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To produce pooled estimates of the global results of tuberculosis (TB) treatment and analyze the predictive factors of successful TB treatment. Studies published between 2014 and 2019 that reported the results of the treatment of pulmonary TB and the factors that influenced these results. The quality of the studies was evaluated according to the Newcastle-Ottawa quality assessment scale. A random effects model was used to calculate the pooled odds ratio (OR) and 95% confidence interval (CI). This review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) in February 2019 under number CRD42019121512. A total of 151 studies met the criteria for inclusion in this review. The success rate for the treatment of drug-sensitive TB in adults was 80.1% (95% CI: 78.4-81.7). America had the lowest treatment success rate, 75.9% (95% CI: 73.8-77.9), and Oceania had the highest, 83.9% (95% CI: 75.2-91.0). In children, the success rate was 84.8% (95% CI: 77.7-90.7); in patients coinfected with HIV, it was 71.0% (95% CI: 63.7-77.8), in patients with multidrug-resistant TB, it was 58.4% (95% CI: 51.4-64.6), in patients with and extensively drug-resistant TB it was 27.1% (12.7-44.5). Patients with negative sputum smears two months after treatment were almost three times more likely to be successfully treated (OR 2.7; 1.5-4.8), whereas patients younger than 65 years (OR 2.0; 1.7-2.4), nondrinkers (OR 2.0; 1.6-2.4) and HIV-negative patients (OR 1.9; 1.6-2.5 3) were two times more likely to be successfully treated. The success of TB treatment at the global level was good, but was still below the defined threshold of 85%. Factors such as age, sex, alcohol consumption, smoking, lack of sputum conversion at two months of treatment and HIV affected the success of TB treatment.

Optimizing wind turbine control is a major challenge due to wind variability and nonlinearity. This research seeks to improve the performance of wind turbines by designing and developing hybrid intelligent controllers that combine advanced artificial intelligence techniques. A control system combining deep neural networks and fuzzy logic was implemented to optimize the efficiency and operational stability of a 3.5 MW wind turbine. This study analyzed several deep learning models (LSTM, GRU, CNN, ANN, and transformers) to predict the generated power, using data from the SCADA system. The structure of the hybrid controller includes a fuzzy inference system with 28 rules based on linguistic variables that consider power, wind speed, and wind direction. Experiments showed that the hybrid-GRU controller achieved the best balance between predictive performance and computational efficiency, with an R2 of 0.96 and 12,119.54 predictions per second. The GRU excels in overall optimization. This study confirms intelligent hybrid controllers’ effectiveness in improving wind turbines’ performance under various operating conditions, contributing significantly to the field of wind energy.

The urgent imperative to mitigate carbon dioxide (CO2) emissions from power generation poses a pressing challenge for contemporary society. In response, there is a critical need to intensify efforts to improve the efficiency of clean energy sources and expand their use, including wind energy. Within this field, it is necessary to address the variability inherent to the wind resource with the application of prediction methodologies that allow production to be managed. At the same time, to extend its use, this clean energy should be made accessible to everyone, including on a small scale, boosting devices that are affordable for individuals, such as Raspberry and other low-cost hardware platforms. This study is designed to evaluate the effectiveness of various machine learning (ML) algorithms, with special emphasis on deep learning models, in accurately forecasting the power output of wind turbines. Specifically, this research deals with convolutional neural networks (CNN), fully connected networks (FC), gated recurrent unit cells (GRU), and transformer-based models. However, the main objective of this work is to analyze the feasibility of deploying these architectures on various computing platforms, comparing their performance both on conventional computing systems and on other lower-cost alternatives, such as Raspberry Pi 3, in order to make them more accessible for the management of this energy generation. Through training and a rigorous benchmarking process, considering accuracy, real-time performance, and energy consumption, this study identifies the optimal technique to accurately model such real-time series data related to wind energy production, and evaluates the hardware implementation of the studied models. Importantly, our findings demonstrate that effective wind power forecasting can be achieved on low-cost hardware platforms, highlighting the potential for widespread adoption and the personal management of wind power generation, thus representing a fundamental step towards the democratization of clean energy technologies.

