Development and prognostic relevance of a histologic grading and staging system for alcohol-related liver disease.

BACKGROUND & AIMS: The SALVE Histopathology Group (SHG) developed and validated a grading and staging system for the clinical and full histological spectrum of alcohol-related liver disease (ALD) and evaluated its prognostic utility in a multinational cohort of 445 patients. METHODS: SALVE grade was described by semiquantitative scores for steatosis, activity (hepatocellular injury and lobular neutrophils) and cholestasis. The histological diagnosis of steatohepatitis due to ALD (histological ASH, hASH) was based on the presence of hepatocellular ballooning and lobular neutrophils. Fibrosis staging was adapted from the Clinical Research Network staging system for non-alcoholic fatty liver disease and the Laennec staging system and reflects the pattern and extent of ALD fibrosis. There are 7 SALVE fibrosis stages (SFS) ranging from no fibrosis to severe cirrhosis. RESULTS: Interobserver κ-value for each grading and staging parameter was >0.6. In the whole study cohort, long-term outcome was associated with activity grade and cholestasis, as well as cirrhosis with very broad septa (severe cirrhosis) (p <0.001 for all parameters). In decompensated ALD, adverse short-term outcome was associated with activity grade, hASH and cholestasis (p = 0.038, 0.012 and 0.001, respectively), whereas in compensated ALD, hASH and severe fibrosis/cirrhosis were associated with decompensation-free survival (p = 0.011 and 0.001, respectively). On multivariable analysis, severe cirrhosis emerged as an independent histological predictor of long-term survival in the whole study cohort. Severe cirrhosis and hASH were identified as independent predictors of short-term survival in decompensated ALD, and also as independent predictors of decompensation-free survival in compensated ALD. CONCLUSION: The SALVE grading and staging system is a reproducible and prognostically relevant method for the histological assessment of disease activity and fibrosis in ALD. LAY SUMMARY: Patients with alcohol-related liver disease (ALD) may undergo liver biopsy to assess disease severity. We developed a system to classify ALD under the microscope by grading ALD activity and staging the extent of liver scarring. We validated the prognostic performance of this system in 445 patients from 4 European centers.

Cholangiopathy aggravation is caused by VDR ablation and alleviated by VDR-independent vitamin D signaling in ABCB4 knockout mice.

BACKGROUND & AIMS: Cholangiopathies are chronic liver diseases in which damaged cholangiocytes trigger a proinflammatory and profibrotic reaction. The nuclear vitamin D receptor (VDR) is highly expressed in cholangiocytes and exerts immune-regulatory functions in these cells. In the present study, we examined the protective function of VDR and other vitamin D signaling pathways in chronic cholangiopathy and cholangiocytes. METHODS: Vdr was invalidated in Abcb4 knockout mice, a widely used animal model of chronic cholangiopathy. The impact of vitamin D signaling on cholangiopathy features was examined in vivo and in cholangiocytes (primary and cell lines). RESULTS: Cholangiopathy features (i.e, cholestasis, ductular reaction and fibrosis) were aggravated in Vdr;Abcb4 double knockout mice compared to the Abcb4 simple knockout, and associated with an overexpression of proinflammatory factors. The proinflammatory phenotype of cholangiocytes was also exacerbated following VDR silencing in vitro. The expression of proinflammatory factors and the severity of cholangiopathy were reduced in the double knockout mice treated with the vitamin D analog calcipotriol or with vitamin D. In vitro, the inflammatory response to TNFα was significantly reduced by calcipotriol in biliary cells silenced for VDR, and this effect was abolished by co-silencing the plasma membrane receptor of vitamin D, protein disulfide-isomerase A3 (PDIA3). CONCLUSIONS: Our results demonstrate an anti-inflammatory role of VDR signaling in cholangiocytes and cholangiopathy. They also provide evidence for PDIA3-mediated anti-inflammatory effects of vitamin D and vitamin D analog in these settings.

KLK3 and TMPRSS2 for molecular lymph-node staging in prostate cancer patients undergoing radical prostatectomy.

