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TA, Ruggiero A, Ruiz-Martinez A, Rummler A, Rurikova Z, Rusakovich NA, Russell HL, Russo G, Rutherfoord JP, Rutherford Colmenares S, Rybacki K, Rybar M, Rye EB, Ryzhov A, Sabater Iglesias JA, Sabatini P, Sadrozinski HFW, Safai Tehrani F, Safarzadeh Samani B, Safdari M, Saha S, Sahinsoy M, Saibel A, Saimpert M, Saito M, Saito T, Salamani D, Salnikov A, Salt J, Salvador Salas A, Salvatore D, Salvatore F, Salzburger A, Sammel D, Sampsonidis D, Sampsonidou D, Sánchez J, Sanchez Pineda A, Sanchez Sebastian V, Sandaker H, Sander CO, Sandesara JA, Sandhoff M, Sandoval C, Sankey DPC, Sano T, Sansoni A, Santi L, Santoni C, Santos H, Santra A, Saoucha KA, Saraiva JG, Sardain J, Sasaki O, Sato K, Sauer C, Sauerburger F, Sauvan E, Savard P, Sawada R, Sawyer C, Sawyer L, Sayago Galvan I, Sbarra C, Sbrizzi A, Scanlon T, Schaarschmidt J, Schäfer U, Schaffer AC, Schaile D, Schamberger RD, Scharf C, Schefer MM, Schegelsky VA, Scheirich D, Schenck F, Schernau M, Scheulen C, Schiavi C, Schioppa EJ, 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MV, Stanislaus B, Stanitzki MM, Stapf B, Starchenko EA, Stark GH, Stark J, Staroba P, Starovoitov P, Stärz S, Staszewski R, Stavropoulos G, Steentoft J, Steinberg P, Stelzer B, Stelzer HJ, Stelzer-Chilton O, Stenzel H, Stevenson TJ, Stewart GA, Stewart JR, Stockton MC, Stoicea G, Stolarski M, Stonjek S, Straessner A, Strandberg J, Strandberg S, Stratmann M, Strauss M, Strebler T, Strizenec P, Ströhmer R, Strom DM, Stroynowski R, Strubig A, Stucci SA, Stugu B, Stupak J, Styles NA, Su D, Su S, Su W, Su X, Sugizaki K, Sulin VV, Sullivan MJ, Sultan DMS, Sultanaliyeva L, Sultansoy S, Sumida T, Sun S, Sun S, Gudnadottir OS, Sur N, Sutton MR, Suzuki H, Svatos M, Swiatlowski M, Swirski T, Sykora I, Sykora M, Sykora T, Ta D, Tackmann K, Taffard A, Tafirout R, Tafoya Vargas JS, Takeva EP, Takubo Y, Talby M, Talyshev AA, Tam KC, Tamir NM, Tanaka A, Tanaka J, Tanaka R, Tanasini M, Tao Z, Tapia Araya S, Tapprogge S, Tarek Abouelfadl Mohamed A, Tarem S, Tariq K, Tarna G, Tartarelli GF, Tas P, 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T, Wenke NS, Wermes N, Wessels M, Wharton AM, White AS, White A, White MJ, Whiteson D, Wickremasinghe L, Wiedenmann W, Wielers M, Wiglesworth C, Wilbern DJ, Wilkens HG, Williams DM, Williams HH, Williams S, Willocq S, Wilson BJ, Windischhofer PJ, Winkel FI, Winklmeier F, Winter BT, Winter JK, Wittgen M, Wobisch M, Wolffs Z, Wollrath J, Wolter MW, Wolters H, Wongel AF, Woodward EL, Worm SD, Wosiek BK, Woźniak KW, Wozniewski S, Wraight K, Wu C, Wu J, Wu M, Wu M, Wu SL, Wu X, Wu Y, Wu Z, Wuerzinger J, Wyatt TR, Wynne BM, Xella S, Xia L, Xia M, Xiang J, Xie M, Xie X, Xin S, Xiong A, Xiong J, Xu D, Xu H, Xu L, Xu R, Xu T, Xu Y, Xu Z, Xu Z, Yabsley B, Yacoob S, Yamaguchi Y, Yamashita E, Yamauchi H, Yamazaki T, Yamazaki Y, Yan J, Yan S, Yan Z, Yang HJ, Yang HT, Yang S, Yang T, Yang X, Yang X, Yang Y, Yang Y, Yang Z, Yao WM, Yap YC, Ye H, Ye H, Ye J, Ye S, Ye X, Yeh Y, Yeletskikh I, Yeo BK, Yexley MR, Yin P, Yorita K, Younas S, Young CJS, Young C, Yu C, Yu Y, Yuan M, Yuan R, Yue L, Zaazoua M, Zabinski B, Zaid E, Zak ZK, Zakareishvili T, Zakharchuk N, Zambito S, Zamora Saa JA, Zang J, Zanzi D, Zaplatilek O, Zeitnitz C, Zeng H, Zeng JC, Zenger DT, Zenin O, Ženiš T, Zenz S, Zerradi S, Zerwas D, Zhai M, Zhang B, Zhang DF, Zhang J, Zhang J, Zhang K, Zhang L, Zhang P, Zhang R, Zhang S, Zhang S, Zhang T, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang Z, Zhang Z, Zhao H, Zhao T, Zhao Y, Zhao Z, Zhemchugov A, Zheng J, Zheng K, Zheng X, Zheng Z, Zhong D, Zhou B, Zhou H, Zhou N, Zhou Y, Zhou Y, Zhu CG, Zhu J, Zhu Y, Zhu Y, Zhuang X, Zhukov K, Zhulanov V, Zimine NI, Zinsser J, Ziolkowski M, Živković L, Zoccoli A, Zoch K, Zorbas TG, Zormpa O, Zou W, Zwalinski L. Study of High-Transverse-Momentum Higgs Boson Production in Association with a Vector Boson in the qqbb Final State with the ATLAS Detector. PHYSICAL REVIEW LETTERS 2024; 132:131802. [PMID: 38613283 DOI: 10.1103/physrevlett.132.131802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/26/2024] [Indexed: 04/14/2024]
Abstract
This Letter presents the first study of Higgs boson production in association with a vector boson (V=W or Z) in the fully hadronic qqbb final state using data recorded by the ATLAS detector at the LHC in proton-proton collisions at sqrt[s]=13 TeV and corresponding to an integrated luminosity of 137 fb^{-1}. The vector bosons and Higgs bosons are each reconstructed as large-radius jets and tagged using jet substructure techniques. Dedicated tagging algorithms exploiting b-tagging properties are used to identify jets consistent with Higgs bosons decaying into bb[over ¯]. Dominant backgrounds from multijet production are determined directly from the data, and a likelihood fit to the jet mass distribution of Higgs boson candidates is used to extract the number of signal events. The VH production cross section is measured inclusively and differentially in several ranges of Higgs boson transverse momentum: 250-450, 450-650, and greater than 650 GeV. The inclusive signal yield relative to the standard model expectation is observed to be μ=1.4_{-0.9}^{+1.0} and the corresponding cross section is 3.1±1.3(stat)_{-1.4}^{+1.8}(syst) pb.
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Zhao H, Jiang G, Li C, Che Y, Long R, Pu J, Zhang Y, Li D, Liao Y, Yu L, Zhao Y, Yuan M, Li Y, Fan S, Liu L, Li Q. Evaluation of Binding and Neutralizing Antibodies for Inactivated SARS-CoV-2 Vaccine Immunization. Diseases 2024; 12:67. [PMID: 38667525 PMCID: PMC11048931 DOI: 10.3390/diseases12040067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/20/2024] [Accepted: 03/22/2024] [Indexed: 04/28/2024] Open
Abstract
The circulating severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variant presents an ongoing challenge for surveillance and detection. It is important to establish an assay for SARS-CoV-2 antibodies in vaccinated individuals. Numerous studies have demonstrated that binding antibodies (such as S-IgG and N-IgG) and neutralizing antibodies (Nabs) can be detected in vaccinated individuals. However, it is still unclear how to evaluate the consistency and correlation between binding antibodies and Nabs induced by inactivated SARS-CoV-2 vaccines. In this study, serum samples from humans, rhesus macaques, and hamsters immunized with inactivated SARS-CoV-2 vaccines were analyzed for S-IgG, N-IgG, and Nabs. The results showed that the titer and seroconversion rate of S-IgG were significantly higher than those of N-IgG. The correlation between S-IgG and Nabs was higher compared to that of N-IgG. Based on this analysis, we further investigated the titer thresholds of S-IgG and N-IgG in predicting the seroconversion of Nabs. According to the threshold, we can quickly determine the positive and negative effects of the SARS-CoV-2 variant neutralizing antibody in individuals. These findings suggest that the S-IgG antibody is a better supplement to and confirmation of SARS-CoV-2 vaccine immunization.
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Liu L, Ma Y, Zhao H, Guo L, Guo Y, Liu CM. Genome-wide association studies identified OsTMF as a gene regulating rice seed germination under salt stress. FRONTIERS IN PLANT SCIENCE 2024; 15:1384246. [PMID: 38601316 PMCID: PMC11004275 DOI: 10.3389/fpls.2024.1384246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 03/15/2024] [Indexed: 04/12/2024]
Abstract
Introduction Salt tolerance during seed germination is an important trait for direct seeding and low-cost rice production. Nevertheless, it is still not clear how seed germination under salt stress is regulated genetically. Methods In this study, genome-wide association studies (GWAS) were performed to decipher the genetic basis of seed germination under salt stress using 541 rice varieties collected worldwide. Results and discussion Three quantitative trait loci (QTLs) were identified including qGRG3-1 on chromosome 3, qGRG3-2 on chromosome 5, and qGRG4 on chromosome 4. Assessment of candidate genes in these loci for their responses to salt stress identified a TATA modulatory factor (OsTMF) in qGRG3-2. The expression of OsTMF was up-regulated in both roots and shoots after exposure to salt stress, and OsTMF knockout mutants exhibited delayed seed germination under salt stress. Haplotype analysis showed that rice varieties carrying OsTMF-Hap2 displayed elevated salt tolerance during seed germination. These results provide important knowledge and resources to improve rice seed germination under salt stress in the future.
