Objective To identify novel biomarkers for predicting the risk of latent tuberculosis infection(LTBI)activation using bio-informatics and machine-learning algorithms and to establish a risk model.Methods The GSE112104 and GSE193777 datasets were obtained from the Gene Expression Omnibus.Differential gene expression and weighted gene co-expression network analyses were per-formed to identify ferroptosis-related differentially expressed genes(FRG-DEGs)associated with LTBI activation.Three machine-learning algorithms,least absolute shrinkage and selection operator,support vector machine-recursive feature elimination,and random forest,were used to identify ferroptosis-related hub genes(FRG-hubs).The reliability of these genes was validated using independent validation datasets and reverse transcription polymerase chain reaction(PCR).A risk model was established using R software.Results In the GSE 112104 dataset,296 genes were upregulated and 1 569 genes were downregulated in active tuberculosis compared to those in LTBI.Among the LTBI progressors,506 genes were upregulated and 1 132 genes were downregulated.Weighted correlation network analysis identified five gene modules,with the blue module showing the strongest correlation with LTBI activation(cor=0.62,P=0.000 04),con-taining 1 340 genes.Intersections with 728 ferroptosis-related genes resulted in eight FRG-DEGs.The machine-learning algorithms iden-tified four FRG-hubs:PLA2G6,GLS2,JUN,and AMN,whose expression decreased with LTBI activation.Reverse transcription PCR con-firmed this trend.A risk model based on these genes yielded an area under the curve of 0.98 to 1.00.Conclusion This study successfully identified novel biomarkers for predicting the risk of LTBI activation and developed an accurate predictive risk model.

Objective:To explore the differences in the standardized z-score amplitude of low-frequency fluctuations (zALFFs) across different brain regions between children with global developmental delay (GDD) and healthy children (HC) using functional near-infrared spectroscopy (fNIRS), and correlating zALFF values with the subjects′ Gesell Developmental Scale (GDS) scores.Methods:Thirty-one children aged 2-4 years with GDD and 29 HC of the same age were studied. fNIRS was used to record both groups′ brain activity in response to natural stimuli and to measure any changes in oxygenated hemoglobin (HbO) levels in cerebral blood flow. zALFF values were calculated and the values of 44 channels were compared between the two groups. The correlations between zALFF values and GDS scores were computed.Results:The zALFF values of the children with GDD were significantly lower than those of the HC in the right frontal pole (channel 10) and the right pre-motor and supplementary motor areas (channel 43). In contrast, the zALFF values in the left pre-motor and supplementary motor areas (channels 24 and 26) were significantly higher in the children with GDD compared to the HC. Spearman ranked correlation analysis revealed that the zALFF values in the right pre-motor and supplementary motor areas (channel 43) were positively correlated with socialization scores on the GDS ( r=0.37, P≤0.05). Conclusions:The delays in cognitive and motor development in children with GDD may be associated with functional abnormalities in the right frontal polar region and the bilateral premotor and supplementary motor areas. zALFF values from the right premotor and supplementary motor areas are positively correlated with social skills.

Objective:To explore the differences in the standardized z-score amplitude of low-frequency fluctuations (zALFFs) across different brain regions between children with global developmental delay (GDD) and healthy children (HC) using functional near-infrared spectroscopy (fNIRS), and correlating zALFF values with the subjects′ Gesell Developmental Scale (GDS) scores.Methods:Thirty-one children aged 2-4 years with GDD and 29 HC of the same age were studied. fNIRS was used to record both groups′ brain activity in response to natural stimuli and to measure any changes in oxygenated hemoglobin (HbO) levels in cerebral blood flow. zALFF values were calculated and the values of 44 channels were compared between the two groups. The correlations between zALFF values and GDS scores were computed.Results:The zALFF values of the children with GDD were significantly lower than those of the HC in the right frontal pole (channel 10) and the right pre-motor and supplementary motor areas (channel 43). In contrast, the zALFF values in the left pre-motor and supplementary motor areas (channels 24 and 26) were significantly higher in the children with GDD compared to the HC. Spearman ranked correlation analysis revealed that the zALFF values in the right pre-motor and supplementary motor areas (channel 43) were positively correlated with socialization scores on the GDS ( r=0.37, P≤0.05). Conclusions:The delays in cognitive and motor development in children with GDD may be associated with functional abnormalities in the right frontal polar region and the bilateral premotor and supplementary motor areas. zALFF values from the right premotor and supplementary motor areas are positively correlated with social skills.

