Friday, September 20, 2024

 

Unveiling the math behind your calendar



Case Western Reserve research explores statistical mysteries of everyday tasks, from Doodle polls to efficient scheduling



Case Western Reserve University





In a world where organizing a simple meeting can feel like herding cats, new research from Case Western Reserve University reveals just how challenging finding a suitable meeting time becomes as the number of participants grows.

The study, published in the European Physical Journal B, dives into the mathematical complexities of this common task, offering new insights into why scheduling often feels so impossible.

“If you like to think the worst about people, then this study might be for you,” quipped researcher Harsh Mathur, professor of physics at the College of Arts and Sciences at CWRU. “But this is about more than Doodle polls. We started off by wanting to answer this question about polls, but it turns out there is more to the story.”

Researchers used mathematical modeling to calculate the likelihood of successfully scheduling a meeting based on several factors: the number of participants (m), the number of possible meeting times (τ) and the number of times each participant is unavailable (r).

What they found: As the number of participants grows, the probability of scheduling a successful meeting decreases sharply.

Specifically, the probability drops significantly when more than five people are involved—especially if participant availability remains consistent.

“We wanted to know the odds,” Mathur said. “The science of probability actually started with people studying gambling, but it applies just as well to something like scheduling meetings. Our research shows that as the number of participants grows, the number of potential meeting times that need to be polled increases exponentially.

“The project had started half in jest but this exponential behavior got our attention. It showed that scheduling meetings is a difficult problem, on par with some of the great problems in computer science.”

‘More to the story’

Interestingly, researchers found a parallel between scheduling difficulties and physical phenomena. They observed that as the probability of a participant rejecting a proposed meeting time increases, there’s a critical point where the likelihood of successfully scheduling the meeting drops sharply. It’s a phenomenon similar to what is known as “phase transitions” in physics, Mathur said, such as ice melting into water.

“Understanding phase transitions mathematically is a triumph of physics,” he said. “It’s fascinating how something as mundane as scheduling can mirror the complexity of phase transitions.”

 

Mathur also noted the study’s broader implications, from casual scenarios like sharing appetizers at a restaurant to more complex settings like drafting climate policy reports, where agreement among many is needed.

“Consensus-building is hard,” Mathur said. “Like phase transitions, it’s complex. But that’s also where the beauty of mathematics lies—it gives us tools to understand and quantify these challenges.”

Mathur said the study contributes insights into the complexities of group coordination and decision-making, with potential applications across various fields.

Joining Mathur in the study were physicists Katherine Brown, of Hamilton College, and Onuttom Narayan, of the University of California, Santa Cruz.

 

Catalogue of fungi in China 3. New taxa of macrofungi from southern Xizang, China



Tsinghua University Press
Several new species of macrofungi from southern Xizang, China 

image: 

Basidiomes of the new species Cuphophyllus dingjieensis, Infundibulicybe jilongensis, Pholiota alpina, Gymnopus jilongensis, Xenasmatella jilongensis and Phanerochaete xizangensis

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Credit: Li-Wei Zhou, Tie-Zheng Wei, State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences




The Himalayas, located on the southern edge of Qinghai-Tibetan Plateau and the border area of Xizang, are the highest (average alt. over 6,000 m) mountain range on the earth. Influenced by the warm and humid air currents from Indian Ocean, the south Himalayas has abundant rains in summer, which provides a suitable habitat for the growth of macrofungi.

 

During a field trip in July 2023, 882 specimens in six counties from the border area of Xizang, China were collected. According to morphological examinations and phylogenetic analyses, the collections were identified as 339 species, belonging to 2 phylum, 5 classes, 16 orders, 40 families, and 169 genera, among which 15 new macrofungal species were revealed and are described in this paper. These species are taxonomically distributed in six orders, 11 families, and 12 genera. In addition, one new combination is proposed. It should be noted that the border area of Xizang is huge, and thus much more efforts should be made to clarify the macrofungal diversity there. The current paper represents the preliminary systematic exploration of macrofungi in this region.

