Catatonia Secondary to Sudden Clozapine Withdrawal: A Case with Three Repeated Episodes and a Literature Review

A literature search identified 9 previously published cases that were considered as possible cases of catatonia secondary to sudden clozapine withdrawal. Two of these 9 cases did not provide enough information to make a diagnosis of catatonia according to the Diagnostic and Statistical Manual, 5th Edition (DSM-5). The Liverpool Adverse Drug Reaction (ADR) Causality Scale was modified to assess ADRs secondary to drug withdrawal. From the 7 published cases which met DSM-5 catatonia criteria, using the modified scale, we established that 3 were definitive and 4 were probable cases of catatonia secondary to clozapine withdrawal. A new definitive case is described with three catatonic episodes which (1) occurred after sudden discontinuation of clozapine in the context of decades of follow-up, (2) had ≥3 of 12 DSM-5 catatonic symptoms and serum creatinine kinase elevation, and (3) required medical hospitalization and intravenous fluids. Clozapine may be a gamma-aminobutyric acid (GABA) receptor agonist; sudden clozapine withdrawal may explain a sudden decrease in GABA activity that may contribute to the development of catatonic symptoms in vulnerable patients. Based on the limited information from these cases, the pharmacological treatment for catatonia secondary to sudden clozapine withdrawal can include benzodiazepines and/or restarting clozapine

Catatonia Secondary to Sudden Clozapine Withdrawal: A Case with Three Repeated Episodes and a Literature Review

A literature search identified 9 previously published cases that were considered as possible cases of catatonia secondary to sudden clozapine withdrawal. Two of these 9 cases did not provide enough information to make a diagnosis of catatonia according to the Diagnostic and Statistical Manual, 5th Edition (DSM-5). The Liverpool Adverse Drug Reaction (ADR) Causality Scale was modified to assess ADRs secondary to drug withdrawal. From the 7 published cases which met DSM-5 catatonia criteria, using the modified scale, we established that 3 were definitive and 4 were probable cases of catatonia secondary to clozapine withdrawal. A new definitive case is described with three catatonic episodes which (1) occurred after sudden discontinuation of clozapine in the context of decades of follow-up, (2) had ≥3 of 12 DSM-5 catatonic symptoms and serum creatinine kinase elevation, and (3) required medical hospitalization and intravenous fluids. Clozapine may be a gamma-aminobutyric acid (GABA) receptor agonist; sudden clozapine withdrawal may explain a sudden decrease in GABA activity that may contribute to the development of catatonic symptoms in vulnerable patients. Based on the limited information from these cases, the pharmacological treatment for catatonia secondary to sudden clozapine withdrawal can include benzodiazepines and/or restarting clozapine

Mechanically transformative electronics, sensors, and implantable devices

Traditionally, electronics have been designed with static form factors to serve designated purposes. This approach has been an optimal direction for maintaining the overall device performance and reliability for targeted applications. However, electronics capable of changing their shape, flexibility, and stretchability will enable versatile and accommodating systems for more diverse applications. Here, we report design concepts, materials, physics, and manufacturing strategies that enable these reconfigurable electronic systems based on temperature-triggered tuning of mechanical characteristics of device platforms. We applied this technology to create personal electronics with variable stiffness and stretchability, a pressure sensor with tunable bandwidth and sensitivity, and a neural probe that softens upon integration with brain tissue. Together, these types of transformative electronics will substantially broaden the use of electronics for wearable and implantable applications

Catatonia Secondary to Sudden Clozapine Withdrawal: A Case with Three Repeated Episodes and a Literature Review

A literature search identified 9 previously published cases that were considered as possible cases of catatonia secondary to sudden clozapine withdrawal. Two of these 9 cases did not provide enough information to make a diagnosis of catatonia according to the Diagnostic and Statistical Manual, 5th Edition (DSM-5). The Liverpool Adverse Drug Reaction (ADR) Causality Scale was modified to assess ADRs secondary to drug withdrawal. From the 7 published cases which met DSM-5 catatonia criteria, using the modified scale, we established that 3 were definitive and 4 were probable cases of catatonia secondary to clozapine withdrawal. A new definitive case is described with three catatonic episodes which (1) occurred after sudden discontinuation of clozapine in the context of decades of follow-up, (2) had ≥3 of 12 DSM-5 catatonic symptoms and serum creatinine kinase elevation, and (3) required medical hospitalization and intravenous fluids. Clozapine may be a gamma-aminobutyric acid (GABA) receptor agonist; sudden clozapine withdrawal may explain a sudden decrease in GABA activity that may contribute to the development of catatonic symptoms in vulnerable patients. Based on the limited information from these cases, the pharmacological treatment for catatonia secondary to sudden clozapine withdrawal can include benzodiazepines and/or restarting clozapine

