Portland couple says they've had two surgeries delayed due to anesthesiology shakeup at Providence KGW.com
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Monthly Archives: December 2023
A Perioperative Blood Management Algorithm Aimed at Conservation of Platelets in Clinical Practice: The Role of the … – Cureus
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Shortage of Anesthesiologists Creates Logjam at Providence Hospitals – Willamette Week
Patients looking to get knee operations, hernia repair or any other non-emergency surgery at Providence hospitals are in for a monthlong wait because the nonprofit system doesnt have enough anesthesiologists.
Providence Portland on the eastside and Providence St. Vincent in the West Hills are taking only emergency, urgent and pregnancy-related cases through the end of the year, a Providence spokeswoman confirmed. The shortage started Nov. 22, when Providence dumped its local contractor, Oregon Anesthesiology Group, and hired Sound Physicians of Tacoma, Wash.
Unfortunately, the new group will not have enough credentialed anesthesia providers to fully cover the ORs at those facilities when the contract begins, Providence managers told staff in an email Nov. 14. We thank you for your understanding and support as we move through this difficult time.
Beyond patients, the clumsy switchover hurts surgeons, who cant operate, and the nurses who assist them. Surgeons are pissed, says one source who works at St. Vincent.
Meantime, the Providence spokeswoman said, nurses can do special projects, work temporarily in other departments, use vacation time, or take unpaid time off.
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Shortage of Anesthesiologists Creates Logjam at Providence Hospitals - Willamette Week
Providence delays hundreds of elective surgeries amid anesthesiology shakeup – KGW.com
Difficult TEE Probe Insertion Requiring Nasal Fiberoptic Guidance – Anesthesiology News
Difficult TEE Probe Insertion Requiring Nasal Fiberoptic Guidance Anesthesiology News
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Difficult TEE Probe Insertion Requiring Nasal Fiberoptic Guidance - Anesthesiology News
Lack of Benefit of Adjusting Adaptively Daily Invitations for the Evaluation of the Quality of Anesthesiologists … – Cureus
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Please choose I'm not a medical professional. Allergy and Immunology Anatomy Anesthesiology Cardiac/Thoracic/Vascular Surgery Cardiology Critical Care Dentistry Dermatology Diabetes and Endocrinology Emergency Medicine Epidemiology and Public Health Family Medicine Forensic Medicine Gastroenterology General Practice Genetics Geriatrics Health Policy Hematology HIV/AIDS Hospital-based Medicine I'm not a medical professional. Infectious Disease Integrative/Complementary Medicine Internal Medicine Internal Medicine-Pediatrics Medical Education and Simulation Medical Physics Medical Student Nephrology Neurological Surgery Neurology Nuclear Medicine Nutrition Obstetrics and Gynecology Occupational Health Oncology Ophthalmology Optometry Oral Medicine Orthopaedics Osteopathic Medicine Otolaryngology Pain Management Palliative Care Pathology Pediatrics Pediatric Surgery Physical Medicine and Rehabilitation Plastic Surgery Podiatry Preventive Medicine Psychiatry Psychology Pulmonology Radiation Oncology Radiology Rheumatology Substance Use and Addiction Surgery Therapeutics Trauma Urology Miscellaneous
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Brain Imaging Reveals Altered Brain Connectivity in Autism – Neuroscience News
Summary: Researchers advanced brain imaging and machine learning to uncover altered functional brain connectivity in individuals with Autism Spectrum Disorder (ASD), acknowledging the diversity within the disorder.
The research distinguishes between shared and individual-specific connectivity patterns in ASD, revealing both common and unique brain alterations. This approach marks a significant shift from group-based analysis to a more personalized understanding of ASD.
The findings open pathways for tailored treatments, addressing the unique needs of individuals with ASD.
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Source: Elsevier
What happens in the brain to cause many neurodevelopmental disorders, including autism spectrum disorder (ASD), remains a mystery. A major limitation for researchers is the lack of biomarkers, or objective biological outputs, for these disorders, and in the case of ASD, for specific subtypes of disease.
