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219 results on '"Boehme M"'

1. Reconciling ionization energies and band gaps of warm dense matter derived with ab initio simulations and average atom models

Author

Massacrier, G., Böhme, M., Vorberger, J., Soubiran, F., and Militzer, B.

Subjects
Physics - Plasma Physics
Abstract

Average atom (AA) models allow one to efficiently compute electronic and optical properties of materials over a wide range of conditions and are often employed to interpret experimental data. However, at high pressure, predictions from AA models have been shown to disagree with results from ab initio computer simulations. Here we reconcile these deviations by developing an innovative type of AA model, AVION, that computes the electronic eigenstates with novel boundary conditions within the ion sphere. Bound and free states are derived consistently. We drop the common AA image that the free-particle spectrum starts at the potential threshold, which we found to be incompatible with ab initio calculations. We perform ab initio simulations of crystalline and liquid carbon and aluminum over a wide range of densities and show that the computed band structure is in very good agreement with predictions from AVION.

Published
2021
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5. Linear-response time-dependent density functional theory approach to warm dense matter with adiabatic exchange--correlation kernels

Author

(0000-0002-9725-9208) Moldabekov, Z., Pavanello, M., Boehme, M. P., Vorberger, J., Dornheim, T., (0000-0002-9725-9208) Moldabekov, Z., Pavanello, M., Boehme, M. P., Vorberger, J., and Dornheim, T.

Abstract

We present a new methodology for the linear-response time-dependent density functional theory (LR-TDDFT) calculation of the dynamic density response function of warm dense matter in an adiabatic approximation that can be used with any available exchange-correlation (XC) functional across Jacob's Ladder and across temperature regimes. The main novelty of the presented approach is that it can go beyond the adiabatic local density approximation (ALDA) and generalized LDA (AGGA) while preserving the self-consistence between the Kohn-Sham (KS) response function and adiabatic XC kernel for extended systems. The key ingredient for the presented method is the combination of the adiabatic XC kernel from the direct perturbation approach with the macroscopic dynamic KS response from the standard LR-TDDFT method using KS orbitals. We demonstrate the application of the method for the example of warm dense hydrogen, for which we perform a detailed analysis of the KS density response function, the RPA result, the total density response function and of the adiabatic XC kernel. The analysis is performed using LDA, GGA, and meta-GGA level approximations for the XC effects. The presented method is directly applicable to disordered systems such as liquid metals, warm dense matter, and dense plasmas.

Published
2023

10. System Analysis of Patient Management During the Pre- and Early Clinical Phase in Severe Head Injury

Author

Baethmann, A., Chapuis, D., Wirth, A., Reulen, H.-J., Steiger, H.-J., Stummer, W., Hölzel, D., Lackner, Ch., Assal, J., Belg, A., Schrödel, M., Müller, N., Ueblacker, P., Chlistalla, A., Schneider, G., Schweiberer, L., Dietz, H.-G., Trappe, A., Göbel, W.-E., Jaksche, H., Messner, V., Grumme, Th., Wenger, P., Müller, H., Stolpe, E., Döffinger, J., Lumenta, Ch., Gerstner, W., Prokscha, G., Sackerer, D., Höfner, F., Eisenstein, T., Lenz, G., Preisz, A., Rothmeier, F., Kirmayer, U., Böhme, M., Horak, W., Weeß, Th., Reulen, H.-J., editor, Steiger, H.-J., editor, Baethmann, Alexander, editor, Plesnila, Nikolaus, editor, Ringel, Florian, editor, and Eriskat, Jörg, editor

Published
1999
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14. Kefir ameliorates specific microbiota-gut-brain axis impairments in a mouse model relevant to autism spectrum disorder

Author

van de Wouw, M, Walsh, CJ, Vigano, GMD, Lyte, JM, Boehme, M, Gual-Grau, A, Crispie, F, Walsh, AM, Clarke, G, Dinan, TG, Cotter, PD, Cryan, JF, van de Wouw, M, Walsh, CJ, Vigano, GMD, Lyte, JM, Boehme, M, Gual-Grau, A, Crispie, F, Walsh, AM, Clarke, G, Dinan, TG, Cotter, PD, and Cryan, JF

Abstract

Autism spectrum disorder (ASD) is one of the most severe developmental disorders, affecting on average 1 in 150 children worldwide. There is a great need for more effective strategies to improve quality of life in ASD subjects. The gut microbiome has emerged as a potential therapeutic target in ASD. A novel modulator of the gut microbiome, the traditionally fermented milk drink kefir, has recently been shown to modulate the microbiota and decrease repetitive behaviour, one of the hallmarks of ASD, in mice. As such, we hypothesized that kefir could ameliorate behavioural deficits in a mouse model relevant to ASD; the BTBR T+ Itpr3tf/J mouse strain. To this end, adult mice were administered either kefir (UK4) or a milk control for three weeks as treatment lead-in, after which they were assessed for their behavioural phenotype using a battery of tests. In addition, we assessed systemic immunity by flow cytometry and the gut microbiome using shotgun metagenomic sequencing. We found that indeed kefir decreased repetitive behaviour in this mouse model. Furthermore, kefir prolonged stress-induced increases in corticosterone 60 min post-stress, which was accompanied by an ameliorated innate immune response as measured by LY6Chi monocyte levels. In addition, kefir increased the levels of anti-inflammatory Treg cells in mesenteric lymph nodes (MLNs). Kefir also increased the relative abundance of Lachnospiraceae bacterium A2, which correlated with reduced repetitive behaviour and increased Treg cells in MLNs. Functionally, kefir modulated various predicted gut microbial pathways, including the gut-brain module S-Adenosylmethionine (SAM) synthesis, as well as L-valine biosynthesis and pyruvate fermentation to isobutanol, which all correlated with repetitive behaviour. Taken together our data show that kefir modulates peripheral immunoregulation, can ameliorate specific ASD behavioural dysfunctions and modulates selective aspects of the composition and function of the gut micro

