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The progression and malignancy of many tumors are associated with increased tissue stiffness. Conversely, the oncogenically transformed cells can be confined in soft stroma. Yet, the underlying mechanisms by which soft matrix confines tumorigenesis and metastasis remain elusive. Here, we show that pancreatic cancer cells are suppressed in the soft extracellular matrix, which is associated with YAP1 degradation through autophagic-lysosomal pathway rather than Hippo signal mediated proteasome pathway. In the soft stroma, PTEN is upregulated and activated, which consequently promotes lysosomal biogenesis, leading to the activation of cysteine-cathepsins for YAP1 degradation. In vitro, purified cathepsin L can directly digest YAP1 under acidic conditions. Lysosomal stress, either caused by chloroquine or overexpression of cystatin A/B, results in YAP1 accumulation and malignant transformation. Likewise, liver fibrosis induced stiffness can promote malignant potential in mice. Clinical data show that down-regulation of lysosomal biogenesis is associated with pancreatic fibrosis and stiffness, YAP1 accumulation, and poor prognosis in PDAC patients. Together, our findings suggest that soft stroma triggers lysosomal flux-mediated YAP1 degradation and induces cancer cell dormancy.

Abstract Background Traditional clustering techniques are typically restricted to either continuous or categorical variables. However, most real-world clinical data are mixed type. This study aims to introduce a clustering technique specifically designed for datasets containing both continuous and categorical variables to offer better clustering compatibility, adaptability, and interpretability than other mixed type techniques. Methods This paper proposed a modified Gower distance incorporating feature importance as weights to maintain equal contributions between continuous and categorical features. The algorithm (DAFI) was evaluated using five simulated datasets with varying proportions of important features and real-world datasets from the 2011–2014 National Health and Nutrition Examination Survey (NHANES). Effectiveness was demonstrated through comparisons with 13 clustering techniques. Clustering performance was assessed using the adjusted Rand index (ARI) for accuracy in simulation studies and the silhouette score for cohesion and separation in NHANES. Additionally, multivariable logistic regression estimated the association between periodontitis (PD) and cardiovascular diseases (CVDs), adjusting for clusters in NHANES. Results In simulation studies, the DAFI-Gower algorithm consistently performs better than baseline methods according to the adjusted Rand index in settings investigated, especially on datasets with more redundant features. In NHANES, 3,760 people were analyzed. DAFI-Gower achieves the highest silhouette score (0.79). Four distinct clusters with diverse health profiles were identified. By incorporating feature importance, we found that cluster formations were more strongly influenced by CVD-related factors. The association between periodontitis and cardiovascular diseases, after adjusting for clusters, reveals significant insights (adjusted OR 1.95, 95% CI 1.50 to 2.55, p = 0.012), highlighting severe periodontitis as a potential risk factor for cardiovascular diseases. Conclusions DAFI performed better than classic clustering baselines on both simulated and real-world datasets. It effectively captures cluster characteristics by considering feature importance, which is crucial in clinical settings where many variables may be similar or irrelevant. We envisage that DAFI offers an effective solution for mixed type clustering.

Abstract Background Pneumothorax is a medical emergency caused by the abnormal accumulation of air in the pleural space—the potential space between the lungs and chest wall. On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum, and we refer to this area as “lung + space.” While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end‐to‐end approach. These models directly map chest radiographs to clinician‐annotated lesion areas, often neglecting the vital domain knowledge that pneumothorax is inherently location‐sensitive. Methods We propose a novel approach that incorporates the lung + space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs. To circumvent the need for additional annotations and to prevent potential label leakage on the target task, our method utilizes external datasets and an auxiliary task of lung segmentation. This approach generates a specific constraint of lung + space for each chest radiograph. Furthermore, we have incorporated a discriminator to eliminate unreliable constraints caused by the domain shift between the auxiliary and target datasets. Results Our results demonstrated considerable improvements, with average performance gains of 4.6%, 3.6%, and 3.3% regarding intersection over union, dice similarity coefficient, and Hausdorff distance. These results were consistent across six baseline models built on three architectures (U‐Net, LinkNet, or PSPNet) and two backbones (VGG‐11 or MobileOne‐S0). We further conducted an ablation study to evaluate the contribution of each component in the proposed method and undertook several robustness studies on hyper‐parameter selection to validate the stability of our method. Conclusions The integration of domain knowledge in DL models for medical applications has often been underemphasized. Our research underscores the significance of incorporating medical domain knowledge about the location‐specific nature of pneumothorax to enhance DL‐based lesion segmentation and further bolster clinicians' trust in DL tools. Beyond pneumothorax, our approach is promising for other thoracic conditions that possess location‐relevant characteristics.

