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Li JP, Tan Y, Sun H, Lin GQ, Chen BB, Wu Y, Xie ZW, Wang YP, Lu AQ, Guan YJ. [Clinical management of thrombocytopenia in cirrhosis]. ZHONGHUA GAN ZANG BING ZA ZHI = ZHONGHUA GANZANGBING ZAZHI = CHINESE JOURNAL OF HEPATOLOGY 2024; 32:489-492. [PMID: 38964889 DOI: 10.3760/cma.j.cn501113-20240326-00158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/06/2024]
Abstract
Thrombocytopenia is one of the common complications of cirrhotic patients, which can induce an increasing bleeding risk and closely correlate with bleeding following invasive procedures. Consequently, how to respond to thrombocytopenia is crucial for improving the prognosis of patients with cirrhosis. This article reviews the main mechanisms of cirrhosis concurrent with thrombocytopenia, as well as the corresponding clinical management strategies.
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Tang CT, Wu Y, Jiang L, Zeng CY, Chen YX. Radiomics analysis based on CT for predicting lymph node metastasis and prognosis in duodenal papillary carcinoma. Insights Imaging 2024; 15:155. [PMID: 38900393 PMCID: PMC11190116 DOI: 10.1186/s13244-024-01732-6] [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: 12/25/2023] [Accepted: 05/13/2024] [Indexed: 06/21/2024] Open
Abstract
OBJECTIVES Radiomics has been demonstrated to be strongly associated with TNM stage and patient prognosis. We aimed to develop a model for predicting lymph node metastasis (LNM) and survival. METHODS For radiomics texture selection, 3D Slicer 5.0.3 software and the least absolute shrinkage and selection operator (LASSO) algorithm were used. Subsequently, the radiomics model, computed tomography (CT) image, and clinical risk model were compared. The performance of the three models was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration plots, and clinical impact curves (CICs). RESULTS For the LNM prediction model, 224 patients with LNM information were used to construct a model that was applied to predict LNM. According to the CT data and clinical characteristics, we constructed a radiomics model, CT imaging model and clinical model. The radiomics model for evaluating LNM status showed excellent calibration and discrimination in the training cohort (AUC = 0.926, 95% CI = 0.869-0.982) and the validation cohort (AUC = 0.872, 95% CI = 0.802-0.941). DeLong's test demonstrated that the difference among the three models was significant. Similarly, DCA and CIC showed that the radiomics model has better clinical utility than the CT imaging model and clinical model. Our model also exhibited good performance in predicting survival-in line with the findings of the model built with clinical risk factors. CONCLUSIONS CT radiomics models exhibited better predictive performance for LNM than models built based on clinical risk characteristics and CT imaging and had comparative clinical utility for predicting patient prognosis. CRITICAL RELEVANCE STATEMENT The radiomics model showed excellent performance and discrimination for predicting LNM and survival of duodenal papillary carcinoma (DPC). KEY POINTS LNM status determines the most appropriate treatment for DPC. Our radiomics model for evaluating the LNM status of DPC performed excellently. The radiomics model had high sensitivity and specificity for predicting survival, exhibiting great clinical value.
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Aaij R, Abdelmotteleb ASW, Abellan Beteta C, Abudinén F, Ackernley T, Adeva B, Adinolfi M, Adlarson P, Agapopoulou C, Aidala CA, Ajaltouni Z, Akar S, Akiba K, Albicocco P, Albrecht J, Alessio F, Alexander M, Alfonso Albero A, Aliouche Z, Alvarez Cartelle P, Amalric R, Amato S, Amey JL, Amhis Y, An L, Anderlini L, Andersson M, Andreianov A, Andreola P, Andreotti M, Andreou D, Anelli AA, Ao D, Archilli F, Argenton M, Arguedas Cuendis S, Artamonov A, Artuso M, Aslanides E, Atzeni M, Audurier B, Bacher D, Bachiller Perea I, Bachmann S, Bachmayer M, Back JJ, Bailly-Reyre A, Baladron Rodriguez P, Balagura V, Baldini W, Baptista de Souza Leite J, Barbetti M, Barbosa IR, Barlow RJ, Barsuk S, Barter W, Bartolini M, Baryshnikov F, Basels JM, Bassi G, Batsukh B, Battig A, Bay A, Beck A, Becker M, Bedeschi F, Bediaga IB, Beiter A, Belin S, Bellee V, Belous K, Belov I, Belyaev I, Benane G, Bencivenni G, Ben-Haim E, Berezhnoy A, Bernet R, Bernet Andres S, Bernstein HC, Bertella C, Bertolin A, Betancourt C, Betti F, Bex J, Bezshyiko I, Bhom J, Bieker MS, Biesuz NV, Billoir P, Biolchini A, Birch M, Bishop FCR, Bitadze A, Bizzeti A, Blago MP, Blake T, Blanc F, Blank JE, Blusk S, Bobulska D, Bocharnikov V, Boelhauve JA, Boente Garcia O, Boettcher T, Bohare A, Boldyrev A, Bolognani CS, Bolzonella R, Bondar N, Borgato F, Borghi S, Borsato M, Borsuk JT, Bouchiba SA, Bowcock TJV, Boyer A, Bozzi C, Bradley MJ, Braun S, Brea Rodriguez A, Breer N, Brodzicka J, Brossa Gonzalo A, Brown J, Brundu D, Buonaura A, Buonincontri L, Burke AT, Burr C, Bursche A, Butkevich A, Butter JS, Buytaert J, Byczynski W, Cadeddu S, Cai H, Calabrese R, Calefice L, Cali S, Calvi M, Calvo Gomez M, Cambon Bouzas J, Campana P, Campora Perez DH, Campoverde Quezada AF, Capelli S, Capriotti L, Caravaca-Mora RC, Carbone A, Carcedo Salgado L, Cardinale R, Cardini A, Carniti P, Carus L, Casais Vidal A, Caspary R, Casse G, Castro Godinez J, Cattaneo M, Cavallero G, Cavallini V, Celani S, Cerasoli J, Cervenkov D, Cesare S, Chadwick AJ, Chahrour I, Charles M, Charpentier P, Chavez Barajas CA, Chefdeville M, Chen C, Chen S, Chernov A, Chernyshenko S, Chobanova V, Cholak S, Chrzaszcz M, Chubykin