The impact of unmet treatment need on oral health related quality of life: a questionnaire survey.

BACKGROUND: Based on the present global burden of oral diseases, unmet dental needs affect a more significant population worldwide. It is characterised by the need for dental care but receiving delayed or no care. The contributing factors include lack of knowledge about oral health, its consequences, and the availability of dental services. We need to find out the scale of the problem of unmet dental needs for the south Indian population. Therefore, the objective was to determine the relationship between the presence of oral disease and the quality of life-related to oral health using the OHIP-14 tool. METHODS: The unmet dental requirements of the south Indian population were determined using a cross-sectional questionnaire survey. Close-ended questions were used to obtain data from two investigators trained to record the answers from the patients. The data was collected using the OHIP-14 questionnaire, which consists of 14 items divided into seven domains with two questions each. Physical pain, psychological impairment, physical disability, psychological disability, social disability, and disability were all considered. An additional analysis of artificial neural network (ANN) was done. RESULTS: The response rate was 100 per cent. N = 1029 people replied to the questionnaire about their unmet dental needs. N = 497 (48.3%) were men, whereas N = 532 (51.7%) were women. The average age was 31.7811.72. As their current occupation, most of the included subjects (60.1%) were students. The respondents had no known personal habits and a mixed diet (94.93%). The average BMI was 24.022.59 (14-30.9). OHIP was present in 62.3% of the population. The average OHIP-14 severity score was 10.97. (8.54). The severity and degree of unmet dental need were substantial (p0.01) due to pain in the mouth/teeth/gums, malocclusion, and gum bleeding. The most common OHIP-14 domains affected by unmet oral needs were psychological discomfort, psychological limitation, social limitation, and feeling handicapped. The analysis of ANN revealed that high OHIP scores were primarily attributed to dental caries, poor oral health, and dental aesthetics. CONCLUSION: The severity and degree of unmet dental needs were significant among the south Indian population. The most common oral health status that impacted OHIP-14 domains were pain, malocclusion, and bleeding gums. These patients were significantly impacted by psychological discomfort and social limitations and felt handicapped.

Beauty and health: an intriguing liaison?

The exposure to different form of beauty coming from visual art, music, nature, architecture, improves mental health and exerts neurotrophic effects on different parts of the brain. This in turn improves physical health, prolongs life expectancy, and reduces the risk of serious degenerative diseases such as Alzheimer's and cancer. These beneficial actions would not be understandable and plausible if one did not accept the mind-body unity. The 'hegemonic' role of the brain in health and illness can be discerned, for example, in the effect of emotions on vital physiological parameters, in the relationships between stress and many medical-clinical pathologies, in the control exercised by the brain over the immune system reflecting also in the inhibition of tumour progression.

Quantitative assessment of tip-toe behavior in individuals with autism spectrum disorder and intellectual disability: A cross-sectional study.

The term "toe walking" describes walking on the toes with a lack of heel strike upon initiation of the stance phase of gait. In individuals with autism spectrum disorder (ASD), this phenomenon, or "tip-toe behavior" (TTB), can be present in a substantial proportion of subjects even during standing. In this study, we investigated TTB in 50 persons with ASD (age range 4-26 years). We evaluated TTB through an observational/report-based assessment protocol. Subsequently, we employed a new structured video-based coding protocol based on standardized video recordings, focusing on static and dynamic conditions. Finally, the findings of the two protocols were compared. Twenty-four subjects with TTB were identified and classified according to three functional groups: TTB1, present only during running (6 subjects); TTB2, present during walking and running (11 subjects); and TTB3, present during standing, walking, and running (7 subjects). Moreover, we found that TTB3 subjects exhibited a significantly higher quantity of TTB compared with subjects in the TTB1 and TTB2 groups during both standing and walking tests. Additionally, a high quantity of TTB in the static test was found to be related to a high quantity of TTB in the dynamic test. Variables such as age, autism severity, intellectual disability, and gender were not significantly associated with the mean percent of TTB both in static and dynamic tests in multivariate analysis. This structured video-based coding approach appears feasible and useful for assessing TTB in individuals with ASD and it has the potential to provide insights into TTB trajectories and aid in designing possible interventions.