 

See the article: https://doi.org/10.1080/21501203.2024.2392014

 

Enhancing artificial intelligence models for the sake of oceanography



Journal of Remote Sensing
Enhancing U-Net for ocean remote sensing applications 

image: 

(A) Standard U-Net framework and its current applications. (B) Proposed advancements in U-Net for semantic segmentation tasks within ocean remote sensing. (C) Enhancement strategies for U-Net in ocean remote sensing forecasting tasks. (D) Approaches for improving U-Net in super-resolution reconstruction tasks specific to ocean remote sensing.

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Credit: [Haoyu Wang, Institute of Oceanology, Chinese Academy of Sciences] (This is an example)




U-Net, a convolutional neural network (CNN) originally intended for medical use can potentially make waves in the ocean remote sensing field

 

There is seldom an issue in our modern world that cannot be solved or helped by technology and artificial intelligence (AI). In this instance, U-Net, a tool used to extract a desired “object” from a medical image, is looked at as potential means of oceanographic research. Although it’s promising, U-Net is not perfect. A few key improvements in the model can make a huge difference when it comes to the field of ocean remote sensing. 

 

Researchers published their findings in the Journal of Remote Sensing in August 2024.

 

The U-Net model appears to have a good enough structure to be a fine candidate for oceanographic research, but at its current state, it is not able to completely fulfill the needs of researchers.

 

To solve the challenges U-Net faces with pivoting to oceanographic research, three main categories need improvement: the model’s segmentation tasks, or ability to categorize each pixel in an image, forecasting tasks and super-resolution tasks.

 

“Through structural improvement and the introduction of new techniques, the U-Net model can gain significant improvement in small target detection, prediction accuracy and image reconstruction quality, further promoting the development of ocean remote sensing research,” said Haoyu Wang, author and researcher.

 

Improving semantic segmentation can improve the U-Net’s ability to detect and identify small targets in the ocean. This can be done by integrating the model with the ability to recognize and identify pixels at a distance away via attention mechanisms. For example, getting the model to recognize the difference between open water and ice formations in the ocean is integral, and U-Net can determine this difference.

 

Forecasting tasks refer to the model’s ability to logically predict an outcome based on physical knowledge and data-driven methods. Previous successes using the U-Net model for oceanic remote sensing include the Sea Ice Prediction Network (SIPNet), which predicts the sea ice concentration of the Antarctic. SIPNet, the U-Net model, used another form of neural network architecture known as “encoder-decoder” that processes an input sequence (encoder) to later be reconstructed back to the original form (decoder). This is often used for summarizing or translating text, but in this case, SIPNet used 8 weeks of data about sea ice concentration to forecast the 8 weeks following. When the encoder-decoder architecture was combined with a temporal-spatial attention module (TSAM), the average difference between the prediction and the actual measurement was less than 3% for a 7-day forecast, showcasing the accuracy U-Net models can have when fully outfitted for the task.

 

Lastly, the improvements suggested for super-resolution tasks include the introduction of a diffusion model to reduce blurring in the images, or “noise.” To reduce noise in images, the correlation between high and low-resolution images has to be identified by taking note of the similarities observed in both resolutions. This also includes making improvements to the model’s capability of extracting features from images. Researchers suggest utilizing a model, PanDiff, to blend the high-res panchromatic (sensitive to all visible colors in the spectrum) and low-resolution multispectral images (images that capture data through spectrums such as infrared and ultraviolet) to be reconstructed by U-Net via the random noise.

 

Further optimization of the U-Net model is necessary to support the goals of researchers in the long term.

 

“The U-Net model’s straightforward and understandable network architecture and superior model fitting capabilities have garnered the most popularity among researchers in the ocean remote sensing community, demonstrating great potential,” said Xiaofeng Li, researcher and author of the study.

 

In addition to the improvements researchers suggest for using U-Net in oceanic research, there is plenty of exploration to be done by combining U-Net with other systems or techniques to further extend an already wide application of the model.

 

Haoyu Wang and Xiaofeng Li of the Institute of Oceanology at the Chinese Academy of Sciences with Haoyu Wang also of the University of Chinese Academy of Sciences contributed to this research.

