6 results
Ecological notes on Actinostella flosculifera (Le Sueur, 1817) (Cnidaria: Actiniaria: Actiniidae) in the South-western Atlantic, Brazil
- Jeferson A. Durán-Fuentes, Flávio Mendes, Rogério Caetano da Costa, Régis Augusto Pescinelli, Sergio R. Floeter, Sérgio N. Stampar
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- Journal:
- Journal of the Marine Biological Association of the United Kingdom / Volume 103 / 2023
- Published online by Cambridge University Press:
- 27 February 2023, e20
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Sea anemones have developed various strategies for interspecific interaction with other organisms and their own ability to obtain food, due to their coevolutionary history, ranging from mutualistic (e.g. clownfish, crustaceans, etc.) and symbiotic associations (zooxanthellae or zoochlorellae) to depredation (e.g. sea slug). This study aims to record some observations on feeding habits and interspecific interactions of Actinostella flosculifera (Le Sueur, 1817) in the locality of Pedra da Sereia in Vila Velha, Espírito Santo, Brazil, and to describe the hunting strategy of the sea slug Spurilla braziliana MacFarland, 1909 and the escape strategy of A. flosculifera. We found that the habitat of A. flosculifera is characterized by shallow pools ~10 cm deep at low tides, and this functions as a trap for many organisms and some biowaste (e.g. bones or fish drifting in from nearby populations) that fall into the oral disc. This is the first report of S. braziliana predating on A. flosculifera. We also report interspecific relationships between A. flosculifera with four species of crustaceans: Omalacantha bicornuta (Latreille, 1825), Menippe cf. nodifrons Stimpson, 1859, Alpheus cf. angulosus McClure, 2002, and Alpheus cf. carlae Anker, 2012.
Prediction of Surgical Risk in General Surgeries: Process Optimization Through Support Vector Machine (SVM) Algorithm
- Flávio Souza, Braulio Couto, Gabriel Henrique Silvestre da Silva, Igor Gonçalves Dias, Rafael Vieira Magno Rigueira, Gustavo Maciel Pimenta, Maurilio Martins, Julio Cesar Mendes, Gabriele Maria Braga, Jéssica Angelina Teixeira, Renata Carvalho Santos, Julia Maria Campos Martins, Karla Silvia de Sousa, Douglas Nascimento de Souza, Gustavo Barros Alves, Vladimir Alexei Rodrigues Rocha
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s355-s356
- Print publication:
- October 2020
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Background: In 5 hospitals in Belo Horizonte (population, 3 million) between July 2016 and June 2018, a survey was performed regarding surgical site infection (SSI). We statistically evaluated SSI incidents and optimized the power to predict SSI through pattern recognition algorithms based on support vector machines (SVMs). Methods: Data were collected on SSIs at 5 different hospitals. The hospital infection control committees (CCIHs) of the hospitals collected all data used in the analysis during their routine SSI surveillance procedures; these data were sent to the NOIS (Nosocomial Infection Study) Project. NOIS uses SACIH software (an automated hospital infection control system) to collect data from hospitals that participate voluntarily in the project. In the NOIS, 3 procedures were performed: (1) a treatment of the database collected for use of intact samples; (2) a statistical analysis on the profile of the hospitals collected; and (3) an assessment of the predictive power of SVM with a nonlinear separation process varying in configurations including kernel function (Laplace, Radial Basis, Hyperbolic Tangent and Bessel) and the k-fold cross-validation–based resampling process (ie, the use of data varied according to the amount of folders that cross and combine the evaluated data, being k = 3, 5, 6, 7, and 10). The data were compared by measuring the area under the curve (AUC; range, 0–1) for each of the configurations. Results: From 13,383 records, 7,565 were usable, and SSI incidence was 2.0%. Most patients were aged 35–62 years; the average duration of surgery was 101 minutes, but 76% of surgeries lasted >2 hours. The mean hospital length of stay without SSI was 4 days versus 17 days for the SSI cases. The survey data showed that even with a low number of SSI cases, the prediction rate for this specific surgery was 0.74, which was 14% higher than the rate reported in the literature. Conclusions: Despite the high noise index of the database, it was possible to sample relevant data for the evaluation of general surgery patients. For the predictive process, our results were >0.50 and were 14% better than those reported in the literature. However, the database requires more SSI case samples because only 2% of positive samples unbalanced the database. To optimize data collection and to enable other hospitals to use the SSI prediction tool, a mobile application was developed (available at www.sacihweb.com).
