Modeling seizure networks in neuron-glia cultures using microelectrode arrays.

Epilepsy is a common neurological disorder, affecting over 65 million people worldwide. Unfortunately, despite resective surgery, over 30 % of patients with drug-resistant epilepsy continue to experience seizures. Retrospective studies considering connectivity using intracranial electrocorticography (ECoG) obtained during neuromonitoring have shown that treatment failure is likely driven by failure to consider critical components of the seizure network, an idea first formally introduced in 2002. However, current studies only capture snapshots in time, precluding the ability to consider seizure network development. Over the past few years, multiwell microelectrode arrays have been increasingly used to study neuronal networks in vitro. As such, we sought to develop a novel in vitro MEA seizure model to allow for study of seizure networks. Specifically, we used 4-aminopyridine (4-AP) to capture hyperexcitable activity, and then show increased network changes after 2 days of chronic treatment. We characterize network changes using functional connectivity measures and a novel technique using dimensionality reduction. We find that 4-AP successfully captures persistently elevated mean firing rate and significant changes in underlying connectivity patterns. We believe this affords a robust in vitro seizure model from which longitudinal network changes can be studied, laying groundwork for future studies exploring seizure network development.

Virtual stimulation of the interictal EEG network localizes the EZ as a measure of cortical excitability.

Introduction: For patients with drug-resistant epilepsy, successful localization and surgical treatment of the epileptogenic zone (EZ) can bring seizure freedom. However, surgical success rates vary widely because there are currently no clinically validated biomarkers of the EZ. Highly epileptogenic regions often display increased levels of cortical excitability, which can be probed using single-pulse electrical stimulation (SPES), where brief pulses of electrical current are delivered to brain tissue. It has been shown that high-amplitude responses to SPES can localize EZ regions, indicating a decreased threshold of excitability. However, performing extensive SPES in the epilepsy monitoring unit (EMU) is time-consuming. Thus, we built patient-specific in silico dynamical network models from interictal intracranial EEG (iEEG) to test whether virtual stimulation could reveal information about the underlying network to identify highly excitable brain regions similar to physical stimulation of the brain. Methods: We performed virtual stimulation in 69 patients that were evaluated at five centers and assessed for clinical outcome 1 year post surgery. We further investigated differences in observed SPES iEEG responses of 14 patients stratified by surgical outcome. Results: Clinically-labeled EZ cortical regions exhibited higher excitability from virtual stimulation than non-EZ regions with most significant differences in successful patients and little difference in failure patients. These trends were also observed in responses to extensive SPES performed in the EMU. Finally, when excitability was used to predict whether a channel is in the EZ or not, the classifier achieved an accuracy of 91%. Discussion: This study demonstrates how excitability determined via virtual stimulation can capture valuable information about the EZ from interictal intracranial EEG.

Mandibular dose-volume predicts time-to-osteoradionecrosis in an actuarial normal-tissue complication probability (NTCP) model: External validation of right-censored clinico-dosimetric and competing risk application across international multi-institutional observational cohorts and online graphical user interface clinical support tool assessment.

Background: Existing studies on osteoradionecrosis of the jaw (ORNJ) have primarily used cross-sectional data, assessing risk factors at a single time point. Determining the time-to-event profile of ORNJ has important implications to monitor oral health in head and neck cancer (HNC) long-term survivors. Methods: Demographic, clinical and dosimetric data were retrospectively obtained for a clinical observational cohort of 1129 patients with HNC treated with radiotherapy (RT) at The University of Texas MD Anderson Cancer Center. ORNJ was diagnosed in 198 patients (18%). A multivariable logistic regression analysis with forward stepwise variable selection identified significant predictors for ORNJ. These predictors were then used to train a Weibull Accelerated Failure Time (AFT) model, which was externally validated using an independent cohort of 265 patients (92 ORNJ cases and 173 controls) treated at Guy's and St. Thomas' Hospitals. Findings: Our model identified that each unit increase in D25% is significantly associated with a 12% shorter time to ORNJ (Adjusted Time Ratio [ATR] 0·88, p<0·005); pre-RT dental extractions was associated to a 27% faster (ATR 0·73, p=0·13) onset of ORNJ; male patients experienced a 38% shorter time to ORNJ (ATR 0·62, p = 0·11). The model demonstrated strong internal calibration (integrated Brier score of 0·133, D-calibration p-value 0.998) and optimal discrimination at 72 months (Harrell's C-index of 0·72). The model also showed good generalization to the independent cohort, despite a slight drop in performance. Interpretation: This study is the first to demonstrate a direct relationship between radiation dose and the time to ORNJ onset, providing a novel characterization of the impact of delivered dose not only on the probability of a late effect (ORNJ), but the conditional risk during survivorship. Funding: This work was supported by various funding sources including NIH, NIDCR, NCI, NAPT, NASA, BCM, Affirmed Pharma, CRUK, KWF Dutch Cancer Society, NWO ZonMw, and the Apache Corporation.