Now, anew studyuses brain imaging and machine learning to identify altered functional brain connectivity (FC) in people with ASD importantly, taking into consideration differences between individuals.
The study appears inBiological Psychiatry, published by Elsevier.
John Krystal, MD, Editor ofBiological Psychiatry, said of the work,ASD has long been known to be a highly heterogeneous condition. While genetic studies have provided some clues to different causes of the disorder in different groups of ASD patients, it has been challenging to separate subtypes of ASD using other types of biomarkers, such as brain imaging.
Brain imaging scans are also extremely heterogenous, varying greatly from one individual to another, making such data difficult to use as a biomarker. Previous studies have identified both increased and decreased FC in people with ASD compared to healthy controls, but because those studies focused on groups of participants, they failed to appreciate heterogeneous autism-related atypical FC.
In the new study, the researchers showed that although heterogenous brain imaging subtypes could be distinguished among participants with ASD.
Xujun Duan, PhD, senior author of the work at the University of Electronic Science and Technology of China, explained,In this study, we used a technique to project altered FC of autism onto two subspaces: an individual-shared subspace, which represents altered connectivity pattern shared across autism, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns.
The investigators found that the individual-shared subspace altered FC of autism reflects differences at the group level, while individual-specific subspace altered FC represents individual variation in autistic traits. These findings suggest a requirement to move beyond group effects and to capture and capitalize on the individual-specific brain features for dissecting clinical heterogeneity.
Dr. Krystal added,Part of the challenge to finding subtypes of ASD has been the enormous complexity of neuroimaging data. This study uses a sophisticated computational approach to identify aspects of brain circuit alterations that are common to ASD and others that are associated with particular ASD traits.
This type of strategy may help to more effectively guide the development of personalized treatments for ASD, i.e., treatments that meet the specific needs of particular patients.
Author: Eileen Leahy Source: Elsevier Contact: Eileen Leahy Elsevier Image: The image is credited to Neuroscience News
Original Research: Open access. Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder by Xujun Duan et al. Biological Psychiatry
Abstract
Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder
Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder.
We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns.
Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode networkrelated and cingulo-opercular networkrelated magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD.
Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.
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Brain Imaging Reveals Altered Brain Connectivity in Autism - Neuroscience News
Mirror Insight: Mice Show Glimpses of Self-Recognition – Neuroscience News
Summary: Mice display behavior akin to self-recognition when viewing their reflections in mirrors. This behavior emerges under specific conditions: familiarity with mirrors, socialization with similar-looking mice, and visible markings on their fur.
The study also identifies a subset of neurons in the hippocampus that are crucial for this self-recognition-like behavior. These findings provide valuable insights into the neural mechanisms behind self-recognition, a previously enigmatic aspect of neurobehavioral research.
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Source: Cell Press
Researchers report December 5 in the journalNeuronthat mice display behavior that resembles self-recognition when they see themselves in the mirror. When the researchers marked the foreheads of black-furred mice with a spot of white ink, the mice spent more time grooming their heads in front of the mirrorpresumably to try and wash away the ink spot.
However, the mice only showed this self-recognition-like behavior if they were already accustomed to mirrors, if they had socialized with other mice who looked like them, and if the ink spot was relatively large.
The team identified a subset of neurons in the hippocampus that are involved in developing and storing this visual self-image, providing a first glimpse of the neural mechanisms behind self-recognition, something that was previously a black box in neurobehavioral research.
To form episodic memory, for example, of events in our daily life, brains form and store information about where, what, when, and who, and the most important component is self-information or status, says neuroscientist and senior author Takashi Kitamura of University of Texas Southwestern Medical Center.
Researchers usually examine how the brain encodes or recognizes others, but the self-information aspect is unclear.
The researchers used a mirror test to investigate whether mice could detect a change in their own appearancein this case, a dollop of ink on their foreheads. Because the ink also provided a tactile stimulus, the researchers tested the black-furred mice with both black and white ink.
