Evaluation of physicians' practice patterns in France concerning fertility preservation in women with endometriosis.

OBJECTIVE: We aim to evaluate the knowledge and physicians' practices concerning fertility preservation in women with endometriosis. DESIGN: Descriptive, observational, national study using an online self-questionnaire, sent by email to French gynaecologists in October 2019 within 2 months. RESULTS: We obtained 110 analyzable responses from mainly surgeons (54 %) and reproductive clinicians (19 %) with a good experience (average 15 years of practice). Amongst these practitioners, 91 % seemed aware of latest French recommendations on endometriosis issued in December 2017. The most commonly used surgical techniques for management of endometriomas were intra-peritoneal cystectomy (51 %), vaporization by plasma energy (29 %) and destruction by bipolar coagulation (8.5 %). Preoperative AMH was systematically or often prescribed by 78 % of the practitioners against 37.3 % who did it postoperatively. Furthermore, 74 % also considered and performed fertility preservation strategy to manage endometriosis. It was offered in situations of bilateral or recurrent endometrioma, but only 33 % offered it in unilateral endometrioma cases. In the cases recorded, vitrification of mature oocytes appears to be the most common fertility preservation technique (used by 87 % of the practitioners). CONCLUSION: We observed in our population of sensitized practitioners a good and adequate knowledge concerning endometriosis physiopathology and recommendations for its management, with good information delivery to women. Operating techniques are adapted although information and education concerning fertility preservation indications seem necessary. The place of multidisciplinary concertation meeting in endometriosis appears essential both for discussion of surgical indications and for fertility preservation possibilities. Creation of dedicated structures should be encouraged.

[Diagnosis of cardiac rhabdomyoma in the first trimester of pregnancy]. / Diagnóstico de un rabdomioma intracardiaco en el primer trimestre de la gestación.

BACKGROUND: Rhabdomyoma is the most common cardiac tumor detected during fetal life and childhood; despite of this, its incidence is very low. CLINICAL CASE: cardiac rhabdomyoma diagnosed at first trimester ultrasound. CONCLUSION: Early diagnosis is important for performing further studies to detect the possible presence of associated anomalies, including TS (tuberous sclerosis). Prognosis is conditioned by number, size and location. In our case, the presence of early hypoplastic right ventricle and pulmonar and tricuspid atresia (probably because of tumor size) conditioned a very bad prognosis, so the patient decided termination of pregnancy.

Effects of atmospheric pollutants (CO, O3, SO2) on the allergenicity of Betula pendula, Ostrya carpinifolia, and Carpinus betulus pollen.

Pollen of Betula pendula, Ostrya carpinifolia, and Carpinus betulus was exposed in vitro to relatively low levels of the air pollutants, namely carbon monoxide, ozone, and sulfur dioxide. The allergenicity of the exposed pollen was compared with that of non-exposed pollen samples to assess if air pollution exposition affects the allergenicity potential of pollen. The immunodetection assays indicated higher IgE recognition by all sera of allergic patients to the pollen protein extracts in all exposed samples in comparison to the non-exposed samples. These results show that the pollen exposition to low pollutants' levels induces increased allergic reaction to sensitized individuals.

Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms.

Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide after a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. The perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. The model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design.

In vitro exposure of Ostrya carpinifolia and Carpinus betulus pollen to atmospheric levels of CO, O3 and SO 2.

Ostrya spp. and Carpinus spp. pollen was in vitro exposed to three atmospheric pollutants: CO, O3 and SO2. Two levels of each pollutant were used, and the first level corresponds to a concentration about the atmospheric hour-limit value acceptable for human health protection in Europe and the second level to about the triple of the first level. Experiments were done under artificial solar light with temperature and relative humidity controlled. The viability of the exposed pollen samples showed a significant decrease. Also, the germination percentage showed a significant decrease in both exposed pollens, and the effect was most pronounced for SO2, followed by O3 and CO. A general decreasing trend in the total soluble protein content of the exposed pollen samples when compared with the control was observed, but it was only statistically significant for the Ostrya spp pollen. The results showed marked effects were observed on the Ostrya spp. and Carpinus spp. pollen when exposed to air pollutant levels that can be considered safe for human health protection.

LECTINPred: web Server that Uses Complex Networks of Protein Structure for Prediction of Lectins with Potential Use as Cancer Biomarkers or in Parasite Vaccine Design.

Lectins (Ls) play an important role in many diseases such as different types of cancer, parasitic infections and other diseases. Interestingly, the Protein Data Bank (PDB) contains +3000 protein 3D structures with unknown function. Thus, we can in principle, discover new Ls mining non-annotated structures from PDB or other sources. However, there are no general models to predict new biologically relevant Ls based on 3D chemical structures. We used the MARCH-INSIDE software to calculate the Markov-Shannon 3D electrostatic entropy parameters for the complex networks of protein structure of 2200 different protein 3D structures, including 1200 Ls. We have performed a Linear Discriminant Analysis (LDA) using these parameters as inputs in order to seek a new Quantitative Structure-Activity Relationship (QSAR) model, which is able to discriminate 3D structure of Ls from other proteins. We implemented this predictor in the web server named LECTINPred, freely available at http://bio-aims.udc.es/LECTINPred.php. This web server showed the following goodness-of-fit statistics: Sensitivity=96.7 % (for Ls), Specificity=87.6 % (non-active proteins), and Accuracy=92.5 % (for all proteins), considering altogether both the training and external prediction series. In mode 2, users can carry out an automatic retrieval of protein structures from PDB. We illustrated the use of this server, in operation mode 1, performing a data mining of PDB. We predicted Ls scores for +2000 proteins with unknown function and selected the top-scored ones as possible lectins. In operation mode 2, LECTINPred can also upload 3D structural models generated with structure-prediction tools like LOMETS or PHYRE2. The new Ls are expected to be of relevance as cancer biomarkers or useful in parasite vaccine design.

Effect of air pollutant NO2 on Betula pendula, Ostrya carpinifolia and Carpinus betulus pollen fertility and human allergenicity.

Pollen of Betula pendula, Ostrya carpinifolia and Carpinus betulus was exposed in vitro to two levels of NO2 (about 0.034 and 0.067 ppm) - both below current atmospheric hour-limit value acceptable for human health protection in Europe (0.11 ppm for NO2). Experiments were performed under artificial solar light with temperature and relative humidity continuously monitored. The viability, germination and total soluble proteins of all the pollen samples exposed to NO2 decreased significantly when compared with the non-exposed. The polypeptide profiles of all the pollen samples showed bands between 15 and 70 kDa and the exposure to NO2 did not produce any detectable changes in these profiles. However, the immunodetection assays indicated higher IgE recognition by patient sera sensitized to the pollen extracts from all exposed samples in comparison to the non-exposed samples. The common reactive bands to the three pollen samples correspond to 58 and 17 kDa proteins.

Naïve Bayes QSDR classification based on spiral-graph Shannon entropies for protein biomarkers in human colon cancer.

Fast cancer diagnosis represents a real necessity in applied medicine due to the importance of this disease. Thus, theoretical models can help as prediction tools. Graph theory representation is one option because it permits us to numerically describe any real system such as the protein macromolecules by transforming real properties into molecular graph topological indices. This study proposes a new classification model for proteins linked with human colon cancer by using spiral graph topological indices of protein amino acid sequences. The best quantitative structure-disease relationship model is based on eleven Shannon entropy indices. It was obtained with the Naïve Bayes method and shows excellent predictive ability (90.92%) for new proteins linked with this type of cancer. The statistical analysis confirms that this model allows diagnosing the absence of human colon cancer obtaining an area under receiver operating characteristic of 0.91. The methodology presented can be used for any type of sequential information such as any protein and nucleic acid sequence.

