Retrieving, Processing, and Visualizing Enormous Email Data

In this project, I will retrieve a large email data set from the web, and then I will place it into a database. I will then analyze and clean up the email data so that it can be visualized. I will run the Python script to retrieve the email data, then will place the data into a database called content.sqlite:

Next, I clean up the email data in content.sqlite by running, which normalizes the email data into structured tables and primary/foreign keys, and sends the data into the database index.sqlite:

I then run the program to calculate basic histogram data on the email messages that were retrieved. I compute the top 5 email list participants and the top 5 email list participants:

Next, I produce a word cloud visualization for the messages that were retrieved, showing the most used words, using, and visualizing via gword.htm:

And finally, I produce a time line visualization of the email messages retrieved, by running the program and visualizing the results on gline.htm:

This retrieval, processing, and visualization project was part of a capstone for a Python certification course. All source codes and data are open source, with credits below.

Photo credit: kOoLiNuS☸ Sparrow App review – 005 via photopin (license).

Source code credit: obtained from under the Copyright Creative Commons Attribution.

Google Geocoding API with a Database and Visualization on Google Maps

Today I will use the Google Geocoding API with a database, and visualize the mapping data on Google Maps. In this project, I have a list of university names with no location information, and I will proceed to add location coordinates to the universities using the Google Geocoding API, load those university names with location data into a database, then visualize that data on Google Maps. Here are the university names in (I added Saint Mary’s University in Halifax in the second row- Go Huskies!):


I then run the Python script to lookup all of the university entries in, call on the Google Geocoding API to add location coordinates, and place all of this data into a database called geodata.sqlite:

I then run to read the database and produce where.js:

Finally, I open where.html to visualize the location markers in Google Maps:

A Map of Information

About this Map

This is a cool map from

This project was performed as part of a Python certification course. All materials in this project are open source, with credits below.

Photo credit: bertboerland Mapping geocoded tweets and flicks pics from me via photopin (license).

Source code and script credit: source codes and scripts obtained from under the Copyright Creative Commons Attribution.

Webcrawl and Pagerank of a Website

Today, I will demonstrate a webcrawl and pagerank of a website. For the parser, I’m using a python code,, which incorporates BeautifulSoup, a Python library for pulling data out of HTML and XML files. I’ll limit the amount of pages to crawl to 100, and will crawl the website, a leading website for anxiety, and a rather large website with 1000’s of web posts. Here is a snippet of the webcrawl:

After running the script, I also ran a pagerank script,, to rank the links that were crawled, based on the links going to that link, and ranking the pages based on the number and quality of the links. I went through 100 iterations. Here is a snippet of running the page ranking script:

Next, I use to visualize the pageranks of the links crawled:

Finally, I further visualize the top 25 links using force.html:

Force-Directed Layout

You can play around with this visual by dragging the nodes (balls) around on the screen, to see their connections to other nodes (links) in different configurations. You can also click on each of the nodes to go to the specific link.

This project was performed as part of a capstone project for a Python certification course. All materials in this project are open source, with credits below.

Photo credit: kolacc20 Very simplified PageRank distribution graph via photopin (license).

Source codes and scripts credit:,,, force.js, force.css, and force.html obtained from under the Copyright Creative Commons Attribution.

A Tour of Data Science Educational Programs

In my quest to become a data scientist, I have embarked on a series of educational journeys, which have included both formal, in-school, educational forums and self-learning, self-paced MOOCS (massive open online courses). Let me start at the beginning of my path to getting an education and training in data science.

Business Intelligence Analytics Advanced Diploma Program

I previously did work as a process engineer for an oil refinery, then switched careers and became a physician, a psychiatrist. After many years seeing patients, teaching medical students, and performing clinical research, I decided it was time to switch to yet another career.

As I was helping my son to pick out courses for his upcoming enrollment at a local community college, I stumbled upon a Business Intelligence Analytics Program at that same community college. As I read through the program description and courses, it looked very interesting to me at the time, and decided to enroll in that program, to learn about business intelligence analytics.

As I went through the program for the first term, it occurred to me that I was more interested in machine learning and predictive analytics, rather than just looking at historical data and presenting the descriptive statistics and what happened last quarter. Although this program gave me a good overview of databases, I found the program lacking in many ways, as the school did not put in the resources to link the students with coop jobs with the local industry in data analytics. So when I was unable to find a coop job in the summer after the first term was completed, I decided that I would need to augment my education, rather than just rely on the program at the school and let the summer go to waste.

MOOCS, Machine Learning, R, and Python

Over the summer break, since I could not find a coop job in data analytics, I decided to look into online learning, also called MOOCS (massive open online courses). I first started with a machine learning MOOC, which was quite informative and hands-on, and became skilled at using R when utilizing various models to apply regression, classification, and clustering to different datasets. From my first success in MOOCS with machine learning and R, I decided to take more courses in computer science and programming.

I particularly liked the MOOC where I was introduced to Python programming. Once I got introduced to Python, I was hooked, as I saw the versatility of Python for web scraping, data parsing, database, analysis, and visualization. Within just 4 weeks of daily Python immersion on MOOCS and solving problems on, I became good enough to call myself a Python programmer.

It was also at that same time that it dawned on me that I had the requisite skills needed as a computer science student to apply to data science master’s programs. The idea of applying to master’s programs was now a reality, since I had accumulated sufficient skills in database, machine learning, and coding, which was required for many of the master’s programs in data science. Also, when I looked at the various job listings for data scientists, almost all of the companies required either a master’s degree, or 2 to 3 years of experience as a data scientist.

That cemented it for me to look into applying to grad school. Fortunately, I did quite well at the community college with the various database courses I took there, and was able to obtain excellent faculty references, which is one of the main requirements for applying to the master’s programs.

MS Analytics Program

As I mentioned in a previous post, I interviewed at an MS Analytics program, but it did not have computer science and coding as part of its curriculum, and was too focused on statistics. Given their total disregard for coding and computer science, I decided against that program (and that decision was mutual, as they did not offer me a position, citing stiff competition). But that statistics professor was way off base, and obviously did not know the definition of a data scientist, and confused them for statisticians. But data scientists are not only experts at statistics…they are also experts at computer science and coding, in addition to having domain expertise.

This being my first exposure to graduate programs in data science, I began to question their ROI (return on investment), especially when I already have two university degrees, one of which is a technical degree in chemical engineering. I was wondering if certificate and diploma programs in data science may be the better option for me, especially with my industry experience and engineering degree. And of course, I could take more MOOCS, as that is how I learned machine learning and how to code! But fortunately, my next interview with a graduate program really impressed me, and I impressed them (how do I know this…read on!).

MSc in Computing and Data Analytics

Good news! I was accepted into a Master of Science Program in Computing and Data Analytics. This program is a well-balanced mix of computer science, statistics, and business intelligence. I had to pass a programming test to get in, as they only accept data science grad students who can actually code…imagine that! I’m very glad to be in this program, and looking forward to starting grad school this Fall. For me, this program was the missing piece in my data science training. For me, I have to get a master’s degree in data science, given my other degrees and industry experience were not in the IT industry.

EMC Data Science Certifications

Even though I am slated to start grad school in a few weeks, I still decided to take the EMC Data Science Associate (EMCDSA) course, as many of the practicing data scientists have the EMCDSA certification. Once I obtain my EMCDSA this summer, I plan on continuing to the next level, and work on obtaining the EMC Data Science Specialist (EMCDSS) certification. The great thing about these are that they are also in MOOC format. Fortunately, I have a group to study these courses, and we meet in-person weekly to go over the material we learned during the week.


So that is my tour of data science educational programs. For me, getting the master’s degree in data science from a well-balanced program is key to my education and training as a data scientist. I don’t believe everyone needs to take my same path, but it is an example of how one person is getting training in this data science field which currently has no official standards for training. For me, the master’s degree will serve as my foundation, while the MOOCS and various data science certifications will augment and enhance my training and experience.

As a word of caution, if you are looking into a master’s program in data science, please pick programs that are well balanced in all the core areas of data science, including computer science (algorithms, coding), statistics, and business intelligence. Skip the ones that ignore computer science, and skip the ones that ignore statistics.

Good luck on your journey to becoming a data scientist, and please contact me should you have any questions.

photo credit: velkr0 classroom via photopin (license)

Why Coding is Important for Data Scientists

As a Data Scientist in training, much of my orientation to the field has been about what skills are needed to become one. In my research and exposure to the field of data science, the knowledge, experience, and skillsets that data scientists have are domain expertise, computer science, and statistics. It appears that the most successful data scientists have expertise in all 3 areas, in addition to their deep knowledge of a specific area(s). So in my pursuit of training as a data scientist, I have these 3 areas in mind when looking at filling-in the gaps in my knowledge and experience.

Master’s Program

I recently had an interview for a master’s program in data science, and I posed the question to them about the focus of their data science master’s program. The statistician professor answered the question by detailing their focus on statistics and machine learning, and how to apply appropriate models to specific problems, and also how to optimize and test such models. I was impressed with this answer, as it is important for data scientists to understand the algorithms that they are applying to datasets. However, there seems to be some in the field who treat the algorithms and analysis as a black box, where the only important features are the selection of the model and the output of the data analysis…they don’t care about how the analysis was performed, or why the chosen algorithm works better than the others. Fortunately, this master’s program was all about understanding the models and optimizing them, which are important skills for a data scientist.

To Code, Or Not To Code

However, when I asked about their approach to computer science and coding, the statistics professor’s reply was:

‘Coding is cheap…we just outsource that, so it is not that important.’

What the heck?

How can you say that understanding analysis and algorithms are important, and not treat it like a black box, then come out and say that coding is cheap?

I have a different opinion. Coding is a basic necessity for all data scientists…if you don’t understand your spoken language, then how are you supposed to communicate your solution, being the data scientist that is a liaison between the business analysts and the backend developers? If you can’t code, then you are not able to harness the power of computers, and thus not able to take advantage of that computing power via elegant and sophisticated algorithms. If you can’t code, then you can’t be innovative, and you can’t create new models for use in the CPUs and the data-lakes that are increasing in power and storage capacity at an exponential rate.

Manual Versus Automated Analysis

If you can’t code, then you will be forced to analyze your data manually, and you spend enormous chunks of time just extracting, cleansing, transforming, and migrating your data (also known as ETL), as you can’t code to automate those processes. If you can’t code, then you waste too much time on prepping your data. If you can’t code, you don’t have time left to perform a business requirements analysis, and no time is left to choose an appropriate model for analysis, and no time left to adequately train the model and optimize and fine-tune it, with different features and dimensions.

How do I know all this? Well, I have tried to do data prepping and analysis via manual methods, due to my previous lack of coding expertise. I previously spent too much time on the data prepping, manipulating, parsing, and migrating data, as I was doing things manually, and it took away time from my other roles as a data scientist, which includes the model selection and fine-tuning.

Data Science and Coding

Now I know better…I have since become proficient at Python, and consider myself a Python programmer. Life is now much easier, when you can code and use computing power to do all the things I used to do manually, like scraping websites, extracting and migrating data, ETL, and analysis. I can now spend most of my time with solution documents, requirements analysis, model selection, model fine-tuning and optimization, implementation planning, project management, and communication of the solution.

Basically, if you can’t code, then you will not be an effective Data Scientist. I’m not saying you have to be an expert coder, as you should leave the complex coding to the software engineers who can code in their sleep. What I’m saying is that you do have to speak the language of your profession, as you can’t be effective as a problem solver, designer, analyst, and communicator if you can’t code.


In summary, real Data Scientists are coders. Some choose not to code, but they still know how to code. And those who choose not to code are most likely thought leaders in the field, where their high level expertise is more valuable than their coding skills. Data Scientists are coders. Don’t hire one without coding skills. And if you can’t code, then you are not a Data Scientist.


photo credit: markus spiske html php java source code via photopin (license)

Analysis of Lottery Draws Between 2009 and 2017

This project entails the analysis of a dataset of historical lottery draws between 2009 and 2017 inclusive, scraped from the website of a lottery by my colleague, Gregory Horne. We had a question whether the winning numbers could be predicted, based on past draws, but needed to know if the winning numbers clustered, or were randomly drawn.

In this lottery, ping-pong balls are labeled with one number, ranging from 1 to 49. One of each number is placed in a barrel. The barrel is spun to mix up all the balls, then one ball is drawn. This is repeated 5 more times for a winning number set of 6 winning numbers. In addition, there is a bonus draw, which gives 7 winning numbers.

We will first analyze the winning numbers from 2009 to 2015, then add the winning numbers from 2016 to 2017, to see how the analysis is changed with new data. Thus, we will analyze two lottery datasets, one from 2009 to 2015, and the other from 2016 to 2017.

We propose to perform cluster analysis on this lottery dataset. We hypothesize that the cluster analysis should be random, and therefore the datapoints should plot in a uniform manner in the feature space. This hypothesis is based on the premise that this specific lottery draw is indeed random in nature. However, if our analysis leads to clustering that is significant, then this can lead to further analysis and speculation on the method of determining winners for this specific lottery.

Please click on the following link for the detailed analysis: Lottery analysis.

photo credit: chrisjtse 41:366:2016 via photopin (license)

Preface to “Anxiety Protocol”

Anxiety-Protocol-2There’s a quiet revolution occurring in psychiatry. The mental illness burden is felt worldwide, yet psychiatry has not been able to help millions of people who suffer from mental illness, as access to psychiatry and mental health services is at a crisis (Fields and Corbett-Dooren, 2014). Psychiatry is fast becoming trivialized due to its inability to deliver treatment to the population. But gone are the days when doctors had the monopoly on medical knowledge. With the advent of the internet, people can now research their symptoms, possible illnesses, and treatment options before even seeing the doctor. When people are suffering from mental illness and can’t access psychiatry, they still need help. As a result, people naturally look to the internet and research their ailments online. For people with anxiety, this book, Anxiety Protocol, and its affiliated website,, represent a new option for those who suffer from anxiety but are not able to receive help. This is an online solution that is based on self-help and natural remedies for anxiety. It is for the newer generations who are not pill-poppers and seek a natural, self-reliant way of getting rid of their anxiety. It is for the new generation who are more health-conscious and looking for healthy living options to treat and prevent anxiety. What we offer here is the latest evidence-based program to help eradicate your anxiety. These are evidence-based services and products that have research showing they are effective at eradicating anxiety…all from the comfort of your laptop, tablet, or smartphone. Imagine: a treatment for anxiety that does not involve going to a doctor’s office or hospital. This is the quiet revolution in psychiatry, where a treatment for anxiety can be effective and delivered without a doctor or therapist, without prescription medications (that have multiple and sometimes severe side effects). Certainly, I’m not advocating you ditch your psychiatrist if you already have one. If you have severe anxiety disorder, then you do need to see a psychiatrist. However, for milder forms of anxiety, this online, natural, and self-reliant intervention will signal the beginning of the quiet revolution in psychiatry, where treatment is delivered virtually, online.

But psychiatry still has much to offer people who have mental illness. These are important and exciting times for the profession, as it tries to figure out the neurobiological underpinnings of mental illness. Currently, clinical psychiatry does not have objective, biological tests to help confirm mental illness. Rather, mental illness is diagnosed based on history and clinical presentation. However, psychiatry is fast becoming a specialty of medicine based on the brain. The mind, and the various problems and illnesses that are from disorders of the mind, can basically be explained at a molecular level, with neurons communicating with each other via synapses, and these synapses connect to one another via neurotransmitters. These neurotransmitters are the chemicals which carry out the message between neurons, and the receptors of these neurotransmitters are the targets of the psychiatric medications prescribed for mental illness…this is the so-called “chemical imbalance” of mental illness. But mental illness is much more complex than a chemical imbalance. In the brain on a macro level, mental processes have specific circuitry, which connect different parts of the brain, and this circuitry is comprised of the neurons which conduct the message between brain areas. Functional neuroimaging is already revealing preliminary evidence that mental illness is associated with disruptions of these brain circuits, and that treatment can normalize these circuits. It is hypothesized that psychotherapy and other alternative treatments can also normalize these disruptions in brain circuitry. In addition to neuroimaging research, genetics research is on the verge of finding the constellation of genes responsible for transmission of mental illness in families. In the next few years, psychiatry should have objective, biological tests to help diagnose mental illness, and cures may be possible. These are exciting times in psychiatry, given it is at the brink of finding the cause (and cure) of mental illness.

