Fc receptor-like A promotes malignant behavior in renal cell carcinoma and correlates with tumor immune infiltration.

BACKGROUND: Our study aims to investigate the mechanisms through which Fc receptor-like A (FCRLA) promotes renal cell carcinoma (RCC) and to examine its significance in relation to tumor immune infiltration. MATERIALS AND METHODS: The correlation between FCRLA and data clinically related to RCC was explored using The Cancer Genome Atlas (TCGA), then validated using Gene Expression Omnibus (GEO) gene chip data. Enrichment and protein-protein interaction (PPI) network analyses were performed for FCRLA and its co-expressed genes. FCRLA was knocked down in RCC cell lines to evaluate its impact on biological behavior. Then the potential downstream regulators of FCRLA were determined by western blotting, and rescue experiments were performed for verification. The relevance between FCRLA and various immune cells was analyzed through GSEA, TIMER, and GEPIA tools. TIDE and ESTIMATE algorithms were used to predict the effect of FCRLA in immunotherapy. RESULTS: Fc receptor-like A was associated with clinical and T stages and could predict the M stage (AUC = 0.692) and 1-3- and 5-year survival rates (AUC = 0.823, 0.834, and 0.862) of RCC patients. Higher expression of FCLRA predicted an unfavorable overall survival (OS) in TCGA-RCC and GSE167573 datasets (p = 0.03, p = 0.04). FCRLA promoted the malignant biological behavior of RCC cells through the pERK1/2/-MMP2 pathway and was associated with tumor immune microenvironment in RCC. CONCLUSION: Fc receptor-like A is positively correlated with poor outcomes in RCC patients and plays an oncogenic role in RCC through the pERK1/2-MMP2 pathway. Patients with RCC might benefit from immunotherapy targeting FCRLA.

Human umbilical cord-derived mesenchymal stromal cells for the treatment of steroid refractory grades III-IV acute graft-versus-host disease with long-term follow-up.

Introduction: Mesenchymal stromal cells (MSCs) have been extensively studied as a potential treatment for steroid refractory acute graft-versus-host disease (aGVHD). However, the majority of clinical trials have focused on bone marrow-derived MSCs. Methods: In this study, we report the outcomes of 86 patients with grade III-IV (82.6% grade IV) steroid refractory aGVHD who were treated with human umbilical cord-derived mesenchymal stromal cells (UC-MSCs). The patient cohort included 17 children and 69 adults. All patients received intravenous infusions of UC-MSCs at a dose of 1 × 106 cells per kg body weight, with a median of 4 infusions (ranging from 1 to 16). Results: The median time between the onset of aGVHD and the first infusion of UC-MSCs was 7 days (ranging from 3 to 88 days). At day 28, the overall response (OR) rate was 52.3%. Specifically, 24 patients (27.9%) achieved complete remission, while 21 (24.4%) exhibited partial remission. The estimated survival probability at 100 days was 43.7%. Following a median follow-up of 108 months (ranging from 61 to 159 months), the survival rate was approximately 11.6% (10/86). Patients who developed acute lower GI tract and liver GVHD exhibited poorer OR rates at day 28 compared to those with only acute lower GI tract GVHD (22.2% vs. 58.8%; p= 0.049). No patient experienced serious adverse events. Discussion: These finding suggest that UC-MSCs are safe and effective in both children and adults with steroid refractory aGVHD. UC-MSCs could be considered as a feasible treatment option for this challenging conditon. (NCT01754454).

VCAT: an integrated variant function annotation tools.

