Low-pass spectral analysis of time-resolved serial femtosecond crystallography data.

Low-pass spectral analysis (LPSA) is a recently developed dynamics retrieval algorithm showing excellent retrieval properties when applied to model data affected by extreme incompleteness and stochastic weighting. In this work, we apply LPSA to an experimental time-resolved serial femtosecond crystallography (TR-SFX) dataset from the membrane protein bacteriorhodopsin (bR) and analyze its parametric sensitivity. While most dynamical modes are contaminated by nonphysical high-frequency features, we identify two dominant modes, which are little affected by spurious frequencies. The dynamics retrieved using these modes shows an isomerization signal compatible with previous findings. We employ synthetic data with increasing timing uncertainty, increasing incompleteness level, pixel-dependent incompleteness, and photon counting errors to investigate the root cause of the high-frequency contamination of our TR-SFX modes. By testing a range of methods, we show that timing errors comparable to the dynamical periods to be retrieved produce a smearing of dynamical features, hampering dynamics retrieval, but with no introduction of spurious components in the solution, when convergence criteria are met. Using model data, we are able to attribute the high-frequency contamination of low-order dynamical modes to the high levels of noise present in the data. Finally, we propose a method to handle missing observations that produces a substantial dynamics retrieval improvement from synthetic data with a significant static component. Reprocessing of the bR TR-SFX data using the improved method yields dynamical movies with strong isomerization signals compatible with previous findings.

Clavicular Metastasis as an Initial Presentation of Papillary Thyroid Cancer.

OBJECTIVE: We present the case of a 44-year-old man with a large neck mass to highlight the unique presentation of papillary thyroid carcinoma (PTC) metastatic to the clavicle. METHODS: We reviewed the medical record for a detailed history and physical examination findings. Our radiology colleagues examined the diagnostic imaging studies performed. The pathology team reviewed the neck mass biopsy and the confirmatory surgical pathology after total resection of the mass. RESULTS: A 44-year-old man presented with an enlarging neck mass. Initial X-rays revealed a large soft tissue density mass that extended to the midline of the right clavicle. A neck ultrasound established a 5.4 × 3.6 cm mass with increased vascularity and calcification extending from the thyroid. A CT scan noted the extension of the mass into the adjacent sternoclavicular junction with osteolysis of the middle third of the clavicle and the superior aspect of the sternal body. Fine-needle aspiration revealed a thyroid neoplasm with follicular features and positive immunostaining consistent with thyroid carcinoma. The patient underwent a composite resection of the tumor, including a segmental osteotomy of approximately two-thirds of the medial clavicle. The pathology report confirmed PTC with extrathyroidal extension and clavicle involvement (staged pT4a pN0), with further genomic findings showing positive KRAS mutation. CONCLUSION: Clavicular metastasis from differentiated thyroid cancer is rare. While the prognosis is generally favorable, various factors, including age greater than 45 years, poor differentiation, follicular thyroid carcinoma, Hurthle cell variant, and extrapulmonary metastasis, have typically been associated with poorer cancer-specific survival.

Selecting XFEL single-particle snapshots by geometric machine learning.

A promising new route for structural biology is single-particle imaging with an X-ray Free-Electron Laser (XFEL). This method has the advantage that the samples do not require crystallization and can be examined at room temperature. However, high-resolution structures can only be obtained from a sufficiently large number of diffraction patterns of individual molecules, so-called single particles. Here, we present a method that allows for efficient identification of single particles in very large XFEL datasets, operates at low signal levels, and is tolerant to background. This method uses supervised Geometric Machine Learning (GML) to extract low-dimensional feature vectors from a training dataset, fuse test datasets into the feature space of training datasets, and separate the data into binary distributions of "single particles" and "non-single particles." As a proof of principle, we tested simulated and experimental datasets of the Coliphage PR772 virus. We created a training dataset and classified three types of test datasets: First, a noise-free simulated test dataset, which gave near perfect separation. Second, simulated test datasets that were modified to reflect different levels of photon counts and background noise. These modified datasets were used to quantify the predictive limits of our approach. Third, an experimental dataset collected at the Stanford Linear Accelerator Center. The single-particle identification for this experimental dataset was compared with previously published results and it was found that GML covers a wide photon-count range, outperforming other single-particle identification methods. Moreover, a major advantage of GML is its ability to retrieve single particles in the presence of structural variability.

Symptomatic Pituitary Metastasis as Initial Manifestation of Renal Cell Carcinoma: Case Report and Review of Literature.

Metastasis to the pituitary gland is extremely rare (∼2% of sellar masses). Clinical, biochemical, and radiologic characteristics of pituitary metastasis are poorly defined and can be difficult to diagnose before surgery. We present an unusual case with pituitary metastasis as the first manifestation of renal cell carcinoma (RCC). A 70-year-old male presented with acute onset of weakness, dizziness, diplopia, and progressively worsening headache. The initial CT head revealed a heterogeneous sellar mass measuring 2.8 × 1.9 × 1.7 cm. A follow-up MRI showed the sellar mass invading the right cavernous sinus. The presumptive diagnosis was a pituitary macroadenoma. Physical examination revealed bilateral 6th cranial nerve palsy and episodes of intermittent binocular horizontal diplopia. Hormonal testing noted possible secondary adrenal insufficiency (AM serum cortisol: 3.3 mcg/dL, ACTH: 8 pg/mL), secondary hypothyroidism (TSH: <0.01 mIU/L, FT4: 0.7 ng/dL), secondary hypogonadism (testosterone: 47 ng/dL, LH: 1.3 mIU/mL, and FSH: 2.3 mIU/mL), and elevated serum prolactin (prolactin: 56.8 ng/ml, normal: 4.0-15.2 ng/ml). IGF-1 level was normal at 110 ng/mL (47-192 ng/mL). The patient was discharged on levothyroxine and hydrocortisone therapy with plans for close surveillance. However, his condition worsened over the next three months, and he was subsequently readmitted with nausea, vomiting, and hypernatremia secondary to diabetes insipidus. Repeat MRI pituitary showed an interval increase in the size of the sellar mass with suprasellar extension and a new mass effect on the optic chiasm. The sellar mass was urgently resected via a trans-sphenoidal approach. The tumor was negative for neuroendocrine markers and pituitary hormone panel, ruling out the diagnosis of pituitary adenoma and triggered workup for metastatic renal cell carcinoma, clear cell type. The diagnosis of renal cell carcinoma was confirmed by the diffuse and strong staining for renal cell carcinoma markers (Pax-8, RCC-1, and CD10). A follow-up CT scan noted large right renal mass measuring 11 × 10 × 11 cm. The patient underwent a cytoreductive robotic right radical nephrectomy for WHO/ISUP histologic grade II clear cell RCC, stage pT2b pNX pM1. He subsequently received fractionated stereotactic radiotherapy to the pituitary gland. He is presently stable with no radiological evidence of progression or new intracranial disease on subsequent imaging. Pituitary metastasis most commonly occurs from breast, lung, or gastrointestinal tumors but also rarely from renal cell carcinoma. Biochemical findings such as panhypopituitarism, acute clinical signs such as headache, visual symptoms, and diabetes insipidus and interval increase in sellar mass in a short time interval should raise suspicion for sellar metastasis.

Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study.

Background: Preterm birth is a major global health challenge, the leading cause of death in children under 5 years of age, and a key measure of a population's general health and nutritional status. Current clinical methods of estimating fetal gestational age are often inaccurate. For example, between 20 and 30 weeks of gestation, the width of the 95% prediction interval around the actual gestational age is estimated to be 18-36 days, even when the best ultrasound estimates are used. The aims of this study are to improve estimates of fetal gestational age and provide personalised predictions of future growth. Methods: Using ultrasound-derived, fetal biometric data, we developed a machine learning approach to accurately estimate gestational age. The accuracy of the method is determined by reference to exactly known facts pertaining to each fetus-specifically, intervals between ultrasound visits-rather than the date of the mother's last menstrual period. The data stem from a sample of healthy, well-nourished participants in a large, multicentre, population-based study, the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st). The generalisability of the algorithm is shown with data from a different and more heterogeneous population (INTERBIO-21st Fetal Study). Findings: In the context of two large datasets, we estimated gestational age between 20 and 30 weeks of gestation with 95% confidence to within 3 days, using measurements made in a 10-week window spanning the second and third trimesters. Fetal gestational age can thus be estimated in the 20-30 weeks gestational age window with a prediction interval 3-5 times better than with any previous algorithm. This will enable improved management of individual pregnancies. 6-week forecasts of the growth trajectory for a given fetus are accurate to within 7 days. This will help identify at-risk fetuses more accurately than currently possible. At population level, the higher accuracy is expected to improve fetal growth charts and population health assessments. Interpretation: Machine learning can circumvent long-standing limitations in determining fetal gestational age and future growth trajectory, without recourse to often inaccurately known information, such as the date of the mother's last menstrual period. Using this algorithm in clinical practice could facilitate the management of individual pregnancies and improve population-level health. Upon publication of this study, the algorithm for gestational age estimates will be provided for research purposes free of charge via a web portal. Funding: Bill & Melinda Gates Foundation, Office of Science (US Department of Energy), US National Science Foundation, and National Institute for Health Research Oxford Biomedical Research Centre.

Enzyme intermediates captured "on the fly" by mix-and-inject serial crystallography.

BACKGROUND: Ever since the first atomic structure of an enzyme was solved, the discovery of the mechanism and dynamics of reactions catalyzed by biomolecules has been the key goal for the understanding of the molecular processes that drive life on earth. Despite a large number of successful methods for trapping reaction intermediates, the direct observation of an ongoing reaction has been possible only in rare and exceptional cases. RESULTS: Here, we demonstrate a general method for capturing enzyme catalysis "in action" by mix-and-inject serial crystallography (MISC). Specifically, we follow the catalytic reaction of the Mycobacterium tuberculosis ß-lactamase with the third-generation antibiotic ceftriaxone by time-resolved serial femtosecond crystallography. The results reveal, in near atomic detail, antibiotic cleavage and inactivation from 30 ms to 2 s. CONCLUSIONS: MISC is a versatile and generally applicable method to investigate reactions of biological macromolecules, some of which are of immense biological significance and might be, in addition, important targets for structure-based drug design. With megahertz X-ray pulse rates expected at the Linac Coherent Light Source II and the European X-ray free-electron laser, multiple, finely spaced time delays can be collected rapidly, allowing a comprehensive description of biomolecular reactions in terms of structure and kinetics from the same set of X-ray data.

Oiling the gate: a mobile application to improve the admissions process from the emergency department to an academic community hospital inpatient medicine service.

The process of admitting patients from the emergency department (ED) to an academic internal medicine (AIM) service in a community teaching hospital is one fraught with variability and disorder. This results in an inconsistent volume of patients admitted to academic versus private hospitalist services and results in frustration of both ED and AIM clinicians. We postulated that implementation of a mobile application (app) would improve provider satisfaction and increase admissions to the academic service. The app was designed and implemented to be easily accessible to ED physicians, regularly updated by academic residents on call, and a real-time source of the number of open AIM admission spots. We found a significant improvement in ED and AIM provider satisfaction with the admission process. There was also a significant increase in admissions to the AIM service after implementation of the app. We submit that the implementation of a mobile app is a viable, cost-efficient, and effective method to streamline the admission process from the ED to AIM services at community-based hospitals.

Conformational landscape of a virus by single-particle X-ray scattering.

