Facial emotion recognition in people with schizophrenia and a history of violence: a mediation analysis.

Evidence for an association between impaired facial emotion recognition and violence in people with schizophrenia is inconclusive. In particular, the role of misidentification patterns involving specific emotions such as anger and the influence of clinical characteristics on this association remain unclear. In this study, we compared facial emotion recognition performance in age- and gender-matched schizophrenia spectrum disorders subjects with (N = 52) and without (N = 52) a history of violence. Data on current symptom severity, Cluster B personality status, past victimization, and alcohol and substance misuse were also collected. Compared to those without, subjects with a history of violence showed worse facial emotion recognition performances, involving anger, fear, disgust, sadness, and happiness. When formally testing the reporting of angry faces, evidence of enhanced sensitivity to anger was not supported. Finally, when the impact of current symptoms was assessed, higher severity of activation symptoms, including motor hyperactivity, elevated mood, excitement and distractibility, mediated the relationship between history of violence and poor facial emotion recognition performance. As a whole, our findings seem to support the role of perceptual deficits involving different emotions as well as of a mediation played by activation symptoms. Facial emotion recognition deficits associated with the propensity to violence, as well certain symptoms mediating their relationship, should be targeted by specific treatment approaches.

Substance use disorders and violent behaviour in patients with severe mental disorders: A prospective, multicentre study.

OBJECTIVE: The relationship between alcohol and substance use and the risk of violence exhibited by patients with mental disorders is under-researched. This prospective cohort study aims to compare patients with severe mental disorders and with different substance use behaviors in terms of sociodemographic and clinical characteristics, hostility, impulsivity and aggressive behaviors. Furthermore, this study aims to assess differences in violent behaviors during a 1-year monitoring follow-up. METHODS: A total of 378 participants with severe mental disorders from Italian residential facilities and from four Departments of Mental Health (244 outpatients and 134 residential patients) were enrolled. Participants were categorized as Persons with Current Substance Use, Persons with Former Substance Use and Persons with Non-Substance Use. All these patients underwent a complex multidimensional assessment, including the lifetime and current substance use; a subsample of outpatients was also assessed with a laboratory substance assay including the testing for specific substances. We assessed the differences among these three groups in hostility, impulsivity and aggressive behaviors. RESULTS: The results of the close 1-year monitoring show a significantly higher risk of violence for patients with severe mental disorders Persons with Current Substance Use compared to Persons with Former Substance Use and Persons with Non-Substance Use. Persons with Current Substance Use showed significantly higher scores for irritability, negativism and verbal assault compared to Persons with Non-Substance Use. Persons with Former Substance Use showed significantly higher scores for lifetime history of aggressive behaviors compared with patients with Persons with Non-Substance Use. CONCLUSION: These findings suggest that patients with comorbid mental illness and substance use disorders should be referred for specific interventions to reduce aggressive behavior and ensure patient well-being and community safety.

Aggressive behavior and metacognitive functions: a longitudinal study on patients with mental disorders.

BACKGROUND: Metacognitive functions play a key role in understanding which psychological variables underlying the personality might lead a person with a severe mental disorder to commit violent acts against others. The aims of this study were to: (a) investigate the differences between patients with poor metacognitive functioning (PM group) and patients with good metacognitive functioning (GM group) in relation to a history of violence; (b) investigate the differences between the two groups in relation to aggressive behavior during a 1-year follow-up; and (c) analyze the predictors of aggressive behavior. METHODS: In a prospective cohort study, patients with severe mental disorders with and without a lifetime history of serious violence were assessed with a large set of standardized instruments and were evaluated bi-monthly with MOAS in order to monitor any aggressive behavior. The total sample included 180 patients: 56% outpatients and 44% inpatients, and the majority were male (75%) with a mean age of 44 (± 9.8) years, and half of them had a history of violence. The sample was split into two groups: poor metacognition (PM) group and good metacognition (GM) group, according to MAI evaluation scores. RESULTS: The PM patients reported a history of violence more frequently than GM patients, during the 1-year follow-up, but no differences between groups in aggressive and violent behavior were found. The strongest predictors of aggressive behavior were: borderline and passive-aggressive personality traits and a history of violence, anger, and hostility. The metacognitive functions alone did not predict aggressive behavior, but metacognitive functions interacted with hostility and angry reactions in predicting aggressive behavior. CONCLUSIONS: This study led to some important conclusions: (a) some aspects closely related to violence are predictive of aggressive behavior only in patients with poor metacognition, thus good metacognition is a protective factor; (b) poor metacognition is associated with a history of violence, which in turn increases the risk of committing aggressive behavior.

