Does India need a surrogacy bill?
Posted by on Friday, 13th January 2012
India surely requires surrogacy bill in coming future for the better understanding of society & its implementation. The surrogacy in the larger context has many factors to understand as well. Being surrogate mother & without the knowledge of its consequences of well being of the child & it’s bearing on the national character, it becomes her moral responsibility to know & understand her action to be pregnant & deliver baby. The question arises has it been clearly understood what is the motive of would be parents?
Are they using surrogacy for being deprived of child because they are infertile or the lady is unable to conceive because of many factors including biological or non-biological and or is unable to bear pregnancy in-spite of being fit to conceive or for some commercial usage later in their (Children’s) lives. This question has question mark and needs full answer before a couple is allowed to go ahead with a motive designed for selfless or selfish cause
More-over, if the surrogate receives compensation beyond the reimbursement of medical and other reasonable expenses, the arrangement is called commercial surrogacy; otherwise it is often referred to as altruistic surrogacy.
Further, the legal tangle that the woman giving birth to a child is the child's legal mother, and the only way for another woman to be recognized as the mother is through adoption (usually requiring the birth mother's formal abandonment of parental rights).
And there needs to finds out about the arrangement, that there may be financial and legal consequences for the parties involved. Sometime the jurisdiction may prevent the genetic mother's adoption of the child even though it leaves the child with no legal mother.
In-case if the intended parents change their mind and do not want the child after all, the surrogate cannot get any reimbursement for expenses, or any promised payment, and she will leave with legal custody of the child.
What does motherhood mean? What is the relationship between genetic motherhood, gestational motherhood, and social motherhood? Is it possible to socially or legally conceive of multiple modes of motherhood and / or the recognition of multiple mothers?
What about Homosexuals, Lesbians, Eunuchs, Trans-sexual and other such couples & their right to go for surrogacy & the intended to be born child.
Now to what extent should the authorities be concerned about exploitation, commoditization and / or coercion when women are paid or not paid to be pregnant and deliver the child especially in cases where there is large wealth and power plays a differential role between intended parents and surrogates?
Should there be an institution where to be surrogate mother can apply for admission & be paid for their services to the couple intending a child? Isn’t it adoption is better solution for the already burdened a country with population explosion and there are many such children waiting to be taken care of by someone?
On one part there is law to curb rape & on the other side is it less than a rape but paid? Although it is easy money to earn by the needy or not so needy woman, nevertheless, what’s the difference in contracting for surrogacy more like contracting for employment / labor, or more like contracting for prostitution, or more like contracting for slavery?
To what extent is it right for society to permit women to make contracts about the use of their bodies? To what extent is it a woman's human right to make contracts regarding the use of her body? Which, if any, of these kinds of contracts should be enforceable?
The role of state needs clear explanation whether it allows or force a woman to carry out "specific performance" of her contract if that requires her to give birth to an embryo she would like to abort, or to abort an embryo she would like to carry to term?
Should a child born via surrogacy have the right to know the identity of any / all of the people involved in that child's conception and delivery?
How about the psychological traumatization of the surrogate mother after relinquishing the baby to the agreed / contracted legally or verbally parents / couple?
All these questions & many more require complete answers for the benefit of society & the parties involved.
Dr Tejinder Mohan Aggarwal
Director
Phoenix Hospital
SCO 8, Sector 16
Panchkula India 134109
(M): 0-931-610-1112
(Ph): +91-172-5011333 (Ext.): 102
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Artificial Intelligence to predict BP using Leucocytapheresis
Posted by on Friday, 7th May 2010
Artificial Intelligence to predict BP using Leucocytapheresis
BP prediction during Leukocytapheresis (LCAP) using Artificial Intelligence
ABSTRACT
INTRODUCTION:
With rapid advances in information technology, computer-aided medical treatment is becoming quite common in hospitals. Several evolved treatments rely on artificial intelligence for prediction of various parameters that affect the blood purification [3]-[5]. During blood purification treatment, it is extremely necessary to monitor circulating blood volume of patient. Till now, most of the hospitals rely on the medical workers to use their own judgment in order to make necessary changes that affect the stability of the patient’s blood pressure. However, this approach is very subjective and could lead to undesirable fluctuation in blood pressure. Hence, a computer-aided method can provide steady blood purification by predicting blood pressure values. This could be an immense valued data to medical workers in providing accurate treatment. Such a method using artificial intelligence is proposed here for evaluation.
