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The delivery of genetic material (DNA and RNA) to cells can cure a wide range of diseases but is limited by the delivery efficiency of the carrier system. Poly β-amino esters (pBAEs) are promising polymer-based vectors that form polyplexes with negatively charged oligonucleotides, enabling cell membrane uptake and gene delivery. pBAE backbone polymer chemistry, as well as terminal oligopeptide modifications, define cellular uptake and transfection efficiency in a given cell line, along with nanoparticle size, polydispersity and zeta potential. Moreover, uptake and transfection efficiency of a given polyplex formulation also vary from cell type to cell type. Therefore, finding the optimal formulation leading to high uptake in a new cell line is dictated by trial and error, and requires time and resources. Machine learning (ML) is an ideal in silico screening tool to learn the non-linearities of complex data sets, like the one presented herein, with the aim of predicting cellular internalisation of pBAE polyplexes. A library of pBAE nanoparticles was fabricated and the uptake studied in 4 different cell lines, on which various ML models were successfully trained. The best performing models were found to be gradient-boosted trees and neural networks. The gradient-boosted trees model was then analysed using SHapley Additive exPlanations, to interpret the model and gain an understanding into the important features and their impact on the predicted outcome.

Background Machine learning methods for suicidal behavior so far have failed to be implemented as a prediction tool. In order to use the capabilities of machine learning to model complex phenomenon, we assessed the predictors of suicide risk using state-of-the-art model explanation methods. Methods Prospective cohort study including a community sample of 1,560 young adults aged between 18 and 24. The first wave took place between 2007 and 2009, and the second wave took place between 2012 and 2014. Sociodemographic and clinical characteristics were assessed at baseline. Incidence of suicide risk at five-years of follow-up was the main outcome. The outcome was assessed using the Mini Neuropsychiatric Interview (MINI) at both waves. Results The risk factors for the incidence of suicide risk at follow-up were: female sex, lower socioeconomic status, older age, not studying, presence of common mental disorder symptoms, and poor quality of life. The interaction between overall health and socioeconomic status in relation to suicide risk was also captured and shows a shift from protection to risk by socioeconomic status as overall health increases. Limitations Proximal factors associated with the incidence of suicide risk were not assessed. Conclusions Our findings indicate that factors related to poor quality of life, not studying, and common mental disorder symptoms of young adults are already in place prior to suicide risk. Most factors present critical non-linear patterns that were identified. These findings are clinically relevant because they can help clinicians to early detect suicide risk.

OBJECTIVE To evaluate whether accelerated brain aging occurs in individuals with mood or psychotic disorders. METHODS A systematic review following PRISMA guidelines was conducted. A meta-analysis was then performed to assess neuroimaging-derived brain age gap in three independent groups: (1) schizophrenia and first-episode psychosis, (2) major depressive disorder, and (3) bipolar disorder. RESULTS A total of 18 papers were included. The random-effects model meta-analysis showed a significantly increased neuroimaging-derived brain age gap relative to age-matched controls for the three major psychiatric disorders, with schizophrenia (3.08; 95%CI [2.32; 3.85]; p

BackgroundThere is still little knowledge of objective suicide risk stratification.MethodsThis study aims to develop models using machine-learning approaches to predict suicide attempt (1) among survey participants in a nationally representative sample and (2) among participants with lifetime major depressive episodes. We used a cohort called the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) that was conducted in two waves and included a nationally representative sample of the adult population in the United States. Wave 1 involved 43 093 respondents and wave 2 involved 34 653 completed face-to-face reinterviews with wave 1 participants. Predictor variables included clinical, stressful life events, and sociodemographic variables from wave 1; outcome included suicide attempt between wave 1 and wave 2.ResultsThe model built with elastic net regularization distinguished individuals who had attempted suicide from those who had not with an area under the ROC curve (AUC) of 0.89, balanced accuracy 81.86%, specificity 89.22%, and sensitivity 74.51% for the general population. For participants with lifetime major depressive episodes, AUC was 0.89, balanced accuracy 81.64%, specificity 85.86%, and sensitivity 77.42%. The most important predictor variables were a diagnosis of borderline personality disorder, post-traumatic stress disorder, and being of Asian descent for the model in all participants; and previous suicide attempt, borderline personality disorder, and overnight stay in hospital because of depressive symptoms for the model in participants with lifetime major depressive episodes. Random forest and artificial neural networks had similar performance.ConclusionsRisk for suicide attempt can be estimated with high accuracy.