¿Cómo utilizamos los hipnóticos en atención primaria? / How do we use hypnotics in primary care?

Objetivos. Describir la utilización de hipnóticos y el perfil del consumidor. Material y métodos. Estudio descriptivo transversal multicéntrico, realizado en atención primaria del área de salud de Toledo. Población: pacientes >=18 años con prescripción de hipnóticos en el año 2007, no terminales. Resultados. Al 8,2% de la población en 2007 se le prescribió algún hipnótico. El principio activo más frecuente fue el lorazepam (58,5%), seguido de lormetazepam (13%). Hemos estudiado 639 pacientes, 68,2% mujeres, con una edad media de 64,2±16,7 años y una mediana de frecuentación de 29 consultas/año (R.I. 23 consultas/año). El 19,4% (DE 3,1) de los pacientes tomaron 2–3 principios activos distintos. Duración del tratamiento mediana de 78,4 semanas (R.I. 88,5). Según historia clínica: el 26,6% tienen 2 o más patologías crónicas, en el 30,9% (DE 3,6) constaba el diagnóstico asociado (8,4% insomnio, 69,4% depresión-ansiedad), tomaban fármacos con posibles interacciones con los hipnóticos 73,8% (DE 3,4), al 15% se revisa el tratamiento, 1,9% (DE 1,06) sufrieron reacciones adversas, 4,9% (DE 0,9) tuvieron caídas durante el tratamiento, el 12,5% (DE 2,3) de los tratamientos superaban la dosis máxima permitida. Conclusiones. El consumidor de hipnóticos de nuestro área es una mujer de 64 años, que vive en el medio rural, hiperfrecuentadora, con depresión, ansiedad y riesgo elevado de interacciones medicamentosas, que toma el hipnótico de forma crónica (AU)

Objectives. To describe the use of hypnotics and the consumer profile. Material and methods. A descriptive cross-sectional multicenter study conducted in primary care in a Health Area of Toledo. Population: non-terminal patients >=18 years with prescription of hypnotics in 2007. Results. A hypnotic drug was prescribed to 8.2% of the population in 2007. The most frequent active principle used was lorazepam (58.5%), followed by lormetazepam (13%). We studied 639 patients, 68.2% women with a mean age of 64.2±16.7 years and a median attendance of 29 visits per year (RI 23 consultations/year). A total of 19.4% (SD 3.1) patients took 2 or 3 different active ingredients. Median treatment duration was 78.4 weeks (RI 88.5). According to the clinical records, 26.6% had 2 or more chronic disorders, in 30.9% (SD 3.6). The associated diagnosis (8.4% insomnia, 69.4% depression-anxiety) was stated in 30.9% (SD 3.6), 73.8% (SD 3.4) took drugs with potential interactions with hypnotics, treatment was revised in 15%, 1.9% (SD 1.06) suffered adverse reactions, 4.9% (SD 0.9) had fallen during the treatment, 12.5% (SD 2.3) of treatments exceeded the maximum permitted dose. Conclusions. The consumer of hypnotics in our area is a woman of 64, who lives in rural areas, is a frequent visitor to the doctor, with anxiety and depression and with high risk of drug interactions, who receive chronic treatment with hypnotic drugs (AU)

Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues.