[Risk factors in police activities: operational criticism in surveillance programs]. / Fattori di rischio nell'attività di Polizia: criticità operative nei protocolli di sorveglianza.

The planning of specific health surveillance programs for police officers is extremely complex due to difficulty in predictability and variety of occupational hazards. Even in the case of conventional occupational risk factors clearly identified by current regulations, particular working conditions may require specific assessment to effectively identify and quantify the risk of occupational exposure. An extensive program of health surveillance, aimed at promoting overall health and effectiveness of the operators, would be really desirable, in order to help better address a number of risks that cannot be easily predicted. The progressive increase in the average age of the working population and the increasing prevalence of chronic degenerative diseases, may also suggest the need for health surveillance procedures designed to verify continued unqualified suitability to police service, providing for the identification of diversified suitability profiles in relation to age and state of health: accordingly, in regard to our field of interest, there is a close link between medico-legal eligibility and occupational medicine.

RISK aversion in Italian forensic and non-forensic patients with schizophrenia spectrum disorders.

BACKGROUND: Goal-directed decision-making is a central component of the broader reward and motivation system, and requires the ability to dynamically integrate both positive and negative feedback from the environment in order to maximize rewards and minimize losses over time. Altered decision-making processes, in which individuals fail to consider the negative consequences of their decisions on both themselves and others, may play a role in driving antisocial behaviour. AIM: The main study aim was to investigate possible differences in loss and risk aversion across matched patients, all with a schizophrenia spectrum disorder (SSD), but who varied according to whether they had a history of serious interpersonal violence or not, and a sample of healthy controls with no history of violence. RESULTS: The sample included 14 forensic and 21 non-forensic patients with SSD, and 41 healthy controls. Among the three decision-making variables under investigation, risk aversion was the only significant predictor of membership of the three groups, with greater risk aversion among non-forensic patients with SSD compared to healthy controls. No differences were observed across groups in loss aversion and choice consistency. CONCLUSIONS: This evidence suggests a new potential treatment target for rehabilitative measures aimed at achieving functional improvements in patients with SSD by selectively leveraging the neuro-cognitive processing of reward.

Personality, Schizophrenia, and Violence: A Longitudinal Study: The Second Wave of the VIORMED Project.

This study investigated the association between maladaptive personality traits, personality disorders (PDs), schizophrenia, and the risk of aggressive behavior. Ninety-four patients with a history of violence and 92 patients with no history of violence underwent a multidimensional baseline assessment. Aggressive behavior was monitored during a 1-year follow-up through the Modified Overt Aggression Scale. The Violent group scored significantly higher than the Control group on the Millon Clinical Multiaxial Inventory (MCMI-III) Antisocial, Sadistic, Borderline, and Paranoid personality scales. Irrespective of any history of violence, patients with PD as a primary diagnosis displayed more aggressive behaviors than those with a primary diagnosis of schizophrenia during the follow-up. Furthermore, the most significant predictor of aggressive behaviors over time was endorsing a primary diagnosis of PD. Identifying the crucial risk factors for violent recidivism would contribute to reducing aggressive behavior in this population.

An Ad Hoc Random Initialization Deep Neural Network Architecture for Discriminating Malignant Breast Cancer Lesions in Mammographic Images.

