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Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group.
Methods
T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9).
Results
PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges’ g = 0.22, pcorrected = .001), and smaller cerebellar WM volume (peak Hedges’ g = 0.14, pcorrected = .008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum (pcorrected = .003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients (pcorrected = .001).
Conclusions
PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.
Routine immunization programs may reduce antibiotic use, but few studies have comprehensively examined their impact on antibiotic utilization. We aimed to explore temporal trends in vaccination and antibiotic use among young children in the United States.
Design:
Ecological study using the Merative® MarketScan Commercial Claims and Encounters database.
Methods:
We analyzed claims data on pediatric vaccine uptake (pneumococcal conjugate, Haemophilus influenzae type b, diphtheria-tetanus-pertussis, and influenza) and antibiotic prescriptions and antibiotic-treated respiratory tract infections among US children <5 years during 2000–2019. Vaccination status was assessed annually, and children were categorized based on receipt of all four vaccines, 1–3 vaccines, or no vaccines. Antibiotic prescriptions were classified by spectrum and drug class. Respiratory infections included otitis media, pharyngitis, pneumonia, sinusitis, and viral infections.
Results:
Among 6.7 million children, vaccine uptake increased from 32.5% receiving all four vaccines in 2004 to 66.8% in 2019. During this period, overall antibiotic prescriptions decreased from 1.89 to 1.01 per person-year, with the greatest reductions in macrolides (73.3%) and broad-spectrum antibiotics (57.0%). Antibiotic-treated respiratory tract infections declined from 2.43 to 1.61 episodes per person-year, with the largest decreases in sinusitis (64.7%) and pharyngitis (39.8%).
Conclusions:
The findings suggest a temporal association between routine childhood immunization uptake and reduced antibiotic utilization. Although immunization programs are primarily aimed at protecting children from vaccine-preventable diseases, their potential role in complementing antimicrobial stewardship efforts and other factors influencing antibiotic reduction warrants further investigation through more rigorous study designs.
Background: Meningiomas are the most common intracranial tumors. Radiotherapy (RT) serves as an adjunct following surgical resection; however, response varies. RTOG-0539 is a prospective, phase 2, trial that stratified patients risk groups based on clinical and pathological criteria, providing key benchmarks for RT outcomes. This is the first study that aims to characterize the molecular landscape of an RT clinical trial in meningiomas. Methods: Tissue from 100 patients was analyzed using DNA methylation, RNA sequencing, and whole-exome sequencing. Copy number variations and mutational profiles were assessed to determine associations with meningioma aggressiveness. Tumors were molecularly classified and pathway analyses were conducted to identify biological processes associated with RT response. Results: High-risk meningiomas exhibited cell cycle dysregulation and hypermetabolic pathway upregulation. 1p loss and 1q gain were more frequent in aggressive meningiomas, and NF2 and non-NF2 mutations co-occurred in some high-risk tumors. Molecular findings led to the reclassification of several cases, highlighting the limitations of histopathologic grading alone. Conclusions: This is the first study to comprehensively characterize the molecular landscape of any RT trial in meningioma, integrating multi-omic data to refine treatment stratification. Findings align with ongoing genomically driven meningioma clinical trials and underscore the need for prospective tissue banking to enhance biomarker-driven treatment strategies.
Background: The WHO grade of meningioma was updated in 2021 to include homozygous deletions of CDKN2A/B and TERT promotor mutations. Previous work including the recent cIMPACT-NOW statement have discussed the potential value of including chromosomal copy number alterations to help refine the current grading system. Methods: Chromosomal copy number profiles were inferred from from 1964 meningiomas using DNA methylation. Regularized Cox regresssion was used to identify CNAs independenly associated with post-surgical and post-RT PFS. Outcomes were stratified by WHO grade and novel CNAs to assess their potential value in WHO critiera. Results: Patients with WHO grade 1 tumours and chromosome 1p loss had similar outcomes to those with WHO grade 2 tumours (median PFS 5.83 [95% CI 4.36-Inf] vs 4.48 [4.09-5.18] years). Those with chromosome 1p loss and 1q gain had similar outcomes to those with WHO grade 3 cases regardless of initial grade (median PFS 2.23 [1.28-Inf] years WHO grade 1, 1.90 [1.23-2.25] years WHO grade 2, compared to 2.27 [1.68-3.05] years in WHO grade 3 cases overall). Conclusions: We advocate for chromosome 1p loss being added as a criterion for a CNS WHO grade of 2 meningioma and addition of 1q gain as a criterion for a CNS WHO grade of 3.
Background: We previously developed a DNA methylation-based risk predictor for meningioma, which has been used locally in a prospective fashion. As a follow-up, we validate this model using a large prospective cohort and introduce a streamlined next-generation model compatible with newer methylation arrays. Methods: The performance of our next-generation predictor was compared with our original model and standard-of-care 2021 WHO grade using time-dependent receiver operating characteristic curves. A nomogram was generated by incorporating our methylation predictor with WHO grade and extent of resection. Results: A total of 1347 meningioma cases were utilized in the study, including 469 prospective cases from 3 institutions and a retrospective cohort of 100 WHO grade 2 cases for model validation. Both the original and next-generation models significantly outperformed 2021 WHO grade in predicting postoperative recurrence. Dichotomizing into grade-specific risk subgroups was predictive of outcome within both WHO grades 1 and 2 tumours (log-rank p<0.05). Multivariable Cox regression demonstrated benefit of adjuvant radiotherapy in high-risk cases specifically, reinforcing its informative role in clinical decision making. Conclusions: This next-generation DNA methylation-based meningioma outcome predictor significantly outperforms 2021 WHO grading in predicting time to recurrence. This will help improve prognostication and inform patient selection for RT.
Background: Meningiomas exhibit considerable heterogeneity. We previously identified four distinct molecular groups (immunogenic, NF2-wildtype, hypermetabolic, proliferative) which address much of this heterogeneity. Despite their utility, the stochasticity of clustering methods and the requirement of multi-omics data limits the potential for classifying cases in the clinical setting. Methods: Using an international cohort of 1698 meningiomas, we constructed and validated a machine learning-based molecular classifier using DNA methylation alone. Original and newly-predicted molecular groups were compared using DNA methylation, RNA sequencing, whole exome sequencing, and clinical outcomes. Results: Group-specific outcomes in the validation cohort were nearly identical to those originally described, with median PFS of 7.4 (4.9-Inf) years in hypermetabolic tumors and 2.5 (2.3-5.3) years in proliferative tumors (not reached in the other groups). Predicted NF2-wildtype cases had no NF2 mutations, and 51.4% had others mutations previously described in this group. RNA pathway analysis revealed upregulation of immune-related pathways in the immunogenic group, metabolic pathways in the hypermetabolic group and cell-cycle programs in the proliferative group. Bulk deconvolution similarly revealed enrichment of macrophages in immunogenic tumours and neoplastic cells in hypermetabolic/proliferative tumours. Conclusions: Our DNA methylation-based classifier faithfully recapitulates the biology and outcomes of the original molecular groups allowing for their widespread clinical implementation.
Background: The combination of PARP inhibitor and immune checkpoint inhibitors have been proposed as a potentially synergistic combinatorial treatment in IDH mutant glioma, targeting dysregulated homologous recombination repair pathways. This study analyzed the cell-free DNA methylome of patients in a phase 2 trial using the PARP inhibitor Olaparib and the PD-1 inhibitor Durvalumab. Methods: Patients with recurrent high-grade IDH-mutant gliomas were enrolled in a phase II open-label study (NCT03991832). Serum was collected at baseline and monthly and cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) was performed. Binomial GLMnet models were developed and model performance was assessed using validation set data. Results: 29 patients were enrolled between 2020–2023. Patients received olaparib 300mg twice daily and durvalumab 1500mg IV every 4 weeks. The overall response rate was 10% via RANO criteria. 144 plasma samples were profiled with cfMeDIP-seq along with 30 healthy controls. The enriched circulating tumour DNA methylome during response periods exhibited a highly specific signature, accurately discriminating response versus failure (AUC 0.98 ± 0.03). Additionally, samples that were taken while on treatment were able to be discriminated from samples off therapy (AUC 0.74 ± 0.11). Conclusions: The cell-free plasma DNA methylome exhibits highly specific signatures that enable accurate prediction of response to therapy.
