Public Projects

hardy-account

by Maulik Patel

I have account agency for insurance. Pls cretae a website for me. With Admin login in python

snowy-website

by Keval Bhatt

Create a visually rich, modern demo website for a premium interior design studio. The goal of this website is to feel luxurious, immersive, and highly visual while keeping text minimal. The design should rely heavily on imagery, spatial transitions, and smooth scrolling experiences that allow visitors to visually explore the brand rather than read large amounts of content. Brand Name: Aurelia Interiors (you can adapt if needed). Overall Design Direction: The website should feel elegant, calm, and premium. Use light, sophisticated tones such as ivory, warm beige, soft taupe, light marble textures, and subtle gold accents. Avoid dark or heavy colors. The entire experience should feel high-end and refined, similar to premium architecture or luxury real estate websites. Navigation (Header): A clean floating header that stays visible as the user scrolls. It should include: Home About Us Services Projects Contact Us The header should be minimal, elegant, and slightly transparent so it blends with the visual background. Core Website Concept: The scrolling experience should simulate moving through a modern luxury house. As the user scrolls down, the perspective subtly transitions through different interior spaces of the house, creating a 3D immersive feeling. Each section of the house represents one of the services offered by the interior design studio. Example flow: Hero Section: A stunning modern luxury home interior with soft lighting and elegant furniture. Subtle animated camera movement or parallax effect to create a cinematic introduction. Display the brand name and a short tagline such as: "Designing Spaces That Feel Like Home." Scrolling Experience: As the user scrolls, the camera transitions through different rooms of the house. Each room highlights a service. Living Room Section: Represents Residential Interior Design Minimal text with elegant overlay labels and subtle animations. Kitchen Section: Represents Space Planning & Functional Design Bedroom Section: Represents Luxury Bedroom Concepts & Custom Furniture Dining Area: Represents Decor Styling & Material Selection Bathroom / Lounge Area: Represents Turnkey Interior Solutions Each section should emphasize imagery, layout ideas, furniture, textures, and decor rather than long explanations. Visual Experience: Use parallax scrolling, depth, shadows, and layered visuals to create a subtle 3D spatial feel as users move through the house. The experience should feel like exploring a beautifully designed home. Projects Section: Display a clean gallery of interior design projects with hover effects and smooth transitions. About Us Section: Short, elegant brand introduction emphasizing craftsmanship, design philosophy, and attention to detail. Contact Section: Minimal and stylish contact form with a luxurious feel. Animations & Effects: Smooth scroll transitions Soft fade-ins for text Parallax depth effects Subtle hover animations on images Typography: Elegant modern fonts that reflect luxury and design sensibility. Keep text minimal and spacious. Overall Experience: The website should feel like exploring a luxury home rather than browsing a typical website. The visuals, layout, and scrolling interaction should invite users to move through the space and imagine how their own interiors could look.

mammoth-organizations

by Priya Mandaviya

Build a SaaS product called "AI Marketing Agent Studio". Goal: A platform where organizations can create marketing campaigns using AI agents. Teams collaborate, generate marketing content, and track campaign performance. Core Features: 1. Multi-Tenant Organization Management - Organizations can create workspaces - Invite team members - Assign roles (Admin, Marketer, Viewer) - Users can switch between organizations - Each organization has separate projects and campaigns 2. AI Marketing Agents Create different AI agents for marketing tasks: - Blog Post Generator - Social Media Caption Generator - Email Campaign Generator - Ad Copy Generator Workflow: User creates campaign → selects AI agent → enters prompt → AI generates content → user edits and saves.

Want to build a web application to get all the jobs present on all the job posting sites to display them on a single dashboard, and a chatbot for help