Artificial Neural Networks (ANNs) is gaining popularity in prediction methods. These are arrays of simultaneous equations that iteratively examine data sets according to learning rules.[Pandya and Macy, 1995] Delta rule is the most commonly and extensively studied prediction method that performs gradient descent optimization and is thus closely related to standard regression models. ANN using the delta rule has been successfully applied in predicting outcome in a variety of complex biomedical problems. ANNs have been widely used in solving real biomedical problems because of their ability to learn the dynamics of complex systems and to identify multidimensional relationships among multiple variables from available input/output samples.
References: IUGR: [7] Gurigen F et al., IEEE Eng. Med. Biol. 1997; 16:55-58, studied ultrasonographic examinations using neural networks (NN) in the detection of intrauterine growth retardation (IUGR). They concluded NN is a very helpful tool for correlating many variables. ECG: [8] Maglaveras N et al., IEEE Trans Biomed Eng. 1998; 45:805-813, worked on adaptive back propagation neural network for real-time ischemia episodes detection, development and performance analysis using the European St-T database.
Coronary artery disease: [9] Shen Z et al., Comp Cardiol 1993; 20:221-224, A neural network approach in the detection of coronary artery disease. Liver transplantation outcomes prediction: [10] Doyle HR et al., Ann Surg 1994; 219:408-415, Predicting out-comes after liver transplantation: A connectionist approach Electronic noise: [11] Kermani BG et al., IEEE Trans Biomed Eng 1999; 46:429-439, Using neural networks and genetic algorithms to enhance performance in electronic noise..
A Neural Network model can facilitate better treatment by predicting the change in circulation of blood volume. ************ The neural network is trained to predict the Ht value after various intervals of time. If the neural network predicts a drop in Ht value, it would indicate that the blood pressure is likely to drop and the medical worker can make the necessary adjustments.
Ulcerative colitis (UC), as well as Crohn’s disease (CD), is one of the prototype nonspecific inflammatory bowel diseases (IBDs) of unknown cause. Drugs such as salazosulfapyridine, 5-aminosalicylic, and immunosuppressants have been used for the treatment of UC. These drugs attenuate the inflammation in the colonic mucosa by their anti-inflammation and immunosuppressive actions, and causes disease remission. However, some patients with UC are even refractory to the strong anti-inflammatory and immunosuppressive actions of steroids. Surgical treatment often has been considered as the ultimate treatment modality for such patients.
Extracorporeal circulation treatment methods have shown to be highly effective for the treatment of UC patients refractory to steroids. One such method is called as granulocyte and monocyte adsorption apheresis (GMA), in which mainly granulocytes and monocytes are removed from the blood. Another method is leukocytapheresis (LCAP), in which granulocytes, monocytes, as well as lymphocytes, are removed from the blood. These treatment modalities have been reported to yield a high therapeutic efficacy in many patients of UC, including those who are refractory to steroids.
Leukocyctapheresis (LCAP) is a blood purification treatment for ulcerative colitis (UC) [1]. LCAP is known to have a low incidence of side effects. During treatment the blood pressure of patient starts decreasing. This phenomenon is caused by change in circulation of blood volume. The continuous reduction of the hematocrit value (Ht) during blood purification process lowers blood pressure. Hence it is a critical factor and is considered to be a major determinant of infusion dose and vice-versa, where artificial neural network (ANN) has an edge over individual judgment.
LCAP is carried out using a column (Cellsorba E) filled with a non-woven fabric made up of polyester fibers. The fabric had a dual structure; an inner layer composed of superfine fibers 0.8-2.8 in diameter, and an outer layer composed of fibers 10-40 in diameter. The blood is filtrated from the outside into the inside of the non-woven fabric wound into a cylindrical shape in the column, and leukocyte components are removed. The blood, with leukocyte removed, is guided out from the column and heated, and the returned to the corresponding vein of the patient’s other arm or leg of the patient. The blood flow rate is set at 30-50, and 2-3 L of blood is treated in each session of LCAP. The treatment is carried out for one hour per session once in a week, for 10 wks.
