Abbott realtime MTB assay for detecting Mycobacterium tuberculosis complex in respiratory specimens: a cost-benefit analysis.

PURPOSE: Molecular screening for Mycobacterium tuberculosis (MTB) can lead to rapid empirical treatment inception and reduce hospitalization time and complementary diagnostic tests. However, in low-prevalence settings, the cost-benefit balance remains controversial due to the high cost. METHODS: We used a Markov model to perform an economic analysis to evaluate the profit after implementing molecular MTB screening (Period B) compared with conventional culture testing (Period A) in respiratory samples from 7,452 consecutive subjects with presumed tuberculosis (TB). RESULTS: The proportion of positivity was comparable between both periods (P > 0.05), with a total of 2.16 and 1.78 samples/patient requested in periods A and B, respectively (P < 0.001). The mean length of hospital stay was 8.66 days (95%CI: 7.63-9.70) in Period B and 11.51 days (95%CI: 10.15-12.87) in Period A (P = 0.001). The healthcare costs associated with diagnosing patients with presumed TB were reduced by €717.95 per patient with PCR screening. The probability of remaining hospitalized and the need for a greater number of outpatient specialty care visits were the variables with the most weight in the model. CONCLUSION: Employing PCR as an MTB screening method in a low-prevalence setting may increase the profits to the system.

Genotypic study of Chlamydia trachomatis for lymphogranuloma venereum diagnosis in rectal specimens from men who have sex with men: a cost-effectiveness analysis.

PURPOSE: The significant proportion of asymptomatic patients and the scarcity of genotypic analysis of lymphogranuloma venereum (LGV), mainly among men who have sex with men (MSM), triggers a high incidence of underdiagnosed patients, highlighting the importance of determining the most appropriate strategy for LGV diagnosis, at both clinical and economical levels. MATERIALS AND METHODS: We conducted L1-L3 serovar detection by molecular biology in stored Chlamydia trachomatis-positive samples from MSM patients with HIV, another STI or belonging to a Pre-exposure prophylaxis program, to make a cost effectiveness study of four diagnostic strategies with a clinical, molecular, or mixed approach. RESULTS: A total of 85 exudates were analyzed: 35urethral (31 symptomatic/4 positive) and 50 rectal (22 symptomatic/25 positive), 70/85 belonging to MSM with associated risk factors. The average cost per patient was €77.09 and €159.55 for clinical (Strategy I) and molecular (Strategy IV) strategies respectively. For molecular diagnosis by genotyping of all rectal exudate samples previously positive for CT (Strategy II), the cost was €123.84. For molecular diagnosis by genotyping of rectal and/or urethral exudate samples from all symptomatic patients (proctitis or urethritis) with a previous positive result for CT (Strategy III), the cost was €129.39. The effectiveness ratios were 0.80, 0.95, 0.91, and 1.00 for each strategy respectively. The smallest ICER was €311.67 for Strategy II compared to Strategy I. CONCLUSIONS: With 30% asymptomatic patients, the most cost-effective strategy was based on genotyping all rectal exudates. With less restrictive selection criteria, thus increasing the number of patients with negative results, the most sensitive strategies tend to be the most cost-effective, but with a high incremental cost-effectiveness ratio.

Hexagonal Hybrid Bismuthene by Molecular Interface Engineering.

High-quality devices based on layered heterostructures are typically built from materials obtained by complex solid-state physical approaches or laborious mechanical exfoliation and transfer. Meanwhile, wet-chemically synthesized materials commonly suffer from surface residuals and intrinsic defects. Here, we synthesize using an unprecedented colloidal photocatalyzed, one-pot redox reaction a few-layers bismuth hybrid of "electronic grade" structural quality. Intriguingly, the material presents a sulfur-alkyl-functionalized reconstructed surface that prevents it from oxidation and leads to a tuned electronic structure that results from the altered arrangement of the surface. The metallic behavior of the hybrid is supported by ab initio predictions and room temperature transport measurements of individual nanoflakes. Our findings indicate how surface reconstructions in two-dimensional (2D) systems can promote unexpected properties that can pave the way to new functionalities and devices. Moreover, this scalable synthetic process opens new avenues for applications in plasmonics or electronic (and spintronic) device fabrication. Beyond electronics, this 2D hybrid material may be of interest in organic catalysis, biomedicine, or energy storage and conversion.

µ -ANT: semantic microaggregation-based anonymization tool.

