Single Label Research Articles

A generalized stochastic block model for overlapping community detection

Over the past two decades, community detection has been extensively explored. Yet, the problem of identifying overlapping communities has not been fully solved. In this paper, we introduce a novel approach, called the generalized stochastic block model, to address this issue by allowing nodes to belong to multiple communities. This approach extends the traditional representation of nodal community assignment from a single community label to a label vector, with each element indicating the membership of a node in a specific community. We develop a Markov chain Monte Carlo algorithm to tackle the model. Through numerical experiments conducted on synthetic and empirical networks, we demonstrate the efficacy of the proposed framework in accurately detecting overlapping communities.

Smart Video Surveillance Using Deep Learning

Abstract: There is a lot of assessment happening in the business about video observation among them; the occupation of CCTV accounts has been blocked. CCTV cameras are put all around the spots for perception and security. In today's digital age, the increasing availability of data and advancements in computer vision have paved the way for numerous applications of object detection. This powerful technology has found its significance in various domains, including video surveillance and image retrieval systems. Object detection enables machines to identify and locate objects within images or video frames, providing valuable insights and aiding in decision-making processes. This article explores the applications, challenges, techniques, and future trends of object detection in the context of video surveillance and image retrieval systems. Object detection is a computer vision technique that involves identifying and localizing objects within images or video frames. Unlike image classification, which assigns a single label to an entire image, object detection goes a step further by detecting and delineating the individual objects present. It enables computers to comprehend visual data and interact with the world, much like humans do

P142 Effect of Atorvastatin on vascular endothelial function in early Systemic Sclerosis: An open label Randomized Controlled trial

Abstract Background/Aims Systemic Sclerosis (SSc) is driven by early endothelial dysfunction, leading to loss of endothelium-dependent vasodilatation. This predisposes to increased stiffness of large arteries. Prospective studies have found a conflicting role of statins in improving vascular function in established SSc. We undertook this study to explore if statins influence endothelium dependent vasodilation in early SSc Methods In this single center open label randomized controlled trial between July 2021 to January 2022, SSc patients (ACR-EULAR 2013) between 18-65 years, with duration < 3 years from the first non-Raynaud’s symptom were included. Those with complicated Raynaud’s, overlap syndromes and cardiovascular comorbidities were excluded. Participants were randomized into statin (atorvastatin 40 mg/day/A40) or no -statin arm. Both groups received stable doses of immunosuppression, during the study period. The primary outcome of flow-mediated dilation (FMD %) of brachial artery, measured by single assessor and secondary outcomes of common carotid intima medial thickness (CIMT), modified Rodnan Skin score (mRSS), Raynaud’s visual analogue scale (RayVAS:0-10), hs CRP, von Willebrand factor and oxidized low-density lipoproteins were measured at baseline and 16 weeks. All patients provided written informed consent; study protocol was approved by IEC(JIP/IEC/2020/026). Considering a change in FMD of 1% between arms as clinically meaningful, sample size was calculated to be 144. Analysis was performed using SPSS v.19.CTRI registration no: REF/2020/07/03495 Results Total 71 eligible patients were randomized into statin(n = 36) and no-statin(n = 35) arms. Baseline parameters were comparable between arms. Baseline mRSS was 14.25. On per-protocol analysis of statin (n = 28) and no-statin (n = 32) arms, there was no significant difference in FMD either at baseline or at 16 weeks (table 1). At week 16, basal brachial artery diameter increased (p = 0.008) in statin arm. Ray VAS scores improved in the statin arm,not achieving significance(p = 0.15). An increase in mRSS from 14 to 16 was noted in no-statin arm , whereas it remained stable in the statin arm (14.5). Secondary endpoints were not met. No adverse events were noted. Conclusion In early SSc, atorvastatin 40 mg/day, added to background immunosuppression, failed to improve endothelial function, assessed using brachial FMD, at 16 weeks. Stabilization of skin tightening was noted in the statin arm. Disclosure A.C. Das: None. M.M. Thabah: None. C. Mariaselvam: None. R. Vijayan: None. A. Anantharaj: None. V. Negi: None.

