The developments in micro-nano-electronics, biology and neuro-sciences make it possible to imagine a new world where vital signs can be monitored continuously, artificial organs can be implanted in human bodies and interfaces between the human brain and the environment can extend the capabilities of men thus making the dream of Dr. Frankenstein become true. This paper surveys some of the most innovative implantable devices and offers some perspectives on the ethical issues that come with the introduction of this technology.
www.theinnovationgroup.it
The Neuroprosthetics is an emerging field in the Health Care & Engineering Sector.
In this Technology a Specialized Chip is implanted in the Brain & by using Electronic & Mechanical Components the Brain Waves in converted into respective Mechanical Movements.
Neuroprosthetics is specifically used for patients suffering from Paralysis, Amoyotropic Lateral Sclerosis & Multiple Sclerosis.
This Field is in its Initial Stage in terms of Research specifically in India.This field requires a lot of research specially for India & Developing Countries.
Neuroprosthetics will be an Transforming World for Health Sector in the future.
15 Trends In Neurotechnologies That Will Change The WorldNikita Lukianets
Below are technologies related to neuro and cognitive under three key areas of accelerating change: Machine Learning & Neural Network Computing, Extended Cognition and Neural Interfaces. Neural network computing will lead to improvements in computer vision and analysis, such as detecting emotions and moods, which may have safety and security applications. Extended cognition involves more direct connection to people's brains, allowing mood, thought patterns and information to be altered in the brain. Neural interfaces get information out of people's brains more efficiently, ultimately allowing a machine-enabled form of telepathy. This presentation covers Michell Zappa research from Policy Horizons Canada
The Neuroprosthetics is an emerging field in the Health Care & Engineering Sector.
In this Technology a Specialized Chip is implanted in the Brain & by using Electronic & Mechanical Components the Brain Waves in converted into respective Mechanical Movements.
Neuroprosthetics is specifically used for patients suffering from Paralysis, Amoyotropic Lateral Sclerosis & Multiple Sclerosis.
This Field is in its Initial Stage in terms of Research specifically in India.This field requires a lot of research specially for India & Developing Countries.
Neuroprosthetics will be an Transforming World for Health Sector in the future.
15 Trends In Neurotechnologies That Will Change The WorldNikita Lukianets
Below are technologies related to neuro and cognitive under three key areas of accelerating change: Machine Learning & Neural Network Computing, Extended Cognition and Neural Interfaces. Neural network computing will lead to improvements in computer vision and analysis, such as detecting emotions and moods, which may have safety and security applications. Extended cognition involves more direct connection to people's brains, allowing mood, thought patterns and information to be altered in the brain. Neural interfaces get information out of people's brains more efficiently, ultimately allowing a machine-enabled form of telepathy. This presentation covers Michell Zappa research from Policy Horizons Canada
Current motorized limb prostheses provide rudimentary functionality for the application in everyday life. Together with a
poor cosmetic appearance, this is the reason why a large percentage of amputees do not use their prosthetic device regularly. This
paper seeks to present an overview of current state of the art research on neural interfaces. The focus lies on non-invasive
recording with EMG and especially High-Density EMG sensors. Additionally, direct machine learning and pattern recognition
algorithms for the decoding of the recorded signals are discussed. Finally, promising research directions for advanced prosthesis
control will be discussed. The bionic arm uses EMG signals to control each action of the hand. In order to control them, we need to
record the EMG signal for different actions. And compare it with real-time values to move the hand in a different manner. There
are separate servo motors to control the actions of each finger separately. So these are programmed by using microcontrollers.
The Dawn of the Age of Artificially Intelligent NeuroprostheticsSagar Hingal
A summary or an overview of the existing technologies that encapsulate the concepts of NeuroScience and Bio-Technology using the enhanced methods of Artificial-intelligence.
In this review paper, there are several case studies and methodologies of implementations of neuroprosthetics as well as how A.I (Artificial Intelligence) is evolved over the period of time and what is next on the future.....