 

The National Natural Science Foundation of China and the Strategic Priority Research Program of the Chinese Academy of Sciences made this research possible.

 

Harnessing egg yolk power: A new approach to paprika oleoresin stability



Tsinghua University Press
Structural, Property, and Loading Characteristics of High-Pressure Homogenized LDL for Paprika Oleoresin Stabilization. 

image: 

This figure illustrates the effects of high-pressure homogenization (HPH) on egg yolk low-density lipoprotein (LDL) used for stabilizing paprika oleoresin (PO). The structural analysis (left) shows protein integrity and secondary structure changes of LDL under different HPH pressures. The central images compare the morphology and appearance of untreated LDL and HPH-treated LDL (LDL-H100). The right section presents property evaluations, including turbidity, particle size, and ζ-potential, demonstrating improved stability with increasing HPH pressure. The bottom section displays load characteristics, highlighting the reduced floating rate and enhanced encapsulation efficiency (EE) of PO with HPH-treated LDL, underscoring the technique's potential to improve PO stability and application in aqueous environments.

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Credit: Jinqiu Wang, Chengdu University, China




Paprika oleoresin (PO), extracted from chili peppers, is renowned for its vibrant color and beneficial health properties, such as antioxidant and anti-inflammatory effects. However, its lipophilic nature and sensitivity to factors like oxygen, heat, and light restrict its use in water-based foods. While previous approaches, including emulsions and liposomes, have aimed to improve PO’s stability, the results have been limited. These persistent challenges underscore the need for new stabilization methods for PO.

The study (DOI: 10.26599/FSAP.2024.9240064), led by scientists from Chengdu University and Huazhong Agricultural University, was published in the journal Food Science of Animal Products on August 23. The research utilized high-pressure homogenization (HPH) to restructure low-density lipoprotein (LDL) from egg yolk, producing a stable aqueous PO solution. By examining microstructure, particle size, encapsulation efficiency, and stability under various conditions, the study confirmed that HPH significantly enhances PO's solubility and stability, offering a greener, safer method of utilizing LDL as a bioactive carrier.

The researchers found that HPH at 100 MPa for 10 cycles decreased the average particle size of the LDL-PO complex by 37.2% and improved encapsulation efficiency by 9.2%. Stability assessments showed notable enhancements in storage, thermal, and UV irradiation resistance, with stability rates increasing from 30.83% to 62.90%, 64.42% to 76.97%, and 77.56% to 92.98%, respectively. Structural analysis revealed that HPH promotes better interaction between LDL and PO, optimizing the dispersion and stability of PO in water without compromising the lipoprotein’s structure.

“The innovative use of HPH to remodel LDL represents a significant advance in the stabilization of natural pigments like PO,” stated Dr. Jinqiu Wang, the study’s lead researcher. “This technique not only boosts LDL’s role as an effective carrier but also broadens the potential uses of natural colorants in various food products, marking a greener and safer approach to food processing.”

The study’s findings suggest that HPH could be extended to stabilize other fat-soluble bioactive compounds, enhancing their application in the food industry. This method offers a promising pathway toward more sustainable and efficient food production, leveraging LDL’s versatility as a carrier for diverse nutrients and active ingredients in aqueous solutions.

This study was supported by the National Natural Science Foundation of China (32072236), and Sichuan Innovation Team Project of National Modern Agricultural Industry Technology System (SCCXTD-2024-24).

 


About Food Science of Animal Products

Food Science of Animal Products, sponsored by Beijing Academy of Food Sciences, published by Tsinghua University Press and exclusively available via SciOpen, is a peer-reviewed, open access international journal that publishes the latest research findings in the field of animal-origin foods, involving food materials such as meat, aquatic products, milk, eggs, animal offals and edible insects. The research scope includes the quality and processing characteristics of food raw materials, the relationships of nutritional components and bioactive substances with human health, product flavor and sensory characteristics, the control of harmful substances during processing or cooking, product preservation, storage and packaging; microorganisms and fermentation, illegal drug residues and food safety detection; authenticity identification; cell-cultured meat, regulations and standards.

