Tensions in primary care cancer detection: An embedded qualitative intervention development study.

BACKGROUND: UK cancer mortality is worse than many other high-income countries, partly due to diagnostic delays in primary care. AIM: To understand beliefs and behaviour of GPs, and systems of general practice teams, to inform the Think Cancer! intervention development. DESIGN AND SETTING: An embedded qualitative study guided by behaviour change models (COM-B and TDF) in primary care in Wales, UK. METHOD: Twenty qualitative, semi-structured telephone interviews with GPs and four face-to-face focus groups with practice teams. Analysis used Framework, results were mapped to multiple, overlapping components of COM-B and TDF. RESULTS: Three themes illustrate (1) complex, multi-level referral considerations facing GPs and practice teams, (2) external influences and constraints, (3) the role of practice systems and culture. Tensions emerged between individual considerations of GPs (Capability, Motivation) and context-dependent external pressures (Opportunity). Detecting cancer was guided not only by external requirements, but also by motivational factors GPs described as part of their cancer diagnostics process. External influences on the diagnosis process often resulted from the primary-secondary care interface and social pressures. GPs adapted their behaviour to deal with this disconnect. Positive practice culture and supportive practice-based systems ameliorated these tensions and complexity. CONCLUSION: By exploring individual GP behaviours together with practice systems and culture we contribute new understandings on how cancer diagnosis operates in primary care and how delays can be improved. We highlight commonly overlooked dynamics and tensions, experienced by GPs as a tension between individual decision-making (Capability, Motivation) and external considerations such as pressures in secondary care (Opportunity).

The cost of cosmetic surgery tourism complications to the NHS: A retrospective analysis.

INTRODUCTION: Medical tourism refers to the process of patients travelling outside of their native country to undergo elective surgical procedures and is a rapidly expanding healthcare phenomenon [1-3]. Whilst a multitude of established Private Healthcare Providers (PHPs) offer cosmetic surgical procedures within the United Kingdom (UK), a growing number of patients are opting to travel outside of the UK to undergo cosmetic surgery. AIM: To assess the number of patients presenting to the Canniesburn Plastic Surgery Unit, with cosmetic surgery tourism complications, from outside of the UK, and the associated costs to NHS Scotland over a five-year period. METHODS: A retrospective case review of a prospectively maintained trauma database, which records all acute referrals, was undertaken analysing patients referred from January 1st 2019 to December 31st 2023 inclusive. RESULTS: 81 patients presented over five years with complications of cosmetic surgery tourism. The most common presenting complaints were wound dehiscence (49.4%) or wound infection (24.7%). The total cost to NHS Scotland was £755,559.68 with an average of £9327.90 per patient. CONCLUSION: This is the largest single centre cohort of cosmetic surgery tourism complications reported within the NHS to date; with rates on the rise, demand grows for increased patient information regarding healthcare tourism risks, a national consensus on the extent of NHS management and urgent international collaboration with policymakers is required to address this issue across borders.

Mis-spliced transcripts generate de novo proteins in TDP-43-related ALS/FTD.

Functional loss of TDP-43, an RNA binding protein genetically and pathologically linked to amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), leads to the inclusion of cryptic exons in hundreds of transcripts during disease. Cryptic exons can promote the degradation of affected transcripts, deleteriously altering cellular function through loss-of-function mechanisms. Here, we show that mRNA transcripts harboring cryptic exons generated de novo proteins in TDP-43-depleted human iPSC-derived neurons in vitro, and de novo peptides were found in cerebrospinal fluid (CSF) samples from patients with ALS or FTD. Using coordinated transcriptomic and proteomic studies of TDP-43-depleted human iPSC-derived neurons, we identified 65 peptides that mapped to 12 cryptic exons. Cryptic exons identified in TDP-43-depleted human iPSC-derived neurons were predictive of cryptic exons expressed in postmortem brain tissue from patients with TDP-43 proteinopathy. These cryptic exons produced transcript variants that generated de novo proteins. We found that the inclusion of cryptic peptide sequences in proteins altered their interactions with other proteins, thereby likely altering their function. Last, we showed that 18 de novo peptides across 13 genes were present in CSF samples from patients with ALS/FTD spectrum disorders. The demonstration of cryptic exon translation suggests new mechanisms for ALS/FTD pathophysiology downstream of TDP-43 dysfunction and may provide a potential strategy to assay TDP-43 function in patient CSF.

