[Progress in the diagnose and treatment of pulmonary arterial thrombosis in situ]

In situ pulmonary arterial thrombosis (ISPAT) refers to the formation of new blood clots in the pulmonary arterial system in the absence of pre-existing clots in the peripheral venous system. With the emergence and prevalence of COVID-19, ISPAT has become an increasingly important cause of pulmonary arterial thrombosis (PAT) alongside thromboembolism. Several factors such as hypoxia, inflammation, endothelial dysfunction, and hypercoagulable state can lead to ISPAT, which is associated with a number of conditions such as thoracic trauma, partial lung resection, pulmonary infectious disease, pulmonary vasculitis, connective tissue diseases, severe pulmonary hypertension, radiation pneumonitis, and acute chest syndrome in sickle cell disease. It is important to differentiate between pulmonary thromboembolism (PTE) and ISPAT for proper disease management and prognosis. In this review, we summarized the characteristics of ISPAT under different disease conditions, the methods to distinguish ISPAT from PTE, and the best treatment strategies. We hoped that this review could improve clinicians' understanding of this independent disease and provide guidance for the refined treatment of patients with PAT.

[Interpretation of specification for service of cancer screening for workers]

As the backbone force of China's social and economic construction, the health status of workers is closely related to the nation's productivity and social development. Currently, cancers have become one of the major diseases threatening the health of workers. However, there are still many shortcomings in the cancer screening services for the workers. To standardize cancer screening services for workers, ensure the quality of screening services, and improve the overall screening effectiveness, 19 institutions, including Peking Union Medical College Hospital of the Chinese Academy of Medical Sciences, have jointly formulated the Group Standard "Specification for service of cancer screening for workers (T/CHAA 023-2023)". This standard follows the principles of "legality, scientific rigor, advancement, and feasibility" and combines the frontier scientific advances in cancer screening. It clarifies the relevant requirements for service principles, service design, service delivery, service management, service evaluation, and improving worker cancer screening. Implementing this group standard will help connect the common screening needs of workers, employers, and cancer screening service providers, standardize the screening process, improve screening quality, and ultimately increase the early diagnosis rate and survival rate of cancer patients. Consequently, this group standard will help safeguard workers' health rights and interests, ensure the labor force resources, promote the comprehensive coordinated and sustainable development of society, and contribute to realizing the "Healthy China 2030" strategic policy.

Terahertz in vivo imaging of human skin: Toward detection of abnormal skin pathologies

Terahertz (THz) imaging has long held promise for skin cancer detection but has been hampered by the lack of practical technological implementation. In this article, we introduce a technique for discriminating several skin pathologies using a coherent THz confocal system based on a THz quantum cascade laser. High resolution in vivo THz images (with diffraction limited to the order of 100 μm) of several different lesion types were acquired and compared against one another using the amplitude and phase values. Our system successfully separated pathologies using a combination of phase and amplitude information and their respective surface textures. The large scan field (50 × 40 mm) of the system allows macroscopic visualization of several skin lesions in a single frame. Utilizing THz imaging for dermatological assessment of skin lesions offers substantial additional diagnostic value for clinicians. THz images contain information complementary to the information contained in the conventional digital images.

Neurosonographic evaluation of corpus callosum-fastigium and tectal length in late-onset small fetuses

Linked article: This Correspondence comments on Lip‐Sosa et al. Click here to view the article.

Hepatitis E virus infection increases the risk of obstetric complications and perinatal adverse outcomes in pregnant women with chronic hepatitis B virus infection

OBJECTIVE

Hepatitis E virus (HEV) infection may occur in pregnant women who had chronic hepatitis B virus (HBV) infection. This study aimed to evaluate whether HEV-HBV co-infection increases the risk of obstetric complications and perinatal adverse outcomes in pregnant women.

PATIENTS AND METHODS

We investigated the clinical data of 3,251 pregnant women with chronic HBV infection. The obstetric complications and perinatal adverse outcomes were compared between patients with HEV-HBV co-infection and patients who had pure chronic HBV infection.

RESULTS

Of the 3,251 pregnant women with chronic HBV infection, 98 patients (3%) had HEV-HBV co-infection. Compared with healthy controls, there is an increased risk of obstetric complications in pregnant women with pure HEV infection [odds ratio (OR)= 3.99, p < 0.001], pure chronic HBV infection (OR = 2.76, p < 0.001), and HEV-HBV co-infection (OR = 5.41,p < 0.001). The rate of obstetric complications and perinatal adverse outcomes is significantly higher in pregnant women with HEV-HBV co-infection compared with those with pure chronic HBV infection or those with pure HEV infection (all p< 0.05). The HEV-HBV co-infection is the most significant risk factor for perinatal adverse outcomes (OR = 15.47, p < 0.001), followed by pure HEV infection (OR = 10.22, p < 0.001), and pure HBV infection (OR = 5.82, p < 0.001).

CONCLUSIONS

HEV infection increases the risk of obstetric complications and perinatal adverse outcomes in pregnant women with chronic HBV infection.

Integrated analysis of single-cell sequencing and weighted co-expression network identifies a novel signature based on cellular senescence-related genes to predict prognosis in glioblastoma

BACKGROUND

Glioblastoma (GBM) is a highly aggressive cancer with heavy mortality rates and poor prognosis. Cellular senescence exerts a pivotal influence on the development and progression of various cancers. However, the underlying effect of cellular senescence on the outcomes of patients with GBM remains to be elucidated.

METHODS

Transcriptome RNA sequencing data with clinical information and single-cell sequencing data of GBM cases were obtained from CGGA, TCGA, and GEO (GSE84465) databases respectively. Single-sample gene set enrichment analysis (ssGSEA) analysis was utilized to calculate the cellular senescence score. WGCNA analysis was employed to ascertain the key gene modules and identify differentially expressed genes (DEGs) associated with the cellular senescence score in GBM. The prognostic senescence-related risk model was developed by least absolute shrinkage and selection operator (LASSO) regression analyses. The immune infiltration level was calculated by microenvironment cell populations counter (MCPcounter), ssGSEA, and xCell algorithms. Potential anti-cancer small molecular compounds of GBM were estimated by "oncoPredict" R package.

RESULTS

A total of 150 DEGs were selected from the pink module through WGCNA analysis. The risk-scoring model was constructed based on 5 cell senescence-associated genes (CCDC151, DRC1, C2orf73, CCDC13, and WDR63). Patients in low-risk group had a better prognostic value compared to those in high-risk group. The nomogram exhibited excellent predictive performance in assessing the survival outcomes of patients with GBM. Top 30 potential anti-cancer small molecular compounds with higher drug sensitivity scores were predicted.

CONCLUSION

Cellular senescence-related genes and clusters in GBM have the potential to provide valuable insights in prognosis and guide clinical decisions.

AI's Role in Improving Social Connection and Oral Health for Older Adults: A Synergistic Approach

KNOWLEDGE TRANSFER STATEMENT

This study explored how artificial intelligence (AI) can revolutionize geriatric care by improving oral health and alleviating social disconnection among isolated older adults. The findings can guide clinicians in integrating AI tools into practices, assist policymakers in developing AI-inclusive health policies, and inform patients about the potential benefits of AI in enhancing their health outcomes and social connection.

Association of Cognitive Reserve Indicator with Cognitive Decline and Structural Brain Differences in Middle and Older Age: Findings from the UK Biobank

BACKGROUND

Cognitive reserve (CR) contributes to preserving cognition when facing brain aging and damage. CR has been linked to dementia risk in late life. However, the association between CR and cognitive changes and brain imaging measures, especially in midlife, is unclear.

OBJECTIVE

We aimed to explore the association of CR with cognitive decline and structural brain differences in middle and older age.

DESIGN

This longitudinal study was from the UK Biobank project where participants completed baseline surveys between 2006 to 2010 and were followed (mean follow-up: 9 years).

SETTING

A population-based study.

PARTICIPANTS

A total of 42,301 dementia-free participants aged 40-70 were followed-up to detect cognitive changes. A subsample (n=34,041) underwent brain magnetic resonance imaging scans.

MEASUREMENTS

We used latent class analysis to generate a CR indicator (categorized as high, moderate, and low) based on education, occupation, and multiple cognitively stimulating activities. Cognitive tests for global and domain-specific cognition were administrated at baseline and follow-up. Total brain, white matter, grey matter, hippocampal, and white matter hyperintensity volumes (TBV, WMV, GMV, HV, and WMHV) were assessed at the follow-up examination. Data were analyzed using mixed-effects models and analysis of covariance.

RESULTS

At baseline, 16,032 (37.9%), 10,709 (25.3%), and 15,560 (36.8%) participants had low, moderate, and high levels of CR, respectively. Compared with low CR, high CR was associated with slower declines in global cognition (β [95% confidence interval]: 0.10 [0.08, 0.11]), prospective memory (0.10 [0.06, 0.15]), fluid intelligence (0.07 [0.04, 0.10]), and reaction time (0.04 [0.02, 0.06]). Participants with high CR had lower TBV, WMV, GMV, and WMHV, but higher HV when controlling for global cognition (corrected P <0.01 for all). The significant relationships between CR and cognition and TBV were present among both middle-aged (<60 years) and older (≥60 years) participants. The CR-cognition association remained significant despite reductions in brain structural properties.

CONCLUSIONS

Higher CR is associated with slower cognitive decline, higher HV, and lower microvascular burden, especially in middle age. Individuals with high CR could tolerate smaller brain volumes while maintaining cognition. The benefit of CR for cognition is independent of structural brain differences. Our findings highlight the contribution of enhancing CR to helping compensate for neuroimaging alterations and ultimately prevent cognitive decline.

