A population model of deep brain stimulation in movement disorders from circuits to cells

Copyright © 2020 Yousif, Bain, Nandi and Borisyuk.For more than 30 years, deep brain stimulation (DBS) has been used to target the symptoms of a number of neurological disorders and in particular movement disorders such as Parkinson's disease (PD) and essential tremor (ET). It is known that the loss of dopaminergic neurons in the substantia nigra leads to PD, while the exact impact of this on the brain dynamics is not fully understood, the presence of beta-band oscillatory activity is thought to be pathological. The cause of ET, however, remains uncertain, however pathological oscillations in the thalamocortical-cerebellar network have been linked to tremor. Both of these movement disorders are treated with DBS, which entails the surgical implantation of electrodes into a patient's brain. While DBS leads to an improvement in symptoms for many patients, the mechanisms underlying this improvement is not clearly understood, and computational modeling has been used extensively to improve this. Many of the models used to study DBS and its effect on the human brain have mainly utilized single neuron and single axon biophysical models. We have previously shown in separate models however, that the use of population models can shed much light on the mechanisms of the underlying pathological neural activity in PD and ET in turn, and on the mechanisms underlying DBS. Together, this work suggested that the dynamics of the cerebellar-basal ganglia thalamocortical network support oscillations at frequency range relevant to movement disorders. Here, we propose a new combined model of this network and present new results that demonstrate that both Parkinsonian oscillations in the beta band and oscillations in the tremor frequency range arise from the dynamics of such a network. We find regions in the parameter space demonstrating the different dynamics and go on to examine the transition from one oscillatory regime to another as well as the impact of DBS on these different types of pathological activity. This work will allow us to better understand the changes in brain activity induced by DBS, and allow us to optimize this clinical therapy, particularly in terms of target selection and parameter setting.Peer reviewe

Some Types of Mappings in Bitopological Spaces

            قدمنا بعض المفاهيم في الفضاءات التبولوجية الثنائية وهي الاقتراب من المجموعة الجزئية من النمط nm-j-ω ، الاتجاه المباشر لمجموعة من النمط nm- j-ω ، التطبيقات المغلقة من النمط nm- j-ω ، صلابة المجموعة من النمط nm- j-ω ، التطبيقات المستمرة من النمط nm- j-ω ، والخط الرئيسي لهذا البحث هو التطبيقات التامة من النمط nm- j-ω في الفضاءات التبولوجية الثنائية. المميزات المتعلقة بهذه المفاهيم والعديد من المبرهنات قد درسنا حيث j = q , δ, a , pre, b, b.            This work, introduces some concepts in bitopological spaces, which are nm-j-ω-converges to a subset, nm-j-ω-directed toward a set, nm-j-ω-closed mappings, nm-j-ω-rigid set, and nm-j-ω-continuous mappings. The mainline idea in this paper is nm-j-ω-perfect mappings in bitopological spaces such that n = 1,2  and m =1,2 n ≠ m. Characterizations concerning these concepts and several theorems are studied, where j = q , δ, a , pre, b, b.

Predicting Cardiovascular Complications in Post-COVID-19 Patients Using Data-Driven Machine Learning Models

The COVID-19 pandemic has globally posed numerous health challenges, notably the emergence of post-COVID-19 cardiovascular complications. This study addresses this by utilizing data-driven machine learning models to predict such complications in 352 post-COVID-19 patients from Iraq. Clinical data, including demographics, comorbidities, lab results, and imaging, were collected and used to construct predictive models. These models, leveraging various machine learning algorithms, demonstrated commendable performance in identifying patients at risk. Early detection through these models promises timely interventions and improved outcomes. In conclusion, this research underscores the potential of data-driven machine learning for predicting post-COVID-19 cardiovascular complications, emphasizing the need for continued validation and research in diverse clinical settings

