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Published1 day agoSharecloseShare pageCopy linkAbout sharingThis video can not be playedTo play this video you need to enable JavaScript in your browser.By Alex BinleyBBC NewsShanghai Disney has become the latest high-profile venue to shut its gates thanks to China’s strict zero-Covid policy, trapping visitors inside.People have been told they will not be allowed out of the theme park until they can show a negative test.It comes after Shanghai reported 10 locally transmitted cases on Saturday.China’s controversial zero-Covid policy has already seen millions of people repeatedly locked down, sometimes in unusual locations.The sudden nature lockdowns have seen people fleeing shops – including a Shanghai branch of Swedish furniture giant Ikea – and workplaces as they try to avoid being trapped inside.However, those awaiting their freedom at Shanghai Disney can console themselves with one positive: rides are continuing to operate for those trapped inside The Happiest Place on Earth.As well as the theme park, surrounding areas such as the shopping street were also abruptly closed shortly after 11:30 local time (3:30 GMT).Videos posted on Chinese social media site Weibo showed people rushing to the park’s gates following the announcement but finding them already locked.Posting on Chinese social media site WeChat, the Shanghai government said the park was barring people from entering and those inside could only leave once they had returned a negative test result.It added that anyone who has visited the park since Thursday must provide three negative test results over three consecutive days.No date has been given for when the park will reopen. Shanghai Disney said tickets will be valid for six months and refunds will be given.The snap closure comes just two days after the park began operating at a reduced capacity to comply with Covid measures.It’s not the first time the park has unexpectedly shut. Last November, 30,000 people were trapped inside after authorities ordered everyone to be tested as part of contact tracing.Almost three years since China reported its first coronavirus case, authorities across the vast nation continue to impose abrupt and extreme measures in a bid to stop any transmission of the virus.Millions of people are under 200 different lockdowns in China, as of October 24, as the country of 1.45 billion consistently records more than 1,000 new Covid cases a day. The numbers are seen as relatively small outbreaks in other parts of the world. However, earlier this month Chinese President Xi Jinping signalled that there would be no easing up of the zero-Covid policy – which aims to wipe out all outbreaks – calling it a “people’s war to stop the spread of the virus”.The Chinese government’s insistence on the increasingly unpopular policy comes as the economy continues to take a hit as a result, with GDP falling by 2.6% in the three months to the end of June from the previous quarter.Have you been forbidden to leave Shanghai Disney because of China’s zero-Covid policy? Email: haveyoursay@bbc.co.uk. Please include a contact number if you are willing to speak to a BBC journalist. You can also get in touch in the following ways:WhatsApp: +44 7756 165803Tweet: @BBC_HaveYourSayOr fill out the form belowPlease read our terms & conditions and privacy policy

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Most deaths from melanoma — the most lethal form of skin cancer — occur in patients who were initially diagnosed with early-stage melanoma and then later experienced a recurrence that is typically not detected until it has spread or metastasized.
A team led by investigators at Massachusetts General Hospital (MGH) recently developed an artificial intelligence-based method to predict which patients are most likely to experience a recurrence and are therefore expected to benefit from aggressive treatment. The method was validated in a study published in npj Precision Oncology.
Most patients with early-stage melanoma are treated with surgery to remove cancerous cells, but patients with more advanced cancer often receive immune checkpoint inhibitors, which effectively strengthen the immune response against tumor cells but also carry significant side effects.
“There is an urgent need to develop predictive tools to assist in the selection of high-risk patients for whom the benefits of immune checkpoint inhibitors would justify the high rate of morbid and potentially fatal immunologic adverse events observed with this therapeutic class,” says senior author Yevgeniy R. Semenov, MD, an investigator in the Department of Dermatology at MGH.
“Reliable prediction of melanoma recurrence can enable more precise treatment selection for immunotherapy, reduce progression to metastatic disease and improve melanoma survival while minimizing exposure to treatment toxicities.”
