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Xu J, Cai X, Miao Z, Yan Y, Chen D, Yang Z, Yue L, Hu W, Zhuo L, Wang J, Xue Z, Fu Y, Xu Y, Zheng J, Guo T, Chen Y. Proteome-wide profiling reveals dysregulated molecular features and accelerated aging in osteoporosis: A 9.8-year prospective study. Aging Cell 2024; 23:e14035. [PMID: 37970652 PMCID: PMC10861190 DOI: 10.1111/acel.14035] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/15/2023] [Accepted: 10/23/2023] [Indexed: 11/17/2023] Open
Abstract
The role of circulatory proteomics in osteoporosis is unclear. Proteome-wide profiling holds the potential to offer mechanistic insights into osteoporosis. Serum proteome with 413 proteins was profiled by liquid chromatography-tandem mass spectrometry (LC-MS/MS) at baseline, and the 2nd, and 3rd follow-ups (7704 person-tests) in the prospective Chinese cohorts with 9.8 follow-up years: discovery cohort (n = 1785) and internal validation cohort (n = 1630). Bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry (DXA) at follow-ups 1 through 3 at lumbar spine (LS) and femoral neck (FN). We used the Light Gradient Boosting Machine (LightGBM) to identify the osteoporosis (OP)-related proteomic features. The relationships between serum proteins and BMD in the two cohorts were estimated by linear mixed-effects model (LMM). Meta-analysis was then performed to explore the combined associations. We identified 53 proteins associated with osteoporosis using LightGBM, and a meta-analysis showed that 22 of these proteins illuminated a significant correlation with BMD (p < 0.05). The most common proteins among them were PHLD, SAMP, PEDF, HPTR, APOA1, SHBG, CO6, A2MG, CBPN, RAIN APOD, and THBG. The identified proteins were used to generate the biological age (BA) of bone. Each 1 SD-year increase in KDM-Proage was associated with higher risk of LS-OP (hazard ratio [HR], 1.25; 95% CI, 1.14-1.36, p = 4.96 × 10-06 ), and FN-OP (HR, 1.13; 95% CI, 1.02-1.23, p = 9.71 × 10-03 ). The findings uncovered that the apolipoproteins, zymoproteins, complements, and binding proteins presented new mechanistic insights into osteoporosis. Serum proteomics could be a crucial indicator for evaluating bone aging.
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Yang H, Zhang B, Wu Z, Pan J, Chen L, Xiu X, Cai X, Liu Z, Zheng Y. Synergistic application of atmospheric and room temperature plasma mutagenesis and adaptive laboratory evolution improves the tolerance of Escherichia coli to L-cysteine. Biotechnol J 2024; 19:e2300648. [PMID: 38403408 DOI: 10.1002/biot.202300648] [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: 11/20/2023] [Revised: 01/16/2024] [Accepted: 01/27/2024] [Indexed: 02/27/2024]
Abstract
L-Cysteine production through fermentation stands as a promising technology. However, excessive accumulation of L-cysteine poses a challenge due to the potential to inflict damage on cellular DNA. In this study, we employed a synergistic approach encompassing atmospheric and room temperature plasma mutagenesis (ARTP) and adaptive laboratory evolution (ALE) to improve L-cysteine tolerance in Escherichia coli. ARTP-treated populations obtained substantial enhancement in L-cysteine tolerance by ALE. Whole-genome sequencing, transcription analysis, and reverse engineering, revealed the pivotal role of an effective export mechanism mediated by gene eamB in augmenting L-cysteine resistance. The isolated tolerant strain, 60AP03/pTrc-cysEf , achieved a 2.2-fold increase in L-cysteine titer by overexpressing the critical gene cysEf during batch fermentation, underscoring its enormous potential for L-cysteine production. The production evaluations, supplemented with L-serine, further demonstrated the stability and superiority of tolerant strains in L-cysteine production. Overall, our work highlighted the substantial impact of the combined ARTP and ALE strategy in increasing the tolerance of E. coli to L-cysteine, providing valuable insights into improving L-cysteine overproduction, and further emphasized the potential of biotechnology in industrial production.
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Cai X, Tang R. Maxillary first molar with two distobuccal root canals and cervical deformity: A case report. Clin Case Rep 2024; 12:e8555. [PMID: 38410658 PMCID: PMC10895546 DOI: 10.1002/ccr3.8555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/16/2024] [Indexed: 02/28/2024] Open
Abstract
The second distobuccal canal in the maxillary first molar is often missed because of the low prevalence rate (0%-4%). The article reports this kind of variation in one case. Promising outcomes have continued up to the present (2-year follow-up).
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Ablikim M, Achasov MN, Adlarson P, Ai XC, Aliberti R, Amoroso A, An MR, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang TT, Chang WL, Che GR, Chelkov G, Chen C, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Cheng WS, Choi SK, Chu X, Cibinetto G, Coen SC, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du MC, Du SX, Duan ZH, Egorov P, Fan YHY, Fan YL, Fang J, Fang SS, Fang WX, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fischer K, Fritsch M, Fritzsch C, Fu CD, Fu JL, Fu YW, Gao H, Gao YN, Gao Y, Garbolino S, Garzia I, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Guan CY, Guan ZL, Guo AQ, Guo LB, Guo MJ, Guo RP, Guo YP, Guskov A, Han TT, Han WY, Hao XQ, Harris FA, He KK, He KL, Heinsius FHH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou XT, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hüsken N, Imoehl W, Jackson J, Jaeger S, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jia XQ, Jia ZK, Jiang HJ, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, K X, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Khoukaz A, Kiuchi R, Kliemt R, Kolcu OB, Kopf B, Kuessner M, Kupsc A, Kühn W, Lane JJ, Larin P, Lavania A, Lavezzi L, Lei TT, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li JW, Li KL, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li QX, Li SX, Li T, Li WD, Li WG, Li XH, Li XL, Li X, Li YG, Li ZJ, Liang C, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Liao YP, Libby J, Limphirat A, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu FH, Liu F, Liu F, Liu GM, Liu H, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu Y, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XY, Ma Y, Ma YM, Maas FE, Maggiora M, Malde S, Malik QA, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Muskalla J, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pei YP, Pelizaeus M, Peng HP, Peters K, Ping JL, Ping RG, Plura S, Pogodin S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin JJ, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Redmer CF, Ren KJ, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi HC, Shi JL, Shi JY, Shi QQ, Shi RS, Shi X, Song JJ, Song TZ, Song WM, Song YJ, Song YX, Sosio S, Spataro S, Stieler F, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZT, Tan YX, Tang CJ, Tang GY, Tang J, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian WH, Tian Y, Tian ZF, Uman I, Wang SJ, Wang B, Wang BL, Wang B, Wang CW, Wang DY, Wang F, Wang HJ, Wang HP, Wang JP, Wang K, Wang LL, Wang M, Wang M, Wang S, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang Y, Wang YD, Wang YF, Wang YH, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei D, Wei DH, Weidner F, Wen SP, Wenzel CW, Wiedner U, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YJ, Wu Z, Xia L, Xian XM, Xiang T, Xiao D, Xiao GY, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu QJ, Xu QN, Xu W, Xu WL, Xu XP, Xu YC, Xu ZP, Xu ZS, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YX, Yang Y, Yang ZW, Yao ZP, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yu XD, Yuan CZ, Yuan L, Yuan SC, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng X, Zeng Y, Zeng YJ, Zhai XY, Zhai YC, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang J, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang P, Zhang QY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang X, Zhang Y, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou LP, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WJ, Zhu YC, Zhu ZA, Zou JH, Zu J. First Measurement of the Decay Asymmetry in the Pure W-Boson-Exchange Decay Λ_{c}^{+}→Ξ^{0}K^{+}. PHYSICAL REVIEW LETTERS 2024; 132:031801. [PMID: 38307076 DOI: 10.1103/physrevlett.132.031801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 11/30/2023] [Indexed: 02/04/2024]
Abstract
Based on 4.4 fb^{-1} of e^{+}e^{-} annihilation data collected at the center-of-mass energies between 4.60 and 4.70 GeV with the BESIII detector at the BEPCII collider, the pure W-boson-exchange decay Λ_{c}^{+}→Ξ^{0}K^{+} is studied with a full angular analysis. The corresponding decay asymmetry is measured for the first time to be α_{Ξ^{0}K^{+}}=0.01±0.16(stat)±0.03(syst). This result reflects the noninterference effect between the S- and P-wave amplitudes. The phase shift between S- and P-wave amplitudes has two solutions, which are δ_{p}-δ_{s}=-1.55±0.25(stat)±0.05(syst) rad or 1.59±0.25(stat)±0.05(syst) rad.
