Research

#Bernie & #Perry

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

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 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.

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

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.

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

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.

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.

The identification and prognosis of the potential for developing Cardiovascular Diseases (CVD) in healthy individuals is a vital aspect of disease management. Accessing the comprehensive health data on CVD currently available within hospital databases holds significant potential for the early detection and diagnosis of CVD, thereby positively impacting disease outcomes. Therefore, the incorporation of machine learning methods holds significant promise in the advancement of clinical practice for the management of Cardiovascular Diseases (CVDs). By providing a means to develop evidence-based clinical guidelines and management algorithms, these techniques can eliminate the need for costly and extensive clinical and laboratory investigations, reducing the associated financial burden on patients and the healthcare system. In order to optimize early prediction and intervention for CVDs, this study proposes the development of novel, robust, effective, and efficient machine learning algorithms, specifically designed for the automatic selection of key features and the detection of early-stage heart disease. The proposed Catboost model yields an F1-score of about 92.3% and an average accuracy of 90.94%. Therefore, Compared to many other existing state-of-art approaches, it successfully achieved and maximized classification performance with higher percentages of accuracy and precision.

Assessing the baseline perfusion and perfusion reserve after acetazolamide (ACZ) challenge is a common method for the evaluation of patients with cerebrovascular disease. Most previous studies using H215O PET applied the bolus injection technique. There is considerable discrepancy regarding the optimal time point of imaging after ACZ injection. The purpose of this study was to continuously monitor cerebral blood flow (CBF) after ACZ using constant-infusion H215O PET. Methods: Four patients with stenoses of an internal carotid artery and 6 with moyamoya disease were studied. H215O was continuously infused, and data were recorded in 1-min frames. After equilibration of H215O, 5 min of baseline data were acquired, and then 1 g of ACZ was administered intravenously and data collection continued for 10–22 min. Arterial blood was continuously drawn for absolute quantification of CBF. Results: The arterial 15O concentration remained generally stable during scanning, and the cerebellar blood flow fluctuations of the 5 baseline scans were small. The scan-to-scan difference was 6% (difference of 2 successive scans/mean). In the nonpathologic areas, the increase in CBF started 1–2 min after administration of ACZ. The largest fraction of the increase occurred from 0 to 10 min. The ratio of CBF in pathologic areas to CBF in cerebellum showed an initial decrease that stabilized after 5 min. Conclusion: A continuous-infusion protocol is a viable alternative to single bolus injections for the assessment of cerebral perfusion status. Such a protocol is advantageous when the time course of CBF after an intervention is not known. With continuous monitoring, the optimal time point for evaluation of a certain parameter can be chosen post hoc. Furthermore, the time course of CBF itself may allow the definition of new parameters for evaluating perfusion status in cerebrovascular patients, both for assessment before a revascularization procedure and for follow-up. A limitation of the present study is the relatively small number of patients with each type of cerebrovascular disease and the lack of healthy subjects.

Purpose To evaluate the precision and accuracy in measured blood lactate concentrations among four commonly used handheld lactate analyzers compared to two stationary analyzers. Methods Venous blood samples were taken at exercise intensities ranging from low to high. The blood lactate concentration was measured simultaneously with four pairs of handheld lactate analyzers (two new units of each brand: Lactate Plus, Lactate Pro2, Lactate Scout 4, and TaiDoc TD-4289), and compared with two stationary analyzers (Biosen C-Line and YSI Sport 1500). Measurements were repeated for a range of blood lactate concentrations (measured with Biosen) from 0.88 to 4.89 mM with a median difference between measurements of 0.10 mM. Results The mean relative differences to the Biosen analyzer were $$-$$ - 7% (Plus), 7% (Pro), $$-$$ - 10% (Scout), 42% (Tai), and $$-$$ - 32% (Ysi). The residual standard errors after linear regression against Biosen were 0.18 mM (Plus), 0.20 mM (Pro), 0.22 mM (Scout), 0.15 mM (Tai), and 0.06 mM (Ysi). Accordingly, a blood lactate concentration of 3 mM measured with Biosen yielded 95% prediction intervals that were 0.72 mM (Plus), 0.80 mM (Pro), 0.87 mM (Scout), 0.60 mM (Tai), and 0.23 mM (Ysi) wide. Conclusion Compared to our two stationary analyzers, the precision of the four handheld lactate analyzers evaluated in this study was poor. Among the four, Tai was the most precise; however, this analyzer had low accuracy with a substantial mean difference to the reference analyzer.

Background In recent years, nitrooxy compounds have been identified as promising inhibitors of methanogenesis in ruminants. However, when animals receive a nitrooxy compound, a high portion of the spared hydrogen is eructated as gas, which partly offsets the energy savings of CH4 mitigation. The objective of the present study was to evaluate the long-term and combined effects of supplementation with N-[2-(nitrooxy)ethyl]-3-pyridinecarboxamide (NPD), a methanogenesis inhibitor, and fumaric acid (FUM), a hydrogen sink, on enteric CH4 production, rumen fermentation, bacterial populations, apparent nutrient digestibility, and lactation performance of dairy goats. Results Twenty-four primiparous dairy goats were used in a randomized complete block design with a 2 × 2 factorial arrangement of treatments: supplementation without or with FUM (32 g/d) or NPD (0.5 g/d). All samples were collected every 3 weeks during a 12-week feeding experiment. Both FUM and NPD supplementation persistently inhibited CH4 yield (L/kg DMI, by 18.8% and 18.1%, respectively) without negative influence on DMI or apparent nutrient digestibility. When supplemented in combination, no additive CH4 suppression was observed. FUM showed greater responses in increasing the molar proportion of propionate when supplemented with NPD than supplemented alone (by 10.2% vs. 4.4%). The rumen microbiota structure in the animals receiving FUM was different from that of the other animals, particularly changed the structure of phylum Firmicutes. Daily milk production and serum total antioxidant capacity were improved by NPD, but the contents of milk fat and protein were decreased, probably due to the bioactivity of absorbed NPD on body metabolism. Conclusions Supplementing NPD and FUM in combination is a promising way to persistently inhibit CH4 emissions with a higher rumen propionate proportion. However, the side effects of this nitrooxy compound on animals and its residues in animal products need further evaluation before it can be used as an animal feed additive.