A, Chulikov V, Ciambrone P, Cicala MF, Cid Vidal X, Ciezarek G, Cifra P, Clarke PEL, Clemencic M, Cliff HV, Closier J, Cobbledick JL, Cocha Toapaxi C, Coco V, Cogan J, Cogneras E, Cojocariu L, Collins P, Colombo T, Comerma-Montells A, Congedo L, Contu A, Cooke N, Corredoira I, Correia A, Corti G, Cottee Meldrum JJ, Couturier B, Craik DC, Cruz Torres M, Currie R, Da Silva CL, Dadabaev S, Dai L, Dai X, Dall'Occo E, Dalseno J, D'Ambrosio C, Daniel J, Danilina A, d'Argent P, Davidson A, Davies JE, Davis A, De Aguiar Francisco O, De Angelis C, de Boer J, De Bruyn K, De Capua S, De Cian M, De Freitas Carneiro Da Graca U, De Lucia E, De Miranda JM, De Paula L, De Serio M, De Simone D, De Simone P, De Vellis F, de Vries JA, Debernardis F, Decamp D, Dedu V, Del Buono L, Delaney B, Dembinski HP, Deng J, Denysenko V, Deschamps O, Dettori F, Dey B, Di Nezza P, Diachkov I, Didenko S, Ding S, Dobishuk V, Docheva AD, Dolmatov A, Dong C, Donohoe AM, Dordei F, Dos Reis AC, Douglas L, Downes AG, Duan W, Duda P, Dudek MW, Dufour L, Duk V, Durante P, Duras MM, Durham JM, Dutta D, Dziurda A, Dzyuba A, Easo S, Eckstein E, Egede U, Egorychev A, Egorychev V, Eirea Orro C, Eisenhardt S, Ejopu E, Ek-In S, Eklund L, Elashri M, Ellbracht J, Ely S, Ene A, Epple E, Escher S, Eschle J, Esen S, Evans T, Fabiano F, Falcao LN, Fan Y, Fang B, Fantini L, Faria M, Farmer K, Fazzini D, Felkowski L, Feng M, Feo M, Fernandez Gomez M, Fernez AD, Ferrari F, Ferreira Rodrigues F, Ferreres Sole S, Ferrillo M, Ferro-Luzzi M, Filippov S, Fini RA, Fiorini M, Firlej M, Fischer KM, Fitzgerald DS, Fitzpatrick C, Fiutowski T, Fleuret F, Fontana M, Fontanelli F, Foreman LF, Forty R, Foulds-Holt D, Franco Sevilla M, Frank M, Franzoso E, Frau G, Frei C, Friday DA, Frontini L, Fu J, Fuehring Q, Fujii Y, Fulghesu T, Gabriel E, Galati G, Galati MD, Gallas Torreira A, Galli D, Gambetta S, Gandelman M, Gandini P, Gao H, Gao R, Gao Y, Gao Y, Gao Y, Garau M, Garcia Martin LM, Garcia Moreno P, García Pardiñas J, Garcia Plana B, Garg KG, Garrido L, Gaspar C, Geertsema RE, Gerken LL, Gersabeck E, Gersabeck M, Gershon T, Ghorbanimoghaddam Z, Giambastiani L, Giasemis FI, Gibson V, Giemza HK, Gilman AL, Giovannetti M, Gioventù A, Gironella Gironell P, Giugliano C, Giza MA, Gkougkousis EL, Glaser FC, Gligorov VV, Göbel C, Golobardes E, Golubkov D, Golutvin A, Gomes A, Gomez Fernandez S, Goncalves Abrantes F, Goncerz M, Gong G, Gooding JA, Gorelov IV, Gotti C, Grabowski JP, Granado Cardoso LA, Graugés E, Graverini E, Grazette L, Graziani G, Grecu AT, Greeven LM, Grieser NA, Grillo L, Gromov S, Gu C, Guarise M, Guittiere M, Guliaeva V, Günther PA, Guseinov AK, Gushchin E, Guz Y, Gys T, Hadavizadeh T, Hadjivasiliou C, Haefeli G, Haen C, Haimberger J, Hajheidari M, Halewood-Leagas T, Halvorsen MM, Hamilton PM, Hammerich J, Han Q, Han X, Hansmann-Menzemer S, Hao L, Harnew N, Harrison T, Hartmann M, Hasse C, He J, Heijhoff K, Hemmer F, Henderson C, Henderson RDL, Hennequin AM, Hennessy K, Henry L, Herd J, Heuel J, Hicheur A, Hill D, Hollitt SE, Horswill J, Hou R, Hou Y, Howarth N, Hu J, Hu J, Hu W, Hu X, Huang W, Hulsbergen W, Hunter RJ, Hushchyn M, Hutchcroft D, Idzik M, Ilin D, Ilten P, Inglessi A, Iniukhin A, Ishteev A, Ivshin K, Jacobsson R, Jage H, Jaimes Elles SJ, Jakobsen S, Jans E, Jashal BK, Jawahery A, Jevtic V, Jiang E, Jiang X, Jiang Y, Jiang YJ, John M, Johnson D, Jones CR, Jones TP, Joshi S, Jost B, Jurik N, Juszczak I, Kaminaris D, Kandybei S, Kang Y, Karacson M, Karpenkov D, Karpov M, Kauniskangas AM, Kautz JW, Keizer F, Keller DM, Kenzie M, Ketel T, Khanji B, Kharisova A, Kholodenko S, Khreich G, Kirn T, Kirsebom VS, Kitouni O, Klaver S, Kleijne N, Klimaszewski K, Kmiec MR, Koliiev S, Kolk L, Konoplyannikov A, Kopciewicz P, Koppenburg P, Korolev M, Kostiuk I, Kot O, Kotriakhova S, Kozachuk A, Kravchenko P, Kravchuk L, Kreps M, Kretzschmar S, Krokovny P, Krupa W, Krzemien W, Kubat J, Kubis S, Kucewicz W, Kucharczyk M, Kudryavtsev V, Kulikova E, Kupsc A, Kutsenko BK, Lacarrere D, Lafferty G, Lai A, Lampis A, Lancierini D, Landesa Gomez C, Lane JJ, Lane R, Langenbruch C, Langer J, Lantwin O, Latham T, Lazzari F, Lazzeroni C, Le Gac R, Lee SH, Lefèvre R, Leflat A, Legotin S, Lehuraux M, Leroy O, Lesiak T, Leverington B, Li A, Li H, Li K, Li L, Li P, Li PR, Li S, Li T, Li T, Li Y, Li Y, Li Z, Lian Z, Liang X, Lin C, Lin T, Lindner R, Lisovskyi V, Litvinov R, Liu G, Liu H, Liu K, Liu Q, Liu S, Liu Y, Liu Y, Liu YL, Lobo Salvia A, Loi A, Lomba Castro J, Long T, Lopes JH, Lopez Huertas A, López Soliño S, Lovell GH, Lucarelli C, Lucchesi D, Luchuk S, Lucio Martinez M, Lukashenko V, Luo Y, Lupato A, Luppi E, Lynch K, Lyu XR, Ma GM, Ma R, Maccolini S, Machefert F, Maciuc F, Mackay I, Madhan Mohan LR, Madurai MM, Maevskiy A, Magdalinski D, Maisuzenko D, Majewski MW, Malczewski JJ, Malde S, Malecki B, Malentacca L, Malinin A, Maltsev T, Manca G, Mancinelli G, Mancuso C, Manera Escalero R, Manuzzi D, Marangotto D, Marchand JF, Marchevski R, Marconi U, Mariani S, Marin Benito C, Marks J, Marshall AM, Marshall PJ, Martelli G, Martellotti G, Martinazzoli L, Martinelli M, Martinez Santos D, Martinez Vidal F, Massafferri A, Materok M, Matev R, Mathad A, Matiunin V, Matteuzzi C, Mattioli KR, Mauri A, Maurice E, Mauricio J, Mayencourt P, Mazurek M, McCann M, Mcconnell L, McGrath TH, McHugh NT, McNab A, McNulty R, Meadows B, Meier G, Melnychuk D, Merk M, Merli A, Meyer Garcia L, Miao D, Miao H, Mikhasenko M, Milanes DA, Minotti A, Minucci E, Miralles T, Mitchell SE, Mitreska B, Mitzel DS, Modak A, Mödden A, Mohammed RA, Moise RD, Mokhnenko S, Mombächer T, Monk M, Monroy IA, Monteil S, Morcillo Gomez