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Abstract During the progression of proliferative vitreoretinopathy (PVR) following ocular trauma, previously quiescent retinal pigment epithelial (RPE) cells transition into a state of rapid proliferation, migration, and secretion. The elusive molecular mechanisms behind these changes have hindered the development of effective pharmacological treatments, presenting a pressing clinical challenge. In this study, by monitoring the dynamic changes in chromatin accessibility and various histone modifications, we chart the comprehensive epigenetic landscape of RPE cells in male mice subjected to traumatic PVR. Coupled with transcriptomic analysis, we reveal a robust correlation between enhancer activation and the upregulation of the PVR-associated gene programs. Furthermore, by constructing transcription factor regulatory networks, we identify the aberrant activation of enhancer-driven RANK-NFATc1 pathway as PVR advanced. Importantly, we demonstrate that intraocular interventions, including nanomedicines inhibiting enhancer activity, gene therapies targeting NFATc1 and antibody therapeutics against RANK pathway, effectively mitigate PVR progression. Together, our findings elucidate the epigenetic basis underlying the activation of PVR-associated genes during RPE cell fate transitions and offer promising therapeutic avenues targeting epigenetic modulation and the RANK-NFATc1 axis for PVR management.

Abstract Background The identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to predict prognosis for acute myeloid leukemia (AML). Methods 952 patients with pathologically-confirmed AML were retrospectively enrolled between 2010 and 2020. CT-derived features (including body composition and subcutaneous fat features), were obtained from the initial chest CT images and were used to build models to predict the prognosis. A CT-derived MSF nomogram was constructed using multivariate Cox regression incorporating CT-based features. The performance of the prediction models was assessed with discrimination, calibration, decision curves and improvements. Results Three CT-derived features, including myosarcopenia, spleen_CTV, and SF_CTV (MSF) were identified as the independent predictors for prognosis in AML (P

Abstract Infectious bursal disease (IBD) of chickens is an acute, high-contact, lytic infectious disease caused by infectious bursal disease virus (IBDV). The attenuated inactivated vaccine produced by DF-1 cells is an effective control method, but the epidemic protection demands from the world poultry industry remain unfulfilled. To improve the IBDV vaccine production capacity and reduce the economic losses caused by IBDV in chicken, cellular metabolic engineering is performed on host cells. In this study, when analyzing the metabolomic after IBDV infection of DF-1 cells and the exogenous addition of reduced glutathione (GSH), we found that glutathione metabolism had an important role in the propagation of IBDV in DF-1 cells, and the glutathione synthetase gene (gss) could be a limiting regulator in glutathione metabolism. Therefore, three stable recombinant cell lines GSS-L, GSS-M, and GSS-H (gss gene overexpression with low, medium, and high mRNA levels) were screened. We found that the recombinant GSS-M cell line had the optimal regulatory effect with a 7.19 ± 0.93-fold increase in IBDV titer. We performed oxidative stress and redox status analysis on different recombinant cell lines, and found that the overexpression of gss gene significantly enhanced the ability of host cells to resist oxidative stress caused by IBDV infection. This study established a high-efficiency DF-1 cells system for IBDV vaccine production by regulating glutathione metabolism, and underscored the importance of moderate gene expression regulation on the virus reproduction providing a way for rational and precise cell engineering. Graphical Abstract

Abstract Background Intracranial extraventricular ependymoma (IEE) is an ependymoma located in the brain parenchyma outside the ventricles. IEE has overlapping clinical and imaging characteristics with glioblastoma multiforme (GBM) but different treatment strategy and prognosis. Therefore, an accurate preoperative diagnosis is necessary for optimizing therapy for IEE. Methods A retrospective multicenter cohort of IEE and GBM was identified. MR imaging characteristics assessed with the Visually Accessible Rembrandt Images (VASARI) feature set and clinicopathological findings were recorded. Independent predictors for IEE were identified using multivariate logistic regression, which was used to construct a diagnostic score for differentiating IEE from GBM. Results Compared to GBM, IEE tended to occur in younger patients. Multivariate logistic regression analysis identified seven independent predictors for IEE. Among them, 3 predictors including tumor necrosis rate (F7), age, and tumor‐enhancing margin thickness (F11), demonstrated higher diagnostic performance with an Area Under Curve (AUC) of more than 70% in distinguishing IEE from GBM. The AUC was 0.85, 0.78, and 0.70, with sensitivity of 92.98%, 72.81%, and 96.49%, and specificity of 65.50%, 73.64%, and 43.41%, for F7, age, and F11, respectively. Conclusion We identified specific MR imaging features such as tumor necrosis and thickness of enhancing tumor margins that could help to differentiate IEE from GBM. Our study results should be helpful to assist in diagnosis and clinical management of this rare brain tumor.

Recently, there has been increasing interest in the use of bacteria for cancer therapy due to their ability to selectively target tumor sites and inhibit tumor growth. However, the complexity of the interaction between bacteria and tumor cells evokes unpredictable therapeutic risk, which induces inflammation, stimulates the up-regulation of cyclooxygenase II (COX-2) protein, and stimulates downstream antiapoptotic gene expression in the tumor microenvironment to reduce the antitumor efficacy of chemotherapy and immunotherapy. In this study, we encapsulated celecoxib (CXB), a specific COX-2 inhibitor, in liposomes anchored to the surface of Escherichia coli Nissle 1917 (ECN) through electrostatic absorption (C@ECN) to suppress ECN-induced COX-2 up-regulation and enhance the synergistic antitumor effect of doxorubicin (DOX). C@ECN improved the antitumor effect of DOX by restraining COX-2 expression. In addition, local T lymphocyte infiltration was induced by the ECN to enhance immunotherapy efficacy in the tumor microenvironment. Considering the biosafety of C@ECN, a hypoxia-induced lysis circuit, pGEX-Pvhb-Lysis, was introduced into the ECN to limit the number of ECNs in vivo. Our results indicate that this system has the potential to enhance the synergistic effect of ECN with chemical drugs to inhibit tumor progression in medical oncology.

