Epstein-Barr virus gp42 antibodies reveal sites of vulnerability for receptor binding and fusion to B cells.

Epstein-Barr virus (EBV) causes infectious mononucleosis and is associated with B cell lymphomas. EBV glycoprotein 42 (gp42) binds HLA class II and activates membrane fusion with B cells. We isolated gp42-specific monoclonal antibodies (mAbs), A10 and 4C12, which use distinct mechanisms to neutralize virus infection. mAb A10 was more potent than the only known neutralizing gp42 mAb, F-2-1, in neutralizing EBV infection and blocking binding to HLA class II. mAb 4C12 was similar to mAb A10 in inhibiting glycoprotein-mediated B cell fusion but did not block receptor binding, and it was less effective in neutralizing infection. Crystallographic structures of gH/gL/gp42/A10 and gp42/4C12 complexes revealed two distinct sites of vulnerability on gp42 for receptor binding and B cell fusion. Passive transfer of mAb A10 into humanized mice conferred nearly 100% protection from viremia and EBV lymphomas after EBV challenge. These findings identify vulnerable sites on EBV that may facilitate therapeutics and vaccines.

Epstein-Barr virus gH/gL has multiple sites of vulnerability for virus neutralization and fusion inhibition.

Epstein-Barr virus (EBV) is nearly ubiquitous in adults. EBV causes infectious mononucleosis and is associated with B cell lymphomas, epithelial cell malignancies, and multiple sclerosis. The EBV gH/gL glycoprotein complex facilitates fusion of virus membrane with host cells and is a target of neutralizing antibodies. Here, we examined the sites of vulnerability for virus neutralization and fusion inhibition within EBV gH/gL. We developed a panel of human monoclonal antibodies (mAbs) that targeted five distinct antigenic sites on EBV gH/gL and prevented infection of epithelial and B cells. Structural analyses using X-ray crystallography and electron microscopy revealed multiple sites of vulnerability and defined the antigenic landscape of EBV gH/gL. One mAb provided near-complete protection against viremia and lymphoma in a humanized mouse EBV challenge model. Our findings provide structural and antigenic knowledge of the viral fusion machinery, yield a potential therapeutic antibody to prevent EBV disease, and emphasize gH/gL as a target for herpesvirus vaccines and therapeutics.

Cognitive Reappraisal and Expressive Suppression Evoke Distinct Neural Connections during Interpersonal Emotion Regulation.

Interpersonal emotion regulation is the dynamic process where the regulator aims to change the target's emotional state, which is presumed to engage three neural systems: cognitive control (i.e., dorsal and ventral lateral PFC, etc.), empathy/social cognition (i.e., dorsal premotor regions, temporal-parietal junction, etc.), and affective response (i.e., insula, amygdala, etc.). This study aimed to identify the underlying neural correlate (especially the interpersonal one), of interpersonal emotion regulation based on two typical strategies (cognitive appraisal, expressive suppression). Thirty-four female dyads (friends) were randomly assigned into two strategy groups, with one assigned as the target and the other as the regulator to downregulate the target's negative emotions using two strategies. A functional near-infrared spectroscopy system was used to simultaneously measure participants' neural activity. Results showed that these two strategies could successfully downregulate the targets' negative emotions. Both strategies evoked intrapersonal and interpersonal neural couplings between the cognitive control, social cognition, and mirror neuron systems (e.g., PFC, temporal-parietal junction, premotor cortex, etc.), whereas cognitive reappraisal (vs expressive suppression) evoked a broader pattern. Further, cognitive reappraisal involved increased interpersonal brain synchronization between the prefrontal and temporal areas at the sharing stage, whereas expressive suppression evoked increased interpersonal brain synchronization associated with the PFC at the regulation stage. These findings indicate that intrapersonal and interpersonal neural couplings associated with regions within the abovementioned systems, possibly involving mental processes, such as cognitive control, mentalizing, and observing, underlie interpersonal emotion regulation based on cognitive reappraisal or expressive suppression.SIGNIFICANCE STATEMENT As significant as intrapersonal emotion regulation, interpersonal emotion regulation subserves parent-child, couple, and leader-follower relationships. Despite enormous growth in research on intrapersonal emotion regulation, the field lacks insight into the neural correlates underpinning interpersonal emotion regulation. This study aimed to probe the underlying neural correlates of interpersonal emotion regulation using a multibrain neuroimaging (i.e., hyperscanning) based on functional near-infrared spectroscopy. Results showed that both cognitive reappraisal and expressive suppression strategies successfully downregulated the target's negative emotions. More importantly, they evoked intrapersonal and interpersonal neural couplings associated with regions within the cognitive control, social cognition, and mirror neuron systems, possibly involving mental processes, such as cognitive control, mentalizing, and observing. These findings deepen our understanding of the neural correlates underpinning interpersonal emotion regulation.