Funding: None
Disclosures: None
Artificial Neural Networks Applied to Prediction to Assess the Likelihood of Surgical Site Infection in Different Surgeries
- Flávio Souza, Braulio Couto, Felipe Leandro Andrade da Conceição, Gabriel Henrique Silvestre da Silva, Igor Gonçalves Dias, Rafael Vieira Magno Rigueira, Gustavo Maciel Pimenta, Maurilio Martins, Julio Cesar Mendes, Vladimir Alexei Rodrigues Rocha, Ana Luiza de Oliveira Rocha, Breno Henrique Colares Silva, Bruna Stella Vieira do Nascimento, Carolina Nunes Dutra, Luiza Pedrosa Gomes, Maria Clara Vilaça, Julia D. O. Matias, Laís L. de Araújo, Luaan S. Rossati, Layna R. Polidoro
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, p. s129
- Print publication:
- October 2020
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Background: Based on data obtained from hospitals in the city of Belo Horizonte (population ~3,000,000), we evaluated relevant factors such as death, age, duration of surgery, potential for contamination and surgical site infection, plastic surgery, and craniotomy. The possibility of predicting surgical site infection (SSI) was then analyzed using pattern recognition algorithms based on MLP (multilayer perceptron). Methods: Data were collected by the hospital infection control committees (CCIHs) in hospitals in Belo Horizonte between 2016 and 2018. The noisy records were filtered, and the occurrences were analyzed. Finally, the predictive power of SSI of 5 types MLP was evaluated experimentally: momentum, backpropagation standard, weight decay, resilient propagation, and quick propagation. The model used 3, 5, 7, and 10 neurons in the occult layer and with resamples varied the number of records for testing (65% and 75%) and for validation (35% and 25%). Comparisons were made by measuring the AUC (area under the curve (range, 0–1). Results: From 1,096 records of craniotomy, 289 were usable for analysis. Moreover, 16% died; averaged age was 56 years (range, 40–65); mean time of surgery was 186 minutes (range, 95–250 minutes); the number of hospitalizations ranged from 1 (90.6%) to 8 (0.3%). Contamination among these cases was rated as follows: 2.7% contaminated, 23.5% potentially contaminated, 72.3% clean. The SSI rate reached 4%. The prediction process in AUCs ranged from 0.7 to 0.994. In plastic surgery, from 3,693 records, 1,099 were intact, with only 1 case of SSI and no deaths. The average age for plastic surgery was 41 years (range, 16–91); the average time of surgery was 218.5 minutes (range, 19–580 minutes); the number of hospitalizations ranged from 1 (77.4%) to 6 times (0.001%). Contamination among these cases was rated as follows: 27.90% potential contamination, 1.67% contaminated, and 0.84% infected. The prediction process ranged in AUCs from 0.2 to 0.4. Conclusions: We identified a high noise index in both surgeries due to subjectivity at the time of data collection. The profiles of each surgery in the statistical analyses were different, which was reflected in the analyzed structures. The MLP for craniotomy surgery demonstrated relevant predictive power and can guide intelligent monitoring software (available in www.sacihweb.com). However, for plastic surgeries, MLPs need more SSI samples to optimize outcomes. To optimize data collection and to enable other hospitals to use the SSI prediction tool, a mobile application was developed.
Disclosures: None
Funding: None
Pattern Recognition Algorithms for Predicting Surgical Site Infection in Abdominal Hysterectomy
- Flávio Souza, Braulio Couto, Felipe Leandro Andrade da Conceição, Gabriel Henrique Silvestre da Silva, Igor Gonçalves Dias, Rafael Vieira Magno Rigueira, Gustavo Maciel Pimenta, Maurilio Martins, Julio Cesar Mendes, Amanda Martins Fagundes, Beatriz Viana Ferreira Escalda, Isabela Marques de Souza, Laura Ferraz de Vasconcelos, Maria Eduarda Rodrigues Medeiros, Thais Azevedo de Almeida
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s344-s345
- Print publication:
- October 2020
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Background: This research represents an experiment based in surgical site infection (SSI) to patients undergoing abdominal hysterectomy surgery procedures in hospitals in Belo Horizonte, (population, 3 million). We statistically evaluated such incidences and studied the SSI prediction power of pattern recognition algorithms, the artificial neural networks based in multilayer perceptron (MLP). Methods: Between July 2016 and June 2018, data on SSI were collected by the hospital infection control committees (CCIH) of the 3 hospitals involved in the research. They collected all data used in the analysis during their routine SSI surveillance procedures. The information was forwarded to the NOIS (Nosocomial Infection Study) Project, which used SACIH (ie, automated hospital infection control system software) to collect data from a sample of hospitals participating voluntarily in the project. After data collection, 3 procedures were performed for SSI prediction: (1) a treatment of the database collected for the use of intact samples; (2) a statistical analysis on the profile of the hospitals collected; and (3) an assessment of the predictive power of 5 types of MLP (ie, backpropagation standard, momentum, resilient propagation, weight decay, and quick propagation). MLPs were tested with 3, 5, 7, and 10 hidden-layer neurons and a database split for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring area under the curve (AUC; range, 0–1) presented for each of the configurations. Results: From 1,166 records collected, only 665 records were enabled for analysis. Regarding statistical data: the average duration of surgery was 100 minutes (range, 31–180); patients were aged 41–49 years; the SSI rate was low (only 10 cases); the average length of stay was 2 days; and there were no deaths among the cases. Moreover, 29% of the operative sites were contaminated and 57% were potentially contaminated, revealing a high rate of potential contamination in the operative sites. The prediction process achieved 0.995. Conclusions: Despite the noise in the database, it was possible to obtain a relevant sampling to evaluate the profile of hospitals in Belo Horizonte. In addition, for the predictive process, although some settings achieved AUC results of 0.5, others achieved and AUC of 0.995, indicating the promise of the automated SSI monitoring framework for abdominal hysterectomy surgery (available in www.sacihweb.com). To optimize data collection and to enable other hospitals to use the SSI prediction tool, a mobile application was developed.