Embedded system for upper-limb exoskeleton based on electromyography controlTELKOMNIKA JOURNAL
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
The mind-to-movement system that allows a quadriplegic man to control a computer using only his thoughts is a scientific milestone. It was reached, in large part, through the brain gate system. This system has become a boon to the paralyzed. The Brain Gate System is based on Cyber kinetics platform technology to sense, transmit, analyze and apply the language of neurons. The principle of operation behind the Brain Gate System is that with intact brain function, brain signals are generated even though they are not sent to the arms, hands and legs.The signals are interpreted and translated into cursor movements, offering the user an alternate Brain Gate pathway to control a computer with thought,just as individuals who have the ability to move their hands use a mouse. The 'Brain Gate' contains tiny spikes that will extend down about one millimetre into the brain after being implanted beneath the skull,monitoring the activity from a small group of neurons.It will now be possible for a patient with spinal cord injury to produce brain signals that relay the intention of moving the paralyzed limbs,as signals to an implanted sensor,which is then output as electronic impulses. These impulses enable the user to operate mechanical devices with the help of a computer cursor. Matthew Nagle,a 25-year-old Massachusetts man with a severe spinal cord injury,has been paralyzed from the neck down since 2001.After taking part in a clinical trial of this system,he has opened e-mail,switched TV channels,turned on lights
A LOW COST EEG BASED BCI PROSTHETIC USING MOTOR IMAGERY ijitcs
Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain
ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to
control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv
Cognitive Suite (i.e. the 1st framework) combined with an embedded software system (i.e. an open source
Arduino board) is able to control the hand through character input associated with the taught actions of
the suite. This system provides evidence of the feasibility of brain signals being a viable approach to
controlling the chosen prosthetic. Results are then presented in the second framework. This latter one
allowed for the training and classification of EEG signals for motor imagery tasks. When analysing the
system, clear visual representations of the performance and accuracy are presented in the results using a
confusion matrix, accuracy measurement and a feedback bar signifying signal strength. Experiments with
various acquisition datasets were carried out and with a critical evaluation of the results given. Finally
depending on the classification of the brain signal a Python script outputs the driving command to the
Arduino to control the prosthetic. The proposed architecture performs overall good results for the design
and implementation of economically convenient BCI and prosthesis.
Current motorized limb prostheses provide rudimentary functionality for the application in everyday life. Together with a
poor cosmetic appearance, this is the reason why a large percentage of amputees do not use their prosthetic device regularly. This
paper seeks to present an overview of current state of the art research on neural interfaces. The focus lies on non-invasive
recording with EMG and especially High-Density EMG sensors. Additionally, direct machine learning and pattern recognition
algorithms for the decoding of the recorded signals are discussed. Finally, promising research directions for advanced prosthesis
control will be discussed. The bionic arm uses EMG signals to control each action of the hand. In order to control them, we need to
record the EMG signal for different actions. And compare it with real-time values to move the hand in a different manner. There
are separate servo motors to control the actions of each finger separately. So these are programmed by using microcontrollers.
The Dawn of the Age of Artificially Intelligent NeuroprostheticsSagar Hingal
A summary or an overview of the existing technologies that encapsulate the concepts of NeuroScience and Bio-Technology using the enhanced methods of Artificial-intelligence.
In this review paper, there are several case studies and methodologies of implementations of neuroprosthetics as well as how A.I (Artificial Intelligence) is evolved over the period of time and what is next on the future.....
Embedded system for upper-limb exoskeleton based on electromyography controlTELKOMNIKA JOURNAL
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
The mind-to-movement system that allows a quadriplegic man to control a computer using only his thoughts is a scientific milestone. It was reached, in large part, through the brain gate system. This system has become a boon to the paralyzed. The Brain Gate System is based on Cyber kinetics platform technology to sense, transmit, analyze and apply the language of neurons. The principle of operation behind the Brain Gate System is that with intact brain function, brain signals are generated even though they are not sent to the arms, hands and legs.The signals are interpreted and translated into cursor movements, offering the user an alternate Brain Gate pathway to control a computer with thought,just as individuals who have the ability to move their hands use a mouse. The 'Brain Gate' contains tiny spikes that will extend down about one millimetre into the brain after being implanted beneath the skull,monitoring the activity from a small group of neurons.It will now be possible for a patient with spinal cord injury to produce brain signals that relay the intention of moving the paralyzed limbs,as signals to an implanted sensor,which is then output as electronic impulses. These impulses enable the user to operate mechanical devices with the help of a computer cursor. Matthew Nagle,a 25-year-old Massachusetts man with a severe spinal cord injury,has been paralyzed from the neck down since 2001.After taking part in a clinical trial of this system,he has opened e-mail,switched TV channels,turned on lights
A LOW COST EEG BASED BCI PROSTHETIC USING MOTOR IMAGERY ijitcs
Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain
ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to
control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv
Cognitive Suite (i.e. the 1st framework) combined with an embedded software system (i.e. an open source
Arduino board) is able to control the hand through character input associated with the taught actions of
the suite. This system provides evidence of the feasibility of brain signals being a viable approach to
controlling the chosen prosthetic. Results are then presented in the second framework. This latter one
allowed for the training and classification of EEG signals for motor imagery tasks. When analysing the
system, clear visual representations of the performance and accuracy are presented in the results using a
confusion matrix, accuracy measurement and a feedback bar signifying signal strength. Experiments with
various acquisition datasets were carried out and with a critical evaluation of the results given. Finally
depending on the classification of the brain signal a Python script outputs the driving command to the
Arduino to control the prosthetic. The proposed architecture performs overall good results for the design
and implementation of economically convenient BCI and prosthesis.