Conglomerate Crop Recommendation by using Multi-label Learning via Ensemble Supervised Clustering Techniques

Existing crop recommender related to either binary classification or multiclass classification. This paper presents conglomerate crop recommendations which consist of a number of different and distinct crops that are grouped together. In this work we focus on transferring knowledge from single label output prediction to multiple label predicted output for a given input data instances. We proposed ESCT algorithm i.e. Ensemble Supervised Clustering Techniques in our research work. ESCT provides a combined approach of conventional clustering and enhanced supervised clustering methodology to optimize the conglomerate recommendation. We are focusing on K-mean clustering for conventional approach and ICCC i.e. Inter cluster correlation coefficient to achieve enhancement in supervised clustering. In conventional K-mean clustering there is a big challenge on how to optimize the k-value of clustering which directly affects the convergence of the clusters. To resolve this problem, we mainly apply function approximation on K-Value which provides us with better clustering and fast convergence. Existing methods for inter-clustering do not adequately address one of the key challenges i.e. exploiting correlations between labels and that is achieved by ICCC algorithm. This model provides learning and prediction of unknown observation by using Back propagation MLL algorithm which provides improved performance.

A methodology for alpha particles identification in liquid scintillation using a cost-efficient Artificial Neural Network

The discrimination of α/β-particles based on Liquid Scintillation (LS) is a measurement technique that can be applied for environmental monitoring. This technique relies on the specific feature of the scintillation process yielding two decay components (fast and delayed) of the photon emission following a particle interaction in a LS sample; indeed, the intensity of the delayed component is higher for α-particles than for β-particles. When operating with a photon detector such as a PhotoMultiplier Tube (PMT), the Pulse-Shape Discrimination (PSD) is the traditional approach, relying on low-pass filtered signals allowing pulse-tail analysis for an α-particle identification. This paper presents a new α-particle detection method based on an Artificial Neural Network (ANN) that directly processes fast pulse trains produced by the delayed scintillation component of a single detector. The ANN is trained using an experimental dataset obtained with a single PMT channel connected in a 3-PMTs detection system from which triple coincidences resulting from α-interactions (241Am) in a LS source are used for the single detector labeling. As we target a real-time implementation, the ANN is designed to minimize the computational resources. The classifier can operate with good performances (91.0% of correct classification, 8.5% false negative and 0.5% false positive) on a single-channel detector.

Cyclopropenes as Chemical Reporters for Dual Bioorthogonal and Orthogonal Metabolic Labeling of DNA.

Dual bioorthogonal labeling enables the investigation and understanding of interactions in the biological environment that are not accessible by a single label. However, applying two bioorthogonal reactions in the same environment remains challenging due to cross-reactivity. We developed a pair of differently modified 2'-deoxynucleosides that solved this issue for dual and orthogonal labeling of DNA. Inverse-electron demand Diels-Alder and photoclick reactions were combined to attach two different fluorogenic labels to genomic DNA in cells. Using a small synthetic library of 1- and 3-methylcyclopropenyl-modified 2'-deoxynucleosides, two 2'-deoxyuridines were identified to be the fastest-reacting ones for each of the two bioorthogonal reactions. Their orthogonal reactivity could be evidenced in vitro. Primer extension experiments were performed with both 2'-deoxyuridines investigating their replication properties as substitutes for thymidine and evaluating subsequent labeling reactions on the DNA level. Finally, dual, orthogonal and metabolic fluorescent labeling of genomic DNA was demonstrated in HeLa cells. An experimental procedure was developed combining intracellular transport and metabolic DNA incorporation of the two 2'-deoxyuridines with the subsequent dual bioorthogonal labeling using a fluorogenic cyanine-styryl tetrazine and a fluorogenic pyrene-tetrazole. These results are fundamental for advanced metabolic labeling strategies for nucleic acids in the future, especially for live cell experiments.