Abstract Machine learning (ML) has achieved substantial success in performing healthcare tasks in which the configuration of every part of the ML pipeline relies heavily on technical knowledge. To help professionals with borderline expertise to better use ML techniques, Automated ML (AutoML) has emerged as a prospective solution. However, most models generated by AutoML are black boxes that are challenging to comprehend and deploy in healthcare settings. We conducted a systematic review to examine AutoML with interpretation systems for healthcare. We searched four databases (MEDLINE, EMBASE, Web of Science, and Scopus) complemented with seven prestigious ML conferences (AAAI, ACL, ICLR, ICML, IJCAI, KDD, and NeurIPS) that reported AutoML with interpretation for healthcare before September 1, 2023. We included 118 articles related to AutoML with interpretation in healthcare. First, we illustrated AutoML techniques used in the included publications, including automated data preparation, automated feature engineering, and automated model development, accompanied by a real‐world case study to demonstrate the advantages of AutoML over classic ML. Then, we summarized interpretation methods: feature interaction and importance, data dimensionality reduction, intrinsically interpretable models, and knowledge distillation and rule extraction. Finally, we detailed how AutoML with interpretation has been used for six major data types: image, free text, tabular data, signal, genomic sequences, and multi‐modality. To some extent, AutoML with interpretation provides effortless development and improves users' trust in ML in healthcare settings. In future studies, researchers should explore automated data preparation, seamless integration of automation and interpretation, compatibility with multi‐modality, and utilization of foundation models.

BackgroundWeight and body shape concerns have become increasingly common among adolescents. Chinese university students show a high risk of eating disorder behaviors. This study aims to analyze the moderating effect of BMI on the relationships between body shape, attitudes, subjective norms, and eating disorder behavioral intentions among Chinese female university students using the Theory of Reasoned Action (TRA) model.MethodsA stratified random sample of 679 female Chinese university students (age, mean ± SD = 19.792 ± 1.007) participated in the study. The surveys comprised the Theory of Reasoned Action Questionnaire (TRA-Q) and the Body Shape Questionnaire (BS-Q) to assess their body shape concerns and behavioral intentions regarding eating disorders. Structural equation modeling was used to test the extended TRA model, with body shape as an additional predictor and BMI as a moderator.ResultsBody shape positively affected attitudes (β = 0.444, p

Background This study aims to evaluate the clinical application value of ultrasound viscoelastic imaging in noninvasive quantitative assessment of chronic kidney disease (CKD).Methods A total of 332 patients with CKD and 190 healthy adults as a control group were prospectively enrolled. Before kidney biopsy, ultrasound viscoelastic imaging was performed to measure the mean stiffness value (Emean), mean viscosity coefficient (Vmean), and mean dispersion coefficient (Dmean) of the renal. CKD patients were divided into three groups based on estimated glomerular filtration rate. The differences in clinic, pathology, ultrasound image parameters between the control and patient groups, or among different CKD groups were compared. The correlation between viscoelastic parameters and pathology were analyzed.Results Emean, Vmean, and Dmean in the control group were less than the CKD group (p 6.61 kPa, Vmean > 1.86 Pa·s, or Dmean > 7.51 m/s/kHz may suggest renal dysfunction. Combining Vmean, Dmean, and Emean can improve the efficiency of identifying CKD.