Blast injuries can occur by a multitude of mechanisms, including improvised explosive devices (IEDs), military munitions, and accidental detonation of chemical or petroleum stores. These injuries disproportionately affect people in low- and middle-income countries (LMICs), where there are often fewer resources to manage complex injuries and mass-casualty events.
Study Objective:
The aim of this systematic review is to describe the literature on the acute facility-based management of blast injuries in LMICs to aid hospitals and organizations preparing to respond to conflict- and non-conflict-related blast events.
Methods:
A search of Ovid MEDLINE, Scopus, Global Index Medicus, Web of Science, CINAHL, and Cochrane databases was used to identify relevant citations from January 1998 through July 2024. This systematic review was conducted in adherence with PRISMA guidelines. Data were extracted and analyzed descriptively. A meta-analysis calculated the pooled proportions of mortality, hospital admission, intensive care unit (ICU) admission, intubation and mechanical ventilation, and emergency surgery.
Results:
Reviewers screened 3,731 titles and abstracts and 173 full texts. Seventy-five articles from 22 countries were included for analysis. Only 14.7% of included articles came from low-income countries (LICs). Sixty percent of studies were conducted in tertiary care hospitals. The mean proportion of patients who were admitted was 52.1% (95% CI, 0.376 to 0.664). Among all in-patients, 20.0% (95% CI, 0.124 to 0.288) were admitted to an ICU. Overall, 38.0% (95% CI, 0.256 to 0.513) of in-patients underwent emergency surgery and 13.8% (95% CI, 0.023 to 0.315) were intubated. Pooled in-patient mortality was 9.5% (95% CI, 0.046 to 0.156) and total hospital mortality (including emergency department [ED] mortality) was 7.4% (95% CI, 0.034 to 0.124). There were no significant differences in mortality when stratified by country income level or hospital setting.
Conclusion:
Findings from this systematic review can be used to guide preparedness and resource allocation for acute care facilities. Pooled proportions for mortality and other outcomes described in the meta-analysis offer a metric by which future researchers can assess the impact of blast events. Under-representation of LICs and non-tertiary care medical facilities and significant heterogeneity in data reporting among published studies limited the analysis.
Discretionary foods that are energy-dense and nutrient-poor contribute to over one third of total energy intake in Australian children and adults, and the typical portion sizes of many discretionary foods have increased significantly in the last two decades(1). The portion size norms (described as a typical perception of how much of a given food people choose to eat at a single eating occasion) are likely to have increased concurrently, with larger sizes now being considered the new normal(2). Public health interventions are urgently needed to reduce the portion size norms and consumption of discretionary foods(3), but the acceptability of such interventions remains unknown. Therefore, this qualitative study aimed to gain insights into consumers’ attitudes towards potential interventions targeted at promoting portion control of discretionary foods. Four online focus group sessions were conducted via Zoom with healthy Australian adults who regularly consume discretionary foods. A question guide was developed to gather participants’ perspectives around four potential public health interventions; reduction of the default serving sizes, increasing serving size options, changes to package sizes, and improving serving size labelling. A female facilitator moderated all focus groups, with a second moderator present to capture other relevant details. Collected data were analysed using a hybrid approach combining deductive and inductive thematic analyses. A total of 35 participants completed the study (19 females, mean age 38 ± 14 years). Participants identified the current food environment as promoting overconsumption; larger serving sizes were reported to be more ubiquitous and better value for money than smaller size options. An overall positive attitude towards the proposed interventions was noted. Out of the four proposed interventions, participants considered the most acceptable intervention to be providing a wider range of serving size options while maintaining a consistent unit price. Other acceptable interventions included reducing the default serving sizes with concurrent price reduction; education and clear guidance around portion size selection (for example, the involvement of health professionals to promote portion control, along with relevant recommendations of appropriate portion sizes from health authorities); more practical on-pack serving size suggestions; and innovative package designs that enable better portion control without contributing to food and plastic waste. In conclusion, participants identified a need for and were in support of interventions aimed at the portion control of discretionary foods. Further research should focus on examining the feasibility and effectiveness of the potential interventions to reduce the purchasing and consumption of large serving sizes. More efforts from public health authorities are required to develop practical and tailored recommendations for consumers around appropriate portion sizes for discretionary foods. Collaboration with the food industry and policy makers is also necessary for implementing public health interventions to reduce the excessive intake of discretionary foods.