Background: Emotional dysregulation is a core feature of borderline personality disorder (BPD). Previous studies have reported that abnormal grey matter volume is associated with the limbic–cortical circuit and default mode network (DMN) in patients with BPD. However, alterations of cortical thickness in adolescents with BPD have not been well evaluated. Objective: The aim of this study was to assess cortical thickness and its association with emotional dysregulation in adolescents with BPD. Method: This prospective study enrolled 52 adolescents with BPD and 39 age- and sex-matched healthy controls (HCs). Assessments included brain magnetic resonance imaging (MRI) acquisition with structural and resting-state functional MRI data, and clinical assessment for emotional dysregulation using the Difficulties in Emotion Regulation Scale (DERS). Cortical thickness and seed-based functional connectivity were analysed with FreeSurfer 7.2 software. Correlation analysis between cortical thickness and the scores from emotional assessment was performed with Spearman analysis. Results: Compared to HCs, there was altered cortical thickness in the DMN and limbic–cortical circuit in adolescents with BPD (Monte Carlo correction, all p

Abstract Non-invasive prediction for KIT/PDGFRA status in GIST is a challenging problem. This study aims to evaluate whether CT based sarcopenia could differentiate KIT/PDGFRA wild-type gastrointestinal stromal tumor (wt-GIST) from the mutant-type GIST (mu-GIST), and to evaluate genetic features of GIST. A total of 174 patients with GIST (wt-GIST = 52) were retrospectively identified between January 2011 to October 2019. A sarcopenia nomogram was constructed by multivariate logistic regression. The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. Genomic data was obtained from our own specimens and also from the open databases cBioPortal. Data was analyzed by R version 3.6.1 and clusterProfiler ( http://cbioportal.org/msk-impact ). There were significantly higher incidence (75.0% vs. 48.4%) and more severe sarcopenia in patients with wt-GIST than in patients with mu-GIST. Multivariate logistic regression analysis showed that sarcopenia score (fitted based on age, gender and skeletal muscle index), and muscle fat index were independent predictors for higher risk of wt-GIST (P

Abstract Background and Purpose Early detection of non‐response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT. Materials and Methods Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non‐response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application. Results This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non‐responders and 101 responders) and 64 patients in the validation cohort (21 non‐responders and 43 responders). For predicting non‐response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance. Conclusion Pretreatment CT radiomics achieved satisfying performance in predicting non‐response to nCRT and could be helpful to assist in treatment planning for patients with LARC.

Abstract Background Maintaining intraoperative haemodynamic stability can reduce cardiovascular complications during surgery for pheochromocytoma and paraganglioma (PPGL). Risk factors such as tumour size and catecholamine levels are reported to predict haemodynamic responses during surgery for PPGL. We hypothesized that additional factors including body composition and genetic information could further improve prediction. Methods Consecutive patients with PPGL confirmed by surgical pathology between June 2010 and June 2019 were retrospectively included. Cross‐sectional computed tomography images at the L3 level were used to assess body composition parameters including skeletal muscle area and visceral fat area. Next‐generation sequencing was performed using a panel containing susceptibility genes of PPGL. Differences in clinical‐genetic characteristics and body composition parameters were analysed and compared in patients with and without intraoperative haemodynamic instability (HDI). Results We included 221 patients with PPGL (median age 47 [38–56] years, and 52% male). Among them, 49.8% had Cluster 2 mutations (related to kinase signalling pathways), 44.8% had sarcopenia, and 52.9% experienced intraoperative HDI. Compared with patients without HDI, more patients with HDI had Cluster 2 mutations (59.8% vs. 38.5%, P = 0.002) and less had sarcopenia (35.9% vs. 54.8%, P = 0.005). Multivariate analysis showed that urine vanillylmandelic acid ≥ 58 μmol/day (adjusted odds ratio [OR] = 1.840, 95% confidence interval [CI] = 1.012–3.347, P = 0.046), tumour size ≥ 4 cm (adjusted OR = 2.278, 95% CI = 1.242–4.180, P = 0.008), and Cluster 2 mutations (adjusted OR = 2.199, 95% CI = 1.128–4.285, P = 0.021) were independent risk factors for intraoperative HDI, while sarcopenia (adjusted OR = 0.475, 95% CI = 0.266–0.846, P = 0.012) decreased the risk. Conclusions Body composition and genotype were associated with intraoperative haemodynamics in patients with PPGL. Our results indicated that inclusion of body composition and genotype in the overall assessment of patients with PPGL helped to predict HDI during surgery, which could assist in implementing preoperative and intraoperative measures to reduce perioperative complications.

Abstract Assessment of adrenal incidentaloma relies on imaging analysis and evaluation of adrenal function. Radiomics as a tool for quantitative image analysis is useful for evaluation of adrenal incidentaloma. In this review, we examined radiomic literature on adrenal incidentaloma including both adrenal functional assessment and structural differentiation of benign versus malignant adrenal tumors. In this review, we summarized the status of radiomic application on adrenal incidentaloma and suggested potential direction for future research.

