Objective To analyze the influenza-like illness (ILI) data in Ordos City from 2017 to 2023 and conduct nucleic acid detection of the virus to understand the local influenza epidemic situation, and to provide a reliable basis for influenza prevention and control in the city. Methods Real-time quantitative polymerase chain reaction (qPCR) was used to identify virus subtypes in ILI throat swab samples. Comparisons of positive rates were conducted using the chi-square test, with a significance level of α=0.05. Results From 2017 to 2023, a total of 3,283,434 outpatient and emergency visits were recorded at the Ordos City Central Hospital, including 74,159 ILI cases, with an ILI proportion of 2.26%. The majority of ILI cases (74.43%) occurred in children aged 0~14 years old. The overall positive rate of influenza virus nucleic acid detection was 10.87%, with the highest proportion being subtype A (seasonal H3) at 43.03%. The highest detection rate was observed in the 5~14 years age group, with statistically significant differences in positive rates across age groups (χ²=155.638, P<0.001). Influenza peaks occurred mainly from November to March of the following year. From January to April, three types of influenza were prevalent alternately or mixed, while from October to December, subtype A (seasonal H3) predominated. Positive rates varied significantly across months (χ²=250.923, P<0.001). The temporal trends of ILI proportions and PCR-positive rates were consistent. Conclusion Influenza in Ordos City exhibits distinct seasonal and age distribution characteristics, with alternating or mixed circulation of three virus types. Continued efforts are needed to strengthen influenza surveillance, especially the prevention and control of influenza in infants and adolescents.

ObjectiveTo investigate the effects of targeted silencing of Runt-related Transcription Factor 3 （RUNX3） on the proliferation and migration of Mouse Hepatic Stellate Cells （HSCs）， as well as subsequent collagen deposition. MethodsMouse hepatic stellate cell line （JS-1） was selected and then morphologically observed and identified under a microscope. After the cells had fully adhered， they were treated with 5 ng/mL of transforming growth factor beta 1 （TGF-β₁） for 24 hours to induce hepatic stellate cell activation. Furthermore， a RUNX3 silencing model was established using RUNX3 lentiviral infection. The experiment was divided into four groups： Control group， TGF-β₁ group， TGF-β₁+siRNA-NC group， and TGF-β₁+siRNA-RUNX3 group. Protein expression changes of RUNX3， alpha-smooth muscle actin （α-SMA）， and Alpha 1 type I collagen （Collagen I） were detected using Western blot method. Cellular immunofluorescence assays were employed to investigate the deposition changes of α-SMA and RUNX3 in hepatic stellate cells. RT-qPCR was utilized to examine the mRNA expression changes of RUNX3， α-SMA， and Collagen I. The proliferative capacity of hepatic stellate cells was assessed using Edu staining. The migratory ability of hepatic stellate cells was evaluated through wound healing assays and Transwell migration experiments. ResultsCompared with Control group， a significant elevation in RUNX3 was observed in the TGF-β₁-induced activated HSCs （P<0.01）. Meanwhile， the protein and mRNA levels of fibrosis-related markers and α-SMA and Collagen I were significantly upregulated （P<0.001）. Additionally， the proliferation and migration capabilities of HSCs were significantly enhanced （P<0.001）. In contrast， when compared to TGF-β₁+siRNA-NC group， TGF-β₁+siRNA-RUNX3 group exhibited a notable decrease in RUNX3 and other related indicators， such as the protein and mRNA levels of α-SMA and Collagen I （P<0.05）. Concurrently， the proliferation and migration capabilities of HSCs were significantly inhibited in TGF-β₁+siRNA-RUNX3 group （P<0.01）. ConclusionSilencing RUNX3 can inhibit the deposition of collagen and the proliferation and migration of hepatic stellate cells. Conversely， RUNX3 promotes the proliferation and migration capabilities of HSCs， thereby facilitating the activation of HSC.