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Yao WM, Yap YC, Ye H, Ye H, Ye J, Ye S, Ye X, Yeh Y, Yeletskikh I, Yeo BK, Yexley MR, Yin P, Yorita K, Younas S, Young CJS, Young C, Yu C, Yu Y, Yuan M, Yuan R, Yue L, Zaazoua M, Zabinski B, Zaid E, Zak ZK, Zakareishvili T, Zakharchuk N, Zambito S, Zamora Saa JA, Zang J, Zanzi D, Zaplatilek O, Zeitnitz C, Zeng H, Zeng JC, Zenger DT, Zenin O, Ženiš T, Zenz S, Zerradi S, Zerwas D, Zhai M, Zhang B, Zhang DF, Zhang J, Zhang J, Zhang K, Zhang L, Zhang P, Zhang R, Zhang S, Zhang S, Zhang T, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang Z, Zhang Z, Zhao H, Zhao T, Zhao Y, Zhao Z, Zhemchugov A, Zheng J, Zheng K, Zheng X, Zheng Z, Zhong D, Zhou B, Zhou H, Zhou N, Zhou Y, Zhu CG, Zhu J, Zhu Y, Zhu Y, Zhuang X, Zhukov K, Zhulanov V, Zimine NI, Zinsser J, Ziolkowski M, Živković L, Zoccoli A, Zoch K, Zorbas TG, Zormpa O, Zou W, Zwalinski L. Measurement of the Centrality Dependence of the Dijet Yield in p+Pb Collisions at sqrt[s_{NN}]=8.16 TeV with the ATLAS Detector. PHYSICAL REVIEW LETTERS 2024; 132:102301. [PMID: 38518341 DOI: 10.1103/physrevlett.132.102301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/07/2023] [Accepted: 12/04/2023] [Indexed: 03/24/2024]
Abstract
ATLAS measured the centrality dependence of the dijet yield using 165 nb^{-1} of p+Pb data collected at sqrt[s_{NN}]=8.16 TeV in 2016. The event centrality, which reflects the p+Pb impact parameter, is characterized by the total transverse energy registered in the Pb-going side of the forward calorimeter. The central-to-peripheral ratio of the scaled dijet yields, R_{CP}, is evaluated, and the results are presented as a function of variables that reflect the kinematics of the initial hard parton scattering process. The R_{CP} shows a scaling with the Bjorken x of the parton originating from the proton, x_{p}, while no such trend is observed as a function of x_{Pb}. This analysis provides unique input to understanding the role of small proton spatial configurations in p+Pb collisions by covering parton momentum fractions from the valence region down to x_{p}∼10^{-3} and x_{Pb}∼4×10^{-4}.
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Ren F, Aliper A, Chen J, Zhao H, Rao S, Kuppe C, Ozerov IV, Zhang M, Witte K, Kruse C, Aladinskiy V, Ivanenkov Y, Polykovskiy D, Fu Y, Babin E, Qiao J, Liang X, Mou Z, Wang H, Pun FW, Ayuso PT, Veviorskiy A, Song D, Liu S, Zhang B, Naumov V, Ding X, Kukharenko A, Izumchenko E, Zhavoronkov A. A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models. Nat Biotechnol 2024:10.1038/s41587-024-02143-0. [PMID: 38459338 DOI: 10.1038/s41587-024-02143-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 01/16/2024] [Indexed: 03/10/2024]
Abstract
Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with a high mortality rate. Putative drug targets in IPF have failed to translate into effective therapies at the clinical level. We identify TRAF2- and NCK-interacting kinase (TNIK) as an anti-fibrotic target using a predictive artificial intelligence (AI) approach. Using AI-driven methodology, we generated INS018_055, a small-molecule TNIK inhibitor, which exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled or topical administration. INS018_055 possesses anti-inflammatory effects in addition to its anti-fibrotic profile, validated in multiple in vivo studies. Its safety and tolerability as well as pharmacokinetics were validated in a randomized, double-blinded, placebo-controlled phase I clinical trial (NCT05154240) involving 78 healthy participants. A separate phase I trial in China, CTR20221542, also demonstrated comparable safety and pharmacokinetic profiles. This work was completed in roughly 18 months from target discovery to preclinical candidate nomination and demonstrates the capabilities of our generative AI-driven drug-discovery pipeline.
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Zhang T, Jin Z, Zhao H, Lai C, Liu Z, Luo P, Dong Z, Wang F. Aqueous alkaline phosphate facilitates the non-exchangeable deuteration of peptides and proteins. RSC Adv 2024; 14:8075-8080. [PMID: 38464689 PMCID: PMC10921277 DOI: 10.1039/d3ra08636d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 02/29/2024] [Indexed: 03/12/2024] Open
Abstract
The incorporation of deuterium into peptides and proteins holds broad applications across various fields, such as drug development and structural characterization. Nevertheless, current methods for peptide/protein deuteration often target exchangeable labile sites or require harsh conditions for stable modification. In this study, we present a late-stage approach utilizing an alkaline phosphate solution to achieve deuteration of non-exchangeable backbone sites of peptides and proteins. The specific deuteration regions are identified through ultraviolet photodissociation (UVPD) and mass spectrometry analysis. This deuteration strategy demonstrates site and structure selectivity, with a notable affinity for labeling the α-helix regions of myoglobin. The deuterium method is particularly suitable for peptides and proteins that remain stable under high pH conditions.
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He S, Liu C, Ren C, Zhao H, Zhang X. Immunological Landscape of Retinal Ischemia-Reperfusion Injury: Insights into Resident and Peripheral Immune Cell Responses. Aging Dis 2024:AD.2024.0129. [PMID: 38502592 DOI: 10.14336/ad.2024.0129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 01/29/2024] [Indexed: 03/21/2024] Open
Abstract
Retinal ischemia-reperfusion injury (RIRI) is a complex condition characterized by immune cell-mediated inflammation and consequent neuronal damage. This review delves into the immune response mechanisms in RIRI, particularly emphasizing the roles played by resident and peripheral immune cells. It highlights the pivotal role of microglia, the primary resident immune cells, in exacerbating neuroinflammation and neuronal damage through their activation and subsequent release of pro-inflammatory mediators. Additionally, the review explores the contributions of other glial cell types, such as astrocytes and Müller cells, in modulating the immune response within the retinal environment. The dual role of the complement system in RIRI is also examined, revealing its complex functions in both safeguarding and impairing retinal health. Inflammasomes, triggered by various danger signals, are discussed as crucial contributors to the inflammatory pathways in RIRI, with an emphasis on the involvement of different NOD-like receptor family proteins. The review further analyzes the infiltration and impact of peripheral immune cells like neutrophils, macrophages, and T cells, which migrate to the retina following ischemic injury. Critical to this discussion is the interplay between resident and peripheral immune cells and its implications for RIRI pathophysiology. Finally, the review outlines future research directions, focusing on basic research and the potential for clinical translation to enhance understanding and treatment of RIRI.
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Huang H, Gu Q, Nie SM, Wang JD, Zhao H, Zhai BW, Zhang MY, Fu YJ. Untargeted metabolomics reveals the regulatory effect of geniposidic acid on lipid accumulation in HepG2 cells and Caenorhabditis elegans and validation in hyperlipidemic hamsters. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 125:155295. [PMID: 38277945 DOI: 10.1016/j.phymed.2023.155295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 12/08/2023] [Accepted: 12/16/2023] [Indexed: 01/28/2024]
Abstract
BACKGROUND Geniposidic acid (GPA) alleviates oxidative stress and inflammation in mice However, whether it can effectively regulate lipid accumulation and prevent hyperlipidemia requires further investigation. PURPOSE This study combined the untargeted metabolomics of cells and a Caenorhabditis elegans model to evaluate the anti-hyperlipidemic potential of GPA by modulating oxidative stress and regulating lipid metabolism. A golden hamster model of hyperlipidemia was used to further validate the lipid-lowering effect and mechanism of action of GPA. METHODS Chemical staining, immunofluorescence, and flow cytometry were performed to examine the effects of GPA on lipid accumulation and oxidative stress. Untargeted metabolomic analysis of cells and C. elegans was performed using ultra-performance liquid chromatography coupled with quadrupole electrostatic field Orbitrap high-resolution mass spectrometry (UPLC-Q-Orbitrap MS) to identify biomarkers altered by GPA action, analyze the affected metabolic pathways, and validate the mechanisms by which GPA regulates lipid metabolism and oxidative stress. A golden hamster model of hyperlipidemia was established to test the lipid-lowering effects of GPA. Body weight, biochemical markers, rate-limiting enzymes, and key proteins were assessed. Hematoxylin and eosin (H&E) and Oil Red O staining were performed. RESULTS Phenotypic data showed that GPA decreased free fatty acid (FFA)-induced lipid buildup and high reactive oxygen species (ROS) levels, reversed the decrease in mitochondrial membrane potential (MMP), and increased the cellular reduced glutathione/oxidized glutathione disulfide (GSH/GSSG) ratio. GPA also reduces high glucose-induced lipid build-up and ROS production in C. elegans. Metabolomic analysis showed that GPA affected purine, lipid, and amino acid metabolism. Moreover, GPA inhibited xanthine oxidase (XOD), glutamate dehydrogenase (GLDH), fatty acid synthase (FAS), phosphorylation of P38 MAPK, and upregulated the expression of SIRT3 and CPT1A protein production to control lipid metabolism and produce antioxidant benefits in cells and golden hamsters. CONCLUSION Current evidence suggests that GPA can effectively regulate lipid metabolism and the oxidative stress response, and has the potential to prevent hyperlipidemia. This study also provided an effective method for evaluating the mechanism of action of GPA.
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Cao L, Feng C, Ye H, Zhao H, Shi Z, Li J, Wu Y, Wang R, Li Q, Liang J, Ji Q, Gu H, Shao M. Differential mRNA profiles reveal the potential roles of genes involved in lactate stimulation in mouse macrophages. Genomics 2024; 116:110814. [PMID: 38432499 DOI: 10.1016/j.ygeno.2024.110814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 01/28/2024] [Accepted: 02/25/2024] [Indexed: 03/05/2024]
Abstract
Lactate is a glycolysis end product, and its levels are markedly associated with disease severity, morbidity, and mortality in sepsis. It modulates key functions of immune cells, including macrophages. In this investigation, transcriptomic analysis was performed using lactic acid, sodium lactate, and hydrochloric acid-stimulated mouse bone marrow-derived macrophages (iBMDM), respectively, to identify lactate-associated signaling pathways. After 24 h of stimulation, 896 differentially expressed genes (DEG) indicated were up-regulation, whereas 792 were down-regulated in the lactic acid group, in the sodium lactate group, 128 DEG were up-regulated, and 41 were down-regulated, and in the hydrochloric acid group, 499 DEG were up-regulated, and 285 were down-regulated. Subsequently, clinical samples were used to further verify the eight genes with significant differences, among which Tssk6, Ypel4, Elovl3, Trp53inp1, and Cfp were differentially expressed in patients with high lactic acid, indicating their possible involvement in lactic acid-induced inflammation and various physiological diseases caused by sepsis. However, elongation of very long chain fatty acids protein 3 (Elovl3) was negatively correlated with lactic acid content in patients. The results of this study provide a necessary reference for better understanding the transcriptomic changes caused by lactic acid and explain the potential role of high lactic acid in the regulation of macrophages in sepsis.
