In recent years, remarkable advances have been made in lung transplantation regarding donor evaluation, perioperative support, and immune regulation, laying a foundation for improving the long-term survival of recipients. The rational application of extended criteria donor lungs and the popularization of ex vivo lung perfusion technology have enhanced the utilization efficiency and safety of marginal donor lungs. Refined assessment of risk factors such as donors’ smoking history and infectious status has reduced the incidence of primary graft dysfunction and postoperative infection. In terms of recipient management, individualized assessment systems have been gradually optimized. The standardized application of intraoperative extracorporeal membrane oxygenation and the development of minimally invasive surgical techniques have effectively alleviated perioperative injuries. The optimization of postoperative immunosuppressive regimens and advances in rejection monitoring technologies have further improved the long-term survival of grafts. Nevertheless, chronic graft dysfunction and recurrent or refractory infections remain the major bottlenecks restricting long-term prognosis. Future research should focus on expanding donor sources, improving the repair quality of marginal donor lungs, promoting precise immunosuppression and anti-infection strategies, and comprehensively enhancing the long-term survival rate and quality of life of lung transplant recipients.

Lung transplantation is an important means to treat end-stage lung disease and improve the survival rate and quality of life of patients. However, many postoperative complications seriously affect the prognosis of recipients. Accurate identification of key prognostic factors and construction of individualized and accurate prediction models are of great significance for postoperative prognosis evaluation, treatment strategy formulation and clinical decision-making. In recent years, the clinical prediction model of lung transplantation has gradually changed from traditional statistical methods to machine learning-driven. Compared with traditional models such as Cox regression and Logistic regression, machine learning models such as random forest, support vector machine and artificial neural network have certain advantages in postoperative survival rate prediction, early warning of complications and pulmonary function evaluation. However, their application is also affected by insufficient sample size and poor interpretability of models. Under the condition of small samples, the traditional model still has important value in prediction accuracy. The appropriate prediction model should be selected according to the clinical status of lung transplantation in China, considering the factors such as sample size, variable complexity and model interpretability. In the future, a multi-center, large-sample lung transplantation database should be constructed to further optimize and tap the potential of machine learning algorithms to improve the robustness and clinical applicability of the model.

Objective To evaluate the effect of donor gender on short-term survival rate of lung transplant recipients. Methods A retrospective analysis was conducted on the data of 1 066 lung transplant recipients. The log-rank test was used to evaluate the differences in short-term fatality among different donor gender groups and donor-recipient gender combination groups. Multivariate Cox regression, propensity score (PS) regression, and propensity score matching (PSM) were employed to control for confounding factors and further assess the differences in fatality. Subgroup analyses were also performed based on donor gender. Results Multivariate Cox regression analysis showed no statistically significant differences in fatality at 30 days, 1 year, 2 years and 3 years postoperatively between male and female donor groups (all P>0.05). After PS regression and PSM, univariate Cox regression analysis indicated that recipients from female donors had a higher fatality at 2 years postoperatively compared to those from male donors, with hazard ratios (95% confidence intervals) of 1.29 (1.01-1.65) and 1.36 (1.03-1.80) respectively. Multivariate Cox regression analysis also revealed no statistically significant differences in fatality at various follow-up time points among different donor-recipient gender combination groups (all P>0.05). Subgroup analyses based on donor sex showed no statistically significant differences in fatality among recipients of different gender within either male or female donor groups (all P>0.05). Conclusions Female donors may reduce the short-term postoperative survival rate of lung transplant recipients, but this negative impact is not sustainable in the long term. At present, there is no evidence to support the inclusion of sex as a factor in lung allocation rules.

Lung transplantation is an important means to treat end-stage lung disease and improve the survival rate and quality of life of patients. However, many postoperative complications seriously affect the prognosis of recipients. Accurate identification of key prognostic factors and construction of individualized and accurate prediction models are of great significance for postoperative prognosis evaluation, treatment strategy formulation and clinical decision-making. In recent years, the clinical prediction model of lung transplantation has gradually changed from traditional statistical methods to machine learning-driven. Compared with traditional models such as Cox regression and Logistic regression, machine learning models such as random forest, support vector machine and artificial neural network have certain advantages in postoperative survival rate prediction, early warning of complications and pulmonary function evaluation. However, their application is also affected by insufficient sample size and poor interpretability of models. Under the condition of small samples, the traditional model still has important value in prediction accuracy. The appropriate prediction model should be selected according to the clinical status of lung transplantation in China, considering the factors such as sample size, variable complexity and model interpretability. In the future, a multi-center, large-sample lung transplantation database should be constructed to further optimize and tap the potential of machine learning algorithms to improve the robustness and clinical applicability of the model.

