An optimized NARX-based model for predicting thermal dynamics and heatwaves in rivers.

The thermal dynamics within river ecosystems represent critical areas of study due to their profound impact on overall aquatic health. With the rising prevalence of heatwaves in rivers, a consequence of climate change, it is imperative to deepen our understanding through comprehensive research efforts. Despite this urgency, there remains a noticeable dearth in studies aimed at refining modeling techniques to precisely characterize the duration and intensity of these events. In response to this gap, the present study endeavors to augment the NARX-based model (Nonlinear Autoregressive network with Exogenous Inputs) to enhance predictive capabilities regarding thermal dynamics and river heatwaves. The optimized NARX-based model included the Bayesian Optimization (BO) algorithm, which allows fine-tuning the number of NARX hidden nodes and lagged input/target values, and the Bayesian Regularization (BR) backpropagation algorithm to improve the NARX calibration process. A long-term dataset spanning from 1991 to 2021, encompassing 18 rivers across the expansive Vistula River Basin, one of Europe's largest river systems, was employed for this study. The performance of the BO-NARX-BR model was compared with that of the widely utilized air2stream model for modeling river water temperature (RWT). The results unequivocally demonstrated the superior performance of the NARX-based model across the calibration and validation periods, and four heatwave years. In the context of river heatwaves, the study revealed an escalating frequency and intensity within the Vistula River Basin. Furthermore, the NARX-based model exhibited superior proficiency in characterizing river heatwaves compared to the air2stream model. This study, as the inaugural examination of river heatwaves in Poland and one of the few globally, furnishes crucial reference points for subsequent research endeavors on this phenomenon.

A novel optimized model based on NARX networks for predicting thermal anomalies in Polish lakes during heatwaves, with special reference to the 2018 heatwave.

In 2018, Europe experienced one of the most severe heatwaves ever recorded. This extreme event's impact on lake surface water temperature (LSWT) in Polish lakes has largely remained unknown. In this study, the impact of the 2018 European heatwave on LSWT in 24 Polish lakes was investigated based on a long-term observed dataset (1987-2020). To capture the LSWT dynamics during the heatwave period and reproduce lake heatwaves, a novel BO-NARX-BR model was developed and evaluated. This model combines the capabilities of the Nonlinear Autoregressive network with Exogenous Inputs (NARX) neural network, the Bayesian Optimization (BO) algorithm for optimizing the number of NARX hidden nodes and lagged input/target values, and the Bayesian Regularization (BR) backpropagation algorithm for the NARX training. The results showed that from April to October 2018, the mean and maximum LSWTs were 2.35 and 3.38 °C warmer than the base-period average (1987-2010) due to the impact of the extreme heatwave. The NARX-based model outperformed another widely used model called air2water in calibration and validation periods. The results also revealed that the BO-NARX-BR model produced significantly better results in capturing lake heatwaves, with computed duration and intensity of lake heatwaves close to the in-situ data. Additionally, LSWT anomaly significantly impacted the duration and intensity of heatwaves that occurred in lakes. Extreme climatic events are gaining increasing importance for the functioning of various elements of the hydrosphere. Such a situation encourages the search for more accurate methods and tools for their prediction. The model applied in the paper corresponds with these assumptions, and its good performance allows for its adaptation to lakes in other regions.

A stacked machine learning model for multi-step ahead prediction of lake surface water temperature.

Lake surface water temperature is one of the most important physical and ecological indices of lakes, which has frequently been used as the indicator to evaluate the impact of climate change on lakes. Knowing the dynamics of lake surface water temperature is thus of great significance. The past decades have witnessed the development of different modeling tools to forecast lake surface water temperature, yet, simple models with fewer input variables, while maintaining high forecasting accuracy are scarce. Impact of forecast horizons on model performance has seldom been investigated. To fill the gap, in this study, a novel machine learning algorithm by stacking multilayer perceptron and random forest (MLP-RF) was employed to forecast daily lake surface water temperature using daily air temperature as the exogenous input variable, with the Bayesian Optimization procedure applied for tuning the hyperparameters. Prediction models were developed using long-term observed data from eight Polish lakes. The MLP-RF stacked model showed very good forecasting capabilities for all lakes and forecast horizons, far better than shallow multilayer perceptron neural network, a model coupling wavelet transform and multilayer perceptron neural network, non-linear regression and air2water models. A reduction in model performance was observed as the forecast horizon increased. However, the model also performs well with a forecast horizon of several days (e.g., 7 days ahead, testing stage: R2 - [0.932, 0.990], RMSE °C - [0.77, 1.83], MAE °C - [0.55, 1.38]). In addition, the MLP-RF stacked model has proven to be reliable for both intermediate temperatures and minimum and maximum peaks. The model proposed in this study will be useful to the scientific community in predicting lake surface water temperature, thus contributing to studies on such sensitive aquatic ecosystems as lakes.

