Optimizing anterior urethral stricture assessment: leveraging AI-assisted three-dimensional sonourethrography in clinical practice.

PURPOSE: This investigation sought to validate the clinical precision and practical applicability of AI-enhanced three-dimensional sonographic imaging for the identification of anterior urethral stricture. METHODS: The study enrolled 63 male patients with diagnosed anterior urethral strictures alongside 10 healthy volunteers to serve as controls. The imaging protocol utilized a high-frequency 3D ultrasound system combined with a linear stepper motor, which enabled precise and rapid image acquisition. For image analysis, an advanced AI-based segmentation process using a modified U-net algorithm was implemented to perform real-time, high-resolution segmentation and three-dimensional reconstruction of the urethra. A comparative analysis was performed against the surgically measured stricture lengths. Spearman's correlation analysis was executed to assess the findings. RESULTS: The AI model completed the entire processing sequence, encompassing recognition, segmentation, and reconstruction, within approximately 5 min. The mean intraoperative length of urethral stricture was determined to be 14.4 ± 8.4 mm. Notably, the mean lengths of the urethral strictures reconstructed by manual and AI models were 13.1 ± 7.5 mm and 13.4 ± 7.2 mm, respectively. Interestingly, no statistically significant disparity in urethral stricture length between manually reconstructed and AI-reconstructed images was observed. Spearman's correlation analysis underscored a more robust association of AI-reconstructed images with intraoperative urethral stricture length than manually reconstructed 3D images (0.870 vs. 0.820). Furthermore, AI-reconstructed images provided detailed views of the corpus spongiosum fibrosis from multiple perspectives. CONCLUSIONS: The research heralds the inception of an innovative, efficient AI-driven sonographic approach for three-dimensional visualization of urethral strictures, substantiating its viability and superiority in clinical application.

Covalent Organic Frameworks for Electrocatalysis：Design, Applications, and Perspectives.

Covalent organic frameworks (COFs) are an innovative class of crystalline porous polymers composed of light elements such as C, N, O, etc., linked by covalent bonds. The distinctive properties of COFs, including designable building blocks, large specific surface area, tunable pore size, abundant active sites, and remarkable stability, have led their widespread applications in electrocatalysis. In recent years, COF-based electrocatalysts have made remarkable progress in various electrocatalytic fields, including the hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, nitrogen reduction reaction, nitrate reduction reaction, and carbon dioxide reduction reaction. This review begins with an introduction to the design and synthesis strategies employed for COF-based electrocatalysts. These strategies include heteroatom doping, metalation of COF and building monomers, encapsulation of active sites within COF pores, and the development of COF-based derived materials. Subsequently, a systematic overview of the recent advancements in the application of COF-based catalysts in electrocatalysis is presented. Finally, the review discusses the main challenges and outlines possible avenues for the future development of COF-based electrocatalysts.

Thermal facial image analyses reveal quantitative hallmarks of aging and metabolic diseases.

Although human core body temperature is known to decrease with age, the age dependency of facial temperature and its potential to indicate aging rate or aging-related diseases remains uncertain. Here, we collected thermal facial images of 2,811 Han Chinese individuals 20-90 years old, developed the ThermoFace method to automatically process and analyze images, and then generated thermal age and disease prediction models. The ThermoFace deep learning model for thermal facial age has a mean absolute deviation of about 5 years in cross-validation and 5.18 years in an independent cohort. The difference between predicted and chronological age is highly associated with metabolic parameters, sleep time, and gene expression pathways like DNA repair, lipolysis, and ATPase in the blood transcriptome, and it is modifiable by exercise. Consistently, ThermoFace disease predictors forecast metabolic diseases like fatty liver with high accuracy (AUC > 0.80), with predicted disease probability correlated with metabolic parameters.

A case of Ph+ acute lymphoblastic leukemia and EGFR mutant lung adenocarcinoma synchronous overlap: may one TKI drug solve two diseases?

BACKGROUND: Philadelphia chromosome positive (Ph+) acute lymphoblastic leukemia (ALL) refers to ALL patients with t(9;22) cytogenetic abnormalities, accounting for about 25% of ALL. Lung adenocarcinoma (LUAD) is the most common pathological type of non-small-cell lung cancer, which has a frequency of approximately 45% cases with mutations in EGFR. Both Ph+ ALL and EGFR mutant LUAD are involved in the pathogenesis of the abnormal activation of the tyrosine kinase pathway. Although the second primary hematological malignancy after the treatment of solid tumors is common in clinics, the synchronous multiple primary malignant tumors of hematological malignancy overlap solid tumors are uncommon, even both tumors involved in the pathogenesis of the abnormal activation of the tyrosine kinase pathway are extremely rare. CASE PRESENTATION: An 84-year-old man with fatigue and dizziness was diagnosed with Ph+ ALL. Meanwhile, a chest CT indicated a space-occupying lesions, characterized by the presence of void, in the right lower lope with the enlargement of mediastinal lymph node and right pleural effusion. After a few weeks, the patient was diagnosed with LUAD with EGFR exon 19 mutation. Both tyrosine kinase inhibitors (TKI) (Flumatinib) and EGFR-TKI (Oxertinib) was used for the patients, and finally have controlled both diseases. CONCLUSION: As far as we know, we for the first time reported a case of Ph+ ALL and EGFR mutant LUAD synchronous overlap, of which pathogenesis is related to abnormal tyrosine kinase activation. This patient was successfully treated with two different TKIs without serious adverse events.

Prediagnosis recognition of acute ischemic stroke by artificial intelligence from facial images.

Stroke is a major threat to life and health in modern society, especially in the aging population. Stroke may cause sudden death or severe sequela-like hemiplegia. Although computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis methods, and artificial intelligence models have been built based on these images, shortage in medical resources and the time and cost of CT/MRI imaging hamper fast detection, thus increasing the severity of stroke. Here, we developed a convolutional neural network model by integrating four networks, Xception, ResNet50, VGG19, and EfficientNetb1, to recognize stroke based on 2D facial images with a cross-validation area under curve (AUC) of 0.91 within the training set of 185 acute ischemic stroke patients and 551 age- and sex-matched controls, and AUC of 0.82 in an independent data set regardless of age and sex. The model computed stroke probability was quantitatively associated with facial features, various clinical parameters of blood clotting indicators and leukocyte counts, and, more importantly, stroke incidence in the near future. Our real-time facial image artificial intelligence model can be used to rapidly screen and prediagnose stroke before CT scanning, thus meeting the urgent need in emergency clinics, potentially translatable to routine monitoring.

The Correlation of Centromere Protein Q with Diagnosis and Prognosis in Hepatocellular Carcinoma.

Introduction: Hepatocellular carcinoma (HCC) is one of the major types of liver cancer. Previous studies have shown that the centromere protein family is associated with malignant biological behaviors such as HCC proliferation. As a member of the centromere protein family, centromere protein Q (CENPQ) is closely associated with immunotherapy and immune cell infiltration in various tumors. However, the role and mechanism of CENPQ in HCC remain unclear. Methods: Multiple public databases and RT-qPCR were used to study the expression of CENPQ in HCC. Based on TCGA data, the correlation between CENPQ and clinicopathological characteristics and prognosis of HCC patients was analyzed, and its diagnostic value was evaluated. The potential biological functions of CENPQ in HCC were explored by functional enrichment analysis of differentially expressed genes. The distribution of tumor-infiltrating immune cell types was assessed using single-sample GSEA, and immune checkpoint gene expression was analyzed using Spearman correlation. Subsequently, loss-of-function experiments were performed to determine the function of CENPQ on the cell cycle and proliferation of HCC cells in vitro. Results: CENPQ was found highly expressed in HCC and correlated with weight, BMI, age, AFP, T stage, pathologic stage, histologic grade, and prothrombin time (all p < 0.05). ROC and Kaplan-Meier analyses indicated that CENPQ may be potentially used as a diagnostic marker for HCC (AUC = 0.881), and its upregulation is associated with decreased OS (p = 0.002), DSS (p < 0.001), and PFI (p = 0.002). Functional enrichment analysis revealed an association of CENPQ with biological processes such as immune cell infiltration, cell cycle, and hippo-merlin signaling deregulation in HCC. Furthermore, knockdown of CENPQ manifested in HCC cells with G0/1 phase cycle arrest and decreased proliferative capacity. Conclusion: CENPQ expression was higher in HCC tissues than in normal liver tissues. It was significantly associated with poor prognosis, immune cell infiltration, cell cycle, and proliferation. Therefore, CENPQ may become a promising prognostic biomarker for HCC patients.

