GNS Healthcare (United States)

Although threats against journalists are commonly understood as discrete acts of intimidation, their enduring role in restructuring everyday newswork remains under-theorized. This article introduces the concept of post-threat journalism to explain how intimidation persists as an ambient structuring condition that reorganizes journalistic practice long after the initial threat. Drawing on in-depth interviews with 12 journalists in Mindanao, the Philippines, the study shows that threats do not simply silence reporting but reshape how journalism is lived and sustained. Journalists adapt through collateral risk management involving family protection, continuous anticipatory vigilance, editorial self-governance, and economic improvisation. These adaptations enable journalism to continue, but in structurally altered forms governed by individualized survival strategies rather than institutional protection. Conceptually, the study advances existing frameworks by demonstrating that threat operates not only as an episodic disruption but as an enduring force that restructures journalism as labor, practice, and lived experience. Empirically, it reveals how press freedom gradually erodes not only through overt repression but through the normalization of vulnerability and anticipatory restraint embedded in routine newswork.

Diabetic wounds are typically difficult to heal. They are usually characterized by prolonged healing time and increased susceptibility to bacterial infection. Therefore, altering the wound microenvironment and improving antibacterial property are effective treatment strategies. In this study, a plant hydrogel with antimicrobial activity and pro-healing properties was designed to integrate silver nanoparticles (AgNPs) with antimicrobial activity into the natural Tofu Chai (Premna microphylla Turcz, PMT) hydrogel, which exhibits strong pro-healing ability and antibacterial infections on the wound surface. In vitro experiments showed that AgNPs-PMT had a significant killing effect on Methicillin-resistant Staphylococcus aureus (MRSA), with an antibacterial efficiency reaching 95.6%. In vivo results showed that AgNPs-PMT efficiently cleared bacteria at the wound site to promote the formation of neovascularization, collagen and granulation tissue, and facilitated wound healing in a diabetic wound model with MRSA infection. On the 11th day, the wound area was only 5.4% of its original size. Overall, AgNPs-PMT demonstrated favorable antibacterial effects against MRSA and showed great potential in the treatment of chronic diabetic wounds.

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients does not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involves assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post-challenge analysis identified a novel expression-based risk marker, PHF19, which has recently been found to have an important biological role in multiple myeloma. Lastly, we show that a simple four feature predictor composed of age, ISS, and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.

Abstract While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients do not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involve assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post challenge analysis identified a novel expression-based risk marker and histone modifier,PHF19, which featured prominently in several independent models. Lastly, we show that a simple four feature predictor composed of age, International Staging System stage (ISS), and expression ofPHF19andMMSETperforms similarly to more complex models with many more gene expression features included.Key pointsMost comprehensive and unbiased assessment of prognostic biomarkers in MM resulting in a robust and parsimonious model.Identification ofPHF19as the expression based biomarker most strongly associated with rapid progression in MM patients.

Abstract Background: In the era of personalized medicine, a major challenge is harnessing longitudinal data across the cancer care continuum, which includes multimodal data sets of biologic, molecular, and clinical information about patients (pts) and their tumors. There is a growing need for new computing analytics, such as machine learning–an important tool in healthcare bio-informatics. We report our approach to building cancer disease models in an unbiased manner through utilization of a causal machine learning and simulation platform. Methods: The Swedish Cancer Institute (SCI) Personalized Medicine Research Program (PMRP) is a prospective registration protocol with the objective of establishing a centralized longitudinal, molecular, and phenotypic data repository. Since 2014, over 1,030 pts have been enrolled, having undergone next-generation sequencing (NGS) profiling of their tumors. Of these pts, we identified 100 breast cancer pts who also have detailed longitudinal clinical annotation within our SCI Breast Cancer Registry. All de-identified data, variables, and data points in the multimodal data types are integrated into normalized data frames to include demographics, cancer risks, tumor specifications, tumor sequencing, initial and subsequent cancer treatments, and outcomes data. A reverse engineering approach, via the Reverse Engineering and Forward Simulation (REFS) platform, is being utilized, focusing on discovering the complex causal mechanisms that determine which therapies will produce the best outcomes for an individual pt. This method goes beyond traditional approaches that rely on data correlations to match treatments to pts. The breast cancer causal model uncovers many of the possible combinations of causal relationships that drive outcomes and enables “what if?” simulations of a variety of interventions, across pts, to determine optimal therapies. Performance metrics and model robustness will be explored using a stratified, n-fold (e.g., 10-fold) cross-validation procedure, which is designed to provide an unbiased estimate of model generalization to new observations. Results: The causal model and simulations can elevate the providers' abilities to better understand treatment responses based on pts' unique clinical data and mutational statuses; study different treatment options to optimize management; and understand the complex interactions among variables that lead to a range of treatment outcomes. Conclusions: Knowledge generated from the simulations of the disease model can potentially streamline and support the clinical decision-making process, to include molecular tumor board deliberations, and ultimately assist providers in arriving at optimal treatment recommendations for pts. Citation Format: Henry Kaplan, Anna Berry, Kristine Rinn, Erin Ellis, George Birchfield, Tanya Wahl, Xiaoyu Liu, Mariko Tameishi, J D. Beatty, Patricia Dawson, Vivek Mehta, Anna Holman, Mary Atwood, Shlece Alexander, Candy Bonham, Lauren Summers, Iya Khalil, Boris Hayete, Diane Wuest, Wei Zheng, Yuhang Liu, Xulong Wang, Thomas David Brown. Machine learning approach to personalized medicine in breast cancer patients: Development of data-driven, personalized, causal modeling through identification and understanding of optimal treatments for predicting better disease outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5299.

BackgroundNonadherence to antipsychotic treatment increases the likelihood of relapse and progressive symptomatology in patients with schizophrenia. Atypical long-acting injectables, including paliperidone palmitate (PP), may increase adherence and improve symptoms. This study compared and assessed predictors of treatment patterns and symptom remission among schizophrenia patients treated with PP versus atypical oral antipsychotic therapy (OAT) in community behavioral health organizations (CBHOs).MethodsThis retrospective cohort analysis evaluated 763 patients with schizophrenia and new (PP-N; N = 174) or continuing (PP-C; N = 308) users of PP, or new users of OAT (N = 281) at enrollment in the REACH-OUT study (2010–2013). Treatment outcomes assessed at 1 year were discontinuation, and adherence, measured by proportion of days covered (PDC) or medication possession ratio (MPR). Remission status was assessed using the Structured Clinical Interview for Symptoms of Remission (SCI-SR). A machine learning platform, Reverse Engineering and Forward Simulation (REFS™), was used to identify predictors of study outcomes. Multivariate Cox and generalized linear regressions estimated the adjusted hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals.ResultsAmong PP-N users, 27% discontinued their initial treatment regimen versus 51% (p < 0.001) of OAT users. PP-N (vs OAT; HR = 0.49 [0.31–0.76]) users and males (HR = 0.65 [0.46–0.92]) had significantly lower rates of discontinuation. Relative to OAT, PP-N had a 36% [31%–42%] higher MPR and a 10-fold increased achievement of PDC ≥80% (OR = 10.46 [5.72–19.76]). PP users were significantly more likely to achieve remission in follow-up (PP-N vs OAT: OR = 2.65 [1.39–5.05]; PP-C vs OAT: OR = 1.83 [1.03–3.25]).ConclusionsRelative to OAT, PP was associated with improved adherence, less frequent treatment discontinuation, and improved symptom remission in this CBHO study population.