Effects of secukinumab combined with tretinoin on metabolism, liver enzymes, and inflammatory factors in patients with moderate to severe psoriasis vulgaris.

Introduction: Psoriasis is a T cell-mediated polygenic chronic inflammatory disease. Interleukin (IL)-17A plays a major role in psoriasis pathogenesis. Secukinumab is a high-affinity human monoclonal antibody against IL-17A. Aim: This article explored efficacy and safety of secukinumab plus tretinoin in moderate to severe psoriasis (MSP) vulgaris, and assessed metabolism, liver function, and inflammation. Material and methods: A total of 135 patients diagnosed with moderate or severe psoriasis vulgaris were enrolled and randomized into three groups at a 1 : 1 : 1 ratio, receiving treatment with rretinoin, secukinumab, or combination therapy for a duration of 16 weeks. Psoriasis area and severity index (PASI) scores, serum T lymphocyte subsets, glucose, lipid, and uric acid (UA) metabolism, liver enzymes, and inflammatory factors (IFs) were measured. Results: Following the therapy, subjects had decreased PASI scores, increased serum CD3+, CD4+, and CD4+/CD8+, decreased serum CD8+, and decreased serum UA and IL-2, IL-6, IL-23, interferon-γ (IFN-γ), and tumor necrosis factor (TNF)-α (p < 0.05). Total cholesterol, triglycerides, low-density lipoprotein, high-density lipoprotein, apolipoproteins A1, B, fasting blood glucose, alanine transaminase, aspartate transaminase, and alkaline phosphatase had no obvious differences among the subjects (p > 0.05). As against the Tretinoin and the Secukinumab groups, the PASI score was visiblysmaller, the changes in serum T lymphocyte subsets were more obvious, and serum UA and IFs were lower in the Combination group following the therapy (p < 0.05). Conclusions: Secukinumab combined with tretinoin is more effective in MSP vulgaris, which can visibly reduce inflammatory response without affecting glucose and lipid metabolism and liver function.

K-CapsNet: K-Nearest Neighbor Based Convolution Capsule Network for Cerenkov Luminescence Tomography Reconstruction.

Cerenkov luminescence tomography (CLT) has received significant attention as a promising imaging modality that can display the three-dimensional (3D) distribution of radioactive probes. However, the reconstruction of CLT suffers from severe ill-posed problem. It is difficult for traditional model-based method to obtain satisfactory result. Recently, deep learning-based method have shown great potential for accurate and efficient CLT reconstruction. In this study, a KNN-based convolution capsule network, named K-CapsNet, is proposed for cerenkov luminescence tomography. In K-CapsNet, the surface photon intensity is encoded in capsule form. The KNN-based convolution and K-means clustering are proposed for efficient encoding. Numerical simulation experiments have been carried out to verify the performance of K-CapsNet, and the results show that it performs superior in source localization and morphological restoration compared with existing methods.

FISTA-NET: Deep Algorithm Unrolling for Cerenkov luminescence tomography.

Cerenkov luminescence tomography (CLT) is a highly sensitive and promising imaging technique that can be used to reconstruct the three-dimensional distribution of radioactive probes in living animals. However, the accuracy of CLT reconstruction is limited by the simplified radiative transfer equation and ill-conditioned inverse problem. To address this issue, we propose a model-based deep learning network that combines the neural network with a model-based approach to enhance the performance of CLT reconstruction. The Fast Iterative Shrinkage Thresholding Algorithm (FISTA), a traditional model-based approach, is expanded into a deep network (known as FISTA-NET). Each layer in the network represents an iteration of the algorithm steps, and connecting these layers can form a deep neural network. In addition, different from the traditional FISTA, the key parameters in FISTA, such as gradient step size and threshold value, can be learned through training data without manual production. To evaluate the performance of FISTA-NET, numerical simulation experiments were conducted, which demonstrate its excellent positioning and shape recovery abilities.Clinical Relevance-This indicates that FISTA-NET strategy can significantly improve the quality of CLT reconstruction, which is further beneficial to the assessment of disease activity and treatment effect based on CLT.

Sparse reconstruction based on dictionary learning and group structure strategy for cone-beam X-ray luminescence computed tomography.

As a dual-modal imaging technology that has emerged in recent years, cone-beam X-ray luminescence computed tomography (CB-XLCT) has exhibited promise as a tool for the early three-dimensional detection of tumors in small animals. However, due to the challenges imposed by the low absorption and high scattering of light in tissues, the CB-XLCT reconstruction problem is a severely ill-conditioned inverse problem, rendering it difficult to obtain satisfactory reconstruction results. In this study, a strategy that utilizes dictionary learning and group structure (DLGS) is proposed to achieve satisfactory CB-XLCT reconstruction performance. The group structure is employed to account for the clustering of nanophosphors in specific regions within the organism, which can enhance the interrelation of elements in the same group. Furthermore, the dictionary learning strategy is implemented to effectively capture sparse features. The performance of the proposed method was evaluated through numerical simulations and in vivo experiments. The experimental results demonstrate that the proposed method achieves superior reconstruction performance in terms of location accuracy, target shape, robustness, dual-source resolution, and in vivo practicability.

Generalized conditional gradient method with adaptive regularization parameters for fluorescence molecular tomography.