Objective To analyze the usage of pediatric drugs in 17 pediatric specialty hospitals from 2016 to 2020, and provide reference and guidance for the development of the essential medicine list （EML） for children and the improvement of the National Reimbursement Drug List （NRDL） in China. Methods Based on the pediatric medication monitoring data from 17 children's specialized hospitals reported to the Chinese Medical Economic Information Network (CMEI) of the Chinese Pharmaceutical Association between 2016 and 2020, this study analyzes the overall situation of the sample hospitals and the clinical use of pediatric drugs according to the major categories of the Anatomical Therapeutic Chemical Classification System （ATC）. Results In the various ATC categories, the trend of systemic use of anti-infective drugs decreasing was significant in the average hospital expenditure, while the trend of respiratory and digestive system and metabolic drugs decreasing was significant in the average hospital DDDS. In 2020, the average number of hospital grade standards for essential drugs （2018 version） accounted for 15.82% of the total number of drug use standards, while the average number of hospital grade standards for medical insurance （2019 version） accounted for 8.23% of the total number of drug use standards. Conclusion The use of pediatric medication in sample hospitals from 2016 to 2020 was generally reasonable, and there would still a certain gap between the actual clinical usage habits with the existing EML and NRDL，which still need to be adjusted.

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers

Objective To identify novel biomarkers for predicting the risk of latent tuberculosis infection(LTBI)activation using bio-informatics and machine-learning algorithms and to establish a risk model.Methods The GSE112104 and GSE193777 datasets were obtained from the Gene Expression Omnibus.Differential gene expression and weighted gene co-expression network analyses were per-formed to identify ferroptosis-related differentially expressed genes(FRG-DEGs)associated with LTBI activation.Three machine-learning algorithms,least absolute shrinkage and selection operator,support vector machine-recursive feature elimination,and random forest,were used to identify ferroptosis-related hub genes(FRG-hubs).The reliability of these genes was validated using independent validation datasets and reverse transcription polymerase chain reaction(PCR).A risk model was established using R software.Results In the GSE 112104 dataset,296 genes were upregulated and 1 569 genes were downregulated in active tuberculosis compared to those in LTBI.Among the LTBI progressors,506 genes were upregulated and 1 132 genes were downregulated.Weighted correlation network analysis identified five gene modules,with the blue module showing the strongest correlation with LTBI activation(cor=0.62,P=0.000 04),con-taining 1 340 genes.Intersections with 728 ferroptosis-related genes resulted in eight FRG-DEGs.The machine-learning algorithms iden-tified four FRG-hubs:PLA2G6,GLS2,JUN,and AMN,whose expression decreased with LTBI activation.Reverse transcription PCR con-firmed this trend.A risk model based on these genes yielded an area under the curve of 0.98 to 1.00.Conclusion This study successfully identified novel biomarkers for predicting the risk of LTBI activation and developed an accurate predictive risk model.

Objective:To investigate the molecular mechanisms of chronic rhinosinusitis (CRS), to identify key cell subgroups and genes, to construct effective diagnostic models, and to screen for potential therapeutic drugs.Methods:Key cell subgroups in CRS were identified through single-cell transcriptomic sequencing data. Essential genes associated with CRS were selected and diagnostic models were constructed by hdWGCNA (high dimensional weighted gene co-expression network analysis) and various machine learning algorithms. Causal inference analysis was performed using Mendelian randomization and colocalization analysis. Potential therapeutic drugs were identified using molecular docking technology, and the results of bioinformatics analysis were validated by immunofluorescence staining. Graphpad Prism, R, Python, and Adobe Illustrator software were used for data and image processing.Results:An increased proportion of basal and suprabasal cells was observed in CRS, especially in eosinophilic CRS with nasal polyps (ECRSwNP), with P=0.001. hdWGCNA revealed that the "yellow module" was closely related to basal and suprabasal cells in CRS. Univariate logistic regression and LASSO algorithm selected 13 key genes ( CTSC, LAMB3, CYP2S1, TRPV4, ARHGAP21, PTHLH, CDH26, MRPS6, TENM4, FAM110C, NCKAP5, SAMD3, and PTCHD4). Based on these 13 genes, an effective CRS diagnostic model was developed using various machine learning algorithms (AUC=0.958). Mendelian randomization analysis indicated a causal relationship between CTSC and CRS (inverse variance weighted: OR=1.06, P=0.006), and colocalization analysis confirmed shared genetic variants between CTSC and CRS (PPH4/PPH3>2). Molecular docking results showed that acetaminophen binded well with CTSC (binding energy:-5.638 kcal/mol). Immunofluorescence staining experiments indicated an increase in CTSC +cells in CRS. Conclusion:This study integrates various bioinformatics methods to identify key cell types and genes in CRS, constructs an effective diagnostic model, underscores the critical role of the CTSC gene in CRS pathogenesis, and provides new targets for the treatment of CRS.

