Pediatric orbital subperiosteal abscess outbreak in Iran: characteristics and causes.

PURPOSE: To evaluate demographics, characteristics, and management of pediatric patients with subperiosteal abscesses (SPA) secondary to orbital cellulitis and discuss the etiology of a dramatic rise in SPA. METHODS: Data were gathered by retrospective chart review of patients admitted to a tertiary referral eye hospital (Farabi Eye Hospital) diagnosed with orbital cellulitis with subperiosteal abscess from October 2022 to March 2023 (six months). Data on demographic information, clinical examination, radiographic evidence of sinusitis, orbital cellulitis, SPA, surgical and non-surgical management taken, isolated bacteria, and duration of hospital stay were gathered. RESULTS: 24 patients were admitted during these six months, with a diagnosis of orbital SPA secondary to paranasal sinusitis, confirmed by an orbital Computed Tomography (CT) scan. The age range was 11 months to 16 years. 75% of patients were male. All patients had a history of flu-like illness before developing orbital cellulitis. All patients had concurrent sinusitis, and 18 underwent initial surgical abscess drainage. The ethmoid sinus was the most involved, and most patients had a medially located SPA. Abscess volume ranged from 0.78 to 7.81 cm3 (mean: 3.52 cm3). One patient had concurrent central retinal artery occlusion due to orbital cellulitis. CONCLUSIONS: In this study, we report a dramatic increase in the incidence of SPA referred to our hospital. Larger abscess volumes and an increased number of cases that needed initial surgical drainage are also of note. An influenza outbreak in the autumn and winter, undiagnosed Corona Virus Disease 2019 (COVID-19) infection, increased antimicrobial resistance due to excessive off-label use of antibiotics during the COVID-19 pandemic, and more virulent bacterial infections are the most probable hypotheses to justify this observation.

Retinal astrocytic hamartoma complicated by branch retinal vein occlusion in a patient with tuberous sclerosis complex.

Purpose: To report a case with branch retinal vein occlusion secondary to a retinal astrocytic hamartoma in a patient with tuberous sclerosis complex. Observations: A fourteen-year-old boy, a known case of tuberous sclerosis complex, with multiple bilateral retinal astrocytic hamartomas was followed by 6 months intervals. In his last follow-up, 6 months after initial presentation, the patient developed angiographic signs of branch retinal vein occlusion (BRVO) in the superotemporal arcade of the right eye distal to one of the retinal astrocytic hamartomas. He underwent targeted retinal laser photocoagulation. No secondary complication related to BRVO was observed during the next six-month follow-up. Conclusion: And Importance: Although the co-occurrence of branch retinal vein occlusion and astrocytic hamartoma may represent an incidental finding, awareness of BRVO as a possible complication associated with retinal astrocytic hamartoma helps timely diagnosis and prompt treatment of this complication, improving the visual prognosis of these patients.

Chronic cortical and electromyographic recordings from a fully implantable device: preclinical experience in a nonhuman primate.

OBJECTIVE: Analysis of intra- and perioperatively recorded cortical and basal ganglia local field potentials in human movement disorders has provided great insight into the pathophysiology of diseases such as Parkinson's, dystonia, and essential tremor. However, in order to better understand the network abnormalities and effects of chronic therapeutic stimulation in these disorders, long-term recording from a fully implantable data collection system is needed. APPROACH: A fully implantable investigational data collection system, the Activa® PC + S neurostimulator (Medtronic, Inc., Minneapolis, MN), has been developed for human use. Here, we tested its utility for extended intracranial recording in the motor system of a nonhuman primate. The system was attached to two quadripolar paddle arrays: one covering sensorimotor cortex, and one covering a proximal forelimb muscle, to study simultaneous cortical field potentials and electromyography during spontaneous transitions from rest to movement. MAIN RESULTS: Over 24 months of recording, movement-related changes in physiologically relevant frequency bands were readily detected, including beta and gamma signals at approximately 2.5 µV/[Formula: see text] and 0.7 µV/[Formula: see text], respectively. The system architecture allowed for flexible recording configurations and algorithm triggered data recording. In the course of physiological analyses, sensing artifacts were observed (∼1 µVrms stationary tones at fixed frequency), which were mitigated either with post-processing or algorithm design and did not impact the scientific conclusions. Histological examination revealed no underlying tissue damage; however, a fibrous capsule had developed around the paddles, demonstrating a potential mechanism for the observed signal amplitude reduction. SIGNIFICANCE: This study establishes the usefulness of this system in measuring chronic brain and muscle signals. Use of this system may potentially be valuable in human trials of chronic brain recording in movement disorders, a next step in the design of closed-loop neurostimulation paradigms.

