Anticipatory anxiety is a negative emotion that arises when individuals encounter potential threats or uncertainties in the future. It is the core symptom of a variety of anxiety disorders, and is closely associated with the occurrence, severity, treatment outcome, and prognosis of anxiety disorders, which has garnered a growing amount of focus in clinical practice. Nevertheless, scientific research on anticipatory anxiety continues to face obstacles such as unclear pathological mechanisms, the absence of simple and consistent self-assessment tools, and effective interventions. To improve understanding of the role of anticipatory anxiety in the diagnosis and treatment of anxiety disorders, this study reviews pertinent domestic and international literature, and briefly introduces the concept, assessment and measurement, activation paradigm, pathological mechanisms, and interventions of anticipatory anxiety.

Objective:To analyze the value of 18F-FDG PET/CT combined with MRI in the diagnosis and differential diagnosis of ductal carcinoma in situ (DCIS) and early stage invasive ductal carcinoma (IDC). Methods:From September 2019 to December 2023, 12 patients with DCIS (all females; age 36-67 years) and 34 patients with early stage IDC (all females; age 36-73 years) in Qingdao Central Hospital were retrospectively analyzed. The general clinical information, MRI features, and 18F-FDG PET/CT features of patients were analyzed. χ2 test, Fisher exact test, and Mann-Whitney U test were used to analyze the data. The independent predictors of DCIS were analyzed by logistic regression analysis. The value of different indicators in diagnosing DCIS was analyzed using ROC curves analysis, and Delong test was used to assess the differences among AUCs. Results:The differences in tumor metabolic volume (MTV; 18.55(10.90, 76.30) vs 4.00(2.00, 11.45)cm 3) and total lesion glycolysis (TLG; 44.85(25.30, 125.30) vs 9.40(6.68, 22.35)g) of breast lesion, enhancement pattern (non-mass enhancement (NME); 8/12 vs 29.4%(10/34)), lobulation sign (0/12 vs 58.8%(20/34)), and apparent diffusion coefficient (ADC; 1.33 (1.16, 1.63)×10 -3vs 1.08 (0.75, 1.28)×10 -3mm 2/s) between DCIS and early stage IDC groups were statistically significant ( Z values: from -3.91 to -2.56, χ2=5.17, all P<0.05). When differentiating DCIS from early stage IDC, NME (odds ratio ( OR)=36.50, 95% CI: 2.15-618.52, P=0.013), ADC ( OR=7.85, 95% CI: 1.11-55.46, P=0.044), and TLG ( OR=1.06, 95% CI: 1.02-1.11, P=0.007) were independent predictors. The AUC of the three predictors combination was 0.941, which was higher than those of single predictors ( Z values: 2.00-2.80, P values: 0.005-0.046). Conclusion:The combination of 18F-FDG PET/CT and MRI improves the efficacy of differential diagnosis between DCIS and early stage IDC, thereby providing a basis for developing personalized treatment plans for patients.

Atopic dermatitis (AD) is a prevalent inflammatory skin disorder in which patients experience recurrent eczematous lesions and intense itching. The colonization of Staphylococcus aureus (S. aureus) is correlated with the severity of the disease, but its role in AD development remains elusive. Using single-cell RNA sequencing, we uncovered that keratinocytes activate a distinct immune response characterized by induction of Il24 when exposed to methicillin-resistant S. aureus (MRSA). Further experiments using animal models showed that the administration of recombinant IL-24 protein worsened AD-like pathology. Genetic ablation of Il24 or the receptor Il20rb in keratinocytes alleviated allergic inflammation and atopic march. Mechanistically, IL-24 acted through its heterodimeric receptors on keratinocytes and augmented the production of IL-33, which in turn aggravated type 2 immunity and AD-like skin conditions. Overall, these findings establish IL-24 as a critical factor for onset and progression of AD and a compelling therapeutic target.
Dermatitis, Atopic/genetics* ; Interleukins/metabolism* ; Animals ; Methicillin-Resistant Staphylococcus aureus/immunology* ; Mice ; Keratinocytes/microbiology* ; Humans ; Interleukin-33/immunology* ; Inflammation/microbiology* ; Staphylococcal Infections/microbiology* ; Disease Models, Animal ; Hypersensitivity/microbiology* ; Mice, Inbred C57BL