Breast cancer is one of the most common cancers in women, with more than 1,300,000 cases and 450,000 deaths each year worldwide. In this context, recent studies showed that early breast cancer detection, along with suitable treatment, could significantly reduce breast cancer death rates in the long term. X-ray mammography is still the instrument of choice in breast cancer screening. In this context, the false-positive and false-negative rates commonly achieved by radiologists are extremely arduous to estimate and control although some authors have estimated figures of up to 20% of total diagnoses or more. The introduction of novel artificial intelligence (AI) technologies applied to the diagnosis and, possibly, prognosis of breast cancer could revolutionize the current status of the management of the breast cancer patient by assisting the radiologist in clinical image interpretation. Lately, a breakthrough in the AI field has been brought about by the introduction of deep learning techniques in general and of convolutional neural networks in particular. Such techniques require no a priori feature space definition from the operator and are able to achieve classification performances which can even surpass human experts. In this paper, we design and validate an ad hoc CNN architecture specialized in breast lesion classification from imaging data only. We explore a total of 260 model architectures in a train-validation-test split in order to propose a model selection criterion which can pose the emphasis on reducing false negatives while still retaining acceptable accuracy. We achieve an area under the receiver operatic characteristics curve of 0.785 (accuracy 71.19%) on the test set, demonstrating how an ad hoc random initialization architecture can and should be fine tuned to a specific problem, especially in biomedical applications.

A random initialization deep neural network for discriminating malignant breast cancer lesions.

Breast cancer is one of the most common cancer in women, with more than 1,300,000 cases and 450,000 deaths each year worldwide. Recent studies show that early breast cancer detection, along with suitable treatment, could significantly reduce breast cancer death rates in the long-term. While the consequences of a false positive diagnosis can be psychologically and socioeconomically burdensome, the result of a false negative diagnosis can be devastating, especially in terms of health detriment. In this context, the false positive and false negative rates commonly achieved by radiologists are extremely arduous to estimate and control, and some authors have estimated figures of up to 20% of total diagnoses or more. Novel ideas in computer-assisted diagnosis have been prompted by the introduction of deep learning techniques in general and of convolutional neural networks (CNN) in particular. In this paper, we design and validate an ad-hoc CNN architecture specialized in breast lesion classification and heuristically explore possible parameter combinations and architecture styles in order to propose a model selection criterion which can pose the emphasis on reducing false negatives while still retaining acceptable accuracy. We achieve good classification performance on the validation and test set, demonstrating how an ad-hoc, random initialization CNN architecture can provide practical aid in the classification and staging of breast cancer.

Publisher Correction: Self-harm behaviour and externally-directed aggression in psychiatric outpatients: a multicentre, prospective study (viormed-2 study).

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Self-harm behaviour and externally-directed aggression In psychiatric outpatients: a multicentre, prospective study (viormed-2 study).

The aim of the project was to investigate differences between outpatients with Severe Mental Disorders (SMDs) with and without a history of Self-Harm behaviour (SHb) and/or Violent behaviour against other people (Vb) in relation to: (a) socio-demographic and clinical characteristics, (b) violent behaviour during a 1-year FU, (c) predictors of SHb and Vb during the FU. Outpatients with SMDs, with and without a history of Vb were enrolled. They were divided in four groups: patients with lifetime Vb (V), patients with both Vb and SHb (V-SH), patients with only SHb (SH) and patients with no history of SHb and Vb (control group, CONT). The frequency and severity of SHb and Vb during the FU were assessed every two weeks by the MOAS. Overall 246 patients were enrolled. BPRS-E Depression item, the SLOF Social acceptability, the BDHI Indirect Aggression, the BIS Motor Impulsiveness and the STAXI-2 Control-Out showed significant correlations with all the four groups (p < 0.030). V and V-SH patient groups reached higher scores in all MOAS sub-scales. Age among the SH group and BPRS-E affect-anxiety subscale among the V group significantly predicted aggression against people. In people with SMDs a history of SHb or Vb is associated with different medium-term outcomes.

Traditional Chinese acupuncture in tension-type headache: a controlled study.

Thirty patients with tension-type headache were randomly chosen to undergo a trial of traditional Chinese acupuncture and sham acupuncture. Five measures were used to assess symptom severity and treatment response: intensity, duration and frequency of headache pain episodes, headache index and analgesic intake. The five measures were assessed during a 4 week baseline period, after 4 and 8 weeks of treatment, and 1, 6 and 12 months thereafter. Before the start of the study, each patient was administered the MMPI. Split-plot ANOVAs showed that, compared to baseline, at 1 month after the end of treatment and for the 12 month follow-up, the frequency of headache episodes, analgesic consumption and the headache index (but not the duration or intensity of headache episodes) significantly decreased over time; however, no difference between acupuncture and placebo treatment was found. No single MMPI scale predicted the response to treatment, but the mean MMPI profile of acupuncture non-responders showed the presence of 'Conversion V'.