Abstract Targeted protein degradation has been widely adopted as a new approach to eliminate both established and previously recalcitrant therapeutic targets. Here, it is reported that the development of small molecule degraders of the envelope (E) protein of dengue virus. Two classes of bivalent E‐degraders are developed by linking two previously reported E‐binding small molecules, GNF‐2, and CVM‐2‐12‐2, to a glutarimide‐based recruiter of the CRL4CRBN ligase to effect proteosome‐mediated degradation of the E protein. ZXH‐2‐107 (based on GNF‐2) is an E‐degrader with ABL inhibitory activity while ZXH‐8‐004 (based on CVM‐2‐12‐2) is a selective and potent E‐degrader. These two compounds provide proof of concept that difficult‐to‐drug targets such as a viral envelope protein can be effectively eliminated using a bivalent degrader and provide starting points for the future development of a new class of direct‐acting antiviral drugs.

IntroductionUltrasound (US) imaging has emerged as a promising tool for assessing age-related muscle changes. This meta-analysis aimed to comprehensively evaluate the associations between US parameters and muscle strength, as well as sarcopenia-related functional performance in older adults by integrating data from multiple studies.MethodsA systematic literature search was conducted in PubMed, Web of Science, and Embase until June 2023. Studies reporting Pearson’s correlation coefficients between US parameters [echo intensity (EI), muscle thickness (MT), cross-sectional area (CSA), pinnations angle (PA), fascicle length (FL)] and measures of muscle strength or physical performance in older adults were included. Effect sizes were pooled using a random-effects model and presented in forest plots. Heterogeneity was assessed using I2, and publication bias was evaluated using Egger’s test.ResultsTwenty-eight studies met the inclusion criteria. Meta-analysis revealed moderate to strong correlations between EI, MT, and CSA with muscle strength. However, no significant associations were found between US parameters and gait speed. For chair stand tests, the strength of associations varied by test type, with weak correlations observed between echo intensity and muscle thickness with sit-to-stand tests. US parameters did not exhibit significant correlations with the Timed Up and Go test.ConclusionUltrasonographic measurements of echo intensity (EI) and muscle thickness (MT) demonstrated moderate to strong correlations with muscle strength and functional assessments related to sarcopenia. To enhance the accuracy of sarcopenia diagnosis and the effectiveness of management strategies, there is a need for larger, longitudinal studies that evaluate a comprehensive range of ultrasonographic parameters.Systematic review registrationhttps://inplasy.com, identifier INPLASY202410086.

Pressure overload-induced pathological cardiac hypertrophy eventually leads to heart failure (HF). Unfortunately, lack of effective targeted therapies for HF remains a challenge in clinical management. Mixed-lineage leukemia 4 (MLL4) is a member of the SET family of histone methyltransferase enzymes, which possesses histone H3 lysine 4 (H3K4)-specific methyltransferase activity. However, whether and how MLL4 regulates cardiac function is not reported in adult HF. Here we report that MLL4 is required for endoplasmic reticulum (ER) stress homeostasis of cardiomyocytes and protective against pressure overload-induced cardiac hypertrophy and HF. We observed that MLL4 is increased in the heart tissue of HF mouse model and HF patients. The cardiomyocyte-specific deletion of Mll4 (Mll4-cKO) in mice leads to aggravated ER stress and cardiac dysfunction following pressure overloading. MLL4 knockdown neonatal rat cardiomyocytes (NRCMs) also display accelerated decompensated ER stress and hypertrophy induced by phenylephrine (PE). The combined analysis of Cleavage Under Targets and Tagmentation sequencing (CUT&Tag-seq) and RNA sequencing (RNA-seq) data reveals that, silencing of Mll4 alters the chromatin landscape for H3K4me1 modification and gene expression patterns in NRCMs. Interestingly, the deficiency of MLL4 results in a marked reduction of H3K4me1 and H3K27ac occupations on Thrombospondin-4 (Thbs4) gene loci, as well as Thbs4 gene expression. Mechanistically, MLL4 acts as a transcriptional activator of Thbs4 through mono-methylation of H3K4 and further regulates THBS4-dependent ER stress response, ultimately plays a role in HF. Our study indicates that pharmacologically targeting MLL4 and ER stress might be a valid therapeutic approach to protect against cardiac hypertrophy and HF.