Though the mirror test was originally developed to test consciousness in different species, the authors note that their experiments only show that mice can detect a change in their own appearance, but this does not necessarily mean that they are self-aware.
They found that mice could indeed detect changes to their appearance, but only under certain conditions. Mice who were familiar with mirrors spent significantly more time grooming their heads (but not other parts of their bodies) in front of the mirror when they were marked with dollops of white ink that were 0.6 cm2or 2 cm2.
However, the mice did not engage in increased head grooming when the ink was blackthe same color as their furor when the ink mark was small (0.2 cm2), even if the ink was white, and mice who were not habituated to mirrors before the ink test did not display increased head grooming in any scenario.
The mice required significant external sensory cues to pass the mirror testwe have to put a lot of ink on their heads, and then the tactile stimulus coming from the ink somehow enables the animal to detect the ink on their heads via a mirror reflection, says first author Jun Yokose of University of Texas Southwestern Medical Center. Chimps and humans dont need any of that extra sensory stimulus.
Using gene expression mapping, the researchers identified a subset of neurons in the ventral hippocampus that were activated when the mice recognized themselves in the mirror. When the researchers selectively rendered these neurons non-functional, the mice no longer displayed the mirror-and-ink-induced grooming behavior.
A subset of these self-responding neurons also became activated when the mice observed other mice of the same strain (and therefore similar physical appearance and fur color), but not when they observed a different strain of mouse that had white fur.
Because previous studies in chimpanzees have suggested that social experience is required for mirror self-recognition, the researchers also tested mice who had been socially isolated after weaning. These socially isolated mice did not display increased head grooming behavior during the ink test, and neither did black-furred mice that were reared alongside white-furred mice.
The gene expression analysis also showed that socially isolated mice did not develop self-responding neuron activity in the hippocampus, and neither did the black-furred mice that were reared by white-furred mice, suggesting that mice need to have social experiences alongside other similar-looking mice in order to develop the neural circuits required for self-recognition.
A subset of these self-responding neurons was also reactivated when we exposed the mice to other individuals of the same strain, says Kitamura.
This is consistent with previous human literature that showed that some hippocampal cells fire not only when the person is looking at themselves, but also when they look at familiar people like a parent.
Next, the researchers plan to try to disentangle the importance of visual and tactile stimuli to test whether mice can recognize changes in their reflection in the absence of a tactile stimulusperhaps by using technology similar to the filters on social media apps that allow people to give themselves puppy-dog faces or bunny ears.
They also plan to study other brain regions that might be involved in self-recognition and to investigate how the different regions communicate and integrate information.
Now that we have this mouse model, we can manipulate or monitor neural activity to comprehensively investigate the neural circuit mechanisms behind how self-recognition-like behavior is induced in mice, says Yokose.
Funding: This research was supported by the Endowed Scholar Program, the Brain & Behavior Research Foundation, the Daiichi Sankyo Foundation of Life Science, and Uehara Memorial Foundation.
Author: Kristopher Benke Source: Cell Press Contact: Kristopher Benke Cell Press Image: The image is credited to Neuroscience News
Original Research: Open access. Visuotactile integration facilitates mirror-induced self-directed behavior through activation of hippocampal neuronal ensembles in mice by Takashi Kitamura et al. Neuron
Abstract
Visuotactile integration facilitates mirror-induced self-directed behavior through activation of hippocampal neuronal ensembles in mice
Remembering the visual features of oneself is critical for self-recognition. However, the neural mechanisms of how the visual self-image is developed remain unknown because of the limited availability of behavioral paradigms in experimental animals.
Here, we demonstrate a mirror-induced self-directed behavior (MSB) in mice, resembling visual self-recognition. Mice displayed increased mark-directed grooming to remove ink placed on their heads when an ink-induced visual-tactile stimulus contingency occurred. MSB required mirror habituation and social experience.
The chemogenetic inhibition of dorsal or ventral hippocampal CA1 (vCA1) neurons attenuated MSB. Especially, a subset of vCA1 neurons activated during the mirror exposure was significantly reactivated during re-exposure to the mirror and was necessary for MSB.