Stereotactic neurosurgery emerged in the mid-20th century following the development of a stereotactic frame by Spiegel and Wycis. Historically women were underrepresented in clinical and academic neurosurgery. There is still a significant deficit of female scientists in this field. This paper aims to demonstrate the career and scientific work of some of the most important women who contributed to the development of stereotactic and functional neurosurgery. Exceptional women from all over the world, represented in this review, assisted the evolution of modern stereotactic and functional neurosurgery as neurosurgeon, neuropathologist, neurologist, neurophysiologist and occupational therapist. Fortunately, we could conclude that, in the last two decades the number of female researches increased significantly.

Among her multiple honors outstand her election as a member of the National Academy of Medicine of the United States of America, the American Academy of Arts and Sciences, and the National Academy of Inventors of the USA. In 2000, she was appointed at the University of Calgary, where she is currently an associate professor of the Department of Clinical Neurosciences and is the Head of the Neuromodulation program of southern Alberta. In 2010, upon her return to Berlin, she worked as a neurologist at the University Hospital Charité where she became head of the Movement Disorders and Neuromodulation Unit.

Gilles de la Tourette Syndrome (GTS) is a chronic tic disorder, characterized by unwanted motor actions and vocalizations. While brain stimulation techniques show promise in reducing tic severity, optimal target networks are not well-defined. Here, we leverage datasets from two independent deep brain stimulation (DBS) cohorts and a cohort of tic-inducing lesions to infer critical networks for treatment and occurrence of tics by mapping stimulation sites and lesions to a functional connectome derived from 1,000 healthy participants. We find that greater tic reduction is linked to higher connectivity of DBS sites (N = 37) with action-related functional resting-state networks, i.e., the cingulo-opercular (r = 0.62; p < 0.001) and somato-cognitive action networks (r = 0.47; p = 0.002). Regions of the cingulo-opercular network best match the optimal connectivity profiles of thalamic DBS. We replicate the significance of targeting cingulo-opercular and somato-cognitive action network connectivity in an independent DBS cohort (N = 10). Finally, we demonstrate that tic-inducing brain lesions (N = 22) exhibit similar connectivity to these networks. Collectively, these results suggest a critical role for these action-related networks in the pathophysiology and treatment of GTS. By leveraging diverse datasets from brain stimulation therapy for Tourette Syndrome and tic-inducing brain lesions, Baldermann et al. reveal a critical role of action-related functional networks in both the treatment and pathophysiology of tic disorders.

This study evaluates the effectiveness of Recurrent Neural Networks (RNNs) and Transformer-based models in predicting the Air Quality Index (AQI). Accurate AQI prediction is critical for mitigating the significant health impacts of air pollution and plays a vital role in public health protection and environmental management. The research compares traditional RNN models, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, with advanced Transformer architectures. Data were collected from a weather station in Cuenca, Ecuador, focusing on key pollutants such as CO, NO2, O3, PM2.5, and SO2. Model performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2). The findings reveal that the LSTM model achieved superior performance, with an R2 of 0.701, an RMSE of 0.087, and an MAE of 0.056, demonstrating superior capability in capturing temporal dependencies within complex datasets. Conversely, while Transformer-based models exhibited potential, they were less effective in handling intricate time-series data, resulting in comparatively lower accuracy. These results position the LSTM model as the most reliable approach for AQI prediction, offering an optimal balance between predictive accuracy and computational efficiency. This research contributes to improving AQI forecasting and underscores the importance of timely interventions to mitigate the harmful effects of air pollution.   Este estudio evalúa la eficacia de las redes neuronales recurrentes (RNN) y los modelos basados en transformadores para predecir el índice de calidad del aire (ICA). La investigación compara los modelos RNN tradicionales, incluidos los de memoria a corto y largo plazo (LSTM) y la unidad recurrente controlada (GRU), con arquitecturas avanzadas de transformadores. El estudio utiliza datos de una estación meteorológica en Cuenca, Ecuador, centrándose en contaminantes como CO, NO2, O3, PM2.5 y SO2. Para evaluar el rendimiento de los modelos, se utilizaron métricas clave como el error cuadrático medio (RMSE), el error absoluto medio (MAE) y el coeficiente de determinación (R2). Los resultados del estudio muestran que el modelo LSTM fue el más preciso, alcanzando un R2 de 0,701, un RMSE de 0,087 y un MAE de 0,056. Esto lo convierte en la mejor opción para capturar dependencias temporales en los datos de series temporales complejas. En comparación, los modelos basados en transformadores demostraron tener potencial, pero no lograron la misma precisión que los modelos LSTM, especialmente en datos temporales más complicados. El estudio concluye que el LSTM es más eficaz en la predicción del ICA, equilibrando tanto la precisión como la eficiencia computacional, o que podría ayudar en intervenciones para mitigar la contaminación del aire.

