Preparing the body's systems for spontaneous regression of cancer tumors with intelligent cell therapy is a modern and promising approach in oncology based on the use of specially modified cells of the immune system to recognize and destroy cancer cells. CAR-T cell therapy is a technology in which T lymphocytes are taken from a patient, genetically modified to express special receptors capable of recognizing antigens on the surface of tumor cells, and then returned to the body to fight cancer. TIL therapy uses lymphocytes extracted directly from the tumor, which are then activated and scaled in vitro and reintroduced into the patient to fight cancer cells. Dendritic cell vaccines stimulate the immune response against cancer. The advantages of intelligent cell therapy include high specificity and the ability to adapt to various tumor types. Elimination of cancer tumors using spontaneous regression provides a key to understanding the natural mechanisms of resistance to cancer cells and potential methods for their natural destruction. Spontaneous regression activates the immune system, which recognizes, modifies and destroys cancer cells. Hormonal changes inside tumor cells cause their spontaneous disappearance. Technological combination of intelligent cell therapy and spontaneous regression is a promising approach in the field of oncology and regenerative medicine. Firstly, the use of cognitive and artificial intelligence systems for diagnostics, monitoring and optimization of cell therapy, secondly, the implementation of machine learning algorithms and big data analysis to identify factors predisposing to spontaneous regression, thirdly, the development of individual therapy protocols based on predictive models, which increases the effectiveness and safety of treatment. Technological combination of intelligent cell therapy and spontaneous regression mechanisms opens new horizons for personalized treatment, increasing effectiveness and minimizing side effects. Increases the effectiveness of therapy by integrating natural regression processes, predicts spontaneous regression, stimulates immune mechanisms using motivated artificial intelligence. In the future, such approaches can become the basis for innovative methods of combating oncological and other serious diseases.

Malignant tumors of various organs differ in morphological, clinical and molecular features. Let's consider the main differences for some organs: 

1. Lungs: squamous cell carcinoma or small cell carcinoma. Metastases to the lymph nodes, brain, bones. 

2. Breast: invasive or non-invasive carcinoma. Characterized by hormonal sensitivity (estrogen or progesterone receptors). Metastases to bone tissue, liver, lungs. 

3. Liver: hepatocellular carcinoma. Associated with cirrhosis, viral hepatitis. Metastases are usually few, primary cancer dominates. 

4. Stomach: adenocarcinoma. Associated with Helicobacter pylori. Metastases to the lymph nodes, liver. 

5. Intestines: adenocarcinoma of the colon. Can develop against the background of polyps. Liver metastases, lymph nodes. 

6. Prostate: Malignant tumors - adenocarcinomas. Metastases - to bones. 

7. Brain: gliomas, metastases from other organs. Metastases from the lungs, mammary gland are the most common. They progress rapidly, causing neurological symptoms. 

General differences include cell type, growth rate, tendency to metastasize and response to treatment. Each tumor has unique characteristics, which are important to consider when diagnosing and choosing a therapy. 

Activation of body systems with the help of intelligent cell therapy for spontaneous regression of cancer tumors is a modern and promising approach in oncology, based on the use of specially modified cells of the immune system to recognize and destroy cancer cells [1-8].

Spontaneous regression processes in malignant tumors are a phenomenon in which the tumor shrinks or completely disappears without specific treatment [9-17]. These processes may manifest themselves differently in different organs. This is due to the peculiarities of tumor biology, immunological mechanisms, and the microenvironment of the organs. The main differences in spontaneous regression in different organs are listed below: 

1. Lungs: spontaneous regression is associated with hypersensitivity or an immune reaction caused by an infection, such as tuberculosis. In some cases, regression is associated with inflammatory processes or antibodies. 

2. Breast: spontaneous disappearance of some invasive carcinomas, especially with hormonal dependence. Associated with changes in hormonal levels and immune response. The mechanism often involves apoptosis of tumor cells. 