In this paper an ANN model is proposed as a predictor of the Ht values after every 1, 3 and 5 minutes. Well-known Multi Layer Perceptron (MLP) neural network (NN) is used here for data evaluation. (Fig.1). MLP is an appropriate instrument to deal with this kind of problem because it can handle the intrinsic nonlinearities involved in these types of biological systems. This in process identifies multidimensional relationships and learns the input/output characteristics from input/output samples. Different input parameter combinations have been tried in order to find the most effective model for prediction.
Currently, a serial Hematocrit Monitor manufactured by CRIT-LINE Monitor (CLM), Hema Metrics Inc Salt Lake City Boston U.S.A is in use due to its particular capabilities. It facilitates the noninvasive monitoring of the Ht value, rate of change in the circulating blood volume (percent blood volume change,BV %), and venous blood oxygen saturation. This is done after every 20-second intervals by measuring the absorption rate of scattered infrared rays in a chamber set within the blood circuit of the blood purification devise. In the Tokushima University Hospital, CLM is used for obtaining Ht values during blood purification. Table1 shows the index of CLM [2].
http://www.google.com/search?hl=en&lr=&q=BP%2FHematocrit+values&btnG=Search
http://www.nature.com/ki/journal/v56/n1/abs/4495544a.html
Berns et al (1999) [13] used interdialytic ambulatory blood pressure (ABP) monitoring to study the effects of partially corrected anemia versus normal hematocrit (hct) on BP in hemodialysis patients. They report that the mean daytime and night time BPs were not different from each other at two, four, and eight months in anemic group or at any time in normal group, and in both groups, most patients had little diurnal change in BP. However this study focuses on BP normal daily activities, i.e. ABP, while here the focus is on BP of an individual as it relates to changes in Ht values during blood transfusion.
http://stroke.ahajournals.org/cgi/content/abstract/18/3/565
L LaRue et al (1987) [14] found no significant difference in BP between hematocrit and stroke subtypes in normotensive individuals either in low (less than or equal to 30, 30-36%) or high (greater than or equal to 47%) hematocrit groups.
http://circ.ahajournals.org/cgi/content/abstract/43/6/876
Martin S et al (1971) [15] studied to assess the importance of an elevated cardiac output in the generation of the hypertension associated with chronic renal failure. It was concluded that the elevation of cardiac index in uremic patients is secondary to anemia and is reversible when the hematocrit is raised over 30%. The high cardiac index is not responsible for hypertension because restoration of cardiac index to normal by transfusion raises blood pressure rather than lowers it.
http://hyper.ahajournals.org/cgi/content/abstract/31/3/848
Giovanni Bertinieri et al (1998) [16] studied reduction in blood viscosity without changing blood volume causes a significant fall in both clinic and 24-hour ambulatory BPs. This is particularly true when, as can often happen, blood pressure is elevated. This emphasizes the importance this variable may have in the determination of blood pressure and the potential therapeutic value of its correction when altered.
http://ajpheart.physiology.org/cgi/content/abstract/289/5/H2136
Judith Martini et al (2005) [17] studied Hematocrit (Hct) relation between Hct and blood pressure and these findings suggest that increasing Hct increases blood viscosity, shear stress, and NO production, leading to vasodilatation and mild hypotension. Larger increases of Hct (>19% of baseline) led blood viscosity to increase >50%, overwhelming the NO effect through a significant viscosity-dependent increase in vascular resistance, causing MAP to rise above baseline values.
LACP Treatment Methodology & Application of Neural Network
The multi-layer perceptron designed for our application had linear function with regard to the input and output layer. The hidden layer neurons had a non-linear function, which was chosen as tanh in this case.
The prediction accuracy depends on the acted structure of the neural network and was examined for total 120 different types of them. The network’s performance also depends on the initial weight values. As a result, for each data set, the network was trained with 20 different initial weight values.
