Late initiation of renal replacement therapy is associated with worse outcomes in acute kidney injury after major abdominal surgery

Introduction Abdominal surgery is probably associated with more likelihood to cause acute kidney injury (AKI). The aim of this study was to evaluate whether early or late start of renal replacement therapy (RRT) defined by simplified RIFLE (sRIFLE) classification in AKI patients after major abdominal surgery will affect outcome. Methods A multicenter prospective observational study based on the NSARF ( National Taiwan University Surgical ICU Associated Renal Failure) Study Group database. 98 patients (41 female, mean age 66.4 +/- 13.9 years) who underwent acute RRT according to local indications for post-major abdominal surgery AKI between 1 January, 2002 and 31 December, 2005 were enrolled The demographic data, comorbid diseases, types of surgery and RRT, as well as the indications for RRT were documented. The patients were divided into early dialysis (sRIFLE-0 or Risk) and late dialysis (LD, sRIFLE -Injury or Failure) groups. Then we measured and recorded patients' outcome including in-hospital mortality and RRT wean-off until 30 June, 2006. Results The in-hospital mortality was compared as endpoint. Fifty-seven patients (58.2%) died during hospitalization. LD (hazard ratio (HR) 1.846; P = 0.027), old age (HR 2.090; P = 0.010), cardiac failure (HR 4.620; P < 0.001), pre-RRT SOFA score (HR 1.152; P < 0.001) were independent indicators for in-hospital mortality. Conclusions The findings of this study support earlier initiation of acute RRT, and also underscore the importance of predicting prognoses of major abdominal surgical patients with AKI by using RIFLE classification

Acute-on-chronic kidney injury at hospital discharge is associated with long-term dialysis and mortality

Existing chronic kidney disease (CKD) is among the most potent predictors of postoperative acute kidney injury (AKI). Here we quantified this risk in a multicenter, observational study of 9425 patients who survived to hospital discharge after major surgery. CKD was defined as a baseline estimated glomerular filtration rate <45ml/min per 1.73m2. AKI was stratified according to the maximum simplified RIFLE classification at hospitalization and unresolved AKI defined as a persistent increase in serum creatinine of more than half above the baseline or the need for dialysis at discharge. A Cox proportional hazard model showed that patients with AKI-on-CKD during hospitalization had significantly worse long-term survival over a median follow-up of 4.8 years (hazard ratio, 3.3) than patients with AKI but without CKD. The incidence of long-term dialysis was 22.4 and 0.17 per 100 person-years among patients with and without existing CKD, respectively. The adjusted hazard ratio for long-term dialysis in patients with AKI-on-CKD was 19.8 compared to patients who developed AKI without existing CKD. Furthermore, AKI-on-CKD but without kidney recovery at discharge had a worse outcome (hazard ratios of 4.6 and 213, respectively) for mortality and long-term dialysis as compared to patients without CKD or AKI. Thus, in a large cohort of postoperative patients who developed AKI, those with existing CKD were at higher risk for long-term mortality and dialysis after hospital discharge than those without. These outcomes were significantly worse in those with unresolved AKI at discharge

Novel Genetic Risk and Metabolic Signatures of Insulin Signaling and Androgenesis in the Anovulation of Polycystic Ovary Syndrome