3. Liver: spontaneous regression of hepatocellular carcinoma is observed. May be accompanied by inflammation or necrosis caused by immune mechanisms or viral infection. 

4. Stomach and intestines: spontaneous regression is associated with inflammatory processes, infections or immune attack. 

5. Prostate: hyperplasia or tumors decrease with immune reactions. 

6. Brain: a decrease in metastases is observed due to immune processes or therapy that coincide with natural changes. 

7. General features of spontaneous regression processes: associated with activation of the immune system, including cytokines, T-lymphocytes, NK cells. In some cases, they are caused by infections that stimulate an immune response against the tumor. May be accompanied by necrosis, inflammation and changes in the tumor microenvironment. Spontaneous regression is more often observed in tumors with high immunogenic activity, such as melanoma, renal cell carcinoma. 

The mechanisms of spontaneous regression remain poorly understood, and this phenomenon is still difficult to predict and use in therapy.

2. Conditions for triggering spontaneous regression of a malignant tumor 

Conditions for triggering spontaneous regression of a malignant tumor are a complex and multifactorial process that includes the interaction of the immune system, tumor cell characteristics, and the microenvironment. The main conditions and factors that contribute to this phenomenon are as follows: 

1. Activation of the immune system: 

- Inflammatory reactions that attract T-lymphocytes, NK cells, and macrophages to tumor tissue. - Release of cytokines (e.g., interferons, interleukins) that stimulate the immune response. 

2. Infections and inflammations: 

- Some infections, such as tuberculosis or viral diseases, can stimulate the immune system and promote tumor cell recognition. 

- Inflammatory processes can lead to tumor tissue necrosis and activation of immune mechanisms. 

3. Immunological reaction against the tumor: 

- Detection of tumor antigens and their presentation to lymphocytes. 

- Formation of antibodies and cytotoxic cells that attack the tumor. 

4. Modification of the tumor microenvironment: 

- Reduction in the level of immune regulatory factors that suppress immunity. 

- Changes in the vascular system that increase the penetration of immune cells. 

5. Genetic and molecular features of tumor cells: 

- Mutations that increase their immunogenicity. 

- Low expression of factors that suppress the immune response. 

6. Triggers of external or internal stimulation: 

- Medical procedures or accidental injuries that cause inflammation. 

- Temporary decrease in immune tolerance. 

7. Hormonal or metabolic changes: 

- Disturbances that affect the growth and immune response of tumor cells. 

In general, spontaneous regression is triggered when the immune system recognizes the tumor as a foreign object and responds to it effectively, overcoming the immune suppression mechanisms that develop in the tumor environment.

3. Physiological process of spontaneous regression of various malignant tumors 

The physiologic process of spontaneous regression of various malignant tumors is a well-documented phenomenon in which the tumor shrinks or completely disappears without targeted treatment. This process is associated with complex immunological, genetic and microbiological mechanisms. Below is an overview of the key aspects of the physiology of spontaneous tumor regression. 

1. Immunological mechanisms 

- Involvement of the immune system: activation of immune cells such as cytotoxic T lymphocytes (CD8+), fasting NK cells and macrophages leads to recognition and destruction of cancer cells. 

- Inflammatory reactions: local inflammation can stimulate the immune response, contributing to the suppression of tumor growth. 

- Immunological memory: sometimes regression is associated with the development of long-term immunological memory, which prevents relapse. 

2. Genetic and molecular factors 

- Genetic mutations: Some tumors may have genetic changes that make them more vulnerable to the immune response or induce apoptosis. 

- Release of antiviral and antimicrobial cytokines: interferons, for example, help suppress tumor growth. 

3. Intracellular and cellular mechanisms 

- Apoptosis: programmed cell death of tumor cells triggered by internal or external signals. 

- Decreased angiogenesis: decreased formation of new blood vessels needed to feed the tumor. 

- Microenvironmental effect: changes in the tumor microenvironment, including suppression of vascular growth and changes in immune cells. 