Simulation
The Neural Network Prediction Method
In each data set, Ht values of first 15 minutes at the beginning of LCAP procedure were not used. This was based on the fact that infusion of normal saline solution lowers the accuracy of measured Ht values during the initial period.
In this study, biological time series signals after 15 minutes of starting LCAP are represented as:
&nb sp; (1)
Where is sampled data at sampling time and is the last Ht value. The Ht data were divided into the consecutive pattern groups for improving the accuracy of prediction. A pattern group is defined as:
&nb sp; (2)
Where is index of group. is sampling time, and is training period. In this study, 60 Ht values were used for the training period. Pattern groups were formed by using Ht values in the following manner:
&nb sp; (3)
In this study, the neural network model is moving average type. The input/output relation of the neural network in the pattern group is as follows:
Input &nb sp; | output
&nb sp; (4)
Where shows the number of input units and shows the prediction time. For example, the sampling interval is 20 seconds, hence means that neural network model will predict Ht value after each 1 minute.
When the number of units in input layer is, the neural network is trained to output based on input. Therefore in pattern group the neural network is trained using vectors. The connection weights were used as initial weights. were small random values. The back propagation algorithm was used as the supervised leaning algorithm for the Multi Layer Perceptron (MLP). When the following condition (Eg.5) was fulfilled, the training was ended.
and &nb sp; (5)
Where is for ideal condition. However it can be set to a value lower than one for tolerance.
Where was indicates the rms error during the training period which is given by
&nb sp; (6)
and is the number of sample in the training set.
Here is the weight matrix for pattern group at iteration during the training. In order to develop a moving average type NN model first the network was trained with all the pattern vectors in pattern group . When the network converges, the weights are obtained. Then the training is carried out using pattern vectors in Pattern Group and is used as initial weights. The sequence is repeated until the training is completed using the group.
The prediction is an output of the neural network model corresponding of input vector.
Error between the measured Ht values and the predicted values was defined as:
&nb sp; (7)
Where is number of pattern of groups.
Results
The prediction accuracy was examined using 120 kinds of NN structures. At first the number of units in the input and hidden layer were varied from 1 to 15. However, the number of units in the input layer always doesn’t to be more than the number of units in the hidden layer in order to avoid over training. Hence, the neural network with the smallest rms error in 120 kinds of structures was chosen as the best NN for the prediction. The NN’s output usually depends on the initial values of connection weights. Therefore initial weights were changed 20 times for each data. The average and standard deviation of rms error in 1, 3, and 5 minutes later were shown in Fig.3, Fig.4, and Fig.5. NN# shows the various NN structures. From Fig.3, Fig.4 and Fig.5, the most suitable NN structure has 1 unit in the input layer and 1 unit in the hidden layer 1 minute later. The most suitable NN structure has 1 unit in the input layer and 1 unit in the hidden layer 3 minutes later. The most suitable NN structure has 2 units in the input layer and 1 unit in the hidden layer 5 minutes later. The results of the prediction using these NN structures at the patient A shown in Fig.6, Fig.7 and Fig.8. Table3 shows the average and deviation of rms error between the measured Ht value and the predicted Ht value about each patients. From Table3, all results were small rms error between the measured Ht data and the predicted Ht data using NN.
Discussion and Conclusion
In this study, it was examined that the change of Ht value 1,3, and 5 minutes later can be predicted using NN. Ht values were predicted using 120 kinds of NN structures, but the best NN structure for prediction was the simple NN structure. The results were achieved since the change of Ht value in LCAP was small. All predictions of Ht values using NN were small rms error in Table3. It is dangerous if there is an error of 1% or more in Ht value in blood purification treatment. Therefore, it seems to be acceptable prediction of Ht values in all cases. This results shows the neural network predicts the Ht successfully.
Dr Tejinder Mohan Aggarwal
MBBS GAMS
DIRECTOR
PHOENIX HOSPITAL
SCO 8 SECTOR 16 PANCHKULA 134109 HARYANA INDIA
FORMER: *Research Associate,
CS & E, Florida Atlantic University (FAU), Boca Raton Fl 33431 USA
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How the brain of patient with Aphasia responds to speech sound?