Although reducing criminal outcomes in individuals with mental illness have long been a priority for governments worldwide, there is still a lack of objective and highly accurate tools that can predict these events at an individual level. Predictive machine learning models may provide a unique opportunity to identify those at the highest risk of criminal activity and facilitate personalized rehabilitation strategies. Therefore, this systematic review and meta-analysis aims to describe the diagnostic accuracy of studies using machine learning techniques to predict criminal and violent outcomes in psychiatry. We performed meta-analyses using the mada, meta, and dmetatools packages in R to predict criminal and violent outcomes in psychiatric patients (n = 2428) (Registration Number: CRD42019127169) by searching PubMed, Scopus, and Web of Science for articles published in any language up to April 2022. Twenty studies were included in the systematic review. Overall, studies used single-nucleotide polymorphisms, text analysis, psychometric scales, hospital records, and resting-state regional cerebral blood flow to build predictive models. Of the studies described in the systematic review, nine were included in the present meta-analysis. The area under the curve (AUC) for predicting violent and criminal outcomes in psychiatry was 0.816 (95% Confidence Interval (CI): 70.57–88.15), with a partial AUC of 0.773, and average sensitivity of 73.33% (95% CI: 64.09–79.63), and average specificity of 72.90% (95% CI: 63.98–79.66), respectively. Furthermore, the pooled accuracy across models was 71.45% (95% CI: 60.88–83.86), with a tau squared (τ2) of 0.0424 (95% CI: 0.0184–0.1553). Based on available evidence, we suggest that prospective models include evidence-based risk factors identified in prior actuarial models. Moreover, there is a need for a greater emphasis on identifying biological features and incorporating novel variables which have not been explored in prior literature. Furthermore, available models remain preliminary, and prospective validation with independent datasets, and across cultures, will be required prior to clinical implementation. Nonetheless, predictive machine learning models hold promise in providing clinicians and researchers with actionable tools to improve how we prevent, detect, or intervene in relevant crime and violent-related outcomes in psychiatry.

Introduction Recent research has suggested an increase in the global prevalence of psychiatric symptoms during the COVID-19 pandemic. This study aimed to assess whether lifestyle behaviors can predict the presence of depression and anxiety in the Brazilian general population, using a model developed in Spain. Methods A web survey was conducted during April-May 2020, which included the Short Multidimensional Inventory Lifestyle Evaluation (SMILE) scale, assessing lifestyle behaviors during the COVID-19 pandemic. Depression and anxiety were examined using the PHQ-2 and the GAD-7, respectively. Elastic net, random forest, and gradient tree boosting were used to develop predictive models. Each technique used a subset of the Spanish sample to train the models, which were then tested internally (vs. the remainder of the Spanish sample) and externally (vs. the full Brazilian sample), evaluating their effectiveness. Results The study sample included 22,562 individuals (19,069 from Brazil, and 3,493 from Spain). The models developed performed similarly and were equally effective in predicting depression and anxiety in both tests, with internal test AUC-ROC values of 0.85 (depression) and 0.86 (anxiety), and external test AUC-ROC values of 0.85 (depression) and 0.84 (anxiety). Meaning of life was the strongest predictor of depression, while sleep quality was the strongest predictor of anxiety during the COVID-19 epidemic. Conclusions Specific lifestyle behaviors during the early COVID-19 epidemic successfully predicted the presence of depression and anxiety in a large Brazilian sample using machine learning models developed on a Spanish sample. Targeted interventions focused on promoting healthier lifestyles are encouraged.

