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Better understanding of interplay among symptoms, cognition and functioning in first-episode psychosis (FEP) is crucial to promoting functional recovery. Network analysis is a promising data-driven approach to elucidating complex interactions among psychopathological variables in psychosis, but has not been applied in FEP.
This study employed network analysis to examine inter-relationships among a wide array of variables encompassing psychopathology, premorbid and onset characteristics, cognition, subjective quality-of-life and psychosocial functioning in 323 adult FEP patients in Hong Kong. Graphical Least Absolute Shrinkage and Selection Operator (LASSO) combined with extended Bayesian information criterion (BIC) model selection was used for network construction. Importance of individual nodes in a generated network was quantified by centrality analyses.
Our results showed that amotivation played the most central role and had the strongest associations with other variables in the network, as indexed by node strength. Amotivation and diminished expression displayed differential relationships with other nodes, supporting the validity of two-factor negative symptom structure. Psychosocial functioning was most strongly connected with amotivation and was weakly linked to several other variables. Within cognitive domain, digit span demonstrated the highest centrality and was connected with most of the other cognitive variables. Exploratory analysis revealed no significant gender differences in network structure and global strength.
Our results suggest the pivotal role of amotivation in psychopathology network of FEP and indicate its critical association with psychosocial functioning. Further research is required to verify the clinical significance of diminished motivation on functional outcome in the early course of psychotic illness.
Thermal analysts have exploited the sensitivity of carbonate mineral decomposition to furnace atmosphere as a diagnostic tool for identifying and quantifying these minerals in mixtures and solid solutions (1-3). However, thermal analysis techniques alone cannot reveal information about the reaction products after each thermal event. In-situ high temperature x-ray diffraction is one technique that can identify these products. Using this technique, Kissinger et al. (4) identified the reaction products of the thermal decomposition of reagent grade FeCO3 (siderite) and MgCO3 (magnesite). However, the thermal behavior of analytical reagent grade carbonates differs from natural minerals (1). Milodowski and Morgan (5) used in-situ XRD to investigate the thermal behavior of the dolomite-ankerite series.
A series of double-perovskite oxides, Sr2RNbO6 (R = Sm, Gd, Dy, Ho, Y, Tm, and Lu) were prepared and their crystal structure and powder diffraction reference patterns were determined using the Rietveld analysis technique. The crystal structure of each of the Sr2RNbO6 phase is reported in this paper. The R = Gd, Ho, and Lu samples were studied using synchrotron radiation, while R = Sm, Dy, Y, and Tm samples were studied using laboratory X-ray diffraction. Members of Sr2RNbO6 are monoclinic with a space group of P21/n and are isostructural with each other. Following the trend of “lanthanide contraction”, from R = Sm to Lu, the lattice parameters “a” of these compounds decreases from 5.84672(10) to 5.78100(3) Å, b from 5.93192(13) to 5.80977(3) Å, c from 8.3142(2) to 8.18957(5) Å, and V decreases from 288.355(11) to 275.057(2) Å3. In this double-perovskite series, the R3+ and Nb5+ ions are structurally ordered. The average Nb–O bond length is nearly constant, while the average R–O bond length decreases with the decreasing ionic radius of R3+. Powder diffraction patterns for these compounds have been submitted to the Powder Diffraction File (PDF).
We present observations of 50 deg2 of the Mopra carbon monoxide (CO) survey of the Southern Galactic Plane, covering Galactic longitudes l = 300–350° and latitudes |b| ⩽ 0.5°. These data have been taken at 0.6 arcmin spatial resolution and 0.1 km s−1spectral resolution, providing an unprecedented view of the molecular clouds and gas of the Southern Galactic Plane in the 109–115 GHz J = 1–0 transitions of 12CO, 13CO, C18O, and C17O.
We present a series of velocity-integrated maps, spectra, and position-velocity plots that illustrate Galactic arm structures and trace masses on the order of ~106 M⊙ deg−2, and include a preliminary catalogue of C18O clumps located between l = 330–340°. Together with the information about the noise statistics of the survey, these data can be retrieved from the Mopra CO website and the PASA data store.
