Sexuality is a basic need of every human being. However, it can somrtimes become deviant. Unlike alcohol or drug abuse, suppression of sexual behavior is not an option, only compulsive character must be suppressed in order to allow a full and satisfying sexuality.

The purpose of this observation is to explore the psychological and physical consequences of sexual addiction.

We report the case of a patient with sexual addiction, in order to describe the impact of this addiction on his psychological and physical health.

Mr. JG is 28 a years old man, he has no particular habits and no significant pathological history. He was addressed by his general practitioner for “excessive masturbation”.

His sexual history is characterized by:

• - An excessive interest in pornographic films since the age of 13,

• - An extremely rich fantasy life,

• - Frequent compulsive masturbation, going up to ten times a day.

Through the years, this hyper sexuality has become more important, and masturbation more frequent, which resulted in:

• - Permanent feeling of embarrassment and shame,

• - Ideas permanently focused on sexual thoughts,

• - Fatigue and chronical physical exhaustion,

• - Constant muscle pain and headache, diminished visual acuity.

• - Relationship difficulties, and social maladjustment,

• - Depression and suicidal thoughts.

The evaluation by the Carnes scale found a score of 16/25, which corresponds to a severe sexual addiction.

The boundary between normal and pathological depends on the models and taboos of society, but for physicians, the major criteria for sexual addiction are mental suffering, social maladjustment, and demand of care.

The aim of our case report is to highlight the possible association of NF1 and psychotic symptoms, to underline the usefulness of a complete somatic examination when confronting with epileptic or mentally retarded teens experiencing an hallucinatory syndrome.

We report the case of a 17-years-old boy, with consanguineous parents and history of mental retardation and generalized epilepsy since the age of three, consulting for auditive and visual hallucinations and an heteroagressiveness since two months. Skin examination found signs of NF1.

Electroencephalography (EEG) showed no epileptic abnormalities and brain MRI showed no abnormalities than could explain the psychotic symptoms that are usually parts of the organic brain syndrome. A medication by 2 mg per day of Risperidone has completely eliminate the psychotic symptoms within two months and is continued for one year at least.

Neurological and psychiatric disorders are often intricate. Psychiatrists should have a better knowledge of both psychiatric and somatic manifestations of NF1 to not pass through the right diagnosis and to ensure to these patients better intellectual, academic and social skills.

Identifying routes of transmission among hospitalized patients during a healthcare-associated outbreak can be tedious, particularly among patients with complex hospital stays and multiple exposures. Data mining of the electronic health record (EHR) has the potential to rapidly identify common exposures among patients suspected of being part of an outbreak.

We retrospectively analyzed 9 hospital outbreaks that occurred during 2011–2016 and that had previously been characterized both according to transmission route and by molecular characterization of the bacterial isolates. We determined (1) the ability of data mining of the EHR to identify the correct route of transmission, (2) how early the correct route was identified during the timeline of the outbreak, and (3) how many cases in the outbreaks could have been prevented had the system been running in real time.

Correct routes were identified for all outbreaks at the second patient, except for one outbreak involving >1 transmission route that was detected at the eighth patient. Up to 40 or 34 infections (78% or 66% of possible preventable infections, respectively) could have been prevented if data mining had been implemented in real time, assuming the initiation of an effective intervention within 7 or 14 days of identification of the transmission route, respectively.

Data mining of the EHR was accurate for identifying routes of transmission among patients who were part of the outbreak. Prospective validation of this approach using routine whole-genome sequencing and data mining of the EHR for both outbreak detection and route attribution is ongoing.

To describe the investigation and control of a rare cluster of Klebsiella pneumoniae carbapenemase–producing Citrobacter freundii in a hospital in southern Florida.

An epidemiologic investigation, review of infection prevention procedures, and molecular studies including whole genome sequencing were conducted.

An outbreak of K. pneumoniae carbapenemase–3-producing C. freundii was identified at a tertiary hospital in Florida in 2014. Of the 6 cases identified, 3 occurred in the same intensive care unit and were caused by the same clone. For 2 of the 3 remaining cases, the isolates had low carbapenem minimum inhibitory concentrations and were unrelated by whole genome sequencing. As a response to the outbreak, supplementary environmental cleaning was implemented, including closure and terminal cleaning of the unit where the 3 cases clustered, in addition to the infection control bundle already in place at the time. No further cases were identified after these additional interventions.

Although C. freundii is not a species that commonly demonstrates carbapenem resistance, our findings suggest that carbapenemase-producing C. freundii may be underdetected even when active surveillance is in place and has a potential to cause hospital outbreak.

Infect Control Hosp Epidemiol 2017;38:320–326

Hospitals are meant to be places for respite and healing; however, technological advances and reliance on monitoring alarms has led to the environment becoming increasingly noisy. The coronary care unit (CCU), like the emergency department, provides care to ill patients while being vulnerable to noise pollution. The World Health Organization (WHO; Geneva, Switzerland) recommends that for optimum rest and healing, sound levels should average approximately 30 decibels (dB) with maximum readings less than 40 dB.

The purpose of this study was to measure and analyze sound levels in three different locations in the CCU, and to review alarm reports in relation to sound levels.

Over a one-month period, sound recorders (Extech SDL600; Extech Instruments; Nashua, New Hampshire USA) were placed in three separate locations in the CCU at the West Roxbury Veterans’ Administration (VA) Hospital (Roxbury, Massachusetts USA). Sound samples were recorded once per second, stored in Comma Separated Values format for Excel (Microsoft Corporation; Redmond, Washington USA), and then exported to Microsoft Excel. Averages were determined, plotted per hour, and alarm histories were reviewed to determine alarm noise effect on total noise for each location, as well as common alarm occurrences.

Patient Room 1 consistently had the lowest average recordings, though all averages were >40 dB, despite decreases between 10:00 pm and 7:00 am. During daytime hours, recordings maintained levels >50 dB. Overnight noise remained above recommended levels 55.25% of the period in Patient Room 1 and 99.61% of the same time period in Patient Room 7. The nurses’ station remained the loudest location of all three. Alarms per hour ranged from 20-26 during the day. Alarms per day averaged: Patient Room 1-57.17, Patient Room 7-122.03, and the nurses’ station - 562.26. Oxygen saturation alarms accounted for 33.59% of activity, and heart-related (including ST segment and pacemaker) accounted for 49.24% of alarms.

The CCU cares for ill patients requiring constant monitoring. Despite advances in technology, measured noise levels for the hospital studied exceeded WHO standards of 40 dB and peaks of 45 dB, even during night hours when patients require rest. Further work is required to reduce noise levels and examine effects on patient satisfaction, clinical outcomes, and length of stay.

RyanKM, GagnonM, HannaT, MelloB, FofanaM, CiottoneG, MolloyM. Noise Pollution: Do We Need a Solution? An Analysis of Noise in a Cardiac Care Unit. Prehosp Disaster Med. 2016;31(4):432–435.