The development of sequencing technology has promoted discovery of variants in the human genome. Identifying functions of these variants is important for us to link genotype to phenotype, and to diagnose diseases. However, it usually requires researchers to visit multiple databases. Here, we presented a one-stop webserver for variant function annotation tools (VCAT, https://biomed.nscc-gz.cn/zhaolab/VCAT/ ) that is the first one connecting variant to functions via the epigenome, protein, drug and RNA. VCAT is also the first one to make all annotations visualized in interactive charts or molecular structures. VCAT allows users to upload data in VCF format, and download results via a URL. Moreover, VCAT has annotated a huge number (1,262,041,068) of variants collected from dbSNP, 1000 Genomes projects, gnomAD, ICGC, TCGA, and HPRC Pangenome project. For these variants, users are able to searcher their functions, related diseases and drugs from VCAT. In summary, VCAT provides a one-stop webserver to explore the potential functions of human genomic variants including their relationship with diseases and drugs.

A new perspective on ductile high-Tcsuperconductors under ambient pressure: few-hydrogen metal-bonded hydrides.

Superconducting materials have garnered widespread attention due to their zero-resistance characteristic and complete diamagnetism. After more than 100 years of exploration, various high-temperature superconducting materials including cuprates, nickelates, iron-based compounds, and ultra-high pressure multi-hydrides have been discovered. However, the practical application of these materials is severely hindered by their poor ductility and/or the need for high-pressure conditions to maintain structural stability. To address these challenges, we first provide a new thought to build high-temperature superconducting materials based on few-hydrogen metal-bonded hydrides under ambient pressure. We then review the related research efforts in this article. Moreover, based on the bonding type of atoms, we classify the existing important superconducting materials and propose the new concepts of pseudo-metal and quasi-metal superconductivity, which are expected to be helpful for the design of new high-temperature superconducting materials in the future.

Deep learning for intracranial aneurysm segmentation using CT angiography.

Objective.This study aimed to employ a two-stage deep learning method to accurately detect small aneurysms (4-10 mm in size) in computed tomography angiography images.Approach.This study included 956 patients from 6 hospitals and a public dataset obtained with 6 CT scanners from different manufacturers. The proposed method consists of two components: a lightweight and fast head region selection (HRS) algorithm and an adaptive 3D nnU-Net network, which is used as the main architecture for segmenting aneurysms. Segments generated by the deep neural network were compared with expert-generated manual segmentation results and assessed using Dice scores.MainResults.The area under the curve (AUC) exceeded 79% across all datasets. In particular, the precision and AUC reached 85.2% and 87.6%, respectively, on certain datasets. The experimental results demonstrated the promising performance of this approach, which reduced the inference time by more than 50% compared to direct inference without HRS.Significance.Compared with a model without HRS, the deep learning approach we developed can accurately segment aneurysms by automatically localizing brain regions and can accelerate aneurysm inference by more than 50%.

Prolonged sleep duration as a predictor of cognitive decline: A meta-analysis encompassing 49 cohort studies.

Despite numerous studies have explored the association between sleep duration and cognition, the link between sleep duration trajectories and cognition remains underexplored. This systematic review aims to elucidate this correlation. We analyzed 55 studies from 14 countries, comprising 36 studies focusing on sleep duration, 20 on insomnia, and 13 on hypersomnia. A total of 10,767,085 participants were included in 49 cohort studies with a mean follow-up duration of 9.1 years. A non-linear association between sleep duration and cognitive decline was identified. Both long (risk ratio (RR):1.35, 95 % confidence intervals (CIs):1.23-1.48) and short sleep durations (RR: 1.12, 95 % CIs:1.03-1.22) were associated with an elevated risk of cognitive decline compared to moderate sleep duration. Additionally, hypersomnia (RR:1.26, 95 % CIs: 1.15-1.39) and insomnia (RR: 1.16, 95 % CIs: 1.002-1.34) were also linked to an increased risk. Moreover, prolonged sleep duration posed a higher risk of cognitive decline than stable sleep duration (RR:1.42, 95 % CIs:1.27-1.59). Importantly, transitioning from short or moderate to long sleep duration, as well as persistent long sleep duration, exhibited higher RRs for cognitive decline (RRs: 1.94, 1.40, and 1.28, respectively) compared to persistent moderate sleep duration. Our findings underscore the significance of prolonged sleep duration, alongside short and long sleep durations, with an elevated risk of cognitive decline. The association is tied to the degree of sleep duration changes. Our study highlights the importance of considering changes in sleep patterns over time, not just static sleep durations.

