The Psychological Symptom Cluster Among Women with Breast Cancer Before and During Adjuvant Therapy

Women with breast cancer commonly experience multiple psychological symptoms (i.e., fatigue, depressive symptom, anxiety) that cluster together throughout their cancer diagnosis and treatment trajectory. Individuals differ substantially in their experience and trajectory of psychological symptoms. A number of factors may contribute to these differences in symptom experience, including demographic and clinical characteristics. In addition, given the contribution of the hypothalamic-pituitary-adrenal (HPA) axis to these contemporaneous symptoms, individual differences in genes that regulate the HPA activity may play a role. This dissertation study aimed to (1) characterize the clustering of psychological symptoms over time among postmenopausal women with early stage breast cancer during the first 18 months of adjuvant therapy, (2) identify distinct subgroups of women with breast cancer based on their experience of a cluster of psychological symptoms , and (3) assess whether distinct demographic and clinical characteristics and variation in genes regulating the HPA axis predict symptom trajectory subgroup membership. This study used symptom and genetic data from postmenopausal women with early stage breast cancer followed from baseline (pre-adjuvant therapy) to 18 months post-initiation of adjuvant therapy. Results showed that most symptom clusters (i.e., psychological, neurocognitive, weight, musculoskeletal, vasomotor, urinary, and sexual) existed before adjuvant therapy and were relatively stable through the first 18 months of adjuvant therapy. The gastrointestinal symptom cluster only appeared at 6 months. Fatigue and symptoms of depression and anxiety clustered together as a psychological symptom cluster over the 18-month period. Two distinct symptom subgroups (“all low” and “all high”) were identified based on the trajectories of fatigue, depressive symptom and anxiety. The “all low” subgroup had stable low severity of fatigue and depressive symptom and a linear decreasing pattern for anxiety over time. The “all high” subgroup had stable high severity of fatigue and depressive symptom and a quadratic pattern for anxiety over time. Women who were younger in age, had less education, and who received chemotherapy had greater likelihood of being in the “all high” symptom subgroup. Variation in genes regulating the HPA axis (i.e., FKBP5 rs9394309, NR3C2 rs5525, CRHR1 rs12944712) were associated with membership in the “all high” symptom subgroup. The results of this study may help to identify women with breast cancer who are at increased risk for psychological symptoms, facilitating the development of individualized and preemptive interventions to better manage their symptoms during adjuvant therapy

AIF Downregulation and Its Interaction with STK3 in Renal Cell Carcinoma

Apoptosis-inducing factor (AIF) plays a crucial role in caspase-independent programmed cell death by triggering chromatin condensation and DNA fragmentation. Therefore, it might be involved in cell homeostasis and tumor development. In this study, we report significant AIF downregulation in the majority of renal cell carcinomas (RCC). In a group of RCC specimens, 84% (43 out of 51) had AIF downregulation by immunohistochemistry stain. Additional 10 kidney tumors, including an oxyphilic adenoma, also had significant AIF downregulation by Northern blot analysis. The mechanisms of the AIF downregulation included both AIF deletion and its promoter methylation. Forced expression of AIF in RCC cell lines induced massive apoptosis. Further analysis revealed that AIF interacted with STK3, a known regulator of apoptosis, and enhanced its phosphorylation at Thr180. These results suggest that AIF downregulation is a common event in kidney tumor development. AIF loss may lead to decreased STK3 activity, defective apoptosis and malignant transformation

Complete chloroplast genome sequence of Rhododendron mariesii and comparative genomics of related species in the family Ericaeae

Rhododendron mariesii Hemsley et Wilson, 1907, a typical member of the family Ericaeae, possesses valuable medicinal and horticultural properties. In this research, the complete chloroplast (cp) genome of R. mariesii was sequenced and assembled, which proved to be a typical quadripartite structure with the length of 203,480 bp. In particular, the lengths of the large single copy region (LSC), small single copy region (SSC), and inverted repeat regions (IR) were 113,715 bp, 7,953 bp, and 40,918 bp, respectively. Among the 151 unique genes, 98 were protein-coding genes, 8 were tRNA genes, and 45 were rRNA genes. The structural characteristics of the R. mariesii cp genome was similar to other angiosperms. Leucine was the most representative amino acid, while cysteine was the lowest representative. Totally, 30 codons showed obvious codon usage bias, and most were A/U-ending codons. Six highly variable regions were observed, such as trnK-pafI and atpE-rpoB, which could serve as potential markers for future barcoding and phylogenetic research of R. mariesii species. Coding regions were more conserved than non-coding regions. Expansion and contraction in the IR region might be the main length variation in R. mariesii and related Ericaeae species. Maximum-likelihood (ML) phylogenetic analysis revealed that R. mariesii was relatively closed to the R. simsii Planchon, 1853 and R. pulchrum Sweet,1831. This research will supply rich genetic resource for R. mariesii and related species of the Ericaeae

Autonomous Navigation for Autonomous Underwater Vehicles Based on Information Filters and Active Sensing

This paper addresses an autonomous navigation method for the autonomous underwater vehicle (AUV) C-Ranger applying information-filter-based simultaneous localization and mapping (SLAM), and its sea trial experiments in Tuandao Bay (Shangdong Province, P.R. China). Weak links in the information matrix in an extended information filter (EIF) can be pruned to achieve an efficient approach-sparse EIF algorithm (SEIF-SLAM). All the basic update formulae can be implemented in constant time irrespective of the size of the map; hence the computational complexity is significantly reduced. The mechanical scanning imaging sonar is chosen as the active sensing device for the underwater vehicle, and a compensation method based on feedback of the AUV pose is presented to overcome distortion of the acoustic images due to the vehicle motion. In order to verify the feasibility of the navigation methods proposed for the C-Ranger, a sea trial was conducted in Tuandao Bay. Experimental results and analysis show that the proposed navigation approach based on SEIF-SLAM improves the accuracy of the navigation compared with conventional method; moreover the algorithm has a low computational cost when compared with EKF-SLAM