Transcranial Direct Current Stimulation (tDCS) over the left dorsolateral prefrontal cortex reduced attentional bias toward natural emotional sounds.

Previous research has indicated that the left dorsolateral prefrontal cortex (DLPFC) exerts an influence on attentional bias toward visual emotional information. However, it remains unclear whether the left DLPFC also play an important role in attentional bias toward natural emotional sounds. The current research employed the emotional spatial cueing paradigm, incorporating natural emotional sounds of considerable ecological validity as auditory cues. Additionally, high-definition transcranial direct current stimulation (HD-tDCS) was utilized to examine the impact of left dorsolateral prefrontal cortex (DLPFC) on attentional bias and its subcomponents, namely attentional engagement and attentional disengagement. The results showed that (1) compared to sham condition, anodal HD-tDCS over the left DLPFC reduced the attentional bias toward positive and negative sounds; (2) anodal HD-tDCS over the left DLPFC reduced the attentional engagement toward positive and negative sounds, whereas it did not affect attentional disengagement away from natural emotional sounds. Taken together, the present study has shown that left DLPFC, which was closely related with the top-down attention regulatory function, plays an important role in auditory emotional attentional bias.

Machine learning for accurate detection of small airway dysfunction-related respiratory changes: an observational study.

BACKGROUND: The use of machine learning(ML) methods would improve the diagnosis of small airway dysfunction(SAD) in subjects with chronic respiratory symptoms and preserved pulmonary function(PPF). This paper evaluated the performance of several ML algorithms associated with the impulse oscillometry(IOS) analysis to aid in the diagnostic of respiratory changes in SAD. We also find out the best configuration for this task. METHODS: IOS and spirometry were measured in 280 subjects, including a healthy control group (n = 78), a group with normal spirometry (n = 158) and a group with abnormal spirometry (n = 44). Various supervised machine learning (ML) algorithms and feature selection strategies were examined, such as Support Vector Machines (SVM), Random Forests (RF), Adaptive Boosting (ADABOOST), Navie Bayesian (BAYES), and K-Nearest Neighbors (KNN). RESULTS: The first experiment of this study demonstrated that the best oscillometric parameter (BOP) was R5, with an AUC value of 0.642, when comparing a healthy control group(CG) with patients in the group without lung volume-defined SAD(PPFN). The AUC value of BOP in the control group was 0.769 compared with patients with spirometry defined SAD(PPFA) in the PPF population. In the second experiment, the ML technique was used. In CGvsPPFN, RF and ADABOOST had the best diagnostic results (AUC = 0.914, 0.915), with significantly higher accuracy compared to BOP (p < 0.01). In CGvsPPFA, RF and ADABOOST had the best diagnostic results (AUC = 0.951, 0.971) and significantly higher diagnostic accuracy (p < 0.01). In the third, fourth and fifth experiments, different feature selection techniques allowed us to find the best IOS parameters (R5, (R5-R20)/R5 and Fres). The results demonstrate that the performance of ADABOOST remained essentially unaltered following the application of the feature selector, whereas the diagnostic accuracy of the remaining four classifiers (RF, SVM, BAYES, and KNN) is marginally enhanced. CONCLUSIONS: IOS combined with ML algorithms provide a new method for diagnosing SAD in subjects with chronic respiratory symptoms and PPF. The present study's findings provide evidence that this combination may help in the early diagnosis of respiratory changes in these patients.