A, Morello G, Morello MJ, Morgenthaler MP, Moron J, Morris AB, Morris AG, Mountain R, Mu H, Mu ZM, Muhammad E, Muheim F, Mulder M, Müller K, Mũnoz-Rojas F, Murta R, Naik P, Nakada T, Nandakumar R, Nanut T, Nasteva I, Needham M, Neri N, Neubert S, Neufeld N, Neustroev P, Newcombe R, Nicolini J, Nicotra D, Niel EM, Nikitin N, Nogga P, Nolte NS, Normand C, Novoa Fernandez J, Nowak G, Nunez C, Nur HN, Oblakowska-Mucha A, Obraztsov V, Oeser T, Okamura S, Okhotnikov A, Oldeman R, Oliva F, Olocco M, Onderwater CJG, O'Neil RH, Otalora Goicochea JM, Ovsiannikova T, Owen P, Oyanguren A, Ozcelik O, Padeken KO, Pagare B, Pais PR, Pajero T, Palano A, Palutan M, Panshin G, Paolucci L, Papanestis A, Pappagallo M, Pappalardo LL, Pappenheimer C, Parkes C, Passalacqua B, Passaleva G, Passaro D, Pastore A, Patel M, Patoc J, Patrignani C, Pawley CJ, Pellegrino A, Pepe Altarelli M, Perazzini S, Pereima D, Pereiro Castro A, Perret P, Perro A, Petridis K, Petrolini A, Petrucci S, Pham H, Pica L, Piccini M, Pietrzyk B, Pietrzyk G, Pinci D, Pisani F, Pizzichemi M, Placinta V, Plo Casasus M, Polci F, Poli Lener M, Poluektov A, Polukhina N, Polyakov I, Polycarpo E, Ponce S, Popov D, Poslavskii S, Prasanth K, Promberger L, Prouve C, Pugatch V, Puill V, Punzi G, Qi HR, Qian W, Qin N, Qu S, Quagliani R, Rabadan Trejo RI, Rachwal B, Rademacker JH, Rama M, Ramírez García M, Ramos Pernas M, Rangel MS, Ratnikov F, Raven G, Rebollo De Miguel M, Redi F, Reich J, Reiss F, Ren Z, Resmi PK, Ribatti R, Ricart GR, Riccardi D, Ricciardi S, Richardson K, Richardson-Slipper M, Rinnert K, Robbe P, Robertson G, Rodrigues E, Rodriguez Fernandez E, Rodriguez Lopez JA, Rodriguez Rodriguez E, Rogovskiy A, Rolf DL, Rollings A, Roloff P, Romanovskiy V, Romero Lamas M, Romero Vidal A, Romolini G, Ronchetti F, Rotondo M, Roy SR, Rudolph MS, Ruf T, Ruiz Diaz M, Ruiz Fernandez RA, Ruiz Vidal J, Ryzhikov A, Ryzka J, Saborido Silva JJ, Sadek R, Sagidova N, Sahoo N, Saitta B, Salomoni M, Sanchez Gras C, Sanderswood I, Santacesaria R, Santamarina Rios C, Santimaria M, Santoro L, Santovetti E, Saputi A, Saranin D, Sarpis G, Sarpis M, Sarti A, Satriano C, Satta A, Saur M, Savrina D, Sazak H, Scantlebury Smead LG, Scarabotto A, Schael S, Scherl S, Schertz AM, Schiller M, Schindler H, Schmelling M, Schmidt B, Schmitt S, Schmitz H, Schneider O, Schopper A, Schulte N, Schulte S, Schune MH, Schwemmer R, Schwering G, Sciascia B, Sciuccati A, Sellam S, Semennikov A, Senghi Soares M, Sergi A, Serra N, Sestini L, Seuthe A, Shang Y, Shangase DM, Shapkin M, Shchemerov I, Shchutska L, Shears T, Shekhtman L, Shen Z, Sheng S, Shevchenko V, Shi B, Shields EB, Shimizu Y, Shmanin E, Shorkin R, Shupperd JD, Silva Coutinho R, Simi G, Simone S, Skidmore N, Skuza R, Skwarnicki T, Slater MW, Smallwood JC, Smith E, Smith K, Smith M, Snoch A, Soares Lavra L, Sokoloff MD, Soler FJP, Solomin A, Solovev A, Solovyev I, Song R, Song Y, Song Y, Song YS, Souza De Almeida FL, Souza De Paula B, Spadaro Norella E, Spedicato E, Speer JG, Spiridenkov E, Spradlin P, Sriskaran V, Stagni F, Stahl M, Stahl S, Stanislaus S, Stein EN, Steinkamp O, Stenyakin O, Stevens H, Strekalina D, Su Y, Suljik F, Sun J, Sun L, Sun Y, Swallow PN, Swientek K, Swystun F, Szabelski A, Szumlak T, Szymanski M, Tan Y, Taneja S, Tat MD, Terentev A, Terzuoli F, Teubert F, Thomas E, Thompson DJD, Tilquin H, Tisserand V, T'Jampens S, Tobin M, Tomassetti L, Tonani G, Tong X, Torres Machado D, Toscano L, Tou DY, Trippl C, Tuci G, Tuning N, Uecker LH, Ukleja A, Unverzagt DJ, Ursov E, Usachov A, Ustyuzhanin A, Uwer U, Vagnoni V, Valassi A, Valenti G, Valls Canudas N, Van Hecke H, van Herwijnen E, Van Hulse CB, Van Laak R, van Veghel M, Vazquez Gomez R, Vazquez Regueiro P, Vázquez Sierra C, Vecchi S, Velthuis JJ, Veltri M, Venkateswaran A, Vesterinen M, Vieira D, Vieites Diaz M, Vilasis-Cardona X, Vilella Figueras E, Villa A, Vincent P, Volle FC, Vom Bruch D, Vorobyev V, Voropaev N, Vos K, Vouters G, Vrahas C, Walsh J, Walton EJ, Wan G, Wang C, Wang G, Wang J, Wang J, Wang J, Wang J, Wang M, Wang NW, Wang R, Wang X, Wang XW, Wang Y, Wang Z, Wang Z, Wang Z, Ward JA, Watson NK, Websdale D, Wei Y, Westhenry BDC, White DJ, Whitehead M, Wiederhold AR, Wiedner D, Wilkinson G, Wilkinson MK, Williams M, Williams MRJ, Williams R, Wilson FF, Wislicki W, Witek M, Witola L, Wong CP, Wormser G, Wotton SA, Wu H, Wu J, Wu Y, Wyllie K, Xian S, Xiang Z, Xie Y, Xu A, Xu J, Xu L, Xu L, Xu M, Xu Z, Xu Z, Xu Z, Yang D, Yang S, Yang X, Yang Y, Yang Z, Yang Z, Yeroshenko V, Yeung H, Yin H, Yu CY, Yu J, Yuan X, Zaffaroni E, Zavertyaev M, Zdybal M, Zeng M, Zhang C, Zhang D, Zhang J, Zhang L, Zhang S, Zhang S, Zhang Y, Zhang Y, Zhang YZ, Zhao Y, Zharkova A, Zhelezov A, Zheng XZ, Zheng Y, Zhou T, Zhou X, Zhou Y, Zhovkovska V, Zhu LZ, Zhu X, Zhu X, Zhu Z, Zhukov V, Zhuo J, Zou Q, Zuliani D, Zunica G. Modification of χ_{c1}(3872) and ψ(2S) Production in pPb Collisions at sqrt[s_{NN}]=8.16 TeV. PHYSICAL REVIEW LETTERS 2024; 132:242301. [PMID: 38949352 DOI: 10.1103/physrevlett.132.242301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 04/17/2024] [Indexed: 07/02/2024]
Abstract
The LHCb Collaboration measures production of the exotic hadron χ_{c1}(3872) in proton-nucleus collisions for the first time. Comparison with the charmonium state ψ(2S) suggests that the exotic χ_{c1}(3872) experiences different dynamics in the nuclear medium than conventional hadrons, and comparison with data from proton-proton collisions indicates that the presence of the nucleus may modify χ_{c1}(3872) production rates. This is the first measurement of the nuclear modification factor of an exotic hadron.
