I recently ordered a book from Amazon concerning Perplexity. In it, I discovered that the Pro version of Perplexity, which I am running, has something named Perplexity Pages. You can accomplish lots of tasks with this aspect of Perplexity, including constructing blogs, which I recently accomplished. See the embedded PDF below, which summarizes in specific detail, the latest research into Peritoneal Dialysis. I employed Perplexity’s built-in graphic tool to make the graphic. Notice that in the prompt I misspelled blog as log, and this web site’s URL which Perplexity corrected for the research.

I noticed the time I was spending on evening dialysis was creeping up, going from a 10-hour-plus range to over eleven hours. (Actual data: 10-52, 10-42, 10-24, 10-47, 10-58, 11-3, 11-0 indicating hours-minutes) (Mean 10-49 and Std Dev 12.44). This indicated something was not right. So I started a three-day regimen of using Heparin, one dose in my 2000 ml evening static fill (4 ml) and one dose in my 6 liter overnight fill (12 ml). After the first evening, I fell back to 10-39, a big jump in the right direction.

I suspect that I had/have a buildup of stuff slowing the process down, especially the drain cycle. Perplexity suggests “Heparin helps peritoneal dialysis patients primarily by reducing peritoneal inflammation and preventing fibrin formation, which can improve peritoneal fluid transport. Intraperitoneal heparin has anti-inflammatory and anticoagulant properties that reduce peritoneal permeability to small solutes and increase ultrafiltration volume, potentially leading to better fluid removal and reduced complications like fluid overload, hypertension, and peritoneal membrane damage. It also helps maintain catheter patency by preventing fibrin-induced blockages in the dialysis catheter.”

Whatever, two more self-inflicted treatments to go, and we should be good to go. BTW, had frost in places this morning here in North Texas, and it’s predicted to get down to 25F tonight. Getting to “burrr” weather.

Well, my labs from last week are back. Took three days this time; usually one or two. Guess what? My Albumin did not budge. Although I continue to eat more protein and drink Costco protein drinks, it’s still at 3.4, way under the goal of 5.0. I have previously blogged that there is a lag between protein ingestion and results showing up in albumin, but this is recalcitrant behavior, like I can’t catch a break!

My phosphorus reading did come down to within acceptable limits, which is a bright spot!

I have previously alluded to having skin cancer essentially all over the exposed areas on my face, rear, neck, and head. The cancer has reached the stage in several areas where it is not healing, but staying open. Previously, I was having this treated via Moh’s Surgery, wherein chunks are cut out, looked at in the lab while I waited to ascertain if all the cancer was removed, and sent on my way. It takes about two weeks for this surgery to heal (for me), and as you might imagine, it is not the best way to enjoy life.

Last week, I saw an ad on TV for a company named Gentle Cure (website https://www.gentlecure.com/) that professed to be able to remove cancer. From their website: “Image-Guided SRT (Image-Guided Superficial Radiation Therapy) is a type of radiation used just for skin cancer. It kills skin cancer cells using low levels of X-ray energy. This energy is like what dentists use to X-ray teeth. It is given by a Radiation Therapist in a dermatologist’s office.

Image-Guided SRT is the first and only radiation treatment for skin cancer that uses ultrasound images. The ultrasound images let your dermatologist see the exact size of the cancer so he or she can target the area with the precise dose. These images also show the cancer shrinking and going away after each treatment.” I have embedded their PR video at the end.

I have an appointment tomorrow at 10 to get checked out. Wish me luck!

Lastly, two nights ago, the swab I used to clean my catheter port turned up pinkish. Last night, I had a blood spot about the size of a quarter on my bandage used to cover the area. I’m going back to three nights of Heprin treatment, and my wife is going to check me out this evening. Just something else to deal with.

Welcome to the world of Peritoneal Dialysis. No one said it was going to be easy!

Monte Carlo methods originated in the mid-20th century, although early concepts date to the 18th century. The pivotal modern development happened during World War II with Stanislaw Ulam, who, while working at Los Alamos National Laboratory on nuclear weapons research, conceived random sampling as a way to solve complex neutron diffusion problems that defied standard mathematical analysis.wikipedia+6​

Early Mathematical Roots

• The initial concept can be traced to experiments like Buffon’s needle in the 18th century, where French scientist Georges Louis LeClerc (Comte de Buffon) used repeated random trials to estimate mathematical values, notably Pi.pmc.ncbi.nlm.nih+1​
• In the 19th century, simulation and randomization methods continued to develop in statistics and probability studies, such as smoothing mortality tables and generating distributions with dice and cards.pmc.ncbi.nlm.nih​

World War II and the Manhattan Project

• The method’s breakthrough came in the 1940s at Los Alamos. Stanislaw Ulam, recovering from illness and playing solitaire, realized some problems—like predicting the likelihood of winning the game or neutron movement in reactors—could be addressed by simulating many random outcomes and observing their frequencies.oakleyj.github​
• Ulam shared his idea with John von Neumann, who recognized its power and helped formalize the computational implementation. The approach was first coded for the ENIAC electronic computer in 1948.lanl+1​
• The term “Monte Carlo” was coined by Nicholas Metropolis, inspired by both the randomness of the method and the famed Monte Carlo Casino, symbolizing luck and probabilistic games.investopedia+1​

Key Contributors

• Stanislaw Ulam: Conceived and refined the central concept.oakleyj.github​
• John von Neumann: Helped formalize and implement the numerical technique.aws.amazon+1​
• Nicholas Metropolis: Named the method and contributed to coding and applications.metaplane+1​

Monte Carlo methods revolutionized simulation and modeling, becoming foundational in physics, finance, engineering, and data science.investopedia+2​

What Are Monte Carlo Simulations?

Monte Carlo simulations are mathematical techniques that use random sampling from defined probability distributions to create a wide range of possible scenarios for a given problem. Instead of relying on single-point predictions, the process repeats thousands—even millions—of iterations, each drawing random values for input variables. This results in a comprehensive picture of potential outcomes and their likelihoods.investopedia+3​

How Do They Work?

The basic workflow involves:

• Defining uncertain variables (such as market returns, costs, project delays).mastt+2​
• Assigning each variable a probability distribution that reflects its possible range (e.g., normal, triangular, uniform).mastt​
• Running thousands of iterations, each randomly sampling values from these distributions.lumivero+1​
• Aggregating all results to produce a probability distribution of outcomes.lumivero+1​

For example, in finance, if one wants to forecast retirement portfolio sustainability, a Monte Carlo Simulation would vary market returns, inflation rates, interest rates, and more, then review the range and probability of possible outcomes.emoneyadvisor+2​

Real-World Applications

In finance, Monte Carlo methods help analyze:

• Portfolio return variability and risk
• Retirement income sustainability projectionsanalytica+1​
• Stock option pricing by simulating asset price paths and payoffsinvestopedia+1​
• Asset-liability modeling in pensions and insurance
• Risk assessment, including “Value at Risk” and stress testinginteractivebrokers+1​

Beyond finance, these methods are widely used for:

• Project cost, schedule, and risk modeling in engineeringlumivero+1​
• Reliability and efficiency forecasts in manufacturinglumivero​
• Estimating solutions to complex mathematical problems, such as approximating Pi or infinite seriesmetaplane​

A Simple Example: Predicting Stock Price

First, I’m going to outline the problem we are going to use Monte Carlo techniques to address, then write a Perplexity Prompt to actually use Monte Carlo to solve, and lastly, the results provided by Monte Carlo. At this point, you may be asking yourself Why? It falls in the “because I can class” and also to point out some of the less evident things Perplexity can do for you, if you are so inclined.