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Tao QQ, Cai X, Xue YY, Ge W, Yue L, Li XY, Lin RR, Peng GP, Jiang W, Li S, Zheng KM, Jiang B, Jia JP, Guo T, Wu ZY. Alzheimer's disease early diagnostic and staging biomarkers revealed by large-scale cerebrospinal fluid and serum proteomic profiling. Innovation (N Y) 2024; 5:100544. [PMID: 38235188 PMCID: PMC10794110 DOI: 10.1016/j.xinn.2023.100544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 11/19/2023] [Indexed: 01/19/2024] Open
Abstract
Amyloid-β, tau pathology, and biomarkers of neurodegeneration make up the core diagnostic biomarkers of Alzheimer disease (AD). However, these proteins represent only a fraction of the complex biological processes underlying AD, and individuals with other brain diseases in which AD pathology is a comorbidity also test positive for these diagnostic biomarkers. More AD-specific early diagnostic and disease staging biomarkers are needed. In this study, we performed tandem mass tag proteomic analysis of paired cerebrospinal fluid (CSF) and serum samples in a discovery cohort comprising 98 participants. Candidate biomarkers were validated by parallel reaction monitoring-based targeted proteomic assays in an independent multicenter cohort comprising 288 participants. We quantified 3,238 CSF and 1,702 serum proteins in the discovery cohort, identifying 171 and 860 CSF proteins and 37 and 323 serum proteins as potential early diagnostic and staging biomarkers, respectively. In the validation cohort, 58 and 21 CSF proteins, as well as 12 and 18 serum proteins, were verified as early diagnostic and staging biomarkers, respectively. Separate 19-protein CSF and an 8-protein serum biomarker panels were built by machine learning to accurately classify mild cognitive impairment (MCI) due to AD from normal cognition with areas under the curve of 0.984 and 0.881, respectively. The 19-protein CSF biomarker panel also effectively discriminated patients with MCI due to AD from patients with other neurodegenerative diseases. Moreover, we identified 21 CSF and 18 serum stage-associated proteins reflecting AD stages. Our findings provide a foundation for developing blood-based tests for AD screening and staging in clinical practice.
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Zhu FY, Huang MY, Zheng K, Zhang XJ, Cai X, Huang LG, Liu ZQ, Zheng YG. Designing a novel (R)-ω-transaminase for asymmetric synthesis of sitagliptin intermediate via motif swapping and semi-rational design. Int J Biol Macromol 2023; 253:127348. [PMID: 37820904 DOI: 10.1016/j.ijbiomac.2023.127348] [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: 06/16/2023] [Revised: 09/25/2023] [Accepted: 10/09/2023] [Indexed: 10/13/2023]
Abstract
The application of (R)-ω-transaminases as biocatalysts for chiral amine synthesis has been hampered by inadequate stereoselectivity and narrow substrate spectrum. Herein, an effective evolution strategy for (R)-ω-transaminase designing for the asymmetric synthesis of sitagliptin intermediate is presented. Since natural transaminases lack activity toward bulky prositagliptin ketone, transaminase scaffolds with catalytic machinery and activity toward the truncated prositagliptin ketone were firstly screened based on substrate walking principle. A transaminase chimera was established synchronously conferring catalytic activity and (R)-selectivity toward prositagliptin ketone through motif swapping, followed by stepwise evolution. The process resulted in a "best" engineered variant MwTAM8, which exhibited 79.2-fold higher activity than the chimeric scaffold MwTAMc. Structural analysis revealed that the heightened activity is mainly due to the enlarged and adaptive substrate pocket and tunnel. The novel (R)-transaminase exhibited unsatisfied industrial operation stability, which is expected to further modify the protein to enhance its tolerance to temperature, pH, and organic solvents to meet sustainable industrial demands. This study underscores a useful evolution strategy of engineering biocatalysts to confer new properties and functions on enzymes for synthesizing high-value drug intermediates.
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Li Y, Cui G, Cai X, Yun G, Zhao Y, Jiang L, Cui S, Zhang J, Liu M, Zeng W, Wang Z, Jiang J. A New Porphyrin-based Covalent Organic Framework with High Iodine Capture Capacity and I-doping Enhanced Conductivity. Chemistry 2023:e202303688. [PMID: 38102885 DOI: 10.1002/chem.202303688] [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: 11/07/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 12/17/2023]
Abstract
Covalent organic frameworks (COFs) are porous organic materials with well-defined and uniform structure. The material is an excellent candidate as a solid adsorbent for iodine adsorption. In the present study, we report the synthesis of COF with porphyrin moiety, TF-TA-COF, by solvothermal reaction, which was characterized by XRD, solid-state 13 C NMR, IR, TGA, and nitrogen adsorption-desorption analysis. TF-TA-COF showed a high specific surface area of 443 m2 g-1 , and exhibited good adsorption performance for iodine vapor, with an adsorption capacity of 2.74 g g-1 . XPS and Raman spectrum indicated that a hybrid of physisorption and chemisorption took place between host COF and iodine molecules. The electric properties of iodine-loaded TF-TA-COF were also studied. After doped with iodine, the conductivity of the material increased by more than 5 orders of magnitude. The photoconductivity of I2 -doped COF was also studied and TF-TA-COF showed doping-enhanced photocurrent generation.
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Qian L, Gu Y, Zhai Q, Xue Z, Liu Y, Li S, Zeng Y, Sun R, Zhang Q, Cai X, Ge W, Dong Z, Gao H, Zhou Y, Zhu Y, Xu Y, Guo T. Multitissue Circadian Proteome Atlas of WT and Per1 -/-/Per2 -/- Mice. Mol Cell Proteomics 2023; 22:100675. [PMID: 37940002 PMCID: PMC10750102 DOI: 10.1016/j.mcpro.2023.100675] [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: 04/27/2023] [Revised: 10/22/2023] [Accepted: 11/03/2023] [Indexed: 11/10/2023] Open
Abstract
The molecular basis of circadian rhythm, driven by core clock genes such as Per1/2, has been investigated on the transcriptome level, but not comprehensively on the proteome level. Here we quantified over 11,000 proteins expressed in eight types of tissues over 46 h with an interval of 2 h, using WT and Per1/Per2 double knockout mouse models. The multitissue circadian proteome landscape of WT mice shows tissue-specific patterns and reflects circadian anticipatory phenomena, which are less obvious on the transcript level. In most peripheral tissues of double knockout mice, reduced protein cyclers are identified when compared with those in WT mice. In addition, PER1/2 contributes to controlling the anticipation of the circadian rhythm, modulating tissue-specific cyclers as well as key pathways including nucleotide excision repair. Severe intertissue temporal dissonance of circadian proteome has been observed in the absence of Per1 and Per2. The γ-aminobutyric acid might modulate some of these temporally correlated cyclers in WT mice. Our study deepens our understanding of rhythmic proteins across multiple tissues and provides valuable insights into chronochemotherapy. The data are accessible at https://prot-rhythm.prottalks.com/.