Published
2021

20. Global Retinoblastoma Presentation and Analysis by National Income Level

Author

Fabian, ID, Abdallah, E, Abdullahi, SU, Abdulqader, RA, Boubacar, SA, Ademola-Popoola, DS, Adio, A, Afshar, AR, Aggarwal, P, Aghaji, AE, Ahmad, A, Akib, MNR, Al Harby, L, Al Ani, MH, Alakbarova, A, Portabella, SA, Al-Badri, SAF, Alcasabas, APA, Al-Dahmash, SA, Alejos, A, Alemany-Rubio, E, Bio, AIA, Carreras, YA, Al-Haddad, C, Al-Hussaini, HHY, Ali, AM, Alia, DB, Al-Jadiry, MF, Al-Jumaly, U, Alkatan, HM, All-Eriksson, C, Al-Mafrachi, AARM, Almeida, AA, Alsawidi, KM, Al-Shaheen, AASM, Al-Shammary, EH, Amiruddin, PO, Antonino, R, Astbury, NJ, Atalay, HT, Atchaneeyasakul, L-O, Atsiaya, R, Attaseth, T, Aung, TH, Ayala, S, Baizakova, B, Balaguer, J, Balayeva, R, Balwierz, W, Barranco, H, Bascaran, C, Popovic, MB, Benavides, R, Benmiloud, S, Guebessi, NB, Berete, RC, Berry, JL, Bhaduri, A, Bhat, S, Biddulph, SJ, Biewald, EM, Bobrova, N, Boehme, M, Boldt, HC, Bonanomi, MTBC, Bornfeld, N, Bouda, GC, Bouguila, H, Boumedane, A, Brennan, RC, Brichard, BG, Buaboonnam, J, Calderon-Sotelo, P, Calle Jara, DA, Camuglia, JE, Cano, MR, Capra, M, Cassoux, N, Castela, G, Castillo, L, Catala-Mora, J, Chantada, GL, Chaudhry, S, Chaugule, SS, Chauhan, A, Chawla, B, Chernodrinska, VS, Chiwanga, FS, Chuluunbat, T, Cieslik, K, Cockcroft, RL, Comsa, C, Correa, ZM, Correa Llano, MG, Corson, TW, Cowan-Lyn, KE, Csoka, M, Cui, X, Da Gama, I, Dangboon, W, Das, A, Das, S, Davanzo, JM, Davidson, A, De Potter, P, Delgado, KQ, Demirci, H, Desjardins, L, Diaz Coronado, RY, Dimaras, H, Dodgshun, AJ, Donaldson, C, Donato Macedo, CR, Dragomir, MD, Du, Y, Du Bruyn, M, Edison, KS, Sutyawan, IWE, El Kettani, A, Elbahi, AM, Elder, JE, Elgalaly, D, Elhaddad, AM, Elhassan, MMA, Elzembely, MM, Essuman, VA, Evina, TGA, Fadoo, Z, Fandino, AC, Faranoush, M, Fasina, O, Fernandez, DDPG, Fernandez-Teijeiro, A, Foster, A, Frenkel, S, Fu, LD, Fuentes-Alabi, SL, Gallie, BL, Gandiwa, M, Garcia, JL, Garcia Aldana, D, Gassant, PY, Geel, JA, Ghassemi, F, Giron, A, Gizachew, Z, Goenz, MA, Gold, AS, Goldberg-Lavid, M, Gole, GA, Gomel, N, Gonzalez, E, Gonzalez Perez, G, Gonzalez-Rodriguez, L, Garcia Pacheco, HN, Graells, J, Green, L, Gregersen, PA, Grigorovski, NDAK, Guedenon, KM, Gunasekera, DS, Gunduz, AK, Gupta, H, Gupta, S, Hadjistilianou, T, Hamel, P, Hamid, SA, Hamzah, N, Hansen, ED, Harbour, JW, Hartnett, ME, Hasanreisoglu, M, Hassan, S, Hederova, S, Hernandez, J, Carcamo Hernandez, LM, Hessissen, L, Hordofa, DF, Huang, LC, Hubbard, GB, Hummlen, M, Husakova, K, Al-Janabi, ANH, Ida, R, Ilic, VR, Jairaj, V, Jeeva, I, Jenkinson, H, Ji, X, Jo, DH, Johnson, KP, Johnson, WJ, Jones, MM, Kabesha, TBA, Kabore, RL, Kaliki, S, Kalinaki, A, Kantar, M, Kao, L-Y, Kardava, T, Kebudi, R, Kepak, T, Keren-Froim, N, Khan, ZJ, Khaqan, HA, Khauv, P, Kheir, WJ, Khetan, V, Khodabande, A, Khotenashvili, Z, Kim, JW, Kim, JH, Kiratli, H, Kivela, TT, Klett, A, Palet, JEKK, Krivaitiene, D, Kruger, M, Kulvichit, K, Kuntorini, MW, Kyara, A, Lachmann, ES, Lam, CPS, Lam, GC, Larson, SA, Latinovic, S, Laurenti, KD, Le, BHA, Lecuona, K, Leverant, AA, Li, C, Limbu, B, Quah, BL, Lopez, JP, Lukamba, RM, Lumbroso, L, Luna-Fineman, S, Lutfi, D, Lysytsia, L, Magrath, GN, Mahajan, A, Majeed, AR, Maka, E, Makan, M, Makimbetov, EK, Manda, C, Martin