Prognostic significance of peripheral and tumor-infiltrating lymphocytes in newly diagnosed stage III/IV non-small-cell lung cancer.

Background and aim: Lymphocytes are effector cells that fight cancer by killing tumor cells. Here, we aim to explore the prognostic significance of both peripheral and tumor-infiltrating lymphocytes (TILs) in newly diagnosed stage III/IV non-small-cell lung cancer (NSCLC). Materials and methods: In total, 105 cases of newly diagnosed stage III/IV NSCLC from July 2017 to October 2022 at the Tianjin Beichen Hospital were retrospectively investigated. Peripheral blood samples at the time of diagnosis and tumor tissue slices from these patients were collected. General peripheral blood cell composition and TILs were measured and analyzed via an automatic blood analyzer and immunofluorescence staining analysis. The overall survival (OS) time of all patients was also obtained and analyzed. Results: The median overall survival (mOS) of all patients is 12 months. The 1-, 2-, and 3-year overall survival rates were 60.5, 28.4, and 18.6%, respectively. Peripheral lymphocyte and neutrophil percentages, serum C-reactive protein (CRP) expression, tumor size, and tumor pathology are the prognostic factors of OS for newly diagnosed stage III/IV NSCLC patients. Moreover, patients with high tumor CD4+ and CD8+ T cell infiltration survived significantly longer compared to patients with low tumor CD4+ and CD8+ T cell infiltration (p < 0.0001 and p = 0.011, respectively). Compared to low tumor CD33+ cell infiltration, high tumor CD33+ cell infiltration was associated with worse OS (p = 0.018). High tumor CD8+ T cell infiltration was associated with lower peripheral lymphocyte number, lower serum CRP expression, smaller tumor size, and better tumor pathology (p = 0.012, p = 0.040, p = 0.012, and p = 0.029, respectively). Conclusion: Increased numbers of peripheral lymphocytes, CD33+ cells, CD4+ TILs, and CD8+ TILs were significantly associated with OS in newly diagnosed stage III/IV NSCLC patients, which were positively associated with several basic clinical factors.

Genetic and phenotypic associations of frailty with cardiovascular indicators and behavioral characteristics.

INTRODUCTION: Frailty Index (FI) is a common measure of frailty, which has been advocated as a routine clinical test by many guidelines. The genetic and phenotypic relationships of FI with cardiovascular indicators (CIs) and behavioral characteristics (BCs) are unclear, which has hampered ability to monitor FI using easily collected data. OBJECTIVES: This study is designed to investigate the genetic and phenotypic associations of frailty with CIs and BCs, and further to construct a model to predict FI. METHOD: Genetic relationships of FI with 288 CIs and 90 BCs were assessed by the cross-trait LD score regression (LDSC) and Mendelian randomization (MR). The phenotypic data of these CIs and BCs were integrated with a machine-learning model to predict FI of individuals in UK-biobank. The relationships of the predicted FI with risks of type 2 diabetes (T2D) and neurodegenerative diseases were tested by the Kaplan-Meier estimator and Cox proportional hazards model. RESULTS: MR revealed putative causal effects of seven CIs and eight BCs on FI. These CIs and BCs were integrated to establish a model for predicting FI. The predicted FI is significantly correlated with the observed FI (Pearson correlation coefficient = 0.660, P-value = 4.96 × 10-62). The prediction model indicated "usual walking pace" contributes the most to prediction. Patients who were predicted with high FI are in significantly higher risk of T2D (HR = 2.635, P < 2 × 10-16) and neurodegenerative diseases (HR = 2.307, P = 1.62 × 10-3) than other patients. CONCLUSION: This study supports associations of FI with CIs and BCs from genetic and phenotypic perspectives. The model that is developed by integrating easily collected CIs and BCs data in predicting FI has the potential to monitor disease risk.

