Statistical Analysis and Machine Learning Used in the Case of Two Behavioral Tests Applied in Zebrafish Exposed to Mycotoxins

Machine learning is a branch of artificial intelligence that allows computer systems to learn directly from examples, data, and experience. Statistical modeling is more about finding connections between variables and consequently the impact of these relationships, while also catering for prediction. It should be clear that these two methodologies are different in terms of their purpose, despite the fact that they use similar means to get there. The evaluation of the machine learning algorithm uses a set of tests to validate its accuracy. Although, for a statistical model, the analysis of regression parameters by confidence intervals, significance tests and other tests can be used to assess the legitimacy of the model. To demonstrate the applications and usefulness of this theory, an experimental study was conducted on zebrafish exposed to mycotoxin. Methods: Patulin (70 µg/L) and kojic acid (100 mg/L, 204 mg/L, and 284 mg/L) were administered by immersion to zebrafish once daily for a period of 7 days before the behavior testing. The following behavioral tests were performed: a novel tank test (NTT) (to assess the explorative behavior and anxiety); and a Y-maze test (which measures the spontaneous explorative behavior). Behavioral tests were performed on separate days. For the behavior tests, the statistical analysis was performed using ANOVA variation analysis (two-way ANOVA). All results are expressed as the mean ± standard error of the mean. The values of the general index F for which p < 0.05 were considered statistically significant. Results: Y-maze—patulin exposure led to an intensification of the locomotor activity and an increased traveled distance and number of arm entries. By increasing the spontaneous alternation between the aquarium’s arms, patulin has shown a stimulating effect on spatial memory. In the case of zebrafish exposed to 100 mg/L kojic acid, the traveled distance was shorter by 27% than the distance attained by those in the control group. The higher doses of kojic acid (204 mg/L and 284 mg/L) led to an increased locomotor activity, distance traveled, number of arm entries, and the spontaneous alternation. The increase in spontaneous alternation demonstrates that 204 mg/L and 284 mg/L kojic acid doses had a stimulating effect on spatial memory. Novel tank test—compared to the control group, the traveled distance of the patulin-exposed fish is slightly reduced. Compared to the control group, the traveled distance of the kojic acid-exposed fish is reduced, due to a shorter mobile time (by 25–27% in the case of fish exposed to 204 mg/L and 284 mg/L kojic acid). Patulin and kojic acid exhibit toxic effects on zebrafish liver, kidney, and myocardium and leads to severe alteration. We continued the analysis by trying some machine learning algorithms on the classification problems in the case of the two behavioral tests MAZE and NTT, after which we concluded that the results were better in the case of the NTT test relative to the MAZE test and that the use of decision tree algorithms leads to amazing results, knowing that their hierarchical structure allows them to learn signals from both classes. Conclusions: The groups exposed to patulin and kojic acid show histological changes in the liver, kidneys, and myocardial muscle tissue. The novel tank test, which assesses exploratory behavior, has been shown to be conclusive in the behavioral analysis of fish that have been given toxins, demonstrating that the intoxicated fish had a decreased explorative behavior and increased anxiety. We were able to detect a machine learning algorithm in the category of decision trees, which can be trained to classify the behavior of fish that were given a toxin in the category of those used in the experiment, only by analyzing the characteristic features of the NTT Behavior Test

Abdominopelvic Actinomycosis&mdash;The Diagnostic and Therapeutic Challenge of the Most Misdiagnosed Disease