Ecological momentary assessment (EMA) may be a valid and acceptable method of assessing dietary intake in young adults(1). EMA may overcome some of the limitations associated with traditional dietary assessment methods such as high respondent burden and memory biases(2) by capturing time-sensitive data via concise dietary surveys. However, most dietary EMA studies either deliver signal-contingent EMAs at fixed intervals or rely on the user’s memory to self-initiate event-contingent EMAs whenever they ate. This may be inappropriate for young adults due to their highly variable eating patterns(1). Young adults are particularly vulnerable to weight gain due to major life transitions and, for this population, dietary information may need to be collected near real-time to improve recall accuracy(3). Therefore, the aim of this study was to examine the feasibility (response rate) and acceptability of an EMA protocol that delivered dietary surveys at times personalised to young adults’ (18–30 years) eating patterns and to compare this to the feasibility and acceptability of EMAs delivered at fixed intervals. A randomised, double-blinded crossover design with two four-day treatment arms was used. In one arm, participants received six EMAs per day at fixed intervals. In the other arm, EMAs were delivered at times tailored to participants’ usual eating schedules (ranged between two to six EMAs per day). Usual eating schedules were determined using time-stamped food and beverage images captured by participants over the four days immediately prior to treatments. EMA questions included, but were not limited to, time of consumption and type of food or beverage group consumed. Response rates were calculated as the percentage of EMAs responded to out of the EMAs delivered. At the end of each arm, participants completed an acceptability survey assessing their opinion of the number of EMAs per day, length of the EMAs, and number of recording days. Twenty-three subjects were included (13 female; mean age 26, SD 2.1 years). Mean response rates of the fixed interval and personalised schedule treatments were 65.1% (SE 3.7%) and 66.3% (SE 3.7%), respectively. Compared to the fixed interval treatment, EMAs delivered during the personalised schedule treatment did not align closer with participants’ eating times; the average time difference between EMA delivery and reported eating time was 1.7 hours for both treatments. Participants from both treatments reported receiving too many EMAs per day but found the length of the EMA and number of days of recording to be ‘just right’. In conclusion, EMAs delivered on a personalised schedule may not improve participant adherence. Due to the irregular nature of young adults’ eating patterns, timing of EMA delivery is difficult to tailor. Future definitive trials should use more sophisticated methods of personalisation such as wearable sensors to trigger event-contingent EMAs.
The 1994 discovery of Shor's quantum algorithm for integer factorization—an important practical problem in the area of cryptography—demonstrated quantum computing's potential for real-world impact. Since then, researchers have worked intensively to expand the list of practical problems that quantum algorithms can solve effectively. This book surveys the fruits of this effort, covering proposed quantum algorithms for concrete problems in many application areas, including quantum chemistry, optimization, finance, and machine learning. For each quantum algorithm considered, the book clearly states the problem being solved and the full computational complexity of the procedure, making sure to account for the contribution from all the underlying primitive ingredients. Separately, the book provides a detailed, independent summary of the most common algorithmic primitives. It has a modular, encyclopedic format to facilitate navigation of the material and to provide a quick reference for designers of quantum algorithms and quantum computing researchers.
