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Published on: Jan 02, 2021
2 minute read
By: Dr Rajan Choudhary

COVID-19 Variant: What We Know About This New Mutation

COVID-19 Variant: What we know about this new mutation

In early December a new variant of COVID -19 was detected in the UK, raising concerns across the world. SARS-CoV-2 has already significantly impacted the world, with 84 million cases worldwide and nearly 2 million deaths. Could the new variant cause further havoc? Is it something we should be worried about? Today we will have a look at what we know so far about the virus.

MUTAGENESIS

To start with we should go over viral mutations. Unlike complex organisms, viruses are highly prone to genetic mutations, even more than bacteria. All organisms including humans, birds, even worms, are prone to mutations as well. However due to their complexity, there is much higher risk of mutations causing significant problems with their cellular and genetic processes, problems that are often incompatible with life, or lead to cancer. For this reason, there are significant genetic roadblocks present to prevent such mutations from occurring.

Viruses on the other hand have genetic replication machinery of poor “quality”, prone to introducing mutations. Since they replicate quickly, with little care on which viral particles survive, it matters little if hundreds of viruses do not survive, as further thousands will and continue to spread in their host. It is for this reason we have such difficulty treating viruses or making viruses against them .

COVID VARIANT

The variant was first picked up by the COVID-19 Genomics UK consortium, which undertakes random genetic sequencing of positive COVID-19 samples across the UK. Since April they have sequenced 140,000 virus genomes, to identify and track outbreaks across the UK. The strain was first identified in September and sequenced in early October. However, the significance of this strain was not realized until the end of the year. By 13th December 1108 cases had been identified across 60 different locations, though the true number is likely much higher. In Norfolk, it accounts for nearly 20% of all samples.

17 Variations have been identified, most significantly in the spike protein the virus uses to bind to the ACE2 receptor found in the lungs. Changes in this protein may have resulted in it being more infectious and spreading more quickly between people. A review of current evidence has shown the rate of transmission was 71% higher than the other variants and may also have a much higher viral load. This has given it an advantage over the other COVID-19 strains- it has already been detected in South Africa, Europe, and America, and it is likely to become the dominant global strain in the near future.

It appears children are more susceptible to catching this virus. The virus propagated at a time when schools were open and running, whilst the rest of the country remained in lockdown. This may have provided a larger pool of children for the virus to spread in, resulting in this change. However, this does not mean that the virus “attacks” children, rather it is able to attach to ACE2 receptors in children’s lungs with greater ease and spread quickly.

VACCINE

The most important question on everyone’s mind – will the vaccine be effective against this new strain? If not, lockdown rules may be extended until new vaccines are discovered, and by then newer strains may leapfrog ahead and make the new vaccines irrelevant again.

So far experts believe that the new variant is unlikely to make vaccines ineffective. The vaccines all produce antibodies against the viral spike protein, but so far it appears the mutation has not changed the shape or function of the spike protein enough for antibodies to fail against it. The antibodies should be able to recognize enough sites on the spike protein to successfully attach, neuter the protein, and present the virus for destruction by the body’s immune system. Unfortunately, it will take some time to fully understand the effects of the mutation, though we can remain hopeful for now

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How Artificial Intelligence Is Transforming Patient Care in India

As a clinician working closely with patients across urban clinics and remote teleconsultation setups, I have seen firsthand how delayed diagnosis, fragmented follow-up, and specialist shortages affect outcomes in India. Artificial intelligence is not a futuristic concept in Indian healthcare anymore. It is actively reshaping how we diagnose diseases, monitor patients, and prevent complications.

AI, when used responsibly under clinical supervision, is becoming a critical support system for doctors and a powerful safety net for patients navigating a complex healthcare ecosystem.

Why India’s Healthcare System Needs AI

India’s healthcare challenges are deeply structural. A large population burdened by lifestyle diseases, combined with uneven access to medical expertise, creates gaps that traditional systems struggle to bridge.

In daily practice, we increasingly see patients presenting late with diabetes, hypertension, heart disease, or cancer. Many ask a simple but important question: why was this not detected earlier? The answer often lies in limited screening, overloaded clinicians, and lack of continuous monitoring.

Chronic conditions dominating Indian clinics today include:

Diabetes affecting over 100 million individuals.
Hypertension rising even among young adults.
Cardiovascular disease driven by late detection.
Increasing cancer incidence with delayed diagnosis.

AI matters here because it supports earlier identification of risk patterns, reduces diagnostic delays, and allows clinicians to focus on decision-making rather than data overload.

How AI Is Changing Medical Diagnosis

One common concern patients raise during consultations is whether AI can truly diagnose diseases accurately. In practice, AI does not replace a doctor. It acts as a high-speed analytical assistant.