A flexible algorithm framework for closed-loop neuromodulation research systems.

Modulation of neural activity through electrical stimulation of tissue is an effective therapy for neurological diseases such as Parkinson's disease and essential tremor. Researchers are exploring improving therapy through adjustment of stimulation parameters based upon sensed data. This requires classifiers to extract features and estimate patient state. It also requires algorithms to appropriately map the state estimation to stimulation parameters. The latter, known as the control policy algorithm, is the focus of this work. Because the optimal control policy algorithms for the nervous system are not fully characterized at this time, we have implemented a generic control policy framework to facilitate exploratory research and rapid prototyping of new neuromodulation strategies.

Design and validation of a fully implantable, chronic, closed-loop neuromodulation device with concurrent sensing and stimulation.

Chronically implantable, closed-loop neuromodulation devices with concurrent sensing and stimulation hold promise for better understanding the nervous system and improving therapies for neurological disease. Concurrent sensing and stimulation are needed to maximize usable neural data, minimize time delays for closed-loop actuation, and investigate the instantaneous response to stimulation. Current systems lack concurrent sensing and stimulation primarily because of stimulation interference to neural signals of interest. While careful design of high performance amplifiers has proved useful to reduce disturbances in the system, stimulation continues to contaminate neural sensing due to biological effects like tissue-electrode impedance mismatch and constraints on stimulation parameters needed to deliver therapy. In this work we describe systematic methods to mitigate the effect of stimulation through a combination of sensing hardware, stimulation parameter selection, and classification algorithms that counter residual stimulation disturbances. To validate these methods we implemented and tested a completely implantable system for over one year in a large animal model of epilepsy. The system proved capable of measuring and detecting seizure activity in the hippocampus both during and after stimulation. Furthermore, we demonstrate an embedded algorithm that actuates neural modulation in response to seizure detection during stimulation, validating the capability to detect bioelectrical markers in the presence of therapy and titrate it appropriately. The capability to detect neural states in the presence of stimulation and optimally titrate therapy is a key innovation required for generalizing closed-loop neural systems for multiple disease states.

A translational platform for prototyping closed-loop neuromodulation systems.

While modulating neural activity through stimulation is an effective treatment for neurological diseases such as Parkinson's disease and essential tremor, an opportunity for improving neuromodulation therapy remains in automatically adjusting therapy to continuously optimize patient outcomes. Practical issues associated with achieving this include the paucity of human data related to disease states, poorly validated estimators of patient state, and unknown dynamic mappings of optimal stimulation parameters based on estimated states. To overcome these challenges, we present an investigational platform including: an implanted sensing and stimulation device to collect data and run automated closed-loop algorithms; an external tool to prototype classifier and control-policy algorithms; and real-time telemetry to update the implanted device firmware and monitor its state. The prototyping system was demonstrated in a chronic large animal model studying hippocampal dynamics. We used the platform to find biomarkers of the observed states and transfer functions of different stimulation amplitudes. Data showed that moderate levels of stimulation suppress hippocampal beta activity, while high levels of stimulation produce seizure-like after-discharge activity. The biomarker and transfer function observations were mapped into classifier and control-policy algorithms, which were downloaded to the implanted device to continuously titrate stimulation amplitude for the desired network effect. The platform is designed to be a flexible prototyping tool and could be used to develop improved mechanistic models and automated closed-loop systems for a variety of neurological disorders.

