Breakthroughs, discoveries, and DIY tips sent six days a week. Whether it's the ocean's deepest hydrothermal vents or tall mountain peaks, bacteria is likely surviving and thriving. Ice caves can host a wide variety of microorganisms and offer biologists a bevy of genetic diversity that still has to be studied. And it could help save lives. A team of scientists in Romania tested antibiotic resistance profiles with a bacterial strain that was hidden in a 5,000-year-old layer of ice inside an underground ice cave.

New Zealand's critically endangered kākāpō are not showing signs of antibiotic resistance. Breakthroughs, discoveries, and DIY tips sent every weekday. With only 237 birds left in the wild, saving New Zealand's critically endangered kākāpō is one of the small country's major conservation projects. These giant, green camouflage experts are threatened by predators, invasive species, human encroachment, and a debilitating illness colloquially called crusty bum disease (exudative cloacitis). Birds that contract it can become infertile, which puts strain on their already small populations.

New antibiotics capable of killing drug-resistant gonorrhoea have been developed by AI. Experts believe that Artificial Intelligence could signify a'second golden age' of antibiotic discovery, after creating two drugs that could be capable of killing superbugs such as gonorrhea and MRSA. Led by Professor James Collins at the Massachusetts Institute of Technology (MIT), a specialist research team used generative AI algorithms to interrogate 36million compounds. The experts then trained the AI to help it learn how bacteria was affected by different molecular structures built of atoms in order to design new antibiotics. In order to do this, they gave it the chemical structure of known compounds and data on their ability to hinder the growth of different bacteria species. Throughout the study, published in the journal Cell, anything too similar to the current antibiotics available, or with the potential to be toxic to human beings, was eradicated.

Infecting roughly 1 in 2 individuals globally, it is the leading cause of peptic ulcer disease, chronic gastritis, and gastric cancer. To investigate whether personalized treatments would be optimal for patients suffering from infection, we developed the H. pylori AI-clinician recommendation system. This system was trained on data from tens of thousands of H. pylori-infected patients from Hp-EuReg, orders of magnitude greater than those experienced by a single real-world clinician. We first used a simulated dataset and demonstrated the ability of our AI Clinician method to identify patient subgroups that would benefit from differential optimal treatments. Next, we trained the AI Clinician on Hp-EuReg, demonstrating on average the AI Clinician reproduces known quality estimates of treatment decision making, for example bismuth and quadruple therapies out-performing triple, with longer durations and higher dose proton pump inhibitor (PPI) showing higher quality estimation on average. Next, we demonstrated that treatment was optimized by recommended personalized therapies in patient subsets, where 65% of patients were recommended a bismuth therapy of either metronidazole, tetracycline, and bismuth salts with PPI, or bismuth quadruple therapy with clarithromycin, amoxicillin, and bismuth salts with PPI, and 15% of patients recommended a quadruple non-bismuth therapy of clarithromycin, amoxicillin, and metronidazole with PPI. Finally, we determined trends in patient variables driving the personalized recommendations using random forest modelling. With around half of the world likely to experience H. pylori infection at some point in their lives, the identification of personalized optimal treatments will be crucial in both gastric cancer prevention and quality of life improvements for countless individuals worldwide.

Synthetic microbiomes offer new possibilities for modulating microbiota, to address the barriers in multidtug resistance (MDR) research. We present a Bayesian optimization approach to enable efficient searching over the space of synthetic microbiome variants to identify candidates predictive of reduced MDR. Microbiome datasets were encoded into a low-dimensional latent space using autoencoders. Sampling from this space allowed generation of synthetic microbiome signatures. Bayesian optimization was then implemented to select variants for biological screening to maximize identification of designs with restricted MDR pathogens based on minimal samples. Four acquisition functions were evaluated: expected improvement, upper confidence bound, Thompson sampling, and probability of improvement. Based on each strategy, synthetic samples were prioritized according to their MDR detection. Expected improvement, upper confidence bound, and probability of improvement consistently produced synthetic microbiome candidates with significantly fewer searches than Thompson sampling. By combining deep latent space mapping and Bayesian learning for efficient guided screening, this study demonstrated the feasibility of creating bespoke synthetic microbiomes with customized MDR profiles.

An antibiogram is a periodic summary of antibiotic resistance results of organisms from infected patients to selected antimicrobial drugs. Antibiograms help clinicians to understand regional resistance rates and select appropriate antibiotics in prescriptions. In practice, significant combinations of antibiotic resistance may appear in different antibiograms, forming antibiogram patterns. Such patterns may imply the prevalence of some infectious diseases in certain regions. Thus it is of crucial importance to monitor antibiotic resistance trends and track the spread of multi-drug resistant organisms. In this paper, we propose a novel problem of antibiogram pattern prediction that aims to predict which patterns will appear in the future. Despite its importance, tackling this problem encounters a series of challenges and has not yet been explored in the literature. First of all, antibiogram patterns are not i.i.d as they may have strong relations with each other due to genomic similarities of the underlying organisms. Second, antibiogram patterns are often temporally dependent on the ones that are previously detected. Furthermore, the spread of antibiotic resistance can be significantly influenced by nearby or similar regions. To address the above challenges, we propose a novel Spatial-Temporal Antibiogram Pattern Prediction framework, STAPP, that can effectively leverage the pattern correlations and exploit the temporal and spatial information. We conduct extensive experiments on a real-world dataset with antibiogram reports of patients from 1999 to 2012 for 203 cities in the United States. The experimental results show the superiority of STAPP against several competitive baselines.

Since the discovery of penicillin in the late 1920s, antibiotics have "revolutionized medicine and saved millions of lives." Unfortunately, the effectiveness of antibiotics is now threatened by the increase of antibiotic-resistant bacteria globally. Antibiotic-resistant infections cause the deaths of up to 1.2 million people annually, making them one of the leading causes of death. There are several factors contributing to this crisis of resistance to antibiotics. These include overusing and misusing antibiotics in treatments.

You are free to share this article under the Attribution 4.0 International license. Computer algorithms can determine antimicrobial resistance of bacteria faster than previous methods, researchers report. This could help treat serious infections more efficiently in the future. Antibiotic-resistant bacteria are on the rise all over the world. Each year, infections caused by multi-drug resistant bacteria lead to at least 300 fatalities in Switzerland alone.

As the world continues to grapple with the Covid-19 pandemic, another health crisis is looming: antibiotic resistance. Bacterial resistance is something that occurs naturally, but widespread antibiotic misuse has propelled antimicrobial resistance (AMR) to major global health threat status; at least 700,000 people are killed by drug-resistant superbugs every year – and by 2050, this number could reach 10 million. A report by the World Health Organization, published earlier this year, also found that none of the 43 antibiotics currently under development "sufficiently address the problem of drug resistance" in the bacteria considered most dangerous to public health. The situation, as it stands, looks bleak – but there is hope. Advances in technology are vastly improving the way researchers discover and develop drugs, and antibiotics are no exception.

Humans have been using antibiotics for about 100 years. For 30 years the competent authorities have been warning of the future problem of antibiotic resistance, since higher doses or different antibiotics are needed to end them. Pathogens, whether bacteria, fungi or protists, that have been traditionally stopped with antibiotics have naturally developed resistance to the drugs used against them. This is due to the process of constant evolution that occurs in nature, but health authorities point out that the misuse and abuse of antibiotics has helped this adaptation to take place much faster than expected. Health authorities suggest that by the end of the 21st century the current antibiotics will no longer be useful and that from 2050 we may already notice the lack of response from many of them. It is estimated that by then about 10 million people will die each year from resistant infections.