7 research outputs found
Técnicas Cirúrgicas e Anestésicas na Histerectomia
Introduction: Hysterectomy, the surgical removal of the uterus, is a common procedure performed on women to treat a variety of gynecological conditions, such as uterine fibroids, endometriosis, abnormal uterine bleeding, and gynecological cancer. Thus, over the years, several surgical techniques have been developed to perform hysterectomy, including the traditional abdominal approach, vaginal hysterectomy, laparoscopic hysterectomy, and robotic hysterectomy. Additionally, the choice of anesthesia is also a crucial aspect in hysterectomy planning, with options including general anesthesia, regional anesthesia (such as spinal or epidural), and local anesthesia techniques. Objective: To compare the clinical results, safety and effectiveness of different surgical techniques and anesthetic approaches used in hysterectomy. Methodology: The Cochrane, Scielo and Medline databases were used, searching for articles published between 2021 and 2024, in Portuguese or English. Final Considerations: In summary, the careful selection of surgical and anesthetic techniques in hysterectomy is essential to optimize clinical results and patient satisfaction. Thus, laparoscopic hysterectomy and the robotic approach emerge as viable and safe alternatives to abdominal hysterectomy, providing lower postoperative morbidity and faster recovery. As for anesthesia, the approach must be personalized, taking into account the safety and efficacy profile of each patient.Introdução: A histerectomia, remoção cirúrgica do útero, é um procedimento comum realizado em mulheres para tratar uma variedade de condições ginecológicas, como miomas uterinos, endometriose, sangramento uterino anormal e câncer ginecológico. Assim, ao longo dos anos, várias técnicas cirúrgicas foram desenvolvidas para realizar a histerectomia, incluindo a abordagem abdominal tradicional, a histerectomia vaginal, a histerectomia laparoscópica e a histerectomia robótica. Além disso, a escolha da anestesia também é um aspecto crucial no planejamento da histerectomia, com opções que incluem anestesia geral, anestesia regional (como a raquianestesia ou a epidural) e técnicas de anestesia local. Objetivo: Comparar os resultados clínicos, a segurança e a eficácia das diferentes técnicas cirúrgicas e abordagens anestésicas utilizadas na histerectomia. Metodologia: Foram utilizadas as bases de dados Cochrane, Scielo e Medline, buscando artigos publicados entre os anos de 2021 e 2024, nos idiomas Português ou Inglês. Considerações Finais: Em síntese, a seleção cuidadosa das técnicas cirúrgicas e anestésicas na histerectomia é fundamental para otimizar os resultados clínicos e a satisfação da paciente. Dessa forma, a histerectomia laparoscópica e a abordagem robótica surgem como alternativas viáveis e seguras à histerectomia abdominal, proporcionando menor morbidade pós-operatória e recuperação mais rápida. Quanto à anestesia, a abordagem deve ser personalizada, levando em consideração o perfil de segurança e eficácia de cada paciente
AI is a viable alternative to high throughput screening: a 318-target study
: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery
Feasibility and effect of high-intensity training on the progression of motor symptoms in adult individuals with Parkinson's disease: A systematic review and meta-analysis.
BackgroundTo determine the feasibility and effect of high-intensity interval training (HIIT) in individuals with Parkinson's and their effect on symptom modification and progression.MethodsWe conducted this systematic review following the Preferred Reporting Items for systematic review and meta-analysis (PRISMA). All studies were searched in seven databases: MEDLINE (PubMed), Cochrane Central Register of Controlled Trials, Web of Science, EMBASE, SPORTDiscus, Virtual Health Library (VHL) and SCOPUS in September 2020 and updated in June 2023. The risk of bias was assessed by the Cochrane Collaboration tool and Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool. We used standardized mean difference (SMD) with a 95% confidence interval (CI) and random effects models, as well as the non-parametric Cochran's Q test and I2 inconsistency test to assess heterogeneity.ResultsA total of 15 randomized clinical trials with 654 participants (mean age, 65.4 years). The majority of studies included high intensity training interventions versus moderate intensity, usual care, or control group. The meta-analysis comparing high-intensity exercise versus control group showed an improvement in the disease severity (MD = -4.80 [95%CI, -6.38; -3.21 high evidence certainty); maximum oxygen consumption (MD = 1.81 [95%CI, 0.36; 3.27] very low evidence certainty) and quality of life (MD = -0.54 [95%CI, -0.94; -0.13] moderate evidence certainty). The results showed that high-intensity exercise compared with moderate intensity exercise group showed a improve motor function and functional mobility measured by the TUG test (MD = -0.38 [95%CI, -0.91; 0.16] moderate evidence certainty) with moderate heterogeneity between studies.ConclusionHigh-intensity exercise performed in both continuous and interval modes when compared with control groups may provide motor function benefits for individuals with Parkinson's disease. HIIT may be feasible, but the intensity of the exercise may influence individuals with Parkinson's disease. However, there was a lack of evidence comparing high intensity and moderate intensity for this population, as the results showed heterogeneity
Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase
Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase