Aurelia Butler

Using artificial intelligence to scan surgery patients’ medical records for signs of risky drinking might help spot those whose alcohol use raises their risk of problems during and after an operation, a new study suggests.
The AI record scan tested in the study could help surgery teams know in advance which patients might need more education about such risks, or treatment to help them reduce their drinking or stop drinking for a period of time before and after surgery.
The findings, published in Alcohol: Clinical and Experimental Research by a team from the University of Michigan, show that using a form of AI called natural language processing to analyze a patient’s entire medical record can spot signs of risky drinking documented in their charts, such as in doctor’s notes, even when they don’t have a diagnosis of an alcohol problem.
Past research has shown that having more than a couple of drinks a day on average is associated with a higher risk of infections, wound complications, pulmonary complications and prolonged hospital stays in people having surgery.
Many people who drink regularly don’t have a problem with alcohol, and when they do they may never receive a formal diagnosis for alcohol use disorder or addiction, which would be easy for a surgical team to spot in their chart.
Scouring records and notes
The researchers, from Michigan Medicine, U-M’s academic medical center, trained their AI model by letting it review 100 anonymous surgical patients’ records to look for risky drinking signs, and comparing its classifications with those of expert human reviewers.

In all, the AI model matched the human expert classification most of the time. The AI model found signs of risky drinking in the notes of 87% of the patients who experts had identified as risky drinkers.
Meanwhile, only 29% of these patients had a diagnosis code related to alcohol in their list of diagnoses. So, many patients with higher risk for complications would have slipped under the radar for their surgical team.
The researchers then allowed the AI model to review more than 53,000 anonymous patient medical records compiled through the Michigan Genomics Initiative. The AI model identified three times more patients with risky alcohol use through this full-text search than the researchers found using diagnosis codes. In all, 15% of patients met criteria via the AI model, compared to 5% via diagnosis codes.
“This evaluation of natural language processing to identify risky drinking in the records of surgical patients could lay the groundwork for efforts to identify other risks in primary care and beyond, with appropriate validation,” said V. G. Vinod Vydiswaran, Ph.D., lead author of the new paper and an associate professor of learning health sciences at the U-M Medical School. “Essentially, this is a way of highlighting for a provider what is already contained in the notes made by other providers, without them having to read the entire record.”
“Given the excess surgical risk that can arise from even a moderate amount of daily alcohol use, and the challenges of implementing robust screening and treatment in the pre-op period, it’s vital that we explore other options for identifying patients who could most benefit from reducing use by themselves or with help, beyond those with a recorded diagnosis,” said senior author Anne Fernandez, Ph.D., an addiction psychologist at the U-M Addiction Center and Addiction Treatment Services and an associate professor of psychiatry.
The new data suggest that surgical clinics that simply review the diagnosis codes listed in their incoming patients’ charts, and flag ones such as alcohol use disorder, alcohol dependence or alcohol-related liver conditions, would be missing many patients with elevated risk.

Alcohol + surgery = added risk
In addition to known risks of surgical complications, Fernandez and colleagues recently published data from a massive Michigan surgical database showing that people who both smoke and have two or more drinks a day were more likely to end up back in the hospital, or back in the operating room, than others. Those with risky drinking who didn’t smoke also were more likely to need a second operation.
She and colleagues also found that 19% of people having surgery may have risky levels of alcohol use, in a review of detailed questionnaire data from people participating in two different studies that enroll people from Michigan Medicine surgery clinics.
The new study used the NLP form of AI not to generate new information, but to look for clues in the pages and pages of provider notes and data that make up a person’s entire medical record.
After validation, Vydiswaran said, the tool could potentially be run on a patient’s record before they are seen in a pre-operative appointment and identify their risk level.
Just knowing that a person has a potentially risky level of drinking isn’t enough, of course.
Fernandez is leading an effort to test a virtual coaching approach to help people scheduled for surgery understand the risks related to their level of drinking and support them in reducing their intake.
“Our goal is to identify people who may be in need of more treatment services, including medication for alcohol use disorder and support during their surgical recovery when alcohol abstinence is necessary,” she said. “We are not aiming to replace the due diligence every provider must do, but to prompt them to talk with patients and get more information to act upon.”
The risks of combining alcohol with the opioid pain medications often used to treat post-surgical pain are very high, she noted.
In addition to current work to validate the model, the team hopes to make their model publicly available, though it would have to be trained on the electronic records system of any health system that seeks to use it.
“These AI tools can do amazing things, but it’s important we use them to do things that could save time for busy clinicians, whether that’s related to alcohol or to drug use, disordered eating, or other chronic conditions,” said Fernandez. “And if we are going to use them to spot potential issues, we need to be ready to offer treatment options too.”
In addition to Fernandez and Vydiswaran, who are members of the U-M Institute for Healthcare Policy and Innovation, the study’s authors are Asher Strayhorn and Katherine Weber of Learning Health Sciences, and Haley Stevens, Jessica Mellinger and G. Scott Winder of Psychiatry.
The study was funded by the National Institute on Alcoholism and Alcohol Abuse, part of the National Institutes of Health (AA026333, AA028315) and by U-M Precision Health, which also runs the Michigan Genomics Initiative. The study used the U-M Data Office for Clinical and Translational Research. More

