Multivariable network associated with cognitive decline and dementia

Metal Body Burden as Cardiovascular Risk Factor in Adults with Metabolic Syndrome and Overweight-Obesity Analysed with an Artificial Neural Network: The Role of Hair Mineralograms

In determining the so-called "body burden", hair has been widely accepted for assessing toxic element exposure. However, its role in assessing essential elements is controversial. This study investigates the possible relationship between hair minerals, metabolic syndrome (MetS) and cardiovascular (CV) risk in non-occupationally exposed subjects with overweight-obesity. Ninety-five voluntary participants (aged 51 ± 12) were recruited in Northern Italy. Hair samples were collected and analysed via inductively coupled plasma mass spectrometry; the total toxicity index (TI) was calculated as well. To evaluate cardiovascular risk factors in the presence or absence of MetS, the following factors were considered via the innovative artificial neural network (ANN) method Auto-CM: hair mineralograms (31 elements) and 25 variables including blood pressure, anthropometric parameters, insulin resistance and biochemical serum markers assessing inflammation. The Framingham risk score, fatty liver index (FLI), visceral adiposity index and CV risk scores were also taken into consideration. As shown by the semantic map, which was subsequently confirmed by an activation and competition system (ACS), obesity parameters are strictly associated with CV risk factors, TI and inflammation; meanwhile, the single mineral elements seem to be unimportant. Data obtained via ANN demonstrate that MetS may be at least partly mediated by altered mineral levels also in the presence of obesity and that waist circumference is a crucial point to be monitored rather than BMI alone. Furthermore, the mineral body burden is one of the important factors for CV risk.

Artificial neural network analysis of bone quality DXA parameters response to teriparatide in fractured osteoporotic patients

Teriparatide is a bone-forming therapy for osteoporosis that increases bone quantity and texture, with uncertain action on bone geometry. No data are available regarding its influence on bone strain. To investigate teriparatide action on parameters of bone quantity and quality and on Bone Strain Index (BSI), also derived from DXA lumbar scan, based on the mathematical model finite element method. Forty osteoporotic patients with fractures were studied before and after two years of daily subcutaneous 20 mcg of teriparatide with dual X-ray photon absorptiometry to assess bone mineral density (BMD), hip structural analysis (HSA), trabecular bone score (TBS), BSI. Spine deformity index (SDI) was calculated from spine X-ray. Shapiro-Wilks, Wilcoxon and Student's t test were used for classical statistical analysis. Auto Contractive Map was used for Artificial Neural Network Analysis (ANNs). In the entire population, the ameliorations after therapy regarded BSI (-13.9%), TBS (5.08%), BMD (8.36%). HSA parameters of femoral shaft showed a worsening. Dividing patients into responders (BMD increase >10%) and non-responders, the first presented TBS and BSI ameliorations (11.87% and -25.46%, respectively). Non-responders presented an amelioration of BSI only, but less than in the other subgroup (-6.57%). ANNs maps reflect the mentioned bone quality improvements. Teriparatide appears to ameliorate not only BMD and TBS, but also BSI, suggesting an increase of bone strength that may explain the known reduction in fracture risk, not simply justified by BMD increase. BSI appears to be a sensitive index of TPD effect. ANNs appears to be a valid tool to investigate complex clinical systems.

Artificial Neural Networks Help to Better Understand the Interplay Between Cognition, Mediterranean Diet, and Physical Performance: Clues from TRELONG Study

BACKGROUND

Nutrition plays an important role in the aging process. Adherence to the Mediterranean diet (MedDiet) has been shown to be associated with lower rates of diseases. Cognitive status seems to be strongly interrelated with physical well-being, so that one influences the other. Physical performance measures are not only associated with clinical and subclinical age-related modifications, but are also able to predict disability, institutionalization, and mortality.

OBJECTIVE

To evaluate prospectively the associations between Mediterranean-Style Dietary Pattern Score (MSDPS), clinical characteristics, and cognition of the population sample of The TREVISO LONGEVA (TRELONG) Study, in Treviso, Italy.

METHODS

Global cognition, physical performance measures, MSDPS, and other clinical features were detected in 2010 in 82 men and 108 women. These characteristics were evaluated in relation to the physical performance measures identified 3.8 years later in 2013 in the same subjects, using a semantic connectivity map, through Auto-CM system, to grasp further and non-linear associations between variables which might remain, otherwise, undetected.

