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Published on: May 31, 2021
1 minute read
By: Dr Rajan Choudhary

REGN-COV2 – Artificial "antibody Cocktail"

REGN-COV2 – Artificial "antibody cocktail"

Recently a new drug was approved for use in COVID patients in India. Known as REGN-COV2, this new therapy is based on monoclonal antibodies targeting the SARS-CoV-2 virus responsible for COVID. So how does it work? And is there any evidence behind the treatment?

Monoclonal antibodies are made from components of the human immune system. When the human body recognizes a bacteria or virus in the body, the immune system recognizes and processes proteins on the surface of that bacteria or virus. This is presented to the adaptive immune system, and antibodies have produced that lock onto these surface proteins, neutralizing their function and also tagging them for destruction by other white blood cells.

If we identify antibodies capable of efficiently tagging and neutralizing a certain virus, we can identify the cell capable of producing that antibody, clone it and produce it in vast quantities. The antibodies produced from this cloned cell line will be able to target the virus at the same efficiency and have the same ability to neutralize the virus. Just like with vaccines, there is a risk of mutations developing, resulting in changes in the surface protein that reduce binding and neutralizing effects of the antibodies, allowing them to escape and continue to cause disease

REGN-COV2 uses two neutralizing antibodies (Casirivimab/imdevimab), in an effort to minimize the ability of a mutant virus to develop resistance against the antibody cocktail, as the virus would need to develop mutations canceling both antibodies out at once. These antibodies neutralize the receptor-binding domain of SARS-CoV-2 spike protein, preventing viral entry through ACE2 receptors.

In a study published in the New England Journal of Medicine (one of the most prestigious journals in the medical field) data from 275 patients showed a drastic reduction in viral load in patients treated with REGN-COV2. This is clinically significant as hospitalized patients with severe symptoms have higher viral counts than non-hospitalized or asymptomatic patients, suggesting the hypoxaemic, shortness of breath symptoms are related to the viral count and resulting exaggerated immune response.

Based off this result the European Medicines Agency approved the use of REGN-COV2 antibody cocktail in patients who do not require supplemental oxygen but are at high risk of progressing to severe COVID-19.

FDA regulations currently recommend it for patients who are COVID positive with risk factors that may result in their hospitalization but do not recommend it for patients who are already hospitalized, requiring O2 therapy because of COVID or need chronic O2 therapy.

Those that fall into the high-risk categories include patients with a high BMI (over 35), diabetes, on immunosuppressive treatment or with an immunosuppressive disease, over 65 years of age or those with chronic diseases such as renal disease, cardiovascular disease, COPD, or other developmental disorders.

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AI-Based Disease Detection India: How SecondMedic Is Transforming Early Diagnosis

India’s healthcare landscape is evolving rapidly, with artificial intelligence emerging as one of the most powerful tools for early disease detection. AI-based disease detection India represents a major shift from reactive healthcare to predictive, preventive, and precise medical analysis. Instead of waiting for symptoms to appear, AI enables clinicians and patients to identify risks early through advanced data interpretation.

Rising chronic diseases, increased diagnostic loads, and limited specialist availability make AI essential for early diagnosis in India. The use of AI in medical imaging, risk scoring, and pattern recognition significantly enhances accuracy while reducing time-consuming manual processes. SecondMedic integrates AI-powered diagnostic tools to help individuals detect health conditions in their earliest stages, enabling timely intervention and improved long-term outcomes.

Why India Needs AI-Based Disease Detection

India faces one of the world’s highest burdens of chronic and lifestyle diseases. Many conditions remain undiagnosed until they reach advanced stages, often due to late screenings, limited access to specialists, or lack of early symptoms.

The need for AI-based detection is driven by:

High incidence of silent diseases like diabetes and hypertension
Overloaded healthcare systems
Limited availability of expert radiologists
Rising lifestyle risk factors
Increasing demand for precision diagnostics
Need for faster, more accurate analysis

AI bridges these gaps by providing early alerts, consistent accuracy, and fast interpretations.

How AI Detects Diseases Early

AI-based disease detection uses machine learning models trained on thousands of medical datasets. These models learn to recognize abnormal patterns and subtle changes that the human eye might overlook.