To combat viruses, bacteria and other pathogens, synthetic biology offers new technological approaches whose performance is being validated in experiments. Researchers from the Würzburg Helmholtz Institute for RNA-based Infection Research and the Helmholtz AI Cooperative applied data integration and artificial intelligence (AI) to develop a machine learning approach that can predict the efficacy of CRISPR technologies more accurately than before. The findings were published today in the journal Genome Biology.
The genome or DNA of an organism incorporates the blueprint for proteins and orchestrates the production of new cells. Aiming to combat pathogens, cure genetic diseases or achieve other positive effects, molecular biological CRISPR technologies are being used to specifically alter or silence genes and inhibit protein production.
One of these molecular biological tools is CRISPRi (from “CRISPR interference”). CRISPRi blocks genes and gene expression without modifying the DNA sequence. As with the CRISPR-Cas system also known as “gene scissors,” this tool involves a ribonucleic acid (RNA), which serves as a guide RNA to direct a nuclease (Cas). In contrast to gene scissors, however, the CRISPRi nuclease only binds to the DNA without cutting it. This binding results in the corresponding gene not being transcribed and thus remaining silent.
Until now, it has been challenging to predict the performance of this method for a specific gene. Researchers from the Würzburg Helmholtz Institute for RNA-based Infection Research (HIRI) in cooperation with the University of Würzburg and the Helmholtz Artificial Intelligence Cooperation Unit (Helmholtz AI) have now developed a machine learning approach using data integration and artificial intelligence (AI) to improve such predictions in the future.
The approach
CRISPRi screens are a highly sensitive tool that can be used to investigate the effects of reduced gene expression. In their study, published today in the journal Genome Biology, the scientists used data from multiple genome-wide CRISPRi essentiality screens to train a machine learning approach. Their goal: to better predict the efficacy of the engineered guide RNAs deployed in the CRISPRi system.
“Unfortunately, genome-wide screens only provide indirect information about guide efficiency. Hence, we have applied a new machine learning method that disentangles the efficacy of the guide RNA from the impact of the silenced gene,” explains Lars Barquist. The computational biologist initiated the study and heads a bioinformatics research group at the Würzburg Helmholtz Institute, a site of the Braunschweig Helmholtz Centre for Infection Research in cooperation with the Julius-Maximilians-Universität Würzburg.

Supported by additional AI tools (“Explainable AI”), the team established comprehensible design rules for future CRISPRi experiments. The study authors validated their approach by conducting an independent screen targeting essential bacterial genes, showing that their predictions were more accurate than previous methods.
“The results have shown that our model outperforms existing methods and provides more reliable predictions of CRISPRi performance when targeting specific genes,” says Yanying Yu, PhD student in Lars Barquist’s research group and first author of the study.
The scientists were particularly surprised to find that the guide RNA itself is not the primary factor in determining CRISPRi depletion in essentiality screens. “Certain gene-specific characteristics related to gene expression appear to have a greater impact than previously assumed,” explains Yu.
The study also reveals that integrating data from multiple data sets significantly improves the predictive accuracy and enables a more reliable assessment of the efficiency of guide RNAs. “Expanding our training data by pulling together multiple experiments is essential to create better prediction models. Prior to our study, lack of data was a major limiting factor for prediction accuracy,” summarizes junior professor Barquist. The approach now published will be very helpful in planning more effective CRISPRi experiments in the future and serve both biotechnology and basic research. “Our study provides a blueprint for developing more precise tools to manipulate bacterial gene expression and ultimately help to better understand and combat pathogens,” says Barquist.
The results at a glance
• Gene features matter: The characteristics of targeted genes have a significant impact on guide RNA depletion in genome-wide screens.