Posted by on Friday, 7th May 2010
How the brain of patient with Aphasia responds to speech sound?
Aphasia or (Aphemia)
• Aphasia (or aphemia) is a loss or impairment of the ability to produce and/or comprehend language, due to brain damage. It is not a result of deafness or muscle paralysis, and it does not necessarily affect intelligence.
• Usually, aphasias are a result of damage to the language centers of the brain (like Broca's area). These areas are almost always located in the left hemisphere, and in most people this is where the ability to produce and comprehend language is found. However in a very small number of people language ability is found in the right hemisphere.
• Damage to these language areas can be caused by a stroke, traumatic brain injury, or other head injury. Aphasia may also develop slowly, as in the case of a brain tumor or progressive neurological disease. All these types of aphasia are classified as secondary.
• Primary aphasia is a relatively rare condition with no known cause and often no other symptoms.
• Aphasia may co-occur with speech disorders such as dysarthria or apraxia of speech, which also result from brain damage.
•
Classification of Aphasia
The locationist model
• Broca's aphasia have damage to the frontal lobe of the brain. These individuals frequently speak in short, meaningful phrases that are produced with great effort. Broca's aphasia is thus characterized as a nonfluent aphasia. Affected people often omit small words such as "is", "and", and "the". For example, a person with Broca's aphasia may say, "Walk dog" meaning, "I will take the dog for a walk". The same sentence could also mean "You take the dog for a walk", or "The dog walked out of the yard", depending on the circumstances. Individuals with Broca's aphasia are able to understand the speech of others to varying degrees. Because of this, they are often aware of their difficulties and can become easily frustrated by their speaking problems. Individuals with Broca's aphasia often have right-sided weakness or paralysis of the arm and leg because the frontal lobe is also important for body movement.
• Damage to the temporal lobe may result in a fluent aphasia that is called Wernicke's aphasia. Individuals with Wernicke's aphasia may speak in long sentences that have no meaning, add unnecessary words, and even create new "words".
• Ludwig Lichtheim proposed five other types of Aphasia
• Pure Word Deafness (all understanding impaired, but expressive channels intact).
• Conduction Aphasia (speech, writing and silent reading intact, but repetition, reading aloud and dictation impaired).
• Apraxia of Speech Which is now considered a separate disorder in itself.
• Transcortical Motor Aphasia (Understanding of speech, writing, repetition and reading intact, but impaired voluntary speech and writing).
• Transcortical Sensory Aphasia,(Impaired comprehension of
• ANOMIA is another type of aphasia proposed under what is commonly known as the Boston-Neoclassical model, which is essentially a difficulty with naming. The sufferer may have difficulties naming certain words, linked by their grammatical type (e.g. difficulty naming verbs and not nouns) or by their semantic category (e.g. difficulty naming words relating to photography but nothing else) or a more general naming difficulty. Sufferers are usually aware and it is comparable to a 'tip of the tongue' sensation experienced by most people.
• A final type of aphasia, global aphasia, results from damage to extensive portions of the language areas of the brain. Individuals with global aphasia have severe communication difficulties and will be extremely limited in their ability to speak or comprehend language
•
The cognitive neuropsychological model:-
The cognitive neuropsychological model builds on cognitive neuropsychology. It assumes that language processing can be broken down into a number of modules, each of which has a specific function. Hence there is a module which recognises phonemes as they are spoken and a module which stores formulated phonemes before they are spoken. Use of this model clinically involves conducting a battery of assessment
Phoneme
In human language, a phoneme is the theoretical representation of a sound. It is a sound of a language as represented (or imagined) without reference to its position in a word or phrase. A phoneme, therefore, is the conception of a sound in the most neutral form possible and distinguishes between different words or morphemes — changing an element of a word from one phoneme to another produces either a different word or obvious nonsense
Any of the following can be considered Aphasia
• inability to comprehend speech
• inability to read (alexia)
• inability to write (agraphia)
• inability to speak, without muscle paralysis
• inability to form words
• inability to name objects (anomia)
• poor enunciation
• excessive creation and use of personal neologisms (jargon aphasia)
• inability to repeat a phrase
• persistent repetition of phrases
• other language impairment
•
Types of Aphasia
• The common types of aphasia are
• Broca's aphasia (expressive aphasia)
• Wernicke's aphasia (receptive aphasia)
• Nominal aphasia (anomic aphasia)
• Global aphasia
• Conduction aphasia
• It is worth noting that a combination of the above is possible.