Efficacy of a novel glioma therapy based on ferroptosis induced by layered double hydroxide loaded with simvastatin

Glioma is the most common primary malignant tumor of the nervous system that starts in the glial cells. Its high invasiveness and recurrence pose major challenges to its effective treatment. Ferroptosis is a new type of programmed cell death characterized by intracellular iron overload and accumulation of lipid peroxides. Existing studies have demonstrated the efficacy of targeted ferroptosis therapy in the treatment of glioma. In this study, folic acid (FA)-modified layered double hydroxide loaded with simvastatin (SIM), a ferroptosis drug, was used to prepare a novel ferroptosis nanodrug (FA-LDH@SIM). The prepared nanodrug improved the therapeutic effect of SIM on glioma. Compared with free SIM, FA-LDH@SIM showed greater cytotoxicity, significantly inhibited glioma cell proliferation, and significantly inhibited glioma invasion and migration ability. Furthermore, SIM could induce changes in certain ferroptosis indicators, including increased intracellular LPO, ROS and MDA level, decreased GSH production, increased divalent iron level, and changes in mitochondrial morphology. Further experiments revealed that SIM induced ferroptosis in tumor cells by down-regulating HMGCR expression and inhibiting the mevalonate pathway to down-regulate GPX4 expression. In addition, the FA-LDH@SIM group significantly inhibited tumor growth after treatment in the animal glioma model. These results indicate that the FA-LDH@SIM nanodrug delivery system exhibits excellent anti-tumor effects both in vitro and in vivo, and is an effective method for the treatment of glioma.

TREM1 as a novel prognostic biomarker and tumor immune microenvironment evaluator in glioma

Glioma is the most malignant tumor in the central nervous system with a poor prognosis. The tumor immune microenvironment plays a crucial role in glioma formation and progress. TREM1, as a vital immune regulator, has not been investigated in glioma. This study aims to explore the role of TREM1 in prognosis and tumor immune microenvironment of glioma. The mRNA expression level of TREM1 was collected from TCGA and GEO databases. The correlations between the clinic-pathological features and TREM1 expression were analyzed using Cox regression analysis. Kaplan-Meier was used to evaluate the effect of TREM1 on OS. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes were performed to analyze the functional annotations and signaling pathways of the TREM1 coexpression genes. ESTIMATE and TIMER explored the correlations between TREM1 and immune cell infiltration. Spearman correlation analysis was conducted to examine the association between the TREM1 and immune checkpoint expression. The expression level of TREM1 was significantly increased in glioma. TREM1 overexpression was positively related to poor prognosis, higher World Health Organization grade, isocitrate dehydrogenase wildtype, and 1p/19q non-codeletion. TREM1 coexpression genes were mainly related to immunoregulation and inflammatory response. TREM1 participated in the initiation and progression of glioma by regulating immune cell infiltration and expression of immune checkpoints. TREM1 is an effective prognostic and diagnostic biomarker in glioma. It can be adopted as a novel predictor for clinical prognosis, pathological characteristics, and immune microenvironment in glioma patients.

[Analysis on the current situation and influencing factors of residents' satisfaction with the built environment of China's Hygienic City Initiative]

Objective: To understand the current situation and the main influencing factors of residents' satisfaction with the built environment of China's Hygienic City Initiative. Methods: From the list of China's hygienic cities (excluding county-level cities), 61 cities were randomly selected in equal proportion and the eligible respondents were randomly selected by using the "Questionnaire Star" network platform to carry out the online questionnaire survey. A self-made satisfaction evaluation scale was used to investigate the satisfaction of the included respondents with the urban built environment and search for relevant data on the city level. The two-level multi-factor mixed effect model was constructed to analyze the influencing factors of residents' satisfaction with the built environment of China's Hygienic City Initiative. Results: The age range of 2 465 respondents was mainly between 18 and 40 years old (79.9%), with males being the main group (45.8%). The total score of residents' satisfaction with the built environment of China's hygienic cities was (69.14±13.24) points. Based on four standardized dimensions of sense of gain, the result showed that the satisfaction of urban governance had the highest score (65.08 points), followed by urban environmental sanitation (63.68 points), urban lifestyle (59.97 points) and urban basic function (59.02 points). The analysis results of the two-level multi-factor mixed effect model showed that compared with residents with an annual average concentration of inhalable fine particles in the environment>48 micrograms/cubic meter, residents with an average concentration between 38 and 48 micrograms/cubic meter [β (95%CI): 1.65 (0.08, 3.21)] and≤37 micrograms/cubic meter or less [β (95%CI): 1.98 (0.53, 3.43)] had higher satisfaction. Compared with residents whose proportion of the secondary industry to GDP was≤40.9%, residents in cities with a larger proportion had a lower satisfaction level [residents with a proportion of 40.9%-48.03%, β (95%CI):-2.21 (-3.93, -0.49); residents with a proportion greater than 48.03%, β (95%CI):-2.58 (-4.58, -0.59)]. Compared with residents with a junior high school or lower education level, residents with a higher education level had a lower satisfaction level [β (95%CI):-2.37 (-4.57, -0.17)]. Residents of universities and above [β (95%CI):-3.82 (-6.05, -1.60)], regularly participate in physical exercise [β (95%CI): 5.78 (4.71, 6.84)] and self-rated good health status [β (95%CI): 6.39 (5.33, 7.45)] had a higher satisfaction level. Conclusion: The satisfaction of residents with the built environment of China's hygienic cities is still acceptable. Satisfaction is related to individual characteristics such as residents' cultural level, type of residence, frequent participation in physical exercise, and self-rated good health status, as well as urban-level factors such as green coverage rate in built-up areas, annual average concentration of inhalable fine particles, and the proportion of GDP in the secondary industry.

Objective Boundary Generation for Gross Target Volume and Organs at Risk Using 3D Multi-Modal Medical Images

PURPOSE/OBJECTIVE(S)

Accurate delineation of Gross Target Volume (GTV) and Organs at Risk (OARs) in medical images is an essential but challenging step in radiotherapy. Deep-learning based automated delineation methods, which learn from manual annotations, are currently prevalent in academic research. However, the limited resolution of medical images and the fuzzy boundaries of lesions and organs present a challenge to the precision of manual annotations. By leveraging the complementary information from multi-modal medical images, this study proposed a novel method to generate objective boundaries of GTV and OARs.

MATERIALS/METHODS

We present a novel method of objective boundary generation, inspired by image matting primarily used for 2D RGB natural images, to process 3D grayscale medical images. The proposed method has the following advantages. 1) It allows for flexible input modalities and assigns weights to each modality according to their relative significance when computing information flows in the matting algorithm. 2) It computes 3D spatial information flow among voxels, which has more advantages over its 2D counterpart. 3) It has a closed-form solution that generates deterministic results. To evaluate the characteristics of the generated boundaries, patients with stage I nasopharyngeal carcinoma (NPC) were studied, with CT images and multi-modal MR images (T1, T1C, T2) aligned using deformable registration. Region of Interests (ROIs), i.e., GTV and parotid gland, were used, with a rough trimap marking extremely few foreground voxels, many background voxels, and a large unknown region. The proposed algorithm leverages the connection between each voxel and its nearest neighbors in the feature space, to propagate the opacity information.

RESULTS

We evaluated the results by employing both qualitative and quantitative methods. Using qualitative evaluation, experienced clinicians confirmed that the results were in agreement with the input data, especially for areas where borders were visible in most modalities (e.g., between air and tumor). For more challenging regions, where boundaries were unclear in the images, the results displayed fine-grained opacity transitions indicating the confidence of each voxel belonging to the ROI. When compared to the delineations made by clinicians, we found our results are usually more compact. We define a precision metric that evaluates the ratio of the matted foreground inside clinicians' delineations versus the entire matted foreground. Using a threshold of 0.4, our binarized result scored 0.95 for GTV and 0.92 for parotid gland.

CONCLUSION

The proposed method demonstrated satisfactory results on challenging ROIs. The objective boundaries generated by this method have advantages in many aspects, including improvement of delineation protocols, enhancement of manual annotation consistency, and increase of deep-learning based automated delineation accuracy.

Potential Dosimetric Predictors of Patient-Reported Quality of Life for Head and Neck Cancer Following Chemoradiation IMRT

PURPOSE/OBJECTIVE(S)

This study aims to identify both acute and late patient patient-reported salivary quality of life outcomes in patients with head and neck cancer treated with chemoradiation therapy on a prospective trial.

MATERIALS/METHODS

A cohort of 40 patients with head and neck cancers were included in the study. All patients underwent concurrent chemoradiation therapy using IMRT delivery (1 patient on one Linac, 24 patients on a different Linac, and 15 on a helical delivery machine). All patients were asked to complete the University of Washington Quality of Life (UOW-QOL) questionnaire at baseline, immediately after treatment, as well as at 1 month, 3 months, 6 months, 12 month and 18 months post-treatment. For the salivary quality of life (QOL) outcome scores, the possible responses were scored on a discreet scale of 100, 70, 30, and 0, with 100 as normal and 0 as dysfunctional. Dosimetric endpoints achieved based on the treatment plan, such as maximum/mean/minimum doses, V30 (percent volume receiving 30 Gy dose), and Dy (dose received to y percent volume) were collected for the bilateral salivary glands, bilateral temporomandibular joint and bilateral submandibular glands. The associations between these dosimetric parameters and the corresponding salivary QOL scores at each time point were analyzed. A Wilcoxon test was performed to identify any differences in the dosimetry and salivary QOL scores among the four different responses.

RESULTS

At short-term follow-up including 1- and 6-month, the distribution of the mean dose received by the right parotid was significantly different between the patients that reported a salivary QOL score of 30 and those that reported 100, with p-values of 0.007 for the 1-month comparison and 0.006 for the 6-month comparison. This was also seen for the V30, with p-values of 0.027 for the 1-month comparison and 0.013 for the 6-month comparison. At 3 months, the maximum dose received by the left temporomandibular joint was significantly different between the patients that reported 30 and those that reported 70, with a p-value of 0.038. At 6 months, the average dose distribution of the right submandibular gland received between the patients that reported a score of 30 and 100 was also significantly different, with a p-value of 0.006. At the long-term follow-up time points of 12 and 18 months, no significant differences were found.

CONCLUSION

The significant differences seen in the data suggest that the dosimetry may have effects on patient reported salivary QOL at short-term follow-up but not long-term. This provides a new perspective into how a patient's QOL over a period of time could be affected by the amount of dose to critical organs. These results also serve as the basis for further investigation into the actual delivered dose, which could differ from the planned dose due to daily anatomic changes over the course of head and neck radiotherapy delivery. These daily volumetric and dosimetric changes may guide early adaptive treatment to improve patient-reported QOL outcomes.