Funding: None
Disclosures: None
Automated Risk Analysis of Surgical Site Infection in Hip Arthroplasty Surgeries
- Flávio Souza, Braulio Couto, Felipe Leandro Andrade da Conceição, Gabriel Henrique Silvestre da Silva, Igor Gonçalves Dias, Rafael Vieira Magno Rigueira, Gustavo Maciel Pimenta, Maurilio Martins, Julio Cesar Mendes, Ana Flavia Viana Quintão, Camila Vieira Brandão, Débora Martins Borges, Eduarda Muzzi Torres Lage, Luiza da Conceição Sabadini, Sabrina de Almeida Lopes
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s135-s136
- Print publication:
- October 2020
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Background: In 7 hospitals in Belo Horizonte, a city with >3,000,000 inhabitants, a survey was conducted between July 2016 and June 2018, focused on surgical site infection (SSI) in patients undergoing arthroplasty surgery procedures. The main objective is to statistically evaluate such incidences and enable a study of the prediction power of SSI through pattern recognition algorithms, the MLPs (multilayer perceptron). Methods: Data were collected on SSI by the hospital infection control committees (CCIHs) of the hospitals involved in the research. All data used in the analysis during their routine SSI surveillance procedures were collected. The information was forwarded to the NOIS (Nosocomial Infection Study) Project, which used SACIH automated hospital infection control system software to collect data from a sample of hospitals participating voluntarily in the project. After data collection, 3 procedures were performed: (1) a treatment of the database collected for the use of intact samples; (2) a statistical analysis on the profile of the hospitals collected; and (3) an assessment of the predictive power of 5 types of MLP (backpropagation standard, momentum, resilient propagation, weight decay, and quick propagation) for SSI prediction. MLPs were tested with 3, 5, 7, and 10 hidden layer neurons and a database split for the resampling process (65% or 75% for testing and 35% or 25% for validation). The results were compared by measuring AUC (area under the curve; range, 0–1) presented for each of the configurations. Results: Of 1,246 records, 535 were intact for analysis. We obtained the following statistics: the average surgery time was 190 minutes (range, 145–217 minutes); the average age of the patients was 67 years (range, 9–103); the prosthetic implant index was 98.13%; the SSI rate was 1.49%, and the death rate was 1.21%. Regarding the prediction power, the maximum prediction power was 0.744. Conclusions: Despite the considerable loss rate of almost 60% of the database samples due to the presence of noise, it was possible to perform relevant sampling for the profile evaluation of hospitals in Belo Horizonte. For the predictive process, some configurations have results that reached 0.744, which indicates the usefulness of the structure for automated SSI monitoring for patients undergoing hip arthroplasty surgery. To optimize data collection and to enable other hospitals to use the SSI prediction tool (available in www.sacihweb.com ), a mobile application was developed.
Funding: None
Disclosures: None
Overdenture with immediate load: mastication and nutrition
- Tânia de Freitas Borges, Francielle Alves Mendes, Terezinha Rezende Carvalho de Oliveira, Célio Jesus do Prado, Flávio Domingues das Neves
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- Journal:
- British Journal of Nutrition / Volume 105 / Issue 7 / 14 April 2011
- Published online by Cambridge University Press:
- 06 December 2010, pp. 990-994
- Print publication:
- 14 April 2011
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Lower conventional complete dentures were converted to overdentures retained by two implants with an immediately loaded bar-clip system (n 16). The masticatory performance test was carried out using ‘Optocal’ after forty chewing strokes. Nutritional condition was evaluated by means of a Mini-Nutritional-Assessment questionnaire. No individual was classified as malnourished. The masticatory performance test demonstrated a statistically significant difference before conversion (baseline) and 3 and 6 months after conversion of the lower conventional complete denture to an implant-retained overdenture (paired-samples t test; P < 0·05). A statistically significant difference before and after 6 months of conversion for nutritional condition (Wilcoxon test; P < 0·05) was also observed. The association between masticatory performance and nutritional assessment demonstrated that a statistically significant difference between masticatory performance of the subgroups classified at baseline as nourished (22·43 %) and those at risk of malnutrition (3·9 %) was only evidenced before conversion (P = 0·006). After the conversion from a lower conventional complete denture to an implant-retained overdenture, at 3 months, the risk of malnutrition and nourished at baseline did not present significant differences in masticatory performance. Nonetheless, the mean masticatory performance potential was 19·86 and 31·98 % for subgroups classified at baseline as at risk of malnutrition and nourished, respectively (P = 0·187). The results before conversion were less favourable for masticatory performance and nutritional condition. The increased retention of the mandibular prosthesis allowed improvement in masticatory performance and nutritional condition.