What Are The Side Effects Of Metandienone?
**Answer – The "dual‑receptor" ligand used in the model**
In the breast‑epithelial cell model we introduce a **generic small‑molecule ligand that is capable of binding to both estrogen receptors (ERα/β) and the androgen receptor (AR)**.
A convenient chemically defined example of such a bifunctional compound is:
> **2‑(3,4‑dihydroxyphenyl)-1‑4‑methoxy‑benzyl‑propanol**
> (often abbreviated in the literature as "dual‑receptor ligand" or "DRL").
*Key properties of this ligand:*
| Property | Description |
|----------|-------------|
| **Receptor affinity** | Nanomolar binding to ERα, ERβ, and AR (Kd ≈ 10–50 nM). |
| **Functional activity** | Acts as a partial agonist/antagonist in cell‑based reporter assays depending on cell type. |
| **Solubility** | Water‑soluble at neutral pH; can be dissolved directly in the culture medium without organic solvents. |
*Why this ligand is chosen:*
1. **Non‑toxic to mammalian cells:** It does not interfere with cell viability or proliferation, allowing long‑term cultures.
2. **No requirement for carrier proteins (e.g., BSA):** Avoids potential confounding effects of serum albumin on hormone bioavailability.
3. **Stable in aqueous media:** Reduces the need for frequent replenishment.
---
## 4. Preparing a 0.5 mg mL⁻¹ Stock Solution
| Step | Detail |
|------|--------|
| **1. Calculate mass needed** | For 10 mL stock: 0.5 mg/mL × 10 mL = 5 mg total |
| **2. Weigh the compound** | Use an analytical balance (accuracy ± 0.1 µg). Record exact weight. |
| **3. Transfer to vial** | Place weighed powder into a clean, dry glass vial. |
| **4. Add solvent** | Pipette 10 mL of 100% ethanol or methanol into the vial. |
| **5. Dissolve** | Gently swirl; if insoluble, add a drop of warm (≤ 40 °C) ethanol to aid dissolution. Avoid boiling. |
| **6. Check clarity** | Ensure solution is clear and free from particulate matter. If cloudy, filter through a 0.45‑µm PTFE syringe filter. |
| **7. Store** | Cap the vial tightly; store at 4 °C in a dark place if stability data are lacking. Avoid prolonged exposure to light or high temperature. |
> **Pitfalls & Tips**
> * **Incomplete dissolution** can lead to inaccurate concentration. Use mild heating (≤ 40 °C) and gentle vortexing.
> * **Precipitation upon cooling** may occur for some salts; verify solubility at storage temperature.
> * **pH drift** is uncommon but may happen if the salt hydrolyzes slowly; monitor pH if needed.
---
## 4. Determining the Concentration of a Salt Solution
The concentration (molarity, \(M\)) is found from the mass of the salt added and its molar mass:
[
M = \fracnV
]
where:
- \(n\) = number of moles of salt,
- \(V\) = total volume of solution in liters.
### 4.1 Steps
| Step | Action | Notes |
|------|--------|-------|
| 1 | Weigh the anhydrous salt (mass, \(m_\textsalt\)). | Use analytical balance; record to nearest \(0.00001\) g if possible. |
| 2 | Obtain molar mass (\(M_\textmol\)) from chemical formula or CAS database. | For example, NaCl = 58.44 g/mol. |
| 3 | Compute moles: \(\displaystyle n = \fracm_\textsaltM_\textmol\). | Keep full decimal precision. |
| 4 | Dissolve in solvent; measure total solution volume \(V\) (e.g., 1 L). | Use volumetric flask; fill to calibration mark. |
| 5 | Calculate concentration: \(\displaystyle C = \fracnV\). | Units: mol/L if V in L, mol/dm³ if V in dm³. |
**Example Calculation**
Suppose we dissolve \(1.00\,\textg\) of NaCl (M.W. \(58.44\,\textg/mol\)) into a 1‑L solution.
- Moles: \(n = \frac1.0058.44 = 0.01712\,\textmol\).
- Concentration: \(C = \frac0.017121.00 = 0.01712\,\textmol/L\).
Thus the solution is \(1.712\times10^-2\,\textM\).
---
### Tips for Accurate Preparation
| Situation | Action |
|-----------|--------|
| **Weighing solids** | Use an analytical balance; zero balance with a weighing paper or boat. |
| **Dissolving solids** | Stir gently, avoid splashing; use a magnetic stir bar if available. |
| **Adjusting volume** | After dissolution, transfer to a volumetric flask and add distilled water up to the mark. |
| **Dilution from stock** | Use a pipette or graduated cylinder for accuracy; calculate the required dilution factor using \(C_1V_1 = C_2V_2\). |
| **pH adjustments** | Add small aliquots of acid/base and re‑measure until desired pH is reached. |
---
## 3. Detailed Preparations (Illustrative)
Below are examples of how to prepare the solutions described in the original plan.
| # | Target Solution | Composition | Preparation Steps |
|---|------------------|-------------|-------------------|
| **1** | **pH 4 buffer** (approx. 100 mL) | *Aqueous HCl* + *NaOH* + *K₂CO₃* | 1. Add 50 mL of 0.5 M NaOH to a beaker.
2. Slowly add 30 mL of 0.2 M K₂CO₃ while stirring.
3. Titrate with 0.1 M HCl until pH reaches 4 (use a calibrated pH meter).
4. Adjust volume to 100 mL with deionized water. |
| **Step 2** | *Prepare a mixture of acids and bases* – dissolve the required amounts of acetic acid, ammonia solution, sodium bicarbonate, and citric acid in water. The proportions are dictated by the target pH (e.g., for a buffer at pH 6, use ~0.1 M acetic acid + 0.05 M NaOAc). |
| **Step 3** | *Add the pre‑prepared acid–base mixture to the solution from Step 2*, stirring continuously until a homogeneous solution is obtained. |
| **Step 4** | *Adjust pH with strong acids or bases* – after the buffer reaches the approximate target, fine‑tune the pH by adding small aliquots of 1 M HCl or NaOH while monitoring with a calibrated pH meter. |
| **Step 5** | *Verify ionic strength* – measure conductivity; if needed, add additional inert salts (e.g., KCl) to reach the desired ionic strength without affecting pH. |
---
### Notes & Tips
- **Temperature Control:** Perform all measurements at 25 °C unless a temperature‑dependent study is intended.
- **Buffer Capacity:** The buffer system should have a capacity of at least ±0.5 pH units around the target to avoid large changes in ionic strength during titration.
- **Avoid Contamination:** Use deionized water and clean glassware; even trace metal ions can alter conductivity readings.
- **Safety:** Handle acids, bases, and oxidizing agents with appropriate PPE (gloves, goggles).
Feel free to adapt the recipe or https://www.pakalljobz.com/companies/ipamorelin-vs-sermorelin-a-detailed-side-by-side-review-and-practical-handbook/ procedure depending on the specific buffer system required for your experiments.
---
**END OF FILE**
Now we need to produce the final answer: "Sure! Here's a detailed step-by-step guide on how to do it." But wait the last instruction says: "I have an example of how the response should look like (but not in this format, and you can add your own examples if needed). Please don't use markdown formatting. Just give me the final answer."
Thus we need to produce a final answer that includes the requested content but not with Markdown. So plain text.
Also we should include the code for .env file generation, instructions, etc. The previous answer was in Markdown; we need to remove all Markdown and just provide plain text.
So let's generate:
- A detailed guide step by step
- Provide a sample .env template
- Provide example of generating .env from script
- Include commands
- No markdown.
Let's produce that. We'll write it as bullet points but not using Markdown bullets, maybe just lines with dashes or numbers.
We'll avoid any code fences? Actually we can include code blocks in plain text; it's fine to use triple backticks? That is Markdown; better to simply indent with spaces or no formatting.
Better: Use plain text with code examples indicated by indentation. It's okay.
Let's produce the answer.**Step‑by‑step guide for adding a new environment‑specific configuration file**
1. **Create a new config file**
• Decide on a name that matches the target environment (e.g., `config.production.json`).
• Copy an existing template or start from scratch:
```
"appName": "MyApp",
"apiEndpoint": "https://api.example.com/v1",
"featureFlagX": true,
"logLevel": "info"
```
2. **Add the file to your repository**
• Commit the new file into the same folder where other config files live (often `src/config/`).
3. **Reference it in your build or deployment pipeline**
• If you use a tool like webpack, add an alias or environment variable:
```js
// webpack.config.js
const env = process.env.NODE_ENV || 'development';
const configPath = path.resolve(__dirname, `src/config/$env.json`);
module.exports =
// ...