4. Microbiota and environmental effects 

- Some evidence suggests that the gut microbiota and external factors may influence the immune response and promote regression. 

5. Hormonal mechanisms 

- In some cases, changes in hormonal status (e.g., decreased levels of hormones that stimulate tumor growth) can promote regression. 

6. Other factors 

- Genetic and epigenetic changes that make the tumor vulnerable. 

- The influence of stress or infectious agents that activate immune responses. 

Spontaneous regression of malignant tumors is a complex interdisciplinary process that includes activation of the immune system, molecular changes, and the tumor microenvironment. This process stimulates the body's own mechanisms to suppress tumor growth.

4. Recording and monitoring of spontaneous regression of malignant tumors 

Recording and monitoring of spontaneous regression of malignant tumors are important steps in the study of this rare phenomenon, allowing to understand its mechanisms and to ensure the accuracy of data. The main directions and methods include: 

1. Documentation of clinical cases 

- Detailed description of the anamnesis, time frame of regression, symptoms and factors preceding the event. 

- Photographic recording of changes in the appearance of the tumor (e.g. reduction in size, disappearance of nodes). 

2. Medical imaging 

- Use of imaging methods: ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET). 

- Regular monitoring of changes in tumor size and structure over time. 

3. Histological and molecular recording 

- Tumor biopsy before and during regression for morphological analysis. 

- Use of fixatives (e.g. formalin), preservation of samples for further examination, including immunohistochemical methods. 

4. Monitoring of immunological parameters 

- Blood and tissue analysis for the presence of immune cells, cytokines, markers of immune system activation. 

- Evaluation of changes in the immune environment in the peripheral blood and inside the tumor. 5. Regular observation and recording.

- Continuous clinical observation of the patient with recording of any changes, symptoms or signs of regression. 

- Maintenance of detailed protocols and databases for analysis of regression patterns and possible factors influencing the process. 

6. Etiological and factorial analysis 

Evaluation of possible triggers, such as infectious agents, immunological reactions, treatment, stressful situations. 

Effective fixation and observation allow not only to document cases of spontaneous regression, but also to reveal the mechanisms of this phenomenon, which is important for application.

5. Directions of Clinical Trials of Spontaneous Regression of Malignant Tumors 

Directions of clinical trials of spontaneous regression of malignant tumors include several key areas:

1. Study of immunological mechanisms 

- Analysis of the role of the immune system in spontaneous regression, including the participation of T-lymphocytes, macrophages and cytokines.

- Identification of biomarkers associated with the activity of the immune response during regression. 

2. Genetic and molecular studies 

- Study of genetic changes and mutations in regressing tumors. 

- Analysis of the expression of genes associated with apoptosis, proliferation and immune regulation. 

3. Clinical observations and retrospective studies 

- Collection of data on cases of spontaneous regression, their characteristics, age, tumor type and conditions. 

- Analysis of factors preceding regression and possible triggers. 

4. Experimental models and preclinical studies 

- Development of animal models that reproduce spontaneous regression. 

- Testing hypotheses about the mechanisms of regression, including the role of immune cells and molecules. 

5. Research into therapeutic approaches that mimic regression 

- Development and evaluation of immunotherapies that stimulate similar processes. 

- Studying methods for activating the immune response to achieve tumor regression. 

These areas of clinical trials help to understand the mechanisms of spontaneous regression and may contribute to the development of new treatments for malignant tumors based on stimulating their own regression processes.

6. Medical indicators that determine the body's readiness for spontaneous regression of a malignant tumor. 

Medical indicators that determine the body's readiness for spontaneous regression of a malignant tumor include a number of immunological and biochemical characteristics indicating the activation of the body's defense mechanisms. These indicators help to understand how well the immune system is able to fight the tumor and whether its natural regression is possible [18-19]. The main ones are: 

1. High activity of immune system cells: 

- Increased number of cytotoxic T-lymphocytes (CD8+ T-cells) and natural killers (NK-cells) in the bloodstream and in tumor tissue. 