OBJECTIVE To identify factors associated with a history of suicide attempt in medical students. METHODS A Web-based survey was sent out to a sample of medical students. A multi-predictor Poisson regression was performed to identify factors associated with a history of suicide attempt. In addition, an elastic net regularization was used to build a risk calculator to identify students at risk for attempted suicide. RESULTS A total of 4,840 participants were included in the study. Prevalence of suicide attempts in the sample was 8.94%. Risk factors associated with past suicide attempt in the multi-predictor Poisson regression were as follows: female gender (P

Problem: Chronological aging in later life is associated with brain degeneration processes and increased risk for disease such as stroke and dementia. With a worldwide tendency of aging populations and increased longevity, mental health, and psychiatric research have paid increasing attention to understanding brain-related changes of aging. Recent findings suggest there is a brain age gap (a difference between chronological age and brain age predicted by brain imaging indices); the magnitude of the gap may indicate early onset of brain aging processes and disease. Artificial intelligence has allowed for a narrowing of the gap in chronological and predicted brain age. However, the factors that drive model predictions of brain age are still unknown, and there is not much about these factors that can be gleaned from the black-box nature of machine learning models. The goal of the present study was to test a brain age regression approach that is more amenable to interpretation by researchers and clinicians.Methods: Using convolutional neural networks we trained multiple regressor models to predict brain age based on single slices of magnetic resonance imaging, which included gray matter- or white matter-segmented inputs. We evaluated the trained models in all brain image slices to generate a final prediction of brain age. Unlike whole-brain approaches to classification, the slice-level predictions allows for the identification of which brain slices and associated regions have the largest difference between chronological and neuroimaging-derived brain age. We also evaluated how model predictions were influenced by slice index and plane, participant age and sex, and MRI data collection site.Results: The results show, first, that the specific slice used for prediction affects prediction error (i.e., difference between chronological age and neuroimaging-derived brain age); second, the MRI site-stratified separation of training and test sets removed site effects and also minimized sex effects; third, the choice of MRI slice plane influences the overall error of the model.Conclusion: Compared to whole brain-based predictive models of neuroimaging-derived brain age, slice-based approach improves the interpretability and therefore the reliability of the prediction of brain age using MRI data.

Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

BACKGROUND Essential workers have been shown to present a higher prevalence of positive screenings for anxiety and depression during the COVID-19 pandemic. Individuals from countries with socioeconomic inequalities may be at increased risk for mental health disorders. OBJECTIVE We aimed to assess the prevalence and predictors of depression, anxiety, and their comorbidity among essential workers in Brazil and Spain during the COVID-19 pandemic. METHODS A web survey was conducted between April and May 2020 in both countries. The main outcome was a positive screening for depression only, anxiety only, or both. Lifestyle was measured using a lifestyle multidimensional scale adapted for the COVID-19 pandemic (Short Multidimensional Inventory Lifestyle Evaluation–Confinement). A multinomial logistic regression model was performed to evaluate the factors associated with depression, anxiety, and the presence of both conditions. RESULTS From the 22,786 individuals included in the web survey, 3745 self-reported to be essential workers. Overall, 8.3% (n=311), 11.6% (n=434), and 27.4% (n=1027) presented positive screenings for depression, anxiety, and both, respectively. After adjusting for confounding factors, the multinomial model showed that an unhealthy lifestyle increased the likelihood of depression (adjusted odds ratio [AOR] 4.00, 95% CI 2.72-5.87), anxiety (AOR 2.39, 95% CI 1.80-3.20), and both anxiety and depression (AOR 8.30, 95% CI 5.90-11.7). Living in Brazil was associated with increased odds of depression (AOR 2.89, 95% CI 2.07-4.06), anxiety (AOR 2.81, 95%CI 2.11-3.74), and both conditions (AOR 5.99, 95% CI 4.53-7.91). CONCLUSIONS Interventions addressing lifestyle may be useful in dealing with symptoms of common mental disorders during the strain imposed among essential workers by the COVID-19 pandemic. Essential workers who live in middle-income countries with higher rates of inequality may face additional challenges. Ensuring equitable treatment and support may be an important challenge ahead, considering the possible syndemic effect of the social determinants of health.

