Academic literature on the topic 'C.M Bellman'

A prótese bucomaxilofacial visa a reabilitação de pacientes que sofreram mutilações na face, restituindo estética e autoestima. Este trabalho visa relatar o caso clínico de um paciente reabilitado com prótese oculopalpebral após sofrer exenteração de órbita, decorrente de um carcinoma espinocelular em pálpebra inferior direita. Paciente, gênero masculino, 56 anos, procurou atendimento odontológico queixando-se de desconforto estético do rosto. Ao exame clínico foi observada ausência do globo ocular, pálpebras e arco superciliar do lado direito, por isto, foi planejada a confecção de uma prótese oculopalpebral. Foi realizada moldagem dos terços superior e médio da face, obteve-se o molde em alginato e, posteriormente, o modelo em gesso. Em seguida, foi confeccionado um globo ocular caracterizado em resina acrílica termopolimerizável. Posteriormente realizou-se, sobre o modelo de gesso, a escultura da área amputada utilizando-se plastilina e cera e após prova e ajustes no paciente, inclusão do conjunto modelo/escultura em mufla e contramufla, com posterior eliminação da peça esculpida. Foi selecionada a cor da pele do paciente e misturou-se uma base ao silicone, que foi incluído na mufla para prensagem. Após a vulcanização do silicone, foram realizados os acabamentos, caracterização e instalação da prótese. Na proservação o paciente relatou grande satisfação com a reconstituição da estética facial. Conclui-se que a prótese bucomaxilofacial é uma alternativa satisfatória para a reabilitação de pacientes que sofreram mutilações faciais, pois restabelece a estética facial, autoestima e convívio social. 
 Descritores: Prótese Maxilofacial; Olho Artificial; Reabilitação; Carcinoma de Células Escamosas.
 Referências
 
 Duncan BGF, Calhoun ME. Facial prostheses in the rehabilitation of burn patients. Nurse Life Care Planner. 2015;15(3):900-5.
 Vieira LM, Oguro P, Dias RB, Pimentel ML, Barretto MRP, Coto NP. Proposition of integrated electrical mechanism anda facial prosthesis for eyelid motion on prosthetic oculopalpebral rehabilitation: technical note. J. 2019; 35(2):659-65.
 Moss OB, Pinheiro BCL, Mendes TCC, Braga FP, Nichthauser B, Leal CMB. Reabilitação oral com prótese bucomaxilofacial em paciente pediátrica submetida à excisão de lesão neoplásica benigna em maxila. Arch Health Invest. 2019;8(11):706-10.
 Petsuksiri J, Frank SJ, Garden AS, Ang KK, Morrison WH, Chao KS, Rosenthal DI, Schwartz DL, Ahamad A, Esmaeli B. Outcomes after radiotherapy for squamous cell carcinoma of the eyelid. 2008;112(1):111-18.
 Dib LL, Oliveira JAP. Reabilitação Bucomaxilofacial - uso de próteses e implantes osseointegrados. In: Cardoso RJA, Gonçalves EAN. Odontologia: arte, ciência e técnica. 6 ed. São Paulo: Artes Médicas; 2002.
 Pinheiro BCL, De Mattos TCB, Dias ST, Braga FP, Leal CMB, Nichthauser B. Reabilitação com prótese ocular em paciente anoftálmico. Full Dent. Sci. 2020;11(42):98-103.
 Wondergem M, Lieben G, Bouman S, van den Brekel MW, Lohuis PJ. Patients' satisfaction with facial prostheses. Br J Oral Maxillofac Surg. 2016;54(4):394-9.
 Lanzara R, Thakur A, Viswambaran M, Khattak A. Fabrication of ocular prosthesis with a digital customization technique - a case report. J Family Med Prim Care. 2019;8(3):1239-42.
 Brandão TB, Filho AJV, Batista VES, Ribeiro ACP, Nary Filho H, Chilvarquer I, et al. Assessment of treatment outcomes for facial prostheses in patients with craniofacial defects: A pilot retrospective study. J Prosthet Dent. 2017;118(2):235-41.
 Koyama S, Sasaki K, Hanawa S, Sato N. The potential of cohesive silicone for facial prosthetic use: a material property study and a clinical report. J Prosthodont. 2011;20(4):299-304.