RESULTS

The Auto-CM system's map showed a close association between better levels of global cognition and MSDPS in 2010 and higher physical performance in 2013. On the other hand, worse levels of global cognition and MSDPS in 2010 were associated with lower physical performance in 2013.

CONCLUSION

The prevention models for successful aging may benefit from integrated programs that include cognitive, physical, and dietary interventions, since these aspects are mutually interrelated.

The role of carboxy-terminal cross-linking telopeptide of type I collagen, dual x-ray absorptiometry bone strain and Romberg test in a new osteoporotic fracture risk evaluation: A proposal from an observational study

The consolidated way of diagnosing and treating osteoporosis in order to prevent fragility fractures has recently been questioned by some papers, which complained of overdiagnosis and consequent overtreatment of this pathology with underestimating other causes of the fragility fractures, like falls. A new clinical approach is proposed for identifying the subgroup of patients prone to fragility fractures. This retrospective observational study was conducted from January to June 2015 at the Nuclear Medicine-Bone Metabolic Unit of the of the Fondazione IRCCS Ca' Granda, Milan, Italy. An Italian population of 125 consecutive postmenopausal women was investigated for bone quantity and bone quality. Patients with neurological diseases regarding balance and vestibular dysfunction, sarcopenia, past or current history of diseases and use of drugs known to affect bone metabolism were excluded. Dual X-ray absorptiometry was used to assess bone quantity (bone mineral density) and bone quality (trabecular bone score and bone strain). Biochemical markers of bone turnover (type I collagen carboxy-terminal telopeptide, alkaline phosphatase, vitamin D) have been measured. Morphometric fractures have been searched by spine radiography. Balance was evaluated by the Romberg test. The data were evaluated with the neural network analysis using the Auto Contractive Map algorithm. The resulting semantic map shows the Minimal Spanning Tree and the Maximally Regular Graph of the interrelations between bone status parameters, balance conditions and fractures of the studied population. A low fracture risk seems to be related to a low carboxy-terminal cross-linking telopeptide of type I collagen level, whereas a positive Romberg test, together with compromised bone trabecular microarchitecture DXA parameters, appears to be strictly connected with fragility fractures. A simple assessment of the risk of fragility fracture is proposed in order to identify those frail patients at risk for osteoporotic fractures, who may have the best benefit from a pharmacological and physiotherapeutic approach.

Peripheral Inflammatory Markers and Antioxidant Response during the Post-Acute and Chronic Phase after Severe Traumatic Brain Injury

Traumatic brain injury (TBI) is a mechanical insult to the brain caused by external forces and associated with inflammation and oxidative stress. The patients may show different profiles of neurological recovery and a combination of oxidative damage and inflammatory processes can affect their courses. It is known that an overexpression of cytokines can be seen in peripheral blood in the early hours/days after the injury, but little is known about the weeks and months encompassing the post-acute and chronic phases. In addition, no information is available about the antioxidant responses mediated by the major enzymes that regulate reactive oxygen species levels: superoxide dismutase, catalase, peroxidases, and GSH-related enzymes. This study investigates the 6-month trends of inflammatory markers and antioxidant responses in 22 severe TBI patients with prolonged disorders of consciousness, consecutively recruited in a dedicated neurorehabilitation facility. Patients with a high degree of neurological impairment often show an uncertain outcome. In addition, the profiles of plasma activities were related to the neurological recovery after 12 months. Venous peripheral blood samples were taken blindly as soon as clinical signs and laboratory markers confirmed the absence of infections, 3 and 6 months later. The clinical and neuropsychological assessment continued up to 12 months. Nineteen patients completed the follow-up. In the chronic phase, persistent high plasma levels of cytokines can interfere with cognitive functioning and higher post-acute levels of cytokines [interferon (IFN)-γ, tumor necrosis factor (TNF)-α, IL1b, IL6] are associated with poorer cognitive recoveries 12 months later. Moreover, higher IFN-γ, higher TNF-α, and lower glutathione peroxidase activity are associated with greater disability. The results add evidence of persistent inflammatory response, provide information about long-term imbalance of antioxidant activity, and suggest that the over-production of cytokines and the alteration of the redox homeostasis in the post-acute phase might adversely affect the neurological and functional recovery. Inflammatory and antioxidant activity markers might offer a feasible way to highlight some of the processes opposing recovery after a severe TBI.