Mental health literacy, mental health experiences and help-seeking behaviours of Chinese elite athletes: a qualitative study.

Introduction: Elite athletes' help-seeking on mental health might be influenced by their mental health literacy (MHL) and mental health experiences. The current study aimed to explore the MHL, experiences and help-seeking behaviours among elite athletes using a qualitative approach. Methods: Face-to-face semi-structured interviews were conducted among 20 Chinese elite athletes, 12 coaches, and 5 team officials. Interview data was analyzed using content analysis. Results: Seven main themes emerged from the analysis. The current study revealed that Chinese elite athletes suffered from various mental health issues and athletes' MHL levels, help-seeking attitudes and intentions, Chinese sports environments, and Chinese cultural background could impact their help-seeking behaviours. Conclusion: Support for Chinese elite athletes' mental health and help-seeking requires improvement.

Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators.

Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses challenges for studying and targeting senescent cells. We present here a machine learning program senescent cell identification (SenCID), which accurately identifies senescent cells in both bulk and single-cell transcriptome. Trained on 602 samples from 52 senescence transcriptome datasets spanning 30 cell types, SenCID identifies six major senescence identities (SIDs). Different SIDs exhibit different senescence baselines, stemness, gene functions, and responses to senolytics. SenCID enables the reconstruction of senescent trajectories under normal aging, chronic diseases, and COVID-19. Additionally, when applied to single-cell Perturb-seq data, SenCID helps reveal a hierarchy of senescence modulators. Overall, SenCID is an essential tool for precise single-cell analysis of cellular senescence, enabling targeted interventions against senescent cells.

Insights into growth stages and genotypes in airborne Pb accumulation in Oryza sativa L. grains: Utilizing isotope fingerprinting alongside a model study.

Atmospheric exposure is an important pathway of accumulation of lead (Pb) in Oryza sativa L. grains. In this study, source contributions of soil, early atmospheric exposure, and late atmospheric exposure, along with their bioaccumulation ratios were examined both in the pot and field experiments using stable Pb isotope fingerprinting technology combined with a three-compartment accumulation model. Furthermore, genotype differences in airborne Pb accumulation among four field-grown rice cultivars were investigated using the partial least squares path model (PLS-PM) linking rice Pb accumulation to agronomic traits. The findings revealed that during the late growth period, the air-foliar-grain transfer of Pb was crucial for rice Pb accumulation. Approximately 69-82% of the Pb found in polished rice was contributed by atmospheric source, with more than 80% accumulating during the late growth stage. The air accumulation ratios of rice grains were genotype-specific and estimated to be 0.364-1.062 m3/g during the late growth. Notably, grain size exhibited the highest standardized total effects on the airborne Pb concentrations in the polished rice, followed by leaf Pb and the upward translocation efficiency of Pb. The present study indicates that mitigating the health risks associated with Pb in rice can be achieved by controlling atmospheric Pb levels during the late growth stage and choosing Japonica inbred varieties characterized by large grain size.

Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud.

The human face is a valuable biomarker of aging, but the collection and use of its image raise significant privacy concerns. Here we present an approach for facial data masking that preserves age-related features using coordinate-wise monotonic transformations. We first develop a deep learning model that estimates age directly from non-registered face point clouds with high accuracy and generalizability. We show that the model learns a highly indistinguishable mapping using faces treated with coordinate-wise monotonic transformations, indicating that the relative positioning of facial information is a low-level biomarker of facial aging. Through visual perception tests and computational 3D face verification experiments, we demonstrate that transformed faces are significantly more difficult to perceive for human but not for machines, except when only the face shape information is accessible. Our study leads to a facial data protection guideline that has the potential to broaden public access to face datasets with minimized privacy risks.

Cell-free DNA methylation reveals cell-specific tissue injury and correlates with disease severity and patient outcomes in COVID-19.

