Vimentin inhibits peste des petits ruminants virus replication by interaction with nucleocapsid protein

The Peste des petits ruminant virus (PPRV) is a member of the Paramyxoviridae family and is classified into the genus Measles virus. PPRV predominantly infects small ruminants, leading to mortality rates of nearly 100%, which have caused significant economic losses in developing countries. Host proteins are important in virus replication, but the PPRV nucleocapsid (N) protein-host interacting partners for regulating PPRV replication remain unclear. The present study confirmed the interaction between PPRV-N and the host protein vimentin by co-immunoprecipitation and co-localization experiments. Overexpression of vimentin suppressed PPRV replication, whereas vimentin knockdown had the opposite effect. Mechanistically, N was subjected to degradation via the ubiquitin/proteasome pathway, where vimentin recruits the E3 ubiquitin ligase NEDD4L to fulfill N-ubiquitination, resulting in the degradation of the N protein. These findings suggest that the host protein vimentin and E3 ubiquitin ligase NEDD4L have an anti-PPRV effect.

Individual connectivity-based parcellations reflect functional properties of human auditory cortex

Neuroimaging studies of the functional organization of human auditory cortex have focused on group-level analyses to identify tendencies that represent the typical brain. Here, we mapped auditory areas of the human superior temporal cortex (STC) in 30 participants by combining functional network analysis and 1-mm isotropic resolution 7T functional magnetic resonance imaging (fMRI). Two resting-state fMRI sessions, and one or two auditory and audiovisual speech localizer sessions, were collected on 3-4 separate days. We generated a set of functional network-based parcellations from these data. Solutions with 4, 6, and 11 networks were selected for closer examination based on local maxima of Dice and Silhouette values. The resulting parcellation of auditory cortices showed high intraindividual reproducibility both between resting state sessions (Dice coefficient: 69-78%) and between resting state and task sessions (Dice coefficient: 62-73%). This demonstrates that auditory areas in STC can be reliably segmented into functional subareas. The interindividual variability was significantly larger than intraindividual variability (Dice coefficient: 57%-68%, p<0.001), indicating that the parcellations also captured meaningful interindividual variability. The individual-specific parcellations yielded the highest alignment with task response topographies, suggesting that individual variability in parcellations reflects individual variability in auditory function. Connectional homogeneity within networks was also highest for the individual-specific parcellations. Furthermore, the similarity in the functional parcellations was not explainable by the similarity of macroanatomical properties of auditory cortex. Our findings suggest that individual-level parcellations capture meaningful idiosyncrasies in auditory cortex organization.

MRI-based radiomics model to preoperatively predict mesenchymal transition subtype in high-grade serous ovarian cancer

AIM

To develop a magnetic resonance imaging (MRI)-based radiomics model for the preoperative identification of mesenchymal transition (MT) subtype in high-grade serous ovarian cancer (HGSOC).

MATERIALS AND METHODS

One hundred and eighty-nine patients with histopathologically confirmed HGSOC were enrolled retrospectively. Among the included patients, 55 patients were determined as the MT subtype and the remaining 134 were non-MT subtype. After extracting a total of 204 features from T2-weighted imaging (T2WI) and contrast-enhanced (CE)-T1WI images, the Mann-Whitney U-test, Spearman correlation test, and Boruta algorithm were adopted to select the optimal feature set. Three classifiers, including logistic regression (LR), support vector machine (SVM), and random forest (RF), were trained to develop radiomics models. The performance of established models was evaluated from three aspects: discrimination, calibration, and clinical utility.

RESULTS

Seven radiomics features relevant to MT subtypes were selected to build the radiomics models. The model based on the RF algorithm showed the best performance in predicting MT subtype, with areas under the curves (AUCs) of 0.866 (95 % confidence interval [CI]: 0.797-0.936) and 0.852 (95 % CI: 0.736-0.967) in the training and testing cohorts, respectively. The calibration curves, supported with Brier scores, indicated very good consistency between observation and prediction. Decision curve analysis (DCA) showed that the RF-based model could provide more net benefit, which suggested favorable utility in clinical application.

CONCLUSION

The RF-based radiomics model provided accurate identification of MT from the non-MT subtype and may help facilitate personalised management of HGSOC.