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Zhao H, Chen Y, Tian S, Wang B, Zhao Y, Ma J. Nomogram for preoperative estimation of symptomatic subdural hygroma risk in pediatric intracranial arachnoid cysts. J Neurosurg Pediatr 2024; 33:285-294. [PMID: 38064705 DOI: 10.3171/2023.11.peds23350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 11/08/2023] [Indexed: 03/03/2024]
Abstract
OBJECTIVE The occurrence and predictors of symptomatic subdural hygroma (SSH) subsequent to the fenestration of pediatric intracranial arachnoid cysts (IACs) are unclear. In this study, the authors aimed to investigate the likelihood of an SSH following IAC fenestration and the impact on operative efficacy with the ultimate goal of constructing a nomogram. METHODS The medical records of 1782 consecutive patients who underwent surgical treatment at the Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine were reviewed. Among these patients, a training cohort (n = 1214) underwent surgery during an earlier period and was used for the development of a nomogram. The remaining patients formed the validation cohort (n = 568) and were used to confirm the performance of the developed model. The development of the nomogram involved the use of potential predictors, while internal validation was conducted using a bootstrap-resampling approach. RESULTS SSH was detected in 13.2% (160 of 1214) of patients in the training cohort and in 11.1% (63 of 568) of patients in the validation cohort. Through multivariate analysis, several factors including Galassi type, IAC distance to the basal cisterns, temporal bulge, midline shift, IAC shape in the coronal view, area of the stoma, and artery location near the stoma were identified as independent predictors of SSH. These 7 predictors were used to construct a nomogram, which exhibited a concordance statistic (C-statistic) of 0.826 and demonstrated good calibration. Following internal validation, the nomogram maintained good calibration and discrimination with a C-statistic of 0.799 (95% CI 0.665-0.841). Patients who had nomogram scores < 30 or ≥ 30 were considered to be at low and high risk of SSH occurrence, respectively. CONCLUSIONS The predictive model and derived nomogram achieved satisfactory preoperative prediction of SSH. Using this nomogram, the risk for an individual patient can be estimated, and the appropriate surgery can be performed in high-risk patients.
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Zhang XJ, Zhao H, Zhang D, Ye DD, Qin J, Zhou YR, Zhang YZ. Blood cell count-derived inflammation indices as predictors of the osteoporotic risk of postmenopausal women. EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES 2024; 28:2207-2216. [PMID: 38567584 DOI: 10.26355/eurrev_202403_35725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
OBJECTIVE We investigated the associations between osteoporosis (OP) and systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) in postmenopausal women. PATIENTS AND METHODS This retrospective study included 966 postmenopausal women. Logistic regression and receiver operating characteristic curve (ROC) analyses were applied to explore the relationships between SII, NLR, MLR, and PLR with the bone mineral density (BMD) and risk of OP. RESULTS Logistic regression analyses showed that SII, PLR, NLR, and MLR had independent negative associations with the OP risk. The ROC curve analysis showed that SII, NLR, and MLR predicted a low BMD, with NLR having the highest predictive value (area under the curve = 0.624). SII > 504.09, PLR > 131.87, NLR > 2.02, and MLR > 0.12 correlated with a particularly high OP risk. CONCLUSIONS High levels of SII, PLR, NLR, and MLR were associated with a high OP risk. In particular, NLR > 2.02 strongly predicted the risk of OP, thereby representing a valuable and convenient inflammatory marker of the OP risk.
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Liu XL, Zhao H, Wang YX, Liu XY, Jiang Y, Tao MF, Liu XY. Detecting and characterizing new endofungal bacteria in new hosts: Pandoraea sputorum and Mycetohabitans endofungorum in Rhizopus arrhizus. Front Microbiol 2024; 15:1346252. [PMID: 38486702 PMCID: PMC10939042 DOI: 10.3389/fmicb.2024.1346252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/12/2024] [Indexed: 03/17/2024] Open
Abstract
The fungus Rhizopus arrhizus (=R. oryzae) is commonly saprotrophic, exhibiting a nature of decomposing organic matter. Additionally, it serves as a crucial starter in food fermentation and can act as a pathogen causing mucormycosis in humans and animals. In this study, two distinct endofungal bacteria (EFBs), associated with individual strains of R. arrhizus, were identified using live/dead staining, fluorescence in situ hybridization, transmission electron microscopy, and 16S rDNA sequencing. The roles of these bacteria were elucidated through antibiotic treatment, pure cultivation, and comparative genomics. The bacterial endosymbionts, Pandoraea sputorum EFB03792 and Mycetohabitans endofungorum EFB03829, were purified from the host fungal strains R. arrhizus XY03792 and XY03829, respectively. Notably, this study marks the first report of Pandoraea as an EFB genus. Compared to its free-living counterparts, P. sputorum EFB03792 exhibited 28 specific virulence factor-related genes, six specific CE10 family genes, and 74 genes associated with type III secretion system (T3SS), emphasizing its pivotal role in invasion and colonization. Furthermore, this study introduces R. arrhizus as a new host for EFB M. endofungorum, with EFB contributing to host sporulation. Despite a visibly reduced genome, M. endofungorum EFB03829 displayed a substantial number of virulence factor-related genes, CE10 family genes, T3SS genes, mobile elements, and significant gene rearrangement. While EFBs have been previously identified in R. arrhizus, their toxin-producing potential in food fermentation has not been explored until this study. The discovery of these two new EFBs highlights their potential for toxin production within R. arrhizus, laying the groundwork for identifying suitable R. arrhizus strains for fermentation processes.
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Aad G, Abbott B, Abeling K, Abicht NJ, Abidi SH, Aboulhorma A, Abramowicz H, Abreu H, Abulaiti Y, Abusleme Hoffman AC, Acharya BS, Adam Bourdarios C, Adamczyk L, Adamek L, Addepalli SV, Addison MJ, Adelman J, Adiguzel A, Adye T, Affolder AA, Afik Y, Agaras MN, Agarwala J, Aggarwal A, Agheorghiesei C, Ahmad A, Ahmadov F, Ahmed WS, Ahuja S, Ai X, Aielli G, Aikot A, Ait Tamlihat M, Aitbenchikh B, Aizenberg I, Akbiyik M, Åkesson TPA, Akimov AV, Akiyama D, Akolkar NN, Al Khoury K, Alberghi GL, Albert J, Albicocco P, Albouy GL, Alderweireldt S, Aleksa M, Aleksandrov IN, Alexa C, Alexopoulos T, Alfonsi F, Algren M, Alhroob M, Ali B, Ali HMJ, Ali S, Alibocus SW, Aliev M, Alimonti G, Alkakhi W, Allaire C, Allbrooke BMM, Allen JF, Allendes Flores CA, Allport PP, Aloisio A, Alonso F, Alpigiani C, Alvarez Estevez M, Alvarez Fernandez A, Alves Cardoso M, Alviggi MG, Aly M, Amaral Coutinho Y, Ambler A, Amelung C, Amerl M, Ames CG, Amidei D, Amor Dos Santos SP, Amos KR, Ananiev V, Anastopoulos C, Andeen T, Anders JK, Andrean SY, Andreazza A, Angelidakis S, Angerami A, Anisenkov AV, Annovi A, Antel C, Anthony MT, Antipov E, Antonelli M, Anulli F, Aoki M, Aoki T, Aparisi Pozo JA, Aparo MA, Aperio Bella L, Appelt C, Apyan A, Aranzabal N, Arcangeletti C, Arce ATH, Arena E, Arguin JF, Argyropoulos S, Arling JH, Arnaez O, Arnold H, Artoni G, Asada H, Asai K, Asai S, Asbah NA, Assahsah J, Assamagan K, Astalos R, Atashi S, Atkin RJ, Atkinson M, Atmani H, Atmasiddha PA, Augsten K, Auricchio S, Auriol AD, Austrup VA, Avolio G, Axiotis K, Azuelos G, Babal D, Bachacou H, Bachas K, Bachiu A, Backman F, Badea A, Bagnaia P, Bahmani M, Bailey AJ, Bailey VR, Baines JT, Baines L, Bakalis C, Baker OK, Bakos E, Bakshi Gupta D, Balakrishnan V, Balasubramanian R, Baldin EM, Balek P, Ballabene E, Balli F, Baltes LM, Balunas WK, Balz J, Banas E, Bandieramonte M, Bandyopadhyay A, Bansal S, Barak L, Barakat M, Barberio EL, Barberis D, Barbero M, Barends KN, Barillari T, Barisits MS, Barklow T, Baron P, Baron Moreno DA, Baroncelli A, Barone G, Barr AJ, Barr JD, Barranco Navarro L, Barreiro F, Barreiro Guimarães da Costa J, Barron U, Barros Teixeira MG, Barsov S, Bartels F, Bartoldus R, Barton AE, Bartos P, Basan A, Baselga M, Bassalat A, Basso