A Novel Combined SLAM Based on RBPF-SLAM and EIF-SLAM for Mobile System Sensing in a Large Scale Environment

Mobile autonomous systems are very important for marine scientific investigation and military applications. Many algorithms have been studied to deal with the computational efficiency problem required for large scale Simultaneous Localization and Mapping (SLAM) and its related accuracy and consistency. Among these methods, submap-based SLAM is a more effective one. By combining the strength of two popular mapping algorithms, the Rao-Blackwellised particle filter (RBPF) and extended information filter (EIF), this paper presents a Combined SLAM—an efficient submap-based solution to the SLAM problem in a large scale environment. RBPF-SLAM is used to produce local maps, which are periodically fused into an EIF-SLAM algorithm. RBPF-SLAM can avoid linearization of the robot model during operating and provide a robust data association, while EIF-SLAM can improve the whole computational speed, and avoid the tendency of RBPF-SLAM to be over-confident. In order to further improve the computational speed in a real time environment, a binary-tree-based decision-making strategy is introduced. Simulation experiments show that the proposed Combined SLAM algorithm significantly outperforms currently existing algorithms in terms of accuracy and consistency, as well as the computing efficiency. Finally, the Combined SLAM algorithm is experimentally validated in a real environment by using the Victoria Park dataset

Altered gut microbiota profile in patients with perimenopausal panic disorder

IntroductionFemales in the perimenopausal period are susceptible to mood disorders. Perimenopausal panic disorder (PPD) is characterized by repeated and unpredictable panic attacks during perimenopause, and it impacts the patient's physical and mental health and social function. Pharmacotherapy is limited in the clinic, and its pathological mechanism is unclear. Recent studies have demonstrated that gut microbiota is strongly linked to emotion; however, the relation between PPD and microbiota is limitedly known.MethodsThis study aimed to discover specific microbiota in PPD patients and the intrinsic connection between them. Gut microbiota was analyzed in PPD patients (n = 40) and healthy controls (n = 40) by 16S rRNA sequencing.ResultsThe results showed reduced α-diversity (richness) in the gut microbiota of PPD patients. β-diversity indicated that PPD and healthy controls had different intestinal microbiota compositions. At the genus level, 30 species of microbiota abundance had significantly different between the PPD and healthy controls. In addition, HAMA, PDSS, and PASS scales were collected in two groups. It was found that Bacteroides and Alistipes were positively correlated with PASS, PDSS, and HAMA.DiscussionBacteroides and Alistipes dysbiosis dominate imbalanced microbiota in PPD patients. This microbial alteration may be a potential pathogenesis and physio-pathological feature of PPD. The distinct gut microbiota can be a potential diagnostic marker and a new therapeutic target for PPD

A Globally and Quadratically Convergent Algorithm for Solving Multilinear Systems with M-tensors

We consider multilinear systems of equations whose coefficient tensors are (Formula presented.)-tensors. Multilinear systems of equations have many applications in engineering and scientific computing, such as data mining and numerical partial differential equations. In this paper, we show that solving multilinear systems with (Formula presented.)-tensors is equivalent to solving nonlinear systems of equations where the involving functions are P-functions. Based on this result, we propose a Newton-type method to solve multilinear systems with (Formula presented.)-tensors. For a multilinear system with a nonsingular (Formula presented.)-tensor and a positive right side vector, we prove that the sequence generated by the proposed method converges to the unique solution of the multilinear system and the convergence rate is quadratic. Numerical results are reported to show that the proposed method is promising

Adverse Events Associated With Anti-IL-23 Agents: Clinical Evidence and Possible Mechanisms

BackgroundAnti-interleukin (IL)-23 agents are widely used for autoimmune disease treatment; however, the safety and risks of specific symptoms have not been systematically assessed.ObjectivesThe aim of this study was to summarize the characteristics and mechanisms of occurrence of five immunological and non-immunological adverse events caused by different anti-IL-23 agents.MethodsThe Cochrane Library, EMBASE, PubMed, and Web of Science databases were searched for eligible randomized clinical trials published from inception through May 1, 2020. Randomized clinical trials that reported at least one type of adverse event after treatment were included, regardless of sex, age, ethnicity, and diagnosis. Two investigators independently screened and extracted the characteristics of the studies, participants, drugs, and adverse event types. The Cochrane Handbook was used to assess the methodological quality of the included randomized clinical trials. Heterogeneity was assessed using the I2 statistic. Meta-regression was applied to determine the sources of heterogeneity, and subgroup analysis was used to identify the factors contributing to adverse events.ResultsForty-eight studies were included in the meta-analysis, comprising 25,624 patients treated with anti-IL-23 agents. Serious immunological or non-immunological adverse events were rare. Anti-IL-12/23-p40 agents appeared to cause adverse events more easily than anti-IL-23-p19 agents. The incidence of cancer did not appear to be related to anti-IL-23 agent treatment, and long-term medication could lead to mental diseases. The prevention of complications should be carefully monitored when administered for over approximately 40 weeks to avoid further adverse reactions, and the incidence of infection was the highest among general immunological adverse events.ConclusionsThe application of anti-IL-23 agents induced a series of immunological and non-immunological adverse events, but these agents tend to be well-tolerated with good safety profiles