Experimental Limits on Solar Reflected Dark Matter with a New Approach on Accelerated-Dark-Matter-Electron Analysis in Semiconductors

Recently a dark matter-electron (DM-electron) paradigm has drawn much attention. Models beyond the standard halo model describing DM accelerated by high energy celestial bodies are under intense examination as well. In this Letter, a velocity components analysis (VCA) method dedicated to swift analysis of accelerated DM-electron interactions via semiconductor detectors is proposed and the first HPGe detector-based accelerated DM-electron analysis is realized. Utilizing the method, the first germanium based constraint on sub-GeV solar reflected DM-electron interaction is presented with the 205.4  kg·day dataset from the CDEX-10 experiment. In the heavy mediator scenario, our result excels in the mass range of 5-15  keV/c^{2}, achieving a 3 orders of magnitude improvement comparing with previous semiconductor experiments. In the light mediator scenario, the strongest laboratory constraint for DM lighter than 0.1  MeV/c^{2} is presented. The result proves the feasibility and demonstrates the vast potential of the VCA technique in future accelerated DM-electron analyses with semiconductor detectors.

Halide-free CO2 cycloaddition onto styrene oxide catalysed by first row transition-metal derivatives of polyoxotungstates

Quaternary ammonium salts of metal derivatives of polyoxometalates [XW11O39M(H2O)]n- (X = P, Si; M = Cr, Mn, Co, Ni, Zn) were successfully tested instead of quaternary ammonium halides as catalysts in the cycloaddition of CO2 to styrene oxide. Remarkably, they gave very satisfactory yields of styrene carbonate at moderate temperature (80 °C).

Plasminogen activator urokinase interacts with the fusion protein and antagonizes the growth of Peste des petits ruminants virus

Peste des petits ruminants is an acute and highly contagious disease caused by the Peste des petits ruminants virus (PPRV). Host proteins play a crucial role in viral replication. However, the effect of fusion (F) protein-interacting partners on PPRV infection is poorly understood. In this study, we found that the expression of goat plasminogen activator urokinase (PLAU) gradually decreased in a time- and dose-dependent manner in PPRV-infected goat alveolar macrophages (GAMs). Goat PLAU was subsequently identified using co-immunoprecipitation and confocal microscopy as an F protein binding partner. The overexpression of goat PLAU inhibited PPRV growth and replication, whereas silencing goat PLAU promoted viral growth and replication. Additionally, we confirmed that goat PLAU interacted with a virus-induced signaling adapter (VISA) to antagonize F-mediated VISA degradation, increasing the production of type I interferon. We also found that goat PLAU reduced the inhibition of PPRV replication in VISA-knockdown GAMs. Our results show that the host protein PLAU inhibits the growth and replication of PPRV by VISA-triggering RIG-I-like receptors and provides insight into the host protein that antagonizes PPRV immunosuppression.IMPORTANCEThe role of host proteins that interact with Peste des petits ruminants virus (PPRV) fusion (F) protein in PPRV replication is poorly understood. This study confirmed that goat plasminogen activator urokinase (PLAU) interacts with the PPRV F protein. We further discovered that goat PLAU inhibited PPRV replication by enhancing virus-induced signaling adapter (VISA) expression and reducing the ability of the F protein to degrade VISA. These findings offer insights into host resistance to viral invasion and suggest new strategies and directions for developing PPR vaccines.

[Clinical application of split liver transplantation: a single center report of 203 cases]

Objective: To investigate the safety and therapeutic effect of split liver transplantation (SLT) in clinical application. Methods: This is a retrospective case-series study. The clinical data of 203 consecutive SLT, 79 living donor liver transplantation (LDLT) and 1 298 whole liver transplantation (WLT) performed at the Third Affiliated Hospital of Sun Yat-sen University from July 2014 to July 2023 were retrospectively analyzed. Two hundred and three SLT liver grafts were obtained from 109 donors. One hundred and twenty-seven grafts were generated by in vitro splitting and 76 grafts were generated by in vivo splitting. There were 90 adult recipients and 113 pediatric recipients. According to time, SLT patients were divided into two groups: the early SLT group (40 cases, from July 2014 to December 2017) and the mature SLT technology group (163 cases, from January 2018 to July 2023). The survival of each group was analyzed and the main factors affecting the survival rate of SLT were analyzed. The Kaplan-Meier method and Log-rank test were used for survival analysis. Results: The cumulative survival rates at 1-, 3-, and 5-year were 74.58%, 71.47%, and 71.47% in the early SLT group, and 88.03%, 87.23%, and 87.23% in the mature SLT group, respectively. Survival rates in the mature SLT group were significantly higher than those in the early SLT group (χ2=5.560,P=0.018). The cumulative survival rates at 1-, 3- and 5-year were 93.41%, 93.41%, 89.95% in the LDLT group and 87.38%, 81.98%, 77.04% in the WLT group, respectively. There was no significant difference among the mature SLT group, the LDLT group and the WLT group (χ2=4.016, P=0.134). Abdominal hemorrhage, infection, primary liver graft nonfunction,and portal vein thrombosis were the main causes of early postoperative death. Conclusion: SLT can achieve results comparable to those of WLT and LDLT in mature technology liver transplant centers, but it needs to go through a certain time learning curve.