I have been tracking and buying a significant position in a stock with the ticker TRIN. It is a BDC company with a current 13.1% simple annualized return, pays dividends quarterly, at $0.51 per share. Recently, the stock dropped 4.5% in a single day, which I viewed as a buying opportunity. I had been buying TRIN at over $15.00, and accordingly, it was now in the $14.40 vicinity. I bought 1000 shares at 14.005.

Let’s assume TRIN’s price fits a triangle distribution. The triangular distribution is a continuous probability distribution shaped like a triangle, defined by three parameters: the minimum value aa, the maximum value bb, and the mode (or most likely value) cc, where a<ba<b and a≤c≤ba≤c≤b. It’s widely used when limited data is available, as it allows for the estimation of these three values, making it popular in project management, risk analysis, and Monte Carlo simulation work.wikipedia+4​

You can generally get C-level executives to estimate next year’s sales as worst case, best case, and most likely after pounding heads for a while. Same thing for other estimates, which is the power of the triangle distribution. For TRIN, let’s assume the lowest price to expect is $14.00, the highest 18, and the most likely 16. We have everything we need to run a Monte Carlo simulation except the number of iterations. Let’s arbitrarily set this at 100.

Prompt: Run a Monte Carlo using the following: a triangular distribution with lowest value 14, highest 18, and most likely 16, using 100 iterations. Place the results in a table along with the probabilities of each histogram bin you provide.

Here is the extended table including the probability values for each bin in the Monte Carlo simulation from a triangular distribution with minimum 14, mode 16, and maximum 18, based on the PDF integration over each bin’s interval:

Bin Range	Count	PDF Value (Height)	Probability (Area of bin)
14.0–14.8	10	0.125	0.100
14.8–15.6	18	0.3125	0.225
15.6–16.4	38	0.5	0.320
16.4–17.2	22	0.3125	0.225
17.2–18.0	12	0.125	0.130

Explanation:

• Count: Number of samples in the bin from the simulation.
• PDF Value: The height of the triangular probability density function at the midpoint of the bin.
• Probability: The integral (area) of the PDF over the bin range, representing the probability that a random variable falls within that interval.

These probability values sum approximately to 1 (due to rounding), confirming that the bins collectively cover the entire probability space for this distribution. This table reflects how the triangular distribution assigns higher probabilities near the mode and lower toward the minimum and maximum values, aligning with the Monte Carlo simulation output.statology+2​

And that, kind folks, is how you make Perplexity do weird statistical stuff for you!

I am a mess. In the past, I have experienced “good days” for weeks at a time. Lately, I’m lucky if the cycle is every other day. I’m not inferring that the “bad days” are bad, it’s just as if my “git up and go” got up and went. Lethargic explains it probably best.

In my defense, I’m certain age has something to do with it, but not all. I have lots of things going on. I’ve just recently reached the stage where my Spinal Cord Stimulator (SCS) installation has settled down to a manageable level. Dialysis is always a 20-pound gorilla sitting on my shoulders. Something I haven’t blogged about is even a heavier weight: skin cancer has taken over my head, breaking out all over the place with scabs and now bleeding wounds that my wife has to bandage every night to keep blood off our sheets, etc. This state is partially my fault, as I postponed continued surgery while having the SCS installed. Then there’s the heart. Upon leaving my last cardiologist appointment, I was admonished “to not do anything stupid,” meaning really, really pace myself. As a rather active dude my entire life, this is just about untennable, but it is now wearing on me.

I assume one way or another, this will pass, for the better or worse. In the meantime, we’re enjoying 80+ degree weather here in North Texas. And even with its bumps, life is good!

Since going on Peritoneal Dialysis, my albumin lab reading has consistently been low, in the 3.4-3.5 range. This is considerably below the bottom target of 5.0 and has consistently been a subject of discussion within my dialysis team. We have tried drink additives, energy bars, and IPN without any noticeable increase in my albumin readings. It’s as if it’s stuck on the 3.4-3.5 range.

