Publisher Health

Some of Rutgers’ top health researchers are calling for a change in our approach to developing COVID-19 vaccines, and vaccines to fight future pandemics, to incorporate both conventional and challenge trails. Conventional randomized controlled trials are where participants receive a vaccine or placebo and then may or may not be exposed as they continue with their lives over the course of the months that follow. Human challenge vaccine trials are where participants receive a vaccine or placebo and are then artificially exposed to the virus.
The commentary by bioethicist Nir Eyal and epidemiologists Tobias Gerhard and Brian Strom (the latter is chancellor of Rutgers Biomedical and Health Sciences) — published in Pharmacoepidemiology and Drug Safety — examines how this parallel approach to vaccine trials can lead to faster and more accurate vaccine assessment and more effective pandemic response.
The researchers say that further vaccine testing could help settle remaining questions about how effective the shots are at blocking infection against old and new virus strains. It could also reveal the most effective dosing and timing between shots, the level of protection compared to natural immunity and how well vaccines work in groups that were underrepresented in initial trials.
While some researchers proposed at the beginning of the COVID-19 pandemic that challenge trials take place, others argued that too little was known about the virus and that conducting the trials would be too dangerous. They were not used for the studies that led to approval of the major COVID-19 vaccines but are now being used in testing.
“The vigorous discussions about vaccine trial designs in the early months of the COVID-19 pandemic unfortunately played out as a mostly adversarial debate between pro-challenge trial and pro-conventional trial supporters. We felt that there was an overlooked third approach that involved combining strengths from both designs and could facilitate better outcomes throughout the remainder of the COVID-19 pandemic and in future pandemics,” says Gerhard, Director, Center for Pharmacoepidemiology and Treatment Science at Rutgers Institute for Health, Health Care Policy and Aging Research (IFH) and Professor, Rutgers Ernest Mario School of Pharmacy.
The Rutgers researchers’ parallel approach, called “Combining Conventional and Challenge trials (CCC),” would involve trials of both types, conducted either simultaneously or at different times.
“In a pandemic, the value of obtaining information as early as possible is so vast that ‘CCC’ is ethically preferable to any single trial, and preparations for a future pandemic should include laying the groundwork for a CCC’,” said Eyal, Henry Rutgers Professor of Bioethics and Director, Center for Population-Level Bioethics at Rutgers IFH.
Eyal and his colleagues say that researchers will be able to collect more information and increase confidence in the efficacy of vaccines.
“When either human challenge- or conventional trials are permitted, it may be even more advisable to combine conventional and challenge testing for surer, faster, and more comprehensive vaccine assessments and a fuller understanding of the infection and the disease,” said Gerhard.
Two trials instead of one would conserve resources, answer more questions, and increase the chance that at least one trial would be successful, they said.
“The added value of faster, more informative completion of testing of the central weapon against a pandemic that threatens an exceptional number of people globally tends to be very high,” Strom said.
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Materials provided by Rutgers University. Original written by Nicole Swenarton. Note: Content may be edited for style and length.

Microfluidic devices are compact testing tools made up of tiny channels carved on a chip, which allow biomedical researchers to test the properties of liquids, particles and cells at a microscale. They are crucial to drug development, diagnostic testing and medical research in areas such as cancer, diabetes and now COVID-19. However, the production of these devices is very labor intensive, with minute channels and wells that often need to be manually etched or molded into a transparent resin chip for testing. While 3D printing has offered many advantages for biomedical device manufacturing, its techniques were previously not sensitive enough to build layers with the minute detail required for microfluidic devices. Until now.
Researchers at the USC Viterbi School of Engineering have now developed a highly specialized 3D printing technique that allows microfluidic channels to be fabricated on chips at a precise microscale not previously achieved. The research, led by Daniel J. Epstein Department of Industrial and Systems Engineering Ph.D. graduate Yang Xu and Professor of Aerospace and Mechanical Engineering and Industrial and Systems Engineering Yong Chen, in collaboration with Professor of Chemical Engineering and Materials Science Noah Malmstadt and Professor Huachao Mao at Purdue University, was published in Nature Communications.
The research team used a type of 3D printing technology known as vat photopolymerization, which harnesses light to control the conversion of liquid resin material into its solid end state.
“After light projection, we can basically decide where to build the parts (of the chip), and because we use light, the resolution can be rather high within a layer. However, the resolution is much worse between layers, which is a critical challenge in the building of microscale channels,” Chen said.
“This is the first time we’ve been able to print something where the channel height is at the 10 micron level; and we can control it really accurately, to an error of plus or minus one micron. This is something that has never been done before, so this is a breakthrough in the 3D printing of small channels,” he said.
Vat photopolymerization makes use of a vat filled with liquid photopolymer resin, out of which a printed item is constructed layer by layer. Ultraviolet light is then flashed onto the object, curing and hardening the resin at each layer level. As this happens, a build platform moves the printed item up or down so additional layers can be built onto it.