A genome-wide CRISPR screening uncovers that TOB1 acts as a key host factor for FMDV infection via both IFN and EGFR mediated pathways

The interaction between foot-and-mouth disease virus (FMDV) and the host is extremely important for virus infection, but there are few researches on it, which is not conducive to vaccine development and FMD control. In this study, we designed a porcine genome-scale CRISPR/Cas9 knockout library containing 93,859 single guide RNAs targeting 16,886 protein-coding genes, 25 long ncRNAs, and 463 microRNAs. Using this library, several previously unreported genes required for FMDV infection are highly enriched post-FMDV selection in IBRS-2 cells. Follow-up studies confirmed the dependency of FMDV on these genes, and we identified a functional role for one of the FMDV-related host genes: TOB1 (Transducer of ERBB2.1). TOB1-knockout significantly inhibits FMDV infection by positively regulating the expression of RIG-I and MDA5. We further found that TOB1-knockout led to more accumulation of mRNA transcripts of transcription factor CEBPA, and thus its protein, which further enhanced transcription of RIG-I and MDA5 genes. In addition, TOB1-knockout was shown to inhibit FMDV adsorption and internalization mediated by EGFR/ERBB2 pathway. Finally, the FMDV lethal challenge on TOB1-knockout mice confirmed that the deletion of TOB1 inhibited FMDV infection in vivo. These results identify TOB1 as a key host factor involved in FMDV infection in pigs.

Associations between low muscle mass and clinical characteristics of health population in China

Objectives

The primary aim of this study is to discern the association between specific clinical parameters and low muscle mass (LMM). We endeavor to elucidate the determinants of LMM and the predictive potency of individual factors.

Methods

In this retrospective cross-sectional study, we encompassed 450 older adult Chinese participants (252 males and 198 females). Muscle mass quantifications were performed using bioelectrical impedance analysis. Comprehensive data encompassing demographic details (age, sex, height, and weight) and laboratory results (complete blood count, thyroid function, liver function, and renal function) were systematically recorded. Logistic regression models, coupled with receiver operating characteristic curve analytics, were employed to ascertain the variables influencing LMM and to evaluate the predictive validity of each parameter on LMM.

Results

Upon confounding adjustment for age, gender, body mass index (BMI), and free thyroxine (FT4) persisted as a determinant of LMM. Specifically, individuals with an FT4 exceeding 1.105 ng/dL exhibited a 1.803-fold increased propensity for LMM relative to those with FT4 values below the specified threshold. Incorporating age, gender, BMI, and FT4 in the diagnostic algorithm enhanced the precision of LMM. The results differ between men and women. In the male population, we can still observe that FT4 has a certain value in the diagnosis of LMM, but this phenomenon is not found in the female population.

Conclusions

Elevated FT4 concentrations, albeit within clinically accepted limits, are inversely associated with muscle mass. As such, FT4 could be postulated as a potential biomarker for LMM in geriatric individuals, especially in the male group.

African Swine Fever Virus I267L Is a Hemorrhage-Related Gene Based on Transcriptome Analysis