Designing a Roadmap for Health Technology Assessment Implementation in Algeria.

Background The scarcity of resources restricts healthcare financing decisions, affecting the population's health. Health technology assessment (HTA) balances restricted budgets with the best possible health outcomes. We aim to characterize the current status of HTA in Algeria and describe the future directions for HTA implementation according to the priorities set by local stakeholders. Methods Stakeholders from the public and private sectors responded to a policy survey about the current and preferred future status of HTA implementation in Algeria. The survey was administered during an online workshop and used a widely accepted international scorecard covering eight domains: capacity building, HTA financing, process and organizational structure, scope of HTA implementation, decision criteria, standardization of methodology, use of local data, and international collaboration. After that, one-on-one interviews with another local expert were conducted to validate and modify the draft recommendations. The interviewees were representatives from government agencies, hospitals, and pharmaceutical companies. Results Thirty-one experts filled out the HTA scorecard survey; most of them were from the public sector (74%). They highlighted that project-based HTA workshops or short courses were the most common form of HTA education in Algeria and recommended the establishment of postgraduate HTA training programs in the future to build sustainable capacities. They reported a lack of funding for HTA research and critical appraisal and recommended an increased public budget for HTA and the introduction of submission fees by manufacturers. There was consensus about the need for local HTA evidence generation in the future. Most of the experts advocated an explicit soft decision threshold. The interviewees further recommended using multi-criteria decision analysis in the short term. The application of quality indicators was believed to improve the reliability of the HTA process. Conclusion The results of our policy research delineate the gap between the current and preferred future status of HTA in Algeria based on insights from multiple stakeholders. The need to improve the educational HTA programs in Algeria, use local data in policy decisions, and increase funding for HTA were the most advocated recommendations.

Focal seizures induce spatiotemporally organized spiking activity in the human cortex.

Epileptic seizures are debilitating because of the clinical symptoms they produce. These symptoms, in turn, may stem directly from disruptions in neural coding. Recent evidence has suggested that the specific temporal order, or sequence, of spiking across a population of cortical neurons may encode information. Here, we investigate how seizures disrupt neuronal spiking sequences in the human brain by recording multi-unit activity from the cerebral cortex in five male participants undergoing monitoring for seizures. We find that pathological discharges during seizures are associated with bursts of spiking activity across a population of cortical neurons. These bursts are organized into highly consistent and stereotyped temporal sequences. As the seizure evolves, spiking sequences diverge from the sequences observed at baseline and become more spatially organized. The direction of this spatial organization matches the direction of the ictal discharges, which spread over the cortex as traveling waves. Our data therefore suggest that seizures can entrain cortical spiking sequences by changing the spatial organization of neuronal firing, providing a possible mechanism by which seizures create symptoms.

Experimental and Finite Element Analysis of Compressive Strength and Diametral Tensile Strength of Luting Cement: An In Vitro Study.

Background Strength parameters greatly influence the selection of luting agents. This study compared the compressive and diametral tensile strengths (DTS) of three luting cements. Materials and methods Three luting cements, conventional glass ionomer (CGI), resin-modified glass ionomer (RMGI), and resin cement (RC), were tested for compressive strength and DTS. Forty-two standardized specimens were prepared, measuring 4 mm by 6 mm for compressive tests and 6 mm by 3 mm for diametral tensile tests. The luting materials were prepared according to the manufacturers' instructions. Result Experimental mean compressive and diametral strengths and standard errors were calculated for each luting agent (n = 10). Analysis of variance was computed (p < 0.05), and multiple comparison tests were performed. RC showed significantly higher compressive strengths and DTS among the three tested luting cements, while the CGI showed the least. The results obtained by finite element analysis (FEA) for both tests closely matched the experimental results. Conclusion In this study, it was concluded that the mean compressive strength and DTS values of all three luting cements were significantly different. The resin luting cement exhibited the highest compressive strength and DTS, while the CGI exhibited the least.