Funding Information: The authors are grateful to all staff in the PCOSAct group for their effort in the collection of blood samples and clinical dataset which used in current study. Special thanks to Prof. Attila Toth from Institute of Physiological Chemistry, Dresden, Germany for the REC114 antibody. This study was supported by the National key Research and Development Program of China (2019YFC1709500); the National Collaboration Project of Critical Illness by Integrating Chinese Medicine and Western Medicine; the Project of Heilongjiang Province Innovation Team “TouYan;” the Yi-Xun Liu and Xiao-Ke Wu Academician Workstation; the Innovation Team of Reproductive Technique with Integrative Chinese Medicine and Western Medicine in Xuzhou City, China; Heilongjiang University of Chinese Medicine from the National Clinical Trial Base; Heilongjiang Provincial Clinical Research Center for Ovary Diseases; the Research Grant Council (T13-602/21-N, C5045-20EF, and 14122021); and Food and Health Bureau in Hong Kong, China (06171026). Ben Willem J. Mol is supported by a National Health and Medical Research Council (NHMRC) Investigator grant (GNT1176437). Ben Willem J. Mol reports consultancy for ObsEva and Merck and travel support from Merck. Xiaoke Wu, Yongyong Shi, and Chi Chiu Wang developed the research question and designed the study. Xiaoke Wu, Yongyong Shi, Yijuan Cao, and Chi Chiu Wang designed the analysis. Yongyong Shi and Zhiqiang Li contributed to the design of the experiment of whole-exome plus targeted SNP sequencing and the analysis, and interpreted the results. Jingshu Gao, Hui Chang, Duojia Zhang, Jing Cong, Yu Wang, Qi Wu, Xiaoxiao Han, Pui Wah Jacqueline Chung, Yiran Li, and Lin Zeng contributed to the experiment of metabolic profile and immunofluorescent staining and the analysis, and interpreted the results. Astrid Borchert and Hartmut Kuhn provided antibody support and advice. Xu Zheng and Lingxi Chen contributed to create the predictive model with deep machine learning. Jian Li, Qi Wu, Hongli Ma, Xu Zheng, and Lingxi Chen contributed to the analysis of the clinical characteristics and interpreted the results. Jian Li, Hongli Ma, Hui Chang, Jing Cong, and Chi Chiu Wang drafted the manuscript. All authors reviewed and revised the manuscript. Xiaoke Wu is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Xiaoke Wu, Chi Chiu Wang, Yijuan Cao, Jian Li, Zhiqiang Li, Hongli Ma, Jingshu Gao, Hui Chang, Duojia Zhang, Jing Cong, Yu Wang, Qi Wu, Xiaoxiao Han, Pui Wah Jacqueline Chung, Yiran Li, Xu Zheng, Lingxi Chen, Lin Zeng, Astrid Borchert, Hartmut Kuhn, Zijiang Chen, Ernest Hung Yu Ng, Elisabet Stener-Victorin, Heping Zhang, Richard S. Legro, Ben Willem J. Mol, and Yongyong Shi declare that they have no conflict of interest or financial conflicts to disclose. Funding Information: This study was supported by the National key Research and Development Program of China ( 2019YFC1709500 ); the National Collaboration Project of Critical Illness by Integrating Chinese Medicine and Western Medicine ; the Project of Heilongjiang Province Innovation Team “TouYan;” the Yi-Xun Liu and Xiao-Ke Wu Academician Workstation; the Innovation Team of Reproductive Technique with Integrative Chinese Medicine and Western Medicine in Xuzhou City , China; Heilongjiang University of Chinese Medicine from the National Clinical Trial Base ; Heilongjiang Provincial Clinical Research Center for Ovary Diseases ; the Research Grant Council ( T13-602/21-N , C5045-20EF , and 14122021 ); and Food and Health Bureau in Hong Kong, China ( 06171026 ). Publisher Copyright: © 2023Peer reviewedPublisher PD

Tandem mass spectrometry data quality assessment by self-convolution

<p>Abstract</p> <p>Background</p> <p>Many algorithms have been developed for deciphering the tandem mass spectrometry (MS) data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on <it>de novo </it>sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified.</p> <p>Results</p> <p>The proposed method measures the qualities of MS data sets based on the symmetric property of b- and y-ion peaks present in a MS spectrum. Self-convolution on MS data and its time-reversal copy was employed. Due to the symmetric nature of b-ions and y-ions peaks, the self-convolution result of a good spectrum would produce a highest mid point intensity peak. To reduce processing time, self-convolution was achieved using Fast Fourier Transform and its inverse transform, followed by the removal of the "DC" (Direct Current) component and the normalisation of the data set. The quality score was defined as the ratio of the intensity at the mid point to the remaining peaks of the convolution result. The method was validated using both theoretical mass spectra, with various permutations, and several real MS data sets. The results were encouraging, revealing a high percentage of positive prediction rates for spectra with good quality scores.</p> <p>Conclusion</p> <p>We have demonstrated in this work a method for determining the quality of tandem MS data set. By pre-determining the quality of tandem MS data before subjecting them to protein identification algorithms, spurious protein predictions due to poor tandem MS data are avoided, giving scientists greater confidence in the predicted results. We conclude that the algorithm performs well and could potentially be used as a pre-processing for all mass spectrometry based protein identification tools.</p

Light Gluino Search for Decays Containing pi+pi- or pi0 from a Neutral Hadron Beam at Fermilab

We report on two null searches, one for the spontaneous appearance of $\pi^+\pi^-$ pairs, another for a single $\pi^0$, consistent with the decay of a long-lived neutral particle into hadrons and an unseen neutral particle. For the lowest level gluon-gluino bound state, known as the $R^0$, we exclude the decays $R^0\to \pi^+\pi^-\tilde{\gamma}$ and $R^0\to \pi^0\tilde{\gamma}$ for the masses of $R^0$ and $\tilde{\gamma}$ in the theoretically allowed range. In the most interesting $R^0$ mass range, $\leq 3 GeV/c^2$, we exclude $R^0$ lifetimes from $3\times 10^{-10}$ seconds to as high as $10^{-3}$ seconds, assuming perturbative QCD production for the $R^0$.Comment: 15 pages, 7 figure

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe