Structural Control Enables Catalytic and Electrocatalytic Activity of Porous Tetrametallic Nanorods.

Integrating more than one type of metal into a nanoparticle that has a well-defined morphology and composition expands the functionalities of nanocatalysts. For a metal core/porous multimetallic shell nanoparticle, the availability of catalytically active surface sites and molecular mass transport can be enhanced, and the multielemental synergy can facilitate intraparticle charge transport. In this work, a reliable and robust synthesis of such a functional tetrametallic nanoparticle type is presented, where a micro- and mesoporous PdPtIr shell is grown on Au nanorods. The effect of critical synthesis parameters, namely temperature and the addition of HCl are investigated on the hydrodynamic size of the micellar pore template as well as on the stability of the metal chloride complexes and various elemental analysis techniques prove composition of the porous multimetallic shell. Due to the synergistic properties, the tetrametallic nanorods possess extensive negative surface charge making them a promising catalyst in reduction reactions. Dye degradation as well as the conversion of p-nitrophenol to p-aminophenol is catalyzed by the supportless nanorods without light illumination. By depositing the particles onto conductive substrates, the nanostructured electrodes show promising electrocatalytic activity in ethanol oxidation reaction. The nanocatalyst presents excellent morphological stability during all the catalytic test reactions.

Clinical Evaluation of Deep Learning for Tumor Delineation on 18F-FDG PET/CT of Head and Neck Cancer.

Artificial intelligence (AI) may decrease 18F-FDG PET/CT-based gross tumor volume (GTV) delineation variability and automate tumor-volume-derived image biomarker extraction. Hence, we aimed to identify and evaluate promising state-of-the-art deep learning methods for head and neck cancer (HNC) PET GTV delineation. Methods: We trained and evaluated deep learning methods using retrospectively included scans of HNC patients referred for radiotherapy between January 2014 and December 2019 (ISRCTN16907234). We used 3 test datasets: an internal set to compare methods, another internal set to compare AI-to-expert variability and expert interobserver variability (IOV), and an external set to compare internal and external AI-to-expert variability. Expert PET GTVs were used as the reference standard. Our benchmark IOV was measured using the PET GTV of 6 experts. The primary outcome was the Dice similarity coefficient (DSC). ANOVA was used to compare methods, a paired t test was used to compare AI-to-expert variability and expert IOV, an unpaired t test was used to compare internal and external AI-to-expert variability, and post hoc Bland-Altman analysis was used to evaluate biomarker agreement. Results: In total, 1,220 18F-FDG PET/CT scans of 1,190 patients (mean age ± SD, 63 ± 10 y; 858 men) were included, and 5 deep learning methods were trained using 5-fold cross-validation (n = 805). The nnU-Net method achieved the highest similarity (DSC, 0.80 [95% CI, 0.77-0.86]; n = 196). We found no evidence of a difference between expert IOV and AI-to-expert variability (DSC, 0.78 for AI vs. 0.82 for experts; mean difference of 0.04 [95% CI, -0.01 to 0.09]; P = 0.12; n = 64). We found no evidence of a difference between the internal and external AI-to-expert variability (DSC, 0.80 internally vs. 0.81 externally; mean difference of 0.004 [95% CI, -0.05 to 0.04]; P = 0.87; n = 125). PET GTV-derived biomarkers of AI were in good agreement with experts. Conclusion: Deep learning can be used to automate 18F-FDG PET/CT tumor-volume-derived imaging biomarkers, and the deep-learning-based volumes have the potential to assist clinical tumor volume delineation in radiation oncology.

First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects.

Vibrational spectroscopy is a powerful approach to visualising interfacial phenomena. However, extracting structural and dynamical information from vibrational spectra is a challenge that requires first-principles simulations, including non-Condon and quantum nuclear effects. We address this challenge by developing a machine-learning enhanced first-principles framework to speed up predictive modelling of infrared, Raman, and sum-frequency generation spectra. Our approach uses machine learning potentials that encode quantum nuclear effects to generate quantum trajectories using simple molecular dynamics efficiently. In addition, we reformulate bulk and interfacial selection rules to express them unambiguously in terms of the derivatives of polarisation and polarisabilities of the whole system and predict these derivatives efficiently using fully-differentiable machine learning models of dielectric response tensors. We demonstrate our framework's performance by predicting the IR, Raman, and sum-frequency generation spectra of liquid water, ice and the water-air interface by achieving near quantitative agreement with experiments at nearly the same computational efficiency as pure classical methods. Finally, to aid the experimental discovery of new phases of nanoconfined water, we predict the temperature-dependent vibrational spectra of monolayer water across the solid-hexatic-liquid phases transition.

Lipid exchange at ER-trans-Golgi contact sites governs polarized cargo sorting.

Oxysterol binding protein (OSBP) extracts cholesterol from the ER to deliver it to the TGN via counter exchange and subsequent hydrolysis of the phosphoinositide PI(4)P. Here, we show that this pathway is essential in polarized epithelial cells where it contributes not only to the proper subcellular distribution of cholesterol but also to the trans-Golgi sorting and trafficking of numerous plasma membrane cargo proteins with apical or basolateral localization. Reducing the expression of OSBP, blocking its activity, or inhibiting a PI4Kinase that fuels OSBP with PI(4)P abolishes the epithelial phenotype. Waves of cargo enrichment in the TGN in phase with OSBP and PI(4)P dynamics suggest that OSBP promotes the formation of lipid gradients along the TGN, which helps cargo sorting. During their transient passage through the trans-Golgi, polarized plasma membrane proteins get close to OSBP but fail to be sorted when OSBP is silenced. Thus, OSBP lipid exchange activity is decisive for polarized cargo sorting and distribution in epithelial cells.

Clinical Investigation of Hereditary and Acquired Thrombophilic Factors in Patients with Venous and Arterial Thromboembolism.

Background: The clinical relevance of thrombophilic laboratory factors, especially the "mild" ones, and the need for their screening is not generally recommended in venous (VTE) and/or arterial (ATE) thromboembolism. Methods: Our aim was to investigate possible associations between comorbidities and 16 inherited/acquired "severe" and "mild" laboratory thrombophilic factors (detailed in introduction) in patients (n=348) with VTE/ATE without a serious trigger (high-risk surgical intervention, active cancer and/or chemo-radiotherapy). Cases with VTE/ATE were enrolled when the thrombotic event occurred under the age of 40, in case of positive family history, recurrent thromboembolism, idiopathic event or unusual location. Patients without a detailed thrombophilia screening or who suffered from both ATE/VTE were excluded to find potential distinct thrombosis type specific thrombophilic risks. The possible role of "mild" factor accumulation was also investigated in VTE (n=266). Results: Elevation of factor VIII clotting activity was associated with VTE rather than ATE. Varicose veins together with postthrombotic syndrome were strongly related to several "mild" factors. Besides "severe" we found that the "mild" thrombophilic factors were also strongly associated with VTE/ATE. Comorbidities/conditions such as diabetes and smoking were generally associated with hyperlipidemia; moreover, both had a correlation with lipoprotein (a) in VTE. We also revealed an important contribution of "mild" factors in increasing trends of several types and localizations of VTE. Conclusion: In summary, besides the "severe" thrombophilic factors, the "mild" ones also seem to play a non-negligible role in the manifestation of thrombosis, especially in combination. Therefore, an extended screening might be useful in the personalized recommendation of antithrombotic prophylaxis.

Composite ligand shells on gold nanoprisms - an ensemble and single particle study.

The attachment of thiolated molecules onto gold surfaces is one of the most extensively used and robust ligand exchange approaches to exploit the nanooptical features of nanoscale and nanostructured plasmonic materials. In this work, the impact of thiol adsorption on the optical properties of wet-chemically synthesized gold nanoprisms is studied both at the ensemble and single particle level to investigate the build-up of more complex ligand layers. Two prototypical ligands with different lengths have been investigated ((16-mercaptohexadecyl)trimethylammonium bromide - MTAB and thiolated polyethylene glycol - mPEG-SH). From ensemble experiments it is found that composite ligand layers are obtained by the sequential addition of the two thiols, and an island-like surface accumulation of the molecules can be anticipated. The single particle experiment derived chemical interface damping and resonance energy changes further support this and show additionally that when the two thiols are used simultaneously, a higher density, intermixed layer is formed. Hence, when working with more than a single type of ligand during surface modification, sequential adsorption is preferred for the combination of accessible essential surface functionalities, whereas for high overall loading the simultaneous use of the different ligand types is favourable.

Structure-Based Design of a Lead Compound Derived from Natural Schweinfurthins with Antitumor Properties That Target Oxysterol-Binding Protein.

Schweinfurthins (SWs) are naturally occurring prenylated stilbenes with promising anticancer properties. They act through a novel mechanism of action similar to that of other families of natural compounds. Their known target, oxysterol-binding protein (OSBP), plays a crucial role in controlling the intracellular distribution of cholesterol. We synthesized 15 analogues of SWs and demonstrated for the first time that their cytotoxicity as well as that of natural derivatives correlates with their affinity for OSBP. Through this extensive SAR study, we selected one synthetic analogue obtained in one step from SW-G. Using its fluorescence properties, we showed that this compound recapitulates the effect of natural SW-G in cells and confirmed that it leads to cell death via the same mechanism. Finally, after pilot PK experiments, we provided the first evidence of its in vivo efficacy in combination with temozolomide in a patient-derived glioblastoma xenograft model.

Laparoscopic resection of ganglioneuroma from the hepatoduodenal ligament: A case report.

INTRODUCTION AND IMPORTANCE: Ganglioneuromas are extremely rare, slow-growing, benign tumors that arising from Schwann cells, ganglion cells, and neuronal or fibrous tissue. Their malignant degeneration occurs very rarely, complete surgical removal is recommended to eliminate possible symptoms or to prevent possible malignant transformation. Reviewing the literature, there is currently insufficient data available on laparoscopic resection of retroperitoneal ganglioneuromas. CASE PRESENTATION: 20-year-old young woman with no previous medical history or regular medication use complaints of abdominal pain. Abdominal CT scan found a cystic mass measuring up to 50 mm in diameter with a thick fluid density and no contrast accumulation, was identified in the porta hepatis region extrahepatically. Ultrasound-guided biopsy was performed, histopathological finding revealed mature benign neurogenic tumor tissue consisting of mature ganglion cells, mature Schwann cells, and branching stroma. CLINICAL DISCUSSION: A laparoscopic surgery was performed, the 5 cm large tumor was excised from the hepatoduodenal ligament. The tumor was removed from the region of the inferior caval vein, portal vein, and the common and proper hepatic arteries. Final histological diagnosis is ganglioneuroma of the hepatoduodenal ligament. After uneventful postoperative period, the patient was discharged home on the 6th day. CONCLUSIONS: Retroperitoneal tumors were previously excised during laparotomy. However, in recent decades, with the development of laparoscopic surgical techniques and tools, laparoscopic removal of some retroperitoneal tumors seems to be the ideal approach. The use of laparoscopy improves visibility of the relationship of the tumor to the surrounding, often vital, structures. Based on a review of the international literature and our own experience, laparoscopic ganglioneuroma resection is the recommended procedure with careful patient selection, as well as appropriate preoperative imaging and diagnostics, and with adequate expertise.

Impact of the COVID-19 pandemic on severe non-SARS-CoV-2 community-acquired pneumonia in Reunion Island: a multicenter retrospective observational study, 2016-2021.

The Coronavirus 2019 (COVID-19) pandemic has had a considerable impact on the incidence of severe community-acquired pneumonia (CAP) worldwide. The aim of this study was to assess the early impact of the COVID-19 pandemic in the Reunion Island. This multicenter retrospective observational study was conducted from 2016 to 2021 in the hospitals of Reunion Island. The incidence of severe non-SARS-CoV-2 CAP, microorganisms, characteristics and outcomes of patients hospitalized in intensive care unit were compared between the pre-COVID-19 period (January 1, 2016 to February 29, 2020) and the early COVID-19 period (March 1, 2020 to October 31, 2021). Over the study period, 389 patients developed severe non-SARS-CoV-2 CAP. The incidence of severe non-SARS-CoV-2 CAP significantly decreased between the two periods (9.16 vs. 4.13 cases per 100,000 person-years). The influenza virus was isolated in 43.5% patients with severe non-SARS-CoV-2 CAP in the pre-COVID-19 period and in none of the 60 patients in the early COVID-19 period (P < 0.0001). The only virus that did not decrease was rhinovirus. Streptococcus pneumoniae was the most frequently isolated bacterial microorganism, with no significant difference between the two periods. In Reunion Island, the COVID-19 pandemic led to a significant decrease in the incidence of influenza, which likely explains the observed decrease in the incidence of severe non-SARS-CoV-2 CAP. The pandemic had no impact on the incidence of other viral and bacterial severe non-SARS-CoV-2 CAP. Monitoring influenza incidence is crucial now that COVID-19 control measures have been removed.

Evaluation of the MACE force field architecture: From medicinal chemistry to materials science.

The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we further evaluate MACE by fitting models for published benchmark datasets. We show that MACE generally outperforms alternatives for a wide range of systems, from amorphous carbon, universal materials modeling, and general small molecule organic chemistry to large molecules and liquid water. We demonstrate the capabilities of the model on tasks ranging from constrained geometry optimization to molecular dynamics simulations and find excellent performance across all tested domains. We show that MACE is very data efficient and can reproduce experimental molecular vibrational spectra when trained on as few as 50 randomly selected reference configurations. We further demonstrate that the strictly local atom-centered model is sufficient for such tasks even in the case of large molecules and weakly interacting molecular assemblies.

Tensor-Reduced Atomic Density Representations.

Density-based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modeling, and the visualization and analysis of material datasets. The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them. This leads to a steep scaling in the size of the representation as the number of elements increases. Graph neural networks, which do not explicitly use density representations, escape this scaling by mapping the chemical element information into a fixed dimensional space in a learnable way. By exploiting symmetry, we recast this approach as tensor factorization of the standard neighbour-density-based descriptors and, using a new notation, identify connections to existing compression algorithms. In doing so, we form compact tensor-reduced representation of the local atomic environment whose size does not depend on the number of chemical elements, is systematically convergable, and therefore remains applicable to a wide range of data analysis and regression tasks.

Hyperactive learning for data-driven interatomic potentials.

Data-driven interatomic potentials have emerged as a powerful tool for approximating ab initio potential energy surfaces. The most time-consuming step in creating these interatomic potentials is typically the generation of a suitable training database. To aid this process hyperactive learning (HAL), an accelerated active learning scheme, is presented as a method for rapid automated training database assembly. HAL adds a biasing term to a physically motivated sampler (e.g. molecular dynamics) driving atomic structures towards uncertainty in turn generating unseen or valuable training configurations. The proposed HAL framework is used to develop atomic cluster expansion (ACE) interatomic potentials for the AlSi10 alloy and polyethylene glycol (PEG) polymer starting from roughly a dozen initial configurations. The HAL generated ACE potentials are shown to be able to determine macroscopic properties, such as melting temperature and density, with close to experimental accuracy.

Spectral Engineering of Hybrid Biotemplated Photonic/Photocatalytic Nanoarchitectures.

Solar radiation is a cheap and abundant energy for water remediation, hydrogen generation by water splitting, and CO2 reduction. Supported photocatalysts have to be tuned to the pollutants to be eliminated. Spectral engineering may be a handy tool to increase the efficiency or the selectivity of these. Photonic nanoarchitectures of biological origin with hierarchical organization from nanometers to centimeters are candidates for such applications. We used the blue wing surface of laboratory-reared male Polyommatus icarus butterflies in combination with atomic layer deposition (ALD) of conformal ZnO coating and octahedral Cu2O nanoparticles (NP) to explore the possibilities of engineering the optical and catalytic properties of hybrid photonic nanoarchitectures. The samples were characterized by UV-Vis spectroscopy and optical and scanning electron microscopy. Their photocatalytic performance was benchmarked by comparing the initial decomposition rates of rhodamine B. Cu2O NPs alone or on the butterfly wings, covered by a 5 nm thick layer of ZnO, showed poor performance. Butterfly wings, or ZnO coated butterfly wings with 15 nm ALD layer showed a 3 to 3.5 times enhancement as compared to bare glass. The best performance of almost 4.3 times increase was obtained for the wings conformally coated with 15 nm ZnO, deposited with Cu2O NPs, followed by conformal coating with an additional 5 nm of ZnO by ALD. This enhanced efficiency is associated with slow light effects on the red edge of the reflectance maximum of the photonic nanoarchitectures and with enhanced carrier separation through the n-type ZnO and the p-type Cu2O heterojunction. Properly chosen biologic photonic nanoarchitectures in combination with carefully selected photocatalyst(s) can significantly increase the photodegradation of pollutants in water under visible light illumination.

Ureteral Complications Requiring Intervention After Kidney Transplant: A Single-Center Experience.

The surgical aspect of kidney transplant can be the surgical technique itself or the use of reconstruction techniques in the case of a complication requiring reoperation. In our study, we examined particularly surgical techniques and reconstruction options for ureteral anastomoses. Data from patients who underwent kidney transplant from 2010 to 2020 (N = 433) were examined retrospectively at follow-up of at least 1 year. We sought an association between the type of ureteral anastomoses and parameters considered to be risk factors based on literature data. We did not note the complicated cases that solved spontaneously and only selected cases where the ureteral anastomosis complication (UAcomp) needed urologic, radiological, or surgical intervention. In a smaller group, we examined the correlation between BK polyomavirus and ureteral stenosis. A total of 9.2% (n = 40) of patients developed UAcomp, 67.5% (n = 27) of whom required reoperation. In complicated cases, the rate of primary ureteral anastomosis type was 60.0% (n = 24) ureteroneocystostomy (UNS) and 40.0% (n = 16) ureteroureterostomy (UU) (P = .184). After UNS, 7.7% (n = 17) of cases required reoperation, and this rate was 4.7% (n = 10) after UU (P = .164). After treatment of the UAcomp, 95.0% (n = 38) of the patients were discharged with a functioning graft, and 5.0% (n = 2) required graftectomy. Complications of ureteral anastomosis with appropriate interventions results in good graft function. The type of ureteral anastomosis is not significantly associated with UAcomp. It is important that the operating surgeon is well versed in UNS and UU techniques to be able to adapt to any situation, be it primary surgery or reoperation.

Functionalized Mesoporous Silica Nanoparticles for Drug-Delivery to Multidrug-Resistant Cancer Cells.

Background: Multidrug resistance is a common reason behind the failure of chemotherapy. Even if the therapy is effective, serious adverse effects might develop due to the low specificity and selectivity of antineoplastic agents. Mesoporous silica nanoparticles (MSNs) are promising materials for tumor-targeting and drug-delivery due to their small size, relatively inert nature, and extremely large specific surfaces that can be functionalized by therapeutic and targeting entities. We aimed to create a fluorescently labeled MSN-based drug-delivery system and investigate their internalization and drug-releasing capability in drug-sensitive MCF-7 and P-glycoprotein-overexpressing multidrug-resistant MCF-7 KCR cancer cells. Methods and Results: To track the uptake and subcellular distribution of MSNs, particles with covalently coupled red fluorescent Rhodamine B (RhoB) were produced (RhoB@MSNs). Both MCF-7 and MCF-7 KCR cells accumulated a significant amount of RhoB@MSNs. The intracellular RhoB@MSN concentrations did not differ between sensitive and multidrug-resistant cells and were kept at the same level even after cessation of RhoB@MSN exposure. Although most RhoB@MSNs resided in the cytoplasm, significantly more RhoB@MSNs co-localized with lysosomes in multidrug-resistant cells compared to sensitive counterparts. To examine the drug-delivery capability of these particles, RhoB@Rho123@MSNs were established, where RhoB-functionalized nanoparticles carried green fluorescent Rhodamine 123 (Rho123) - a P-glycoprotein substrate - as cargo within mesopores. Significantly higher Rho123 fluorescence intensity was detected in RhoB@Rho123@MSN-treated multidrug-resistant cells than in free Rho123-exposed counterparts. The exceptional drug-delivery potential of MSNs was further verified using Mitomycin C (MMC)-loaded RhoB@MSNs (RhoB@MMC@MSNs). Exposures to RhoB@MMC@MSNs significantly decreased the viability not only of drug-sensitive but of multidrug-resistant cells and the elimination of MDR cells was significantly more robust than upon free MMC treatments. Conclusion: The efficient delivery of Rho123 and MMC to multidrug-resistant cells via MSNs, the amplified and presumably prolonged intracellular drug concentration, and the consequently enhanced cytotoxic effects envision the enormous potential of MSNs to defeat multidrug-resistant cancer.