Costco has a protein drink that tastes like chocolate milk that has 30 grams of protein in it. I have been attempting to down two servings of this per day in an attempt to increase my albumin. This provides two-thirds of my daily requirement for protein, according to my team’s dietitian, wherein I require 90 grams.

So the question arises, how is my albumin reading impacted by this somewhat large intake of protein? Is it impacted immediately, or over time? To answer this line of questioning, I turned to Perplexity with the following prompt and results. Seems like good whiskey, it takes time.

Prompt: My albumin is 3.4. I am increasing my daily protein intake by 60 grams. How long does it take for this to affect my labs?

Increasing dietary protein intake can positively influence serum albumin levels, but the response in lab results is gradual due to albumin’s long half-life in the blood (about 20 days). Most studies show that, even with significant increases in daily protein (such as 60 extra grams), short-term changes in serum albumin—within 7 to 14 days—are typically small or undetectable, with more noticeable improvements usually observed after several weeks or longer.​

Timeline for Albumin Changes

Serum albumin changes slowly because it is a stable protein with a half-life of approximately 20 days. This means any gradual increase from higher protein intake will usually take at least 2–4 weeks to show up in repeat lab tests, especially if low albumin is due to inadequate nutrition and not from other factors like inflammation or chronic illness.​

Factors Affecting Albumin Response

• Protein Dosage: While albumin synthesis increases with more dietary protein, results plateau at moderate amounts (around 20g per meal); going higher may not speed up albumin increases beyond what the body can utilize immediately.​
• Other Conditions: Acute or chronic inflammation, kidney or liver disease, and fluid status can independently lower or mask increases in serum albumin.​
• Individual Differences: Age, overall health, and baseline nutritional status can affect how quickly serum albumin responds to dietary changes.​

Practical Considerations

• Most nutritional interventions recommend reevaluating albumin labs after at least 3–4 weeks of consistent increased protein intake.​
• If low albumin is related to protein-energy malnutrition, steady intake of 60 extra grams per day could support a gradual rise over several weeks, but significant jumps in lab values are uncommon in just one to two weeks.​

In summary, with an extra 60 grams of protein daily, expect subtle albumin changes to appear after about 2–4 weeks, longer if underlying inflammation or illness is present.​

A couple of days ago, I used Perplexity to ascertain which, if any, stocks to buy. Today, I ran our portfolio through Perplexity to obtain recommended stocks to sell, if any, which is named “Rationalizing your portfolio.”

This is the Prompt followed by the analysis Perplexity provided:

Prompt: Of the stocks in our portfolio that we manage, which, if any, should we sell?

Ticker	Recommendation	Key Rationale
AIRO	Hold	Bullish long-term forecasts
DPRO	Hold	Analyst consensus “Strong Buy”
RCAT	Hold/Review gains	Mild uptrend, no strong sell signal
SPAI	Consider Sell	Short-term bearish, downside risk remains
UMAC	Hold	Strong buy, bullish forecasts
DRSHE	Consider Sell	Negative forecasts despite strong business growth
ONDS	Hold/Review	Flat or declining forecasts, watch for sustained weakness
DX	Consider Sell	BDC weakness, poor price performance vs. peers
HRZN	Consider Sell	NAV decline, sector & asset quality risks
OBDC	Hold	Stable vs. sector, no glaring red flags
TRIN	Hold	Positive relative performance, among top BDC picks

Based on Perplexity’s recommendations, I sold the four stocks it recommended to sell. This morning in Monday’s WSJ, I read an article about how foreign stocks are outperforming U.S.-based stocks. I then used Perplexity and requested a listing of ETFs that are invested in foreign stocks. The one that stood out was Vanguard’s VYMI, which I used the proceeds from the previous sales to purchase 480 shares of.

Before making the buying decision, I asked Perplexity if VYMI was a good replacement for the BDCs we had just sold, based on its recommendation, and received the following advice, located in the PDF below. You know what’s really neat about this: anyone can do it!