Robust memory of face moral values is encoded in the human caudate tail: a simultaneous EEG-fMRI study.

Moral judgements about people based on their actions is a key component that guides social decision making. It is currently unknown how positive or negative moral judgments associated with a person's face are processed and stored in the brain for a long time. Here, we investigate the long-term memory of moral values associated with human faces using simultaneous EEG-fMRI data acquisition. Results show that only a few exposures to morally charged stories of people are enough to form long-term memories a day later for a relatively large number of new faces. Event related potentials (ERPs) showed a significant differentiation of remembered good vs bad faces over centerofrontal electrode sites (value ERP). EEG-informed fMRI analysis revealed a subcortical cluster centered on the left caudate tail (CDt) as a correlate of the face value ERP. Importantly neither this analysis nor a conventional whole-brain analysis revealed any significant coding of face values in cortical areas, in particular the fusiform face area (FFA). Conversely an fMRI-informed EEG source localization using accurate subject-specific EEG head models also revealed activation in the left caudate tail. Nevertheless, the detected caudate tail region was found to be functionally connected to the FFA, suggesting FFA to be the source of face-specific information to CDt. A further psycho-physiological interaction analysis also revealed task-dependent coupling between CDt and dorsomedial prefrontal cortex (dmPFC), a region previously identified as retaining emotional working memories. These results identify CDt as a main site for encoding the long-term value memories of faces in humans suggesting that moral value of faces activates the same subcortical basal ganglia circuitry involved in processing reward value memory for objects in primates.

Coexistence of intrathyroid thymic carcinoma and papillary thyroid carcinoma: a case report and literature review.

Background: Intrathyroid thymic carcinoma (ITTC) is a rare neoplasm of the thyroid, which accounts for less than 0.15% of all thyroid malignancies. The coexistence of ITTC and papillary thyroid carcinoma (PTC) is an extremely rare condition reported only in a limited number of cases. Case summary: A 26-year-old female presented with a growing neck mass, hoarseness, and dysphagia over four months. Ultrasonography revealed that the entire left lobe and the isthmus of the thyroid were replaced with a hypoechoic mass. Moreover, it revealed two hypoechoic nodules in the right thyroid. The patient underwent a total thyroidectomy and paratracheal lymph node dissection. Histopathological examinations revealed the coexistence of ITTC and PTC in the same thyroid. In immunohistochemical analyses, the ITTC was positive for CD5, P63, CD117, and CK 5/6 and negative for thyroglobulin, calcitonin, and TTF 1. At the same time, PTC was positive for TTF 1 and thyroglobulin and negative for CD5, P63, and CK 5/6. The patient received postoperative radiotherapy and remained well with no evidence of recurrence during one month follow-up. Conclusion: Distinguishing ITTC from other thyroid malignancies before the surgery is challenging due to its non-specific presentations. Therefore, the diagnosis relies on postoperative studies, especially immunohistochemistry. The recommended treatment approach to improve survival in ITTC cases is total thyroidectomy combined with cervical lymph node dissection, followed by postoperative radiotherapy. The coexistence of ITTC and PTC may indicate the similarity in the underlying mechanisms of these tumors. However, further investigations are needed to understand this potential correlation.

The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis.