MJ, Basson CR, Bates RL, Batlamous S, Batley JR, Batool B, Battaglia M, Battulga D, Bauce M, Bauer M, Bauer P, Bazzano Hurrell LT, Beacham JB, Beau T, Beauchemin PH, Becherer F, Bechtle P, Beck HP, Becker K, Beddall AJ, Bednyakov VA, Bee CP, Beemster LJ, Beermann TA, Begalli M, Begel M, Behera A, Behr JK, Beirer JF, Beisiegel F, Belfkir M, Bella G, Bellagamba L, Bellerive A, Bellos P, Beloborodov K, Belyaev NL, Benchekroun D, Bendebba F, Benhammou Y, Benoit M, Bensinger JR, Bentvelsen S, Beresford L, Beretta M, Bergeaas Kuutmann E, Berger N, Bergmann B, Beringer J, Bernardi G, Bernius C, Bernlochner FU, Bernon F, Berry T, Berta P, Berthold A, Bertram IA, Bethke S, Betti A, Bevan AJ, Bhamjee M, Bhatta S, Bhattacharya DS, Bhattarai P, Bhopatkar VS, Bi R, Bianchi RM, Bianco G, Biebel O, Bielski R, Biglietti M, Billoud TRV, Bindi M, Bingul A, Bini C, Biondini A, Birch-Sykes CJ, Bird GA, Birman M, Biros M, Biryukov S, Bisanz T, Bisceglie E, Biswal JP, Biswas D, Bitadze A, Bjørke K, Bloch I, Blocker C, Blue A, Blumenschein U, Blumenthal J, Bobbink GJ, Bobrovnikov VS, Boehler M, Boehm B, Bogavac D, Bogdanchikov AG, Bohm C, Boisvert V, Bokan P, Bold T, Bomben M, Bona M, Boonekamp M, Booth CD, Borbély AG, Bordulev IS, Borecka-Bielska HM, Borgna LS, Borissov G, Bortoletto D, Boscherini D, Bosman M, Bossio Sola JD, Bouaouda K, Bouchhar N, Boudreau J, Bouhova-Thacker EV, Boumediene D, Bouquet R, Boveia A, Boyd J, Boye D, Boyko IR, Bracinik J, Brahimi N, Brandt G, Brandt O, Braren F, Brau B, Brau JE, Brener R, Brenner L, Brenner R, Bressler S, Britton D, Britzger D, Brock I, Brooijmans G, Brooks WK, Brost E, Brown LM, Bruce LE, Bruckler TL, Bruckman de Renstrom PA, Brüers B, Bruni A, Bruni G, Bruschi M, Bruscino N, Buanes T, Buat Q, Buchin D, Buckley AG, Bulekov O, Bullard BA, Burdin S, Burgard CD, Burger AM, Burghgrave B, Burlayenko O, Burr JTP, Burton CD, Burzynski JC, Busch EL, Büscher V, Bussey PJ, Butler JM, Buttar CM, Butterworth JM, Buttinger W, Buxo Vazquez CJ, Buzykaev AR, Cabrera Urbán S, Cadamuro L, Caforio D, Cai H, Cai Y, Cairo VMM, Cakir O, Calace N, Calafiura P, Calderini G, Calfayan P, Callea G, Caloba LP, Calvet D, Calvet S, Calvet TP, Calvetti M, Camacho Toro R, Camarda S, Camarero Munoz D, Camarri P, Camerlingo MT, Cameron D, Camincher C, Campanelli M, Camplani A, Canale V, Canesse A, Cantero J, Cao Y, Capocasa F, Capua M, Carbone A, Cardarelli R, Cardenas JCJ, Cardillo F, Carli T, Carlino G, Carlotto JI, Carlson BT, Carlson EM, Carminati L, Carnelli A, Carnesale M, Caron S, Carquin E, Carrá S, Carratta G, Carrio Argos F, Carter JWS, Carter TM, Casado MP, Caspar M, Castiglia EG, Castillo FL, Castillo Garcia L, Castillo Gimenez V, Castro NF, Catinaccio A, Catmore JR, Cavaliere V, Cavalli N, Cavasinni V, Cekmecelioglu YC, Celebi E, Celli F, Centonze MS, Cepaitis V, Cerny K, Cerqueira AS, Cerri A, Cerrito L, Cerutti F, Cervato B, Cervelli A, Cesarini G, Cetin SA, Chadi Z, Chakraborty D, Chan J, Chan WY, Chapman JD, Chapon E, Chargeishvili B, Charlton DG, Charman TP, Chatterjee M, Chauhan C, Chekanov S, Chekulaev SV, Chelkov GA, Chen A, Chen B, Chen B, Chen H, Chen H, Chen J, Chen J, Chen M, Chen S, Chen SJ, Chen X, Chen X, Chen Y, Cheng CL, Cheng HC, Cheong S, Cheplakov A, Cheremushkina E, Cherepanova E, Cherkaoui El Moursli R, Cheu E, Cheung K, Chevalier L, Chiarella V, Chiarelli G, Chiedde N, Chiodini G, Chisholm AS, Chitan A, Chitishvili M, Chizhov MV, Choi K, Chomont AR, Chou Y, Chow EYS, Chowdhury T, Chu KL, Chu MC, Chu X, Chudoba J, Chwastowski JJ, Cieri D, Ciesla KM, Cindro V, Ciocio A, Cirotto F, Citron ZH, Citterio M, Ciubotaru DA, Ciungu BM, Clark A, Clark PJ, Clavijo Columbie JM, Clawson SE, Clement C, Clercx J, Clissa L, Coadou Y, Cobal M, Coccaro A, Barrue RFC, Coelho Lopes De Sa R, Coelli S, Cohen H, Coimbra AEC, Cole B, Collot J, Conde Muiño P, Connell MP, Connell SH, Connelly IA, Conroy EI, Conventi F, Cooke HG, Cooper-Sarkar AM, Cordeiro Oudot Choi A, Cormier F, Corpe LD, Corradi M, Corriveau F, Cortes-Gonzalez A, Costa MJ, Costanza F, Costanzo D, Cote BM, Cowan G, Cranmer K, Cremonini D, Crépé-Renaudin S, Crescioli F, Cristinziani M, Cristoforetti M, Croft V, Crosby JE, Crosetti G, Cueto A, Cuhadar Donszelmann T, Cui H, Cui Z, Cunningham WR, Curcio F, Czodrowski P, Czurylo MM, Da Cunha Sargedas De Sousa MJ, Da Fonseca Pinto JV, Da Via C, Dabrowski W, Dado T, Dahbi S, Dai T, Dal Santo D, Dallapiccola C, Dam M, D'amen G, D'Amico V, Damp J, Dandoy JR, Daneri MF, Danninger M, Dao V, Darbo G, Darmora S, Das SJ, D'Auria S, David C, Davidek T, Davis-Purcell B, Dawson I, Day-Hall HA, De K, De Asmundis R, De Biase N, De Castro S, De Groot N, de Jong P, De la Torre H, De Maria A, De Salvo A, De Sanctis U, De Santo A, De Vivie De Regie JB, Dedovich DV, Degens J, Deiana AM, Del Corso F, Del Peso J, Del Rio F, Deliot F, Delitzsch CM, Della Pietra M, Della Volpe D, Dell'Acqua A, Dell'Asta L, Delmastro M, Delsart PA, Demers S, Demichev M, Denisov SP, D'Eramo L, Derendarz D, Derue F, Dervan P, Desch K, Deutsch C, Di Bello FA, Di Ciaccio A, Di Ciaccio L, Di Domenico A, Di Donato C, Di Girolamo A, Di Gregorio G, Di Luca A, Di Micco B, Di Nardo R, Diaconu C, Diamantopoulou M, Dias FA, Vale TDD, Diaz MA, Diaz Capriles FG, Didenko M, Diehl EB, Diehl L, Díez Cornell S, Diez Pardos C, Dimitriadi C, Dimitrievska A, Dingfelder J, Dinu IM, Dittmeier SJ, Dittus F, Djama F, Djobava T, Djuvsland JI, Doglioni C, Dohnalova A, Dolejsi J, Dolezal Z, Dona KM, Donadelli M, Dong B, Donini J, D'Onofrio A, D'Onofrio M, Dopke J, Doria A, Dos Santos Fernandes N, Dougan P, Dova MT, Doyle AT, Draguet MA, Dreyer E, Drivas-Koulouris I, Drobac AS, Drozdova M, Du D, du Pree TA, Dubinin F, Dubovsky M, Duchovni E, Duckeck G, Ducu OA, Duda D, Dudarev A, Duden ER, D'uffizi M, Duflot L, Dührssen M, Dülsen C, Dumitriu AE, Dunford M, Dungs S, Dunne K, Duperrin A, Duran Yildiz H, Düren M, Durglishvili A, Dwyer BL, Dyckes GI, Dyndal M, Dysch S, Dziedzic BS, Earnshaw ZO, Eberwein GH, Eckerova B, Eggebrecht S, Egidio Purcino De Souza E, Ehrke LF, Eigen G, Einsweiler K, Ekelof T, Ekman PA, El Farkh S, El Ghazali Y, El Jarrari H, El Moussaouy A, Ellajosyula V, Ellert M, Ellinghaus F, Elliot AA, Ellis N, Elmsheuser J, Elsing M, Emeliyanov D, Enari Y, Ene I, Epari S, Erdmann J, Erland PA, Errenst M, Escalier M, Escobar C, Etzion E, Evans G, Evans H, Evans LS, Evans MO, Ezhilov A, Ezzarqtouni S, Fabbri F, Fabbri L, Facini G, Fadeyev V, Fakhrutdinov RM, Falciano S, Falda Ulhoa Coelho LF, Falke PJ, Faltova J, Fan C, Fan Y, Fang Y, Fanti M, Faraj M, Farazpay Z, Farbin A, Farilla A, Farooque T, Farrington SM, Fassi F, Fassouliotis D, Faucci Giannelli M, Fawcett WJ, Fayard L, Federic P, Federicova P, Fedin OL, Fedotov G, Feickert M, Feligioni L, Fellers DE, Feng 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A, Renda M, Rendel MB, Renner F, Rennie AG, Rescia AL, Resconi S, Ressegotti M, Rettie S, Reyes Rivera JG, Reynolds E, Rezanova OL, Reznicek P, Ribaric N, Ricci E, Richter R, Richter S, Richter-Was E, Ridel M, Ridouani S, Rieck P, Riedler P, Rijssenbeek M, Rimoldi A, Rimoldi M, Rinaldi L, Rinn TT, Rinnagel MP, Ripellino G, Riu I, Rivadeneira P, Rivera Vergara JC, Rizatdinova F, Rizvi E, Roberts BA, Roberts BR, Robertson SH, Robinson D, Robles Gajardo CM, Robles Manzano M, Robson A, Rocchi A, Roda C, Rodriguez Bosca S, Rodriguez Garcia Y, Rodriguez Rodriguez A, Rodríguez Vera AM, Roe S, Roemer JT, Roepe-Gier AR, Roggel J, Røhne O, Rojas RA, Roland CPA, Roloff J, Romaniouk A, Romano E, Romano M, Romero Hernandez AC, Rompotis N, Roos L, Rosati S, Rosser BJ, Rossi E, Rossi E, Rossi LP, Rossini L, Rosten R, Rotaru M, Rottler B, Rougier C, Rousseau D, Rousso D, Roy A, Roy-Garand S, Rozanov A, Rozen Y, Ruan X, Rubio Jimenez A, Ruby AJ, Ruelas Rivera VH, Ruggeri TA, Ruggiero A, Ruiz-Martinez A, Rummler A, Rurikova Z, Rusakovich NA, Russell HL, Russo G, Rutherfoord JP, Rutherford Colmenares S, Rybacki K, Rybar M, Rye EB, Ryzhov A, Sabater Iglesias JA, Sabatini P, Sabetta L, Sadrozinski HFW, Safai Tehrani F, Safarzadeh Samani B, Safdari M, Saha S, Sahinsoy M, Saimpert M, Saito M, Saito T, Salamani D, Salnikov A, Salt J, Salvador Salas A, Salvatore D, Salvatore F, Salzburger A, Sammel D, Sampsonidis D, Sampsonidou D, Sánchez J, Sanchez Pineda A, Sanchez Sebastian V, Sandaker H, Sander CO, Sandesara JA, Sandhoff M, Sandoval C, Sankey DPC, Sano T, Sansoni A, Santi L, Santoni C, Santos H, Santpur SN, Santra A, Saoucha KA, Saraiva JG, Sardain J, Sasaki O, Sato K, Sauer