 Soares LHS, Bello CV, Reis AKL, Nunes RR, Mason EM. Tumores malignos de pálpebra. Arq Bras Oftalmol. 2001;64(1):287-9.
 Grant GT, Aita-Holmes C, Liacouras P, Garnes J, Wilson WO Jr. Digital capture, design, and manufacturing of a facial prosthesis: Clinical report on a pediatric patient. J Prosthet Dent. 2015;114(1):138-41.
 Sohaib A, Amano K, Xiao K, Yates JM, Whitford C, Werger S. Colour quality of facial prostheses in additive manufacturing. Int J Adv Manuf Technol. 2018; 96(2):881-94.
 Veerareddy C, Nair KC, Reddy R. Simplified Technique for Orbital Prosthesis fabrication: a case report. J Prosthodont. 2012;21(1):561-68.
 Bellamy K, Limbert G, Waters MG, Middleton J. An elastomeric material for facial prostheses: synthesis, experimental and numerical testing aspects. 2003;24(27):5061-66.
 Papaspyrou G, Yildiz C, Bozzato V, Bohr C, Schneider M, Hecker D, Schick B, Al Kadah B. Prosthetic supply of facial defects: long-term experience and retrospective analysis on 99 patients. Eur Arch Otorhinolaryngol. 2018;275(2):607-13.
 Chang TL, Garrett N, Roumanas E, Beumer J 3rd. Treatment satisfaction with facial prostheses. J Prosthet Dent. 2005;94(3):275-80. 
 Nomura T, Sato J, Matsuura M, Kawaguchi K, Sekiguchi R, Horie A, Seto K. Lightweight acrylic resin facial prosthesis for maxillofacial defects: a fabrication and retention method. J Prosthet Dent. 2013;110(4):326-30.
 Dings JPJ, Merkx MAW, de Clonie Maclennan-Naphausen MTP, van de Pol P, Maal TJJ, Meijer GJ. Maxillofacial prosthetic rehabilitation: A survey on the quality of life. J Prosthet Dent. 2018;120(5):780-86.
 Ariani N, Visser A, Teulings MR, Dijk M, Rahardjo TB, Vissink A, van der Mei HC. Efficacy of cleansing agents in killing microorganisms in mixed species biofilms present on silicone facial prostheses--an in vitro study. Clin Oral Investig. 2015;19(9):2285-93.
 Goiato MC, Pesqueira AA, dos Santos DM, Zavanelli AC, Ribeiro Pdo P. Color stability comparison of silicone facial prostheses following disinfection. J Prosthodont. 2009;18(3):242-44. 
 Jebreil K. Accetability of orbital prostheses. J Prosthet Dent. 1980;43(1):82-5.

BackgroundMultiple genomics-based biomarkers of response to immune checkpoint inhibition have been reported or proposed, including tumor mutation/neoantigen frequency, PD-L1 expression, T cell receptor repertoire clonality, interferon gene signature expression, HLA expression, and others.1 Although genomics associations of response have been reported, the primary studies have used a variety of data generation and processing techniques. There is a need for data harmonization and assessment of generalizability of potential biomarkers across multiple datasets.MethodsWe acquired patient-level RNA sequencing FASTQ data files from 10 data sets reported in seven pan-cancer PD-1 and CTLA-4 immune checkpoint inhibition trials with matched clinical annotations.2–7 We applied a common bioinformatics workflow for quality control, mapping to reference (STAR), generating gene expression matrices (SALMON), T cell receptor repertoire inference (MiXCR), extraction of immune gene signatures and immune subtypes,8 and differential gene expression analysis (DESeq2). We analyzed i) immunogenomics features proposed as biomarkers, and ii) gene expression signatures built from each trial for association with overall survival across the set of trials using univariable Cox proportional hazards regression. In all, we assessed 9 total immunogenomics features/signatures. P-values were adjusted for multiple testing using the Benjamini-Hochberg method.ResultsOf the 9 immunogenomics features assessed, cytolytic activity score and expression of the Follicular Dendritic Cell Secreted Protein gene (FDCSP) were associated with survival in two of seven studies, respectively (adjusted p < 0.05) (figure 1). No proposed biomarkers were significantly associated with survival in more than two studies. The sets of genes significantly associated with clinical benefit across the studies were highly disjoint, with only three genes significant in three studies and thirteen genes significant in two studies (figure 2). No genes were significantly associated with clinical benefit in more than three of seven studies.Abstract 75 Figure 1Association of immunogenomics features and proposed biomarkers with