# Document Chat Assistant — Product Requirements (Prompt for Tool Evaluation) ## Goal Build a production-ready **Chat with Documents assistant** that allows users to upload a large collection of files and ask natural language questions to retrieve accurate information from them. The system should understand document content, identify relevant information, and generate clear answers based on the available documents. This document describes the **expected capabilities and behaviors** of the system so that it can be tested using existing production-ready tools in the market. --- # Core Capabilities ## 1. Document Understanding The system should be able to process different types of documents and understand the content inside them. Examples of supported content include: - Text paragraphs - Structured tables - Lists and bullet points - Headings and sections - Multi-page documents - Spreadsheet-style data The assistant should treat the documents as a knowledge base and use them to answer questions. --- ## 2. Question Answering Users should be able to ask questions in natural language such as: - Product related questions - Information lookup - Comparison questions - Summary requests - Clarification questions - Follow‑up questions based on previous answers The system should respond with **clear and accurate answers based only on the available documents**. --- ## 3. Table Understanding Many documents contain structured data in tables. The assistant should be able to: - Understand table structure - Read rows and columns - Extract values from tables - Compare numbers - Identify patterns or differences in rows If a question is related to numerical or tabular information, the system should return answers based on that data. --- ## 4. Multi‑Document Reasoning Some questions may require information from **multiple documents**. The assistant should be able to: - Combine information from different sources - Compare data between files - Provide consolidated answers - Mention the relevant sources used for the answer --- ## 5. Document Discovery Users may also want to discover documents themselves. The assistant should support queries such as: - Finding documents related to a topic - Identifying which document contains certain information - Listing available documents - Suggesting relevant files for further reading --- ## 6. Conversation Context The assistant should support **multi‑turn conversations**. Example behavior: User question → assistant answers User follow‑up → assistant understands context and continues the conversation. The system should remember recent conversation context so follow‑up questions make sense. --- ## 7. Answer Quality Generated answers should: - Be concise and easy to read - Be factually grounded in the documents - Avoid hallucinating information not present in documents - Clearly present structured information when necessary Responses may include: - Text explanations - Bullet points - Short summaries - Tables when relevant --- ## 8. Source Awareness Whenever possible, the assistant should indicate **which document or section the information came from**. This helps users verify answers and explore the original source. --- ## 9. Large Knowledge Base Support The system should be able to operate with a **large document repository**, potentially containing thousands of files. Expected capabilities include: - Fast search across many documents - Accurate retrieval of relevant information - Stable performance as the dataset grows --- ## 10. User Experience Expectations The assistant interface should allow users to: - Ask questions easily - View clear answers - See referenced sources - Continue conversation naturally The experience should feel similar to interacting with a knowledgeable assistant that has read all the uploaded documents. --- # Evaluation Objective This requirement document is intended to test and evaluate **existing AI document assistant tools available in the market**. The goal is to observe: - How accurately the tool retrieves information - How well it understands structured data - Whether it handles multi‑document queries - How natural and helpful the generated responses are The system should behave like a **reliable knowledge assistant for document collections**.