Bone ranks as the third most frequent tissue affected by cancer metastases, following the lung and liver. Bone metastases are often painful and may result in pathological fracture, which is a major cause of morbidity and mortality in cancer patients. To quantify fracture risk, finite element (FE) analysis has shown to be a promising tool, but metastatic lesions are typically not specifically segmented and therefore their mechanical properties may not be represented adequately. Deep learning methods potentially provide the opportunity to automatically segment these lesions and change the mechanical properties more adequately. In this study, our primary focus was to gain insight into the performance of an automatic segmentation algorithm for femoral metastatic lesions using deep learning methods and the subsequent effects on FE outcomes. The aims were to determine the similarity between manual segmentation and automatic segmentation; the differences in predicted failure load between FE models with automatically segmented osteolytic and mixed lesions and the models with CT-based lesion values (the gold standard); and the effect on the BOne Strength (BOS) score (failure load adjusted for body weight) and subsequent fracture risk assessments. From two patient cohorts, a total number of 50 femurs with osteolytic and mixed metastatic lesions were included in this study. The femurs were segmented from CT images and transferred into FE meshes. The material behavior was implemented as non-linear isotropic. These FE models were considered as gold standard (Finite Element no Segmented Lesion: FE-no-SL), whereby the local calcium equivalent density of both femur and metastatic lesion was extracted from CT-values. Lesions in the femur were manually segmented by two biomechanical experts after which final lesion segmentation for each femur was obtained based on consensus of opinions between two observers. Subsequently, a self-configuring variant of the popular deep learning model U-Net known as nnU-Net was used to automatically segment metastatic lesions within the femur. For these models with segmented lesions (Finite Element with Segmented Lesion: FE-with-SL), the calcium equivalent density within the metastatic lesions was set to zero after being segmented by the neural network, simulating absence of load-bearing capacity of these lesions. The models (either with or without automatically segmented lesions) were loaded incrementally in axial direction until failure was simulated. Dice coefficient was used to evaluate the similarity of the manual and automatic segmentation. Mean calcium equivalent density values within the automatically segmented lesions were calculated. Failure loads and patterns were determined. Furthermore, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for both groups by comparing the predictions to the occurrence or absence of actual fracture within the patient cohorts. The automatic segmentation algorithm performed in a none-robust manner. Dice coefficients describing the similarity between consented manual and automatic segmentations were relatively low (mean 0.45 ± standard deviation 0.33, median 0.54). Failure load difference between the FE-no-SL and FE-with-SL groups varied from 0 % to 48 % (mean 6.6 %). Correlation analysis of failure loads between the two groups showed a strong relationship (R2 > 0.9). From the 50 cases, four cases showed clear deviations for which models with automatic lesion segmentation (FE-with-SL) showed considerably lower failure loads. In the whole database including osteolytic and mixed lesions, sensitivity and NPV remained the same, but specificity and PPV decreased from 94 % to 83 %, and from 78 % to 54 % respectively from FE-no-SL to FE-with-SL. This study indicates that the nnU-Net yielded none-robust outcomes in femoral lesion segmentation and that other segmentation algorithms should be considered. However, the difference in failure pattern and failure load between FE models with automatically segmented osteolytic and mixed lesions were relatively small in most cases with a few exceptions. On the other hand, the accuracy of fracture risk assessment using the BOS score was lower compared to the FE-no-SL. In conclusion, this study showed that automatic lesion segmentation is a none-solved issue and therefore, quantifying lesion characteristics and the subsequent effect on the fracture risk using deep learning will remain challenging.

miRNAs as key modulators between normal cells and tumor microenvironment interactions.

The tumor microenvironment (TME) is well-defined target for understanding tumor progression and various cell types. Major elements of the tumor microenvironment are the followings: endothelial cells, fibroblasts, signaling molecules, extracellular matrix, and infiltrating immune cells. MicroRNAs (miRNAs) are a group of small noncoding RNAs with major functions in the gene expression regulation at post-transcriptional level that have also appeared to exerts key functions in the cancer initiation/progression in diverse biological processes and the tumor microenvironment. This study summarized various roles of miRNAs in the complex interactions between the tumor and normal cells in their microenvironment.

Targeting long noncoding RNAs in neuroblastoma: Progress and prospects.

Neuroblastoma (NB) is the third most prevalent tumor that mostly influences infants and young children. Although different treatments have been developed for the treatment of NB, high-risk patients have been reported to have low survival rates. Currently, long noncoding RNAs (lncRNAs) have shown an attractive potential in cancer research and a party of investigations have been performed to understand mechanisms underlying tumor development through lncRNA dysregulation. Researchers have just newly initiated to exhibit the involvement of lncRNAs in NB pathogenesis. In this review article, we tried to clarify the point we stand with respect to the involvement of lncRNAs in NB. Moreover, implications for the pathologic roles of lncRNAs in the development of NB have been discussed. It seems that some of these lncRNAs have promising potential to be applied as biomarkers for NB prognosis and treatment.

Androgens' Role in Severity and Mortality Rates of COVID-19.

By the end of December 2019 new corona virus began to spread from Wuhan, China and caused a worldwide pandemic. COVID-19 deaths and prevalence represented sex discrepant patterns with higher rate of deaths and infection in males than females which could be justified by androgen-mediated mechanisms. This review aimed to assess the role of androgens in COVID-19 severity and mortality. Androgens increase expressions of Type II transmembrane Serine Protease (TMPRSS2) and Angiotensin Converting Enzyme 2 (ACE2), which both facilitate new corona virus entry into host cell and their expression is higher in young males than females. According to observational studies, prevalence of COVID-19 infections and deaths was more in androgenic alopecic patients than patients without androgenic alopecia. The COVID-19 mortality rates in aged men (>60 years) were substantially higher than aged females and even young males caused by high inflammatory activities such as cytokine storm due to hypogonadism in this population. Use of anti-androgen and TMPRSS2 inhibitor drugs considerably modified COVID-19 symptoms. Androgen deprivation therapy also improved COVID-19 symptoms in prostate cancer: overall the role of androgens in severity of COVID-19 and its associated mortality seemed to be very important. So, more studies in variety of populations are required to define the absolute role of androgens.