On the other hand, it is also the worst of times for psychiatry, given so many people with mental illness suffer without treatment. This book, Anxiety Protocol, and its affiliated website,, respond to those individuals who often go unheard. It’s been developed to deliver treatment online and virtually. It is our sincere hope that we can reach the millions who suffer from anxiety, and provide them with a natural, online, and self-reliant solution to their ailment.


Carlo Carandang, MD

Author, Anxiety Protocol

September 2014

Book Review: Anxiety Protocol

Anxiety Protocol, The Foundation: Understanding Anxiety, Author: Carlo Carandang, MD

Book Reviewer: Sheik Hosenbocus, MD

anxiety-protocol-ecover-rev2 A large and ever growing number of people suffer daily from the debilitating effects of anxiety affecting their quality of life. A majority suffers in silence. Without the appropriate tools and in their attempts to cope, they often resort to maladaptive strategies. Many find solace in the use of various substances including alcohol, cigarettes, marijuana, to mention a few. This goes on for years and years as anxiety is a life-long condition. The lack of user friendly, down to earth tools out there has been a major perpetrator of this problem. The Anxiety Protocol will undoubtedly now addresses this gap. A major strength of this book is its encouragement on the use of modern technology to enable people young and old to research and manage their ailment on line where they will learn self-help skills and alternative strategies. This is an easy to read book that offers a comprehensive, up to date and holistic exploration of the different anxiety disorders backed by the latest research and is extremely well referenced. The affiliated website enables those who do not have timely access to a family doctor or specialist to find out about the latest evidence-based solutions that can help them deal more effectively with their anxiety.

The book contains six sections divided into 22 chapters. Each chapter is a comprehensive review in its own right. Section 1, Chapter 1 presents a general overview and classification of anxiety disorders citing a comprehensive list of symptoms to characterize each disorder. Each subsequent chapter (2-9) provides a thorough exploration of a separate anxiety disorder, illustrated by specific detailed case examples containing the presenting symptoms, full case history, diagnosis and clinical course.  For each disorder there is a comprehensive list of management strategies including self-help, psychotherapy, pharmacotherapy and goes beyond the traditional treatment strategies for each disorder by expanding on a variety of natural remedies and providing research based information on a variety of herbal and nutritional products. This in itself is a unique and welcomed feature of this book making it very appealing to many. Section 1, chapter 10 provides a brief up to date account of the neurobiology of anxiety disorders to demystify misconceptions and provide a better understanding of the “chemical imbalance” in the brain usually referred to by many but only few can comprehend.

Section 2 expands on the treatment of anxiety with chapter 11 focusing on the psychotherapeutic approach to anxiety disorders. The emphasis here is placed on cognitive behavioral therapy (CBT) as the first line treatment for all anxiety disorders, citing a higher level of evidence of effectiveness than any other type of psychotherapy. Chapter 12 explores the various classes of medications used to treat specific anxiety disorders and provides the reader with a helpful table listing all the medications, the type of anxiety they treat and the mechanisms of action. In this chapter the author refers to prescription medication as a “last resort treatment” reserved for the most severe cases or those who have failed psychotherapy. This may sound controversial to those who consider medication in association with or as an adjunct to psychotherapy. According to the author this statement has been explicitly written to educate, raise awareness and encourage physicians to avoid the indiscriminate use of psychotropic medications for anxiety before even trying other strategies especially CBT whose beneficial effects have also proven to be longer lasting than any medication as well as self–help and/or alternative interventions. The book is meant not only to educate about anxiety but also hopes at the same time to change the way physicians think about treatment or resort too quickly to medication.