• A few less common varieties include
• Transcortical motor aphasia
• Subcortical motor aphasia
• Transcortical sensory aphasia
• Subcortical sensory aphasia
• Mixed transcortical aphasia
• Acquired eleptiform aphasia (Landau Kleffner Syndrome
What are sound waves?
• ~ Sound waves are waves of air pressure.
• ~ If one plots air pressure, then the peaks correspond to points of maximum compression, and the
• troughs, points of maximum rarefaction.
• ~ Amplitude is the difference between minimum and maximum pressure and is perceived as
• loudness.
• ~ Frequency is the number of peaks that go by a fixed point in one second.
• ~ The normal range of frequencies audible to humans is 20 to 20,000 Hz (the number of waves
• per second). We are most sensitive to frequencies between 2000 to 4000 Hz, the frequency range
• of the spoken words.
• The Physiology of the Senses
• Transformations For Perception and Action
• Tutis Vilis http://www.physpharm.fmd.uwo.ca/undergrad/sensesweb/
How does sound energy reach inner ear?
What is the function of round window?
Auditory afferents activation
How is the frequency of sound coded?
• Helmholtz noted that the basilar membrane is narrow and stiff (like a high string on a piano) near the oval window, and wide and floppy (like a low string) at the other end.
• Because of this, each portion of the basilar membrane vibrates maximally for a particular frequency of sound.
• High frequency sounds maximally displace the hair cells near the oval window while low frequency sounds maximally displace hair cells at the other end.
• Thus sound frequency is topographically represented on the basilar membrane (place coding).
• (i.e. frequency is coded by which neuron is activated, not necessarily by its firing rate. This is like labeled lines in the sense of touch)
How is loudness coded?
The cue to sound direction
Role of Superior Olive in sound localization
The columnar structure in primary auditory cortex
What happens beyond auditory cortex?
The probable sequence of activity that occurs when a person repeats a written word
What happens to each information in each area?
Neuroplasticity-Treatment in Aphasia
An overview of neuroplasticity in Aphasia
• After a stroke or traumatic brain injury, a zone of residual speech function exists between damaged and undamaged regions within language processing areas in brain.
• Within this zone, there are areas that can be improved using precise patterns of stimulation.
• The stimulation of undamaged neurons in this area can increase their functionality—an adaptation that contributes or responds to speech sound in a patient with Aphasia.
• Neuroplasticity is referred to as the ability of the brain neurons to compensate for sustained injuries thereby adjusting their activity according to stimulation from the environment. This proven ability of the brain to regenerate itself is the basis for the treatment of Aphasics.
• Healthy neurons may also be stimulated to adjust their activity to process hearing information, an adaptation that contributes to the hypothesis of treatment of Aphasia.
How it works
• The acoustic characteristics of speech supply a listener with cues enabling identification of both the phonetic content of the message as well as information pertaining to who is speaking and the intention of the message.
• Linguistic information is necessary to distinguish the meaning of the message (consonants and vowels).
• Paralinguistic information conveys the intention, or how the message is expressed (e.g., statement versus question, angry versus happy emotional state).
• Paralinguistic acoustic elements add a multidimensional aspect to speech that is separate from the phonetic information of the verbal message.
• Acoustic characteristics: The source-filter model of speech production states that speech comprises:
• (i) vibration of the vocal folds reacting to airflow from the lungs (source)
• (ii) the shape of the vocal tract, tongue, lips, and jaw (filter) (Fant, 1960).
• Generally but not exclusively, paralinguistic information is conveyed by the source.
• linguistic information is conveyed by particular filter shapes.