Predictors of Response to Cholinesterase Inhibitors Treatment of Alzheimer's Disease: Date Mining from the TREDEM Registry

BACKGROUND

The pharmacological treatment of Alzheimer's disease (AD) is based largely on cholinesterase inhibitors (ChEI).

OBJECTIVE

To investigate whether or not some non-pharmacological and contextual factors measured prior to starting treatment such as past occupation, lifestyles, marital status, degree of autonomy and cognitive impairment, living alone or with others, and the degree of brain atrophy are associated with a better response to ChEI treatment.

METHODS

Eighty-four AD and six AD with cerebrovascular disease (AD + CVD) outpatients of Treviso Dementia (TREDEM) Registry, with an average cholinesterase inhibitors treatment length of four years, were considered. The outpatients had undergone a complete evaluation and some non-pharmacological and contextual factors were collected. We defined responder a patient with a delta score T0 - T1 equal or inferior to 2.0 points per year of MMSE and a non-responder a patient with a delta score T0 - T1 superior to 2.0 points per year. In order to identify hidden relationships between variables related to response and non-response, we use a special kind of artificial neural network called Auto-CM, able to create a semantic connectivity map of the variables considered in the study.

RESULTS

A higher cognitive profile, a previous intellectual occupation, healthier lifestyles, being married and not living alone, a higher degree of autonomy, and lower degree of brain atrophy at baseline resulted in affecting the response to long-term ChEI therapy.

CONCLUSION

Non-pharmacological and contextual factors appear to influence the effectiveness of treatment with ChEI in the long term.

The constellation of dietary factors in adolescent acne: a semantic connectivity map approach

BACKGROUND

Different lifestyle and dietetic factors have been linked with the onset and severity of acne.

OBJECTIVE

To assess the complex interconnection between dietetic variables and acne.

METHODS

This was a reanalysis of data from a case-control study by using a semantic connectivity map approach. 563 subjects, aged 10-24 years, involved in a case-control study of acne between March 2009 and February 2010, were considered in this study. The analysis evaluated the link between a moderate to severe acne and anthropometric variables, family history and dietetic factors. Analyses were conducted by relying on an artificial adaptive system, the Auto Semantic Connectivity Map (AutoCM).

RESULTS

The AutoCM map showed that moderate-severe acne was closely associated with family history of acne in first degree relatives, obesity (BMI ≥ 30), and high consumption of milk, in particular skim milk, cheese/yogurt, sweets/cakes, chocolate, and a low consumption of fish, and limited intake of fruits/vegetables.

CONCLUSION

Our analyses confirm the link between several dietetic items and acne. When providing care, dermatologists should also be aware of the complex interconnection between dietetic factors and acne.

Data Mining of Determinants of Intrauterine Growth Retardation Revisited Using Novel Algorithms Generating Semantic Maps and Prototypical Discriminating Variable Profiles

OBJECTIVES

Intra-uterine growth retardation is often of unknown origin, and is of great interest as a "Fetal Origin of Adult Disease" has been now well recognized. We built a benchmark based upon a previously analysed data set related to Intrauterine Growth Retardation with 46 subjects described by 14 variables, related with the insulin-like growth factor system and pro-inflammatory cytokines, namely interleukin-6 and tumor necrosis factor-α.

DESIGN AND METHODS

We used new algorithms for optimal information sorting based on the combination of two neural network algorithms: Auto-contractive Map and Activation and Competition System. Auto-Contractive Map spatializes the relationships among variables or records by constructing a suitable embedding space where 'closeness' among variables or records reflects accurately their associations. The Activation and Competition System algorithm instead works as a dynamic non linear associative memory on the weight matrices of other algorithms, and is able to produce a prototypical variable profile of a given target.

RESULTS

Classical statistical analysis, proved to be unable to distinguish intrauterine growth retardation from appropriate-for-gestational age (AGA) subjects due to the high non-linearity of underlying functions. Auto-contractive map succeeded in clustering and differentiating completely the conditions under study, while Activation and Competition System allowed to develop the profile of variables which discriminated the two conditions under study better than any other previous form of attempt. In particular, Activation and Competition System showed that ppropriateness for gestational age was explained by IGF-2 relative gene expression, and by IGFBP-2 and TNF-α placental contents. IUGR instead was explained by IGF-I, IGFBP-1, IGFBP-2 and IL-6 gene expression in placenta.