survival in 10 publicly available datasets from 7 clinical trials with immune checkpoint blockade. Nine immunogenomics features were tested in 10 publicly available RNAseq data sets from 7 published clinical trials with immune checkpoint blockade for their correlation with outcome. SKCM, skin cutaneous melanoma; BLCA, bladder cancer; Kidney, kidney cancer; Ureter, ureteral cancer; GBM, glioblastomaAbstract 75 Figure 2Association of gene expression of single genes with survival in 10 publicly available datasets from 7 clinical trials with immune checkpoint inhibitorsConclusionsNo proposed biomarkers were highly generalizable across studies. We expect that integrated modeling incorporating multiple immunogenomics features will be required to build a robust and generalizable biomarker for ICI response. Further work is needed to analyze determinants of response and clinical benefit.AcknowledgementsWe would like to thank SITC for funding for this work as part of the Sparkathon TimIOS collaborative project.ReferencesZappasodi R, Wolchok JD, Merghoub T. Strategies for Predicting Response to Checkpoint Inhibitors. Curr Hematol Malig Rep 2018;13(5):383–95.Liu D, Schilling B, Liu D, Sucker A, Livingstone E, Jerby-Arnon L, Zimmer L, Gutzmer R, Satzger I, Loquai C, Grabbe S, Vokes N, Margolis CA, Conway J, He MX, Elmarakeby H, Dietlein F, Miao D, Tracy A, Gogas H, Goldinger SM, Utikal J, Blank CU, Rauschenberg R, von Bubnoff D, Krackhardt A, Weide B, Haferkamp S, Kiecker F, Izar B, Garraway L, Regev A, Flaherty K, Paschen A, Van Allen EM, Schadendorf D. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med 2019;25(12):1916–27.Gide TN, Quek C, Menzies AM, Tasker AT, Shang P, Holst J, Madore J, Lim SY, Velickovic R, Wongchenko M, Yan Y, Lo S, Carlino MS, Guminski A, Saw RPM, Pang A, McGuire HM, Palendira U, Thompson JF, Rizos H, Silva IPD, Batten M, Scolyer RA, Long GV, Wilmott JS. distinct immune cell populations define response to anti-pd-1 monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy. Cancer Cell 2019;35(2):238–55 e6.Cloughesy TF, Mochizuki AY, Orpilla JR, Hugo W, Lee AH, Davidson TB, Wang AC, Ellingson BM, Rytlewski JA, Sanders CM, Kawaguchi ES, Du L, Li G, Yong WH, Gaffey SC, Cohen AL, Mellinghoff IK, Lee EQ, Reardon DA, O’Brien BJ, Butowski NA, Nghiemphu PL, Clarke JL, Arrillaga-Romany IC, Colman H, Kaley TJ, de Groot JF, Liau LM, Wen PY, Prins RM. Neoadjuvant anti-PD-1 immunotherapy promotes a survival benefit with intratumoral and systemic immune responses in recurrent glioblastoma. Nat Med. 2019;25(3):477–86.Riaz N, Havel JJ, Makarov V, Desrichard A, Urba WJ, Sims JS, Hodi FS, Martin-Algarra S, Mandal R, Sharfman WH, Bhatia S, Hwu WJ, Gajewski TF, Slingluff CL, Jr., Chowell D, Kendall SM, Chang H, Shah R, Kuo F, Morris LGT, Sidhom JW, Schneck JP, Horak CE, Weinhold N, Chan TA. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 2017;171(4):934–49 e16.Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, Berent-Maoz B, Pang J, Chmielowski B, Cherry G, Seja E, Lomeli S, Kong X, Kelley MC, Sosman JA, Johnson DB, Ribas A, Lo RS. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 2016;165(1):35–44.Rosenberg JE, Hoffman-Censits J, Powles T, van der Heijden MS, Balar AV, Necchi A, Dawson N, O’Donnell PH, Balmanoukian A, Loriot Y, Srinivas S, Retz MM, Grivas P, Joseph RW, Galsky MD, Fleming MT, Petrylak DP, Perez-Gracia JL, Burris HA, Castellano D, Canil C, Bellmunt J, Bajorin D, Nickles D, Bourgon R, Frampton GM, Cui N, Mariathasan S, Abidoye O, Fine GD, Dreicer R. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet. 2016;387(10031):1909–20.Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, Porta-Pardo E, Gao GF, Plaisier CL, Eddy JA, Ziv E, Culhane AC, Paull EO, Sivakumar IKA, Gentles AJ, Malhotra R, Farshidfar F, Colaprico A, Parker JS, Mose LE, Vo NS, Liu J, Liu Y, Rader J, Dhankani V, Reynolds SM, Bowlby R, Califano A, Cherniack AD, Anastassiou D, Bedognetti D, Mokrab Y, Newman AM, Rao A, Chen K, Krasnitz A, Hu H, Malta TM, Noushmehr H, Pedamallu CS, Bullman S, Ojesina AI, Lamb A, Zhou W, Shen H, Choueiri TK, Weinstein JN, Guinney J, Saltz J, Holt RA, Rabkin CS, Cancer Genome Atlas Research N, Lazar AJ, Serody JS, Demicco EG, Disis ML, Vincent BG, Shmulevich I. The Immune Landscape of Cancer. Immunity 2018;48(4):812–30e14.