BackgroundPatients with obsessive-compulsive disorder (OCD) are at increased risk for suicide attempt (SA) compared to the general population. However, the significant risk factors for SA in this population remains unclear – whether these factors are associated with the disorder itself or related to extrinsic factors, such as comorbidities and sociodemographic variables. This study aimed to identify predictors of SA in OCD patients using a machine learning algorithm.MethodsA total of 959 outpatients with OCD were included. An elastic net model was performed to recognize the predictors of SA among OCD patients, using clinical and sociodemographic variables.ResultsThe prevalence of SA in our sample was 10.8%. Relevant predictors of SA founded by the elastic net algorithm were the following: previous suicide planning, previous suicide thoughts, lifetime depressive episode, and intermittent explosive disorder. Our elastic net model had a good performance and found an area under the curve of 0.95.ConclusionsThis is the first study to evaluate risk factors for SA among OCD patients using machine learning algorithms. Our results demonstrate an accurate risk algorithm can be created using clinical and sociodemographic variables. All aspects of suicidal phenomena need to be carefully investigated by clinicians in every evaluation of OCD patients. Particular attention should be given to comorbidity with depressive symptoms.

To train deep learning models in a supervised fashion, we need a significant amount of training data, but in most medical imaging scenarios, there is a lack of annotated data available. In this paper, we compare state-of-the-art semi-supervised classification methods in a medical imaging scenario. We evaluate the performance of different approaches in a chest radiograph classification task using the ChestX-ray14 dataset. We adapted methods based on pseudo-labeling and consistency regularization to perform multi-label classification and to use a state-of-the-art model architecture in chest radiograph classification. Our proposed approaches resulted in average AUCs up to 0.6691 with only 25 labeled samples per class, and an average AUC of 0.7182 when using only 2% of the labeled data, achieving results superior to previous approaches on semi-supervised chest radiograph classification.

Suicide is one of the leading causes of death among young people, and its rates have failed to decline over the years. Such numbers are alarming and highlight the need for more precise means of evaluation, adequate management, and early identification of patients with suicide risk. The main aim of this chapter is to present the possibility of applying precision medicine concepts, such as machine learning tools and big data, in the evaluation and identification of these patients, by predicting and classifying patients with high suicide risk. Additionally, this chapter aims to briefly describe updated concepts and the literature about suicide, concerning risk and protective factors, preventive strategies, and potential treatments for people with suicide risk.

While deep learning models become more widespread, their ability to handle unseen data and generalize for any scenario is yet to be challenged. In medical imaging, there is a high heterogeneity of distributions among images based on the equipment that generates them and their parametrization. This heterogeneity triggers a common issue in machine learning called domain shift, which represents the difference between the training data distribution and the distribution of where a model is employed. A high domain shift often results in a poor generalization performance from the models. In this work, we evaluate the extent of which domain shift damages model performance on four of the largest datasets of chest radiographs. We show how training and testing with different datasets (e.g., training in ChestX-ray14 and testing in CheXpert) drastically affects model performance, posing a big question over the reliability of deep learning models trained on public datasets. We also show that models trained on CheXpert and MIMIC-CXR generalized better to other datasets.

Neuroprogression is associated with structural and functional brain changes that occur in parallel with cognitive and functioning impairments. There is substantial evidence showing early white matter changes, as well as trajectory-related gray matter alterations. Several structures, including prefrontal, parietal, temporal cortex, and limbic structures, seem to be altered over the course of bipolar disorder, especially associated with the number of episodes and length of the disease. An important limitation is that most of the studies used either a cross-sectional design or a short follow-up period, which may be insufficient to identify all neuroprogressive changes over time. In addition, the heterogeneity of patients with bipolar disorder is another challenge to determine which subjects will have a more pernicious trajectory. Larger studies and the use of new techniques, such as machine learning, may help to enable more discoveries and evidence on the role of neuroprogression in BD.