Bioelectronic Medicines | Personalized Medicine | Drug Delivery SystemPankaj Saha
Bioelectronic Medicine is a
the new treatment procedure for diseases by using electrical pulses instead of the drug product. In this, there is a small implantation of the electrical devices, that
will generate and develop periodical digital doses to the nerve bundles, which will produce the
therapeutics effect for fighting against the diseases, and that will last for hours to full of
the day that is dependent on the mechanism of the drug therapy. The implantable device like
electronic brains and nerve-stimulating implants are not new devices because they
have already been used in the treatment procedure of diseases. It is used for the treatment
of the disorders like epilepsy, and parkinsonism and also for controlling the bladder.
In this new class of medicine, whole of the treatment procedure is fully dependent on the
accuracy, determination and the modulation of the electric signaling pattern in the whole of the
nervous system. More targeted modulation can be achieved during the chronic disease, because
of the functions controlled by the peripheral nervous are extensive during the time of chronic
diseases (Bansal Niharika et al, 2019). A small implantable device is attached to the nerves of
the individual, in the viscera of the peripheral nervous system. The device has an ability to
decode and regulate the neural signaling pattern and provide the therapeutics effects to target
at a specific organ (Bansal Niharika et al, 2019)
Impact of adaptive filtering-based component analysis method on steady-state ...IAESIJAI
The significance of brain computer interface (BCI) systems is immensely high, especially for disabled people and patients with nervous system failure. Therefore, in this study, adaptive filtering-based component analysis (AFCA) model is presented to enhance target box identification efficiency at varied flickering frequencies in a visual stimulation process by efficient acquisition of electroencephalogram (EEG) signals for the application of steady-state visually evoked potential based BCI system. Furthermore, optimization of proposed AFCA model is performed based on the maximized reproducibility of correlated components. A multimedia authoring and management using your eyes and mind (MAMEM) steady-state visual evoked potential (SSVEP) dataset is utilized for efficient training of EEG signals and background entities are eliminated using adaptive filters in a pre-processing stage. Additionally, spatial filtering components are obtained to detect target flickering box based on the obtained quality features. Performance is measured by acquisition of SSVEP signals in terms of reconstruction efficiency, classification accuracy and information transfer rate (ITR) using proposed AFCA model. Mean classification accuracy for all 11 subject is 93.48% and ITR is 308.23 bpm. Further, classification accuracy is relatively higher than various SSVEP classification algorithms.
Tracking times in temporal patterns embodied in intra-cortical data for cont...IJECEIAES
Brain-machines capture brain signals in order to restore communication and movement to disabled people who suffer from brain palsy or motor disorders. In brain regions, the ensemble firing of populations of neurons represents spatio-temporal patterns that are transformed into outgoing spatio-temporal patterns which encode complex cognitive task. This transformation is dynamic, non-stationary (time-varying) and highly nonlinear. Hence, modeling such complex biological patterns requires specific model structures to uncover the underlying physiological mechanisms and their influences on system behavior. In this study, a recent multi-electrode technology allows the record of the simultaneous neuron activities in behaving animals. Intra-cortical data are processed according to these steps: spike detection and sorting, than desired action extraction from the rate of the obtained signal. We focus on the following important questions about (i) the possibility of linking the brain signal time events with some time-delayed mapping tools; (ii) the use of some suitable inputs than others for the decoder; (iii) a consideration of separated data or a special representation founded on multi-dimensional statistics. This paper concentrates mostly on the analysis of parallel spike train when certain critical hypotheses are ignored by the data for the working method. We have made efforts to define explicitly whether the underlying hypotheses are actually achieved. In this paper, we propose an algorithm to define the embedded memory order of NARX recurrent neural networks to the hand trajectory tracking process. We also demonstrate that this algorithm can improve performance on inference tasks.
