Research

Gaslighting occurs in the workplace, in politics, and in intimate relationships. But how do you spot it? Unlike overt bullying, gaslighting can be difficult to recognize.

Reading Time: 10 minutesWith more young people talking openly about mental health, gaslighting has become an increasingly common topic among this age group over the last few years. Gaslighting and other forms of emotional abuse have been depicted in films and TV and discussed on reality shows like The Bachelorette. Gaslighting was even chosen as the 2022 Word

PDF | Aim is to discuss gaslighting and to provide advices how to recognize the abuser and how to defend oneself from the gaslighting. | Find, read and cite all the research you need on ResearchGate

Gaslighting is emotional abuse and mental manipulation that can leave you questioning everything about yourself.

Your partner may have some signs of a gaslighter, or even just a couple. How do you know you've reached the point when you need to leave?

The longer you stay, the longer it takes to recover.

Whether in relationships or in politics, a psychoanalyst explains how to tell if you’re being gaslit.

Reading Time: 10 minutesWith more young people talking openly about mental health, gaslighting has become an increasingly common topic among this age group over the last few years. Gaslighting and other forms of emotional abuse have been depicted in films and TV and discussed on reality shows like The Bachelorette. Gaslighting was even chosen as the 2022 Word

Back in the news is an essay that socialist presidential candidate Bernie Sanders wrote for an alternative newspaper, talking about rape fantasies.

#Bernie & #Perry

Notes for Nonparametric Statistics. MSc in Statistics for Data Science. Carlos III University of Madrid.

Background Bacterial ghosts are the evacuated bacterial cellular membranes from most of the genetic and protein contents which preserved their surface characters. Recently, bacterial ghosts exploited for different biomedical applications, for instance, vaccination. The purpose of this study is to measure the immunogenic protective response of bacterial ghosts of Salmonella Typhimurium in animals and to allow future testing this response in humans. The immunologic response was qualitatively, quantitatively, and functionally measured. We have measured the humoral and cellular immune responses, such as immunoglobulins elevation (IgG), increased granulocytes, serum antibacterial activity, clearance of virulence in feces and liver, and the survival rate. Results The bacterial ghosts’ vaccine was able to protect 100% of subcutaneously vaccinated rats and 75% of adjuvant subcutaneously vaccinated rats. The lowest survival rate was in the orally vaccinated group (25%). The maximum level of serum IgG titers, as well as serum and feces bactericidal activity (100% eradication), was exhibited in the subcutaneously vaccinated group with adjuvant vaccines followed by the subcutaneously vaccinated one. Additionally, the highest granulocytes’ number was observed in the adjuvant vaccine subcutaneously immunized group. The bacterial load in liver homogenate was eliminated in the subcutaneously vaccinated rats after the virulence challenge. Conclusions The bacterial ghosts of Salmonella enterica serovar Typhimurium that prepared by Tween 80 Protocol showed an effective vaccine candidate that protected animals, eliminated the virulence in feces and liver. These findings show that chemically induced bacterial ghosts of Salmonella Typhimurium can be a promising vaccine.

Objective To compare the safety and accuracy of cortical bone trajectory screw placement between the robot-assisted and fluoroscopy-assisted approaches. Methods This retrospective study was conducted between November 2018 and June 2020, including 81 patients who underwent cortical bone trajectory (CBT) surgery for degenerative lumbar spine disease. CBT was performed by the same team of experienced surgeons. The patients were randomly divided into two groups—the fluoroscopy-assisted group (FA, 44 patients) and the robot-assisted group (RA, 37 patients). Robots for orthopedic surgery were used in the robot-assisted group, whereas conventional fluoroscopy-guided screw placement was used in the fluoroscopy-assisted group. The accuracy of screw placement and rate of superior facet joint violation were assessed using postoperative computed tomography (CT). The time of single screw placement, intraoperative blood loss, and radiation exposure to the surgical team were also recorded. The χ2 test and Student’s t-test were used to analyze the significance of the variables (P < 0.05). Results A total of 376 screws were inserted in 81 patients, including 172 screws in the robot-assisted group and 204 pedicle screws in the fluoroscopy-assisted group. Screw placement accuracy was higher in the RA group (160, 93%) than in the FA group (169, 83%) (P = 0.003). The RA group had a lower violation of the superior facet joint than the FA group. The number of screws reaching grade 0 in the RA group (58, 78%) was more than that in the FA group (56, 64%) (P = 0.041). Screw placement time was longer in the FA group (7.25 ± 0.84 min) than in the RA group (5.58 ± 1.22 min, P < 0.001). The FA group had more intraoperative bleeding (273.41 ± 118.20 ml) than the RA group (248.65 ± 97.53 ml, P = 0.313). The radiation time of the FA group (0.43 ± 0.07 min) was longer than the RA group (0.37 ± 0.10 min, P = 0.001). Furthermore, the overall learning curve tended to decrease. Conclusions Robot-assisted screw placement improves screw placement accuracy, shortens screw placement time, effectively improves surgical safety and efficiency, and reduces radiation exposure to the surgical team. In addition, the learning curve of robot-assisted screw placement is smooth and easy to operate.

