Strong doping reduction on wafer-scale CVD graphene devices via Al2O3ALD encapsulation.

We present the electrical characterization of wafer-scale graphene devices fabricated with an industrially-relevant, contact-first integration scheme combined with Al2O3encapsulation via atomic layer deposition. All the devices show a statistically significant reduction in the Dirac point position,Vcnp, from around +47 V to between -5 and 5 V (on 285 nm SiO2), while maintaining the mobility values. The data and methods presented are relevant for further integration of graphene devices, specifically sensors, at the back-end-of-line of a standard CMOS flow.

Gate Controlled Photocurrent Generation Mechanisms in High-Gain In2Se3 Phototransistors.

Photocurrent in photodetectors incorporating van der Waals materials is typically produced by a combination of photocurrent generation mechanisms that occur simultaneously during operation. Because of this, response times in these devices often yield to slower, high gain processes, which cannot be turned off. Here we report on photodetectors incorporating the layered material In2Se3, which allow complete modulation of a high gain, photogating mechanism in the ON state in favor of fast photoconduction in the OFF state. While photoconduction is largely gate independent, photocurrent from the photogating effect is strongly modulated through application of a back gate voltage. By varying the back gate, we demonstrate control over the dominant mechanism responsible for photocurrent generation. Furthermore, because of the strong photogating effect, these direct-band gap, multilayer phototransistors produce ultrahigh gains of (9.8 ± 2.5) × 10(4) A/W and inferred detectivities of (3.3 ± 0.8) × 10(13) Jones, putting In2Se3 among the most sensitive 2D materials for photodetection studied to date.

Type 2 diabetic patients with Graves' disease have more frequent and severe Graves' orbitopathy.

BACKGROUND AND AIMS: Due to the worldwide increasing prevalence of diabetes (DM), patients with both diabetes and Graves' disease (GD) have become more frequent. Sporadic reports indicate that Graves' orbitopathy (GO), a GD complication that affects orbital soft tissues, can be severe in DM patients. The relationship between these diseases is not well understood. This study aims at evaluating the association of GD and GO with autoimmune and non-autoimmune diabetes (DM) and to assess diabetic features that influence GD and GO prevalence and severity. METHODS AND RESULTS: This retrospective study evaluated GD, GO and DM association in 1211 consecutive GD patients (447 with GO and 77 with DM). A case-control study was carried out to evaluate DM relationship with GO severity by comparing at 1:2 ratio GO patients with or without DM. A strong association was found between GD and T1DM (p = 0.01) but not T2DM. Instead, the presence of GO was strongly associated with T2DM (p = 0.01). Moreover, GO was more frequently severe in GD patients with T2DM (11/30 or 36.6%) than in those without T2DM (1/60 or 1.7%, p = 0.05). T2DM was the strongest risk factor for severe GO (OR = 34.1 vs. 4.4 p < 0.049 in cigarette smokers). DM duration, obesity and vascular complications, but not metabolic control were significant determinants of GO severity. CONCLUSIONS: GD is associated with T1DM but not with T2DM, probably because of the common autoimmune background. GO, in contrast, is more frequent and severe in T2DM, significantly associated with obesity, diabetes duration and diabetic vasculopathy but not metabolic control.

Auto-Contractive Maps: an artificial adaptive system for data mining. An application to Alzheimer disease.

This article presents a new paradigm of Artificial Neural Networks (ANNs): the Auto-Contractive Maps (Auto-CM). The Auto-CM differ from the traditional ANNs under many viewpoints: the Auto-CM start their learning task without a random initialization of their weights, they meet their convergence criterion when all their output nodes become null, their weights matrix develops a data driven warping of the original Euclidean space, they show suitable topological properties, etc. Further two new algorithms, theoretically linked to Auto-CM are presented: the first one is useful to evaluate the complexity and the topological information of any kind of connected graph: the H Function is the index to measure the global hubness of the graph generated by the Auto-CM weights matrix. The second one is named Maximally Regular Graph (MRG) and it is an development of the traditionally Minimum Spanning Tree (MST). Finally, Auto-CM and MRG, with the support of the H Function, are applied to a real complex dataset about Alzheimer disease: this data come from the very known Nuns Study, where variables measuring the abilities of normal and Alzheimer subject during their lifespan and variables measuring the number of the plaques and of the tangles in their brain after their death. The example of the Alzheimer data base is extremely useful to figure out how this new approach can help to re design bottom-up the overall structure of factors related to a complex disease like this.