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.

BACKGROUND/OBJECTIVES: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer. METHODS: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines. RESULTS: Forty four studies from 2006 to 2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%). CONCLUSION: Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Berberine and berberine nanoformulations in cancer therapy: Focusing on lung cancer.

Lung cancer is the second most prevalent cancer and ranks first in cancer-related death worldwide. Due to the resistance development to conventional cancer therapy strategies, including chemotherapy, radiotherapy, targeted therapy, and immunotherapy, various natural products and their extracts have been revealed as alternatives. Berberine (BBR), which is present in the stem, root, and bark of various trees, could exert anticancer activities by regulating tumor cell proliferation, apoptosis, autophagy, metastasis, angiogenesis, and immune responses via modulating several signaling pathways within the tumor microenvironment. Due to its poor water solubility, poor pharmacokinetics/bioavailability profile, and extensive p-glycoprotein-dependent efflux, BBR application in (pre) clinical studies is restricted. To overcome these limitations, BBR can be encapsulated in nanoparticle (NP)-based drug delivery systems, as monotherapy or combinational therapy, and improve BBR therapeutic efficacy. Nanoformulations also facilitate the selective delivery of BBR into lung cancer cells. In addition to the anticancer activities of BBR, especially in lung cancer, here we reviewed the BBR nanoformulations, including polymeric NPs, metal-based NPs, carbon nanostructures, and others, in the treatment of lung cancer.

Differential gene expression underlying epileptogenicity in patients with gliomas.

Background: Seizures are a common sequela for patients suffering from gliomas. Molecular properties are known to influence the initiation of seizures that may influence tumor growth. Different levels of gene expression with seizures related to gliomas remain unclear. We analyzed RNA sequencing of gliomas to further probe these differences. Methods: Total RNA sequencing was obtained from The Cancer Genome Atlas-Lower-Grade Glioma project, comprised of 2021 World Health Organization classification low-grade gliomas, including IDH-mutant and IDH-wild type, to distinguish differential expression in patients who did and did not experience seizures. Utilizing QIAGEN Ingenuity Pathways Analysis, we identified canonical and functional pathways to characterize differential expression. Results: Of 289 patients with gliomas, 83 (28.7%) had available information regarding seizure occurrence prior to intervention and other pertinent variables of interest. Of these, 50 (60.2%) were allocated to the seizure group. When comparing the level of RNA expression from these tumors between the seizure and non-seizure groups, 52 genes that were significantly differentially regulated were identified. We found canonical pathways that were altered, most significantly RhoGDI and semaphorin neuronal repulsive signaling. Functional gene analysis revealed tumors that promoted seizures had significantly increased functional gene sets involving neuronal differentiation and synaptogenesis. Conclusions: In the setting of gliomas, differences in tumor gene expression exist between individuals with and without seizures, despite similarities in patient demographics and other tumor characteristics. There are significant differences in gene expression associated with neuron development and synaptogenesis, ultimately suggesting a mechanistic role of a tumor-neuron synapse in seizure initiation.

Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation.

PURPOSE: The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors. METHODS: Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure. RESULTS: Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.

Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning.

BACKGROUND: Radiotherapy is a core treatment modality for oropharyngeal cancer (OPC), where the primary gross tumor volume (GTVp) is manually segmented with high interobserver variability. This calls for reliable and trustworthy automated tools in clinician workflow. Therefore, accurate uncertainty quantification and its downstream utilization is critical. METHODS: Here we propose uncertainty-aware deep learning for OPC GTVp segmentation, and illustrate the utility of uncertainty in multiple applications. We examine two Bayesian deep learning (BDL) models and eight uncertainty measures, and utilize a large multi-institute dataset of 292 PET/CT scans to systematically analyze our approach. RESULTS: We show that our uncertainty-based approach accurately predicts the quality of the deep learning segmentation in 86.6% of cases, identifies low performance cases for semi-automated correction, and visualizes regions of the scans where the segmentations likely fail. CONCLUSIONS: Our BDL-based analysis provides a first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation.