Objective To explore the effect of adeno-associated virus sense transfection up-regulating the expression level of the growth and differential factor 11 (GDF11) in vivo on aortic injury in type 2 diabetic mellitus rats (T2DM).Methods Nine-week-old male SD rats were randomly selected to establish a T2DM model by using high-sugar and high-fat chow plus small-dose streptozotocin (STZ) combined induction.Both normal rats and dia-betic model rats were randomly divided into five groups:blank control group (Control group) , negative virus con-trol group (NC group), GDF11 adeno-associated virus group (GDF11 group), diabetic group (DM group), and diabetic + GDF11 adeno-associated virus group (DM+GDF11 group) .After 8 weeks of feeding, the serum con-centrations of insulin (INS) , advanced glycosylation end products (AGES) , recombinant growth transforming fac-tor 11 (GDF11), total cholesterol (T-CHO), triglycerides (TG), low-density lipoproteins (LDL-C), high-densi-ty lipoproteins (HDL-C) , asymmetric dimethylarginine (ADMA) , and malondialdehyde (MDA) were assayed in the rats;periodic acid-schiff stain(PAS stain) was used to observe the sites of glycogen deposition, and hematoxy-lin-eosin staining (HE) was used to observe vascular damage.Scanning electron microscopy was used to observe the damage of vascular endothelial cells and vascular elastic fibers, and protein blotting and immunohistochemistry were used to detect the expression levels of vascular injury-related proteins.Protein blotting and immunohistochem-istry were used to detect the expression levels of vascular injury-related proteins.Results The biochemical inde-xes showed that the serum concentrations of AGES, T-CHO, TG, LDL-C and MDA were higher in the DM group than those in the Control group (P<0.05), the concentrations of INS, GDF11, HDL-C and ADMA were signifi-cantly lower than those in the Control group (P<0.05) , and the concentrations of AGES and HDL-C were not sig-nificantly lower in the DM+GDF11 group compared with the DM group (P<0.05) .HDL-C was not significantly different from the DM group, and several other data were improved (P<0.05) .Pathological staining suggested that PAS staining in the DM group suggested that glycogen particles deposited in the endothelium and subendotheli-um of the aorta, HE staining observed thickening of the aortic mesentery, endothelial cells and elastic fibers broke off in an irregular alignment, and electron microscopy observed endothelial damage in the vasculature and elastic fi-bers broke off in the DM group, and these changes attenuated in the DM+GDF11 group.Protein blotting and im-munohistochemistry indicated that the expression of endothelial cell-associated proteins decreased in the DM group (P<0.05) , and mesenchymal markers elevated in the DM group (P<0.05) , these proteins were regressed in the DM+GDF11 group, and the difference was statistically significant (P <0.05).Conclusion Increasing the expression level of GDF11 in vivo can improve aortic vascular injury caused by diabetes mellitus, which is inferred that it may be related to the inhibition of endothelial mesenchymal transition to protect the function of vascular endo-thelial cells and thus improve vascular injury.

Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) functions as a key regulator in inflammation and cell death and is involved in mediating a variety of inflammatory or degenerative diseases. A number of allosteric RIPK1 inhibitors (RIPK1i) have been developed, and some of them have already advanced into clinical evaluation. Recently, selective RIPK1i that interact with both the allosteric pocket and the ATP-binding site of RIPK1 have started to emerge. Here, we report the rational development of a new series of type-II RIPK1i based on the rediscovery of a reported but mechanistically atypical RIPK3i. We also describe the structure-guided lead optimization of a potent, selective, and orally bioavailable RIPK1i, 62, which exhibits extraordinary efficacies in mouse models of acute or chronic inflammatory diseases. Collectively, 62 provides a useful tool for evaluating RIPK1 in animal disease models and a promising lead for further drug development.

Gastric cancer(GC)is one of the most common gastrointestinal tumors.As a newly discovered type of non-coding RNAs,transfer RNA(tRNA)-derived small RNAs(tsRNAs)play a dual biological role in cancer.Our previous studies have demonstrated the potential of tRF-23-Q99P9P9NDD as a diagnostic and prognostic biomarker for GC.In this work,we confirmed for the first time that tRF-23-Q99P9P9NDD can promote the proliferation,migration,and invasion of GC cells in vitro.The dual luciferase reporter gene assay confirmed that tRF-23-Q99P9P9NDD could bind to the 3'untranslated region(UTR)site of acyl-coenzyme A dehydrogenase short/branched chain(ACADSB).In addition,ACADSB could rescue the effect of tRF-23-Q99P9P9NDD on GC cells.Next,we used Gene Ontology(GO),the Kyoto Encyclopedia of Genes and Genomes(KEGG),and Gene Set Enrichment Analysis(GSEA)to find that downregulated ACADSB in GC may promote lipid accumulation by inhibiting fatty acid catabolism and ferroptosis.Finally,we verified the correlation between ACADSB and 12 ferroptosis genes at the transcriptional level,as well as the changes in reactive oxygen species(ROS)levels by flow cytometry.In summary,this study proposes that tRF-23-Q99P9P9NDD may affect GC lipid metabolism and ferroptosis by targeting ACADSB,thereby promoting GC progression.It provides a theoretical basis for the diagnostic and prognostic monitoring value of GC and opens up new possibilities for treatment.