Objective To develop and validate a deep learning model based on chest CT images to accurately distinguish between drug-resistant(DR-TB)and-sensitive tuberculosis(DS-TB).Methods A retrospective study was conducted on 722 cases of confirmed secondary tuberculosis admitted in our center from January 2019 to December 2022.According to the results of antimicrobial susceptibility test,they were divided into 357 DS-TB cases and 365 DR-TB cases.Pre-existing U-Net segmentation model was employed to segment the lung parenchyma regions in CT images.The dataset was randomly partitioned into a training set and a testing set in an 8:2 ratio.Six 3D deep learning architectures(3D Swin Transformer,3D ShuffleNet v2,3D ViT,3D MobileNet v2,3D DenseNet,and 3D ResNet18)were employed to evaluate the discriminative efficiency between DS-TB and DR-TB.Hyperparameters were optimized by five-fold cross-validation on the training set to construct the optimal model.The performance of the constructed model was assessed using area under the curve(AUC),sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV),and F1-score.Six radiologists independently evaluated DR-TB identification on the test set,and their performance was compared with the best-performing deep learning model.Results The AUC value in DR-TB prediction was 0.583,0.704,0.698,0.758,0.736,and 0.841,respectively,for 3D Swin Transformer,3D ShuffleNet v2,3D ViT,3D MobileNet v2,3D DenseNet,and 3D ResNet18.The 3D ResNet18 model demonstrated optimal performance,achieving a sensitivity of 0.935(95%CI:0.880～0.987),a specificity of 0.642(95%CI:0.492～0.757),a PPV of 0.750(95%CI:0.663～0.835),an NPV of 0.896(95%CI:0.809～0.976),an AUC value of 0.841,and a F1-score of 0.832.The radiologists got a F1-score of 0.571,0.450,0.675,0.623,0.617 and 0.635,respectively,and the F1-score of the 3D ResNet18 model is all higher than that of the radiologists.The highest-performing radiologist achieved sensitivity,specificity,PPV and NPV of 0.701(95%CI:0.605～0.802),0.567(95%CI:0.447～0.684),0.651(95%CI:0.549～0.757),and 0.623(95%CI:0.500～0.754),with all these values lower than those of the 3D ResNet18 model(P<0.05).Class activation mapping showed that the 3D ResNet18 model could focus on key lesion areas.The class activation mapping demonstrated that the 3D ResNet18 model could effectively focus on critical lesion regions.Conclusion Our 3D ResNet18 model shows the best predictive performance in identifying DR-TB,and is expected to assist clinical diagnosis for DR-TB.

Objective To analyze the molecular epidemiology and colistin-resistant genes of carbapenem-resistant Klebsiella pneumoniae(CRKP)by whole-genome sequencing,and to provide reference for clinical diagnosis and treatment.Methods 57 CRKP strains isolated from clinical specimens of hospitalized patients in a tertiary general first-class hospital in Anhui Province from 2021 to 2023 were collected and antimicrobial susceptibility testing was performed.Multilocus sequence typing,capsule serotype,resistance genes,and virulence genes of CRKP strains were analyzed by whole-genome sequencing technique,and single nucleotide polymorphism analysis was conducted on sequences of all strains.Colistin resistance-related genes were amplified by polymerase chain reaction(PCR).Results 57 CRKP strains exhibited resistance to 14 antimicrobial agents,with the exception of tigecycline.The se-quencing results showed that 93.0％(53/57)of CRKP carried blaKPC-2,and the ST11 type CRKP strain had the highest detection rate(51/57,89.5％).Single nucleotide polymorphism clustering analysis showed that the 57 CRKP strains were divided into 11 clone groups,of which 4 clone groups were all ST11-KL64 type CRKP.40(70.2％)CRKP strains carried multiple virulence genes.Five strains of CRKP were colistin-resistant strains,the resistance mechanism involved the insertion of ISKpn26 element at site 70 of the mgrB gene.Conclusion The CRKP strain is primarily characterized by the production of KPC-2 ST11-KL64,with disseminated transmission in intensive care unit.The insertion of ISKpn26 element leading to mgrB gene mutation is related to resistance of CRKP to colistin in this region.

Porcine deltacoronavirus(PDCoV)is the main pathogen of porcine deltacoronavirus dis-ease.After infection,pigsmanifest a series of main symptoms,such as persistent vomiting,watery diarrhea and severe dehydration.Pigs at almost all growth stages are likely to be infected with the virus,especially suckling piglets are much sensitive to the virus.Once PDCoV infects the host,it u-sually causes significant immunosuppression.In recent years,studies on the immunosuppressive mechanism of PDCoV have gradually attracted widespread attention.The results showed that mul-tiple proteins of PDCoV were involved in the regulation of host innate immunity,revealing the mechanism of these proteins in regulating host innate immunity.In this paper,the interaction mechanism between PDCoV protein and host innate immunity were rsummarized,which will pro-vide a theoretical basis for further understanding the pathogenesis of PDCoV and effective preven-tion and control of porcine delta coronavirus disease.

