Involvement in surface antigen expression by a moonlighting FG-repeat nucleoporin in trypanosomes

Components of the nuclear periphery coordinate a multitude of activities, including macromolecular transport, cell-cycle progression, and chromatin organization. Nuclear pore complexes (NPCs) mediate nucleocytoplasmic transport, mRNA processing, and transcriptional regulation, and NPC components can define regions of high transcriptional activity in some organisms at the nuclear periphery and nucleoplasm. Lineage-specific features underpin several core nuclear functions and in trypanosomatids, which branched very early from other eukaryotes, unique protein components constitute the lamina, kinetochores, and parts of the NPCs. Here we describe a phenylalanine-glycine (FG)-repeat nucleoporin, TbNup53b, that has dual localizations within the nucleoplasm and NPC. In addition to association with nucleoporins, TbNup53b interacts with a known trans-splicing component, TSR1, and has a role in controlling expression of surface proteins including the nucleolar periphery-located, procyclin genes. Significantly, while several nucleoporins are implicated in intranuclear transcriptional regulation in metazoa, TbNup53b appears orthologous to components of the yeast/human Nup49/Nup58 complex, for which no transcriptional functions are known. These data suggest that FG-Nups are frequently co-opted to transcriptional functions during evolution and extend the presence of FG-repeat nucleoporin control of gene expression to trypanosomes, suggesting that this is a widespread and ancient eukaryotic feature, as well as underscoring once more flexibility within nucleoporin function

Carbon Nanotube–Liposome Complexes in Hydrogels for Controlled Drug Delivery via Near-Infrared Laser Stimulation

Externally controllable drug delivery systems are crucial for a variety of biological applications where the dosage and timing of drug delivery need to be adjusted based on disease diagnosis and progression. Here, we have developed an externally controllable drug delivery system by combining three extensively used platforms: hydrogels, liposomes, and single-walled carbon nanotubes (SWCNTs). We have developed carbon nanotube–liposome complexes (CLCs) and incorporated these structures into a 3D alginate hydrogel for use as an optically controlled drug delivery system. The CLC structures were characterized by using a variety of imaging and spectroscopic techniques, and an optimal SWCNT/lipid ratio was selected. The optimal CLCs were loaded with a model drug (FITC-Dex), incorporated into a hydrogel, and their release profile was studied. It was shown that release of the drug cargo can be triggered by using an NIR laser stimulation tuned to the optical resonance of a particular SWCNT species. It was further shown that the amount of released cargo can be tuned by varying the NIR stimulation time. This system demonstrates the externally controlled delivery of drug cargo and can be used for different applications including cancer chemotherapy delivery

Effect of Opioid vs Nonopioid Medications on Pain-Related Function in Patients With Chronic Back Pain or Hip or Knee Osteoarthritis Pain

Importance: Limited evidence is available regarding long-term outcomes of opioids compared with nonopioid medications for chronic pain. Objective: To compare opioid vs nonopioid medications over 12 months on pain-related function, pain intensity, and adverse effects. Design, Setting, and Participants: Pragmatic, 12-month, randomized trial with masked outcome assessment. Patients were recruited from Veterans Affairs primary care clinics from June 2013 through December 2015; follow-up was completed December 2016. Eligible patients had moderate to severe chronic back pain or hip or knee osteoarthritis pain despite analgesic use. Of 265 patients enrolled, 25 withdrew prior to randomization and 240 were randomized. Interventions: Both interventions (opioid and nonopioid medication therapy) followed a treat-to-target strategy aiming for improved pain and function. Each intervention had its own prescribing strategy that included multiple medication options in 3 steps. In the opioid group, the first step was immediate-release morphine, oxycodone, or hydrocodone/acetaminophen. For the nonopioid group, the first step was acetaminophen (paracetamol) or a nonsteroidal anti-inflammatory drug. Medications were changed, added, or adjusted within the assigned treatment group according to individual patient response. Main Outcomes and Measures: The primary outcome was pain-related function (Brief Pain Inventory [BPI] interference scale) over 12 months and the main secondary outcome was pain intensity (BPI severity scale). For both BPI scales (range, 0-10; higher scores = worse function or pain intensity), a 1-point improvement was clinically important. The primary adverse outcome was medication-related symptoms (patient-reported checklist; range, 0-19). Results: Among 240 randomized patients (mean age, 58.3 years; women, 32 [13.0%]), 234 (97.5%) completed the trial. Groups did not significantly differ on pain-related function over 12 months (overall P = .58); mean 12-month BPI interference was 3.4 for the opioid group and 3.3 for the nonopioid group (difference, 0.1 [95% CI, -0.5 to 0.7]). Pain intensity was significantly better in the nonopioid group over 12 months (overall P = .03); mean 12-month BPI severity was 4.0 for the opioid group and 3.5 for the nonopioid group (difference, 0.5 [95% CI, 0.0 to 1.0]). Adverse medication-related symptoms were significantly more common in the opioid group over 12 months (overall P = .03); mean medication-related symptoms at 12 months were 1.8 in the opioid group and 0.9 in the nonopioid group (difference, 0.9 [95% CI, 0.3 to 1.5]). Conclusions and Relevance: Treatment with opioids was not superior to treatment with nonopioid medications for improving pain-related function over 12 months. Results do not support initiation of opioid therapy for moderate to severe chronic back pain or hip or knee osteoarthritis pain