Abstract Objective Whether preoperative serum carbohydrate antigen 19–9 (CA19-9) is an independent prognostic factor and there are interactions of serum CA19-9 with carcinoembryonic antigen (CEA) on the risk of recurrence in colorectal cancer (CRC) patients are still not clarified. Methods Consecutive patients with CRC who underwent curative resection for stage II-III colorectal adenocarcinoma at five hospitals were collected. Based on Cox models, associations of preoperative CA19-9 with recurrence-free survival (RFS) and overall survival (OS) were evaluated in patients with or without elevated CEA, and interactions between CEA and CA19-9 were also calculated. Restricted cubic spline (RCS) curves were used to evaluate the associations between preoperative CA19-9 and CRC outcomes on a continuous scale. Results A total of 5048 patients (3029 [60.0%] men; median [interquartile range, IQR] age, 61.0 [51.0, 68.0] years; median [IQR] follow-up duration 46.8 [36.5–62.4] months) were included. The risk of recurrence increased with the elevated level of preoperative CA19-9, with the slope steeper in patients with normal CEA than those with elevated CEA. Worse RFS was observed for elevated preoperative CA19-9 (> 37 U/mL) (n = 738) versus normal preoperative CA19-9 (≤ 37 U/mL) (n = 4310) (3-year RFS rate: 59.4% versus 78.0%; unadjusted hazard ratio [HR]: 2.02; 95% confidence interval [CI]:1.79 to 2.28), and significant interaction was found between CA19-9 and CEA (P for interaction = 0.001). Increased risk and interaction with CEA were also observed for OS. In the Cox multivariable analysis, elevated CA19-9 was associated with shorter RFS and OS regardless of preoperative CEA level, even after adjustment for other prognostic factors (HR: 2.08, 95% CI:1.75 to 2.47; HR: 2.25, 95% CI:1.80 to 2.81). Subgroup analyses and sensitivity analyses yielded largely similar results. These associations were maintained in patients with stage II disease (n = 2724). Conclusions Preoperative CA19-9 is an independent prognostic factor in CRC patients. Preoperative CA19-9 can be clinically used as a routine biomarker for CRC patients, especially with preoperative normal serum CEA.

Models for predicting axillary lymph node metastasis (ALNM) in breast cancer patients are lacking. We aimed to develop an efficient model to accurately predict ALNM. Three hundred fifty-five breast cancer patients were recruited and randomly divided into the training and validation sets. Univariate and multivariate logistic regressions were applied to identify predictors of ALNM. We developed nomograms based on these variables to predict ALNM. The performance of the nomograms was tested using the receiver operating characteristic curve and calibration curve, and a decision curve analysis was performed to assess the clinical utility of the prediction models. The nomograms that included clinical N stage (cN), pathological grade (pathGrade), and hemoglobin accurately predicted ALNM in the training and validation sets (area under the curve [AUC] 0.80 and 0.80, respectively). We then explored the importance of the cN and pathGradesignatures used in the integrated model and developed new nomograms by removing the two variables. The results suggested that the combine-pathGrade nomogram also accurately predicted ALNM in the training and validation sets (AUC 0.78 and 0.78, respectively), but the combine-cN nomogram did not (AUC 0.64 and 0.60, in training and validation sets, respectively). We described a cN-based ALNM prediction model in breast cancer patients, presenting a novel efficient clinical decision nomogram for predicting ALNM.

IntroductionStudies have found a varying degree of cognitive, psychosocial, and functional impairments in patients with unruptured intracranial aneurysms (UIAs), whereas the neural correlates underlying these impairments remain unknown.MethodsTo examine the brain morphological alterations and white matter lesions in patients with UIA, we performed a range of structural analyses to examine the brain morphological alterations in patients with UIA compared with healthy controls (HCs). Twenty-one patients with UIA and 23 HCs were prospectively enrolled into this study. Study assessment consisted of a brain magnetic resonance imaging (MRI) scan with high-resolution T1-weighted and T2-weighted imaging data, a Montreal Cognitive Assessment (MoCA), and laboratory tests including blood inflammatory markers and serum lipids. Brain MRI data were processed for cortical thickness, local gyrification index (LGI), volume and shape of subcortical nuclei, and white matter lesions.ResultsCompared to the HCs, patients with UIA showed no significant differences in cortical thickness but decreased LGI values in the right posterior cingulate cortex, retrosplenial cortex, cuneus, and lingual gyrus. In addition, decreased LGI values correlated with decreased MoCA score (r = 0.498, p = 0.021) and increased white matter lesion scores (r = −0.497, p = 0.022). The LGI values were correlated with laboratory values such as inflammatory markers and serum lipids. Patients with UIA also showed significant regional atrophy in bilateral thalami as compared to the HCs. Moreover, the LGI values were significantly correlated with thalamic volume in the HCs (r = 0.4728, p = 0.0227) but not in the patients with UIA (r = 0.11, p = 0.6350).DiscussionThe decreased cortical gyrification, increased white matter lesions, and regional thalamic atrophy in patients with UIA might be potential neural correlates of cognitive changes in UIA.

Abstract The scale-up of animal cell cultivation is important but remains complex and challenging. In the present study, we propose a novel scale-up strategy for baby hamster Syrian kidney-21 (BHK-21) cell cultivation based on similar hydrodynamic environments. The hydrodynamic characteristics of the different scale bioreactors were determined by computational fluid dynamics (CFD) and further correlated with the agitation speed. The optimal hydrodynamic environment for cell cultivation and vaccine production was determined from the cultivation experiments of BHK-21 cells in 5-L laboratory-scale bioreactors equipped with different impellers at various agitation speeds. BHK-21 cell cultivation was scaled up from 5-L to 42-, 350-, and 1000-L bioreactors by adjusting the agitation speed to make the hydrodynamic features similar to those in the 5-L bioreactor, especially for the shear rate in the impeller zone (γ imp) and energy dissipation rate in the tank bulk zone (ε tan). The maximum cell density and cell aggregation rate in these scaled-up bioreactors were in the range of 4.6 × 106 ~ 4.8 × 106 cells/mL and 16 ~ 20%, which are comparable to or even better than those observed in the 5-L bioreactor (maximum cell density 4.8 × 106 cells/mL, cell aggregation rate 21%). The maximum virus titer of 108.0 LD50/mL achieved in the 1000-L bioreactor was close to 108.3 LD50/mL that obtained in the 5-L bioreactor. Hence, the scale-up strategy proposed in this study is feasible and can efficiently facilitate the scale-up processes of animal cell cultivation.