create an chating application

create a fully function AI dsa tutor with the following functionalities Your project **“AI-Powered DSA Learning & Document Analysis Platform”** combines two major systems: * **DSA Tutor (Intelligent Learning Assistant)** * **Project SISO (RAG-based Document Intelligence System)** Together they form a **multi-capability AI platform** for learning, coding practice, and document analysis. Below is a **clear, structured list of the combined functionalities**. --- # Combined Functionalities of AI Tutor + Project SiSo ## 1. Intelligent DSA Learning System Provides **concept explanations for Data Structures and Algorithms**. **Capabilities** * Step-by-step explanations * Text-based diagrams * Concept breakdown from basic → advanced * Context-aware follow-up questions **Example** User asks: *“Explain Dynamic Programming”* System provides: * Definition * Intuition * Example problems * Diagram explanation * Complexity discussion --- # 2. Coding Practice & Evaluation Engine Users can **solve coding problems and receive automated evaluation**. ### Features * Multi-language coding support * Python * Java * C++ * JavaScript * Code analysis includes: * Correctness validation * Time complexity analysis * Space complexity analysis * Optimal vs brute-force detection * Improvement suggestions **Example** User submits solution for **Two Sum** System evaluates: ``` Correctness: Passed Time Complexity: O(n²) Optimal Solution: O(n) using HashMap ``` --- # 3. MCQ Test Generation & Knowledge Assessment System can generate **adaptive quizzes for concept reinforcement.** ### Capabilities * Dynamic MCQ generation * Difficulty levels (Easy / Medium / Hard) * Instant evaluation * Explanation for correct answer * Performance feedback --- # 4. Conversational AI Tutor Acts as a **general AI assistant for learning discussions.** ### Capabilities * Free chat mode * Doubt solving * Interview preparation * Concept comparisons Example queries: * “Difference between BFS and DFS” * “Explain heap vs priority queue” --- # 5. Intelligent Intent Detection The system automatically **detects what the user wants**. ### Possible intents | User Input | System Mode | | ---------------------- | ------------- | | Explain Graphs | Learning Mode | | Solve LeetCode problem | Coding Mode | | Give MCQs on Trees | Test Mode | | General question | Chat Mode | This routing ensures **correct handler execution**. --- # 6. Multi-Document Question Answering (RAG) Project SiSo allows users to **ask questions across multiple documents.** ### Supported documents * DOCX * Study material * Notes * Resume * Job descriptions ### Process ``` Document Upload → Parsing + OCR → Chunking → Embedding generation → Weaviate indexing → Retrieval → LLM answer generation ``` --- # 7. Citation-Based Answer Generation All answers from documents include **precise citations.** Format: ``` [doc_id:page_number:chunk_index] ``` Example: ``` The transformer architecture relies on self-attention [doc1:3:2] ``` This ensures **traceability and reliability of information**. --- # 8. Resume vs Job Description Analysis The system performs **career gap analysis**. ### Features * Resume parsing * JD parsing * Skill gap identification * Improvement suggestions Example output: ``` Missing Skills: - Kubernetes - Docker - GraphQL Recommendation: Learn container orchestration and API frameworks. ``` --- # 9. Study Material Question Answering Students can upload **lecture notes or textbooks**. Capabilities: * Ask conceptual questions * Extract summaries * Generate explanations * Retrieve exact references Example: ``` Q: Explain Gradient Descent ``` Answer retrieved from uploaded lecture notes. --- # 10. High-Availability AI Architecture System ensures **reliable LLM responses**. ### Model pipeline Primary model ``` Google Gemini ``` Fallback model ``` Ollama (Local Llama 3) ``` Mechanism: ``` Gemini request → if failure → automatic fallback to Ollama ``` --- # 11. Asynchronous Processing Pipeline Heavy tasks run **as background jobs**. Architecture: ``` User Request ↓ FastAPI API ↓ Redis Queue ↓ Worker ↓ Processing ↓ Result Retrieval ``` Benefits: * Scalable * Non-blocking API * Handles large documents --- # 12. Hybrid Search Retrieval System Uses **two retrieval methods together**. | Method | Purpose | | ------------- | ---------------- | | Vector Search | semantic meaning | | BM25 | keyword matching | This hybrid search improves **retrieval accuracy**. --- # 13. Multi-Document Knowledge Indexing The system supports **grouped document search**. Metadata stored in vector database: ``` doc_id doc_group_id source_file page_number chunk_index ``` This allows: * Cross-document answers * grouped search results --- # 14. Job Status Tracking System Users can track processing progress. Endpoints: ``` POST /submit_job GET /job_status/{job_id} GET /get_result/{doc_id} ``` Example status: ``` Processing: Chunking documents Processing: Generating embeddings Completed ``` --- # 15. Interactive Frontend Workflow The UI supports **three stages**: ### 1️�� Input Stage * Upload documents * Enter questions ### 2️⃣ Processing Stage * Progress polling * Loading indicators ### 3️⃣ Result Stage * AI generated answers * Citation references * Analysis reports --- # Final Combined Platform Vision The system ultimately acts as a **unified AI knowledge platform** capable of: | Domain | Capability | | ------------ | --------------------- | | Learning | DSA tutor | | Coding | Practice + evaluation | | Testing | MCQ quizzes | | Conversation | AI discussion | | Documents | RAG Q&A | | Career | Resume analysis | --- ✅ **In one sentence (interview ready):** > The platform integrates an AI-driven DSA tutoring system with a Retrieval-Augmented Generation (RAG) document intelligence engine, enabling interactive algorithm learning, automated code evaluation, adaptive testing, multi-document question answering with citations, and AI-based resume-job description gap analysis within a unified scalable architecture.

okay i need to create a project , i need you to create a automation system using which we can automate article genration tasks , so basically in that we will need to enter a simple automation tool that activates every Monday and thursday , it will already have the data of the user's industry and it will choose , it will do a websearch using apis ( either duck duck go or gemini or somehting that will be used to fetch the latest news about that industry withing last 72 to 84 hour ) , and based on that it will create a article about that in simple terms and easy to understand words and it will get autmatically publihsind on my website