Radiotherapy is used as a treatment for people with oropharyngeal cancer. It is important to distinguish the areas where cancer is present so the radiotherapy treatment can be targeted at the cancer. Computational methods based on artificial intelligence can automate this task but need to be able to distinguish areas where it is unclear whether cancer is present. In this study we compare these computational methods that are able to highlight areas where it is unclear whether or not cancer is present. Our approach accurately predicts how well these areas are distinguished by the models. Our results could be applied to improve the computational methods used during radiotherapy treatment. This could enable more targeted treatment to be used in the future, which could result in better outcomes for people with oropharyngeal cancer.

Microglia in Glioblastomas: Molecular Insight and Immunotherapeutic Potential.

Glioblastoma (GBM) is one of the most aggressive and devastating primary brain tumors, with a median survival of 15 months following diagnosis. Despite the intense treatment regimen which routinely includes maximal safe neurosurgical resection followed by adjuvant radio- and chemotherapy, the disease remains uniformly fatal. The poor prognosis associated with GBM is multifactorial owing to factors such as increased proliferation, angiogenesis, and metabolic switching to glycolytic pathways. Critically, GBM-mediated local and systemic immunosuppression result in inadequate immune surveillance and ultimately, tumor-immune escape. Microglia-the resident macrophages of the central nervous system (CNS)-play crucial roles in mediating the local immune response in the brain. Depending on the specific pathological cues, microglia are activated into either a pro-inflammatory, neurotoxic phenotype, known as M1, or an anti-inflammatory, regenerative phenotype, known as M2. In either case, microglia secrete corresponding pro- or anti-inflammatory cytokines and chemokines that either promote or hinder tumor growth. Herein, we review the interplay between GBM cells and resident microglia with a focus on contemporary studies highlighting the effect of GBM on the subtypes of microglia expressed, the associated cytokines/chemokines secreted, and ultimately, their impact on tumor pathogenesis. Finally, we explore how understanding the intricacies of the tumor-immune landscape can inform novel immunotherapeutic strategies against this devastating disease.

Persistent GDNF Expression 45 Months after Putaminal Infusion of AAV2-GDNF in a Patient with Parkinson's Disease.

OBJECTIVE: Gene therapy by convection-enhanced delivery of type 2 adeno-associated virus-glial cell derived neurotrophic factor (AAV2-GDNF) to the bilateral putamina seeks to increase GDNF gene expression and treat Parkinson's disease (PD). METHODS: A 63-year-old man with advanced PD received AAV2-GDNF in a clinical trial. He died from pneumonia after anterior cervical discectomy and fusion 45 months later. An autopsy included brain examination for GDNF transgene expression. Putaminal catecholamine concentrations were compared to in vivo 18F-Fluorodopa (18F-FDOPA) positron emission tomography (PET) scanning results before and 18 months after AAV2-GDNF infusion. RESULTS: Parkinsonian progression stabilized clinically. Postmortem neuropathology confirmed PD. Bilateral putaminal regions previously infused with AAV2-GDNF expressed the GDNF gene. Total putaminal dopamine was 1% of control, confirming the striatal dopaminergic deficiency suggested by baseline 18F-DOPA-PET scanning. Putaminal regions responded as expected to AAV2-GDNF. CONCLUSION: After AAV2-GDNF infusion, infused putaminal regions showed increased GDNF gene expression, tyrosine hydroxylase immunoreactive sprouting, catechol levels, and 18F-FDOPA-PET signal, suggesting the regenerative potential of AAV2-GDNF in PD.

Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review.

Background/purpose: The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. The purpose of this study was to scope existing literature related to UQ in RT, identify areas of improvement, and determine future directions. Methods: We followed the PRISMA-ScR scoping review reporting guidelines. We utilized the population (human cancer patients), concept (utilization of AI UQ), context (radiotherapy applications) framework to structure our search and screening process. We conducted a systematic search spanning seven databases, supplemented by manual curation, up to January 2024. Our search yielded a total of 8980 articles for initial review. Manuscript screening and data extraction was performed in Covidence. Data extraction categories included general study characteristics, RT characteristics, AI characteristics, and UQ characteristics. Results: We identified 56 articles published from 2015-2024. 10 domains of RT applications were represented; most studies evaluated auto-contouring (50%), followed by image-synthesis (13%), and multiple applications simultaneously (11%). 12 disease sites were represented, with head and neck cancer being the most common disease site independent of application space (32%). Imaging data was used in 91% of studies, while only 13% incorporated RT dose information. Most studies focused on failure detection as the main application of UQ (60%), with Monte Carlo dropout being the most commonly implemented UQ method (32%) followed by ensembling (16%). 55% of studies did not share code or datasets. Conclusion: Our review revealed a lack of diversity in UQ for RT applications beyond auto-contouring. Moreover, there was a clear need to study additional UQ methods, such as conformal prediction. Our results may incentivize the development of guidelines for reporting and implementation of UQ in RT.