Resting-state fMRI network efficiency as a mediator in the relationship between the glymphatic system and cognitive function in obstructive sleep apnea hypopnea syndrome: Insights from a DTI-ALPS investigation.

INTRODUCTION: Obstructive sleep apnea hypopnea syndrome (OSAHS) is associated with cognitive impairment and physiological complications, necessitating further understanding of its mechanisms. This study investigates the relationship between glymphatic system function, brain network efficiency, and cognitive impairment in OSAHS patients using diffusion tensor image analysis along the perivascular space (DTI-ALPS) and resting-state fMRI. MATERIALS AND METHODS: This study included 31 OSAHS patients and 34 age- and gender-matched healthy controls (HC). All participants underwent GE 3.0T magnetic resonance imaging (MRI) with diffusion tensor image (DTI) and resting-state fMRI scans. The DTI-ALPS index and brain functional networks were assessed. Differences between groups and correlations with clinical characteristics were analyzed. Additionally, the mediating role of brain network efficiency was explored. Finally, receiver operating characteristics (ROC) analysis assessed diagnostic performance. RESULTS: OSAHS patients had significantly lower ALPS-index (1.268 vs. 1.431, p < 0.0001) and moderate negative correlation with Apnea Hypopnea Index (AHI) (r = -0.389, p = 0.031), as well as moderate positive correlation with Montreal Cognitive Assessment (MoCA) (r = 0.525, p = 0.002). Moreover, global efficiency (Eg) of the brain network was positively correlated with the ALPS-index and MoCA scores in OSAHS patients (r = 0.405, p = 0.024; r = 0.56, p = 0.001, respectively). Furthermore, mediation analysis showed that global efficiency partially mediated the impact of glymphatic system dysfunction on cognitive impairment in OSAHS patients (indirect effect = 4.58, mediation effect = 26.9 %). The AUROC for identifying OSAHS and HC was 0.80 (95 % CI 0.69 to 0.91) using an ALPS-index cut-off of 1.35. CONCLUSIONS: OSAHS patients exhibit decreased ALPS-index, indicating impaired glymphatic system function. Dysfunction of the glymphatic system can affect cognitive function in OSAHS by disrupting brain functional network, suggesting a potential underlying pathological mechanism. Additionally, preliminary findings suggest that the ALPS-index may offer promise as a potential indicator for OSAHS.

GPSFun: geometry-aware protein sequence function predictions with language models.

Knowledge of protein function is essential for elucidating disease mechanisms and discovering new drug targets. However, there is a widening gap between the exponential growth of protein sequences and their limited function annotations. In our prior studies, we have developed a series of methods including GraphPPIS, GraphSite, LMetalSite and SPROF-GO for protein function annotations at residue or protein level. To further enhance their applicability and performance, we now present GPSFun, a versatile web server for Geometry-aware Protein Sequence Function annotations, which equips our previous tools with language models and geometric deep learning. Specifically, GPSFun employs large language models to efficiently predict 3D conformations of the input protein sequences and extract informative sequence embeddings. Subsequently, geometric graph neural networks are utilized to capture the sequence and structure patterns in the protein graphs, facilitating various downstream predictions including protein-ligand binding sites, gene ontologies, subcellular locations and protein solubility. Notably, GPSFun achieves superior performance to state-of-the-art methods across diverse tasks without requiring multiple sequence alignments or experimental protein structures. GPSFun is freely available to all users at https://bio-web1.nscc-gz.cn/app/GPSFun with user-friendly interfaces and rich visualizations.

Genome-scale annotation of protein binding sites via language model and geometric deep learning.

Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven't fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.

Effect of oxide interactions on chromium speciation transformation during simulated municipal solid waste incineration.