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.

Prenatal Environmental Stressors and DNA Methylation Levels in Placenta and Peripheral Tissues of Mothers and Neonates Evaluated by Applying Artificial Neural Networks.

Exposure to environmental stressors during pregnancy plays an important role in influencing subsequent susceptibility to certain chronic diseases through the modulation of epigenetic mechanisms, including DNA methylation. Our aim was to explore the connections between environmental exposures during gestation with DNA methylation of placental cells, maternal and neonatal buccal cells by applying artificial neural networks (ANNs). A total of 28 mother-infant pairs were enrolled. Data on gestational exposure to adverse environmental factors and on mother health status were collected through the administration of a questionnaire. DNA methylation analyses at both gene-specific and global level were analyzed in placentas, maternal and neonatal buccal cells. In the placenta, the concentrations of various metals and dioxins were also analyzed. Analysis of ANNs revealed that suboptimal birth weight is associated with placental H19 methylation, maternal stress during pregnancy with methylation levels of NR3C1 and BDNF in placentas and mother's buccal DNA, respectively, and exposure to air pollutants with maternal MGMT methylation. Associations were also observed between placental concentrations of lead, chromium, cadmium and mercury with methylation levels of OXTR in placentas, HSD11B2 in maternal buccal cells and placentas, MECP2 in neonatal buccal cells, and MTHFR in maternal buccal cells. Furthermore, dioxin concentrations were associated with placental RELN, neonatal HSD11B2 and maternal H19 gene methylation levels. Current results suggest that exposure of pregnant women to environmental stressors during pregnancy could induce aberrant methylation levels in genes linked to several pathways important for embryogenesis in both the placenta, potentially affecting foetal development, and in the peripheral tissues of mothers and infants, potentially providing peripheral biomarkers of environmental exposure.

Application of Artificial Neural Networks (ANN) to Elucidate the Connections among Smell, Obesity with Related Metabolic Alterations, and Eating Habit in Patients with Weight Excess.

Obesity is a severe health problem linked to an increased risk of comorbidity and mortality and its etiopathogenesis includes genetic, epigenetic, microbiota composition, and environmental factors, such as dietary habits. The olfactory system plays an important role in controlling food intake and meal size, influencing body weight and energy balance. This study aims to identify the connection between olfactory function and clinical and nutritional aspects related to weight excess in a group of 68 patients with overweight or obesity. All participants underwent the evaluation of olfactory function, anthropometric data (weight, height, BMI, waist circumference), clinical data (hypertension, disglycemia, dyslipidemia, metabolic syndrome), and adherence to the Mediterranean diet (Mediterranean Diet Score). A fourth-generation artificial neural network data mining approach was used to uncover trends and subtle associations between variables. Olfactory tests showed that 65% of patients presented hyposmia. A negative correlation was found between olfactory scores and systolic blood pressure, fasting plasma glucose, and triglycerides levels, but a positive correlation was found between olfactory scores and the Mediterranean diet score. The methodology of artificial neural networks and the semantic connectivity map "Auto-Contractive Map" highlighted the underlying scheme of the connections between the variables considered. In particular, hyposmia was linked to obesity and related metabolic alterations and the male sex. The female sex was connected with normosmia, higher adherence to the Mediterranean diet, and normal values of blood pressure, lipids, and glucose levels. These results highlight an inverse correlation between olfactory skills and BMI and show that a normosmic condition, probably because of greater adherence to the Mediterranean diet, seems to protect not only from an excessive increase in body weight but also from associated pathological conditions such as hypertension and metabolic syndrome.