• Repeated constant external stimulation thru’ adjusted sound waves and words in high or low pitch in the form of time phased locked activity and concomitantly noting down the evoked potential status of language area of brain as a reference in treatment could provide a hope to the aphasic patients.
Dr Tejinder Mohan Aggarwal
MBBS GAMS
DIRECTOR
PHOENIX HOSPITAL
SCO 8 SECTOR 16 PANCHKULA 134109 HARYANA INDIA
FORMER: *Research Associate,
CS & E, Florida Atlantic University (FAU), Boca Raton Fl 33431 USA
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HEALTHY MARRIAGE KUNDLI 'MEDICAL HOROSCOPE'
Posted by on Tuesday, 27th April 2010
Dear Doctor
PHOENIX HOSPITAL & DIAGNOSTIC CENTRE, SCO 8, SECTOR 16, PANCHKULA HARYANA 134109 INDIA is launching a new concept and we are pretty sure you would agree, appreciate & participate that no doubt the marriage is settled in heaven & celebrated on earth, nevertheless, the health of would be couple is most important than comparing the horoscope of girl & boy. The horoscope is not going to give the details of health part of each person.
In present time & changing scenario knowing actual health details would be most satisfying & tension free at the time of wedding both for the girl’s as well as boy’s family.
Won’t you agree that just by comparing the compatibility of horoscope of both the boy and girl & thereby knowing their Gunas / Doshas does not in anyway solve the purpose except finding little solace and leaving rest in the hands of God? Such kind of compatibility does not give any kind of details of the diseases like HIV, VDRL, TB and Cardiac Problems etc. etc.
It is in the mutual interest of boy or girl and their families to know prior to nuptial knot the health picture which sometime later leads to unsavory situation and many a times divorce. By this even if need be congenital abnormality / chromosomal defects type diseases could also be known.
Therefore, PHOENIX HOSPITAL is launching
*HEALTHY
MARRIAGE KUNDLI
(MEDICAL HOROSCOPE)
OF WOULD BE COUPLE
‘Please visit for free consultation & counseling’
*Conditions apply
*Charges as applicable
Regards
Dr T M Aggrwal
Ph: 0172-5054321, 5011333
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Combinatorial Multi-Component Therapies of Drugs using Pruned GMDH Algorithm
Posted by on Monday, 1st March 2010
Combinatorial Multi-Component Therapies of Drugs using Pruned GMDH Algorithm
Tejinder M Aggrwal1, Abhijit Pandya1 and Larry Liebovitch2
1Florida Atlantic University, Department of Computer Science and Engineering, Boca Raton, FL 33431
2Florida Atlantic University, Center for Complex Systems and Brain Sciences, Center for Molecular Biology and Biotechnology, Department of Psychology, Department of Biomedical Science, Boca Raton, FL 33431
ABSTRACT
Multi-component therapies, originating through deliberate mixing of drugs in a clinical setting, through happenstance, and through rational design, have a successful history in a number of areas of medicine, including cancer, infectious diseases, and CNS disorders. Use of single drug for complex biological processes, where in fact redundancy and multi-functionality are the norm, fundamentally limits the therapeutic index that can be achieved by a most potent and highly selective drug. Thus, it will almost certainly be necessary to use even new “targeted” pharmaceuticals in combinations. Drugs designed for a specific target are always found to have multiple effects. Rather to hope that one bullet can be designed to hit only one target, nonlinear interactions across genomic and proteomic networks could be used to design Combinatorial Multi-Component Therapies (CMCT) that are more targeted with fewer side effects.
This paper reviews the opportunities and challenges inherent in the application of non-linear interactions of neural networking using pruned GMDH Algorithm with specific reference to the possibility of achieving combinatorial selectivity with multi-component drugs. Using a nonlinear model of how the output effect depends on multiple input drugs, an artificial neural network can accurately predict the effect of all 215 = 32,768 combinations of drug inputs using only the limited data of the output effect of the drugs presented one-at-a-time and pairs-at-a-time. Systematic combination screening may ultimately be useful for exploring the connectivity of biological pathways. When performed this approach may result in the discovery of new combination drug regimens having least side effects targeting multiple actions.