BackgroundThe predictive role of sarcopenia in cancer prognosis is an area of increasing concern. However, the influence of sex difference on the predictive role of sarcopenia in cancer prognosis has not been clearly defined. This retrospective cohort study investigated the effect of preoperative sarcopenia on the long-term outcomes of patients with gastric cancer (GC) based on sexual dimorphism.MethodsPreoperative abdominal computed tomography (CT) scans from 379 GC patients who underwent radical gastrectomy were carefully analyzed. The patients were categorized into sarcopenia and non-sarcopenia groups according to the L3 skeletal muscle index (L3 SMI) measured on CT scans. Moreover, other indexes which can be used to evaluate the muscle area or the muscle quality, including skeletal muscle area (SMA), visceral fat area (VFA), subcutaneous fat area (SFA), skeletal muscle radiation attenuation (SM-RA), visceral fat index (VFI), subcutaneous fat index (SFI), and subcutaneous and visceral ratio (SV), were obtained from CT scans.ResultsThere were 254 men and 125 women included in our study. After calculation, we defined sex-specific SMI-related mortality cutoff as 39.73 and 32.97 cm2/m2 for men and women. Univariable analysis showed that pathological tumor-node-metastasis (pTNM), depth of invasion, lymph node metastasis, differentiation degree, preoperative sarcopenia (for men), SMA (for men), L3 SMI, SFA (for women), SFI (for women), SV (for women), and SM-RA (especially for men) were significant independent predictors of overall survival (OS). Multivariable analysis showed that pTNM, depth of invasion, poor differentiation, and SM-RA were significantly associated with 5-year OS in GC patients. However, CT-determined sarcopenia was associated with significantly worse OS only in men, and SFA was significantly associated with 5-year OS only in women.ConclusionSM-RA is a reliable prognostic factor in patients with GC after radical gastrectomy. The impact of indexes mentioned above on survival outcomes is dependent on sex. CT-determined preoperative sarcopenia, a muscle-related indicator, was associated with outcomes in men. Adipose-related indicator (SFA), instead, was associated with outcomes in women.

Background and PurposeComputerized tomography (CT) scans are commonly performed to assist in diagnosis and treatment of locally advanced rectal cancer (LARC). This study assessed the usefulness of pretreatment CT-based radiomics for predicting pathological complete response (pCR) of LARC to neoadjuvant chemoradiotherapy (nCRT).Materials and MethodsPatients with LARC who underwent nCRT followed by total mesorectal excision surgery from July 2010 to December 2018 were enrolled in this retrospective study. A total of 340 radiomic features were extracted from pretreatment contrast-enhanced CT images. The most relevant features to pCR were selected using the least absolute shrinkage and selection operator (LASSO) method and a radiomic signature was generated. Predictive models were built with radiomic features and clinico-pathological variables. Model performance was assessed with decision curve analysis and was validated in an independent cohort.ResultsThe pCR was achieved in 44 of the 216 consecutive patients (20.4%) in this study. The model with the best performance used both radiomics and clinical variables including radiomic signatures, distance to anal verge, lymphocyte-to-monocyte ratio, and carcinoembryonic antigen. This combined model discriminated between patients with and without pCR with an area under the curve of 0.926 and 0.872 in the training and the validation cohorts, respectively. The combined model also showed better performance than models built with radiomic or clinical variables alone.ConclusionOur combined predictive model was robust in differentiating patients with and without response to nCRT.

AimTo evaluate the feasibility of computed tomography (CT) - derived measurements of body composition parameters to predict the risk factor of non-objective response (non-OR) in patients with hepatocellular carcinoma (HCC) undergoing anti-PD-1 immunotherapy and hepatic artery infusion chemotherapy (immune-HAIC).MethodsPatients with histologically confirmed HCC and treated with the immune-HAIC were retrospectively recruited between June 30, 2019, and July 31, 2021. CT-based estimations of body composition parameters were acquired from the baseline unenhanced abdominal CT images at the level of the third lumbar vertebra (L3) and were applied to develop models predicting the probability of OR. A myosteatosis nomogram was built using the multivariate logistic regression incorporating both myosteatosis measurements and clinical variables. Receiver operating characteristic (ROC) curves assessed the performance of prediction models, including the area under the curve (AUC). The nomogram’s performance was assessed by the calibration, discrimination, and decision curve analyses. Associations among predictors and gene mutations were also examined by correlation matrix analysis.ResultsFifty-two patients were recruited to this study cohort, with 30 patients having a OR status after immune-HAIC treatment. Estimations of myosteatosis parameters, like SM-RA (skeletal muscle radiation attenuation), were significantly associated with the probability of predicting OR (P=0.007). The SM-RA combined nomogram model, including serum red blood cell, hemoglobin, creatinine, and the mean CT value of visceral fat (VFmean) improved the prediction probability for OR disease with an AUC of 0.713 (95% CI, 0.75 to 0.95) than the clinical model nomogram with AUC of 0.62 using a 5-fold cross-validation methodology. Favorable clinical potentials were observed in the decision curve analysis.ConclusionsThe CT-based estimations of myosteatosis could be used as an indicator to predict a higher risk of transition to the Non-OR disease state in HCC patients treated with immune-HAIC therapy. This study demonstrated the therapeutic relevance of skeletal muscle composition assessments in the overall prediction of treatment response and prognosis in HCC patients.

ObjectivesTo assess the accuracy of computed tomography (CT)-based machine learning models for differentiating subclinical pheochromocytoma (sPHEO) from lipid-poor adenoma (LPA) in patients with adrenal incidentalomas.Patients and MethodsThe study included 188 tumors in the 183 patients with LPA and 92 tumors in 86 patients with sPHEO. Pre-enhanced CT imaging features of the tumors were evaluated. Machine learning prediction models and scoring systems for differentiating sPHEO from LPA were built using logistic regression (LR), support vector machine (SVM) and random forest (RF) approaches.ResultsThe LR model performed better than other models. The LR model (M1) including three CT features: CTpre value, shape, and necrosis/cystic changes had an area under the receiver operating characteristic curve (AUC) of 0.917 and an accuracy of 0.864. The LR model (M2) including three CT features: CTpre value, shape and homogeneity had an AUC of 0.888 and an accuracy of 0.832. The S2 scoring system (sensitivity: 0.859, specificity: 0.824) had comparable diagnostic value to S1 (sensitivity: 0.815; specificity: 0.910).ConclusionsOur results indicated the potential of using a non-invasive imaging method such as CT-based machine learning models and scoring systems for predicting histology of adrenal incidentalomas. This approach may assist the diagnosis and personalized care of patients with adrenal tumors.