Neuropsychological and Behavioral Profile in Sleep-Related Hypermotor Epilepsy (SHE) and Disorders of Arousal (DOA): A Multimodal Analysis.

STUDY OBJECTIVES: Disorder of arousal (DOA) and sleep-related hypermotor epilepsy (SHE) are complex, often bizarre, involuntary sleep behaviors, whose differential diagnosis may be challenging because they share some clinical features, such as sleep fragmentation. Mounting evidence highlights the critical role of sleep in cognitive functions. Controversial findings are raised about the cognitive profile in SHE; however, no studies have investigated the cognitive profile in DOA. This study aimed to assess whether sleep instability affects cognitive functions in patients with SHE or DOA. METHODS: This study analyzed 11 patients with DOA, 11 patients with SHE, and 22 healthy controls (HC). They underwent full-night video polysomnography (vPSG) and comprehensive neuropsychological and behavioral evaluation. Differences in the variables of interest among the SHE group, DOA group, and their respective control groups were evaluated. The auto-contractive map (auto-CM) system was used to evaluate the strength of association across the collected data. RESULTS: The SHE group had reduced sleep efficiency and increased wake after sleep onset (WASO); both the SHE and DOA groups showed increased % of N2 and REM sleep compared to the HC group. Neuropsychological and behavioral evaluations showed a different cognitive profile in the SHE group with respect to the HC group. The auto-CM showed that Pittsburgh Sleep Quality Index (PSQI), Beck depression inventory (BDI), MWCST_PE, Epworth sleepiness scale (ESS), WASO, N1, and % REM were strictly correlated with SHE, whereas the SE and arousal index (AI) were strictly related to DOA. CONCLUSIONS: Patients with SHE and DOA present different cognitive and psychiatric profiles, with subtle and selective cognitive impairments only in those with SHE, supporting the discriminative power of cognitive and psychiatric assessment in these two conditions.

Do artificial neural networks love sex? How the combination of artificial neural networks with evolutionary algorithms may help to identify gender influence in rheumatic diseases.

Although medical research has been performed predominantly on men both in preclinical and clinical studies, continuous efforts have been made to overcome this gender bias. Examining retrospectively 21 data sets containing sex as one of the descriptive variables, it was possible to verify how many times our AI protocol decided to keep gender information in the predictive model. The data sets pertained a vast array of diseases such as dyspeptic syndrome, atrophic gastritis, venous thrombosis, gastroesophageal reflux disease, irritable bowel syndrome, Alzheimer diseases and mild cognitive impairment, myocardial infarction, gastrointestinal bleeding, gastric cancer, hypercortisolism, AIDS, COVID diagnosis, extracorporeal membrane oxygenation in intensive therapy, among others. The sample size of these data sets ranged between 80 and 3147 (average 600). The number of variables ranged from 19 to 101 (average 41). Gender resulted to be part of the heuristic predictive model 19 out of 21 times. This means that also for highly adaptive and potent tools like Artificial Neural Networks, information on sex carries a specific value. In the field of rheumatology, there is a specific example in psoriatic arthritis that shows that the presence of gender information allows a significantly better accuracy of ANNs in predicting diagnosis from clinical data (from 87.7% to 94.47%). The results of this study confirm the importance of gender information in building high performance predictive model in the field of Artificial Intelligence (AI). Therefore, also for AI, sex counts.

Artificial neural networks reveal sex differences in gene methylation, and connections between maternal risk factors and symptom severity in autism spectrum disorder.

Aim and methods: Artificial neural networks were used to unravel connections among blood gene methylation levels, sex, maternal risk factors and symptom severity evaluated using the Autism Diagnostic Observation Schedule 2 (ADOS-2) score in 58 children with autism spectrum disorder (ASD). Results: Methylation levels of MECP2, HTR1A and OXTR genes were connected to females, and those of EN2, BCL2 and RELN genes to males. High gestational weight gain, lack of folic acid supplements, advanced maternal age, preterm birth, low birthweight and living in rural context were the best predictors of a high ADOS-2 score. Conclusion: Artificial neural networks revealed links among ASD maternal risk factors, symptom severity, gene methylation levels and sex differences in methylation that warrant further investigation in ASD.