The number of Americans with visual impairment or blindness is expected to jump to more than 8 million by the year 2050, according to research led by the USC Gayle and Edward Roski Eye Institute conducted back in 2016.
With the youngest baby boomers reaching 65 years old by 2029, age-related eye diseases and conditions are expected to swell during what’s being called the “silver tsunami.”
According to medical experts, it’s safe to say many of those cases will be caused by retinal degenerative diseases, the progressive degeneration of the light-sensitive photoreceptors in your retina.
Based on these estimates, there is an unmet need for new technologies that treat vision loss due to diseases of photoreceptor degeneration.
While there are no successful non-invasive therapeutics currently available for the treatment of vision loss, researchers at USC have come up with a new idea to address this growing problem.
Currently, ophthalmologists use electronic technology to directly stimulate retinal neurons by implanting electrode devices inside the eye, a technique that requires expensive and invasive surgery.

Medical imaging is an important part of modern healthcare, enhancing both the precision, reliability and development of treatment for various diseases. Artificial intelligence has also been widely used to further enhance the process.
However, conventional medical image diagnosis employing AI algorithms require large amounts of annotations as supervision signals for model training. To acquire accurate labels for the AI algorithms — radiologists, as part of the clinical routine, prepare radiology reports for each of their patients, followed by annotation staff extracting and confirming structured labels from those reports using human-defined rules and existing natural language processing (NLP) tools. The ultimate accuracy of extracted labels hinges on the quality of human work and various NLP tools. The method comes at a heavy price, being both labour intensive and time consuming.
An engineering team at the University of Hong Kong (HKU) has developed a new approach “REFERS” (Reviewing Free-text Reports for Supervision), which can cut human cost down by 90%, by enabling the automatic acquisition of supervision signals from hundreds of thousands of radiology reports at the same time. It attains a high accuracy in predictions, surpassing its counterpart of conventional medical image diagnosis employing AI algorithms.
The innovative approach marks a solid step towards realizing generalized medical artificial intelligence. The breakthrough was published in Nature Machine Intelligence in the paper titled “Generalized radiograph representation learning via cross-supervision between images and free-text radiology reports.”
“AI-enabled medical image diagnosis has the potential to support medical specialists in reducing their workload and improving the diagnostic efficiency and accuracy, including but not limited to reducing the diagnosis time and detecting subtle disease patterns,” said Professor YU Yizhou, leader of the team from HKU’s Department of Computer Science under the Faculty of Engineering.
“We believe abstract and complex logical reasoning sentences in radiology reports provide sufficient information for learning easily transferable visual features. With appropriate training, REFERS directly learns radiograph representations from free-text reports without the need to involve manpower in labelling.” Professor Yu remarked.
For training REFERS, the research team uses a public database with 370,000 X-Ray images, and associated radiology reports, on 14 common chest diseases including atelectasis, cardiomegaly, pleural effusion, pneumonia and pneumothorax. The researchers managed to build a radiograph recognition model using 100 radiographs only, and attains 83% accuracy in predictions. When the number was increased to 1,000, their model exhibits amazing performance with an accuracy of 88.2%, which surpasses its counterpart trained with 10,000 radiologist annotations (accuracy at 87.6%). When 10,000 radiographs were used, the accuracy is at 90.1%. In general, an accuracy above 85% in predictions is useful in real-world clinical applications.
REFERS achieves the goal by accomplishing two report-related tasks, i.e., report generation and radiograph-report matching. In the first task, REFERS translates radiographs into text reports by first encoding radiographs into an intermediate representation, which is then used to predict text reports via a decoder network. A cost function is defined to measure the similarity between predicted and real report texts, based on which gradient-based optimization is employed to train the neural network and update its weights.
As for the second task, REFERS first encodes both radiographs and free-text reports into the same semantic space, where representations of each report and its associated radiographs are aligned via contrastive learning.
“Compared to conventional methods that heavily rely on human annotations, REFERS has the ability to acquire supervision from each word in the radiology reports. We can substantially reduce the amount of data annotation by 90% and the cost to build medical artificial intelligence. It marks a significant step towards realizing generalized medical artificial intelligence, ” said the paper’s first author Dr ZHOU Hong-Yu.
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Materials provided by The University of Hong Kong. Note: Content may be edited for style and length.

For cancer patients, Covid-19 poses a particular risk due to their often compromised immune systems, weakened by therapy or disease, which is why vaccination is very important for their protection. Now, a recent study led by MedUni Vienna shows that, due to Omicron, there is an increasing number of breakthrough infections in people with cancer, especially while they are undergoing cancer therapy. The researchers of the study, which has just been published in the journal Cancer Cell, emphasise that adhering to protective measures and development of vaccines adapted to virus variants is important to affected individuals.
A total of 3,959 patients who are or have been treated for cancer at University Hospital Vienna and at the Franz Tappeiner Hospital in Merano (Italy) were examined in the study. Eighty-five (85) percent of patients had received at least one vaccination with one of the SARS-CoV-2 vaccines authorised in the EU. Between February 2020 and February 2022, a total of 950 of the 3,959 cancer patients (24 percent) had become infected with the SARS-CoV-2 virus. As the research shows, the number of breakthrough infections increased significantly with the emergence of the Omicron variant in January 2022: 70 percent of infected patients were vaccinated. Thus, the risk of vaccine breakthrough for cancer patients due to Omicron tripled compared with the Delta variant that prevailed between October and December. Breakthrough infections were significantly more common among those who were undergoing systemic treatment than among those without ongoing cancer therapy.
To obtain reasons for the higher rate of breakthrough infections with Omicron compared to Delta, the researchers examined, among other things, the concentration of protective antibodies in the blood in samples from 78 cancer patients and 25 healthy individuals. Strikingly, both in people with solid tumors and blood cancers, there was greatly reduced inhibition of the Omicron variant by specific vaccine antibodies as compared to people without cancer. However, there was also a trend toward shorter hospital stays for vaccinated versus unvaccinated patients. In addition, breakthrough infections only required intensive medical care in rare cases.
Protective measures still important for cancer patients
Matthias Preusser, Head of the Division of Oncology at the Department of Medicine I at MedUni Vienna and University Hospital Vienna, says, “The increasing rates of breakthrough infections and hospitalisations of vaccinated cancer patients associated with Omicron underscore the need for further protective measures not only to effectively combat the ongoing pandemic, but also to prepare for the potential emergence of additional Sars-CoV-2 variants. Vaccines adapted to the particular Sars-CoV-2 variant could help to better protect cancer patients and maintain life-sustaining cancer treatment during the pandemic.”
As study director, Preusser conducted the study in cooperation with the Haemato-Oncological Day Clinic at the Franz Tappeiner Hospital in Merano (Italy), the Center for Pathophysiology, Infectiology and Immunology at MedUni Vienna, the Clinical Institute of Laboratory Medicine, the Department of Infections and Tropical Medicine (Department of Medicine I) at MedUni Vienna, the Department of Artificial Intelligence and Human Interfaces and Intelligent Data Analytics Lab Salzburg at the University of Salzburg, and the Faculty of Economics at the University of Klagenfurt.
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Materials provided by Medical University of Vienna. Note: Content may be edited for style and length.

A research team led by scientists from the Hong Kong University of Science and Technology (HKUST) has developed an innovative technology for in vivo imaging of the important biological processes involved in the injury and repair of spinal cords, paving the way for a better understanding of the pathology and potential treatment of spinal cord injury (SCI).
A tight bundle of neural cells (neurons and glia) and nerve pathways (axons), the spinal cord serves as a primary information highway between the brain and the peripheral nerves in the rest of our body. Damage to the spinal cord is a devastating and largely irreversible neurological trauma, and can result in lifelong disability and paralysis with no available cure.
While Imaging plays an important role in understanding spinal cord functions and its response to pathological insults and therapeutic procedures, there is currently no effective method to capture the injured spinal cord at the level of cellular processes without activating the immune response. Conventional imaging techniques require the patients to have their spinal cord tissue removed to increase image resolution, or run the risk of triggering immune responses in spinal cord tissue, which may affect the disease process being investigated.
Now, a research team led by Prof. QU Jianan, professor of Department of Electrical & Computer Engineering, and Prof. LIU Kai, associate professor of Division of Life Science at HKUST, has demonstrated a new approach to achieve long-term, repetitive, stable, high-resolution, and inflammation-free in vivo spinal cord imaging in mouse models.
In their proposed protocol, ligamentum flavum (LF) — the ligaments connecting adjacent vertebrae in our spine — is retained to protect the underlying spinal cord tissue and reduce the risk of imaging window activating inflammation. But retaining the LF layer also means sacrificing the imaging quality, because the layer introduces optical scattering and results in decreased penetration depth of spinal cord imaging.
To solve this problem, the team applied iodixanol, an FDA-approved non-toxic compound, as an optical clearing medium for the imaging window and greatly enhanced its transparency as well as image contrast and resolution. Compared with the prior methods, the iodixanol-based optical clearing technique allows the researchers to remove less tissue above the spinal cord without compromising imaging quality, thus significantly extending the number of imaging sessions to up to 15 sessions over 167 days.
Using this optically cleared intervertebral window, the team studied neuron-glia dynamics and observed strengthened contact of microglia with the nodes of Ranvier during axonal degeneration, opening a promising way to study the interaction between immune cells and nodes of Ranvier under normal and injury conditions. The results were recently published in Nature Communications.
“Considering the difficulties associated with long-term and repetitive spinal cord imaging, this innovation will be an important and widely used tool for the study of spinal cord injury,” said Prof. Qu, who is an expert of optical engineering and science with extensive experience in in vivo linear and nonlinear optical spectroscopy and imaging of biological tissues from a variety of animal models.
“By avoiding surgery-induced inflammation, we can track microglia from resting to activation stages and understand its functional interaction with degenerating and regenerating axons in the spinal cord,” added Prof. Liu, whose research interests include the cellular and molecular mechanisms of axonal regeneration in the adult mammalian central nervous system. “In vivo imaging in living animal models will reveal new biological insights leading to efficient therapeutic strategies for SCI treatment.”

It is an age old dream of medicine: if arbitrary kinds of tissue could be produced artificially from stem cells, then injuries could be healed with the body’s own cells, and one day it might even be possible to produce artificial organs. However, it is difficult to get cells into the desired shape. The methods that have existed so far can be divided into two fundamentally different categories: Either one first creates small tissue building blocks, such as round cell agglomerates or flat cell sheets, and then assembles them, or one initially creates a fine, porous scaffold that is then cultivated with cells. Both approaches have advantages and disadvantages.
At TU Wien (Vienna), a third approach has now been developed: Using a special laser-based 3D printing technique, micro-scaffolds with a diameter of less than a third of a millimetre can be produced, which can accommodate thousands of cells. In this way, a high cell density is present from the start, but one still has the flexibility adapt the shape and mechanical properties of the structure.
With scaffold or without?
“The scaffold-based approaches that have been developed so far have great advantages: If you first make a porous scaffold, you can precisely define its mechanical properties,” says Dr Olivier Guillaume, lead author of the current study, who is researching at TU Wien in the team of Prof Aleksandr Ovsianikov at the Institute of Materials Science and Technology. “The scaffold can be soft or hard as needed, it consists of biocompatible materials that are degraded in the body. They can even be equipped with special biomolecules that promote tissue formation.”
The downside, however, is that it is difficult to quickly and completely populate such a scaffold with cells. A lot of manual work is still needed here today, even though research is already being done on automated processes. Especially with large scaffolds, it takes a long time for the cells to migrate into the interior of the structure; often the cell density remains very low and inhomogeneous.
The situation is completely different if no such scaffold is used. It is also possible to simply grow small cell agglomerates, which are then joined together in the desired shape so that they eventually merge. With this technique, the number of cells is large from the start, but there are hardly any possibilities to intervene in the process. For example, it can happen that the cell spheres change their size or shape and the tissue ends up with different properties than desired.

CU Boulder researchers have discovered a new way to inhibit the most commonly mutated gene underlying human tumor growth, opening the door to new therapeutic strategies for cancer and a host of other diseases.
The discovery, published April 5 in the journal Cell Reports, marks an important step forward in the decades-long quest to target transcription factors (TFs), a notoriously hard-to-block class of proteins which, when mutated or dysregulated, can disrupt cell function and drive illness.
“This class of proteins represents one of the most high-impact therapeutic targets in biomedicine,” said senior author and biochemistry Professor Dylan Taatjes. “We provide a completely new strategy for blocking transcription factor function that could have broad applications to many diseases, including and beyond cancer.”
More than 1,500 transcription factors exist in the human body, each responsible for binding to specific sequences in DNA and transcribing or “decoding” the body’s genetic blueprint to instruct a cell what to do.
Different TFs act in different kinds of cells (muscle, skin, blood, etc.), regulating everything from inflammation to cholesterol metabolism to wound healing to controlled cell death, which is key to inhibiting cancer.
When a TF is mutated, those instructions can go awry, turning a beneficial protein into a harmful one “like Jekyll and Hyde,” said Taatjes.