CONCLUSION

This further analysis provided further insight into the placental key-players of fetal growth within the insulin-like growth factor and cytokine systems. Our previous published analysis could identify only which variables were predictive of fetal growth in general, and identified only some relationships.

Di-(2-ethylhexyl) phthalate metabolites in urine show age-related changes and associations with adiposity and parameters of insulin sensitivity in childhood

OBJECTIVES

Phthalates might be implicated with obesity and insulin sensitivity. We evaluated the levels of primary and secondary metabolites of Di-(2-ethylhexyl) phthalate (DEHP) in urine in obese and normal-weight subjects both before and during puberty, and investigated their relationships with auxological parameters and indexes of insulin sensitivity.

DESIGN AND METHODS

DEHP metabolites (MEHP, 6-OH-MEHP, 5-oxo-MEHP, 5-OH-MEHP, and 5-CX-MEHP), were measured in urine by RP-HPLC-ESI-MS. Traditional statistical analysis and a data mining analysis using the Auto-CM analysis were able to offer an insight into the complex biological connections between the studied variables.

RESULTS

The data showed changes in DEHP metabolites in urine related with obesity, puberty, and presence of insulin resistance. Changes in urine metabolites were related with age, height and weight, waist circumference and waist to height ratio, thus to fat distribution. In addition, clear relationships in both obese and normal-weight subjects were detected among MEHP, its products of oxidation and measurements of insulin sensitivity.

CONCLUSION

It remains to be elucidated whether exposure to phthalates per se is actually the risk factor or if the ability of the body to metabolize phthalates is actually the key point. Further studies that span from conception to elderly subjects besides further understanding of DEHP metabolism are warranted to clarify these aspects.

The new Qualitative Scoring MMSE Pentagon Test (QSPT) as a valid screening tool between autopsy-confirmed dementia with Lewy bodies and Alzheimer's disease

Visual-constructional apraxia is a prominent feature of dementia with Lewy bodies (DLB) that might help to clinically distinguish it from Alzheimer's disease (AD). The main goal of this study was to assess performance on the copy intersecting-pentagon item of the Mini-Mental State Examination with the new Qualitative Scoring method for the Pentagon copy Test (QSPT). In order to determine which aspects of the drawings might differentiate DLB from AD, pentagon drawings of autopsy-verified DLB (n = 16) and AD (n = 15) patients were assessed using the QSPT. The qualitative scoring encompasses the assessment of different parameters of the drawing, such as number of angles, distance/intersection, closure/opening, rotation, and closing-in. The QSPT scores were compared between groups using linear analyses and artificial neural network analyses at four different time points. Linear analyses showed that during the first evaluation, number of angles was the only parameter that showed a significant difference between DLB and AD patients. A gradual decline in other parameters and total pentagon score occurred in both groups during subsequent years, with greater decline for the DLB group. The artificial neural network analyses using auto-contractive maps showed that, with disease progression, DLB became related to relatively lower qualitative pentagon scores, whereas AD became related to relatively higher qualitative scores. These findings suggest that the QSPT might be a sensitive measure of visuo-constructive abilities able to differentiate DLB from AD at disease onset and as the diseases progress, but further studies on larger population are necessary in order to establish its clinical relevance.

Variations in inflammatory genes are associated with periodontitis

BACKGROUND

Periodontitis is a multi-factorial disease and several risk-factors such as infections, inflammatory responses, oral hygiene, smoke, aging and individual predisposition are involved in the disease. Pathogens trigger chronic inflammation with cytokines release which in turn leads to the destruction of the connective and the teeth supporting bone. The identification of genetic factors controlling oral inflammation may increase our understanding of genetic predisposition to periodontitis.Single nucleotide polymorphisms in the promoter region of Vascular Endothelial Growth Factor, Alpha-1-Antichymotripsin, hydroxy-methyl-glutaryl CoA reductase, Interferon alpha, Interleukin-1 Beta, Interleukin 10, Interleukin 6 and Tumor Necrosis Factor- alpha genes from a case/control study were investigated.