Section 3, Chapter 13 and 15 deal exclusively with alternative treatments for anxiety with a comprehensive list of practical self-help interventions. Chapter 14 covers an extensive list of various natural supplements that have proven useful in anxiety disorders. Each is described in detail backed by recent studies and placebo controlled RCTs. including positive effects, dosages and adverse effects. Section 4 provides an extensive coverage of anxiety disorders resulting from trauma and stress including adjustment disorders complete with case examples and treatment strategies. Section 5 is important as it deals specifically with anxiety disorders in special population including children (chapters 18 &19) and the elderly (chapter 20) focusing on the special criteria, clinical course and treatment of anxiety disorders in these population with well written case examples.

In conclusion Anxiety Protocol is a creative, innovative and comprehensive book on anxiety. The author presents his readers with a vast array of self-help skills and alternative methods of managing their anxiety including online support via its websites, and This is quite unique and in keeping with the type of treatment that people usually aspire to but have not been able to access so far. Anxiety Protocol has broken the ice and now makes it easily and readily accessible. A weakness of the book is with regards to children namely, a lack of specificity of the disorders and specifically the ages that some of the techniques could be most helpful and that could also serve as a guide to parents and clinicians. The book’s greatest achievement is the provision of comprehensive alternate strategies including natural remedies and self-help strategies as well as the provision of an affiliated website to provide specific management strategies and tools putting these at easy reach of most people. The information contained in the book is well researched and referenced and aimed at those who for different reasons may not have access to a well-qualified therapist or physician or those sitting on a wait list. This book now provides hope for them and as such should be made widely available and easily accessible to the population at large as well all mental health practitioners and physicians. There is a lot of knowledge to be gained by reading the Anxiety Protocol. Overall this book is highly recommended as a useful resource in many different ways.

Sheik Hosenbocus, MD, FRCPC

Clinical Assistant Professor

Department of Psychiatry

University of British Columbia

January 2015


Note froAnxiety-Protocol-2m the author, Dr. Carlo Carandang: Dr. Sheik Hosenbocus kindly reviewed the 1st book of Anxiety Protocol, The Foundation: Understanding Anxiety, but did not review the 2nd book of Anxiety Protocol, Moving Forward: Treatment Plan, as the 2nd book was not completed at the time of his review. The 2nd book, Anxiety Protocol, Moving Forward: Treatment Plan, is a concise yet comprehensive, 8-chapter self-help course designed to help you eradicate your anxiety quickly, while the 1st book of Anxiety Protocol, The Foundation: Understanding Anxiety, is a comprehensive reference for increasing your knowledge of everything related to anxiety.

My Review Of KalmPro

Anxiety affects millions of people, but not everyone with anxiety needs prescription medications. Prescription medications for anxiety include antidepressants and benzodiazepines. However, antidepressants and benzodiazepines have multiple, significant side effects, and are expensive. Indeed, a recent study has questioned the efficacy of antidepressants for anxiety, as their effect may have been overestimated due to publication bias, where journals tend to only publish positive studies, while not publishing the negative ones (Roest et al., 2015).

Fortunately, there are alternatives to prescription medications for anxiety. Herbal and dietary supplements (natural supplements) are gaining popularity for mental health problems like anxiety, as they are associated with less side effects and are less expensive. A novel natural supplement for anxiety has recently been formulated, called KalmPro, at What is different about this natural supplement for anxiety is that the ingredients were formulated from studies showing effectiveness and safety for the treatment of anxiety.

KalmPro has multiple ingredients which have been combined into a 750mg pill, with the recommended daily dosage being 1 to 2 pills daily. The all-natural ingredients include a carbohydrate (inositol), an amino-acid (l-theanine), and three herbs (lemon balm, passionflower, and lavender).

Only two natural supplements have several placebo-controlled randomized controlled trials (RCTs) showing effectiveness for anxiety: kava and inositol (Hofmann, 2012). However, kava is associated with liver toxicity and liver failure, so kava is not recommended for treatment of anxiety. However, KalmPro has inositol, which has multiple studies showing effectiveness for panic disorder and obsessive compulsive disorder (OCD) (Palatnik et al., 2001; Fux et. al., 1996). Inositol is a sweet-tasting carbohydrate. It is a natural compound with virtually no side effects.