Cyclopropene als Chemische Reporter für die Duale Bioorthogonale und Orthogonale Metabolische Markierung von DNA

AbstractDie duale bioorthogonale Markierung ermöglicht die Untersuchung und das Verständnis von Wechselwirkungen in der biologischen Umgebung, die mit einer einzigen Markierung nicht zugänglich sind. Eine Herausforderung bleibt jedoch die Anwendung von zwei bioorthogonalen Reaktionen in der gleichen Umgebung aufgrund von Kreuzreaktivität. Um dieses Problem für die duale und orthogonale Markierung von DNA zu lösen, haben wir zwei unterschiedlich modifizierte 2′‐Desoxynukleoside entwickelt. Mit Hilfe der Diels‐Alder‐Reaktion mit inversem Elektronenbedarf und Photoklick‐Reaktion wurde genomische DNA in Zellen mit zwei verschiedenen fluorogenen Markern verknüpft. Aus einer kleinen synthetischen Bibliothek aus 1‐ und 3‐methylcyclopropenyl‐modifizierten 2′‐Desoxynukleosiden wurden zwei 2′‐Desoxyuridine identifiziert, die am schnellsten in jeweils einer der beiden bioorthogonalen Reaktionen reagieren. Ihre orthogonale Reaktivität konnte in vitro nachgewiesen werden. Ihre Replikationseigenschaften als Ersatz für Thymidin wurden in Primerverlängerungsexperimenten untersucht, um die anschließenden Markierungsreaktionen an DNA abzuschätzen. Schließlich wurde eine duale, orthogonale und metabolische Fluoreszenzmarkierung von genomischer DNA in HeLa‐Zellen nachgewiesen. Es wurde ein experimentelles Protokoll entwickelt, das den intrazellulären Transport und den metabolischen DNA‐Einbau der beiden 2′‐Desoxyuridine mit der anschließenden dualen bioorthogonalen Markierung unter Verwendung eines fluorogenen Cyanin‐Styryl‐Tetrazins und eines fluorogenen Pyren‐Tetrazols kombiniert. Diese Ergebnisse sind von grundlegender Bedeutung für fortgeschrittene metabolische Markierungsstrategien für Nukleinsäuren in der Zukunft, insbesondere in lebenden Zellen.

Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes.

Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are transparent and in line with clinical, diagnostic rules. We demonstrate our approach for autism spectrum disorders (ASD), a neurodevelopmental condition with increasing prevalence. We use unstructured data from the Centers for Disease Control and Prevention (CDC) surveillance records labeled by a CDC-trained clinician with ASD A1-3 and B1-4 criterion labels per sentence and with ASD cases labels per record using Diagnostic and Statistical Manual of Mental Disorders (DSM5) rules. One rule-based and three deep ML algorithms and six ensembles were compared and evaluated using a test set with 6773 sentences (N = 35 cases) set aside in advance. Criterion and case labeling were evaluated for each ML algorithm and ensemble. Case labeling outcomes were compared also with seven traditional tests. Performance for criterion labeling was highest for the hybrid BiLSTM ML model. The best case labeling was achieved by an ensemble of two BiLSTM ML models using a majority vote. It achieved 100% precision (or PPV), 83% recall (or sensitivity), 100% specificity, 91% accuracy, and 0.91 F-measure. A comparison with existing diagnostic tests shows that our best ensemble was more accurate overall. Transparent ML is achievable even with small datasets. By focusing on intermediate steps, deep ML can provide transparent decisions. By leveraging data redundancies, ML errors at the intermediate level have a low impact on final outcomes.

Nanoparticle Metrology of Silicates Using Time-Resolved Multiplexed Dye Fluorescence Anisotropy, Small Angle X-ray Scattering, and Molecular Dynamics Simulations.

