clinical model
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2022 ◽  
Vol 20 (1) ◽  
Jianqiu Kong ◽  
Junjiong Zheng ◽  
Jieying Wu ◽  
Shaoxu Wu ◽  
Jinhua Cai ◽  

Abstract Background Preoperative diagnosis of pheochromocytoma (PHEO) accurately impacts preoperative preparation and surgical outcome in PHEO patients. Highly reliable model to diagnose PHEO is lacking. We aimed to develop a magnetic resonance imaging (MRI)-based radiomic-clinical model to distinguish PHEO from adrenal lesions. Methods In total, 305 patients with 309 adrenal lesions were included and divided into different sets. The least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction, feature selection, and radiomics signature building. In addition, a nomogram incorporating the obtained radiomics signature and selected clinical predictors was developed by using multivariable logistic regression analysis. The performance of the radiomic-clinical model was assessed with respect to its discrimination, calibration, and clinical usefulness. Results Seven radiomics features were selected among the 1301 features obtained as they could differentiate PHEOs from other adrenal lesions in the training (area under the curve [AUC], 0.887), internal validation (AUC, 0.880), and external validation cohorts (AUC, 0.807). Predictors contained in the individualized prediction nomogram included the radiomics signature and symptom number (symptoms include headache, palpitation, and diaphoresis). The training set yielded an AUC of 0.893 for the nomogram, which was confirmed in the internal and external validation sets with AUCs of 0.906 and 0.844, respectively. Decision curve analyses indicated the nomogram was clinically useful. In addition, 25 patients with 25 lesions were recruited for prospective validation, which yielded an AUC of 0.917 for the nomogram. Conclusion We propose a radiomic-based nomogram incorporating clinically useful signatures as an easy-to-use, predictive and individualized tool for PHEO diagnosis.

2022 ◽  
Nallammai Muthiah ◽  
Arka Mallela ◽  
Lena Vodovotz ◽  
Nikhil Sharma ◽  
Emefa Akwayena ◽  

Introduction Epilepsy impacts 470,000 children in the United States, and children with epilepsy are estimated to expend 6 times more on healthcare than those without epilepsy. For patients with antiseizure medication (ASM)-resistant epilepsy and unresectable seizure foci, vagus nerve stimulation (VNS) is a treatment option. Predicting response to VNS has been historically challenging. We aimed to create a clinical prediction score which could be utilized in a routine outpatient clinical setting. Methods We performed an 11-year, single-center retrospective analysis of patients <21 years old with ASM-resistant epilepsy who underwent VNS. The primary outcome was >50% seizure frequency reduction after one year. Univariate and multivariate logistic regressions were performed to assess clinical factors associated with VNS response; 70% and 30% of the sample were used to train and validate the multivariate model, respectively. A prediction score was developed based on the multivariate regression. Sensitivity, specificity, and area under the receiver operating curve (AUC) were calculated. Results This analysis included 365 patients. Multivariate logistic regression revealed that variables associated with VNS response were: <4 years of epilepsy duration before VNS (p=0.008) and focal motor seizures (p=0.037). The variables included in the clinical prediction score were: epilepsy duration before VNS, age at seizure onset, number of pre-VNS ASMs, if VNS was the patient's first therapeutic epilepsy surgery, and predominant seizure semiology. The final AUC was 0.7013 for the "fitted" sample and 0.6159 for the "validation" sample. Conclusions We developed a clinical model to predict VNS response in one of the largest samples of pediatric VNS patients to date. While the presented clinical prediction model demonstrated an acceptable AUC in the training cohort, clinical variables alone likely do not accurately predict VNS response. This score may be useful upon further validation, though its predictive ability underscores the need for more robust biomarkers of treatment response.

