Week 10 — A&P Lab / Scientific Inquiry · "Feel the Fatigue"

Course: Anatomy & Physiology I (BIOL 2301 + BIOL 2101) · Silver Oak University (fictional sample) · Prof. Navarro
Objective: Objective 5 — relate muscle structure to function; tie a measurable performance drop to the ATP supply behind the sliding-filament cycle · SLO A (relate structure to function) · SLO B (use physiological terminology correctly)
Worth 50 points · Labs group = 15% of the grade · Lab 10
Format: a simple at-home physiology measurement (no equipment, nothing to buy) — you'll measure your own grip fatigue across repeated trials, build a fatigue curve, tie it to ATP and the cross-bridge cycle, and then catch the AI's mistakes when it narrates a muscle contraction.

This is the course's signature weekly component. Every instructional week has one A&P lab. This week's is an at-home measurement of your own muscle — earlier and later weeks use a virtual anatomy atlas, a virtual microscope, and PhET physiology simulations. The reference links below are links to external sites — nothing to buy or download.

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Part 1 — The Big Picture

This week you learned the machinery of a contraction: the sarcomere, the sliding-filament model, and the cross-bridge cycle that needs ATP to pull and to release. Today you'll feel that machinery hit its limit. When you squeeze your hand over and over as fast as you can, the count drops trial after trial — that's muscle fatigue, and it tracks with your fibers running low on ATP (and building waste like lactic acid) faster than they can regenerate it. You'll measure the drop, graph it, and explain it with this week's mechanism.

The scientific habit this builds: make a prediction → collect repeated measurements → compute simple summary numbers → interpret them with a mechanism (not just "I got tired"). That's physiology: a number you can explain.

Background (optional, ~10 min): OpenStax A&P §10.3, "Muscle Fiber Contraction and Relaxation" — the cross-bridge cycle, why ATP is needed to detach the myosin head, and what's known about fatigue: 🔗 https://openstax.org/books/anatomy-and-physiology-2e/pages/10-3-muscle-fiber-contraction-and-relaxation . For a labeled muscle/sarcomere reference, InnerBody's Muscular System: 🔗 https://www.innerbody.com/image/musfov.html

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Part 2 — Your Scientific Question & Hypothesis

Even a simple at-home measurement starts like any inquiry — with a question and a prediction you'll test against evidence (here, your own grip).

The question: When a muscle is worked repeatedly without rest, does its performance decline in a measurable, predictable way — and can the steps of contraction explain it?

Before you start, write your hypothesis / prediction:

I predict that my squeeze count will __ (rise / stay the same / fall) from Trial 1 to Trial 5, by roughly _ %. I also predict that when I ask an AI to narrate the steps of a contraction, it will make at least ___ ordering or labeling error(s) I can catch.

(There's no single "right" number — you're predicting the trend and how reliable the AI will be, then checking both.)

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Part 3 — Materials & Procedure

You need (all free, no equipment):
- Your hand (and a soft object to squeeze if you like — a rolled sock, a stress ball, or just your own clenched fist).
- A timer (your phone) that shows seconds.
- Optional references: OpenStax §10.3 and InnerBody Muscular (linked above).
- An approved chatbot (Gemini, Claude, or ChatGPT) for Part 6.

Procedure (about 5 minutes of measuring):
1. Pick one hand (your dominant hand is fine). Sit comfortably with your forearm resting.
2. Trial 1: for exactly 10 seconds, open and close your fist (one full squeeze-and-release = one rep) as fast as you can while still going all the way. Count the reps. Record the count.
3. Rest exactly 15 seconds. (Short rest on purpose — we want to build fatigue, not erase it.)
4. Repeat for Trials 2, 3, 4, and 5, each a 10-second all-out count with a 15-second rest between. Record each count.
5. Do the simple math in Part 4 (average; the drop from Trial 1 to Trial 5; the percent decline). Then make a quick line graph: trial number on the x-axis, squeeze count on the y-axis — that's your fatigue curve.

Safety: stop if you feel pain (not just normal burning/tiredness). This is a fatigue measurement, not a max-effort strength test. If a hand injury makes this unsafe, substitute toe-taps or ankle pumps with the same timing, or use the InnerBody/OpenStax reference to reason through a provided dataset (see the key) instead.

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Part 4 — Data Table & Quick Math (fill this in)

Trial	Squeezes in 10 s (your count)
Trial 1	______
Trial 2	______
Trial 3	______
Trial 4	______
Trial 5	______

Now compute (show your arithmetic):
- Average squeezes/trial = (Trial 1 + Trial 2 + Trial 3 + Trial 4 + Trial 5) ÷ 5 = __
- Total drop = (Trial 1) − (Trial 5) = _ squeezes
- Percent decline = (Total drop ÷ Trial 1) × 100 = ___ %
- Fatigue curve: sketch the 5 points as a line graph (trial # vs. count). Does it fall steadily, level off, or stay flat?

(Your exact numbers will differ from anyone else's — that's fine. What matters is the trend and that your math is correct. A worked example with sample numbers is in the instructor key.)

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Part 5 — Identify the Reasoning

Answer in a sentence or two each:
1. Did your count fall across the trials? Using this week's mechanism, explain why repeated contractions lead to fatigue — name ATP and the cross-bridge cycle (what does the myosin head need ATP for?).
2. Your filaments never got shorter, yet your hand "got weaker." Reconcile this with the sliding-filament model — what actually changes during a contraction, and why does fatigue reduce force/speed rather than filament length?
3. Structure → function: postural muscles (like the ones holding your head up all day) resist fatigue far better than your sprinting grip did. What about their fibers (think mitochondria, blood supply, aerobic ATP) explains that difference?