Expanding the Color Range of Photoresponsive Multicolor Luminescent System Through Host-Guest Interaction.

Photoresponsive multicolor luminescent systems offer interesting functions, which have led to applications in anticounterfeiting and biological imaging. However, expanding the color range of these materials remains a challenging task. Herein, a carbazole-modified dithienylethene derivative (DTE-CZ) that exhibits modulated fluorescence color changes through the photocyclization reaction and photolysis reaction is synthesized. DTE-CZ emits orange fluorescence, and it can release a fluorophore which emits blue fluorescence by the photolysis reaction, resulting in the color change. Upon complexation of DTE-CZ with cucurbit[10]uril (CB[10]), the fluorescence wavelength will have a blue shift and the photolysis reaction will be inhibited. Benefiting from the influence of CB[10] and the photolysis reaction of free guests, the color range of the photoresponsive system which is composed of free guests and host-guest complexes is further extended. White light emission along with a color shift from yellow-green to blue was achieved by adjusting the ratio of free guests to host-guest complexes. Finally, the photoresponsive multicolor systems are utilized to construct a photostimulated PVA film and an information encryption system. This work provides an alternative strategy for the preparing of photoresponsive multicolor luminescent system and modulation of its color range.

Association of serum uric acid levels with risk of epilepsy: A national population-based study.

BACKGROUND: Uric acid (UA) serves as a crucial endogenous antioxidant in the body, offering protection against oxidative stress, whichmaycontributetoepilepsypathogenesis. The association between serum UA levels and epilepsy remains uncertain. This study aimed to examine the potential connections between serum UA levels and epilepsy in US adults. METHODS: Utilizing data from the National Health and Nutrition Examination Survey (NHANES) 2013-2018, a cross-sectional analysis was conducted. Weighted logistic regression analyses were employed to assess the potential link between serum UA levels and the risk of epilepsy. Additionally, sensitivity analyses were conducted to evaluate the reliability of the results. RESULTS: We included 15,373 participants, of whom 136 (0.79 %) had epilepsy. Following adjustment for multiple variables, participants with serum UA levels <4.1 mg/dl had an odds ratio of 2.24 (95 % CI: 1.12-4.47, P = 0.023) for epilepsy compared to those with serum UA levels of 5.8-6.5 mg/dl. The results of the sensitivity analyses corroborated the initial findings. CONCLUSIONS: Our study revealed a significant association between lower serum UA levels and heightened risks of epilepsy, suggesting that low UA levels may serve as an independent risk factor for epilepsy. A marginal increase in UA levels within the normal range may act as a protective factor against epilepsy.

Investigation on the vibrational relaxation and ultrafast electronic dynamics of S1 state in 2,4-difluoroanisole.

Intramolecular vibrational energy redistribution (IVR) has a profound impact on dynamic processes. We have studied two types of IVR processes, restricted and dissipative, and ultrafast dynamics of the S1 state of 2,4-difluoroanisole using time-resolved photoelectron spectroscopy and time-of-flight mass spectroscopy. The restricted IVR occurs in the intermediate regime of 219 cm-1 vibrational level, and the dissipative IVR occurs in the statistical regime of 1200 cm-1. The lifetimes of IVR processes are measured to be 90 and 11 ps, respectively, depending on the internal energies of the S1 state and differ by a factor of eight. Similar subsequent dynamics were observed at two vibrational levels in the S1 state. The population undergoes IVR following the initial excitation and subsequently leaks into a triplet state, accompanied by intersystem crossing within ∼400 ps followed by a slower nonradiative relaxation of the triplet state on the nanosecond time scale. Furthermore, the values of 3s and 3px Rydberg states of 2,4-difluoroanisole were experimentally determined to be 5.02 and 6.28 eV.

Ultrafast restricted intramolecular rotation in molecules with aggregation induced emission.