2022 ◽  
Vol 23 (2) ◽  
pp. 835
Bang M. Tran ◽  
Samantha L. Grimley ◽  
Julie L. McAuley ◽  
Abderrahman Hachani ◽  
Linda Earnest ◽  

The global urgency to uncover medical countermeasures to combat the COVID-19 pandemic caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has revealed an unmet need for robust tissue culture models that faithfully recapitulate key features of human tissues and disease. Infection of the nose is considered the dominant initial site for SARS-CoV-2 infection and models that replicate this entry portal offer the greatest potential for examining and demonstrating the effectiveness of countermeasures designed to prevent or manage this highly communicable disease. Here, we test an air–liquid-interface (ALI) differentiated human nasal epithelium (HNE) culture system as a model of authentic SARS-CoV-2 infection. Progenitor cells (basal cells) were isolated from nasal turbinate brushings, expanded under conditionally reprogrammed cell (CRC) culture conditions and differentiated at ALI. Differentiated cells were inoculated with different SARS-CoV-2 clinical isolates. Infectious virus release into apical washes was determined by TCID50, while infected cells were visualized by immunofluorescence and confocal microscopy. We demonstrate robust, reproducible SARS-CoV-2 infection of ALI-HNE established from different donors. Viral entry and release occurred from the apical surface, and infection was primarily observed in ciliated cells. In contrast to the ancestral clinical isolate, the Delta variant caused considerable cell damage. Successful establishment of ALI-HNE is donor dependent. ALI-HNE recapitulate key features of human SARS-CoV-2 infection of the nose and can serve as a pre-clinical model without the need for invasive collection of human respiratory tissue samples.

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0260356
Mie S. Berke ◽  
Louise K. D. Fensholdt ◽  
Sara Hestehave ◽  
Otto Kalliokoski ◽  
Klas S. P. Abelson

Complete Freund’s adjuvant (CFA)-induced arthritis in rats is a common animal model for studying chronic inflammatory pain. However, modelling of the disease is associated with unnecessary pain and impaired animal wellbeing, particularly in the immediate post-induction phase. Few attempts have been made to counteract these adverse effects with analgesics. The present study investigated the effect of buprenorphine on animal welfare, pain-related behaviour and model-specific parameters during the disease progression in a rat model of CFA-induced monoarthritis. The aim was to reduce or eliminate unnecessary pain in this model, in order to improve animal welfare and to avoid suffering, without compromising the quality of the model. Twenty-four male Sprague Dawley rats were injected with 20 μl of CFA into the left tibio-tarsal joint to induce monoarthritis. Rats were treated with either buprenorphine or carprofen for 15 days during the disease development, and were compared to a saline-treated CFA-injected group or a negative control group. Measurements of welfare, pain-related behaviour and clinical model-specific parameters were collected. The study was terminated after 3 weeks, ending with a histopathologic analysis. Regardless of treatment, CFA-injected rats displayed mechanical hyperalgesia and developed severe histopathological changes associated with arthritis. However, no severe effects on general welfare were found at any time. Buprenorphine treatment reduced facial pain expression scores, improved mobility, stance and lameness scores and it did not supress the CFA-induced ankle swelling, contrary to carprofen. Although buprenorphine failed to demonstrate a robust analgesic effect on the mechanical hyperalgesia in this study, it did not interfere with the development of the intended pathology.

Abbe H. Crawford ◽  
John C.W. Hildyard ◽  
Sophie A.M. Rushing ◽  
Dominic J. Wells ◽  
Maria Diez-Leon ◽  

Duchenne muscular dystrophy (DMD), a fatal musculoskeletal disorder, is associated with neurodevelopmental disorders and cognitive impairment caused by brain dystrophin deficiency. Dog models of DMD represent key translational tools to study dystrophin biology and to develop novel therapeutics. However, characterization of dystrophin expression and function in the canine brain is lacking. We studied the DE50-MD canine model of DMD that has a missense mutation in the donor splice site of exon 50. Using a battery of cognitive tests, we detected a neurocognitive phenotype in DE50-MD dogs including reduced attention, problem-solving and exploration of novel objects. Through a combination of capillary immunoelectrophoresis, immunolabelling, qPCR and RNAScope in situ hybridization we show that regional dystrophin expression in the adult canine brain reflects that of humans, and that the DE50-MD dog lacks full length dystrophin (Dp427) protein expression but retains expression of the two shorter brain-expressed isoforms, Dp140 and Dp71. Thus, the DE50-MD dog is a translationally-relevant pre-clinical model to study the consequences of Dp427 deficiency in the brain and to develop therapeutic strategies for the neurological sequelae of DMD.