Study and analysis of motion artifacts for ambulatory electroencephalographyIJECEIAES
Motion artifacts contribute complexity in acquiring clean electroencephalography (EEG) data. It is one of the major challenges for ambulatory EEG. The performance of mobile health monitoring, neurological disorders diagnosis and surgeries can be significantly improved by reducing the motion artifacts. Although different papers have proposed various novel approaches for removing motion artifacts, the datasets used to validate those algorithms are questionable. In this paper, a unique EEG dataset was presented where ten different activities were performed. No such previous EEG recordings using EMOTIV EEG headset are available in research history that explicitly mentioned and considered a number of daily activities that induced motion artifacts in EEG recordings. Quantitative study shows that in comparison to correlation coefficient, the coherence analysis depicted a better similarity measure between motion artifacts and motion sensor data. Motion artifacts were characterized with very low frequency which overlapped with the Delta rhythm of the EEG. Also, a general wavelet transform based approach was presented to remove motion artifacts. Further experiment and analysis with more similarity metrics and longer recording duration for each activity is required to finalize the characteristics of motion artifacts and henceforth reliably identify and subsequently remove the motion artifacts in the contaminated EEG recordings.
Brain Computer Interface for User Recognition And Smart Home ControlIJTET Journal
This project discussed about a brain controlled biometric based on Brain–computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a biometric technology can be controlled. The intention of the project work is to develop a user recognition machine that can assist the work independent on others. Here, we are analyzing the brain wave signals. Human brain consists of millions of interconnected neurons. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human thoughts, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) will receive the brain wave raw data and it will extract and process the signal using Mat lab platform. Then the control commands will be transmitted to the robotic module to process. With this entire system, we can operate the home application according to the human thoughts and it can be turned by blink muscle contraction.
Recognition of new gestures using myo armband for myoelectric prosthetic appl...IJECEIAES
Myoelectric prostheses are a viable solution for people with amputations. The chal- lenge in implementing a usable myoelectric prosthesis lies in accurately recognizing different hand gestures. The current myoelectric devices usually implement very few hand gestures. In order to approximate a real hand functionality, a myoelectric prosthesis should implement a large number of hand and finger gestures. However, increasing number of gestures can lead to a decrease in recognition accuracy. In this work a Myo armband device is used to recognize fourteen gestures (five build in gestures of Myo armband in addition to nine new gestures). The data in this research is collected from three body-able subjects for a period of 7 seconds per gesture. The proposed method uses a pattern recognition technique based on Multi-Layer Perceptron Neural Network (MLPNN). The results show an average accuracy of 90.5% in recognizing the proposed fourteen gestures.
This paper presents the activities of children in Epilepsy in which a person has repeated or recurrent
seizures over time. In this, the particular person suffers from symptoms such as violent shaking, non controllable
jerky movements of the arms and legs, loss of alertness etc. But in the proposed model, we are using the wearable
sensors which will be worn on the brain and palms to detect the epileptic attacks and the GSM is used so that the
message is send through mobile to their relatives or friends that an attack has occurred. Proper medicines can cure
this disorder so we are going to suggest medicines according to the type of seizure. The readings will be displayed
on flex grid. The sensors include two 3-axis accelerometer sensors, temperature sensor, moisture sensor. This
model will utilize the ARM LPC2138 processor and the programming will be developed in keil3. Software
includes Visual Basics. So we will be doing the monitoring as the seizure will be detected and their parents or
relatives will be knowing about it. We are going to detect generalized tonic clonic seizures, absence seizures.
Keywords — Epilepsy, flex grid, GSM, sensors, types of seizures.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Smartphone based wearable sensors for cyborgs using neural network enginedayanabenny
Now we can imagine a situation where human beings wear electronic skin as touch sensors for health monitoring system. There is a recent advancement in developing a system that uses wireless sensors placed on body and collected sensor information are dealt with smart phone applications. This application uses cloud computing, location data and a neural network engine to determine the current state of a patient is dangerous or not. Electronic skin modules mount spatially on human beings. The neural network engine fuses the information from multiple sensors.
Smartphone based wearable sensors for cyborgs using neural network engineDayana Benny
Now we can imagine a situation where human beings wear electronic skin as touch sensors for health monitoring system. There is a recent advancement in developing a system that uses wireless sensors placed on body and collected sensor information are dealt with smart phone applications. This application uses cloud computing, location data and a neural network engine to determine the current state of a patient is dangerous or not. Electronic skin modules mount spatially on human beings. The neural network engine fuses the information from multiple sensors.
Similar to Dr. Frankenstein’s Dream Made Possible: Implanted Electronic Devices (20)
Si sente oggi molto parlare del nuovo trend dell’Internet delle cose, di sensori e oggetti intelligenti, di nuove opportunità di controllo olistico di macchine produttive, abitazioni,
elettrodomestici. Qual è la dimensione attuale del fenomeno dell’Internet of Everything? Quali sono le previsioni di evoluzione?