The brain structure of patients with recent onset psychosis and depression can offer important biological insights into these illnesses and how they might develop.
In a new study published today in Biological Psychiatry, researchers at the University of Birmingham show that by examining structural MRI scans of the brain, it’s possible to identify patients most susceptible to poor outcomes.
By identifying these patients in the early stages of their illness, clinicians will be able to offer more targeted and effective treatments.
“Currently, the way we diagnose most mental health disorders is based on a patient’s history, symptoms, and clinical observations, rather than on biological information,” says lead author Paris Alexandros Lalousis. “That means patients might have similar underlying biological mechanisms in their illness, but different diagnoses. By understanding those mechanisms more fully, we can give clinicians better tools to use in planning treatments.”
In the study, the researchers used data from around 300 patients with recent onset psychosis and recent onset depression taking part in the PRONIA study. PRONIA is a European Union-funded cohort study investigating prognostic tools for psychoses which is taking place across seven European research centres including Birmingham.
The researchers used a machine learning algorithm to assess data from patients’ brain scans and sort these into groups, or clusters. Two clusters were identified based on the scans, each of which contained both patients with psychosis and patients with depression. Each cluster revealed distinctive characteristics which related strongly to their likelihood of recovery.
In the first cluster, lower volumes of grey matter — the darker tissue inside the brain involved in muscle control and functions such as memory, emotions, and decision-making — were associated with patients who went on to have poorer outcomes. In the second group, in contrast, higher levels of grey matter signalled patients who were more likely to recover well from their illness.
A second algorithm was then used to predict the patients’ condition nine months following the initial diagnosis. The researchers found a higher level of accuracy in predicting outcomes when using the biologically based clusters compared to traditional diagnostic systems.
Evidence also showed that patients in the cluster with lower volumes of grey matter in their brain scans may have higher levels of inflammation, poorer concentration, and other cognitive impairments previously associated with depression and schizophrenia.
Finally, the team tested the clusters in other large cohort studies in Germany and the US and were able to show that the same identified clusters could be used to predict patient outcomes.
“While the PRONIA study contained people who were recently diagnosed with their illness, the other datasets we used contained people with chronic conditions,” explains Lalousis. “We found that the longer the duration of illness, the more likely it was that a patient would fit into the first cluster with lower grey matter volume. That really adds to the evidence that structural MRI scans may be able to offer useful diagnostic information to help guide targeted treatment decisions.”=
The next step for the team is to start to validate the clusters in the clinic, gathering patient data in real time, before planning larger scale clinical trials.
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Materials provided by University of Birmingham. Note: Content may be edited for style and length.

Treating pollutants, such as endocrine disruptors, is an effective way to protect the environment. Endocrine disruptors are chemicals that alter the hormonal systems and the development of organisms that are exposed to them, even in small quantities. Doctoral student Julie Robitaille and Professor Valérie Langlois of the Institut national de la recherche scientifique (INRS) are working on an effluent analysis tool to predict their harmful effects.
“There is interest in Quebec and around the world in finding ways to track endocrine disruptors. These methods could even identify where the contamination is coming from in a given area — whether it’s from agricultural, hospital, municipal, or industrial environments,” says Professor Valérie Langlois.
The pair of researchers is also working with municipal and industrial partners to monitor drinking water and wastewater to plan for potential infrastructure changes. Unlike many current techniques that test on fish, their method does not involve animal testing. Instead, their approach uses human cell lines, genetically modified in the laboratory to be sensitive to certain hormones.
“When an endocrine disruptor activates the receptors on these cells, they emit a small light. That’s how we determine whether the wastewater could be posing a risk to the hormonal system,” explains Julie Robitaille, a doctoral student in water sciences. However, she says that further research is needed to reveal how their cellular findings translate to aquatic species.
The contaminant cocktail
The challenge in monitoring wastewater comes from the “cocktail” of endocrine disruptors it contains. “You can’t just look at whether each individual substance is present. You need to analyze whether the entire mixture has any effect, since these contaminants can have different consequences when combined with other chemicals,” says the PhD student.
To test the effects of the pollutant mix, the researchers turned to bioassays, using the biological analyses to measure the reactions of their cell lines when exposed to wastewater samples, without knowing exactly which contaminants they contained.
Robitaille used several techniques to demonstrate the effectiveness of this type of approach, one of which involved making an inventory of all the tools available to regulatory authorities. She conducted this literature review in collaboration with scientists who are members of the Intersectoral Centre for Endocrine Disruptor Analysis (ICEDA). The publication appeared in the Environmental Research journal’s 2022 Special Issue on Endocrine Disrupting Chemicals.
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Materials provided by Institut national de la recherche scientifique – INRS. Original written by Audrey-Maude Vézina. Note: Content may be edited for style and length.