RESULTS

The C allele of Vascular Endothelial Growth Factor, A allele of Interleukin 10 and GG genotype of Tumor Necrosis Factor-α were individually associated with chronic periodontitis. However, the concomitant presence of the three genetic markers in the same subjects appeared to play a synergistic role and increased several folds the risk of the disease.

CONCLUSIONS

Our findings offer new tools to implement the screening of unaffected subjects with an increased susceptibility of periodontitis and increase our understanding regarding the genetic inflammatory background related to familiarity of the disease.

Staging dementia from symptom profiles on a care partner website

BACKGROUND

The World Wide Web allows access to patient/care partner perspectives on the lived experience of dementia. We were interested in how symptoms that care partners target for tracking relate to dementia stage, and whether dementia could be staged using only these online profiles of targeted symptoms.

OBJECTIVES

To use clinical data where the dementia stage is known to develop a model that classifies an individual's stage of dementia based on their symptom profile and to apply this model to classify dementia stages for subjects from a Web-based dataset.

METHODS

An Artificial Neural Network (ANN) was used to identify the relationships between the dementia stages and individualized profiles of people with dementia obtained from the 60-item SymptomGuide (SG). The clinic-based training dataset (n=320), with known dementia stages, was used to create an ANN model for classifying stages in Web-based users (n=1930).

RESULTS

The ANN model was trained in 66% of the 320 Memory Clinic patients, with the remaining 34% used to test its accuracy in classification. Training and testing staging distributions were not significantly different. In the 1930 Web-based profiles, 309 people (16%) were classified as having mild cognitive impairment, 36% as mild dementia, 29% as moderate, and 19% as severe. In both the clinical and Web-based symptom profiles, most symptoms became more common as the stage of dementia worsened (eg, mean 5.6 SD 5.9 symptoms in the MCI group versus 11.9 SD 11.3 in the severe). Overall, Web profiles recorded more symptoms (mean 7.1 SD 8.0) than did clinic ones (mean 5.5 SD 1.8). Even so, symptom profiles were relatively similar between the Web-based and clinical datasets.

CONCLUSION

Symptoms targeted for online tracking by care partners of people with dementia can be used to stage dementia. Even so, caution is needed to assure the validity of data collected online as the current staging algorithm should be seen as an initial step.

Monocyte chemoattractant protein-1 promoter polymorphism and plasma levels in alzheimer's disease

Background

Neurodegenerative disorders such Alzheimer's disease (AD) are often characterized by senile plaques and neurofibrillary tangle. In addition, reactive astrogliosis, microglia activation and a chronic inflammation are found in AD brain. Activated microglia has been reported to express a large number of beta chemokines including monocyte chemoattractant protein-1 (MCP-1). The potential role of MCP-1 in AD pathogenesis is supported by the over expression of MCP-1 associated with an increase of amyloid deposition in transgenic mice. MCP-1 protein may be regulated by a single nucleotide polymorphism (SNP) occurring at position −2518 of the MCP-1 gene promoter. In this paper we correlated the A-2518G MCP-1 SNP distribution in three different populations: AD, control and MCI (mild cognitive impairment) population to evaluate whether this SNP might be a risk factor for AD or for MCI-AD conversion. MCP-1 plasma levels were also measured and correlated to the cognitive impairment (CIND) and AD risk.

Results

No differences in genotype distribution and allele frequencies of A-2518G MCP-1 SNP among AD patients, MCI subjects and controls were observed even after APOEe4 variation adjustment with logistic regression. However in MCI subjects, followed up for two years, this SNP appears to influence the progression of the disease; being the G allele slightly more frequent in MCI patients that developed AD. MCP-1 plasma levels were different among CIND (cognitive impairment but no dementia), AD and controls. The MCP-1 A-2518G promoter polymorphism did not affect MCP-1 plasma levels within the three populations.

Conclusions

MCP-1 G allele did not affect the risk of AD, but slightly influenced MCI conversion to AD and MCP-1 plasma levels were increased in subjects with preclinical AD.