MOTIVATION: Detailed patient data are crucial for medical research. Yet, these healthcare data can only be released for secondary use if they have undergone anonymization. RESULTS: We present and describe µ-ANT, a practical and easily configurable anonymization tool for (healthcare) data. It implements several state-of-the-art methods to offer robust privacy guarantees and preserve the utility of the anonymized data as much as possible. µ-ANT also supports the heterogenous attribute types commonly found in electronic healthcare records and targets both practitioners and software developers interested in data anonymization. AVAILABILITY AND IMPLEMENTATION: (source code, documentation, executable, sample datasets and use case examples) https://github.com/CrisesUrv/microaggregation-based_anonymization_tool.

Qualitative assessment of SARS-CoV-2-specific antibody avidity by lateral flow immunochromatographic IgG/IgM antibody assay.

Knowledge of the precise timing of SARS-CoV-2 infection may be of clinical and epidemiological relevance. The presence of low-avidity IgGs has conventionally been considered an indicator of recent infection. Here, we carried out qualitative assessment of SARS-CoV-2-specific antibody avidity using an urea (6M) dissociation test performed on a lateral flow immunochromatographic IgG/IgM device. We included a total of 76 serum specimens collected from 57 COVID-19 patients, of which 39 tested positive for both IgG and IgM and 37 only for IgG. Sera losing IgG reactivity after urea treatment (n = 28) were drawn significantly earlier (P = .04) after onset of symptoms than those which preserved it (n = 48). This assay may be helpful to estimate the time of acquisition of infection in patients with mild to severe COVID-19.

Suitability of two rapid lateral flow immunochromatographic assays for predicting SARS-CoV-2 neutralizing activity of sera.

Assessment of commercial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoassays for their capacity to provide reliable information on sera neutralizing activity is an emerging need. We evaluated the performance of two commercially available lateral flow immunochromatographic assays (LFIC; Wondfo SARS-CoV-2 Antibody test and the INNOVITA 2019-nCoV Ab test) in comparison with a SARS-CoV-2 neutralization pseudotyped assay for coronavirus disease 2019 (COVID-19) diagnosis in hospitalized patients and investigate whether the intensity of the test band in LFIC associates with neutralizing antibody (NtAb) titers. Ninety sera were included from 51 patients with moderate to severe COVID-19. A green fluorescent protein (GFP) reporter-based pseudotyped neutralization assay (vesicular stomatitis virus coated with SARS-CoV-2 spike protein) was used. Test line intensity was scored using a 4-level scale (0 to 3+). The overall sensitivity of LFIC assays was 91.1% for the Wondfo SARS-CoV-2 Antibody test, 72.2% for the INNOVITA 2019-nCoV IgG, 85.6% for the INNOVITA 2019-nCoV IgM, and 92.2% for the NtAb assay. Sensitivity increased for all assays in sera collected beyond day 14 after symptoms onset (93.9%, 79.6%, 93.9%, and 93.9%, respectively). Reactivities equal to or more intense than the positive control line (≥2+) in the Wondfo assay had a negative predictive value of 100% and a positive predictive value of 96.4% for high NtAb50 titers (≥1/160). Our findings support the use of LFIC assays evaluated herein, particularly the Wondfo test, for COVID-19 diagnosis. We also find evidence that these rapid immunoassays can be used to predict high SARS-CoV-2-S NtAb50 titers.

Give more data, awareness and control to individual citizens, and they will help COVID-19 containment.

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.

WS2 /MoS2 Heterostructures through Thermal Treatment of MoS2 Layers Electrostatically Functionalized with W3 S4 Molecular Clusters.

The preparation of 2D stacked layers combining flakes of different nature gives rise to countless numbers of heterostructures where new band alignments, defined at the interfaces, control the electronic properties of the system. Among the large family of 2D/2D heterostructures, the one formed by the combination of the most common semiconducting transition metal dichalcogenides, WS2 /MoS2 , has awakened great interest owing to its photovoltaic and photoelectrochemical properties. Solution as well as dry physical methods have been developed to optimize the synthesis of these heterostructures. Here, a suspension of negatively charged MoS2 flakes is mixed with a methanolic solution of a cationic W3 S4 -core cluster, giving rise to a homogeneous distribution of the clusters over the layers. In a second step, a calcination of this molecular/2D heterostructure under N2 leads to the formation of clean WS2 /MoS2 heterostructures, where the photoluminescence of both counterparts is quenched, proving an efficient interlayer coupling. Thus, this chemical method combines the advantages of a solution approach (simple, scalable, and low-cost) with the good quality interfaces reached by using more complicated traditional physical methods.

Ethical Value-Centric Cybersecurity: A Methodology Based on a Value Graph.