• Data integration improves predictions: Combining data from multiple CRISPRi screens significantly improves the accuracy of prediction models and enables more reliable estimates of guide RNA efficiency.
• Designing better CRISPRi experiments: The study provides valuable insights for designing more effective CRISPRi experiments by predicting guide RNA efficiency, enabling precise gene-silencing strategies.
Funding
The study was supported by funds from the Bavarian State Ministry of Science and Art through the bayresq.net research network. More

New research conducted by students and a professor at the University of Cincinnati Blue Ash College finds that smartphone usage can increase and even become unhealthy for those who have obsessive-compulsive disorder (OCD), a psychiatric disorder with symptoms related to unwanted and distressing thoughts that can lead to repetitive and disruptive behaviors.
UC Blue Ash undergraduate students Kaley Aukerman, Madi Kenna and Ryan Padgett recently co-authored the research that was published online in Current Psychology. It evaluates the impact of OCD symptoms in predicting how someone would score in Problematic Smartphone Use (PSU).
The students worked on the project with Alex Holte, PhD, assistant professor of psychology at UC Blue Ash. They surveyed more than 400 people and asked them to complete multiple measures assessing various levels of obsessive-compulsive behavior, fear of missing out, inhibitory anxiety, boredom proneness and PSU.
The research found that individuals with clinically significant levels of OCD are more prone to have PSU in comparison to those with non-clinical levels of OCD. The group also documented that fear of missing out and boredom influenced the relationship between OCD and PSU.
“There is a theoretical model known as compensatory internet use theory and it suggests that people will compensate for negative emotions by using technology,” says Holte. “Individuals who have OCD desire certainty. So, they might have a fear related to their OCD that they can use their phone to check and confirm or deny that fear.”
The study also shows the chain of actions that can occur by theorizing OCD predicted boredom proneness, fear of missing out and inhibitory anxiety. These factors can lead someone with OCD to checking and re-checking their phone over and over.
The research conducted by the students opens new doors in studying how people with OCD can be impacted by their smartphone use and how using a smartphone can become a behavioral addiction. Holte said he felt the findings were important enough to submit for publication and was pleasantly surprised when Current Psychology responded favorably, and quickly. The research was published online this past fall, just months after it was submitted.

“It is really rare for undergrad students to get published, just because the publication process typically takes a long time,” said Holte. “I think my first publication took two or three years after I submitted.”
For Aukerman, Kenna and Padgett, having the research published was a nice surprise, but being part of the research process and learning how to document their findings has been a valuable learning experience.
“The really big jump is getting used to scientific literature and being able to write and format it, because it’s not your simple conversation,” Padgett said. “Professor Holte was really good in helping us take our research and describe it in detail, in the sort of way that is expected for scientific research.”
Padgett is studying neuroscience with plans to eventually pursue a master’s degree in psychology or neuropsychology. He said he appreciates the mentoring that Professor Holte has provided and is excited to continue learning about the research process.
Next steps for the students and professor will be to study how some people look at smartphones as a refuge that takes them away from their troubles, while others consider it a burden that requires their frequent attention. More

Newcastle University research is helping shed light on the important interaction between users and remote drivers that oversee the operation of automated vehicles.
Automated vehicles (AVs), also known as driverless vehicles, hold the promise of transforming mobility, offering numerous benefits such as safer roads, increased accessibility, enhanced productivity, economic growth, and contributions to decarbonisation.
While lower-level automation systems provide assistance to drivers, higher-level automation (SAE Level 4) allows vehicles to operate without on-board driver input. A crucial failsafe mechanism for Level 4 Automated Vehicles (L4 AV) involves remote driving through a teleoperation system controlled by a remote driver. However, understanding end-users’ needs and requirements in this context remains a significant research gap.
Publishing their findings in the journal Transportation Research Part F: Traffic Psychology and Behaviour,an international research team led by Newcastle University studied the preferences of potential end-users for a 5G-enabled L4 AV with a remote teleoperation system as a failsafe mechanism.
The researchers conducted qualitative semi-structured interviews with 29 potential end-users to explore the interaction between drivers, automation, and remote drivers in L4 AVs.
The results show that end-users support the failsafe feature of remote driving, envisioning positive applications for night driving, long distances, motorways, and more. The exploration of L4 AV as a ‘designated driver’ to reduce alcohol-impaired driving garnered interest, and concerns were raised about the reliability of the teleoperation system, remote driver performance, 5G network connection, cybersecurity, and privacy issues.
The findings reveal that end-users expressed a desire to understand how remote teleoperator drivers operate the vehicle remotely, highlighting the importance of clear communication.

