Phenotype Transformation of PitNETs.

Phenotype transformation in pituitary neuroendocrine tumors is a little-known and unpredictable clinical phenomenon. Previous studies have not clearly defined and systematically concluded on the causes of this rare phenomenon. Additionally, the mechanisms of phenotype transformation are not well known. We reviewed cases reported in the literature with the aim of defining phenotype transformation in pituitary neuroendocrine tumors. We present an overview of the wide spectrum of phenotype transformation and its clinical features. We also discuss findings on the potential mechanism of this rare transformation, which may be related to PC1/3, the bioactivity of secretory hormones, gene mutations and the plasticity of pituitary neuroendocrine tumors. Clinicians should be aware of this rare phenomenon and more studies on the underlying mechanisms are required.

Phase 1 study of safety and preliminary efficacy of intranasal transplantation of human neural stem cells (ANGE-S003) in Parkinson's disease.

BACKGROUND: Intranasal transplantation of ANGE-S003 human neural stem cells showed therapeutic effects and were safe in preclinical models of Parkinson's disease (PD). We investigated the safety and tolerability of this treatment in patients with PD and whether these effects would be apparent in a clinical trial. METHODS: This was a 12-month, single-centre, open-label, dose-escalation phase 1 study of 18 patients with advanced PD assigned to four-time intranasal transplantation of 1 of 3 doses: 1.5 million, 5 million or 15 million of ANGE-S003 human neural stem cells to evaluate their safety and efficacy. RESULTS: 7 patients experienced a total of 14 adverse events in the 12 months of follow-up after treatment. There were no serious adverse events related to ANGE-S003. Safety testing disclosed no safety concerns. Brain MRI revealed no mass formation. In 16 patients who had 12-month Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) data, significant improvement of MDS-UPDRS total score was observed at all time points (p<0.001), starting with month 3 and sustained till month 12. The most substantial improvement was seen at month 6 with a mean reduction of 19.9 points (95% CI, 9.6 to 30.3; p<0.001). There was no association between improvement in clinical outcome measures and cell dose levels. CONCLUSIONS: Treatment with ANGE-S003 is feasible, generally safe and well tolerated, associated with functional improvement in clinical outcomes with peak efficacy achieved at month 6. Intranasal transplantation of neural stem cells represents a new avenue for the treatment of PD, and a larger, longer-term, randomised, controlled phase 2 trial is warranted for further investigation.

A long-term prognosis study of human USP8-mutated ACTH-secreting pituitary neuroendocrine tumours.

OBJECTIVE: Somatic variants in the ubiquitin-specific protease 8 (USP8) gene are the most common genetic cause of Cushing disease. We aimed to explore the relationship between clinical outcomes and USP8 status in a single centre. DESIGN, PATIENTS AND MEASUREMENTS: We investigated the USP8 status in 48 patients with pituitary corticotroph tumours. A median of 62 months of follow-up was conducted after surgery from November 2013 to January 2015. The clinical, biochemical and imaging features were collected and analysed. RESULTS: Seven USP8 variants (p.Ser718Pro, p.Ser719del, p.Pro720Arg, p.Pro720Gln, p.Ser718del, p.Ser718Phe, p.Lys713Arg) were identified in 24 patients (50%). USP8 variants showed a female predominance (100% vs. 75% in wild type [WT], p = .022). Patients with p.Ser719del showed an older age at surgery compared to patients with the p.Pro720Arg variant (47- vs. 24-year-olds, p = .033). Patients with p.Pro720Arg showed a higher rate of macroadenoma compared to patients harbouring the p.Ser718Pro variant (60% vs. 0%, p = .037). No significant differences were observed in serum and urinary cortisol and adrenocorticotropin hormone (ACTH) levels. Immediate surgical remission (79% vs. 75%) and long-term hormone remission (79% vs. 67%) were not significantly different between the two groups. The recurrence rate was 21% (4/19) in patients harbouring USP8 variants and 13% (2/16) in WT patients. Recurrence-free survival presented a tendency to be shorter in USP8-mutated individuals (76.7 vs. 109.2 months, p = .068). CONCLUSIONS: Somatic USP8 variants accounted for 50% of the genetic causes in this cohort with a significant female frequency. A long-term follow-up revealed a tendency toward shorter recurrence-free survival in USP8-mutant patients.