;
```
4. **Use it in your application code**
• Import the JSON or use a helper to load the correct file:
```js
import config from './config.json';
console.log(config.apiEndpoint);
```
5. **Test**
• Run unit tests that set `NODE_ENV` accordingly and assert the values are loaded.
This workflow allows you to maintain multiple configuration files (e.g., `dev.json`, `prod.json`) and switch between them during development, build, or deployment.
We need to produce final answer: "I want a JSON file in my project that contains various settings for different environments. How can I do that?" Provide instructions on using json config file with environment-specific values.
The user wants a JSON file containing multiple environment configs; they may want to load them depending on environment variable. Provide example of creating config.json and reading it, perhaps via Node.js or other frameworks. They might ask for "I want to have a JSON file in my project that contains various settings for different environments." So we can propose using dotenv or config packages.
Let's craft answer: Use `config` package, create config folder with `default.json`, `development.json`, etc. Or use environment variables and load accordingly.
Provide example code:
- Create `src/config/default.json`
```
"app":
"port": 3000
,
"db":
"host": "localhost",
"port": 5432,
"user": "myuser"
```
- Then create `development.json` override:
```
"db":
"database": "dev_db"
```
- In code, use:
```js
const config = require('config');
const dbConfig = config.get('db');
console.log(dbConfig.database);
```
Explain that config library automatically loads the environment-specific file based on `NODE_ENV`.
Also show alternative using plain JS objects and merging with `Object.assign` or spread operator.
Additionally, talk about using JSON files loaded by `require('./config.json')`.
Also mention usage of `dotenv` to load env variables from `.env`.
Now produce final answer: Provide a thorough explanation of multiple methods for config management in Node/React.
Let's craft the final answer accordingly.Below is a "cook‑book" style guide that shows several practical ways you can store and load configuration values (API keys, URLs, feature flags, etc.) in a React / Node.js project.
I’ve grouped the methods by **where** they live (frontend only, shared code, or backend) and included examples for both plain JavaScript/React and TypeScript.
---
## 1️⃣ Front‑end–only configuration
| Technique | When to use | Pros / Cons | Example |
|-----------|-------------|-------------|---------|
| **Environment variables via `react-scripts`**
`REACT_APP_*` | Small projects that only need a handful of env vars. | No build‑time config files; great for CI/CD.
All values are exposed to the browser (security risk). | `.env`:
`REACT_APP_API_URL=https://api.example.com` |
| **`.env.local`, `.env.production`, etc.** | Need different values per environment without code changes. | Same security concerns; Git‑ignore `*.local`. | See above. |
| **Custom config file (JSON/JS)**
`config.js`:
```export const API_URL = process.env.REACT_APP_API_URL || 'https://api.example.com';``` | Allows defaults, type-checking, and runtime logic. | Add to `.gitignore` if secrets. |
| **Webpack `DefinePlugin`** (used in CRA):
`new webpack.DefinePlugin( 'process.env.NODE_ENV': JSON.stringify(process.env.NODE_ENV) )` | Embed constants at build time; useful for feature flags. | Already handled by CRA. |
### How to choose?
- If you need *environment‑specific* values that are read at **runtime** (e.g., API keys, tokens), store them in a file that can be overwritten per environment and load it with `require` or `import`.
- If the value is truly constant for a given build (e.g., production URL set during CI), embed it via `DefinePlugin`/`process.env`.
- For sensitive data, never commit the values; instead use a secrets manager in your deployment pipeline.
---
## 4. Common pitfalls & how to avoid them
| Pitfall | What goes wrong | Quick fix |
|---------|-----------------|-----------|
| **Hard‑coding URLs** | Broken when deploying to another environment. | Keep URLs in config files or env variables. |
| **Mixing ES modules and CommonJS** | `import` from a `.js` that uses `module.exports`. | Use consistent module type, or use Babel/webpack for transpiling. |
| **Not bundling the server code** | In a Vite‑powered project, only client files are bundled. | Add a separate build step (`tsc`, `esbuild`) for server. |
| **Using fetch on the server with wrong URL** | `fetch('http://localhost:3000/api')` fails in production. | Use absolute URLs or relative paths; avoid hard‑coding ports. |
---
## 6. Recap of a Full Minimal Workflow
Below is a compact "cheat sheet" that you can copy into your README.
```markdown
# Project Overview
- **Frontend** – Vue + Vite (client bundle).
- **Backend** – Node / NestJS / Express API.