To help achieve this, Semenov and his colleagues assessed the effectiveness of algorithms based on machine learning, a branch of artificial intelligence, that used data from patient electronic health records to predict melanoma recurrence.
Specifically, the team collected 1,720 early-stage melanomas — 1,172 from the Mass General Brigham healthcare system (MGB) and 548 from the Dana-Farber Cancer Institute (DFCI) — and extracted 36 clinical and pathologic features of these cancers from electronic health records to predict patients’ recurrence risk with machine learning algorithms. Algorithms were developed and validated with various MGB and DFCI patient sets, and tumor thickness and rate of cancer cell division were identified as the most predictive features.
“Our comprehensive risk prediction platform using novel machine learning approaches to determine the risk of early-stage melanoma recurrence reached high levels of classification and time to event prediction accuracy,” says Semenov. “Our results suggest that machine learning algorithms can extract predictive signals from clinicopathologic features for early-stage melanoma recurrence prediction, which will enable the identification of patients who may benefit from adjuvant immunotherapy.”
Additional Mass General co-authors include Ahmad Rajeh, Michael R. Collier, Min Seok Choi, Munachimso Amadife, Kimberly Tang, Shijia Zhang, Jordan Phillips, Nora A. Alexander, Yining Hua, Wenxin Chen, Diane, Ho, Stacey Duey, and Genevieve M. Boland.
This work was supported by the Melanoma Research Alliance, the National Institutes of Health, the Department of Defense, and the Dermatology Foundation.
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Aging involves complicated plot twists and a large cast of characters: inflammation, stress, metabolism changes, and many others. Now, a team of Salk Institute and UC San Diego scientists reveal another factor implicated in the aging process — a class of lipids called SGDGs (3-sulfogalactosyl diacylglycerols) that decline in the brain with age and may have anti-inflammatory effects.
The research, published in Nature Chemical Biology on October 20, 2022, helps unravel the molecular basis of brain aging, reveals new mechanisms underlying age-related neurological diseases, and offers future opportunities for therapeutic intervention.
“These SGDGs clearly play an important role in aging, and this finding opens up the possibility that there are other critical aging pathways we’ve been missing,” says co-corresponding author Alan Saghatelian, professor in Salk’s Clayton Foundation Laboratories for Peptide Biology and holder of the Dr. Frederik Paulsen Chair. “This is a pretty clear case of something that should be dug into more in the future.”
SGDGs are a class of lipids, also called fats. Lipids contribute to the structure, development, and function of healthy brains, while badly regulated lipids are linked to aging and diseased brains. However, lipids, unlike genes and proteins, are not well understood and have often been overlooked in aging research. Saghatelian specializes in discovering new lipids and determining their structures.
His lab, in collaboration with Professor Dionicio Siegel at UC San Diego, made three discoveries involving SGDGs: In the brain, lipid levels are very different in older mice than in younger mice; all SGDG family members and related lipids change significantly with age; and SGDGs may be regulated by processes that are known to regulate aging.
To reach these findings, the team took an unusual, exploratory approach that combined the large-scale study of lipids (lipidomics) with structural chemistry and advanced data analytics. They first obtained lipid profiles of mouse brains at five ages, ranging from one to 18 months, using liquid chromatography-mass spectrometry. Technological advances in this instrumentation vastly expanded the number of data points available to the scientists, and advanced data analysis allowed them to determine age-related patterns in the enormous lipid profiles. The team then constructed SGDG molecules and tested them for biological activity.

Weill Cornell Medicine researchers have developed a computational method to map the architecture of human tissues in unprecedented detail. Their approach promises to accelerate studies on organ-scale cellular interactions and could enable powerful new diagnostic strategies for a wide range of diseases.
The method, published Oct. 31 in Nature Methods, grew out of the scientists’ frustration with the gap between classical microscopy and modern single-cell molecular analysis. “Looking at tissues under the microscope, you see a bunch of cells that are grouped together spatially — you see that organization in images almost immediately,” said lead author Junbum Kim, a graduate student in physiology and biophysics at Weill Cornell Medicine. “Now, cell biologists have gained the ability to examine individual cells in tremendous detail, down to which genes each cell is expressing, so they’re focused on the cells instead of focusing on the tissue structure,” he said.
