Robust Nuclear Spin Polarization via Ground-State Level Anticrossing of Boron Vacancy Defects in Hexagonal Boron Nitride.

Nuclear spin polarization plays a crucial role in quantum information processing and quantum sensing. In this work, we demonstrate a robust and efficient method for nuclear spin polarization with boron vacancy (V_{B}^{-}) defects in hexagonal boron nitride (h-BN) using ground-state level anticrossing (GSLAC). We show that GSLAC-assisted nuclear polarization can be achieved with significantly lower laser power than excited-state level anticrossing, making the process experimentally more viable. Furthermore, we have demonstrated direct optical readout of nuclear spins for V_{B}^{-} in h-BN. Our findings suggest that GSLAC is a promising technique for the precise control and manipulation of nuclear spins in V_{B}^{-} defects in h-BN.

The limits of fair medical imaging AI in real-world generalization.

As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Previous research established AI's capacity to infer demographic data from chest X-rays, leading to a key concern: do models using demographic shortcuts have unfair predictions across subpopulations? In this study, we conducted a thorough investigation into the extent to which medical AI uses demographic encodings, focusing on potential fairness discrepancies within both in-distribution training sets and external test sets. Our analysis covers three key medical imaging disciplines-radiology, dermatology and ophthalmology-and incorporates data from six global chest X-ray datasets. We confirm that medical imaging AI leverages demographic shortcuts in disease classification. Although correcting shortcuts algorithmically effectively addresses fairness gaps to create 'locally optimal' models within the original data distribution, this optimality is not true in new test settings. Surprisingly, we found that models with less encoding of demographic attributes are often most 'globally optimal', exhibiting better fairness during model evaluation in new test environments. Our work establishes best practices for medical imaging models that maintain their performance and fairness in deployments beyond their initial training contexts, underscoring critical considerations for AI clinical deployments across populations and sites.

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" - there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification.

Demographic bias in misdiagnosis by computational pathology models.

Despite increasing numbers of regulatory approvals, deep learning-based computational pathology systems often overlook the impact of demographic factors on performance, potentially leading to biases. This concern is all the more important as computational pathology has leveraged large public datasets that underrepresent certain demographic groups. Using publicly available data from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, as well as internal patient data, we show that whole-slide image classification models display marked performance disparities across different demographic groups when used to subtype breast and lung carcinomas and to predict IDH1 mutations in gliomas. For example, when using common modeling approaches, we observed performance gaps (in area under the receiver operating characteristic curve) between white and Black patients of 3.0% for breast cancer subtyping, 10.9% for lung cancer subtyping and 16.0% for IDH1 mutation prediction in gliomas. We found that richer feature representations obtained from self-supervised vision foundation models reduce performance variations between groups. These representations provide improvements upon weaker models even when those weaker models are combined with state-of-the-art bias mitigation strategies and modeling choices. Nevertheless, self-supervised vision foundation models do not fully eliminate these discrepancies, highlighting the continuing need for bias mitigation efforts in computational pathology. Finally, we demonstrate that our results extend to other demographic factors beyond patient race. Given these findings, we encourage regulatory and policy agencies to integrate demographic-stratified evaluation into their assessment guidelines.

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" - there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification.

Profiles of Free and Bound Phenolics and Their Antioxidant Capacity in Rice Bean (Vigna umbellata).

Rice bean (Vigna umbellata) is a medicinal and dietary legume rich in polyphenols. In this study, the free and bound phenolics in rice bean were extracted by water, 80% methanol, and acid, base, and composite enzymatic hydrolysis, respectively. The polyphenol profiles of the extracted fractions were analyzed. The outcome demonstrated that base hydrolysis was the most effective way to liberate bound phenolics from rice bean (14.18 mg GAE/g DW), which was 16.68 and 56.72 folds higher than those extracted by acid and enzymatic hydrolysis, respectively. The bound polyphenols released by base hydrolysis contributed to 71.15% of the total phenolic content. A total of 35 individual phenolics was identified, of which isoquercitrin, procyanidin B1, rutin, taxifolin, and catechin were the main monomeric phenolics in the free fraction, while gallic acid, protocatechuic acid, p-hydroxybenzoic acid, catechin, and phloroglucinol were the main monomeric phenolics in the bound fraction. In comparison to the free phenolics extracted by water and 80% methanol and the bound phenolics extracted using acid and composite enzymatic hydrolysis, the bound phenolics from base hydrolysis had a superior antioxidant capacity. The antioxidant activity of rice bean is primarily attributed to individual phenolics such as catechin, abundant both in free and bound fractions, and also p-hydroxybenzoic acid, gallic acid, and protocatechuic acid in bound fractions. The bound phenolics of rice bean were first reported and showed large differences with the composition of free phenolics. This work suggests that the bound fraction of rice bean must be taken into account in assessing its potential benefits to health.