Trace levels of oxytetracycline (OTC)—a veterinary antibiotic and feed additive—are widespread in the environment. Studies revealed that OTC potentially impairs thyroid function, which may affect neurobehaviour; however, the impact of exposure to environmental concentrations of OTC on adult neurobehaviour is unknown. In this study, the effects of OTC on zebrafish after 30-day exposure were investigated. The total swimming distance was significantly increased under vibration and light/dark stimulation, while time spent in the white area was prolonged during the black/white preference test, indicating that the zebrafish became bolder and more impulsive under low OTC exposure. Additionally, monoamine neurotransmitter (5-hydroxytryptamine, dopamine, norepinephrine) levels were decreased and gene expression of monoamine oxidase (mao) involved in neurotransmitter metabolism was upregulated at the transcription level after OTC exposure. Because triiodothyronine (T3) levels were enhanced following exposure to OTC, we speculated that T3 may mediate OTC damage to the nervous system. Our simulated molecular docking analysis showed that OTC combined with the sodium iodide cotransporter protein may result in excessive T3 synthesis. We further exposed zebrafish to T3, and they exhibited similar behaviour to the OTC exposure group. In conclusion, environmental OTC may activate monoamine oxidase and enhance the metabolism of monoaminergic neurotransmitters via T3, thereby inducing abnormal neurobehaviour.

BackgroundIt is prudent to identify the risk for progressive disease (PD) in patients with non-small-cell lung cancer (NSCLC) who undergo platinum-based chemotherapy. The present study aimed to develop a CT imaging-based sarcopenic nomogram for predicting the risk of PD prior to chemotherapy treatment.MethodsWe retrospectively enrolled patients with NSCLC who underwent platinum-based chemotherapy. Imaging-based body composition parameters such as skeletal muscle index (SMI) for assessment of sarcopenia were obtained from pre-chemotherapy chest CT images at the level of the eleventh thoracic vertebral body (T11). Sarcopenic nomogram was constructed using multivariate logistic regression and performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve.ResultsSixty (14.7%) of the 408 patients in the study cohort developed PD during chemotherapy. The prediction nomogram for developing PD achieved a moderate efficiency with an area under the curve (AUC) of 0.75 (95% CI: 0.69-0.80) for the training cohort, and 0.76 (95%CI: 0.68-0.84) for the validation cohort, as well as a good performance of consistence (bootstrap for training cohort: 0.75 ± 0.02; validation cohort: 0.74 ± 0.06). Favorable clinical application was observed in the decision curve analysis.ConclusionOur CT-based sarcopenic nomogram showed the potential for an individualized prediction of progression for patients with NSCLC receiving platinum-based chemotherapy.

PurposeEarly recurrence of glioblastoma after standard treatment makes patient care challenging. This study aimed to assess preoperative magnetic resonance imaging (MRI) radiomics for predicting early recurrence of glioblastoma.Patients and MethodsA total of 122 patients (training cohort: n = 86; validation cohort: n = 36) with pathologically confirmed glioblastoma were included in this retrospective study. Preoperative brain MRI images were analyzed for both radiomics and the Visually Accessible Rembrandt Image (VASARI) features of glioblastoma. Models incorporating MRI radiomics, the VASARI parameters, and clinical variables were developed and presented in a nomogram. Performance was assessed based on calibration, discrimination, and clinical usefulness.ResultsThe nomogram consisting of the radiomic signatures, the VASARI parameters, and blood urea nitrogen (BUN) values showed good discrimination between the patients with early recurrence and those with later recurrence, with an area under the curve of 0.85 (95% CI, 0.77-0.94) in the training cohort and 0.84 [95% CI, 0.71-0.97] in the validation cohort. Decision curve analysis demonstrated favorable clinical application of the nomogram.ConclusionThis study showed the potential usefulness of preoperative brain MRI radiomics in predicting the early recurrence of glioblastoma, which should be helpful in personalized management of glioblastoma.

Solid-state batteries (SSBs) have proven to have the potential to be a proper substitute for conventional lithium-ion batteries due to their promising features. In order for the SSBs to be market-ready, the prognostics and health management (PHM) of battery systems plays a critical role in achieving such a goal. PHM ensures the reliability and availability of batteries during their operational time with acceptable safety margin. In the past two decades, much of the focus has been directed towards the PHM of lithium-ion batteries, while little attention has been given to PHM of solid-state batteries. Hence, this report presents a holistic review of the recent advances and current trends in PHM techniques of solid-state batteries and the associated challenges. For this purpose, notable commonly employed physics-based, data-driven, and hybrid methods are discussed in this report. The goal of this study is to bridge the gap between liquid state and SSBs and present the crucial aspects of SSBs that should be considered in order to have an accurate PHM model. The primary focus is given to the ML-based data-driven methods and the requirements that are needed to be included in the models, including anode, cathode, and electrolyte materials.

Abstract The cap protein is encoded by the orf2 gene of porcine circovirus type 2 (PCV2) has the main antigen epitope of PCV2 and can form virus-like particles (VLPs), which are expressed in insect cells. PCV2-VLPs can effectively inhibit PCV2 replication as a subunit vaccine. In this study, a robust and reliable fed-batch process was successfully developed for the production of PCV2-VLPs by Sf9 cells. The feeding solution, feeding strategy, and cell density at infection were optimized to maximize the final PCV2-VLPs production yields. The cell density at infection and the volumetric PCV2-VLPs production reached 12 × 106 cells/mL and 110 mg/L, respectively, which yielded 3- and 3.6-fold enhancements compared to the batch culture. The PCV2-VLPs produced in fed-batch culture were not different from the PCV2-VLPs produced in a batch culture in an immunity test. A highly efficient production process was produced for PCV2-VLPs subunit vaccines, which could provide an effective means for the industrial production of PCV2 vaccines.