- Increased activity of macrophages capable of destroying cancer cells. 

2. Changes in the cytokine profile: 

- Increased levels of interferons (for example, IFN-γ), which stimulate antitumor immunity. 

- Increased levels of interleukins (e.g. IL-2) that promote proliferation of T cells and NK cells. 

3. Presence of antibodies to tumor antigens: 

- Presence of specific antibodies in the blood indicating an active immune response against the tumor.

4. Presence of tumor antigens and immune complexes: 

- Detection of tumor-specific antigens in the blood or tissue indicating an immune system response to the tumor site. 

5. Intracellular and interstitial parameters: 

- High proliferative activity of lymphocytes in tumor tissue. 

- Presence of apoptotic cells within the tumor indicating an active process of cancer cell destruction.

6. Genetic and molecular markers: 

- Genetic predisposition to an enhanced immune response (e.g. certain HLA alleles). 

- High expression of genes associated with immune activation. 

7. General body parameters: 

- No severe concomitant diseases or immunodeficiency. 

- Good functional status of the body (e.g., ECOG or Karnofsky scale). 

These parameters together help to assess the body's potential ability to spontaneous tumor regression. Research in this area is ongoing, with the aim of determining the precise predictors and mechanisms of this phenomenon.

7. Preparation of body systems for spontaneous regression of malignant tumors using intelligent cell therapy 

Preparation of body systems for spontaneous regression of malignant tumors using intelligent cell therapy includes a set of measures aimed at strengthening and modulating the immune system to increase the likelihood of natural destruction of tumor cells. This approach involves not only the use of cellular preparations, but also the integration of modern technologies and methods to optimize the immune response. The main stages and areas of preparation include: 

1. Diagnostic assessment of the body's immune status: 

- Analysis of the immune cell profile (T-lymphocytes, NK cells, macrophages). 

 Determination of the levels of cytokines, antibodies and tumor antigens. 

- Genetic and molecular characterization of the immune system to identify the potential for regression.

2. Modulation of the immune system: 

- Use of immunomodulators (e.g. interferons, interleukins) to increase the activity of T- and NK-cells. 

- Administration of immunostimulants that stimulate the development and activation of immune effectors. 

- Correction of the imbalance between regulatory and active T cells. 

3. Personalization of cell therapy: 

- Isolation, modification, and expansion of autologous immune cells (e.g., T lymphocytes, dendritic cells) to enhance their antitumor activity. 

- Use of genetic modification technologies (e.g., CAR-T or TCR therapy) to increase the specificity and effectiveness of cells. 

4. Providing a microenvironment favorable for regression: 

- Monitoring the condition of surrounding tissues and reducing inflammatory processes or immune suppression. 

- Use of drugs that reduce tumor immune tolerance (e.g., checkpoint inhibitors). 

5. Maintaining general health and eliminating concomitant factors: 

- Improving nutrition, correcting metabolism, eliminating chronic infections and inflammation. 

- Adequate therapy of concomitant diseases to increase overall resistance. 

6. Monitoring and correction of therapy: 

- Constant monitoring of immune parameters and tumor condition. 

- Adaptation of protocols depending on the dynamics of the body's response. 

7. The intelligent aspect of cell therapy involves the use of modern information technologies, such as artificial intelligence systems, bioinformatics and personalized models, to predict the body's response, optimize protocols and timely activation [20]. 

The goal of intelligent cell therapy is to create conditions inside the body that maximally promote the activation of immune mechanisms and the destruction of tumor cells, which can lead to spontaneous regression without the need for aggressive drug intervention. However, it is important to emphasize that such approaches require an individual approach, interdisciplinary research and clinical confirmation.