C, Sauerburger F, Sauvan E, Savard P, Sawada R, Sawyer C, Sawyer L, Sayago Galvan I, Sbarra C, Sbrizzi A, Scanlon T, Schaarschmidt J, Schacht P, Schaefer D, Schäfer U, Schaffer AC, Schaile D, Schamberger RD, Scharf C, Schefer MM, Schegelsky VA, Scheirich D, Schenck F, Schernau M, Scheulen C, Schiavi C, Schioppa EJ, Schioppa M, Schlag B, Schleicher KE, Schlenker S, Schmeing J, Schmidt MA, Schmieden K, Schmitt C, Schmitt S, Schoeffel L, Schoening A, Scholer PG, Schopf E, Schott M, Schovancova J, Schramm S, Schroeder F, Schroer T, Schultz-Coulon HC, Schumacher M, Schumm BA, Schune P, Schuy AJ, Schwartz HR, Schwartzman A, Schwarz TA, Schwemling P, Schwienhorst R, Sciandra A, Sciolla G, Scuri F, Sebastiani CD, Sedlaczek K, Seema P, Seidel SC, Seiden A, Seidlitz BD, Seitz C, Seixas JM, Sekhniaidze G, Sekula SJ, Selem L, Semprini-Cesari N, Sengupta D, Senthilkumar V, Serin L, Serkin L, Sessa M, Severini H, Sforza F, Sfyrla A, Shabalina E, Shaheen R, Shahinian JD, Shaked Renous D, Shan LY, Shapiro M, Sharma A, Sharma AS, Sharma P, Sharma S, Shatalov PB, Shaw K, Shaw SM, Shcherbakova A, Shen Q, Sherwood P, Shi L, Shi X, Shimmin CO, Shinner JD, Shipsey IPJ, Shirabe S, Shiyakova M, Shlomi J, Shochet MJ, Shojaii J, Shope DR, Shrestha B, Shrestha S, Shrif EM, Shroff MJ, Sicho P, Sickles AM, Sideras Haddad E, Sidoti A, Siegert F, Sijacki D, Sikora R, Sili F, Silva JM, Silva Oliveira MV, Silverstein SB, Simion S, Simoniello R, Simpson EL, Simpson H, Simpson LR, Simpson ND, Simsek S, Sindhu S, Sinervo P, Singh S, Sinha S, Sinha S, Sioli M, Siral I, Sitnikova E, Sivoklokov SY, Sjölin J, Skaf A, Skorda E, Skubic P, Slawinska M, Smakhtin V, Smart BH, Smiesko J, Smirnov SY, Smirnov Y, Smirnova LN, Smirnova O, Smith AC, Smith EA, Smith HA, Smith JL, Smith R, Smizanska M, Smolek K, Snesarev AA, Snider SR, Snoek HL, Snyder S, Sobie R, Soffer A, Solans Sanchez CA, Soldatov EY, Soldevila U, Solodkov AA, Solomon S, Soloshenko A, Solovieva K, Solovyanov OV, Solovyev V, Sommer P, Sonay A, Song WY, Sonneveld JM, Sopczak A, Sopio AL, Sopkova F, Sothilingam V, Sottocornola S, Soualah R, Soumaimi Z, South D, Soybelman N, Spagnolo S, Spalla M, Sperlich D, Spigo G, Spinali S, Spiteri DP, Spousta M, Staats EJ, Stabile A, Stamen R, Stampekis A, Standke M, Stanecka E, Stange MV, Stanislaus B, Stanitzki MM, Stapf B, Starchenko EA, Stark GH, Stark J, Starko DM, Staroba P, Starovoitov P, Stärz S, Staszewski R, Stavropoulos G, Steentoft J, Steinberg P, Stelzer B, Stelzer HJ, Stelzer-Chilton O, Stenzel H, Stevenson TJ, Stewart GA, Stewart JR, Stockton MC, Stoicea G, Stolarski M, Stonjek S, Straessner A, Strandberg J, Strandberg S, Stratmann M, Strauss M, Strebler T, Strizenec P, Ströhmer R, Strom DM, Strom LR, Stroynowski R, Strubig A, Stucci SA, Stugu B, Stupak J, Styles NA, Su D, Su S, Su W, Su X, Sugizaki K, Sulin VV, Sullivan MJ, Sultan DMS, Sultanaliyeva L, Sultansoy S, Sumida T, Sun S, Sun S, Sunneborn Gudnadottir O, Sur N, Sutton MR, Suzuki H, Svatos M, Swiatlowski M, Swirski T, Sykora I, Sykora M, Sykora T, Ta D, Tackmann K, Taffard A, Tafirout R, Tafoya Vargas JS, Takeva EP, Takubo Y, Talby M, Talyshev AA, Tam KC, Tamir NM, Tanaka A, Tanaka J, Tanaka R, Tanasini M, Tao Z, Tapia Araya S, Tapprogge S, Tarek Abouelfadl Mohamed A, Tarem S, Tariq K, Tarna G, Tartarelli GF, Tas P, Tasevsky M, Tassi E, Tate AC, Tateno G, Tayalati Y, Taylor GN, Taylor W, Teagle H, Tee AS, Teixeira De Lima R, Teixeira-Dias P, Teoh JJ, Terashi K, Terron J, Terzo S, Testa M, Teuscher RJ, Thaler A, Theiner O, Themistokleous N, Theveneaux-Pelzer T, Thielmann O, Thomas DW, Thomas JP, Thompson EA, Thompson PD, Thomson E, Tian Y, Tikhomirov V, Tikhonov YA, Timoshenko S, Timoshyn D, Ting EXL, Tipton P, Tlou SH, Tnourji A, Todome K, Todorova-Nova S, Todt S, Togawa M, Tojo J, Tokár S, Tokushuku K, Toldaiev O, Tombs R, Tomoto M, Tompkins L, Topolnicki KW, Torrence E, Torres H, Torró Pastor E, Toscani M, Tosciri C, Tost M, Tovey DR, Traeet A, Trandafir IS, Trefzger T, Tricoli A, Trigger IM, Trincaz-Duvoid S, Trischuk DA, Trocmé B, Troncon C, Truong L, Trzebinski M, Trzupek A, Tsai F, Tsai M, Tsiamis A, Tsiareshka PV, Tsigaridas S, Tsirigotis A, Tsiskaridze V, Tskhadadze EG, Tsopoulou M, Tsujikawa Y, Tsukerman II, Tsulaia V, Tsuno S, Tsur O, Tsuri K, Tsybychev D, Tu Y, Tudorache A, Tudorache V, Tuna AN, Turchikhin S, Turk Cakir I, Turra R, Turtuvshin T, Tuts PM, Tzamarias S, Tzanis P, Tzovara E, Ukegawa F, Ulloa Poblete PA, Umaka EN, Unal G, Unal M, Undrus A, Unel G, Urban J, Urquijo P, Usai G, Ushioda R, Usman M, Uysal Z, Vacavant L, Vacek V, Vachon B, Vadla KOH, Vafeiadis T, Vaitkus A, Valderanis C, Valdes Santurio E, Valente M, Valentinetti S, Valero A, Valiente Moreno E, Vallier A, Valls Ferrer JA, Van Arneman DR, Van Daalen TR, Van Der Graaf A, Van Gemmeren P, Van Rijnbach M, Van Stroud S, Van Vulpen I, Vanadia M, Vandelli W, Vandenbroucke M, Vandewall ER, Vannicola D, Vannoli L, Vari R, Varnes EW, Varni C, Varol T, Varouchas D, Varriale L, Varvell KE, Vasile ME, Vaslin L, Vasquez GA, Vasyukov A, Vazeille F, Vazquez Schroeder T, Veatch J, Vecchio V, Veen MJ, Veliscek I, Veloce LM, Veloso F, Veneziano S, Ventura A, Ventura Gonzalez S, Verbytskyi A, Verducci M, Vergis C, Verissimo De Araujo M, Verkerke W, Vermeulen JC, Vernieri C, Vessella M, Vetterli MC, Vgenopoulos A, Viaux Maira N, Vickey T, Vickey Boeriu OE, Viehhauser GHA, Vigani L, Villa M, Villaplana Perez M, Villhauer EM, Vilucchi E, Vincter MG, Virdee GS, Vishwakarma A, Visibile A, Vittori C, Vivarelli I, Vladimirov V, Voevodina E, Vogel F, Vokac P, Volkotrub Y, Von Ahnen J, Von Toerne E, Vormwald B, Vorobel V, Vorobev K, Vos M, Voss K, Vossebeld JH, Vozak M, Vozdecky L, Vranjes N, Vranjes Milosavljevic M, Vreeswijk M, Vuillermet R, Vujinovic O, Vukotic I, Wada S, Wagner C, Wagner JM, Wagner W, Wahdan S, Wahlberg H, Wakida M, Walder J, Walker R, Walkowiak W, Wall A, Wamorkar T, Wang AZ, Wang C, Wang C, Wang H, Wang J, Wang RJ, Wang R, Wang R, Wang SM, Wang S, Wang T, Wang WT, Wang W, Wang X, Wang X, Wang X, Wang Y, Wang Y, Wang Z, Wang Z, Wang Z, Warburton A, Ward RJ, Warrack N, Watson AT, Watson H, Watson MF, Watton E, Watts G, Waugh BM, Weber C, Weber HA, Weber MS, Weber SM, Wei C, Wei Y, Weidberg AR, Weik EJ, Weingarten J, Weirich M, Weiser C, Wells CJ, Wenaus T, Wendland B, Wengler T, Wenke NS, Wermes N, Wessels M, Wharton AM, White AS, White A, White MJ, Whiteson D, Wickremasinghe L, Wiedenmann W, Wiel C, Wielers M, Wiglesworth C, Wilbern DJ, Wilkens HG, Williams DM, Williams HH, Williams S, Willocq S, Wilson BJ, Windischhofer PJ, Winkel FI, Winklmeier F, Winter BT, Winter JK, Wittgen M, Wobisch M, Wolffs Z, Wollrath J, Wolter MW, Wolters H, Wongel AF, Worm SD, Wosiek BK, Woźniak KW, Wozniewski S, Wraight K, Wu C, Wu J, Wu M, Wu M, Wu SL, Wu X, Wu Y, Wu Z, Wuerzinger J, Wyatt TR, Wynne BM, Xella S, Xia L, Xia M, Xiang J, Xie M, Xie X, Xin S, Xiong J, Xu D, Xu H, Xu L, Xu R, Xu T, Xu Y, Xu Z, Xu Z, Yabsley B, Yacoob S, Yamaguchi Y, Yamashita E, Yamauchi H, Yamazaki T, Yamazaki Y, Yan J, Yan S, Yan Z, Yang HJ, Yang HT, Yang S, Yang T, Yang X, Yang X, Yang Y, Yang Y, Yang Z, Yao WM, Yap YC, Ye H, Ye H, Ye J, Ye S, Ye X, Yeh Y, Yeletskikh I, Yeo BK, Yexley MR, Yin P, Yorita K, Younas S, Young CJS, Young C, Yu Y, Yuan M, Yuan R, Yue L, Zaazoua M, Zabinski B, Zaid E, Zakareishvili T, Zakharchuk N, Zambito S, Zamora Saa JA, Zang J, Zanzi D, Zaplatilek O, Zeitnitz C, Zeng H, Zeng JC, Zenger DT, Zenin O, Ženiš T, Zenz S, Zerradi S, Zerwas D, Zhai M, Zhang B, Zhang DF, Zhang J, Zhang J, Zhang K, Zhang L, Zhang P, Zhang R, Zhang S, Zhang T, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Z, Zhang Z, Zhao H, Zhao P, Zhao T, Zhao Y, Zhao Z, Zhemchugov A, Zheng J, Zheng K, Zheng X, Zheng Z, Zhong D, Zhou B, Zhou H, Zhou N, Zhou Y, Zhu CG, Zhu J, Zhu Y, Zhu Y, Zhuang X, Zhukov K, Zhulanov V, Zimine NI, Zinsser J, Ziolkowski M, Živković L, Zoccoli A, Zoch K, Zorbas TG, Zormpa O, Zou W, Zwalinski L. Search for New Phenomena in Two-Body Invariant Mass Distributions Using Unsupervised Machine Learning for Anomaly Detection at sqrt[s]=13 TeV with the ATLAS Detector. PHYSICAL REVIEW LETTERS 2024; 132:081801. [PMID: 38457710 DOI: 10.1103/physrevlett.132.081801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 12/13/2023] [Indexed: 03/10/2024]
Abstract