A Generalized Deep Learning Method for Synthetic CT Generation

PURPOSE/OBJECTIVE(S)

The application of deep learning to generate synthetic CT (sCT) has been widely studied in radiotherapy. Existing methods generally involve data from two different image modalities, such as CBCT-CT or MRI-CT, the quality of sCT is adversely affected by source image quality. We propose a unique method of synthesizing MRI and CBCT into sCT based on single-modal CT for training, and call it SmGAN.

MATERIALS/METHODS

We used planning CT of a group of 35 head and neck cases to as training data. We then applied two different spatial transformations to the planning CT image to produce the transformed CT1 and CT2. And We used a random style enhancement technique (Shuffle Remap) to modify the image distribution of CT1 which we termed CT1+E. CT1+E was used to simulate the patient's "image of the day" while CT2 to simulate the "planning image". After feeding both CT1+E and CT2 into the generator, we obtained the sCT predicted by the generator. The generator was trained using the Mean Absolute Error (MAE) loss between sCT and CT1. In the actual clinical process, we use the patient's CBCT or MRI instead of CT1+E and the patient's planning CT instead of CT2 as the input of the generator. After processing, we get an sCT that can maintain the spatial position of the image taken on the day, while presenting features similar to the planning CT. The evaluation data we have includes 10 pairs of MRI-Def_CT and 10 pairs of CBCT-Def_CT Head and Neck patients. Def_CT is obtained from the planning CT based on the spatial position deformation of MRI and CBCT. To evaluate the accuracy of sCT based on MRI and CBCT with Def CT, we use a range of metrics, including Hounsfield Unit (HU) difference, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and gamma pass rate. All results will be benchmarks against the advanced method RegGAN for comparison.

RESULTS

Compared to RegGAN, the results of SmGAN were significantly better. The mean absolute errors within the body were (44.7±216.2 HU vs. 36.7±131.4 HU) and (64.9±123.7 HU vs. 58.2±152.8 HU) for the CBCT-SCT and MRI-SCT, respectively (Table 1). In addition, experimental results show that SmGAN also outperforms RegGAN in dose calculation accuracy. For example, under the 10% threshold, SmGAN's gamma pass rate of 1mm and 1% is 0.926±0.02, compared with gamma rate of 0.896±0.02 for RegGAN.

CONCLUSION

We proposed a generalized deep learning model for synthetic CT generation, based on CBCT or MRI images. The proposed algorithm achieved high accuracy of dosimetric metrics, as well as excellent IMRT QA verification results. Compared to other existing synthetic CT generation methods, the proposed SmGAN required a single-modal image for training, which is considered as a major breakthrough in the industry, and is expected to have wide spread of clinical applications.

An Adaptive Multi-Feature Fusion Network for Predicting Overall Survival of Patients with Head and Neck Cancer

PURPOSE/OBJECTIVE(S)

Accurate prognostic prediction could allow personalized treatment to achieve optimal clinical outcome. We aimed to develop a highly predictive overall survival model, considering the complementary relationships between clinical information, traditional radiomics and deep image information, to further improve the overall prediction accuracy by constructing a richer feature set and adaptive weighting.

MATERIALS/METHODS

A total of 427 patients with Oropharyngeal Cancer (OPC) patients from the TCIA database were included. 341 cases were used for training, 86 cases were used as an independent cohort. Patient characteristics, including TMN, age, gender, HPV status, smoking or drinking status, etc. were considered as potential predictors. Traditional radiomics features of gross tumor volume (GTV) was extracted from planning CT using open-source software. In addition, a two-dimensional convolutional network (2D_CNN) was designed to extract deep image features. An adaptive multi-feature fusion network was developed to predict overall survival of patients based on three types of features. The fusion network integrates an attention mechanism to the channel dimension to obtain proper weighting of each channel in the feature graph through the fully connected network by focusing on effective feature channels and automatic learning according to the loss, thus improving the utilization rate of effective features. The model performance was evaluated using the area-under-ROC-curve (AUC), accuracy, precision, recall, f1-score.

RESULTS

The AUCs of predictive models based on clinical features, traditional radiomics features and deep image features were 0.7, 0.61 and 0.72, respectively. Combining patient characteristics, radiomic features and deep imaging features, the AUCs of the prediction models was significantly improved to 0.85 and 0.86 (with attention mechanisms) for the independent test cohort (Table 1).

CONCLUSION

The proposed adaptive multi-channel network assigned effective weights to the potential predictors, selectively enhanced useful features while suppressed irrelevant features, enabling more accurate feature map weights. We demonstrated the improved predictive value, with a multi-channel fusion network integrated with an attention mechanism, for overall survival of OPC patients.

Quality and Safety Considerations in Image Guided Radiation Therapy: An ASTRO Safety White Paper Update

PURPOSE/OBJECTIVE(S)

This updated report on image guided radiation therapy (IGRT) is based on a consensus-based white paper previously published by the American Society for Radiation Oncology (ASTRO) addressing patient safety. In the past decade, IGRT technology and procedures have progressed significantly and are now more commonly used. The use of IGRT has now extended beyond high-precision treatments, such as stereotactic radiosurgery and stereotactic body radiation therapy, and into routine clinical practice for many treatment techniques and anatomic sites. Therefore, quality and treatment planning and delivery considerations for these techniques are paramount for patient safety.

MATERIALS/METHODS

In 2021, ASTRO convened an interdisciplinary task force to assess the original IGRT white paper and update content where appropriate. Recommendations were created using a consensus-building methodology, and task force members indicated their level of agreement based on a 5-point Likert scale from "strongly agree" to "strongly disagree." A prespecified threshold of ≥75% of raters who selected "strongly agree" or "agree" indicated consensus.

RESULTS

The IGRT white paper was published (Pract Radiat Oncol. 2022 Dec) and endorsed by the American Association of Physicists in Medicine (AAPM), American Association of Medical Dosimetrists, and American Society of Radiologic Technologists. Since the first IGRT paper was published by ASTRO in 2013, significant technological advancement has taken place. New and updated considerations in personnel requirements, staffing, education and training, equipment and technological requirements, quality management and assurance, IGRT program management, and safety considerations were reported. A 17-point consensus was reached and recommended in 5 areas surrounding program development, quality assurance, quality management, treatment delivery, and vendor engagement (Table 5, Summary of key recommendations).

CONCLUSION

This IGRT white paper builds on the previous version and uses other guidance documents to primarily focus on processes related to quality and safety. IGRT requires an interdisciplinary team-based approach, staffed by appropriately trained specialists, as well as significant personnel resources, specialized technology, and implementation time. A thorough feasibility analysis of resources is required and should be discussed with all personnel before undertaking new imaging techniques. A comprehensive quality-assurance program must be developed to ensure IGRT is performed safely and effectively. As IGRT technologies continue to improve or emerge, existing practice guidelines should be updated regularly according to the latest AAPM Task Group reports. Patient safety in the application of IGRT is everyone's responsibility, and professional organizations, regulators, vendors, and end-users must demonstrate strong commitments to ensure that the highest levels of safety are achieved.

Early Prediction of Radiation Treatment Response via Longitudinal Analysis of CBCT Radiomic Features for Locally Advanced Rectal Cancer

PURPOSE/OBJECTIVE(S)

Patients respond to the same radiation treatment course differently due to inter- and intra- patient variability in radiosensitivity. Despite widespread use of AI/ML in radiation oncology, there is a lack of monitoring strategies used during treatment courses to evaluate early predictors of treatment response in a systematic fashion. This work advances a straightforward, yet effective, method for the early detection of treatment response through systematically analyzing daily CBCT radiomic features. The goal is to aid clinicians in assessing the treatment efficacy routinely with a view towards optimizing personalized treatment.

MATERIALS/METHODS

We included a cohort of 30 patients diagnosed with locally advanced rectal cancer who underwent neo-adjuvant fractionated radiation treatment (RT) with a prescription dose of 50.4 Gy (28 fractions), followed by total mesorectal excision surgery after completion of ChemoRT. Daily IGRT imaging was acquired prior to each fraction resulting in a total of 840 CBCTs. Patients were divided into responder (14 patients) and non-responder (16 patients) groups based on post-RT pathological response. Mutual information algorithms were utilized to rigorously register daily CBCT images to the planning CT, and longitudinal radiomic features of the target were extracted from the daily CBCTs during the entire treatment course. All longitudinal features for a given patient were standardized with Z-Score normalization, followed by linear fitting using the least square method, resulting in radiomic feature trends (RFT) represented by slope values. Statistical significance was established via a two-sample U test and P-value with a threshold of 0.05. Logistic regression was performed to eliminate RFT with accuracy rates lower than 0.5. The final trending model was developed using random forest. For each patient at fraction N, our investigation involved independent 27 group experiments, where each experiment considered image group from fraction #1 to N, to confirm the effectiveness and stability of the model.

RESULTS

The proposed RFT demonstrated a high level of precision and consistency for post-RT response based on longitudinal CBCT images for LARC patients. The trending model yielded an accuracy of 0.9556, 95% CI (0.94, 0.972) when each daily image was considered, the prediction consistency was 0.964. Given the first 14 experiments (considering group images of fraction #1-15), the prediction accuracy was 0.9357, 95% CI (0.915, 0.956) and the prediction consistency was 0.952.

CONCLUSION

A strategy for monitoring and early prediction of LARC patients' radioresponse was evaluated via longitudinal CBCT assessment. Our trending models demonstrate a significant difference between the responder vs non-responder groups as early as the 15th fraction. Our strategy achieved superior accuracy and consistency to predict post-RT response of LARC patients.

Ceruloplasmin regulating fibrosis in orbital fibroblasts provides a novel therapeutic target for Graves' orbitopathy

PURPOSE

In diagnosing the pathogenesis of Graves' orbitopathy (GO), there is a growing interest in fibrosis generated by orbital fibroblasts (OFs); nevertheless, the involvement of ceruloplasmin (CP) in OFs remains unknown.