Dataset of weekly intra-treatment diffusion weighted imaging in head and neck cancer patients treated with MR-Linac.

Radiation therapy (RT) is a crucial treatment for head and neck squamous cell carcinoma (HNSCC); however, it can have adverse effects on patients' long-term function and quality of life. Biomarkers that can predict tumor response to RT are being explored to personalize treatment and improve outcomes. While tissue and blood biomarkers have limitations, imaging biomarkers derived from magnetic resonance imaging (MRI) offer detailed information. The integration of MRI and a linear accelerator in the MR-Linac system allows for MR-guided radiation therapy (MRgRT), offering precise visualization and treatment delivery. This data descriptor offers a valuable repository for weekly intra-treatment diffusion-weighted imaging (DWI) data obtained from head and neck cancer patients. By analyzing the sequential DWI changes and their correlation with treatment response, as well as oncological and survival outcomes, the study provides valuable insights into the clinical implications of DWI in HNSCC.

Prevalence of Imposter Syndrome and Its Risk Factors Among University of Sharjah Medical Students.

Background Imposter syndrome describes an internal experience of intellectual fraud, where individuals attribute their academic or occupational endeavors and achievements primarily to luck rather than to their diligent efforts. Additionally, the stringent standards and prerequisites set by medical institutions create an environment conducive to impostorism among medical students. This study aimed to evaluate the prevalence and severity of imposter syndrome among medical students at the University of Sharjah. Methodology This research was designed as a descriptive cross-sectional study. A total of 400 participants enrolled in the study using non-probability convenience sampling, but 399 participants, 49.4% (197) from colleges of medicine and 50.6% (202) from dentistry, successfully completed the questionnaire. Participants completed a questionnaire containing the Clance Imposter Phenomenon Scale. Statistical associations between variables were tested using the chi-square test. Individuals with chronic medical conditions or those using medications with known psychiatric side effects were excluded. Results The analyzed sample comprised 399 students, with 64.7% females and 35.3% males. Most respondents were from year 2 (21.3%, 85), while the fewest were from year 5 (18.3%, 73). The majority of students fell into the categories of moderate (46.4%, 185) and frequent (35.8%, 143) imposter experiences. Among all investigated characteristics, pure academic factors such as field of study (p = 0.001), study phases (p = 0.032), advisor's attitude (p = 0.029), and comparison with peers' performance and grades (p = 0.024 and <0.001, respectively) exhibited the highest significant association with the severity of imposter syndrome. Conclusions This study revealed a high prevalence of imposter syndrome among medical students, emphasizing the need for comprehensive strategies and interventions targeting academically associated risk factors to alleviate the burden of imposter syndrome.

HCC-Check: A Novel Diagnostic Tool for Early Detection of Hepatocellular Carcinoma Based on Cytokeratin-1 and Epithelial Membrane Antigen: A Cross-Sectional Study.