Create a task management system for Make sure there is drag and drop functionality for task status

garnet-mg

by 苗宇

我想写一个国自然面上项目，先前的具体做法是对12例MG患者进行了外周血scRNAseq+scTCR+scBCR测序，并在GEO数据库中下载了相同平台的健康人样本进行整合分析，发现了MG患者中CD8T发生克隆扩增，而且克隆扩增的T细胞与HC中克隆扩增的T细胞之间进行GSEA分析发现病毒性心肌炎相关的通路被显著激活；我想提出假设MG 克隆扩增 T 细胞是具有心肌自身抗原交叉反应性。后续可以添加机制验证实验，题目定为（重症肌无力克隆扩增CD8⁺T细胞介导心肌交叉免疫损伤的机制研究）。（C4T细胞我并未发现克隆扩增，可能是scTCR的缘故导致整体CD4T都很少，所以我觉得可能是单克隆的主要原因，另外文献中很多有提到Cd4T克隆扩增的现象） 另外我们先前已经对另一个项目的分析中针对500例内部样本MG患者进行了bulk TCR测序，其中混杂有100个左右的病毒性心肌炎患者，可以针对前面鉴定的TCR进一步数据层面的验证）；你觉得我顺着这个思路做怎么样呢，如果可行，你帮我根据文献报道帮我写一个立项依据出来，下面的立项依据是我去年写的另一个项目的，你可以参考下格式。 1. 项目的立项依据（研究意义、国内外研究现状及发展动态分析，需结合科学研究发展趋势来论述科学意义；或结合国民经济和社会发展中迫切需要解决的关键科技问题来论述其应用前景。附主要参考文献目录）； 1.1研究意义 视神经脊髓炎谱系疾病（Neuromyelitis optica spectrum disorders，NMOSD）是一种罕见的主要累及视神经和脊髓的自身免疫性疾病。这种疾病以急性视神经炎和横贯性脊髓炎为主要临床特征，患者往往会因反复发作而产生严重的后遗症，如视力下降或失明、肢体瘫痪甚至死亡。在80%以上的患者中，NMOSD是由高度特异性的血清自身抗体（AQP4-IgG）攻击视神经和脊髓中星形胶质细胞上的水通道蛋白4（Aquaporin-4，AQP4）所致[1]。 自2004年AQP4-IgG报道以来，NMOSD被认为是一种体液免疫驱动的疾病[2]。目前该病的治疗策略大多依赖于使用免疫抑制剂，包括靶向CD19或CD20的B细胞耗竭疗法[3, 4]。尽管这种疗法在大多数NMOSD患者中可通过清除血液中的B细胞来缓解症状，但我们在临床研究中发现仍有部分患者即使B细胞数量减少至零，病情仍然会严重恶化，这意味着存在B细胞之外的因素影响疾病的进程。事实上，越来越多的证据表明，T细胞与NMOSD的病理生理学有关[5, 6]。有研究指出，NMOSD患者外周血中Th17细胞的比例与疾病残疾程度之间存在显著正相关[7]。我们在先前的研究中也发现了NMOSD患者的TCRβ库相对于健康对照发生非常显著的克隆扩增[8]。这一发现进一步印证了T细胞在NMOSD病程中起到的重要作用。尽管如此，T细胞在NMOSD进展过程中的详细动态变化仍有待揭示。 在本研究的前期，我们发现NMOSD患者的TCRβ特征与疾病严重程度存在显著负相关。为了探究疾病进展背后T细胞的动态变化过程，本研究将选择不同残疾严重程度的NMOSD患者作为研究对象，拟通过免疫组库、转录组、单细胞转录组和单细胞VDJ测序等技术手段进行多组学分析，以揭示T细胞在疾病进展中的演化过程和特征变化，识别与基因表达、细胞类型和免疫反应相关的差异。这些结果可能为我们理解NMOSD的免疫机制和病程进展提供新的视角。 1.2 国内外研究现状及发展动态 1.2.1 B细胞和浆细胞在NMOSD中的作用 约80%的患者可检测到自身抗体AQP4-IgG[9, 10]。AQP4-IgG被认为在进入中枢神经系统（CNS）并与星形胶质细胞末端的AQP4结合时具有致病性，从而刺激一系列炎症事件，导致NMOSD中的轴突和神经元损伤。越来越多的证据表明，AQP4-IgG在外周合成，而不是在鞘内合成，随后通过破坏的血脑屏障进入中枢神经系统[11]。因此，NMOSD可以被认为是一种主要由外周抗体介导的自身免疫性疾病。 AQP4-IgG+的NMOSD患者发病机制如图1所示[12]。AQP4肽可以被自身反应性T细胞识别，然后极化为辅助性T细胞亚型17（Th17）表型，并为被构象完整的AQP4蛋白激活的B细胞提供帮助。进而，这些B细胞分化为能够分泌AQP4-IgG的成熟浆细胞。这个过程发生在中枢神经系统之外[12]。AQP4-IgG在血液中循环并进入CNS，在那里与星形胶质细胞足突上表达的AQP4蛋白相互作用。这种相互作用可以激活补体，导致血脑屏障破坏、星形胶质细胞膜受损、少突胶质细胞死亡、髓鞘丢失以及神经元损伤[13-15]。星形胶质细胞损伤的机制主要包括补体依赖的细胞毒作用和抗体依赖的细胞毒作用。补体依赖性细胞毒性可能是NMOSD的主要机制。 图1 NMOSD的发病机制示意图[16] AQP4-IgG的致病作用凸显了B细胞和浆细胞在NMOSD中的重要性。AQP4-IgG的直接致病性也支持B细胞参与的证据[17]。由于B细胞发挥多种关键作用，NMOSD被认为是体液介导的。B细胞能够产生多种促炎细胞因子，包括IL-6、TNF-α和粒细胞巨噬细胞集落刺激因子，这些因子可驱动炎症反应[18]。B细胞作为NMOSD发病机制的核心，并且与补体系统是当前可用的NMOSD疗法的关键靶标。 