BACKGROUND: The recently identified methylation patterns specific to cell type allows the tracing of cell death dynamics at the cellular level in health and diseases. This study used COVID-19 as a disease model to investigate the efficacy of cell-specific cell-free DNA (cfDNA) methylation markers in reflecting or predicting disease severity or outcome. METHODS: Whole genome methylation sequencing of cfDNA was performed for 20 healthy individuals, 20 cases with non-hospitalized COVID-19 and 12 cases with severe COVID-19 admitted to intensive care unit (ICU). Differentially methylated regions (DMRs) and gene ontology pathway enrichment analyses were performed to explore the locus-specific methylation difference between cohorts. The proportion of cfDNA derived from lung and immune cells to a given sample (i.e. tissue fraction) at cell-type resolution was estimated using a novel algorithm, which reflects lung injuries and immune response in COVID-19 patients and was further used to evaluate clinical severity and patient outcome. RESULTS: COVID­19 patients had globally reduced cfDNA methylation level compared with healthy controls. Compared with non-hospitalized COVID-19 patients, the cfDNA methylation pattern was significantly altered in severe patients with the identification of 11,156 DMRs, which were mainly enriched in pathways related to immune response. Markedly elevated levels of cfDNA derived from lung and more specifically alveolar epithelial cells, bronchial epithelial cells, and lung endothelial cells were observed in COVID-19 patients compared with healthy controls. Compared with non-hospitalized patients or healthy controls, severe COVID-19 had significantly higher cfDNA derived from B cells, T cells and granulocytes and lower cfDNA from natural killer cells. Moreover, cfDNA derived from alveolar epithelial cells had the optimal performance to differentiate COVID-19 with different severities, lung injury levels, SOFA scores and in-hospital deaths, with the area under the receiver operating characteristic curve of 0.958, 0.941, 0.919 and 0.955, respectively. CONCLUSION: Severe COVID-19 has a distinct cfDNA methylation signature compared with non-hospitalized COVID-19 and healthy controls. Cell type-specific cfDNA methylation signature enables the tracing of COVID-19 related cell deaths in lung and immune cells at cell-type resolution, which is correlated with clinical severities and outcomes, and has extensive application prospects to evaluate tissue injuries in diseases with multi-organ dysfunction.

Serum alpha-fetoprotein response as a preoperative prognostic indicator in unresectable hepatocellular carcinoma with salvage hepatectomy following conversion therapy: a multicenter retrospective study.

Background: This study evaluates the efficacy of alpha-fetoprotein (AFP) response as a surrogate marker for determining recurrence-free survival (RFS) in patients with unresectable hepatocellular carcinoma (uHCC) who undergo salvage hepatectomy following conversion therapy with tyrosine kinase inhibitor (TKI) and anti-PD-1 antibody-based regimen. Methods: This multicenter retrospective study included 74 patients with uHCC and positive AFP (>20 ng/mL) at diagnosis, who underwent salvage hepatectomy after treatment with TKIs and anti-PD-1 antibody-based regimens. The association between AFP response-defined as a ≥ 80% decrease in final AFP levels before salvage hepatectomy from diagnosis-and RFS post-hepatectomy was investigated. Results: AFP responders demonstrated significantly better postoperative RFS compared to non-responders (P<0.001). The median RFS was not reached for AFP responders, with 1-year and 2-year RFS rates of 81.3% and 70.8%, respectively. In contrast, AFP non-responders had a median RFS of 7.43 months, with 1-year and 2-year RFS rates at 37.1% and 37.1%, respectively. Multivariate Cox regression analysis identified AFP response as an independent predictor of RFS. Integrating AFP response with radiologic tumor response facilitated further stratification of patients into distinct risk categories: those with radiologic remission experienced the most favorable RFS, followed by patients with partial response/stable disease and AFP response, and the least favorable RFS among patients with partial response/stable disease but without AFP response. Sensitivity analyses further confirmed the association between AFP response and improved RFS across various cutoff values and in patients with AFP ≥ 200 ng/mL at diagnosis (all P<0.05). Conclusion: The "20-80" rule based on AFP response could be helpful for clinicians to preoperatively stratify the risk of patients undergoing salvage hepatectomy, enabling identification and management of those unlikely to benefit from this procedure.

Identification of the metaphyseal skeletal stem cell building trabecular bone.