Cholestasis is associated with a higher rate of complications in both medical and surgical necrotizing enterocolitis.

OBJECTIVE: To evaluate the relationship between cholestasis and outcomes in medical and surgical necrotizing enterocolitis (NEC). STUDY DESIGN: A retrospective analysis of prospectively collected data from 1472 infants with NEC [455 medical (mNEC) and 1017 surgical (sNEC)] from the Children's Hospital Neonatal Database. RESULTS: The prevalence of cholestasis was lower in mNEC versus sNEC (38.2% vs 70.1%, p < 0.001). In both groups, cholestasis was associated with lower birth gestational age [mNEC: OR 0.79 (95% CI 0.68-0.92); sNEC: OR 0.86 (95% CI 0.79-0.95)] and increased days of parenteral nutrition [mNEC: OR 1.08 (95% CI 1.04-1.13); sNEC: OR 1.01 (95% CI 1.01-1.02)]. For both groups, the highest direct bilirubin was associated with the composite outcome mortality or length of stay >75th percentile [mNEC: OR 1.21 (95% CI 1.06-1.38); sNEC: OR 1.06 (95% CI 1.03-1.09)]. CONCLUSION: Cholestasis with both medical NEC and surgical NEC is associated with adverse patient outcomes including increased mortality or extreme length of stay.

Mortality risk from United States coal electricity generation.

Policy-makers seeking to limit the impact of coal electricity-generating units (EGUs, also known as power plants) on air quality and climate justify regulations by quantifying the health burden attributable to exposure from these sources. We defined "coal PM2.5" as fine particulate matter associated with coal EGU sulfur dioxide emissions and estimated annual exposure to coal PM2.5 from 480 EGUs in the US. We estimated the number of deaths attributable to coal PM2.5 from 1999 to 2020 using individual-level Medicare death records representing 650 million person-years. Exposure to coal PM2.5 was associated with 2.1 times greater mortality risk than exposure to PM2.5 from all sources. A total of 460,000 deaths were attributable to coal PM2.5, representing 25% of all PM2.5-related Medicare deaths before 2009 and 7% after 2012. Here, we quantify and visualize the contribution of individual EGUs to mortality.

A fully automated FAIMS-DIA mass spectrometry-based proteomic pipeline.

Here, we present a standardized, "off-the-shelf" proteomics pipeline working in a single 96-well plate to achieve deep coverage of cellular proteomes with high throughput and scalability. This integrated pipeline streamlines a fully automated sample preparation platform, a data-independent acquisition (DIA) coupled with high-field asymmetric waveform ion mobility spectrometer (FAIMS) interface, and an optimized library-free DIA database search strategy. Our systematic evaluation of FAIMS-DIA showing single compensation voltage (CV) at -35 V not only yields the deepest proteome coverage but also best correlates with DIA without FAIMS. Our in-depth comparison of direct-DIA database search engines shows that Spectronaut outperforms others, providing the highest quantifiable proteins. Next, we apply three common DIA strategies in characterizing human induced pluripotent stem cell (iPSC)-derived neurons and show single-shot mass spectrometry (MS) using single-CV (-35 V)-FAIMS-DIA results in >9,000 quantifiable proteins with <10% missing values, as well as superior reproducibility and accuracy compared with other existing DIA methods.

The short-chain fatty acid acetate coordinates with CD30 to modulate T-cell survival.