Sensory Profile of Children and Adolescents with Autism Spectrum Disorder and Tip-Toe Behavior: Results of an Observational Pilot Study.

Atypical sensory processing is frequently reported in persons with autism spectrum disorders (ASD), and it is one of the described diagnostic criteria for ASD. There is also mounting literature supporting the presence of motor impairments in individuals with ASD. Among these motor signs, tip-toe behavior (TTB) is a possible clinical finding, but its etiology is not clearly understood. It is suggested that TTB in ASD could be a sign of a sensory modulation impairment, but evidence is lacking and controversial. The main aim of this pilot study is to explore sensory features in a sample (4 females; 28 males) of children and adolescents with ASD (age range: 7-18). All participants also presented Intellectual Disability. Participants were divided in two groups, matched for age and gender, on the basis of the presence or absence of TTB (16 ASD TTB group vs. 16 ASD NO-TTB group) and then evaluated by using the Short Sensory Profile. We found that both ASD groups tend to significantly present sensory-related behavioral symptoms, but ASD TTB individuals more frequently showed the specific pattern of "under responsive/seeks sensation" than ASD NO-TTB individuals. These preliminary findings support that sensory-motor features might be taken into consideration when rehabilitation for TTB in children and adolescents with ASD is necessary.

Hyperandrogenism and menstrual imbalance are the best predictors of metformin response in PCOS patients.

BACKGROUND: Moving from the correlation between insulin-resistance and PCOS, metformin has been administered in some PCOS women improving ovulatory and metabolic functions and decreasing androgen levels. Inconsistency and unpredictability of response to metformin limit its extensive use. Aim of this study was to identify reliable predictors of response to metformin therapy for weight loss and reduction in plasma androgen levels using ANNs (artificial neural networks). METHODS: One hundred eight consecutive women with PCOS (ESHRE/ASRM 2003 Rotterdam criteria) treated with metformin 1500 mg/day, at inclusion and every 6 months underwent to a complete clinical, endocrine/metabolic assessment and ultrasonographic evaluation. Therapy outcomes were BMI reduction (≥1 kg/m2) in overweight/obese and free-androgen-index (FAI) decrease (≥1%) in hyperandrogenemic women. Semantic connectivity maps (SCMs) were obtained through Auto-CM, a fourth generation ANN, to compare patients' baseline clinical features to the treatment outcomes. Multivariate logistic regression analysis was used to assess the major predictor in drop-out patients and the associated risk. RESULTS: At 6 months 54 out of 103 (52,4%) obese patients showed BMI reduction and 45 out of 89 (50,6%) hyperandrogenemic women showed FAI decrease. The further response rates at 12 months were 30,6 and 47%, respectively. SCMs showed a clear polarization for both the outcomes with elevated accuracy. Treatment responsiveness resulted strictly related to oligo-amenorrhea and hyperandrogenemia at baseline. In addition, lower serum testosterone levels at baseline were found to be the major predictor of treatment discontinuation. CONCLUSIONS: In women with PCOS, menstrual pattern imbalance and ovarian androgens excess are the best predictors of metformin response. They may pave the way for a rethinking of the criteria for evaluating hyperandrogenism in order to better define the large population included in the diagnosis of PCOS. Baseline plasma testosterone level can serve as a sensitive marker to predict treatment compliance.

Assessment of New Coronary Features on Quantitative Coronary Angiographic Images With Innovative Unsupervised Artificial Adaptive Systems: A Proof-of-Concept Study.