Closed-loop neuromodulation is presumed to be the logical evolution for improving the effectiveness of deep brain stimulation (DBS) treatment protocols (Widge et al., 2018). Identifying symptom-relevant biomarkers that provide meaningful feedback to stimulator devices is an important initial step in this direction. This report demonstrates a technique for assaying neural circuitry hypothesized to contribute to OCD and DBS treatment outcomes. We computed phase-lag connectivity between LFPs and EEGs in thirteen treatment-refractory OCD patients. Simultaneous recordings from scalp EEG and externalized DBS electrodes in the ventral capsule/ventral striatum (VC/VS) were collected at rest during the perioperative treatment stage. Connectivity strength between midfrontal EEG sensors and VC/VS electrodes correlated with baseline OCD symptoms and 12-month posttreatment OCD symptoms. Results are qualified by a relatively small sample size, and limitations regarding the conclusiveness of VS and mPFC as neural generators given some concerns about volume conduction. Nonetheless, findings are consistent with treatment-relevant tractography findings and theories that link frontostriatal hyperconnectivity to the etiopathogenesis of OCD. Findings support the continued investigation of connectivity-based assays for aiding in determination of optimal stimulation location, and are an initial step towards the identification of biomarkers that can guide closed-loop neuromodulation systems. •Phase-lag connectivity may inform closed-loop neuromodulation.•Change in frontostriatal (hyper)connectivity may be a therapeutic mechanism of DBS.•Phase-lag connectivity between frontal and striatal regions predicts OCD severity.•Network-level metrics may be useful for guiding on-demand neuromodulation.•Findings support frontostriatal theories of OCD etiopathogenesis.

Self-injurious behavior (SIB) is associated with diverse psychiatric conditions. Sometimes, (e.g., in patients with autism spectrum disorder or acquired brain injuries) SIB is the most dominant symptom, severely restricting the psychosocial functioning and quality of life of the patients and inhibiting appropriate patient care. In severe cases, it can lead to permanent physical injuries or even death. Primary therapy consists of medical treatment and if implementable, behavioral therapy. For patients with severe SIB refractory to conventional therapy neuromodulation can be considered as a last recourse. In scientific literature, several successful lesioning and deep brain stimulation targets have been described that can indicate a common underlying neuronal pathway. The objectives of this study were to evaluate the short- and long-term clinical outcome of patients with severe, therapy refractory SIB who underwent DBS with diverse underlying psychiatric disorders and to correlate these outcomes with the activated connectivity networks. We retrospectively analyzed ten patients with SIB who underwent DBS surgery with diverse psychiatric conditions including autism spectrum disorder, organic personality disorder after hypoxic or traumatic brain injury or Tourette syndrome. DBS targets were chosen according to the underlying disorder, patients were either stimulated in the nucleus accumbens, amygdala, posterior hypothalamus, medial thalamus or ventrolateral thalamus. Clinical outcome was measured six months after surgery and at long-term follow-up after ten or more years using the Early Rehabilitation Barthel index (ERBI) and time of restraint. Connectivity patterns were analyzed using normative connectome. Based on previous literature the orbitofrontal cortex, superior frontal gyrus, the amygdala and the hippocampus were chosen as regions of interest. This analysis showed a significant improvement in the functionality of the patients with DBS in the short- and long-term follow-up. Good clinical outcome correlated with higher connectivity to the amygdala and hippocampus. These findings may suggest a common pathway, which can be relevant when planning a surgical procedure in patients with SIB.