Assessing the use of anti-PD1 monotherapy as adjuvant therapy and determinants of treatment choice in stage III cutaneous melanoma in the US.

BACKGROUND: The objective of this study was to describe real-world adjuvant therapy (AT) use by disease substage and assess determinants of treatment choice among patients with stage III melanoma. METHODS: This non-interventional retrospective study included survey responses and data from patient records provided by US medical oncologists. Survey responses, patient demographic/clinical characteristics, treatment utilization, and reasons for treatment were reported descriptively. The association between patient and disease characteristics and AT selection was assessed using logistic and multinomial regression models, overall and stratified by AJCC8 substage (IIIA vs. IIIB/C/D) and type of AT received (anti-PD1 monotherapy, BRAF/MEK, no AT), respectively. RESULTS: In total 152 medical oncologists completed the survey and reviewed the charts of 507 patients (168 stage IIIA; 339 stages IIIB/IIIC/IIID); 405 (79.9%) patients received AT (360/405 (88.9%) received anti-PD1 therapy; 45/405 (11.1%) received BRAF/MEK therapy). Physicians reported clinical guidelines (61.2%), treatment efficacy (37.5%), and ECOG performance status (31.6%) as drivers of AT prescription. Patient-level data confirmed that improving patient outcomes (79%) was the main reason for anti-PD1 prescription; expected limited treatment benefit (37%), patient refusal (36%), and toxicity concerns (30%) were reasons for not prescribing AT. In multivariable analyses stage IIIB/IIIC/IIID disease significantly increased the probability of receiving AT (odds ratio [OR] 1.74) and anti-PD1 therapy (OR 1.82); ECOG 2/3 and Medicaid/no insurance decreased the probability of AT receipt (OR 0.37 and 0.42, respectively) and anti-PD1 therapy (OR 0.41 and 0.42, respectively) among all patients and patients with stage IIIA disease. CONCLUSION: Most patients were given AT with a vast majority treated with an anti-PD1 therapy. Physician- and patient-level evidence confirmed the impact of disease substage on AT use, with stage IIIA patients, patients without adequate insurance coverage, and worse ECOG status having a lower probability of receiving AT.

NLSI: An innovative method to locate epidemic sources on the SEIR propagation model.

Epidemics pose a significant threat to societal development. Accurately and swiftly identifying the source of an outbreak is crucial for controlling the spread of an epidemic and minimizing its impact. However, existing research on locating epidemic sources often overlooks the fact that epidemics have an incubation period and fails to consider social behaviors like self-isolation during the spread of the epidemic. In this study, we first take into account isolation behavior and introduce the Susceptible-Exposed-Infected-Recovered (SEIR) propagation model to simulate the spread of epidemics. As the epidemic reaches a certain threshold, government agencies or hospitals will report the IDs of some infected individuals and the time when symptoms first appear. The reported individuals, along with their first and second-order neighbors, are then isolated. Using the moment of symptom onset reported by the isolated individuals, we propose a node-level classification method and subsequently develop the node-level-based source identification (NLSI) algorithm. Extensive experiments demonstrate that the NLSI algorithm is capable of solving the source identification problem for single and multiple sources under the SEIR propagation model. We find that the source identification accuracy is higher when the infection rate is lower, and a sparse network structure is beneficial to source localization. Furthermore, we discover that the length of the isolation period has little impact on source localization, while the length of the incubation period significantly affects the accuracy of source localization. This research offers a novel approach for identifying the origin of the epidemic associated with our defined SEIR model.

Locating sources of Szegedy's quantum network.

Source location in quantum networks is a critical area of research with profound implications for cutting-edge fields such as quantum state tomography, quantum computing, and quantum communication. In this study, we present groundbreaking research on the technique and theory of source location in Szegedy's quantum networks. We develop a linear system evolution model for a Szegedy's quantum network system using matrix vectorization techniques. Subsequently, we propose a highly precise and robust source-location algorithm based on compressed sensing specifically tailored for Szegedy's quantum network. To validate the effectiveness and feasibility of our algorithm, we conduct numerical simulations on various model and real networks, yielding compelling results. These findings underscore the potential of our approach in practical applications.

The locatability of Pearson algorithm for multi-source location in complex networks.

