Dementia is thought to occur when proteins called amyloid-β, tau, and α-synuclein accumulate in the brain and form oligomers. A research group from the Department of Translational Neuroscience, Osaka City University Graduate School of Medicine, had previously shown in a study using mice that the antibiotic rifampicin removes oligomers from the brain and improves cognitive function. However, the drug has been associated with side effects such as liver damage. Resveratrol, a naturally occurring antioxidant in plants, is used as a supplement in Europe and the United States. “To combat the negative side effects of the existing drug rifampicin, we thought of combining it with the hepatoprotective effects of resveratrol,” illustrates Professor Takami Tomiyama, who acted as lead investigator for the current study.

This time, the research group administered a fixed dose combination of rifampicin and resveratrol intranasally five days a week for a total of four weeks to mice models of Alzheimer’s disease, frontotemporal dementia, and dementia with Lewy bodies, and observed their cognitive functions and brain pathology. The results showed that the combination significantly improved the cognitive function of the mice, inhibited the accumulation of oligomers, and restored synaptophysin levels — presynaptic proteins that facilitate synapses. Additionally, blood levels of liver enzymes, a marker of hepatic damage that normally increases with rifampicin, remained normal in the fixed-dose combination. Furthermore, increased levels of brain-derived neurotrophic factor (BDNF) expression were observed in the hippocampus, which was not seen with rifampicin alone. These results indicate that this fixed-dose combination is superior to rifampicin alone in terms of both safety and efficacy.

The results of this study were published online in the Swiss scientific journal Frontiers in Neuroscience on December 13, 2021.

“The number of patients with dementia has been increasing all over the world, with some sources predicting a doubling of patients every 20 years. However, there is still no effective treatment for the disease,” states Specially Appointed Lecturer Tomohiro Umeda, first author of the study. “Recent studies have shown that abnormalities begin to appear in the brains of dementia patients more than 20 years before the onset of the disease.” By investigating new therapeutic purposes with existing drugs in a process called drug repositioning, the research team hopes to diagnose and prevent dementia before the neurons start dying.

Furthermore, based on the team’s previous research experience, nasal administration of a fixed dose combination of rifampicin and resveratrol would increase drug transferability to the brain and further enhance both safety and medicinal effects. The dosage used in this study was 0.02 mg of rifampicin per mouse per day, or 1 mg/kg/day assuming a mouse weight of 20g. “Converted to a human dosage based on body surface area, it becomes 0.081 mg/kg/day,” states Prof. Tomiyama, “currently, rifampicin is prescribed at 10 mg/kg/day as an antibiotic, and compared to this, we confirmed an effect at a much lower dosage.”

The development of a fixed-dose combination of rifampicin and resveratrol nasal spray is currently being carried out by Medilabo RFP, a venture company originating from the research team’s laboratory. Following the publication of this paper, Medilabo RFP has begun preparations for global clinical trials. In November 2021, with the support of the Japan External Trade Organization (JETRO), Medilabo RFP has established a subsidiary in Massachusetts, USA.

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The study appears in Biological Psychiatry, published by Elsevier.

Microglia, the brain’s own immune cells, were once thought of as a homogenous population that was either “activated” or “inactivated,” with either pro-inflammatory or neuroprotective effects. But the cells are now recognized to have a vast array of phenotypes depending on environmental conditions with myriad functional consequences. Microglia are increasingly appreciated as critical players in neurologic and psychiatric disorders.

Fatemeh Haghighi, PhD, senior author of the new work, said: “To address this gap in knowledge, we set out to characterize the DNA methylation landscape of human primary microglia cells and factors that contribute to variations in the microglia methylome.”

DNA methylation is the main form of epigenetic regulation, which determines the pattern of which genes are being turned “on” or “off” in various circumstances over time.

The researchers studied isolated microglia cells from post-mortem human brain tissue from 22 donors of various age, including 1 patient with schizophrenia, 13 with mood disorder, and 8 controls with no psychiatric disorder, taken from 4 brain regions. They analyzed the microglia using genome-scale methylation microarrays.

Unsurprisingly, microglia showed DNA methylation profiles that were distinct from other cells in the central nervous system. But less expected, said Haghighi, “we found that interindividual differences rather than brain region differences had a much larger effect on the DNA methylation variability.” In addition, an exploratory analysis showed differences in the methylation profile of microglia from brains of subjects with psychiatric disorders compared to controls.

John Krystal, MD, Editor of Biological Psychiatry, said of the work, “These promising data point to pathology of the microglia, key immune cells of the brain, in the biology of depression.”