BACKGROUND: IGTS is a rare phenomenon of paradoxical germ cell tumor (GCT) growth during or following treatment despite normalization of tumor markers. We sought to evaluate the frequency, clinical characteristics and outcome of IGTS in patients in 21 North-American and Australian institutions. METHODS: Patients with IGTS diagnosed from 2000-2017 were retrospectively evaluated. RESULTS: Out of 739 GCT diagnoses, IGTS was identified in 33 patients (4.5%). IGTS occurred in 9/191 (4.7%) mixed-malignant GCTs, 4/22 (18.2%) immature teratomas (ITs), 3/472 (0.6%) germinomas/germinomas with mature teratoma, and in 17 secreting non-biopsied tumours. Median age at GCT diagnosis was 10.9 years (range 1.8-19.4). Male gender (84%) and pineal location (88%) predominated. Of 27 patients with elevated markers, median serum AFP and Beta-HCG were 70 ng/mL (range 9.2-932) and 44 IU/L (range 4.2-493), respectively. IGTS occurred at a median time of 2 months (range 0.5-32) from diagnosis, during chemotherapy in 85%, radiation in 3%, and after treatment completion in 12%. Surgical resection was attempted in all, leading to gross total resection in 76%. Most patients (79%) resumed GCT chemotherapy/radiation after surgery. At a median follow-up of 5.3 years (range 0.3-12), all but 2 patients are alive (1 succumbed to progressive disease, 1 to malignant transformation of GCT). CONCLUSION: IGTS occurred in less than 5% of patients with GCT and most commonly after initiation of chemotherapy. IGTS was more common in patients with IT-only on biopsy than with mixed-malignant GCT. Surgical resection is a principal treatment modality. Survival outcomes for patients who developed IGTS are favourable.
Universal health coverage is a key health target in the Sustainable Development Goals (SDGs) that has the means to link equitable social and economic development. As a concept firmly based on equity, it is widely accepted at international and national levels as important for populations to attain ‘health for all’ especially for marginalised groups. However, implementing universal coverage has been fraught with challenges and the increasing privatisation of health care provision adds to the challenge because it is being implemented in a health system that rests on a property regime that promotes inequality. This paper asks the question, ‘What does an equitable health system look like?’ rather than the usual ‘How do you make the existing health system more equitable?’ Using an ethnographic approach, the authors explored via interviews, focus group discussions and participant observation a health system that uses the commons approach such as which exists with indigenous peoples and found features that helped make the system intrinsically equitable. Based on these features, the paper proposes an alternative basis to organise universal health coverage that will better ensure equity in health systems and ultimately contribute to meeting the SDGs.
As part of a multi-wavelength study, we report on a 50 ks Chandra/ACIS observation of the Guitar Nebula, a bow shock nebula associated with the radio pulsar B2224+65. We see a “hot spot” at the tip of the bow shock. We also notice a “jet” of X-ray emission at position angle (PA) −69°. However, the proper motion of the pulsar and the axis of optical emission is at PA 52°.1. We discuss the resulting interpretations of the relativistic pulsar wind and the surrounding ISM.
We have investigated the solidified microstructure of nucleation-generated grains obtained via complete melting of Si films on SiO2 at high nucleation temperatures. This was achieved using a high-temperature-capable hot stage in conjunction with excimer laser irradiation. As predicted by the direct-growth model that considers (1) the evolution in the temperature of the solidifying interface and (2) the subsequent modes of growth (consisting of amorphous, defective, and epitaxial) as key factors, we were able to observe the appearance of “normal” grains that possess a single-crystal core area. These grains, which are in contrast to previously reported flower-shaped grains that fully make up the microstructure of the solidified films obtained via irradiation at lower preheating temperatures (and amongst which these “normal” grains emerge), indicate that epitaxial growth of nucleated crystals must have taken place within the grains. We discuss the implications of our findings regarding (1) the validity of the direct-growth model, (2) the nature of the heterogeneous nucleation mechanism, and (3) the alternative explanations and assumptions that have been previously employed in order to explain the microstructure of Si films obtained via nucleation and growth within the complete melting regime.
We present an updated status of the EDGE project, which is a survey of 125 local galaxies in the 12CO(1−0) and 13CO(1−0) lines. We combine the molecular data of the EDGE survey with the stellar and ionized gas maps of the CALIFA survey to give a comprehensive view of the dependence of the star formation efficiency, or equivalently, the molecular gas depletion time, on various local environments, such as the stellar surface density, metallicity, and radius from the galaxy center. This study will provide insight into the parameters that drive the star formation efficiency in galaxies at z ~ 0.
We present observations of the first 10° of longitude in the Mopra CO survey of the southern Galactic plane, covering Galactic longitude l = 320–330° and latitude b = ±0.5°, and l = 327–330°, b = +0.5–1.0°. These data have been taken at 35-arcsec spatial resolution and 0.1 km s−1 spectral resolution, providing an unprecedented view of the molecular clouds and gas of the southern Galactic plane in the 109–115 GHz J = 1–0 transitions of 12CO, 13CO, C18O, and C17O. Together with information about the noise statistics from the Mopra telescope, these data can be retrieved from the Mopra CO website and the CSIRO-ATNF data archive.