Our society is being shaped in a non-negligible way by the technological advances of recent years, especially in information and communications technologies (ICTs). The pervasiveness and democratization of ICTs have allowed people from all backgrounds to access and use them, which has resulted in new information-based assets. At the same time, this phenomenon has brought a new class of problems, in the form of activists, criminals and state actors that target the new assets to achieve their goals, legitimate or not. Cybersecurity includes the research, tools and techniques to protect information assets. However, some cybersecurity measures may clash with the ethical values of citizens. We analyze the synergies and tensions between some of these values, namely security, privacy, fairness and autonomy. From this analysis, we derive a value graph, and then we set out to identify those paths in the graph that lead to satisfying all four aforementioned values in the cybersecurity setting, by taking advantage of their synergies and avoiding their tensions. We illustrate our conceptual discussion with examples of enabling technologies. We also sketch how our methodology can be generalized to any setting where several potentially conflicting values have to be satisfied.

Nonanuclear Spin-Crossover Complex Containing Iron(II) and Iron(III) Based on a 2,6-Bis(pyrazol-1-yl)pyridine Ligand Functionalized with a Carboxylate Group.

The synthesis and magnetostructural characterization of [Fe(III)3(µ3-O)(H2O)3[Fe(II)(bppCOOH)(bppCOO)]6](ClO4)13·(CH3)2CO)6·(solvate) (2) are reported. This compound is obtained as a secondary product during synthesis of the mononuclear complex [Fe(II)(bppCOOH)2](ClO4)2 (1). The single-crystal X-ray diffraction structure of 2 shows that it contains the nonanuclear cluster of the formula [Fe(III)3(µ3-O)(H2O)3[Fe(II)(bppCOOH)(bppCOO)]6](13+), which is formed by a central Fe(III)3O core coordinated to six partially deprotonated [Fe(II)(bppCOOH)(bppCOO)](+) complexes. Raman spectroscopy studies on single crystals of 1 and 2 have been performed to elucidate the spin and oxidation states of iron in 2. These studies and magnetic characterization indicate that most of the iron(II) complexes of 2 remain in the low-spin (LS) state and present a gradual and incomplete spin crossover above 300 K. On the other hand, the Fe(III) trimer shows the expected antiferromagnetic behavior. From the structural point of view, 2 represents the first example in which bppCOO(-) acts as a bridging ligand, thus forming a polynuclear magnetic complex.

Two-color single-photon emission from InAs quantum dots: toward logic information management using quantum light.

In this work, we propose the use of the Hanbury-Brown and Twiss interferometric technique and a switchable two-color excitation method for evaluating the exciton and noncorrelated electron-hole dynamics associated with single photon emission from indium arsenide (InAs) self-assembled quantum dots (QDs). Using a microstate master equation model we demonstrate that our single QDs are described by nonlinear exciton dynamics. The simultaneous detection of two-color, single photon emission from InAs QDs using these nonlinear dynamics was used to design a NOT AND logic transference function. This computational functionality combines the advantages of working with light/photons as input/output device parameters (all-optical system) and that of a nanodevice (QD size of ∼ 20 nm) while also providing high optical sensitivity (ultralow optical power operational requirements). These system features represent an important and interesting step toward the development of new prototypes for the incoming quantum information technologies.

Correction of the tip convolution effects in the imaging of nanostructures studied through scanning force microscopy.

AFM images are always affected by artifacts arising from tip convolution effects, resulting in a decrease in the lateral resolution of this technique. The magnitude of such effects is described by means of geometrical considerations, thereby providing better understanding of the convolution phenomenon. We demonstrate that for a constant tip radius, the convolution error is increased with the object height, mainly for the narrowest motifs. Certain influence of the object shape is observed between rectangular and elliptical objects with the same height. Such moderate differences are essentially expected among elongated objects; in contrast they are reduced as the object aspect ratio is increased. Finally, we propose an algorithm to study the influence of the size, shape and aspect ratio of different nanometric motifs on a flat substrate. Indeed, with this algorithm, convolution artifacts can be extended to any kind of motif including real surface roughness. From the simulation results we demonstrate that in most cases the real motif's width can be estimated from AFM images without knowing its shape in detail.

FRR: fair remote retrieval of outsourced private medical records in electronic health networks.

Cloud computing is emerging as the next-generation IT architecture. However, cloud computing also raises security and privacy concerns since the users have no physical control over the outsourced data. This paper focuses on fairly retrieving encrypted private medical records outsourced to remote untrusted cloud servers in the case of medical accidents and disputes. Our goal is to enable an independent committee to fairly recover the original private medical records so that medical investigation can be carried out in a convincing way. We achieve this goal with a fair remote retrieval (FRR) model in which either t investigation committee members cooperatively retrieve the original medical data or none of them can get any information on the medical records. We realize the first FRR scheme by exploiting fair multi-member key exchange and homomorphic privately verifiable tags. Based on the standard computational Diffie-Hellman (CDH) assumption, our scheme is provably secure in the random oracle model (ROM). A detailed performance analysis and experimental results show that our scheme is efficient in terms of communication and computation.