Background Essential workers have been shown to present a higher prevalence of positive screenings for anxiety and depression during the COVID-19 pandemic. Individuals from countries with socioeconomic inequalities may be at increased risk for mental health disorders. Objective We aimed to assess the prevalence and predictors of depression, anxiety, and their comorbidity among essential workers in Brazil and Spain during the COVID-19 pandemic. Methods A web survey was conducted between April and May 2020 in both countries. The main outcome was a positive screening for depression only, anxiety only, or both. Lifestyle was measured using a lifestyle multidimensional scale adapted for the COVID-19 pandemic (Short Multidimensional Inventory Lifestyle Evaluation–Confinement). A multinomial logistic regression model was performed to evaluate the factors associated with depression, anxiety, and the presence of both conditions. Results From the 22,786 individuals included in the web survey, 3745 self-reported to be essential workers. Overall, 8.3% (n=311), 11.6% (n=434), and 27.4% (n=1027) presented positive screenings for depression, anxiety, and both, respectively. After adjusting for confounding factors, the multinomial model showed that an unhealthy lifestyle increased the likelihood of depression (adjusted odds ratio [AOR] 4.00, 95% CI 2.72-5.87), anxiety (AOR 2.39, 95% CI 1.80-3.20), and both anxiety and depression (AOR 8.30, 95% CI 5.90-11.7). Living in Brazil was associated with increased odds of depression (AOR 2.89, 95% CI 2.07-4.06), anxiety (AOR 2.81, 95%CI 2.11-3.74), and both conditions (AOR 5.99, 95% CI 4.53-7.91). Conclusions Interventions addressing lifestyle may be useful in dealing with symptoms of common mental disorders during the strain imposed among essential workers by the COVID-19 pandemic. Essential workers who live in middle-income countries with higher rates of inequality may face additional challenges. Ensuring equitable treatment and support may be an important challenge ahead, considering the possible syndemic effect of the social determinants of health.

Big data and machine learning are gaining traction in health sciences research. They might provide predictive models for both clinical practice and public health systems. Big data is a broad term used to denote volumes of large and complex measurements. Beyond genomics and other “omic” fields, big data includes administrative, molecular, clinical, environmental, sociodemographic, and even social media information. Machine learning, also known as pattern recognition, represents a range of techniques used to analyze big data by identifying patterns of interaction among features. Compared with traditional statistical methods that provide primarily average group-level results, machine learning algorithms allow predictions and stratification of clinical outcomes at the level of an individual subject. In the present chapter, we provide a concise historical perspective of some important events in health sciences and the analytical methods used to find causes and treatment of illnesses. The overall aim is to understand why big data and machine learning have recently become promising methods to define, predict, and treat illnesses, and how they can transform the way we conceptualize care in health sciences.

We explore a type of transfer learning in Convolutional Neural Networks where a network trained on a primary representation of examples (e.g. photographs) is capable of generalizing to a secondary representation (e.g. sketches) without fully training on the latter. We show that the network is able to improve classification on classes for which no examples in the secondary representation were provided, an evidence that the model is exploiting and generalizing concepts learned from examples in the primary representation. We measure this lateral representation learning on a CNN trained on the ImageNet dataset and use overlapping classes in the TU-Berlin and Caltech- 256 datasets as secondary representations, showing that the effect can’t be fully explained by the network learning newly specialized kernels. This phenomenon can potentially be used to train classes in domain adaptation tasks for which few examples in a target representation are available.

Recent advances in deep learning methods have come to define the state-of-the-art for many medical imaging applications, surpassing even human judgment in several tasks. Those models, however, when trained to reduce the empirical risk on a single domain, fail to generalize when applied to other domains, a very common scenario in medical imaging due to the variability of images and anatomical structures, even across the same imaging modality. In this work, we extend the method of unsupervised domain adaptation using self-ensembling for the semantic segmentation task and explore multiple facets of the method on a small and realistic publicly-available magnetic resonance (MRI) dataset. Through an extensive evaluation, we show that self-ensembling can indeed improve the generalization of the models even when using a small amount of unlabelled data., Comment: 15 pages, 9 figures

Graffiti is an urban phenomenon that can be useful as an indicator of social and economic factors of a geographic region or community. Automatically identifying this urban writings can be useful for understanding cities and their communities. In this paper we investigate the use of Convolutional Neural Networks aiming at classifying weakly labeled images to identify the presence or absence of graffiti art in images. We propose the use of a VGG-16 architecture pre-trained on the ImageNet dataset and show a novel approach to fine-tuning the network over graffiti examples extracted from Flickr. Experiments using this approach show accuracy comparable to that of ImageNet classes.

This paper evaluates the deep learning architecture AlexNet applied to the diagnosis of disorders from leaf images using a recent dataset containing five apple tree disorders. It extends previous work by providing a more extensive testing and a dataset validation by using visualization methods. We show that previous results likely overestimate general accuracy, but that the model is able to learn relevant features from the images.