In this work, the ultrafast intramolecular rotation behavior of 1,1,2,3,4,5-hexaphenylsilole has been investigated in several solutions with different viscosities using femtosecond transient absorption spectroscopy combined with density functional theory and time-dependent density functional theory calculations. It is demonstrated that the nonradiative process, which competes with radiative decay, involves two main stages, namely the restricted intramolecular rotation and internal conversion processes. The intramolecular rotation depends on viscosity and presents a significant restriction. The restricted rotational rate is determined to be dozens of picoseconds. The following nonradiative process is strongly dominated by intramolecular rotation. The nonradiative decay rate will decrease with the increase in viscosity, leading to a rise in the radiative probability and photoluminous yield. These results have borne out the mechanism of ultrafast restricted intramolecular rotation of aggregation induced emission and provided a detailed photophysical picture of nonradiative processes.

Machine learning models based on CT radiomics features for distinguishing benign and malignant vertebral compression fractures in patients with malignant tumors.

BACKGROUND: Radiomics has become an important tool for distinguishing benign and malignant vertebral compression fractures (VCFs). It is more clinically significant to concentrate on patients who have malignant tumors and differentiate between benign and malignant VCFs. PURPOSE: To explore the value of multiple machine learning (ML) models based on CT radiomics features for differentiating benign and malignant VCFs in patients with malignant tumors. MATERIAL AND METHODS: This study retrospectively analyzed 78 patients with malignant tumors accompanied by VCFs, 45 patients with benign VCFs, and 33 patients with malignant VCFs. A total of 140 lesions (86 benign lesions, 54 malignant lesions) were ultimately included in this study. All patients were divided into training sets (n = 98) and validation sets (n = 42) according to the 7:3 ratio. The radiomics features were screened and dimensioned, and multiple radiomics ML models were constructed. The receiver operating characteristic (ROC) curve was performed to assess the diagnostic performance. RESULTS: Five radiomics features were included in the model. All the ML models built have good diagnostic efficiency, among which the support vector machine (SVM) model performs better. The area under the curve (AUC), sensitivity, specificity, and accuracy in the training set were 0.908, 0.816, 0.883, and 0.857, respectively, while those in the validation set were 0.911, 0.647, 0.92, and 0.81, respectively. CONCLUSION: A variety of ML models built based on CT radiomics features have good value for differentiating benign and malignant VCFs in malignant tumor patients, and the SVM model has a better performance.

A 3D and Explainable Artificial Intelligence Model for Evaluation of Chronic Otitis Media Based on Temporal Bone Computed Tomography: Model Development, Validation, and Clinical Application.

BACKGROUND: Temporal bone computed tomography (CT) helps diagnose chronic otitis media (COM). However, its interpretation requires training and expertise. Artificial intelligence (AI) can help clinicians evaluate COM through CT scans, but existing models lack transparency and may not fully leverage multidimensional diagnostic information. OBJECTIVE: We aimed to develop an explainable AI system based on 3D convolutional neural networks (CNNs) for automatic CT-based evaluation of COM. METHODS: Temporal bone CT scans were retrospectively obtained from patients operated for COM between December 2015 and July 2021 at 2 independent institutes. A region of interest encompassing the middle ear was automatically segmented, and 3D CNNs were subsequently trained to identify pathological ears and cholesteatoma. An ablation study was performed to refine model architecture. Benchmark tests were conducted against a baseline 2D model and 7 clinical experts. Model performance was measured through cross-validation and external validation. Heat maps, generated using Gradient-Weighted Class Activation Mapping, were used to highlight critical decision-making regions. Finally, the AI system was assessed with a prospective cohort to aid clinicians in preoperative COM assessment. RESULTS: Internal and external data sets contained 1661 and 108 patients (3153 and 211 eligible ears), respectively. The 3D model exhibited decent performance with mean areas under the receiver operating characteristic curves of 0.96 (SD 0.01) and 0.93 (SD 0.01), and mean accuracies of 0.878 (SD 0.017) and 0.843 (SD 0.015), respectively, for detecting pathological ears on the 2 data sets. Similar outcomes were observed for cholesteatoma identification (mean area under the receiver operating characteristic curve 0.85, SD 0.03 and 0.83, SD 0.05; mean accuracies 0.783, SD 0.04 and 0.813, SD 0.033, respectively). The proposed 3D model achieved a commendable balance between performance and network size relative to alternative models. It significantly outperformed the 2D approach in detecting COM (P≤.05) and exhibited a substantial gain in identifying cholesteatoma (P<.001). The model also demonstrated superior diagnostic capabilities over resident fellows and the attending otologist (P<.05), rivaling all senior clinicians in both tasks. The generated heat maps properly highlighted the middle ear and mastoid regions, aligning with human knowledge in interpreting temporal bone CT. The resulting AI system achieved an accuracy of 81.8% in generating preoperative diagnoses for 121 patients and contributed to clinical decision-making in 90.1% cases. CONCLUSIONS: We present a 3D CNN model trained to detect pathological changes and identify cholesteatoma via temporal bone CT scans. In both tasks, this model significantly outperforms the baseline 2D approach, achieving levels comparable with or surpassing those of human experts. The model also exhibits decent generalizability and enhanced comprehensibility. This AI system facilitates automatic COM assessment and shows promising viability in real-world clinical settings. These findings underscore AI's potential as a valuable aid for clinicians in COM evaluation. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2000036300; https://www.chictr.org.cn/showprojEN.html?proj=58685.