About SciOpen 

SciOpen is an open access resource of scientific and technical content published by Tsinghua University Press and its publishing partners. SciOpen provides end-to-end services across manuscript submission, peer review, content hosting, analytics, identity management, and expert advice to ensure each journal’s development. By digitalizing the publishing process, SciOpen widens the reach, deepens the impact, and accelerates the exchange of ideas.

 

Chilling discovery: Temperature fluctuations mar fish quality




Tsinghua University Press
Impact of temperature fluctuations on the quality and volatile components of large yellow croaker. 

image: 

This diagram illustrates the experimental setup and findings of the study on large yellow croaker (Pseudosciaena crocea) under various temperature fluctuation conditions during cold chain logistics. The research involved chemical and microbiological analyses alongside volatile odor component profiling, highlighting the significant impact of temperature changes on fish freshness. Key findings suggest that shifts in volatile compounds could serve as potential indicators for monitoring seafood quality.

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Credit: Yanbo Wang, Gongshang University, China




Large yellow croaker is a highly nutritious and economically valuable mariculture species, but its perishable nature poses significant challenges in storage and transport. Cold chain logistics play a crucial role in maintaining seafood quality, but frequent temperature fluctuations during transit and handling can trigger microbial growth, enzymatic activities, and chemical reactions that degrade fish quality. Addressing these challenges requires deeper insights into how temperature variations affect seafood and identifying reliable indicators of spoilage. This study aims to fill that gap, providing essential data to improve cold chain management and reduce quality loss.

This research (DOI: 10.26599/FSAP.2023.9240057), conducted by a team from Zhejiang Gongshang University and published in Food Science of Animal Products on June19, investigates the effects of temperature fluctuations during cold chain logistics on large yellow croaker. The study replicates real-world cold chain conditions, showing that inconsistent temperatures significantly compromise both the microbial and chemical integrity of the fish. The results highlight a critical need for stringent temperature control throughout the cold chain to prevent spoilage, offering valuable insights into how transport conditions impact seafood quality and setting the stage for improved preservation practices.

The research team developed a cold chain simulation model to examine the impact of temperature fluctuations ranging from 4°C to 16°C on large yellow croaker. Results showed that higher temperature fluctuations led to increased total viable counts (TVC), total volatile basic nitrogen (TVB-N) content, K values, and thiobarbituric acid reactive substances (TBARS) values—all key indicators of spoilage. The study identified 81 volatile compounds, including aldehydes, ketones, and nitrogenous compounds, which varied significantly with temperature changes. Notably, 2-pentanone and ethyl acetate emerged as potential markers of quality deterioration, offering a novel approach to monitoring seafood freshness. The findings underscore the importance of maintaining stable temperatures to reduce spoilage and preserve the nutritional and sensory properties of seafood. This work provides a scientific foundation for refining cold chain practices, enhancing seafood safety, and reducing waste, making it highly relevant to the seafood industry.

Dr. Yanbo Wang, the study’s lead author, emphasized the significance of the findings: “Our study clearly demonstrates how temperature fluctuations can dramatically affect seafood quality, reinforcing the need for stringent cold chain management. Identifying specific volatile compounds as markers of spoilage provides a new tool for monitoring seafood freshness throughout the supply chain. These insights are critical for producers and distributors to minimize quality loss during transport and ensure food safety. By advancing our understanding of temperature effects on seafood, we can drive improvements across the entire cold chain logistics system.”

The implications of this research extend beyond quality control, potentially transforming the seafood supply chain. By pinpointing key indicators of spoilage, such as TVC and specific volatile compounds, the study paves the way for enhanced monitoring systems that detect quality degradation early. These insights could inspire new packaging and transport technologies that minimize temperature fluctuations, ultimately reducing food waste and bolstering consumer safety. The research also highlights the urgent need for updated regulatory standards to ensure proper temperature management in cold chain logistics, safeguarding seafood quality from ocean to table.

This study was supported by the National Natural Science Foundation of China (32072290), the Zhejiang Provincial Natural Science Foundation of China (LZ22C200003), and the Fundamental Research Funds for the Provincial Universities of Zhejiang (XRK22003).