This work provides a study on how Convolutional Neural Networks, trained to identify objects primarily in photos, perform when applied to more abstract representations of the same objects. Our main goal is to better understand the generalization abilities of these networks and their learned inner representations. We show that both GoogLeNet and AlexNet networks are largely unable to recognize abstract sketches that are easily recognizable by humans. Moreover, we show that the measured efficacy vary considerably across different classes and we discuss possible reasons for this.

The automation of the monitoring, inspection and underwater maintenance tasks by underwater robots require a mapping and localization system. One challenge of these systems is how to recognize previously visited place in sensory information. This paper proposes a extended version of a method to detect loop closure dealing with acoustic images acquired by a forward looking sonar (FLS). The method builds a graph of Gaussian probability density function. This structure represents both shape and topological relation. We improve the image segmentation step adding a local parameters adjustment regard to intensity peak analyze of acoustic beams and changed the graph matching metric. We evaluate the method in a real dataset acquired by a underwater vehicle performing navigation in a harbor area.

The use of robots in underwater exploration is increasing in the last years. The automation of the monitoring, inspection and underwater maintenance tasks require a good mapping and localization system. One of the key issues of these systems is how to summarize the sensory information in order to recognize an area that has already been visited. This paper proposes a description method of acoustic images acquired by a forward looking sonar (FLS) using a graph of Gaussian probability density function. This structure represents both shape and topological relation. Furthermore, we also presented a method to match the descriptors in a efficient way. We evaluated the method in a real dataset acquired by a underwater vehicle performing autonomous navigation and mapping tasks in a marine environment.

The expansion of the indications for laparoscopic surgery to include high-risk patient, acute and malignant pathology, and more complex procedures may prolong the hospital stay. Cox multiple stepwise regression analysis model was employed to determine independent predictors of prolonged postoperative hospital stay (more than 3 days) following advanced laparoscopic procedures among 10 variables. Some 130 patients had undergone advanced laparoscopic surgical procedures between November 2000 and August 2003. The median postoperative hospital stay was 3 days (interquartile range 2-5), and 81 patients (62.3%) were discharged within 3 days of surgery. The independent predictors of prolonged postoperative hospital stay were ASA score of 3 or 4 (odds ratio [OR] = 4.610, P = 0.0002) and preoperative hospital stay (OR = 0.151 per day, P = 0.001). Independent predictors of duration of preoperative hospital stay were emergency admission to hospital (OR = 9.516, 95% CI 5.770-13.261, P < 0.0001) and an underlying malignant pathology (OR = 7.948, 95% CI 3.623-12.273, P = 0.0004). Advanced laparoscopic surgery is associated with a short postoperative hospital stay in the majority of patients. Prolongation of the postoperative hospital stay (more than 3 days) may be expected if the patient had been in the hospital with an acute or malignant disease for more than 6 days prior to surgery and in patients with high comorbidity. The duration of surgery has no impact on the duration of the postoperative hospital stay.

Background Two robotic laparoscopic camera-holders, Endo Assist and Aesop 3000, are compared from a system design viewpoint measuring the time taken to perform certain tasks by the operator. Methods EndoAssist and Aesop 3000 robots were tested in a simulated environment. EndoAssist was controlled via a headset-mounted motion axis selection sensor, while Aesop was voice activated. A series of simple and complex tasks were performed moving the camera to different targets. The performance of each task was video taped, and the time from onset to the end of the task was taken from the recording. Results The results showed the EndoAssist robot to be significantly quicker for most of the tasks studied. This was attributed to increased accuracy of movement in EndoAssist in comparison to the voice recognition errors evident while operating Aesop. Conclusion The time taken to perform tasks yields significantly more information about the integrated human-robot system than simply studying the speed of movement of the robot.

Background: Robot and operator should be considered as a system. Evaluation of this system, not of the robot itself, will assess the quality of the robot. EndoAssist and Aesop, laparoscopic camera-holder surgical robots, were compared. Methods: Diagonal, vertical, sideways, zoom and complex movement tasks were timed. The whole procedure was videotaped in a simulated environment and the time of each movement was taken from the tape. Results: EndoAssist performed simple downward, sideways and diagonal tasks as well as complex movement faster than Aesop. There was no difference between the robots for zoom movements. Aesop was quicker only in the preprogrammed mode for most complex tasks. Conclusion: EndoAssist performed better overall due to its greater accuracy and reduced number of erratic movements.