Short-term forecasts of streamflow in the UK based on a novel hybrid artificial intelligence algorithm.

In recent years, the growing impact of climate change on surface water bodies has made the analysis and forecasting of streamflow rates essential for proper planning and management of water resources. This study proposes a novel ensemble (or hybrid) model, based on the combination of a Deep Learning algorithm, the Nonlinear AutoRegressive network with eXogenous inputs, and two Machine Learning algorithms, Multilayer Perceptron and Random Forest, for the short-term streamflow forecasting, considering precipitation as the only exogenous input and a forecast horizon up to 7 days. A large regional study was performed, considering 18 watercourses throughout the United Kingdom, characterized by different catchment areas and flow regimes. In particular, the predictions obtained with the ensemble Machine Learning-Deep Learning model were compared with the ones achieved with simpler models based on an ensemble of both Machine Learning algorithms and on the only Deep Learning algorithm. The hybrid Machine Learning-Deep Learning model outperformed the simpler models, with values of R2 above 0.9 for several watercourses, with the greatest discrepancies for small basins, where high and non-uniform rainfall throughout the year makes the streamflow rate forecasting a challenging task. Furthermore, the hybrid Machine Learning-Deep Learning model has been shown to be less affected by reductions in performance as the forecasting horizon increases compared to the simpler models, leading to reliable predictions even for 7-day forecasts.

A nonlinear autoregressive exogenous (NARX) model to predict nitrate concentration in rivers.

Forecasting nitrate concentration in rivers is essential for environmental protection and careful treatment of drinking water. This study shows that nonlinear autoregressive with exogenous inputs neural networks can provide accurate models to predict nitrate plus nitrite concentrations in waterways. The Susquehanna River and the Raccoon River, USA, were chosen as case studies. Water discharge, water temperature, dissolved oxygen, and specific conductance were considered exogenous inputs. The forecasting sensitivity to changes in the exogenous input parameters and time series length was also assessed. For Kreutz Creek at Strickler station (Pennsylvania), the prediction accuracy increased with the number of exogenous input variables, with the best performance achieved considering all the variables (R2 = 0.77). The predictions were accurate also for the Raccoon River (Iowa), although only the water discharge was considered exogenous input (South Raccoon River at Redfield-R2 = 0.94). Both short- and long-term predictions were satisfactory.

Previous Surgery and the Temporal Evolution of Functional Outcomes and Complication Rates in Total Ankle Replacement.

Given that most ankle replacements are post-traumatic in origin, it is important to investigate if prior interventions affect a patient's functional outcomes or increase the possibility of complications. Prior ankle surgeries create scar tissue and zones of impaired vascularity which could ultimately interfere with surgical healing. The purpose of this study is to assess the pain and functional temporal outcomes of patients with and without prior surgeries in the ipsilateral ankle. We retrospectively identified a consecutive series of 100 primary total ankle replacements (TARs) who were followed for a minimum of 3 years, with follow-up time points of 0, 6, 12, and 36 months. We documented prior surgical interventions and several patient-reported outcomes. Outcomes were measured using American Orthopaedic Foot and Ankle Society (AOFAS) score, Visual Analogue Scale (VAS), 12-Item Short Form Study (SF-12), and range of motion scores. The 2 groups showed no difference on the temporal evolution of outcomes. An irrigation and debridement of previous open fractures was the only presurgical intervention that showed a statistically significant difference in temporal evolution of functional and pain outcomes between intervention and nonintervention groups. No significant correlations were found between all outcomes and the time between the last intervention and ankle replacement surgery. A preoperative discussion should center on potential complications and predicted functional outcomes. The presence, type, and timing of an intervention prior to an ankle replacement do not strongly affect the temporal outcomes of pain and functional outcome scores. Furthermore, the complication rate is not affected by prior surgeries.