Internet fino ad oggi è servita a collegare tra loro le persone tramite PC e device mobile: in futuro, la rete crescerà molto di più e si espanderà in domini inesplorati, creando legami sempre più stretti tra il mondo fisico e quello digitale. Appliance domestiche intelligenti, sistemi di riscaldamento e condizionamento, sensori per monitorare le condizioni ambientali, attuatori per lanciare azioni da remoto: molti di
questi oggetti saranno dotati di un proprio indirizzo IP, trasmetteranno dati, saranno raggiungibili in qualsiasi momento e da qualunque luogo, parleranno con noi. Come
mostra la figura successiva, le componenti che rendono possibile questo scenario ci sono già tutte: sensori, attuatori, reti wireless, batterie, strumenti analitici avanzati,
processi già codificati di localizzazione, controllo, automazione e alert.
www.theinnovationgroup.it
Looking into the Crystal Ball: From Transistors to the Smart EarthThe Innovation Group
This paper is based on a keynote talk presented by Prof. Sangiovanni-Vincentelli at the 50th DAC. It discusses the evolution of cyber-physical and bio-cyber systems leading us to a smarter planet, and it predicts how EDA and embedded systems have to expand into this new field.
Dall’analisi di TIG sulle relazioni finanziarie del primo trimestre 2014: utili in crescita e una ripresa dei
margini di interesse. Rimangono sospesi i risultati negativi di MPS, Banco Popolare e Banca Carige.
www.theinnovationgroup.it
The IBM global C-suite Study draws
on a decade of research with over
20,000 interviews
Watch Peter J Korsten interview on http://youtu.be/FYUCDJG9eNM
Thanks to: IBM Institute for Business Value
www.theinnovationgroup.it
Best Ayurvedic medicine for Gas and IndigestionSwastikAyurveda
Here is the updated list of Top Best Ayurvedic medicine for Gas and Indigestion and those are Gas-O-Go Syp for Dyspepsia | Lavizyme Syrup for Acidity | Yumzyme Hepatoprotective Capsules etc
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
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2. algorithm that has to mimic the very different behaviors of the
two cell populations. An alpha cell tends to react to rapid
electrical events (spikes), while the beta cell tends to react in
bursts of voltage spikes, punctuated by low voltage silent
periods that last for seconds or even minutes. When glucose
concentrations rise, the beta cells remain in the high voltage
burst state longer, secreting more insulin as a result [9, 10]. The
“bionic pancreas” mimics this biological process by detecting
the user's glucose level via a sensor every five minutes. If it
reports a high level of glucose, the silicon beta cell generates a
signal that drives a motor that pushes a syringe dispensing
insulin into the tissue beneath the skin until the glucose reading
at the sensor drops. If the sensor reports a low glucose value,
the silicon alpha cell activates the second pump to administer
glucagon instead.
Fig. 2 Nanobiosensor macro assembly (courtesy Sandro Carrara,
EPFL, Lausanne, CH)
This approach differs from today's dominant method of
delivering only insulin using a relatively simple control system.
The artificial pancreas system eliminates the need for multiple
insulin injections and administers the insulin in a more
biologically faithful way. This approach reduces complications
and alleviates the need for patients to worry about what they
eat and drink. This system has been used in a wide range of
implantable biosensor devices for monitoring and controlling
drugs absorption [13-15] and delivering the drug right dose
when needed.
IV. MONITORING AND CONTROLLING THE BRAIN
A challenging goal in neuroscience is reading out, or
decoding, mental content from brain activity [16-19].
Functional magnetic resonance imaging (fMRI) studies have
already decoded orientation [20] position [21] and object
category [22] from activity in the visual cortex [23-26].
However, these studies typically used relatively simple stimuli
(for example, gratings) or images drawn from fixed categories
(for example, faces and houses), and decoding was based on
previous measurements of brain activity evoked by the same
stimuli.
Today, brain mapping and interfaces with the external
world are possible by reading the signals emitted by the cells in
the brain. The target is to let the brain interact directly with
prosthetic devices or with humanoid robots. The target of
Brain-to-Machine Interfaces (BMI) is shown in Figure 3. The
BMI for clinical applications should be implanted in the
patient’s body as much as possible. Wireless telemetry offers a
viable solution for this purpose. The prosthesis not only should
have the functionality of the human arm in terms of power and
accuracy of the actuators, but should also be equipped with the
touch and position sensors from which signals can be
transmitted back to the subject’s brain. BMIs are characterized
according to whether they utilize invasive (i.e. intra-cranial) or
non-invasive methods of electrophysiological recordings.