International experience on the use of artificial neural networks in gastroenterology.

In this paper, we reconsider the scientific background for the use of artificial intelligence tools in medicine. A review of some recent significant papers shows that artificial neural networks, the more advanced and effective artificial intelligence technique, can improve the classification accuracy and survival prediction of a number of gastrointestinal diseases. We discuss the 'added value' the use of artificial neural networks-based tools can bring in the field of gastroenterology, both at research and clinical application level, when compared with traditional statistical or clinical-pathological methods.

Prediction of high on-treatment platelet reactivity in clopidogrel-treated patients with acute coronary syndromes.

BACKGROUND: About 40% of clopidogrel-treated patients display high platelet reactivity (HPR). Alternative treatments of HPR patients, identified by platelet function tests, failed to improve their clinical outcomes in large randomized clinical trials. A more appealing alternative would be to identify HPR patients a priori, based on the presence/absence of demographic, clinical and genetic factors that affect PR. Due to the complexity and multiplicity of these factors, traditional statistical methods (TSMs) fail to identify a priori HPR patients accurately. The objective was to test whether Artificial Neural Networks (ANNs) or other Machine Learning Systems (MLSs), which use algorithms to extract model-like 'structure' information from a given set of data, accurately predict platelet reactivity (PR) in clopidogrel-treated patients. METHODS: A complete set of fifty-nine demographic, clinical, genetic data was available of 603 patients with acute coronary syndromes enrolled in the prospective GEPRESS study, which showed that HPR after 1month of clopidogrel treatment independently predicted adverse cardiovascular events in patients with Syntax Score >14. Data were analysed by MLSs and TSMs. ANNs identified more variables associated PR at 1month, compared to TSMs. RESULTS: ANNs overall accuracy in predicting PR, although superior to other MLSs was 63% (95% CI 59-66). PR phenotype changed in both directions in 35% of patients across the 3 time points tested (before PCI, at hospital discharge and at 1month). CONCLUSIONS: Despite their ability to analyse very complex non-linear phenomena, ANNs or MLS were unable to predict PR accurately, likely because PR is a highly unstable phenotype.

Chronic exposure to high glucose and impairment of K(+)-channel function in perifused rat pancreatic islets.

Prolonged exposure to high glucose levels impairs the ability of pancreatic islets to secrete insulin as a response to that stimulus. Because glucose, like other insulin secretagogues, elicits insulin secretion by inhibiting the ATP-sensitive K+ channels, in this study, we investigated the effect of prolonged (24-h) exposure of rat pancreatic islets to high (16.7 mM) glucose concentration on 86Rb efflux (used as a tracer for K+). The data obtained indicate that islets exposed to high glucose concentration have impaired function of the glucose-sensitive K+ channel, this phenomenon is temporarily related to a defective response of glucose-induced insulin release, and these alterations are reversible.

Glucose modulates glucose transporter affinity, glucokinase activity, and secretory response in rat pancreatic beta-cells.

Pancreatic islets were cultured for 24 h in medium containing either low (1.4), normal (5.5), or high (16.7 mM) glucose, and then insulin secretion was measured at the end of 1 h incubation at 37 degrees C. Insulin release in the absence of glucose was 64 +/- 20, 152 +/- 11, and 284 +/- 30 pg.islet-1.h-1 (mean +/- SE, n = 6, G1.4 and G16.7 vs. G.5.5, P < 0.05) and the response to 22 mM glucose stimulation was 640 +/- 136, 2460 +/- 276, and 1890 +/- 172 pg.islet-1.h-1, respectively (n = 6, G1.4 vs. G5.5, P < 0.01, G16.7 vs. G5.5, P = 0.065). The 50% maximal response of insulin secretion (increment over baseline) was reached at an average glucose concentration of 9.9 +/- 0.7 mM in islets preexposed to G5.5, and at glucose 13.3 +/- 0.9 and 4.8 +/- 0.4 mM (P < 0.05 in respect to G5.5) in islets preexposed to G1.4 and G16.7, respectively. To investigate the molecular mechanism responsible for this altered glucose sensitivity, we measured, in parallel experiments, the kinetic characteristics of glucose transport, glucokinase, and glucose utilization. Glucose transport was measured by evaluating 3-O-methylglucose uptake. The apparent Km of the low-affinity transporter (GLUT2) was 16.6 +/- 2.4 mM in isolated pancreatic cells cultured at 5.5 mM glucose.(ABSTRACT TRUNCATED AT 250 WORDS)