Prediction of spring flows using nonlinear autoregressive exogenous (NARX) neural network models.

In the Mediterranean area, climate changes have led to long and frequent droughts with a drop in groundwater resources. An accurate prediction of the spring discharge is an essential task for the proper management of the groundwater resources and for the sustainable development of large areas of the Mediterranean basin. This study shows an unprecedented application of non-linear AutoRegressive with eXogenous inputs (NARX) neural networks to the prediction of spring flows. In particular, discharge prediction models were developed for 9 monitored springs located in the Umbria region, along the carbonate ridge of the Umbria-Marche Apennines. In the modeling, the precipitation was also considered as an exogenous input parameter. Good performances were achieved for all the springs and for both short-term and long-term predictions, passing from a lag time equal to 1 month (R2 = 0.9012-0.9842, RAE = 0.0933-0.2557) to 12 months (R2 = 0.9005-0.9838, RAE = 0.0963-0.2409). The forecasting sensitivity to changes in the temporal resolution, passing from weekly to monthly, was also assessed. The good results achieved recommend the use of the NARX network for spring discharge prediction in other areas characterized by karst aquifers.

Shortcut nitrification-denitrification and biological phosphorus removal in acetate- and ethanol-fed moving bed biofilm reactors under microaerobic/aerobic conditions.

This study investigated the feasibility of coupling simultaneous partial nitrification and denitrification (SPND) to biological phosphorus removal in continuous-flow intermittently-aerated moving bed biofilm reactors (MBBRs) fed with different carbon sources, i.e. ethanol and acetate. Bacterial cultivation at pH 8.2 (±0.2), 26-28 °C and SRT of 4 day and microaerobic/aerobic MBBR operation allowed to achieve average dissolved organic carbon (DOC), total inorganic nitrogen (TIN) and P-PO43- removal efficiencies (REs) of 100%, 81-88% and 83-86% at HRT of 1 day, dissolved oxygen (DO) range of 0.2-3 mg L-1 and feed C/N and C/P ratios of 3.6 and 11, respectively. Acetate supplementation favored a diversified microbial community, while overgrowth of heterotrophs was observed when increasing feed C/N ratio in ethanol-fed MBBR. Illumina sequencing displayed the presence of putative phosphorus accumulating organisms (PAOs) such as Hydrogenophaga and Pseudomonas in MBBR biofilm and suspended biomass, whereas no typical NOB was identified during the study.

Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network.

In the Mediterranean area, the high water demand frequently leads to an excessive exploitation of the water resource, which involves a qualitative degradation of the freshwaters stored in coastal karst aquifers, as a result of phenomena such as sea saltwater intrusion. In this study, the NARX network was used to predict the daily groundwater level fluctuation for 76 monitored wells located on the Apulian territory. A preliminary analysis on reference wells was performed in order to assess the impact on the groundwater level prediction of two input parameters, rainfall and evapotranspiration, and the sensitivity to changes of training algorithm and input time delay. Based on the findings of the preliminary analysis, a comprehensive regional analysis and extensive sub-regional analyses were performed, proving the reliability of the NARX-BR network for the groundwater level prediction in wells located on different hydrogeological structures. The accurate results obtained for the Apulia region suggest the NARX network application for groundwater level prediction in other areas affected by groundwater resource management issues.

Simultaneous nitrification, denitrification and phosphorus removal in a continuous-flow moving bed biofilm reactor alternating microaerobic and aerobic conditions.

A continuous-flow moving bed biofilm reactor (IAMBBR) alternating microaerobic and aerobic conditions was used to remove carbon, nitrogen and phosphorus through simultaneous nitrification and denitrification coupled to phosphorus removal (SNDPR). The IAMBBR was operated under different dissolved oxygen (DO) ranges (0.2-2, 0.2-3 and 0.2-4 mg L-1) and feed C/N ratios (2.8, 3.6 and 4.2) at HRT of 1 day. At a DO range of 0.2-3 mg L-1 and feed C/N ratio of 3.6, the IAMBBR achieved simultaneous removal of dissolved organic carbon (DOC), total inorganic nitrogen (TIN) and P-PO43- with average efficiencies of 100%, 62% and 75%, respectively. Illumina sequencing revealed the coexistence of nitrifiers and P-accumulating denitrifiers (e.g. Hydrogenophaga) in the IAMBBR biofilm. Batch activity tests showed that phosphorus uptake did not occur under stable anaerobic or anoxic conditions, nor under aerobic conditions in absence of nitrate.