To assess the effectiveness of infection control preparedness for human infection with influenza A H7N9 in Hong Kong.
A descriptive study of responses to the emergence of influenza A H7N9.
A university-affiliated teaching hospital.
Healthcare workers (HCWs) with unprotected exposure (not wearing N95 respirator during aerosol-generating procedure) to a patient with influenza A H7N9.
A bundle approach including active and enhanced surveillance, early airborne infection isolation, rapid molecular diagnostic testing, and extensive contact tracing for HCWs with unprotected exposure was implemented. Seventy HCWs with unprotected exposure to an index case were interviewed especially regarding their patient care activities.
From April 1, 2013, through May 31, 2014, a total of 126 (0.08%) of 163,456 admitted patients were tested for the H7 gene by reverse transcription-polymerase chain reaction per protocol. Two confirmed cases were identified. Seventy (53.8%) of 130 HCWs had unprotected exposure to an index case, whereas 41 (58.6%) and 58 (82.9%) of 70 HCWs wore surgical masks and practiced hand hygiene after patient care, respectively. Sixteen (22.9%) of 70 HCWs were involved in high-risk patient contacts. More HCWs with high-risk patient contacts received oseltamivir prophylaxis (P=0.088) and significantly more had paired sera collected for H7 antibody testing (P<0.001). Ten (14.3%) of 70 HCWs developed influenza-like illness during medical surveillance, but none had positive results by reverse transcription-polymerase chain reaction. Paired sera was available from 33 of 70 HCWs with unprotected exposure, and none showed seroconversion against H7N9.
Despite the delay in airborne precautions implementation, no patient-to-HCW transmission of influenza A H7N9 was demonstrated.
Medicine is a science of uncertainty and an art of probability.
Sir William Osler
Decision trees and Markov cohort models, as described and illustrated in the previous chapters, are essentially macrosimulation models. Such models simulate cohorts or groups of subjects. A number of limitations exist to the use of these models. Markov cohort models, for example, have ‘no memory’, implying that subjects in a particular state are a homogeneous group. Techniques to overcome these limitations, such as expanding the number of states, using tunnel states, or using alternative modeling techniques, were discussed in Chapter 10. These techniques can get very complex when dealing with extensive heterogeneity within a population. Microsimulation using Monte Carlo analysis provides another powerful technique to account for heterogeneity across subjects. Microsimulation with Monte Carlo analysis was introduced in Chapter 10 as an alternative method for evaluating a Markov model. In this chapter it will be discussed at greater length in the context of simulating heterogeneity.
In the previous chapters we represented uncertainty with probabilities. Implicitly the assumption was that, even though we were unsure of whether an event would take place, we could nevertheless predict or estimate the probability (or relative frequency) that it would occur. In essence we were using deterministic models. In reality, however, we are also uncertain of the degree of uncertainty. In other words, rather than dealing with a fixed probability we are actually dealing with a distribution of possible values of probabilities. Not only are we uncertain about the probabilities we use in our models, but we are also uncertain about the effectiveness outcomes and cost estimates included in the analysis. Thus, every parameter value we enter into our models is better represented as a probabilistic variable rather than a deterministic variable. If there is a single uncertain parameter, e.g., the relative risk reduction of an intervention, then the 95% confidence interval (CI) of this parameter is commonly used to indicate the uncertainty of the effect. Uncertainty in two or more components requires more complex methods, such as Monte Carlo probabilistic sensitivity analysis, which we will also discuss in this chapter.
Values are what we care about. As such, values should be the driving force for our decision making. They should be the basis for the time and effort we spend thinking about decisions. But this is not the way it is. It is not even close to the way it is.
Value judgments underlie virtually all clinical decisions. Sometimes the decision rests on a comparison of probability alone, such as the probability of surviving an acute episode of illness. In such cases, there is a single outcome measure – the probability of immediate survival – that can be averaged out to arrive at an optimal decision. In most cases, however, decisions between alternative strategies require not only estimates of the probabilities of the associated outcomes, but also value judgments about how to weigh the benefits versus the harms, and how to incorporate other factors like individual preferences for convenience, timing, who makes decisions, who else is affected by the decision, and the like. Consider the following examples.