As an important substrate for cell metabolism, the short-chain fatty acid acetate emerges as a regulator of cell fate and function. However, its role in T-cell survival and its underlying mechanisms remain largely unknown. Here, we demonstrate that acetate modulates T-cell apoptosis via potentiation of α-tubulin acetylation. We further show that acetate treatment effectively increases the expression of the tumor necrosis factor receptor (TNFR) family member CD30 by enhancing its gene transcription. Moreover, CD30 physically associates with and stabilizes the deacetylase HDAC6, which deacetylates α-tubulin to decrease microtubule stability. Proteomic profiling of CD30 knockout (Cd30-/-) T-cells reveals elevated expression of anti-apoptotic BCL2 family proteins and thus promotes T-cell survival via a microtubule-Bcl-2 axis. Taken together, our results demonstrate that acetate is a regulator of T-cell survival by controlling levels of acetylated α-tubulin. This suggests that therapeutic manipulation of acetate metabolism may facilitate optimal T-cell responses in pathological conditions.

The autism-associated loss of δ-catenin functions disrupts social behavior.

δ-catenin is expressed in excitatory synapses and functions as an anchor for the glutamatergic AMPA receptor (AMPAR) GluA2 subunit in the postsynaptic density. The glycine 34 to serine (G34S) mutation in the δ-catenin gene has been found in autism spectrum disorder (ASD) patients and results in loss of δ-catenin functions at excitatory synapses, which is presumed to underlie ASD pathogenesis in humans. However, how the G34S mutation causes loss of δ-catenin functions to induce ASD remains unclear. Here, using neuroblastoma cells, we identify that the G34S mutation increases glycogen synthase kinase 3ß (GSK3ß)-dependent δ-catenin degradation to reduce δ-catenin levels, which likely contributes to the loss of δ-catenin functions. Synaptic δ-catenin and GluA2 levels in the cortex are significantly decreased in mice harboring the δ-catenin G34S mutation. The G34S mutation increases glutamatergic activity in cortical excitatory neurons while it is decreased in inhibitory interneurons, indicating changes in cellular excitation and inhibition. δ-catenin G34S mutant mice also exhibit social dysfunction, a common feature of ASD. Most importantly, pharmacological inhibition of GSK3ß activity reverses the G34S-induced loss of δ-catenin function effects in cells and mice. Finally, using δ-catenin knockout mice, we confirm that δ-catenin is required for GSK3ß inhibition-induced restoration of normal social behavior in δ-catenin G34S mutant animals. Taken together, we reveal that the loss of δ-catenin functions arising from the ASD-associated G34S mutation induces social dysfunction via alterations in glutamatergic activity and that GSK3ß inhibition can reverse δ-catenin G34S-induced synaptic and behavioral deficits.

Cases in Precision Medicine: Is There an Obligation to Return Reinterpreted Genetic Results to Former Patients?

Interpretation of many genetic test results can change over time as new data accumulate. Hence, physicians who order genetic tests may subsequently receive revised reports with important implications for patients' medical treatment-even for patients who are no longer in their care. Several of the ethical principles underlying medical practice suggest an obligation to reach out to former patients with this information. Discharging that obligation can be accomplished, at a minimum, by attempting to contact the former patient with their last known contact information.

Mis-spliced transcripts generate de novo proteins in TDP-43-related ALS/FTD.

