When should we give up on expectant management for patients with proximal ureteral stones?

Background:Proximal ureteral stones (PUS) have relatively low rates of spontaneous expulsion. However, some patients do well on expectant management. Our aim was to compare risk factors for surgical intervention in patients with PUS who underwent primary intervention to those subjected to expectant management.Materials and methods:We retrospectively reviewed the medical charts of patients presented to the emergency room with symptoms of renal colic and underwent computerized tomography between August 2016 and August 2017. A total of 97 consecutive patients were identified with up to 10mm PUS. We collected patient demographics, clinical, and imaging data, and performed binary regression analysis for risk of intervention.Results:The average age was 49years (range 17-97) and average stone size was 7.1mm (range 3-10). Forty-one patients underwent immediate intervention while the remaining 56 patients were treated conservatively. Of the 56 patients treated conservatively, 26 underwent delayed intervention while 30 reported spontaneous stone expulsion. On univariate analysis of all 97 patients, statistically significant risk factors for intervention were found based on stone size, age, serum lymphocyte, platelet counts, and stone density. Of these risk factors, stone size ≥ 7mm (p = 0.012, odds ratio = 5.4) and platelet count ≤ 230K/μL (p = 0.027, odds ratio = 4.9) remained statistically significant on multivariate analysis.Conclusion:Stone size and platelet count were found to be risk factors for surgical intervention in patients with up to 10mm PUS. These findings may assist in identifying patients who are more suitable for conservative approach.     

The trans-Golgi network-associated human ubiquitin-protein ligase POSH is essential for HIV type 1 production

HIV type 1 (HIV-1) was shown to assemble either at the plasma membrane or in the membrane of late endosomes. Now, we report an essential role for human ubiquitin ligase POSH (Plenty of SH3s; hPOSH), a trans-Golgi network-associated protein, in the targeting of HIV-1 to the plasma membrane. Small inhibitory RNA-mediated silencing of hPOSH ablates virus secretion and Gag plasma membrane localization. Reintroduction of native, but not a RING finger mutant, hPOSH restores virus release and Gag plasma membrane localization in hPOSH-depleted cells. Furthermore, expression of the RING finger mutant hPOSH inhibits virus release and induces accumulation of intracellular Gag in normal cells. Together, our results identify a previously undescribed step in HIV biogenesis and suggest a direct function for hPOSH-mediated ubiquitination in protein sorting at the trans-Golgi network. Consequently, hPOSH may be a useful host target for therapeutic intervention.     

Recovery from diabetes in mice by beta cell regeneration

The mechanisms that regulate pancreatic beta cell mass are poorly understood. While autoimmune and pharmacological destruction of insulin-producing beta cells is often irreversible, adult beta cell mass does fluctuate in response to physiological cues including pregnancy and insulin resistance. This plasticity points to the possibility of harnessing the regenerative capacity of the beta cell to treat diabetes. We developed a transgenic mouse model to study the dynamics of beta cell regeneration from a diabetic state. Following doxycycline administration, transgenic mice expressed diphtheria toxin in beta cells, resulting in apoptosis of 70%-80% of beta cells, destruction of islet architecture, and diabetes. Withdrawal of doxycycline resulted in a spontaneous normalization of blood glucose levels and islet architecture and a significant regeneration of beta cell mass with no apparent toxicity of transient hyperglycemia. Lineage tracing analysis indicated that enhanced proliferation of surviving beta cells played the major role in regeneration. Surprisingly, treatment with Sirolimus and Tacrolimus, immunosuppressants used in the Edmonton protocol for human islet transplantation, inhibited beta cell regeneration and prevented the normalization of glucose homeostasis. These results suggest that regenerative therapy for type 1 diabetes may be achieved if autoimmunity is halted using regeneration-compatible drugs.     

Leadership as a component of crowd control in a hospital dealing with a mass-casualty incident: lessons learned from the October 2000 riots in Nazareth

INTRODUCTION: Crowd control is essential to the handling of mass-casualty incidents (MCIs). This is the task of the police at the site of the incident. For a hospital, responsibility falls on its security forces, with the police assuming an auxiliary role. Crowd control is difficult, especially when the casualties are due to riots involving clashes between rioters and police. This study uses data regarding the October 2000 riots in Nazareth to draw lessons about the determinants of crowd control on the scene and in hospitals. METHODS: Data collected from formal debriefings were processed to identify the specifics of a MCI due to massive riots. The transport of patients to the hospital and the behavior of their families were considered. The actions taken by the Hospital Manager to control crowds on the hospital premises also were analyzed. RESULTS: During 10 days of riots (01-10 October 2000), 160 casualties, including 10 severely wounded, were evacuated to the Nazareth Italian Hospital. The Nazareth English Hospital received 132 injured patients, including one critically wounded, nine severely wounded, 26 moderately injured, and 96 mildly injured. All victims were evacuated from the scene by private vehicles and were accompanied by numerous family members. This obstructed access to hospitals and hampered the care of the casualties in the emergency department. The hospital staff was unable to perform triage at the emergency department's entrance and to assign the wounded to immediate treatment areas or waiting areas. All of the wounded were taken by their families directly into the "immediate care"location where a great effort was made to prioritize the severely injured. In order to control the events, the hospital's managers enlisted prominent individuals within the crowds to aid with control. At one point, the mayor was enlisted to successfully achieve crowd control. CONCLUSIONS: During riots, city, community, and even makeshift leaders within a crowd can play a pivotal role in helping hospital management control crowds. It may be advisable to train medical teams and hospital management to recognize potential leaders, and gain their cooperation in such an event. To optimize such cooperation, community leaders also should be acquainted with the roles of public health agencies and emergency services systems.     