African swine fever (ASF) is an acute and severe disease transmitted among domestic pigs and wild boars. This disease is notorious for its high mortality rate and has caused great losses to the world's pig industry in the past few years. After infection, pigs can develop symptoms such as high fever, inflammation, and acute hemorrhage, finally leading to death. African swine fever virus (ASFV) is the causal agent of ASF; it is a large DNA virus with 150-200 genes. Elucidating the functions of each gene could provide insightful information for developing prevention and control methods. Herein, to investigate the function of I267L, porcine alveolar macrophages (PAMs) infected with an I267L-deleted ASFV strain (named ∆I267L) and wild-type ASFV for 18 h and 36 h were taken for transcriptome sequencing (RNA-seq). The most distinct different gene that appeared at both 18 hpi (hours post-infection) and 36 hpi was F3; it is the key link between inflammation and coagulation cascades. KEGG analysis (Kyoto encyclopedia of genes and genomes analysis) revealed the complement and coagulation cascades were also significantly affected at 18 hpi. Genes associated with the immune response were also highly enriched with the deletion of I267L. RNA-seq results were validated through RT-qPCR. Further experiments confirmed that ASFV infection could suppress the induction of F3 through TNF-α, while I267L deletion partially impaired this suppression. These results suggest that I267L is a pathogenicity-associated gene that modulates the hemorrhages of ASF by suppressing F3 expression. This study provides new insights into the molecular mechanisms of ASFV pathogenicity and potential targets for ASFV prevention and control.

An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice

General practice plays a prominent role in primary health care (PHC). However, evidence has shown that the quality of PHC is still unsatisfactory, and the accuracy of clinical diagnosis and treatment must be improved in China. Decision making tools based on artificial intelligence can help general practitioners diagnose diseases, but most existing research is not sufficiently scalable and explainable. An explainable and personalized cognitive reasoning model based on knowledge graph (CRKG) proposed in this article can provide personalized diagnosis, perform decision making in general practice, and simulate the mode of thinking of human beings utilizing patients' electronic health records (EHRs) and knowledge graph. Taking abdominal diseases as the application point, an abdominal disease knowledge graph is first constructed in a semiautomated manner. Then, the CRKG designed referring to dual process theory in cognitive science involves the update strategy of global graph representations and reasoning on a personal cognitive graph by adopting the idea of graph neural networks and attention mechanisms. For the diagnosis of diseases in general practice, the CRKG outperforms all the baselines with a precision@1 of 0.7873, recall@10 of 0.9020 and hits@10 of 0.9340. Additionally, the visualization of the reasoning process for each visit of a patient based on the knowledge graph enhances clinicians' comprehension and contributes to explainability. This study is of great importance for the exploration and application of decision making based on EHRs and knowledge graph.

Building gender-specific sexually transmitted infection risk prediction models using CatBoost algorithm and NHANES data

BACKGROUND AND AIMS

Sexually transmitted infections (STIs) are a significant global public health challenge due to their high incidence rate and potential for severe consequences when early intervention is neglected. Research shows an upward trend in absolute cases and DALY numbers of STIs, with syphilis, chlamydia, trichomoniasis, and genital herpes exhibiting an increasing trend in age-standardized rate (ASR) from 2010 to 2019. Machine learning (ML) presents significant advantages in disease prediction, with several studies exploring its potential for STI prediction. The objective of this study is to build males-based and females-based STI risk prediction models based on the CatBoost algorithm using data from the National Health and Nutrition Examination Survey (NHANES) for training and validation, with sub-group analysis performed on each STI. The female sub-group also includes human papilloma virus (HPV) infection.

METHODS

The study utilized data from the National Health and Nutrition Examination Survey (NHANES) program to build males-based and females-based STI risk prediction models using the CatBoost algorithm. Data was collected from 12,053 participants aged 18 to 59 years old, with general demographic characteristics and sexual behavior questionnaire responses included as features. The Adaptive Synthetic Sampling Approach (ADASYN) algorithm was used to address data imbalance, and 15 machine learning algorithms were evaluated before ultimately selecting the CatBoost algorithm. The SHAP method was employed to enhance interpretability by identifying feature importance in the model's STIs risk prediction.

RESULTS

The CatBoost classifier achieved AUC values of 0.9995, 0.9948, 0.9923, and 0.9996 and 0.9769 for predicting chlamydia, genital herpes, genital warts, gonorrhea, and overall STIs infections among males. The CatBoost classifier achieved AUC values of 0.9971, 0.972, 0.9765, 1, 0.9485 and 0.8819 for predicting chlamydia, genital herpes, genital warts, gonorrhea, HPV and overall STIs infections among females. The characteristics of having sex with new partner/year, times having sex without condom/year, and the number of female vaginal sex partners/lifetime have been identified as the top three significant predictors for the overall risk of male STIs. Similarly, ever having anal sex with a man, age and the number of male vaginal sex partners/lifetime have been identified as the top three significant predictors for the overall risk of female STIs.