Cancer Therapy by Silver Nanoparticles: Fiction or Reality?

As an emerging new class, metal nanoparticles and especially silver nanoparticles hold great potential in the field of cancer biology. Due to cancer-specific targeting, the consequently attenuated side-effects and the massive anti-cancer features render nanoparticle therapeutics desirable platforms for clinically relevant drug development. In this review, we highlight those characteristics of silver nanoparticle-based therapeutic concepts that are unique, exploitable, and achievable, as well as those that represent the critical hurdle in their advancement to clinical utilization. The collection of findings presented here will describe the features that distinguish silver nanoparticles from other anti-cancer agents and display the realistic opportunities and implications in oncotherapeutic innovations to find out whether cancer therapy by silver nanoparticles is fiction or reality.

Test-retest repeatability and interobserver variation of healthy tissue metabolism using 18F-FDG PET/CT of the thorax among lung cancer patients.

OBJECTIVES: The aim of this study was to assess the test-retest repeatability and interobserver variation in healthy tissue (HT) metabolism using 2-deoxy-2-[18F]fluoro-d-glucose (18F-FDG) PET/computed tomography (PET/CT) of the thorax in lung cancer patients. METHODS: A retrospective analysis was conducted in 22 patients with non-small cell lung cancer who had two PET/CT scans of the thorax performed 3 days apart with no interval treatment. The maximum, mean and peak standardized uptake values (SUVs) in different HTs were measured by a single observer for the test-retest analysis and two observers for interobserver variation. Bland-Altman plots were used to assess the repeatability and interobserver variation. Intrasubject variability was evaluated using within-subject coefficients of variation (wCV). RESULTS: The wCV of test-retest SUVmean measurements in mediastinal blood pool, bone marrow, skeletal muscles and lungs was less than 20%. The left ventricle (LV) showed higher wCV (>60%) in all SUV parameters with wide limits of repeatability. High interobserver agreement was found with wCV of less than 10% in SUVmean of all HT, but up to 22% was noted in the LV. CONCLUSION: HT metabolism is stable in a test-retest scenario and has high interobserver agreement. SUVmean was the most stable metric in organs with low FDG uptake and SUVpeak in HTs with moderate uptake. Test-retest measurements in LV were highly variable irrespective of the SUV parameters used for measurements.

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE.

We demonstrate that fast and accurate linear force fields can be built for molecules using the atomic cluster expansion (ACE) framework. The ACE models parametrize the potential energy surface in terms of body-ordered symmetric polynomials making the functional form reminiscent of traditional molecular mechanics force fields. We show that the four- or five-body ACE force fields improve on the accuracy of the empirical force fields by up to a factor of 10, reaching the accuracy typical of recently proposed machine-learning-based approaches. We not only show state of the art accuracy and speed on the widely used MD17 and ISO17 benchmark data sets, but we also go beyond RMSE by comparing a number of ML and empirical force fields to ACE on more important tasks such as normal-mode prediction, high-temperature molecular dynamics, dihedral torsional profile prediction, and even bond breaking. We also demonstrate the smoothness, transferability, and extrapolation capabilities of ACE on a new challenging benchmark data set comprised of a potential energy surface of a flexible druglike molecule.

Case Report: Medullary Thyroid Cancer Workup Initiated by Unexpectedly High Procalcitonin Level-Endocrine Training Saves Life in the COVID-19 Unit.

Background: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel coronavirus that has caused a worldwide pandemic. The majority of medullary thyroid cancers present as a thyroid nodule. At the time of diagnosis, cervical lymph nodes and distant metastases are frequently detected. Case Report: Here, we present a case of a 46-year-old man with coronavirus disease (COVID) pneumonia, who had persistently high serum procalcitonin levels despite normal C-reactive protein levels. The attending infectologist happened to be a colleague who spent some time, as part of her internal medicine rotation, in the Endocrine Ward and recalled that medullary thyroid cancer might be the cause. This led to the timely workup and treatment of the medullary cancer.