Background and Purpose: The Active Connection Matrixes (ACMs) are unsupervised artificial adaptive systems able to extract from digital images features of interest (edges, tissue differentiation, etc.) unnoticeable with conventional systems. In this proof-of-concept study, we assessed the potentiality of ACMs to increase measurement precision of morphological structures (e.g., stenosis and lumen diameter) and to grasp morphological features (arterial walls) from quantitative coronary angiography (QCA), unnoticeable on the original images. Methods: Archive images of QCA and intravascular ultrasound (IVUS) of 10 patients (8 men, age 69.1 ± 9.7 years) who underwent both procedures for clinical reasons were retrospectively analyzed. Arterial features derived from "IVUS images," "conventional QCA images," and "ACM-reprocessed QCA images" were measured in 21 coronary segments. Portions of 1-mm length (263 for lumen and 526 for arterial walls) were head-to-head compared to assess quali-quantitative between-methods agreement. Results: When stenosis was calculated on "ACM-reprocessed QCA images," the bias vs. IVUS (gold standard) did not improve, but the correlation coefficient of the QCA-IVUS relationship increased from 0.47 to 0.83. When IVUS-derived lumen diameters were compared with diameters obtained on ACM-reprocessed QCA images, the bias (-0.25 mm) was significantly smaller (p < 0.01) than that observed with original QCA images (0.58 mm). ACMs were also able to extract arterial wall features from QCA. The bias between the measures of arterial walls obtained with IVUS and ACMs, although significant (p < 0.01), was small [0.09 mm, 95% CI (0.03, 0.14)] and the correlation was fairly good (r = 0.63; p < 0.0001). Conclusions: This study provides proof of concept that ACMs increase the measurement precision of coronary lumen diameter and allow extracting from QCA images hidden features that mirror well the arterial walls derived by IVUS.

Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children.

INTRODUCTION: Accurate assessment of resting energy expenditure (REE) can guide optimal nutritional prescription in critically ill children. Indirect calorimetry (IC) is the gold standard for REE measurement, but its use is limited. Alternatively, REE estimates by predictive equations/formulae are often inaccurate. Recently, predicting REE with artificial neural networks (ANN) was found to be accurate in healthy children. We aimed to investigate the role of ANN in predicting REE in critically ill children and to compare the accuracy with common equations/formulae. STUDY METHODS: We enrolled 257 critically ill children. Nutritional status/vital signs/biochemical values were recorded. We used IC to measure REE. Commonly employed equations/formulae and the VCO2-based Mehta equation were estimated. ANN analysis to predict REE was conducted, employing the TWIST system. RESULTS: ANN considered demographic/anthropometric data to model REE. The predictive model was good (accuracy 75.6%; R2 = 0.71) but not better than Talbot tables for weight. After adding vital signs/biochemical values, the model became superior to all equations/formulae (accuracy 82.3%, R2 = 0.80) and comparable to the Mehta equation. Including IC-measured VCO2 increased the accuracy to 89.6%, superior to the Mehta equation. CONCLUSIONS: We described the accuracy of REE prediction using models that include demographic/anthropometric/clinical/metabolic variables. ANN may represent a reliable option for REE estimation, overcoming the inaccuracies of traditional predictive equations/formulae.

Bone Strain Index predicts fragility fracture in osteoporotic women: an artificial intelligence-based study.

BACKGROUND: We applied an artificial intelligence-based model to predict fragility fractures in postmenopausal women, using different dual-energy x-ray absorptiometry (DXA) parameters. METHODS: One hundred seventy-four postmenopausal women without vertebral fractures (VFs) at baseline (mean age 66.3 ± 9.8) were retrospectively evaluated. Data has been collected from September 2010 to August 2018. All subjects performed a spine x-ray to assess VFs, together with lumbar and femoral DXA for bone mineral density (BMD) and the bone strain index (BSI) evaluation. Follow-up exams were performed after 3.34 ± 1.91 years. Considering the occurrence of new VFs at follow-up, two groups were created: fractured versus not-fractured. We applied an artificial neural network (ANN) analysis with a predictive tool (TWIST system) to select relevant input data from a list of 13 variables including BMD and BSI. A semantic connectivity map was built to analyse the connections among variables within the groups. For group comparisons, an independent-samples t-test was used; variables were expressed as mean ± standard deviation. RESULTS: For each patient, we evaluated a total of n = 6 exams. At follow-up, n = 69 (39.6%) women developed a VF. ANNs reached a predictive accuracy of 79.56% within the training testing procedure, with a sensitivity of 80.93% and a specificity of 78.18%. The semantic connectivity map showed that a low BSI at the total femur is connected to the absence of VFs. CONCLUSION: We found a high performance of ANN analysis in predicting the occurrence of VFs. Femoral BSI appears as a useful DXA index to identify patients at lower risk for lumbar VFs.