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Wu Y, Zhou JX, Xi L, Ding Y, Chen G, Wang M, Rong R. [Cytological diagnosis of 3 cases of primary mediastinal yolk sac tumor with pleural effusion]. ZHONGHUA BING LI XUE ZA ZHI = CHINESE JOURNAL OF PATHOLOGY 2024; 53:619-621. [PMID: 38825911 DOI: 10.3760/cma.j.cn112151-20231010-00245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
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Ablikim M, Achasov MN, Adlarson P, Afedulidis O, Ai XC, Aliberti R, Amoroso A, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Bao HR, 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, Che GR, Chelkov G, Chen C, Chen CH, Chen C, Chen G, Chen HS, Chen HY, Chen ML, Chen SJ, Chen SL, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Chen ZY, Choi SK, Cibinetto G, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng CQ, 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 YH, Fang J, Fang J, Fang SS, Fang WX, Fang Y, Fang YQ, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Feng YT, Fritsch M, Fu CD, Fu JL, Fu YW, Gao H, Gao XB, Gao YN, Gao Y, Garbolino S, Garzia I, Ge L, 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, Gutierrez J, Han KL, Han TT, Hao XQ, Harris FA, He KK, He KL, Heinsius FH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou XT, Hou YR, Hou ZL, Hu BY, Hu HM, Hu JF, Hu SL, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hölzken F, Hüsken N, In der Wiesche N, Jackson J, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji W, Ji XB, Ji XL, Ji YY, Jia XQ, Jia ZK, Jiang D, Jiang HB, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao JK, Jiao Z, Jin S, Jin Y, Jing MQ, Jing XM, Johansson T, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kavatsyuk M, Ke BC, Khachatryan V, Khoukaz A, Kiuchi R, Kolcu OB, Kopf B, Kuessner M, Kui X, Kumar N, Kupsc A, Kühn W, Lane JJ, Larin P, Lavezzi L, Lei TT, Lei ZH, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li QM, Li QX, Li R, Li SX, Li T, Li WD, Li WG, Li X, Li XH, Li XL, Li XZ, Li X, Li YG, Li ZJ, Li ZX, Liang C, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Libby J, Limphirat A, Lin CC, 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 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 X, Liu Y, Liu Y, Liu YB, Liu ZA, Liu ZD, 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 H, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma T, Ma XT, Ma XY, Ma Y, Ma YM, Maas FE, Maggiora M, Malde S, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Moses B, Muchnoi NY, Muskalla J, Nefedov Y, Nerling F, Nie LS, Nikolaev IB, Ning Z, Nisar S, Niu QL, Niu WD, Niu Y, Olsen SL, Ouyang Q, Pacetti 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Zhang HH, Zhang HQ, Zhang HR, Zhang HY, Zhang J, Zhang J, Zhang JJ, Zhang JL, Zhang JQ, Zhang JS, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang LM, Zhang L, Zhang P, Zhang QY, Zhang RY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang Y, Zhang YT, Zhang YH, Zhang YM, Zhang Y, Zhang Y, Zhang ZD, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhang ZZ, Zhao G, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao N, Zhao RP, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng BM, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou JY, Zhou LP, Zhou S, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu KS, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WD, Zhu YC, Zhu ZA, Zou JH, Zu J. First Study of Antihyperon-Nucleon Scattering Λ[over ¯]p→Λ[over ¯]p and Measurement of Λp→Λp Cross Section. PHYSICAL REVIEW LETTERS 2024; 132:231902. [PMID: 38905649 DOI: 10.1103/physrevlett.132.231902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/04/2024] [Accepted: 05/07/2024] [Indexed: 06/23/2024]
Abstract
Using (10.087±0.044)×10^{9} J/ψ events collected with the BESIII detector at the BEPCII storage ring, the processes Λp→Λp and Λ[over ¯]p→Λ[over ¯]p are studied, where the Λ/Λ[over ¯] baryons are produced in the process J/ψ→ΛΛ[over ¯] and the protons are the hydrogen nuclei in the cooling oil of the beam pipe. Clear signals are observed for the two reactions. The cross sections in -0.9≤cosθ_{Λ/Λ[over ¯]}≤0.9 are measured to be σ(Λp→Λp)=(12.2±1.6_{stat}±1.1_{syst}) and σ(Λ[over ¯]p→Λ[over ¯]p)=(17.5±2.1_{stat}±1.6_{syst}) mb at the Λ/Λ[over ¯] momentum of 1.074 GeV/c within a range of ±0.017 GeV/c, where the θ_{Λ/Λ[over ¯]} are the scattering angles of the Λ/Λ[over ¯] in the Λp/Λ[over ¯]p rest frames. Furthermore, the differential cross sections of the two reactions are also measured, where there is a slight tendency of forward scattering for Λp→Λp, and a strong forward peak for Λ[over ¯]p→Λ[over ¯]p. We present an approach to extract the total elastic cross sections by extrapolation. The study of Λ[over ¯]p→Λ[over ¯]p represents the first study of antihyperon-nucleon scattering, and these new measurements will serve as important inputs for the theoretical understanding of the (anti)hyperon-nucleon interaction.
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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, 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, 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, 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, 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, Urrejola P, Usai G, Ushioda R, Usman M, Uysal Z, 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, 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, Vavricka R, 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, Voevodina E, Vogel F, Voigt JC, Vokac P, Volkotrub Y, Von Ahnen J, Von Toerne E, Vormwald B, Vorobel V, Vorobev K, Vos M, Voss K, Vozak M, Vozdecky L, Vranjes N, Vranjes Milosavljevic M, Vreeswijk M, Vu NK, 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, Wallin EJ, 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, Waterhouse S, 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, 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, Woodward EL, Worm SD, Wosiek BK, Woźniak KW, Wozniewski S, Wraight K, Wu C, 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, 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 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, Zimine NI, Zinsser J, Ziolkowski M, Živković L, Zoccoli A, Zoch K, Zorbas TG, Zormpa O, Zou W, Zwalinski L. Combination of Searches for Resonant Higgs Boson Pair Production Using pp Collisions at sqrt[s]=13 TeV with the ATLAS Detector. PHYSICAL REVIEW LETTERS 2024; 132:231801. [PMID: 38905640 DOI: 10.1103/physrevlett.132.231801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 05/07/2024] [Indexed: 06/23/2024]
Abstract
A combination of searches for a new resonance decaying into a Higgs boson pair is presented, using up to 139 fb^{-1} of pp collision data at sqrt[s]=13 TeV recorded with the ATLAS detector at the LHC. The combination includes searches performed in three decay channels: bb[over ¯]bb[over ¯], bb[over ¯]τ^{+}τ^{-}, and bb[over ¯]γγ. No excess above the expected Standard Model background is observed and upper limits are set at the 95% confidence level on the production cross section of Higgs boson pairs originating from the decay of a narrow scalar resonance with mass in the range 251 GeV-5 TeV. The observed (expected) limits are in the range 0.96-600 fb (1.2-390 fb). The limits are interpreted in the type-I two-Higgs-doublet model and the minimal supersymmetric standard model, and constrain parameter space not previously excluded by other searches.
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Xie Y, Li J, Tao Q, Wu Y, Liu Z, Chen Y, Zeng C. Identification of subclusters and prognostic genes based on GLS-associated molecular signature in ulcerative colitis. Sci Rep 2024; 14:13102. [PMID: 38849409 PMCID: PMC11161595 DOI: 10.1038/s41598-024-63891-2] [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: 01/17/2024] [Accepted: 06/03/2024] [Indexed: 06/09/2024] Open
Abstract
Ulcerative colitis (UC) is a chronic and recurrent inflammatory disease that affects the colon and rectum. The response to treatment varies among individuals with UC. Therefore, the aim of this study was to identify and explore potential biomarkers for different subtypes of UC and examine their association with immune cell infiltration. We obtained UC RNA sequencing data from the GEO database, which included the training set GSE92415 and the validation set GSE87473 and GSE72514. UC patients were classified based on GLS and its associated genes using consensus clustering analysis. We identified differentially expressed genes (DEGs) in different UC subtypes through a differential expression analysis of the training cohort. Machine learning algorithms, including Weighted Gene Co-Expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were utilized to identify marker genes for UC. The CIBERSORT algorithm was used to determine the abundance of various immune cells in UC and their correlation with UC signature genes. Finally, we validated the expression of GLS through in vivo and ex vivo experiments. The expression of GLS was found to be elevated in patients with UC compared to normal patients. GLS and its related genes were able to classify UC patients into two subtypes, C1 and C2. The C1 subtype, as compared to the C2 subtype, showed a higher Mayo score and poorer treatment response. A total of 18 DEGs were identified in both subtypes, including 7 up-regulated and 11 down-regulated genes. Four UC signature genes (CWH43, HEPACAM2, IL24, and PCK1) were identified and their diagnostic value was validated in a separate cohort (AUC > 0.85). Furthermore, we found that UC signature biomarkers were linked to the immune cell infiltration. CWH43, HEPACAM2, IL24, and PCK1 may serve as potential biomarkers for diagnosing different subtypes of UC, which could contribute to the development of targeted molecular therapy and immunotherapy for UC.
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Zhao X, Leng D, Wang H, Jin H, Wu Y, Qin Z, Wu D, Wei X. An Acid-Responsive Iron-Based Nanocomposite for OSCC Treatment. J Dent Res 2024; 103:612-621. [PMID: 38684484 DOI: 10.1177/00220345241238154] [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] [Indexed: 05/02/2024] Open
Abstract
Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer, characterized by invasiveness, local lymph node metastasis, and poor prognosis. Traditional treatment and medications have limitations, making the specific inhibition of OSCC growth, invasion, and metastasis a challenge. The tumor microenvironment exhibits mildly acidity and high concentrations of H2O2, and its exploitation for cancer treatment has been widely researched across various cancers, but research in the oral cancer field is relatively limited. In this study, by loading ultra-small Prussian blue nanoparticles (USPBNPs) into mesoporous calcium-silicate nanoparticles (MCSNs), we developed an acid-responsive iron-based nanocomposite, USPBNPs@MCSNs (UPM), for the OSCC treatment. UPM demonstrated excellent dual enzyme activities, generating toxic ·OH in a mildly acidic environment, effectively killing OSCC cells and producing O2 in a neutral environment to alleviate tissue hypoxia. The results showed that UPM could effectively inhibit the proliferation, migration, and invasion of OSCC cells, as well as the growth of mice solid tumors, without obvious systemic toxicity. The mechanisms may involve UPM inducing ferroptosis of OSCC cells by downregulating the xCT/GPX4/glutathione (GSH) axis, characterized by intracellular iron accumulation, reactive oxygen species accumulation, GSH depletion, lipid peroxidation, and abnormal changes in mitochondrial morphology. Therefore, this study provides empirical support for ferroptosis as an emerging therapeutic target for OSCC and offers a valuable insight for future OSCC treatment.