METHODS

Differentially expressed genes (DEGs) were identified through bioinformatic analysis. OFs were isolated from orbital tissue and identified with immunofluorescent staining. The levels of DEGs were validated in GO tissue samples and TGF-β-challenged OFs, and CP was selected for the following laboratory investigations. CP overexpression or knockdown was achieved, and cell viability and fibrosis-associated proteins were investigated to assess the cell phenotype and function. Signaling pathways were subsequently investigated to explore the mechanism of CP function in OFs.

RESULTS

CP and cathepsin C (CTSC) are two overlapped DEGs in GSE58331 and GSE105149. OFs were isolated and identified through fibrotic biomarkers. CP and CTSC were downregulated in GO tissue samples and TGF-β-challenged OFs. CP overexpression or knockdown was achieved in OFs by transducing a CP overexpression vector or small interfering RNA against CP (si1-CP or si2-CP) and verified using a qRT-PCR. CP overexpression inhibited cell viability and reduced the levels of α-SMA, vimentin, fibronectin, and collagen I, whereas CP knockdown exerted opposite effects on OFs. CP overexpression inhibited the phosphorylation of Smad3, Erk1/2, p38, JNK, and AKT; conversely, CP knockdown exerted opposite effects on the phosphorylation of factors mentioned above.

CONCLUSION

CP was downregulated in GO and suppressed the expression of fibrosis-associated proteins in both GO and normal OFs. CP might serve as a promising therapeutic agent in the treatment regimens for GO.

The evaluation of six genes combined value in glioma diagnosis and prognosis

PURPOSE

Glioma is the most common and fatal type of brain tumour. Owing to its aggressiveness and lethality, early diagnosis and prediction of patient survival are very important. This study aimed to identify key genes and biomarkers for glioma that can guide clinicians in making rapid diagnosis and prognostication.

METHODS

Data mining of The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data, and Genotype-Tissue Expression Project brain expression data revealed significantly differentially expressed genes (DEGs), and the risk scores of individual patients were calculated. WGCNA was utilized to screen for genes most related to clinical diagnosis. Prognostic genes associated with glioma were selected via combining the LASSO regression with univariate and multivariate Cox regression and protein-protein interaction network analyses. Then, a nomogram was constructed. And CGGA dataset was utilized to validated. The protein expression levels of the signature were detected using the human protein atlas. Drug response prediction was carried out using the package "pRRophetic".

RESULTS

A six-gene signature (KLF6, CHI3L1, SERPINE1, ANGPT2, TGFBR1, and PTX3) was identified and used to stratify patients into low- and high-risk groups. Survival, ROC curve, and Cox analyses clarified that the six hub genes were a favourable independent prognostic factor for patients with glioma. A nomogram was set up by integrating clinical parameters with risk signatures, showing high precision for predicting 2-, 3-, 4-, 5-years survival. In addition, the expression of most genes was consistent with protein expression. Furthermore, the sensitivity to the top ten drugs in the GDSC database of the high-risk group was significantly higher than the low-risk group.

CONCLUSION

Based on genetic profiles and clinicopathological features, including age, grade, isocitrate dehydrogenase mutation status, we constructed a comprehensive prognostic model for patients with glioma. These signatures can be regarded as biomarkers to predict the prognosis of gliomas, possibly providing more therapeutic strategies for future clinical research.

A Novel Multi-Objective Based Feature Selection Method for Response Prediction

PURPOSE/OBJECTIVE(S)

Accurate response prediction is essential towards personalized treatment in radiation therapy. Excessive imaging features, extracted from medical images, pose a great challenge in radiomic analyses. Feature selection is an essential step to remove redundant and irrelevant features for model construction.

MATERIALS/METHODS

We proposed a novel multi-objective based radiomic feature selection method (MRMOPSO), where the number of features, sensitivity, and specificity are jointly considered as optimization objectives for feature selection. The MRMOPSO innovated by three aspects: 1) Fisher score initialize the feature population to speed up the convergence; 2) Min-redundancy particle generation operations to reduce the redundancy between radiomic features, a truncation strategy was also introduced; 3) Particle selection operation guided by elitism strategies to improve local search ability of the algorithm. We evaluated the effectiveness of the proposed MRMOPSO method using a cohort of oropharyngeal cancer patients from The Cancer Imaging Archive (TCIA). 357 patients were used for model training and additional 64 patients were used for independent evaluation. The proposed methods were compared with (a) classical feature selection methods, i.e., Lasso, minimal-redundancy-maximal-relevance criterion (mRMR), F-score, and mutual information (MI), (b) single-objective feature selection methods, i.e., genetic algorithm (GA), particle swarm optimization algorithm (PSO) and (c) multi-objective feature selection methods, i.e., multiple objective particle swarm optimization (MOPSO), nondominated sorting genetic algorithm II (NSGA II).

RESULTS

The other feature selection methods yielded AUCs, sensitivity, specificity of (0.48-0.71), (0.49-0.86), (0.33-0.67), respectively. The MRMOPSO achieved significantly highly AUC of 0.84 with smaller number of selected features on the independent dataset (Table 1). Additionally, the MRMOPSO remarkably improved the sensitivity (0.81), specificity (0.81) and achieved an excellent balance between sensitively and specificity.

CONCLUSION

We demonstrated a novel multi-objective based radiomic feature selection method. The proposed algorithm effectively reduced feature dimension, and achieved superior AUC with simultaneous improved sensitivity and specificity, for radiomic response prediction.

Impact of Prophylactic Pelvic Lymph Node Irradiation in De-Novo Oligometastatic Prostate Cancer

PURPOSE/OBJECTIVE(S)

To evaluate the impact of prophylactic pelvic nodal irradiation in de-novo oligometastatic prostate cancer treated with radiotherapy (RT) for both primary tumor and all metastatic lesions.

MATERIALS/METHODS

This was a single-center prospective cohort study. De novo oligometastatic prostate cancer patients with RT for both primary tumor and all metastatic lesions were included. Kaplan-Meier method, log rank test and cox regression were used to calculate OS and PFS. PFS included PSA failure, local or distant failure assessed by imaging.

RESULTS

This study analyzed 202 patients from 10/2011 to 1/2022 with median follow-up of 48 months. A total of 126 (62.4%) patients were treated with pelvic lymph node RT. The dose was 47.5 Gy with 1.9 Gy per fraction. Among them, 66 (32.7%) patients were treated with whole pelvic RT (WPRT), which the upper limit was at the aortic bifurcation. 60 (29.7%) patients were treated with mini-WPRT, which the upper limit was at the lower margin of obturator foramen. The incidence of diarrhea (P = 0.038) and leukocyte reduction (P = 0.040) in the WPRT subgroup during radiotherapy was significantly higher than that in the mini-WPRT and non-pelvic RT subgroup. For the whole cohort, the median OS and PFS were not reached. The subgroup analysis showed that the elective pelvic nodal irradiation could improve PFS (P = 0.042). However, there was no difference of PFS between standard WPRT and mini-WPRT.

CONCLUSION

The study suggests that for de-novo oligometastatic prostate cancer, elective pelvic nodal irradiation may improve PFS. For patients who cannot tolerate WPRT, mini-WPRT may be an alternative option. However, it needs to be verified in the prospective RCT study.

Unsupervised Domain Adaptation of Auto-Segmentation on Multi-Source MRIs

PURPOSE/OBJECTIVE(S)

Deep learning has achieved great success in medical image segmentation. Most existing deep learning (DL) approaches make no adjustments to the model prior to inference. These models can perform well on the data of the same distribution, but their performance usually degrades when applied to the images from different source, i.e., different scanners. To tackle the problem caused by domain shift, we proposed an unsupervised domain adaptation (UDA) method based on entropy minimization and physical consistency constraints.

MATERIALS/METHODS

The proposed method combines feature-level and instance-level domain adaptation techniques to transfer knowledge from the source to the target domain. Specifically, the feature-level adaptation technique uses a graph-based entropy minimization to reduce the discrepancy between the source and target domains. The instance-level adaptation technique employs a novel consistency loss to regularize the physical consistency of the same object, such as volume, length, and centroid, thus improving the segmentation accuracy of the target domain. A collection of 93 abdominal MR images, comprising 45 cases from a 0.35T MRI scanner (TRUFI) and 48 cases from a 1.5T MRI scanner (T2), was utilized to evaluate the effectiveness of the proposed method. The contours of 6 organs-at-risk were delineated by a senior radiation oncologist, serving as the ground truth. Three models, the source model (SRC) trained on the source domain, the target model (TGT) trained on the target domain, and the UDA model adapted from the source domain to the target domain, were compared on the target domain using the Dice Similarity Coefficient (DSC).

RESULTS

In the experiment of 0.35T-to-1.5T, the proposed UDA method outperformed the source model, achieving an average DSC score of 0.82 ± 0.11, compared to 0.58 ± 0.23 (SRC) and 0.85 ± 0.09 (TGT), respectively. In the inverse experiment 1.5T-to-0.35T, the UDA model achieved an average DSC score of 0.79±0.13, compared to DSCs of 0.52 ± 0.25 and 0.81 ± 0.11 for the SRC and TGT respectively. The UDA method yielded a significant improvement of 46%, compared to the SRC. Particularly, OARs (organ at risk) with higher deformability such as the stomach and duodenum achieved a 58% and 63% improvement in performance, respectively.

CONCLUSION

This work presents a compelling approach of UDA for auto-segmentation on multi-source MRIs. Experimental results demonstrate that the UDA effectively improve the segmentation performance of the source model in the target domain, resulting in a more robust segmentation model.

Feature Selection Based on Unsupervised Clustering Mechanism on Multiple-Sequence MRIs for Predicting Neoadjuvant Chemoradiation Response in Locally Advanced Rectal Cancer

PURPOSE/OBJECTIVE(S)

Accurate response prediction allows for personalized cancer management. We developed an unsupervised clustering mechanism to improve effectiveness and efficiency in feature selection operation for accurate patient stratification.