Chromium released during municipal solid waste incineration (MSWI) is toxic and carcinogenic. The removal of chromium from simulated MSWI flue gas by four sorbents (CaO, bamboo charcoal (BC), powdered activated carbon (PAC), and Al2O3) and the effects of four oxides (SiO2, Al2O3, Fe2O3, and CaO) on chromium speciation transformation were investigated. The results showed that the removal rates of total Cr by the four sorbents were Al2O3 < CaO < PAC < BC, while the removal rates of Cr(VI) by the four sorbents were Al2O3 < PAC < BC < CaO. CaO had a strong oxidizing effect on Cr(III), while BC and PAC had a better-reducing effect on Cr(VI). SiO2 was better for the reduction of Na2CrO4 and K2CrO4 above 1000°C due to its strong acidity, and the addition of CaO significantly inhibited the reduction of Cr(VI). MgCrO4 decomposed above 700°C to form MgCr2O4, and the reaction between MgCrO4 and oxides also existed in the form of a more stable trivalent spinel. Furthermore, when investigating the effect of oxides on the oxidation of Cr(III) in CrCl3, it was discovered that CaO promoted the conversion of Cr(III) to Cr(VI), while the presence of chlorine caused chromium to exist in the form of Cr(V), and increasing the content of CaO and extending the heating time facilitated the oxidation of Cr(III). In addition, silicate, aluminate, and ferrite were generated after the addition of SiO2, Al2O3, and Fe2O3, which reduced the alkalinity of CaO and had an important role in inhibiting the oxidation of Cr(III). The acidic oxides can not only promote the reduction of Cr(VI) but also have an inhibitory effect on the oxidation of Cr(III) ascribed to alkali metals/alkaline earth metals, and the proportion of acidic oxides can be increased moderately to reduce the generation of harmful substances in the hazardous solid waste heat treatment.

54 K Spin Transition Temperature Shift in a Fe6L4 Octahedral Cage Induced by Optimal Fitted Multiple Guests.

Spin-crossover (SCO) coordination cages are at the forefront of research for their potential in crafting next-generation molecular devices. However, due to the scarcity of SCO hosts and their own limited cavities, the interplay between the SCO host and the multiple guests binding has remained elusive. In this contribution, we present a family of pseudo-octahedral coordination cages (M6L4, M = ZnII, CoII, FeII, and NiII) assembled from a tritopic tridentate ligand L with metal ions. The utilization of FeII ion leads to the successful creation of the Fe6L4-type SCO cage. Host-guest studies of these M6L4 cages reveal their capacity to encapsulate four adamantine-based guests. Notably, the spin transition temperature T1/2 of Fe6L4 is dependent on the multiple guests encapsulated. The inclusion of adamantine yields an unprecedented T1/2 shift of 54 K, a record shift in guest-mediated SCO coordination cages to date. This drastic shift is ascribed to the synergistic effect of multiple guests coupled with their optimal fit within the host. Through a straightforward thermodynamic cycle, the binding affinities of the high-spin (HS) and low-spin (LS) states are separated from their apparent binding constant. This result indicates that the LS state has a stronger binding affinity for the multiple guests than the HS state. Exploring the SCO thermodynamics of host-guest complexes allows us to examine the optimal fit of multiple guests to the host cavity. This study reveals that the T1/2 of the SCO host can be manipulated by the encapsulation of multiple guests, and the SCO cage is an ideal candidate for determining the multiple guest fit.

Serum soluble alpha-klotho klotho and cognitive functioning in older adults aged 60 and 79: an analysis of cross-sectional data of the National Health and Nutrition Examination Survey 2011 to 2014.