Ameliorative Effect of Ellagic Acid on Aging in Rats with the Potential Mechanism Relying on the Gut Microbiota and Urolithin A-Producing Ability.

Ellagic acid (EA) exhibits potential antiaging activity. Differences in individual ability to produce urolithins may result in large interindividual variability in the health effects of EA. Therefore, the effects and mechanism of EA on d-galactose-induced aging, considering urolithin A-producing ability, were investigated. Our results showed that EA improved cognitive impairment and hippocampal damage, increased the GABA (by 107.84-117.86%) and 5-HT (by 72.56-100.85%) levels, and suppressed the inflammatory and oxidative stress in aging rats. Thirteen plasma metabolites and 12 brain metabolites were improved by EA administration in aging rats. In particular, EA showed a better anti-aging effect in high-UroA-producing rats than in the low counterparts, while antibiotic intervention almost offset EA-alleviated aging induced by d-gal. Furthermore, the lower ratio of Firmicutes and Bacteroidota as well as the greater abundances of Akkermansia (by 139.21%), Bifidobacterium (by 88.04%), Clostridium_sensu_stricto_1 (by 183.47%), Lactobacillus (by 97.23%), and Turicibacter (by 83.06%) were observed in the high-UroA-producing group compared with the model group (p < 0.05). These findings provide novel insights into the anti-aging effects of EA and suggest that the ability of the gut microbiota responding to EA largely determines EA's anti-aging performance.

Steady-state auditory motion based potentials evoked by intermittent periodic virtual sound source and the effect of auditory noise on EEG enhancement.

Hearing is one of the most important human perception forms, and humans can capture the movement of sound in complex environments. On the basis of this phenomenon, this study explored the possibility of eliciting a steady-state brain response in an intermittent periodic motion sound source. In this study, a novel discrete continuous and orderly change of sound source positions stimulation paradigm was designed based on virtual sound using head-related transfer functions (HRTFs). And then the auditory motion stimulation paradigms with different noise levels were designed by changing the signal-to-noise ratio (SNR). The characteristics of brain response and the effects of different noises on brain response were studied by analyzing electroencephalogram (EEG) signals evoked by the proposed stimulation. Experimental results showed that the proposed paradigm could elicit a novel steady-state auditory evoked potential (AEP), i.e., steady-state motion auditory evoked potential (SSMAEP). And moderate noise could enhance SSMAEP amplitude and corresponding brain connectivity. This study enriches the types of AEPs and provides insights into the mechanism of brain processing of motion sound sources and the impact of noise on brain processing.

Monitoring gait at home with radio waves in Parkinson's disease: A marker of severity, progression, and medication response.

Parkinson's disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people's bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients' daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.

Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals.

There are currently no effective biomarkers for diagnosing Parkinson's disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. The model was evaluated on a large dataset comprising 7,671 individuals, using data from several hospitals in the United States, as well as multiple public datasets. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and external test sets, respectively. The AI model can also estimate PD severity and progression in accordance with the Movement Disorder Society Unified Parkinson's Disease Rating Scale (R = 0.94, P = 3.6 × 10-25). The AI model uses an attention layer that allows for interpreting its predictions with respect to sleep and electroencephalogram. Moreover, the model can assess PD in the home setting in a touchless manner, by extracting breathing from radio waves that bounce off a person's body during sleep. Our study demonstrates the feasibility of objective, noninvasive, at-home assessment of PD, and also provides initial evidence that this AI model may be useful for risk assessment before clinical diagnosis.

Potential of Establishing the Corresponding Human Microbial Community in Pseudo Germ-Free Mice through Fecal Microbe Transfer from Three Urolithin Metabotypes.