Functional loss of TDP-43, an RNA-binding protein genetically and pathologically linked to ALS and FTD, leads to inclusion of cryptic exons in hundreds of transcripts during disease. Cryptic exons can promote degradation of affected transcripts, deleteriously altering cellular function through loss-of-function mechanisms. However, the possibility of de novo protein synthesis from cryptic exon transcripts has not been explored. Here, we show that mRNA transcripts harboring cryptic exons generate de novo proteins both in TDP-43 deficient cellular models and in disease. Using coordinated transcriptomic and proteomic studies of TDP-43 depleted iPSC-derived neurons, we identified numerous peptides that mapped to cryptic exons. Cryptic exons identified in iPSC models were highly predictive of cryptic exons expressed in brains of patients with TDP-43 proteinopathy, including cryptic transcripts that generated de novo proteins. We discovered that inclusion of cryptic peptide sequences in proteins altered their interactions with other proteins, thereby likely altering their function. Finally, we showed that these de novo peptides were present in CSF from patients with ALS. The demonstration of cryptic exon translation suggests new mechanisms for ALS pathophysiology downstream of TDP-43 dysfunction and may provide a strategy for novel biomarker development.

RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple self-cleaving ribozymes.

Self-cleaving ribozymes are RNA molecules that catalyze the cleavage of their own phosphodiester backbones. These ribozymes are found in all domains of life and are also a tool for biotechnical and synthetic biology applications. Self-cleaving ribozymes are also an important model of sequence-to-function relationships for RNA because their small size simplifies synthesis of genetic variants and self-cleaving activity is an accessible readout of the functional consequence of the mutation. Here, we used a high-throughput experimental approach to determine the relative activity for every possible single and double mutant of five self-cleaving ribozymes. From this data, we comprehensively identified non-additive effects between pairs of mutations (epistasis) for all five ribozymes. We analyzed how changes in activity and trends in epistasis map to the ribozyme structures. The variety of structures studied provided opportunities to observe several examples of common structural elements, and the data was collected under identical experimental conditions to enable direct comparison. Heatmap-based visualization of the data revealed patterns indicating structural features of the ribozymes including paired regions, unpaired loops, non-canonical structures, and tertiary structural contacts. The data also revealed signatures of functionally critical nucleotides involved in catalysis. The results demonstrate that the data sets provide structural information similar to chemical or enzymatic probing experiments, but with additional quantitative functional information. The large-scale data sets can be used for models predicting structure and function and for efforts to engineer self-cleaving ribozymes.

The autism-associated loss of δ-catenin functions disrupts social behaviors.

δ-catenin is expressed in excitatory synapses and functions as an anchor for the glutamatergic AMPA receptor (AMPAR) GluA2 subunit in the postsynaptic density. The glycine 34 to serine (G34S) mutation in the δ-catenin gene is found in autism spectrum disorder (ASD) patients and induces loss of δ-catenin functions at excitatory synapses, which is presumed to underlie ASD pathogenesis in humans. However, how the G34S mutation causes loss of δ-catenin functions to induce ASD remains unclear. Here, using neuroblastoma cells, we discover that the G34S mutation generates an additional phosphorylation site for glycogen synthase kinase 3ß (GSK3ß). This promotes δ-catenin degradation and causes the reduction of δ-catenin levels, which likely contributes to the loss of δ-catenin functions. Synaptic δ-catenin and GluA2 levels in the cortex are significantly decreased in mice harboring the δ-catenin G34S mutation. The G34S mutation increases glutamatergic activity in cortical excitatory neurons while it is decreased in inhibitory interneurons, indicating changes in cellular excitation and inhibition. δ-catenin G34S mutant mice also exhibit social dysfunction, a common feature of ASD. Most importantly, inhibition of GSK3ß activity reverses the G34S-induced loss of δ-catenin function effects in cells and mice. Finally, using δ-catenin knockout mice, we confirm that δ-catenin is required for GSK3ß inhibition-induced restoration of normal social behaviors in δ-catenin G34S mutant animals. Taken together, we reveal that the loss of δ-catenin functions arising from the ASD-associated G34S mutation induces social dysfunction via alterations in glutamatergic activity and that GSK3ß inhibition can reverse δ-catenin G34S-induced synaptic and behavioral deficits. Significance Statement: δ-catenin is important for the localization and function of glutamatergic AMPA receptors at synapses in many brain regions. The glycine 34 to serine (G34S) mutation in the δ-catenin gene is found in autism patients and results in the loss of δ-catenin functions. δ-catenin expression is also closely linked to other autism-risk genes involved in synaptic structure and function, further implying that it is important for the autism pathophysiology. Importantly, social dysfunction is a key characteristic of autism. Nonetheless, the links between δ-catenin functions and social behaviors are largely unknown. The significance of the current research is thus predicated on filling this gap by discovering the molecular, cellular, and synaptic underpinnings of the role of δ-catenin in social behaviors.