OBJECTIVES: Klotho, consisting of membrane klotho and soluble alpha-klotho, is found to be associated with better cognitive outcomes in small samples of the aged population. We aimed to examine the association of serum soluble alpha-klotho with cognitive functioning among older adults using a nationally representative sample of U.S. older adults. METHOD: A total of 2,173 U.S. older adults aged 60-79 years in the National Health and Nutrition Examination Survey from 2011 to 2014 were included in this cross-sectional analysis. Serum soluble alpha-klotho was measured in the laboratory and analyzed with an ELISA kit. Cognitive function was measured using the Consortium to Establish a Registry for Alzheimer's Disease Word Learning subtest (CERAD-WL) immediate and delayed memory, the Animal fluency test (AFT), and the Digit Symbol Substitution Test (DSST). Test-specific and global cognition z-scores were calculated based on sample means and standard deviations. Multivariable linear regression models were applied to examine the association of quartiles and continuous value of serum soluble alpha-klotho with test-specific and global cognition z-scores. Subgroup analysis was conducted by sex. The following covariates were included in the analysis- age, sex, race/ethnicity, education, depressive symptoms, smoking status, body mass index (BMI), physical activity, stroke, prevalent coronary heart disease, total cholesterol, and systolic blood pressure. All the information was self-reported or obtained from health exams. RESULTS: Serum soluble alpha-klotho level in the lowest quartile was associated with lower z-scores for DSST (beta [ß] =-0.13, 95% confidence interval [CI]: -0.25, -0.01). For subgroup analysis, serum soluble alpha-klotho level in the lowest quartile was associated with lower z-scores for DSST (ß=-0.16, 95% CI: -0.32, -0.003) and global cognition (ß=-0.14, 95% CI: -0.28, -0.01) among female participants. No association was found between continuous serum soluble alpha-klotho and cognitive functioning among the participants. CONCLUSIONS: Lower serum soluble alpha-klotho quartile was associated with poorer cognitive functioning among older women. Future studies are expected to examine the longitudinal association between klotho levels and cognitive outcomes.

Diffusion Tensor and Kurtosis MRI-Based Radiomics Analysis of Kidney Injury in Type 2 Diabetes.

BACKGROUND: Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) can provide quantitative parameters that show promise for evaluation of diabetic kidney disease (DKD). The combination of radiomics with DTI and DKI may hold potential clinical value in detecting DKD. PURPOSE: To investigate radiomics models of DKI and DTI for predicting DKD in type 2 diabetes mellitus (T2DM) and evaluate their performance in automated renal parenchyma segmentation. STUDY TYPE: Prospective. POPULATION: One hundred and sixty-three T2DM patients (87 DKD; 63 females; 27-80 years), randomly divided into training cohort (N = 114) and validation cohort (N = 49). FIELD STRENGTH/SEQUENCE: 1.5-T, diffusion spectrum imaging (DSI) with 9 different b-values. ASSESSMENT: The images of DSI were processed to generate DKI and DTI parameter maps, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). The Swin UNETR model was trained with 5-fold cross-validation using 100 samples for renal parenchyma segmentation. Subsequently, radiomics features were automatically extracted from each parameter map. The performance of the radiomics models on the validation cohort was evaluated by utilizing the receiver operating characteristic (ROC) curve. STATISTICAL TESTS: Mann-Whitney U test, Chi-squared test, Pearson correlation coefficient, least absolute shrinkage and selection operator (LASSO), dice similarity coefficient (DSC), decision curve analysis (DCA), area under the curve (AUC), and DeLong's test. The threshold for statistical significance was set at P < 0.05. RESULTS: The DKI_MD achieved the best segmentation performance (DSC, 0.925 ± 0.011). A combined radiomics model (DTI_FA, DTI_MD, DKI_FA, DKI_MD, and DKI_RD) showed the best performance (AUC, 0.918; 95% confidence interval [CI]: 0.820-0.991). When the threshold probability was greater than 20%, the combined model provided the greatest net benefit. Among the single parameter maps, the DTI_FA exhibited superior diagnostic performance (AUC, 887; 95% CI: 0.779-0.972). DATA CONCLUSION: The radiomics signature constructed based on DKI and DTI may be used as an accurate and non-invasive tool to identify T2DM and DKD. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Metal-bonded perovskite lead hydride with phonon-mediated superconductivity exceeding 46 K under ambient pressure.

