Objective: To explore the effect of C1q / tumor necrosis factor-related protein 9 ( CTRP9 ) on the expression of genes and proteins related to lipid metabolism of brown adipose tissue (BAT) in mice after cold stimulation． Methods : C57BL /6J male mice were injected with adenovirus Ad-GFP (control group) or Ad-CTRP9 ( experience group) into the scapular region and kept for 7 days．After cold stimulation at 4 ℃ for 10 hours，the expression levels of BAT marker genes and proteins were detected by real time PCR and Western blot. Results: Overexpression of CTRP9 induced by cold stimulation significantly increased the mRNA level of iodothyronine deiodinase 2 (Dio2) in BAT (P＜0. 01) ．Additionally，there was no significant difference in the expression of BAT marker genes ( UCP-1，PGC-1 α , PRDM16 and ARβ3) ，and liposynthesis and lipolysis related genes (PPARγ , HSL and ATGL) ．Uncoupling protein 1 (UCP-1) protein expression was upregualted in Ad-CTRP9 compared to the Ad-GFP control group ，while the expression of lipolysis related protein adipose triglyceride lipase ( ATGL) decreased significantly (P＜0. 05) ． Conclusion In cold environment，overexpression of CTRP9 promotes the accumulation of UCP-1 protein in BAT，upregulates the expression of thyroid hormone signal related gene Dio2，and inhibits triglyceride hydrolysis to maintain a constant body temperature.

Nonpharmaceutical interventions (NPIs) have been commonly deployed to prevent and control the spread of the coronavirus disease 2019 (COVID-19), resulting in a worldwide decline in influenza prevalence. However, the influenza risk in China warrants cautious assessment. We conducted a cross-sectional, seroepidemiological study in Shandong Province, Northern China in mid-2021. Hemagglutination inhibition was performed to test antibodies against four influenza vaccine strains. A combination of descriptive and meta-analyses was adopted to compare the seroprevalence of influenza antibodies before and during the COVID-19 pandemic. The overall seroprevalence values against A/H1N1pdm09, A/H3N2, B/Victoria, and B/Yamagata were 17.8% (95% CI 16.2%-19.5%), 23.5% (95% CI 21.7%-25.4%), 7.6% (95% CI 6.6%-8.7%), and 15.0 (95% CI 13.5%-16.5%), respectively, in the study period. The overall vaccination rate was extremely low (2.6%). Our results revealed that antibody titers in vaccinated participants were significantly higher than those in unvaccinated individuals (P < 0.001). Notably, the meta-analysis showed that antibodies against A/H1N1pdm09 and A/H3N2 were significantly low in adults after the COVID-19 pandemic (P < 0.01). Increasing vaccination rates and maintaining NPIs are recommended to prevent an elevated influenza risk in China.

Background::Non-smokers account for a large proportion of lung cancer patients, especially in Asia, but the attention paid to them is limited compared with smokers. In non-smokers, males display a risk for lung cancer incidence distinct from the females—even after excluding the influence of smoking; but the knowledge regarding the factors causing the difference is sparse. Based on a large multicenter prospective cancer screening cohort in China, we aimed to elucidate the interpretable sex differences caused by known factors and provide clues for primary and secondary prevention.Methods::Risk factors including demographic characteristics, lifestyle factors, family history of cancer, and baseline comorbidity were obtained from 796,283 Chinese non-smoking participants by the baseline risk assessment completed in 2013 to 2018. Cox regression analysis was performed to assess the sex difference in the risk of lung cancer, and the hazard ratios (HRs) that were adjusted for different known factors were calculated and compared to determine the proportion of excess risk and to explain the existing risk factors.Results::With a median follow-up of 4.80 years, 3351 subjects who were diagnosed with lung cancer were selected in the analysis. The lung cancer risk of males was significantly higher than that of females; the HRs in all male non-smokers were 1.29 (95% confidence interval [CI]: 1.20-1.38) after adjusting for the age and 1.38 (95% CI: 1.28-1.50) after adjusting for all factors, which suggested that known factors could not explain the sex difference in the risk of lung cancer in non-smokers. Known factors were 7% (|1.29-1.38|/1.29) more harmful in women than in men. For adenocarcinoma, women showed excess risk higher than men, contrary to squamous cell carcinoma; after adjusting for all factors, 47% ([1.30-1.16]/[1.30-1]) and 4% ([7.02-6.75]/[7.02-1])) of the excess risk was explainable in adenocarcinoma and squamous cell carcinoma. The main causes of gender differences in lung cancer risk were lifestyle factors, baseline comorbidity, and family history.Conclusions::Significant gender differences in the risk of lung cancer were discovered in China non-smokers. Existing risk factors did not explain the excess lung cancer risk of all non-smoking men, and the internal causes for the excess risk still need to be explored; most known risk factors were more harmful to non-smoking women; further exploring the causes of the sex difference would help to improve the prevention and screening programs and protect the non-smoking males from lung cancers.

