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BACKGROUND: Non-pharmacological interventions, as complements to pharmacological treatments, are widely employed for managing symptom clusters in patients with lung cancer. Although numerous systematic reviews and meta-analyses have explored the effects of these interventions, most studies have centred on the broader cancer population and specific symptom clusters. This review aims to consolidate existing non-pharmacological interventions and assess their effectiveness in managing symptom clusters among lung cancer patients. METHODS: A comprehensive literature search, encompassing eight databases from inception to October 1, 2024, was conducted. Two independent reviewers carried out the study selection, quality assessment, and data extraction. Methodological quality was evaluated using the Cochrane Risk-of-Bias 2 tool and the Risk of Bias in Non-randomized Studies of Interventions. The findings were synthesized narratively based on intervention type and supplemented by meta-analysis using RevMan 5.4 software. The study protocol was registered with PROSPERO (CRD42023467406). RESULTS: This systematic review comprised 15 relevant studies involving 1,692 patients, published between 2011 and 2024. The analysis revealed the effectiveness of psychological, educational, and complementary or alternative medicine interventions in alleviating the severity of most symptom clusters. However, the efficacy of exercise-based and multimodal interventions remained inconclusive. The meta-analysis demonstrated a positive impact of non-pharmacological interventions on depression compared with the control conditions (SMD = -0.30, 95% CI [-0.46, -0.15], p

BACKGROUND: The intricate balance between the advantages and risks of low-dose computed tomography (LDCT) impedes the utilization of lung cancer screening (LCS). Guiding shared decision-making (SDM) for well-informed choices regarding LCS is pivotal. There has been a notable increase in research related to SDM. However, these studies possess limitations. For example, they may ignore the identification of decision support and needs from the perspective of health care providers and high-risk groups. Additionally, these studies have not adequately addressed the complete SDM process, including pre-decisional needs, the decision-making process, and post-decision experiences. Furthermore, the East-West divide of SDM has been largely ignored. This study aimed to explore the decisional needs and support for shared decision-making for LCS among health care providers and high-risk groups in China. METHODS: Informed by the Ottawa Decision-Support Framework, we conducted qualitative, face-to-face in-depth interviews to explore shared decision-making among 30 lung cancer high-risk individuals and 9 health care providers. Content analysis was used for data analysis. RESULTS: We identified 4 decisional needs that impair shared decision-making: (1) LCS knowledge deficit; (2) inadequate supportive resources; (3) shared decision-making conceptual bias; and (4) delicate doctor-patient bonds. We identified 3 decision supports: (1) providing information throughout the LCS process; (2) providing shared decision-making decision coaching; and (3) providing decision tools. CONCLUSIONS: This study offers valuable insights into the decisional needs and support required to undergo LCS among high-risk individuals and perspectives from health care providers. Future studies should aim to design interventions that enhance the quality of shared decision-making by offering LCS information, decision tools for LCS, and decision coaching for shared decision-making (e.g., through community nurses). Simultaneously, it is crucial to assess individuals needs for effective deliberation to prevent conflicts and regrets after arriving at a decision.

OBJECTIVE: To investigate current screening behaviors among high-risk individuals and analyse the factors that influence them. METHODS: A cross-sectional of 1652 high-risk individuals were recruited in Fujian Province, China from February to October 2022. Socio-demographic characteristics of participants were collected and other survey measures included a lung cancer and lung cancer screening knowledge questionnaire and a stage of adoption algorithm. Standardized measures on surveys were comprised of the: Lung Cancer Screening Health Belief Scales, Cataldo Lung Cancer Stigma Scale, Generalized Anxiety Disorder Scale-7, Patient Health Questionnaire-9, and the Patient Trust in the Medical Profession Scale. Factors associated with screening behavior were identified using binary logistic regression analysis. RESULTS: Lung cancer screening behavior stages were largely reported as Stage 1 and Stage 2 (64.4%). The facilitators of lung cancer screening included urban residence (OR = 1.717, 95% CI: 1.224-2.408), holding administrative positions (OR = 16.601, 95% CI: 2.118-130.126), previous lung cancer screening behavior (OR = 10.331, 95% CI: 7.463-14.302), media exposure focused on lung cancer screening (OR = 1.868, 95% CI: 1.344-2.596), a high level of knowledge about lung cancer and lung cancer screening (OR = 1.256, 95% CI: 1.185-1.332), perceived risk of lung cancer (OR = 1.123, 95% CI: 1.029-1.225) and lung cancer screening health beliefs (OR = 1.090, 95% CI: 1.067-1.113). A barrier to lung cancer screening was found to be social influence (influence of friends or family) (OR = 0.669, 95% CI: 0.465-0.964). CONCLUSIONS: This study found a low participation rate in lung cancer screening and identified eight factors that affected lung cancer screening behaviors among high-risk individuals. Findings suggest targeted lung cancer screening programs should be developed based on identified influencing factors in order to effectively promote awareness and uptake of lung cancer screening.