Earlier cancer diagnosis in primary care: a feasibility economic analysis of ThinkCancer!

BACKGROUND: UK cancer survival rates are much lower compared with other high-income countries. In primary care, there are opportunities for GPs and other healthcare professionals to act more quickly in response to presented symptoms that might represent cancer. ThinkCancer! is a complex behaviour change intervention aimed at primary care practice teams to improve the timely diagnosis of cancer. AIM: To explore the costs of delivering the ThinkCancer! intervention to expedite cancer diagnosis in primary care. DESIGN & SETTING: Feasibility economic analysis using a micro-costing approach, which was undertaken in 19 general practices in Wales, UK. METHOD: From an NHS perspective, micro-costing methodology was used to determine whether it was feasible to gather sufficient economic data to cost the ThinkCancer! INTERVENTION: Owing to the COVID-19 pandemic, ThinkCancer! was mainly delivered remotely online in a digital format. Budget impact analysis (BIA) and sensitivity analysis were conducted to explore the costs of face-to-face delivery of the ThinkCancer! intervention as intended pre-COVID-19. RESULTS: The total costs of delivering the ThinkCancer! intervention across 19 general practices in Wales was £25 030, with an average cost per practice of £1317 (standard deviation [SD]: 578.2). Findings from the BIA indicated a total cost of £34 630 for face-to-face delivery. CONCLUSION: Data collection methods were successful in gathering sufficient health economics data to cost the ThinkCancer! INTERVENTION: Results of this feasibility study will be used to inform a future definitive economic evaluation alongside a pragmatic randomised controlled trial (RCT).

Mechanistic forecasts of species responses to climate change: The promise of biophysical ecology.

A core challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models based on biophysical ecology have a long history of development and application, their use in global change biology remains limited despite their enormous promise and increasingly accessible software. We contend that greater understanding and training in the theory and methods of biophysical ecology is vital to expand their application. Our review shows how biophysical models can be implemented to understand and predict climate change impacts on species' behavior, phenology, survival, distribution, and abundance. It also illustrates the types of outputs that can be generated, and the data inputs required for different implementations. Examples range from simple calculations of body temperature at a particular site and time, to more complex analyses of species' distribution limits based on projected energy and water balances, accounting for behavior and phenology. We outline challenges that currently limit the widespread application of biophysical models relating to data availability, training, and the lack of common software ecosystems. We also discuss progress and future developments that could allow these models to be applied to many species across large spatial extents and timeframes. Finally, we highlight how biophysical models are uniquely suited to solve global change biology problems that involve predicting and interpreting responses to environmental variability and extremes, multiple or shifting constraints, and novel abiotic or biotic environments.

Patterns of lipid-injectable emulsion use in neonatal intensive care units across the United States: A multi-institution survey.