Quantitative Structure-Activity Relationship (QSAR) models have been used in Pharmaceutical design and Medicinal Chemistry for the discovery of anti-parasite drugs. QSAR models predict biological activity using as input different types of structural parameters of molecules. Topological Indices (TIs) are a very interesting class of these parameters. We can derive TIs from graph representations based on only nodes (atoms) and edges (chemical bonds). TIs are not time-consuming in terms of computational resources because they depend only on atom-atom connectivity information. This information expressed in the molecular graphs can be tabulated in the form of adjacency matrices easy to manipulate with computers. Consequently, TIs allow the rapid collection, annotation, retrieval, comparison and mining of molecular structures within large databases. The interest in TIs has exploded because we can use them to describe also macromolecular and macroscopic systems represented by complex networks of interactions (links) between the different parts of a system (nodes) such as: drug-target, protein-protein, metabolic, host-parasite, brain cortex, parasite disease spreading, Internet, or social networks. In this work, we review and comment on the following topics related to the use of TIs in anti-parasite drugs and target discovery. The first topic reviewed was: Topological Indices and QSAR for antiparasitic drugs. This topic included: Theoretical Background, QSAR for anti-malaria drugs, QSAR for anti-Toxoplasma drugs. The second topic was: TOMO-COMD approach to QSAR of antiparasitic drugs. We included in this topic: TOMO-COMD theoretical background and TOMO-COMD models for antihelmintic activity, Trichomonas, anti-malarials, anti-trypanosome compounds. The third section was inserted to discuss Topological Indices in the context of Complex Networks. The last section is devoted to the MARCH-INSIDE approach to QSAR of antiparasitic drugs and targets. This begins with a theoretical background for drugs and parameters for proteins. Next, we reviewed MARCH-INSIDE models for Pharmaceutical Design of antiparasitic drugs including: flukicidal drugs and anti-coccidial drugs. We close MARCH-NSIDE topic with a review of multi-target QSAR of antiparasitic drugs, MARCH-INSIDE assembly of complex networks of antiparasitic drugs. We closed the MARCH-INSIDE section discussing the prediction of proteins in parasites and MARCH-INSIDE web-servers for Protein-Protein interactions in parasites: Plasmod-PPI and Trypano-PPI web-servers. We closed this revision with an important section devoted to review some legal issues related to QSAR models.

Review of MARCH-INSIDE & complex networks prediction of drugs: ADMET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers.

In this communication we carry out an in-depth review of a very versatile QSPR-like method. The method name is MARCH-INSIDE (MARkov CHains Ivariants for Network Selection and DEsign) and is a simple but efficient computational approach to the study of QSPR-like problems in biomedical sciences. The method uses the theory of Markov Chains to generate parameters that numerically describe the structure of a system. This approach generates two principal types of parameters Stochastic Topological Indices (sto-TIs). The use of these parameters allows the rapid collection, annotation, retrieval, comparison and mining structures of molecular, macromolecular, supramolecular, and non-molecular systems within large databases. Here, we review and comment by the first time on the several applications of MARCH-INSIDE to predict drugs ADMET, Activity, Metabolizing Enzymes, and Toxico-Proteomics biomarkers discovery. The MARCH-INSIDE models reviewed are: a) drug-tissue distribution profiles, b) assembling drug-tissue complex networks, c) multi-target models for anti-parasite/anti-microbial activity, c) assembling drug-target networks, d) drug toxicity and side effects, e) web-server for drug metabolizing enzymes, f) models in drugs toxico-proteomics. We close the review with some legal remarks related to the use of this class of QSPR-like models.

QSAR for RNases and theoretic-experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein.

The toxicity and low success of current treatments for Leishmaniosis determines the search of new peptide drugs and/or molecular targets in Leishmania pathogen species (L. infantum and L. major). For example, Ribonucleases (RNases) are enzymes relevant to several biologic processes; then, theoretical and experimental study of the molecular diversity of Peptide Mass Fingerprints (PMFs) of RNases is useful for drug design. This study introduces a methodology that combines QSAR models, 2D-Electrophoresis (2D-E), MALDI-TOF Mass Spectroscopy (MS), BLAST alignment, and Molecular Dynamics (MD) to explore PMFs of RNases. We illustrate this approach by investigating for the first time the PMFs of a new protein of L. infantum. Here we report and compare new versus old predictive models for RNases based on Topological Indices (TIs) of Markov Pseudo-Folding Lattices. These group of indices called Pseudo-folding Lattice 2D-TIs include: Spectral moments pi ( k )(x,y), Mean Electrostatic potentials xi ( k )(x,y), and Entropy measures theta ( k )(x,y). The accuracy of the models (training/cross-validation) was as follows: xi ( k )(x,y)-model (96.0%/91.7%)>pi ( k )(x,y)-model (84.7/83.3) > theta ( k )(x,y)-model (66.0/66.7). We also carried out a 2D-E analysis of biological samples of L. infantum promastigotes focusing on a 2D-E gel spot of one unknown protein with M<20, 100 and pI <7. MASCOT search identified 20 proteins with Mowse score >30, but not one >52 (threshold value), the higher value of 42 was for a probable DNA-directed RNA polymerase. However, we determined experimentally the sequence of more than 140 peptides. We used QSAR models to predict RNase scores for these peptides and BLAST alignment to confirm some results. We also calculated 3D-folding TIs based on MD experiments and compared 2D versus 3D-TIs on molecular phylogenetic analysis of the molecular diversity of these peptides. This combined strategy may be of interest in drug development or target identification.