The study participants also indicated that they prefer drivers to be focused and not multitasking during teleoperation. In addition, they require remote drivers based in the same country as the L4 AV to prevent issues such as unfamiliar road layouts, different traffic rules, cultural driving style variations, liability concerns, and time differences from affecting performance.
Study lead author, Dr Shuo Li, Research Associate at Newcastle University’s School of Engineering, said: “As we journey into the realm of connected and automated vehicles, our research provides comprehensive insights and highlights key aspects of the new driver-automation-remote driver interaction in 5G-enabled Level 4 Automated Vehicles. Offering end-users a transparent, qualified, and location-aware remote driving experience is not only an added feature but also crucial for safety and acceptance of automated mobility.”
Study co-author, Professor Phil Blythe CBE, Professor of Intelligent Transport Systems, and head of the Future Mobility Group, Newcastle University, added: “Newcastle University and it’s regional partners are at the leading edge of investigating what is needed to practically and safely introduce Automated Vehicles and in particular the challenge of Connected and Automated Logistics — which will deliver significant benefits to the region and the sector in general. These research findings on the use of remote, teleoperations to supervise driverless AV’s is a critical cog in the automation machine and will, through our on-going work, also inform on workload and thus potentially how many vehicles an individual teleoperator can safely handle. Overall this is part of our wider objective to ensure the Newcastle University and the NE remain at the forefront of automation and future logistics.”
The experts recommend future research that explores the potential role of L4 AV as a ‘designated driver’ and its impact on road safety. This work has been funded through DCMS 5G CAL and CCAV and Innovate UK V-CAL projects. These projects are regional consortium developing the concept and technologies for Connected and Autonomous Logistics and demonstrating them on routes between VANTEC logistics and Nissan. The work is also supported by the CCAV and Innovate UK SAMS project. The project aims to redefine urban mobility by deploying and testing autonomous zero-emission shuttles in a real-world setting. More

Scientists from Yale and the University of Cologne were able to show that statistical models created by artificial intelligence (AI) predict very accurately whether a medication responds in people with schizophrenia. However, the models are highly context-dependent and cannot be generalized.
In a recent study, scientists have been investigating the accuracy of AI models that predict whether people with schizophrenia will respond to antipsychotic medication.
Statistical models from the field of artificial intelligence (AI) have great potential to improve decision-making related to medical treatment. However, data from medical treatment that can be used for training these models are not only rare, but also expensive. Therefore, the predictive accuracy of statistical models has so far only been demonstrated in a few data sets of limited size. In the current work, the scientists are investigating the potential of AI models and testing the accuracy of the prediction of treatment response to antipsychotic medication for schizophrenia in several independent clinical trials.
The results of the new study, in which researchers from the Faculty of Medicine of the University of Cologne and Yale were involved, show that the models were able to predict patient outcomes with high accuracy within the trial in which they were developed. However, when used outside the original trial, they did not show better performance than random predictions. Pooling data across trials did not improve predictions either.The study ‘Illusory generalizability of clinical prediction models’ was published in Science.
The study was led by leading scientists from the field of precision psychiatry. This is an area of psychiatry in which data-related models, targeted therapies and suitable medications for individuals or patient groups are supposed to be determined.
“Our goal is to use novel models from the field of AI to treat patients with mental health problems in a more targeted manner,” says Dr Joseph Kambeitz, Professor of Biological Psychiatry at the Faculty of Medicine of the University of Cologne and the University Hospital Cologne. “Although numerous initial studies prove the success of such AI models, a demonstration of the robustness of these models has not yet been made.”
And this safety is of great importance for everyday clinical use.
“We have strict quality requirements for clinical models and we also have to ensure that models in different contexts provide good predictions,” says Kambeitz. The models should provide equally good predictions, whether they are used in a hospital in the USA, Germany or Chile.
The results of the study show that a generalization of predictions of AI models across different study centres cannot be ensured at the moment. This is an important signal for clinical practice and shows that further research is needed to actually improve psychiatric care. In ongoing studies, the researchers hope to overcome these obstacles. In cooperation with partners from the USA, England and Australia, they are working on the one hand to examine large patient groups and data sets in order to improve the accuracy of AI models and on the use of other data modalities such as biological samples or new digital markers such as language, motion profiles and smartphone usage. More