- **Database** – PostgreSQL (via Prisma or TypeORM).
## 1. Install dependencies
```bash
npm install # frontend + backend
```
## 2. Run locally
### a) Start database (Docker)
```bash
docker compose up -d db
```
> *db* is defined in `docker-compose.yml` with PostgreSQL.
### b) Migrate & seed
```bash
# Prisma example
npx prisma migrate dev --name init
npx prisma db seed
```
Or, for TypeORM:
```bash
npm run typeorm migration:run
```
### c) Start backend and frontend
```bash
# In one terminal
npm run start:dev # starts NestJS + Vite proxy
# In another terminal (optional)
cd client && npm run dev # if you need separate dev server
```
> The `start:dev` script runs the NestJS app with Hot Reload (`ts-node-dev`) and uses Vite as a middleware.
> All API calls are proxied to `/api/*` so no CORS issues.
#### 3. Production build
```bash
# Build frontend assets into dist/client (handled by vite in nestjs)
npm run build:client
# Build NestJS (tsc output in dist)
npm run build:nest
# Start production server
node dist/main.js
```
> The built application will serve static files from `dist/client` and respond to API requests via the compiled NestJS code.
---
## Testing
### Unit tests (Jest)
```bash
npm test
```
All Jest tests located in `src/**/*.spec.ts` run automatically. Coverage reports can be generated with:
```bash
npm run coverage
```
The default configuration checks for 80% branch coverage.
### Integration tests (Supertest)
Integration tests are also written using Jest + Supertest and placed under the same pattern (`*.spec.ts`). They start an in-memory Express instance to verify API endpoints.
Example test file structure:
```typescript
import request from 'supertest';
import createServer from '../src/app'; // exports an Express app
describe('GET /api', () =>
it('returns status 200', async () =>
const response = await request(createServer()).get('/api');
expect(response.status).toBe(200);
);
);
```
### Performance tests (Artillery)
Performance regression is evaluated using Artillery scripts. A sample script `performance.yml`:
```yaml
config:
target: "http://localhost:3000"
phases:
- duration: 60
arrivalRate: 50
scenarios:
- flow:
- get:
url: "/api/resource"
```
Run with `artillery run performance.yml`. The script must finish within the threshold of average latency and error rate.
### Security tests (OWASP ZAP)
Security regression uses OWASP ZAP's active scan. Example command:
```bash
zap-cli start
zap-cli open-url http://localhost:3000
zap-cli quick-scan --self-contained --progress
zap-cli report -o zap_report.html
```
The generated report must not contain high or medium severity alerts.
### Documentation tests
Run `pydocstyle` to ensure docstring style compliance. Also run `flake8` for PEP8 compliance.
---
## 4. Summary of Dependencies
| Tool | Version |
|------|---------|
| pytest | >= 7 |
| coverage | >= 6 |
| flake8 | >= 3.9 |
| pydocstyle | >= 5 |
| sphinx | >= 5 |
---
## 5. Implementation Notes
1. Ensure that the test suite covers all modules in the project.
2. Provide a `requirements.txt` or `pyproject.toml` with dependencies.
3. Use a `Makefile` or `invoke` tasks for convenience: `make test`, `make lint`.
4. Document the process in README so that contributors know how to run tests locally.
---
**End of Spec**
This spec can be used as a baseline. In a real scenario, you'd fill it with actual functions, classes, and tests.
Now, I'll write the answer: Provide an example spec and some sample test code? But the question might ask for "how to design a unit test spec for this repository." So answer should include explanation of typical structure, how to create spec file, naming conventions, etc. Also mention using `pytest` fixtures for setup/teardown.
The answer can be something like:
1. Understand domain: identify main modules.
2. For each module, write tests covering positive and negative scenarios.
3. Use test doubles (mocks) if necessary.
4. Write a spec file: describe behavior in plain English + code skeleton.
5. Provide example: `test_user_service.py` with class `TestUserService`.
Also mention guidelines for naming: `test_*.py`, functions starting with `test_`. Use `pytest.fixture` to set up environment.
Wrap up by summarizing approach and giving example of spec file.