Begue, N, Mason, L, Mason, JO, Matende, IO, Materin, M, Mattosinho, CCDS, Matua, M, Mayet, I, Mbumba, FB, McKenzie, JD, Medina-Sanson, A, Mehrvar, A, Mengesha, AA, Menon, V, Mercado, GJVD, Mets, MB, Midena, E, Mishra, DKC, Mndeme, FG, Mohamedani, AA, Mohammad, MT, Moll, AC, Montero, MM, Morales, RA, Moreira, C, Mruthyunjaya, P, Msina, MS, Msukwa, G, Mudaliar, SS, Muma, K, Munier, FL, Murgoi, G, Murray, TG, Musa, KO, Mushtaq, A, Mustak, H, Muyen, OM, Naidu, G, Nair, AG, Naumenko, L, Roth, PAN, Nency, YM, Neroev, V, Ngo, H, Nieves, RM, Nikitovic, M, Nkanga, ED, Nkumbe, H, Nuruddin, M, Nyaywa, M, Obono-Obiang, G, Oguego, NC, Olechowski, A, Oliver, SCN, Osei-Bonsu, P, Ossandon, D, Paez-Escamilla, MA, Pagarra, H, Painter, SL, Paintsil, V, Paiva, L, Pal, BP, Palanivelu, MS, Papyan, R, Parrozzani, R, Parulekar, M, Morales, CRP, Paton, KE, Pawinska-Wasikowska, K, Pe'er, J, Pena, A, Peric, S, Pham, CTM, Philbert, R, Plager, DA, Pochop, P, Polania, RA, Polyakov, VG, Pompe, MT, Pons, JJ, Prat, D, Prom, V, Purwanto, I, Qadir, AO, Qayyum, S, Qian, J, Rahman, A, Rahman, S, Rahmat, J, Rajkarnikar, P, Ramanjulu, R, Ramasubramanian, A, Ramirez-Ortiz, MA, Raobela, L, Rashid, R, Reddy, MA, Reich, E, Renner, LA, Reynders, D, Ribadu, D, Riheia, MM, Ritter-Sovinz, P, Rojanaporn, D, Romero, L, Roy, SR, Saab, RH, Saakyan, S, Sabhan, AH, Sagoo, MS, Said, AMA, Saiju, R, Salas, B, San Roman Pacheco, S, Sanchez, GL, Sayalith, P, Scanlan, TA, Schefler, AC, Schoeman, J, Sedaghat, A, Seregard, S, Seth, R, Shah, AS, Shakoor, SA, Sharma, MK, Sherief, ST, Shetye, NG, Shields, CL, Siddiqui, SN, Cheikh, SS, Silva, S, Singh, AD, Singh, N, Singh, U, Singha, P, Sitorus, RS, Skalet, AH, Soebagjo, HD, Sorochynska, T, Ssali, G, Stacey, AW, Staffieri, SE, Stahl, ED, Stathopoulos, C, Kranjc, BS, Stones, DK, Strahlendorf, C, Suarez, MEC, Sultana, S, Sun, X, Sundy, M, Superstein, R, Supriyadi, E, Surukrattanaskul, S, Suzuki, S, Svojgr, K, Sylla, F, Tamamyan, G, Tan, D, Tandili, A, Tarrillo Leiva, FF, Tashvighi, M, Tateshi, B, Tehuteru, ES, Teixeira, LF, Teh, KH, Theophile, T, Toledano, H, Trang, DL, Traore, F, Trichaiyaporn, S, Tuncer, S, Tyau-Tyau, H, Umar, AB, Unal, E, Uner, OE, Urbak, SF, Ushakova, TL, Usmanov, RH, Valeina, S, Wijsard, MVH, Varadisai, A, Vasquez, L, Vaughan, LO, Veleva-Krasteva, NV, Verma, N, Victor, AA, Viksnins, M, Villacos Chafla, EG, Vishnevskia-Dai, V, Vora, T, Wachtel, AE, Wackernagel, W, Waddell, K, Wade, PD, Wali, AH, Wang, Y-Z, Weiss, A, Wilson, MW, Wime, ADC, Wiwatwongwana, A, Wiwatwongwana, D, Dod, CW, Wongwai, P, Xiang, D, Xiao, Y, Yam, JC, Yang, H, Yanga, JM, Yaqub, MA, Yarovaya, VA, Yarovoy, AA, Ye, H, Yousef, YA, Yuliawati, P, Zapata