MATERIALS/METHODS

Forty-three locally advanced rectal cancer (LARC) patients underwent neoadjuvant chemoradiation were included, pre-treatment T2 and ADC MRIs were acquired for each patient. An initial feature space consisting of 200 radiomic features extracted from manually delineated GTVs from two sequences of MR images. Additional 960 high-order radiomic features extracted from a 3D convolutional neural network (CNN). To remove redundant and irrelevant features, we developed an unsupervised clustering-based feature selection operation to determine the combination of features with potential best performance. The normal process of feature selection involves searching new feature combinations and training new classifiers for evaluating their performance via an iterative process based on selected feature set, the overall time cost is tremendous. To balance the computational cost and search efficiency, firstly, we proposed an unsupervised clustering analysis metric- Comprehensive Cluster Analysis Index (CCAI) through the K-means algorithm, where the average distances between the sample points and the cluster centroids and so on, to construct a multiple linear regression model. Secondly, we extracted sample points by varying the number of features and feature ratios between radiomic features and 3D-CNN features in the output of feature selection. Thirdly, we optimized the model using the sampling points to calculate the CCAI. Two typical feature combination search algorithms, the random forest recursive feature elimination (RF-RFE) and the differential evolution (DE), were used to perform feature selection with CCAI.

RESULTS

The accuracy, area-under-curve (AUC) and specificity, based on combined 3D-CNN and radiomic features extracted from combined T2 and ADC images, were 0.852, 0.871, and 0.735, respectively. Our experiments illustrated higher predictive power (AUC = 0.846) based on high-order abstract features extracted from the CNN on ADC and T2 images, compared to the traditional radiomic model (AUC = 0.714). Additionally, the predictive models constructed based on radiomics and CNN features extracted from ADC images were more predictable in terms of treatment responses than the radiomic and CNN imaging features extracted from T2 images. The average computational time of DE and RF-RFE were 50.5s and 128.6s in one single computation, the average computational time were 24.2s and 91.3s with CCAI, respectively.

CONCLUSION

We proposed an unsupervised clustering analysis mechanism to improve the effectiveness of feature selection while decreasing its time cost markedly, which highlight the correlation and complementarity between low- and high-level imaging features, achieving better predictive accuracy.

Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder

Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental disorders, which are characterized by complex and unique neuroelectrophysiological mechanisms in psychiatric neurosciences. The understanding of the brain functional differences between GAD and DD is crucial for the accurate diagnosis and clinical efficacy evaluation. The aim of this study was to reveal the differences in functional brain imaging between GAD and DD based on multidimensional electroencephalogram (EEG) characteristics. To this end, 10 min resting-state EEG signals were recorded from 38 GAD and 34 DD individuals. Multidimensional EEG features were subsequently extracted, which include power spectrum density (PSD), fuzzy entropy (FE), and phase lag index (PLI). Then, a direct statistical analysis (i.e., ANOVA) and three ensemble learning models (i.e., Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost)) were used on these EEG features for the differential recognitions. Our results showed that DD has significantly higher PSD values in the alpha1 and beta band, and a higher FE in the beta band, in comparison with GAD, along with the aberrant functional connections in all four bands between GAD and DD. Moreover, machine learning analysis further revealed that the distinct features predominantly occurred in the beta band and functional connections. Here, we show that DD has higher power and more complex brain activity patterns in the beta band and reorganized brain functional network structures in all bands compared to GAD. In sum, these findings move towards the practical identification of brain functional differences between GAD and DD.

Unveiling tumor immune evasion mechanisms: abnormal expression of transporters on immune cells in the tumor microenvironment

The tumor microenvironment (TME) is a crucial driving factor for tumor progression and it can hinder the body's immune response by altering the metabolic activity of immune cells. Both tumor and immune cells maintain their proliferative characteristics and physiological functions through transporter-mediated regulation of nutrient acquisition and metabolite efflux. Transporters also play an important role in modulating immune responses in the TME. In this review, we outline the metabolic characteristics of the TME and systematically elaborate on the effects of abundant metabolites on immune cell function and transporter expression. We also discuss the mechanism of tumor immune escape due to transporter dysfunction. Finally, we introduce some transporter-targeted antitumor therapeutic strategies, with the aim of providing new insights into the development of antitumor drugs and rational drug usage for clinical cancer therapy.

Brain network analysis reveals convergent and divergent aberrations between mild stroke patients with cortical and subcortical infarcts during cognitive task performing

Although consistent evidence has revealed that cognitive impairment is a common sequela in patients with mild stroke, few studies have focused on it, nor the impact of lesion location on cognitive function. Evidence on the neural mechanisms underlying the effects of mild stroke and lesion location on cognitive function is limited. This prompted us to conduct a comprehensive and quantitative study of functional brain network properties in mild stroke patients with different lesion locations. Specifically, an empirical approach was introduced in the present work to explore the impact of mild stroke-induced cognitive alterations on functional brain network reorganization during cognitive tasks (i.e., visual and auditory oddball). Electroencephalogram functional connectivity was estimated from three groups (i.e., 40 patients with cortical infarctions, 48 patients with subcortical infarctions, and 50 healthy controls). Using graph theoretical analysis, we quantitatively investigated the topological reorganization of functional brain networks at both global and nodal levels. Results showed that both patient groups had significantly worse behavioral performance on both tasks, with significantly longer reaction times and reduced response accuracy. Furthermore, decreased global and local efficiency were found in both patient groups, indicating a mild stroke-related disruption in information processing efficiency that is independent of lesion location. Regarding the nodal level, both divergent and convergent node strength distribution patterns were revealed between both patient groups, implying that mild stroke with different lesion locations would lead to complex regional alterations during visual and auditory information processing, while certain robust cognitive processes were independent of lesion location. These findings provide some of the first quantitative insights into the complex neural mechanisms of mild stroke-induced cognitive impairment and extend our understanding of underlying alterations in cognition-related brain networks induced by different lesion locations, which may help to promote post-stroke management and rehabilitation.

Functionalized nanoparticles crossing the brain-blood barrier to target glioma cells

Glioma is the most common tumor of the central nervous system (CNS), with a 5-year survival rate of <35%. Drug therapy, such as chemotherapeutic and immunotherapeutic agents, remains one of the main treatment modalities for glioma, including temozolomide, doxorubicin, bortezomib, cabazitaxel, dihydroartemisinin, immune checkpoint inhibitors, as well as other approaches such as siRNA, ferroptosis induction, etc. However, the filter function of the blood-brain barrier (BBB) reduces the amount of drugs needed to effectively target CNS tumors, making it one of the main reasons for poor drug efficacies in glioma. Thus, finding a suitable drug delivery platform that can cross the BBB, increase drug aggregation and retainment in tumoral areas and avoid accumulation in non-targeted areas remains an unsolved challenge in glioma drug therapy. An ideal drug delivery system for glioma therapy should have the following features: (1) prolonged drug life in circulation and effective penetration through the BBB; (2) adequate accumulation within the tumor (3) controlled-drug release modulation; (4) good clearance from the body without significant toxicity and immunogenicity, etc. In this regard, due to their unique structural features, nanocarriers can effectively span the BBB and target glioma cells through surface functionalization, providing a new and effective strategy for drug delivery. In this article, we discuss the characteristics and pathways of different nanocarriers for crossing the BBB and targeting glioma by listing different materials for drug delivery platforms, including lipid materials, polymers, nanocrystals, inorganic nanomaterials, etc.

[Analysis of the characteristics of viral infections in children with diarrhea in Beijing from 2018 to 2022]

Objective: To explore the characteristics of viral infections in children with diarrhea in Beijing from 2018 to 2022. Methods: Real-time PCR and enzyme-linked immunosorbent assay were used to detect viral nucleic acid of Norovirus (NoV), Sappovirus (SaV), Astrovirus (AstV), Enteric Adenovirus (AdV) or antigen of Rotavirus (RV) in 748 stool samples collected from Beijing Capital Institute of Pediatrics from January 2018 to December 2021. Subsequently, the reverse transcription PCR or PCR method was used to amplify the target gene of the positive samples after the initial screening, followed by sequencing, genotyping and evolution analysis, so as to obtain the characteristics of these viruses. Phylogenetic analysis was performed using Mega 6.0. Results: From 2018 to 2021, the overall detection rate of the above five common viruses was 37.6%(281/748)in children under 5 years old in Beijing. NoV, Enteric AdV and RV were still the top three diarrhea-related viruses, followed by AstV and SaV, accounting for 41.6%, 29.2%, 27.8%, 8.9% and 7.5%, respectively. The detection rate of co-infections with two or three diarrhea-related viruses was 4.7% (35/748). From the perspective of annual distribution, the detection rate of Enteric AdV was the highest in 2021, while NoV was predominant in the other 4 years. From the perspective of genetic characteristics, NoV was predominant by GII.4, and after the first detection of GII.4[P16] in 2020, it occupied the first two gene groups together with GII.4[P31]. Although the predominant RV was G9P[8], the rare epidemic strain G8P[8] was first detected in 2021. The predominant genotypes of Enteric AdV and AstV were Ad41 and HAstV-1. SaV was sporadic spread with a low detection rate. Conclusion: Among the diarrhea-related viruses infected children under 5 years of age in Beijing, the predominant strains of NoV and RV have changed and new sub-genotypes have been detected for the first time, while the predominant strains of AstV and Enteric AdV are relatively stable.

Ramelteon improves blood-brain barrier of focal cerebral ischemia rats to prevent post-stroke depression via upregulating occludin

Post-stroke depression (PSD) negatively affects the prognosis of post-stroke animals. Ramelteon has neuroprotection for chronic ischemia animals, but the effect and the biological mechanism of it on PSD is still unclear. This study explored the effects of ramelteon with prophylactic administration on blood-brain barrier in rats with middle cerebral artery occlusion (MCAO) and the oxygen-glucose deprivation/reperfusion (OGD/R) bEnd.3 cells and found that ramelteon pretreatment improved the depressive-like behaviors and decreased infarct area in MCAO rats. Also, this study found ramelteon pretreatment improved viability and inhibited permeability in OGD/R cells. In addition, this study found that MCP-1, TNF-α, and IL-1 levels were raised in the MCAO rats and that occludin protein and mRNA levels were decreased in the MCAO and the OGD/R models, while the Egr-1 level was up-regulated. All of these were antagonized by ramelteon pretreatment. In addition, overexpression of Egr-1 could reverse the effect of 100nM ramelteon pretreatment on FITC and occludin levels in OGD/R cells. In short, this study has demonstrated that the protective effect on PSD of ramelteon pretreatment on MCAO rats is related to the development of BBB permeability and that ramelteon regulates occludin to protect the BBB by inhibiting Egr-1.

Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder

To investigate the differences in functional brain network structures between patients with a high level of generalized anxiety disorder (HGAD) and those with a low level of generalized anxiety disorder (LGAD), a resting-state electroencephalogram (EEG) was recorded in 30 LGAD patients and 21 HGAD patients. Functional connectivity between all pairs of brain regions was determined by the Phase Lag Index (PLI) to construct a functional brain network. Then, the characteristic path length, clustering coefficient, and small world were calculated to estimate functional brain network structures. The results showed that the PLI values of HGAD were significantly increased in alpha2, and significantly decreased in the theta and alpha1 rhythms, and the small-world attributes for both HGAD patients and LGAD patients were less than one for all the rhythms. Moreover, the small-world values of HGAD were significantly lower than those of LGAD in the theta and alpha2 rhythms, which indicated that the brain functional network structure would deteriorate with the increase in generalized anxiety disorder (GAD) severity. Our findings may play a role in the development and understanding of LGAD and HGAD to determine whether interventions that target these brain changes may be effective in treating GAD.

Diabetes, Edentulism, and Cognitive Decline: A 12-Year Prospective Analysis

Diabetes mellitus (DM) is a recognized risk factor for dementia, and increasing evidence shows that tooth loss is associated with cognitive impairment and dementia. However, the effect of the co-occurrence of DM and edentulism on cognitive decline is understudied. This 12-y cohort study aimed to assess the effect of the co-occurrence of DM and edentulism on cognitive decline and examine whether the effect differs by age group. Data were drawn from the 2006 to 2018 Health and Retirement Study. The study sample included 5,440 older adults aged 65 to 74 y, 3,300 aged 75 to 84 y, and 1,208 aged 85 y or older. Linear mixed-effect regression was employed to model the rates of cognitive decline stratified by age cohorts. Compared with their counterparts with neither DM nor edentulism at baseline, older adults aged 65 to 74 y (β = -1.12; 95% confidence interval [CI], -1.56 to -0.65; P < 0.001) and those aged 75 to 84 y with both conditions (β = -1.35; 95% CI, -2.09 to -0.61; P < 0.001) had a worse cognitive function. For the rate of cognitive decline, compared to those with neither condition from the same age cohort, older adults aged 65 to 74 y with both conditions declined at a higher rate (β = -0.15; 95% CI, -0.20 to -0.10; P < 0.001). Having DM alone led to an accelerated cognitive decline in older adults aged 65 to 74 y (β = -0.09; 95% CI, -0.13 to -0.05; P < 0.001); having edentulism alone led to an accelerated decline in older adults aged 65 to 74 y (β = -0.13; 95% CI, -0.17 to -0.08; P < 0.001) and older adults aged 75 to 84 (β = -0.10; 95% CI, -0.17 to -0.03; P < 0.01). Our study finds the co-occurrence of DM and edentulism led to a worse cognitive function and a faster cognitive decline in older adults aged 65 to 74 y.

Comparison of clinical characteristics with spontaneous intracranial hypotension complicated with subdural hematoma between surgical treatment and non-surgical treatment

OBJECTIVES

The aim of this current study was to analyze the possible surgical predictive factors about spontaneous intracranial hypotension (SIH) complicated with subdural hematoma (SDH).

METHODS

178 patients diagnosed with both SIH and SDH were retrospectively reviewed. All the participants were divided into as EBP group (BPG) and surgical treatment group (STG). The demographic, clinical manifestations, radiographic and treatment data of the two groups were collected and compared by the electrical medical history system.

RESULTS

The male patients accounted for the majority in the STG (77.88 %), which was significantly larger than those in the BPG(p = 0.004). Patients who regularly smoked and drunk alcohol in the STG group were significantly greater than those in the BPG group (p_(smoking)= 0.049, p_(drinking)= 0.014). The headache occurring in the temporal position in the STG accounted for 44.44 % which was significantly greater than those in the BPG (25.17 %, p = 0.040). The durations days of symptoms were 54.85 ± 34.78 and 33.72 ± 34.97 in the STG and BPG respectively (p = 0.005). The midline shift degree in the STG was also significantly greater than that in the BPG (p = 0.001). The largest hematoma depth in the STG was significantly greater than that in the BPG (p = 0.000). The frequency of blood patch in the STG was significantly greater than that in the BPG(p = 0022).

CONCLUSION

Male, unhealthy living habits, temporal headache, duration of headache, midline shift, hematoma depth and blood patch frequency were possible related factors with the poor outcome of blood patch and surgical treatment.

Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression

Depression has become one of the most common mental illnesses, causing serious physical and mental harm. However, there remain unclear and uniform physiological indicators to support the diagnosis of clinical depression. This study aimed to use machine learning techniques to investigate the abnormal multidimensional EEG features in patients with depression. Resting-state EEG signals were recorded from 41 patients with depression and 34 healthy controls. Multiple dimensional characteristics were extracted, including power spectral density (PSD), fuzzy entropy (FE), and phase lag index (PLI). These three different dimensional characteristics with statistical differences between two groups were ranked by three machine learning algorithms. Then, the ranked characteristics were placed into the classifiers according to the importance of features to obtain the optimal feature subset with the highest classification accuracy. The results showed that the optimal feature subset contained 86 features with the highest classification accuracy of 98.54% ± 0.21%. According to the statistics of the optimal feature subset, PLI had the largest number of features among the three categories, and the number of beta features was bigger than other rhythms. Moreover, compared to the healthy controls, the PLI values in the depression group increased in theta and beta rhythms, but decreased in alpha1 and alpha2 rhythms. The PSD of theta and beta rhythms were significantly greater in depression group than that in healthy controls, and the FE of beta rhythm showed the same trend. These findings indicate that the distribution of abnormal multidimensional features is potentially useful for the diagnosis of depression and understanding of neural mechanisms.

The role and molecular mechanism of gut microbiota in Graves' orbitopathy

PURPOSE

Graves' orbitopathy (GO) is an autoimmune orbital disorder. Gut microbiota dysfunction plays a vital role in autoimmune diseases, including Graves' disease (GD) and GO. In the present study, we aimed to investigate the change of gut microbiota in GD/GO using mouse model.

METHODS

The murine model of GD/GO was established by the challenge of adenovirus expressing thyroid-stimulating hormone (TSH) receptor (TSHR) (Ad-TSHR). The histological changes of orbital and thyroid tissues were analyzed by hematoxylin and eosin (H&E), Masson staining, and immunohistochemistry (IHC) staining. The fecal samples were collected for 16S rRNA gene sequencing and bioinformatics analysis.

RESULTS

The GD/GO model was established successfully, as manifested as the broadened eyelid, exophthalmia and conjunctive redness, severe inflammatory infiltration among thyroid glands and between extraocular muscle space, hypertrophic extraocular muscles, elevated thyroxine (T4) and decreased TSH, and positive CD34, CD40, collagen I, and α-SMA staining. A total of 222 operational taxonomic units (OUTs) were overlapped between mice in the Ad-NC and Ad-TSHR groups. The microbial composition of the samples in the two groups was mainly Bacteroidia and Clostridia, and the Ad-NC group had a significantly lower content of Bacteroidia and higher content of Clostridia. KEGG orthology analysis results revealed differences in dehydrogenase, aspartic acid, bile acid, chalcone synthase, acetyltransferase, glutamylcyclotransferase, glycogenin, and 1-phosphatidylinositol-4-phosphate 5-kinase between two groups; enzyme commission (EC) analysis results revealed differences in several dehydrogenase, oxidase, thioxy/reductase between two groups; MetaCyc pathways analysis results revealed differences in isoleucine degradation, oxidation of C1 compounds, tricarboxylic acid (TCA) cycle IV, taurine degradation, and biosynthesis of paromamine, heme, colonic acid building blocks, butanediol, lysine/threonine/methionine, and histidine/purine/pyrimidine between two groups.

CONCLUSION

This study induced a mouse model of GD/GO by Ad-TSHR challenge, and gut microbiota characteristics were identified in the GD/GO mice. The Bacteroidia and Clostridia abundance was changed in the GD/GO mice. These findings may lay a solid experimental foundation for developing personalized treatment regimens for GD patients according to the individual gut microbiota. Given the potential impact of regional differences on intestinal microbiota, this study in China may provide a reference for the global overview of the gut-thyroid axis hypothesis.

Antioxidants in brain tumors: current therapeutic significance and future prospects

Brain cancer is regarded among the deadliest forms of cancer worldwide. The distinct tumor microenvironment and inherent characteristics of brain tumor cells virtually render them resistant to the majority of conventional and advanced therapies. Oxidative stress (OS) is a key disruptor of normal brain homeostasis and is involved in carcinogenesis of different forms of brain cancers. Thus, antioxidants may inhibit tumorigenesis by preventing OS induced by various oncogenic factors. Antioxidants are hypothesized to inhibit cancer initiation by endorsing DNA repair and suppressing cancer progression by creating an energy crisis for preneoplastic cells, resulting in antiproliferative effects. These effects are referred to as chemopreventive effects mediated by an antioxidant mechanism. In addition, antioxidants minimize chemotherapy-induced nonspecific organ toxicity and prolong survival. Antioxidants also support the prooxidant chemistry that demonstrate chemotherapeutic potential, particularly at high or pharmacological doses and trigger OS by promoting free radical production, which is essential for activating cell death pathways. A growing body of evidence also revealed the roles of exogenous antioxidants as adjuvants and their ability to reverse chemoresistance. In this review, we explain the influences of different exogenous and endogenous antioxidants on brain cancers with reference to their chemopreventive and chemotherapeutic roles. The role of antioxidants on metabolic reprogramming and their influence on downstream signaling events induced by tumor suppressor gene mutations are critically discussed. Finally, the review hypothesized that both pro- and antioxidant roles are involved in the anticancer mechanisms of the antioxidant molecules by killing neoplastic cells and inhibiting tumor recurrence followed by conventional cancer treatments. The requirements of pro- and antioxidant effects of exogenous antioxidants in brain tumor treatment under different conditions are critically discussed along with the reasons behind the conflicting outcomes in different reports. Finally, we also mention the influencing factors that regulate the pharmacology of the exogenous antioxidants in brain cancer treatment. In conclusion, to achieve consistent clinical outcomes with antioxidant treatments in brain cancers, rigorous mechanistic studies are required with respect to the types, forms, and stages of brain tumors. The concomitant treatment regimens also need adequate consideration.

