Cancer is a large group of diseases that can start in almost any organ or tissue of the body when abnormal cells grow uncontrollably, and go beyond their usual boundaries to invade adjoining parts of the body. According to the World Health Organization (WHO), it is the second most common cause of death globally, accounting for millions of deaths every year. Lung, prostate, colorectal, stomach and liver cancer are the most common types of cancer in men, while breast, colorectal, lung, cervical and thyroid cancer are the most common among women. However, these are not necessarily the deadliest forms of cancer.

What makes cancer the deadliest depends upon how many people have it and what percentage of those people actually survive. Cancer researchers determine this on the basis of five-year relative survival. This is the percentage of people who are expected to survive the effects of a given cancer, excluding their risk of other possible causes of death, for five years past a diagnosis. It is also important to note that what makes cancer really deadly is that practically no cure for it. A cure for cancer would imply that there are no cancerous cells remaining in the body.

Here are the 5 deadliest cancers in the U.S., according to SEER five-year relative survival data for cases diagnosed between 2014 and 2020.

1. Pancreatic cancer occurs when cells in your pancreas, a gland in your abdomen that aids digestion, mutate and multiply out of control, forming a tumour. Major risk factors include smoking, obesity, diabetes, chronic pancreatitis, certain genetic mutations and environmental chemical exposure.

2. Esophageal cancer develops in the oesophagus, which is the tube that connects your throat to your stomach.

3. Liver cancer and intrahepatic bile duct cancer originate in the liver or bile ducts, often linked to hepatitis infections, heavy alcohol use, obesity, and aflatoxin exposure.

4. Lung and bronchus cancer primarily caused by smoking, secondhand smoke, and environmental pollutants, affects the lungs and airways, making it the leading cause of cancer death in the US.

5. Acute myeloid leukaemia (AML) is an aggressive blood and bone marrow cancer that progresses rapidly, often linked to genetic mutations, radiation exposure, and certain chemicals.

ALSO READ: Why Are Lifestyle Factors Making Millennials Vulnerable To Cancer?

While GLP-1 drugs such as Ozempic, Wegovy and Zepbound are helping with weight loss and diabetes management, men already at risk of androgenetic alopecia may face a 7% higher risk of hair loss with higher GLP-1 activity, according to a new study.

Hair thinning has been reported by both men and women using GLP-1 drugs, with researchers largely attributing the shedding to rapid weight loss associated with the medications.

However, researchers at NYU Langone Health have now identified a potential genetic link between GLP-1 activity and androgenetic alopecia, an inherited form of hair loss affecting the top and front of the scalp.

"Our study provides the first genetic link between GLP-1 use and an increased risk of male-pattern hair loss from androgenetic alopecia, an association long suspected but until now not shown scientifically," said Lynn Petukhova, assistant professor in the Department of Dermatology and the Department of Population Health at NYU Grossman School of Medicine.

What Did The Study Find?

Researchers used large, publicly available genetic databases, including:

• eQTLGen: 31,684 mostly white men and women.
• Complex Traits Genetics group: 205,327 mostly white men

The researchers compared GLP1R-related genetic data with markers associated with androgenetic alopecia. They found that genetic variants linked to naturally higher levels of GLP-1 receptor proteins were more common in men with androgenetic alopecia.

The team also adjusted the analysis for hypertension, which may affect blood flow to the scalp and hair follicle growth; and accounted for insulin resistance and lower testosterone levels.

After these adjustments, the 7% higher risk remained.

Published online in the Journal of Investigative Dermatology, the findings suggest that GLP-1 activity and male-pattern hair loss may share a biological link.

Could Screening Help Prevent Hair Loss?

The findings could eventually help identify people who may be more vulnerable to hair loss before they are prescribed GLP-1 medications.

"Our findings suggest that, if future experiments prove successful, some men, and possibly women too, could be screened and benchmarked for their risk of hair loss before being prescribed GLP-1 medications," Petukhova said.

The researchers also suggested that some GLP-1 users could potentially receive combination treatments to prevent hair loss, such as minoxidil or another drug.

