Traumatic Hystero-Neurasthenia in Professor Charcot's Leçons du Mardi.

At the end of the 19th century, several authors became interested in the physical and psychological symptoms resulting from traumatic life events. Oppenheim presented 42 detailed clinical observations. He suggested the term "traumatic neurosis." Charcot, who was interested in male hysteria, published over 20 cases of traumatic hysteria between 1878 and 1893. The symptoms were considered to have a dynamic or functional origin. The role of horror and terror during the trauma was emphasized. However, Charcot opposed the idea of traumatic neuroses as specific syndromes as he considered them to be only an etiological form of hystero-neurasthenia. In The Tuesday Lessons (Les Leçons du Mardi), he presents several observations. They are surprising when compared with the current criteria for posttraumatic stress disorder (PTSD). Although he had rejected this new entity, a hundred years before the appearance of the Diagnostic and Statistical Manual of Mental Disorders, Third Edition, Revised, Charcot described most of the symptoms mentioned for a diagnosis of PTSD such as intrusion (reliving the trauma, nightmares, and severe emotional distress), avoidance, negative changes in thinking and mood (negative thoughts, lack of interest, etc.), arousal, and reactivity (trouble sleeping, trouble concentrating, being easily startled or frightened, irritability, etc.).

Customization of biomedical terminologies.

Within the biomedical area over one hundred terminologies exist and are merged in the Unified Medical Language System Metathesaurus, which gives over 1 million concepts. When such huge terminological resources are available, the users must deal with them and specifically they must deal with irrelevant parts of these terminologies. We propose to exploit seed terms and semantic distance algorithms in order to customize the terminologies and to limit within them a semantically homogeneous space. An evaluation performed by a medical expert indicates that the proposed approach is relevant for the customization of terminologies and that the extracted terms are mostly relevant to the seeds. It also indicates that different algorithms provide with similar or identical results within a given terminology. The difference is due to the terminologies exploited. A special attention must be paid to the definition of optimal association between the semantic similarity algorithms and the thresholds specific to a given terminology.

Grouping the pharmacovigilance terms with a hybrid approach.

Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs. It leads to the safety survey of pharmaceutical products. The pharmacovigilance process benefits from the traditional statistical approaches and also from the qualitative information on semantic relations between close ADR terms, such as SMQs or hierarchical levels of MedDRA. In this work, our objective is to detect the semantic relatedness between the ADR MedDRA terms. To achieve this, we combine two approaches: semantic similarity algorithms computed within structured resources and terminology structuring methods applied to a raw list of the MedDRA terms. We compare these methods between them and study their differences and complementarity. The results are evaluated against the gold standard manually compiled within the pharmacovigilance area and also with an expert. The combination of the methods leads to an improved recall.

Traumatic memory reactivation with or without propranolol for PTSD and comorbid MD symptoms: a randomised clinical trial.

Post-traumatic stress disorder (PTSD) is difficult to treat but one promising strategy is to block memory reconsolidation of the traumatic event. This study aimed to evaluate the efficacy of traumatic memory reactivation under the influence of propranolol, a noradrenergic beta-receptor blocker, in reducing PTSD symptoms as well as comorbid major depression (MD) symptoms. We conducted a double blind, placebo-controlled, randomised clinical trial in 66 adults diagnosed with longstanding PTSD. Propranolol or a placebo was administered 90 min before a brief memory reactivation session, once a week for 6 consecutive weeks. Measures included the SCID PTSD module, the PTSD Check List (PCL-S) and the Beck Depression Inventory-II (BDI-II). PTSD symptoms decreased both in the pre-reactivation propranolol group (39.28%) and the pre-reactivation placebo group (34.48 %). During the 6 treatment sessions, PCL-S and BDI-II scores decreased to similar extent in both groups and there were no treatment differences. During the 3-month follow-up period, there were no treatment effects for the mean PCL-S and BDI-II scores. However, in patients with severe PTSD symptoms (PCL-S ≥ 65) before treatment, PCL-S and BDI-II scores continued to decline 3 months after the end of treatment in the propranolol group while they increased in the placebo group. Repeated traumatic memory reactivation seemed to be effective for PTSD and comorbid MD symptoms. However, the efficacy of propranolol was not greater than that of placebo 1 week post treatment. Furthermore, in this traumatic memory reactivation, PTSD symptom severity at baseline might have influenced the post-treatment effect of propranolol.

Exploitation of semantic methods to cluster pharmacovigilance terms.

Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs. This activity is usually performed within dedicated databases (national, European, international...), in which the ADRs declared for patients are usually coded with a specific controlled terminology MedDRA (Medical Dictionary for Drug Regulatory Activities). Traditionally, the detection of adverse drug reactions is performed with data mining algorithms, while more recently the groupings of close ADR terms are also being exploited. The Standardized MedDRA Queries (SMQs) have become a standard in pharmacovigilance. They are created manually by international boards of experts with the objective to group together the MedDRA terms related to a given safety topic. Within the MedDRA version 13, 84 SMQs exist, although several important safety topics are not yet covered. The objective of our work is to propose an automatic method for assisting the creation of SMQs using the clustering of semantically close MedDRA terms. The experimented method relies on semantic approaches: semantic distance and similarity algorithms, terminology structuring methods and term clustering. The obtained results indicate that the proposed unsupervised methods appear to be complementary for this task, they can generate subsets of the existing SMQs and make this process systematic and less time consuming.