IntroductionThis study aimed to evaluate the effects of varied resistance training modalities on physical fitness components, body composition, maximal strength assessed by one-repetition maximum (1RM), isokinetic muscle functions of the shoulder and knee joints, and biomechanical properties of core muscles.MethodsForty participants were randomly assigned to four groups: control group (CG, n = 10), compound set training group (CSG, n = 10), pyramid set training group (PSG, n = 10), and superset training group (SSG, n = 10). Excluding the CG, the other three groups underwent an 8-week resistance training program, three sessions per week, at 60%–80% of 1RM intensity for 60–90 min per session. Assessments included body composition, physical fitness components, 1RM, isokinetic muscle functions, and biomechanical properties (muscle frequency, stiffness, etc.) of the rectus abdominis and external oblique muscles.ResultsThe PSG demonstrated the most significant improvement in relative peak torque during isokinetic testing of the shoulder and knee joints. Compared to the CG, all exercise groups exhibited positive effects on back strength, sprint performance, 1RM, and core muscle biomechanics. Notably, the PSG showed superior enhancement in external oblique stiffness. However, no significant differences were observed among the exercise groups for rectus abdominis biomechanical properties.DiscussionStructured resistance training effectively improved maximal strength, functional performance, and core muscle biomechanics. The pyramidal training modality conferred specific benefits for isokinetic muscle functions and external oblique stiffness, suggesting its efficacy in enhancing force production capabilities and core stability.

This paper proposed a two-way coupled computational fluid dynamics (CFD) and discrete element method (DEM) approach to analyze the evolution of clogging in slurry shield tunneling quantitatively. The interactions between clay particles and slurry were considered by exchanging three interaction forces, including buoyancy force, pressure gradient force, and drag force. The CFD-DEM coupling approach was first benchmarked by comparing cutterhead torque and total thrust with field monitored data of a practical slurry shield tunnel project. The evolution process of the particle phase and the fluid phase over time was presented. The results indicated that fewer than 70% of the particles can be washed away in time by the circulating slurry. About 9% of the particles adhered to the submerged wall, resulting in increased cutterhead torque and thrust. Through parametric analysis, the influence of the shield driving parameters on the clay clogging behavior is further explored. The time history of cutterhead torque or thrust can be used as a criterion for judging whether clogging has occurred. Additionally, a new assessment method of clogging risk and an optimization strategy of driving parameters were proposed, which were intended to provide some guidance for similar projects.

Abstract Molecular folding regulation with environmental stimuli is critical in living and artificial molecular machine systems. Herein, we described a macrocycle, cyclo[4] (1,3-(4,6-dimethyl)benzene)[4](1,3-(4,6-dimethyl)benzene)(4-pyridine). Under 298 K, it has three stable stiff atropisomers with names as 1 (C s symmetry), 2 (C s symmetry), and 3 (C 4v symmetry). At 393 K, 1 can reversibly transform into 2, but at 473 K, it can irrevocably transform into 3. At 338 K, 3 and (PhCN)2PdCl2 complex to produce the metal-organic cage 4. Only at 338 K does the combination of 1 or 2 and (PhCN)2PdCl2 create a gel-like structure. Heating both gels to 473 K transforms them into 4. In addition to offering a thermally accelerated method for modifying self-assembled systems using macrocyclic building blocks, this study also has the potential to develop the nanoscale transformation material with a thermal response.