We investigate the nanometrology of sub-nanometre particle sizes in industrially manufactured sodium silicate liquors at high pH using time-resolved fluorescence anisotropy. Rather than the previous approach of using a single dye label, we investigate and quantify the advantages and limitations of multiplexing two fluorescent dye labels. Rotational times of the non-binding rhodamine B and adsorbing rhodamine 6G dyes are used to independently determine the medium microviscosity and the silicate particle radius, respectively. The anisotropy measurements were performed on the range of samples prepared by diluting the stock solution of silicate to concentrations ranging between 0.2 M and 2 M of NaOH and on the stock solution at different temperatures. Additionally, it was shown that the particle size can also be measured using a single excitation wavelength when both dyes are present in the sample. The recovered average particle size has an upper limit of 7.0 ± 1.2 Å. The obtained results were further verified using small-angle X-ray scattering, with the recovered particle size equal to 6.50 ± 0.08 Å. To disclose the impact of the dye label on the measured complex size, we further investigated the adsorption state of rhodamine 6G on silica nanoparticles using molecular dynamics simulations, which showed that the size contribution is strongly impacted by the size of the nanoparticle of interest. In the case of the higher radius of curvature (less curved) of larger particles, the size contribution of the dye label is below 10%, while in the case of smaller and more curved particles, the contribution increases significantly, which also suggests that the particles of interest might not be perfectly spherical.

Prediction of rhinitis with class imbalance based on heterogeneous ensemble learning

Common clinical rhinitis is characterized by different types of cases and class imbalance. Its prediction belongs to multiple output classification. Low recognition rate and poor generalization performance often occur for minority class. Therefore, we propose a novel integrated classification model, ARF-OOBEE, which transforms the multi-output classification to multi-label classification and multi-class classification. The multi-label classifier automatically adjusts the number and depth of integrated forest learners according to the imbalance ratio of single class label in a subset. It can effectively reduce the impact of class imbalance on classification and improve prediction performance of both majority or minority class concurrently. Also, we build a multi-class classification based on out-of-bag Extra-Tree to accomplish finer classification for the predicted labels. In addition, we calculate the feature importance for rhinitis on the grounds of the purity of nodes in decision-making tree inside Random Forest and study the correlation between rhinitis features. We conduct 12 folds cross-validation experiments on 461 cases of clinical rhinitis. The outcomes show that the evaluation indicators of ARF-OOBEE, such as Sensitivity, Specificity, Accuracy, F1-Score, AUC, and G-Mean are 74.9%,86.5%,92.0%,78.3%,95.3%, and 79.9%, respectively. In comparison to the other methods, ARF-OOBEE has better evaluation indicator and is more effective for the early clinical diagnosis of rhinitis.

Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number

Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician’s office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen’s κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

T-NET: Weakly Supervised Graph Learning for Combatting Human Trafficking

Human trafficking (HT) for forced sexual exploitation, often described as modern-day slavery, is a pervasive problem that affects millions of people worldwide. Perpetrators of this crime post advertisements (ads) on behalf of their victims on adult service websites (ASW). These websites typically contain hundreds of thousands of ads including those posted by independent escorts, massage parlor agencies and spammers (fake ads). Detecting suspicious activity in these ads is difficult and developing data-driven methods is challenging due to the hard-to-label, complex and sensitive nature of the data. In this paper, we propose T-Net, which unlike previous solutions, formulates this problem as weakly supervised classification. Since it takes several months to years to investigate a case and obtain a single definitive label, we design domain-specific signals or indicators that provide weak labels. T-Net also looks into connections between ads and models the problem as a graph learning task instead of classifying ads independently. We show that T-Net outperforms all baselines on a real-world dataset of ads by 7% average weighted F1 score. Given that this data contains personally identifiable information, we also present a realistic data generator and provide the first publicly available dataset in this domain which may be leveraged by the wider research community.

COMBAT: Alternated Training for Effective Clean-Label Backdoor Attacks

Backdoor attacks pose a critical concern to the practice of using third-party data for AI development. The data can be poisoned to make a trained model misbehave when a predefined trigger pattern appears, granting the attackers illegal benefits. While most proposed backdoor attacks are dirty-label, clean-label attacks are more desirable by keeping data labels unchanged to dodge human inspection. However, designing a working clean-label attack is a challenging task, and existing clean-label attacks show underwhelming performance. In this paper, we propose a novel mechanism to develop clean-label attacks with outstanding attack performance. The key component is a trigger pattern generator, which is trained together with a surrogate model in an alternating manner. Our proposed mechanism is flexible and customizable, allowing different backdoor trigger types and behaviors for either single or multiple target labels. Our backdoor attacks can reach near-perfect attack success rates and bypass all state-of-the-art backdoor defenses, as illustrated via comprehensive experiments on standard benchmark datasets. Our code is available at https://github.com/VinAIResearch/COMBAT.

