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2025.02 Study Archive
2025.02 Study Archive
01. Engaging homeostatic plasticity to treat depression. Mol Psychiatry. 2018
Why does the paper emphasize the functional shift of GABAB receptors rather than the simple blockade of NMDARs?
Why does the paper highlight NMDAR blockade on pyramidal neurons rather than on inhibitory interneurons?
How can both NMDAR agonists and antagonists produce similar antidepressant effects despite their opposite actions?
Why are GABABR agonists, such as baclofen, not used as antidepressants and fail to show consistent antidepressant efficacy?
Which AMPAR subunit does the paper highlight as critical, and why
02. Kai-Xin-San ameliorates fluoxetine-resistant depressive-like behaviors by modulating tryptophan-kynurenine metabolic homeostasis in a rodent model. J Ethnopharmacol. 2025
What was the strategy for creating an efficient TRD model (grouping/criteria/duration)?
What are the key enzymes and metabolites related to the tryptophan-kynurenine/serotonin pathways?
How was the Agonist/Antagonist function of the compound inferred from molecular docking?
What is the importance of the hippocampus in depression (neuroplasticity)?
How were the effects of the parent drug compounds distinguished from their active metabolites?
How was the conditioned medium study for cell-to-cell interaction conducted?
03. Synergistic stress-relieving and cognitive-enhancing effects of walnut peptide and theanine in human brain organoid and mouse stress models. Phytomedicine. 2025
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04. GABAergic Signaling Underlying REM Sleep Deprivation-Induced Spatial Working Memory Deficits. Brain Behav. 2025
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Lisa M. Monteggia
BDNF Signaling in Context: From Synaptic Regulation to Psychiatric Disorders. Cell. 2021
Neurobiology of Depression. Neuron. 2002
Ketamine and rapid antidepressant action: new treatments and novel synaptic signaling mechanisms. Neuropsychopharmacology. 2023
TRD
Preclinical models of treatment-resistant depression: challenges and perspectives.Pharmacol Rep. 2023
Rodent models of treatment-resistant depression. Eur J Pharmacol. 2015
Why I Keep You in CC
Why I Keep You in CC
Sharing = Transparency
Research and education must be transparent. By sharing, we reduce misunderstandings and also lower the chance of data manipulation or selective reporting.
Sharing = Memory
Someone in the lab will always remember the information. By sharing, we support each other’s memory and maintain continuity as a group.
Sharing = Review
When several people read the same message, mistakes or missing points can be found more easily. This increases accuracy and reliability.
Sharing = New Ideas
Even if the content is not directly related to your own project, you may find a new perspective or apply the idea to your research.
Sharing = Synchronization
Sharing helps align the knowledge, information, and academic level of lab members. This keeps everyone moving forward together without the need to explain everything one by one.
Sharing = Communication Efficiency
I don’t need to send separate emails to different people. Instead of passing messages A→B→C, sharing connects A → B(C) at once, making communication more efficient.
Purpose of Data Meeting
Purpose of Data Meeting
First, this meeting is about sharing your data and gathering insights from all lab members. It’s a chance for everyone to contribute and for us to look at our data from multiple perspectives.
Second, and importantly, it’s about refining the quality of our work before it goes outside the lab. By discussing and constructively critiquing each other’s findings now, we ensure that our data is as strong and credible as possible before any external submission.
Third, this is also a chance to sharpen your communication skills. Presenting your data clearly here will prepare you for future conferences and publications.
Using artificial intelligence for systematic review: the example of elicit
Using artificial intelligence for systematic review: the example of elicit
Repeatability Problem: Getting different results every time
A major issue was that the search results were inconsistent. When researchers ran the exact same search query at three different times, they got a completely different number of articles each time
Reliability & Comprehensiveness Problem: Missing important papers
Elicit failed to find 14 important articles that were identified by the traditional method.
Technical Reasons for the Problems
No Keyword Search: Unlike traditional databases, Elicit does not search for specific keywords. Instead, it uses a "semantic search" to find papers that are related in meaning, which can cause the results to vary.
Relies on a Single Database: Elicit only searches for articles within one database called "Semantic Scholar".
Incorrect Citations: The tool made serious referencing errors. For example, it cited a research plan (a protocol) as if it were a final study to support a conclusion. This type of error shows that human oversight is essential to verify its work
Artificial intelligence-assisted academic writing
Artificial intelligence-assisted academic writing
1. Key Concerns
Plagiarism: AI may copy existing text.
Hallucination: AI sometimes creates false or nonsensical information.
Fake References: AI may produce incorrect or completely made-up citations.
2. Journal and Publisher Guidelines
Transparency: Always disclose when and how AI was used (best in the methods section).
No Authorship: AI cannot be listed as an author because it has no responsibility.
Human Creativity First: Main ideas and analysis must come from the researchers.
3. Ethical Use Levels
Tier 1 (Safe): Grammar, spelling, readability, translation → must be checked by a human.
Tier 2 (Conditional): Outlines, summaries, clarity, brainstorming → only valid if researchers review and keep control of the ideas.
Tier 3 (Not Recommended): Letting AI write new content, propose new concepts, or interpret data → this reduces critical thinking and can create errors.
4. Researcher Checklist
Before using AI in writing, ask yourself:
Are the main ideas and analysis truly mine?
Does AI use harm my own writing and thinking skills?
Is all content and every reference accurate and reliable?
Have I clearly disclosed how AI was used?
How to Use Google Calendar
How to Use Google Calendar
Google Calendar is an important tool to manage your study and research.
Write your personal plans in your own calendar, and use the lab calendar for research-related events.
Always put your name in front of the event so that others know who you are. (Example: [YANG] Piezo sleep)
The calendar should include not only your plans but also your reflections. Use colors like a traffic light: green for plans, red for completed tasks, and yellow for unfinished ones. This makes it easy to see the progress at a glance.
You do not need to write every small routine task. Instead, record important events and project updates that the PI or other lab members should know.
Also, mark your vacations in the same way, and invite others to the event if needed.
In the end, researchers who manage their time well achieve better results. Time is the most valuable resource.
Use Google Calendar as a tool for both planning and reflection to grow as a successful researcher.
KSBNS 2025 – Pre-Conference Checklist
KSBNS 2025 – Pre-Conference Checklist
1. Review the Program
Identify the sessions you're interested in, including the date, time, and room number.
Red highlights in the Excel file indicate recommended sessions. *To help you enjoy the conference in a more organized and thorough way, I have compiled a detailed version of the program schedule.
2. Research the Speakers
Look up the speakers of the sessions you plan to attend.
If possible, gather their profiles or links to related presentation materials.
3. Prepare for Networking
Bring business cards or prepare a short self-introduction to use in networking opportunities.
4. Check Local Amenities
You can find nearby restaurants, cafes, or quiet spots to relax near the venue.
5. Prepare Questions and Note-taking Tools
Write down questions in advance for each session.
Bring a notebook or other tools to jot down key points.
6. Pre-read Key Papers
Read 1–2 recent papers from the presenters of your target sessions.
This will help you engage more deeply and facilitate meaningful conversations during networking.
Imaging Mass Spectrometer (TimsTOF flex MALDI-2)
Imaging Mass Spectrometer (TimsTOF flex MALDI-2)
2025 Summer Intensive Study Topics
2025 Summer Intensive Study Topics
Maximizing the Application of BDNF
Deepening Knowledge of Orexin
Upgrading Statistical Skills for Behavioral Testing
Classifying Subtypes Based on Treatment Responsiveness
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