CONCLUSIONS

This study demonstrated the effectiveness of the CatBoost classifier in predicting STI risks among both male and female populations. The SHAP algorithm revealed key predictors for each infection, highlighting consistent demographic characteristics and sexual behaviors across different STIs. These insights can guide targeted prevention strategies and interventions to alleviate the impact of STIs on public health.

The CUL5 E3 ligase complex negatively regulates central signaling pathways in CD8+ T cells

CD8+ T cells play an important role in anti-tumor immunity. Better understanding of their regulation could advance cancer immunotherapies. Here we identify, via stepwise CRISPR-based screening, that CUL5 is a negative regulator of the core signaling pathways of CD8+ T cells. Knocking out CUL5 in mouse CD8+ T cells significantly improves their tumor growth inhibiting ability, with significant proteomic alterations that broadly enhance TCR and cytokine signaling and their effector functions. Chemical inhibition of neddylation required by CUL5 activation, also enhances CD8 effector activities with CUL5 validated as a major target. Mechanistically, CUL5, which is upregulated by TCR stimulation, interacts with the SOCS-box-containing protein PCMTD2 and inhibits TCR and IL2 signaling. Additionally, CTLA4 is markedly upregulated by CUL5 knockout, and its inactivation further enhances the anti-tumor effect of CUL5 KO. These results together reveal a negative regulatory mechanism for CD8+ T cells and have strong translational implications in cancer immunotherapy.

Generation and optimization of off-the-shelf immunotherapeutics targeting TCR-Vβ2+ T cell malignancy

Current treatments for T cell malignancies encounter issues of disease relapse and off-target toxicity. Using T cell receptor (TCR)Vβ2 as a model, here we demonstrate the rapid generation of an off-the-shelf allogeneic chimeric antigen receptor (CAR)-T platform targeting the clone-specific TCR Vβ chain for malignant T cell killing while limiting normal cell destruction. Healthy donor T cells undergo CRISPR-induced TRAC, B2M and CIITA knockout to eliminate T cell-dependent graft-versus-host and host-versus-graft reactivity. Second generation 4-1BB/CD3zeta CAR containing high affinity humanized anti-Vβ scFv is expressed efficiently on donor T cells via both lentivirus and adeno-associated virus transduction with limited detectable pre-existing immunoreactivity. Our optimized CAR-T cells demonstrate specific and persistent killing of Vβ2+ Jurkat cells and Vβ2+ patient derived malignant T cells, in vitro and in vivo, without affecting normal T cells. In parallel, we generate humanized anti-Vβ2 antibody with enhanced antibody-dependent cellular cytotoxicity (ADCC) by Fc-engineering for NK cell ADCC therapy.

Measurement and analysis of leakage neutron spectra from Lead slab samples with D-T neutrons

The leakage neutron spectra from three different sizes of Lead samples were measured by a TOF technique at 60° and 120°. The essential characteristic properties of the experimental measurement spectra can be reproduced well by MCNP code simulations with the ENDF/B-VIII.0, CENDL-3.2, JENDL-5.0, JEFF-3.3 and TENDL-2021 evaluated nuclear data libraries. The calculated results of JENDL-5.0 and JEFF-3.3 libraries agree better with the experimental data in the whole energy range. The results from ENDF/B-VIII.0 and CENDL-3.2 are overestimated in the 4-9 MeV range at 60° and in the 4-12.5 MeV range at 120°. The differences of the leakage neutron spectra by MCNP simulations using five evaluated nuclear data libraries mainly originate from the differences of the spectrum distributions of neutron reaction channels in these libraries. And the secondary neutron energy distribution and angular distribution from the five libraries have been present to explain it.

A few-shot disease diagnosis decision making model based on meta-learning for general practice

BACKGROUND

Diagnostic errors have become the biggest threat to the safety of patients in primary health care. General practitioners, as the "gatekeepers" of primary health care, have a responsibility to accurately diagnose patients. However, many general practitioners have insufficient knowledge and clinical experience in some diseases. Clinical decision making tools need to be developed to effectively improve the diagnostic process in primary health care. The long-tailed class distributions of medical datasets are challenging for many popular decision making models based on deep learning, which have difficulty predicting few-shot diseases. Meta-learning is a new strategy for solving few-shot problems.