BACKGROUND: Perioperative treatment has become an increasingly important aspect of the management of patients with non-small cell lung cancer (NSCLC). Small-scale clinical studies performed in recent years have shown improvements in the major pathological remission rate after neoadjuvant therapy, suggesting that it will soon become an important part of NSCLC treatment. Nevertheless, neoadjuvant immunotherapy may be accompanied by serious adverse reactions that lead to delay or cancelation of surgery, additional illness, and even death, and have therefore attracted much attention. The purpose of the clinical recommendations is to form a diagnosis and treatment plan suitable for the current domestic medical situation for the immune-related adverse event (irAE). METHODS: This recommendation is composed of experts in thoracic surgery, oncologists, thoracic medicine and irAE related departments (gastroenterology, respirology, cardiology, infectious medicine, hematology, endocrinology, rheumatology, neurology, dermatology, emergency section) to jointly complete the formulation. Experts make full reference to the irAE guidelines, large-scale clinical research data published by thoracic surgery, and the clinical experience of domestic doctors and publicly published cases, and repeated discussions in multiple disciplines to form this recommendation for perioperative irAE. RESULTS: This clinical recommendation covers the whole process of prevention, evaluation, examination, treatment and monitoring related to irAE, so as to guide the clinical work comprehensively and effectively. CONCLUSIONS Perioperative irAE management is an important part of immune perioperative treatment of lung cancer. With the continuous development of immune perioperative treatment, more research is needed in the future to optimize the diagnosis and treatment of perioperative irAE.

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and is the fourth-leading cause of cancer-related deaths worldwide. HCC is refractory to many standard cancer treatments and the prognosis is often poor, highlighting a pressing need to identify biomarkers of aggressiveness and potential targets for future treatments. Kinesin family member 2C (KIF2C) is reported to be highly expressed in several human tumors. Nevertheless, the molecular mechanisms underlying the role of KIF2C in tumor development and progression have not been investigated. In this study, we found that KIF2C expression was significantly upregulated in HCC, and that KIF2C up-regulation was associated with a poor prognosis. Utilizing both gain and loss of function assays, we showed that KIF2C promoted HCC cell proliferation, migration, invasion, and metastasis both in vitro and in vivo. Mechanistically, we identified TBC1D7 as a binding partner of KIF2C, and this interaction disrupts the formation of the TSC complex, resulting in the enhancement of mammalian target of rapamycin complex1 (mTORC1) signal transduction. Additionally, we found that KIF2C is a direct target of the Wnt/β-catenin pathway, and acts as a key factor in mediating the crosstalk between Wnt/β-catenin and mTORC1 signaling. Thus, the results of our study establish a link between Wnt/β-catenin and mTORC1 signaling, which highlights the potential of KIF2C as a therapeutic target for the treatment of HCC.
Adult ; Aged ; Animals ; Carcinoma, Hepatocellular/pathology* ; Cell Line, Tumor ; Cell Movement ; Cell Proliferation ; Epithelial-Mesenchymal Transition/genetics* ; Female ; Gene Expression Regulation, Neoplastic ; Humans ; Intracellular Signaling Peptides and Proteins/metabolism* ; Kinesins/metabolism* ; Liver Neoplasms/pathology* ; Male ; Mice ; Mice, Inbred BALB C ; Middle Aged ; Neoplasm Staging ; Prognosis ; Protein Binding ; RNA, Small Interfering/metabolism* ; Survival Analysis ; Tumor Burden ; Wnt Signaling Pathway ; Xenograft Model Antitumor Assays ; beta Catenin/metabolism*

