A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis

Nailfold capillaroscopy is widely used in assessing health conditions, highlighting the pressing need for an automated nailfold capillary analysis system. In this study, we present a pioneering effort in constructing a comprehensive nailfold capillary dataset-321 images, 219 videos from 68 subjects, with clinic reports and expert annotations-that serves as a crucial resource for training deep-learning models. Leveraging this dataset, we finetuned three deep learning models with expert annotations as supervised labels and integrated them into a novel end-to-end nailfold capillary analysis pipeline. This pipeline excels in automatically detecting and measuring a wide range of size factors, morphological features, and dynamic aspects of nailfold capillaries. We compared our outcomes with clinical reports. Experiment results showed that our automated pipeline achieves an average of sub-pixel level precision in measurements and 89.9% accuracy in identifying morphological abnormalities. These results underscore its potential for advancing quantitative medical research and enabling pervasive computing in healthcare. Our data and code are available at https://github.com/THU-CS-PI-LAB/ANFC-Automated-Nailfold-Capillary.Comment: Dataset, code, pretrained models: https://github.com/THU-CS-PI-LAB/ANFC-Automated-Nailfold-Capillar

Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images

PurposeTo develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs).Material and methodsThe study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the “One-vs-Rest” strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model.ResultsFollowing pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary “One-vs-Rest” strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively.ConclusionThe DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set

Correlated states in twisted double bilayer graphene

Electron-electron interactions play an important role in graphene and related systems and can induce exotic quantum states, especially in a stacked bilayer with a small twist angle. For bilayer graphene where the two layers are twisted by a "magic angle", flat band and strong many-body effects lead to correlated insulating states and superconductivity. In contrast to monolayer graphene, the band structure of untwisted bilayer graphene can be further tuned by a displacement field, providing an extra degree of freedom to control the flat band that should appear when two bilayers are stacked on top of each other. Here, we report the discovery and characterization of such displacement-field tunable electronic phases in twisted double bilayer graphene. We observe insulating states at a half-filled conduction band in an intermediate range of displacement fields. Furthermore, the resistance gap in the correlated insulator increases with respect to the in-plane magnetic fields and we find that the g factor according to spin Zeeman effect is ~2, indicating spin polarization at half filling. These results establish the twisted double bilayer graphene as an easily tunable platform for exploring quantum many-body states

On-Site Quantification and Infection Risk Assessment of Airborne SARS-CoV-2 Virus Via a Nanoplasmonic Bioaerosol Sensing System in Healthcare Settings

On-site quantification and early-stage infection risk assessment of airborne severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with high spatiotemporal resolution is a promising approach for mitigating the spread of coronavirus disease 2019 (COVID-19) pandemic and informing life-saving decisions. Here, a condensation (hygroscopic growth)-assisted bioaerosol collection and plasmonic photothermal sensing (CAPS) system for on-site quantitative risk analysis of SARS-CoV-2 virus-laden aerosols is presented. The CAPS system provided rapid thermoplasmonic biosensing results after an aerosol-to-hydrosol sampling process in COVID-19-related environments including a hospital and a nursing home. The detection limit reached 0.25 copies/µL in the complex aerosol background without further purification. More importantly, the CAPS system enabled direct measurement of the SARS-CoV-2 virus exposures with high spatiotemporal resolution. Measurement and feedback of the results to healthcare workers and patients via a QR-code are completed within two hours. Based on a dose-responseµ model, it is used the plasmonic biosensing signal to calculate probabilities of SARS-CoV-2 infection risk and estimate maximum exposure durations to an acceptable risk threshold in different environmental settings

Factors affecting the early establishment of neonatal intestinal flora and its intervention measures

In recent years, it has become evident that early-life intestinal flora plays a pivotal role in determining human health. Consequently, it is imperative to explore the establishment of neonatal intestinal flora and its influencing factors. Early neonatal intestinal flora is influenced by a multitude of factors, including maternal and infant-related factors, as well as external environment. This review summarizes the colonization mechanism of intestinal flora in the early life of newborns and discussed their influence on the establishment of neonatal intestinal flora, taking into account factors such as delivery mode, gestational age and feeding mode. Additionally, this review delves into the natural or artificial reconstruction of intestinal flora colonization defects in infants born via cesarean section and premature infants, with the goal of establishing a theoretical foundation for preventing and treating issues related to neonatal intestinal flora colonization and associated diseases

The NMDA receptor intracellular C-terminal domains reciprocally interact with allosteric modulators

N-methyl-d-aspartate receptors (NMDARs) have multiple prominent roles in CNS function but their excessive or insufficient activity contributes to neuropathological/psychiatric disorders. Consequently, a variety of positive and negative allosteric modulators (PAMs and NAMs, respectively) have recently been developed. Although these modulators bind to extracellular domains, in the present report we find that the NMDAR's intracellular C-terminal domains (CTDs) significantly influence PAM/NAM activity. GluN2 CTD deletion robustly affected NAM and PAM activity with both enhancing and inhibiting effects that were compound-specific and NMDAR subunit-specific. In three cases, individual PAMs became NAMs at specific GluN2-truncated receptors. In contrast to GluN2, GluN1 CTD removal only reduced PAM activity of UBP684 and CIQ, and did not affect NAM activity. Consistent with these findings, agents altering phosphorylation state or intracellular calcium levels displayed receptor-specific and compound-specific effects on PAM activity. It is possible that the GluN2's M4 domain transmits intracellular modulatory signals from the CTD to the M1/M4 channel gating machinery and that this site is a point of convergence in the direct or indirect actions of several PAMs/NAMs thus rendering them sensitive to CTD status. Thus, allosteric modulators are likely to have a marked and varied sensitivity to post-translational modifications, protein-protein associations, and intracellular ions. The interaction between PAM activity and NMDAR CTDs appears reciprocal. GluN1 CTD-deletion eliminated UBP684, but not pregnenolone sulfate (PS), PAM activity. And, in the absence of agonists, UBP684, but not PS, was able to promote movement of fluorescently-tagged GluN1-CTDs. Thus, it may be possible to pharmacologically target NMDAR metabotropic activity in the absence of channel activation

Layer-by-Layer Epitaxy of Multilayer MoS2 Wafers

Two-dimensional (2D) semiconductor of MoS2 has great potential for advanced electronics technologies beyond silicon1-9. So far, high-quality monolayer MoS2 wafers10-12 are already available and various demonstrations from individual transistors to integrated circuits have also been shown13-15. In addition to the monolayer, multilayers have narrower band gaps but improved carrier mobilities and current capacities over the monolayer5,16-18. However, achieving high-quality multilayer MoS2 wafers remains a challenge. Here we report the growth of high quality multilayer MoS2 4-inch wafers via the layer-by-layer epitaxy process. The epitaxy leads to well-defined stacking orders between adjacent epitaxial layers and offers a delicate control of layer numbers up to 6. Systematic evaluations on the atomic structures and electronic properties were carried out for achieved wafers with different layer numbers. Significant improvements on device performances were found in thicker-layer field effect transistors (FETs), as expected. For example, the average field-effect mobility ({\mu}FE) at room temperature (RT) can increase from ~80 cm2V-1s-1 for monolayer to ~110/145 cm2V-1s-1 for bilayer/trilayer devices. The highest RT {\mu}FE=234.7 cm2V-1s-1 and a record-high on-current densities of 1.704 mA{\mu}m-1 at Vds=2 V were also achieved in trilayer MoS2 FETs with a high on/off ratio exceeding 107. Our work hence moves a step closer to practical applications of 2D MoS2 in electronics.Comment: 13 pages,4 Figure