AimsTo investigate the trajectory, influencing factors and dynamic relationships between fear of cancer recurrence (FCR) and quality of life (QOL) in lung cancer patients.DesignProspective longitudinal study.MethodsLongitudinal data from 310 lung cancer patients across three hospitals in China were assessed at 1, 3, 6 and 12 months postoperatively (T1 -T4 ). Descriptive statistics characterised patient demographics, clinical characteristics, levels of FCR and QOL. A linear mixed-effects model was employed to analyse FCR trajectories, identify influencing factors on these trajectories, and predict the impact of FCR on QOL.ResultsFCR changed significantly over time, with a slight decrease during T1 -T2 , an increase at T3 and gradual decline at T4 . Higher fear levels were associated with female sex, suburban or rural residency, being a family breadwinner, presence of comorbidities and negative coping behaviours, and low family resilience. QOL negatively correlated with FCR, and FCR predicted lower QOL.ConclusionsAt 3 and 6 months postoperatively, lung cancer patients, especially women, suburban or rural residents, family breadwinners, those with comorbidities, negative coping behaviours and low family resilience, reported high levels of FCR. Healthcare providers should pay special attention to lung cancer patients especially during the period of 3-6 months post-surgery and offer tailored interventions to improve their QOL.Implications for the profession and patient careUnderstanding the FCR trajectories, its influencing factors and its negative impacts on QOL can guide the development of targeted interventions to reduce fear and enhance well-being in patients with cancer.ImpactIdentifying the trajectories and influencing factors of fear of lung cancer recurrence in patients at different time points informs future research on targeted interventions to improve QOL.Reporting methodThe study adhered to the guidelines outlined in the Statement on Reporting Observational Longitudinal Research.

BackgroundPrevious clinical studies have found that negative mental states such as depression and anxiety are closely related to COVID-19 infection. We used Mendelian randomization (MR) to explore the relationship between depression, anxiety, and COVID-19 infection.MethodsOur data were based on publicly available GWAS databases. The COVID-19 samples were obtained from the COVID-19 Host Genetics Initiative (HGI). The depression samples were obtained from the Psychiatric Genomics Consortium (PGC). The anxiety samples were derived from the Finngen database. We used inverse-variance weighting (IVW) as the primary analysis method, with weighted median, MR Egger, and multivariate MRI adjustment.ResultsThere was no causal effect of different COVID-19 infection statuses on depression and anxiety as determined by MR analysis. In addition, in the reverse MR analysis, we found a significant causal effect of anxiety on severe symptoms after COVID-19 infection. The results of the MR Egger regression, weighted median, and weighted mode methods were consistent with the IVW method. Based on sensitivity analyses, horizontal pleiotropy was unlikely to influence the final results.ConclusionOur findings indicate that anxiety is a risk factor for severe symptoms following COVID-19 infection. However, the mechanism of interaction between the two needs further investigation.

The vapor–liquid equilibrium (VLE) data of the ternary system methanol–methyl formate–methylal was measured at atmospheric pressure using a modified Rose equilibrium kettle with vapor–liquid double circulation method. The experiment data were correlated with the NRTL, UNIQUAC, and Wilson activity coefficient model equations. The results shown that the root mean square deviation (RMSD) between the calculated and simulated values of the three models followed the order: UNIQUAC ≈ NRTL < Wilson, and except for the RMSD (T) in the range of 0.4–0.5, the others are less than 0.01. In addition, the NRTL model was selected to link with Aspen Plus software to simulate the separation process of polyformaldehyde (POM) waste liquid. The simulation results show that the methyl formate in POM waste stream can be purified by simple distillation, while the methylal separated from the POM waste liquid, which was affected by factors like the azeotropic behavior of binary components, necessitates a complex distillation process. Under optimal operating conditions, the recovery yield of methyl formate through direct distillation can reach 99.7%, with an economic benefit of 6960.1 CNY per ton of waste liquid. Although the economic benefit of the multi-component distillation reach 7281.2 CNY, the increase in the number of equipment and the complexity of the process have negative impacts. [ABSTRACT FROM AUTHOR]