KalmPro also has l-theanine, an amino acid found in green tea. L-theanine reduces anxiety symptoms in healthy subjects, decreases anxiety in people with psychosis, and improves concentration while decreasing anxiety (Unno et al., 2013; Ritsner et al., 2011; Kobayashi et al., 1998). Green tea has been associated with anxiety reduction for centuries, and l-theanine is the responsible ingredient.

KalmPro has several herbs, one of which is lemon balm. Lemon balm is an herb in the mint family. Lemon balm combined with Valerian led to decreased anxiety in healthy subjects, and was effective for mild to moderate anxiety disorders and sleep problems (Kennedy et al., 2006; Cases et al., 2011). Another herb in KalmPro is passionflower, a flowering plant. Passionflower was as effective as oxazepam for generalized anxiety disorder, and reduced anxiety in surgery patients pre-operatively (Akhondzadeh et al., 2001; Movafegh et. al., 2008). The last herb in KalmPro is lavender, a flowering plant in the mint family. Lavender was more effective than placebo for generalized anxiety disorder, had less side effects than paroxetine, had a side effect profile comparable to the placebo, was as effective as lorazepam for generalized anxiety disorder, and does not have the sedative or addictive potential of benzodiazepines (Kasper et al., 2014; Woelk and Schläfke, 2010). Lavender has a few RCTs which are positive for anxiety, and with more positive studies, it may eventually have the type of evidence backing kava and inositol for anxiety treatment.

In summary, KalmPro has natural herbs and dietary supplements which have research studies showing effectiveness for generalized anxiety disorder, panic disorder, obsessive compulsive disorder, mild to moderate anxiety disorders, stress in healthy people, stress before surgery, and sleep problems. In addition to treating anxiety effectively, KalmPro is safe and well tolerated, according to the studies showing the safety of each natural ingredient. KalmPro can be considered for mild to moderate cases of anxiety. However, severe cases of anxiety need medical attention, preferably from a psychiatrist.

Spoiler Inside: References: SelectShow

What Treatments Help For Treatment Resistant Depression?

Treatment resistant depression (TRD), also known as treatment refractory depression, is defined as failure to respond to 1st and 2nd line treatments. The first question to consider is if the diagnosis of major depressive disorder is correct. It may be a misdiagnosis, and may be the reason for the lack of response to treatment. For example, it could be a medical problem like hypothyroidism, electrolyte abnormalities, and anemia (B12 or iron deficiency), just to name a few. The depressive symptoms could also be caused by a medication or a substance of abuse. Once the medical problems are ruled out, then the psychiatric diagnosis may be incorrect. For example, if the diagnosis is adjustment disorder with depressed mood, then psychotropic medication may not alleviate the depressive symptoms…typically psychotherapy and addressing the stressor is helpful.

If indeed you have the right diagnosis of major depressive disorder, then the psychiatrist needs to consider if adequate dose and duration were tried with the previous treatments, and if not, to optimize the dose and duration. If symptoms continue after the optimization, then the psychiatrist may consider switching to a different class of antidepressant, such as switching from a selective serotonin reuptake inhibitor (ie sertraline- Zoloft) to a norepinephrine serotonin reuptake inhibitor (ie venlafaxine- Effexor). If this does not work, then the psychiatrist may consider augmenting the antidepressant with another medication such as lithium, thyroid hormone, or lamotrigine (Lamictal). Lamotrigine is a promising medication for TRD, and can even be used as monotherapy for TRD. Lamotrigine is advantageous for TRD in that it has a tolerable side effect profile when compared to other psychotropics for TRD like lithium. Lamotrigine just has to be dosed slowly to prevent severe rash from occurring.

Finally, electroconvulsive therapy (ECT), vagus nerve stimulation (VNS), and repetitive transcranial magnetic stimulation (rTMS) are last resort treatments for TRD. The most important aspect of TRD treatment is finding a psychiatrist who specializes in TRD and performs research in TRD. You can find these TRD-specialized psychiatrists at most university hospitals, as the university hospitals have all the infrastructure and expertise necessary to carry out the complex TRD treatments like ECT and VNS. rTMS is becoming more available in psychiatrists’ offices.

photo credit: danniatherton Image Library Danni Atherton Canberra ACT via photopin (license)