Gustatory Cortex Is Involved in Evidence Accumulation during Food Choice.

Food choice is one of the most fundamental and most frequent value-based decisions for all animals including humans. However, the neural circuitry involved in food-based decisions is only recently being addressed. Given the relatively fast dynamics of decision formation, electroencephalography (EEG)-informed fMRI analysis is highly beneficial for localizing this circuitry in humans. Here, by using the EEG correlates of evidence accumulation in a simultaneously recorded EEG-fMRI dataset, we found a significant role for the right temporal-parietal operculum (PO) and medial insula including gustatory cortex (GC) in binary choice between food items. These activations were uncovered by using the "EEG energy" (power 2 of EEG) as the BOLD regressor and were missed if conventional analysis with the EEG signal itself were to be used, in agreement with theoretical predictions for EEG and BOLD relations. No significant positive correlations were found with higher powers of EEG (powers 3 or 4) pointing to specificity and sufficiency of EEG energy as the main correlate of the BOLD response. This finding extends the role of cortical areas traditionally involved in palatability processing to value-based decision-making and offers the "EEG energy" as a key regressor of BOLD response in simultaneous EEG-fMRI designs.

The effect of variations in CT scan protocol on femoral finite element failure load assessment using phantomless calibration.

Recently, it was shown that fracture risk assessment in patients with femoral bone metastases using Finite Element (FE) modeling can be performed using a calibration phantom or air-fat-muscle calibration and that non-patient-specific calibration was less favorable. The purpose of this study was to investigate if phantomless calibration can be used instead of phantom calibration when different CT protocols are used. Differences in effect of CT protocols on Hounsfield units (HU), calculated bone mineral density (BMD) and FE failure loads between phantom and two methods of phantomless calibrations were studied. Five human cadaver lower limbs were scanned atop a calibration phantom according to a standard scanning protocol and seven additional commonly deviating protocols including current, peak kilovoltage (kVp), slice thickness, rotation time, field of view, reconstruction kernel, and reconstruction algorithm. The HUs of the scans were calibrated to BMD (in mg/cm3) using the calibration phantom as well as using air-fat-muscle and non-patient-specific calibration, resulting in three models for each scan. FE models were created, and failure loads were calculated by simulating an axial load on the femur. HU, calculated BMD and failure load of all protocols were compared between the three calibration methods. The different protocols showed little variation in HU, BMD and failure load. However, compared to phantom calibration, changing the kVp resulted in a relatively large decrease of approximately 10% in mean HU and BMD of the trabecular and cortical region of interest (ROI), resulting in a 13.8% and 13.4% lower failure load when air-fat-muscle and non-patient-specific calibrations were used, respectively. In conclusion, while we observed significant correlations between air-fat-muscle calibration and phantom calibration as well as between non-patient-specific calibration and phantom calibration, our sample size was too small to prove that either of these calibration approaches was superior. Further studies are necessary to test whether air-fat-muscle or non-patient-specific calibration could replace phantom calibration in case of different scanning protocols.

Evaluation of inter- and intra-operator reliability of manual segmentation of femoral metastatic lesions.

PURPOSE: Accurate identification of metastatic lesions is important for improvement in biomechanical models that calculate the fracture risk of metastatic bones. The aim of this study was therefore to assess the inter- and intra-operator reliability of manual segmentation of femoral metastatic lesions. METHODS: CT scans of 54 metastatic femurs (19 osteolytic, 17 osteoblastic, and 18 mixed) were segmented two times by two operators. Dice coefficients (DCs) were calculated adopting the quantification that a DC˃0.7 indicates good reliability. RESULTS: Generally, rather poor inter- and intra-operator reliability of lesion segmentation were found. Inter-operator DCs were 0.54 (± 0.28) and 0.50 (± 0.32) for the first and second segmentations, respectively, whereas intra-operator DCs were 0.56 (± 0.28) for operator I and 0.71 (± 0.23) for operator II. Larger lesions scored significantly higher DCs in comparison with smaller lesions. Of the femurs with larger mean segmentation volumes, 83% and 93% were segmented with good inter- and intra-operator DCs (> 0.7), respectively. There was no difference between the mean DCs of osteolytic, osteoblastic, and mixed lesions. CONCLUSION: Manual segmentation of femoral bone metastases is very challenging and resulted in unsatisfactory mean reliability values. There is a need for development of a segmentation protocol to reduce the inter- and intra-operator segmentation variation as the first step and use of computer-assisted segmentation tools as a second step as this study shows that manual segmentation of femoral metastatic lesions is highly challenging.