An optimized experimental protocol based on neuro-evolutionary algorithms application to the classification of dyspeptic patients and to the prediction of the effectiveness of their treatment.

OBJECTIVE: This paper aims to present a specific optimized experimental protocol (EP) for classification and/or prediction problems. The neuro-evolutionary algorithms on which it is based and its application with two selected real cases are described in detail. The first application addresses the problem of classifying the functional (FD) or organic (OD) forms of dyspepsia; the second relates to the problem of predicting the 6-month follow-up outcome of dyspeptic patients treated by helicobacter pylori (HP) eradication therapy. METHODS AND MATERIAL: The database built by the multicentre observational study, performed in Italy by the NUD-look Study Group, provided the material studied: a collection of data from 861 patients with previously uninvestigated dyspepsia, being referred for upper gastrointestinal endoscopy to 42 Italian Endoscopic Services. The proposed EP makes use of techniques based on advanced neuro-evolutionary systems (NESs) and is structured in phases and steps. The use of specific input selection (IS) and training and testing (T and T) techniques together with genetic doping (GenD) algorithm is described in detail, as well as the steps taken in the two benchmark and optimization protocol phases. RESULTS: In terms of accuracy results, a value of 79.64% was achieved during optimization, with mean benchmark values of 64.90% for the linear discriminant analysis (LDA) and 68.15% for the multi layer perceptron (MLP), for the classification task. A value of 88.61% was achieved during optimization for the prediction task, with mean benchmark values of 49.32% for the LDA and 70.05% for the MLP. CONCLUSIONS: The proposed EP has led to the construction of inductors that are viable and usable on medical data which is representative but highly not linear. In particular, for the classification problem, these new inductors may be effectively used on the basal examination data to support doctors in deciding whether to avoid endoscopic examinations; whereas, in the prediction problem, they may support doctors' decisions about the advisability of eradication therapy. In both cases the variables selected indicate the possibility of reducing the data collection effort and also of providing information that can be used for general investigations on symptom relevance.

Comparison of combined therapies in treatment of secondary failure to glyburide.

OBJECTIVE: To compare the effectiveness of alternative combined treatments in patients with non-insulin-dependent diabetes mellitus (NIDDM) with secondary failure to sulfonylureas. RESEARCH DESIGN AND METHODS: A crossover study was carried out by randomly assigning 16 NIDDM patients to a combined treatment with the addition of either a single low-dose bedtime injection of 0.2 U/kg body wt NPH insulin or an oral three times a day administration of 1.5 g/day metformin to the previously ineffective glyburide treatment. RESULTS: Both combined therapies significantly (P less than 0.01) reduced fasting plasma glucose (FPG), postprandial plasma glucose (PPPG) and percentage of HbA1. The addition of metformin was more effective than the addition of insulin (P less than 0.01) in improving PPPG in the 8 patients with higher post-glucagon C-peptide levels. In contrast, the efficacy of neither combined therapy was related to patient age, age of diabetes onset, duration of the disease, percentage of ideal body weight, and FPG. The addition of insulin but not metformin caused a significant (P less than 0.01) increase of mean body weight. Neither combined treatment caused changes in serum cholesterol and triglyceride levels. No symptomatic hypoglycemic episode was reported in any of the 16 patients. CONCLUSIONS: The addition of bedtime NPH insulin or metformin was effective in improving the glycemic control in most NIDDM patients with secondary failure to glyburide. The combination of metformin and sulfonylurea was more effective in reducing PPPG and did not induce any increase of body weight.