Lopez, AM, Zein, E, Zhang, C, Zhang, Y, Zhao, J, Zheng, X, Zhilyaeva, K, Zia, N, Ziko, OAO, Zondervan, M, Bowman, R, Fabian, ID, Abdallah, E, Abdullahi, SU, Abdulqader, RA, Boubacar, SA, Ademola-Popoola, DS, Adio, A, Afshar, AR, Aggarwal, P, Aghaji, AE, Ahmad, A, Akib, MNR, Al Harby, L, Al Ani, MH, Alakbarova, A, Portabella, SA, Al-Badri, SAF, Alcasabas, APA, Al-Dahmash, SA, Alejos, A, Alemany-Rubio, E, Bio, AIA, Carreras, YA, Al-Haddad, C, Al-Hussaini, HHY, Ali, AM, Alia, DB, Al-Jadiry, MF, Al-Jumaly, U, Alkatan, HM, All-Eriksson, C, Al-Mafrachi, AARM, Almeida, AA, Alsawidi, KM, Al-Shaheen, AASM, Al-Shammary, EH, Amiruddin, PO, Antonino, R, Astbury, NJ, Atalay, HT, Atchaneeyasakul, L-O, Atsiaya, R, Attaseth, T, Aung, TH, Ayala, S, Baizakova, B, Balaguer, J, Balayeva, R, Balwierz, W, Barranco, H, Bascaran, C, Popovic, MB, Benavides, R, Benmiloud, S, Guebessi, NB, Berete, RC, Berry, JL, Bhaduri, A, Bhat, S, Biddulph, SJ, Biewald, EM, Bobrova, N, Boehme, M, Boldt, HC, Bonanomi, MTBC, Bornfeld, N, Bouda, GC, Bouguila, H, Boumedane, A, Brennan, RC, Brichard, BG, Buaboonnam, J, Calderon-Sotelo, P, Calle Jara, DA, Camuglia, JE, Cano, MR, Capra, M, Cassoux, N, Castela, G, Castillo, L, Catala-Mora, J, Chantada, GL, Chaudhry, S, Chaugule, SS, Chauhan, A, Chawla, B, Chernodrinska, VS, Chiwanga, FS, Chuluunbat, T, Cieslik, K, Cockcroft, RL, Comsa, C, Correa, ZM, Correa Llano, MG, Corson, TW, Cowan-Lyn, KE, Csoka, M, Cui, X, Da Gama, I, Dangboon, W, Das, A, Das, S, Davanzo, JM, Davidson, A, De Potter, P, Delgado, KQ, Demirci, H, Desjardins, L, Diaz Coronado, RY, Dimaras, H, Dodgshun, AJ, Donaldson, C, Donato Macedo, CR, Dragomir, MD, Du, Y, Du Bruyn, M, Edison, KS, Sutyawan, IWE, El Kettani, A, Elbahi, AM, Elder, JE, Elgalaly, D, Elhaddad, AM, Elhassan, MMA, Elzembely, MM, Essuman, VA, Evina, TGA, Fadoo, Z, Fandino, AC, Faranoush, M, Fasina, O, Fernandez, DDPG, Fernandez-Teijeiro, A, Foster, A, Frenkel, S, Fu, LD, Fuentes-Alabi, SL, Gallie, BL, Gandiwa, M, Garcia, JL, Garcia Aldana, D, Gassant, PY, Geel, JA, Ghassemi, F, Giron, A, Gizachew, Z, Goenz, MA, Gold, AS, Goldberg-Lavid, M, Gole, GA, Gomel, N, Gonzalez, E, Gonzalez Perez, G, Gonzalez-Rodriguez, L, Garcia Pacheco, HN, Graells, J, Green, L, Gregersen, PA, Grigorovski, NDAK, Guedenon, KM, Gunasekera, DS, Gunduz, AK, Gupta, H, Gupta, S, Hadjistilianou, T, Hamel, P, Hamid, SA, Hamzah, N, Hansen, ED, Harbour, JW, Hartnett, ME, Hasanreisoglu, M, Hassan, S, Hederova, S, Hernandez, J, Carcamo Hernandez, LM, Hessissen, L, Hordofa, DF, Huang, LC, Hubbard, GB, Hummlen, M, Husakova, K, Al-Janabi, ANH, Ida, R, Ilic, VR, Jairaj, V, Jeeva, I, Jenkinson, H, Ji, X, Jo, DH, Johnson, KP, Johnson, WJ, Jones, MM, Kabesha, TBA, Kabore, RL, Kaliki, S, Kalinaki, A, Kantar, M, Kao, L-Y, Kardava, T, Kebudi, R, Kepak, T, Keren-Froim, N, Khan, ZJ, Khaqan, HA, Khauv, P, Kheir, WJ, Khetan, V, Khodabande, A, Khotenashvili, Z, Kim, JW, Kim, JH, Kiratli, H, Kivela, TT, Klett, A, Palet, JEKK, Krivaitiene, D, Kruger, M, Kulvichit, K, Kuntorini, MW, Kyara, A, Lachmann, ES, Lam, CPS, Lam, GC, Larson, SA, Latinovic, S, Laurenti, KD, Le, BHA, Lecuona, K, Leverant, AA, Li, C, Limbu, B, Quah, BL, Lopez, JP, Lukamba, RM, Lumbroso, L, Luna-Fineman, S, Lutfi, D, Lysytsia, L, Magrath, GN, Mahajan, A, Majeed, AR, Maka, E, Makan, M, Makimbetov, EK, Manda, C, Martin Begue, N, Mason, L, Mason, JO, Matende, IO, Materin, M, Mattosinho, CCDS, Matua, M, Mayet, I, Mbumba, FB, McKenzie, JD, Medina-Sanson, A, Mehrvar, A, Mengesha, AA, Menon, V, Mercado, GJVD, Mets, MB, Midena, E, Mishra, DKC, Mndeme, FG, Mohamedani, AA, Mohammad, MT, Moll, AC, Montero, MM, Morales, RA, Moreira, C, Mruthyunjaya, P, Msina, MS, Msukwa, G, Mudaliar, SS, Muma, K, Munier, FL, Murgoi, G, Murray, TG, Musa, KO, Mushtaq, A, Mustak, H, Muyen, OM, Naidu, G, Nair, AG, Naumenko, L, Roth, PAN, Nency, YM, Neroev, V, Ngo, H, Nieves, RM, Nikitovic, M, Nkanga, ED, Nkumbe, H, Nuruddin, M, Nyaywa, M, Obono-Obiang, G, Oguego, NC, Olechowski, A, Oliver, SCN, Osei-Bonsu, P, Ossandon, D, Paez-Escamilla, MA, Pagarra, H, Painter, SL, Paintsil, V, Paiva, L, Pal, BP, Palanivelu, MS, Papyan, R, Parrozzani, R, Parulekar, M, Morales, CRP, Paton, KE, Pawinska-Wasikowska, K, Pe'er, J, Pena, A, Peric, S, Pham, CTM, Philbert, R, Plager, DA, Pochop, P, Polania, RA, Polyakov, VG, Pompe, MT, Pons, JJ, Prat, D, Prom, V, Purwanto, I, Qadir, AO, Qayyum, S, Qian, J, Rahman, A, Rahman, S, Rahmat, J, Rajkarnikar, P, Ramanjulu, R, Ramasubramanian, A, Ramirez-Ortiz, MA, Raobela, L, Rashid, R, Reddy, MA, Reich, E, Renner, LA, Reynders, D, Ribadu, D, Riheia, MM, Ritter-Sovinz, P, Rojanaporn, D, Romero, L, Roy, SR, Saab, RH, Saakyan, S, Sabhan, AH, Sagoo, MS, Said, AMA, Saiju, R, Salas, B, San Roman Pacheco, S, Sanchez, GL, Sayalith, P, Scanlan, TA, Schefler, AC, Schoeman, J, Sedaghat, A, Seregard, S, Seth, R, Shah, AS, Shakoor, SA, Sharma, MK, Sherief, ST, Shetye, NG, Shields, CL, Siddiqui, SN, Cheikh, SS, Silva, S, Singh, AD, Singh, N, Singh, U, Singha, P, Sitorus, RS, Skalet, AH, Soebagjo, HD, Sorochynska, T, Ssali, G, Stacey, AW, Staffieri, SE, Stahl, ED, Stathopoulos, C, Kranjc, BS, Stones, DK, Strahlendorf, C, Suarez, MEC, Sultana, S, Sun, X, Sundy, M, Superstein, R, Supriyadi, E, Surukrattanaskul, S, Suzuki, S, Svojgr, K, Sylla, F, Tamamyan, G, Tan, D, Tandili, A, Tarrillo Leiva, FF, Tashvighi, M, Tateshi, B, Tehuteru, ES, Teixeira, LF, Teh, KH, Theophile, T, Toledano, H, Trang, DL, Traore, F, Trichaiyaporn, S, Tuncer, S, Tyau-Tyau, H, Umar, AB, Unal, E, Uner, OE, Urbak, SF, Ushakova, TL, Usmanov, RH, Valeina, S, Wijsard, MVH, Varadisai, A, Vasquez, L, Vaughan, LO, Veleva-Krasteva, NV, Verma, N, Victor, AA, Viksnins, M, Villacos Chafla, EG, Vishnevskia-Dai, V, Vora, T, Wachtel, AE, Wackernagel, W, Waddell, K, Wade, PD, Wali, AH, Wang, Y-Z, Weiss, A, Wilson, MW, Wime, ADC, Wiwatwongwana, A, Wiwatwongwana, D, Dod, CW, Wongwai, P, Xiang, D, Xiao, Y, Yam, JC, Yang, H, Yanga, JM, Yaqub, MA, Yarovaya, VA, Yarovoy, AA, Ye, H, Yousef, YA, Yuliawati, P, Zapata Lopez, AM, Zein, E, Zhang, C, Zhang, Y, Zhao, J, Zheng, X, Zhilyaeva, K, Zia, N, Ziko, OAO, Zondervan, M, and Bowman, R