Advancing neuromodulation using a dynamic control framework.

The current state of neuromodulation can be cast in a classical dynamic control framework such that the nervous system is the classical "plant", the neural stimulator is the controller, tools to collect clinical data are the sensors, and the physician's judgment is the state estimator. This framework characterizes the types of opportunities available to advance neuromodulation. In particular, technology can potentially address two dominant factors limiting the performance of the control system: "observability," the ability to observe the state of the system from output measurements, and "controllability," the ability to drive the system to a desired state using control actuation. Improving sensors and actuation methods are necessary to address these factors. Equally important is improving state estimation by understanding the neural processes underlying diseases. Development of enabling technology to utilize control theory principles facilitates investigations into improving intervention as well as research into the dynamic properties of the nervous system and mechanisms of action of therapies. In this paper, we provide an overview of the control system framework for neuromodulation, its practical challenges, and investigational devices applying this framework for limited applications. To help motivate future efforts, we describe our chronically implantable, low-power neural stimulation system, which integrates sensing, actuation, and state estimation. This research system has been implanted and used in an ovine to address novel research questions.

Emerging technology for advancing the treatment of epilepsy using a dynamic control framework.

We briefly describe a dynamic control system framework for neuromodulation for epilepsy, with an emphasis on its practical challenges and the preliminary validation of key prototype technologies in a chronic animal model. The current state of neuromodulation can be viewed as a classical dynamic control framework such that the nervous system is the classical "plant", the neural stimulator is the controller/actuator, clinical observation, patient diaries and/or measured bio-markers are the sensor, and clinical judgment applied to these sensor inputs forms the state estimator. Technology can potentially address two main factors contributing to the performance limitations of existing systems: "observability," the ability to observe the state of the system from output measurements, and "controllability," the ability to drive the system to a desired state. In addition to improving sensors and actuator performance, methods and tools to better understand disease state dynamics and state estimation are also critical for improving therapy outcomes. We describe our preliminary validation of key "observability" and "controllability" technology blocks using an implanted research tool in an epilepsy disease model. This model allows for testing the key emerging technologies in a representative neural network of therapeutic importance. In the future, we believe these technologies might enable both first principles understanding of neural network behavior for optimizing therapy design, and provide a practical pathway towards clinical translation.

On the design of robotic hands for brain-machine interface.

Brain-machine interface (BMI) is the latest solution to a lack of control for paralyzed or prosthetic limbs. In this paper the authors focus on the design of anatomical robotic hands that use BMI as a critical intervention in restorative neurosurgery and they justify the requirement for lower-level neuromusculoskeletal details (relating to biomechanics, muscles, peripheral nerves, and some aspects of the spinal cord) in both mechanical and control systems. A person uses his or her hands for intimate contact and dexterous interactions with objects that require the user to control not only the finger endpoint locations but also the forces and the stiffness of the fingers. To recreate all of these human properties in a robotic hand, the most direct and perhaps the optimal approach is to duplicate the anatomical musculoskeletal structure. When a prosthetic hand is anatomically correct, the input to the device can come from the same neural signals that used to arrive at the muscles in the original hand. The more similar the mechanical structure of a prosthetic hand is to a human hand, the less learning time is required for the user to recreate dexterous behavior. In addition, removing some of the nonlinearity from the relationship between the cortical signals and the finger movements into the peripheral controls and hardware vastly simplifies the needed BMI algorithms. (Nonlinearity refers to a system of equations in which effects are not proportional to their causes. Such a system could be difficult or impossible to model.) Finally, if a prosthetic hand can be built so that it is anatomically correct, subcomponents could be integrated back into remaining portions of the user's hand at any transitional locations. In the near future, anatomically correct prosthetic hands could be used in restorative neurosurgery to satisfy the user's needs for both aesthetics and ease of control while also providing the highest possible degree of dexterity.