The catastrophic consequences of nuclear reactor core meltdowns necessitate a comprehensive understanding of fuel rod behavior during severe accidents. This study employs the moving particle semi-implicit (MPS) method to simulate fuel rod melting and molten material migration, capturing complex phenomena such as phase changes, chemical reactions, and fluid dynamics. The developed MPS code integrates models for heat transfer, eutectic reactions, and surface tension, allowing for detailed three-dimensional simulations. Validation of the code is achieved through comparisons with analytical solutions and experimental data from the FROMA experiments. Simulations of Al–Zn substitute material rods show accurate temperature responses and phase transitions, highlighting the impact of radiation heat transfer and molten pool depth on melting behavior. Additional simulations of a single rod and a 2 × 2 rod bundle in a pressurized water reactor under extreme conditions reveal the significant role of atomic diffusion. Mass transfer between the cladding and fuel pellet expands the melting area and facilitates the formation of low-melting-point substances. The high-temperature behavior of the rod bundle demonstrates characteristic patterns, including the downward migration of molten zirconium and the dissolution of unmelted cladding. Analysis of molten UO2 migration within the rod bundle channel illustrates the influence of initial temperature and volume on migration dynamics and Zr cladding dissolution rates. This study offers valuable insights into understanding and mitigating the risks associated with core meltdowns, thereby enhancing nuclear reactor safety. [ABSTRACT FROM AUTHOR]

Airborne warning and control systems (AWACS) serve as critical command and control centres in air combat operations, making them prime targets for strategic attacks. These enemy attacks typically rely on the accurate determination of AWACS combat positions using different direction‐finding devices. In particular, passive monopulse angle measurement systems locate AWACS by measuring the angles of signals emitted by AWACS radiation sources, thus rendering them vulnerable to attacks. Given the criticality of the airborne radars of AWACS in battle command and control operations, they must function continuously to monitor air and sea targets. Hence, AWACS cannot effectively evade electronic reconnaissance systems through tactics such as radar shutdown. To explore alternative measures, the authors investigate the deception mechanisms of an integrated frequency diverse array AWACS (FDA‒AWACS) against passive monopulse angle measurements. Using an established FDA signal model, the principles underlying two common monopulse angle measurement methods are first outlined. Subsequently, the angle estimation formulae typically used by these monopulse angle measurement systems to interpret received FDA radiation signals are derived. Additionally, the transmit beampatterns, amplitude patterns, and angle measurement deception capabilities of several typical FDAs are examined. Simulation results indicate that the FDA‒AWACS can theoretically deceive passive monopulse angle measurement systems to a certain extent. However, one‐dimensional uniform linear FDAs and other arrays using sinusoidal frequency offsets exhibit limited deception abilities. In contrast, arrays utilising cubic and quartic frequency offset achieve angle measurement errors exceeding 2° in far‐field scenarios. [ABSTRACT FROM AUTHOR]

At present,the reliability of a single indicator is evaluated by a conventional reliability model.The results have only two states:the system is completely normal and the system is completely faulty.It is difficult to determine what state the valve system is between "good" and "failed".At the same time,ignoring the impact of non-critical state of the system on system reliability and evaluating method with a single indicator will result in insufficient evaluation results.Therefore,a fuzzy evaluation method based on triple index was proposed.The valve system failure probability PF,the failure probability considering the influence of non-critical states of the system PEF and the mean time between failures MTBF as the indicators to evaluate the reliability gray number clustering were considered.The failure probability considering the influence of the non-critical state of the system was obtained by the cumulative failure probability of the system and the number of expected state transitions.The experimental results indicated that the method can well describe the influence of the non-critical state of the system on the reliability and can determine the reliable level of the system.