BACKGROUND: Lymph-node (LN) metastasis in prostate cancer (PC) is a main risk factor for tumor recurrence after radical prostatectomy (RP). Molecular analysis facilitates detection of small-volume LN metastases with higher sensitivity than histopathology. We aimed to prospectively evaluate six candidate gene markers for detection of pelvic LN metastases and to determine their ability to predict biochemical recurrence-free survival (bRFS) in patients treated with RP. METHODS: The expression of kallikrein 2, 3, and 4 (KLK2, KLK3, and KLK4), prostate-specific membrane antigen (PSMA), transmembrane serine protease 2 (TMPRSS2) and transient receptor potential cation channel subfamily M member 8 (TRPM8) was assessed using qPCR. We analyzed LNs from 111 patients (intermediate PC, n = 32 (29%); high-risk PC, n = 79 (71%)) who underwent RP and extended pelvic lymph-node dissection without neoadjuvant treatment. RESULTS: Overall, 2411 LNs were examined by molecular and histopathologic examination. Histopathology detected 69 LN metastases in 28 (25%) patients. KLK2 and KLK3 diagnostically performed best and classified all pN1-patients correctly as molecular node-positive (molN1/pN1). The concordance on LN level was best for KLK3 (96%). KLK2, KLK3, KLK4, PSMA, TMPRSS2, and TRPM8 reclassified 27 (24%), 32 (29%), 29 (26%), 8 (7%), 13 (12%), and 23 (21%) pN0-patients, respectively, as node-positive (pN0/molN1). On multivariable cox regression analysis molecular LN status (molN1 vs. molN0) using KLK3 (HR 4.0, p = 0.04) and TMPRSS2 (HR 5.1, p = 0.02) were independent predictors of bRFS. Median bRFS was shorter in patients with only molecular positive LNs (molN1/pN0) for KLK3 (24 months, p = 0.001) and for TMPRSS2 (12 months, p < 0.001) compared to patients with negative nodes (molN0/pN0) (median bRFS not reached). CONCLUSIONS: For diagnostic purposes, KLK3 showed highest concordance with histopathology for detection of LN metastases in PC patients undergoing RP. For prognostic purposes, KLK3 and TMPRSS2 expression were superior to histopathologic LN status and other transcripts tested for molecular LN status. We suggest a combined KLK3/TMPRSS2 panel as a valuable diagnostic and prognostic tool for molecular LN analysis.

In-vivo multilaboratory investigation of the optical properties of the human head.

The in-vivo optical properties of the human head are investigated in the 600-1100 nm range on different subjects using continuous wave and time domain diffuse optical spectroscopy. The work was performed in collaboration with different research groups and the different techniques were applied to the same subject. Data analysis was carried out using homogeneous and layered models and final results were also confirmed by Monte Carlo simulations. The depth sensitivity of each technique was investigated and related to the probed region of the cerebral tissue. This work, based on different validated instruments, is a contribution to fill the existing gap between the present knowledge and the actual in-vivo values of the head optical properties.

Application of multiple artificial neural networks for the determination of the optical properties of turbid media.

We determined the optical properties of turbid media from simulated spatially resolved reflectance (SRR) curves using an artificial neural network (ANN). In order to improve the performance of our method, multiple ANNs were applied for this problem. First, Monte Carlo (MC) simulations were performed using random optical properties which are relevant for biological tissue. For a better performance of the ANN in respect of SRR measurements, the exact setup geometry was taken into account for the MC simulations. Second, the performed simulations were classified into different categories according to their shape. Third, multiple ANNs which were adjusted to these categories, were used to solve the inverse problem, i.e., the determination of the optical properties from SRR curves. Finally, these ANNs were applied to determine the optical properties of simulated SRR curves out of the range 0.5 mm(-1) < µ(s)(') < 5 mm(-1) and 0.0001 mm(-1) < µ(a)<1 mm(-1). The average relative error was 2.9% and 6.1% for the reduced scattering coefficient µs' and for the absorption coefficient µ(a), respectively.

Computation of the optical properties of turbid media from slope and curvature of spatially resolved reflectance curves.

The optical properties of turbid media were calculated from the curvature at the radial distance ρ(O) and the slope at the radial distance ρ* of simulated spatially resolved reflectance curves (ρ(O) (ρ*) denotes a decrease of the spatially resolved reflectance curve of 0.75 (2.4) orders of magnitude relative to the reflectance value at 1.2 mm). We found correlations between the curvature at ρ(O) and the reduced scattering coefficient as well as the slope at ρ* and the absorption coefficient. For the determination of the optical properties we used these two correlations. The calculation of the reduced scattering coefficient from the curvature at ρ(O) is practically independent from the absorption coefficient. Knowing the reduced scattering coefficient within a certain accuracy allows the determination of the absorption coefficient from the slope at ρ*. Additionally, we investigated the performance of an artificial neural network for the determination of the optical properties using the above explained correlations. This means we used the derivatives as input data. Our artificial neural network was capable to learn the mapping between the optical properties and the derivatives. In effect, the results for the determined optical properties improved in comparison to the above explained method. Finally, the procedure was compared to an artificial neural network that was trained without using the derivatives.

Non-invasive determination of the absorption coefficient of the brain from time-resolved reflectance using a neural network.

We investigated the performance of a neural network for derivation of the absorption coefficient of the brain from simulated non-invasive time-resolved reflectance measurements on the head. A five-layered geometry was considered assuming that the optical properties (except the absorption coefficient of the brain) and the thickness of all layers were known with an uncertainty. A solution of the layered diffusion equation was used to train the neural network. We determined the absorption coefficient of the brain with an RMS error of <6% from reflectance data at a single distance calculated by diffusion theory. By applying the neural network to reflectance curves obtained from Monte Carlo simulations, similar errors were found.