Abdominopelvic actinomycosis is a rare chronic or subacute bacterial infection caused by Actinomyces israelii, a Gram-positive anaerobic bacterium that normally colonizes the digestive and genital tracts, clinically presented as an inflammatory mass or abscess formation. Methods: We reviewed the medical records of the patients from our clinic with abdominopelvic actinomycosis who underwent surgery between 2002 and 2022. In this period, 28 cases (9 men and 19 women) were treated. The mean age was 43.36 years and they were hospitalized for abdominopelvic tumors or inflammatory tumors in 15 cases and inflammatory disease in 13 cases. Results: Causes of actinomycosis in the studied group were an intra-uterine contraceptive device in 17 cases, foreign bodies in 2 cases, diabetes in 4 cases, stenting of the bile duct in 1 case, and immunodepression. For 6 patients, we performed surgery by open approach and for 21 patients by a laparoscopic approach. For nine patients, abdominopelvic actinomycosis had been mimicking a colon malignancy (cecum and ascending colon, four cases; transverse colon, two cases; and on the greater omentum, three cases) and for six patients, a pelvic tumor (advanced ovarian cancer). After surgery the patients underwent specific treatment with antibiotics, with good results. In two cases we discovered and treated hepatic actinomycosis, one case by a laparoscopic approach and one case by a percutaneous approach. In our lot we noticed three recurrences that required reintervention in patients who had had short-term antibiotics due to non-compliance with treatment out of four such cases. Conclusions: For abdominopelvic malignancies, actinomycosis should be included in the differential diagnosis, as well as for inflammatory bowel diseases and bowel obstructions. We have a wide range of patients considering the rarity of this condition. Long-term antibiotics are necessary to prevent recurrence

Laparoscopic Hartmann Procedure—A Surgery That Still Saves Lives

Background: A Hartmann operation, which is the intervention by which the lower part of the sigmoid and the upper part of the rectum are resected with the closing of the rectal stump and end colostomy, has as its indications: advanced or complicated rectosigmoid neoplasm, moderate biological condition of the patient, peritoneal sepsis, intestinal occlusion and fragile colonic wall, especially in the context of inflammatory changes. The Hartmann procedure can save lives even at the cost of a stoma reversal failure. Methods: The cases operated with the Hartmann procedure by an open approach or laparoscopic approach in our clinic, between 1 January 2016 and 31 December 2020, were admitted in this study and their medical records were reviewed, also making a comparison between the two types of approach. Univariate statistical comparisons but also a multivariate analysis was performed. Results: We performed 985 operations for intestinal and colonic occlusion (7.15% of the total operations in the clinic), 531 (54%) were non-tumor occlusions and 454 (46%) were occlusive tumors (88 Hartmann operations). Of these, 7.3% were laparoscopically performed (7 laparoscopic Hartmann operations and 23 diagnostic laparoscopies). A total of 11 cases (18%) also had colonic perforation. We compared laparoscopic Hartmann with open Hartmann and observed the benefits of laparoscopy for postoperative morbidity and mortality. The presence of pulmonary and cardiac morbidities is associated with the occurrence of general postoperative morbidities, while peritonitis is statistically significantly associated with the occurrence of local complications that are absent after the laparoscopic approach. Conclusions: The Hartmann procedure is still nowadays an operation widely used in emergency situations. Laparoscopy may become standard for the Hartmann procedure and reversal of the Hartmann procedure, but the percentage of laparoscopy remains low due to advanced or complicated colorectal cancer, poor general condition both at the first and second intervention, and the difficulties of reversal of the Hartmann procedure

Statistical Analysis and Machine Learning Used in the Case of Two Behavioral Tests Applied in Zebrafish Exposed to Mycotoxins