LFighter: Defending against the label-flipping attack in federated learning.

Federated learning (FL) provides autonomy and privacy by design to participating peers, who cooperatively build a machine learning (ML) model while keeping their private data in their devices. However, that same autonomy opens the door for malicious peers to poison the model by conducting either untargeted or targeted poisoning attacks. The label-flipping (LF) attack is a targeted poisoning attack where the attackers poison their training data by flipping the labels of some examples from one class (i.e., the source class) to another (i.e., the target class). Unfortunately, this attack is easy to perform and hard to detect, and it negatively impacts the performance of the global model. Existing defenses against LF are limited by assumptions on the distribution of the peers' data and/or do not perform well with high-dimensional models. In this paper, we deeply investigate the LF attack behavior. We find that the contradicting objectives of attackers and honest peers on the source class examples are reflected on the parameter gradients corresponding to the neurons of the source and target classes in the output layer. This makes those gradients good discriminative features for the attack detection. Accordingly, we propose LFighter, a novel defense against the LF attack that first dynamically extracts those gradients from the peers' local updates and then clusters the extracted gradients, analyzes the resulting clusters, and filters out potential bad updates before model aggregation. Extensive empirical analysis on three data sets shows the effectiveness of the proposed defense regardless of the data distribution or model dimensionality. Also, LFighter outperforms several state-of-the-art defenses by offering lower test error, higher overall accuracy, higher source class accuracy, lower attack success rate, and higher stability of the source class accuracy. Our code and data are available for reproducibility purposes at https://github.com/NajeebJebreel/LFighter.

Impact of basal heart rate on long-term prognosis of heart transplant patients.

Previous studies in patients with heart failure have shown that an elevated basal heart rate (HR) is associated with a poor outcome. Our aim with this study was to investigate if this relationship is also present in heart transplantation (HTx) recipients. From 2003 until 2010, 256 HTx performed in our center were recruited. Patients who required pacemaker, heart-lung transplants, pediatrics, retransplants, and those patients with a survival of less than 1 year were excluded. The final number included in the analysis was 191. Using the HR obtained by EKG during elective admission at 1 year post-HTx and the survival rate, an ROC-curve was performed. The best point under the curve was achieved with 101 beats per minute (bpm), so patients were divided in two groups according to their HR. A comparison between survival curves of both groups was performed (Kaplan-Meier). Subsequently, a multivariate analysis considering HR and other variables with influence on survival according to the literature was carried out. A total of 136 patients were included in the group with HR ≤100 bpm, and 55 in the one with HR >100 bpm. There were no basal differences in both groups except for primary graft failure, which was more frequent in the >100 bpm group (30.9 vs. 17%, P = 0.033). Patients with ≤100 bpm had a better long prognosis (P < 0.001). The multivariate analysis proved that high HR was an independent predictor of mortality. Our study shows that HR should be considered as a prognosis factor in HTx patients.

Fair detection of poisoning attacks in federated learning on non-i.i.d. data.

Reconciling machine learning with individual privacy is one of the main motivations behind federated learning (FL), a decentralized machine learning technique that aggregates partial models trained by clients on their own private data to obtain a global deep learning model. Even if FL provides stronger privacy guarantees to the participating clients than centralized learning collecting the clients' data in a central server, FL is vulnerable to some attacks whereby malicious clients submit bad updates in order to prevent the model from converging or, more subtly, to introduce artificial bias in the classification (poisoning). Poisoning detection techniques compute statistics on the updates to identify malicious clients. A downside of anti-poisoning techniques is that they might lead to discriminate minority groups whose data are significantly and legitimately different from those of the majority of clients. This would not only be unfair, but would yield poorer models that would fail to capture the knowledge in the training data, especially when data are not independent and identically distributed (non-i.i.d.). In this work, we strive to strike a balance between fighting poisoning and accommodating diversity to help learning fairer and less discriminatory federated learning models. In this way, we forestall the exclusion of diverse clients while still ensuring detection of poisoning attacks. Empirical work on three data sets shows that employing our approach to tell legitimate from malicious updates produces models that are more accurate than those obtained with state-of-the-art poisoning detection techniques. Additionally, we explore the impact of our proposal on the performance of models on non-i.i.d local training data.