METHODS AND MATERIALS

In this study, a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML) is proposed. The MAML algorithm is applied in a knowledge graph-based disease diagnosis model to find the optimal model parameters. Moreover, FSDD-MAML can learn learning rates for all modules of the knowledge graph-based disease diagnosis model. For n-way, k-shot learning tasks, the inner loop of FSDD-MAML performs multiple gradient update steps to learn internal features in disease classification tasks using n×k examples, and the outer loop of FSDD-MAML optimizes the meta-objective to find the associated optimal parameters and learning rates. FSDD-MAML is compared with the original knowledge graph-based disease diagnosis model and other meta-learning algorithms based on an abdominal disease dataset.

RESULT

Meta-learning algorithms can greatly improve the performance of models in top-1 evaluation compared with top-3, top-5, and top-10 evaluations. The proposed decision making model FSDD-MAML outperforms all the other models, with a precision@1 of 90.02 %. We achieve state-of-the-art performance in the diagnosis of all diseases, and the prediction performance for few-shot diseases is greatly improved. For the two groups with the fewest examples of diseases, FSDD-MAML achieves relative increases in precision@1 of 29.13 % and 21.63 % compared with the original knowledge graph-based disease diagnosis model. In addition, we analyze the reasoning process of several few-shot disease predictions and provide an explanation for the results.

CONCLUSION

The decision making model based on meta-learning proposed in this paper can support the rapid diagnosis of diseases in general practice and is especially capable of helping general practitioners diagnose few-shot diseases. This study is of profound significance for the exploration and application of meta-learning to few-shot disease assessment in general practice.

Quantitative and large-format histochemistry to characterize peripheral artery compositional gradients

The femoropopliteal artery (FPA) is a long, flexible vessel that travels down the anteromedial compartment of the thigh as the femoral artery and then behind the kneecap as the popliteal artery. This artery undergoes various degrees of flexion, extension, and torsion during normal walking movements. The FPA is also the most susceptible peripheral artery to atherosclerosis and is where peripheral artery disease manifests in 80% of cases. The connection between peripheral artery location, its mechanical flexion, and its physiological or pathological biochemistry has been investigated for decades; however, histochemical methods remain poorly leveraged in their ability to spatially correlate normal or abnormal extracellular matrix and cells with regions of mechanical flexion. This study generates new histological image processing pipelines to quantitate tissue composition across high-resolution FPA regions-of-interest or low-resolution whole-section cross-sections in relation to their anatomical locations and flexions during normal movement. Comparing healthy ovine femoral, popliteal, and cranial-tibial artery sections as a pilot, substantial arterial contortion was observed in the distal popliteal and cranial tibial regions of the FPA which correlated with increased vascular smooth muscle cells and decreased elastin content. These methods aim to aid in the quantitative characterization of the spatial distribution of extracellular matrix and cells in large heterogeneous tissue sections such as the FPA. RESEARCH HIGHLIGHTS: Large-format histology preserves artery architecture. Elastin and smooth muscle content is correlated with distance from heart and contortion during flexion. Cell and protein analyses are sensitive to sectioning plane and image magnification.

[Interaction analysis of the influence of different factors and benzene exposure on workers' alanine aminotransferase]

Objective: To investigate the main factors that influence ALT abnormalities in workers exposed to benzene. Methods: In June 2022, data of 613 enterprises with benzene hazards and 585 enterprises with non-benzene hazards in Tianjin in 2021 were collected, and occupational health examination data of 13018 workers with benzene exposure and 13018 workers with non-benzene exposure were collected, and the region, enterprise type, industry classification and enterprise scale of the employer were analyzed. And occupational health examination data of workers with benzene exposure and non-benzene exposure. The effects of personal general situation, occupational history, enterprise information and benzene exposure on alanine aminotransferase were evaluated by additive interaction. Results: Compared with the group of non-benzene-exposed workers, the personal general conditions, occupational history, company information were higher in the benzene-exposed workers, and the differences were statistically significant (P<0.05). The quantitative analysis of additive interaction found that gender (RERI=2.632, 95%CI: 1.966-3.297; AP=0.383, 95%CI: 0.311-0.456; S=1.813, 95%CI: 1.530-2.149), age (RERI=1.142, 95%CI: 0.928-1.356; AP=0.462, 95% CI: 0.371-0.552; S=4.461, 95%CI: 1.800-11.053), length of service (RERI=-1.199, 95%CI: -1.653--0.745; AP=-0.456, 95%CI: -0.640--0.271; S=0.576, 95%CI: 0.479-0.693), region (RERI=0.421, 95% CI: 0.148-0.694; AP=0.161, 95%CI: 0.053-0.268; S=1.350, 95%CI: 1.057-1.726), industry classification (RERI=0.627, 95%CI: 0.345-0.910; AP=0.232, 95%CI: 0.132-0.332; S=1.584, 95%CI: 1.233-2.035) and benzene exposure had a statistically significant additive interaction with abnormal serum ALT. Conclusion: Emphasis should be placed on male workers under the age of 40 in the petrochemical industry, oil storage and transportation, and power production, so as to protect the health of workers more specifically and reduce the risk of disability due to disease.