Using a manifold-based analysis of experimental diffraction snapshots from an X-ray free electron laser, we determine the three-dimensional structure and conformational landscape of the PR772 virus to a detector-limited resolution of 9 nm. Our results indicate that a single conformational coordinate controls reorganization of the genome, growth of a tubular structure from a portal vertex and release of the genome. These results demonstrate that single-particle X-ray scattering has the potential to shed light on key biological processes.

Trajectories of the ribosome as a Brownian nanomachine.

A Brownian machine, a tiny device buffeted by the random motions of molecules in the environment, is capable of exploiting these thermal motions for many of the conformational changes in its work cycle. Such machines are now thought to be ubiquitous, with the ribosome, a molecular machine responsible for protein synthesis, increasingly regarded as prototypical. Here we present a new analytical approach capable of determining the free-energy landscape and the continuous trajectories of molecular machines from a large number of snapshots obtained by cryogenic electron microscopy. We demonstrate this approach in the context of experimental cryogenic electron microscope images of a large ensemble of nontranslating ribosomes purified from yeast cells. The free-energy landscape is seen to contain a closed path of low energy, along which the ribosome exhibits conformational changes known to be associated with the elongation cycle. Our approach allows model-free quantitative analysis of the degrees of freedom and the energy landscape underlying continuous conformational changes in nanomachines, including those important for biological function.

Radiation dose reduction in medical x-ray CT via Fourier-based iterative reconstruction.

PURPOSE: A Fourier-based iterative reconstruction technique, termed Equally Sloped Tomography (EST), is developed in conjunction with advanced mathematical regularization to investigate radiation dose reduction in x-ray CT. The method is experimentally implemented on fan-beam CT and evaluated as a function of imaging dose on a series of image quality phantoms and anonymous pediatric patient data sets. Numerical simulation experiments are also performed to explore the extension of EST to helical cone-beam geometry. METHODS: EST is a Fourier based iterative algorithm, which iterates back and forth between real and Fourier space utilizing the algebraically exact pseudopolar fast Fourier transform (PPFFT). In each iteration, physical constraints and mathematical regularization are applied in real space, while the measured data are enforced in Fourier space. The algorithm is automatically terminated when a proposed termination criterion is met. Experimentally, fan-beam projections were acquired by the Siemens z-flying focal spot technology, and subsequently interleaved and rebinned to a pseudopolar grid. Image quality phantoms were scanned at systematically varied mAs settings, reconstructed by EST and conventional reconstruction methods such as filtered back projection (FBP), and quantified using metrics including resolution, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs). Pediatric data sets were reconstructed at their original acquisition settings and additionally simulated to lower dose settings for comparison and evaluation of the potential for radiation dose reduction. Numerical experiments were conducted to quantify EST and other iterative methods in terms of image quality and computation time. The extension of EST to helical cone-beam CT was implemented by using the advanced single-slice rebinning (ASSR) method. RESULTS: Based on the phantom and pediatric patient fan-beam CT data, it is demonstrated that EST reconstructions with the lowest scanner flux setting of 39 mAs produce comparable image quality, resolution, and contrast relative to FBP with the 140 mAs flux setting. Compared to the algebraic reconstruction technique and the expectation maximization statistical reconstruction algorithm, a significant reduction in computation time is achieved with EST. Finally, numerical experiments on helical cone-beam CT data suggest that the combination of EST and ASSR produces reconstructions with higher image quality and lower noise than the Feldkamp Davis and Kress (FDK) method and the conventional ASSR approach. CONCLUSIONS: A Fourier-based iterative method has been applied to the reconstruction of fan-bean CT data with reduced x-ray fluence. This method incorporates advantageous features in both real and Fourier space iterative schemes: using a fast and algebraically exact method to calculate forward projection, enforcing the measured data in Fourier space, and applying physical constraints and flexible regularization in real space. Our results suggest that EST can be utilized for radiation dose reduction in x-ray CT via the readily implementable technique of lowering mAs settings. Numerical experiments further indicate that EST requires less computation time than several other iterative algorithms and can, in principle, be extended to helical cone-beam geometry in combination with the ASSR method.