ObjectiveTo investigate the role of runt-related transcription factor 3 （RUNX3） in transforming growth factor-β₁ （TGF-β₁）-induced activation of mouse primary pulmonary fibroblasts （PFs）， and its effects on fibroblast activation protein （FAP） expression， cell proliferation， and collagen synthesis. MethodsPFs were isolated from C57BL/6 mice and cultured. A RUNX3 knockdown model was established using small interfering RNA （siRNA）. Cells were assigned to the control group （Control）， TGF-β₁-treated group （TGF-β₁）， negative control group （TGF-β₁+siRNA-NC）， and RUNX3-silenced group （TGF-β₁+si-RUNX3）. In addition， a RUNX3 overexpression rescue experiment was performed based on TGF-β₁ stimulation. Protein and mRNA levels of RUNX3， FAP， and typeⅠcollagen （COL1A1） were measured by Western blot and reverse transcription quantitative real-time PCR （RT-qPCR）. Cell proliferation was assessed using CCK-8 and EdU assays. Co-expression of COL1A1 and FAP was examined by double immunofluorescence staining. ResultsCompared with the Control group， RUNX3， FAP， and COL1A1 expression levels were upregulated in PFs in the TGF-β₁ group （P<0.01）. The CCK-8 assay showed that the absorbance value was reduced in the RUNX3 knockdown group compared with the negative control group （P<0.01）. Consistently， the EdU assay demonstrated a lower proportion of EdU-positive cells in the RUNX3 knockdown group than in the negative control group （P<0.01）. Immunofluorescence double staining revealed decreased fluorescence intensities of COL1A1 and FAP in the RUNX3 knockdown group relative to the negative control. Under RUNX3 overexpression conditions， these fluorescence signals exhibited a partial rebound （P<0.01）. ConclusionRUNX3 in TGF-β1-induced PFs may promote cell proliferation and collagen synthesis by positively regulating FAP expression. Targeting the RUNX3/FAP axis may represent a potential therapeutic strategy for pulmonary fibrosis.

With the rapid development of medical big data and artificial intelligence,Clinical Prediction Models(CPMs)have become pivotal tools for disease prevention,diagnosis,and treatment.Current research predominantly focuses on the economic analysis of pharmacological or public health interventions,yet a comprehensive methodological framework for the economic evaluation of CPMs has been notably absent.The Guidelines for Economic Evaluation of Clinical Prediction Models(hereafter the Guidelines),jointly initiated by the Chinese Research Hospital Association and Peking University,Tsinghua University,and Capital Medical University,adheres to the WHO Handbook for Guideline Development and the Reporting Items for Practice Guidelines in Healthcare(RIGHT)standards.A multidisciplinary collaboration,including a steering committee,expert panel,secretariat,and external review group,was established to develop the guideline following evidence-based principles and procedures.Consensus recommendations were formulated through the Delphi method.It describes the background,objectives,target group,and the development methodology and process,ensuring the entire compilation process of the Guidelines is transparent and standardized.Through comprehensive evidence retrieval,systematic evidence appraisal,and a scientific approach to forming recommendations,the scientific rigor and validity of the Guidelines were further enhanced.

Objective: To investigate the effect of targeted silencing of DNA methyltransferase 3A(DNMT3A) on collagen deposition, proliferation and migration activity of mouse lung fibroblasts(PFs). Methods: In order to ensure the proliferation and migration activity of primary fibroblasts, the lung tissues of neonatal C57 suckling mice were taken, PFs were extracted after being sheared, and the morphology was observed and identified under the microscope. PFs cells were activated by 5 ng/ml TGF-β1for 24 h after cell attachment, and DNMT3A silencing model was constructed by small interfering RNA; The experiment was divided into control group, TGF-β1group, TGF-β1+ siRNA-NC group and TGF-β1+ siRNA-DNMT3A group. The protein expressions of DNMT3A, α-smooth muscle actin(α-SMA) and Collagen Ⅰ were detected by Western blot; Real time quantitative reverse transcription polymerase chain reaction(RT-qPCR) was used to detect the mRNA expression changes ofDNMT3A,α-SMAandCollagenⅠ. The proliferation ability of PFs was detected by CCK-8 and EdU staining; the migration ability of PFs was detected by scratch test and Transwell migration test. Results: Compared with the control group, TGF-β1induced the increase of DNMT3A in the activated PFs cell group(P<0.01), the protein and mRNA levels of fibrosis and proliferation related indicators α-SMA and Collagen Ⅰ also increased(allP<0.05), and the proliferation and migration ability of PFs increased(allP<0.000 1). Compared with the siRNA-NC group, the protein expression levels of DNMT3A(P<0.000 1) and related indicators α-SMA(P<0.01) and Collagen Ⅰ(P<0.01) significantly decreased in the DNMT3A silencing group by Western blot, and the mRNA levels ofDNMT3A,α-SMAandCollagenⅠby RT-qPCR also decreased(allP<0.001), and the proliferation(P<0.01) and migration ability(P<0.05) of PFs cells decreased compared with the control group. Conclusion Silencing DNMT3A can inhibit the deposition of collagen and the proliferation of PFs. DNMT3A can promote the proliferation and migration of PFs, and then promote the activation of PFs and the development of pulmonary fibrosis. This process may be regulated by DNA methylation modification.