Skeletal stem cells (SSCs) that are capable of self-renewal and multipotent differentiation contribute to bone development and homeostasis. Several populations of SSCs at different skeletal sites have been reported. Here, we identify a metaphyseal SSC (mpSSC) population whose transcriptional landscape is distinct from other bone mesenchymal stromal cells (BMSCs). These mpSSCs are marked by Sstr2 or Pdgfrb+Kitl-, located just underneath the growth plate, and exclusively derived from hypertrophic chondrocytes (HCs). These HC-derived mpSSCs have properties of self-renewal and multipotency in vitro and in vivo, producing most HC offspring postnatally. HC-specific deletion of Hgs, a component of the endosomal sorting complex required for transport, impairs the HC-to-mpSSC conversion and compromises trabecular bone formation. Thus, mpSSC is the major source of BMSCs and osteoblasts in bone marrow, supporting the postnatal trabecular bone formation.

Multiple diffusive behaviors of the random walk in inhomogeneous environments.

Anomalous diffusive behaviors are observed in highly inhomogeneous but relatively stable environments such as intracellular media and are increasingly attracting attention. In this paper we develop a coupled continuous-time random walk model in which the waiting time is power-law coupled with the local environmental diffusion coefficient. We provide two forms of the waiting time density, namely, a heavy-tailed density and an exponential density. For different waiting time densities, anomalous diffusions with the diffusion exponent between 0 and 2 and Brownian yet non-Gaussian diffusion can be realized within the present model. The diffusive behaviors are analyzed and discussed by deriving the mean-squared displacement and probability density function. In addition we derive the effective jump length density corresponding to the decoupled form to help distinguish the diffusion types. Our model unifies two kinds of anomalous diffusive behavior with different characteristics in the same inhomogeneous environment into a theoretical framework. The model interprets the random motion of particles in a complex inhomogeneous environment and reproduces the experimental results of different biological and physical systems.

Tissue-specific profiling of age-dependent miRNAomic changes in Caenorhabditis elegans.

Ageing exhibits common and distinct features in various tissues, making it critical to decipher the tissue-specific ageing mechanisms. MiRNAs are essential regulators in ageing and are recently highlighted as a class of intercellular messengers. However, little is known about the tissue-specific transcriptomic changes of miRNAs during ageing. C. elegans is a well-established model organism in ageing research. Here, we profile the age-dependent miRNAomic changes in five isolated worm tissues. Besides the diverse ageing-regulated miRNA expression across tissues, we discover numerous miRNAs in the tissues without their transcription. We further profile miRNAs in the extracellular vesicles and find that worm miRNAs undergo inter-tissue trafficking via these vesicles in an age-dependent manner. Using these datasets, we uncover the interaction between body wall muscle-derived mir-1 and DAF-16/FOXO in the intestine, suggesting mir-1 as a messenger in inter-tissue signalling. Taken together, we systematically investigate worm miRNAs in the somatic tissues and extracellular vesicles during ageing, providing a valuable resource to study tissue-autonomous and nonautonomous functions of miRNAs in ageing.

Application of AI in biological age prediction.

The development of anti-aging interventions requires quantitative measurement of biological age. Machine learning models, known as "aging clocks," are built by leveraging diverse aging biomarkers that vary across lifespan to predict biological age. In addition to traditional aging clocks harnessing epigenetic signatures derived from bulk samples, emerging technologies allow the biological age estimating at single-cell level to dissect cellular diversity in aging tissues. Moreover, imaging-based aging clocks are increasingly employed with the advantage of non-invasive measurement, making it suitable for large-scale human cohort studies. To fully capture the features in the ever-growing multi-modal and high-dimensional aging-related data and uncover disease associations, deep-learning based approaches, which are effective to learn complex and non-linear relationships without relying on pre-defined features, are increasingly applied. The use of big data and AI-based aging clocks has achieved high accuracy, interpretability and generalizability, guiding clinical applications to delay age-related diseases and extend healthy lifespans.

Quantifying the energy landscape in weakly and strongly disordered frictional media.