 


About Food Science of Animal Products

Food Science of Animal Products, sponsored by Beijing Academy of Food Sciences, published by Tsinghua University Press and exclusively available via SciOpen, is a peer-reviewed, open access international journal that publishes the latest research findings in the field of animal-origin foods, involving food materials such as meat, aquatic products, milk, eggs, animal offals and edible insects. The research scope includes the quality and processing characteristics of food raw materials, the relationships of nutritional components and bioactive substances with human health, product flavor and sensory characteristics, the control of harmful substances during processing or cooking, product preservation, storage and packaging; microorganisms and fermentation, illegal drug residues and food safety detection; authenticity identification; cell-cultured meat, regulations and standards.

About SciOpen 

SciOpen is an open access resource of scientific and technical content published by Tsinghua University Press and its publishing partners. SciOpen provides end-to-end services across manuscript submission, peer review, content hosting, analytics, identity management, and expert advice to ensure each journal’s development. By digitalizing the publishing process, SciOpen widens the reach, deepens the impact, and accelerates the exchange of ideas.

 

Edible insects show promise as sustainable nutritional source



Tsinghua University Press
Comparative Health Effects of Ruspolia nitidula and Clupea harengus Meals on Rats. 

image: 

This image illustrates the impact of Ruspolia nitidula (grasshopper) and Clupea harengus (fish) meals on rat health. Rats fed with grasshopper meal showed optimal improvements in libido, sleep, hair health, ejaculation, and copulation, whereas those fed with fish meal exhibited moderate enhancements in similar health markers.

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Credit: Ngnaniyyi Abdoul, University of Dschang, Cameroon




As the global population grows and traditional livestock production increasingly strains environmental resources, there is a rising interest in alternative protein sources. Edible insects, particularly grasshoppers, are abundant in regions like Cameroon and provide essential nutrients, including proteins, amino acids, and minerals vital for health and growth. Addressing these challenges calls for in-depth studies on the nutritional benefits of insects such as Ruspolia nitidula.

Conducted by the University of Dschang, Cameroon, and published (DOI: 10.26599/FSAP.2024.9240068) in the journal Food Science of Animal Products on August 30, this study examined the effects of substituting traditional Clupea harengus fish meal with Ruspolia nitidula grasshopper meal in rat diets. Over 12 weeks, researchers evaluated how this dietary change impacted libido, sleep, hair growth, and overall health, assessing the insect meal's potential as a viable alternative protein source.

The study demonstrated that replacing fish meal with Ruspolia nitidula grasshopper meal resulted in significant health improvements in rats. Those on the grasshopper diet exhibited enhanced libido, with increased intromissions and ejaculations compared to rats on fish meal or protein-deficient diets. Sleep quality also improved, with rats experiencing longer, more restful sleep. Hair quality was notably superior, with 94.58% of hairs in optimal condition in the grasshopper-fed group, compared to just 5.55% and 0.27% in the fish meal and protein-deficient groups. Additionally, the grasshopper-fed rats showed greater body weight gain, indicating overall better health and nutrition. These findings underscore the grasshopper meal's potential as a sustainable and nutritionally superior alternative protein source.

Dr. Ngnaniyyi Abdoul, the study's lead researcher, remarked, "Our findings highlight the significant potential of edible insects like Ruspolia nitidula as alternative protein sources. The grasshopper meal not only meets nutritional needs but also offers substantial health benefits, including improved libido, better sleep, and enhanced hair quality, with far-reaching implications for both animal and human diets."

This research emphasizes the potential of Ruspolia nitidula as a sustainable, nutrient-rich protein alternative. Beyond animal feed, the findings suggest that grasshopper meal could play a role in addressing human malnutrition, particularly in low-resource settings. With ecological advantages and health benefits, edible insects present a compelling solution for future food security and dietary enhancement.

The North Cameroon Association for Ecological and Food Transition (ABC-ECOLO) for funding this study.