Searches for new resonances are performed using an unsupervised anomaly-detection technique. Events with at least one electron or muon are selected from 140 fb^{-1} of pp collisions at sqrt[s]=13 TeV recorded by ATLAS at the Large Hadron Collider. The approach involves training an autoencoder on data, and subsequently defining anomalous regions based on the reconstruction loss of the decoder. Studies focus on nine invariant mass spectra that contain pairs of objects consisting of one light jet or b jet and either one lepton (e,μ), photon, or second light jet or b jet in the anomalous regions. No significant deviations from the background hypotheses are observed. Limits on contributions from generic Gaussian signals with various widths of the resonance mass are obtained for nine invariant masses in the anomalous regions.
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Zhao H, Zhou JT, Ong YS. Word2Pix: Word to Pixel Cross-Attention Transformer in Visual Grounding. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:1523-1533. [PMID: 35749327 DOI: 10.1109/tnnls.2022.3183827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Current one-stage methods for visual grounding encode the language query as one holistic sentence embedding before fusion with visual features for target localization. Such a formulation provides insufficient ability to model query at the word level, and therefore is prone to neglect words that may not be the most important ones for a sentence but are critical for the referred object. In this article, we propose Word2Pix: a one-stage visual grounding network based on the encoder-decoder transformer architecture that enables learning for textual to visual feature correspondence via word to pixel attention. Each word from the query sentence is given an equal opportunity when attending to visual pixels through multiple stacks of transformer decoder layers. In this way, the decoder can learn to model the language query and fuse language with the visual features for target prediction simultaneously. We conduct the experiments on RefCOCO, RefCOCO+, and RefCOCOg datasets, and the proposed Word2Pix outperforms the existing one-stage methods by a notable margin. The results obtained also show that Word2Pix surpasses the two-stage visual grounding models, while at the same time keeping the merits of the one-stage paradigm, namely, end-to-end training and fast inference speed. Code is available at https://github.com/azurerain7/Word2Pix.
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Cheng X, Liu B, Zhao H, Zhang H, Wang J, Li Z, Li B, Chen Z, Hu J. Interfacial effect between Ni 2P/CdS for simultaneously heightening photocatalytic hydrogen production and lignocellulosic biomass photorefining. J Colloid Interface Sci 2024; 655:943-952. [PMID: 37949744 DOI: 10.1016/j.jcis.2023.11.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/04/2023] [Accepted: 11/06/2023] [Indexed: 11/12/2023]
Abstract
Photorefining of biomass is increasingly recognized as a pivotal technology for the simultaneous production of hydrogen and value-added chemicals. The intrinsic recalcitrance of lignocellulosic biomass puts high demands on the rational design of bifunctional photocatalyst. Herein, Ni2P/CdS with a strong interfacial effect in this work was designed to overcome lignocellulosic biomass photorefining. The strong interfacial effect between Ni2P and CdS not only improved the light absorbance, but also optimized the spatial redistribution of photogenerated electrons and holes. Therefore, Ni2P/CdS exhibited an unprecedented H2 evolution activity (ca. 199.7 mmol·h-1·g-1) in the presence of lactic acid as the traditional sacrificial agent. Considerable H2 generation was also achieved in the presence of lignin (ca. 322.8 μmol·h-1·g-1), cellulose (ca. 534.3 μmol·h-1·g-1) and hemicellulose (ca. 382.2 μmol·h-1·g-1) as the electron donor respectively. Theoretical calculation results indicated that establishing the interfacial effect between Ni2P and CdS optimized their work functions. This optimization fosters improved the redistribution between electrons and holes, as a result, photocatalytic hydrogen production from biomass solution was greatly enhanced. Significantly, Ni2P/CdS showed dual functionalities to produce H2 and value-added compounds from raw biomass directly. This present work demonstrates the potential of raw biomass photorefining through astutely designing photocatalysts.
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Jian Z, Song T, Zhang Z, Ai Z, Zhao H, Tang M, Liu K. An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation. SENSORS (BASEL, SWITZERLAND) 2024; 24:928. [PMID: 38339644 PMCID: PMC10857237 DOI: 10.3390/s24030928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 01/26/2024] [Accepted: 01/28/2024] [Indexed: 02/12/2024]
Abstract
Fluorescence in situ hybridization (FISH) is a powerful cytogenetic method used to precisely detect and localize nucleic acid sequences. This technique is proving to be an invaluable tool in medical diagnostics and has made significant contributions to biology and the life sciences. However, the number of cells is large and the nucleic acid sequences are disorganized in the FISH images taken using the microscope. Processing and analyzing images is a time-consuming and laborious task for researchers, as it can easily tire the human eyes and lead to errors in judgment. In recent years, deep learning has made significant progress in the field of medical imaging, especially the successful application of introducing the attention mechanism. The attention mechanism, as a key component of deep learning, improves the understanding and interpretation of medical images by giving different weights to different regions of the image, enabling the model to focus more on important features. To address the challenges in FISH image analysis, we combined medical imaging with deep learning to develop the SEAM-Unet++ automated cell contour segmentation algorithm with integrated attention mechanism. The significant advantage of this algorithm is that it improves the accuracy of cell contours in FISH images. Experiments have demonstrated that by introducing the attention mechanism, our method is able to segment cells that are adherent to each other more efficiently.
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Liu SP, Liu C, Xu B, Zhou H, Zhao H. Disulfidptosis and its Role in Peripheral Blood Immune Cells after a Stroke: A New Frontier in Stroke Pathogenesis. Curr Neurovasc Res 2024; 21:CNR-EPUB-138184. [PMID: 38299279 DOI: 10.2174/0115672026286243240105115419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 11/01/2023] [Indexed: 02/02/2024]
Abstract
BACKGROUND Stroke-Induced Immunodepression (SIID) is characterized by apoptosis in blood immune populations, such as T cells, B cells, NK cells, and monocytes, leading to the clinical presentation of lymphopenia. Disulfidptosis is a novel form of programmed cell death characterized by accumulating disulfide bonds in the cytoplasm, resulting in cellular dysfunction and eventual cell death. OBJECTIVE In this study, we investigated the association between disulfidptosis and stroke by analyzing gene sequencing data from peripheral blood samples of stroke patients. METHODS Differential gene expression analysis identified a set of disulfidptosis-related genes (DRGs) significantly associated with stroke. Initial exploration identified 32 DRGs and their interactions. Our study encompassed several analyses to understand the molecular mechanisms of DRGs in stroke. Weighted Gene Co-Expression Network Analysis (WGCNA) uncovered modules of co-expressed genes in stroke samples, and differentially expressed gene (DEG) analysis highlighted 1643 key genes. RESULTS These analyses converged on four hub genes of DRGs (SLC2A3, SLC2A14, SLC7A11, NCKAP1) associated with stroke. Immune cell composition analysis indicated positive correlations between hub genes and macrophages M1, M2, and neutrophils and negative associations with CD4+ and CD8+ T cells, B cells, and NK cells. Sub-cluster analysis revealed two distinct clusters with different immune cell expression profiles. Gene Set Enrichment Analysis (GSEA) demonstrated enrichment of apoptosis-related pathways, neurotrophin signaling, and actin cytoskeleton regulation. Associations between hub genes and apoptosis, necroptosis, ferroptosis, and cuproptosis, were also identified. CONCLUSION These results suggest that the DRG hub genes are interconnected with various cell death pathways and immune processes, potentially contributing to stroke pathological development.