Artificial intelligence based approaches to evaluate actual evapotranspiration in wetlands.

Wetlands are extraordinary ecosystems and important climate regulators that also contribute to reduce natural disaster risk. Unfortunately, wetlands are declining much faster than forests. The safeguarding of the wetlands also needs knowledge of the dynamics that control the water balance of these environments. Therefore, an accurate estimation of evapotranspiration in wetlands is an essential task. When adequate experimental data are available, some algorithms deriving from Artificial Intelligence research represent a promising alternative to the most common estimation techniques. In this study, starting from daily measurements of climatic variables such as net solar radiation, depth to water, wind speed, mean relative humidity, maximum temperature, minimum temperature, and mean temperature, using the Random Forest, Additive Regression of Decision Stump, Multilayer Perceptron and k-Nearest Neighbors algorithms, 24 estimation models, different in input variables, have been developed and compared. The data have been provided by USGS. They have been obtained from a measuring site in wetlands of Indian River County, Florida using the eddy-covariance technique. The accuracy of these models based on AI algorithms remains good even if the number of input variables is reduced from 7 to 3. Net solar radiation, mean temperature and mean relative humidity or wind speed measurements allow obtaining a sufficiently accurate estimation model. Random Forest and k-Nearest Neighbors provide slightly better performance than Additive Regression of Decision Stump and Multilayer Perceptron. The analyzed models show in most cases the lowest accuracy in the range 2-4 mm/day, while the highest accuracy is obtained in the ranges 0-2 mm/day and 6-8 mm/day, with the exception of the models based on the Additive Regression, which show similar levels of accuracy in the different considered sub-intervals.

Effect of carbon-to-nitrogen ratio on simultaneous nitrification denitrification and phosphorus removal in a microaerobic moving bed biofilm reactor.

In this study, long-term simultaneous nitrification denitrification (SND) and phosphorous removal were investigated in a continuous-flow microaerobic MBBR (mMBBR) operated at a dissolved oxygen (DO) concentration of 1.0 (±0.2) mg L-1. The mMBBR performance was evaluated at different feed carbon-to-nitrogen (C/N) ratios (2.7, 4.2 and 5.6) and HRTs (2 days and 1 day). Stable long-term mMBBR operation and chemical oxygen demand (COD), total inorganic nitrogen (TIN) and phosphorous (P-PO43-) removal efficiencies up to 100%, 68% and 72%, respectively, were observed at a feed C/N ratio of 4.2. Lower TIN removal efficiency and unstable performance were observed at feed C/N ratios of 2.7 and 5.6, respectively. HRT decrease from 2 days to 1 day resulted in transient NH4+ accumulation and higher NO2-/NO3- ratio in the effluent. Batch activity tests showed that biofilm cultivation at a feed C/N ratio of 4.2 resulted in the highest denitrifying activity (189 mg N gVSS-1 d-1), whereas the highest nitrifying activity (316 mg N gVSS-1 d-1) was observed after cultivation at a feed C/N ratio of 2.7. Thermodynamic modeling with Visual MINTEQ and stoichiometric evaluations revealed that P removal was mainly biological and can be attributed to the P-accumulating capacity of denitrifying bacteria.

Sirolimus-Eluting Magnesium Resorbable Scaffold Implantation in Patients with Acute Myocardial Infarction.

To date, very little is known about the performance of a sirolimus-eluting bioresorbable magnesium scaffold (Mg-BRS) in patients with acute myocardial infarction (AMI). A multicenter cohort analysis was performed on 69 consecutive AMI subjects treated with Mg-BRS. Procedural success was obtained in all cases, and no in-hospital events were reported. At 9-month follow-up, no cardiac death, target-vessel myocardial infarction, ischemia-driven target lesion revascularization, or Mg-BRS thrombosis was reported. Although our analysis showed encouraging results, larger studies and longer-term follow-up are needed to better understand the potential benefits associated with the use of a Mg-BRS in AMI patients.

