Effect of adrenocorticotropic hormone stimulation during adrenal vein sampling for the subtyping of primary aldosteronism: a prospective study.

OBJECTIVE: Adrenal venous sampling (AVS) is key for primary aldosteronism subtype identification. However, the value of adrenocorticotropic hormone (ACTH) stimulation in AVS is still controversial. METHODS: In this prospective study, we investigated the role of continuous ACTH infusion on the performance and interpretation of bilateral simultaneous AVS using a standard protocol in 59 primary aldosteronism patients. We analyzed the selectivity index and lateralization index in AVS pre and post-ACTH and estimated the prognosis of patients who underwent adrenalectomy with different cutoff points of lateralization index post-ACTH. RESULTS: The confirmed success rate of bilateral adrenal vein catheterization increased from 84% pre-ACTH to 95% post-ACTH. Fifty percent of the patients had a decline in lateralization index post-ACTH, 30% of patients showed unilateral primary aldosteronism pre-ACTH but bilateral primary aldosteronism post-ACTH according to lateralization index at least 2 pre-ACTH and lateralization index at least 4 post-ACTH. The outcomes of the patients with primary aldosteronism after adrenalectomy indicated that all patients achieved clinical and biochemical success regardless of lateralization index at least 4 or less than 4 post-ACTH. Receiver operating characteristic curves showed that lateralization index cutoff 2.58 post-ACTH stimulation yielded the best threshold in lateralization with a sensitivity of 73.1% and a specificity of 92.9%. CONCLUSION: ACTH stimulation increased the AVS success rates in patients with primary aldosteronism, reduced lateralization index in some cases and decreased the proportion of identified unilateral primary aldosteronism, resulting in some patients losing the opportunity for disease cure. Compared with lateralization index at least 4, a lower cutoff point of lateralization index at least 2.58 after ACTH stimulation has better accuracy of lateralization diagnosis.

Impaired Cognitive Function in Patients With Autonomous Cortisol Secretion in Adrenal Incidentalomas.

CONTEXT: Glucocorticoids have potent effects on the central nervous system. However, while patients with Cushing syndrome frequently report impairments in cognitive function, studies investigating cognitive function in patients with autonomous cortisol secretion (ACS) in adrenal incidentalomas (AIs) are scarce. OBJECTIVE: The aim of the present study was to evaluate neurocognitive function in patients with ACS. METHODS: We prospectively recruited 63 patients with AI, 36 patients with nonfunctional adrenal adenoma (NFA) (46.5 ± 10.5 years), and 27 patients with ACS (48.6 ± 9.1 years); these patients underwent a battery of validated neuropsychological tests. ACS was diagnosed when serum cortisol levels after a 1-mg dexamethasone suppression test (cortisol1 mg DST) ≥ 50 nmol/L. RESULTS: Patients with ACS had higher frequency of subjective memory complaints (40.7% vs 13.9%, P < 0.05) and higher proportion of mild cognitive impairment (22.2% vs 2.8%, P < 0.05) than patients with NFA. Furthermore, patients with ACS had worse performance on working memory and the visuospatial/constructional domain than patients with NFA (all P < 0.05). Serum cortisol1 mg DST was negatively correlated with working memory and visuospatial/constructional domains (r = -0.307 and -0.306, respectively, all P < 0.05). Performance on working memory and visuospatial/constructional domains gradually deteriorated with increases in serum cortisol1 mg DST (all P values for trend < 0.05). Multivariate linear regression analysis showed that serum cortisol1 mg DST was a significant risk factor for impairment of working memory and visuospatial/constructional domains (B = -0.006 and -0.043, respectively, all P < 0.05). CONCLUSION: This study is the first to report that ACS is accompanied by impaired cognitive function. Consequently, cognitive function assessment should be incorporated into the clinical evaluation of patients with ACS. CLINICAL TRIAL REGISTRATION NUMBER: NCT05357456.

Study on sand liquefaction induced by Songyuan earthquake with a magnitude of M5.7 in China.

A large-scale sand liquefaction producing typical and novel surface phenomena was found at the epicenter of Songyuan M5.7 earthquake occurring on May 28, 2018. Field survey and experimental test encompassing boring sampling, standard penetration test (SPT), cone penetration test (CPT), scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray fluorescence (XRF) were performed to ascertain the liquefaction damage and site characteristic. Cone penetration test is an excellent assay for the identification of liquefied sand layer and acquisition of physio-mechanical parameter. Moreover, the assay is applicable for in-situ post-earthquake investigation. Factors promoting the formation and controlling the distribution of the sand liquefaction were analyzed. The liquefaction impacted an 80 km2 area, and was primarily embodied as sand boil and water sprout on rice field, despite producing no significant structural damage. Due to the simple profile of local soil layer, ground motion, geomorphic condition, and groundwater level were the main factors governing the distribution of the liquefaction. Majority of the liquefied sand layer was discovered at the depth less than 10 m. However, deep layer liquefaction at the depth greater than 18 m was also discovered, which was demonstrated by the upward movement of liquefied sand towards the upper silty clay layer at the depth of 17 m. Most importantly, we have identified loess liquefaction, a phenomenon which had not been reported previously in Northeast China. Lastly, it is important to highlight the risk of significant liquefaction damage at Songyuan. Hence, investigating the liquefaction risk is potentially beneficial for augmenting planning on earthquake mitigation, engineering reconnaissance, and design project.