Fluorescence molecular tomography (FMT) is an optical imaging technology with the ability of visualizing the three-dimensional distribution of fluorescently labelled probes in vivo. However, due to the light scattering effect and ill-posed inverse problems, obtaining satisfactory FMT reconstruction is still a challenging problem. In this work, to improve the performance of FMT reconstruction, we proposed a generalized conditional gradient method with adaptive regularization parameters (GCGM-ARP). In order to make a tradeoff between the sparsity and shape preservation of the reconstruction source, and to maintain its robustness, elastic-net (EN) regularization is introduced. EN regularization combines the advantages of L1-norm and L2-norm, and overcomes the shortcomings of traditional Lp-norm regularization, such as over-sparsity, over-smoothness, and non-robustness. Thus, the equivalent optimization formulation of the original problem can be obtained. To further improve the performance of the reconstruction, the L-curve is adopted to adaptively adjust the regularization parameters. Then, the generalized conditional gradient method (GCGM) is used to split the minimization problem based on EN regularization into two simpler sub-problems, which are determining the direction of the gradient and the step size. These sub-problems are addressed efficiently to obtain more sparse solutions. To assess the performance of our proposed method, a series of numerical simulation experiments and in vivo experiments were implemented. The experimental results show that, compared with other mathematical reconstruction methods, GCGM-ARP method has the minimum location error (LE) and relative intensity error (RIE), and the maximum dice coefficient (Dice) in the case of different sources number or shape, or Gaussian noise of 5%-25%. This indicates that GCGM-ARP has superior reconstruction performance in source localization, dual-source resolution, morphology recovery, and robustness. In conclusion, the proposed GCGM-ARP is an effective and robust strategy for FMT reconstruction in biomedical application.

ABPO-TVSCAD: alternating Bregman proximity operators approach based on TVSCAD regularization for bioluminescence tomography.

Objective.Bioluminescence tomography (BLT) is a promising non-invasive optical medical imaging technique, which can visualize and quantitatively analyze the distribution of tumor cells in living tissues. However, due to the influence of photon scattering effect and ill-conditioned inverse problem, the reconstruction result is unsatisfactory. The purpose of this study is to improve the reconstruction performance of BLT.Approach.An alternating Bregman proximity operators (ABPO) method based on TVSCAD regularization is proposed for BLT reconstruction. TVSCAD combines the anisotropic total variation (TV) regularization constraints and the non-convex smoothly clipped absolute deviation (SCAD) penalty constraints, to make a trade-off between the sparsity and edge preservation of the source. ABPO approach is used to solve the TVSCAD model (ABPO-TVSCAD for short). In addition, to accelerate the convergence speed of the ABPO, we adapt the strategy of shrinking the permission source region, which further improves the performance of ABPO-TVSCAD.Main results.The results of numerical simulations andin vivoxenograft mouse experiment show that our proposed method achieved superior accuracy in spatial localization and morphological reconstruction of bioluminescent source.Significance.ABPO-TVSCAD is an effective and robust reconstruction method for BLT, and we hope that this method can promote the development of optical molecular tomography.

Elastic net-based non-negative iterative three-operator splitting strategy for Cerenkov luminescence tomography.

Cerenkov luminescence tomography (CLT) provides a powerful optical molecular imaging technique for non-invasive detection and visualization of radiopharmaceuticals in living objects. However, the severe photon scattering effect causes ill-posedness of the inverse problem, and the location accuracy and shape recovery of CLT reconstruction results are unsatisfactory for clinical application. Here, to improve the reconstruction spatial location accuracy and shape recovery ability, a non-negative iterative three operator splitting (NNITOS) strategy based on elastic net (EN) regularization was proposed. NNITOS formalizes the CLT reconstruction as a non-convex optimization problem and splits it into three operators, the least square, L1/2-norm regularization, and adaptive grouping manifold learning, then iteratively solved them. After stepwise iterations, the result of NNITOS converged progressively. Meanwhile, to speed up the convergence and ensure the sparsity of the solution, shrinking the region of interest was utilized in this strategy. To verify the effectiveness of the method, numerical simulations and in vivo experiments were performed. The result of these experiments demonstrated that, compared to several methods, NNITOS can achieve superior performance in terms of location accuracy, shape recovery capability, and robustness. We hope this work can accelerate the clinical application of CLT in the future.

The Correlation Between Probiotics and Anxiety and Depression Levels in Cancer Patients: A Retrospective Cohort Study.

Objective: Studies 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. Methods: We 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. Results: Demographic 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. Conclusion: Our 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.

Effects of bulking agents on greenhouse gases and related genes in sludge composting.

The effect of organic bulking agents on CO2, NH3, N2O and CH4 emission and related genes was evaluated in 40 days sludge composting with wood chip, wheat straw and rice husk, respectively. The results showed wood chip had the highest C/N of 111.3, total porosity of 93.13% and aeration porosity of 78.98% among three bulking agents. Wheat straw had the highest water-holding porosity of 25.62%, which could be critical factor increasing CH4 production and reducing NH3 emission. Moreover, there was no significant difference in N2O emission rates in three composting systems with three bulking agents. RDA analysis showed a negative correlation between mcrA and NH + 4-N. Nitrate content in raw feedstock was dominant factor limiting N2O yield due to low amoA. The continuous increase of oxidation-reduction potential was significantly positive correlated with pmoA and negative correlation with nirK and norB, which reduced N2O and CH4 production in the curing period.