Post-COVID-19 Effects on Female Fertility: An In-Depth Scientific Investigation

This study aimed to comprehensively investigate the post-COVID-19 effects on female fertility in patients with a history of severe COVID-19 infection. Data were collected from 340 patients who had previously experienced severe COVID-19 symptoms and sought medical assistance at private clinics and fertility centers in various provinces of Iraq. A comparative control group of 280 patients, who had not contracted COVID-19 or had mild cases, was included. The study assessed ovarian reserve, hormonal imbalances, and endometrial health in the post-recovery phase. The findings revealed a significant decrease in ovarian reserve, hormonal disturbances, and endometrial abnormalities among patients with a history of severe COVID-19 infection compared to the control group. This in-depth investigation sheds light on the potential long-term impacts of severe COVID-19 on female fertility. The results emphasize the need for further research and targeted interventions to support women affected by post-COVID-19 fertility issues. Understanding these effects is crucial for providing appropriate medical care and support to women on their reproductive journey after recovering from severe COVID-19

Identifying Risk Factors for Post-COVID-19 Mental Health Disorders: A Machine Learning Perspective

In this study, we leveraged machine learning techniques to identify risk factors associated with post-COVID-19 mental health disorders. Our analysis, based on data collected from 669 patients across various provinces in Iraq, yielded valuable insights. We found that age, gender, and geographical region of residence were significant demographic factors influencing the likelihood of developing mental health disorders in post-COVID-19 patients. Additionally, comorbidities and the severity of COVID-19 illness were important clinical predictors. Psychosocial factors, such as social support, coping strategies, and perceived stress levels, also played a substantial role. Our findings emphasize the complex interplay of multiple factors in the development of mental health disorders following COVID-19 recovery. Healthcare providers and policymakers should consider these risk factors when designing targeted interventions and support systems for individuals at risk. Machine learning-based approaches can provide a valuable tool for predicting and preventing adverse mental health outcomes in post-COVID-19 patients. Further research and prospective studies are needed to validate these findings and enhance our understanding of the long-term psychological impact of the COVID-19 pandemic. This study contributes to the growing body of knowledge regarding the mental health consequences of the COVID-19 pandemic and underscores the importance of a multidisciplinary approach to address the diverse needs of individuals on the path to recovery. Keywords: COVID-19, mental health, risk factors, machine learning, Ira

Integrative AI-Driven Strategies for Advancing Precision Medicine in Infectious Diseases and Beyond: A Novel Multidisciplinary Approach

Precision medicine, tailored to individual patients based on their genetics, environment, and lifestyle, shows promise in managing complex diseases like infections. Integrating artificial intelligence (AI) into precision medicine can revolutionize disease management. This paper introduces a novel approach using AI to advance precision medicine in infectious diseases and beyond. It integrates diverse fields, analyzing patients' profiles using genomics, proteomics, microbiomics, and clinical data. AI algorithms process vast data, providing insights for precise diagnosis, treatment, and prognosis. AI-driven predictive modeling empowers healthcare providers to make personalized and effective interventions. Collaboration among experts from different domains refines AI models and ensures ethical and robust applications. Beyond infections, this AI-driven approach can benefit other complex diseases. Precision medicine powered by AI has the potential to transform healthcare into a proactive, patient-centric model. Research is needed to address privacy, regulations, and AI integration into clinical workflows. Collaboration among researchers, healthcare institutions, and policymakers is crucial in harnessing AI-driven strategies for advancing precision medicine and improving patient outcomes

Complete Coherent Control of a Quantum Dot Strongly Coupled to a Nanocavity

Strongly coupled quantum dot-cavity systems provide a non-linear configuration of hybridized light-matter states with promising quantum-optical applications. Here, we investigate the coherent interaction between strong laser pulses and quantum dot-cavity polaritons. Resonant excitation of polaritonic states and their interaction with phonons allow us to observe coherent Rabi oscillations and Ramsey fringes. Furthermore, we demonstrate complete coherent control of a quantum dot-photonic crystal cavity based quantum-bit. By controlling the excitation power and phase in a two-pulse excitation scheme we achieve access to the full Bloch sphere. Quantum-optical simulations are in good agreement with our experiments and provide insight into the decoherence mechanisms

Ultrafast polariton-phonon dynamics of strongly coupled quantum dot-nanocavity systems

We investigate the influence of exciton-phonon coupling on the dynamics of a strongly coupled quantum dot-photonic crystal cavity system and explore the effects of this interaction on different schemes for non-classical light generation. By performing time-resolved measurements, we map out the detuning-dependent polariton lifetime and extract the spectrum of the polariton-to-phonon coupling with unprecedented precision. Photon-blockade experiments for different pulse-length and detuning conditions (supported by quantum optical simulations) reveal that achieving high-fidelity photon blockade requires an intricate understanding of the phonons' influence on the system dynamics. Finally, we achieve direct coherent control of the polariton states of a strongly coupled system and demonstrate that their efficient coupling to phonons can be exploited for novel concepts in high-fidelity single photon generation

Di masa COVID-19, bagaimana cara melindungi diri sendiri dan orang lain?