Three urolithin metabotypes (UMs) have been defined in the population according to final urolithins converted by gut microbiota. Currently, it is difficult to establish the cause-and-effect relationship between urolithins and microbiota in human studies. Studies on the health effects of ellagic acid (EA) in animal models rarely consider the differences in the urolithin production. Therefore, the objective of this study is to establish human microbiota-associated (HMA) mice, imitating the microbiota composition of the three UMs. Antibiotic-induced pseudo germ-free mice were gavaged with fecal bacteria of the three UM donors for four weeks. The results showed that the ability to produce corresponding urolithins was successfully transferred from the donor of the three UMs to HMA mice. The three UM HMA mice adopted a humanized microbiota profile similar to their corresponding donor. The family Eggerthellaceae and genera Eggerthella and Gordonibacter were successfully transferred and colonized from UM-A/B donors to HMA mice. Overall, the three UM HMA mouse models were successfully established, which provide a basis for exploring the health effects of EA.

Free and Bound Phenolic Profiles of Rosa roxburghii Tratt Leaves and Their Antioxidant and Inhibitory Effects on α-Glucosidase.

Rosa roxburghii Tratt (R. roxburghii) tea is a traditional Chinese beverage. This study aims to investigate and compare the phenolics in free and bound forms of two cultivars of R. roxburghii leaves, and their bioactivities. The total phenolic content of free and bound fractions was 72.71 and 17.75 mg GAE/g DW in Gui Nong No. 5 (GNN5) and 94.28 and 11.19 mg GAE/g DW in Seedless Cili (SC). A total of 37 phenolic compounds were characterized and quantified by UPLC-Q-Exactive Orbitrap/MS with ellagic acid, quercitrin, isoquercitrin, and quininic acid in free fraction, while gallic acid, ellagic acid, and hyperoside were main compounds in bound fraction. The free fraction with higher phenolic contents also showed excellent performances on antioxidant activities and α-glucosidase inhibitory potency than bound phenolics. Therefore, the results highlight that R. roxburghii leaves are a promising source enriched in phenolic constituents for functional beverages and nutritional foods.

Contactless In-Home Monitoring of the Long-Term Respiratory and Behavioral Phenotypes in Older Adults With COVID-19: A Case Series.

Currently, there is a limited understanding of long-term outcomes of COVID-19, and a need for in-home measurements of patients through the whole course of their disease. We study a novel approach for monitoring the long-term trajectories of respiratory and behavioral symptoms of COVID-19 patients at home. We use a sensor that analyzes the radio signals in the room to infer patients' respiration, sleep and activities in a passive and contactless manner. We report the results of continuous monitoring of three residents of an assisted living facility for 3 months, through the course of their disease and subsequent recovery. In total, we collected 4,358 measurements of gait speed, 294 nights of sleep, and 3,056 h of respiration. The data shows differences in the respiration signals between asymptomatic and symptomatic patients. Longitudinally, we note sleep and motor abnormalities that persisted for months after becoming COVID negative. Our study represents a novel phenotyping of the respiratory and behavioral trajectories of COVID recovery, and suggests that the two may be integral components of the COVID-19 syndrome. It further provides a proof-of-concept that contactless passive sensors may uniquely facilitate studying detailed longitudinal outcomes of COVID-19, particularly among older adults.

Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study.

BACKGROUND: Prehospitalization documentation is a challenging task and prone to loss of information, as paramedics operate under disruptive environments requiring their constant attention to the patients. OBJECTIVE: The aim of this study is to develop a mobile platform for hands-free prehospitalization documentation to assist first responders in operational medical environments by aggregating all existing solutions for noise resiliency and domain adaptation. METHODS: The platform was built to extract meaningful medical information from the real-time audio streaming at the point of injury and transmit complete documentation to a field hospital prior to patient arrival. To this end, the state-of-the-art automatic speech recognition (ASR) solutions with the following modular improvements were thoroughly explored: noise-resilient ASR, multi-style training, customized lexicon, and speech enhancement. The development of the platform was strictly guided by qualitative research and simulation-based evaluation to address the relevant challenges through progressive improvements at every process step of the end-to-end solution. The primary performance metrics included medical word error rate (WER) in machine-transcribed text output and an F1 score calculated by comparing the autogenerated documentation to manual documentation by physicians. RESULTS: The total number of 15,139 individual words necessary for completing the documentation were identified from all conversations that occurred during the physician-supervised simulation drills. The baseline model presented a suboptimal performance with a WER of 69.85% and an F1 score of 0.611. The noise-resilient ASR, multi-style training, and customized lexicon improved the overall performance; the finalized platform achieved a medical WER of 33.3% and an F1 score of 0.81 when compared to manual documentation. The speech enhancement degraded performance with medical WER increased from 33.3% to 46.33% and the corresponding F1 score decreased from 0.81 to 0.78. All changes in performance were statistically significant (P<.001). CONCLUSIONS: This study presented a fully functional mobile platform for hands-free prehospitalization documentation in operational medical environments and lessons learned from its implementation.