However, “it’s crucial for researchers to learn more about the details of tissue structure; fundamental changes in the relationships between cells within a tissue drive both healthy and diseased organ function,” said senior author Dr. Olivier Elemento, director of the Englander Institute for Precision Medicine and a professor of physiology and biophysics and of computational genomics in computational biomedicine at Weill Cornell Medicine.
Manually combining single cell data with maps of tissue structure is slow and tedious, though. Machine learning algorithms have shown some potential for automating the process, but they’re limited by the data used to train them. To address that, Kim and his colleagues developed an unsupervised computational strategy, using a combination of single-cell gene expression profiles and cells’ locations to define structural regions within a tissue.
Co-senior author Dr. André Rendeiro, a postdoctoral fellow at Weill Cornell Medicine during the study and currently a principal investigator at the Research Center for Molecular Medicine of the Austrian Academy of Sciences in Vienna, Austria, compares the new method to mapping a city such as New York: “One way to go about it would be to go to every intersection and count each kind of building: is it residential, is it commercial … is it a shop or restaurant?” Putting all of those data into one matrix, and the buildings’ locations into another, one could then combine the two matrices and look for patterns.
“Essentially, we could start to make a general statement about where the different neighborhoods are and where their borders are based on the abundance of, say, residential versus commercial buildings — just as anyone walking through the Upper East Side, Midtown or Downtown would do based on their observations,” said Dr. Rendeiro.
The researchers used the new method to generate detailed maps of several types of tissues, identifying and quantifying new aspects of microanatomy — the patterns that emerge at small scale when cells interact and that determine the ultimate function of tissue. Collaborating with a colleague at the University of North Carolina at Chapel Hill who studies lung disease, they also demonstrated that their technique could draw fine shades of distinction between different disease states in a tissue.
While cancer and other chronic diseases often cause major changes in tissue structure, detailed microanatomy could also help in diagnosing and treating more acute conditions. Rendeiro points to severe COVID-19 as one example, where “there are a lot of immune cells that move into the neighborhood, and there’s really dramatic change in the lung tissue.” The team is now applying their new technique to a wide range of tissues to understand how changes in tissue organization underlie its function in healthy state and dysfunction in disease.
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Having biopsies taken and endless tests run is nobody’s idea of a good time, even if it’s necessary for monitoring your health. Now, researchers from Japan report the development of a new technique that could make testing for cancer a lot less invasive.
In a study published in September in the Journal of the American Chemical Society, researchers from Tokyo Medical and Dental University (TMDU) have revealed a new technique for assessing the cancer-related marker using breast cancer cell lines.
Detecting cancer-related markers is a powerful way to determine diagnosis, prognosis, and treatment success. Modern techniques are capable of detecting these markers in patient samples such as blood and urine, which provides a noninvasive way to monitor and evaluate patients.
“Circulating tumor cells (CTCs), which are cancer cells found in the blood, are one of the main targets used to evaluate cancer patients’ blood samples,” states Miyuki Tabata, first author of the study. “However, it can be challenging to isolate these cells from the blood, and current approaches do not adequately detect both epithelial cell and mesenchymal cell markers, which are important for determining the stage of the cancer.”
To create a system that can quickly and easily detect cancer-related markers on CTCs (and potentially other factors in the blood), the researchers used an apparatus called an ion-sensitive field effect transistor (ISFET), which is a tiny electrical circuit that is activated by a change in pH. They coated these transistors with breast cancer cells and then added an antibody linked to a chemical reporter that causes a change in pH if the antibody recognizes the cells.
“We found that the chemical reporter glucose oxidase successfully detected the expression of epidermal growth factor receptor (EGFR), a marker of poor cancer prognosis, on CTC membranes,” says Yuji Miyahara, senior author of the study. “Furthermore, the strength of the chemical signal correlated with the amount of EGFR expressed by the cells.”
