RESUMO Um dos principais desafios no manejo clínico do câncer de próstata é identificar qual tumor é agressivo e precisa de tratamento invasivo. Assim, ser capaz de prever qual irá progredir para recorrência bioquímica é uma ótima estratégia para estratificar pacientes com câncer de próstata. Pensando nisso, criamos uma fórmula matemática que leva em consideração os dados clínicos e patológicos resultando em uma variável quantitativa, denominada densidade de PSA da lesão, que tem potencial para predizer recidiva bioquímica. Para testar se nossa variável é capaz de predizer recorrência bioquímica, usamos uma coorte de 219 pacientes com câncer de próstata, associando nossas variáveis e parâmetros clássicos como a recorrência bioquímica. PSA total, peso da lesão, volume e densidade de PSA clássico foram associados com recorrência bioquímica (p<0,05). O escore ISUP também foi associado à recorrência bioquímica na biópsia e na amostra cirúrgica (p<0,001). O aumento da densidade do PSA da lesão foi significativamente associado à recidiva bioquímica (p=0,03). As variáveis ??derivadas da fórmula, PSA 15% e PSA 152, também foram associadas positivamente à recorrência bioquímica (p=0,01 e p=0,002 respectivamente). A análise de regressão logística mostra que a densidade do PSA clássico, do PSA da lesão e PSA total, juntos, podem explicar até 13% dos casos de recorrência. A densidade de PSA da lesão por si só poderia explicar até 7% dos casos de recorrência. Em conclusão, esta nova abordagem matemática pode ser uma ferramenta útil para prever a recorrência da doença no câncer de próstata.

ABSTRACT A main challenge in the clinical management of prostate cancer is to identify which tumor is aggressive and needs invasive treatment. Thus, being able to predict which cancer will progress to biochemical recurrence is a great strategy to stratify prostate cancer patients. With that in mind, we created a mathematical formula that takes into account the patients clinical and pathological data resulting in a quantitative variable, called PSA density of the lesion, which has the potential to predict biochemical recurrence. To test if our variable is able to predict biochemical recurrence, we use a cohort of 219 prostate cancer patients, associating our new variable and classic parameters of prostate cancer with biochemical recurrence. Total PSA, lesion weight, volume and classic PSA density were positively associated with biochemical recurrence (p<0.05). ISUP score was also associated with biochemical recurrence in both biopsy and surgical specimen (p<0.001). The increase of PSA density of the lesion was significantly associated with the biochemical recurrence (p=0.03). Variables derived from the formula, PSA 15% and PSA 152, were also positive associated with the biochemical recurrence (p=0.01 and p=0.002 respectively). Logistic regression analysis shows that classic PSA density, PSA density of the lesion and total PSA, together, can explain up to 13% of cases of biochemical recurrence. PSA density of the lesion alone would have the ability to explain up to 7% of cases of biochemical recurrence. In conclusion, this new mathematical approach could be a useful tool to predict disease recurrence in prostate cancer.

Abstract The aim of this study was to identify resistance to bacterial halo blight in Arabica coffee lines carrying Coffea racemosa genes. Eighteen Arabica coffee lines derivative from the genotype C1195-5-6-2, and the cultivars IAPAR 59 and IPR 99 were evaluated for resistance to bacterial halo blight in two trials carried out in field conditions, in Londrina, PR, Brazil. The cultivars Mundo Novo IAC 376-4 and Catuaí Vermelho IAC 81 were included as susceptible controls. Ten lines and the cultivar IAPAR 59 showed resistance to bacterial halo blight. The cultivar IPR 99 presented intermediate reaction, and the controls were very susceptible. This is the first study to show that lines derivative from the genotype C1195-5-6-2, which has C. racemosa genes, could be a source of resistance to bacterial halo blight in coffee breeding programs.

A method for HPLC determination of sulfadimethoxyne in milk is presented. The analyte isolation and concentration were performed by solid-phase extraction through a C-8 cartridge, pre-conditioned with hexane, methanol and water and eluted with MeOH. The recovery determination was done with a spiked solution of 20, 50 or 100 µg L-1. In this concentration range, the recovery was 83.2% with a RDS of 15.4%. For quantification, a Zorbax Eclipse XDB-C8 (4.6 mm x 150 mm, 5 µm), a mobile phase of MeCN: 0.01 mol L-1 KH2PO4 aq. (1:4), and a variable wavelength detector (275 nm) were used.