We study locating propagation sources in complex networks. We proposed an multi-source location algorithm for different propagation dynamics by using sparse observations. Without knowing the propagation dynamics and any dynamic parameters, we can calculate node centrality based on the character that positive correlation between inform time of nodes and geodesic distance between nodes and sources. The algorithm is robust and have high location accuracy for any number of sources. We study locatability of the proposed source location algorithm and present a corresponding strategy to select observer nodes based on greedy algorithm. All simulations on both model and real-world networks proved the feasibility and validity of this algorithm.

Effect of Fecal Microbiota, Live-Jslm (REBYOTA [RBL]) on Health-Related Quality of Life in Patients With Recurrent Clostridioides difficile Infection: Results From the PUNCH CD3 Clinical Trial.

Background: Recurrence of Clostridioides difficile infection (rCDI) is common, prolonging disease morbidity and leading to poor quality of life. We evaluated disease-specific health-related quality of life (HRQL) in patients with rCDI treated with fecal microbiota, live-jslm (REBYOTA [RBL]; Rebiotix) versus placebo. Methods: This was a secondary analysis of a randomized, double-blind, placebo-controlled phase 3 study (PUNCH CD3). The disease-specific Clostridioides difficile Quality of Life Survey (Cdiff32) was administered at baseline and at weeks 1, 4, and 8. Changes in Cdiff32 total and domain (physical, mental, social) scores from baseline to week 8 were compared between RBL and placebo and for responders and nonresponders. Results: Findings were analyzed in a total of 185 patients (RBL, n = 128 [69.2%]; placebo, n = 57 [30.8%]) with available Cdiff32 data. Patients from both arms showed significant improvements in Cdiff32 scores relative to baseline across all outcomes and at all time points (all P < .001); RBL-treated patients showed significantly greater improvements in mental domain than those receiving placebo. In adjusted analyses, RBL-treated patients showed greater improvements than placebo in total score and physical and mental domains (all P < .05). Similar improvement in mental domain was observed among responders, while nonresponders showed numerical improvements with RBL but not placebo. Conclusions: In a phase 3 double-blinded clinical trial, RBL-treated patients reported more substantial and sustained disease-specific HRQL improvements than placebo-treated patients. Clinical Trials Registration: ClinicalTrials.gov NCT03244644 (https://clinicaltrials.gov/ct2/show/NCT03244644).

EHR Data Quality Assessment Tools and Issue Reporting Workflows for the 'All of Us' Research Program Clinical Data Research Network.

The All of Us (AoU) Research Program aggregates electronic health records (EHR) data from 300,00+ participants spanning 50+ distinct data sites. The diversity and size of AoU's data network result in multifaceted obstacles to data integration that may undermine the usability of patient EHR. Consequently, the AoU team implemented data quality tools to regularly evaluate and communicate EHR data quality issues at scale. The use of systematic feedback and educational tools ultimately increased site engagement and led to quantitative improvements in EHR quality as measured by program- and externally-defined metrics. These improvements enabled the AoU team to save time on troubleshooting EHR and focus on the development of alternate mechanisms to improve the quality of future EHR submissions. While this framework has proven effective, further efforts to automate and centralize communication channels are needed to deepen the program's efforts while retaining its scalability.

CD-Based Indices for Link Prediction in Complex Network.

Lots of similarity-based algorithms have been designed to deal with the problem of link prediction in the past decade. In order to improve prediction accuracy, a novel cosine similarity index CD based on distance between nodes and cosine value between vectors is proposed in this paper. Firstly, node coordinate matrix can be obtained by node distances which are different from distance matrix and row vectors of the matrix are regarded as coordinates of nodes. Then, cosine value between node coordinates is used as their similarity index. A local community density index LD is also proposed. Then, a series of CD-based indices include CD-LD-k, CD*LD-k, CD-k and CDI are presented and applied in ten real networks. Experimental results demonstrate the effectiveness of CD-based indices. The effects of network clustering coefficient and assortative coefficient on prediction accuracy of indices are analyzed. CD-LD-k and CD*LD-k can improve prediction accuracy without considering the assortative coefficient of network is negative or positive. According to analysis of relative precision of each method on each network, CD-LD-k and CD*LD-k indices have excellent average performance and robustness. CD and CD-k indices perform better on positive assortative networks than on negative assortative networks. For negative assortative networks, we improve and refine CD index, referred as CDI index, combining the advantages of CD index and evolutionary mechanism of the network model BA. Experimental results reveal that CDI index can increase prediction accuracy of CD on negative assortative networks.