Ranking and combining multiple predictors without labeled data

In a broad range of classification and decision-making problems, one is given the advice or predictions of several classifiers, of unknown reliability, over multiple questions or queries. This scenario is different from the standard supervised setting, where each classifier’s accuracy can be assessed using available labeled data, and raises two questions: Given only the predictions of several classifiers over a large set of unlabeled test data, is it possible to (i) reliably rank them and (ii) construct a metaclassifier more accurate than most classifiers in the ensemble? Here we present a spectral approach to address these questions. First, assuming conditional independence between classifiers, we show that the off-diagonal entries of their covariance matrix correspond to a rank-one matrix. Moreover, the classifiers can be ranked using the leading eigenvector of this covariance matrix, because its entries are proportional to their balanced accuracies. Second, via a linear approximation to the maximum likelihood estimator, we derive the Spectral Meta-Learner (SML), an unsupervised ensemble classifier whose weights are equal to these eigenvector entries. On both simulated and real data, SML typically achieves a higher accuracy than most classifiers in the ensemble and can provide a better starting point than majority voting for estimating the maximum likelihood solution. Furthermore, SML is robust to the presence of small malicious groups of classifiers designed to veer the ensemble prediction away from the (unknown) ground truth.Every day, multiple decisions are made based on input and suggestions from several sources, either algorithms or advisers, of unknown reliability. Investment companies handle their portfolios by combining reports from several analysts, each providing recommendations on buying, selling, or holding multiple stocks (1, 2). Central banks combine surveys of several professional forecasters to monitor rates of inflation, real gross domestic product growth, and unemployment (3–6). Biologists study the genomic binding locations of proteins by combining or ranking the predictions of several peak detection algorithms applied to large-scale genomics data (7). Physician tumor boards convene a number of experts from different disciplines to discuss patients whose diseases pose diagnostic and therapeutic challenges (8). Peer-review panels discuss multiple grant applications and make recommendations to fund or reject them (9). The examples above describe scenarios in which several human advisers or algorithms provide their predictions or answers to a list of queries or questions. A key challenge is to improve decision making by combining these multiple predictions of unknown reliability. Automating this process of combining multiple predictors is an active field of research in decision science (cci.mit.edu/research), medicine (10), business (refs. 11 and 12 and www.kaggle.com/competitions), and government (www.iarpa.gov/Programs/ia/ACE/ace.html and www.goodjudgmentproject.com), as well as in statistics and machine learning.Such scenarios, whereby advisers of unknown reliability provide potentially conflicting opinions, or propose to take opposite actions, raise several interesting questions. How should the decision maker proceed to identify who, among the advisers, is the most reliable? Moreover, is it possible for the decision maker to cleverly combine the collection of answers from all of the advisers and provide even more accurate answers?In statistical terms, the first question corresponds to the problem of estimating prediction performances of preconstructed classifiers (e.g., the advisers) in the absence of class labels. Namely, each classifier was constructed independently on a potentially different training dataset (e.g., each adviser trained on his/her own using possibly different sources of information), yet they are all being applied to the same new test data (e.g., list of queries) for which labels are not available, either because they are expensive to obtain or because they will only be available in the future, after the decision has been made. In addition, the accuracy of each classifier on its own training data is unknown. This scenario is markedly different from the standard supervised setting in machine learning and statistics. There, classifiers are typically trained on the same labeled data and can be ranked, for example, by comparing their empirical accuracy on a common labeled validation set. In this paper we show that under standard assumptions of independence between classifier errors their unknown performances can still be ranked even in the absence of labeled data.The second question raised above corresponds to the problem of combining predictions of preconstructed classifiers to form a metaclassifier with improved prediction performance. This problem arises in many fields, including combination of forecasts in decision science and crowdsourcing in machine learning, which have each derived different approaches to address it. If we had external knowledge or historical data to assess the reliability of the available classifiers we could use well-established solutions relying on panels of experts or forecast combinations (11–14). In our problem such knowledge is not always available and thus these solutions are in general not applicable. The oldest solution that does not require additional information is majority voting, whereby the predicted class label is determined by a rule of majority, with all advisers assigned the same weight. More recently, iterative likelihood maximization procedures, pioneered by Dawid and Skene (15), have been proposed, in particular in crowdsourcing applications (16–23). Owing to the nonconvexity of the likelihood function, these techniques often converge only to a local, rather than global, maximum and require careful initialization. Furthermore, there are typically no guarantees on the quality of the resulting solution.In this paper we address these questions via a spectral analysis that yields four major insights:

1. Under standard assumptions of independence between classifier errors, in the limit of an infinite test set, the off-diagonal entries of the population covariance matrix of the classifiers correspond to a rank-one matrix.
2. The entries of the leading eigenvector of this rank-one matrix are proportional to the balanced accuracies of the classifiers. Thus, a spectral decomposition of this rank-one matrix provides a computationally efficient approach to rank the performances of an ensemble of classifiers.
3. A linear approximation of the maximum likelihood estimator yields an ensemble learner whose weights are proportional to the entries of this eigenvector. This represents an efficient, easily constructed, unsupervised ensemble learner, which we term Spectral Meta-Learner (SML).
4. An interest group of conspiring classifiers (a cartel) that maliciously attempts to veer the overall ensemble solution away from the (unknown) ground truth leads to a rank-two covariance matrix. Furthermore, in contrast to majority voting, SML is robust to the presence of a small-enough cartel whose members are unknown.

In addition, we demonstrate the advantages of spectral approaches based on these insights, using both simulated and real-world datasets. When the independence assumptions hold approximately, SML is typically better than most classifiers in the ensemble and their majority vote, achieving results comparable to the maximum likelihood estimator (MLE). Empirically, we find SML to be a better starting point for computing the MLE that consistently leads to improved performance. Finally, spectral approaches are also robust to cartels and therefore helpful in analyzing surveys where a biased subgroup of advisers (a cartel) may have corrupted the data.