Sihang Cheng ◽  
Xiang Yu ◽  
Xinyue Chen ◽  
Zhengyu Jin ◽  
Huadan Xue ◽  

Objective: To develop and evaluate a machine learning-based CT radiomics model for the prediction of hepatic encephalopathy (HE) after transjugular intrahepatic portosystemic shunt (TIPS). Methods: A total of 106 patients who underwent TIPS placement were consecutively enrolled in this retrospective study. Region of interests (ROIs) were drawn on unenhanced, arterial phase, and portal venous Phase CT images, and radiomics features were extracted, respectively. A radiomics model was established to predict the occurrence of HE after TIPS by using random forest algorithm and ten-fold cross-validation. Receiver operating characteristic (ROC) curves were performed to validate the capability of the radiomics model and clinical model on the training, test and original datasets, respectively. Results: The radiomics model showed favorable discriminatory ability in the training cohort with an area under the curve (AUC) of 0.899 (95% CI, 0.848 to 0.951), while in the test cohort, it was confirmed with an AUC of 0.887 (95% CI, 0.760 to 1.00). After applying this model to original dataset, it had an AUC of 0.955 (95% CI, 0.896 to 1.00). A clinical model was also built with an AUC of 0.649 (95% CI, 0.530 to 0.767) in the original dataset, and a Delong test demonstrated its relative lower efficiency when compared with the radiomics model (p < 0.05). Conclusion: Machine learning-based CT radiomics model performed better than traditional clinical parameter-based models in the prediction of post-TIPS HE. Advances in knowledge: Radiomics model for the prediction of post-TIPS HE was built based on feature extraction from routine acquired preoperative CT images and feature selection by random forest algorithm, which showed satisfied performance and proved the advantages of machine learning in this field.

2022 ◽  
Vol 13 (1) ◽  
Jeffrey W. Grimm ◽  
Katherine North ◽  
Madeleine Hopkins ◽  
Kyle Jiganti ◽  
Alex McCoy ◽  

Abstract Background There are sex differences in addiction behaviors. To develop a pre-clinical animal model to investigate this, the present study examined sex differences in sucrose taking and seeking using Long-Evans rats. Methods Five experiments were conducted using separate groups of subjects. The first two examined sucrose or saccharin preference in two-bottle home cage choice tests. Experiment three assessed sucrose intake in a binge model with sucrose available in home cage bottles. Experiments four and five utilized operant-based procedures. In experiment four rats responded for sucrose on fixed and progressive ratio (FR, PR) schedules of reinforcement over a range of concentrations of sucrose. A final component of experiment four was measuring seeking in the absence of sucrose challenged with the dopamine D1 receptor antagonist SCH23390. Experiment five assessed responding for water on FR and PR schedules of reinforcement. Results When accounting for body weight, female rats consumed more sucrose than water; but there was no sex difference in saccharin preference over a range of saccharin concentrations. When accounting for body weight, females consumed more sucrose than males in the binge model, and only females increased binge intake over 14 days of the study. Females responded at higher rates for sucrose under both FR and PR schedules of reinforcement. Females responded at higher rates in extinction (seeking); SCH23390 reduced sucrose seeking of both females and males. Females responded at higher rates for water on FR and PR schedules than males, although rates of responding were low and decreased over sessions. Conclusions Across bottle-choice, binge intake, and operant procedures, female Long-Evans rats consumed more sucrose and responded at higher rates for sucrose. Although females also responded more for water, the vigor of responding did not explain the consistent sex difference in sucrose taking and seeking. The sex difference in sucrose taking was also not explained by sweet preference, as there was no sex difference in saccharin preference. These data provide a pre-clinical model to further evaluate sex differences in addiction behaviors and manipulations designed to reduce them.