Leveraging Local Variance for Pseudo-Label Selection in Semi-supervised Learning

Semi-supervised learning algorithms that use pseudo-labeling have become increasingly popular for improving model performance by utilizing both labeled and unlabeled data. In this paper, we offer a fresh perspective on the selection of pseudo-labels, inspired by theoretical insights. We suggest that pseudo-labels with a high degree of local variance are more prone to inaccuracies. Based on this premise, we introduce the Local Variance Match (LVM) method, which aims to optimize the selection of pseudo-labels in semi-supervised learning (SSL) tasks. Our methodology is validated through a series of experiments on widely-used image classification datasets, such as CIFAR-10, CIFAR-100, and SVHN, spanning various labeled data quantity scenarios. The empirical findings show that the LVM method substantially outpaces current SSL techniques, achieving state-of-the-art results in many of these scenarios. For instance, we observed an error rate of 5.41% on CIFAR-10 with a single label for each class, 35.87% on CIFAR-100 when using four labels per class, and 1.94% on SVHN with four labels for each class. Notably, the standout error rate of 5.41% is less than 1% shy of the performance in a fully-supervised learning environment. In experiments on ImageNet with 100k labeled data, the LVM also reached state-of-the-art outcomes. Additionally, the efficacy of the LVM method is further validated by its stellar performance in speech recognition experiments.

Double Immunohistochemical Labelling of PRAME and Melan A in Slow Mohs Biopsy Margin Assessment of Lentigo Maligna and Lentigo Maligna Melanoma.

Introduction: Lentigo maligna (LM) and lentigo maligna melanoma (LMM) predominantly affect the head and neck areas in elderly patients, presenting as challenging ill-defined pigmented lesions with indistinct borders. Surgical margin determination for complete removal remains intricate due to these characteristics. Morphological examination of surgical margins is the key form of determining successful treatment in LM/LMM and underpin the greater margin control provided through the Slow Mohs micrographic surgery (SMMS) approach. Recent assessments have explored the use of immunohistochemistry (IHC) markers, such as Preferentially Expressed Antigen in Melanoma (PRAME), to aid in LM/LMM and margin evaluation, leveraging the selectivity of PRAME labelling in malignant melanocytic neoplasms. Methods: A Novel double-labelling (DL) method incorporating both PRAME and MelanA IHC was employed to further maximise the clinical applicability of PRAME in the assessment of LM/LMM in SMMS biopsies. The evaluation involved 51 samples, comparing the results of the novel DL with respective single-labelling (SL) IHC slides. Results: The findings demonstrated a significant agreement of 96.1% between the DL method and SL slides across the tested samples. The benchmark PRAME SL exhibited a sensitivity of 91.3% in the SMMS specimens and 67.9% in histologically confirmed positive margins. Discussion: This study highlights the utility of PRAME IHC and by extension PRAME DL as an adjunctive tool in the assessment of melanocytic tumours within staged excision margins in SMMS samples.

Dynamic multi-label feature selection algorithm based on label importance and label correlation