Apelin is an endogenous ligand that binds to the G protein-coupled receptor angiotensin-like-receptor 1 (APJ). Apelin and APJ are widely distributed in organs and tissues and are involved in multiple physiological and pathological processes including cardiovascular regulation, neuroendocrine stress response, energy metabolism, etc. Additionally, apelin/APJ axis was found to play an important role in cancer development and progression. Apela is a newly identified endogenous ligand for APJ. Several studies have revealed the potential role of Apela in cancers. In this article, we review the current studies focusing on the role of apelin/APJ signaling and Apela in different cancers. Potential mechanisms by which apelin/APJ and Apela mediate the regulation of cancer development and progression were also mentioned. The Apelin/APJ signaling and Apela may serve as potential therapeutic candidates for treatment of cancer.

Potato (Solanum tuberosum L.) growth and production is highly dependent on potassium (K) levels in the soil. Southwest China is the largest potato production region but it has low availability of soil potassium. To assess the genetic variation in K use efficiency, 20 potato genotypes were collected to compare the yield and K content in a pot experiment. Moreover, ‘Huayu-5’ and ‘Zhongshu-19’ were cultivated in five K applications to investigate the K distribution and sucrose in different organs. The results indicated that there were highly significant effects of K, genotype and K×G interactions on tuber yield, plant and tuber K content, plant K uptake efficiency and K harvest index. Cluster analysis classified 20 potato genotypes into four types: DH (high efficiency at low and high K application), LKH (high efficiency at low K application), HKH (high efficiency at high K application) and DL (low efficiency at low and high K application). The potassium distribution percentage in the tubers of the potassium-efficient genotype was higher than that of the potassium-inefficient genotype under low potassium application. The sucrose content in the tuber gently declined as the application of K rose in both cultivars, and that in the tuber of ‘Huayu-5’ was higher than that in ‘Zhongshu-19’. ‘Huayu-5’ reached the highest yield when the potassium application was 159.45 kg ha-1, and ‘Zhongshu-19’ reached the highest yield when the potassium application was 281.4 kg ha-1. This study indicated that genetic variation for K utilization efficiency existed among 20 genotypes, and yield in low K application and relative yield were suitable criteria for screening K utilization efficiency genotypes.

BackgroundClear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery.MethodsPatients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis.ResultsA total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765–0.9585) and 0.8088 (95% CI: 0.7064–0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353–0.8987) and 0.8017 (95% CI: 0.6878–0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646–0.9824) and an AUC of 0.9099 (95% CI: 0.8324–0.9873) for the training cohort and validation cohort, respectively.ConclusionWe developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.

Objective: Early detection of platinum resistance for ovarian cancer treatment remains challenging. This study aims to develop a machine learning model incorporating genomic data such as Single-Nucleotide Polymorphisms (SNPs) of Human Sulfatase 1 (SULF1) with a CT radiomic model based on pre-treatment CT images, to predict platinum resistance for ovarian cancer (OC) treatment. Methods: A cohort of 102 patients with pathologically confirmed OC was retrospectively enrolled into this study from January 2006 to February 2018. All patients had platinum-based chemotherapy after maximal cyto-reductive surgery. This cohort was separated into two groups according to treatment response, i.e., the group with platinum-resistant disease (PR group) and the group with platinum-sensitive disease (PS group). We genotyped 12 SNPs of SULF1 for all OC patients using Mass Array Method. Radiomic features, SNP data and clinicopathological data of the 102 patients were used to build the differentiation models. The study participants were divided into two cohorts: the training cohort (n = 71) and the validation cohort (n = 31). Feature selection and predictive modeling were performed using least absolute shrinkage and selection operator (LASSO), Random Forest Classifier and Support Vector Machine methods. Model performance for predicting platinum resistance was assessed with respect to its calibration, discrimination, and clinical application. Results: For prediction of platinum resistance, the approach combining the radiomics, clinicopathological data and SNP data demonstrated higher classification efficiency, with an AUC value of 0.993 (95 % CI: 0.83 to 0.98) in the training cohort and 0.967 (95 % CI: 0.83 to 0.98) in validation cohort, than the performance with only the SNPs of SULF1 model (AUC: training, 0.843 [95 %CI: 0.738-0.948]; validation, 0.815 [0.601-1.000]), or with only the radiomic model (AUC: training, 0.874 [95 %CI: 0.789-0.960]; validation, 0.832 [95 %CI: 0.687-0.976]). This integrated approach also showed good calibration and favorable clinical utility. Conclusions: A predictive model combining pretreatment CT radiomics with genomic data such as SNPs of SULF1 could potentially help to predict platinum resistance in ovarian cancer treatment.

Autoimmune encephalitic syndromes include mutism, somnolence, movement disorder, and behavioral, or psychiatric symptoms. When paired with bilateral basal ganglia lesions on magnetic resonance imaging, these support the diagnosis of basal ganglia encephalitis (BGE). BGE is a rare but distinct entity of putative autoimmune etiology, with specific basal ganglia inflammation and acute movement disorders. A previous study identified dopamine-2 receptors (D2R) antibody to be positive in most BGE children, indicating that the D2R antibody may trigger the downstream pathological process in BGE patients. The highest levels of D2R are found in the striatum, the nucleus accumbens, and the olfactory tubercle. D2R antibody-positive BGE is widely reported in children. Here we present a 17-year-old girl with a typical clinical feature of basal ganglia encephalitis, who benefited from immune therapy.