BACKGROUND: Lipid-injectable emulsions (ILEs) are a necessity for neonates dependent on parenteral nutrition (PN). In this manuscript, we describe the patterns of ILE use in neonatal intensive care units (NICUs) in the United States (US). METHODS: An electronic survey was sent to 488 NICUs across the US between December 2020 and March 2021. Survey fields included availability and utilization of various ILE in neonates. RESULTS: The response rate was 22% (107 out of 488). Soybean oil ILE (SO-ILE) and soybean oil, medium-chain triglycerides, olive oil, fish oil ILE (SO, MCT, OO, FO-ILE) had similar availability (87% vs 86%, respectively), and SO, MCT, OO, FO-ILE was more commonly used (SO-ILE, 71% vs SO, MCT, OO, FO-ILE, 86%). Fish oil-ILE (FO-ILE) was used by 55% of centers. SO-ILE was most frequently used with PN and needs <4 weeks without cholestasis (79%). The most common reason for SO, MCT, OO, FO-ILE use was cholestasis (71%). ILE minimization was used by 28% of SO-ILE and 22% of SO, MCT, OO, FO-ILE users; 95% of these centers restrict SO, MCT, OO, FO-ILE to doses ≤2 g/kg/day. Twenty-two percent of centers started FO-ILE at direct bilirubin of >5 mg/dl. CONCLUSION: The results of this survey reveal significant variability in ILE usage across the US. Lipid minimization with SO, MCT, OO, FO-ILE and initiation of FO-ILE for cholestasis at higher bilirubin thresholds are prevalent. Such reports are crucial for a better understanding of ILE use in the NICU and in future ILE development.

ProtPipe: A Multifunctional Data Analysis Pipeline for Proteomics and Peptidomics.

Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, personalized medicine, systems biology and biomedical applications. By combining MS with different proteomics approaches such as immunopurification MS, immunopeptidomics, and total protein proteomics, researchers can gain insights into protein-protein interactions, immune responses, cellular processes, and disease mechanisms. The application of MS-based proteomics in these areas continues to advance our understanding of protein function, cellular signaling, and complex biological systems. Data analysis for mass spectrometry is a critical process that includes identifying and quantifying proteins and peptides and exploring biological functions for these proteins in downstream analysis. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analysis. ProtPipe provides downstream analysis including identifying differential abundance proteins and peptides, pathway enrichment analysis, protein-protein interaction analysis, and MHC1-peptide binding affinity. ProtPipe generates annotated tables and diagnostic visualizations from statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. ProtPipe is well-documented open-source software and is available at https://github.com/NIH-CARD/ProtPipe , accompanied by a web interface.

Predicting higher-order mutational effects in an RNA enzyme by machine learning of high-throughput experimental data.

Ribozymes are RNA molecules that catalyze biochemical reactions. Self-cleaving ribozymes are a common naturally occurring class of ribozymes that catalyze site-specific cleavage of their own phosphodiester backbone. In addition to their natural functions, self-cleaving ribozymes have been used to engineer control of gene expression because they can be designed to alter RNA processing and stability. However, the rational design of ribozyme activity remains challenging, and many ribozyme-based systems are engineered or improved by random mutagenesis and selection (in vitro evolution). Improving a ribozyme-based system often requires several mutations to achieve the desired function, but extensive pairwise and higher-order epistasis prevent a simple prediction of the effect of multiple mutations that is needed for rational design. Recently, high-throughput sequencing-based approaches have produced data sets on the effects of numerous mutations in different ribozymes (RNA fitness landscapes). Here we used such high-throughput experimental data from variants of the CPEB3 self-cleaving ribozyme to train a predictive model through machine learning approaches. We trained models using either a random forest or long short-term memory (LSTM) recurrent neural network approach. We found that models trained on a comprehensive set of pairwise mutant data could predict active sequences at higher mutational distances, but the correlation between predicted and experimentally observed self-cleavage activity decreased with increasing mutational distance. Adding sequences with increasingly higher numbers of mutations to the training data improved the correlation at increasing mutational distances. Systematically reducing the size of the training data set suggests that a wide distribution of ribozyme activity may be the key to accurate predictions. Because the model predictions are based only on sequence and activity data, the results demonstrate that this machine learning approach allows readily obtainable experimental data to be used for RNA design efforts even for RNA molecules with unknown structures. The accurate prediction of RNA functions will enable a more comprehensive understanding of RNA fitness landscapes for studying evolution and for guiding RNA-based engineering efforts.