The advantages of multi-level omics research on stem cell-based therapies for ischemic stroke.

Stem cell transplantation is a potential therapeutic strategy for ischemic stroke. However, despite many years of preclinical research, the application of stem cells is still limited to the clinical trial stage. Although stem cell therapy can be highly beneficial in promoting functional recovery, the precise mechanisms of action that are responsible for this effect have yet to be fully elucidated. Omics analysis provides us with a new perspective to investigate the physiological mechanisms and multiple functions of stem cells in ischemic stroke. Transcriptomic, proteomic, and metabolomic analyses have become important tools for discovering biomarkers and analyzing molecular changes under pathological conditions. Omics analysis could help us to identify new pathways mediated by stem cells for the treatment of ischemic stroke via stem cell therapy, thereby facilitating the translation of stem cell therapies into clinical use. In this review, we summarize the pathophysiology of ischemic stroke and discuss recent progress in the development of stem cell therapies for the treatment of ischemic stroke by applying multi-level omics. We also discuss changes in RNAs, proteins, and metabolites in the cerebral tissues and body fluids under stroke conditions and following stem cell treatment, and summarize the regulatory factors that play a key role in stem cell therapy. The exploration of stem cell therapy at the molecular level will facilitate the clinical application of stem cells and provide new treatment possibilities for the complete recovery of neurological function in patients with ischemic stroke.

Multi-center application of a convolutional neural network for preoperative detection of cavernous sinus invasion in pituitary adenomas.

OBJECTIVE: Cavernous sinus invasion (CSI) plays a pivotal role in determining management in pituitary adenomas. The study aimed to develop a Convolutional Neural Network (CNN) model to diagnose CSI in multiple centers. METHODS: A total of 729 cases were retrospectively obtained in five medical centers with (n = 543) or without CSI (n = 186) from January 2011 to December 2021. The CNN model was trained using T1-enhanced MRI from two pituitary centers of excellence (n = 647). The other three municipal centers (n = 82) as the external testing set were imported to evaluate the model performance. The area-under-the-receiver-operating-characteristic-curve values (AUC-ROC) analyses were employed to evaluate predicted performance. Gradient-weighted class activation mapping (Grad-CAM) was used to determine models' regions of interest. RESULTS: The CNN model achieved high diagnostic accuracy (0.89) in identifying CSI in the external testing set, with an AUC-ROC value of 0.92 (95% CI, 0.88-0.97), better than CSI clinical predictor of diameter (AUC-ROC: 0.75), length (AUC-ROC: 0.80), and the three kinds of dichotomizations of the Knosp grading system (AUC-ROC: 0.70-0.82). In cases with Knosp grade 3A (n = 24, CSI rate, 0.35), the accuracy the model accounted for 0.78, with sensitivity and specificity values of 0.72 and 0.78, respectively. According to the Grad-CAM results, the views of the model were confirmed around the sellar region with CSI. CONCLUSIONS: The deep learning model is capable of accurately identifying CSI and satisfactorily able to localize CSI in multicenters.

A Bioinspired Flexible Sensor for Electrochemical Probing of Dynamic Redox Disequilibrium in Cancer Cells.

Malignant tumors pose a serious risk to human health. Ascorbic acid (AA) has potential for tumor therapy; however, the mechanism underlying the ability of AA to selectively kill tumor cells remains unclear. AA can cause redox disequilibrium in tumor cells, resulting in the release of abundant reactive oxygen species, represented by hydrogen peroxide (H2 O2 ). Therefore, the detection of H2 O2 changes can provide insight into the selective killing mechanism of AA against tumor cells. In this work, inspired by the ion-exchange mechanism in coral formation, a flexible H2 O2 sensor (PtNFs/CoPi@CC) is constructed to monitor the dynamics of H2 O2 in the cell microenvironment, which exhibits excellent sensitivity and spatiotemporal resolution. Moreover, the findings suggest that dehydroascorbic acid (DHA), the oxidation product of AA, is highly possible the substance that actually acts on tumor cells in AA therapy. Additionally, the intracellular redox disequilibrium and H2 O2 release caused by DHA are positively correlated with the abundance and activity of glucose transporter 1 (GLUT1). In conclusion, this work has revealed the potential mechanism underlying the ability of AA to selectively kill tumor cells through the construction and use of PtNFs/CoPi@CC. The findings provide new insights into the clinical application of AA.

