Lung cancer is a common malignant tumor disease with high clinical disability and death rates. Currently, lung cancer diagnosis mainly relies on manual pathology section analysis, but the low efficiency and subjective nature of manual film reading can lead to certain misdiagnoses and omissions. With the continuous development of science and technology, artificial intelligence (AI) has been gradually applied to imaging diagnosis. Although there are reports on AI-assisted lung cancer diagnosis, there are still problems such as small sample size and untimely data updates. Therefore, in this study, a large amount of recent data was included, and meta-analysis was used to evaluate the value of AI for lung cancer diagnosis. With the help of STATA16.0, the value of AI-assisted lung cancer diagnosis was assessed by specificity, sensitivity, negative likelihood ratio, positive likelihood ratio, diagnostic ratio, and plotting the working characteristic curves of subjects. Meta-regression and subgroup analysis were used to investigate the value of AI-assisted lung cancer diagnosis. The results of the meta-analysis showed that the combined sensitivity of the AI-aided diagnosis system for lung cancer diagnosis was 0.87 [95% CI (0.82, 0.90)], specificity was 0.87 [95% CI (0.82, 0.91)] (CI stands for confidence interval.), the missed diagnosis rate was 13%, the misdiagnosis rate was 13%, the positive likelihood ratio was 6.5 [95% CI (4.6, 9.3)], the negative likelihood ratio was 0.15 [95% CI (0.11, 0.21)], a diagnostic ratio of 43 [95% CI (24, 76)] and a sum of area under the combined subject operating characteristic (SROC) curve of 0.93 [95% CI (0.91, 0.95)]. Based on the results, the AI-assisted diagnostic system for CT (Computerized Tomography), imaging has considerable diagnostic accuracy for lung cancer diagnosis, which is of significant value for lung cancer diagnosis and has greater feasibility of realizing the extension application in the field of clinical diagnosis.

The outbreak of COVID-19 at the end of 2019 has had a significant impact on people. While attention is paid to the immense physical harm it has caused, the psychological impact should not be underestimated. The main purpose of this study was to explore the stress, anxiety, and depression levels of different groups of college students during the COVID-19 pandemic. We conducted an online questionnaire survey of college students by using the Perceived Stress Scale (PSS-10), the Patient Health Questionnaire-9 items (PHQ-9), and the Self-rating Anxiety Scale (SAS). A total of 1,586 questionnaires were collected and analyzed in R language. The results showed that students with moderate to severe stress (PSS-10 ≥ 14) accounted for 67.50%; the detection rate of depression (PHQ-9 ≥ 5) reached 43.77%; and 20.60% of students had anxiety (SAS standard score ≥ 50). There were significant differences in PSS-10/SAS among different genders, majors, whether returning to school or not, and those with different psychological experiences (negative or positive, P < 0.05). It is notable that the median of female, medical student, non-resumption of schooling, and negative experience was higher than that of positive experience (P < 0.05). The results of principal component analysis showed that there were significant differences in PHQ-9, PSS-10, and SAS between the resumption of schooling group and the non-resumption group. Therefore, it is inferred that the stress and anxiety level of college students during the COVID-19 pandemic is generally high, especially for those who have not yet resumed school. Long-term negative emotions can easily lead to serious mental diseases such as cognitive impairment. Education departments should attach great importance to the mental health of college students, and it is necessary to provide precise psychological interventions for groups experiencing greater pressure levels and marked anxiety and depression.

The targeted therapeutic effect of nano drug delivery system for glioblastoma has been hampered by the weak enhanced permeability and retention (EPR) effect of glioblastoma and the low delivering efficiency of NDDS in glioblastoma tissue. In this study, a tumor-penetrating peptide (RGERPPR), the specific ligand of neuropilin-1 overexpressed on glioblastoma and endothelial cells, was used as a targeting moiety to enhance the anti-glioblastoma effect of doxorubicin liposomes. Firstly, RGERPPR-PEG-DSPE was synthesized and used to prepare the RGERPPR peptide-functionalized liposomes (RGE-LS), which showed vesicle sizes of around 90 nm and narrow size distributions. The cellular uptake and in vivo near-infrared fluorescence imaging test displayed that RGE-LS exhibited increased uptake by glioblastoma cells and intracranial glioblastoma tissues. The cytotoxicity assay and anti-glioblastoma study proved that RGERPPR functionalization significantly enhanced the in vitro inhibitory effect of doxorubicin liposomes on glioblastoma cells and prolonged the median survival time of nude mice bearing intracranial glioblastoma. Finally, the immunofluorescence analysis evidenced that RGE-LS were able to penetrate through tumor vessels and stroma and deep into the whole tumor tissue. The results indicated that tumor-penetrating peptide functionalization is an effective strategy for enhancing the anti-glioblastoma effect of doxorubicin liposomes.