Objective:To explore the causes of the crosstalk in the gross α and gross β measurement using an MPC 9604 low background α/β counter.Methods:With the A4 copy paper (70 g/m 2), polyethylene (PE) films (8.7 g/m 2), and 304 stainless steel seperately as shielding materials, the gross α and gross β experiments, gamma spectrometry experiments and solid state nuclear track detection (SSNTD) experiments were conducted by using 241Am and 40K standard materials. A comprehensive analysis encompassing statistical analysis and nuclear physics analysis was performed to reveal the impact of contributing factors on the crosstalk in the gross α and gross β measurement with an MPC 9604 low background α/β counter. Results:241Am powder source experimental result: when two sheets of copy paper were used in the experiment, α-rays did not generate one count in the β channel of the low background α/β counter. The same test with the shielding material of two layers of PE films showed that the α count rate further decreased by about 36.5%, while the β count rate hardly changed. The gross α and gross β experiments and γ spectrometry with the shielding material of stainless steel demonstrated that the characteristic γ ray peaking at 59.5 keV of the 241Am powder source did not generate one count in the β channel. 40K powder source experimental result: when the source was covered with steel of total thickness of 0.965 mm in the gross α and gross β experiments, the γ rays of 40K did not generate one count in the β channel. Compared with naked 40K powder source, when source was covered with one and two sheets of copy paper, the gross α count rate decreased approximately from 3.30 × 10 -3 to 1.50 × 10 -3 and 1.75 × 10 -3, respectively. The SSNTD indicated the presence of other α nuclides in 40K powder source. Conclusions:The β counting in the β channel with the 241Am powder source using MPC 9604 low background α/β counter was, instead of α-rays, caused by the internal conversion electrons and the characteristic X rays of 11.870-22.402 keV emitted from the 241Am powder source, thus this is not a true α/β crosstalk. The α counting in α channel with the 40K powder source, except the contribution of impurity α nuclides, was mainly attributed to the α signals arising from β particles when the amplitude of the piled-up β pules exceeded the discrimination threshold of the detector, therefore it is a true crosstalk.

Organ transplantation has become an effective treatment for multiple end-stage diseases. However, the recipients of organ transplantation need to take immunosuppressive drugs for a long time after operation, which leads to low immune function and relatively high incidence of bacterial, viral and fungal infections. Traditional microbial detection methods, such as pathogen culture, immunological detection and polymerase chain reaction, have been widely applied in infection detection, whereas these methods may cause problems, such as long detection time and presumed pathogens. Metagenomic next-generation sequencing has been widely adopted in infection prevention and control in organ transplantation in recent years due to high detection rate and comprehensive detection of pathogen spectrum. In this article, the application of metagenomic next-generation sequencing in the prevention and control of infection in solid organ transplantation was reviewed, aiming to provide reference for the diagnosis and treatment of transplantation-related infection.

Lung transplantation is an effective means of treating various end-stage lung diseases.However,compared with other solid organ transplants,the survival rate after lung transplantation is relatively low.The main reason is the numerous complications after lung transplantation,among which infection is one of the most common complications.Respiratory viral infections are an important type of infection after lung transplantation,which severely affect the survival time and quality of life of lung transplant recipients.Early identification,early prevention,and active diagnosis and treatment are of great significance in reducing the incidence and fatality of respiratory viral infections after lung transplantation.This article reviews the epidemiology,risk factors,prevention and treatment principles,and specific prevention and treatment progress of common viruses in respiratory viral infections after lung transplantation at home and abroad,in order to provide a reference for the prevention and treatment of respiratory viral infections after lung transplantation in clinical practice.

