ObjectiveStudies have shown a correlation between gut microbiota and anxiety and depression levels. However, these studies are mainly animal studies or clinical studies of non-cancer patients, there is still a lack of relevant studies in cancer patients. The main objective of this trial was to analyze the correlation between probiotics and anxiety and depression levels in cancer patients.MethodsWe screened all cancer patients consecutively admitted to the inpatient department of the First Affiliated Hospital, Zhejiang University School of Medicine in May 2020. A total of 292 cancer patients met our inclusion criteria. Then, we followed up all patients for 24 weeks. Patients who had incomplete data or loss of follow-up were excluded. In addition, in patients who took probiotics, those did not take probiotics consistently or did not take specific probiotics were excluded. Ultimately, the number of patients enrolled was 82 in probiotics cohort and 100 in non-probiotics cohort. The 17-item Hamilton Depression Scale (HAMD-17) questionnaire was used to measure the depression levels of the patients, and we also used Hamilton Anxiety Scale (HAMA) questionnaire to assess the patients’ anxiety levels. A logistic regression model was used to analyze whether the difference in baseline data of two cohorts would affect the final result.ResultsDemographic and clinical characteristics of all cancer patients enrolled in probiotics cohort and non-probiotics cohort were similar except the cancer therapy (P = 0.004). According to the HAMA score, we divided cancer patients into non-anxiety group (HAMA score < 14) and anxiety group (HAMA score ≥ 14). Similarly, cancer patients were also divided into non-depression group (HAMD-17 score ≤ 7) and depression group (HAMD-17 score > 7). The results demonstrated that there was no statistical difference in the proportion of patients with anxiety (6.1 and 13.0%, respectively, P = 0.121) and depression (30.5 and 23.0%, respectively, P = 0.254) between probiotics and non-probiotics cohorts. The results of logistic regression model analysis further proved that the baseline difference in cancer therapy did not affect the conclusions.ConclusionOur results still suggest that there is no significant correlation between probiotics and anxiety and depression levels in cancer patients. Therefore, we do not recommend supplementing probiotics for cancer patients to prevent anxiety and depression. Moreover, high-quality RCTs are also needed to further confirm the conclusions of this study.

Čerenkov luminescence tomography (CLT) is a highly sensitive and promising technique for three-dimensional non-invasive detection of radiopharmaceuticals in living organisms. However, the severe photon scattering effect causes ill-posedness of the inverse problem, and the results of CLT reconstruction are still unsatisfactory. In this work, a multi-stage cascade neural network is proposed to improve the performance of CLT reconstruction, which is based on the attention mechanism and introduces a special constraint. The network cascades an inverse sub-network (ISN) and a forward sub-network (FSN), where the ISN extrapolates the distribution of internal Čerenkov sources from the surface photon intensity, and the FSN is used to derive the surface photon intensity from the reconstructed Čerenkov source, similar to the transmission process of photons in living organisms. In addition, the FSN further optimizes the reconstruction results of the ISN. To evaluate the performance of our proposed method, numerical simulation experiments and in vivo experiments were carried out. The results show that compared with the existing methods, this method can achieve superior performance in terms of location accuracy and shape recovery capability.

Cerenkov Luminescence Tomography (CLT) is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes. However, due to severe ill-posed inverse problem, obtaining accurate reconstruction results is still a challenge for traditional model-based methods. The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source, which effectively improves the performance of CLT reconstruction. However, the previously proposed deep learning-based methods cannot work well when the order of input is disarranged. In this paper, a novel 3D graph convolution-based residual network, GCR-Net, is proposed, which can obtain a robust and accurate reconstruction result from the photon intensity of the surface. Additionally, it is proved that the network is insensitive to the order of input. The performance of this method was evaluated with numerical simulations and in vivo experiments. The results demonstrated that compared with the existing methods, the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing three-dimensional information.