 1 and child 2 were respectively found to harbor a c. 347G>A (p.R116Q) variant and a c. 274G>A (p.A92T) variant of the HSD17B10 gene, and child 3 was found to harbor compound heterozygous variants of the ACAT1 gene, namely c. 547G>A (p.G183R) and a c. 331G>C (p.A111P). Among these, the c. 274G>A (p.A92T) and c. 331G>C (p.A111P) variants were unreported previously. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), they were respectively classified as variant of unknown significance (PP3_Strong+ PM2_supporting) and likely pathogenic (PM3+ PM2_Supporting+ PP3_Moderate+ PP4). Conclusion:Both the HSD17B10 deficiency and BKD can lead to Isoleucine metabolism disorders, which may be difficult to distinguish clinically. Genetic testing can further confirm the diagnosis. Discoveries of the HSD17B10: c. 274G>A (p.A92T) variant and the ACAT1: c. 331G>C (p.A111P) variant have enriched the mutational spectrum of the two diseases.