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Part 6 — AI-Critique Moment (required — this is the BYOAI step)

Now bring in your approved chatbot (Gemini, Claude, or ChatGPT) and be the physiologist who checks its work.

1. Paste this to the chatbot: "Explain the steps of a skeletal-muscle contraction in order, and tell me which filament is thick and which is thin, and what ATP is used for."
2. Check everything it says against this week's sequence and the OpenStax §10.3 reference:
   - Did it start with the nerve signal (ACh at the NMJ) — not with calcium? Chatbots often put calcium first or skip the NMJ entirely.
   - Did it keep the order ACh → muscle action potential → Ca²⁺ from the SR → troponin/tropomyosin → cross-bridge/power stroke?
   - Did it say actin is thin and myosin is thick (not the reverse)?
   - Did it say the filaments slide (not "shorten")?
   - Did it note that ATP powers the power stroke AND the detachment of the myosin head (not "only at relaxation")?
3. Write 2–3 sentences reporting what the AI got right and at least one ordering or labeling error you caught and corrected (with the correct step or term). If it happened to get everything right, say how you verified the order against the §10.3 sequence — that's the skill.

The habit all term: the tool drafts, you judge. A chatbot will confidently scramble the steps or swap actin and myosin — catching it is the point, and in physiology the order is the meaning.

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Part 7 — What to Submit

Submit a single document (or text entry) with: your hypothesis/prediction, your completed Part 4 data table + the three computed numbers + your fatigue-curve sketch, your Part 5 answers, and your Part 6 AI-critique paragraph. Due Sunday, Nov 8, 11:59 p.m. (50 points).

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Instructor answer key — REMOVE BEFORE PUBLISHING TO STUDENTS

Students' raw counts will vary; grade the trend, the arithmetic, and the mechanism, not the specific numbers. The worked example below uses a representative dataset; every number in it was pre-computed and independently re-verified in Python (see the Quality-gate line).

Worked example (representative student data): Trials = 24, 21, 18, 15, 12 squeezes.
- Average = (24 + 21 + 18 + 15 + 12) ÷ 5 = 90 ÷ 5 = 18 squeezes/trial.
- Total drop = 24 − 12 = 12 squeezes.
- Percent decline = (12 ÷ 24) × 100 = 50 %.
- Fatigue curve: a steady downward line (about −3 squeezes per trial here) — a classic fatigue trend. (Real data often levels off a bit by the last trial; either a steady decline or a decline-then-plateau is acceptable and correctly interpreted.)

Part 5 — model answers:
1. Yes — the count falls because each contraction's cross-bridge cycle needs ATP: the myosin head uses ATP to perform the power stroke and, critically, to detach and re-cock for the next pull. With rapid, repeated contractions, fibers spend ATP (and oxygen) faster than they regenerate it and accumulate waste (e.g., lactic acid), so fewer/weaker cycles complete per trial → fatigue.
2. In the sliding-filament model, the filaments slide past each other and overlap more (the Z discs move closer); their lengths never change. Fatigue doesn't shorten the filaments — it reduces how many cross-bridges can cycle (less ATP available), so the muscle produces less force/fewer fast reps, which is what the dropping count measures.
3. Postural/endurance muscles are rich in fatigue-resistant fibers — lots of mitochondria and a generous blood supply, so they make ATP aerobically and can keep going for hours. The grip sprint leans on fast, powerful fibers that rely on quick anaerobic ATP and tire in seconds — same machine, tuned for a different job (structure → function).
- Part 6 (AI-critique): full credit for a specific catch — most commonly the AI placing calcium before the nerve signal, omitting the NMJ/ACh step, swapping actin (thin) and myosin (thick), saying the filaments "shorten," or claiming ATP is only needed for relaxation. Full credit also if the student verified the order step-by-step against OpenStax §10.3.

Grading rubric — 50 points

Criterion	Full	Partial	None
Hypothesis / prediction — a clear prediction about both the trend and the AI's reliability (6)	6	3–4	0–2
Data table + math (Part 4) — five trials recorded; average, total drop, and percent decline computed correctly; fatigue curve drawn (18)	18	9–15	0–7
Reasoning (Part 5) — ATP/cross-bridge explanation of fatigue, the slide-not-shorten reconciliation, and a sound structure→function point on fiber types (14)	14	7–11	0–5
AI-critique (Part 6) — names a specific ordering/labeling error caught and corrected with the right step or term (8)	8	4–6	0–3
Physiological language — uses standard terms correctly throughout (sarcomere, actin/myosin, cross-bridge, ATP) (4)	4	2	0–1

Quality gate (self-checked): the mechanism is verified against standard physiology (OpenStax §10.2–10.3 + InnerBody Muscular) — the sarcomere is the contractile unit (Z-disc boundaries; actin = thin, myosin = thick); the sliding-filament model has the filaments slide/overlap, not shorten; the contraction sequence is ACh at the NMJ → muscle action potential → Ca²⁺ from the SR → Ca²⁺ binds troponin / tropomyosin uncovers actin → cross-bridge/power stroke (ATP) → relaxation; ATP is required for the power stroke, the head's detachment, and the calcium pumps (no ATP → rigor). Anatomy-accuracy gate: PASS. Quantitative gate: PASS — the worked-example numbers were pre-computed and independently re-verified in /tmp Python: average = (24+21+18+15+12)/5 = 90/5 = 18; total drop = 24−12 = 12; percent decline = 12/24×100 = 50% (steady −3/trial). Students' own arithmetic is graded the same way against their recorded counts.

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Provenance: built clean-room with the founder's Course Maker method; at-home grip-fatigue protocol; reference links (OpenStax §10.2–10.3, InnerBody Muscular) verified live 2026-06-27; no license/CC claims. Silver Oak University and Prof. Navarro are fictional.