ObjectiveTo investigate the chemical hazards in the production line of lithium batteries, so as to provide a scientific basis for the management of occupational-health risk and to promote the healthy and sustainable development of the lithium battery industry. MethodsAn on-site survey on the process flow of the production of lithium battery was conducted in an enterprise. Volatile organic compounds (VOCs) in the occupational environment were collected by Summa canisters, carbonates and N-methyl pyrrolidone (NMP) were collected using activated carbon tubes, and airborne metals were collected using filter membranes. VOCs, carbonates and NMP were detected by gas chromatography-mass spectrometry (GC-MS), and airborne metal elements in the dust samples were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). ResultsNon-targeted environmental monitoring results indicated that NMP was detected in the negative /positive electrode coating, assembly and drying filling workstations, dimethyl carbonate (DMC) was detected in the assembly, drying and electrolyte injection workstations, and ethyl methyl carbonate (EMC) was detected solely in the electrolyte injection workstation. Semi-quantitative analyses of VOCs identified 136 pollutants, including acrylonitrile and halohydrocarbons. Quantitative targeted environmental monitoring results revealed the highest geometric mean (GM) concentration of EMC (31.450 mg·m^-3) was found in the assembly and drying workstations, diethyl carbonate (DEC) was detected in all workstations. While vinylene carbonate (VC) and ethylene carbonate (EC) were detected only in electrolyte injection, assembly and drying workstations. NMP was detected in all positive electrode coating samples, with a GM concentration of 5.68 mg·m^-3 (concentration range: 4.0‒ 7.4 mg·m^-³). Lithium was exclusively detected in dust samples from the liquid injection workstation (GM: 0.014 μg·m^-³). ConclusionNMP, EMC, DEC, and other chemicals are identified at the key workstations such as the positive electrode coating, electrolyte injection, assembly and drying in the lithium production line. Furthermore, semi-quantitative VOCs analyses identified 136 pollutants, demonstrating a characteristic of multicomponent chemical exposure.

With the rapid development of medical big data and artificial intelligence,Clinical Prediction Models(CPMs)have become pivotal tools for disease prevention,diagnosis,and treatment.Current research predominantly focuses on the economic analysis of pharmacological or public health interventions,yet a comprehensive methodological framework for the economic evaluation of CPMs has been notably absent.The Guidelines for Economic Evaluation of Clinical Prediction Models(hereafter the Guidelines),jointly initiated by the Chinese Research Hospital Association and Peking University,Tsinghua University,and Capital Medical University,adheres to the WHO Handbook for Guideline Development and the Reporting Items for Practice Guidelines in Healthcare(RIGHT)standards.A multidisciplinary collaboration,including a steering committee,expert panel,secretariat,and external review group,was established to develop the guideline following evidence-based principles and procedures.Consensus recommendations were formulated through the Delphi method.It describes the background,objectives,target group,and the development methodology and process,ensuring the entire compilation process of the Guidelines is transparent and standardized.Through comprehensive evidence retrieval,systematic evidence appraisal,and a scientific approach to forming recommendations,the scientific rigor and validity of the Guidelines were further enhanced.

Objective:To investigate the correlation between the hemodynamic pattern of non-culprit vessel stenosis and long-term vessel-oriented composite outcome(VOCO) in patients with acute ST-segment elevation myocardial infarction (STEMI).Methods:From January 2019 to December 2021, 233 consecutive patients with STEMI and non-culprit vessel stenosis were prospectively enrolled at Shanghai East Hospital. The median follow-up duration was 3.9 years. The 367 non-culprit vessels of the 233 patients were divided into the VOCO group (33 vessels, 9.0%) and the non-VOCO group (334 vessels, 91.0%). Parameters pertaining to the hemodynamic pattern of non-culprit vessel stenosis between the two groups were compared. Receiver operating characteristic (ROC) curves were used to assess the correlation between hemodynamic pattern and VOCO, and Cox multivariate regression and logistic multivariate regression analyses were applied to identify independent predictors of VOCO.Results:The 233 enrolled patients were aged (62.5±12.9) years, with 193 males (82.8%). In the VOCO group, the maximum quantitative flow ratio (QFR) decreased within 20 mm of the QFR-assessed segment, the difference in QFR across the entire vessel, the length of functionally significant vessel, and the maximum gradient of QFR decrease (dQFR/dsmax) were significantly greater than those in the non-VOCO group. ROC curve analysis showed that the optimal threshold for predicting VOCO using dQFR/dsmax was 0.009 6 (area under the curve: 0.691, 95% CI: 0.606-0.775, P<0.001). Multivariable Cox regression analysis revealed that dQFR/dsmax was an independent predictor of VOCO ( HR=1.199, 95% CI: 1.070-1.343, P=0.002). When anatomical and functional stenosis severities were included in the model, a high pullback pressure gradient (PPG) index ( HR=1.572, 95% CI: 1.052-2.351, P=0.027) emerged as an independent predictor of VOCO. Multivariable logistic regression analysis revealed that a low PPG index( OR=2.851, 95% CI: 1.945-4.178, P<0.001) was an independent predictor of QFR≤0.80 without long-term VOCO. Conclusion:In patients with STEMI, localized hemodynamic patterns of coronary artery stenosis, characterized by high dQFR/dsmax and high PPG index, are associated with long-term VOCO.