Nutrition and Autism Spectrum Disorder: Between False Myths and Real Research-Based Opportunities.

Autism Spectrum Disorder (ASD) is a multicomplex disorder characterized by an umbrella of specific issues in the areas of social communication, restricted interests, and repetitive behaviors [...].

Artificial Neural Networks Analysis of polysomnographic and clinical features in Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS): from sleep alteration to "Brain Fog".

STUDY OBJECTIVES: PANS (pediatric acute onset neuropsychiatric syndrome) is thought to be the result of several mechanisms and multiple etiologies, ranging from endocrine/metabolic causes to postinfectious autoimmune and neuroinflammatory disorders. Sleep disorders represent one of the most frequent manifestations of PANS, involving around 80% of patients. The present study describes the clinical and polysomnographic features in a group of PANS children identifying the relationships between sleep disorders and other PANS symptoms. METHODS: All participants underwent a clinical evaluation including comprehensive sleep history, polysomnography, cognitive assessment and blood chemistry examination. A data mining approach with fourth-generation artificial neural networks has been used in order to discover subtle trends and associations among variables. RESULTS: Polysomnography showed abnormality in 17 out of 23 recruited subjects (73.9%). In particular, 8/17 children (47%) had ineffective sleep, 10/17 (58.8%) fragmented sleep, 8/17 (47.1%) periodic limb movement disorder (PLMD) and 11/17 (64.7%) REM-sleep without atonia (RSWA). Most subjects presented more than one sleep disturbances. Notably, among the 19/23 patients diagnosed with Tic/Tourette disorder, 8/19 (42.1%) show PLMD and 10/19 (52.6%) RSWA. Artificial neural network methodology and the auto-contractive map exploited the links among the full spectrum of variables revealing the simultaneous connections among them, facing the complexity of PANS phenotype. CONCLUSION: Disordered sleep represents, for prevalence and impact on quality of life, a cardinal symptom in patients with PANS. Thus, considering the weight of sleep disturbances on diagnosis and prognosis of PANS, we could consider the possibility of including them among the major diagnostic criteria.

Patterns of Restricted and Repetitive Behaviors in Autism Spectrum Disorders: A Cross-Sectional Video Recording Study. Preliminary Report.

BACKGROUND: Several instruments have been proposed to investigate restricted, repetitive behaviors (RRBs) in individuals with Autism Spectrum Disorder (ASD). Systematic video observations may overcome questionnaire and interview limitations to investigate RRBs. This study aimed to analyze stereotypic patterns through video recordings and to determine the correlation between the number and appearance of RRBs to ASD severity. METHODS: Twenty health professionals wearing a body cam recorded 780 specific RRBs during everyday activities of 67 individuals with ASD (mean age: 14.2 ± 3.72 years) for three months. Each stereotypy was classified according to its complexity pattern (i.e., simple or complex) based on body parts and sensory channels involved. RESULTS: The RRBs spectrum for each subject ranged from one to 33 different patterns (mean: 11.6 ± 6.82). Individuals with a lower number of stereotypies shown a lower ASD severity compared to subjects with a higher number of stereotypies (p = 0.044). No significant differences were observed between individuals exhibiting simple (n = 40) and complex patterns (n = 27) of stereotypies on ASD severity, age, sex, and the number of stereotypes. CONCLUSIONS: This study represents the first attempt to systematically document expression patterns of RRBs with a data-driven approach. This may provide a better understanding of the pathophysiology and management of RRBs.