The self-responding vCA1 neurons were also reactivated when mice were exposed to a conspecific of the same strain.
These results suggest that visual self-image may be developed through social experience and mirror habituation and stored in a subset of vCA1 neurons.
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Mirror Insight: Mice Show Glimpses of Self-Recognition - Neuroscience News
AI Revolutionizes Neuron Tracking in Moving Animals – Neuroscience News
Summary: Researchers developed an AI-based method to track neurons in moving and deforming animals, a significant advancement in neuroscience research. This convolutional neural network (CNN) method overcomes the challenge of tracking brain activity in organisms like worms, whose bodies constantly change shape.
By employing targeted augmentation, the AI significantly reduces the need for manual image annotation, streamlining the neuron identification process. Tested on the roundworm Caenorhabditis elegans, this technology has not only increased analysis efficiency but also deepened insights into complex neuronal behaviors.
Key Facts:
Source: EPFL
Recent advances allow imaging of neurons inside freely moving animals. However, to decode circuit activity, these imaged neurons must be computationally identified and tracked. This becomes particularly challenging when the brain itself moves and deforms inside an organisms flexible body, e.g. in a worm. Until now, the scientific community has lacked the tools to address the problem.
Now, a team of scientists from EPFL and Harvard have developed a pioneering AI method to track neurons inside moving and deforming animals. The study, now published inNature Methods, was led bySahand Jamal Rahiat EPFLs School of Basic Sciences.
The new method is based on a convolutional neural network (CNN), which is a type of AI that has been trained to recognize and understand patterns in images. This involves a process called convolution, which looks at small parts of the picture like edges, colors, or shapes at a time and then combines all that information together to make sense of it and to identify objects or patterns.
The problem is that to identify and track neurons during a movie of an animals brain, many images have to be labeled by hand because the animal appears very differently across time due to the many different body deformations. Given the diversity of the animals postures, generating a sufficient number of annotations manually to train a CNN can be daunting.
To address this, the researchers developed an enhanced CNN featuring targeted augmentation. The innovative technique automatically synthesizes reliable annotations for reference out of only a limited set of manual annotations. The result is that the CNN effectively learns the internal deformations of the brain and then uses them to create annotations for new postures, drastically reducing the need for manual annotation and double-checking.
The new method is versatile, being able to identify neurons whether they are represented in images as individual points or as 3D volumes. The researchers tested it on the roundwormCaenorhabditis elegans, whose 302 neurons have made it a popular model organism in neuroscience.
Using the enhanced CNN, the scientists measured activity in some of the worms interneurons (neurons that bridge signals between neurons). They found that they exhibit complex behaviors, for example changing their response patterns when exposed to different stimuli, such as periodic bursts of odors.
The team have made their CNN accessible, providing a user-friendly graphical user interface that integrates targeted augmentation, streamlining the process into a comprehensive pipeline, from manual annotation to final proofreading.
By significantly reducing the manual effort required for neuron segmentation and tracking, the new method increases analysis throughput three times compared to full manual annotation, says Sahand Jamal Rahi.
The breakthrough has the potential to accelerate research in brain imaging and deepen our understanding of neural circuits and behaviors.
Other contributors
Swiss Data Science Center
Author: Nik Papageorgiou Source: EPFL Contact: Nik Papageorgiou EPFL Image: The image is credited to Neuroscience News
Original Research: The findings will appear in Nature Methods
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AI Revolutionizes Neuron Tracking in Moving Animals - Neuroscience News
AI Vulnerabilities Exposed: Adversarial Attacks More Common and Dangerous Than Expected – Neuroscience News
Summary: A new study reveals that artificial intelligence systems are more susceptible to adversarial attacks than previously believed, making them vulnerable to manipulation that can lead to incorrect decisions.
Researchers found that adversarial vulnerabilities are widespread in AI deep neural networks, raising concerns about their use in critical applications. To assess these vulnerabilities, the team developed QuadAttacK, a software that can test neural networks for susceptibility to adversarial attacks.
