Short stature(SS) and disorder of sex development(DSD) are two types of conditions characterized by high clinical heterogeneity and complex etiology. There is interplay and mutual influence between the pathways regulated by growth hormone and sex hormones in skeletal and gonadal development. Causing co-occurrence of SS and DSD, as seen in conditions such as Turner syndrome, mixed gonadal dysgenesis, Noonan syndrome, and Prader-Willi syndrome. Patients with these disorders are often accompanied by distinctive facial features, endocrine and metabolic disturbances, cardiovascular disease, and other systemic complications. Genetic factors involved include chromosomal numerical and structural abnormalities; mutations in genes such as SHOX, CHD7, SOX8, and PTPN11, dysregulation of the RAS/mitogen activated protein kinase signaling pathway, and defects in imprinted genes. This article aims to systematically review the relevant research progress, in order to provide a reference for the clinical diagnosis and treatment strategies of patients with coexisting SS and DSD.

To evaluate and project the disease burden of early-onset colorectal cancer (EOCRC), and to identify its major risk factors.

To explore the clinical value of a multimodal predictive model based on multiparametric magnetic resonance imaging(MRI) radiomics combined with deep learning(DL) features for the preoperative noninvasive assessment of mismatch repair-deficient(MMRd) status in endometrial cancer(EC).

Developmental dysplasia of hip(DDH)usually occurs in children,and delayed diagnosis of DDH might lead to serious complications and influence long-term prognosis.The application of artificial intelligence(AI)in medical images helps to quantitatively individualize image data,reduce bias generated by manual analysis and achieve early and accurate diagnosis of children DDH.The research progresses of AI in imaging diagnosis of children DDH were reviewed in this article.

Reconstruction of low-dose CT image faced problems such as low resolution,blurred artifacts and strong noise.Deep learning reconstruction met the needs of reducing radiation dose and improving imaging quality,promoted reconstruction of CT image towards high efficiency,intelligence and integration of clinical diagnosis and treatment.The research progresses in deep learning low-dose CT imaging reconstruction were reviewed in this article.

Objective To evaluate the predictive value of a nomogram model developed by integrating clinical and ultrasound multiparameters for HER-2-positive breast cancer.Methods This study retrospectively enrolled 343 patients with pathologically confirmed breast cancer from three medical centers and randomly divided them into training and validation cohorts.Univariate analysis,LASSO regression,and multivariate logistic regres-sion were conducted on the training set to identify independent prognostic factors and construct a nomogram model.Bootstrap resampling with 1000 iterations was performed to evaluate the model's robustness.Model calibration was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit test.Receiver operating characteristic(ROC)curves were generated to evaluate model discrimination,and the area under the curve(AUC)along with other performance metrics were calculated.Decision curve analysis was employed to assess the clinical utility of the model,and the validation cohort was used for external validation.Results Univariate,LASSO,and multivariate regression analyses demonstrated that age,TTP(time to peak),and the presence of a filling defect sign were independent predictors of HER-2-positive breast cancer(all P<0.05).Based on these independent predictors,a nomogram model was constructed.Bootstrap validation with 1,000 resamples indicated that the model's predictive performance was stable.The Hosmer-Lemeshow test confirmed satisfactory model calibration,while the calibration curve illustrated accurate prediction probabilities.The area under the curve(AUC)for the training set was 0.863(95%CI:0.806～0.920),and for the validation set,it was 0.846(95%CI:0.764～0.929),indicating strong discriminative and generalization capabilities.Additionally,the clinical decision curve analysis demonstrated favor-able clinical utility.Conclusion A nomogram model integrating clinical and multimodal ultrasound parameters demonstrates potential utility in predicting HER-2-positive breast cancer.

One of the key components of adsorbents for blood purification is the microsphere adsorbent. Microsphere adsorbents should meet the following requirements: stable physical and chemical structures, easy for functional modification to endow the adsorbents with specific adsorption functions or characteristics, with good biocompatibility and with low non-specific adsorption, as well as with enough mechanical strength. Microsphere adsorbents prepared from polysaccharide bio-based materials fulfill the above requirements and have been widely used in the field of blood adsorption. In this article, adsorbents prepared from polysaccharide bio-based materials such as cellulose, agarose, alginate, as well as adsorbents prepared from the aforementioned materials and carbon materials and the application of the said bio-based adsorbents in blood adsorption is reviewed. The future development is also discussed, aiming to provide guidance and reference for the preparation, functional modification and application research of bio-based adsorbents for blood adsorption.
Microspheres ; Adsorption ; Humans

Mechanical pain is one of the most common causes of clinical pain, but there remains a lack of effective treatment for debilitating mechanical and chronic forms of neuropathic pain. Recently, neurotoxin GsMTx4, a selective mechanosensitive (MS) channel inhibitor, has been found to be effective, while the underlying mechanism remains elusive. Here, with multiple rodent pain models, we demonstrated that a GsMTx4-based 17-residue peptide, which we call P10581, was able to reduce mechanical hyperalgesia and neuropathic pain. The analgesic effects of P10581 can be as strong as morphine but is not toxic in animal models. The anti-hyperalgesic effect of the peptide was resistant to naloxone (an μ-opioid receptor antagonist) and showed no side effects of morphine, including tolerance, motor impairment, and conditioned place preference. Pharmacological inhibition of TRPV4 by P10581 in a heterogeneous expression system, combined with the use of Trpv4 knockout mice indicates that TRPV4 channels may act as the potential target for the analgesic effect of P10581. Our study identified a potential drug for curing mechanical pain and exposed its mechanism.

T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.