Glyburide and tolbutamide induce desensitization of insulin release in rat pancreatic islets by different mechanisms.

Insulin secretion was studied in rat pancreatic islets after 24-h exposure to various glyburide or tolbutamide concentrations. Glucose-induced insulin release was significantly (P < 0.05) reduced in islets cultured with 0.1 microM glyburide or 100 microM tolbutamide (2098 +/- 187, 832 +/- 93, and 989 +/- 88 pg/islet.h in control, glyburide-exposed, and tolbutamide-exposed islets, respectively). When glyburide-treated islets were stimulated with glyburide or tolbutamide, insulin release was also impaired compared to that in control islets (P < 0.05). In contrast, tolbutamide-exposed islets showed an impaired response to tolbutamide, but a normal response to glyburide. To investigate the mechanism of the sulfonylurea-induced impairment of insulin secretion, we measured insulin release and Rb+ efflux (a marker of the K+ channel activity) in a perifusion system and islet Ca2+ uptake under static conditions. Insulin release in response to 16.7 mM glucose increased in control islets from 9.4 +/- 1.1 to 131 +/- 19 pg/islet.min (first phase secretion peak). Simultaneously, the fractional 86Rb+ efflux declined from 0.015 +/- 0.002% to 0.006 +/- 0.001% (change in decrement, -63.5%). Glucose-induced insulin release in glyburide- and tolbutamide-treated islets was significantly reduced (first phase peak, 22.1 +/- 5 and 39.7 +/- 8 pg/islet.min, respectively; P < 0.05), and the fractional 86Rb+ efflux decrement was -21 +/- 6% for glyburide (P < 0.005 vs. control islets) and -65 +/- 4% (not different from control) for tolbutamide. When glyburide- or tolbutamide-exposed islets were stimulated with the corresponding sulfonylurea, insulin release was impaired compared to that in control islets (P < 0.05), but, again, 86Rb+ efflux was impaired (P < 0.05) only in glyburide-exposed islets. When 45Ca2+ uptake was studied, the increase in glucose concentration from 2.8 to 16.7 mM increased calcium uptake in control islets from 1.76 +/- 0.58 to 7.27 +/- 1.36 pmol/islet.2 min (n = 4). Preexposure to 0.1 microM glyburide did not change calcium uptake at a glucose concentration of 2.8 mM (1.44 +/- 0.45 pmol/islet.2 min) but significantly reduced calcium uptake stimulated by 16.7 mM glucose (3.21 +/- 0.35 pmol/islet.2 min; n = 4; P < 0.005 compared to control islets). In contrast, preexposure to 100 microM tolbutamide did not change either basal or glucose-stimulated calcium uptake (1.44 +/- 0.45 and 6.90 +/- 0.81 pmol/islet.2 min, respectively; n = 4). These data show that in vitro chronic exposure of pancreatic islets to the sulfonylureas glyburide and tolbutamide impairs their ability to respond to a subsequent glucose or sulfonylurea stimulation.(ABSTRACT TRUNCATED AT 400 WORDS)

Influence of tumor necrosis factor-alpha on the modulation by interferon-gamma of HLA class II molecules in human thyroid cells and its effect on interferon-gamma binding.

Cytokines are important modulators of immunological reactions, but it has been postulated that they might act on other unrelated epithelial cells. We studied the effects of recombinant interferon-gamma (rIFN gamma) and recombinant tumor necrosis factor-alpha (rTNF alpha) on normal human thyroid cells. We found that the combination of these two cytokines enhanced HLA class II molecule expression on these cells compared with the effect of rIFN gamma alone. This was proven by both immunofluorescence as well as a more sensitive and quantitative RIA. rTNF alpha alone had no effect on HLA class II molecule induction on the same thyrocytes, suggesting a synergistic rather than an additive action in combination with rIFN gamma. The addition of 600 U/ml rTNF alpha to low dose rIFN gamma (10 U/mL) enhanced class II expression by 50%, as quantified by RIA. We also demonstrated that normal thyrocytes possess distinct receptors for the two cytokines and that rTNF alpha probably augments IFN gamma binding, since it increased when the cells were first incubated with rTNF alpha. This increased binding provides an explanation for the synergistic action of rTNF alpha in enhancing class II molecule expression by rIFN gamma. We conclude that the presence of receptors for these cytokines on human thyroid cells gives a direct demonstration of their potential biological action on cells normally not involved in the immunological circuit. The phenomenon might also explain their direct or indirect involvement in vivo, such as in influencing inappropriate HLA class II molecule expression in epithelial cells affected by autoimmunity.