Clinical and Radiological Outcomes of Transfibular Total Ankle Arthroplasty.

BACKGROUND:: Ankle arthritis is a highly limiting pathology that causes pain and functional limitation with subsequent deterioration of quality of life. With recent advances in surgical instrumentation and techniques, prosthetic replacement of the ankle has proven to be a valid alternative to arthrodesis with comparable outcomes. The purpose of this study was to evaluate clinical and radiological findings in a transfibular total ankle replacement with follow-up of at least 2 years. METHODS:: This prospective study included 89 patients who underwent transfibular total ankle arthroplasty from May 2013 to February 2016. The mean age was 53.2 ± 13.5 years. All patients were followed for at least 24 months postoperatively with an average follow-up of 42.0 ± 23.5 months. Patients were assessed clinically and radiographically preoperatively and at 6, 12, and 24 months postoperatively. RESULTS:: At 24 months postoperatively, patients demonstrated statistically significant improvement in the American Orthopaedic Foot & Ankle Society (AOFAS) Ankle Hindfoot Score from 33.8 ± 14.3 to 88.5 ± 6.6 ( P < .001), visual analog scale (VAS) from 80.5 ± 17.0 to 14.1 ± 9.2 ( P < .001), and Short Form-12 Physical and Mental Composite Scores from 29.9 ± 6.7 and 43.3 ± 8.6 to 47.0 ± 7.6 and 53.3 ± 8.1, respectively ( P < .001). In addition, ankle dorsiflexion and plantarflexion improved from 6.2 ± 5.5 and 9.6 ± 5.8 degrees to 24.2 ± 7.9 and 18.1 ± 7.6 degrees, respectively ( P < .001). Radiographically, patients demonstrated maintained neutral alignment of the ankle at 24 months. No patient demonstrated any radiographic evidence of tibial or talar lucency at final follow-up. Seven patients underwent reoperation for removal of symptomatic hardware (6 fibular plates, 1 syndesmotic screw); 2 patients experienced delayed wound healing. The first one was treated with plate removal and flap coverage. The second one required fibular plate removal. One patient developed a postoperative prosthetic infection requiring operative debridement, removal of implants, and placement of an antibiotic spacer. CONCLUSION:: This study found that transfibular total ankle replacement was a safe and effective option for patients with ankle arthritis resulting in improvements in patient-reported outcomes, range of motion, and radiographic parameters. However, further studies are required to determine the mid- and long-term performance of these implants. LEVEL OF EVIDENCE:: Level IV, case series.

One-Year Results Following a Pre-Specified ABSORB Implantation Strategy in ST-Elevation Myocardial Infarction (BVS STEMI STRATEGY-IT Study).

BACKGROUND: data from clinical experiences with Absorb bioresorbable scaffold (BRS) in STEMI raised concerns among clinicians about the device safety because a noteworthy scaffold thrombosis (ScT) rate was reported at early and long-term follow-up. Nevertheless, pre-specified technical suggestions of how to perform an optimal BRS procedure in STEMI were lacking. In this study we sought to assess the 1-year results following a pre-specified BRS implantation strategy in ST-elevation myocardial infarction (STEMI) patients undergoing primary PCI (pPCI). METHODS: This is a prospective, multicenter study on 505 STEMI patients undergoing pPCI with Absorb following a dedicated implantation protocol. The primary end-point (a device oriented composite end-point (DOCE) of cardiac death, target-vessel myocardial infarction (TV-MI) and ischemia-driven target lesion revascularization (ID-TLR) within 30 days) was already reported. We here present DOCE, its singular components and ScT rates (secondary end-points) at 1-year. RESULTS: According to the study protocol direct Absorb implantation was feasible in 47 (9.3%) patients while post-dilatation was performed in 468 (92.7%) cases. The hierarchical DOCE rate at 1-year was 1.2% (0.4% cardiac death, 0.4% TV-MI and 0.8% ID-TLR) versus 0.6% at 30-day. Two episodes (0.4%) of ScT (one probable subacute and one late definite) were reported. At 1-year, 99.2% patients were on dual antiplatelet therapy (95% with ticagrelor or prasugrel). CONCLUSIONS: A pre-specified Absorb implantation strategy in STEMI patients was associated with persistent low DOCE and ScT rates at 1-year. Longer term follow-up is needed to assess the role of this strategy on preventing very-late events (NCT02601781).