Fig. 3. Brain machine Interface possible applications
A. Non-invasive monitoring system
Non-invasive systems primarily exploit
electroencephalograms (EEGs) to control computer cursors or
other devices. This approach has proved useful for helping
paralyzed or ‘locked in’ patients develop ways of
communication with the external world [27]. However, despite
having the great advantage of not exposing the patient to the
risks of brain surgery, EEG-based techniques provide
communication channels of limited capacity and spatial
resolution. In fact brain electrical signal can be recorded from
the scalp with a large number of electrodes - 16 to 256 - with
good temporal resolution, which can be composed with
electromyographic (EMG) signals, even if this often requires
extensive user training. Their typical transfer rate is currently
5–25 bits [21, 22]. Although such a transfer rate might not be
sufficient to control the movements of prosthetic arms or legs
that have multiple degrees of freedom, from field data, it seems
feasible to recognize EEG patterns related to particular
voluntary intentions. Recently, adaptive algorithms that
constantly update the classifier parameters during training have
been implemented [26].
Several strategies have also been proposed to provide
feedback to users of EEG-based BMIs. For instance, virtual-
reality systems can provide a realistic feedback that can be
efficient for BMI training [28]. In a recent demonstration of
this approach, subjects navigated through a virtual environment
by imagining themselves walking [29].
In an effort to improve the resolution of brain potentials
monitored by the BMIs, more invasive recording methods,
such as electrocorticograms (ECoGs) recorded by subdural
BARE
CARBON NANOTUBES
CNTs + PROBE ENZYMES
10.3 ± 1.14 nm
3. electrodes, have been introduced. ECoGs sample neuronal
activity from smaller cortical areas than conventional EEGs. In
addition, they contain higher-frequency gamma rhythms (>30
Hz). Consequently, ECoG-based BMIs are expected to have
better accuracy and shorter training times than BMIs based on
EEGs [30].
Fig. 4. ECoG array recordings of neuronal signals at frequencies up to ~300
Hz from the cortical surface of the brain
EEG-based BMIs have been implemented as solutions for
patients suffering from various degrees of paralysis. These
BMIs (in the case of patients with advanced amyotrophic
lateral sclerosis) enable control of computer cursors, which the
patients use to communicate with the external world or to
indicate their intentions. The first successful and most well-
received application of this approach was based on the
utilization of slow cortical potentials to control a computer-
aided spelling system [30, 31]. BMIs based on mu and beta
rhythms have also been tested in severely paralyzed people
[32]. One study reported that a tetraplegic patient, aided by a
BMI that detected beta waves in his sensorimotor cortex and
activated a functional electrical stimulation device, learned to
grasp objects using his paralyzed hand [33].
In addition to using EEGs, imaging techniques such as
fMRI, have been explored as a new source of brain-derived
signals to drive BMIs [34]. Although fMRI-based BMIs are not
suitable for everyday use and suffer from temporal delays of
several seconds, they have good spatial resolution and, most
importantly, can sample the activity of deep brain structures.
Recently, fMRI was used to measure brain activation during
the operation of a BCI based on slow cortical potentials [35].
Myoelectric systems that make use of voluntary activations of
unaffected muscles, in partially paralyzed and amputees
subjects [36–39], and use these signals to control limb
prostheses and exoskeletons, offer an alternative to the existing
non-invasive BMIs. Currently, these systems are more practical
for everyday situations than EEG based BMIs [36].
In summary, paralyzed patients can re-acquire basic forms
of communication and motor control using EEG-based
systems. Yet motor recovery, obtained using these systems, has
been limited. No clear breakthrough that could significantly
enhance the power of EEG-based BMIs has been reported in
the literature [36]. This by no means reduces the clinical utility
of these systems. Some of them have improved the quality of
life of patients, such as the BCI for spelling [37]. But if the
goal of a BMI is to restore movements with multiple degrees of
freedom through the control of an artificial prosthesis, the
message from published evidence is clear: this task will require
recording of high resolution signals from the brain, and this can
be done using invasive approaches [38-40].
B. Invasive monitoring system
Invasive BMI approaches are based on recordings from
ensembles of single brain cells (also known as single units) or
on the activity of multiple neurons (also known as multi-units)
[41-46]. These approaches have their roots in the pioneering
studies conducted by Fetz and colleagues in the 1960s and
1970s [47, 48]. In these experiments, monkeys learned to
control the activity of their cortical neurons voluntarily, aided
by biofeedback indicating the firing rate of single neurons. A
few years after these experiments, Schmidt raised the
possibility that voluntary motor commands could be extracted
from raw cortical neural activity and used to control a
prosthetic device designed to restore motor functions in
severely paralyzed patients [49]. Largely owing to technical
difficulties associated with obtaining the needed cortical
signals and implementing real-time interfaces quickly enough,
thorough experimental testing of Schmidt’s proposition took
almost two decades. These bottlenecks were overcome because
of a series of experimental and technological breakthroughs
that led to a new electrophysiological methodology for chronic,
multi-site, multi-electrode recordings [50–52]. A BMI
approach that relies on long-term recordings from large
populations of neurons (100–400 units) evolved from
experiments carried out in 1995 [52]. After the introduction of
this approach, a series of studies demonstrated that neuronal
readout of tactile stimuli could be accomplished using pattern-
recognition algorithms [51,52].