<p style='text-indent:20px;'>This paper is concerned with the existence and multiplicity of constant sign solutions for the following fully nonlinear equation</p><p style='text-indent:20px;'><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ \begin{equation*} \left\{ \begin{array}{l} -\mathcal{M}_\mathcal{C}^{\pm}(D^2u) = \mu f(u) \ \ \ \ \text{in} \ \ \Omega,\\ u = 0 \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{on}\ \partial\Omega, \end{array} \right. \end{equation*} $\end{document} </tex-math> </disp-formula></p><p style='text-indent:20px;'>where <inline-formula><tex-math id="M3">\begin{document}$ \Omega\subset\mathbb{R}^N $\end{document}</tex-math></inline-formula> is a bounded regular domain with <inline-formula><tex-math id="M4">\begin{document}$ N\geq3 $\end{document}</tex-math></inline-formula>, <inline-formula><tex-math id="M5">\begin{document}$ \mathcal{M}_\mathcal{C}^{\pm} $\end{document}</tex-math></inline-formula> are general Hamilton-Jacobi-Bellman operators, <inline-formula><tex-math id="M6">\begin{document}$ \mu $\end{document}</tex-math></inline-formula> is a real parameter. By using bifurcation theory, we determine the range of parameter <inline-formula><tex-math id="M7">\begin{document}$ \mu $\end{document}</tex-math></inline-formula> of the above problem which has one or multiple constant sign solutions according to the behaviors of <inline-formula><tex-math id="M8">\begin{document}$ f $\end{document}</tex-math></inline-formula> at <inline-formula><tex-math id="M9">\begin{document}$ 0 $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M10">\begin{document}$ \infty $\end{document}</tex-math></inline-formula>, and whether <inline-formula><tex-math id="M11">\begin{document}$ f $\end{document}</tex-math></inline-formula> satisfies the signum condition <inline-formula><tex-math id="M12">\begin{document}$ f(s)s>0 $\end{document}</tex-math></inline-formula> for <inline-formula><tex-math id="M13">\begin{document}$ s\neq0 $\end{document}</tex-math></inline-formula>.</p>

Abstract Background Biologic therapies have transformed the outlook for RA but the significant health economic impact of these therapies has highlighted the need to define predictive markers of response. Rituximab (RTX) is licensed for use following failure of csDMARDs and TNF inhibitor (TNFi) therapy. However, in this increasing therapeutically resistant cohort only 30% of patients achieve an ACR50 response. The observation in early RA that 50% of patients show low/absence of synovial B-cells prompted us to test the hypothesis that in these patients a biologic agent targeting alternative pathways maybe more effective. We report results from the first pathobiology-driven randomised controlled trial (RCT) in RA (R4RA) evaluating whether patient stratification according to the synovial B-cell rich/poor status enriches for response/non response to RTX. Methods R4RA is a phase IV open-label RCT conducted in 19 European centres recruiting patients failing or intolerant to csDMARD therapy and at least one TNFi. Synovial tissue was obtained at trial entry and used to classify patients as B-cell rich or poor using both histological and RNA-seq classification criteria. Patients were randomised to receive RTX or tocilizumab (TCZ). The study was