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Sun R, Tan L, Ding X, A J, Xue Z, Cai X, Li S, Guo T. A pathway activity-based proteomic classifier stratifies prostate tumors into two subtypes. Clin Proteomics 2023; 20:50. [PMID: 37950160 PMCID: PMC10638831 DOI: 10.1186/s12014-023-09441-w] [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: 01/26/2023] [Accepted: 10/25/2023] [Indexed: 11/12/2023] Open
Abstract
Prostate cancer (PCa) is the second most common cancer in males worldwide. The risk stratification of PCa is mainly based on morphological examination. Here we analyzed the proteome of 667 tumor samples from 487 Chinese PCa patients and characterized 9576 protein groups by PulseDIA mass spectrometry. Then we developed a pathway activity-based classifier concerning 13 proteins from seven pathways, and dichotomized the PCa patients into two subtypes, namely PPS1 and PPS2. PPS1 is featured with enhanced innate immunity, while PPS2 with suppressed innate immunity. This classifier exhibited a correlation with PCa progression in our cohort and was further validated by two published transcriptome datasets. Notably, PPS2 was significantly correlated with poor biochemical recurrence (BCR)/metastasis-free survival (log-rank P-value < 0.05). The PPS2 was also featured with cell proliferation activation. Together, our study presents a novel pathway activity-based stratification scheme for PCa.
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Ablikim M, Achasov MN, Adlarson P, Ai XC, Aliberti R, Amoroso A, An MR, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bloms J, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang TT, Chang WL, Che GR, Chelkov G, Chen C, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Cheng WS, Choi SK, Chu X, Cibinetto G, Coen SC, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du SX, Duan ZH, Egorov P, Fan YL, Fang J, Fang SS, Fang WX, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fischer K, Fritsch M, Fritzsch C, Fu CD, Fu JL, Fu YW, Gao H, Gao YN, Gao Y, Garbolino S, Garzia I, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Gu YT, Guan CY, Guan ZL, Guo AQ, Guo LB, Guo MJ, Guo RP, Guo YP, Guskov A, Han TT, Han WY, Hao XQ, Harris FA, He KK, He KL, Heinsius FHH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou XT, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hüsken N, Imoehl W, Irshad M, Jackson J, Jaeger S, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jia XQ, Jia ZK, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Khoukaz A, Kiuchi R, Kliemt R, Kolcu OB, Kopf B, Kuessner MK, Kui X, Kupsc A, Kühn W, Lane JJ, Larin P, Lavania A, Lavezzi L, Lei TT, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li JW, Li KL, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li QX, Li SX, Li T, Li WD, Li WG, Li XH, Li XL, Li X, Li YG, Li ZJ, Li ZX, Liang C, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Libby J, Limphirat A, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu FH, Liu F, Liu F, Liu GM, Liu H, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu Y, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XY, Ma Y, Ma YM, Maas FE, Maggiora M, Malde S, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pei YP, Pelizaeus M, Peng HP, Peters K, Ping JL, Ping RG, Plura S, Pogodin S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin JJ, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Redmer CF, Ren KJ, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi HC, Shi JL, Shi JY, Shi QQ, Shi RS, Shi X, Song JJ, Song TZ, Song WM, Song YJ, Song YX, Sosio S, Spataro S, Stieler F, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZT, Tan YX, Tang CJ, Tang GY, Tang J, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian WH, Tian Y, Tian ZF, Uman I, Wang SJ, Wang B, Wang BL, Wang B, Wang CW, Wang DY, Wang F, Wang HJ, Wang HP, Wang JP, Wang K, Wang LL, Wang M, Wang M, Wang S, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang Y, Wang YD, Wang YF, Wang YH, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei D, Wei DH, Weidner F, Wen SP, Wenzel CW, Wiedner UW, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YJ, Wu Z, Xia L, Xian XM, Xiang T, Xiao D, Xiao GY, Xiao H, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu QJ, Xu QN, Xu W, Xu WL, Xu XP, Xu YC, Xu ZP, Xu ZS, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YX, Yang Y, Yang ZW, Yao ZP, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yu XD, Yuan CZ, Yuan L, Yuan SC, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng X, Zeng Y, Zeng YJ, Zhai XY, Zhai YC, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang P, Zhang QY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang XY, Zhang Y, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou LP, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WJ, Zhu YC, Zhu ZA, Zou JH, Zu J. Test of CP Symmetry in Hyperon to Neutron Decays. PHYSICAL REVIEW LETTERS 2023; 131:191802. [PMID: 38000397 DOI: 10.1103/physrevlett.131.191802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 10/03/2023] [Indexed: 11/26/2023]
Abstract
The quantum entangled J/ψ→Σ^{+}Σ[over ¯]^{-} pairs from (1.0087±0.0044)×10^{10} J/ψ events taken by the BESIII detector are used to study the nonleptonic two-body weak decays Σ^{+}→nπ^{+} and Σ[over ¯]^{-}→n[over ¯]π^{-}. The CP-odd weak decay parameters of the decays Σ^{+}→nπ^{+} (α_{+}) and Σ[over ¯]^{-}→n[over ¯]π^{-} (α[over ¯]_{-}) are determined to be 0.0481±0.0031_{stat}±0.0019_{syst} and -0.0565±0.0047_{stat}±0.0022_{syst}, respectively. The decay parameter α[over ¯]_{-} is measured for the first time, and the accuracy of α_{+} is improved by a factor of 4 compared to the previous results. The simultaneously determined decay parameters allow the first precision CP symmetry test for any hyperon decay with a neutron in the final state with the measurement of A_{CP}=(α_{+}+α[over ¯]_{-})/(α_{+}-α[over ¯]_{-})=-0.080±0.052_{stat}±0.028_{syst}. Assuming CP conservation, the average decay parameter is determined as ⟨α_{+}⟩=(α_{+}-α[over ¯]_{-})/2=-0.0506±0.0026_{stat}±0.0019_{syst}, while the ratios α_{+}/α_{0} and α[over ¯]_{-}/α[over ¯]_{0} are -0.0490±0.0032_{stat}±0.0021_{syst} and -0.0571±0.0053_{stat}±0.0032_{syst}, where α_{0} and α[over ¯]_{0} are the decay parameters of the decays Σ^{+}→pπ^{0} and Σ[over ¯]^{-}→p[over ¯]π^{0}, respectively.
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Ablikim M, Achasov MN, Adlarson P, Ai XC, Aliberti R, Amoroso A, An MR, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang TT, Chang WL, Che GR, Chelkov G, Chen C, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Cheng WS, Choi SK, Chu X, Cibinetto G, Coen SC, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du MC, Du SX, Duan ZH, Egorov P, Fan YHY, Fan YL, Fang J, Fang SS, Fang WX, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fischer K, Fritsch M, Fritzsch C, Fu CD, Fu JL, Fu YW, Gao H, Gao YN, Gao Y, Garbolino S, Garzia I, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Guan CY, Guan ZL, Guo AQ, Guo LB, Guo MJ, Guo RP, Guo YP, Guskov A, Han TT, Han WY, Hao XQ, Harris FA, He KK, He KL, Heinsius FHH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou XT, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hüsken N, Imoehl W, Jackson J, Jaeger S, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jia XQ, Jia ZK, Jiang HJ, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Khoukaz A, Kiuchi R, Kliemt R, Kolcu OB, Kopf B, Kuessner M, Kupsc A, Kühn W, Lane JJ, Larin P, Lavania A, Lavezzi L, Lei TT, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li JW, Li KL, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li QX, Li SX, Li T, Li WD, Li WG, Li XH, Li XL, Li X, Li YG, Li ZJ, Liang C, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Liao YP, Libby J, Limphirat A, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu FH, Liu F, Liu F, Liu GM, Liu H, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu Y, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XY, Ma Y, Ma YM, Maas FE, Maggiora M, Malde S, Malik QA, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Muskalla J, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pei YP, Pelizaeus M, Peng HP, Peters K, Ping JL, Ping RG, Plura S, Pogodin S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qiao XK, Qin JJ, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Redmer CF, Ren KJ, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi HC, Shi JL, Shi JY, Shi QQ, Shi RS, Shi X, Song JJ, Song TZ, Song WM, Song YJ, Song YX, Sosio S, Spataro S, Stieler F, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZT, Tan YX, Tang CJ, Tang GY, Tang J, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian WH, Tian Y, Tian ZF, Uman I, Wang SJ, Wang B, Wang BL, Wang B, Wang CW, Wang DY, Wang F, Wang HJ, Wang HP, Wang JP, Wang K, Wang LL, Wang M, Wang M, Wang S, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang Y, Wang YD, Wang YF, Wang YH, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei D, Wei DH, Weidner F, Wen SP, Wenzel CW, Wiedner U, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YJ, Wu Z, Xia L, Xian XM, Xiang T, Xiao D, Xiao GY, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu QJ, Xu QN, Xu W, Xu WL, Xu XP, Xu YC, Xu ZP, Xu ZS, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YX, Yang Y, Yang ZW, Yao ZP, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yu XD, Yuan CZ, Yuan L, Yuan SC, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng X, Zeng Y, Zeng YJ, Zhai XY, Zhai YC, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang P, Zhang QY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang X, Zhang Y, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou LP, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WJ, Zhu YC, Zhu ZA, Zou JH, Zu J. Measurement of Energy-Dependent Pair-Production Cross Section and Electromagnetic Form Factors of a Charmed Baryon. PHYSICAL REVIEW LETTERS 2023; 131:191901. [PMID: 38000396 DOI: 10.1103/physrevlett.131.191901] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/29/2023] [Accepted: 09/15/2023] [Indexed: 11/26/2023]
Abstract
We study the process e^{+}e^{-}→Λ_{c}^{+}Λ[over ¯]_{c}^{-} at twelve center-of-mass energies from 4.6119 to 4.9509 GeV using data samples collected by the BESIII detector at the BEPCII collider. The Born cross sections and effective form factors (|G_{eff}|) are determined with unprecedented precision after combining the single and double-tag methods based on the decay process Λ_{c}^{+}→pK^{-}π^{+}. Flat cross sections around 4.63 GeV are obtained and no indication of the resonant structure Y(4630), as reported by Belle, is found. In addition, no oscillatory behavior is discerned in the |G_{eff}| energy dependence of Λ_{c}^{+}, in contrast to what is seen for the proton and neutron cases. Analyzing the cross section together with the polar-angle distribution of the Λ_{c}^{+} baryon at each energy point, the moduli of electric and magnetic form factors (|G_{E}| and |G_{M}|) are extracted and separated. For the first time, the energy dependence of the form factor ratio |G_{E}/G_{M}| is observed, which can be well described by an oscillatory function.
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Qiu S, Zhu Y, Xie B, Chen W, Wang D, Cai X, Sun Z, Wu T. Prediabetes Progression and Regression on Objectively- Measured Physical Function: A Prospective Cohort Study. Diabetes Metab J 2023; 47:859-868. [PMID: 37915187 PMCID: PMC10695714 DOI: 10.4093/dmj.2022.0377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/29/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGRUOUND Prediabetes leads to declines in physical function in older adults, but the impact of prediabetes progression or regression on physical function is unknown. This study assessed this longitudinal association, with physical function objectivelymeasured by grip strength, walking speed, and standing balance, based on the Health and Retirement Study enrolling United States adults aged >50 years. METHODS Participants with prediabetes were followed-up for 4-year to ascertain prediabetes status alteration (maintained, regressed, or progressed), and another 4-year to assess their impacts on physical function. Weak grip strength was defined as <26 kg for men and <16 kg for women, slow walking speed was as <0.8 m/sec, and poor standing balance was as an uncompleted fulltandem standing testing. Logistic and linear regression analyses were performed. RESULTS Of the included 1,511 participants with prediabetes, 700 maintained as prediabetes, 306 progressed to diabetes, and 505 regressed to normoglycemia over 4 years. Grip strength and walking speed were declined from baseline during the 4-year followup, regardless of prediabetes status alteration. Compared with prediabetes maintenance, prediabetes progression increased the odds of developing weak grip strength by 89% (95% confidence interval [CI], 0.04 to 2.44) and exhibited larger declines in grip strength by 0.85 kg (95% CI, -1.65 to -0.04). However, prediabetes progression was not related to impairments in walking speed or standing balance. Prediabetes regression also did not affect any measures of physical function. CONCLUSION Prediabetes progression accelerates grip strength decline in aging population, while prediabetes regression may not prevent physical function decline due to aging.
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Ablikim M, Achasov MN, Adlarson P, Aliberti R, Amoroso A, An MR, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bloms J, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang TT, Chang WL, Che GR, Chelkov G, Chen C, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Cheng WS, Choi SK, Chu X, Cibinetto G, Coen SC, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du SX, Duan ZH, Egorov P, Fan YL, Fang J, Fang SS, Fang WX, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fischer K, Fritsch M, Fritzsch C, Fu CD, Fu YW, Gao H, Gao YN, Gao Y, Garbolino S, Garzia I, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Gu YT, Guan CY, Guan ZL, Guo AQ, Guo LB, Guo RP, Guo YP, Guskov A, Hou XT, Han WY, Hao XQ, Harris FA, He KK, He KL, Heinsius FH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hüsken N, Imoehl W, Irshad M, Jackson J, Jaeger S, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jia ZK, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, Kui X, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Khoukaz A, Kiuchi R, Kliemt R, Koch L, Kolcu OB, Kopf B, Kuessner M, Kupsc A, Kühn W, Lane JJ, Lange JS, Larin P, Lavania A, Lavezzi L, Lei TT, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li JW, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li SX, Li T, Li WD, Li WG, Li XH, Li XL, Li X, Li YG, Li ZJ, Li ZX, Li ZY, Liang C, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Libby J, Limphirat A, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu D, Liu FH, Liu F, Liu F, Liu GM, Liu H, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XY, Ma Y, Maas FE, Maggiora M, Maldaner S, Malde S, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Pei YP, Pelizaeus M, Peng HP, Peters K, Ping JL, Ping RG, Plura S, Pogodin S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin JJ, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Redmer CF, Ren KJ, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi HC, Shi JY, Shi QQ, Shi RS, Shi X, Song JJ, Song TZ, Song WM, Song YX, Sosio S, Spataro S, Stieler F, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZT, Tan YX, Tang CJ, Tang GY, Tang J, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian WH, Tian Y, Tian ZF, Uman I, Wang B, Wang BL, Wang B, Wang CW, Wang DY, Wang F, Wang HJ, Wang HP, Wang K, Wang LL, Wang M, Wang M, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WH, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang Y, Wang YD, Wang YF, Wang YH, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei D, Wei DH, Weidner F, Wen SP, Wenzel CW, Wiedner U, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YJ, Wu Z, Xia L, Xian XM, Xiang T, Xiao D, Xiao GY, Xiao H, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu QJ, Xu WL, Xu XP, Xu YC, Xu ZP, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YX, Yang Y, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu T, Yu XD, Yuan CZ, Yuan L, Yuan SC, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng X, Zeng Y, Zeng YJ, Zhai XY, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang P, Zhang QY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang XY, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou LP, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WJ, Zhu YC, Zhu ZA, Zou JH, Zu J. Determination of Spin-Parity Quantum Numbers for the Narrow Structure near the pΛ[over ¯] Threshold in e^{+}e^{-}→pK^{-}Λ[over ¯]+c.c. PHYSICAL REVIEW LETTERS 2023; 131:151901. [PMID: 37897776 DOI: 10.1103/physrevlett.131.151901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 08/22/2023] [Accepted: 09/15/2023] [Indexed: 10/30/2023]
Abstract
A narrow structure in the pΛ[over ¯] system near the mass threshold, named as X(2085), is observed in the process e^{+}e^{-}→pK^{-}Λ[over ¯] with a statistical significance greater than 20σ. Its spin and parity are determined for the first time to be J^{P}=1^{+} in an amplitude analysis, with a statistical significance greater than 5σ over other quantum numbers (0^{-},1^{-} and 2^{+}). The pole positions of X(2085) are measured to be M_{pole}=(2084_{-2}^{+4}±9) MeV and Γ_{pole}=(58_{-3}^{+4}±25) MeV, where the first uncertainties are statistical and the second ones are systematic. The analysis is based on the study of the process e^{+}e^{-}→pK^{-}Λ[over ¯] and uses the data samples collected with the BESIII detector at the center-of-mass energies sqrt[s]=4.008, 4.178, 4.226, 4.258, 4.416, and 4.682 GeV with a total integrated luminosity of 8.35 fb^{-1}.
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Ablikim M, Achasov MN, Adlarson P, Ai XC, Aliberti R, Amoroso A, An MR, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang TT, Chang WL, Che GR, Chelkov G, Chen C, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Cheng WS, Choi SK, Chu X, Cibinetto G, Coen SC, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du MC, Du SX, Duan ZH, Egorov P, Fan YL, Fang J, Fang SS, Fang WX, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fischer K, Fritsch M, Fritzsch C, Fu CD, Fu JL, Fu YW, Gao H, Gao YN, Gao Y, Garbolino S, Garzia I, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Gu YT, Guan CY, Guan ZL, Guo AQ, Guo LB, Guo MJ, Guo RP, Guo YP, Guskov A, Han TT, Han WY, Hao XQ, Harris FA, He KK, He KL, Heinsius FHH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou XT, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hüsken N, Imoehl W, Irshad M, Jackson J, Jaeger S, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jia XQ, Jia ZK, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, X K, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Khoukaz A, Kiuchi R, Kliemt R, Kolcu OB, Kopf B, Kuessner MK, Kupsc A, Kühn W, Lane JJ, Larin P, Lavania A, Lavezzi L, Lei TT, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li JW, Li KL, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li QX, Li SX, Li T, Li WD, Li WG, Li XH, Li XL, Li X, Li YG, Li ZJ, Li ZX, Liang C, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Liao YP, Libby J, Limphirat A, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu FH, Liu F, Liu F, Liu GM, Liu H, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu Y, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XY, Ma Y, Ma YM, Maas FE, Maggiora M, Malde S, Malik QA, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pei YP, Pelizaeus M, Peng HP, Peters K, Ping JL, Ping RG, Plura S, Pogodin S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin JJ, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Redmer CF, Ren KJ, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi HC, Shi JL, Shi JY, Shi QQ, Shi RS, Shi X, Song JJ, Song TZ, Song WM, Song YJ, Song YX, Sosio S, Spataro S, Stieler F, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZT, Tan YX, Tang CJ, Tang GY, Tang J, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian WH, Tian Y, Tian ZF, Uman I, Wang SJ, Wang B, Wang BL, Wang B, Wang CW, Wang DY, Wang F, Wang HJ, Wang HP, Wang JP, Wang K, Wang LL, Wang M, Wang M, Wang S, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang Y, Wang YD, Wang YF, Wang YH, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei D, Wei DH, Weidner F, Wen SP, Wenzel CW, Wiedner UW, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YJ, Wu Z, Xia L, Xian XM, Xiang T, Xiao D, Xiao GY, Xiao H, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu QJ, Xu QN, Xu W, Xu WL, Xu XP, Xu YC, Xu ZP, Xu ZS, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YX, Yang Y, Yang ZW, Yao ZP, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yu XD, Yuan CZ, Yuan L, Yuan SC, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng X, Zeng Y, Zeng YJ, Zhai XY, Zhai YC, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang P, Zhang QY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang XY, Zhang Y, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou LP, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WJ, Zhu YC, Zhu ZA, Zou JH, Zu J. Precise Measurement of the e^{+}e^{-}→D_{s}^{*+}D_{s}^{*-} Cross Sections at Center-of-Mass Energies from Threshold to 4.95 GeV. PHYSICAL REVIEW LETTERS 2023; 131:151903. [PMID: 37897771 DOI: 10.1103/physrevlett.131.151903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/16/2023] [Accepted: 08/29/2023] [Indexed: 10/30/2023]
Abstract
The process e^{+}e^{-}→D_{s}^{*+}D_{s}^{*-} is studied with a semi-inclusive method using data samples at center-of-mass energies from threshold to 4.95 GeV collected with the BESIII detector operating at the Beijing Electron Positron Collider. The Born cross sections of the process are measured for the first time with high precision in this energy region. Two resonance structures are observed in the energy-dependent cross sections around 4.2 and 4.4 GeV. By fitting the cross sections with a coherent sum of three Breit-Wigner amplitudes and one phase-space amplitude, the two significant structures are assigned masses of (4186.8±8.7±30) and (4414.6±3.4±6.1) MeV/c^{2}, widths of (55±15±53) and (122.5±7.5±8.1) MeV, where the first errors are statistical and the second ones are systematic. The inclusion of a third Breit-Wigner amplitude is necessary to describe a structure around 4.79 GeV.
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Jin L, Wan Q, Ouyang S, Zheng L, Cai X, Zhang X, Shen J, Jia D, Liu Z, Zheng Y. Isomerase and epimerase: overview and practical application in production of functional sugars. Crit Rev Food Sci Nutr 2023:1-16. [PMID: 37807720 DOI: 10.1080/10408398.2023.2260888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
The biosynthesis of functional sugars has gained significant attention due to their potential health benefits and increasing demand in the food industry. Enzymatic synthesis has emerged as a promising approach, offering high catalytic efficiency, chemoselectivity, and stereoselectivity. However, challenges such as poor thermostability, low catalytic efficiency, and food safety concerns have limited the commercial production of functional sugars. Protein engineering, including directed evolution and rational design, has shown promise in overcoming these barriers and improving biocatalysts for large-scale production. Furthermore, enzyme immobilization has proven effective in reducing costs and facilitating the production of functional sugars. To ensure food safety, the use of food-grade expression systems has been explored. However, downstream technologies, including separation, purification, and crystallization, still pose challenges in terms of efficiency and cost-effectiveness. Addressing these challenges is crucial to optimize the overall production process. Despite the obstacles, the future outlook for functional sugars is promising, driven by increasing awareness of their health benefits and continuous technological advancements. With further research and technological breakthroughs, industrial-scale production of functional sugars through biosynthesis will become a reality, leading to their widespread incorporation in various industries and products.
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Hu J, Ren M, Cai X, Lyu JJ, Shen XX, Kong YY. [Clinicopathological and prognostic features of subungual melanoma in situ]. ZHONGHUA BING LI XUE ZA ZHI = CHINESE JOURNAL OF PATHOLOGY 2023; 52:1006-1011. [PMID: 37805391 DOI: 10.3760/cma.j.cn112151-20230226-00152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/09/2023]
Abstract
Objective: To investigate the clinicopathological characteristics, immunohistochemical profiles, molecular features, and prognosis of subungual melanoma in situ (SMIS). Methods: Thirty cases of SMIS were collected in Fudan University Shanghai Cancer Center, Shanghai, China from 2018 to 2022. The clinicopathological characteristics and follow-up data were retrospectively analyzed. Histopathologic evaluation and immunohistochemical studies were carried out. By using Vysis melanoma fluorescence in situ hybridization (FISH) probe kit, combined with 9p21(CDKN2A) and 8q24(MYC) assays were performed. Results: There were 8 males and 22 females. The patients' ages ranged from 22 to 65 years (median 48 years). All patients presented with longitudinal melanonychia involving a single digit. Thumb was the most commonly affected digit (16/30, 53.3%). 56.7% (17/30) of the cases presented with Hutchinson's sign. Microscopically, melanocytes proliferated along the dermo-epithelial junction. Hyperchromatism and nuclear pleomorphism were two of the most common histological features. The melanocyte count ranged from 30 to 185. Most cases showed small to medium nuclear enlargement (29/30, 96.7%). Pagetoid spread was seen in all cases. Intra-epithelial mitoses were identified in 56.7% (17/30) of the cases. Involvement of nailfold was found in 19 cases, 4 of which were accompanied by cutaneous adnexal extension. The positive rates of SOX10, PNL2, Melan A, HMB45, S-100, and PRAME were 100.0%, 100.0%, 96.0%, 95.0%, 76.9%, and 83.3%, respectively. FISH analysis was positive in 6/9 of the cases. Follow-up data were available in 28 patients, and all of them were alive without disease. Conclusions: SMIS mainly shows small to medium-sized cells. High melanocyte count, hyperchromatism, nuclear pleomorphism, Pagetoid spreading, intra-epithelial mitosis, nailfold involvement, and cutaneous adnexal extension are important diagnostic hallmarks. Immunohistochemistry including SOX10 and PRAME, combined with FISH analysis, is valuable for the diagnosis of SMIS.
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Ablikim M, Achasov MN, Adlarson P, Aliberti R, Amoroso A, An MR, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bloms J, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang TT, Chang WL, Che GR, Chelkov G, Chen C, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Cheng WS, Choi SK, Chu X, Cibinetto G, Coen SC, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du SX, Duan ZH, Egorov P, Fan YL, Fang J, Fang SS, Fang WX, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Fischer K, Fritsch M, Fritzsch C, Fu CD, Fu JL, Fu YW, Gao H, Gao YN, Gao Y, Garbolino S, Garzia I, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Gu YT, Guan CY, Guan ZL, Guo AQ, Guo LB, Guo RP, Guo YP, Guskov A, Hou XT, Han TT, Han WY, Hao XQ, Harris FA, He KK, He KL, Heinsius FHH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hüsken N, Imoehl W, Irshad M, Jackson J, Jaeger S, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji XB, Ji XL, Ji YY, Jia ZK, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Johansson T, Kui X, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Khoukaz A, Kiuchi R, Kliemt R, Koch L, Kolcu OB, Kopf B, Kuessner MK, Kupsc A, Kühn W, Lane JJ, Lange JS, Larin P, Lavania A, Lavezzi L, Lei TT, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li JW, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li SX, Li T, Li WD, Li WG, Li XH, Li XL, Li X, Li YG, Li ZJ, Li ZX, Li ZY, Liang C, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Libby J, Limphirat A, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu D, Liu FH, Liu F, Liu F, Liu GM, Liu H, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JL, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu Y, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XY, Ma Y, Maas FE, Maggiora M, Maldaner S, Malde S, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pei YP, Pelizaeus M, Peng HP, Peters K, Ping JL, Ping RG, Plura S, Pogodin S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin JJ, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Redmer CF, Ren KJ, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi HC, Shi JL, Shi JY, Shi QQ, Shi RS, Shi X, Song JJ, Song TZ, Song WM, Song YJ, Song YX, Sosio S, Spataro S, Stieler F, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZT, Tan YX, Tang CJ, Tang GY, Tang J, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian WH, Tian ZF, Uman I, Wang B, Wang BL, Wang B, Wang CW, Wang DY, Wang F, Wang HJ, Wang HP, Wang K, Wang LL, Wang M, Wang M, Wang S, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WH, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang Y, Wang YD, Wang YF, Wang YH, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei D, Wei DH, Weidner F, Wen SP, Wenzel CW, Wiedner UW, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YJ, Wu Z, Xia L, Xian XM, Xiang T, Xiao D, Xiao GY, Xiao H, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu QJ, Xu QN, Xu W, Xu WL, Xu XP, Xu YC, Xu ZP, Xu ZS, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YX, Yang Y, Yang ZW, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu T, Yu XD, Yuan CZ, Yuan L, Yuan SC, Yuan XQ, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng X, Zeng Y, Zeng YJ, Zhai XY, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang LM, Zhang LQ, Zhang L, Zhang P, Zhang QY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang XY, Zhang Y, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou LP, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WJ, Zhu YC, Zhu ZA, Zou JH, Zu J. First Experimental Study of the Purely Leptonic Decay D_{s}^{*+}→e^{+}ν_{e}. PHYSICAL REVIEW LETTERS 2023; 131:141802. [PMID: 37862669 DOI: 10.1103/physrevlett.131.141802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/25/2023] [Accepted: 09/05/2023] [Indexed: 10/22/2023]
Abstract
Using 7.33 fb^{-1} of e^{+}e^{-} collision data taken with the BESIII detector at the BEPCII collider, we report the first experimental study of the purely leptonic decay D_{s}^{*+}→e^{+}ν_{e}. Our data contain a signal of this decay with a statistical significance of 2.9σ. The branching fraction of D_{s}^{*+}→e^{+}ν_{e} is measured to be (2.1_{-0.9_{stat}}^{+1.2}±0.2_{syst})×10^{-5}, corresponding to an upper limit of 4.0×10^{-5} at the 90% confidence level. Taking the total width of the D_{s}^{*+} [(0.070±0.028) keV] predicted with the radiative D_{s}^{*+} decay from the lattice QCD calculation as input, the decay constant of the D_{s}^{*+} is determined to be f_{D_{s}^{*+}}=(214_{-46_{stat}}^{+61}±44_{syst}) MeV, corresponding to an upper limit of 354 MeV at the 90% confidence level.
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Qiu S, Cai X, Sun Z, Wu T. Comment on 'Prediabetes is an independent risk factor for sarcopenia in older men, but not in older women: the Bunkyo Health Study' by Kaga et al. J Cachexia Sarcopenia Muscle 2023; 14:2452-2453. [PMID: 37435695 PMCID: PMC10570061 DOI: 10.1002/jcsm.13293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/13/2023] Open
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Li Y, Liu J, Wang GZ, Yu W, Cai X, Li H, Cheng Y, Song XY, Fu XL. Exploration of Multiomic Profiles and Biomarkers as Predictors of Neoadjuvant Chemoradiotherapy Responsiveness in Esophageal Squamous Cell Carcinoma. Int J Radiat Oncol Biol Phys 2023; 117:e315. [PMID: 37785133 DOI: 10.1016/j.ijrobp.2023.06.2347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The current gold standard of care for resectable locally advanced esophageal cancer is neoadjuvant chemoradiotherapy (NCRT) followed by surgery. Given that only 30-40% of patients with esophageal squamous cell carcinoma (ESCC) achieved a pathologic complete response (pCR) following neoadjuvant chemoradiotherapy, it is critical to understand the biological basis of NCRT resistance in esophageal cancer and identify biomarkers for these patients in order to further personalize treatment plans. We aim to depict the biological landscape of ESCC responsiveness and resistance to neoadjuvant chemoradiotherapy. MATERIALS/METHODS Endoscopic biopsied specimens of the primary tumors and paired peripheral blood samples were obtained from 24 patients before neoadjuvant chemoradiotherapy and tested for whole exosome sequencing, RNA sequencing, and DIA mass spectrometry. Genomic data were analyzed for significantly mutated genes, copy number alterations, MSI, TMB, and mutational signatures. Transcriptomics and proteomics data were used to examine differentially activated pathways. GSEA and ActivePathways were used for the single omics level and joint multi-omics analysis, respectively. Tumor microenvironment (TME) characteristics were deconvoluted by xCell upon RNA-seq data. Treatment resistance biomarkers were identified and validated in a separate cohort using mIHC. RESULTS In the study cohort, 54% (13/24) of the patients achieved pCR. WES data suggested that FBXW7 was more frequently mutated in the pCR group (fisher test p-value = 0.029), and the most significant cytoband loss in the pCR group was 9p21.3 (qval = 0.001). Differences in TMB, MSI, and mutational signatures were not significant between groups. Combined transcriptomics and proteomics analysis revealed that type I interferon signaling pathways and RIG-I-like receptor signaling pathways(p<0.05) were enriched in non-pCR tumors. Esophageal cancer cohort RNA-seq data from TCGA verified the correlation between the genetic variances (FBXW7 mutation and 9p21.3 loss) and the decreased expression of type I interferon signaling pathway genes. In TME analysis, tolerogenic dendritic cells and exhausted T cell signatures were significantly enriched in non-pCR tumors, indicating an immunosuppressive status in treatment resistant patients. Based on proteomics PPI network and differential expression genes from RNA-seq data, a biomarker panel consisted of 12 proteins predictive of non-pCR tumors was identified: STAT1, EIF2AK2, MX1, BST2, TRIM21, SAMHD1, IFI44L, GBP1, PARP14, ISG15, IFIT3, and HLA-B. The expression of selected genes was validated by mIHC in an independent cohort. CONCLUSION Through a multiomics approach, we described the biological characteristics of ESCC with distinct responses to neoadjuvant chemoradiotherapy and proposed a panel of 12 proteins as predictive biomarkers for non-pCR patients.
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Ablikim M, Achasov MN, Adlarson P, Ahmed S, Albrecht M, Aliberti R, Amoroso A, An Q, Bai Y, Bakina O, Ferroli RB, Balossino I, Ban Y, Begzsuren K, Berger N, Bertani M, Bettoni D, Bianchi F, Bloms J, Bortone A, Boyko I, Briere RA, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang WL, Chelkov G, Chen G, Chen HS, Chen ML, Chen SJ, Chen XR, Chen YB, Chen ZJ, Cheng WS, Cibinetto G, Cossio F, Dai HL, Dai JP, Dai XC, Dbeyssi A, de Boer RE, Dedovich D, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du SX, Fang J, Fang SS, Fang Y, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Fritsch M, Fu CD, Gao YN, Gao Y, Gao Y, Garzia I, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Greco M, Gu LM, Gu MH, Gu S, Gu YT, Guan CY, Guo AQ, Guo LB, Guo RP, Guo YP, Guskov A, Han TT, Hao XQ, Harris FA, He KL, Heinsius FHH, Heinz CH, Heng YK, Herold C, Himmelreich M, Holtmann T, Hou YR, Hou ZL, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang LQ, Huang XT, Huang YP, Hussain T, Imoehl W, Irshad M, Jaeger S, Janchiv S, Ji Q, Ji QP, Ji XB, Ji XL, Jiang XS, Jiao JB, Jiao Z, Jin S, Jin Y, Johansson T, Kalantar-Nayestanaki N, Kang XS, Kappert R, Kavatsyuk M, Ke BC, Keshk IK, Khoukaz A, Kiese P, Kiuchi R, Kliemt R, Kolcu OB, Kopf B, Kuemmel M, Kuessner MK, Kupsc A, Kurth MG, Kühn W, Lane JJ, Larin P, Lavania A, Lavezzi L, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li JQ, Li JW, Li K, Li LK, Li L, Li PL, Li PR, Li SY, Li WD, Li WG, Li XH, Li XL, Li ZY, Liang H, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Libby J, Limphirat A, Liu BJ, Liu CX, Liu D, Liu FH, Liu F, Liu F, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JY, Liu K, Liu KY, Liu L, Liu MH, Liu Q, Liu SB, Liu S, Liu T, Liu WM, Liu X, Liu YB, Liu ZA, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JD, Lu JG, Lu XL, Lu Y, Lu YP, Luo CL, Luo MX, Luo T, Luo XL, Lusso S, Lyu XR, Ma FC, Ma HL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma RT, Ma XX, Ma XY, Maas FE, Maggiora M, Maldaner S, Malde S, Malik QA, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Min TJ, Mitchell RE, Mo XH, Muchnoi NY, Muramatsu H, Nakhoul S, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pelizaeus M, Peng HP, Peters K, Ping JL, Ping RG, Pitka A, Poling R, Prasad V, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin LQ, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu SQ, Qu SQ, Ravindran K, Redmer CF, Rivetti A, Rodin V, Rolo M, Rong G, Rosner C, Sarantsev A, Schelhaas Y, Schnier C, Schoenning K, Scodeggio M, Shan DC, Shan W, Shan XY, Shao M, Shen CP, Shen PX, Shen XY, Shi HC, Shi RS, Shi X, Shi XD, Song WM, Song YX, Sosio S, Spataro S, Su KX, Sun GX, Sun JF, Sun L, Sun SS, Sun T, Sun WY, Sun YJ, Sun YK, Sun YZ, Sun ZT, Tan YH, Tan YX, Tang CJ, Tang GY, Tang J, Teng JX, Thoren V, Uman I, Wang B, Wang BL, Wang CW, Wang DY, Wang HP, Wang K, Wang LL, Wang M, Wang M, Wang WH, Wang WP, Wang X, Wang XF, Wang XL, Wang Y, Wang YD, Wang YF, Wang YQ, Wang Z, Wang ZY, Wang Z, Wang Z, Wei DH, Weidenkaff P, Weidner F, Wen SP, White DJ, Wiedner UW, Wilkinson G, Wolke M, Wollenberg L, Wu JF, Wu LH, Wu LJ, Wu X, Wu Z, Xia L, Xiao H, Xiao SY, Xiao ZJ, Xie XH, Xie YG, Xie YH, Xing TY, Xu GF, Xu JJ, Xu QJ, Xu W, Xu XP, Xu YC, Yan F, Yan L, Yan WB, Yan WC, Yan X, Yang HJ, Yang HX, Yang L, Yang SL, Yang YH, Yang Y, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yuan CZ, Yuan L, Yuan W, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng Y, Zhang BX, Zhang GY, Zhang H, Zhang HH, Zhang HH, Zhang HY, Zhang JJ, Zhang JQ, Zhang JW, Zhang JY, Zhang JZ, Zhang J, Zhang J, Zhang L, Zhang SF, Zhang XD, Zhang XY, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZY, Zhao G, Zhao J, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao Q, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng YH, Zhong B, Zhong C, Zhou LP, Zhou Q, Zhou X, Zhou XK, Zhou XR, Zhu AN, Zhu J, Zhu K, Zhu KJ, Zhu SH, Zhu WJ, Zhu WJ, Zhu YC, Zhu ZA, Zou BS, Zou JH. Search for Λ[over ¯]-Λ Baryon-Number-Violating Oscillations in the Decay J/ψ→pK^{-}Λ[over ¯]+c.c. PHYSICAL REVIEW LETTERS 2023; 131:121801. [PMID: 37802947 DOI: 10.1103/physrevlett.131.121801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/14/2023] [Accepted: 08/29/2023] [Indexed: 10/08/2023]
Abstract
We report on the first search for Λ[over ¯]-Λ oscillations in the decay J/ψ→pK^{-}Λ[over ¯]+c.c. by analyzing 1.31×10^{9} J/ψ events accumulated with the BESIII detector at the BEPCII collider. The J/ψ events are produced using e^{+}e^{-} collisions at a center of mass energy sqrt[s]=3.097 GeV. No evidence for hyperon oscillations is observed. The upper limit for the oscillation rate of Λ[over ¯] to Λ hyperons is determined to be P(Λ)=[B(J/ψ→pK^{-}Λ+c.c.)/B(J/ψ→pK^{-}Λ[over ¯]+c.c.)]<4.4×10^{-6} corresponding to an oscillation parameter δm_{ΛΛ[over ¯]} of less than 3.8×10^{-18} GeV at the 90% confidence level.
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Wu Y, Liu X, Wang X, Yu L, Yan H, Xie Y, Pu Q, Cai X, Kong Y, Yang Z. A Nomogram Prognostic Model for Advanced Hepatocellular Carcinoma Based on the Interaction Between CD8 +T Cell Counts and Age. Onco Targets Ther 2023; 16:753-766. [PMID: 37752911 PMCID: PMC10519212 DOI: 10.2147/ott.s426195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/06/2023] [Indexed: 09/28/2023] Open
Abstract
Objective CD8+T cells are essential components of the adaptive immune system and are crucial in the body's immune system. This study aimed to investigate how the prognosis of patients with advanced hepatocellular carcinoma (HCC) was affected by their CD8+ T cell counts and age and established an effective nomogram model to predict the overall survival (OS). Methods A total of 427 patients with advanced HCC from Beijing Ditan Hospital, Capital Medical University, were enrolled in this study and randomly divided into training and validation groups, with 300 and 127 individuals in each group, respectively. Cox regression analysis was used to screen for independent risk factors for advanced HCC, and the interactive relationship between CD8+T cells and patient age was examined to establish a nomogram prediction model. Results Cox multivariate regression and interaction analyses indicated that tumor number, tumor size, aspartate aminotransferase (AST), C-reactive protein (CRP), relationship of CD8+T cell counts and age were independent predictors of 6-month OS in patients with advanced HCC, and the nomogram model was established based on these factors. The area under the receiver operating characteristic curve (AUC) of the nomogram model for predicting the 3-month, 6-month, and 12-month OS rates were 0.821, 0.802, and 0.756, respectively. Moreover, in clinical practice, patients with true-positive survival benefit more than true-positive death, therefore, we selected 25% as the clinical decision threshold probability based on probability density functions (PDFs) and clinical utility curves (CUCs), which can distinguish approximately 92% of patients who died and 37% of patients who survived. Conclusion The nomogram model based on CD8+T cell counts and age accurately assessed the prognosis of patients with advanced HCC and suggested that high CD8+T cell levels are beneficial to the survival of patients with advanced HCC.
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Cai X, Xue Z, Zeng FF, Tang J, Yue L, Wang B, Ge W, Xie Y, Miao Z, Gou W, Fu Y, Li S, Gao J, Shuai M, Zhang K, Xu F, Tian Y, Xiang N, Zhou Y, Shan PF, Zhu Y, Chen YM, Zheng JS, Guo T. Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome. Cell Rep Med 2023; 4:101172. [PMID: 37652016 PMCID: PMC10518601 DOI: 10.1016/j.xcrm.2023.101172] [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: 10/12/2022] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 09/02/2023]
Abstract
Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global burden on healthcare systems. Here, we measured over 400 proteins from ∼20,000 proteomes using data-independent acquisition mass spectrometry for 7,890 serum samples from a longitudinal cohort of 3,840 participants with two follow-up time points over 10 years. We then built a machine-learning model for predicting the risk of developing MetS within 10 years. Our model, composed of 11 proteins and the age of the individuals, achieved an area under the curve of 0.774 in the validation cohort (n = 242). Using linear mixed models, we found that apolipoproteins, immune-related proteins, and coagulation-related proteins best correlated with MetS development. This population-scale proteomics study broadens our understanding of MetS and may guide the development of prevention and targeted therapies for MetS.
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Yang H, Zhang B, Wu ZD, Chen LF, Pan JY, Xiu XL, Cai X, Liu ZQ, Zheng YG. Combinatorial Metabolic Engineering of Escherichia coli for Enhanced L-Cysteine Production: Insights into Crucial Regulatory Modes and Optimization of Carbon-Sulfur Metabolism and Cofactor Availability. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:13409-13418. [PMID: 37639615 DOI: 10.1021/acs.jafc.3c03709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Microbial production of valuable compounds can be enhanced by various metabolic strategies. This study proposed combinatorial metabolic engineering to develop an effective Escherichia coli cell factory dedicated to L-cysteine production. First, the crucial regulatory modes that control L-cysteine levels were investigated to guide metabolic modifications. A two-stage fermentation was achieved by employing multi-copy gene expression, improving the balance between production and growth. Subsequently, carbon flux distribution was further optimized by modifying the C1 unit metabolism and the glycolytic pathway. The modifications of sulfur assimilation demonstrated superior performance of thiosulfate utilization pathways in enhancing L-cysteine titer. Furthermore, the studies focusing on cofactor availability and preference emphasized the vital role of synergistic enhancement of sulfur-carbon metabolism in L-cysteine overproduction. In a 5 L bioreactor, the strain BW15-3/pED accumulated 12.6 g/L of L-cysteine. This work presented an effective metabolic engineering strategy for the development of L-cysteine-producing strains.
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Xue Z, Zhu T, Zhang F, Zhang C, Xiang N, Qian L, Yi X, Sun Y, Liu W, Cai X, Wang L, Dai X, Yue L, Li L, Pham TV, Piersma SR, Xiao Q, Luo M, Lu C, Zhu J, Zhao Y, Wang G, Xiao J, Liu T, Liu Z, He Y, Wu Q, Gong T, Zhu J, Zheng Z, Ye J, Li Y, Jimenez CR, A J, Guo T. DPHL v.2: An updated and comprehensive DIA pan-human assay library for quantifying more than 14,000 proteins. PATTERNS (NEW YORK, N.Y.) 2023; 4:100792. [PMID: 37521047 PMCID: PMC10382975 DOI: 10.1016/j.patter.2023.100792] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 04/29/2023] [Accepted: 06/12/2023] [Indexed: 08/01/2023]
Abstract
A comprehensive pan-human spectral library is critical for biomarker discovery using mass spectrometry (MS)-based proteomics. DPHL v.1, a previous pan-human library built from 1,096 data-dependent acquisition (DDA) MS data of 16 human tissue types, allows quantifying of 10,943 proteins. Here, we generated DPHL v.2 from 1,608 DDA-MS data. The data included 586 DDA-MS data acquired from 18 tissue types, while 1,022 files were derived from DPHL v.1. DPHL v.2 thus comprises data from 24 sample types, including several cancer types (lung, breast, kidney, and prostate cancer, among others). We generated four variants of DPHL v.2 to include semi-tryptic peptides and protein isoforms. DPHL v.2 was then applied to two colorectal cancer cohorts. The numbers of identified and significantly dysregulated proteins increased by at least 21.7% and 14.2%, respectively, compared with DPHL v.1. Our findings show that the increased human proteome coverage of DPHL v.2 provides larger pools of potential protein biomarkers.
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Cai X, Tan ZQ, Liu B, Song GL. [A case of synovial sarcoma of larynx]. ZHONGHUA ER BI YAN HOU TOU JING WAI KE ZA ZHI = CHINESE JOURNAL OF OTORHINOLARYNGOLOGY HEAD AND NECK SURGERY 2023; 58:721-723. [PMID: 37455121 DOI: 10.3760/cma.j.cn115330-20221205-00732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
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