Multi-label distribution is a popular direction in current machine learning research and is relevant to many practical problems. In multi-label learning, samples are usually described by high-dimensional features, many of which are redundant or invalid. This paper proposes a multi-label static feature selection algorithm to solve the problems caused by high-dimensional features of multi-label learning samples. This algorithm is based on label importance and label relevance, and improves the neighborhood rough set model. One reason for using neighborhood rough sets is that feature selection using neighborhood rough sets does not require any prior knowledge of the feature space structure. Another reason is that it does not destroy the neighborhood and order structure of the data when processing multi-label data. The method of mutual information is used to achieve the extension from single labels to multiple labels in the multi-label neighborhood; through this method, the label importance and label relevance of multi-label data are connected. In addition, in the multi-label task scenario, features may be interdependent and interrelated, and features often arrive incrementally or can be extracted continuously; we call these flow features. Traditional static feature selection algorithms do not handle flow features well. Therefore, this paper proposes a dynamic feature selection algorithm for flow features, which is based on previous static feature selection algorithms. The proposed static and dynamic algorithms have been tested on a multi-label learning task set and the experimental results show the effectiveness of both algorithms.

Mitoquinone mesylate as post-exposure prophylaxis against SARS-CoV-2 infection in humans: an exploratory single center pragmatic open label non-randomized pilot clinical trial with matched controls

An ongoing important need exists to rapidly develop novel therapeutics for COVID-19 that will retain antiviral efficacy in the setting of rapidly evolving SARS-CoV-2 variants and potential future development of resistance of SARS-COV-2 to remdesivir and protease inhibitors. To date, there is no FDA-approved treatment for post-exposure prophylaxis against SAR-CoV-2. We have shown that the mitochondrial antioxidant mitoquinone/mitoquinol mesylate (Mito-MES), a dietary supplement, has antiviral activity against SARS-CoV-2 invitro and in SARS-CoV-2 infected K18-hACE2 mice. In this exploratory, pragmatic open label clinical trial (ClinicalTrials.gov identifier NCT05381454), we studied whether Mito-MES is an effective post-exposure prophylaxis treatment in people who had high-grade unmasked exposures to SARS-CoV-2 within 5 days prior to study entry. Participants were enrolled in real-world setting in Los Angeles, United States between May 1 and December 1, 2022 and were assigned to either mito-MES 20mg daily for 14 days (n=40) or no mito-MES (controls) (n=40). The primary endpoint was development of SARS-CoV-2 infection based on 4 COVID-19 diagnostic tests [rapid antigen tests (RATs) or PCR] performed during the study period (14 days post exposure). Out of 40 (23 females; 57.5%) study participants who took Mito-MES, 12 (30%) developed SARS-CoV-2 infection compared to 30 of the 40 controls (75%) (difference-45.0%, 95% confidence intervals (CI):-64.5%,-25.5%). Out of 40 (19 females; 47.5%) study participants in the control group, 30 (75.0%) had at least one positive COVID-19 diagnostic test and 23 (57.5%) were symptomatic. With regards to key secondary outcomes, among symptomatic SARS-CoV-2 infections, the median duration of viral symptoms was lower in the Mito-MES group (median 3.0, 95% CI 2.75, 3.25) compared to the control group (median 5.0, 95% CI 4.0, 7.0). None of the study participants was hospitalized or required oxygen therapy. Mito-MES was well tolerated and no serious side effect was reported in any study participant. This work describes antiviral activity of mito-MES in humans. Mito-MES was well tolerated in our study population and attenuated transmission of SARS-CoV-2 infection. Given established safety of Mito-MES in humans, our results suggest that randomized control clinical trials of Mito-MES as post-exposure prophylaxis against SARS-CoV-2 infection are warranted. This work was supported in part by National Institutes of Health grant R01AG059501 (TK), National Institutes of Health grant R01AG059502 04S1 (TK), NIH/National Center for Advancing Translational Sciences (NCATS) UCLA CTSI Grant Number UL1TR001881 and California HIV/AIDS Research Program grant OS17-LA-002 (TK).