Bifidobacterium spp. and their metabolite lactate protect against acute pancreatitis via inhibition of pancreatic and systemic inflammatory responses

Severe acute pancreatitis (SAP) is a critical illness characterized by a severe systemic inflammatory response resulting in persistent multiple organ failure and sepsis. The intestinal microbiome is increasingly appreciated to play a crucial role in modulation of AP disease outcome, but limited information is available about the identity and mechanism of action for specific commensal bacteria involved in AP-associated inflammation. Here we show that Bifidobacteria, particularly B. animalis, can protect against AP by regulating pancreatic and systemic inflammation in germ-free (GF) and oral antibiotic-treated (Abx) mouse models. Colonization by B. animalis and administration of its metabolite lactate protected Abx and GF mice from AP by reducing serum amylase concentration, ameliorating pancreatic lesions and improving survival rate after retrograde injection of sodium taurocholate. B. animalis relieved macrophage-associated local and systemic inflammation of AP in a TLR4/MyD88- and NLRP3/Caspase1-dependent manner through its metabolite lactate. Supporting our findings from the mouse study, clinical AP patients exhibited a decreased fecal abundance of Bifidobacteria that was inversely correlated with the severity of systemic inflammatory responses. These results may shed light on the heterogeneity of clinical outcomes and drive the development of more efficacious therapeutic interventions for AP, and potentially for other inflammatory disorders.

[Indirubin relieves inflammatory injury of chondrocytes in a mouse model of osteoarthritis]

OBJECTIVE

To investigate the effect of indirubin for relieving joint inflammation and injury in a rat model of osteoarthritis.

METHODS

Articular cartilage chondrocytes were isolated from adult rat knee joint and cultured in the presence of interleukin-1β (IL-1β) and 0.1, 0.5, 1.0, or 2.0 μmol/L indirubin. The cells were transfected with NPAS2 siRNA or a non-specific siRNA, and the cell proliferation and apoptosis were evaluated using tetramethylthiazole blue staining and flow cytometry. The protein expression levels of Bax, Bcl-2, ACAN, COL2A1, MMP-13 and NPAS2 were detected with Western blotting, and the levels of NO, PGE2 and TNF-α in the culture supernatant were determined with ELISA. The mRNA expression levels of NPAS2, ACAN, COL2A1 and MMP-13 were detected using fluorescence quantitative PCR. In a C57BL/6 mouse model of osteoarthritis, the effect of indirubin on BAX, Bcl-2, ACAN and MMP-13 protein expressions in the bone and joint tissues were evaluated with Western blotting.

RESULTS

Treatment with 0.1 μmol/L indirubin produced no significant changes in chondrocyte proliferation, apoptosis, caspase-3 activity, or BAX and Bcl-2 protein expressions. At higher doses (0.5, 1.0 and 2.0 μmol/L), indirubin significantly promoted cell proliferation, increased Bcl-2 protein expression, and lowered cell apoptosis rate, caspase-3 activity and Bax protein expression (P < 0.05). Indirubin treatment at 0.5 μmol/L up-regulated the protein and mRNA expressions of NPAS2, ACAN and COL2A1, and down-regulated the expressions of MMP-13, NO, PGE2 and TNF-α (P < 0.05). Interference of NPAS2 expression significantly attenuated the protective effect of 0.5 μmol/L indirubin against IL-1β-induced chondrocyte injury. The mouse model of osteoarthritis showed obviously increased protein levels of BAX and MMP-13 (P < 0.01) and decreased levels of Bcl-2 (P < 0.05) and ACAN (P < 0.01) in the knee joint, and indirubin treatment of the mouse models significantly inhibited the increase of BAX and MMP-13 protein expressions (P < 0.01) and up-regulated the protein expressions of Bcl-2 and ACAN (P < 0.05).

CONCLUSION

Indirubin has a protective effect on osteoarthritis tissue and alleviates inflammation and damage of osteoarthritis chondrocytes possibly through NPAS2.

[Progress in clinical diagnosis and treatment of multiple primary lung cancer]

With the application of high-resolution chest imaging system and lung cancer screening program, patients with multiple primary lung cancer (MPLC) are becoming a growing population in clinical practice. However, the diagnostic criteria of MPLC and its differentiation from intrapulmonary metastasis of lung cancer (IM) are still controversial, especially in cases with similar histology. On the basis of reviewing the existing literature, this paper discusses the changes of the diagnostic criteria of MPLC and the differential diagnosis methods of imaging, histology and molecular genetics of MPLC and IM, and briefly introduces the application of multidisciplinary diagnosis, algorithm, predictive model and artificial intelligence in the differential diagnosis of MPLC. In addition, we also discuss the latest progress in the treatment of MPLC. Radical surgery is the main method for the treatment of MPLC. Stereotactic body radiation therapy (SBRT) is safe and feasible for inoperable MPLC patients, and targeted therapy and immunotherapy can also be used in MPLC after appropriate patient selection.

Preparation and Evaluation of the Cytoprotective Activity of Micelles with DSPE-PEG-C60 as a Carrier Against Doxorubicin-Induced Cytotoxicity

To deliver doxorubicin (DOX) with enhanced efficacy and safety in vivo, fullerenol-modified micelles were prepared with the amphiphilic polymer DSPE-PEG-C60 as a carrier, which was synthesized by linking C60(OH)₂₂ with DSPE-PEG-NH₂. Studies of its particle size, PDI, zeta potential, and encapsulation efficiency were performed. DOX was successfully loaded into the micelles, exhibiting a suitable particle size [97 nm, 211 nm, 260 nm, vector: DOX = 5:1, 10:1; 15:1 (W/W)], a negative zeta potential of around −30 mv, and an acceptable encapsulation efficiency [86.1, 95.4, 97.5%, vector: DOX = 5:1, 10:1; 15:1 (W/W)]. The release behaviors of DOX from DSPE-PEG-C60 micelles were consistent with the DSPE-PEG micelles, and it showed sustained release. There was lower cytotoxicity of DSPE-PEG-C60 micelles on normal cell lines (L02, H9c2, GES-1) than free DOX and DSPE-PEG micelles. We explored the protective role of DSPE-PEG-C60 on doxorubicin-induced cardiomyocyte damage in H9c2 cells, which were evaluated with a reactive oxygen species (ROS) assay kit, JC-1, and an FITC annexin V apoptosis detection kit for cellular oxidative stress, mitochondrial membrane potential, and apoptosis. The results showed that H9c2 cells exposed to DSPE-PEG-C60 micelles displayed decreased intracellular ROS, an increased ratio of red fluorescence (JC-1 aggregates) to green fluorescence (JC-1 monomers), and a lower apoptotic ratio than the control and DSPE-PEG micelle cells. In conclusion, the prepared DOX-loaded DSPE-PEG-C60 micelles have great promise for safe, effective tumor therapy.

[Soluble egg antigen of Schistosoma japonicum induces macrophage apoptosis in mice]

OBJECTIVE

To investigate the dynamic changes of macrophage numbers and apoptosis during Schistosoma japonicum infection, and to investigate the possible mechanisms of macrophage apoptosis induced by S. japonicum soluble egg antigen (SEA).

METHODS

C57BL/6 mice at ages of 6~8 weeks were randomly divided into 4 groups, including three experimental groups and a normal control group. Each mouse in the experimental groups was infected with (12 ± 1) cercariae of S. japonicum via the abdominal skin, and all mice in an experimental group were sacrificed 3, 5, 8 weeks post-infection, respectively, while mice in the control group were not infected with S. japonicum cercariae and sacrificed on the day of S. japonicum infection in the experimental group. Mouse liver specimens and peritoneal exudation cells were sampled in each group, and the dynamic changes of macrophage numbers and apoptosis were detected. Mouse peritoneal macrophages were isolated, purified and treated with S. japonicum SEA, PBS and ovalbumin (OVA) in vitro, and the macrophage apoptosis was detected using flow cytometry. The mRNA and protein expression of BCL-2 protein family members were determined in macrophages using real-time quantitative PCR (qP-CR) and Western blotting assays, and the activation of caspase 3 was determined using flow cytometry and Western blotting. In addition, macrophages were in vitro treated with S. japonicum SEA in presence of a caspase inhibitor, H₂O₂ or N-acetyl-L-cysteine, and the apoptosis of macrophages was detected using flow cytometry.

RESULTS

The total macrophage numbers continued to increase in mouse liver [(0.873 ± 0.106) × 106, (2.737 ± 0.460) × 10⁶ and (3.107 ± 0.367) × 10⁶ cells, respectively; F = 81.900, P < 0.01] and peritoneal specimens [(5.282 ± 1.136) × 105, (7.500 ± 1.200) × 10⁵ and (12.800 ± 0.800) × 10⁵ cells, respectively; F = 55.720, P < 0.01] 3, 5 and 8 weeks post-infection with S. japonicum, and the numbers of apoptotic macrophages also continued to increase in mouse liver [(0.092 ± 0.018) × 10⁶, (0.186 ± 0.025) × 10⁶ and (0.173 ± 0.0270) × 10⁶ cells; F = 57.780, P < 0.01] and peritoneal specimens [(0.335 ± 0.022) × 10⁵, (0.771 ± 0.099) × 10⁵ and (1.094 ± 0.051) × 10⁵ cells; F = 49.460, P < 0.01] 3, 5 and 8 weeks post-infection with S. japonicum. The apoptotic rate of SEA-treated macrophages [(24.330 ± 0.784)%] was significantly higher than that of PBS-[(18.500 ± 1.077)%] and OVA-treated macrophages [(18.900 ± 1.350)%] (both P values < 0.01). There were no significant differences in the mRNA or protein expression of Bcl-2 [Bcl - 2 mRNA expression: (1.662 ± 0.943) vs. (1.000 ± 0.000), t = 1.215, P > 0.05; BCL protein expression: (0.068 ± 0.004) vs. (0.070 ± 0.005), t = 0.699, P > 0.05], Bax [Bax mRNA expression: (0.711 ± 0.200) vs. (1.000 ± 0.000), t = 2.507, P > 0.05; BAX protein expression: (0.089 ± 0.005) vs. (0.097 ± 0.003), t = 2.232, P > 0.05] and Bak [Bak mRNA expression: (1.255 ± 0.049) vs. (1.00 ± 0.00), t = 0.897, P > 0.05; BAK protein expression: (0.439 ± 0.048) vs. (0.571 ± 0.091), t = 2.231, P > 0.05] between in SEA- and PBS-treated macrophages. S. japonicum SEA induced macrophage apoptosis in the presence of a caspase inhibitor (F = 0.411, P > 0.05); however, SEA failed to induce macrophage apoptosis in the presence of H₂O₂ or NAC (F = 11.880 and 9.897, both P values < 0.05).

CONCLUSIONS

S. japonicum SEA may induce macrophage apoptosis through promoting reactive oxygen species expression during S. japonicum infections in mice.

POS0071 THE EFFICACY OF PIRFENIDONE COMBINED WITH IMMUNOSUPPRESSANT THERAPY IN THE CONNECTIVE TISSUE DISEASE-ASSOCIATED PULMONARY INTERSTITIAL DISEASE, A 24-WEEK PROSPECTIVE CONTROLLED COHORT STUDY

Background

Pulmonary interstitial disease(ILD)is very common in connective tissue disease (CTD). Different subtypes display significant differentiations in prognosis. Pirfenidone (PFD), the targeted anti-fibrosis and anti-inflammatory drug, started to apply in CTD-ILD, while its strategy of combination with immunotherapy, bridging time and service time are worth discussing.

Objectives

To evaluate the efficacy and safety of PFD combined with immunosuppressant (IS) in the treatment of several CTD- ILD.

Methods

111 CTD-ILD patients were involved from Aug 2019 to Dec 2021 (ClinicalTrials.gov Identifier NCT04928586), including systemic sclerosis (SSc, n=30), inflammatory myopathy (IIM, n=51), rheumatoid arthritis (RA, n=17) and other CTDs (such as systemic lupus erythematosus, sjogren’s syndrome, n=13). Patients were treated with relative stable dose of glucocorticoid (GC) and/or IS since screening.After the evaluation of HRCT, pulmonary function (FVC% and DLCo%) and basic disease activity, PFD was added or not and followed up regularly for 24 weeks. The changes of lung function and imaging were recorded in different subgroups.

Results

At baseline, The FVC% in the SSc-PFD (n=14), IIM-PFD (n=25) pts were lower than that of SSc-no-PFD (n=16), IIM-no-PFD (n=26) respectively, that was 81.06 vs 99.63(p = 0.014), 78.23 vs 91.12, (p= 0.010). And IIM-PFD group present a lower baseline DLCo% than IIM-no-PFD pts (64.25 vs 72.82, p =0.034). DLCo% in the other CTDs was lower than SSc, IIM, RA pts (54.58 vs 65.55, 68.71, 66.89, p=0.036), while there were no significant differences in baseline FVC% among these disease.

After 24 weeks of treatment, FVC% in SSc-PFD group was improved by 7.15%, while that was -0.91% in SSc-no-PFD pts (p=0.042). The elevation in FVC% was also significant between the IIM-PFD and IIM-no-PFD control (8.00 vs 1.40, p=0.016). On the other hand, DLCo% of RA-PFD obviously enhanced 7.40%, compared with RA-no-PFD decrease 5.50% from baseline(p=0.002) (Figure 1).

Figure 1.

The changes of FVC% and DLco% in PFD treated CTD-ILD for 24 weeks. (A) FVC% changes in SSc, IIM, RA and other CTD-ILD from baseline.; (B) DLco% changes in SSc, IIM, RA and other CTD-ILD from baseline. * p< 0.05, compared to no PFD treatment group.

When performing subgroup analysis with manifestations in HRCT, the change of FVC% in SSc-PFD pts with usual interstitial pueumonia tendency(SSc-PFD-UIP) was higher than SSc-PFD-non-UIP group (8.05 vs -3.20, p=0.014). However, the non-UIP tendency in IIM-PDF pts displayed superior therapeutic effects than IIM-PDF-UIP pts (10.50 vs 1.00, p=0.005). In addition, DLCo% improved significantly in RA-PDF-non-UIP subgroup, compared with RA-PDF-UIP pts (10.40 vs -4.45, p=0.017).

According to whether the baseline FVC% and DLCo% value was less than 70%, the improvement of FVC% was significantly higher in PFD treatment group than no-PFD pts of SSc and IIM with baseline High-FVC%(6.60 vs 0.10, p=0.047),(6.30 vs 1.10, p= 0.089).In RA-PFD pts, DLCo% showed a significant increase in baseline DLCo% less than 70% subgroup, compared to RA-no-PDF (7.40% vs -6.60%, p=0.011).The basic IS were considerable between PFD andno-PFD pts throughout the study and no differences of GS and IS dosages at baseline and follow up among all subgroups.

Multiple linear regression analysis found that baseline FVC%<70%(HR=4.56,6.81) and prescription of PFD(HR=4.56,4.37) could positively affect the changes of FVC% and DLCo% (all p<0.05).

Conclusion

The response of pulmonary function to PFD were varied in different CTD-ILD subsets. SSc and IIM pts acquired obviously improvement on FVC%, especially with high baseline FVC group. DLco% elevated in RA-PFD, highlight those baseline Low-DLco and No-UIP pts.

Acknowledgements

Funded by ECCM Program of Clinical Research Center of Shandong University (No. 2021SDUCRCB010)

Disclosure of Interests

None declared

Understanding the Complex Interactions between Coffee, Tea Intake and Neurologically Relevant Tissues Proteins in the Development of Anxiety and Depression

OBJECTIVES

Coffee and tea intake might be associated with psychiatry diseases. However, it is unclear whether the effect of coffee/tea on anxiety and depression depending on the different types of proteins.

DESIGN

This was a cross-sectional study.

SETTING

Our datasets were downloaded from online.

PARTICIPANTS

Phenotypic and genotypic data for coffee intake(N=376,196) and tea intake (N=376,078) were derived from UK Biobank. GWAS data of proteins (N=1,537) from neurologically relevant tissues (brain, cerebrospinal fluid (CSF) and plasma) were obtained from a recently published study.

MEASUREMENTS

Multivariate linear analysis was then used to evaluate the potential interaction effect between coffee/tea intake and proteins polygenetic risk score (PRS) on the risks of anxiety and depression controlling for age, sex, Townsend deprivation index (TDI), smoke, drinking and education level.

RESULTS

34 coffee intake-proteins interactions and 15 tea intake-proteins interactions were observed in anxiety individuals, such as coffee intake-c-Jun interaction (β=0.0169, P=4.131×10-3), coffee intake-Fas interaction (β=-0.0190, P=8.132×10-4), tea intake-sL-Selectin interaction (β=0.0112, P=5.412×10-3) and tea intake-IL-1F6 (β=0.0083, P=4.471×10-2). 25 coffee intake-proteins and 14 tea intake-proteins interactions were observed in depression individuals, including coffee intake- IL-1 sRI (β=0.0171, P=4.888×10-3) and coffee intake-NXPH1 interaction (β=0.0156, P=9.819×10-3), tea intake-COLEC12 interaction (β=0.0127, P=3.280×10-3), and tea intake-Layilin interaction (β=0.0117, P=7.926×10-3).

CONCLUSIONS

Our results suggested the important role of multiple proteins in neurologically relevant tissues in the associations between coffee/tea intake and psychiatry diseases, providing entry points to explore the mechanisms underlying anxiety and depression.

[Study on the genetic characteristics of enteric viral pathogens of sporadic adult diarrhea in Chaoyang district, Beijing in 2019]

Objective: To analyze the distribution and genetic characteristics of sporadic adult diarrhea virus in Chaoyang District, Beijing. Methods: Fecal samples from 177 adult patients with sporadic diarrhea were collected from 4 enteric outpatient clinics in Chaoyang District, Beijing from May to December 2019. Nucleic acid detection of Norovirus, Sappovirus, Rotavirus, Enteric Adenovirus and Astrovirus in the samples was performed by real-time quantitative PCR. The positive samples were amplified by RT-PCR/PCR and sequenced. The phylogenetic analysis was performed by neighbor-Joining (NJ) methods of Mega 6.0 software. Results: There were 60 of 177 (33.90%) adult sporadic diarrhea samples positive for enteric viral pathogens. Among them, 47 cases were infected with single virus, including 29 cases of Norovirus, 9 cases of Sappovirus, 8 cases of Astrovirus and 1 case of Enteric Adenovirus, in addition with 13 cases of multiple infections. None of rotavirus was detected. Partial sequences were successfully obtained for analysis, including 16 cases of GI Norovirus (7 subtypes and GI.3[P13] predominant), 10 cases of GII Norovirus (5 subtypes and GII.6[P7] predominant), 12 cases of Sappovirus (4 subtypes and GI.2 predominant), and 7 cases of Astrovirus (2 subtypes and AST-1 predominant). Conclusion: Norovirus, Astrovirus and Sappovirus are main pathogens among sporadic adult diarrhea in Beijing in 2019, and and different pathogenic gene subtypes show diverse characteristics.