Prevalence and correlates of negative side effects from vaping nicotine:Findings from the 2020 ITC four country smoking and vaping survey

IntroductionThis study examined prevalence and correlates of self-reported negative side effects from nicotine vaping product (NVP) use among people who currently or recently vape.MethodsThis cross-sectional study analysed data from 3906 adults (aged 18+ years) from the 2020 ITC Four Country Smoking and Vaping Survey (Canada, US, England and Australia) who reported they had ever smoked cigarettes and were either currently vaping daily/weekly or had vaped in the last month. Participants were asked about experiencing and seeking medical advice for any negative side effects from vaping in the past month. Logistic regressions were used to estimate prevalence and identify correlates.ResultsOverall, 87.1 % reported no negative side effects from vaping. The most common side effects were throat irritation (5.8 %), cough (5.5 %), and mouth irritation (4.1 %). The top two that led to seeking medical advice were: mouth irritation (46.8 %) and loss of taste (45.2 %). Those more likely to self-report side effects were younger, male, currently smoking (vs quit), vaping for &lt;6 months (vs &gt;1 year), using disposables or cartridges/pods (vs tanks), using vapes with nicotine (vs without nicotine), using menthol/mint flavour (vs sweet flavour), currently smoking (vs quit), believing vaping causes various diseases (e.g., heart disease), and believing that vaping is equally/more harmful than smoking.ConclusionNegative side effects associated with NVP use were rare and mainly minor in all four countries. Shorter duration of vaping, concurrent smoking while vaping and perceptions of greater vaping harms relative to smoking were associated with more reported negative side effects attributed to vaping.<p/

Machine learning-based mortality prediction models using national liver transplantation registries are feasible but have limited utility across countries

Many countries curate national registries of liver transplant (LT) data. These registries are often used to generate predictive models; however, potential performance and transferability of these models remain unclear. We used data from 3 national registries and developed machine learning algorithm (MLA)-based models to predict 90-day post-LT mortality within and across countries. Predictive performance and external validity of each model were assessed. Prospectively collected data of adult patients (aged ≥18 years) who underwent primary LTs between January 2008 and December 2018 from the Canadian Organ Replacement Registry (Canada), National Health Service Blood and Transplantation (United Kingdom), and United Network for Organ Sharing (United States) were used to develop MLA models to predict 90-day post-LT mortality. Models were developed using each registry individually (based on variables inherent to the individual databases) and using all 3 registries combined (variables in common between the registries [harmonized]). The model performance was evaluated using area under the receiver operating characteristic (AUROC) curve. The number of patients included was as follows: Canada, n = 1214; the United Kingdom, n = 5287; and the United States, n = 59,558. The best performing MLA-based model was ridge regression across both individual registries and harmonized data sets. Model performance diminished from individualized to the harmonized registries, especially in Canada (individualized ridge: AUROC, 0.74; range, 0.73-0.74; harmonized: AUROC, 0.68; range, 0.50-0.73) and US (individualized ridge: AUROC, 0.71; range, 0.70-0.71; harmonized: AUROC, 0.66; range, 0.66-0.66) data sets. External model performance across countries was poor overall. MLA-based models yield a fair discriminatory potential when used within individual databases. However, the external validity of these models is poor when applied across countries. Standardization of registry-based variables could facilitate the added value of MLA-based models in informing decision making in future LTs.</p

Machine learning-based mortality prediction models using national liver transplantation registries are feasible but have limited utility across countries