When light goes through a material, it often behaves in unpredictable ways. This phenomenon is the subject of an entire field of study called “nonlinear optics,” which is now integral to technological and scientific advances from laser development and optical frequency metrology, to gravitational wave astronomy and quantum information science.
In addition, recent years have seen nonlinear optics applied in optical signal processing, telecommunications, sensing, spectroscopy, light detection and ranging. All these applications involve the miniaturization of devices that manipulate light in nonlinear ways onto a small chip, enabling complex light interactions chip-scale.
Now, a team of scientists at EPFL and the Max Plank Institute has brought nonlinear optical phenomena into a transmission electron microscope (TEM), a type of microscope that uses electrons for imaging instead of light. The study was led by Professor Tobias J. Kippenberg at EPFL and Professor Claus Ropers, Director of the Max Planck Institute for Multidisciplinary Sciences. It is now published in Science.
At the heart of the study are “Kerr solitons,” waves of light that hold their shape and energy as they move through a material, like a perfectly formed surf wave traveling across the ocean. This study used a particular type of Kerr solitons called “dissipative,” which are stable, localized pulses of light that last tens of femtoseconds (a quadrillionth of a second) and form spontaneously in the microresonator. Dissipative Kerr solitons can also interact with electrons, which made them crucial for this study.
The researchers formed dissipative Kerr solitons inside a photonic microresonator, a tiny chip that traps and circulates light inside a reflective cavity, creating the perfect conditions for these waves. “We generated various nonlinear spatiotemporal light patterns in the microresonator driven by a continuous-wave laser,” explains EPFL researcher Yujia Yang, who led the study. “These light patterns interacted with a beam of electrons passing by the photonic chip, and left fingerprints in the electron spectrum.”
Specifically, the approach demonstrated the coupling between free electrons and dissipative Kerr solitons, which allowed the researchers to probe soliton dynamics in the microresonator cavity and perform ultrafast modulation of electron beams.
“Our ability to generate dissipative Kerr solitons [DKS] in a TEM extends the use of microresonator-base frequency combs to unexplored territories,” says Kippenberg. “The electron-DKS interaction could enable high repetition-rate ultrafast electron microscopy and particle accelerators empowered by a small photonic chip.”
Ropers adds: “Our results show electron microscopy could be a powerful technique for probing nonlinear optical dynamics at the nanoscale. This technique is non-invasive and able to directly access the intracavity field, key to understanding nonlinear optical physics and developing nonlinear photonic devices.”
The photonic chips were fabricated in the Center of MicroNanoTechnology (CMi) and the Institute of Physics cleanroom at EPFL. The experiments were conducted at the Göttingen Ultrafast Transmission Electron Microscopy (UTEM) Lab. More

Soft robots, medical devices, and wearable devices have permeated our daily lives. KAIST researchers have developed a fluid switch using ionic polymer artificial muscles that operates at ultra-low power and produces a force 34 times greater than its weight. Fluid switches control fluid flow, causing the fluid to flow in a specific direction to invoke various movements.
KAIST (President Kwang-Hyung Lee) announced on the 4th of January that a research team under Professor IlKwon Oh from the Department of Mechanical Engineering has developed a soft fluidic switch that operates at ultra-low voltage and can be used in narrow spaces.
Artificial muscles imitate human muscles and provide flexible and natural movements compared to traditional motors, making them one of the basic elements used in soft robots, medical devices, and wearable devices. These artificial muscles create movements in response to external stimuli such as electricity, air pressure, and temperature changes, and in order to utilize artificial muscles, it is important to control these movements precisely.
Switches based on existing motors were difficult to use within limited spaces due to their rigidity and large size. In order to address these issues, the research team developed an electro-ionic soft actuator that can control fluid flow while producing large amounts of force, even in a narrow pipe, and used it as a soft fluidic switch.
The ionic polymer artificial muscle developed by the research team is composed of metal electrodes and ionic polymers, and it generates force and movement in response to electricity. A polysulfonated covalent organic framework (pS-COF) made by combining organic molecules on the surface of the artificial muscle electrode was used to generate an impressive amount of force relative to its weight with ultra-low power (~0.01V).
As a result, the artificial muscle, which was manufactured to be as thin as a hair with a thickness of 180 µm, produced a force more than 34 times greater than its light weight of 10 mg to initiate smooth movement. Through this, the research team was able to precisely control the direction of fluid flow with low power.
Professor IlKwon Oh, who led this research, said, “The electrochemical soft fluidic switch that operate at ultra-low power can open up many possibilities in the fields of soft robots, soft electronics, and microfluidics based on fluid control.” He added, “From smart fibers to biomedical devices, this technology has the potential to be immediately put to use in a variety of industrial settings as it can be easily applied to ultra-small electronic systems in our daily lives.”
The results of this study, in which Dr. Manmatha Mahato, a research professor in the Department of Mechanical Engineering at KAIST, participated as the first author, were published in the international academic journal Science Advances on December 13, 2023. (Paper title: Polysulfonated Covalent Organic Framework as Active Electrode Host for Mobile Cation Guests in Electrochemical Soft Actuator)
This research was conducted with support from the National Research Foundation of Korea’s Leader Scientist Support Project (Creative Research Group) and Future Convergence Pioneer Project. More