Bone strain index as a predictor of further vertebral fracture in osteoporotic women: An artificial intelligence-based analysis.

BACKGROUND: Osteoporosis is an asymptomatic disease of high prevalence and incidence, leading to bone fractures burdened by high mortality and disability, mainly when several subsequent fractures occur. A fragility fracture predictive model, Artificial Intelligence-based, to identify dual X-ray absorptiometry (DXA) variables able to characterise those patients who are prone to further fractures called Bone Strain Index, was evaluated in this study. METHODS: In a prospective, longitudinal, multicentric study 172 female outpatients with at least one vertebral fracture at the first observation were enrolled. They performed a spine X-ray to calculate spine deformity index (SDI) and a lumbar and femoral DXA scan to assess bone mineral density (BMD) and bone strain index (BSI) at baseline and after a follow-up period of 3 years in average. At the end of the follow-up, 93 women developed a further vertebral fracture. The further vertebral fracture was considered as one unit increase of SDI. We assessed the predictive capacity of supervised Artificial Neural Networks (ANNs) to distinguish women who developed a further fracture from those without it, and to detect those variables providing the maximal amount of relevant information to discriminate the two groups. ANNs choose appropriate input data automatically (TWIST-system, Training With Input Selection and Testing). Moreover, we built a semantic connectivity map usingthe Auto Contractive Map to provide further insights about the convoluted connections between the osteoporotic variables under consideration and the two scenarios (further fracture vs no further fracture). RESULTS: TWIST system selected 5 out of 13 available variables: age, menopause age, BMI, FTot BMC, FTot BSI. With training testing procedure, ANNs reached predictive accuracy of 79.36%, with a sensitivity of 75% and a specificity of 83.72%. The semantic connectivity map highlighted the role of BSI in predicting the risk of a further fracture. CONCLUSIONS: Artificial Intelligence is a useful method to analyse a complex system like that regarding osteoporosis, able to identify patients prone to a further fragility fracture. BSI appears to be a useful DXA index in identifying those patients who are at risk of further vertebral fractures.

Communication improvement reduces BPSD: a music therapy study based on artificial neural networks.

BACKGROUND: Literature showed the effects of music therapy on behavioral disturbances, cognitive functions, and on quality of life in people with dementia. Especially, relational active music therapy approach is oriented to reduce behavioral disturbances increasing communication, especially non-verbal communication. OBJECTIVE: This study aimed at exploring the connection between the baseline characteristics of responders and the positive outcome of the intervention, but also the close relationship between the behavioral disturbances and the core of the therapeutic intervention (the relationship/communication improvement). METHOD: Linear correlation index between input variables and the presence of a critical improvement of behavioral symptoms according Neuropsychiatric Inventory and a semantic connectivity map were used to determine, respectively, variables predictive of the response and complex connections between clinical variables and the relational nature of active music therapy intervention. The dataset was composed of 27 variables and 70 patients with a moderate-severe stage of dementia and behavioral disturbances. RESULTS: The main predictive factor is the Barthel Index, followed by NPI and some of its sub-items (mainly, Disinhibition, Depression, Hallucinations, Irritability, Aberrant Motor Activity, and Agitation). Moreover, the semantic map underlines how the improvement in communication/relationship is directly linked to "responder" variable. "Responder" variable is also connected to "age," "Mini Mental State Examination," and sex ("female"). CONCLUSIONS: The study confirms the appropriateness of active music therapy in the reduction of behavioral disturbances and also highlights how unsupervised artificial neural networks models can support clinical practice in defining predictive factors and exploring the correlation between characteristics of therapeutic-rehabilitative interventions and related outcomes.