有研究发现NMOSD患者CD19intCD27highCD38high B细胞亚群在患者外周血中选择性克隆扩增，该亚群具有浆细胞样细胞功能，并分泌AQP4-IgG[19]。另有研究发现，NMOSD中B细胞向浆细胞分化失调。当与外周辅助性T细胞共培养时，来自NMOSD的记忆B细胞分化为浆细胞的潜力增强。因此，B细胞谱系的转录谱偏向浆细胞样表型[20]。 1.2.2 自身反应性T细胞在NMOSD中的作用 事实上，越来越多的证据表明，T细胞与NMOSD的病理生理学有关[5-7]。Nishiyama等人发现T细胞在NMOSD的发病机制中扮演着重要角色[21]，单独的AQP4特异性抗体并不能引发中枢神经系统的炎症性脱髓鞘病变[13, 22]。然而，将AQP4特异性T细胞与AQP4-IgG同时转移到大鼠体内，则会产生与NMOSD相似的炎性组织损伤[23, 24]。 NMOSD血清中的AQP4特异性抗体是IgG1，这是一种T细胞依赖的IgG亚类[25]，T细胞介导的CNS炎症允许这些抗体进入中枢神经系统[26]，这表明AQP4特异性的CD4+ T细胞参与了这种适应性体液反应的发生。目前已经在NMOSD患者中发现了AQP4特异性T细胞的存在[27]。有研究发现，来自NMOSD患者的T细胞比来自健康对照的T细胞表现出对AQP4更强的增殖能力，并且对AQP4p61-80的反应最强烈[28]。据报道，与健康人相比，NMOSD患者急性期外周血T细胞总数中的调节性T细胞（Tregs），特别是初始Tregs的比例明显下降[5]。另外有研究发现，AQP4抗原刺激可使免疫反应极化为Th17反应，并分泌Th17相关细胞因子，如IL-6和IL-21[29]。Th17细胞可通过分泌IL-17、促进内皮活化和刺激中性粒细胞跨内皮迁移来损害血脑屏障的完整性[30]。此外，申请人在先前的研究中发现，相对于健康对照，AQP4-IgG+ NMOSD患者的TCRβ库多样性和CDR3长度的显著降低。并提出AQP4-IgG+ NMOSD疾病的发生可能是受到CMV病毒感染而激活自身反应性T细胞所诱发[8]。总之，这些观察结果凸显了AQP4特异性T细胞在NMOSD发病机制中作为适应性体液和细胞免疫反应驱动因素的潜在作用。 综上所述，本研究基于NMOSD疾病进展过程中TCRβ库的多样性显著降低的现象提出科学假设：在NMOSD的疾病进展过程中，特定的T细胞亚群经历了克隆扩增，这一过程对于疾病进展起到了促进作用。 然而，关于疾病进展过程中T细胞发挥的重要作用面临以下两个关键问题： 1）疾病进展中T细胞的动态变化过程及其对B细胞的影响：目前尚未清楚T细胞在NMOSD进展过程中的详细动态变化，我们需要揭示T细胞的动态演变（包括细胞比例和分子特征的动态变化）以及它如何影响B细胞和疾病的长期发展。 2）疾病进展过程中影响T细胞多样性的调控机制：本研究前期发现NMOSD患者的TCRβ库多样性随着疾病的残疾进展逐渐降低。这意味着随着疾病进展，一些T细胞发生克隆扩增，但我们需要更加明确这背后的调控机制。 为了解决这些问题，我们计划以不同严重程度的NMOSD患者为研究对象，通过多组学分析，包括免疫组库、转录组、单细胞转录组和单细胞VDJ测序等技术手段，来揭示T细胞在NMOSD患者中的演化过程、调控机制以及与B细胞多样性和疾病进展的关系。这将有助于我们更好地理解AQP4-IgG+ NMOSD的免疫机制，从而促进疾病的诊断、治疗和预后监测的发展。 参考文献： [1] JARIUS S, PAUL F, WEINSHENKER B G, et al. Neuromyelitis optica [J]. Nat Rev Dis Primers, 2020, 6(1): 85. [2] LENNON V A, WINGERCHUK D M, KRYZER T J, et al. A serum autoantibody marker of neuromyelitis optica: distinction from multiple sclerosis [J]. Lancet, 2004, 364(9451): 2106-12. [3] KITLEY J, PALACE J. Therapeutic options in neuromyelitis optica spectrum disorders [J]. Expert Rev Neurother, 2016, 16(3): 319-29. [4] PAPADOPOULOS M C, BENNETT J L, VERKMAN A S. Treatment of neuromyelitis optica: state-of-the-art and emerging therapies [J]. Nat Rev Neurol, 2014, 10(9): 493-506. [5] MA X, QIN C, CHEN M, et al. Regulatory T cells protect against brain damage by alleviating inflammatory response in neuromyelitis optica spectrum disorder [J]. J Neuroinflammation, 2021, 18(1): 201. [6] SHI Z, QIU Y, ZHAO