Fig. 5. Rhesus monkey brain and neuronal cortical electrodes
These developments paved the way to the first experiment
in which neuronal population activity recorded in rats enacted
movements of a robotic device that had a single degree of
freedom. A similar BMI approach was shown to work on
rhesus monkeys [53–54] (Figure 5). As a result of these
experimental efforts, in less than six years several laboratories
reported BMIs that reproduced primate arm reaching [54] and
the combination of reaching and grasping movements, using
either computer cursors or robotic manipulators as actuators.
There are several important differences that distinguish these
BMIs. These include: the number of cortical implants (e.g. uni-
site or multi-site recordings); the cortical location of implants
(e.g. frontal or parietal cortex, or both); the type of neural
signal recorded (local field potentials versus single-unit or
multi-unit signals); and the size of the neural sample. With the
exception of the ones described in [55], all BMIs tested in
monkeys have relied on single cortical site recordings either of
local field potentials or of small samples (<30) of neurons or
4. multi-units. Most of these small-sample, single-area BMIs
utilized neural signals recorded in the primary motor cortex,
although one group has focused on BMIs that processed neural
signals recorded in the posterior parietal cortex [64]. In [56] a
BMI strategy was recently implemented based on single-unit
recordings made during intra-operative placement of deep-
brain stimulators in Parkinson patients [56].
Fig. 6 Experimental setup and stability of ensemble recordings. (A)
Schematics for manual control (MC) and brain control (BC). (B) Stability of
putative single units across multiple days.C) Stability of firing properties
across time. (courtesy: Josè Carmena UC Berkeley US)
Extracting motor control signals from the firing patterns of
populations of neurons and using these control signals to
reproduce motor behaviors in artificial actuators are the two
key operations that a clinically viable BMI should perform
flawlessly [53, 54]. To be accepted by patients, BMI devices
will also have to act in the same way and feel the same as the
subjects’ own limbs. Recent findings suggest that this task
might be accomplished by creating conditions under which the
brain undergoes experience-dependent plasticity and
assimilates the prosthetic limb as if it were part of the subject’s
own body. Until recently, such plasticity was achieved using
visual feedback (Figure 6 [53]). However, a more efficient way
to assimilate the prosthetic limb in the brain representation
could be to use multiple artificial feedback signals, derived
from pressure and position sensors placed on the prosthetic
limb. These feedback signals would effectively train the brain
to incorporate the properties of the artificial limb into the
tuning characteristic of neurons located in cortical and
subcortical areas that maintain representations of the subject’s
body. Such plasticity will result in sensory and motor areas of
the brain representing the prosthetic device.
V. OPTOGENETICS
To improve understanding of psychiatric and neurological
disorders, it is important to identify which neural circuits may
be responsible, to pinpoint the precise nature of the causally
important aberrations in these circuits and to modulate circuit
and behavioral dysfunction with precise and specific
interventions. However, such a deep, circuit-level
understanding of neuropsychiatric disorders, or indeed even of
normal neural circuit function, has been challenging for
traditional methods. The complexity of neural circuitry has
historically precluded the use of genetically and temporally
precise manipulations to probe detailed mechanisms of
function and dysfunction.
Optogenetics [57-59] involves the use of microbial opsins,
or related tools, that can be activated by illumination to
manipulate cells with high specificity and temporal precision
even within intact tissue or live animals. Optogenetic
approaches have been used to dissect neural circuits in animals
to identify symptoms that are relevant to fear, anxiety,
depression, schizophrenia, addiction, social dysfunction,
Parkinson disease and epilepsy. Successful probing of complex
diseases in this way depends on the validity of using animals
used to identify the crucial circuit elements and activity
patterns that are involved in each cluster of symptoms, and the
precision and efficiency of interventions designed to selectively
target these elements or patterns. However, several limitations,
caveats and considerations are in order. A very important
limitation of optogenetics is the production of heat with
illumination. Heated neurons may not only alter their activity
in a nonspecific manner but may also be detrimental to cell
health. Appropriate controls, as well as assessment of light
source stability and performance, must be carefully and
frequently examined to ensure precise and reliable light output
and interpretation of light effects [59].