However, more research is needed to understand how GLP-1 activity may affect hair follicle growth. Petukhova also plans to investigate whether the genetic link seen in men applies to women.

Hair shedding has been reported among GLP-1 users and has been linked by researchers to rapid weight loss.

In many cases, hair growth resumes several months after weight stabilizes.

The new findings add a potential genetic explanation to these reports, but further research is needed to establish the underlying biological mechanism.

What Is Androgenetic alopecia?

Androgenetic alopecia is a common form of hair loss that becomes more likely with age. It can begin as early as the teenage years and affects both men and women, with hair loss in women most commonly observed after menopause.

Surveys suggest that 12% of Americans have used GLP-1 drugs specifically for weight loss, including one-fifth of women aged 50 to 64. Since Ozempic was approved by the US Food and Drug Administration in 2020, prescriptions for the medication have more than tripled.

Researchers from the Indian Institute of Technology Madras (IIT Madras) and Christian Medical College (CMC), Vellore, have developed three artificial intelligence (AI)-based tools designed to help detect and assess kidney diseases earlier.

The technologies could help doctors identify kidney conditions more quickly, analyse medical images more consistently and assess the extent of kidney tumours in greater detail.

What Did The Researchers Develop?

The team developed three complementary technologies:

A machine learning model that uses clinical and laboratory information to predict a person's risk of chronic kidney disease (CKD).

Together, the tools have been designed to support clinicians in detecting kidney disease and assessing its severity.

How Can The AI Tools Help?

Kidney diseases can remain asymptomatic during their early stages and may go undetected until significant damage has occurred.

The researchers say their AI-based tools could assist physicians by providing rapid and consistent analysis, potentially supporting earlier diagnosis and more informed treatment decisions.

“The team aimed to develop intelligent systems that would help clinicians make quicker and more informed decisions. We used machine learning along with clinical knowledge to develop tools that would assist in the earlier detection of kidney diseases and give more detailed information specific to the patient,” said Prof. G.L. Samuel, Department of Mechanical Engineering, IIT Madras, in a statement.

AI System Trained On More Than 12,000 CT Images

The CT image classifier was trained using more than 12,000 images and can distinguish between healthy kidneys, cysts, stones and tumours.

The researchers also developed a 3D imaging framework using open-source software to measure tumour burden.

According to the team, the approach offers an inexpensive and repeatable way to assess the extent of a tumour, which could provide additional information for treatment planning.

The CKD prediction model was implemented as a user-friendly prototype interface with the aim of facilitating future clinical translation. The team also worked on improving the model's accuracy and interpretability for doctors.

“Early detection is of paramount importance when dealing with kidney diseases; these AI tools can help detect at-risk patients early and plan their treatment more effectively. The patient-specific imaging framework is of significant promise as it goes beyond the standard measurements to give a more comprehensive picture of the extent of the disease,” said Jennifer Delighta, Research Scholar, IIT Madras.

Towards A Kidney Digital Twin

The research could also contribute to the development of a kidney Digital Twin — a technology that combines AI-assisted image analysis with patient-specific 3D anatomical models.

Such virtual models could eventually help researchers and clinicians monitor disease progression, forecast changes and support more personalized treatment planning.

However, the technologies are still being developed. The researchers plan to test the models using additional patient datasets to validate their performance and establish partnerships with healthcare institutions for potential real-world deployment.

The team is also exploring the long-term integration of these AI technologies with minimally invasive wearable sensing systems and Digital Twin platforms for personalised kidney health monitoring.

On September 3, 1928, Scottish scientist Alexander Fleming returned to his laboratory after a holiday. He famously noticed the antibacterial effect of mold contaminating a Staphylococcus culture.

Fleming identified the mold as belonging to the Penicillium genus and found that it produced a substance capable of inhibiting bacterial growth, which he named penicillin.

However, Fleming’s discovery was only the beginning. Penicillin proved difficult to isolate, purify and mass-produce. During World War II, scientists Howard Florey and Ernst Chain built on Fleming’s work to develop large-scale production methods, converting penicillin into a life-saving medicine and ushering in the modern antibiotic era.