Background Predicting survival of recipients after liver transplantation is regarded as one of the most important challenges in contemporary medicine. Hence, improving on current prediction models is of great interest.Nowadays, there is a strong discussion in the medical field about machine learning (ML) and whether it has greater potential than traditional regression models when dealing with complex data. Criticism to ML is related to unsuitable performance measures and lack of interpretability which is important for clinicians. Methods In this paper, ML techniques such as random forests and neural networks are applied to large data of 62294 patients from the United States with 97 predictors selected on clinical/statistical grounds, over more than 600, to predict survival from transplantation. Of particular interest is also the identification of potential risk factors. A comparison is performed between 3 different Cox models (with all variables, backward selection and LASSO) and 3 machine learning techniques: a random survival forest and 2 partial logistic artificial neural networks (PLANNs). For PLANNs, novel extensions to their original specification are tested. Emphasis is given on the advantages and pitfalls of each method and on the interpretability of the ML techniques. Results Well-established predictive measures are employed from the survival field (C-index, Brier score and Integrated Brier Score) and the strongest prognostic factors are identified for each model. Clinical endpoint is overall graft-survival defined as the time between transplantation and the date of graft-failure or death. The random survival forest shows slightly better predictive performance than Cox models based on the C-index. Neural networks show better performance than both Cox models and random survival forest based on the Integrated Brier Score at 10 years. Conclusion In this work, it is shown that machine learning techniques can be a useful tool for both prediction and interpretation in the survival context. From the ML techniques examined here, PLANN with 1 hidden layer predicts survival probabilities the most accurately, being as calibrated as the Cox model with all variables. Trial registration Retrospective data were provided by the Scientific Registry of Transplant Recipients under Data Use Agreement number 9477 for analysis of risk factors after liver transplantation.

Author Alessandra Larini Author Carlo Aldinucci Author Velio Bocci Publication Institute of General Physiology, University of Siena, 53100, Siena, Italy PDF Download PDF Document Ozone as a modulator of the immune system Abstract In order to clarify the immunomodulating properties of ozone, we have investigated: a) the effects of stimulation on isolated peripheral human blood mononuclear cells (PBMC) from normal donors with either ozone or ozonated serum; b) the range (in terms of O3 concentrations) of the therapeutic window; c) the stimulatory and toxic effects and d) the pattern, of both proinflammatory and immunosuppressive cytokine production up to 86 hours after Read more

Non-invasive cross-sectional imaging techniques play a crucial role in the assessment of the varied manifestations of vascular disease. Vascular imaging encompasses a wide variety of pathology. Designing vascular imaging protocols can be challenging owing to the non-uniform velocity of blood in the aorta, differences in cardiac output between patients, and the effect of different disease states on blood flow. In this review, we provide the rationale behind—and a practical guide to—designing and implementing straightforward vascular computed tomography (CT) and magnetic resonance imaging (MRI) protocols.Teaching Points • There is a wide range of vascular pathologies requiring bespoke imaging protocols. • Variations in cardiac output and non-uniform blood velocity complicate vascular imaging. • Contrast media dose, injection rate and duration affect arterial enhancement in CTA. • Iterative CT reconstruction can improve image quality and reduce radiation dose. • MRA is of particular value when imaging small arteries and venous studies.

Learn the science behind peak and fade intervals and how they can improve athlete performance. Get insights to elevate your training.

Evaluate the cardiovascular properties of MANP in AA with Hypertension

Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable.

Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable.

Background: Timely and accurate outcome prediction plays a vital role in guiding clinical decisions on acute ischemic stroke. Early condition deterioration and severity after the acute stage are determinants for long-term outcomes. Therefore, predicting early outcomes is crucial in acute stroke management. However, interpreting the predictions and transforming them into clinically explainable concepts are as important as the predictions themselves. Objective: This work focused on machine learning model analysis in predicting the early outcomes of ischemic stroke and used model explanation skills in interpreting the results. Methods: Acute ischemic stroke patients registered on the Stroke Registry of the Chang Gung Healthcare System (SRICHS) in 2009 were enrolled for machine learning predictions of the two primary outcomes: modified Rankin Scale (mRS) at hospital discharge and in-hospital deterioration. We compared 4 machine learning models, namely support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), and deep neural network (DNN), with the area under the curve (AUC) of the receiver operating characteristic curve. Further, 3 resampling methods, random under sampling (RUS), random over sampling, and the synthetic minority over-sampling technique, dealt with the imbalanced data. The models were explained based on the ranking of feature importance and the SHapley Additive exPlanations (SHAP). Results: RF performed well in both outcomes (discharge mRS: mean AUC 0.829, SD 0.018; in-hospital deterioration: mean AUC 0.710, SD 0.023 on original data and 0.728, SD 0.036 on resampled data with RUS for imbalanced data). In addition, DNN outperformed other models in predicting in-hospital deterioration on data without resampling (mean AUC 0.732, SD 0.064). In general, resampling contributed to the limited improvement of model performance in predicting in-hospital deterioration using imbalanced data. The features obtained from the National Institutes of Health Stroke Scale (NIHSS), white blood cell differential counts, and age were the key features for predicting discharge mRS. In contrast, the NIHSS total score, initial blood pressure, having diabetes mellitus, and features from hemograms were the most important features in predicting in-hospital deterioration. The SHAP summary described the impacts of the feature values on each outcome prediction. Conclusions: Machine learning models are feasible in predicting early stroke outcomes. An enriched feature bank could improve model performance. Initial neurological levels and age determined the activity independence at hospital discharge. In addition, physiological and laboratory surveillance aided in predicting in-hospital deterioration. The use of the SHAP explanatory method successfully transformed machine learning predictions into clinically meaningful results.