2022 ◽  
Vol 11 ◽  
Bao Feng ◽  
Liebin Huang ◽  
Yu Liu ◽  
Yehang Chen ◽  
Haoyang Zhou ◽  

ObjectiveThis study aims to differentiate preoperative Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL) by transfer learning radiomics nomogram (TLRN) with whole slide images of GC as source domain data.Materials and MethodsThis study retrospectively enrolled 438 patients with histopathologic diagnoses of Borrmann type IV GC and PGL. They received CT examinations from three hospitals. Quantitative transfer learning features were extracted by the proposed transfer learning radiopathomic network and used to construct transfer learning radiomics signatures (TLRS). A TLRN, which integrates TLRS, clinical factors, and CT subjective findings, was developed by multivariate logistic regression. The diagnostic TLRN performance was assessed by clinical usefulness in the independent validation set.ResultsThe TLRN was built by TLRS and a high enhanced serosa sign, which showed good agreement by the calibration curve. The TLRN performance was superior to the clinical model and TLRS. Its areas under the curve (AUC) were 0.958 (95% confidence interval [CI], 0.883–0.991), 0.867 (95% CI, 0.794–0.922), and 0.921 (95% CI, 0.860–0.960) in the internal and two external validation cohorts, respectively. Decision curve analysis (DCA) showed that the TLRN was better than any other model. TLRN has potential generalization ability, as shown in the stratification analysis.ConclusionsThe proposed TLRN based on gastric WSIs may help preoperatively differentiate PGL from Borrmann type IV GC.Borrmann type IV gastric cancer, primary gastric lymphoma, transfer learning, whole slide image, deep learning.

2022 ◽  
Michelle Martinez ◽  
Kevin P. Uribe ◽  
Valeria Garcia ◽  
Omar Lira ◽  
Felix Matos-Ocasio ◽  

In recent years, there has been a dramatic increase in nicotine vapor consumption via electronic nicotine delivery systems (i.e., e-cigarettes), particularly in adolescents. While recent work has focused on the health effects of nicotine vapor exposure, its effects on the brain and behavior remain unclear. In this study, we assessed the effects that cessation from repeated nicotine vapor exposure had on behavioral and neuronal measures of withdrawal. For Experiment 1, fifty-six adult male rats were tested for plasma cotinine levels, somatic withdrawal signs, and anxiety-like behavior in the elevated plus maze, immediately following precipitated withdrawal from repeated exposure to 12 or 24 mg/mL nicotine vapor. In Experiment 2, twelve adult male rats were tested for intracranial self-stimulation (ICSS) across 14 days of exposure to 24 mg/mL nicotine vapor and across the 14 days immediately following nicotine exposure. Results revealed that plasma cotinine, somatic signs, anxiety-like behavior, and ICSS stimulation thresholds were all observed to be elevated during withdrawal in the 24 mg/mL nicotine group, when compared to vehicle controls (50/50 vegetable glycerin/propylene glycol). The data suggest that cessation from repeated nicotine vapor exposure using our preclinical model leads to nicotine dependence and withdrawal, and demonstrates that the vapor system described in these experiments is a viable pre-clinical model of e-cigarette use in humans. Further characterization of the mechanisms driving nicotine vapor abuse and dependence is needed to improve policies and educational campaigns related to e-cigarette use.

2022 ◽  
Vol 12 (1) ◽  
Akira Shinaoka ◽  
Kazuyo Kamiyama ◽  
Kiyoshi Yamada ◽  
Yoshihiro Kimata

AbstractMost protocols for lymphatic imaging of the lower limb conventionally inject tracer materials only into the interdigital space; however, recent studies indicate that there are four independent lymphatic vessel groups (anteromedial, anterolateral, posteromedial, and posterolateral) in the lower limb. Thus, three additional injection sites are needed for lymphatic imaging of the entire lower limb. We aimed to validate a multiple injection designed protocol and demonstrate its clinical benefits. Overall, 206 lower limbs undergoing indocyanine green fluorescent lymphography with the new injection protocol were registered retrospectively. To assess the influence of predictor variables on the degree of severity, multivariable logistic regression models were used with individual known risk factors. Using a generalized linear model, the area under the curve (AUC) of the conventional clinical model, comprising known severity risk factors, was compared with that of the modified model that included defects in the posterolateral and posteromedial groups. Multivariable logistic regression models showed a significant difference for the posteromedial and posterolateral groups. The AUC of the modified model was significantly improved compared to that of the conventional clinical model. Finding defects in the posteromedial and posterolateral groups is a significant criterion for judging lymphedema severity and introducing a new lymphedema severity classification.

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