Importantly, these results corresponded to the levels of EGFR detected using other techniques, indicating that the ISFET approach accurately identifies cancer-related marker expression on cells.
“These results provide proof of concept that an ISFET-based system can be used to efficiently assess patient cancer status based on liquid biopsy samples,” states Tabata.
Given that ISFETs can be manufactured to be the size of a single cell and assembled into vast arrays, this technique has the potential to enable high-throughput analysis of cancer cells at single-cell resolution. In addition, the use of multiple antibodies for the chemical enzyme detection step could enable the simultaneous analysis of multiple cancer-related markers.
The article, “Detection of epidermal growth factor receptor expression in breast cancer cell lines using an ion-sensitive field effect transistor in combination with enzymatic chemical signal amplification,” was published in the Journal of the American Chemical Society at DOI: 10.1021/jacs.2c06122
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Researchers at University of California San Diego School of Medicine, with colleagues from the ACTIV-2 trial, part of the U.S. government’s response to COVID-19, investigated whether symptoms of COVID-19 recurred following a two-day symptom-free period in persons who did not receive any treatment for the disease.
They sought to determine whether such recurrences might be different from those that have been documented in persons treated with Paxlovid, a phenomenon known as Paxlovid rebound.
Writing in the Oct 27, 2022 issue of JAMA Network Open, the scientists tracked 13 defined COVID-19 symptoms for 29 days in 158 untreated study participants. They found that more than one-third of the participants who reported complete resolution of symptoms for at least two consecutive days subsequently reported a return of symptoms.
“It is clear that COVID-19 has waxing and waning of symptoms, whether they are treated or not,” said the study’s lead author Davey M. Smith, MD, head of Infectious Diseases and Global Public Health at UC San Diego School of Medicine and an infectious disease specialist at UC San Diego Health.
In late-2021, the U.S. Food and Drug Administration issued an emergency use authorization for Paxlovid, a combination of two oral antiviral drugs (nirmatrelvir and ritonavir) that has since become the primary frontline treatment for COVID-19. Clinical trial data suggests Paxlovid significantly reduces the risk of severe illness, hospitalization and death.
But treatment efficacy has proven ephemeral in some cases, with a minority of patients experiencing either a reemergence of COVID symptoms after the five-day course of Paxlovid treatment or testing positive for the SARS-CoV-2 virus after a previous, negative test.
Earlier this year, Smith and colleagues published a study that suggested Paxlovid rebound did not appear to involve drug resistance or impaired immunity, but might be a case of insufficient drug exposure. Other studies have reported similar results.
“COVID rebound is a real phenomenon. It is complex, involving multiple factors, and its biological underpinnings remain unclear. More research is required,” said Smith. “In this latest study, however, we wanted to see if symptom rebound also occurred during the natural history of COVID-19.”
To do so, they assessed eligible participants in a 2020 clinical trial assessing the safety and efficacy of an investigational drug to treat COVID-19. These study volunteers, however, had received a placebo. During the trial, participants completed a daily symptom diary, including weeks after initial resolution of their COVID-19 symptoms. At 28 days post-enrollment in the study, 108 of 158 participants (68 percent) said their symptoms had completely resolved, but 48 participants (30 percent of the total group) subsequently reported that at least one defined symptom had returned.
Most (85 percent) of the participants with recurring symptoms described them as mild; 15 percent as moderate; none as severe. The most common relapsing symptoms were cough, fatigue and headache. Eight of the 158 participants were hospitalized for their illnesses, but there were no deaths and none involved participants who had achieved symptom resolution, then experienced recurrence.
Co-authors include: Jonathan Z. Li, Harvard Medical School; Carlee Moser, Eunice Yeh and Michael D. Hughes, Harvard University; and Judith S. Currier and Kara W. Chew, UCLA.
Funding for this research came, in part, from the US Government’s Response to COVID-19 and the National Institutes of Health (grants UM1 AI068634, UM1 AI068636, and UM1 AI106701).