Distribution of Urolithins Metabotypes in Healthy Chinese Youth: Difference in Gut Microbiota and Predicted Metabolic Pathways.

This is the first study to report the distribution of urolithin metabotypes (UMs) in Asian people, specifically in the Chinese. As was reported for Europeans and Latin Americans, three UMs were observed, UM-A (54.3%), UM-B (31.4%), and UM-0 (14.3%), in 35 healthy Chinese youth. The richness and diversity of gut microbiota were lower in UM-0 than in UM-A and UM-B at the genus level. Gordonibacter in UM-A and UM-B was significantly higher than that in UM-0. The Akkermansia was not found in UM-0. The correlation analysis between the type and content of urolithins and the gut microbiota at the genus level showed that 27 genera were significantly positively correlated with urolithin A and 20 genera were significantly positively associated with isourolithin A and urolithin B. In addition, different KEGG pathways such as TCA cycle, energy metabolism, and some disease were found between the gut microbiome of the three UMs. Further research is needed to explore the mechanisms of metabotypes and the differential health benefits or illness predisposition of the three UMs.

Phenolic Compounds Profile and Antioxidant Capacity of Pitahaya Fruit Peel from Two Red-Skinned Species (Hylocereus polyrhizus and Hylocereus undatus).

Pitahaya peel is a good source of bioactive polyphenols. However, the bound phenolics and their antioxidant activity remain unclear. The bound phenolics of pitahaya peel from two red-skinned species with red pulp (RP) and white pulp (WP) were released with different methods (acid, base, and composite enzymes hydrolysis). The results revealed that base hydrolysis was the most efficient method for releasing the bound phenolics from RP (11.6 mg GAE/g DW) and WP (10.5 mg GAE/g DW), which was 13.04-fold and 8.18-fold for RP and 75.07-fold and 10.94-fold for WP compared with acid hydrolysis and enzymatic hydrolysis, respectively. A total of 37 phenolic compounds were identified by UPLC-TOF/MS with most chlorogenic acid, caffeic acid, ferulic acid and p-coumaric acid in RP, whereas chlorogenic acid, caffeic acid, ferulic acid, rutin and isoquercitrin were the main compounds in WP. Regardless of the hydrolysis method, the extracts having the highest phenolic content showed the strongest antioxidant activities. The work shows that hydrolysis methods have a significant effect on the release of phenolics, and the contents of major characteristic bound phenolic compounds are related to the ecological type of pitahaya.

A Novel Polysaccharide Isolated From Fresh Longan (Dimocarpus longan Lour.) Activates Macrophage via TLR2/4-Mediated PI3/AKT and MyD88/TRAF6 Pathways.

A novel immunomodulatory polysaccharide (LP4) with a molecular weight 6.31 × 104 g/mol was purified from fresh longan pulp. It was composed of mannose, glucose, glucuronic acid, galactose, xylose, arabinose, galacturonic acid, fucose, and rhamnose in a molar percentage of 36:31:10:7:4:4:3:2:2, and mainly linked by (1→6)-ß-Man, (1→4)-ß-Glc and (1→6)-α-Glc. LP4 can obviously enhance the phagocytosis of macrophages and promote the proliferation of lymphocytes. After treating macrophages with LP4 (12.5-50 µg/ml), the production of IL-1ß and TNF-α was significantly increased. These increases of cytokines were suppressed when the TLR2/TLR4 receptors were inhibited by anti-TLR2 and/or anti-TLR4 antibodies. Moreover, the mRNA expression of INOS, AKT, PI3K, TRAF6 and MyD88 was significantly suppressed by TLR2/TLR4 antibodies. These results indicated that LP4 induced macrophage activation mainly via the TLR2 and TLR4-induced PI3K/AKT and MyD88/TRAF6 pathways.