Interpersonal emotion regulation and physiological synchrony: cognitive reappraisal versus expressive suppression.

The present study aimed to explore the effectiveness of two typical intrapersonal strategies (cognitive reappraisal, CR; expressive suppression, ES) on interpersonal emotion regulation (IER), and uncover the physiological synchrony pattern underlying this. A sample of 90 friend dyads (N = 180) was randomly assigned to the CR, the ES, or the control group. In each dyad, the target underwent a negative emotional task (induce sadness by recalling a negative event), and the regulator was assigned to implement the CR strategy, the ES strategy, or no action to down-regulate the targets' negative emotions. Self-reported results showed that compared to the control group, both CR and ES strategies decreased the targets' negative emotions, and increased the targets' positive emotions, indicating a successful IER effect. And the ECG results revealed that relative to the control condition, both CR and ES strategies evoked stronger physiological synchrony (heart rate synchrony and heart rate variation synchrony) during the emotion regulation stage of IER. Overall, these findings demonstrated the similar efficacy of reappraisal and suppression strategies implemented by the regulators to improve the targets' negative emotions, and suggested that the physiological synchrony might have an important relational meaning during the IER process.

Development of an emergency science popularization capacity index system for nurses in China-A Delphi study.

AIM: To construct a popularization of an emergency science capacity index system for nurses based on the iceberg model. DESIGN: A mixed-method research design incorporating quantitative and qualitative components was used. METHODS: In this study, the first draft of the questionnaire was developed through literature analysis and semi-structured interviews. According to the Delphi expert inclusion criteria, 20 experts with rich experience in the field of science popularization were selected, and the system of emergency science popularization ability of nurses was ultimately developed through two rounds of consultation according to the Delphi method. The weights of various indicators were determined through hierarchical analysis. RESULTS: Following two rounds of Delphi surveys, a total of 3 primary, 6 secondary, and 26 tertiary indicators were identified. The indicators were found to be highly significant, with variation coefficients ranging from 0.063 to 0.140. The Kendall's harmonization coefficients for the primary, secondary, and tertiary indicators were 0.350, 0.341, and 0.146, respectively. The popularization of the emergency science capacity system has a certain scientific and practical significance, which is of guidance to the competency of nurses in public health emergencies. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. This study did not involve patients, service users, caregivers, or members of the public.

[Clinical and genetic characteristics of four children with Kabuki syndrome due to de novo variants of KMT2D gene].