Value of deep-learning image reconstruction at submillisievert CT for evaluation of the female pelvis

AIM

To assess the value of deep-learning reconstruction (DLR) at submillisievert computed tomography (CT) for the evaluation of the female pelvis, with standard dose (SD) hybrid iterative reconstruction (IR) images as reference.

MATERIALS AND METHODS

The present study enrolled 50 female patients consecutively who underwent contrast-enhanced abdominopelvic CT for clinically indicated reasons. Submillisievert pelvic images were acquired using a noise index of 15 for low-dose (LD) scans, which were reconstructed with DLR (body and body sharp), hybrid-IR, and model-based IR (MBIR). Additionally, SD scans were reconstructed with a noise index of 7.5 using hybrid-IR. Radiation dose, quantitative image quality, overall image quality, image appearance using a five-point Likert scale (1-5: worst to best), and lesion evaluation in both SD and LD images were analysed and compared.

RESULTS

The submillisievert pelvic CT examinations showed a 61.09 ± 4.13% reduction in the CT dose index volume compared to SD examinations. Among the LD images, DLR (body sharp) had the highest quantitative quality, followed by DLR (body), MBIR, and hybrid-IR. LD DLR (body) had overall image quality comparable to the reference (p=0.084) and favourable image appearance (p=0.209). In total, 40 pelvic lesions were detected in both SD and LD images. LD DLR (body and body sharp) exhibited similar diagnostic confidence (p=0.317 and 0.096) compared with SD hybrid-IR.

CONCLUSION

DLR algorithms, providing comparable image quality and diagnostic confidence, are feasible in submillisievert abdominopelvic CT. The DLR (body) algorithm with favourable image appearance is recommended in clinical settings.

Priority list of potential endocrine-disrupting chemicals in food chemical contaminants: a docking study and in vitro/epidemiological evidence integration

Diet is an important exposure route of endocrine-disrupting chemicals (EDCs), but many unfiltered potential EDCs remain in food. The in silico prediction of EDCs is a popular method for preliminary screening. Potential EDCs in food were screened using Endocrine Disruptome, an open-source platform for inverse docking, to predict the binding probabilities of 587 food chemical contaminants with 18 human nuclear hormone receptor (NHR) conformations. In total, 25 contaminants were bound to multiple NHRs such as oestrogen receptor α/β and androgen receptor. These 25 compounds mainly include pesticides and per- and polyfluoroalkyl substances (PFASs). The prediction results were validated with the in vitro data. The structural features and the crucial amino acid residues of the four NHRs were also validated based on previous literature. The findings indicate that the screening has good prediction efficiency. In addition, the epidemic evidence about endocrine interference of PFASs in food on children was further validated through this screening. This study provides preliminary screening results for EDCs in food and a priority list for in vitro and in vivo research.

Predicting Radiation Esophagitis using 18F-FAPI-04 PET/CT in Patients with LA-ESCC Treated with Concurrent Chemoradiotherapy

PURPOSE/OBJECTIVE(S)

This prospective study examined whether 18F-FAPI-04 PET/CT can predict the development and severity of radiation esophagitis (RE) in patients with locally advanced esophageal squamous cell carcinoma (LA-ESCC) treated with concurrent chemoradiotherapy.

MATERIALS/METHODS

From June 2021 to March 2022, images were prospectively collected from LA-ESCC patients who underwent 18F-FAPI-04 PET/CT examinations before and during radiotherapy. The development of RE was evaluated weekly according to Radiation Therapy Oncology Group criterion. The target-to-background ratio in blood (TBRblood) was analyzed at each time point and correlated with the onset and severity of RE. Factors that predicted RE were identified by multivariate logistic analyses.