The findings highlight the need to enhance AI robustness against such attacks, particularly in applications with potential human life implications.
Key Facts:
Source: North Carolina State University
Artificial intelligence tools hold promise for applications ranging from autonomous vehicles to the interpretation of medical images. However, a new study finds these AI tools are more vulnerable than previously thought to targeted attacks that effectively force AI systems to make bad decisions.
At issue are so-called adversarial attacks, in which someone manipulates the data being fed into an AI system in order to confuse it. For example, someone might know that putting a specific type of sticker at a specific spot on a stop sign could effectively make the stop sign invisible to an AI system. Or a hacker could install code on an X-ray machine that alters the image data in a way that causes an AI system to make inaccurate diagnoses.
For the most part, you can make all sorts of changes to a stop sign, and an AI that has been trained to identify stop signs will still know its a stop sign, says Tianfu Wu, co-author of a paper on the new work and an associate professor of electrical and computer engineering at North Carolina State University.
However, if the AI has a vulnerability, and an attacker knows the vulnerability, the attacker could take advantage of the vulnerability and cause an accident.
The new study from Wu and his collaborators focused on determining how common these sorts of adversarial vulnerabilities are in AI deep neural networks. They found that the vulnerabilities are much more common than previously thought.
Whats more, we found that attackers can take advantage of these vulnerabilities to force the AI to interpret the data to be whatever they want, Wu says.
Using the stop sign example, you could make the AI system think the stop sign is a mailbox, or a speed limit sign, or a green light, and so on, simply by using slightly different stickers or whatever the vulnerability is.
This is incredibly important, because if an AI system is not robust against these sorts of attacks, you dont want to put the system into practical use particularly for applications that can affect human lives.
To test the vulnerability of deep neural networks to these adversarial attacks, the researchers developed a piece of software called QuadAttacK. The software can be used to test any deep neural network for adversarial vulnerabilities.
Basically, if you have a trained AI system, and you test it with clean data, the AI system will behave as predicted. QuadAttacKwatches these operations and learns how the AI is making decisions related to the data. This allows QuadAttacKto determine how the data could be manipulated to fool the AI.
QuadAttacKthen begins sending manipulated data to the AI system to see how the AI responds. If QuadAttacKhas identified a vulnerability it can quickly make the AI see whatever QuadAttacKwants it to see.
In proof-of-concept testing, the researchers used QuadAttacKto test four deep neural networks: two convolutional neural networks (ResNet-50 and DenseNet-121) and two vision transformers (ViT-B and DEiT-S). These four networks were chosen because they are in widespread use in AI systems around the world.
We were surprised to find that all four of these networks were very vulnerable to adversarial attacks, Wu says. We were particularly surprised at the extent to which we could fine-tune the attacks to make the networks see what we wanted them to see.
The research team has made QuadAttacKpublicly available, so that the research community can use it themselves to test neural networks for vulnerabilities. The program can be found here:https://thomaspaniagua.github.io/quadattack_web/.
Now that we can better identify these vulnerabilities, the next step is to find ways to minimize those vulnerabilities, Wu says. We already have some potential solutions but the results of that work are still forthcoming.
The paper, QuadAttacK: A Quadratic Programming Approach to Learning Ordered Top-KAdversarial Attacks, will be presented Dec. 16 at the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), which is being held in New Orleans, La. First author of the paper is Thomas Paniagua, a Ph.D. student at NCState. The paper was co-authored by Ryan Grainger, a Ph.D. student at NCState.
Funding: The work was done with support from the U.S. Army Research Office, under grants W911NF1810295 and W911NF2210010; and from the National Science Foundation, under grants 1909644, 2024688 and 2013451.
Author: Matt Shipman Source: North Carolina State University Contact: Matt Shipman North Carolina State University Image: The image is credited to Neuroscience News
Original Research: The findings will be presented at the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS)
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AI Vulnerabilities Exposed: Adversarial Attacks More Common and Dangerous Than Expected - Neuroscience News