Radiation encephalopathy (RE) is deemed to be a disease induced only by radiotherapy (RT), with the effects of chemotherapeutic agents on the brains of nasopharyngeal carcinoma (NPC) patients being largely overlooked. In this study, we investigated structural and functional brain alterations in NPC patients following RT with or without chemotherapy. Fifty-six pre-RT, 37 post-RT, and 108 post-CCRT (concomitant chemo-radiotherapy) NPC patients were enrolled in this study. A surface-based local gyrification index (LGI) was obtained from high resolution MRI and was used to evaluate between-group differences in cortical folding. Seed-based functional connectivity (FC) analysis of resting-state fMRI data was also conducted to investigate the functional significance of the cortical folding alterations. Compared with the Pre-RT group, patients in the Post-CCRT group showed LGI reductions in widespread brain regions including the bilateral temporal lobes, insula, frontal lobes, and parietal lobes. Compared with the Post-RT group, patients in the Post-CCRT group showed LGI reductions in the right insula, which extended to the adjacent frontal lobe. Seed-based FC analysis showed that patients in the Post-CCRT group had lower FC between the insula and the left middle frontal gyrus than patients in the Pre-RT group. The follow-up results showed that patients in the Post-CCRT group had a much higher RE incidence rate (20.4%) than patients in the Post-RT group (2.7%; P = 0.01). These findings indicate that chemotherapy potentially facilitated the occurrence of RE in NPC patients who underwent radiotherapy.

Background: Conventional methods for predicting treatment response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) are limited.Methods: This study retrospectively recruited 134 LARC patients who underwent standard nCRT followed by total mesorectal excision surgery in our institution. Based on pre-operative axial T2-weighted images, machine learning radiomics was performed. A receiver operating characteristic (ROC) curve was performed to test the efficiencies of the predictive model.Results: Among the 134 patients, 32 (23.9%) achieved pathological complete response (pCR), 69 (51.5%) achieved a good response, and 91 (67.9%) achieved down-staging. For prediction of pCR, good-response, and down-staging, the predictive model demonstrated high classification efficiencies, with an AUC value of 0.91 (95% CI: 0.83–0.98), 0.90 (95% CI: 0.83–0.97), and 0.93 (95% CI: 0.87–0.98), respectively.Conclusion: Our machine learning radiomics model showed promise for predicting response to nCRT in patients with LARC. Our predictive model based on the commonly used T2-weighted images on pelvic Magnetic Resonance Imaging (MRI) scans has the potential to be adapted in clinical practice.Novelty and Impact Statements: Methods for predicting the response of the locally advanced rectal cancer (LARC, T3-4, or N+) to neoadjuvant chemoradiotherapy (nCRT) is lacking. In the present study, we developed a new machine learning radiomics method based on T2-weighted images. As a non-invasive tool, this method facilitates prediction performance effectively. It achieves a satisfactory overall diagnostic accuracy for predicting of pCR, good response, and down-staging show an AUC of 0.908, 0.902, and 0.930 in LARC patients, respectively.

Pheochromocytomas (PCCs) are rare neuroendocrine tumors that originate from chromaffin cells of the adrenal medulla, and paragangliomas (PGLs) are extra-adrenal pheochromocytomas. These can be mainly found in clinical syndromes including multiple endocrine neoplasia (MEN), von Hippel–Lindau (VHL) syndrome, neurofibromatosis-1 (NF-1) and familial paraganglioma (FPGL). PCCs and PGLs are thought to have the highest degree of heritability among human tumors, and it has been estimated that 60% of the patients have genetic abnormalities. This review provides an overview of the clinical syndrome and the genetic screening strategies of PCCs and PGLs. Comprehensive screening principles and strategies, along with specific screening based on clinical symptoms, biochemical tests and immunohistochemistry, are discussed.

Microvascular invasion (MVI) is regarded as one of the independent risk factors for recurrence and poor prognosis of hepatocellular carcinoma (HCC). The presence of MVI in HCCs was evaluated on the basis of pathological reports of surgical specimens and was defined as tumor within a vascular space lined by endothelium that was visible only on microscopy. The aim of the study was to investigate the usefulness of intravoxel incoherent motion (IVIM) diffusion weighted (DW) magnetic resonance (MR) imaging in predicting MVI of HCC. Preoperative IVIM DW imaging and Gd-EOB-DTPA-enhanced MRI (DCE-MRI) of 51 patients were analyzed. Standard apparent diffusion coefficient (ADC), D (the true diffusion coefficient), D* (the pseudodiffusion coefficient) and f (the perfusion fraction), relative enhancement (RE) and radiological features were evaluated and analyzed. Univariate analysis revealed that HCCs with MVI had a higher portion of an irregular tumor shape than HCCs without MVI (p = 0.009), the Standard ADC, D value were significantly lower in HCCs with MVI (p = 0.022, p = 0.007, respectively). Multivariate analysis revealed that an irregular shape (p = 0.012) and D value ≤ 1.16×10-3mm2/sec (p = 0.048) were independent predictors for MVI. Combining the two factors of an irregular shape and D value, a sensitivity of 94.4% and specificity of 63.6% for predicting MVI was obtained. In conclusion, we found that an irregular shape and D value ≤ 1.16×10-3mm2/sec may suggest the presence of MVI in HCCs.