An incisional hernia is a common condition that can be repaired by laparoscopic surgery, with the use of a prosthetic mesh. There are certain situations, however, in which the use of a mesh might be contraindicated, inadvisable, unnecessary, or unavailable. In this paper, we report on a new laparoscopic technique for the suture repair of incisional hernias that may be safely used under such conditions in selected patients.

Surgical relief of gastric outlet obstruction (GOO) or small bowel obstruction in patients who had undergone major resection or palliative bypass surgery for malignancy is conventionally achieved at a laparotomy. The potential role of minimally invasive surgery in the management of these complications has not been previously explored.Between 2003 and 2004, 4 consecutive patients, age range 37 to 72 years, where admitted with gastric outlet or proximal small bowel obstruction following previous open surgery for suspected intra-abdominal malignancy, under the care of one surgeon. The respective past histories of these patients were recurrent GOO and concomitant distal biliary obstruction following a previous open gastric bypass elsewhere for metastatic pancreatic head cancer; persistent adhesive small bowel obstruction following radical gastrectomy for gastric cancer; GOO secondary to intra-abdominal recurrence 6 months after hepatobiliary resection for hilar cholangiocarcinoma; and GOO following previous pancreatico-duodenectomy for suspected pancreatic head cancer. Their respective surgical management consisted of a laparoscopic re-do gastric bypass and concomitant cholecystojejunostomy; adhesiolysis and revision of the Roux-en-Y enteric anastomosis; a Devine exclusion gastroenterostomy; and resection and refashioning of the gastroenterostomy.There were no conversions to open surgery and no postoperative complications. The median operating time was 240 minutes (range, 145 to 300 minutes). Oral free fluid intake was resumed on postoperative day (POD) 1, while diet was resumed between POD 2 and 4. The median postoperative hospital stay was 15.5 days (range, 14 to 25 days).Previous laparotomy and major resection or palliation of malignancy do not preclude the application of the laparoscopic approach for the management of upper gastrointestinal obstruction. Laparoscopic adhesiolysis and revision of enteroenteric and gastroenteric anastomoses are feasible management options in the hands of those experienced with complex laparoscopic surgery.

Laparoscopic gastric bypass for relief of gastric outlet obstruction (GOO) is feasible and safe. However, comparative data to confirm the benefits of the laparoscopic approach remain scarce. Between 1998 and 2003, 26 patients underwent 15 laparoscopic (surgeon A) and 12 open (surgeon B) gastrojejunostomies (GJs) for GOO. The indications for surgery included malignant (n = 17) and benign (n = 10) diseases. There were no conversions to open surgery in the laparoscopic group, and no operative mortality occurred in either group. The groups were comparable for age, sex, American Society of Anesthesiology (ASA) score, frequencies of previous abdominal surgery and of malignant or benign disease, and type of GJ fashioned. There were no differences between the laparoscopic and open groups with regard to the operating time (median, 90 vs 111 min; P = 0.113), and patients receiving intraoperative blood transfusion. However, laparoscopic surgery was associated with significantly shorter durations of postoperative intravenous hydration (60 vs 234 h; P = 0.001), opiate analgesia (49 vs 128 h; P = 0.025), and hospital stay (3 vs 15 days; P = 0.005). Operative morbidity occurred more frequently following open surgery (33% vs 13%; P = 0.219). Laparoscopic GJ for the relief of GOO is associated with a smoother and more rapid postoperative recovery and shorter hospital stay compared with open surgery. In experienced hands, the laparoscopic approach to GJ should become the new gold standard.

The unstructured scenario, the extraction of significant features, the imprecision of sensors along with the impossibility of using GPS signals are some of the challenges encountered in underwater environments. Given this adverse context, the Simultaneous Localization and Mapping techniques (SLAM) attempt to localize the robot in an efficient way in an unknown underwater environment while, at the same time, generate a representative model of the environment. In this paper, we focus on key topics related to SLAM applications in underwater environments. Moreover, a review of major studies in the literature and proposed solutions for addressing the problem are presented. Given the limitations of probabilistic approaches, a new alternative based on a bio-inspired model is highlighted.