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Pan W, Pang LJ, Cai HL, Wu Y, Zhang W, Fang JC. Retraction Note: MiR-1294 acts as a tumor suppressor in clear cell renal cell carcinoma through targeting HOXA6. EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES 2024; 28:3700. [PMID: 38884519 DOI: 10.26355/eurrev_202406_36395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
The article "MiR-1294 acts as a tumor suppressor in clear cell renal cell carcinoma through targeting HOXA6", by W. Pan, L.-J. Pang, H.-L. Cai, Y. Wu, W. Zhang, J.-C. Fang, published in Eur Rev Med Pharmacol Sci 2019; 23 (9): 3719-3725-DOI: 10.26355/eurrev_201905_17797-PMID: 31114997 has been retracted by the Editor in Chief. Following some concerns raised on PubPeer regarding a possible manipulation in Figures 2 and 3, the Editor in Chief has started an investigation to assess the validity of the results as well as possible figure manipulation. The authors have been informed about the journal's investigation but remained unresponsive. The journal investigation revealed duplications in panels miR-1294 mimic - Caki01 and Caki01 of Figure 2D and in the Western blots of Figure 3 with previously published articles. Consequently, the Editor in Chief mistrusts the results presented and has decided to retract the article. This article has been retracted. The Publisher apologizes for any inconvenience this may cause. https://www.europeanreview.org/article/17797.
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Loor-Torres R, Duran M, Toro-Tobon D, Chavez MM, Ponce O, Jacome CS, Torres DS, Perneth SA, Montori V, Golembiewski E, Osorio MB, Fan JW, Ospina NS, Wu Y, Brito JP. A Systematic Review of Natural Language Processing Methods and Applications in Thyroidology. MAYO CLINIC PROCEEDINGS. DIGITAL HEALTH 2024; 2:270-279. [PMID: 38938930 PMCID: PMC11210322 DOI: 10.1016/j.mcpdig.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
This study aimed to review the application of natural language processing (NLP) in thyroid-related conditions and to summarize current challenges and potential future directions. We performed a systematic search of databases for studies describing NLP applications in thyroid conditions published in English between January 1, 2012 and November 4, 2022. In addition, we used a snowballing technique to identify studies missed in the initial search or published after our search timeline until April 1, 2023. For included studies, we extracted the NLP method (eg, rule-based, machine learning, deep learning, or hybrid), NLP application (eg, identification, classification, and automation), thyroid condition (eg, thyroid cancer, thyroid nodule, and functional or autoimmune disease), data source (eg, electronic health records, health forums, medical literature databases, or genomic databases), performance metrics, and stages of development. We identified 24 eligible NLP studies focusing on thyroid-related conditions. Deep learning-based methods were the most common (38%), followed by rule-based (21%), and traditional machine learning (21%) methods. Thyroid nodules (54%) and thyroid cancer (29%) were the primary conditions under investigation. Electronic health records were the dominant data source (17/24, 71%), with imaging reports being the most frequently used (15/17, 88%). There is increasing interest in NLP applications for thyroid-related studies, mostly addressing thyroid nodules and using deep learning-based methodologies with limited external validation. However, none of the reviewed NLP applications have reached clinical practice. Several limitations, including inconsistent clinical documentation and model portability, need to be addressed to promote the evaluation and implementation of NLP applications to support patient care in thyroidology.
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Yin Z, Zhao Q, Lv X, Zhang X, Wu Y. Circular RNA ath-circ032768, a competing endogenous RNA, response the drought stress by targeting miR472-RPS5 module. PLANT BIOLOGY (STUTTGART, GERMANY) 2024; 26:544-559. [PMID: 38588338 DOI: 10.1111/plb.13645] [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: 11/21/2023] [Accepted: 02/21/2024] [Indexed: 04/10/2024]
Abstract
CircRNAs (circular RNAs) reduce the abundance of miRNAs through ceRNA (competing endogenous RNA), to regulate many physiological processes and stress responses in plants. However, the role of circRNA in drought stress is poorly understood. Through ring identification and sequencing verification of ath-circ032768, bioinformatics analysis predicted the interaction of ath-circ032768-miR472-RPS5, and further obtained transgenic plants overexpressing ath-circ032768 and silencing STTM-miR472. The change in drought stress was analysed using biochemical and molecular biological methods. Sequencing and biological analysis confirmed that ath-circ032768, miR472 and RPS5 were responsive to drought stress, and changes in gene expression were consistent with the prediction of ceRNA. The silencing vectors ath-circ032768 and STTM-miR472 were constructed using molecular biology techniques, and stable transgenic plants with drought tolerance obtained. Further physiological and biochemical studies showed that ath-circ032768 could bind to miR472, and that miR472 could bind to the RPS5 gene, resulting in decreased expression of RPS5. Hence, ath-circ032768 can competitively inhibit degradation of RPS5 by miR472 through ceRNA. This process is accompanied by increased expression of DREB2A, RD29A and RD29B genes. Through the ath-circ032768-miR472-RPS5 pathway, the RPS5 stress resistance protein interacts with DREB2A protein to enhance expression of downstream drought resistance genes, RD29A and RD29B, and participate in the regulation mechanism of plant drought resistance, thereby improving drought tolerance of plants.
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Mu J, Wu Y, Wu C, Piao H, Jin B. Relationship between mandibular third molars and mandibular angle and condylar fractures. Med Oral Patol Oral Cir Bucal 2024:26604. [PMID: 38794934 DOI: 10.4317/medoral.26604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 04/22/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Assess the correlation between the position of the third molar (M3) and fractures of the mandibular angle and condyle using panoramic radiographs to offer valuable data references for oral clinical research. MATERIAL AND METHODS A retrospective cross-sectional study was undertaken, involving the collection of 409 cases of mandibular fracture in the Yanbian University Hospital. The case records and panoramic radiographs of mandibular angle fracture (78 cases) and condylar fracture (106 cases) were evaluated. RESULTS In the comparative analysis between the mandibular angle fracture group and the condylar fracture group, statistical significance was observed in the variables of M3 existence (P = 0.002), eruption of M3 from the alveolar cavity (P = 0.003), P&G position classification (P = 0.001), deep impactions (Classes IC, IIC, IIIB, and IIIC) (P < 0.001), and the presence of impacted M3 in both groups (P < 0.001).Regarding M3 roots, the mandibular angle fracture group exhibited the highest prevalence of multiple roots at 75.4%, surpassing the 64.6% observed in the condylar fracture group. The prevalence of proximal angles in the mandibular angle group and the condyle group was the highest, accounting for 64.6% and 61.5%, respectively. The percentage of M3 in the two groups was 80% and 43.1%, respectively, with a significant difference (P < 0.001). CONCLUSIONS Impacted mandibular third molars (M3) elevate the risk of mandibular angle fractures, while their absence or normal eruption reduces this risk and protects against condylar process fractures. The fracture risk is influenced by the M3's position: P&G Class II and Class B impactions, where M3s emerge partially from the alveolar bone, are significantly associated with mandibular angle fractures. In contrast, the absence of M3 or its placement in P&G Class I and Class A positions tends to correlate with a higher incidence of condylar process fractures.