OBJECTIVE: To explore the clinical and genetic characteristics of four children with Kabuki syndrome (KS) due to variants of KMT2D gene. METHODS: Four children with KS diagnosed at the Children's Hospital of Shanxi Province between January 2020 and December 2022 were selected as the study subjects. Whole exome sequencing was carried out for the children and their family members. Candidate variants were verified by Sanger sequencing and pathogenicity analysis. RESULTS: The KS phenotype scores for the four children were 7, 8, 6, and 6, respectively. Child 2 also presented with a rare solitary kidney malformation. Genetic testing revealed that all children had harbored novel de novo variants of the KMT2D gene, including c.16472_16473del, c.858dup, c.11899C>T, and c.12844C>T, respectively. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), all of the variants were classified as pathogenic. CONCLUSION: For children showing phenotypes such as distinctive facial features, intellectual disability, developmental delay, cardiac abnormalities, and urinary system anomalies, KS should be considered. Early diagnosis and intervention can be achieved through genetic testing, especially in the presence of KMT2D gene mutations.

AMPK/mTOR/p70S6K axis prevents apoptosis of Porphyromonas gingivalis-infected gingival epithelial cells via BadSer136 phosphorylation.

Epithelial disruption is the initiation of most infectious disease. Regulation of epithelium apoptosis may play a key role in balance the survival competition between resident bacteria and host cells. The role of the mTOR/p70S6K pathway in preventing apoptosis of human gingival epithelial cells (hGECs) infected with Porphyromonas gingivalis (Pg) was investigated in order to further understand the survival strategy of the epithelial cells in during Pg infecting. hGECs was challenged with Pg for 4, 12, and 24 h. Additionally, hGECs was pretreated with LY294002 (PI3K signaling inhibitor) or Compound C (AMPK inhibitor) for 12 h and exposed them to Pg for 24 h. Subsequently, apoptosis was detected using flow cytometry, and expression and activity of Bcl-2, Bad, Bax, PI3K, AKT, AMPK, mTOR, and p70S6K proteins were analyzed using western blotting. Pg-infecting did not increase apoptosis of hGECs; but the expression ratio of Bad to Bcl-2 was increased after infecting. In contrast, BadSer136 phosphorylation was promoted, accompanied by a significant reduction of mTOR/p70S6K and PI3K/AKT signaling, along with the upregulation of AMPKThr172 signaling. Morrover, the PI3K inhibitor LY294002 promoted Pg-mediated reduction of mTOR/p70S6K expression, and the increase of AMPK signaling and BadSer136 phosphorylation rate, eventually decreasing apoptosis. While Compound C inhibited Pg-mediated activation of AMPK and downregulation of mTOR/p70S6K signaling, significantly reduced the BadSer136 phosphorylation rate, thereby increasing apoptosis. Thus, hGECs prevent apoptosis via an inherent cellular-homeostasis, pro-survival mechanism during Pg infection, the AMPK/mTOR/p70S6K pathway helps prevent apoptosis in hGECs infected with Pg by regulating BadSer136 phosphorylation.

Chlorogenic acid, rutin, and quercetin from Lysimachia christinae alleviate triptolide-induced multi-organ injury in vivo by modulating immunity and AKT/mTOR signal pathway to inhibit ferroptosis and apoptosis.

Drug-induced organ injury is one of the key factors causing organ failure and death in the global public. Triptolide (TP) is the main immunosuppressive component of Tripterygium wilfordii Hook. f. (Leigongteng, LGT) for the first-line management of autoimmune conditions, but it can cause serious multi-organ injury. Lysimachia christinae (Jinqiancao, JQC) is a detoxifying Chinese medicine and could suppress LGT's toxicity. It contains many immune enhancement and organ protection components including chlorogenic acid (CA), rutin (Rut), and quercetin (Que). This study aimed to explore the protection of combined treatments of these organ-protective ingredients of JQC on TP-induced liver, kidney, and heart injury and initially explore the mechanisms. Molecular docking showed that CA, Rut, and Que bounded protein kinase B (AKT)/mechanistic target of rapamycin (mTOR) pathway-related molecules intimately and might competitively antagonize TP. Corresponding in vivo results showed that the combination activated TP-inhibited protein of AKT/mTOR pathway, and reversed TP-induced excessive ferroptosis (excessive Fe 2+ and lipid peroxidation malondialdehyde accumulation, decreased levels of antioxidant enzymes catalase, glutathione peroxidase, glutathione-s transferase, reduced glutathione, and superoxide dismutase, and down-regulated P62/nuclear factor erythroid-2-related factor 2/heme oxygenase-1 pathway), and apoptosis (activated apoptotic factor Fas and Bax and inhibited Bcl-2) in the organ of mice to varying degrees. In conclusion, the combined treatments of CA, Rut, and Que from JQC inhibited TP-induced multi-organ injury in vivo, and the mechanism may largely involve immunomodulation and activation of the AKT/mTOR pathway-mediated cell death reduction including ferroptosis and apoptosis inhibition.