Early radiological assessment of locally advanced pancreatic cancer treated with electrochemotherapy.

AIM: To report early imaging assessment of ablated area post electrochemotherapy (ECT) in patients with locally advanced pancreatic cancer (LAPC). METHODS: ECT was performed in 19 LAPC patients enrolled in an approved ongoing clinical phase I/II study. Before and after ECT, 18 patients underwent computed tomography (CT) scan, 11 patients underwent morphological and functional magnetic resonance (MR) scan (dynamic contrast enhanced-MRI) calculating wash-in slope (WIS) and wash-out slope (WOS); diffusion weighted imaging calculating pseudo-diffusivity (Dp), perfusion fraction (fp) and tissue diffusivity (Dt); 10 patients underwent positron emission tomography (PET). Response evaluation criteria in solid tumour (RECIST) on MR and CT were used to assess tumour therapy response. Choi on CT, PET response criteria in solid tumors (PERCIST) on PET and functional parameters on MR were used to evaluate treatment response. RESULTS: For each patient no significant reduction was measurable by CT and MR using RECIST. According Choi criteria a partial response was obtained in 18/18 (100.0%) patients. According PERCIST criteria 6/10 (60.0%) patients showed a partial response, 3/10 (30.0%) stable disease and 1/10 (10.0%) progression disease. Moreover, using functional MR parameters, a significant reduction of viable tumour after ECT can be observed. According ΔWIS and ΔWOS 9/11 (81.8%) patients exhibited a partial response and 2/11 (18.2%) stable disease; 8/11 (72.7%) patients were considered in partial response by ΔDp evaluation and 3/11 (27.3%) in stable disease; according ΔDt 7/11 (63.6%) patients showed a partial response, 1/11 (9.1%) showed progression of disease and 3/11 (27.3%) were stable. Perfusion fraction fp showed a significant reduction after ECT only in four patients. No significant difference was observed after ECT in signal intensity of T1-weighted images and T2-weighted images, and in equilibrium-phase of contrast study, according to χ2 test was observed. A good correlation was reported between ΔHounsfield unit and Δmaximum standardized uptake value and between Δfp and ΔWOS, with a significant statistically difference (P < 0.05) using Spearman correlation coefficient. CONCLUSION: Perfusion and diffusion MR derived parameters, Choi, PERCIST criteria are more performant than morphological MR and CT criteria to assess ECT treatment response.

Diagnostic accuracy of magnetic resonance, computed tomography and contrast enhanced ultrasound in radiological multimodality assessment of peribiliary liver metastases.

PURPOSE: We compared diagnostic performance of Magnetic Resonance (MR), Computed Tomography (CT) and Ultrasound (US) with (CEUS) and without contrast medium to identify peribiliary metastasis. METHODS: We identified 35 subjects with histological proven peribiliary metastases who underwent CEUS, CT and MR study. Four radiologists evaluated the presence of peribiliary lesions, using a 4-point confidence scale. Echogenicity, density and T1-Weigthed (T1-W), T2-W and Diffusion Weighted Imaging (DWI) signal intensity as well as the enhancement pattern during contrast studies on CEUS, CT and MR so as hepatobiliary-phase on MRI was assessed. RESULTS: All lesions were detected by MR. CT detected 8 lesions, while US/CEUS detected one lesion. According to the site of the lesion, respect to the bile duct and hepatic parenchyma: 19 (54.3%) were periductal, 15 (42.8%) were intra-periductal and 1 (2.8%) was periductal-intrahepatic. According to the confidence scale MRI had the best diagnostic performance to assess the lesion. CT obtained lower diagnostic performance. There was no significant difference in MR signal intensity and contrast enhancement among all metastases (p>0.05). There was no significant difference in CT density and contrast enhancement among all metastases (p>0.05). CONCLUSIONS: MRI is the method of choice for biliary tract tumors but it does not allow a correct differential diagnosis among different histological types of metastasis. The presence of biliary tree dilatation without hepatic lesions on CT and US/CEUS study may be an indirect sign of peribiliary metastases and for this reason the patient should be evaluated by MRI.

Clinical and microbiological outcomes in haematogenous spondylodiscitis treated conservatively.