Direct effects of biguanides on glucose utilization in vitro.

The effect of the biguanides metformin and phenformin on glucose utilization in isolated cells was studied with IM-9 human lymphocytes. Both agents stimulated glucose consumption from the incubation media. Detectable effects of metformin were seen at 33 mumol/L and detectable effects of phenformin were seen at 1.7 mumol/L. Both agents, at similar concentrations, also stimulated [3H] 2-deoxy-D-glucose uptake. Studies with phenformin indicated that biguanides increase the Vmax of uptake without changing the Km. In contrast to the biguanides, IM-9 cells insulin did not influence either glucose consumption or [3H] 2-deoxy-D-glucose uptake. These data provide evidence, therefore, that biguanides may directly influence the cellular utilization of glucose.

Nicotinamide partially reverses the interleukin-1 beta inhibition of glucose-induced insulin release in pancreatic islets.

Interleukin-1 beta (IL-1 beta) is known to inhibit glucose-induced insulin release by pancreatic islets. We studied the effect of nicotinamide, an inhibitor of poly[adenosine diphosphate (ADP)-ribose] synthetase and a free-radical scavenger, on this IL-1 beta-induced inhibition using rat pancreatic islets. In static experiments, groups of five islets were incubated for 24 hours in culture medium CMRL-1066, with or without 50 U/mL IL-1 beta, in the presence or absence of nicotinamide (dose range, 0 to 50 mmol/L), and then exposed for 1 hour to either 1.4 or 19.4 mmol/L glucose, 10 mmol/L arginine, or 10 mumols/L glyburide. Basal insulin secretion was 183 +/- 32 pg/islet/h (mean +/- SE, n = 7) and 176 +/- 39 (n = 7) in control islets and in islets exposed to 50 U/mL IL-1 beta, respectively. Glucose-stimulated insulin secretion was significantly reduced (185 +/- 41) in IL-1 beta-exposed islets in comparison to control islets (2,037 +/- 363). In parallel, arginine-stimulated insulin release was inhibited by IL-1 beta exposure (166 +/- 31 pg/islet/h, mean +/- SE, n = 3) in comparison to control islets (1,679 +/- 307). In contrast, IL-1 beta exposure did not significantly reduce glyburide-induced insulin secretion (1,516 +/- 231 and 1,236 +/- 214 in control and IL-1 beta-exposed islets, respectively; mean +/- SE, n = 3). When islets were simultaneously exposed to IL-1 beta and increasing concentrations of nicotinamide, a dose-dependent recovery of glucose-induced insulin secretion was observed, with the maximum effect at 25 mmol/L nicotinamide (1,007 +/- 123, P less than .001).(ABSTRACT TRUNCATED AT 250 WORDS)

Glucose transport, phosphorylation, and utilization in isolated porcine pancreatic islets.