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Shen XY, Li XP, Wang Y, Wu Y, Li Y, Yang YC, Wei LH, Fan Y, Tang ZQ. [Analysis of sequential chemotherapy efficacy in ovarian epithelial carcinoma, fallopian tube carcinoma and primary peritoneal carcinoma]. ZHONGHUA FU CHAN KE ZA ZHI 2024; 59:383-390. [PMID: 38797568 DOI: 10.3760/cma.j.cn112141-20231201-00235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Objective: To explore the sequential chemotherapy efficacy of different chemotherapeutic regimens in ovarian epithelial carcinoma, fallopian tube carcinoma, and primary peritoneal carcinoma. Methods: A retrospective analysis was conducted on clinical and pathological data of 100 patients with platinum-sensitive ovarian epithelial carcinoma, fallopian tube carcinoma, and primary peritoneal carcinoma treated at Peking University Peopel's Hospital from January 1992 to January 2019. All patients underwent staging surgery or cytoreductive surgery followed by adjuvant chemotherapy. Based on different postoperative adjuvant chemotherapy regimens, patients were divided into the sequential chemotherapy group (70 cases) and the conventional chemotherapy group (30 cases). Clinical and pathological characteristics, chemotherapy efficacy, adverse reactions, and prognosis were compared between the two groups. Results: (1) Clinical and pathological characteristics: the age, tumor types (including ovarian epithelial carcinoma, fallopian tube carcinoma, and primary peritoneal carcinoma), pathological types, International Federation of Gynecology and Obstetrics (FIGO) stage, postoperative residual disease size, presence of neoadjuvant chemotherapy, and total number of chemotherapy cycles were compared between the sequential chemotherapy group and the conventional chemotherapy group. There were no statistically significant differences observed in these characteristics between the two groups (all P>0.05). (2) Chemotherapy efficacy: the median sum of complete response (CR)+partial response (PR) duration in the sequential chemotherapy group was 80.0 months (range: 39 to 369 months), whereas in the conventional chemotherapy group, it was 28.0 months (range: 13 to 52 months). A statistically significant difference was observed between the two groups (Z=-7.82, P<0.001). (3) Chemotherapy adverse reactions: in the sequential chemotherapy group, 55 cases (79%, 55/70) experienced bone marrow suppression and 20 cases (29%, 20/70) had neurological symptoms. In the conventional chemotherapy group, these adverse reactions occurred in 11 cases (37%, 11/30) and 2 cases (7%, 2/30), respectively. Statistically significant differences were observed between the two groups for both bone marrow suppression and neurological symptoms (all P<0.05). For the other chemotherapy adverse reactions compared between the two groups, no statistically significant differences were observed (all P>0.05). (4) Prognosis: during the follow-up period, the recurrence rate in the sequential chemotherapy group was 73% (51/70) and in the conventional chemotherapy group was 100% (30/30). The median sum of recurrence-free interval was 70.5 months (range: 19 to 330 months) in the sequential chemotherapy group and 15.0 months (range: 6 to 40 months) in the conventional chemotherapy group. Statistically significant differences were observed between the two groups for both recurrence rate and median recurrence-free interval (all P<0.01).In the sequential chemotherapy group, the median progression-free survival (PFS) time was 84.0 months (range: 34 to 373 months), and the median overall survival (OS) time was 87.0 months (range: 45 to 377 months). In contrast, in the conventional chemotherapy group, the median PFS time was 30.5 months (range: 14 to 60 months), and the median OS time was 37.5 months (range: 18 to 67 months). Statistically significant differences were observed between the two groups for both PFS and OS (all P<0.001). In the sequential chemotherapy group, the 3-year, 5-year, and 10-year OS rates were 100% (70/70), 93% (65/70), and 21% (15/70), respectively. In contrast, in the conventional chemotherapy group, the OS rates were 50% (15/30) at 3 years, 3% (1/30) at 5 years, and 0 at 10 years, respectively. The two groups were compared respectively, and the differences were statistically significant (all P<0.05). Conclusions: Sequential chemotherapy significantly prolongs PFS and OS in patients with ovarian epithelial carcinoma, fallopian tube carcinoma, and primary peritoneal carcinoma. The efficacy is superior to that of the conventional chemotherapy, with manageable adverse reactions. The use of sequential chemotherapy as first-line treatment for patients with ovarian epithelial carcinoma, fallopian tube carcinoma, and primary peritoneal carcinoma is recommended.
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Xie Q, Chen Q, Chen A, Peng C, Hu Y, Lin F, Peng X, Huang J, Zhang J, Keloth V, Zhou X, He H, Ohno-Machado L, Wu Y, Xu H, Bian J. Me-LLaMA: Foundation Large Language Models for Medical Applications. RESEARCH SQUARE 2024:rs.3.rs-4240043. [PMID: 38826372 PMCID: PMC11142305 DOI: 10.21203/rs.3.rs-4240043/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Recent advancements in large language models (LLMs) such as ChatGPT and LLaMA have hinted at their potential to revolutionize medical applications, yet their application in clinical settings often reveals limitations due to a lack of specialized training on medical-specific data. In response to this challenge, this study introduces Me-LLaMA, a novel medical LLM family that includes foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions - Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction tuning of LLaMA2 using large medical datasets. Our methodology leverages a comprehensive domain-specific data suite, including a large-scale, continual pre-training dataset with 129B tokens, an instruction tuning dataset with 214k samples, and a new medical evaluation benchmark (MIBE) across six critical medical tasks with 12 datasets. Our extensive evaluation using the MIBE shows that Me-LLaMA models achieve overall better performance than existing open-source medical LLMs in zero-shot, few-shot and supervised learning abilities. With task-specific instruction tuning, Me-LLaMA models outperform ChatGPT on 7 out of 8 datasets and GPT-4 on 5 out of 8 datasets. In addition, we investigated the catastrophic forgetting problem, and our results show that Me-LLaMA models outperform other open-source medical LLMs in mitigating this issue. Me-LLaMA is one of the largest open-source medical foundation LLMs that use both biomedical and clinical data. It exhibits superior performance across both general and medical tasks compared to other open-source medical LLMs, rendering it an attractive choice for medical AI applications. We release our models, datasets, and evaluation scripts at: https://github.com/BIDS-Xu-Lab/Me-LLaMA.
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Xie Y, Wu Y, Tao Q, Chen Y, Zeng C. Causal effects of circulating glutamine on colitis, IBD, and digestive system cancers: a Mendelian randomisation study. J Cancer 2024; 15:3738-3749. [PMID: 38911392 PMCID: PMC11190753 DOI: 10.7150/jca.96085] [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: 03/08/2024] [Accepted: 05/01/2024] [Indexed: 06/25/2024] Open
Abstract
Backgrounds: There is growing evidence linking glutamine levels to the risk of gastrointestinal diseases, yet the presence of a causal relationship remains uncertain. In this study, we employed a Mendelian randomization (MR) approach to investigate potential causal associations between glutamine and colitis, inflammatory bowel disease (IBD), and digestive tumors. Methods: Genetic instrumental variables for glutamine exposure were identified from a genome-wide association study (GWAS) involving 114,751 participants. We pooled statistics from GWAS of gastrointestinal diseases in European populations, encompassing colitis (cases=1193, controls=461,740), IBD (cases=31,665, controls=33,977), Crohn's disease (cases=17,897, controls=33,977), ulcerative colitis (cases=1,239, controls=990), oesophageal cancer (cases=740, controls=372,016), gastric cancer (cases=6,563, controls=195,745), liver cell carcinoma (cases=168, controls=372,016), hepatic bile duct cancer (cases=418, controls=159,201), pancreatic cancer (cases=1,196, controls=475,049), and colon cancer (cases=1,494, controls=461,439). To ensure the validity of our findings, we utilized several analytical approaches including inverse variance weighted, weighted median, weighted mode, MR-Egger, and simple mode method. Results: Using the IVW method, we found that glutamine levels were inversely associated with colon cancer (OR = 0.998; 95% CI: 0.997-1.000; P = 0.027), colitis (OR = 0.998; 95% CI: 0.997-1.000; P = 0.020), and IBD (OR = 0.551; 95% CI: 0.343-0.886; P = 0.014). Subgroup analysis revealed a negative association between glutamine and Crohn's disease (OR = 0.375; 95% CI: 0.253-0.557; P = 1.11E-06), but not with ulcerative colitis (OR = 0.508; 95% CI: 0.163-1.586; P = 0.244). Glutamine levels showed no significant correlation with oesophageal cancer (OR = 1.000; 95% CI: 0.999-1.001; P = 0.566), gastric cancer (OR = 0.966; 95% CI: 0.832-1.121; P = 0.648), liver cell carcinoma (OR = 1.000; 95% CI: 0.999-1.000; P = 0.397), hepatic bile duct cancer (OR = 0.819; 95% CI: 0.499-1.344; P = 0.430), and pancreatic cancer (OR = 1.130; 95% CI: 0.897-1.423; P = 0.301). Sensitivity analyses also supports this finding, affirming the reliability and robustness of our study. Conclusions: This study suggests that blood glutamine levels in European populations may lower the risk of colon cancer, colitis, and IBD, particularly Crohn's disease. Nevertheless, additional research involving a diverse range of ancestries is imperative to corroborate this causal relationship.