 


About Food Science of Animal Products

Food Science of Animal Products, sponsored by Beijing Academy of Food Sciences, published by Tsinghua University Press and exclusively available via SciOpen, is a peer-reviewed, open access international journal that publishes the latest research findings in the field of animal-origin foods, involving food materials such as meat, aquatic products, milk, eggs, animal offals and edible insects. The research scope includes the quality and processing characteristics of food raw materials, the relationships of nutritional components and bioactive substances with human health, product flavor and sensory characteristics, the control of harmful substances during processing or cooking, product preservation, storage and packaging; microorganisms and fermentation, illegal drug residues and food safety detection; authenticity identification; cell-cultured meat, regulations and standards.

About SciOpen 

SciOpen is an open access resource of scientific and technical content published by Tsinghua University Press and its publishing partners. SciOpen provides end-to-end services across manuscript submission, peer review, content hosting, analytics, identity management, and expert advice to ensure each journal’s development. By digitalizing the publishing process, SciOpen widens the reach, deepens the impact, and accelerates the exchange of ideas.

 

Machine learning could help reduce hospitalizations by nearly 30% during a pandemic, study finds


New research shows machine learning can be more effective than current methods to distribute scarce treatments to patients most vulnerable during a public health crisis New research shows machine learning can be more effective than current methods 


University of Colorado Anschutz Medical Campus




A new study sheds light on a promising approach using machine learning to more effectively allocate medical treatments during a pandemic or any time there’s a shortage of therapeutics.  

The findings, published today in JAMA Health Forum, found a significant reduction in expected hospitalizations when using machine learning to help distribute medication using the COVID-19 pandemic to test the model. The model proves to reduce hospitalizations relatively by about 27 percent compared to actual and observed care.

“During the pandemic, the healthcare system was at a breaking point and many health care facilities relied on a first-come, first-serve or a patient’s health history to implement who received treatments,” said the paper’s senior author Adit Ginde, MD, professor of emergency medicine at the University of Colorado Anschutz Medical Campus.

“However, these methods often don’t address the complex interactions that can occur in patients when taking medications to determine expected clinical effectiveness and may overlook patients who would benefit the most from treatment. We show that machine learning in these scenarios is a way to use real-time, real-world evidence to inform public health decision making,” Ginde adds.

In the study, the researchers showed that using machine learning that looks at how individual patients benefit differently from treatment can provide doctors, health systems and public health officials with more accurate information in real-time than traditional allocation score models. Mengli Xiao, PhD, assistant professor in Biostatistics and Informatics, developed the mAb allocation system based on machine learning.

“Existing allocation methods primarily target patients who have a high-risk profile for hospitalizations without treatments. They could overlook patients who benefit most from treatments. We developed a mAb allocation point system based on treatment effect heterogeneity estimates from machine learning. Our allocation prioritizes patient characteristics associated with large causal treatment effects, seeking to optimize overall treatment benefits when resources are limited” said Xiao, who is also faculty at the Center of Innovative Design and Analysis (CIDA).

Specifically, the researchers looked at the effectiveness of adding a novel Policy Learning Trees (PLTs)-based method for optimizing the allocation of COVID-19 neutralizing monoclonal antibodies (mAbs) during periods of resource constraint.

The PLT approach was designed to decide which treatments to assign to individuals in a way that maximizes the overall benefits for the population (ensuring those who are at the highest risk of hospitalization are sure to receive treatments, especially when treatment is scarce). This is done by taking into account how different factors affect the effectiveness of the treatment.

The researchers compared the machine learning approach with real-world decisions and a standard point allocation system used during the pandemic. They found the PLTs-based model demonstrated a significant reduction in expected hospitalizations compared to the observed allocation. This improvement also surpassed the performance of the Monoclonal Antibody Screening Score, which observes antibodies for diagnosis.

“Using an innovative approach like machine learning expands beyond crises like the COVID-19 pandemic and shows we can provide personalized public health decisions even when resources are limited in any scenario. To do so, though, it’s important that robust, real-time data platforms, like what we developed for this project, are implemented to provide data-driven decisions,” adds Ginde, a leader in the Colorado Clinical and Translational Sciences Institute at CU Anschutz.   