Microglia-neuron interactions promote chronic itch via the NLRP3-IL-1ß-GRPR axis.

BACKGROUND: Spinal astrocytes contribute to chronic itch via sensitization of itch-specific neurons expressing gastrin-releasing peptide receptor (GRPR). However, whether microglia-neuron interactions contribute to itch remains unclear. In this study, we aimed to explore how microglia interact with GRPR+ neurons and promote chronic itch. METHODS: RNA sequencing, quantitative real-time PCR, western blot, immunohistochemistry, RNAscope ISH, pharmacologic and genetic approaches were performed to examine the roles of spinal NLRP3 (The NOD-like receptor family, pyrin-containing domain 3) inflammasome activation and IL-1ß-IL1R1 signaling in chronic itch. Grpr-eGFP and Grpr KO mice were used to investigate microglia-GRPR+ neuron interactions. RESULTS: We observed NLRP3 inflammasome activation and IL-1ß production in spinal microglia under chronic itch conditions. Blockade of microglial activation and the NLRP3/caspase-1/IL-1ß axis attenuated chronic itch and neuronal activation. Type 1 IL-1 receptor (IL-1R1) was expressed in GRPR+ neurons, which are essential for the development of chronic itch. Our studies also find that IL-1ß+ microglia are localized in close proximity to GRPR+ neurons. Consistently, intrathecal injection of IL1R1 antagonist or exogenous IL-1ß indicate that the IL-1ß-IL-1R1 signaling pathway enhanced the activation of GRPR+ neurons. Furthermore, our results demonstrate that the microglial NLRP3/caspase-1/IL-1ß axis contributes to several different chronic itches triggered by small molecules and protein allergens from the environment and drugs. CONCLUSION: Our findings reveal a previously unknown mechanism in which microglia enhances the activation of GRPR+ neurons through the NLRP3/caspase-1/IL-1ß/IL1R1 axis. These results will provide new insights into the pathophysiology of pruritus and novel therapeutic strategies for patients with chronic itch.

Diagnostic efficiency of [68 Ga]Ga-DOTA-FAPI-04 in differentiating periprosthetic hip joint infection and aseptic failure.

PURPOSE: To assess the efficiency of [68 Ga]Ga-DOTA-FAPI-04 in diagnosing periprosthetic hip joint infection and establish a diagnostic standard of clinical significance based on uptake pattern. METHODS: [68 Ga]Ga-DOTA-FAPI-04 PET/CT was performed in patients with symptomatic hip arthroplasty from December 2019 to July 2022. The reference standard was based on the 2018 Evidence-Based and Validation Criteria. Two diagnostic criteria, SUVmax and uptake pattern, were used to diagnose PJI. Meanwhile, original data were imported into IKT-snap to draw the view of interest, A.K. was used to extract features of clinical cases, and unsupervised clustering analysis was applied according to the groups. RESULTS: A total of 103 patients were included, 28 of whom had PJI. The area under the curve of SUVmax was 0.898, which was better than that of all of the serological tests. The cutoff value of SUVmax was 7.53, and the sensitivity and specificity were 100 and 72%, respectively. The sensitivity, specificity and accuracy of the uptake pattern were 100, 93.1 and 95%, respectively. In radiomics analysis, the features of PJI were significantly different from those of aseptic failure. CONCLUSION: The efficiency of [68 Ga]Ga-DOTA-FAPI-04 PET/CT in diagnosing PJI showed promising results, and the diagnostic criteria of the uptake pattern were more clinically instructive. Radiomics also showed certain application prospects in the field of PJI. TRIAL REGISTRATION NUMBER: Trial registration: ChiCTR2000041204. Registered 24 September 2019.