INTRODUCTION: Spondylodiscitis refers to infections of the intervertebral disc and the adjacent vertebral body. Although it is still considered a rare condition, its rate is projected to increase. Mortality rate is considered to be low, but an estimated one third of the survivors experience residual disabilities. Literature shows that uncomplicated spondylodiscitis can be adequately treated by early antibiotic therapy and immobilization. The aim of the study is to evaluate the outcome of conservative treatment in patients with haematogenous spondylodiscitis. MATERIALS AND METHODS: All patients with haematogenous spondylodiscitis observed in two orthopaedic centres were retrospectively considered. The medical records, radiologic imaging, bacteriology results, treatment, and complications of all patients were reviewed. RESULTS: Thirty patients (median age 64 years, range 15-77, females 56.7%) were considered in the study, eight (26.7%) showed residual back pain at median follow-up of 117 weeks (range 104-189). A significant difference in SF-36 physical (P < 0.001), SF-36 mental function (P < 0.002), and Oswestry Disability Index (ODI) (P < 0.001) scores was observed among patients with residual local pain compared to the ones who had not. Methicillin-resistant Staphylococcus aureus (MRSA) infection and symptoms duration before the diagnosis were associated with an increased risk of persistent back pain and permanent disability. The most important negative determinants of SF-36 mental function were the age of patients (ρ = 0.36, P < 0.05), the duration of symptoms before the diagnosis (ρ = 0.44, P < 0.05) and MRSA infection (P = 0.006). Spondylodiscitis sustained by MRSA and the duration of symptoms before the diagnosis influenced negatively the physical status (P = 0.002) and ODI (ρ = 0.36, P < 0.05), respectively. CONCLUSIONS: Conservative approaches are safe and effective for patients without complications. A delayed diagnosis and MRSA infections are related to poor clinical outcome among patients treated by conservative treatment; this must be carried out scrupulously with close patient monitoring.

Diagnostic performance of magnetic resonance imaging and 3D endoanal ultrasound in detection, staging and assessment post treatment, in anal cancer.

We compared Magnetic Resonance Imaging (MRI) and 3D Endoanal Ultrasound (EAUS) imaging performance to confirm anal carcinoma and to monitor treatment response.58 patients with anal cancer were retrospectively enrolled. All patients underwent clinical examination, anoscopic examination; EAUS and contrast-enhanced MRI study before and after treatment. Four radiologists evaluated the presence of lesions, using a 4-point confidence scale, features of the lesion and nodes on EAUS images, T1-weighted (T1-W), T2-weighted (T2-W) and diffusion-weighted images (DWI) signal intensity (SI), the apparent diffusion coefficient (ADC) map for nodes and lesion, as well as enhancement pattern during dynamic MRI were assessed.All lesions were detected by EAUS while MRI detected 93.1% of anal cancer. MRI showed a good correlation with EAUS, anoscopy and clinical examination. The residual tissue not showed significant difference in EAUS assessment and T2-W SI in pre and post treatment. We found significant difference in dynamic study, in SI of DWI, in ADC map and values among responder's patients in pre and post treatment. The neoplastic nodes were hypoecoic on EAUS, with hyperintense signal on T2-W sequences and hypointense signal on T1-W. The neoplastic nodes showed SI on DWI sequences and ADC value similar to anal cancer. We found significant difference in nodes status in pre and post therapy on DWI data.3D EAUS and MRI are accurate techniques in anal cancer staging, although EAUS is more accurate than MRI for T1 stage. MRI allows correct detection of neoplastic nodes and can properly stratify patients into responders or non responders.

Multidetector computer tomography in the pancreatic adenocarcinoma assessment: an update.

Ductal adenocarcinoma of the pancreas is one of the most aggressive forms of cancer, with only a minority of cases being resectable at the moment of their diagnosis. The accurate detection and characterization of pancreatic carcinoma is very important for patient management. Multidetector-row computed tomography (MDCT) has become the cross-sectional modality of choice in the diagnosis, staging, treatment planning, and follow-up of patients with pancreatic tumors. However, approximately 11% of ductal adenocarcinomas still remain undetected at MDCT because of the lack of attenuation gradient between the lesion and the adjacent pancreatic parenchyma. In this systematic literature review we investigate the current evolution of the CT technique, limitations, and perspectives in the evaluation of pancreatic carcinoma.