To establish a TaqMan-based multiplex RT-qPCR method for the identification of Japa-nese encephalitis virus(JEV),Porcine reproductive and respiratory syndrome virus(PRRSV),and Classical swine fever virus(CSFV),this study designed and synthesized three pairs of specific primers and probes based on the conserved sequences of JEV E,PRRSV ORF6,and CSFV E2 a-vailable in the NCBI GenBank.By optimizing the reaction system and protocol,a multiplex RT-qPCR method for detecting these three viruses was developed and applied to the detection of clini-cal samples.The results showed that the established TaqMan multiplex RT-qPCR specifically am-plified the gene fragments of JEV,PRRSV,and CSFV,and did not amplify other non-target genes,indicating good specificity of the method.Intra-assay and inter-assay repeatability tests showed that the coefficient of variation(Cv)values were all below 3％,demonstrating that the method has ex-cellent repeatability.Sensitivity tests revealed that the minimum detectable amount for the recom-binant plasmids of the three viruses was 100 copies/pL.Using the established method,a total of 969 samples,including blood,aborted fetuses,semen,and deceased pigs,from 26 pig farms in Guizhou Province were tested.The detection rates were 34.3％(332/969)for JEV,28.3％(274/969)for PRRSV,and 19.8％(192/969)for CSFV.The co-infection rates were 10.1％(98/969)for JEV and PRRSV,12.1％(117/969)for JEV and CSFV,and 14.6％(141/969)for CSFV and PRRSV.Additionally,the triple co-infection rate of JEV,PRRSV,and CSFV was 7.9％(77/969).These results indicate that the TaqMan multiplex RT-qPCR method developed in this study is ef-fective for detecting these three viruses in pig farms,providing technical support for identifying vi-ral causes of reproductive disorders.

Japanese encephalitis virus(JEV)belongs to the genus Flavivirus and the family Flavi-viridae,and is classified as a single-stranded,positive-sense RNA virus.The disease known as Japa-nese encephalitis(JE),which results from JEV infection,is a viral zoonosis that is prevalent worldwide and poses a significant public health concern.JEV infection activates a variety of signa-ling pathways,leading to a series of changes that are crucial to the virus's pathogenesis.Among these pathways,Toll-like receptors(TLRs)are particularly significant,and their diverse range and complex signal transduction mechanisms present substantial challenges for the prevention and con-trol of JEV.Currently,there is no specific treatment for JEV.Although some vaccines have been developed to prevent JE,eradicating JEV remains difficult due to its zoonotic transmission cycle and the limited efficacy of the available vaccines.This article reviews the alterations in various TLR signaling pathways induced by JEV infection in the host,aiming to provide insights into the patho-genic mechanisms of JEV and to identify potential new antiviral targets.

Porcine reproductive and respiratory syndrome virus(PRRSV)is the pathogen of porcine reproductive and respiratory syndrome(PRRS),and its infection mainly causes abortion,stillbirth in sows and piglet respiratory infections,which are widely prevalent in the world and seriously jeopardize the development of the world's animal husbandry industry.PRRSV infection of the host is capable of inducing significant immunosuppression,and in recent years,the study of the mecha-nism of PRRSV immunosuppression has become a hot topic,with studies showing that numerous PRRSV proteins are involved in the regulation of host innate immunity and elucidating the mecha-nism by which PRRSV proteins modulate host innate immunity.In this paper,we reviewed the progress of research on the interaction mechanism between PRRSV proteins and host innate im-munity to provide a theoretical basis for a comprehensive understanding of the pathogenesis of PRRSV and the prevention and control of PRRS.

Porcine deltacoronavirus(PDCoV)is the main pathogen of porcine deltacoronavirus dis-ease.After infection,pigsmanifest a series of main symptoms,such as persistent vomiting,watery diarrhea and severe dehydration.Pigs at almost all growth stages are likely to be infected with the virus,especially suckling piglets are much sensitive to the virus.Once PDCoV infects the host,it u-sually causes significant immunosuppression.In recent years,studies on the immunosuppressive mechanism of PDCoV have gradually attracted widespread attention.The results showed that mul-tiple proteins of PDCoV were involved in the regulation of host innate immunity,revealing the mechanism of these proteins in regulating host innate immunity.In this paper,the interaction mechanism between PDCoV protein and host innate immunity were rsummarized,which will pro-vide a theoretical basis for further understanding the pathogenesis of PDCoV and effective preven-tion and control of porcine delta coronavirus disease.