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.

Background Hypoxic and cold environments have been shown to improve the function and performance of athletes. However, it is unclear whether the combination of subalpine conditions and cold temperatures may have a greater effect. The present study aims to investigate the effects of 6 weeks of training in a sub-plateau cold environment on the physical function and athletic ability of elite parallel giant slalom snowboard athletes. Methods Nine elite athletes (four males and five females) participated in the study. The athletes underwent 6 weeks of high intensity ski-specific technical training (150 min/session, six times/week) and medium-intensity physical training (120 min/session, six times/week) prior to the Beijing 2021 Winter Olympic Games test competition. The physiological and biochemical parameters were collected from elbow venous blood samples after each 2-week session to assess the athletes’ physical functional status. The athletes’ athletic ability was evaluated by measuring their maximal oxygen uptake, Wingate 30 s anaerobic capacity, 30 m sprint run, and race performance. Measurements were taken before and after participating in the training program for six weeks. The repeated measure ANOVA was used to test the overall differences of blood physiological and biochemical indicators. For indicators with significant time main effects, post-hoc tests were conducted using the least significant difference (LSD) method. The paired-samples t-test was used to analyze changes in athletic ability indicators before and after training. Results (1) There was a significant overall time effect for red blood cells (RBC) and white blood cells (WBC) in males; there was also a significant effect on the percentage of lymphocytes (LY%), serum testosterone (T), and testosterone to cortisol ratio (T/C) in females (p < 0.001 − 0.015, ${\eta }_{p}^{2}=0.81-0.99$ η p 2 = 0 . 81 − 0 . 99 ). In addition, a significant time effect was also found for blood urea(BU), serum creatine kinase (CK), and serum cortisol levels in both male and female athletes (p = 0.001 − 0.029, ${\eta }_{p}^{2}=0.52-0.95$ η p 2 = 0 . 52 − 0 . 95 ). (2) BU and CK levels in males and LY% in females were all significantly higher at week 6 (p = 0.001 − 0.038), while WBC in males was significantly lower (p = 0.030). T and T/C were significantly lower in females at week 2 compared to pre-training (p = 0.007, 0.008, respectively), while cortisol (C) was significantly higher in males and females at weeks 2 and 4 (p(male) = 0.015, 0.004, respectively; p(female) = 0.024, 0.030, respectively). (3) There was a noticeable increase in relative maximal oxygen uptake, Wingate 30 s relative average anaerobic power, 30 m sprint run performance, and race performance in comparison to the pre-training measurements (p < 0.001 − 0.027). Conclusions Six weeks of sub-plateau cold environment training may improve physical functioning and promote aerobic and anaerobic capacity for parallel giant slalom snowboard athletes. Furthermore, male athletes had a greater improvement of physical functioning and athletic ability when trained in sub-plateau cold environments.

pdfslide.net ist die beste Quelle für alle Informationen die Sie suchen. Von allgemeinen Themen bis hin zu speziellen Sachverhalten, finden Sie auf pdfslide.net alles. Wir hoffen, dass Sie hier das Gesuchte finden!

Background PfAP2-EXP2 is located within chromosome 6 of Plasmodium falciparum recently identified to be undergoing an extensive selective sweep in West African isolates. The gene encoding this transcription factor, PfAP2-EXP2, is essential and thus likely subject to purifying selection that limits variants in the parasite population despite its genomic location. Methods 72 Plasmodium falciparum field samples and 801 clinical sequences from the Pf6 MalariaGEN dataset of Ghanaian origin, were integrated and analysed. Results A total of 14 single nucleotide variants of which 5 were missense variants, were identified after quality checks and filtering. Except for one, all identified variants were rare among the clinical samples obtained in this study (Minor allelic frequency < 0.01). Further results revealed a considerably low dN/dS value (0.208) suggesting the presence of purifying selection. Further, all the mutant amino acids were wildtype residues in AP2-EXP2 orthologous proteins—tentatively suggesting a genus-level conservation of amino acid residues. Computational analysis and predictions corroborated these findings. Conclusions Despite the recent extensive selective sweep within chromosome 6 of West African isolates, PfAP2-EXP2 of Ghanaian origin exhibits low nucleotide diversity and very low dN/dS consistent with purifying selection acting to maintain the function of an essential gene. The conservation of AP2-EXP2 is an important factor that makes it a potential drug target. Graphical Abstract

With increasing demand and environmental concerns, researchers are exploring new materials that can perform as well or better than traditional materials while r

Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture is an open access book designed to provide needed guidance for GHG measurement methods in smallholder agriculture