Some treatment decisions are straightforward. For example, what should be done for an elderly patient with a fractured hip? Inserting a metal pin has dramatically altered the management: instead of lying in bed for weeks or months waiting for the fracture to heal while blood clots and pneumonia threatened, the patient is now ambulatory within days. The risks of morbidity and mortality are both greatly reduced. However, many treatment decisions are complex. They involve uncertainties and trade-offs that need to be carefully weighed before choosing. Tragic outcomes may occur no matter which choice is made, and the best that can be done is to minimize the overall risks. Such decisions can be difficult and uncomfortable to make. For example, consider the following historical dilemma.
Benjamin Franklin and smallpox
Benjamin Franklin argued implicitly in favor of the application to individual patients of probabilities based on previous experience with similar groups of patients. Before Edward Jenner’s discovery in 1796 of cowpox vaccination for smallpox, it was known that immunity from smallpox could be achieved by a live smallpox inoculation, but the procedure entailed a risk of death. When a smallpox epidemic broke out in Boston in 1721, the physician Zabdiel Boylston consented, at the urging of the clergyman Cotton Mather, to inoculate several hundred citizens. Mather and Boylston reported their results (1):
Out of about ten thousand Bostonians, five thousand seven hundred fifty-nine took smallpox the natural way. Of these, eight hundred eighty-five died, or one in seven. Two hundred eighty-six took smallpox by inoculation. Of these, six died, or one in forty-seven.
Before ordering a test ask: What will you do if the test is positive? What will you do if the test is negative? If the answers are the same, then don’t do the test.
Poster in an Emergency Department
In the previous chapter we looked at how to interpret diagnostic information such as symptoms, signs, and diagnostic tests. Now we need to consider when such information is helpful in decision making. Even if they reduce uncertainty, tests are not always helpful. If used inappropriately to guide a decision, a test may mislead more than it leads. In general, performing a test to gain additional information is worthwhile only if two conditions hold: (1) at least one decision would change given some test result, and (2) the risk to the patient associated with the test is less than the expected benefit that would be gained from the subsequent change in decision. These conditions are most likely to be fulfilled when we are confronted with intermediate probabilities of the target disease, that is, when we are in a diagnostic ‘gray zone.’ Tests are least likely to be helpful either when we are so certain a patient has the target disease that the negative result of an imperfect test would not dissuade us from treating, or, conversely, when we are so certain that the patient does not have the target disease that a positive result of an imperfect test would not persuade us to treat. These concepts are illustrated in Figure 6.1, which divides the probability of a disease into three ranges:
do not treat (for the target disease) and do not test, because even a positive test would not persuade us to treat;
test, because the test will help with treatment decisions or with follow-up; and
treat and do not test, because even a negative test would not dissuade us from treating.
Treat implies patient management as if disease is present and may imply initiating medical therapy, performing a therapeutic procedure, advising a lifestyle or other adjuvant intervention, or a combination of these. Do not treat implies patient management as if disease is absent and usually means risk factor management, lifestyle advice, self-care and/or watchful waiting.
In previous chapters we have seen several applications of decision trees to solve clinical problems under conditions of uncertainty. Decision trees work well in analyzing chance events with limited recursion and a limited time horizon. The limited number of sequential decisions or chance nodes allows one to capture all the necessary information to maximize expected utility. However, when events can occur repeatedly over an extended time period, the decision-tree framework can become unmanageable. Many decision situations involve events occurring over the lifetime of the patient, thus extending far into the future. Life spans vary, but conventional trees require us to specify a fixed time horizon. The probabilities and utilities of these events may change over time and must be accounted for. This is the case for most chronic conditions. Examples include heart disease, Alzheimer’s disease, various cancers, diabetes, asthma, osteoporosis, human immunodeficiency virus (HIV), inflammatory bowel disease, multiple sclerosis and more. This chapter offers a methodology for dealing with recurring events and extended (variable) time horizons.
Consider a patient with peripheral arterial disease (PAD: obstruction of the arteries to the legs) for whom a decision has to be made for either bypass surgery or percutaneous intervention (PI). We assume that conservative treatment through an exercise regimen has not provided sufficient relief. A very simplified decision tree is presented in Figure 10.1. Following the choice of treatment, the patient may die as a result of the procedure (captured in the ‘mortality’ branches) or survive the procedure. If the patient survives, treatment may fail and the patient returns to the pre-procedure prognosis, or treatment may be successful and the patient is relieved of symptoms. If we consider some fixed time horizon like a year or five years, we can assign utilities to the three possible outcomes (success, failure, death) and calculate expected utilities to choose a preferred treatment. In the current structure, there is no explicit allowance for the time horizon we are considering, nor for the timing of the various events. Even if we consider a fixed time horizon of, say, five years, there surely is a different implication for prognosis if failure occurs in the first year versus the fifth year.