RESULTS

Thirty patients (median age, 66.5 years [interquartile range: 56¨C71 years]; 22 men) were evaluated. Significantly higher TBRblood (during radiotherapy, mean: 3.06 vs 7.11, P = 0.003) and change in TBRblood compared with pre-RT (ΔTBRblood, mean: 0.67 vs 4.81, P = 0.002) were observed in patients with RE than patients without RE. Those with grade 3 RE had a significantly higher TBRblood (during radiotherapy, mean: 4.55 vs 9.66, P = 0.003) and ΔTBRblood (mean: 2.16 vs 7.50, P = 0.003) compared with those with RE

CONCLUSION

The ΔTBRblood on 18F-FAPI-04 PET/CT may be effective at identifying patients at risk for the development of RE, especially grade 3 RE.

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Disease Burden and Epidemiological Trends of Chronic Kidney Disease at the Global, Regional, National Levels from 1990 to 2019

BACKGROUND

Chronic kidney disease (CKD) is a serious public health issue worldwide, but the disease burden of CKD caused by different etiologies and changing trends has not been fully examined.

METHODS

We collected data from Global Burden of Disease Study 2019 (GBD 2019), including incident cases, age-standardized incidence rate (ASIR), disability-adjusted life years (DALYs), and age-standardized DALY rate between 1990 and 2019 by region, etiology, age, and sex, and calculated the estimated annual percentage change (EAPC) of the rate to evaluate the epidemiological trends.

RESULTS

Globally, incident cases of CKD increased from 7.80 million in 1990 to 18.99 million in 2019, and DALYs increased from 21.50 million to 41.54 million. ASIR increased with an EAPC of 0.69 (95% uncertainty interval [UI] 0.49-0.89) and reached 233.65 per 100,000 in 2019, while the age-standardized DALY rate increased with an EAPC of 0.30 (95% UI 0.17-0.43) and reached 514.86 per 100,000. North Africa and the Middle East, central Latin America, and North America had the highest ASIR in 2019. Central Latin America had the highest age-standardized DALY rate, meanwhile. Almost all countries experienced an increase in ASIR, and over 50% of countries had an increasing trend in age-standardized DALY rate from 1990 to 2019. CKD due to diabetes mellitus type 2 and hypertension accounted for the largest disease burden with 85% incident cases and 66% DALYs in 2019 of known causes, with the highest growth in age-standardized DALY rate and a similar geographic pattern to that of total CKD. Besides, the highest incidence rate of total and four specific CKDs were identified in people aged 70 plus years, who also had the highest DALY rate with a stable trend after 2010. Females had a higher ASIR, while males had a higher age-standardized DALY rate, the gap of which was most distinctive in CKD due to hypertension.

CONCLUSION

The disease burden of CKD remains substantial and continues to grow globally. From 1990 to 2019, global incident cases of CKD have more than doubled and DALYs have almost doubled, and surpassed 40 million years. CKD due to diabetes mellitus type 2 and hypertension contributed nearly 2/3 of DALYs in 2019 of known causes, and had witnessed the highest growth in age-standardized DALY rate. Etiology-specific prevention strategies should be placed as a high priority on the goal of precise control of CKD.

Differential Roles of Interleukin-6 in Severe Acute Respiratory Syndrome-Coronavirus-2 Infection and Cardiometabolic Diseases

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection can lead to a cytokine storm, unleashed in part by pyroptosis of virus-infected macrophages and monocytes. Interleukin-6 (IL-6) has emerged as a key participant in this ominous complication of COVID-19. IL-6 antagonists have improved outcomes in patients with COVID-19 in some, but not all, studies. IL-6 signaling involves at least 3 distinct pathways, including classic-signaling, trans-signaling, and trans-presentation depending on the localization of IL-6 receptor and its binding partner glycoprotein gp130. IL-6 has become a therapeutic target in COVID-19, cardiovascular diseases, and other inflammatory conditions. However, the efficacy of inhibition of IL-6 signaling in metabolic diseases, such as obesity and diabetes, may depend in part on cell type-dependent actions of IL-6 in controlling lipid metabolism, glucose uptake, and insulin sensitivity owing to complexities that remain to be elucidated. The present review sought to summarize and discuss the current understanding of how and whether targeting IL-6 signaling ameliorates outcomes following SARS-CoV-2 infection and associated clinical complications, focusing predominantly on metabolic and cardiovascular diseases.