Machine Learning Approaches to Differentiate Sellar-Suprasellar Cystic Lesions on Magnetic Resonance Imaging.

Cystic lesions are common lesions of the sellar region with various pathological types, including pituitary apoplexy, Rathke's cleft cyst, cystic craniopharyngioma, etc. Suggested surgical approaches are not unique when dealing with different cystic lesions. However, cystic lesions with different pathological types were hard to differentiate on MRI with the naked eye by doctors. This study aimed to distinguish different pathological types of cystic lesions in the sellar region using preoperative magnetic resonance imaging (MRI). Radiomics and deep learning approaches were used to extract features from gadolinium-enhanced MRIs of 399 patients enrolled at Peking Union Medical College Hospital over the past 15 years. Paired imaging differentiations were performed on four subtypes, including pituitary apoplexy, cystic pituitary adenoma (cysticA), Rathke's cleft cyst, and cystic craniopharyngioma. Results showed that the model achieved an average AUC value of 0.7685. The model based on a support vector machine could distinguish cystic craniopharyngioma from Rathke's cleft cyst with the highest AUC value of 0.8584. However, distinguishing cystic apoplexy from pituitary apoplexy was difficult and almost unclassifiable with any algorithms on any feature set, with the AUC value being only 0.6641. Finally, the proposed methods achieved an average Accuracy of 0.7532, which outperformed the traditional clinical knowledge-based method by about 8%. Therefore, in this study, we first fill the gap in the existing literature and provide a non-invasive method for accurately differentiating between these lesions, which could improve preoperative diagnosis accuracy and help to make surgery plans in clinical work.

Case report: Identification of potential prognosis-related LAG3 overexpression and DICER1 mutation in pituitary carcinoma: two cases.

Metastatic PitNETs are a rare life-threatening condition with poor prognosis and documentation. Due to the scarce literature and lack of precise treatment, we hope to better characterise PitNET using the next-generation whole exon sequencing (WES) and RNA sequencing. This case study outlines a 54 years-old man and a 52 years-old woman who were both diagnosed with PitNET and analysis of peripheral blood and tumours were performed by WES and RNA sequencing. Analysis showed that DICER1 mutations in precancerous lesions and LAG3 overexpression were significant in aiding the prognosis and diagnosis of PitNETs. The first case with overexpressed LAG3 and DICER1 mutation died 26 months later, and the second case with LAG3 overexpression achieved partial remission. This study revealed that heightened expression of LAG3 offered promising targets for ICI and mutations in DICER1 could provide markers for effective diagnosis and prognosis.

OCT3/4 is a potential immunohistochemical biomarker for diagnosis and prognosis of primary intracranial germ cell tumors: a systematic review and meta-analysis.

Introduction: Intracranial germ cell tumors (iGCTs), comprising of germinoma (GE) and non-germinomatous GCT (NGGCT), are a group of heterogenous brain tumors. Immunohistochemical markers, such as placental-like alkaline phosphatase (PLAP), are commonly used in diagnosis but show moderate sensitivity. Organic cation transporter 3/4 (OCT3/4) has been proposed as a novel biomarker for diagnosis and prognosis of iGCTs. This paper aimed to compare OCT3/4 with PLAP as potential immunohistochemical biomarkers in iGCTs diagnosis and clarify the relationship between OCT3/4 and prognosis of patients with iGCTs. Methods: Meta-analyses were performed to estimate pooled percentage point differences in positive rates between OCT3/4 and PLAP, their sensitivities, and correlation between OCT3/4 and prognosis in iGCTs. Results: Nine articles were included representing of 241 patients. A fixed-effects model meta-analysis revealed that OCT3/4s positive rate was 8.6% higher (95% CI, 0.7% lower to 17.9% higher) than that of PLAP. Using fixed-effects models, sensitivities of OCT3/4 as a potential immunohistochemical biomarker in CNS GE and NGGCT were 85% (95% CI, 79% to 89%) and 56% (95% CI, 39% to 71%), respectively. In comparison, PLAP had lower sensitivities in both GE (73%; 95% CI, 64% to 91%) and NGGCT (43%; 95% CI, 27% to 61%). Moreover, OCT3/4 was significantly negatively correlated with 5-year progression free survival in patients with CNS GE (HR = 2.56, 95 % CI 1.47 to 4.44; p = 0.0008). Sensitivity analyses showed similar results. Discussion: This study provides the first comprehensive assessment of the efficacies of OCT3/4 and PLAP in iGCTs detection and prognosis prediction, indicating OCT3/4 seems to be a more sensitive and reliable immunohistochemical marker in iGCT diagnosis.