Objective:To explore the advantages of the novel individualized 3D printing artificial vertebral body in spine reconstruction and to evaluate its clinical effect.Methods:From January 2017 to December 2018, the 15 patients who underwent total vertebrectomy and spine reconstruction with individualized 3D printing artificial vertebral body were analyzed retrospectively. There were 8 males and 7 females, with the mean age 39.5 years (range: 20-57), including 12 primary tumors and 3 metastatic tumors. According to tumor location and surrounding soft tissue invasion range, simple posterior or combined anterior and posterior approach were used for total vertebral resection, and the defection was reconstructed by 3D printing artificial vertebral body. The operation time, intraoperative bleeding volume, postoperative stability of artificial vertebral body and bone ingrowth of adjacent vertebral body, preoperative and postoperative neurological changes, preoperative and postoperative VAS score, local control and survival of patients were analyzed.Results:The mean operation time was 412.0 min (range: 135-740 min), and the mean blood loss was 4 140.0ml (range: 100-14 000 ml). The mean follow-up time was 23.2 months (range: 12-35 months), and no one loss to follow-up. One case had pleural rupture, one case had cerebrospinal fluid leakage and one case had L5 nerve root palsy. All patients recovered after active symptomatic treatment. Compare with the preoperative VAS score (4.7±1.1), the differences of VAS score at 7 d postoperative and last follow-up (1.6±0.6 and 1.0±0.5) were significantly reduced ( P<0.001). Three patients with Frankel grade C gradually recovered to grade D, and no change were found in grade D and Grade E patients, there was no significant improved at last follow-up. Preliminary bone growth was found between the artificial vertebral body and the adjacent vertebral body 3 months after operation. The bone growth was more obvious at 12 months post-operation, and the artificial vertebral body fused with the adjacent vertebral bodies to form bone integration. At 24 months post-operation, the integration of the artificial vertebral body was more accurate. During the follow-up period, there was no loosening or displacement of the artificial vertebral body and no failure of internal fixation. A case of hemangioendothelioma and a case of epithelioid angiosarcoma died at 33 months and 35 months postoperatively. One patient with chondrosarcoma had local recurrence at16 months post-operation. After treated with arotinib, the tumor did not progress. The other 12 patients had no tumor recurrence or distant metastasis. Conclusion:After spinal tumor resection, individualized 3D printing artificial vertebral body can be used to accurate restoration of spinal continuity, and provide nice interface matching and bone growth between artificial vertebral body and the adjacent vertebral endplates. Moreover, the immediate and long-term stability of the artificial vertebral body can meet the needs of spinal reconstruction.

Objective:To systematically understand the global research progress in the construction and validation of lung cancer risk prediction models.Methods:"lung neoplasms" , "lung cancer" , "lung carcinoma" , "lung tumor" , "risk" , "malignancy" , "carcinogenesis" , "prediction" , "assessment" , "model" , "tool" , "score" , "paradigm" , and "algorithm" were used as search keywords. Original articles were systematically searched from Chinese databases (CNKI, and Wanfang) and English databases (PubMed, Embase, Cochrane, and Web of Science) published prior to December 2018. The language of studies was restricted to Chinese and English. The inclusion criteria were human oriented studies with complete information for model development, validation and evaluation. The exclusion criteria were informal publications such as conference abstracts, Chinese dissertation papers, and research materials such as reviews, letters, and news reports. A total of 33 papers involving 27 models were included. The population characteristics of all included studies, study design, predicting factors and the performance of models were analyzed and compared.Results:Among 27 models, the number of American-based, European-based and Asian-based model studies was 12, 6 and 9, respectively. In addition, there were 6 Chinese-based model studies. According to the factors fitted into the models, these studies could be divided into traditional epidemiological models (11 studies), clinical index models (6 studies), and genetic index models (10 studies). 15 models were not validated after construction or were cross-validated only in the internal population, and the extrapolation effect of models was not effectively evaluated; 8 models were validated in single external population; only 4 models were verified in multiple external populations (3-7); the area under the curve (AUC) of models ranged from 0.57 to 0.90.Conclusion:Research on risk prediction models for lung cancer is in development stage. In addition to the lack of external validation of existing models, the exploration of potential clinical indicators was also limited.