AI analyzes:

Blood test patterns
Vital signs and wearable data
Imaging scans (X-rays, MRIs, CT scans)
Medical history
Genetic predispositions
Lifestyle habits

Through advanced algorithms, AI can identify risks long before symptoms appear, giving patients critical time for prevention and treatment.

AI in Medical Imaging: A Major Breakthrough for India

Medical imaging AI has transformed diagnosis speed and accuracy. In India, where access to radiologists is uneven, AI helps bridge diagnostic gaps.

AI-assisted imaging helps detect:

Lung infections and tuberculosis
Early-stage cancer indicators
Cardiac abnormalities
Brain tumors and neurological issues
Bone fractures and musculoskeletal conditions
Liver and kidney anomalies

SecondMedic uses AI-supported imaging interpretation to enhance precision and reduce reporting delays.

AI for Chronic Disease Prediction

Chronic illnesses often develop silently. By analyzing long-term trends, AI can predict disease progression and alert patients earlier.

AI helps forecast:

Prediabetes to diabetes progression
Heart attack risk
Hypertension development
Chronic kidney disease
Thyroid dysfunction
Metabolic health decline

These predictions allow individuals to take preventive action far in advance.

Personalized Disease Detection with AI

AI enables personalized diagnostics by incorporating each user’s biological and lifestyle data into prediction models.

Personalized AI detection considers:

Age and family history
Diet and activity levels
Sleep patterns
Stress levels
Blood markers
Genetic factors

This creates a highly individualized health risk profile.

SecondMedic’s AI engine creates a personalized risk score for each user, allowing targeted preventive strategies.

AI for Cancer Early Detection

Cancer often goes undiagnosed until it reaches advanced stages. AI helps detect early warning signs by analyzing subtle abnormalities.

AI supports early cancer detection in:

Breast cancer (mammograms)
Cervical cancer (Pap tests and visual scans)
Lung cancer (X-rays and CT scans)
Colon cancer indicators
Skin cancer lesion analysis

This improves survival rates by supporting early diagnosis.

Real-Time Monitoring with AI

Continuous monitoring is essential for early disease detection. AI integrates with wearable devices and digital health tools to track vital parameters in real time.

AI monitors:

Heart rate trends
Oxygen levels
Blood pressure variations
Stress levels
Sleep quality
Blood glucose fluctuations (connected devices)

Real-time alerts notify users of abnormalities that require attention.

AI in Public Health Disease Detection

AI is also used at the population level to identify disease patterns and outbreaks.

AI supports public health by:

Predicting outbreak patterns
Analyzing environmental health impact
Tracking regional disease trends
Supporting government screening programs

This strengthens India’s preventive health strategy.

How SecondMedic Uses AI for Disease Detection

SecondMedic integrates AI tools throughout its digital healthcare ecosystem, helping individuals access early detection and preventive insights.

SecondMedic’s AI capabilities include:

Risk scoring for diseases
AI analysis of medical reports
Predictive analytics dashboards
Early-warning alerts
Integration with wearables
AI-supported doctor consultations

This helps users understand risks clearly and take action early.

Challenges in AI-Based Disease Detection

While AI offers powerful benefits, it must be used responsibly.

Challenges include:

Requirement of high-quality medical data
Need for clinical validation
Maintaining data privacy
Avoiding algorithmic bias
Ensuring user awareness and understanding

SecondMedic follows ethical AI practices aligned with DPDP Act and ABDM standards.

Future of AI-Based Disease Detection in India

AI will continue to redefine diagnostics in India over the next decade.

Future developments include:

Deep AI for full-body scan interpretation
Genomic-based AI predictions
Emotion and mental health detection through AI
AI-assisted virtual triage systems
At-home AI diagnostic kits
National integrated AI health platforms

SecondMedic aims to lead in these innovations by integrating advanced predictive tools.

Conclusion

AI-based disease detection India is shaping a new era of proactive healthcare. By analyzing health patterns, detecting abnormalities early, and providing accurate risk assessment, AI empowers individuals to act before diseases progress. SecondMedic uses AI-driven diagnostic tools to support early detection, preventive care, and long-term health protection.

To explore AI-powered diagnostic support, visit www.secondmedic.com

References

NITI Aayog – AI for Healthcare in India
WHO – AI in Early Disease Detection
ICMR – Chronic Disease Patterns India
IMARC – Indian AI Healthcare Market
FICCI – AI and Precision Medicine India

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