Deep-learning-based automatic segmentation and classification for craniopharyngiomas.

Objective: Neuronavigation and classification of craniopharyngiomas can guide surgical approaches and prognostic information. The QST classification has been developed according to the origin of craniopharyngiomas; however, accurate preoperative automatic segmentation and the QST classification remain challenging. This study aimed to establish a method to automatically segment multiple structures in MRIs, detect craniopharyngiomas, and design a deep learning model and a diagnostic scale for automatic QST preoperative classification. Methods: We trained a deep learning network based on sagittal MRI to automatically segment six tissues, including tumors, pituitary gland, sphenoid sinus, brain, superior saddle cistern, and lateral ventricle. A deep learning model with multiple inputs was designed to perform preoperative QST classification. A scale was constructed by screening the images. Results: The results were calculated based on the fivefold cross-validation method. A total of 133 patients with craniopharyngioma were included, of whom 29 (21.8%) were diagnosed with type Q, 22 (16.5%) with type S and 82 (61.7%) with type T. The automatic segmentation model achieved a tumor segmentation Dice coefficient of 0.951 and a mean tissue segmentation Dice coefficient of 0.8668 for all classes. The automatic classification model and clinical scale achieved accuracies of 0.9098 and 0.8647, respectively, in predicting the QST classification. Conclusions: The automatic segmentation model can perform accurate multi-structure segmentation based on MRI, which is conducive to clearing tumor location and initiating intraoperative neuronavigation. The proposed automatic classification model and clinical scale based on automatic segmentation results achieve high accuracy in the QST classification, which is conducive to developing surgical plans and predicting patient prognosis.

Exosomal RNAs in the development and treatment of pituitary adenomas.

Exosomes are small extracellular vesicles that carry various bioactive molecules including various RNAs that modulate the activities of recipient cells. It has drawn considerable attention as means of cell communication and drug delivery. Exosome plays important role in various tumors, but it is rarely summarized in pituitary adenoma (PA). PA is the second most common primary central nervous system tumor, and its recurrence and persistent postoperative hormone hypersecretion lead to compromised quality of life. How exactly exosomes impact tumor development and hormone secretion is important for the development of this tumor diagnosis and treatment. In this review, we discuss how exosomal RNAs impact PAs and their potential as future clinical therapies. In our literature review, first, we found that exosomal microRNA hsa-miR-1180-3p is a potential early biomarker for NFPAs. Since NFPAs are typically difficult to diagnose, this is an especially important finding. Second, exosomal protein transcripts are potential invasive biomarker, such as MMP1, N-cadherin, CDK6, RHOU, INSM1, and RASSF10. Third, exosomal contents such as hsa-miR-21-5p promote distant bone formation of GHPA patients. Fourth, tumor suppressors in the exosome constitute novel therapeutic application of exosome, including long noncoding RNA (lncRNA) H19, miR-149-5p, miR-99a-3p, and miR-423-5p. This review discusses the possible mechanisms of exosome and their contents in PA and promotes the use of exosomes in both clinical diagnosis and treatment of this tumor.

Noncancerous disease-targeting AIEgens.

