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:The pathogenesis of diabetic peripheral neuropathy has not yet been clarified,and TAM(Tyro3,Axl,and MerTK)receptor tyrosine kinases can control apoptotic cells and suppress inflammatory responses in the central nervous system. OBJECTIVE:To investigate the difference of Mer receptor tyrosine kinase(MerTK)levels in plasma and sciatic nerve tissue of Sprague-Dawley rats with type 2 diabetes and diabetic peripheral neuropathy,and to study the correlation between MerTK and diabetic peripheral neuropathy. METHODS:Forty male Sprague-Dawley were randomly divided into control group with 15 rats,type 2 diabetes group with 10 rats,and diabetic peripheral neuropathy group with 15 rats.The control group was fed with ordinary diet,while the experimental groups were fed with high-fat and high-sugar diet.After 6 weeks,intraperitoneal injection of streptozotocin at the minimum dose of 35 mg/kg was administered in the two experimental groups.After 14 days,tail vein blood was collected to detect blood glucose.If blood glucose≥16.7 mmol/L,the model of type 2 diabetes was successfully established.Rats in the diabetic peripheral neuropathy group continued to be fed with a high-sugar and high-fat diet for 8 weeks.The sciatic nerve conduction velocity of rats was detected through live isolation under anesthesia.Blood samples were collected from the abdominal aorta,and the sciatic nerve tissue was collected.Histological changes of nerve fibers in each group were observed under a light microscope to confirm the success of diabetic peripheral neuropathy modeling.ELISA was used to detect peripheral blood glucose,blood lipids and serum MerTK levels in rats;hematoxylin-eosin staining was used to observe the histological changes in the sciatic nerve;immunofluorescence,immunohistochemistry and western blot were used to detect the expression of MerTK in the sciatic nerve tissue. RESULTS AND CONCLUSION:The Sprague-Dawley rat models of type 2 diabetes and type 2 diabetes peripheral neuropathy were successfully constructed,and the modeling rate of diabetic peripheral neuropathy was 80%.Compared with the control group,the blood glucose levels of rats in the type 2 diabetes and diabetic peripheral neuropathy groups were significantly higher(P<0.000 1),while the blood glucose level in the diabetic peripheral neuropathy group was higher than that in the type 2 diabetes group;and the sciatic nerve conduction velocity was significantly decreased(P<0.05),which was lower in the diabetic peripheral neuropathy group than the type 2 diabetes group.Histological examination:Compared with the control group,the sciatic nerve nuclei were reduced in the type 2 diabetes group,with some vacuolar degeneration and phagocytosis;in the diabetic peripheral neuropathy group,the cell body was swollen,the nuclear spacing was increased,vacuolar degeneration was observed,and the myelin sheath was partitioned and unsmooth,and lattice-like axons appeared.Serum MerTK levels were significantly higher in the diabetic peripheral neuropathy group than the control group.Expression of MerTK in the sciatic nerve tissue was significantly upregulated in the diabetic peripheral neuropathy group compared with the control group(P<0.05).To conclude,elevated levels of MerTK in plasma and sciatic nerve tissue of rats with diabetic peripheral neuropathy are presumably related to its anti-inflammatory and immunomodulatory effects.

Objective:To explore the clinical manifestations of IgG4-related diseases（IgG4-RD） complicated with moderately differentiated adenosquamous carcinoma of the parotid gland, the diagnostic criteria for IgG4-related diseases and parotid malignant tumors, treatment regimens, and the application of fine-needle aspiration in disease diagnosis, so as to reduce clinical misdiagnosis and missed diagnosis. Methods:A retrospective analysis was conducted on the case data of a patient with IgG4-related diseases（IgG4-RD） complicated with moderately differentiated adenosquamous carcinoma of the parotid gland admitted to our department in March 2024. The clinical characteristics, imaging findings, preoperative puncture results, and postoperative pathological features were analyzed, and relevant literatures on both diseases were reviewed and summarized. Results:The elderly male patient was admitted due to "a mass in the parotid area in front of the right ear for more than 3 months". Through clinical examination, imaging examination, laboratory examination, and preoperative needle biopsy, the diagnosis of "right parotid moderately differentiated adenosquamous carcinoma complicated with IgG4-related disease" was considered. It was also considered that IgG4-related disease did not involve other organs before surgery, so no systemic hormone therapy was given before or after surgery. After surgery combined with postoperative radiotherapy, follow-up showed that neither the parotid tumor nor IgG4-related disease recurred. Conclusion:"IgG4-related disease complicated with moderately differentiated adenosquamous carcinoma"is a rare clinical disease. Both lack typical clinical manifestations and specific imaging features, and the diagnosis is mostly unclear before surgery. Pathological examination is of great significance in the diagnosis of the disease, while fine-needle aspiration has limited value in the diagnosis, which should attract the attention of clinicians. In addition, for patients with both diseases, individualized treatment plans should be formulated.
Humans ; Parotid Neoplasms/pathology* ; Male ; Carcinoma, Adenosquamous/pathology* ; Immunoglobulin G4-Related Disease/complications* ; Parotid Gland/pathology* ; Retrospective Studies ; Aged ; Biopsy, Fine-Needle ; Immunoglobulin G

To investigate the correlation between vitamin D nutritional status and insulin resistance in pubertal adolescents.