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Zhao H, Sun Q, Jiang XH, Yuan XH, Peng JS. [Application progress of clinical outcome assessment measures in patients with gastric cancer]. ZHONGHUA WEI CHANG WAI KE ZA ZHI = CHINESE JOURNAL OF GASTROINTESTINAL SURGERY 2024; 27:92-98. [PMID: 38262907 DOI: 10.3760/cma.j.cn441530-20230308-00070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
Gastric cancer is a common tumor of the gastrointestinal tract, and the global trend in morbidity and mortality are not encouraging. Especially in advanced gastric cancer, patient survival outcome is an essential clinical concern and a vital outcome indicator in clinical outcome assessment. This article reviews the definition of clinical outcome assessment and the measurement tools that can be applied in gastric cancer patients, describes the detailed classification of clinical outcome assessment tools, and reviews the current status of the application of clinical outcome assessment in gastric cancer, analyzing the effects and shortcomings of its application, to provide a reference for the clinical staff in choosing the appropriate tools, and assisting in the comprehensive and holistic assessment of clinical outcomes for the promotion of the development of precision medicine.
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Li X, Zeng F, Yue R, Ma D, Meng Z, Li Q, Zhang Z, Zhang H, Liao Y, Liao Y, Jiang G, Zhao H, Yu L, Li D, Zhang Y, Liu L, Li Q. Heterologous Booster Immunization Based on Inactivated SARS-CoV-2 Vaccine Enhances Humoral Immunity and Promotes BCR Repertoire Development. Vaccines (Basel) 2024; 12:120. [PMID: 38400104 PMCID: PMC10891849 DOI: 10.3390/vaccines12020120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/13/2024] [Accepted: 01/17/2024] [Indexed: 02/25/2024] Open
Abstract
Recent studies have indicated that sequentially administering SARS-CoV-2 vaccines can result in increased antibody and cellular immune responses. In this study, we compared homologous and heterologous immunization strategies following two doses of inactivated vaccines in a mouse model. Our research demonstrates that heterologous sequential immunization resulted in more immune responses displayed in the lymph node germinal center, which induced a greater number of antibody-secreting cells (ASCs), resulting in enhanced humoral and cellular immune responses and increased cross-protection against five variant strains. In further single B-cell analysis, the above findings were supported by the presence of unique B-cell receptor (BCR) repertoires and diversity in CDR3 sequence profiles elicited by a heterologous booster immunization strategy.
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Liu YQ, Zhang C, Zhao H, Wang GQ. [Clinical significance and research progress of quantitative hepatitis B virus core antibody measurement]. ZHONGHUA GAN ZANG BING ZA ZHI = ZHONGHUA GANZANGBING ZAZHI = CHINESE JOURNAL OF HEPATOLOGY 2024; 32:83-86. [PMID: 38320797 DOI: 10.3760/cma.j.cn501113-20231130-00255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Hepatitis B virus core antibodies are specific antibodies produced after viral infection that appear early and last for a long time, and its levels in serum are measured by the double-antigen sandwich chemiluminescent microparticle immunoassay method, which has higher sensitivity and specificity, providing new clinical indicators for hepatitis B patients diagnosis, treatment, and drug withdrawal management. This article reviews the clinical significance and research progress of quantitative hepatitis B core antibody measurement and expounds on its research applications and prospects in clinical practice.
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Zhao H, Li Y, Liu W, Zhang G, Wang Y. The Dynamic Evolution Model of the Chemical and Carbon Isotopic Composition of C 1-3 during the Hydrocarbon Generation Process. Molecules 2024; 29:476. [PMID: 38257388 PMCID: PMC10820989 DOI: 10.3390/molecules29020476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/05/2024] [Accepted: 01/11/2024] [Indexed: 01/24/2024] Open
Abstract
A new approach is presented in this paper for the dynamic modeling of the chemical and isotopic evolution of C1-3 during the hydrocarbon generation process. Based on systematic data obtained from published papers for the pyrolysis of various hydrocarbon sources (type I kerogen/source rock, type II kerogen/source rock, type III kerogen/source rock, crude oil, and asphalt, etc.), the empirical evolution framework of the chemical and isotopic composition of C1-3 during the hydrocarbon generation process was built. Although the empirical framework was built only by fitting a large amount of pyrolysis data, the chemical and isotopic composition of C1-3 derived from the pyrolysis experiments all follow evolution laws, convincing us that it is applicable to the thermal evolution process of various hydrocarbon sources. Based on the simplified formula of the isotopic composition of mixed natural gas at different maturities (δ13Cmixed), δ13Cmixed = X×niA×δ13CiA+Y×niB×δ13CiBX×niA+Y×niB, it can be derived that the cumulative isotopic composition of alkane generated in a certain maturity interval can be expressed by the integral of the product of the instantaneous isotopic composition and instantaneous yield at a certain maturity point, and then divided by the cumulative yield of alkane generated in the corresponding maturity interval. Thus, the cumulative isotopic composition (A(X)), cumulative yield (B(X)), instantaneous isotope (C(X)), and instantaneous yield (D(x)) in the dynamic model, comply with the following formula during the maturity interval of (X0~X). A(X) = ∫X0XCX×DXdxB(X), where A(X) and B(X) can be obtained by the fitting of pyrolysis data, and D(x) can also be obtained from the derivation of B(X). The dynamic model was applied on the pyrolysis data of Pingliang Shale to illustrate the quantitative evolution of the cumulative yield, instantaneous yield, cumulative isotope, and instantaneous isotope of C1-3 with increasing maturity. The dynamic model can quantify the yield of methane, ethane, and propane, as well as δ13C1, δ13C2, and δ13C3, respectively, during the hydrocarbon generation process. This model is of great significance for evaluating the natural gas resources of hydrocarbon source rock of different maturities and for identifying the origin and evolutionary process of hydrocarbons by chemical and isotopic data. Moreover, this model provides an approach to study the dynamic evolution of the isotope series of C1-3 (including reversed isotopic series), which is promising for revealing the mechanism responsible for isotopic reversal when combined with post-generation studies.
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PC, Mcpartland CM, McPherson RA, Mehlhase S, Mehta A, Melini D, Mellado Garcia BR, Melo AH, Meloni F, Mendes Jacques Da Costa AM, Meng HY, Meng L, Menke S, Mentink M, Meoni E, Merlassino C, Merola L, Meroni C, Merz G, Meshkov O, Metcalfe J, Mete AS, Meyer C, Meyer JP, Middleton RP, Mijović L, Mikenberg G, Mikestikova M, Mikuž M, Mildner H, Milic A, Milke CD, Miller DW, Miller LS, Milov A, Milstead DA, Min T, Minaenko AA, Minashvili IA, Mince L, Mincer AI, Mindur B, Mineev M, Mino Y, Mir LM, Miralles Lopez M, Mironova M, Mishima A, Missio MC, Mitani T, Mitra A, Mitsou VA, Miu O, Miyagawa PS, Miyazaki Y, Mizukami A, Mkrtchyan T, Mlinarevic M, Mlinarevic T, Mlynarikova M, Mobius S, Mochizuki K, Moder P, Mogg P, Mohammed AF, Mohapatra S, Mokgatitswane G, Moleri L, Mondal B, Mondal S, Mönig K, Monnier E, Monsonis Romero L, Montejo Berlingen J, Montella M, Montereali F, Monticelli F, Monzani S, Morange N, De Carvalho ALM, Moreno Llácer M, Moreno Martinez C, Morettini P, Morgenstern S, Morii 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Siegert F, Sijacki D, Sikora R, Sili F, Silva JM, Silva Oliveira MV, Silverstein SB, Simion S, Simoniello R, Simpson EL, Simpson H, Simpson LR, Simpson ND, Simsek S, Sindhu S, Sinervo P, Singh S, Sinha S, Sinha S, Sioli M, Siral I, Sitnikova E, Sivoklokov SY, Sjölin J, Skaf A, Skorda E, Skubic P, Slawinska M, Smakhtin V, Smart BH, Smiesko J, Smirnov SY, Smirnov Y, Smirnova LN, Smirnova O, Smith AC, Smith EA, Smith HA, Smith JL, Smith R, Smizanska M, Smolek K, Snesarev AA, Snider SR, Snoek HL, Snyder S, Sobie R, Soffer A, Solans Sanchez CA, Soldatov EY, Soldevila U, Solodkov AA, Solomon S, Soloshenko A, Solovieva K, Solovyanov OV, Solovyev V, Sommer P, Sonay A, Song WY, Sonneveld JM, Sopczak A, Sopio AL, Sopkova F, Sothilingam V, Sottocornola S, Soualah R, Soumaimi Z, South D, Spagnolo S, Spalla M, Sperlich D, Spigo G, Spina M, Spinali S, Spiteri DP, Spousta M, Staats EJ, Stabile A, Stamen R, Stamenkovic M, Stampekis A, Standke M, Stanecka E, Stange MV, Stanislaus B, Stanitzki MM, Stapf B, Starchenko EA, Stark GH, Stark J, Starko DM, Staroba P, Starovoitov P, Stärz S, Staszewski R, Stavropoulos G, Steentoft J, Steinberg P, Stelzer B, Stelzer HJ, Stelzer-Chilton O, Stenzel H, Stevenson TJ, Stewart GA, Stewart JR, Stockton MC, Stoicea G, Stolarski M, Stonjek S, Straessner A, Strandberg J, Strandberg S, Strauss M, Strebler T, Strizenec P, Ströhmer R, Strom DM, Strom LR, Stroynowski R, Strubig A, Stucci SA, Stugu B, Stupak J, Styles NA, Su D, Su S, Su W, Su X, Sugizaki K, Sulin VV, Sullivan MJ, Sultan DMS, Sultanaliyeva L, Sultansoy S, Sumida T, Sun S, Sun S, Gudnadottir OS, Sur N, Sutton MR, Suzuki H, Svatos M, Swiatlowski M, Swirski T, Sykora I, Sykora M, Sykora T, Ta D, Tackmann K, Taffard A, Tafirout R, Tafoya Vargas JS, Takeva EP, Takubo Y, Talby M, Talyshev AA, Tam KC, Tamir NM, Tanaka A, Tanaka J, Tanaka R, Tanasini M, Tao Z, Tapia Araya S, Tapprogge S, Tarek Abouelfadl Mohamed A, Tarem S, Tariq K, Tarna G, Tartarelli GF, Tas P, Tasevsky M, Tassi E, Tate AC, Tateno G, Tayalati Y, Taylor GN, Taylor W, Teagle H, Tee AS, Teixeira De Lima R, Teixeira-Dias P, Teoh JJ, Terashi K, Terron J, Terzo S, Testa M, Teuscher RJ, Thaler A, Theiner O, Themistokleous N, Theveneaux-Pelzer T, Thielmann O, Thomas