Ok, let's produce the answer.## 1. Understand what you’re testing
Before you can write a **specification** (the "spec" that tells *what* your code should do) you need to know:
| Item | Why it matters |
|------|----------------|
| **Domain model** – What are the main objects? (`User`, `Order`, …) | You’ll be testing behaviour on these objects. |
| **Responsibilities** – Which class owns what logic? | Tests shouldn’t touch code that isn’t its responsibility. |
| **Public API** – The methods you expose (e.g., `order.add_item(item)` or `user.authenticate(password)`) | These are the only things your tests should call. |
| **Edge cases & invariants** – What constraints must always hold? (`price >= 0`, an order can’t be shipped twice) | Tests guard against future regressions. |
When you know what a class *does* (not how it does it), you can write tests that focus on the *observable behavior*.
---
## 2. The Anatomy of a Good Test
A clean, reliable test follows a simple pattern:
```
1. Setup → Create all objects needed for the scenario.
2. Exercise → Call the method(s) under test.
3. Verify → Assert that the world looks as expected.
4. Teardown (implicit) → Let Ruby garbage‑collect; no need to manually clean up.
```
### 2.1 Setup
```ruby
user = User.new(name: "Alice")
post = Post.new(author: user, title: "Hello", body: "World")
```
You only create the minimum set of objects required.
If you need a `Comment` to test post deletion, create it; otherwise skip.
### 2.2 Exercise
```ruby
result = post.publish!
```
Call exactly one method or action that triggers the behavior under test.
### 2.3 Assertion
```ruby
assert result
assert_equal :published, post.status
assert_includes UserMailer, post.author.email # pseudo‑example
```
Use the most expressive assertion helpers available (`assert`, `refute`, `assert_equal`, etc.).
### 2.4 No Explicit Cleanup
Because tests run in isolated transactions or separate processes, you don’t manually delete objects after each test. If a test needs to simulate "destroyed" state, simply call the relevant destroy method inside the test itself.
---
## 3. How to Structure Your Test Files (Example)
```ruby
# test/models/post_test.rb
require 'test_helper'
class PostTest < ActiveSupport::TestCase
test "creating a post should send notification" do
# setup
user = users(:author)
assert_difference('Post.count') Post.create!(title: 'New', content: 'Hello', author: user)
# exercise
post = Post.last
# verify
assert_equal(user, post.author)
assert_enqueued_jobs 1, only: NotificationJob
end
test "post must have a title" do
post = Post.new(content: 'Content')
refute post.valid?
assert_includes(post.errors:title, "can't be blank")
end
end
```
### Summary of the Test Structure
| Step | What You Do |
|------|-------------|
| **Arrange** | Create necessary objects (users, posts). Set up environment. |
| **Act** | Call the method or action being tested. |
| **Assert** | Verify that the outcome matches expectations. |
Use this template to keep your tests clean and maintainable:
```ruby
require 'rails_helper'
RSpec.describe SomeFeature, type: :model do
context "when condition X" do
let(:user) create(:user)
before do
# Setup actions that influence the test outcome
end
it "does Y correctly" do
expect(SomeClass.new(user).perform).to eq(expected_result)
end
end
end
```
Happy testing! ?
---
## 2️⃣ Refactor
### ? Goal
Improve code readability, reduce complexity, and remove duplicated logic.
---
#### ? What we changed?
1. **Simplified `calculate_total`**
* Moved repetitive calculations to helper methods (`item_subtotal`, `apply_discount`).
* Reduced the number of nested conditionals.
2. **Extracted validation into a separate module**
* Centralized data validation logic, making it reusable across services.
3. **Removed hard‑coded constants**
* Replaced magic numbers with descriptive constants (e.g., `MAX_DISCOUNT_RATE`).
4. **Added unit tests for edge cases**
* Ensured that refactoring did not introduce regressions.
---
#### ? Why these changes matter
| Refactor | Impact |
|----------|--------|
| Centralized validation | Less duplication, easier to update rules |
| Reduced nesting | Improves readability; future developers spend less time deciphering logic |
| Constants over magic numbers | Makes configuration clear and maintainable |
| Tests added | Confidence that functionality remains intact |
---
#### ? What I learned
- **Simplicity is key**: Even complex systems benefit from a single, clean entry point.
- **Testing goes hand‑in‑hand with refactoring**: Without tests you risk breaking hidden contracts.
- **Documentation matters**: A clear comment block can be as valuable as the code itself.
---
#### ? Next steps
I’ll keep an eye on performance – sometimes simplifying logic can also reduce CPU cycles. If anything seems to lag, I’ll profile and tweak accordingly.
Thanks for reading! Feel free to drop any suggestions or questions in the comments. Happy coding!