A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study.

OBJECTIVES: To develop and validate a CT-based deep learning radiomics nomogram (DLRN) for outcome prediction in clear cell renal cell carcinoma (ccRCC), and its performance was compared with the Stage, Size, Grade, and Necrosis (SSIGN) score, the University of California, Los Angeles, Integrated Staging System (UISS), the Memorial Sloan-Kettering Cancer Center (MSKCC), and the International Metastatic Renal Cell Database Consortium (IMDC). METHODS: A multicenter of 799 localized (training/ test cohort, 558/241) and 45 metastatic ccRCC patients were studied. A DLRN was developed for predicting recurrence-free survival (RFS) in localized ccRCC patients, and another DLRN was developed for predicting overall survival (OS) in metastatic ccRCC patients. The performance of the two DLRNs was compared with that of the SSIGN, UISS, MSKCC, and IMDC. Model performance was assessed with Kaplan-Meier curves, time-dependent area under the curve (time-AUC), Harrell's concordance index (C-index), and decision curve analysis (DCA). RESULTS: In the test cohort, the DLRN achieved higher time-AUCs (0.921, 0.911, and 0.900 for 1, 3, and 5 years, respectively), C-index (0.883), and net benefit than SSIGN and UISS in predicting RFS for localized ccRCC patients. The DLRN provided higher time-AUCs (0.594, 0.649, and 0.754 for 1, 3, and 5 years, respectively) than MSKCC and IMDC in predicting OS for metastatic ccRCC patients. CONCLUSIONS: The DLRN can accurately predict outcomes and outperformed the existing prognostic models in ccRCC patients. CLINICAL RELEVANCE STATEMENT: This deep learning radiomics nomogram may facilitate individualized treatment, surveillance, and adjuvant trial design for patients with clear cell renal cell carcinoma. KEY POINTS: • SSIGN, UISS, MSKCC, and IMDC may be insufficient for outcome prediction in ccRCC patients. • Radiomics and deep learning allow for the characterization of tumor heterogeneity. • The CT-based deep learning radiomics nomogram outperforms the existing prognostic models in ccRCC outcome prediction.

Predicting pathological complete response of neoadjuvant radiotherapy and targeted therapy for soft tissue sarcoma by whole-tumor texture analysis of multisequence MRI imaging.

OBJECTIVES: To construct effective prediction models for neoadjuvant radiotherapy (RT) and targeted therapy based on whole-tumor texture analysis of multisequence MRI for soft tissue sarcoma (STS) patients. METHODS: Thirty patients with STS of the extremities or trunk from a prospective phase II trial were enrolled for this analysis. All patients underwent pre- and post-neoadjuvant RT MRI examinations from which whole-tumor texture features were extracted, including T1-weighted with fat saturation and contrast enhancement (T1FSGd), T2-weighted with fat saturation (T2FS), and diffusion-weighted imaging (DWI) sequences and their corresponding apparent diffusion coefficient (ADC) maps. According to the postoperative pathological results, the patients were divided into pathological complete response (pCR) and non-pCR (N-pCR) groups. pCR was defined as less than 5% of residual tumor cells by postoperative pathology. Delta features were defined as the percentage change in a texture feature from pre- to post-neoadjuvant RT MRI. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance. RESULTS: Five of 30 patients (16.7%) achieved pCR. The Delta_Model (AUC 0.92) had a better predictive ability than the Pre_Model (AUC 0.78) and Post_Model (AUC 0.76) and was better than AJCC staging (AUC 0.52) and RECIST 1.1 criteria (AUC 0.52). The Combined_Model (pre, post, and delta features) had the best predictive performance (AUC 0.95). CONCLUSION: Whole-tumor texture analysis of multisequence MRI can well predict pCR status after neoadjuvant RT and targeted therapy in STS patients, with better performance than RECIST 1.1 and AJCC staging. KEY POINTS: • MRI multisequence texture analysis could predict the efficacy of neoadjuvant RT and targeted therapy for STS patients. • Texture features showed incremental value beyond routine clinical factors. • The Combined_Model with features at multiple time points showed the best performance.