AI in Imaging and Diagnostics

AI systems can rapidly analyse:

X-rays and CT scans.
MRI images.
Mammograms.
Pathology slides.
Cardiac and neurological imaging.

These tools flag abnormalities within seconds, allowing doctors to prioritise critical findings. Clinical studies published in peer-reviewed journals have shown that AI models can match specialist-level accuracy for specific imaging tasks when used correctly.

From a physician’s perspective, the real benefit is not speed alone. It is consistency. AI reduces the risk of missed findings during high-volume diagnostic workflows, especially in resource-constrained settings.

Can AI Monitor Patients Outside Hospitals

Patients managing chronic illness often ask whether technology can help them avoid repeated hospital visits. AI-enabled remote monitoring is one of the most meaningful advances in this area.

AI-Supported Remote Patient Monitoring

AI continuously evaluates trends in:

Blood pressure.
Heart rate variability.
Blood glucose patterns.
Oxygen saturation.
Physical activity and sleep quality.

Rather than reacting to a single abnormal value, AI identifies worsening trends over time. Clinically, this allows early intervention before complications escalate.

Evidence from global health system studies shows that continuous monitoring can significantly reduce avoidable hospital admissions, particularly for diabetes, heart disease, and elderly patients.

Using AI to Predict and Prevent Chronic Diseases

Preventive healthcare remains underdeveloped in India. Most patients seek care after symptoms appear. AI helps shift this model.

By analysing medical history, lifestyle habits, vitals, and environmental factors, predictive models can estimate:

Future heart attack risk.
Progression of diabetes.
Decline in kidney function.
Stroke probability.
Asthma exacerbation triggers.

Patients often ask if AI can really prevent disease. Prevention here means early warnings. When risk patterns are detected early, doctors can adjust treatment plans, recommend lifestyle changes, and prevent irreversible damage.

Personalised Treatment in a Diverse Indian Population

Indian patients differ widely in genetics, diet, stress patterns, and cultural habits. Standardised treatment protocols often fall short.

AI supports personalised care by analysing:

Medication responses.
Dietary intake.
Blood markers.
Sleep and stress trends.
Coexisting medical conditions.

For example:

In diabetes care, AI helps personalise carbohydrate distribution and medication timing.
In hypertension, it identifies sodium sensitivity and stress-related spikes.
In hormonal conditions like PCOS, it aligns nutrition and activity with cycle patterns.

From a clinical standpoint, personalised insights improve adherence and reduce relapse rates.

AI-Enabled Telemedicine and Smarter Consultations

Telemedicine has become an essential part of care delivery in India. Patients frequently ask whether online consultations are as effective as in-person visits.

AI enhances telemedicine by:

Structuring symptom inputs before consultations.
Routing patients to the appropriate specialist.
Generating concise medical summaries for doctors.
Supporting follow-up reminders and medication adherence checks.

When used correctly, AI reduces diagnostic delays and improves consultation efficiency without compromising safety.

Expanding Healthcare Access Beyond Cities

A major question in public health is whether AI can truly improve rural healthcare access. In practice, it already is.

AI enables:

Remote diagnostics supported by portable devices.
Virtual specialist consultations for rural clinics.
Smartphone-based imaging and screening tools.
AI-guided triage in underserved regions.

By reducing dependence on physical proximity to specialists, AI helps bridge longstanding geographical barriers in India’s healthcare system.

Safety, Ethics, and the Role of Doctors in AI Care

Patients rightly express concern about safety, privacy, and over-reliance on technology. These concerns are valid.

Responsible AI use in healthcare requires:

Transparent algorithms.
Explicit patient consent.
High-quality, verified medical datasets.
Strict data privacy safeguards.
Continuous clinical supervision.

In ethical practice, AI outputs never replace medical judgment. Doctors remain accountable for decisions. Human-in-the-loop verification is essential to ensure patient safety and trust.

What This Transformation Means for Indian Patients

Artificial intelligence is fundamentally changing patient care in India by making healthcare more proactive, more precise, and more accessible. From early diagnosis to personalised treatment and continuous monitoring, AI empowers both patients and clinicians with data-backed clarity.

SecondMedic’s patient-first approach integrates AI as a clinical support system, not a replacement for doctors. By combining medical expertise with digital intelligence, the goal remains simple: better outcomes, earlier intervention, and care that adapts to each patient’s real-world needs.

As clinicians, our responsibility is to ensure that technology serves patients ethically and effectively. When used with care and oversight, AI has the potential to redefine healthcare delivery across India in a way that is inclusive, preventive, and sustainable.

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