PURPOSE: The secondary damage of spinal cord injury (SCI) starts from the collapse of the blood spinal cord barrier (BSCB) to chronic and devastating neurological deficits. Thereby, the retention of the integrity and permeability of BSCB is well-recognized as one of the major therapies to promote functional recovery after SCI. Previous studies have demonstrated that activation of hypoxia inducible factor-1α (HIF-1α) provides anti-apoptosis and neuroprotection in SCI. Endogenous HIF-1α, rapidly degraded by prolylhydroxylase, is insufficient for promoting functional recovery. Dimethyloxalylglycine (DMOG), a highly selective inhibitor of prolylhydroxylase, has been reported to have a positive effect on axon regeneration. However, the roles and underlying mechanisms of DMOG in BSCB restoration remain unclear. Herein, we aim to investigate pathological changes of BSCB restoration in rats with SCI treated by DOMG and evaluate the therapeutic effects of DMOG. METHODS: The work was performed from 2022 to 2023. In this study, Allen's impact model and human umbilical vein endothelial cells were employed to explore the mechanism of DMOG. In the phenotypic validation experiment, the rats were randomly divided into 3 groups: sham group, SCI group, and SCI + DMOG group (10 rats for each). Histological analysis via Nissl staining, Basso-Beattie-Bresnahan scale, and footprint analysis was used to evaluate the functional recovery after SCI. Western blotting, TUNEL assay, and immunofluorescence staining were employed to exhibit levels of tight junction and adhesion junction of BSCB, HIF-1α, cell apoptosis, and endoplasmic reticulum (ER) stress. The one-way ANOVA test was used for statistical analysis. The difference was considered statistically significant at p < 0.05. RESULTS: In this study, we observed the expression of HIF-1α reduced in the SCI model. DMOG treatment remarkably augmented HIF-1α level, alleviated endothelial cells apoptosis and disruption of BSCB, and enhanced functional recovery post-SCI. Besides, the administration of DMOG offset the activation of ER stress induced by SCI, but this phenomenon was blocked by tunicamycin (an ER stress activator). Finally, we disclosed that DMOG maintained the integrity and permeability of BSCB by inhibiting ER stress, and inhibition of HIF-1α erased the protection from DMOG. CONCLUSIONS Our findings illustrate that the administration of DMOG alleviates the devastation of BSCB and HIF-1α-induced inhibition of ER stress.
Spinal Cord Injuries/pathology* ; Animals ; Apoptosis/drug effects* ; Amino Acids, Dicarboxylic/therapeutic use* ; Recovery of Function/drug effects* ; Rats ; Rats, Sprague-Dawley ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism* ; Male ; Spinal Cord/blood supply*

An 11-year-old boy presented with erythematous plaques over the bilateral mandibular and mental regions for 2 years, accompanied by cough and dyspnea for more than 2 months. Chest computed tomography angiography revealed marked stenosis of the right pulmonary artery, irregular aortic caliber, and aortic wall thickening. Histopathological examination of the skin lesion, including immunohistochemistry and special stains, confirmed a chronic suppurative inflammation. Whole-exome sequencing was negative. A final diagnosis of granuloma faciale and Takayasu arteritis was established. Combination therapy with systemic tocilizumab, prednisone, and methotrexate, along with topical 0.1% tacrolimus ointment, resulted in a favorable clinical response. This report summarizes the clinical features of a pediatric case of granuloma faciale and Takayasu arteritis and reviews the etiology, diagnostic approach, and current treatment strategies for the disorders, aiming to enhance clinicians' understanding of these conditions.
Humans ; Male ; Child ; Takayasu Arteritis/diagnosis* ; Facial Dermatoses/diagnosis*