Combining personality traits with traditional risk factors for coronary stenosis: an artificial neural networks solution in patients with computed tomography detected coronary artery disease

Background. Coronary artery disease (CAD) is a complex, multifactorial disease in which personality seems to play a role but with no definition in combination with other risk factors. Objective. To explore the nonlinear and simultaneous pathways between traditional and personality traits risk factors and coronary stenosis by Artificial Neural Networks (ANN) data mining analysis. Method. Seventy-five subjects were examined for traditional cardiac risk factors and personality traits. Analyses were based on a new data mining method using a particular artificial adaptive system, the autocontractive map (AutoCM). Results. Several traditional Cardiovascular Risk Factors (CRF) present significant relations with coronary artery plaque (CAP) presence or severity. Moreover, anger turns out to be the main factor of personality for CAP in connection with numbers of traditional risk factors. Hidden connection map showed that anger, hostility, and the Type D personality subscale social inhibition are the core factors related to the traditional cardiovascular risk factors (CRF) specifically by hypertension. Discussion. This study shows a nonlinear and simultaneous pathway between traditional risk factors and personality traits associated with coronary stenosis in CAD patients without history of cardiovascular disease. In particular, anger seems to be the main personality factor for CAP in addition to traditional risk factors.

Artificial Adaptive Systems and predictive medicine: a revolutionary paradigm shift

An individual patient is not the average representative of the population. Rather he or she is a person with unique characteristics. An intervention may be effective for a population but not necessarily for the individual patient. The recommendation of a guideline may not be right for a particular patient because it is not what he or she wants, and implementing the recommendation will not necessarily mean a favourable outcome.The author will describe a reconfiguration of medical thought which originates from non linear dynamics and chaos theory. The coupling of computer science and these new theoretical bases coming from complex systems mathematics allows the creation of "intelligent" agents able to adapt themselves dynamically to problem of high complexity: the Artificial Adaptive Systems, which include Artificial Neural Networks( ANNs ) and Evolutionary Algorithms ( EA).ANNs and EA are able to reproduce the dynamical interaction of multiple factors simultaneously, allowing the study of complexity; they can also help medical doctors in making decisions under extreme uncertainty and to draw conclusions on individual basis and not as average trends. These tools can allow a more efficient Technology Transfer from the Science of Medicine to the Real World overcoming many obstacles responsible for the present translational failure. They also contribute to a new holistic vision of the human subject contrasting the statistical reductionism which tends to squeeze or even delete the single subject sacrificing him to his group of belongingness. A remarkable contribution to this individual approach comes from Fuzzy Logic, according to which there are no sharp limits between opposite things, like health and disease. This approach allows to partially escape from probability theory trap in situations where is fundamental to express a judgment based on a single case and favours a novel humanism directed to the management of the patient as individual subject.

Predictive diagnostics and personalized medicine for the prevention of chronic degenerative diseases

Progressive increase of mean age and life expectancy in both industrialized and emerging societies parallels an increment of chronic degenerative diseases (CDD) such as cancer, cardiovascular, autoimmune or neurodegenerative diseases among the elderly. CDD are of complex diagnosis, difficult to treat and absorbing an increasing proportion in the health care budgets worldwide. However, recent development in modern medicine especially in genetics, proteomics, and informatics is leading to the discovery of biomarkers associated with different CDD that can be used as indicator of disease's risk in healthy subjects. Therefore, predictive medicine is merging and medical doctors may for the first time anticipate the deleterious effect of CDD and use markers to identify persons with high risk of developing a given CDD before the clinical manifestation of the diseases. This innovative approach may offer substantial advantages, since the promise of personalized medicine is to preserve individual health in people with high risk by starting early treatment or prevention protocols. The pathway is now open, however the road to an effective personalized medicine is still long, several (diagnostic) predictive instruments for different CDD are under development, some ethical issues have to be solved. Operative proposals for the heath care systems are now needed to verify potential benefits of predictive medicine in the clinical practice. In fact, predictive diagnostics, personalized medicine and personalized therapy have the potential of changing classical approaches of modern medicine to CDD.

Pathophysiology of age-related diseases

A Symposium regarding the Pathophysiology of Successful and Unsuccessful Ageing was held in Palermo, Italy on 7-8 April 2009. Three lectures from that Symposium by G. Campisi, L. Ginaldi and F. Licastro are here summarized. Ageing is a complex process which negatively impacts on the development of various bodily systems and its ability to function. A long life in a healthy, vigorous, youthful body has always been one of humanity's greatest dreams. Thus, a better understanding of the pathophysiology of age-related diseases is urgently required to improve our understanding of maintaining good health in the elderly and to program possible therapeutic intervention.