OBJECTIVES:To characterize the imaging and clinicopathological features of primitive neuroectodermal tumors (PNETs) arising in intra-abdominal and retroperitoneal regions. METHODS:Eighteen patients with histopathologically proven intra-abdominal and retroperitoneal PNET were enrolled; computed tomography was performed for all cases, and magnetic resonance imaging was performed for a single case. Typical computed tomography and magnetic resonance imaging findings, including morphology, texture and enhancement features, as well as clinicopathological characteristics and prognosis data were retrospectively analyzed. RESULTS:Of eighteen PNET patients, fifteen were male and three were female, with a median age of 36 years (range, 2-65 years). The onset of symptoms was most often nonspecific and insidious. The mean tumor diameter was 7.2 cm (range, 3.0-12.1 cm), with necrosis in fifteen cases, cystic changes in eight, partition structure in five, calcification in five, hemorrhage in two, and mural nodules in one. Contrast enhanced computed tomography showed multiple tiny feeding arteries within the masses in six cases, resulting in a crab-like appearance, and mild ring enhancement pattern in five cases. Eleven cases showed surrounding invasion and metastasis. Of the eighteen PNET cases, nine cases showed smooth, well-defined margins, and nine cases had irregular, ill-defined margins. A median survival was 10.0±1.6 months. However, chemotherapy had efficacy on patients even those with advanced disease. CONCLUSIONS:Primary intra-abdominal and retroperitoneal PNETs are rare, and imaging features documented here may help the diagnosis of this severe disease. Notably, two signs present in retroperitoneal PNET tumors, including a mild ring enhancement pattern and a crab-like appearance of the tiny feeding arteries, may have the potential to help us improve the ability to make a relatively reliable diagnosis.

Sudden death syndrome (SDS) is caused by the fungal pathogen, Fusarium virguliforme, and is a major threat to soybean production in North America. There are two major components of this disease: (i) root necrosis and (ii) foliar SDS. Root symptoms consist of root necrosis with vascular discoloration. Foliar SDS is characterized by interveinal chlorosis and leaf necrosis, and in severe cases by flower and pod abscission. A major toxin involved in initiating foliar SDS has been identified. Nothing is known about how root necrosis develops. In order to unravel the mechanisms used by the pathogen to cause root necrosis, the transcriptome of the pathogen in infected soybean root tissues of a susceptible cultivar, 'Essex', was investigated. The transcriptomes of the germinating conidia and mycelia were also examined. Of the 14,845 predicted F. virguliforme genes, we observed that 12,017 (81%) were expressed in germinating conidia and 12,208 (82%) in mycelia and 10,626 (72%) in infected soybean roots. Of the 10,626 genes induced in infected roots, 224 were transcribed only following infection. Expression of several infection-induced genes encoding enzymes with oxidation-reduction properties suggests that degradation of antimicrobial compounds such as the phytoalexin, glyceollin, could be important in early stages of the root tissue infection. Enzymes with hydrolytic and catalytic activities could play an important role in establishing the necrotrophic phase. The expression of a large number of genes encoding enzymes with catalytic and hydrolytic activities during the late infection stages suggests that cell wall degradation could be involved in root necrosis and the establishment of the necrotrophic phase in this pathogen.

Banana Fusarium wilt is a soil-spread fungal disease caused by Fusarium oxysporum. In China, the main virulence fungi in banana are F. oxysporum race 1 (F1, weak virulence) and race 4 (F4, strong virulence). To date, no proteomic analyses have compared the two races, but the difference in virulence between F1 and F4 might result from their differentially expressed proteins. Here we report the first comparative proteomics of F1 and F4 cultured under various conditions, and finally identify 99 protein species, which represent 59 unique proteins. These proteins are mainly involved in carbohydrate metabolism, post-translational modification, energy production, and inorganic ion transport. Bioinformatics analysis indicated that among the 46 proteins identified from F4 were several enzymes that might be important for virulence. Reverse transcription PCR analysis of the genes for 15 of the 56 proteins revealed that their transcriptional patterns were similar to their protein expression patterns. Taken together, these data suggest that proteins involved in carbohydrate metabolism and ion transport may be important in the pathogenesis of banana Fusarium wilt. Some enzymes such as catalase-peroxidase, galactosidase and chitinase might contribute to the strong virulence of F4. Overexpression or knockout of the genes for the F4-specific proteins will help us to further understand the molecular mechanism of Fusarium-induced banana wilt.

Abstract Background In computer-aided diagnosis (CAD) studies utilizing multireader multicase (MRMC) designs, missing data might occur when there are instances of misinterpretation or oversight by the reader or problems with measurement techniques. Improper handling of these missing data can lead to bias. However, little research has been conducted on addressing the missing data issue within the MRMC framework. Methods We introduced a novel approach that integrates multiple imputation with MRMC analysis (MI-MRMC). An elaborate simulation study was conducted to compare the efficacy of our proposed approach with that of the traditional complete case analysis strategy within the MRMC design. Furthermore, we applied these approaches to a real MRMC design CAD study on aneurysm detection via head and neck CT angiograms to further validate their practicality. Results Compared with traditional complete case analysis, the simulation study demonstrated the MI-MRMC approach provides an almost unbiased estimate of diagnostic capability, alongside satisfactory performance in terms of statistical power and the type I error rate within the MRMC framework, even in small sample scenarios. In the real CAD study, the proposed MI-MRMC method further demonstrated strong performance in terms of both point estimates and confidence intervals compared with traditional complete case analysis. Conclusion Within MRMC design settings, the adoption of an MI-MRMC approach in the face of missing data can facilitate the attainment of unbiased and robust estimates of diagnostic capability.

Abstract The rising prevalence of diabetes mellitus has casted a spotlight on one of its significant sequelae: cognitive impairment. Sodium-glucose cotransporter-2 (SGLT2) inhibitors, originally developed for diabetes management, are increasingly studied for their cognitive benefits. These benefits may include reduction of oxidative stress and neuroinflammation, decrease of amyloid burdens, enhancement of neuronal plasticity, and improved cerebral glucose utilization. The multifaceted effects and the relatively favorable side-effect profile of SGLT2 inhibitors render them a promising therapeutic candidate for cognitive disorders. Nonetheless, the application of SGLT2 inhibitors for cognitive impairment is not without its limitations, necessitating more comprehensive research to fully determine their therapeutic potential for cognitive treatment. In this review, we discuss the role of SGLT2 in neural function, elucidate the diabetes-cognition nexus, and synthesize current knowledge on the cognitive effects of SGLT2 inhibitors based on animal studies and clinical evidence. Research gaps are proposed to spur further investigation.