Objective To analyze the clinical characteristics associated with prenatal diagnosis of FBN 1 gene mutations in a family.This study explores the correlation between gene mutations and their corresponding clini-cal phenotypes,emphasizing the significance of prenatal diagnosis in providing a foundation for subsequent follow-up and intervention.Methods Genomic DNA was extracted from the amniotic fluid of the fetus and the peripheral blood of the parents for trio-whole exome sequencing.The candidate variant identified was subsequently validated using Sanger sequencing.Results The pedigree comprised four generations and nine family members,with four individuals exhibiting slender limbs and toes.Among these,three showed congenital lens dislocation or subluxation.No abnormalities in the cardiovascular system were observed.Genetic testing of symptomatic individuals revealed a heterozygous mutation(c.6158G>T)in the FBN 1 gene.Conclusions The FBN 1 c.6158G>T(p.C2053F)muta-tion was identified as the pathogenic variant responsible for the condition in this family,exhibiting autosomal domi-nant inheritance.To our knowledge,this is the first reported case of the FBN 1 c.6158G>T(p.C2053F)mutation in China.Prenatal diagnosis can facilitate early confirmation of the condition and provide a foundation for subsequent in-terventions and follow-up care.

Xiaoaiping (XAP) Injection demonstrates the anti-prostate cancer (PCa) effects, yet the underlying mechanism remains unclear. This study aims to investigate the impact of XAP on PCa and elucidate its mechanism of action. PCa cell proliferation was evaluated using a cell counting kit-8 (CCK-8) assay. Cell apoptosis was assessed through Hoechst staining and Western blotting assays. Proteomics technology was employed to identify key molecules and significant signaling pathways modulated by XAP in PCa cells. To further validate potential key genes and important pathways, a series of assays were conducted, including acridine orange (AO) staining, transmission electron microscopy, and immunofluorescence assays. The molecular mechanism of XAP against PCa in vivo was examined using a PC3 xenograft mouse model. Results demonstrated that XAP significantly inhibited cell proliferation in multiple PCa cell lines. In C4-2 and prostate cancer cell line-3 (PC3) cells, XAP induced cellular apoptosis, evidenced by reduced B-cell lymphoma 2 (Bcl-2) levels and elevated Bcl-2-associated X (Bax) levels. Proteomic, immunofluorescence, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) investigations revealed a strong correlation between forkhead box O3a (FoxO3a) autophagic degradation and the anti-PCa action of XAP. XAP hindered autophagy by reducing the expression levels of autophagy-related protein 5 (Atg5)/autophagy-related protein 12 (Atg12) and enhancing FoxO3a expression and nuclear translocation. Furthermore, XAP exhibited potent anti-PCa action in PC3 xenograft mice and triggered FoxO3a nuclear translocation in tumor tissue. These findings suggest that XAP induces PCa apoptosis via inhibition of FoxO3a autophagic degradation, potentially offering a novel perspective on XAP injection as an effective anticancer therapy for PCa.
Male ; Humans ; Prostatic Neoplasms/physiopathology* ; Autophagy/drug effects* ; Animals ; Drugs, Chinese Herbal/pharmacology* ; Proteomics ; Mice ; Apoptosis/drug effects* ; Cell Line, Tumor ; Cell Proliferation/drug effects* ; Forkhead Box Protein O3/genetics* ; Xenograft Model Antitumor Assays ; Mice, Nude ; Mice, Inbred BALB C

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective To analyze the clinical characteristics associated with prenatal diagnosis of FBN 1 gene mutations in a family.This study explores the correlation between gene mutations and their corresponding clini-cal phenotypes,emphasizing the significance of prenatal diagnosis in providing a foundation for subsequent follow-up and intervention.Methods Genomic DNA was extracted from the amniotic fluid of the fetus and the peripheral blood of the parents for trio-whole exome sequencing.The candidate variant identified was subsequently validated using Sanger sequencing.Results The pedigree comprised four generations and nine family members,with four individuals exhibiting slender limbs and toes.Among these,three showed congenital lens dislocation or subluxation.No abnormalities in the cardiovascular system were observed.Genetic testing of symptomatic individuals revealed a heterozygous mutation(c.6158G>T)in the FBN 1 gene.Conclusions The FBN 1 c.6158G>T(p.C2053F)muta-tion was identified as the pathogenic variant responsible for the condition in this family,exhibiting autosomal domi-nant inheritance.To our knowledge,this is the first reported case of the FBN 1 c.6158G>T(p.C2053F)mutation in China.Prenatal diagnosis can facilitate early confirmation of the condition and provide a foundation for subsequent in-terventions and follow-up care.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

ObjectiveTo analyze the fertility characteristics of the registered population in a neighborhood in Jing’an District， Shanghai from 2013 to 2022， and to provide a reference for population development planning and allocation of public health resources. MethodsData on newborns and puerperae registered in the neighborhood were collected through the Shanghai Birth Medical Information System and Public Security Department information system. Statistical analysis was conducted using Excel 2010， and the Joinpoint regression model was used to describe the changes. ResultsFrom 2013 to 2022， the crude birth rate， general fertility rate， and total fertility rate showed a declining trend， with average annual percent change （AAPC） values of -7.62%， -6.99%， and -4.54%， respectively. The average age of first childbirth and the age of childbearing among women gradually increased， with AAPC values of 1.24% and 1.28%， respectively. In the past 10 years， the total number of newborns showed a downward trend， with an AAPC value of -6.15%. After the implementation of the two-child policy， the proportion of second children showed an increasing trend， with an AAPC value of 7.37%. ConclusionThe fertility level of the population in the neighborhood is declining. The two-child policy has not significantly improved fertility rates， and the age of childbearing in women continues to rise. Diverse measures are needed to encourage childbirths and promote healthy births.