Intercalation-Induced Disintegrated Layer-By-Layer Growth of Ultrathin Ternary Mo(Te1-xSx)2 Plates.

Nanometer-thick transition-metal dichalcogenides (TMDs) have attracted increasing research interest because of their exotic physical properties, but their high-yield and large-scale synthesis remains a challenge for their practical device applications. In this study, we realize the high-yield synthesis of nanometer-thick single-crystalline Mo(Te1-xSx)2 plates by a facile chemical vapor deposition method. Adding S powders in the precursors can result in the products varying from well-faceted MoTe2 hexagonal plates to irregular Mo(Te1-xSx)2 plates with randomly stacked nanometer-thick layer steps. Moreover, their lateral dimension increases from several µm for binary MoTe2 to several tens of µm for ternary Mo(Te1-xSx)2. More interestingly, such irregular Mo(Te1-xSx)2 plates can form few layers by ultrasonic exfoliation. Our detailed electron microscopy analyses show that three kinds of S forms influence the ternary growth. In particular, elemental S8 intercalations play an important role in the growth and exfoliation of ultrathin Mo(Te1-xSx)2 plates. This study enriches the fundamental understanding of zero-valent intercalation in TMDs and provides a new insight into secure high-yield nanometer-thick TMDs, which is critical for practical applications.

Structural characterization of an active polysaccharide of longan and evaluation of immunological activity.

An active polysaccharide (LPD2) was isolated from longan pulp by comparing the effects of polysaccharides on the phagocytosis of macrophages. LPD2 was composed of arabinose, mannose, glucose, and galactose in a molar ratio of 0.25:0.49:1:0.5 with average molecular weight of 9.64 × 106 Da. The main linkages of the sugar residues of LPD2 were (1→4)-ß-Glc and (1→6)-ß-Man. LPD2 significantly enhanced the lymphocytes proliferation, phagocytosis and NO and IL-6 secretion by macrophage. The anti-TLR2 and anti-TLR4 mAbs markedly suppressed LPD2-mediated NO and IL-6 production. Furthermore, anti-TLR4 or anti-TLR2 plus anti-TLR4 treatment significantly decreased LPD2-induced increase of MyD88, IRAK4, TRAF6 and INOS mRNA expression. Moreover, western blotting analysis showed that LPD2 enhanced the expression of target proteins in MyD88/IRAK4-TRAF6- INOS pathways. These results suggested that LPD2 induced macrophage activation partly via the TLR2- and TLR4-mediated MyD88/IRAK4-TRAF6 signaling pathways. Knowing the structural features and activities of active polysaccharide of longan gives the insights into longan polysaccharide application as an immunomodulatory agent.

Insulin Receptor Substrate Suppression by the Tyrphostin NT157 Inhibits Responses to Insulin-Like Growth Factor-I and Insulin in Breast Cancer Cells.

Insulin and insulin-like growth factor (IGF) signaling systems regulate breast cancer growth, progression, and metastasis. The insulin receptor substrates 1 and 2 (IRS1/2) transduce signaling from the type I IGF receptor (IGF-IR) and insulin receptor (InR) to mediate the biological effects of receptor activation. In breast cancer, IRS-1 plays a critical role in cancer cell proliferation while IRS-2 is associated with motility and metastasis. NT157, a small-molecule tyrphostin, downregulates IRS proteins in several model systems. In breast cancer cells, NT157 treatment suppressed IRS protein expression in a dose-dependent manner. Exposure to NT157 inhibited the activation of downstream signaling mediated by the IRS proteins. NT157 induced a MAPK-dependent serine phosphorylation of IRS proteins which resulted in disassociation between IRS proteins and their receptors resulting in IRS degradation. In estrogen receptor-α-positive (ERα+) breast cancer cells (MCF-7 and T47D), NT157 also resulted in cytoplasmic ERα downregulation likely because of disruption of an IRS-1-IGF-IR/InR/ERα complex. NT157 decreased S phase fraction, monolayer, and anchorage-independent growth after IGF/insulin treatment in ERα+ breast cancer cells. NT157 downregulation of IRS protein expression also sensitized ERα+ breast cancer cells to rapamycin. Moreover, NT157 inhibited the growth of tamoxifen-resistant ERα+ breast cancer cells. Given that both IGF-IR and InR play a role in cancer biology, targeting of IRS adaptor proteins may be a more effective strategy to inhibit the function of these receptors.