Z, et al. CD8(+) T cell subpopulations and pro-inflammatory cytokines in neuromyelitis optica spectrum disorder [J]. Ann Clin Transl Neurol, 2021, 8(1): 43-53. [7] AGASING A M, WU Q, KHATRI B, et al. Transcriptomics and proteomics reveal a cooperation between interferon and T-helper 17 cells in neuromyelitis optica [J]. Nat Commun, 2020, 11(1): 2856. [8] MIAO Y, SHI Z, ZHANG W, et al. Immune Repertoire Profiling Reveals Its Clinical Application Potential and Triggers for Neuromyelitis Optica Spectrum Disorders [J]. Neurol Neuroimmunol Neuroinflamm, 2023, 10(5). [9] HAMID S H M, WHITTAM D, MUTCH K, et al. What proportion of AQP4-IgG-negative NMO spectrum disorder patients are MOG-IgG positive? A cross sectional study of 132 patients [J]. J Neurol, 2017, 264(10): 2088-94. [10] PAPADOPOULOS M C, VERKMAN A S. Aquaporin water channels in the nervous system [J]. Nat Rev Neurosci, 2013, 14(4): 265-77. [11] MAJED M, FRYER J P, MCKEON A, et al. Clinical utility of testing AQP4-IgG in CSF: Guidance for physicians [J]. Neurol Neuroimmunol Neuroinflamm, 2016, 3(3): e231. [12] WEINSHENKER B G, WINGERCHUK D M. Neuromyelitis Spectrum Disorders [J]. Mayo Clin Proc, 2017, 92(4): 663-79. [13] PAPADOPOULOS M C, VERKMAN A S. Aquaporin 4 and neuromyelitis optica [J]. Lancet Neurol, 2012, 11(6): 535-44. [14] DUAN T, SMITH A J, VERKMAN A S. Complement-independent bystander injury in AQP4-IgG seropositive neuromyelitis optica produced by antibody-dependent cellular cytotoxicity [J]. Acta Neuropathol Commun, 2019, 7(1): 112. [15] SABATINO J J, JR., PRöBSTEL A K, ZAMVIL S S. B cells in autoimmune and neurodegenerative central nervous system diseases [J]. Nat Rev Neurosci, 2019, 20(12): 728-45. [16] PITTOCK S J, ZEKERIDOU A, WEINSHENKER B G. Hope for patients with neuromyelitis optica spectrum disorders - from mechanisms to trials [J]. Nat Rev Neurol, 2021, 17(12): 759-73. [17] WILSON R, MAKUCH M, KIENZLER A K, et al. Condition-dependent generation of aquaporin-4 antibodies from circulating B cells in neuromyelitis optica [J]. Brain, 2018, 141(4): 1063-74. [18] SIRIRATNAM P, HUDA S, BUTZKUEVEN H, et al. A comprehensive review of the advances in neuromyelitis optica spectrum disorder [J]. Autoimmun Rev, 2023, 22(12): 103465. [19] CHIHARA N, ARANAMI T, SATO W, et al. Interleukin 6 signaling