Porcine islets have been proposed as a donor source for human transplantation, mainly because of both structural and biological similarities of porcine and human insulin. However, the in vitro function of these islets is poorly characterized. In the present study, we first examined insulin release in response to glucose in static incubation experiments. Increasing glucose concentrations up to 8.3 mmol/L stimulated insulin release; however, this elevation was only twofold, and a paradoxical decline was observed at glucose concentrations higher than 8.3 mmol/L. In cultured porcine islets, a greater insulin secretion may be elicited by agents that increase intracellular cyclic adenosine monophosphate (cAMP) levels. To investigate the possible reasons for the porcine islet low response to glucose in vitro, we then evaluated in parallel experiments glucose transport, phosphorylation, and utilization. Glucose transport studies (using 3-O-methyl glucose uptake at 15 degrees C for 15 seconds) indicated the presence of both a high-affinity (Km, 1.2 +/- 0.6 mmol/L) and a low-affinity (Km, 11.8 +/- 1.9 nmol/L, n = 5) component. Glucose phosphorylation, evaluated by measuring the rate of glucose-6-phosphate formation in a fluorimetric assay, indicated that glucokinase activity had a maximum (Vmax) of 7.97 +/- 0.94 nmol/microgram DNA/h and a Km of 8.3 +/- 0.9 mmol/L (mean +/- SE, n = 8).(ABSTRACT TRUNCATED AT 250 WORDS)

Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography.

The aim of this study was to evaluate the capability of improved artificial neural networks (ANN) and additional novel training methods in distinguishing between benign and malignant breast lesions in contrast-enhanced magnetic resonance-mammography (MRM). A total of 604 histologically proven cases of contrast-enhanced lesions of the female breast at MRI were analyzed. Morphological, dynamic and clinical parameters were collected and stored in a database. The data set was divided into several groups using random or experimental methods [Training & Testing (T&T) algorithm] to train and test different ANNs. An additional novel computer program for input variable selection was applied. Sensitivity and specificity were calculated and compared with a statistical method and an expert radiologist. After optimization of the distribution of cases among the training and testing sets by the T & T algorithm and the reduction of input variables by the Input Selection procedure a highly sophisticated ANN achieved a sensitivity of 93.6% and a specificity of 91.9% in predicting malignancy of lesions within an independent prediction sample set. The best statistical method reached a sensitivity of 90.5% and a specificity of 68.9%. An expert radiologist performed better than the statistical method but worse than the ANN (sensitivity 92.1%, specificity 85.6%). Features extracted out of dynamic contrast-enhanced MRM and additional clinical data can be successfully analyzed by advanced ANNs. The quality of the resulting network strongly depends on the training methods, which are improved by the use of novel training tools. The best results of an improved ANN outperform expert radiologists.

Contribution of artificial neural networks to the classification and treatment of patients with uninvestigated dyspepsia.

OBJECTIVES: To verify whether symptoms reported by patients with uninvestigated dyspepsia might be helpful in either classifying functional from organic dyspepsia (1st experiment), or recognising which Helicobacter pylori infected patients may benefit from eradication therapy (2nd experiment). METHODS: We compared the performance of artificial neural networks and linear discriminant analysis in two experiments on a database including socio-demographic features, past medical history, alarming symptoms, and symptoms at presentation of 860 patients with uninvestigated dyspepsia enrolled in a large observational multi-centre Italian study. RESULTS: In the 1st experiment, the best prediction for organic disease was given by the Sine Net model (specificity of 87.6% with 13 patients misclassified) and the best prediction for functional dyspepsia by the FF Bp model (sensitivity of 83.4% with 56 patients misclassified). The highest global accuracy of linear discriminant analysis was 65.1%, with 150 patients misclassified. In the 2nd experiment, the highest predictive performance was provided by the SelfDASn model: all infected patients who became symptom-free after successful eradicating treatment were correctly classified, whereas nine errors were made in forecasting patients who did not benefit from such a therapy. The highest global performance of linear discriminant analysis was 53.2%, with 37 patients misclassified. CONCLUSIONS: In patients with uninvestigated dyspepsia, artificial neural networks might have potential for categorising those affected by either organic or functional dyspepsia, as well as for identifying all Helicobacter pylori infected dyspeptic patients who will benefit from eradication.

Artificial neural network assessment of substitutive pharmacological treatments in hospitalised intravenous drug users.