powered to test in the B cell poor population superiority of TCZ over RTX at 16 weeks. The primary and co-primary end-points were defined respectively as Clinical Disease Activity Index (CDAI) ≥50% improvement from baseline and Major Treatment response (MTR)= CDAI improvement ≥ 50% and CDAI ≤10.1. Results The trial recruited to target (n = 164) with a power of 89.5%. In the B cell poor cohort a numerically higher number of patients achieved the primary endpoint and a significantly higher number of patients achieved co-primary endpoint (MTR). Classification of patients as B cell poor/rich according to RNA-seq criteria enhanced the difference between TCZ and RTX, with a significantly higher number of TCZ treated patients reaching both CDAI 50% improvement and CDAI MTR in the B-cell poor group. Conclusion In a RA B cell poor population failing csDMARDs and TNFi therapy, TCZ is more effective than RTX. This first biopsy-driven RCT suggests clinical utility for integrating molecular pathology profiling into treatment algorithms to allocate targeted therapies. Disclosures F. Humby: Honoraria; Roche, Pfizer. Grants/research support; Pfizer. P. Durez: BMS,Bristol-Myers Squibb, Celltrion, Eli Lilly, Hospira, Mundipharma, Pfizer, Samsung, Sanofi, UCB. M. Buch: Consultancies; Pfizer, Roche, UCB, AbbVie, Eli Lilly, Sandoz, and Sanofi. Grants/research support; Pfizer, Roche, UCB, AbbVie, Eli Lilly, Sandoz, and Sanofi. M. Lewis: None. M. Bombardieri: None. H. Rizvi: None. S. Kelly: None. L. Fossati: None. R. Hands: None. G. Giorli: None. A. Mahto: None. C. Montecucco: None. B. Lauwerys: None. V.C. Romao: None. A.G. Pratt: Member of speakers’ bureau; Eli Lilly and Janssen-Cilag Ltd. Grants/research support; Pfizer. S. Bugatti: None. N. Ng: None. F. Rivellese: None. P. Ho: None. M. Bellan: None. P. Sainaghi: None. P. Verschueren: None. N. Gendi: None. B. Dasgupta: Abbvie, BMS, GSK, Roche, Roche Chugai, Sanofi, Sanofi Aventis, Sanofi-Aventis. A. Cauli: BMS, Celgene, Lilly, Lilly MSD, MSD, Novartis, Pfizer, Sanofi, Sigma Wesseumen, UCB. C. John: None. A. Nerviani: None. G. Thornborn: None. D. Holroyd: None. M. Congia: None. C. Thompson: None. P. Reynolds: None. J. Cañete: None. R. J. Moots: Biogen, Bristol-Myers Squibb, Chugai, Novartis, Pfizer Inc, Roche, Sandoz, UCB. P.C. Taylor: AbbVie, Biogen, Celgene, Eli Lilly and Company, Fresenius, Fresenius SE & Co, Galapagos, Gilead. GlaxoSmithKline, Janssen, Lilly, Nordic Pharma, Pfizer, Pfizer Inc, Roche, Sanofi, UCB. C. Edwards: Abbvie, Biogen, BMS, Fresenius, Janssen, Lilly, MSD, Novartis, Pfizer, Roche, UCB. J. Isaacs: None. P. Sasieni: None. J. E. Fonesca: None. E. Choy: AbbVie, Abbvie, Roche, Chugai, Amgen, Eli Lilly, Janssen, Novartis, Regeneron, R-Pharm and Sanofi, Amgen, Amgen, Roche, Chugai, Bristol-Myers Squibb, Eli-Lilly Janssen, Pfizer, Regeneron, Sanofi and UCB., AstraZeneca, Bio-Cancer, Bio-Cancer, Biogen, Novartis, Sanofi, Roche, Pfizer and UCB ,Biogen, BMS, Boehringer Ingelheim, Celgene, Chugai Pharma, Eli Lilly, Ferring Pharmaceuticals, GSK, Hospira, Janssen, Jazz Pharmaceuticals, Merck Sharp & Dohme, Merrimack Pharmaceutical, Napp, Novartis, Novimmune, ObsEva, Pfizer, Regeneron, Roche, R-Pharm, Sanofi, SynAct Pharma, Tonix, Union Chimique Belge. C. Pitzalis: None. NIHR have funded the study.