Coronaviruses are a big identified group of viruses that could result in sickness in humans and animals. It was confirmed that many of these viruses caused respiratory diseases among humans and their symptoms range from popular colds to more serious diseases, such as the Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS). The recently detected Coronavirus (called SARS-CoV-2) causes the COVID-19 pandemic, which causes a serious threat worldwide. There was no previous knowledge of this virus before the outbreak of Wuhan city in China in December 2019.  However, there is progress in defining, understanding and dealing with this virus.  In this review, we are focusing on the common questions regarding coronavirus transition and spread, and how to prevent the infection.  Coronavirus adalah kelompok besar virus yang dapat menyebabkan penyakit pada manusia dan hewan. Telah dipastikan bahwa banyak dari virus ini menyebabkan penyakit pernapasan pada manusia dan gejalanya berkisar dari pilek populer hingga penyakit yang lebih serius, seperti sindrom pernapasan Timur Tengah (MERS) dan sindrom pernapasan akut yang parah (SARS). Virus Corona yang baru terdeteksi (disebut SARS-CoV-2) menyebabkan pandemi COVID-19, menyebabkan ancaman serius di seluruh dunia. Belum ada pengetahuan sebelumnya tentang virus ini sebelum merebak di kota Wuhan (China) pada Desember 2019 lalu. Namun, ada kemajuan dalam pendefinisian, pemahaman, dan penanganan virus ini. Dalam ulasan ini, kami berfokus pada pertanyaan umum mengenai transisi dan penyebaran virus corona, serta cara mencegah infeksi

Effect of drip irrigation circuits design and lateral line length on: II-flow velocity and velocity head

The objectives of the work were to study the effect of drip irrigation circuits (DIC) and lateral lines lengths (LLL) on: Flow velocity (FV) and velocity head (VH). Laboratory tests were con- ducted at Irrigation Devices and Equipments Tests Laboratory, Agricultural Engineering Research Institute, Agriculture Research Center, Giza, Egypt. The experimental design of laboratory experiments was split in randomized complete block design with three replicates. Laboratory tests carried out on three irrigation lateral lines 40, 60, 80 m (LLL1, LLL2; LLL3) under the following three drip irrigation circuits (DIC): a) one manifold for lateral lines or closed circuits with one manifold of drip irrigation system (CM1DIS); b) closed circuits with two manifolds for lateral lines (CM2DIS), and c) traditional drip irrigation system (TDIS) as a control. Concerning FV values, DIC and LLL treatments could state in the following ascending orders: TDIS \u3c CM1DIS \u3c CM2DIS and LLL1 \u3c LLL2 \u3c LLL3, respectively. FV varied from 0.593 m·sec–1 to 1.376 m·sec–1. i.e. FV \u3c 5 ft·sec–1 and this is necessary to avoid the effect of water hammer in the main and sub-main lines, but in lateral line, it can cause silt and clay precipitation problems. The differences in FV among DIC and LLL were significant at the 1% level. The effect of interaction: DIC X LLL on FV values, were significant at the 1% level. The maximum and minimum values of FV were noticed in these interactions: CM2DIS X LLL3 and TDIS X LLL1, respectively. The following ascending orders TDIS \u3c CM1DIS \u3c CM2DIS and LLL1 \u3c LLL2 \u3c LLL3 expressed their effects on VH respectively. Differences in VH among DIC and/or LLL were significant at the 1% with few exceptions. The effects of interactions: DIC X LLL on VH were significant at the 1% level in some cases. The maximum and minimum values of VH were found in the interactions: CM2DIS X LLL3 and TDIS X LLL1, respectively