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Raoultella planticola has been considered a relatively harmless Gram-negative bacteria, rarely associated with clinical infection. However, in recent years, the frequency at which severe infection by R. planticola and drug-resistant strains are reported in literature has increased. Here, we present one case of acute cholecystitis caused by R. planticola, and review all previously reported cases of the infection in an attempt to identify new trends in biological and clinical features of R. planticola infections.

IGF-1 and IGFBP-3 may influence risk of prostate cancer through their roles in cellular growth, metabolism and apoptosis, however, epidemiologic results have been inconsistent. The role of obesity in prostate cancer risk is not clearly understood, but hyperinsulinemia-related increases in bioactive IGF-1 levels, associated with obesity, could be a component of the relationship between the IGF-axis and prostate cancer. We conducted a nested case-control study in the prospective Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial to examine associations between IGF-1 and IGFBP-3 and risk of prostate cancer. A total of 727 incident prostate cancer cases and 887 matched controls were selected for this analysis. There was no clear overall association between IGF-1, IGFBP-3 and IGF-1:IGFBP-3 molar ratio (IGFmr) and prostate cancer risk, however, IGFmr was associated with risk in obese men (BMI > 30, p-trend 5 0.04), with a greater than 2-fold increased risk in the highest IGFmr quartile (OR 2.34, 95% CI 1.10-5.01). Risk was specifically increased for aggressive disease in obese men (OR 2.80, 95% CI 1.11-7.08). In summary, our large prospective study showed no overall association between the insulin-like growth factor axis and prostate cancer risk, however, IGFmr was related to risk for aggressive prostate cancer in obese men. ' 2007 Wiley-Liss, Inc.Key words: prostate; cancer; insulin-like growth factors Prostate cancer is the most commonly diagnosed non-skin cancer and the second leading cause of cancer mortality among men in the United States.1 Age, race and family history of prostate cancer are well established risk factors for this disease (reviewed in Ref.2), while genetic factors, steroid hormones, growth factors, obesity and dietary factors are also potentially important components in the etiology of this disease (reviewed in Refs. 2-7).The role of the IGF axis in prostate cancer is uncertain, [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] including its relative importance in prostate cancer initiation and progression towards aggressive disease. 27 Insulin-like growth factors (IGF) are involved in cellular metabolism, differentiation, proliferation, transformation and apoptosis, during both normal and malignant growth of the prostate. 28 Insulin-like growth factor-1 (IGF-1) has mitogenic and anti-apoptotic effects, increases transcription of IGF-1, and has been related to prostate cancer progression. 29,30 The majority of circulating IGF-1 is bound to one of the 6 insulin-like growth factor binding proteins (IGFBPs), which regulate the availability and ligand function of IGF-1 for its receptor, IGF-1R.31,32 IGFBP-3, the most abundant of these proteins, 33 has independent anti-mitogenic and pro-apoptotic activity in prostate cells. 34,35 There are also questions about the role of obesity in prostate carcinogenesis, with several large prospective cohort studies suggesting that obesity is associated particularly with aggressive prostate cancer and prostate cancer mortality. [...

Advances in our understanding of the role of magnetic resonance imaging (MRI) for the detection of prostate cancer have enabled its integration into clinical routines in the past two decades. The Prostate Imaging Reporting and Data System (PI-RADS) is an established imaging-based scoring system that scores the probability of clinically significant prostate cancer on MRI to guide management. Image fusion technology allows one to combine the superior soft tissue contrast resolution of MRI, with real-time anatomical depiction using ultrasound or computed tomography. This allows the accurate mapping of prostate cancer for targeted biopsy and treatment. Machine learning provides vast opportunities for automated organ and lesion depiction that could increase the reproducibility of PI-RADS categorisation, and improve co-registration across imaging modalities to enhance diagnostic and treatment methods that can then be individualised based on clinical risk of malignancy. In this article, we provide a comprehensive and contemporary review of advancements, and share insights into new opportunities in this field.

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A targeted intervention can significantly improve early discharges and should be replicable at other academic medical centers. Reinforcement is needed for these gains to be sustainable, however.

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