Adrenal gland involvement in 11-ketotestosterone production analyzed using LC-MS/MS

Introduction11-ketotestosterone (11KT), which is derived by the bioconversion of testosterone via 11β-hydroxytestosterone (11OHT), is a potent agonist of the human androgen receptor. The adrenal gland is considered an important organ in 11KT production because CYP11B1, which catalyzes testosterone to 11OHT, is expressed in the adrenal glands. The present study aimed to demonstrate adrenal gland involvement in 11KT production in prepubertal children, a topic which has not yet been addressed by any previous studies.MethodsThree, retrospective, observational studies were performed. Study 1 enrolled patients aged 8 months to 7 years with severe Kawasaki disease (KD) who were treated with mPSL pulse. Studies 2 and 3 included patients who had received a corticotropin-releasing hormone (CRH) stimulation test and adrenocorticotropic hormone (ACTH) stimulation test, respectively. Samples were collected before and after treatment or drug administration, and serum 11KT, 11OHT, and other 11-oxygenated androgens were measured by LC-MS/MS. Steroid hormone values before and after medication were analyzed using the Wilcoxon signed rank test.ResultsStudies 1, 2, and 3 included twenty patients with severe KD, eight patients with a CRH stimulation test, and eight patients with an ACTH stimulation test, respectively. Study 1 demonstrated that the median (IQR) 11KT level was significantly higher before, than after, mPSL pulse (0.39 (0.28-0.47) nmol/L versus 0.064 (0.012-0.075) nmol/L; P < 0.001). Studies 2 and 3 indicated no significant difference in the median 11KT value before and after the CRH or ACTH stimulation test while the 11OHT value was significantly higher after the test.ConclusionIn conclusion, the mediation of 11KT production by ACTH demonstrated the importance of the adrenal glands in the synthesis of this androgen in prepubertal children

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早大学位記番号:新8425早稲田大

EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning

Graph Contrastive Learning (GCL) is unsupervised graph representation learning that can obtain useful representation of unknown nodes. The node representation can be utilized as features of downstream tasks. However, GCL is vulnerable to poisoning attacks as with existing learning models. A state-of-the-art defense cannot sufficiently negate adverse effects by poisoned graphs although such a defense introduces adversarial training in the GCL. To achieve further improvement, pruning adversarial edges is important. To the best of our knowledge, the feasibility remains unexplored in the GCL domain. In this paper, we propose a simple defense for GCL, EdgePruner. We focus on the fact that the state-of-the-art poisoning attack on GCL tends to mainly add adversarial edges to create poisoned graphs, which means that pruning edges is important to sanitize the graphs. Thus, EdgePruner prunes edges that contribute to minimizing the contrastive loss based on the node representation obtained after training on poisoned graphs by GCL. Furthermore, we focus on the fact that nodes with distinct features are connected by adversarial edges in poisoned graphs. Thus, we introduce feature similarity between neighboring nodes to help more appropriately determine adversarial edges. This similarity is helpful in further eliminating adverse effects from poisoned graphs on various datasets. Finally, EdgePruner outputs a graph that yields the minimum contrastive loss as the sanitized graph. Our results demonstrate that pruning adversarial edges is feasible on six datasets. EdgePruner can improve the accuracy of node classification under the attack by up to 5.55% compared with that of the state-of-the-art defense. Moreover, we show that EdgePruner is immune to an adaptive attack

Giant enhancement of cryogenic thermopower by polar structural instability in the pressurized semimetal MoTe2

We found that a high mobility semimetal 1T'-MoTe2 shows a significant pressure-dependent change in the cryogenic thermopower in the vicinity of the critical pressure, where the polar structural transition disappears. With the application of a high pressure of 0.75 GPa, while the resistivity becomes as low as 10 {\mu}{\Omega}cm, thermopower reached the maximum value of 60 {\mu}VK-1 at 25 K, leading to a giant thermoelectric power factor of 300 {\mu}WK-2cm-1. Based on semiquantitative analyses, the origin of this behavior is discussed in terms of inelastic electron-phonon scattering enhanced by the softening of zone center phonon modes associated with the polar structural instability.Comment: 13 pages, 4 figures Physical review B (accepted

Ultra-low-dose estrogen therapy for female hypogonadism.

In females, endogenous estrogen secretion increases gradually before pubertal development. The benefits of low-dose estrogen therapy in patients with Turner syndrome were originally discussed by Ross et al. and Quigley et al. These seminal studies used ethinyl estradiol (EE2), starting at a dose of 25 ng/kg/d. We hypothesized that the initial dosage of estrogen could be titrated to more closely mimic physiological increments of endogenous estrogen. Therefore, our recent study initiated EE2 treatment at a dosage of 1-2 ng/kg/d, an ultra-low-dose estrogen therapy in pediatric patients with Turner syndrome. The ultra-low-dose estrogen therapy in this syndrome produced a good final height outcome but achieved suboptimal bone mineral density (BMD). In the present review, we have explained our findings to clarify the merits and demerits of this new therapy and to promote further discussion and research. This type of ultra-low-dose estrogen therapy, initiated at an early age, could be ideal for estrogen replacement in female patients with hypogonadism, such as Turner syndrome

R-HTDetector: Robust Hardware-Trojan Detection Based on Adversarial Training

Hardware Trojans (HTs) have become a serious problem, and extermination of them is strongly required for enhancing the security and safety of integrated circuits. An effective solution is to identify HTs at the gate level via machine learning techniques. However, machine learning has specific vulnerabilities, such as adversarial examples. In reality, it has been reported that adversarial modified HTs greatly degrade the performance of a machine learning-based HT detection method. Therefore, we propose a robust HT detection method using adversarial training (R-HTDetector). We formally describe the robustness of R-HTDetector in modifying HTs. Our work gives the world-first adversarial training for HT detection with theoretical backgrounds. We show through experiments with Trust-HUB benchmarks that R-HTDetector overcomes adversarial examples while maintaining its original accuracy