8. Control of spontaneous regression by a motivated adaptive reflexive erudite AI assistant 

Using a motivated adaptive reflexive erudite AI assistant with a multimodal ontology to identify the body's readiness for spontaneous regression of a specific malignant tumor based on medical indicators is an advanced technology that combines modern achievements in the field of artificial intelligence, ontologies and medical diagnostics [21-22]. Below is an analysis of the key components and possible approaches to the implementation of such a system. 

1. Motivated adaptive AI assistant 

- Motivation: the system must have built-in goals and motivation to search for and analyze data related to tumor regression. 

- Adaptability: the ability to learn and adjust its models based on new data, individual patient characteristics and the dynamics of the condition. 

- Reflexivity: the ability to evaluate one's own decisions and conclusions, as well as adjust them. 

2. Erudite AI assistant 

- Has an extensive knowledge base in the field of medicine, oncology, biology and statistics. 

- Uses expert knowledge to interpret data and form hypotheses. 

3. Multimodal ontology 

- Integration of various types of data: medical images (MRI, CT, ultrasound), laboratory parameters, genetic data, clinical observations. 

- Structuring knowledge using ontologies to ensure interrelation and semantic consistency. 

4. Identifying the body's readiness for spontaneous regression 

- Analysis of medical indicators: marker levels, genetic mutations, immunological indicators, features of the tumor histotype. 

- Monitoring the dynamics of these indicators over time. 

- Using machine learning and statistical analysis methods to determine signs preceding spontaneous regression. 

5. System architecture: 

- Collection and integration of multimodal data using interfaces and sensors. 

- Processing and preliminary analysis of data using multimodal neural networks. 

- Using ontological models for semantic data unification. 

- Using adaptive machine learning models trained on individual patient data. 

- Internal reflexive system for assessing the accuracy of predictions and adjusting the model. 

- Visualization of results and recommendations for doctors. 

- Creating a knowledge base and ontologies in the medical field. 

- Developing modules for processing multimodal data. 

- Implementation of machine learning methods (e.g. deep neural networks, random forests) for pattern analysis. 

- Building a feedback system for continuous learning and improvement of the model. 

Using such a system will improve the accuracy of prediction and identification of patients who have the potential for spontaneous tumor regression, which can significantly affect the choice of treatment and monitoring tactics. An important aspect is ensuring the reliability and interpretability of AI solutions, as well as compliance with ethical and legal standards in medical practice.

The future of intelligent regression of malignant tumors is a promising direction in oncology based on the integration of modern technologies and fundamental research. 

1. Personalized medicine and forecasting 

- Using artificial intelligence (AI) to analyze genomic, proteomic and clinical data of the patient. - Creating models that predict the likelihood of spontaneous regression or the success of therapeutic interventions. 

- Individual treatment protocols optimized for the characteristics of a particular patient. 

2. Mechanisms of spontaneous regression and their stimulation 

- In-depth study of immunological and molecular factors that contribute to regression. 

- Development of methods for pharmacological or genetic stimulation of natural regression processes. 

- Implementation of gene therapy technologies and immunomodulators to activate tumor self-regulation mechanisms. 

3. Integration of artificial intelligence and big data 

- Using machine learning to identify patterns preceding regression. 

- Constantly updating knowledge bases and algorithms based on new clinical data. 

- Development of decision support systems for doctors and patients. 

4. Biomarkers and diagnostics 

- Identification of biomarkers predicting the tendency to regression. 

- Creation of non-invasive methods for monitoring the tumor condition and the effectiveness of therapy. 

- Use of liquid biopsy and nanotechnology to track the dynamics of the process. 

5. Ethical and clinical aspects 

Development of standards and protocols for the safe use of regression stimulation technologies. - Conducting clinical trials to evaluate the effectiveness and safety of new methods. 

Ensuring patient awareness and consent. 

In general, the future of intelligent regression of malignant tumors involves the creation of complex, dynamically adaptive systems that can not only predict and stimulate natural regression processes, but also integrate them into individual treatment regimens. Such a strategy has the potential to radically change approaches to oncological therapy, increase its effectiveness and reduce side effects.