Another limitation of optogenetics is the potential for
toxicity at very high expression levels or long-term expression.
With so many variables, it is important to carefully validate
with imaging, physiology, or c-FOS (protein encoded for by
the FOS gene) staining that neurons are being manipulated
with the strength and specificity intended before interpreting
any experimental results.
VI. CONCLUSIONS AND ETHICAL CONSIDERATIONS
The field of micro-devices and algorithms for monitoring
and controlling body functions is exciting and wonderfully rich
of challenging scientific and engineering problems. Results can
only be achieved by leveraging multiple disciplines ranging
from medicine, to biology, from chemistry to material science,
from computer science to electronics and mechanical
engineering. We do believe that this field will grow to become
a major scientific and economic sector in the years to come.
However, there are deep ethical issues that need to be
addressed. The question will certainly loom that if functions
can be restored for those in need, is it right to use these
technologies to enhance the abilities of healthy individuals as
well? It is essential that devices are safe to use and pose few, if
any, risks to the individual counter-balanced with benefit. But
the ethical problems that these technologies pose are not vastly
different from those presented by existing therapies such as
antidepressants. In brain-controlled prosthetic devices, signals
from the brain are decoded by a computer that sits in the
device. These signals are then used to predict what a user
intends to do. Invariably, predictions will sometimes fail. Who
is responsible for involuntary acts? This question was already
considered years ago, when the automatic pilot was introduced
in airplanes but today becomes more pressing as the host is the
human brain. In [60] other possible questions are underlined: is
it the fault of the computer or the user? Will a user need some
kind of “license” and “obligatory insurance” to operate
5. prosthesis? What if machines change the brain? To the
philosophical question: "What is a man?" Aristotle answered:
"Man is a rational animal." If our brain is driven by a decoder
that takes for us decisions on the base of an optimal searching
algorithm, are we still men or are we robots then? The classic
approach of biomedical ethics is to weigh the benefits for the
patient against the risk of the intervention and to respect the
patient's autonomous decisions [61]. This should also hold for
the proposed expansion of deep brain stimulation [DBS] to
treat patients with psychiatric disorders [62].
What is enhancement and what is treatment depends on
defining “normality” and “disease”, and this is notoriously
difficult. In [60] several opinions on this issue are considered.
Christopher Boorse, a philosopher at the University of
Delaware, defines disease as a statistical deviation from
"species-typical functioning" [63]. As deafness is measurably
different from the norm, it is thus considered a disease. This
definition has been influential and has been used as a criterion
for allocation of medical resources [64]. From this perspective,
for instance, the intended medical application of cochlear
implants seems ethically unproblematic. Nevertheless, Anita
Silvers, a philosopher at San Francisco State University and a
disability scholar and activist, has described such treatments as
a "tyranny of the normal" [65], designed to adjust people who
are deaf to a world designed by the hearing, ultimately
implying the inferiority of deafness [65]. Although many have
expressed excitement at the expanded development and testing
of brain–machine interface devices to enhance otherwise
deficient abilities, Silvers suspects that prostheses could be
used for a "policy of normalizing". These serious concerns
should not prevent further research on brain–machine
interfaces. Still, whether brain-technology applications are a
proper option remains dependent on technological
developments and on addressing important safety issues. One
issue that is perhaps more pressing is how to ensure that risks
are minimized during research. Animal experimentation, where
allowed (i.e. in Europe is not allowed if not under conditions
established by EU regulation), will probably not address the
full extent of psychological and neurological effects that
implantable brain–machine interfaces could have [66-70].
Research on human subjects will be needed, but testing
neuronal motor prostheses in healthy people is ethically
unjustifiable because of the risk of bleeding, swelling,
inflammation and other, unknown, long-term effects. People
with paralysis, who might benefit most from this research, are
also not the most appropriate research subjects. Because of the
very limited medical possibilities and often severe disabilities,
such individuals may be vulnerable to taking on undue risk
[60]. Brain–machine interfaces promise therapeutic benefit and
should be pursued. The technologies pose ethical challenges,
but these are conceptually similar to those that bioethicists have
addressed for other therapies. The limit is the respect of the
human dignity as already underlined, by Immanuel Kant in
[71]: “Act in such a way that you use the humanity in your own
person and in the person of any third party at all times as an
end in itself and never simply as a means to an end”. In this
common objective, ethics and neuroscience research can
cooperate for the treatment of chronic disease.
VII. ACKNOWLEDGMENTS
The authors wish to acknowledge Jose Carmena and
Michel Maharabitz from UC Berkeley, US for the inspiring
discussions on the BMI and Optogenetics techniques and for
the photos.
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