Background: Hepatocellular carcinoma is frequently diagnosed in advanced stages, leading to a poorer prognosis. Therefore, early diagnosis and identification of biomarkers may significantly improve outcomes. Methods: This cross-sectional study enrolled 486 participants distributed among 3 groups: F1 to F3 = 184, F4 = 183, and hepatocellular carcinoma = 119. Liver fibrosis staging was performed using FibroScan, while imaging features were used for hepatocellular carcinoma detection. Epithelial membrane antigen and cytokeratin-1 levels in serum were quantified through Western blot and ELISA, respectively. Results: Patients diagnosed with hepatocellular carcinoma exhibited significantly elevated levels of epithelial membrane antigen and cytokeratin-1 compared to non-hepatocellular carcinoma patients, with a highly significant statistical difference (P < .0001). Epithelial membrane antigen demonstrated diagnostic performance with an area under the curve of 0.75, a sensitivity of 69.0%, and a specificity of 68.5%. Cytokeratin-1 for the identification of hepatocellular carcinoma showed a sensitivity of 79.0% and a specificity of 81.4%, resulting in an area under the curve of 0.87. The developed HCC-Check, which incorporates epithelial membrane antigen, cytokeratin-1, albumin, and alpha-fetoprotein, displayed a higher area under the curve of 0.95 to identify hepatocellular carcinoma, with a sensitivity of 89.8% and a specificity of 83.9%. Notably, HCC-Check values exceeding 2.57 substantially increased the likelihood of hepatocellular carcinoma, with an estimated odds ratio of 50.65, indicating a higher susceptibility to hepatocellular carcinoma development than those with lower values. The HCC-Check diagnostic test exhibited high precision in identifying patients with hepatocellular carcinoma, particularly those with small tumor sizes (<5 cm) and a single nodule, as reflected in area under the curve values of 0.92 and 0.85, respectively. HCC-Check was then applied to the validation study to test its accuracy and reproducibility, showing superior area under the curves for identifying different stages of hepatocellular carcinoma. These outcomes underscore the effectiveness of the test in the early detection of hepatocellular carcinoma. Conclusion: The HCC-Check test presents a highly accurate diagnostic method for detecting hepatocellular carcinoma in its early stages.

Guanidine-to-piperidine switch affords high affinity small molecule NPFF ligands with preference for NPFF1-R and NPFF2-R subtypes.

The Neuropeptide FF (NPFF) receptor system is known to modulate opioid actions and has been shown to mediate opioid-induced hyperalgesia and tolerance. The lack of subtype selective small molecule compounds has hampered further exploration of the pharmacology of this receptor system. The vast majority of available NPFF ligands possess a highly basic guanidine group, including our lead small molecule, MES304. Despite providing strong receptor binding, the guanidine group presents a potential pharmacokinetic liability for in vivo pharmacological tool development. Through structure-activity relationship exploration, we were able to modify our lead molecule MES304 to arrive at guanidine-free NPFF ligands. The novel piperidine analogues 8b and 16a are among the few non-guanidine based NPFF ligands known in literature. Both compounds displayed nanomolar NPFF-R binding affinity approaching that of the parent molecule. Moreover, while MES304 was non-subtype selective, these two analogues presented new starting points for subtype selective scaffolds, whereby 8b displayed a 15-fold preference for NPFF1-R, and 16a demonstrated an 8-fold preference for NPFF2-R. Both analogues showed no agonist activity on either receptor subtype in the in vitro functional activity assay, while 8b displayed antagonistic properties at NPFF1-R. The calculated physicochemical properties of 8b and 16a were also shown to be more favorable for in vivo tool design. These results indicate the possibility of developing potent, subtype selective NPFF ligands devoid of a guanidine functionality.

Prognostic value of tumor volume doubling time in lung-metastatic adenoid cystic carcinoma.

OBJECTIVES: Lung metastases in adenoid cystic carcinoma (ACC) usually have indolent growth and the optimal timing to start systemic therapy is not established. We assessed ACC lung metastasis tumor growth dynamics and compared the prognostic value of time to progression (TTP) and tumor volume doubling time (TVDT). METHODS: The study included ACC patients with ≥1 pulmonary metastasis (≥5 mm) and at least 2 chest computed tomography scans. Radiology assessment was performed from the first scan showing metastasis until treatment initiation or death. Up to 5 lung nodules per patient were segmented for TVDT calculation. To assess tumor growth rate (TGR), the correlation coefficient (r) and coefficient of determination (R2) were calculated for measured lung nodules. TTP was assessed per RECIST 1.1; TVDT was calculated using the Schwartz formula. Overall survival was analyzed using the Kaplan-Meier method. RESULTS: The study included 75 patients. Sixty-seven patients (89%) had lung-only metastasis on first CT scan. The TGR was overall constant (median R2 = 0.974). Median TTP and TVDT were 11.2 months and 7.5 months. Shorter TVDT (<6 months) was associated with poor overall survival (HR = 0.48; p = 0.037), but TTP was not associated with survival (HR = 1.02; p = 0.96). Cox regression showed that TVDT but not TTP significantly correlated with OS. TVDT calculated using estimated tumor volume correlated with TVDT obtained by segmentation. CONCLUSION: Most ACC lung metastases have a constant TGR. TVDT may be a better prognostic indicator than TTP in lung-metastatic ACC. TVDT can be estimated by single longitudinal measurement in clinical practice.