We investigate the "roughness" of the energy landscape of a system that diffuses in a heterogeneous medium with a random position-dependent friction coefficient α(x). This random friction acting on the system stems from spatial inhomogeneity in the surrounding medium and is modeled using the generalized Caldira-Leggett model. For a weakly disordered medium exhibiting a Gaussian random diffusivity D(x) = kBT/α(x) characterized by its average value ⟨D(x)⟩ and a pair-correlation function ⟨D(x1)D(x2)⟩, we find that the renormalized intrinsic diffusion coefficient is lower than the average one due to the fluctuations in diffusivity. The induced weak internal friction leads to increased roughness in the energy landscape. When applying this idea to diffusive motion in liquid water, the dissociation energy for a hydrogen bond gradually approaches experimental findings as fluctuation parameters increase. Conversely, for a strongly disordered medium (i.e., ultrafast-folding proteins), the energy landscape ranges from a few to a few kcal/mol, depending on the strength of the disorder. By fitting protein folding dynamics to the escape process from a metastable potential, the decreased escape rate conceptualizes the role of strong internal friction. Studying the energy landscape in complex systems is helpful because it has implications for the dynamics of biological, soft, and active matter systems.

CircPRDM5 inhibits the proliferation, migration, invasion, and glucose metabolism of gastric cancer cells by reducing GCNT4 expression in a miR-485-3p-dependent manner.

Circular RNA (circRNA) plays a key part in the pathological process of gastric cancer (GC). The study is organized to analyze the function of circPRDM5 in GC cell tumor properties. Expression levels of circPRDM5, miR-485-3p, glucosaminyl (N-acetyl) transferase 4 (GCNT4), ki67, E-cadherin, N-cadherin, and hexokinase 2 (HK2) were analyzed by quantitative real-time polymerase chain reaction (PCR), Western blotting or immunohistochemistry assay. Cell proliferation was assessed by cell colony formation assay and 5-ethynyl-2'-deoxyuridine assay. Cell migration and invasion were investigated by transwell assay. Glycolysis was evaluated by the Seahorse XF Glycolysis Stress Test Kit. Dual-luciferase reporter assay and RNA pull-down assay were performed to identify the associations among circPRDM5, miR-485-3p, and GCNT4. Xenograft mouse model assay was conducted to determine the effects of circPRDM5 on tumor formation in vivo. CircPRDM5 and GCNT4 expression were downregulated, while miR-485-3p expression was upregulated in GC tissues and cells when compared with paracancerous tissues or human gastric epithelial cells. CircPRDM5 overexpression inhibited proliferation, migration, invasion, and glucose metabolism of GC cells; however, circPRDM5 depletion had the opposite effects. CircPRDM5 repressed tumor properties of GC cells in vivo. MiR-485-3p restoration relieved circPRDM5-induced effects in GC cells. GCNT4 overexpression remitted the promoting effects of miR-485-3p mimics on GC cell malignancy. CircPRDM5 acted as a sponge for miR-485-3p, and GCNT4 was identified as a target gene of miR-485-3p. Moreover, circPRDM5 regulated GCNT4 expression by interacting with miR-485-3p.CircPRDM5 acted as a miR-485-3p sponge to inhibit GC progression by increasing GCNT4 expression, proving a potential target for GC therapy.

The Divergent Effects of Resilience Qualities and Resilience Support in Predicting Pre-Competition Anxiety and Championship Performance.

Purpose: Psychological resilience is vital to the development of sport talents. Qualitative research has consistently demonstrated that sport resilience encapsulates a mixed package of resilience qualities (reflecting positive traits and characteristics) and resilience support (reflecting perceived support and related resources). Ironically, sport resilience research adopting quantitative methods has been assessing resilience as a unidimensional construct, with little attention to the multi-facet nature of resilience and its effects on performance. In the present research, we tested a novel proposition that resilience qualities predict reduced pre-competition cognitive anxiety and contribute to performance more than resilience support. Methods & Results: Across two samples of competitive table tennis players (Study 1: N = 196 competing at province level; Study 2: N = 106 competing at national level), we consistently found resilience qualities, rather than resilience support, predicted lower levels of pre-competition cognitive anxiety and superior performance at a national championship. Results also suggest that pre-competition cognitive anxiety mediated the relationship between resilience qualities and performance. Conclusion: The findings provide the first evidence supporting the divergent effects of resilience qualities and resilience support in predicting pre- competition anxiety and championship performance and call for the consideration of such a distinction when designing and delivering resilience programs.