Many countries curate national registries of liver transplant (LT) data. These registries are often used to generate predictive models; however, potential performance and transferability of these models remain unclear. We used data from 3 national registries and developed machine learning algorithm (MLA)-based models to predict 90-day post-LT mortality within and across countries. Predictive performance and external validity of each model were assessed. Prospectively collected data of adult patients (aged ≥18 years) who underwent primary LTs between January 2008 and December 2018 from the Canadian Organ Replacement Registry (Canada), National Health Service Blood and Transplantation (United Kingdom), and United Network for Organ Sharing (United States) were used to develop MLA models to predict 90-day post-LT mortality. Models were developed using each registry individually (based on variables inherent to the individual databases) and using all 3 registries combined (variables in common between the registries [harmonized]). The model performance was evaluated using area under the receiver operating characteristic (AUROC) curve. The number of patients included was as follows: Canada, n = 1214; the United Kingdom, n = 5287; and the United States, n = 59,558. The best performing MLA-based model was ridge regression across both individual registries and harmonized data sets. Model performance diminished from individualized to the harmonized registries, especially in Canada (individualized ridge: AUROC, 0.74; range, 0.73-0.74; harmonized: AUROC, 0.68; range, 0.50-0.73) and US (individualized ridge: AUROC, 0.71; range, 0.70-0.71; harmonized: AUROC, 0.66; range, 0.66-0.66) data sets. External model performance across countries was poor overall. MLA-based models yield a fair discriminatory potential when used within individual databases. However, the external validity of these models is poor when applied across countries. Standardization of registry-based variables could facilitate the added value of MLA-based models in informing decision making in future LTs.</p

Associations of Cannabis Use, High-Risk Alcohol Use, and Depressive Symptomology with Motivation and Attempts to Quit Cigarette Smoking Among Adults: Findings from the 2020 ITC Four Country Smoking and Vaping Survey

This study assessed independent and interaction effects of the frequency of cannabis use, high-risk alcohol use, and depressive symptomology on motivation and attempts to quit cigarette smoking among adults who regularly smoked. Cross-sectional data are from the 2020 International Tobacco Control Four Country Smoking and Vaping Survey and included 7044 adults (ages 18 + years) who smoked cigarettes daily in Australia (n = 1113), Canada (n = 2069), England (n = 2444), and the United States (USA) (n = 1418). Among all respondents, 33.1% of adults reported wanting to quit smoking “a lot,” and 29.1% made a past-year quit attempt. Cannabis use was not significantly associated with either outcome (both p ≥ 0.05). High-risk alcohol use was significantly associated with decreased odds of motivation to quit (p = 0.02) and making a quit attempt (p = 0.004). Depressive symptomology was associated with increased odds for both outcomes (both p < 0.001). There were no significant 2- or 3-way interactions between cannabis use, alcohol consumption, and depressive symptomatology. Overall, just over a quarter of adults who smoked daily reported making a recent quit attempt, and most were not highly motivated to quit. Longitudinal research should investigate whether there are linkages between cannabis use, risky alcohol consumption, and/or depression on successful long-term smoking cessation

Machine learning-based mortality prediction models using national liver transplantation registries are feasible but have limited utility across countries

Many countries curate national registries of liver transplant (LT) data. These registries are often used to generate predictive models; however, potential performance and transferability of these models remain unclear. We used data from 3 national registries and developed machine learning algorithm (MLA)-based models to predict 90-day post-LT mortality within and across countries. Predictive performance and external validity of each model were assessed. Prospectively collected data of adult patients (aged ≥18 years) who underwent primary LTs between January 2008 and December 2018 from the Canadian Organ Replacement Registry (Canada), National Health Service Blood and Transplantation (United Kingdom), and United Network for Organ Sharing (United States) were used to develop MLA models to predict 90-day post-LT mortality. Models were developed using each registry individually (based on variables inherent to the individual databases) and using all 3 registries combined (variables in common between the registries [harmonized]). The model performance was evaluated using area under the receiver operating characteristic (AUROC) curve. The number of patients included was as follows: Canada, n = 1214; the United Kingdom, n = 5287; and the United States, n = 59,558. The best performing MLA-based model was ridge regression across both individual registries and harmonized data sets. Model performance diminished from individualized to the harmonized registries, especially in Canada (individualized ridge: AUROC, 0.74; range, 0.73-0.74; harmonized: AUROC, 0.68; range, 0.50-0.73) and US (individualized ridge: AUROC, 0.71; range, 0.70-0.71; harmonized: AUROC, 0.66; range, 0.66-0.66) data sets. External model performance across countries was poor overall. MLA-based models yield a fair discriminatory potential when used within individual databases. However, the external validity of these models is poor when applied across countries. Standardization of registry-based variables could facilitate the added value of MLA-based models in informing decision making in future LTs.</p