OBJECTIVES: Verrucous epidermal nevus (VEN), seborrheic keratosis (SK), verruca plana (VP), verruca vulgaris (VV), and nevus sebaceous (NS) are common verrucous proliferative skin diseases with similar clinical appearances, often posing diagnostic challenges. Dermoscopy and reflectance confocal microscopy (RCM) can aid in their differentiation, yet their specific features under these tools have not been systematically described. This study aims to summarize and analyze the dermoscopic and RCM features of VEN, SK, VP, VV, and NS. METHODS: A total of 121 patients with histopathologically confirmed verrucous proliferative skin diseases were enrolled. Dermoscopy and RCM imaging was used to observe and analyze the microscopic features of these conditions. RESULTS: Under dermoscopy, the 5 diseases displayed distinct characteristics: VEN typically showed gyriform structures; SK was characterized by gyriform structures, comedo-like openings, and milia-like cysts; VP and VV featured dotted vessels and frogspawn-like structures; NS presented as brownish-yellow globules. RCM revealed shared features such as hyperkeratosis and acanthosis across all 5 diseases. Specific features included gyriform structures and elongated rete ridges in VEN; pseudocysts and gyriform structures in SK; evenly distributed ring-like structures in VP; vacuolated cells and papillomatous proliferation in VV; and frogspawn-like structures in NS. CONCLUSIONS These 5 verrucous proliferative skin conditions exhibit distinguishable features under both dermoscopy and RCM. The combination of these 2 noninvasive imaging modalities holds significant clinical value for the differential diagnosis of verrucous proliferative skin diseases.
Humans ; Dermoscopy/methods* ; Diagnosis, Differential ; Microscopy, Confocal/methods* ; Male ; Female ; Adult ; Middle Aged ; Adolescent ; Keratosis, Seborrheic/pathology* ; Young Adult ; Warts/diagnosis* ; Child ; Aged ; Skin Diseases/pathology* ; Nevus, Sebaceous of Jadassohn/diagnosis* ; Skin Neoplasms/diagnosis* ; Child, Preschool

The elevated polyamines, amine-rich molecules with diverse functions in pathophysiology processes, are implicated in contributing to tumorigenesis and progression. Whether and how they affect the efficacy of chemotherapy is incompletely understood. Our screening assays reveal that the supplement with a low dose of spermidine (Spd), one of the polyamines, enhances ferroptosis in prostate cancer cells as evidenced by increased lipid peroxidation and intracellular Fe²⁺ levels in vitro. Combination treatment with Spd and a low dose of ferroptosis inducer erastin synergistically augments anti-tumor efficacy with undetectable toxicity in mice. Analysis of RNA-seq data indicates that heme oxygenase 1 (HMOX1), an enzyme that catalyzes the cleavage of heme to release Fe²⁺, is significantly upregulated in response to Spd and erastin cotreatment. Spd mediated the hypusine modification of the eukaryotic initiation factor 5A (EIF5A) promotes the translation of the nuclear factor erythroid 2-related factor 2 (NRF2), subsequently leading to elevation of HMOX1. Moreover, Spd and erastin significantly inhibit proteasome activity which results in a decrease in proteasomal degradation of NRF2, although many proteasome-related genes are induced either by Spd or Spd plus erastin. Thus, in addition to its pro-oncogenic activity, the supplement of Spd improves antitumor activity in combination with ferroptosis inducers and offers an optional approach to cancer treatment.

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.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Nitrogen oxides(NOx=NO+NO2)are important precursors of ozone(O3),and NOx and its oxides together constitute reactive nitrogen oxides(NOy)in the atmosphere.A comprehensive understanding of the total NOy level in the atmosphere is of great significance for a deeper understanding of the atmospheric nitrogen cycle and oxidation,as well as for formulating strategies for air pollution prevention and control.In this work,a thermal decomposition-broadband cavity enhanced absorption spectroscopy(TD-BBCEAS)technique for online measurement of total NOy in the atmosphere was developed.With this method,the NOy was efficiently converted into NO2,and the total NOy concentration in the atmosphere was indirectly obtained by measuring NO2.Focusing on the key factors affecting the measurement of total NOy,the influence of NO titration efficiency and other NOy component TD efficiency on measurement accuracy was emphasized.By changing the oxygen(O2)flow rate through the mercury lamp to alter the O3 concentration for titrating NO,the conversion efficiency of NO was evaluated.At O2 flow rate of 6 mL/min,the conversion efficiency of NO was greater than 99%.TD efficiency testing and analysis on NO2,peroxyacetyl nitrate(PAN),nitric acid(HNO3),and nitrous acid(HONO),which account for a large proportion of atmospheric NOy components,was carried out using 680℃as the optimal TD temperature for efficient conversion of NOy.With NO and HONO sample gases as typical verification gases,the conversion efficiency of NOy and the accuracy of NOy measurement by TD-BBCEAS system were verified by switching the on and off modes of mercury lamp and TD device.At integration time of 60 s,the detection limit of the system for NOy was 2.83×1010 molecules/cm3(60 s,2σ).A comparative measurement of actual atmospheric NOy was conducted between the TD-BBCEAS system and the NOy analyzer.The observation results showed a correlation coefficient(R2)of 0.98 and a slope of 0.93,further verifying the feasibility and accuracy of applying the TD-BBCEAS system to measurement of total NOy.