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Azimuthal Angle Correlations of Muons Produced via Heavy-Flavor Decays in 5.02 TeV Pb+Pb and pp Collisions with the ATLAS Detector. PHYSICAL REVIEW LETTERS 2024; 132:202301. [PMID: 38829073 DOI: 10.1103/physrevlett.132.202301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/13/2024] [Indexed: 06/05/2024]
Abstract
Angular correlations between heavy quarks provide a unique probe of the quark-gluon plasma created in ultrarelativistic heavy-ion collisions. Results are presented of a measurement of the azimuthal angle correlations between muons originating from semileptonic decays of heavy quarks produced in 5.02 TeV Pb+Pb and pp collisions at the LHC. The muons are measured with transverse momenta and pseudorapidities satisfying p_{T}^{μ}>4 GeV and |η^{μ}|<2.4, respectively. The distributions of azimuthal angle separation Δϕ for muon pairs having pseudorapidity separation |Δη|>0.8, are measured in different Pb+Pb centrality intervals and compared to the same distribution measured in pp collisions at the same center-of-mass energy. Results are presented separately for muon pairs with opposite-sign charges, same-sign charges, and all pairs. A clear peak is observed in all Δϕ distributions at Δϕ∼π, consistent with the parent heavy-quark pairs being produced via hard-scattering processes. The widths of that peak, characterized using Cauchy-Lorentz fits to the Δϕ distributions, are found to not vary significantly as a function of Pb+Pb collision centrality and are similar for pp and Pb+Pb collisions. This observation will provide important constraints on theoretical descriptions of heavy-quark interactions with the quark-gluon plasma.
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Wu Y. [The overdiagnosis of thyroid cancer should not be overinterpreted]. ZHONGHUA YI XUE ZA ZHI 2024; 104:1561-1565. [PMID: 38742341 DOI: 10.3760/cma.j.cn112137-20231025-00889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Recently, the topic of "overdiagnosis" of thyroid cancer has once again sparked controversy in the medical academic community, as well as extensive discussions from patients and the general population. To some extent, the overdiagnosis of thyroid cancer has been over interpreted. This article provides a detailed discussion on how to correctly understand the overdiagnosis of thyroid cancer from the perspective of a thyroid surgeon. The author believes that there are two elements to the occurrence of overdiagnosis of thyroid cancer: the presence of a large "silent pool" of inert tumors in the human body and medical methods to improve detection rates. In recent years, thyroid cancer has become the fastest growing malignant tumor in the world in terms of incidence rate. The exponential growth mode makes thyroid cancer over diagnosed and inevitably becomes a hot topic in academia. However, the overdiagnosis of thyroid cancer is not contradictory to the early screening and diagnosis of thyroid cancer. The overdiagnosis of thyroid cancer should be viewed scientifically and rationally, especially avoiding overinterpretation.
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Wu Y, Xu W, Yang W. Response to the comment on "Clinicopathological features and prognostic factors of salivary gland myoepithelial carcinoma: institutional experience of 42 cases". Int J Oral Maxillofac Surg 2024:S0901-5027(24)00144-9. [PMID: 38740538 DOI: 10.1016/j.ijom.2024.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 04/14/2024] [Accepted: 04/29/2024] [Indexed: 05/16/2024]
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Shi H, Song L, Wu Y, Shen R, Zhang C, Liao X, Wang Q, Zhu J. Edaravone Alleviates Traumatic Brain Injury by Inhibition of Ferroptosis via FSP1 Pathway. Mol Neurobiol 2024:10.1007/s12035-024-04216-2. [PMID: 38733490 DOI: 10.1007/s12035-024-04216-2] [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: 01/28/2024] [Accepted: 05/04/2024] [Indexed: 05/13/2024]
Abstract
Traumatic brain injury (TBI) is a highly severe form of trauma with complex series of reactions in brain tissue which ultimately results in neuronal damage. Previous studies proved that neuronal ferroptosis, which was induced by intracranial haemorrhage and other reasons, was one of the most primary causes of neuronal damage following TBI. However, the association between neuronal mechanical injury and ferroptosis in TBI and relevant treatments remain unclear. In the present study, we first demonstrated the occurrence of neuronal ferroptosis in the early stage of TBI and preliminarily elucidated that edaravone (EDA), a cerebroprotective agent that eliminates oxygen radicals, was able to inhibit ferroptosis induced by TBI. A cell scratching model was established in PC12 cells, and it was confirmed that mechanical injury induced ferroptosis in neurons at the early stage of TBI. Ferroptosis suppressor protein 1 (FSP1) plays a significant role in inhibiting ferroptosis, and we found that iFSP, a ferroptosis agonist which is capable to inhibit FSP1 pathway, attenuated the anti-ferroptosis effect of EDA. In conclusion, our results suggested that EDA inhibited neuronal ferroptosis induced by mechanical injury in the early phase of TBI by activating FSP1 pathway, which could provide evidence for future research on prevention and treatment of TBI.
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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 HJ, Jiang LL, 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, 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 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 Observation of a Three-Resonance Structure in e^{+}e^{-}→Nonopen Charm Hadrons. PHYSICAL REVIEW LETTERS 2024; 132:191902. [PMID: 38804946 DOI: 10.1103/physrevlett.132.191902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 03/10/2024] [Accepted: 03/29/2024] [Indexed: 05/29/2024]
Abstract
We report the measurement of the inclusive cross sections for e^{+}e^{-}→nOCH (where nOCH denotes non-open charm hadrons) with improved precision at center-of-mass (c.m.) energies from 3.645 to 3.871 GeV. We observe three resonances: R(3760), R(3780), and R(3810) with significances of 8.1σ, 13.7σ, and 8.8σ, respectively. The R(3810) state is observed for the first time, while the R(3760) and R(3780) states are observed for the first time in the nOCH cross sections. Two sets of resonance parameters describe the energy-dependent line shape of the cross sections well. In set I [set II], the R(3810) state has mass (3805.7±1.1±2.7) [(3805.7±1.1±2.7)] MeV/c^{2}, total width (11.6±2.9±1.9) [(11.5±2.8±1.9)] MeV, and an electronic width multiplied by the nOCH decay branching fraction of (10.9±3.8±2.5) [(11.0±3.4±2.5)] eV. In addition, we measure the branching fractions B[R(3760)→nOCH]=(25.2±16.1±30.4)%[(6.4±4.8±7.7)%] and B[R(3780)→nOCH]=(12.3±6.6±8.3)%[(10.4±4.8±7.0)%] for the first time. The R(3760) state can be interpreted as an open-charm (OC) molecular state, but containing a simple four-quark state component. The R(3810) state can be interpreted as a hadrocharmonium state.
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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, Bao HR, 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 WL, Che GR, Chelkov G, Chen C, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SL, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, 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 YH, Fang J, Fang SS, Fang WX, Fang Y, Fang YQ, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Feng YT, Fischer K, Fritsch M, 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, Gutierrez J, Han KL, 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 BY, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Hussain T, Hüsken N, In der Wiesche N, 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 HB, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao Z, Jin S, Jin Y, Jing MQ, Jing XM, Johansson T, Kui X, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kavatsyuk M, Ke BC, Khachatryan V, Khoukaz A, Kiuchi R, Kolcu OB, Kopf B, Kuessner M, Kupsc A, Kühn W, Lane JJ, Larin P, 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 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 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 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 H, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, 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, Moses B, Muchnoi NY, Muskalla J, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu QL, Niu WD, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pei YP, Pelizaeus M, Peng HP, Peng YY, Peters K, Ping JL, Ping RG, Plura S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin JJ, Qin LQ, Qin XS, Qin ZH, Qiu JF, Qu SQ, Redmer CF, Ren KJ, Rivetti A, 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, 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, Wan Y, Wang SJ, Wang B, Wang BL, Wang B, Wang CW, Wang DY, Wang F, Wang HJ, Wang JP, Wang K, Wang LL, Wang M, Wang M, Wang NY, 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 YL, 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 YH, 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 Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng SH, 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 HC, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang J, Zhang J, 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 Y, Zhang Y, Zhang YT, Zhang YH, Zhang Y, Zhang Y, Zhang ZD, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao RP, 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 of X(2370) as 0^{-+} from J/ψ→γK_{S}^{0}K_{S}^{0}η^{'}. PHYSICAL REVIEW LETTERS 2024; 132:181901. [PMID: 38759175 DOI: 10.1103/physrevlett.132.181901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/05/2024] [Accepted: 03/28/2024] [Indexed: 05/19/2024]
Abstract
Based on (10087±44)×10^{6} J/ψ events collected with the BESIII detector, a partial wave analysis of the decay J/ψ→γK_{S}^{0}K_{S}^{0}η^{'} is performed. The mass and width of the X(2370) are measured to be 2395±11(stat)_{-94}^{+26}(syst) MeV/c^{2} and 188_{-17}^{+18}(stat)_{-33}^{+124}(syst) MeV, respectively. The corresponding product branching fraction is B[J/ψ→γX(2370)]×B[X(2370)→f_{0}(980)η^{'}]×B[f_{0}(980)→K_{S}^{0}K_{S}^{0}]=(1.31±0.22(stat)_{-0.84}^{+2.85}(syst))×10^{-5}. The statistical significance of the X(2370) is greater than 11.7σ and the spin parity is determined to be 0^{-+} for the first time. The measured mass and spin parity of the X(2370) are consistent with the predictions of the lightest pseudoscalar glueball.