Ice shelves affect the stability of ice sheets by supporting the mass balance of ice upstream of the grounding line. Marine ice, formed from supercooled water freezing at the base of ice shelves, contributes to mass gain and affects ice dynamics. Direct measurements of marine ice thickness are rare due to the challenges of borehole drilling. Here we assume hydrostatic equilibrium to estimate marine ice distribution beneath the Amery Ice Shelf (AIS) using meteoric ice-thickness data obtained from radio-echo sounding collected during the Chinese National Antarctic Research Expedition between 2015 and 2019. This is the first mapping of marine ice beneath the AIS in nearly 20 years. Our new estimates of marine ice along two longitudinal bands beneath the northwest AIS are spatially consistent with earlier work but thicker. We also find a marine ice layer exceeding 30 m of thickness in the central ice shelf and patchy refreezing downstream of the grounding line. Thickness differences from prior results may indicate time-variation in basal melting and freezing patterns driven by polynya activity and coastal water intrusions masses under the ice shelf, highlighting that those changes in ice–ocean interaction are impacting ice-shelf stability.
Despite advances in antiretroviral treatment (ART), human immunodeficiency virus (HIV) can detrimentally affect everyday functioning. Neurocognitive impairment (NCI) and current depression are common in people with HIV (PWH) and can contribute to poor functional outcomes, but potential synergies between the two conditions are less understood. Thus, the present study aimed to compare the independent and combined effects of NCI and depression on everyday functioning in PWH. We predicted worse functional outcomes with comorbid NCI and depression than either condition alone.
Methods:
PWH enrolled at the UCSD HIV Neurobehavioral Research Program were assessed for neuropsychological performance, depression severity (≤minimal, mild, moderate, or severe; Beck Depression Inventory-II), and self-reported everyday functioning.
Results:
Participants were 1,973 PWH (79% male; 66% racial/ethnic minority; Age: M = 48.6; Education: M = 13.0, 66% AIDS; 82% on ART; 42% with NCI; 35% BDI>13). ANCOVA models found effects of NCI and depression symptom severity on all functional outcomes (ps < .0001). With NCI and depression severity included in the same model, both remained significant (ps < .0001), although the effects of each were attenuated, and yielded better model fit parameters (i.e., lower AIC values) than models with only NCI or only depression.
Conclusions:
Consistent with prior literature, NCI and depression had independent effects on everyday functioning in PWH. There was also evidence for combined effects of NCI and depression, such that their comorbidity had a greater impact on functioning than either alone. Our results have implications for informing future interventions to target common, comorbid NCI and depressed mood in PWH and thus reduce HIV-related health disparities.
This chapter covers quantum algorithmic primitives for loading classical data into a quantum algorithm. These primitives are important in many quantum algorithms, and they are especially essential for algorithms for big-data problems in the area of machine learning. We cover quantum random access memory (QRAM), an operation that allows a quantum algorithm to query a classical database in superposition. We carefully detail caveats and nuances that appear for realizing fast large-scale QRAM and what this means for algorithms that rely upon QRAM. We also cover primitives for preparing arbitrary quantum states given a list of the amplitudes stored in a classical database, and for performing a block-encoding of a matrix, given a list of its entries stored in a classical database.
This chapter covers the multiplicative weights update method, a quantum algorithmic primitive for certain continuous optimization problems. This method is a framework for classical algorithms, but it can be made quantum by incorporating the quantum algorithmic primitive of Gibbs sampling and amplitude amplification. The framework can be applied to solve linear programs and related convex problems, or generalized to handle matrix-valued weights and used to solve semidefinite programs.
This chapter covers quantum algorithmic primitives related to linear algebra. We discuss block-encodings, a versatile and abstract access model that features in many quantum algorithms. We explain how block-encodings can be manipulated, for example by taking products or linear combinations. We discuss the techniques of quantum signal processing, qubitization, and quantum singular value transformation, which unify many quantum algorithms into a common framework.
In the Preface, we motivate the book by discussing the history of quantum computing and the development of the field of quantum algorithms over the past several decades. We argue that the present moment calls for adopting an end-to-end lens in how we study quantum algorithms, and we discuss the contents of the book and how to use it.
This chapter covers the quantum adiabatic algorithm, a quantum algorithmic primitive for preparing the ground state of a Hamiltonian. The quantum adiabatic algorithm is a prominent ingredient in quantum algorithms for end-to-end problems in combinatorial optimization and simulation of physical systems. For example, it can be used to prepare the electronic ground state of a molecule, which is used as an input to quantum phase estimation to estimate the ground state energy.