Abstract

IMPORTANCE: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. OBJECTIVES: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. DESIGN, SETTING, AND PARTICIPANTS: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. MAIN OUTCOMES AND MEASURES: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. RESULTS: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis

Published
2020

27. Analysis of drug-drug interactions in spontaneous adverse drug reaction reports from EudraVigilance focusing on psychiatric drugs and somatic medication.

Author

Dubrall, D., Weber, R., Boehme, M., Schmid, M., Sachs, B., and Scholl, C.

Subjects
DRUG side effects, PLATELET aggregation inhibitors, PSYCHIATRIC drugs, MOOD stabilizers, SEROTONIN uptake inhibitors
Abstract

Introduction: Patients with severe mental illnesses (SMI) are often exposed to polymedication. Additionally, the risk of somatic diseases is twice as high in patients with SMI as in individuals without a psychiatric disorder. Furthermore, drug–drug interactions (DDI) between psychiatric drugs and somatic medications are a well-known cause of adverse drug reactions (ADR). Objectives: The aim of this study was to analyse whether already known DDI related to psychiatric drugs and somatic medication still occur in everyday clinical practice. Methods: Therefore we identified all spontaneous ADR reports contained in the European ADR database EudraVigilance from Germany received between 01/2017 and 12/2021 reported for patients older than 17 years in which antidepressants, antipsychotics and mood stabilizers were reported as suspected/interacting (n= 9,665). ADR reports referring to intentional overdoses and suicide attempts were excluded (n= 9,276 left). We used the ABDATA drug information system in order to identify all potential DDI (pDDI). The identified reports with pDDI were then assessed individually to determine whether the respective DDI occurred. Results: 1,271 reports with 728 potentially interacting drugs pairs related to psychiatric drugs and somatic medications with 2,655 pDDI were found. Restricted to potentially interacting drug pairs with more than 10 reports, (i) hyponatremias related to antidepressants and diuretics (n= 362, 32.6%), (ii) bleeding events related to selective serotonin reuptake inhibitors (SSRI) and platelet aggregation inhibitors, anticoagulants or non-steroidal antiinflammatory drugs (NSAID) (n= 295, 17.5%), and (iii) increased beta-blocker effects related to SSRIs and beta-blockers (n= 126, 11.3%) were the most frequently identified pDDI. After individual case assessment, in 33.3% (14/42), 23.7% (45/190) and 17.4% (8/46) of the reports bleeding events related to SSRIs and anticoagulants, SSRIs and platelet aggregation inhibitors and SSRIs and NSAIDs were reported. Hyponatremia was reported in 7.6% (22/289) of the reports related to antidepressants and diuretics and increased beta-blocker effects in 6.9% (8/116) of the reports related to SSRIs and beta-blockers. Conclusions: According to our analysis, well-known DDI still occur in the treatment of psychiatric patients with psychiatric drugs and somatic medication. Whenever possible, alternative drug combinations with a lower potential of DDIs may be considered or appropriate monitoring measures should be conducted. Disclosure of Interest: None Declared [ABSTRACT FROM AUTHOR]

Published
2024
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31. Targeting the gut microbiome to reverse microglia activation and stress-induced immune priming in ageing

Author

Boehme, M., primary, Van de Wouw, M., additional, Van Sandhu, K., additional, Lyons, K., additional, Fouhy, F., additional, Olavarria Ramirez, L., additional, Van Leuven, L., additional, Golubeva, A., additional, Scott, K.A., additional, Stanton, C., additional, Dinan, T.G., additional, Schellekens, H., additional, and Cryan, J.F., additional

Published
2018
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36. A precise and accurate 'astronomical' age of the Ries impact crater, Germany: A cautious note on argon dating of impact material

Author

Rocholl, A., Ovtcharova, M., Schaltegger, U., Wijbrans, J., Pohl, J., Harzhauser, M., Prieto, J., Ulbig, A., and Boehme, M.

Abstract

Accurate and precise dating of meteorite impacts is a prime intent of stratigraphic, paleoclimatic, and paleonto-logical research. Robust age estimates, however, exist only for 11 of 174 confirmed impacts structures (ref. 1) including the Ries crater in Germany, one of the best studied impact structures on Earth. This is probably also the impact site with the highest number of radiometric age determinations. So far, published ages are exclusively based on the analysis of impact-generated melts, i.e. suevite glass and/or moldavites (tektites) (e.g., ref. 2-7) and feldspar melt (ref. 8). However, despite more than 60 individual isotopic age data obtained during 50 years of research there is no consensus as to the accurate age of the impact. For example, 40Ar-39Ar ages range from 15.2 Ma (ref. 2) to 14.3 Ma (ref. 6,7), with the young ages representing most of the recent age determinations. Accordingly, the long-standing estimate of about 15 +- 0.2 Ma (ref. 2,3) has been criticized recently as being too old by more than 0.5 Ma (e.g., ref. 1, 5-8) and only confirmed once (ref. 9). Ages < 14.6 Ma, however, pose severe problems on geophysical and biostratigraphic interpretations. Most significantly and central to the discussion, they are in conflict with a reversed magnetic field during impact (ref. 10,11), because a normal magnetic field persisted from about 14.6 to 14.15 Ma (ref. 12), i.e. impact ages < 14.6 Ma are impossible from a paleomagnetic point of view.