Combination or multi-component therapy, in which one or more drugs are used at the same time, was first explored at a theoretical level (Loewe, 1928) and typically has several goals, such as: reducing the frequency at which acquired resistance arises by combining drugs with minimal cross-resistance; lowering the doses of drugs with non-overlapping toxicity and similar therapeutic effects so as to achieve efficacy with fewer side effects; using one or more chemotherapeutic drugs to sensitize cells to the action of additional drugs; exploiting additivity, or better-yet, synergism, in the biochemical activities of two drugs so as to achieve significantly greater potency than is possible with either drug on its own.
. Neural networks are generally considered "black boxes" of memory (Pandya and Macy, 1995). In other words a researcher may know the precise values of inputs, the precise values of outputs and the precise values of the connections weights without any knowledge of precise mathematical expressions for the relationships, because, such modeling is quite difficult with complex networks. Most of the programs available for neural networks do not design the network by assigning weights but they train the networks to give desired output for given input, and then record the weights.
The algorithm developed in this paper provides a solution to the above problem. Each neuron in the hidden layer can be represented using a quadratic polynomial equation involving any two neurons from the previous layer. This gives insight into the network and clearly defines the relationship between the neurons in a layer and the neurons in a previous layer making it easier to understand even for complex networks. Such visualization shows the dynamics of learning allow for comparison of different networks and show differences due to regularization and optimization procedures.
GMDH (Ivakhnenko, 1971) algorithm forms a basis for the algorithm proposed in this work. However several modifications are made to the basic GMDH algorithm to meet all the goals of the proposed algorithm and provide a pruned network. The new algorithm follows a similar method as that used in regression analysis in order to calculate the weights for the neuron functions. Though not originally designed for the purpose of calculating weights in a neural network, it can be easily adapted for this modern purpose. The proposed algorithm combines the best procedures from the variations on the GMDH method (Kondo & Pandya, 2000) in order to quickly produce the smallest, most accurate network possible.
This algorithm is then applied to analyze Drug Test Data (Liebovitch et al, 2006). The development of a new drug is a complex and expensive process. Current estimates place the total development costs of a new drug (including the writing off of false starts, clinical trials and tests required by regulatory authorities) somewhere in the region of 800 million dollars. As using combination of drugs to determine which combination can provide a better therapeutic effect is an expensive procedure, the algorithm developed in this work is applied to train the network using a small training set to determine which pathways in these networks interact and can maximize therapeutic effects.
The pruned GMDH is used to train a network on inputs of drugs presented one-at-time and predict the output when the input set includes pairs-at-a-time, three at a time etc. This algorithm was successful in developing the network for an input set of drugs which was limited to one-at-a-time. The algorithm was then used to train the network when the input set was changed to one-at-a-time and pairs-at-a-time, where it was able to predict the output for test set (includes drugs provided three-at-a-time, four-at-a-time etc) with an accuracy rate of 91%. The test results suggest that this approach may be of great value in the analysis of combination of drugs to produce maximized therapeutic effects.
REFERENCES:
1. Ivakhnenko, A.G. “Polynomial Theory of Complex Systems.” IEEE Transactions on Systems, Man, and Cybernetics, vol. 1, pp. 364-378, 1971
2. Kondo T, Pandya A.S, “GMDH type neural networks with radial basis functions and their application to medical image recognition of stomach”. Proc. of the 39th SICE Annual Conference, International Session Paper, 313A-4, pp. 1-6, 2000
3. Liebovitch L, Nicholas and Pandya A. S. “Developing Combinatorial Multi-Component Therapies (CMCT) of Drugs that are More Specific and Have Fewer Side Effects than Traditional One Drug Therapies”, pre-print, 2006
4. Loewe, “Quantitation Probleme der Pharmakologie”. Ergeb Physiol Biol Chem Exp Pharmakol 27, pp. 47-187, 1928
5. Pandya, A. S. and Macy, R. B., “Pattern Recognition using Neural Networks in C++”, IEEE Press and CRC Press, 1995
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