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Lu SM, Zhou FY, Xie LY, Ma YL, Zhao GP, Gao X, Wu Y, Wang JG. Photoionization process of the hydrogenlike carbon ion embedded in warm and hot dense plasmas. Phys Rev E 2024; 109:055205. [PMID: 38907490 DOI: 10.1103/physreve.109.055205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/30/2024] [Indexed: 06/24/2024]
Abstract
Complicated many-body interactions between ions and surrounding particles exist in warm and hot dense plasmas. It will significantly alter the atomic structures and dynamic properties of the embedded ions. Recently, the atomic-state-dependent (ASD) screening model has been proposed and shown to be valid for investigating the screening effect in warm and hot dense plasmas over a wide range of electron densities and temperatures. By employing the ASD model, we investigate the photoionization process for the hydrogenlike carbon ion embedded in warm and hot dense plasmas with corresponding Coulomb coupling parameter ranges of 0.05 ≤ Γ ≤ 1.16, where Γ characterizes the ratio of the average potential to thermal energy. It is found that there are stronger plasma screening effects on the ionization energy and photoionization cross section due to the negative-energy electron distributions considered in the ASD model compared to those considering only free electrons. The present results from the ASD model show reasonable agreement with the classical Debye-Hückel (DH) model in weakly coupled plasmas. However, significant deviations of the ionization energy and cross section between these two models are observed in moderately and strongly coupled plasmas, due to the approximate treatment of the plasma-electron density distribution of the DH model. In the region of low photoelectron energies, the positions of the shape resonance peaks of the cross sections obtained from the ASD model differ significantly from those of the DH model due to the different screening effects.
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Peng C, Yang X, Smith KE, Yu Z, Chen A, Bian J, Wu Y. Model tuning or prompt Tuning? a study of large language models for clinical concept and relation extraction. J Biomed Inform 2024; 153:104630. [PMID: 38548007 PMCID: PMC11065560 DOI: 10.1016/j.jbi.2024.104630] [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: 12/20/2023] [Revised: 02/24/2024] [Accepted: 03/25/2024] [Indexed: 04/04/2024]
Abstract
OBJECTIVE To develop soft prompt-based learning architecture for large language models (LLMs), examine prompt-tuning using frozen/unfrozen LLMs, and assess their abilities in transfer learning and few-shot learning. METHODS We developed a soft prompt-based learning architecture and compared 4 strategies including (1) fine-tuning without prompts; (2) hard-prompting with unfrozen LLMs; (3) soft-prompting with unfrozen LLMs; and (4) soft-prompting with frozen LLMs. We evaluated GatorTron, a clinical LLM with up to 8.9 billion parameters, and compared GatorTron with 4 existing transformer models for clinical concept and relation extraction on 2 benchmark datasets for adverse drug events and social determinants of health (SDoH). We evaluated the few-shot learning ability and generalizability for cross-institution applications. RESULTS AND CONCLUSION When LLMs are unfrozen, GatorTron-3.9B with soft prompting achieves the best strict F1-scores of 0.9118 and 0.8604 for concept extraction, outperforming the traditional fine-tuning and hard prompt-based models by 0.6 ∼ 3.1 % and 1.2 ∼ 2.9 %, respectively; GatorTron-345 M with soft prompting achieves the best F1-scores of 0.8332 and 0.7488 for end-to-end relation extraction, outperforming other two models by 0.2 ∼ 2 % and 0.6 ∼ 11.7 %, respectively. When LLMs are frozen, small LLMs have a big gap to be competitive with unfrozen models; scaling LLMs up to billions of parameters makes frozen LLMs competitive with unfrozen models. Soft prompting with a frozen GatorTron-8.9B model achieved the best performance for cross-institution evaluation. We demonstrate that (1) machines can learn soft prompts better than hard prompts composed by human, (2) frozen LLMs have good few-shot learning ability and generalizability for cross-institution applications, (3) frozen LLMs reduce computing cost to 2.5 ∼ 6 % of previous methods using unfrozen LLMs, and (4) frozen LLMs require large models (e.g., over several billions of parameters) for good performance.
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Yu Z, Peng C, Yang X, Dang C, Adekkanattu P, Gopal Patra B, Peng Y, Pathak J, Wilson DL, Chang CY, Lo-Ciganic WH, George TJ, Hogan WR, Guo Y, Bian J, Wu Y. Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias. J Biomed Inform 2024; 153:104642. [PMID: 38621641 PMCID: PMC11141428 DOI: 10.1016/j.jbi.2024.104642] [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: 10/25/2023] [Revised: 04/09/2024] [Accepted: 04/12/2024] [Indexed: 04/17/2024]
Abstract
OBJECTIVE To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different disease groups, and examine population-level extraction ratio. METHODS We developed SDoH corpora using clinical notes identified at the University of Florida (UF) Health. We systematically compared 7 transformer-based large language models (LLMs) and developed an open-source package - SODA (i.e., SOcial DeterminAnts) to facilitate SDoH extraction from clinical narratives. We examined the performance and potential bias of SODA for different race and gender groups, tested the generalizability of SODA using two disease domains including cancer and opioid use, and explored strategies for improvement. We applied SODA to extract 19 categories of SDoH from the breast (n = 7,971), lung (n = 11,804), and colorectal cancer (n = 6,240) cohorts to assess patient-level extraction ratio and examine the differences among race and gender groups. RESULTS We developed an SDoH corpus using 629 clinical notes of cancer patients with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH, and another cross-disease validation corpus using 200 notes from opioid use patients with 4,342 SDoH concepts/attributes. We compared 7 transformer models and the GatorTron model achieved the best mean average strict/lenient F1 scores of 0.9122 and 0.9367 for SDoH concept extraction and 0.9584 and 0.9593 for linking attributes to SDoH concepts. There is a small performance gap (∼4%) between Males and Females, but a large performance gap (>16 %) among race groups. The performance dropped when we applied the cancer SDoH model to the opioid cohort; fine-tuning using a smaller opioid SDoH corpus improved the performance. The extraction ratio varied in the three cancer cohorts, in which 10 SDoH could be extracted from over 70 % of cancer patients, but 9 SDoH could be extracted from less than 70 % of cancer patients. Individuals from the White and Black groups have a higher extraction ratio than other minority race groups. CONCLUSIONS Our SODA package achieved good performance in extracting 19 categories of SDoH from clinical narratives. The SODA package with pre-trained transformer models is available at https://github.com/uf-hobi-informatics-lab/SODA_Docker.
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Wu Y, Li X, Guo F, Wu X. Multiple maxillofacial desmoid tumours: a case report. Int J Oral Maxillofac Surg 2024:S0901-5027(24)00098-5. [PMID: 38693024 DOI: 10.1016/j.ijom.2024.04.004] [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: 12/21/2023] [Revised: 03/30/2024] [Accepted: 04/16/2024] [Indexed: 05/03/2024]
Abstract
Desmoid tumors (DTs) are rare benign neoplasms but cause significant mortality due to their locally infiltrative nature and propensity to recur. Most DTs occur in the extremities and trunk. Head and neck DTs are uncommon but can have a significant impact on a patient's facial appearance. However, there is limited information available about the diagnosis and treatment for multiple DTs located in head and neck. We report the first case of multiple maxillofacial DTs in a 14-year-old boy. He had painless submandibular masses for three months and MRI imaging reveals abnormal high signals on the submandibular and bilateral zygomatic regions. Considering facial aesthetics, via intraoral incision we obtained a biopsy from the largest mass. Pathological examination confirmed a diagnosis of DT. We selected the wait-and-see strategy and clinically monitored the rest of the masses. During the subsequent 1-year follow-up, the masses were stagnant and appeared to involute. According to the development and outcome of this case, a conservative treatment for craniofacial DTs is suggested; however, greater clarity concerning management and prognosis could derive from prospective study of a larger patient cohort in the future.
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