Artificial neural networks (ANNs) provide better solutions than linear discriminant analysis (LDA) to problems of classification and estimation involving a large number of non-homogeneous (categorical and metric) variables. In this study, we compared the ability of traditional LDA and a feed-forward back-propagation (FF-BP) ANN with self-momentum to predict pharmacological treatments received by intravenous drug users (IDUs) hospitalised for coexisting medical illness. When medical staff considered detoxification appropriate they usually suggested methadone (MET) and (or) benzodiazepines (BDZ). Given four different treatment options (MET, BDZ, MET+BDZ, no treatment) as dependent variables and 38 independent variables, the FF-BP ANN provided the best prediction of the consultant's decision (overall accuracy: 62.7%). It achieved the highest level of predictive accuracy for the BDZ option (90.5%), the lowest for no treatment (29.6), often misclassifying no treatment as BDZ. The LDA yielded a lower mean accuracy (50.3%). When the untreated group was excluded, ANN improved its absolute recognition rate by only 1.2% and the BDZ group remained the best predicted. In contrast, LDA improved its absolute recognition rate from 50.3 to 58.9%, maximum 65.7% for the BDZ group. In conclusion, the FF-BP ANN was more accurate than the statistical model (discriminant analysis) in predicting the pharmacological treatment of IDUs.

[Iodine deficiency and iodine prophylaxis experience in Sicily]. / Carenza iodica ed esperienza di iodoprofilassi in Sicilia.

Twenty years ago we described an area of goiter endemia in North-Eastern Sicily. In this area endemic goiter was associated to a variable degree of iodine deficiency and, in some places, also to an increased thiocyanate urine excretion. Our studies have demonstrated a strict relationship between iodine deficiency and congenital hypothyroidism (both permanent and transient), an increased prevalence of autonomously functioning thyroid nodules and an increased prevalence of thyroid cancer (follicular and anaplastic histotypes). A number of cases of endemic cretinism have also been described. An active iodine prophylaxis program has been carried in the town of Troina in the years 1979-87, by iodinating the municipal water supply. This intervention caused the disappearance of goiter in schoolchildren in only five years. In the last 20 years the prevalence of goiter has decreased in all endemic areas probably because of the "silent prophylaxis", due to improved socio-economic conditions and industrial food consumption. Today the persistence of endemic goiter confirms the inadequacy of the silent iodine prophylaxis and the need to immediately introduce an active iodine prophylaxis in Sicily.

The I.F.A.S.T. model allows the prediction of conversion to Alzheimer disease in patients with mild cognitive impairment with high degree of accuracy.

This paper presents the results obtained with the innovative use of special types of artificial neural networks (ANNs) assembled in a novel methodology named IFAST (implicit function as squashing time) capable of compressing the temporal sequence of electroencephalographic (EEG) data into spatial invariants. The aim of this study is to test the potential of this parallel and nonlinear EEG analysis technique in providing an automatic classification of mild cognitive impairment (MCI) subjects who will convert to Alzheimer's disease (AD) with a high degree of accuracy. Eyes-closed resting EEG data (10-20 electrode montage) were recorded in 143 amnesic MCI subjects. Based on 1-year follow up, the subjects were retrospectively classified to MCI converted to AD and MCI stable. The EEG tracks were successively filtered according to four different frequency ranges, in order to evaluate the hypotheses that a specific range, corresponding to specific brain wave type, could provide a better classification (0.12 Hz, 12.2 - 29.8 Hz; 30.2 - 40 Hz, and finally Notch Filter 48 - 50 Hz). The spatial content of the EEG voltage was extracted by IFAST step-wise procedure using ANNs. The data input for the classification operated by ANNs were not the EEG data, but the connections weights of a nonlinear auto-associative ANN trained to reproduce the recorded EEG tracks. These weights represented a good model of the peculiar spatial features of the EEG patterns at scalp surface. The classification based on these parameters was binary and performed by a supervised ANN. The best results distinguishing between MCI stable and MCI/AD reached to 85.98%.(012 Hz band). And confirmed the working hypothesis that a correct automatic classification can be obtained extracting spatial information content of the resting EEG voltage by ANNs and represent the basis for research aimed at integrating spatial and temporal information content of the EEG. These results suggest that this low-cost procedure can reliably distinguish eyes-closed resting EEG data in individual MCI subjects who will have different prognosis at 1-year follow up, and is promising for a large-scale periodic screening of large populations at amnesic MCI subjects at risk of AD.