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Hand gestures constitute another important mode of human communication that could be adopted for human-computer interactions. Recent progress in camera systems, image analysis, and machine learning have made optical-based gesture recognition a more attractive option in most contexts than approaches relying on wearable sensors or data gloves, as used by Anderton in Minority Report. However, current methods are hindered by a variety of limitations, including high computational complexity, low speed, poor accuracy, or a low number of recognizable gestures. To tackle these issues, a team led by Zhiyi Yu of Sun Yat-sen University, China, recently developed a new hand gesture recognition algorithm that strikes a good balance between complexity, accuracy, and applicability. As detailed in their paper, which was published in the Journal of Electronic Imaging, the team adopted innovative strategies to overcome key challenges and realize an algorithm that can be easily applied in consumer-level devices.

One of the main features of the algorithm is adaptability to different hand types. The algorithm first tries to classify the hand type of the user as either slim, normal, or broad based on three measurements accounting for relationships between palm width, palm length, and finger length. If this classification is successful, subsequent steps in the hand gesture recognition process only compare the input gesture with stored samples of the same hand type. “Traditional simple algorithms tend to suffer from low recognition rates because they cannot cope with different hand types. By first classifying the input gesture by hand type and then using sample libraries that match this type, we can improve the overall recognition rate with almost negligible resource consumption,” explains Yu.

Another key aspect of the team’s method is the use of a “shortcut feature” to perform a prerecognition step. While the recognition algorithm is capable of identifying an input gesture out of nine possible gestures, comparing all the features of the input gesture with those of the stored samples for all possible gestures would be very time consuming. To solve this problem, the prerecognition step calculates a ratio of the area of the hand to select the three most likely gestures of the possible nine. This simple feature is enough to narrow down the number of candidate gestures to three, out of which the final gesture is decided using a much more complex and high-precision feature extraction based on “Hu invariant moments.” Yu says, “The gesture prerecognition step not only reduces the number of calculations and hardware resources required but also improves recognition speed without compromising accuracy.”

The team tested their algorithm both in a commercial PC processor and an FPGA platform using an USB camera. They had 40 volunteers make the nine hand gestures multiple times to build up the sample library, and another 40 volunteers to determine the accuracy of the system. Overall, the results showed that the proposed approach could recognize hand gestures in real time with an accuracy exceeding 93%, even if the input gesture images were rotated, translated, or scaled. According to the researchers, future work will focus on improving the performance of the algorithm under poor lightning conditions and increasing the number of possible gestures.

Gesture recognition has many promising fields of application and could pave the way to new ways of controlling electronic devices. A revolution in human-computer interaction might be close at hand!

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Researchers investigated how neutrophils are able to differentiate between bacteria to be destroyed and other compounds in the bloodstream, such as cholesterol particles. They tested a library consisting of 23 different protein-based nanoparticles in mice with ALI which revealed a set of “rules” that predict uptake by neutrophils. Neutrophils don’t take up symmetrical, rigid particles, such as viruses, but they do take up particles that exhibited “protein clumping,” which the researchers call nanoparticles with agglutinated protein (NAPs).

“We want to utilize the existing function of neutrophils that identifies and eliminates invaders to inform how to design a ‘Trojan horse’ nanoparticle that overactive neutrophils will intake and deliver treatment to alleviate ALI and ARDS,” said study lead author Jacob Myerson, PhD, a postdoctoral research fellow in the Department of Systems Pharmacology and Translational Therapeutics. “In order to build this ‘Trojan horse’ delivery system, though, we had to determine how neutrophils identify which particles in the blood to take up.”

ALI and ARDS are life-threatening forms of respiratory failure with high morbidity and mortality rates. Prior to COVID-19, there were 190,000 annual cases of ARDS in the U.S. and 75,000 deaths, with the ARDS being caused by pneumonia, sepsis, and trauma. However, COVID has increased ARDS cases into the millions. When ALI or ARDS occurs, the lung’s air sacs recruit neutrophils to the lungs in order to eliminate circulating microbes. This process causes neutrophils to release compounds that further aggravate lung injury and damage the air sacs, so patients develop low blood oxygen levels. Unfortunately, despite the severity of ALI/ARDS, there is no effective drug to control it, and treatment currently focuses on supporting patients while the lungs naturally, but slowly, heal.

To address ARDS and other medical problems, researchers at Penn and elsewhere have been using nanoparticles to concentrate drugs in injured or diseased organs. Such nanoparticles are also being used for gene therapy and immunotherapy.

The researchers note that while the development of viable therapies for ALI/ARDS using nanoparticles to deliver treatments via neutrophils are a long way off, this research represents a significant step in understanding the condition and function of the immune system.

“Now that we have determined that neutrophils patrol for nanoparticles with agglutinated protein, our next step is to understand how and why other microbes, like viruses, which are rigid and symmetrical, evolved to evade neutrophils,” said senior author Jacob Brenner, MD, PhD, an associate professor of Pulmonary Medicine in the Division of Pulmonary, Allergy, and Critical Care. “With this knowledge, we can continue to utilize this unique combination of material science and engineering, to create disease-specific therapies that target more advanced and complicated pathologies.”

The research was funded by the National Institutes of Health (R01 HL125462).