Objective:To investigate the correlation between the hemodynamic pattern of non-culprit vessel stenosis and long-term vessel-oriented composite outcome(VOCO) in patients with acute ST-segment elevation myocardial infarction (STEMI).Methods:From January 2019 to December 2021, 233 consecutive patients with STEMI and non-culprit vessel stenosis were prospectively enrolled at Shanghai East Hospital. The median follow-up duration was 3.9 years. The 367 non-culprit vessels of the 233 patients were divided into the VOCO group (33 vessels, 9.0%) and the non-VOCO group (334 vessels, 91.0%). Parameters pertaining to the hemodynamic pattern of non-culprit vessel stenosis between the two groups were compared. Receiver operating characteristic (ROC) curves were used to assess the correlation between hemodynamic pattern and VOCO, and Cox multivariate regression and logistic multivariate regression analyses were applied to identify independent predictors of VOCO.Results:The 233 enrolled patients were aged (62.5±12.9) years, with 193 males (82.8%). In the VOCO group, the maximum quantitative flow ratio (QFR) decreased within 20 mm of the QFR-assessed segment, the difference in QFR across the entire vessel, the length of functionally significant vessel, and the maximum gradient of QFR decrease (dQFR/dsmax) were significantly greater than those in the non-VOCO group. ROC curve analysis showed that the optimal threshold for predicting VOCO using dQFR/dsmax was 0.009 6 (area under the curve: 0.691, 95% CI: 0.606-0.775, P<0.001). Multivariable Cox regression analysis revealed that dQFR/dsmax was an independent predictor of VOCO ( HR=1.199, 95% CI: 1.070-1.343, P=0.002). When anatomical and functional stenosis severities were included in the model, a high pullback pressure gradient (PPG) index ( HR=1.572, 95% CI: 1.052-2.351, P=0.027) emerged as an independent predictor of VOCO. Multivariable logistic regression analysis revealed that a low PPG index( OR=2.851, 95% CI: 1.945-4.178, P<0.001) was an independent predictor of QFR≤0.80 without long-term VOCO. Conclusion:In patients with STEMI, localized hemodynamic patterns of coronary artery stenosis, characterized by high dQFR/dsmax and high PPG index, are associated with long-term VOCO.

The COVID-19 epidemic has spread to the whole world for three years and has had a serious impact on human life, health and economic activities. China's epidemic prevention and control has gone through the following stages: emergency unconventional stage, emergency normalization stage, and the transitional stage from the emergency normalization to the "Category B infectious disease treated as Category B" normalization, and achieved a major and decisive victory. The designated hospitals for prevention and control of COVID-19 epidemic in Tianjin has successfully completed its tasks in all stages of epidemic prevention and control, and has accumulated valuable experience. This article summarizes the experience of constructing a hospital infection prevention and control system during the "Category B infectious disease treated as Category A" period in designated hospital. The experience is summarized as the "Cluster" hospital infection prevention and control system, namely "three rings" outside, middle and inside, "three districts" of green, orange and red, "three things" before, during and after the event, "two-day pre-purification" and "two-director system", and "one zone" management. In emergency situations, we adopt a simplified version of the cluster hospital infection prevention and control system. In emergency situations, a simplified version of the "Cluster" hospital infection prevention and control system can be adopted. This system has the following characteristics: firstly, the system emphasizes the characteristics of "cluster" and the overall management of key measures to avoid any shortcomings. The second, it emphasizes the transformation of infection control concepts to maximize the safety of medical services through infection control. The third, it emphasizes the optimization of the process. The prevention and control measures should be comprehensive and focused, while also preventing excessive use. The measures emphasize the use of the least resources to achieve the best infection control effect. The fourth, it emphasizes the quality control work of infection control, pays attention to the importance of the process, and advocates the concept of "system slimming, process fattening". Fifthly, it emphasizes that the future development depends on artificial intelligence, in order to improve the quality and efficiency of prevention and control to the greatest extent. Sixth, hospitals need to strengthen continuous training and retraining. We utilize diverse training methods, including artificial intelligence, to ensure that infection control policies and procedures are simple. We have established an evaluation and feedback mechanism to ensure that medical personnel are in an emergency state at all times.