Rapidly-Customizable, Scalable 3D-Printed Wireless Optogenetic Probes for Versatile Applications in Neuroscience

Optogenetics is an advanced neuroscience technique that enables the dissection of neural circuitry with high spatiotemporal precision. Recent advances in materials and microfabrication techniques have enabled minimally invasive and biocompatible optical neural probes, thereby facilitating in vivo optogenetic research. However, conventional fabrication techniques rely on cleanroom facilities, which are not easily accessible and are expensive to use, making the overall manufacturing process inconvenient and costly. Moreover, the inherent time-consuming nature of current fabrication procedures impede the rapid customization of neural probes in between in vivo studies. Here, a new technique stemming from 3D printing technology for the low-cost, mass production of rapidly customizable optogenetic neural probes is introduced. The 3D printing production process, on-the-fly design versatility, and biocompatibility of 3D printed optogenetic probes as well as their functional capabilities for wireless in vivo optogenetics is detailed. Successful in vivo studies with 3D printed devices highlight the reliability of this easily accessible and flexible manufacturing approach that, with advances in printing technology, can foreshadow its widespread applications in low-cost bioelectronics in the future. © 2020 Wiley-VCH GmbH1

The RSNA Pediatric Bone Age Machine Learning Challenge.

Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze AI model creation, and identify innovators in medical imaging. Materials and Methods The goal of this challenge was to solicit individuals and teams to create an algorithm or model using ML techniques that would accurately determine skeletal age in a curated data set of pediatric hand radiographs. The primary evaluation measure was the mean absolute distance (MAD) in months, which was calculated as the mean of the absolute values of the difference between the model estimates and those of the reference standard, bone age. Results A data set consisting of 14 236 hand radiographs (12 611 training set, 1425 validation set, 200 test set) was made available to registered challenge participants. A total of 260 individuals or teams registered on the Challenge website. A total of 105 submissions were uploaded from 48 unique users during the training, validation, and test phases. Almost all methods used deep neural network techniques based on one or more convolutional neural networks (CNNs). The best five results based on MAD were 4.2, 4.4, 4.4, 4.5, and 4.5 months, respectively. Conclusion The RSNA Pediatric Bone Age Machine Learning Challenge showed how a coordinated approach to solving a medical imaging problem can be successfully conducted. Future ML challenges will catalyze collaboration and development of ML tools and methods that can potentially improve diagnostic accuracy and patient care. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Siegel in this issue

The RSNA Pediatric Bone Age Machine Learning Challenge.

Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze AI model creation, and identify innovators in medical imaging. Materials and Methods The goal of this challenge was to solicit individuals and teams to create an algorithm or model using ML techniques that would accurately determine skeletal age in a curated data set of pediatric hand radiographs. The primary evaluation measure was the mean absolute distance (MAD) in months, which was calculated as the mean of the absolute values of the difference between the model estimates and those of the reference standard, bone age. Results A data set consisting of 14 236 hand radiographs (12 611 training set, 1425 validation set, 200 test set) was made available to registered challenge participants. A total of 260 individuals or teams registered on the Challenge website. A total of 105 submissions were uploaded from 48 unique users during the training, validation, and test phases. Almost all methods used deep neural network techniques based on one or more convolutional neural networks (CNNs). The best five results based on MAD were 4.2, 4.4, 4.4, 4.5, and 4.5 months, respectively. Conclusion The RSNA Pediatric Bone Age Machine Learning Challenge showed how a coordinated approach to solving a medical imaging problem can be successfully conducted. Future ML challenges will catalyze collaboration and development of ML tools and methods that can potentially improve diagnostic accuracy and patient care. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Siegel in this issue