How Did Penicillin Resistance Develop?

As penicillin came into widespread medical use in the 1940s, resistance to the drug also emerged.

“Penicillin acts through a beta-lactam ring, which targets the bacteria, but then the organisms started producing an enzyme known as beta-lactamase,” Dr NK Ganguly, former Director General of the Indian Council of Medical Research (ICMR) told HealthandMe.

“This beta-lactamase broke the ring, so various derivative varieties of penicillins were synthesized,” he explained.

But as new penicillin derivatives were developed, bacteria also evolved or acquired mechanisms, including different beta-lactamases, that could break down these drugs.

As a result, penicillin became less effective against many bacteria. However, it remains effective against certain organisms and infections, including:

• Syphilis
• Group A beta-haemolytic Streptococcus
• Streptococcus pneumoniae (pneumococcus)

“Penicillin remains the gold standard for certain infections and indications, including neonatal sepsis, childhood pneumonia, rheumatic heart disease prophylaxis and resurging cases of syphilis,” Dr Ganguly said.

The Growing Threat Of Resistance

The story of penicillin resistance is an early example of a much broader problem the world is grappling with today: antimicrobial resistance (AMR), which threatens the effectiveness of modern healthcare.

Decades after antibiotics transformed medicine, the bacteria these drugs were designed to target have evolved significant resistance. This threat has been further compounded by the misuse and overuse of antibiotics across sectors.

According to the World Health Organization (WHO), approximately 1 in 6 laboratory-confirmed bacterial infections worldwide were resistant to antibiotic treatments in 2023.

Low- and middle-income countries bear the heaviest burden of infectious disease but face severe shortages of specialized antibiotics.

A global study covering 82 countries, led by the Murdoch Children’s Research Institute (MCRI), found that antibiotic resistance increased across every region between 2004 and 2022. As a result, critical treatments for routine childhood infections are becoming increasingly ineffective.

“The discovery of antibiotics is perhaps the most significant, life-changing breakthrough in the history of medicine,” Dr. Rajeev Jayadevan, Ex-President of IMA Cochin and Convener of the Research Cell, Kerala, told HealthandMe.

“However, bacteria possess natural evolutionary mechanisms to resist antibiotics as part of their survival machinery. Unfortunately, overuse in human healthcare, veterinary medicine and agriculture has allowed bacteria to continuously adapt and evade treatment,” he added.

Major Drivers Of Antimicrobial Resistance

Antimicrobial resistance is driven by a combination of clinical, agricultural, industrial and environmental factors:

1. Inappropriate syndromic and empirical prescribing
   • Viral infections
   • Unnecessary STD treatment
2. Self-medication and suboptimal adherence
3. Informal healthcare providers
4. Substandard and falsified medicines
5. Agricultural and veterinary misuse
   • Growth promoters in animal feed
   • Use of human-critical antibiotics in livestock
6. Industrial and environmental contamination
   • Manufacturing effluents
   • Fermentation and industrial waste
   • Effluents and sewage
7. Lack of public awareness

What Can Be Done?

So, are we running out of effective antibiotics? Not entirely, yet "the development of newer antibiotics has not kept pace with bacterial evolution" Dr Rajeev said.

Resistance is also making some infections increasingly difficult to treat. To counter, stronger national policies are needed the unnecessary antibiotic prescribing while ensuring that patients who genuinely need specialized antibiotics can access them.

Improving access will require coordinated action at both local and national levels. This includes:

• Ensuring quality-assured antibiotics are available and affordable in frontline healthcare settings
• Procuring the right antibiotics in adequate quantities
• Strengthening surveillance of antibiotic use
• Setting country-specific prescribing targets based on local disease burden and public health needs.

“Beyond discovering new drugs, the long-term solution lies in regulating antibiotic use globally—because antimicrobial resistance knows no boundaries. A resistant organism originating in one region can rapidly spread worldwide,” Dr Rajeev said.