promotes anti-aquaporin 4 autoantibody production from plasmablasts in neuromyelitis optica [J]. Proc Natl Acad Sci U S A, 2011, 108(9): 3701-6. [20] HOSHINO Y, NOTO D, SANO S, et al. Dysregulated B cell differentiation towards antibody-secreting cells in neuromyelitis optica spectrum disorder [J]. J Neuroinflammation, 2022, 19(1): 6. [21] NISHIYAMA S, ITO T, MISU T, et al. A case of NMO seropositive for aquaporin-4 antibody more than 10 years before onset [J]. Neurology, 2009, 72(22): 1960-1. [22] JARIUS S, WILDEMANN B. AQP4 antibodies in neuromyelitis optica: diagnostic and pathogenetic relevance [J]. Nat Rev Neurol, 2010, 6(7): 383-92. [23] ZEKA B, HASTERMANN M, KAUFMANN N, et al. Aquaporin 4-specific T cells and NMO-IgG cause primary retinal damage in experimental NMO/SD [J]. Acta Neuropathol Commun, 2016, 4(1): 82. [24] ZEKA B, HASTERMANN M, HOCHMEISTER S, et al. Highly encephalitogenic aquaporin 4-specific T cells and NMO-IgG jointly orchestrate lesion location and tissue damage in the CNS [J]. Acta Neuropathol, 2015, 130(6): 783-98. [25] NURIEVA R I, CHUNG Y. Understanding the development and function of T follicular helper cells [J]. Cell Mol Immunol, 2010, 7(3): 190-7. [26] BRADL M, MISU T, TAKAHASHI T, et al. Neuromyelitis optica: pathogenicity of patient immunoglobulin in vivo [J]. Ann Neurol, 2009, 66(5): 630-43. [27] CHANG H, CONG H, WANG H, et al. Thymic Involution and Altered Naive CD4 T Cell Homeostasis in Neuromyelitis Optica Spectrum Disorder [J]. Front Immunol, 2021, 12: 645277. [28] VARRIN-DOYER M, SPENCER C M, SCHULZE-TOPPHOFF U, et al. Aquaporin 4-specific T cells in neuromyelitis optica exhibit a Th17 bias and recognize Clostridium ABC transporter [J]. Ann Neurol, 2012, 72(1): 53-64. [29] LINHARES U C, SCHIAVONI P B, BARROS P O, et al. The ex vivo production of IL-6 and IL-21 by CD4+ T cells is directly associated with neurological disability in neuromyelitis optica patients [J]. J Clin Immunol, 2013, 33(1): 179-89. [30] KEBIR H, KREYMBORG K, IFERGAN I, et al. Human TH17 lymphocytes promote blood-brain barrier disruption and central nervous system inflammation [J]. Nat Med, 2007, 13(10): 1173-5.

polar-application

by Monish Raj

i like to make an mobile application for travel

I am planning to build an Local Service Marketplace Website