Machine learning is a branch of artificial intelligence that allows computer systems to learn directly from examples, data, and experience. Statistical modeling is more about finding connections between variables and consequently the impact of these relationships, while also catering for prediction. It should be clear that these two methodologies are different in terms of their purpose, despite the fact that they use similar means to get there. The evaluation of the machine learning algorithm uses a set of tests to validate its accuracy. Although, for a statistical model, the analysis of regression parameters by confidence intervals, significance tests and other tests can be used to assess the legitimacy of the model. To demonstrate the applications and usefulness of this theory, an experimental study was conducted on zebrafish exposed to mycotoxin. Methods: Patulin (70 &micro;g/L) and kojic acid (100 mg/L, 204 mg/L, and 284 mg/L) were administered by immersion to zebrafish once daily for a period of 7 days before the behavior testing. The following behavioral tests were performed: a novel tank test (NTT) (to assess the explorative behavior and anxiety); and a Y-maze test (which measures the spontaneous explorative behavior). Behavioral tests were performed on separate days. For the behavior tests, the statistical analysis was performed using ANOVA variation analysis (two-way ANOVA). All results are expressed as the mean &plusmn; standard error of the mean. The values of the general index F for which p &lt; 0.05 were considered statistically significant. Results: Y-maze&mdash;patulin exposure led to an intensification of the locomotor activity and an increased traveled distance and number of arm entries. By increasing the spontaneous alternation between the aquarium&rsquo;s arms, patulin has shown a stimulating effect on spatial memory. In the case of zebrafish exposed to 100 mg/L kojic acid, the traveled distance was shorter by 27% than the distance attained by those in the control group. The higher doses of kojic acid (204 mg/L and 284 mg/L) led to an increased locomotor activity, distance traveled, number of arm entries, and the spontaneous alternation. The increase in spontaneous alternation demonstrates that 204 mg/L and 284 mg/L kojic acid doses had a stimulating effect on spatial memory. Novel tank test&mdash;compared to the control group, the traveled distance of the patulin-exposed fish is slightly reduced. Compared to the control group, the traveled distance of the kojic acid-exposed fish is reduced, due to a shorter mobile time (by 25&ndash;27% in the case of fish exposed to 204 mg/L and 284 mg/L kojic acid). Patulin and kojic acid exhibit toxic effects on zebrafish liver, kidney, and myocardium and leads to severe alteration. We continued the analysis by trying some machine learning algorithms on the classification problems in the case of the two behavioral tests MAZE and NTT, after which we concluded that the results were better in the case of the NTT test relative to the MAZE test and that the use of decision tree algorithms leads to amazing results, knowing that their hierarchical structure allows them to learn signals from both classes. Conclusions: The groups exposed to patulin and kojic acid show histological changes in the liver, kidneys, and myocardial muscle tissue. The novel tank test, which assesses exploratory behavior, has been shown to be conclusive in the behavioral analysis of fish that have been given toxins, demonstrating that the intoxicated fish had a decreased explorative behavior and increased anxiety. We were able to detect a machine learning algorithm in the category of decision trees, which can be trained to classify the behavior of fish that were given a toxin in the category of those used in the experiment, only by analyzing the characteristic features of the NTT Behavior Test

Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey

Background: Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons' knowledge and perception of using AI-based tools in clinical decision-making processes. Methods: An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society's website and Twitter profile. Results: 650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons' preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust. Discussion: The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI

Time for a paradigm shift in shared decision-making in trauma and emergency surgery? Results from an international survey

Background Shared decision-making (SDM) between clinicians and patients is one of the pillars of the modern patient-centric philosophy of care. This study aims to explore SDM in the discipline of trauma and emergency surgery, investigating its interpretation as well as the barriers and facilitators for its implementation among surgeons. Methods Grounding on the literature on the topics of the understanding, barriers, and facilitators of SDM in trauma and emergency surgery, a survey was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was sent to all 917 WSES members, advertised through the society’s website, and shared on the society’s Twitter profile. Results A total of 650 trauma and emergency surgeons from 71 countries in five continents participated in the initiative. Less than half of the surgeons understood SDM, and 30% still saw the value in exclusively engaging multidisciplinary provider teams without involving the patient. Several barriers to effectively partnering with the patient in the decision-making process were identified, such as the lack of time and the need to concentrate on making medical teams work smoothly. Discussion Our investigation underlines how only a minority of trauma and emergency surgeons understand SDM, and perhaps, the value of SDM is not fully accepted in trauma and emergency situations. The inclusion of SDM practices in clinical guidelines may represent the most feasible and advocated solutions