Objective To explore the effect of coronavirus disease 2019 (COVID-19) on the frequency of chest CT scan. Methods A retrospective study was conducted to extract information on the number of outpatient, emergency, and inpatient visits and patients who had chest CT imaging examination from January 1 to December 31, 2020 and in the same period in 2019 through the hospital’s medical data platform for analysis, and the chi-square test was used to analyze whether the difference in the proportion of patients who had chest CT imaging examination between 2019 and 2020 was statistically significant. Results The proportion of outpatients and emergency patients with chest CT examination was significantly higher in 2020 than in 2019 (2.48% vs 1.47%, χ² = 581.7, P < 0.000). The proportion of inpatients who underwent chest CT examination was significantly higher in 2020 than in 2019 (35.47% vs 28.01%, χ² = 182.0, P < 0.000). Conclusion Under the COVID-19 epidemic, the proportion of chest CT examination in this hospital in 2020 shows a significant upward trend compared with the same period in 2019, which will increase the collective dose due to medical exposure, and the hospital should pay attention to the determination of the legitimacy of chest CT scan.

Objective:To evaluate the application value of left ventricular pressure-strain loop (PSL) in patients with rheumatoid arthritis (RA) by the PSL.Methods:From April 2020 to June 2021, 75 RA patients(case group) and 35 healthy physical examination person(control group) were selected from the Affiliated Hospital of Inner Mongolia Medical University.According to the disease activity scores 28(DAS28) joint disease range of activity score, the patients were divided into low range of activity group (16 cases), medium range of activity group (35 cases) and high range of activity group (24 cases). Echocardiographic examinations were performed on all patients, conventional ultrasound data were collected and the left ventricular PSL technology was used to analyze the myocardial work parameters, including global work index (GWI), global constructive work (GCW), global waste work (GWW) and global work efficiency (GWE). The mean values of myocardial work index (MWI), constructive work (CW), waste work (WW) and work efficiency (WE) were calculated. The differences of myocardial work parameters between the case groups and the control group were compared, and the correlation analysis of myocardial work parameters with global longitudinal strain of left ventricle(LVGLS), left ventricular ejection fraction(LVEF) and DAS28 scores were conducted.Results:①There were no statistically significant differences of clinical data between each case group and the control group (all P>0.05). ②Compared with the control group and low disease activity group, the LVGLS of medium and high disease activity groups decreased obviously (all P<0.05). ③The GWI and GCW of the medium and high disease activity groups were significantly lower than those of the control group (all P<0.05), and there were no statistically significant differences of GWW and GWE between the case groups and the control group (all P>0.05). The mid-segment MWI, apical MWI and CW of the medium and high disease activity case groups, and the mid-segment CW of the medium disease activity case group were lower than those of the control group (all P<0.05). ④In the case group, GWI, GCW and LVEF were significantly and positively correlated ( rs1=0.253, P=0.008; rs2=0.261, P=0.024), and were significantly negatively correlated with LVGLS ( rs1=-0.525, P<0.001; rs2=0.455, P<0.001). There were no significant correlations between myocardial work parameters and DAS28 score (all P>0.05). Conclusions:PSL can early reflect to the impaired cardiac function of RA patients with moderate and high disease activity, and provide a new imaging method for clinical evaluation of the cardiac function of patients.

Objective To discuss ⁷Be and a 77.2 keV full-energy peak with short half-life found in the water sample from the 3D water phantom of a proton therapy system. Methods We measured the water sample from the 3D water phantom of a proton therapy system according to Determination of Radionuclides in Water by Gamma Spectrometry (GB/T 16140—2018). Results The activity concentration of ⁷Be in the water sample was 1.30 × 10¹ Bq·L⁻¹ on December 24, 2018; 4.3 × 10¹ Bq·L⁻¹ on March 22, 2019; and 1.41 × 10¹ Bq·L⁻¹ at the time of sampling on December 19, 2018. On December 24, 2018, the net peak area of the 77.2 keV full-energy peak in the sample was 683 ± 45, and the measurement time was 26123.02 s; on March 22, 2019, the net peak area decreased to the background level of 194 ± 49, and the measurement time was 86400.00 s. Conclusion In the 3D water phantom of the proton therapy system, ⁷Be can be generated from the spallation reaction between high-energy neutrons and oxygen in water. In addition, we find a full-energy peak at 77.2 keV with short half-life. The activity concentration of ⁷Be in the water sample is lower than the exemption level, but the activity concentration at sampling may not be the maximum activity concentration in the process of quality control. The inductive radionuclide ⁷Be produced in the 3D water phantom should be identified and properly evaluated in the assessment of occupational radiation hazards of proton therapy system.