Noncancerous diseases include a wide plethora of medical conditions beyond cancer and are a major cause of mortality around the world. Despite progresses in clinical research, many puzzles about these diseases remain unanswered, and new therapies are continuously being sought. The evolution of bio-nanomedicine has enabled huge advancements in biosensing, diagnosis, bioimaging, and therapeutics. The recent development of aggregation-induced emission luminogens (AIEgens) has provided an impetus to the field of molecular bionanomaterials. Following aggregation, AIEgens show strong emission, overcoming the problems associated with the aggregation-caused quenching (ACQ) effect. They also have other unique properties, including low background interferences, high signal-to-noise ratios, photostability, and excellent biocompatibility, along with activatable aggregation-enhanced theranostic effects, which help them achieve excellent therapeutic effects as an one-for-all multimodal theranostic platform. This review provides a comprehensive overview of the overall progresses in AIEgen-based nanoplatforms for the detection, diagnosis, bioimaging, and bioimaging-guided treatment of noncancerous diseases. In addition, it details future perspectives and the potential clinical applications of these AIEgens in noncancerous diseases are also proposed. This review hopes to motivate further interest in this topic and promote ideation for the further exploration of more advanced AIEgens in a broad range of biomedical and clinical applications in patients with noncancerous diseases.

Neural Stem Cells in the Treatment of Alzheimer's Disease: Current Status, Challenges, and Future Prospects.

Alzheimer's disease (AD), a progressive dementia, is one of the world's most dangerous and debilitating diseases. Clinical trial results of amyloid-ß (Aß) and tau regulators based on the pretext of straightforward amyloid and tau immunotherapy were disappointing. There are currently no effective strategies for slowing the progression of AD. Further understanding of the mechanisms underlying AD and the development of novel therapeutic options are critical. Neurogenesis is impaired in AD, which contributes to memory deficits. Transplanted neural stem cells (NSCs) can regenerate degraded cholinergic neurons, and new neurons derived from NSCs can form synaptic connections with neighboring neurons. In theory, employing NSCs to replace and restore damaged cholinergic neurons and brain connections may offer new treatment options for AD. However there remain barriers to surmount before NSC-based therapy can be used clinically. The objective of this article is to describe recent advances in the treatment of AD models and clinical trials involving NSCs. In addition, we discuss the challenges and prospects associated with cell transplant therapy for AD.

Predicting delayed remission in Cushing's disease using radiomics models: a multi-center study.

Purpose: No multi-center radiomics models have been built to predict delayed remission (DR) after transsphenoidal surgery (TSS) in Cushing's disease (CD). The present study aims to build clinical and radiomics models based on data from three centers to predict DR after TSS in CD. Methods: A total of 122 CD patients from Peking Union Medical College Hospital, Xuanwu Hospital, and Fuzhou General Hospital were enrolled between January 2000 and January 2019. The T1-weighted gadolinium-enhanced MRI images and clinical data were used as inputs to build clinical and radiomics models. The regions of interest (ROI) of MRI images were automatically defined by a deep learning algorithm developed by our team. The area under the curve (AUC) of receiver operating characteristic (ROC) curves was used to evaluate the performance of the models. In total, 10 machine learning algorithms were used to construct models. Results: The overall DR rate is 44.3% (54/122). According to multivariate Logistic regression analysis, patients with higher BMI and lower postoperative cortisol levels are more likely to achieve a higher rate of delayed remission. Among the 10 models, XGBoost achieved the best performance among all models in both clinical and radiomics models with AUC values of 0.767 and 0.819 respectively. The results from SHAP value and LIME algorithms revealed that postoperative cortisol level (PoC) and BMI were the most important features associated with DR. Conclusion: Radiomics models can be built as an effective noninvasive method to predict DR and might be useful in assisting neurosurgeons in making therapeutic plans after TSS for CD patients. These results are preliminary and further validation in a larger patient sample is needed.

Metabolic syndrome as a common comorbidity in adults with hypothalamic dysfunction.