Objective:To develop a lung cancer risk prediction model for female non-smokers.Methods:Based on the Kailuan prospective dynamic cohort (2006.05-2015.12), a nested case-control study was conducted. Participants diagnosed with primary pathologically confirmed lung cancer during follow-up were identified as the case group, and others were identified as the control group. A total of 24 701 subjects were included in the study, including 86 lung cancer cases and 24 615 control population, respectively. Questionnaires, physical examinations, and laboratory tests were conducted to collect relevant information. Multivariable-adjusted logistic regressions were conducted to develop a lung cancer risk prediction model. Area Under the Curve (AUC) and Hosmer-Lemeshow tests were used to evaluate discrimination and calibration, respectively. Ten-fold cross-validation was used for internal validation.Results:Two sets of models were developed: the simple model (including age and monthly income) and the metabolic index model [including age, monthly income, fasting blood glucose (FBG), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C)].The AUC (95%CI) [0.745 (0.719-0.771)] of the metabolic index model was higher than that of the simple prediction model [0.688 (0.660-0.716)] ( P=0.004). Both the simple model ( PHL=0.287) and the metabolic index model ( PHL=0.134) were well-calibrated. The results of ten-fold cross-validation indicated sufficient stability, with an average AUC of 0.699 and a standard error (SD) of 0.010. Conclusion:By incorporating metabolic markers, accurate and reliable lung cancer risk prediction model for female non smokers could be developed.

Objective:To systematically understand the global research progress in the construction and validation of lung cancer risk prediction models.Methods:"lung neoplasms" , "lung cancer" , "lung carcinoma" , "lung tumor" , "risk" , "malignancy" , "carcinogenesis" , "prediction" , "assessment" , "model" , "tool" , "score" , "paradigm" , and "algorithm" were used as search keywords. Original articles were systematically searched from Chinese databases (CNKI, and Wanfang) and English databases (PubMed, Embase, Cochrane, and Web of Science) published prior to December 2018. The language of studies was restricted to Chinese and English. The inclusion criteria were human oriented studies with complete information for model development, validation and evaluation. The exclusion criteria were informal publications such as conference abstracts, Chinese dissertation papers, and research materials such as reviews, letters, and news reports. A total of 33 papers involving 27 models were included. The population characteristics of all included studies, study design, predicting factors and the performance of models were analyzed and compared.Results:Among 27 models, the number of American-based, European-based and Asian-based model studies was 12, 6 and 9, respectively. In addition, there were 6 Chinese-based model studies. According to the factors fitted into the models, these studies could be divided into traditional epidemiological models (11 studies), clinical index models (6 studies), and genetic index models (10 studies). 15 models were not validated after construction or were cross-validated only in the internal population, and the extrapolation effect of models was not effectively evaluated; 8 models were validated in single external population; only 4 models were verified in multiple external populations (3-7); the area under the curve (AUC) of models ranged from 0.57 to 0.90.Conclusion:Research on risk prediction models for lung cancer is in development stage. In addition to the lack of external validation of existing models, the exploration of potential clinical indicators was also limited.

Objective:To develop a lung cancer risk prediction model for female non-smokers.Methods:Based on the Kailuan prospective dynamic cohort (2006.05-2015.12), a nested case-control study was conducted. Participants diagnosed with primary pathologically confirmed lung cancer during follow-up were identified as the case group, and others were identified as the control group. A total of 24 701 subjects were included in the study, including 86 lung cancer cases and 24 615 control population, respectively. Questionnaires, physical examinations, and laboratory tests were conducted to collect relevant information. Multivariable-adjusted logistic regressions were conducted to develop a lung cancer risk prediction model. Area Under the Curve (AUC) and Hosmer-Lemeshow tests were used to evaluate discrimination and calibration, respectively. Ten-fold cross-validation was used for internal validation.Results:Two sets of models were developed: the simple model (including age and monthly income) and the metabolic index model [including age, monthly income, fasting blood glucose (FBG), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C)].The AUC (95%CI) [0.745 (0.719-0.771)] of the metabolic index model was higher than that of the simple prediction model [0.688 (0.660-0.716)] ( P=0.004). Both the simple model ( PHL=0.287) and the metabolic index model ( PHL=0.134) were well-calibrated. The results of ten-fold cross-validation indicated sufficient stability, with an average AUC of 0.699 and a standard error (SD) of 0.010. Conclusion:By incorporating metabolic markers, accurate and reliable lung cancer risk prediction model for female non smokers could be developed.