Taxus yunnanensis genome offers insights into gymnosperm phylogeny and taxol production.

Taxol, a natural product derived from Taxus, is one of the most effective natural anticancer drugs and the biosynthetic pathway of Taxol is the basis of heterologous bio-production. Here, we report a high-quality genome assembly and annotation of Taxus yunnanensis based on 10.7 Gb sequences assembled into 12 chromosomes with contig N50 and scaffold N50 of 2.89 Mb and 966.80 Mb, respectively. Phylogenomic analyses show that T. yunnanensis is most closely related to Sequoiadendron giganteum among the sampled taxa, with an estimated divergence time of 133.4-213.0 MYA. As with most gymnosperms, and unlike most angiosperms, there is no evidence of a recent whole-genome duplication in T. yunnanensis. Repetitive sequences, especially long terminal repeat retrotransposons, are prevalent in the T. yunnanensis genome, contributing to its large genome size. We further integrated genomic and transcriptomic data to unveil clusters of genes involved in Taxol synthesis, located on the chromosome 12, while gene families encoding hydroxylase in the Taxol pathway exhibited significant expansion. Our study contributes to the further elucidation of gymnosperm relationships and the Taxol biosynthetic pathway.

A powerful procedure that controls the false discovery rate with directional information.

In many multiple testing applications in genetics, the signs of the test statistics provide useful directional information, such as whether genes are potentially up- or down-regulated between two experimental conditions. However, most existing procedures that control the false discovery rate (FDR) are P-value based and ignore such directional information. We introduce a novel procedure, the signed-knockoff procedure, to utilize the directional information and control the FDR in finite samples. We demonstrate the power advantage of our procedure through simulation studies and two real applications.

Effects of acupuncture on patients with Alzheimer's disease: Protocol for a systematic review and meta-analysis.

BACKGROUND: Alzheimer's disease (AD) is the leading progressive neurodegenerative disease worldwide, accompanied with nonreversible cognitive impairments. Acupuncture, as the traditional Chinese technique, is widely applied in clinical trials for AD. The aim of this review is to evaluate the efficacy and safety of acupuncture in the aspect of antidementia. METHODS: Randomized controlled trials (RCTs) related to acupuncture treatment targeting AD will be collected. We will search the following 4 databases of electronic English resources, including PubMed, Embase, MEDLINE, Web of Science, and 4 Chinese databases, namely CNKI, CBM, VIP, and Wanfang database. All the RCTs will be searched from their inception to November 2018. After screening the studies, a meta-analysis of RCTs will be carried out. Subsequently, the assessment of bias risk, data synthesis, subgroup analysis will be conducted using RevMan V.5.3.5 software if the setting condition is met. RESULTS: This systematic view and meta-analysis will assess the efficacy and safety of acupuncture intervention on AD patients, which is fundamentally based on current published evidence, and provide a high-quality synthesis for clinical practitioners of treating AD with acupuncture, CONCLUSION:: The summary of our systematic view will determine whether acupuncture intervention could be an efficient and feasible approach to the treatment of AD patients.

Accelerated low-rank representation for subspace clustering and semi-supervised classification on large-scale data.

The scalability of low-rank representation (LRR) to large-scale data is still a major research issue, because it is extremely time-consuming to solve singular value decomposition (SVD) in each optimization iteration especially for large matrices. Several methods were proposed to speed up LRR, but they are still computationally heavy, and the overall representation results were also found degenerated. In this paper, a novel method, called accelerated LRR (ALRR) is proposed for large-scale data. The proposed accelerated method integrates matrix factorization with nuclear-norm minimization to find a low-rank representation. In our proposed method, the large square matrix of representation coefficients is transformed into a significantly smaller square matrix, on which SVD can be efficiently implemented. The size of the transformed matrix is not related to the number of data points and the optimization of ALRR is linear with the number of data points. The proposed ALRR is convex, accurate, robust, and efficient for large-scale data. In this paper, ALRR is compared with state-of-the-art in subspace clustering and semi-supervised classification on real image datasets. The obtained results verify the effectiveness and superiority of the proposed ALRR method.