Published
2011

37. Prevailing stratospheric easterly wind direction in the Paratethys during the Lower Badenian: Ar-Ar- and Nd-isotopic evidence from rhyolitic ash layers in the Upper Freshwater Molasse, S-Germany

Author

Rocholl, A., Boehme, M., Guenther, D., Höfer, H., and Ulbig, A.

Abstract

Paleo-wind patterns are essential puzzle-stones in reconstructing paleoclimatic condi-tions. It was previously suggested (Ref. 1) that bentonite-related, plinian-type rhyolitic tuffs embedded in ca. 15 Ma old sediments of the Upper Freshwater Molasse (UFM) in southern Germany derived from the Pannonian Basin, E-Hungary. Such a scenario implies westward transport of the volcanic ashes by up to more than 1.000 km and will imply easterly winds at stratospheric highs (∼20 km).

Published
2008

43. Electronic structure of $\mathrm{WSe_2}$ : A combined photoemission and inverse photoemission study

Author

Traving, M., Boehme, M., Kipp, L., Skibowski, M., Starrost, F., Krasovskii, E. E., Perlov, A., and Schattke, W.

Subjects
Condensed Matter::Materials Science, Condensed Matter::Strongly Correlated Electrons, ddc:530
Abstract

Physical review / B 55(16), 10392 - 10399 (1997). doi:10.1103/PhysRevB.55.10392, We report on a complete determination of the experimental and theoretical valence- and conduction-band structure of WSe2. The results of combined angle-resolved photoemission and inverse photoemission are discussed in the context of fully relativistic linear-muffin-tin-orbital and extended linear-augmented-plane-wave calculations. We measure an indirect band gap of 1.2 eV, and find the valence-band maximum to be located at the center of the Brillouin zone and the conduction-band minimum at 0.55ΓK., Published by Inst., Woodbury, NY

Published
1997
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44. Web-accessible proteome databases for microbial research

Author

Pleissner, K.P., Eifert, T., Buettner, S., Schmidt, Frank, Boehme, M., Meyer, T.F., Kaufmann, S.H.E., Jungblut, P.R., Pleissner, K.P., Eifert, T., Buettner, S., Schmidt, Frank, Boehme, M., Meyer, T.F., Kaufmann, S.H.E., and Jungblut, P.R.

Abstract

The analysis of proteomes of biological organisms represents a major challenge of the post-genome era. Classical proteomics combines two-dimensional electrophoresis (2-DE) and mass spectrometry (MS) for the identification of proteins. Novel technologies such as isotope coded affinity tag (ICAT)-liquid chromatography/mass spectrometry (LC/MS) open new insights into protein alterations. The vast amount and diverse types of proteomic data require adequate web-accessible computational and database technologies for storage, integration, dissemination, analysis and visualization. A proteome database system (http://www.mpiib-berlin.mpg.de/2D-PAGE) for microbial research has been constructed which integrates 2-DE/MS, ICAT-LC/MS and functional classification data of proteins with genomic, metabolic and other biological knowledge sources. The two-dimensional polyacrylamide gel electrophoresis database delivers experimental data on microbial proteins including mass spectra for the validation of protein identification. The ICAT-LC/MS database comprises experimental data for protein alterations of mycobacterial strains BCG vs. H37Rv. By formulating complex queries within a functional protein classification database "FUNC_CLASS" for Mycobacterium tuberculosis and Helicobacter pylori the researcher can gather precise information on genes, proteins, protein classes and metabolic pathways. The use of the R language in the database architecture allows high-level data analysis and visualization to be performed "on-the-fly". The database system is centrally administrated, and investigators without specific bioinformatic competence in database construction can submit their data. The database system also serves as a template for a prototype of a European Proteome Database of Pathogenic Bacteria. Currently, the database system includes proteome information for six strains of microorganisms

Published
2004

50. Intraoperative endothelial damage is associated with increased risk of stenoses in infrainguinal vein grafts

Author

Nielsen, T G, Hesse, B, Boehme, M W, Schroeder, T V, Nielsen, T G, Hesse, B, Boehme, M W, and Schroeder, T V

Abstract

Saphenous vein injury is believed to predispose to vein graft stenoses. The aims of this study were to assess the endothelial injury associated with infrainguinal vein bypass surgery by serial measurements of serum thrombomodulin concentration as well as by platelet scintigraphy, and to relate these findings with the postoperative development of stenoses.

Published
2001

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