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Materials provided by University of California – San Diego. Original written by Scott LaFee and Nicole Mlynaryk. Note: Content may be edited for style and length.

Newborns across the United States are screened to check for hearing loss. This test is important because it helps families better understand their child’s health, but it’s often not accessible to children in other countries because the screening device is expensive.
A team led by researchers at the University of Washington has created a new hearing screening system that uses a smartphone and low-cost earbuds instead. The team tested this device with 114 patients, including 52 babies up to 6 months old. The researchers also tested the device on pediatric patients with known hearing loss. Their tool performed as well as the commercial device, and it correctly identified all patients with hearing loss.
The team published these results Oct. 31 in Nature Biomedical Engineering.
“There is a huge amount of health inequity in the world. I grew up in a country where there was no hearing screening available, in part because the screening device itself is pretty expensive,” said senior author Shyam Gollakota, a UW professor in the Paul G. Allen School of Computer Science & Engineering. “The project here is to leverage the ubiquity of mobile devices people across the world already have — smartphones and $2 to $3 earbuds — to make newborn hearing screening something that’s accessible to all without sacrificing quality.”
Because babies can’t tell doctors whether they can hear a given sound, these tests rely on the mechanics of the ear.
“When an external sound is played, hair cells in the inner ear move and vibrate. The result is a very quiet sound that our instruments can pick up,” said co-author Dr. Randall Bly, an associate professor of otolaryngology-head and neck surgery at the UW School of Medicine who practices at Seattle Children’s Hospital. “This screening is very sensitive, meaning that if there is a concern about a patient’s hearing, they will be referred for a more thorough evaluation with a specialist.”
For the test, doctors send two different tones into the ear at the same time. Based on those tones, the hair cells in the ear vibrate and create a third tone, which is what the doctors are listening for.

Hair loss is undesirable for many men — and women — because one’s hairstyle is often closely tied to their self-confidence. And while some people embrace it, others wish they could regrow their lost strands. Now, researchers reporting in ACS’ Nano Letters have used artificial intelligence (AI) to predict compounds that could neutralize baldness-causing reactive oxygen species in the scalp. Using the best candidate, they constructed a proof-of-concept microneedle patch and effectively regenerated hair on mice.
Most people with substantial hair loss have the condition androgenic alopecia, also called male- or female-pattern baldness. In this condition, hair follicles can be damaged by androgens, inflammation or an overabundance of reactive oxygen species, such as oxygen free radicals. When the levels of oxygen free radicals are too high, they can overwhelm the body’s antioxidant enzymes that typically keep them in check. Superoxide dismutase (SOD) is one of these enzymes, and researchers have recently created SOD mimics called “nanozymes.” But so far, those that have been reported aren’t very good at removing oxygen free radicals. So, Lina Wang, Zhiling Zhu and colleagues wanted to see whether machine learning, a form of AI, could help them design a better nanozyme for treating hair loss.
The researchers chose transition-metal thiophosphate compounds as potential nanozyme candidates. They tested machine-learning models with 91 different transition-metal, phosphate and sulfate combinations, and the techniques predicted that MnPS3 would have the most powerful SOD-like ability. Next, MnPS3 nanosheets were synthesized through chemical vapor transport of manganese, red phosphorus and sulfur powders. In initial tests with human skin fibroblast cells, the nanosheets significantly reduced the levels of reactive oxygen species without causing harm.
Based on these results, the team prepared MnPS3 microneedle patches and treated androgenic alopecia-affected mouse models with them. Within 13 days, the animals regenerated thicker hair strands that more densely covered their previously bald backsides than mice treated with testosterone or minoxidil. The researchers say that their study both produced a nanozyme treatment for regenerating hair, and indicated the potential for computer-based methods for use in the design of future nanozyme therapeutics.
The authors acknowledge funding from the National Natural Science Foundation of China and the Natural Science Foundation of Shandong Province China.
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When individuals with varying degrees of chronic kidney disease who were not on dialysis answered annual questionnaires about their symptoms, researchers found that one-third could be categorized as having a “Worse symptom score and worsening trajectory” of symptoms. As reported in CJASN, these patients had especially high risks of later needing dialysis and of dying before dialysis initiation.