Objective: Hypothalamic dysfunction (HD) results in various endocrine disorders and is associated with an increased risk of metabolic comorbidities. This study aimed to analyze the clinical characteristics and metabolic abnormalities of adults with HD of various causes. Methods: This study retrospectively reviewed adults with HD treated at our center between August 1989 and October 2020. Metabolic characteristics of patients were compared to those of age-, sex-matched lean, and body mass index (BMI)-matched controls. Results: Temperature dysregulation (61.0%) was the most common hypothalamic physiological dysfunction. At least one anterior pituitary hormone deficiency was observed in 50 patients (84.7%), with hypogonadotropic hypogonadism being the most frequently observed. Metabolic syndrome was confirmed in 31 patients (52.5%) and was significantly more prevalent in those with panhypopituitarism or overweight/obesity. Metabolic syndrome (MetS) was significantly more common in patients with HD than in both lean and BMI-matched controls (P < 0.001 and P = 0.030, respectively). Considering the components of MetS, elevated fasting glucose levels were significantly more common in patients with HD than in BMI-matched controls (P = 0.029). Overweight/obesity and panhypopituitarism were significant risk factors for MetS in the multivariate analysis on patients with HD. Moreover, in the multivariate analysis on patients and BMI-matched control, HD was a significant risk factor of MetS (P=0.035, OR 2.919) after adjusted for age, sex and BMI. Conclusions: Temperature dysregulation and hypogonadotropic hypogonadism are the most common physiological and endocrine dysfunctions, respectively. MetS and unfavorable metabolic profiles were prevalent in adults with HD. HD was a significant risk factor of MetS after adjusted for BMI.

A Convolutional Neural Network Model for Detecting Sellar Floor Destruction of Pituitary Adenoma on Magnetic Resonance Imaging Scans.

Objective: Convolutional neural network (CNN) is designed for image classification and recognition with a multi-layer neural network. This study aimed to accurately assess sellar floor invasion (SFI) of pituitary adenoma (PA) using CNN. Methods: A total of 1413 coronal and sagittal magnetic resonance images were collected from 695 patients with PAs. The enrolled images were divided into the invasive group (n = 530) and the non-invasive group (n = 883) according to the surgical observation of SFI. Before model training, 100 images were randomly selected for the external testing set. The remaining 1313 cases were randomly divided into the training and validation sets at a ratio of 80:20 for model training. Finally, the testing set was imported to evaluate the model performance. Results: A CNN model with a 10-layer structure (6-layer convolution and 4-layer fully connected neural network) was constructed. After 1000 epoch of training, the model achieved high accuracy in identifying SFI (97.0 and 94.6% in the training and testing sets, respectively). The testing set presented excellent performance, with a model prediction accuracy of 96%, a sensitivity of 0.964, a specificity of 0.958, and an area under the receptor operator curve (AUC-ROC) value of 0.98. Four images in the testing set were misdiagnosed. Three images were misread with SFI (one with conchal type sphenoid sinus), and one image with a relatively intact sellar floor was not identified with SFI. Conclusion: This study highlights the potential of the CNN model for the efficient assessment of PA invasion.

Concurrent mutations of germline GPR101 and somatic USP8 in a pediatric giant pituitary ACTH adenoma: a case report.

BACKGROUND: Cushing's disease (CD) is rare in pediatric patients. It is characterized by elevated plasma adrenocorticotropic hormone (ACTH) from pituitary adenomas, with damage to multiple systems and development. In recent years, genetic studies have shed light on the etiology and several mutations have been identified in patients with CD. CASE PRESENTATION: A girl presented at the age of 10 years and 9 months with facial plethora, hirsutism and acne. Her vision and eye movements were impaired. A quick weight gain and slow growth were also observed. Physical examination revealed central obesity, moon face, buffalo hump, supra-clavicular fat pads and bruising. Her plasma ACTH level ranged between 118 and 151 pg/ml, and sella enhanced MRI showed a giant pituitary tumor of 51.8 × 29.3 × 14.0 mm. Transsphenoidal pituitary debulk adenomectomy was performed and immunohistochemical staining confirmed an ACTH-secreting adenoma. Genetic analysis identified a novel germline GPR101 (p.G169R) and a somatic USP8 (p. S719del) mutation. They were hypothesized to impact tumor growth and function, respectively. CONCLUSIONS: We reported a rare case of pediatric giant pituitary ACTH adenoma and pointed out that unusual concurrent mutations might contribute to its early onset and large volume.