Pharmacokinetics of single dose doxycycline in the rectum, vagina, and urethra: implications for prevention of bacterial sexually transmitted infections

Clinical trials showed a single oral dose of doxycycline taken after sex protects against STIs among men who have sex with men (MSM) but not women. Pharmacokinetic data at vaginal, rectal and penile sites of STI exposure are lacking. We examined vaginal, rectal and urethral doxycycline concentrations in men and women to better inform STI prevention. Doxycycline pharmacokinetics in male and female participants 18-59 years of age were evaluated in blood and urine and on rectal and vaginal swabs collected at 1, 2, 4, 8, 24, 48, 72, 96 and 168h after receiving a 200mg oral doxycycline dose in a non-randomised single dose open label single centre study in Atlanta, Georgia. Rectal, vaginal, and cervical biopsies and male urethral swabs were collected 24h after dosing (Trial registration: NCT04860505). Doxycycline was measured by liquid chromatography-mass spectrometry. Eleven male and nine female participants participated in the study. Doxycycline concentrations on rectal and vaginal swabs collected up to 96h after dosing were approximately twice those of plasma and remained above minimum inhibitory concentrations (MICs) for at least four, three, and two days for Chlamydia trachomatis, Treponema pallidum, and tetracycline-sensitive Neisseria gonorrhoeae, respectively. Geometric mean doxycycline concentrations in male urethral secretions (1.166μg/mL; 95% CI 0.568-2.394μg/mL), male rectal (0.596μg/g; 0.442-0.803μg/g), vaginal (0.261μg/g; 0.098-0.696μg/g) and cervical tissue (0.410μg/g; 0.193-0.870μg/g) in biopsies collected 24h after dosing exceeded MICs. Plasma and urine doxycycline levels defined adherence markers up to four and seven days postdosing, respectively. No adverse events were reported in this study. Doxycycline efficiently distributes to the rectum, vagina and urethra. Findings can help explain efficacy of STI prevention by doxycycline. Funded by CDC intramural funds, CDC contract HCVJCG-2020-45044 (to CFK).

Development of finely tuned liposome nanoplatform for macrophage depletion

BackgroundImmunotherapy with clodronate-encapsulated liposomes, which induce macrophage depletion, has been studied extensively. However, previously reported liposomal formulation-based drugs (Clodrosome® and m-Clodrosome®) are limited by their inconsistent size and therapeutic efficacy. Thus, we aimed to achieve consistent therapeutic effects by effectively depleting macrophages with uniform-sized liposomes.ResultsWe developed four types of click chemistry-based liposome nanoplatforms that were uniformly sized and encapsulated with clodronate, for effective macrophage depletion, followed by conjugation with Man-N3 and radiolabeling. Functionalization with Man-N3 improves the specific targeting of M2 macrophages, and radioisotope labeling enables in vivo imaging of the liposome nanoplatforms. The functionalized liposome nanoplatforms are stable under physiological conditions. The difference in the biodistribution of the four liposome nanoplatforms in vivo were recorded using positron emission tomography imaging. Among the four platforms, the clodronate-encapsulated mannosylated liposome effectively depleted M2 macrophages in the normal liver and tumor microenvironment ex vivo compared to that by Clodrosome® and m-Clodrosome®.ConclusionThe newly-developed liposome nanoplatform, with finely tuned size control, high in vivo stability, and excellent ex vivo M2 macrophage targeting and depletion effects, is a promising macrophage-depleting agent.Graphical

SIMPEL: using stable isotopes to elucidate dynamics of context specific metabolism

The capacity to leverage high resolution mass spectrometry (HRMS) with transient isotope labeling experiments is an untapped opportunity to derive insights on context-specific metabolism, that is difficult to assess quantitatively. Tools are needed to comprehensively mine isotopologue information in an automated, high-throughput way without errors. We describe a tool, Stable Isotope-assisted Metabolomics for Pathway Elucidation (SIMPEL), to simplify analysis and interpretation of isotope-enriched HRMS datasets. The efficacy of SIMPEL is demonstrated through examples of central carbon and lipid metabolism. In the first description, a dual-isotope labeling experiment is paired with SIMPEL and isotopically nonstationary metabolic flux analysis (INST-MFA) to resolve fluxes in central metabolism that would be otherwise challenging to quantify. In the second example, SIMPEL was paired with HRMS-based lipidomics data to describe lipid metabolism based on a single labeling experiment. Available as an R package, SIMPEL extends metabolomics analyses to include isotopologue signatures necessary to quantify metabolic flux.