DW, Thomas JP, Thompson EA, Thompson PD, Thomson E, Tian Y, Tikhomirov V, Tikhonov YA, Timoshenko S, Timoshyn D, Ting EXL, Tipton P, Tlou SH, Tnourji A, Todome K, Todorova-Nova S, Todt S, Togawa M, Tojo J, Tokár S, Tokushuku K, Toldaiev O, Tombs R, Tomoto M, Tompkins L, Topolnicki KW, Torrence E, Torres H, Torró Pastor E, Toscani M, Tosciri C, Tost M, Tovey DR, Traeet A, Trandafir IS, Trefzger T, Tricoli A, Trigger IM, Trincaz-Duvoid S, Trischuk DA, Trocmé B, Troncon C, Truong L, Trzebinski M, Trzupek A, Tsai F, Tsai M, Tsiamis A, Tsiareshka PV, Tsigaridas S, Tsirigotis A, Tsiskaridze V, Tskhadadze EG, Tsopoulou M, Tsujikawa Y, Tsukerman II, Tsulaia V, Tsuno S, Tsur O, Tsuri K, Tsybychev D, Tu Y, Tudorache A, Tudorache V, Tuna AN, Turchikhin S, Turk Cakir I, 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Zakharchuk N, Zambito S, Zamora Saa JA, Zang J, Zanzi D, Zaplatilek O, Zeitnitz C, Zeng H, Zeng JC, Zenger DT, Zenin O, Ženiš T, Zenz S, Zerradi S, Zerwas D, Zhai M, Zhang B, Zhang DF, Zhang J, Zhang J, Zhang K, Zhang L, Zhang P, Zhang R, Zhang S, Zhang T, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Z, Zhang Z, Zhao H, Zhao P, Zhao T, Zhao Y, Zhao Z, Zhemchugov A, Zheng K, Zheng X, Zheng Z, Zhong D, Zhou B, Zhou H, Zhou N, Zhou Y, Zhu CG, Zhu J, Zhu Y, Zhu Y, Zhuang X, Zhukov K, Zhulanov V, Zimine NI, Zinsser J, Ziolkowski M, Živković L, Zoccoli A, Zoch K, Zorbas TG, Zormpa O, Zou W, Zwalinski L. Observation of WZγ Production in pp Collisions at sqrt[s]=13 TeV with the ATLAS Detector. PHYSICAL REVIEW LETTERS 2024; 132:021802. [PMID: 38277610 DOI: 10.1103/physrevlett.132.021802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 09/30/2023] [Accepted: 11/08/2023] [Indexed: 01/28/2024]
Abstract
This Letter reports the observation of WZγ production and a measurement of its cross section using 140.1±1.2 fb^{-1} of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. The WZγ production cross section, with both the W and Z bosons decaying leptonically, pp→WZγ→ℓ^{'}^{±}νℓ^{+}ℓ^{-}γ (ℓ^{(^{'})}=e, μ), is measured in a fiducial phase-space region defined such that the leptons and the photon have high transverse momentum and the photon is isolated. The cross section is found to be 2.01±0.30(stat)±0.16(syst) fb. The corresponding standard model predicted cross section calculated at next-to-leading order in perturbative quantum chromodynamics and at leading order in the electroweak coupling constant is 1.50±0.06 fb. The observed significance of the WZγ signal is 6.3σ, compared with an expected significance of 5.0σ.
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Li S, Xing X, Wang L, Xu J, Ren C, Li Y, Wang J, Liu Z, Zhao H, Zhao W, Ji X. Remote ischemic conditioning reduces adverse events in patients with acute ischemic stroke complicating acute myocardial infarction: a randomized controlled trial. Crit Care 2024; 28:5. [PMID: 38167175 PMCID: PMC10759604 DOI: 10.1186/s13054-023-04786-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 12/20/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Acute ischemic stroke (AIS) complicating an acute myocardial infarction (AMI) is not uncommon, but can severely worsen the clinical prognosis. This study aimed to investigate whether remote ischemic conditioning (RIC) could provide clinical benefits to patients with AIS complicating AMI. METHODS Subjects with AIS complicating AMI were recruited in this double-blind, randomized, controlled trial; assigned to the RIC and sham groups; and respectively underwent twice daily RIC and sham RIC for 2 weeks. All subjects received standard medical therapy. The primary endpoint was the rate of major adverse cardiac and cerebrovascular events (MACCEs) within 3 months after enrollment. MACCEs comprise of death from all causes, unstable anginas, AMI, acute ischemic strokes, and transient ischemic attacks. RESULTS Eighty subjects were randomly assigned; 37 patients in the RIC group and 40 patients in the sham-RIC group completed the 3-month follow-up and were included in the final analysis. Both RIC and sham RIC procedures were well tolerated. At 3-month follow-up, 11 subjects (29.7%) in the RIC group experienced MACCEs compared to 21 (52.5%) in the sham group (hazard ratio [HR], 0.396; 95% confidence interval, 0.187-0.838; adjusted p < 0.05). Six subjects (16.2%) in the RIC group had died at the 3-month follow up, significantly lower than the 15 (37.5%) deaths in the sham group (adjusted HR 0.333; 95% CI 0.126-0.881; p = 0.027). Seventeen subjects (45.9%) in the RIC group and 6 subjects (15.0%) in the sham group achieved functional independence (mRS score ≤ 2) at 3-month follow-up (adjusted OR 12.75; 95% CI 2.104-77.21; p = 0.006). CONCLUSIONS Among patients with acute ischemic stroke complicating acute myocardial infarction, treatment with remote ischemic conditioning decreased the major adverse cardiac and cerebrovascular events and improved functional outcomes at 90 days. TRIAL REGISTRATION URL: www. CLINICALTRIALS gov . Unique identifier: NCT03868007. Registered 8 March 2019.
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Peng X, Zhou Q, Wang CQ, Zhang ZM, Luo Z, Xu SY, Feng B, Fang ZF, Lin Y, Zhuo Y, Jiang XM, Zhao H, Tang JY, Wu D, Che LQ. Dietary supplementation of proteases on growth performance, nutrient digestibility, blood characteristics and gut microbiota of growing pigs fed sorghum-based diets. Animal 2024; 18:101052. [PMID: 38181459 DOI: 10.1016/j.animal.2023.101052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 12/02/2023] [Accepted: 12/04/2023] [Indexed: 01/07/2024] Open
Abstract
Low-tannin sorghum is an excellent energy source in pig diets. However, sorghum contains several anti-nutritional factors that may have negative effects on nutrient digestibility. The impacts of proteases on growth performance, nutrient digestibility, blood parameters, and gut microbiota of growing pigs fed sorghum-based diets were studied in this study. Ninety-six pigs (20.66 ± 0.65 kg BW) were allocated into three groups (eight pens/group, four pigs/pen): (1) CON (control diet, sorghum-based diet included 66.98% sorghum), (2) PRO1 (CON + 200 mg/kg proteases), (3) PRO2 (CON + 400 mg/kg proteases) for 28 d. No differences were observed in growth performance and apparent total tract digestibility (ATTD) of nutrients between CON and PRO1 groups. Pigs fed PRO2 diet had increased (P < 0.05) BW on d 21 and 28, and increased (P < 0.05) average daily gain during d 14-21 and the overall period compared with pigs fed CON diet. In addition, pigs fed PRO2 diet had improved (P < 0.05) ATTD of gross energy, CP, and DM compared with pigs fed CON and PRO1 diets. Pigs fed PRO2 diet had lower (P < 0.05) plasma globulin (GLB) level and higher (P < 0.05) plasma glucose, albumin (ALB) and immunoglobulin G levels, and ALB/GLB ratio than pigs fed CON and PRO1 diets. Furthermore, pigs fed PRO2 diet had decreased (P < 0.05) the relative abundance of Acidobacteriota at the phylum level and increased (P < 0.05) the relative abundance of Prevotella_9 at the genus level. The linear discriminant analysis effect size analysis also showed that pigs fed PRO2 diet had significantly enriched short-chain fatty acid-producing bacteria, such as Subdoligranulum and Parabacteroides. In conclusion, protease supplementation at 400 mg/kg improved the growth performance of growing pigs fed sorghum-based diets, which may be attributed to the improvement of nutrient digestibility, host metabolism, immune status and associated with the altered gut microbiota profiles.
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Zhao H, Tang T, Lu Y, Li X, Sun L, Chen S, Ma L, Luo Y, Wang K, Zhao M. Development and Validation of Data-Level Innovation Data-Balancing Machine Learning Models for Predicting Optimal Implantable Collamer Lens Size and Postoperative Vault. Ophthalmol Ther 2024; 13:267-286. [PMID: 37943481 PMCID: PMC10776515 DOI: 10.1007/s40123-023-00841-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 10/20/2023] [Indexed: 11/10/2023] Open
Abstract
INTRODUCTION There are only four sizes of implantable collamer lens (ICL) available for selection, which cannot completely fit all patients as a result of the discontinuity of ICL sizes. Sizing an optimal ICL and predicting postoperative vault are still unresolved problems. This study aimed to develop and validate innovative data-level data-balancing machine learning-based models for predicting ICL size and postoperative vault. METHODS The patients were randomly assigned to training and internal validation sets in a 4:1 ratio. Feature selection was performed using analysis of variance (ANOVA) and Kruskal-Wallis feature importance methods. Traditional linear regression model and machine learning-based models were used. The accuracy of models was assessed using the area under the curve (AUC) and confusion matrix. RESULTS A total of 564 patients (1127 eyes) were eligible for this study, consisting of 808 eyes in the training set, 202 eyes in the internal validation set, and 117 eyes in the external validation set. Compared with the traditional linear regression method, the machine learning model bagging tree showed the best performance for ICL size selection, with an accuracy of 84.5% (95% confidence interval (CI) 83.2-85.8%), and the AUC ranged from 0.88 to 0.99; the prediction accuracy of 12.1 mm and 13.7 mm ICL sizes was improved by 49% and 59%, respectively. The bagging tree model achieved the best accuracy [90.2%, (95% CI 88.9-91.5%)] for predicting the postoperative vault, and the AUC ranged from 0.90 to 0.94. The prediction accuracies of internal and external validation dataset for ICL sizing were 82.2% (95% CI 81.1-83.3%) and 82.1% (95% CI 81.1-83.1%), respectively. CONCLUSIONS The innovative data-level data balancing-based machine learning model can be used to predict ICL size and postoperative vault more accurately, which can assist surgeons in choosing optimal ICL size, thus reducing risks of postoperative complications and secondary surgery.
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