Multi-target spectral moment: QSAR for antiviral drugs vs. different viral species.

The antiviral QSAR models have an important limitation today. They predict the biological activity of drugs against only one viral species. This is determined by the fact that most of the current reported molecular descriptors encode only information about the molecular structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this work, we use Markov Chain theory to calculate new multi-target spectral moments to fit a QSAR model for drugs active against 40 viral species. The model is based on 500 drugs (including active and non-active compounds) tested as antiviral agents in the recent literature; not all drugs were predicted against all viruses, but only those with experimental values. The database also contains 207 well-known compounds (not as recent as the previous ones) reported in the Merck Index with other activities that do not include antiviral action against any virus species. We used Linear Discriminant Analysis (LDA) to classify all these drugs into two classes as active or non-active against the different viral species tested, whose data we processed. The model correctly classifies 5129 out of 5594 non-active compounds (91.69%) and 412 out of 422 active compounds (97.63%). Overall training predictability was 92.34%. The validation of the model was carried out by means of external predicting series, the model classifying, thus, 2568 out of 2779 non-active compounds and 224 out of 229 active compounds. Overall training predictability was 92.82%. The present work reports the first attempts to calculate within a unified framework the probabilities of antiviral drugs against different virus species based on a spectral moment analysis.

Prediction of enzyme classes from 3D structure: a general model and examples of experimental-theoretic scoring of peptide mass fingerprints of Leishmania proteins.

The number of protein and peptide structures included in Protein Data Bank (PDB) and Gen Bank without functional annotation has increased. Consequently, there is a high demand for theoretical models to predict these functions. Here, we trained and validated, with an external set, a Markov Chain Model (MCM) that classifies proteins by their possible mechanism of action according to Enzyme Classification (EC) number. The methodology proposed is essentially new, and enables prediction of all EC classes with a single equation without the need for an equation for each class or nonlinear models with multiple outputs. In addition, the model may be used to predict whether one peptide presents a positive or negative contribution of the activity of the same EC class. The model predicts the first EC number for 106 out of 151 (70.2%) oxidoreductases, 178/178 (100%) transferases, 223/223 (100%) hydrolases, 64/85 (75.3%) lyases, 74/74 (100%) isomerases, and 100/100 (100%) ligases, as well as 745/811 (91.9%) nonenzymes. It is important to underline that this method may help us predict new enzyme proteins or select peptide candidates that improve enzyme activity, which may be of interest for the prediction of new drugs or drug targets. To illustrate the model's application, we report the 2D-Electrophoresis (2DE) isolation from Leishmania infantum as well as MADLI TOF Mass Spectra characterization and theoretical study of the Peptide Mass Fingerprints (PMFs) of a new protein sequence. The theoretical study focused on MASCOT, BLAST alignment, and alignment-free QSAR prediction of the contribution of 29 peptides found in the PMF of the new protein to specific enzyme action. This combined strategy may be used to identify and predict peptides of prokaryote and eukaryote parasites and their hosts as well as other superior organisms, which may be of interest in drug development or target identification.