The paper in JAMA Health Forum will be the 15th publication to come out of a project called Monoclonal Antibody (mAB) Colorado, which was funded by a grant from the National Institutes of Health (NIH) and National Center for Advancing Translational Sciences (NCATS). The project focused on doing the most good for the most people, using real world evidence for data-driven decisions during the COVID-19 pandemic.

The researchers hope this paper will encourage public health entities, policymakers and disaster management agencies to look into methods like machine learning to implement in case of a future public health crisis.

About the University of Colorado Anschutz Medical Campus

The University of Colorado Anschutz Medical Campus is a world-class medical destination at the forefront of transformative science, medicine, education and patient care. The campus encompasses the University of Colorado health professional schools, more than 60 centers and institutes, and two nationally ranked independent hospitals - UCHealth University of Colorado Hospital and Children's Hospital Colorado – which see more than 2 million adult and pediatric patient visits yearly. Innovative, interconnected and highly collaborative, the University of Colorado Anschutz Medical Campus delivers life-changing treatments, patient care and professional training and conducts world-renowned research fueled by $705 million in research grants. For more information, visit www.cuanschutz.edu.


Stimulant, antidepressant, and opioid telehealth prescription trends between 2019 and 2022



JAMA Network




About The Study: From 2019 to 2022, overall prescription volumes for stimulant and antidepressant medications increased, while prescription volume for opioids decreased. Concurrently, the proportion of telehealth prescriptions climbed across medications, increasing by a factor of 188 in opioids and more than 20 for antidepressants. These findings align with existing research highlighting the shift toward telehealth and the rise in stimulant and opioid telehealth prescribing during the pandemic. While in-person prescribing remains the most common, increasing telehealth utilization across medications suggests a growing acceptance, need, or preference for remote services.

Corresponding Author: To contact the corresponding author, Ashwini Nagappan, MBE, email ashwininagappan@ucla.edu.

To access the embargoed study: Visit our For The Media website at this link https://media.jamanetwork.com/

(doi:10.1001/jamanetworkopen.2024.33334)

Editor’s Note: Please see the article for additional information, including other authors, author contributions and affiliations, conflict of interest and financial disclosures, and funding and support.

#  #  #

Embed this link to provide your readers free access to the full-text article This link will be live at the embargo time http://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2024.33334?utm_source=For_The_Media&utm_medium=referral&utm_campaign=ftm_links&utm_term=091324

About JAMA Network Open: JAMA Network Open is an online-only open access general medical journal from the JAMA Network. On weekdays, the journal publishes peer-reviewed clinical research and commentary in more than 40 medical and health subject areas. Every article is free online from the day of publication. 

 

Adolescents and young adults’ sources of contraceptive information



JAMA Network




About The Study: This study’s results suggest discrepancies between preferred and actual sources of contraceptive information for assigned female at birth adolescents and young adults in the U.S. Findings underscore the role of clinicians in supporting informed contraceptive decision-making among adolescents and young adults. Clinicians were the most commonly preferred source, and receiving information from them was associated with having sufficient information to choose a contraceptive method; however, clinicians were the source with the largest discrepancy between preferred and actual use. 

Corresponding Author: To contact the corresponding author, Elizabeth Pleasants, DrPH, MPH, email b_pleasants@berkeley.edu.

To access the embargoed study: Visit our For The Media website at this link https://media.jamanetwork.com/

(doi:10.1001/jamanetworkopen.2024.33310)

Editor’s Note: Please see the article for additional information, including other authors, author contributions and affiliations, conflict of interest and financial disclosures, and funding and support.

#  #  #

Embed this link to provide your readers free access to the full-text article This link will be live at the embargo time http://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2024.33310?utm_source=For_The_Media&utm_medium=referral&utm_campaign=ftm_links&utm_term=091324

About JAMA Network Open: JAMA Network Open is an online-only open access general medical journal from the JAMA Network. On weekdays, the journal publishes peer-reviewed clinical research and commentary in more than 40 medical and health subject areas. Every article is free online from the day of publication.