For the study, Moustapha Faye, MD (CHRU Nancy, Université Cheikh Anta Diop de Dakar) and his colleagues, investigators of the CKD-REIN cohort study, assessed symptoms annually using the Kidney Disease Quality of Life-36 questionnaire that was completed by 2,787 adults in France with CKD who were not on dialysis.
The prevalence of each symptom ranged from 24% (chest pain) to 83% (fatigue), and 98% of participants reported at least one symptom. After a median follow-up of 5.3 years, 690 participants initiated kidney replacement therapy (KRT) such as dialysis, and 490 died before KRT. The team identified two profiles of symptom trajectories: a “Worse symptom score and worsening trajectory” in 31% of participants, characterized by a low initial symptom score that worsened more than 10 points (on a scale of 0-100) over time, and a “Better symptom score and stable trajectory” in 69% of participants, characterized by a high initial score that remained stable over time.
Participants in the “Worse symptom score and worsening trajectory” category had more risk factors for CKD progression at baseline, worse quality of life, and a higher risk of KRT and death before KRT than other participants.
“In addition to the already existing classifications of CKD, it is possible to actively monitor symptoms and classify patients according to their progression. This monitoring should involve practitioners and patients,” said Dr. Faye. “This active symptom tracking will allow early therapeutic interventions to be planned to help manage different symptoms.”
An accompanying editorial notes that “in addition to disease management, Faye et al. provide further evidence of the need to care for the unpleasant symptoms that cause suffering and affect the well-being of patients with advanced CKD.”
Additional study authors include Karine Legrand, PhD; Lisa Le Gall; Karen Leffondre, PhD; Abdou Y. Omorou, MD, PhD; Natalia Alencar de Pinho,PhD; Christian Combe, MD, PhD; Denis Fouque, MD, PhD; Christian Jacquelinet, PhD; Maurice Laville,PhD; Sophie Liabeuf, PhD; Ziad A Massy, MD, PhD; Elodie Speyer, PhD; Roberto Pecoits Filho, PhD; Bénédicte Stengel, MD, PhD; Luc Frimat, MD, PhD; and Carole Ayav, MD; and the CKD-REIN Study Group.
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The growth factor IGF plays a key role in human growth. In the absence of IGF signaling, we become dwarfs. Later in life, IGF is involved in age-related diseases, e.g. cancer and cardiovascular disease. In both cases, IGF must be converted from an inactive to an active form. This is what PAPP-A is able to do.
“Seven years ago we discovered that the protein STC2 blocks the activity of PAPP-A, thus indirectly inhibiting the activity of the IGF growth factor. To block the activity, STC2 must form a complex with PAPP-A. We have studied this complex, and we now know its three-dimensional structure. It is fascinating to see what a molecule, we know biochemically very well, actually looks like. PAPP-A is heart-shaped with an inner ‘chamber’. But from a research point of view, the shape is not the most interesting feature. Rather, it is the interactions between the different elements of the molecule,” Professor Claus Oxvig explains.
There are still many unanswered questions about the molecular mechanisms, which regulate how much IGF is converted into the active form. It is likely that complex formation between PAPP-A and STC2 is highly regulated. Such a hypothesis is supported by earlier findings showing that natural human variants of STC2, in which just a single amino acid is substituted, form the complex with PAPP-A slightly slower. The consequence of this is that slightly more IGF can be activated by PAPP-A, resulting in an increase in height of up to 2.1 cm.
The first-author of the publication reporting the PAPP-A·STC2 structure, graduate student Sara Dam Kobberø, has used cryo-electron microscopy (cryo-EM) to determine the structure of the large protein complex. The Danish National Cryo-EM Research Infrastructure (EMBION, AU) has allowed this study, which has also involved participants from the University of Copenhagen.
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Materials provided by Aarhus University. Original written by Lisbeth Heilesen. Note: Content may be edited for style and length.