Scoring function for DNA-drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories.

We introduce here a new class of invariants for MD trajectories based on the spectral moments pi(k)(L) of the Markov matrix associated to lattice network-like (LN) graph representations of Molecular Dynamics (MD) trajectories. The procedure embeds the MD energy profiles on a 2D Cartesian coordinates system using simple heuristic rules. At the same time, we associate the LN with a Markov matrix that describes the probabilities of passing from one state to other in the new 2D space. We construct this type of LNs for 422 MD trajectories obtained in DNA-drug docking experiments of 57 furocoumarins. The combined use of psoralens+ultraviolet light (UVA) radiation is known as PUVA therapy. PUVA is effective in the treatment of skin diseases such as psoriasis and mycosis fungoides. PUVA is also useful to treat human platelet (PTL) concentrates in order to eliminate Leishmania spp. and Trypanosoma cruzi. Both are parasites that cause Leishmaniosis (a dangerous skin and visceral disease) and Chagas disease, respectively; and may circulate in blood products collected from infected donors. We included in this study both lineal (psoralens) and angular (angelicins) furocoumarins. In the study, we grouped the LNs on two sets; set1: DNA-drug complex MD trajectories for active compounds and set2: MD trajectories of non-active compounds or no-optimal MD trajectories of active compounds. We calculated the respective pi(k)(L) values for all these LNs and used them as inputs to train a new classifier that discriminate set1 from set2 cases. In training series the model correctly classifies 79 out of 80 (specificity=98.75%) set1 and 226 out of 238 (Sensitivity=94.96%) set2 trajectories. In independent validation series the model correctly classifies 26 out of 26 (specificity=100%) set1 and 75 out of 78 (sensitivity=96.15%) set2 trajectories. We propose this new model as a scoring function to guide DNA-docking studies in the drug design of new coumarins for anticancer or antiparasitic PUVA therapy.

Multi-target spectral moment: QSAR for antifungal drugs vs. different fungi species.

The most important limitation of antifungal QSAR models is that they predict the biological activity of drugs against only one fungal species. This is determined due the fact that most of the up-to-date reported molecular descriptors encode only information about the molecular structure. Consequently, predicting the probability with which a drug is active against different fungal species with a single unifying model is a goal of major importance. Herein, we use the Markov Chain theory to calculate new multi-target spectral moments to fit a QSAR model that predicts the antifungal activity of more than 280 drugs against 90 fungi species. Linear discriminant analysis (LDA) was used to classify drugs into two classes as active or non-active against the different tested fungal species whose data we processed. The model correctly classifies 12 434 out of 12 566 non-active compounds (98.95%) and 421 out of 468 active compounds (89.96%). Overall training predictability was 98.63%. Validation of the model was carried out by means of external predicting series, the model classifying, thus, 6216 out of 6277 non-active compounds and 215 out of 239 active compounds. Overall training predictability was 98.7%. The present is the first attempt to calculate, within a unifying framework, the probabilities of antifungal action of drugs against many different species based on spectral moment's analysis.

A case report on the role of plasma exchange in the management of a massive amlodipine besylate intoxication.

Therapeutic plasma exchange is a procedure used to remove pathologic substances from a patient's blood that has proven useful in some cases of drug overdose. Overdose by calcium channel blocker antihypertensive agents has been shown to be a cause of significant morbidity and can often times prove fatal. These agents cause systemic hypotension by inhibiting cell membrane calcium channels, which leads to a slowing of intracardiac electric conduction with consequent impairment of myocardial function and widespread vasodilation. Shock and metabolic acidosis result from the persistent hypotension. In high doses, calcium channel blocking agents cause insulin resistance. We describe the case of a previously healthy young woman who ingested a massive dose of amlodipine and was treated by therapeutic plasma exchange after non-responsiveness to conventional therapy. The case illustrates the need for utilization of therapeutic plasma exchange in the emergency management of certain cases of severe amlodipine overdose.