In the search for high-temperature superconductivity in hydrides, a plethora of multi-hydrogen superconductors have been theoretically predicted, and some have been synthesized experimentally under ultrahigh pressures of several hundred GPa. However, the impracticality of these high-pressure methods has been a persistent issue. In response, we propose a new approach to achieve high-temperature superconductivity under ambient pressure by implanting hydrogen into lead to create a stable few-hydrogen binary perovskite, Pb4H. This approach diverges from the popular design methodology of multi-hydrogen covalent high critical temperature (Tc) superconductors under ultrahigh pressure. By solving the anisotropic Migdal-Eliashberg equations, we demonstrate that perovskite Pb4H presents a phonon-mediated superconductivity exceeding 46 K with inclusion of spin-orbit coupling, which is six times higher than that of bulk Pb (7.22 K) and comparable to that of MgB2, the highestTcachieved experimentally at ambient pressure under the Bardeen, Cooper, and Schrieffer framework. The highTccan be attributed to the strong electron-phonon coupling strength of 2.45, which arises from hydrogen implantation in lead that induces several high-frequency optical phonon modes with a relatively large phonon linewidth resulting from H atom vibration. The metallic-bonding in perovskite Pb4H not only improves the structural stability but also guarantees better ductility than the widely investigated multi-hydrogen, iron-based and cuprate superconductors. These results suggest that there is potential for the exploration of new high-temperature superconductors under ambient pressure and may reignite interest in their experimental synthesis in the near future.

Development and External Validation of a Gait Test Based Diagnostic Model for Detecting Mild Cognitive Impairment.

OBJECTIVE: To address the lack of large-scale screening tools for mild cognitive impairment (MCI), this study aimed to assess the discriminatory ability of several gait tests for MCI and develop a screening tool based on gait test for MCI. DESIGN: A diagnostic case-control test. SETTING: The general community. PARTICIPANTS: We recruited 134 older adults (≥65 years) for the derivation sample, comprising -69 individuals in the cognitively normal group and -65 in the MCI group (N=134). An additional 70 participants were enrolled for the validation sample. INTERVENTIONS: All participants completed gait tests consisting of a single task (ST) and 3 dual tasks (DTs): counting backwards, serial subtractions 7, and naming animals. MAIN OUTCOME MEASURES: Binary logistic regression analyses were used to develop models, and the efficacy of each model was assessed using receiver operating characteristic (ROC) curve and area under the curve (AUC). The best effective model was the final diagnostic model and validated using ROC curve and calibration curve. RESULTS: The DT gait test incorporating serial subtractions 7 as the cognitive task demonstrated the highest efficacy with the AUC of 0.906 and the accuracy of 0.831 in detecting MCI with "years of education" being adjusted. Furthermore, the model exhibited consistent performance across different age and sex groups. In external validation, the model displayed robust discrimination (AUC=0.913) and calibration (calibrated intercept=-0.062, slope=1.039). CONCLUSIONS: The DT gait test incorporating serial subtractions 7 as the cognitive task demonstrated robust discriminate ability for MCI. This test holds the potential to serve as a large-scale screening tool for MCI, aids in the early detection and intervention of cognitive impairment in older adults.

HmsU-Net: A hybrid multi-scale U-net based on a CNN and transformer for medical image segmentation.

Accurate medical image segmentation is of great significance for subsequent diagnosis and analysis. The acquisition of multi-scale information plays an important role in segmenting regions of interest of different sizes. With the emergence of Transformers, numerous networks adopted hybrid structures incorporating Transformers and CNNs to learn multi-scale information. However, the majority of research has focused on the design and composition of CNN and Transformer structures, neglecting the inconsistencies in feature learning between Transformer and CNN. This oversight has resulted in the hybrid network's performance not being fully realized. In this work, we proposed a novel hybrid multi-scale segmentation network named HmsU-Net, which effectively fused multi-scale features. Specifically, HmsU-Net employed a parallel design incorporating both CNN and Transformer architectures. To address the inconsistency in feature learning between CNN and Transformer within the same stage, we proposed the multi-scale feature fusion module. For feature fusion across different stages, we introduced the cross-attention module. Comprehensive experiments conducted on various datasets demonstrate that our approach surpasses current state-of-the-art methods.