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Some diseases are described in the literature as sometimes acute, sometimes chronic: How do general practitioners take medical decisions when faced with “fluctuating” conditions? A qualitative study

Background Although there is a clear distinction between the management of typically acute diseases (e.g., a sore throat) and the management of typically chronic diseases (e.g., diabetes), there is also a ‘fluctuating’ zone that includes many medical conditions with a major impact on the healthcare system and the management of which is usually judged to be unsatisfactory. Objective To understand how general practitioners (GPs) identify and manage acute, chronic and ‘fluctuating’ (i.e., subacute or recurrent acute) conditions on a day-to-day basis. Methods In a qualitative focus group study, 33 French GPs were invited to discuss their management of typical acute diseases, typical chronic diseases, and ‘fluctuating’ conditions. Data saturation was achieved after five focus groups. Thematic content analyses and matrix analyses (disease management key factors vs. diseases studied) were performed. Results The disease management key factors were classified into in seven themes: physician-patient negotiation; complex consultations; greater vigilance for patients with multimorbidity; the absence of standard treatments; patient education over time; inapplicable guidelines; and difficult multidisciplinary coordination. The specific management key factors of ‘fluctuating’ conditions were frequent, erratically scheduled consultations; consultations focused on social relationships, work, and the family; the lack of effective drug treatment in most cases; a break in the patient pathway; a lack of initial medical education about these diseases; and medical guidelines judged to be inappropriate with regard to actual practice. Conclusions These results challenge the acute/chronic dichotomy that is still applied to disease management but also highlight possible ways of improving the management of ‘fluctuating’ conditions.

Does the registration system reform reduce the finance sector’s risk spillover effect in China’s stock market—Causal inference based on dual machine learning

With growing uncertainty in global trade, improving access to domestic capital markets has become an important way to manage financial risk spillovers. This study examines how the registration system reform affects the finance sector's risk spillovers to other 10 sectors in China’s stock market using a dual machine learning model. The findings include: (1) The finance sector's risk spillovers vary over time and are heterogeneous. Risk spillovers rapidly intensify two months after the outbreak of the COVID-19 pandemic, with the average net ∆CoVaR value changing from −0.001 to −0.006. The finance sector mainly accepts risk from the public utility sector and exports risk to the other 9 sectors, with the highest spillovers going to the communication and information technology sectors, showing extreme net ΔCoVaR values around −0.02. (2) The registration system reform increases the finance sector's risk spillover effect, and this conclusion remains the same after a series of robustness tests. (3) Sector heterogeneity tests show that the reform boosts the finance sector's risk spillovers to cyclical sectors and sectors with a low proportion of strategic emerging companies but reduces risk spillovers to midstream and supportive sectors. Finally, some suggestions and implications are proposed.

Iridium Polypyridyl Carboxylates as Excited-State PCET Catalysts for the Functionalization of Unactivated C–H Bonds

Prevalence of the depression among heart failure patients in Ethiopia, 2024: A systematic review and meta-analysis

Background Heart failure (HF) is a major clinical condition contributing to high morbidity, mortality, and healthcare burden. Depression is increasingly recognized as a nontraditional risk factor for HF. However, data on its prevalence among HF patients in Ethiopia remain limited. This study aimed to estimate the pooled prevalence of depression in Ethiopian HF patients. Methods The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, using data abstraction from various electronic sources (PubMed, Web of Science, Google Scholar, Scopus, Science direct, African journal, and online University repositories studies). Studies reporting the prevalence of depression among heart failure patients found until 28th November, 2024 were included. Analysis was conducted using STATA version 17 software, with assessment of heterogeneity and publication bias. A random-effects meta-analysis model was used to estimate the pooled prevalence of depression. Results This study revealing a pooled prevalence of depression among Heart failure patients in Ethiopia is 43.93%. Subgroup analyses based on region, type of institution, and sampling method showed different prevalence rates: the Southern Nations, Nationalities, and Peoples’ Region had the highest rate at 60.13%, while Addis Ababa had the lowest at 35.18%. In terms of study institution types, teaching hospitals reported the highest prevalence at 46.81%, whereas referral hospitals showed the lowest rate at 31.05%. When considering sampling techniques, consecutive sampling yielded the highest prevalence at 55.09%, compared to just 15.40% for systematic random sampling. The analysis indicated a publication bias (p = 0.003), which warranted the use of trim and fill methods. Conclusion and recommendations The rate of depression among heart failure patients in Ethiopia is notably high, highlighting the necessity for targeted interventions from the Ministry of Health to tackle this concern. It is essential to create multi-sectorial strategies that offer context-specific solutions, such as rehabilitation programs, to help reduce depression in heart failure patients. The review protocol was registered in PROSPERO ID: CRD42023405077.

The impact of historical redlining policies on community composition and the COVID-19 pandemic in Boston

The COVID-19 pandemic has accentuated racial and socioeconomic disparities that have existed in the U.S. because of structural racism. We aimed to understand how the legacy of historical redlining policies was associated with COVID-19 incidence in Boston ZIP code tabulation areas (ZCTAs) from March 2020 to December 2022. Data were extracted from the Institute for Social Research at the University of Michigan, the Boston Public Health Commission, the Boston Planning & Development Agency, and the American Community Survey. We ran a generalized linear model accounting for time to explore the association between historical redlining, characterized as Homeowners’ Loan Corporation (HOLC) grade, and monthly COVID-19 incidence rate. Models were adjusted for the proportion of the ZCTA population identified as non-White, were older than 75, homeowners, and foreign-born. We also accounted for the median household value in each ZCTA. We found no significant association between HOLC grade and monthly COVID-19 incidence rate in Boston (IRR: 0.95; 95% CI: 0.80, 1.12). Downstream effects of redlining, such as gentrification and higher median home value, were associated with a higher monthly COVID-19 incidence (IRR: 1.12; 95% CI: 1.01, 1.25). These results highlight the unique ways Boston’s historical racist policies have manifested themselves in health outcomes today. Place-based context and history is important when examining redlining in public health research.

Correction: Exercise prescription for the prevention and treatment of chronic diseases in primary care: Protocol of the RedExAP study

Synthetic CRISPR Networks Driven by Transcription Factors via Structure-Switching DNA Translators

The relationship between pollen monodiets and the activities of proteolytic systems in the fat body and hemolymph of honeybee workers

The homogenization of landscapes through the introduction of large-scale farms, the decline of biodiversity conditioned by high summer temperatures and dry weather, as well as the expansion of alien species determine the monodiet feeding of honeybees. In this study, we investigated the effect of monopollen feeding regimens (containing hazel, rapeseed, pine, buckwheat, Phacelia, and goldenrod) on the activity of the proteolytic system in the tergite 3, tergite 5 or sternite apian fat body, and hemolymph. We showed that pollen from rapeseed, Phacelia, buckwheat, and goldenrod increased the activities of acidic, neutral, and alkaline proteases and their inhibitors in the fat body and hemolymph when compared to the group fed with sugar candy only. The activities of proteases and their inhibitors in bees fed with pollen from hazel and pine were usually higher compared to the activities of honeybees fed with sugar candy only, but lower than in workers fed sugar candy with the pollen of entomophilous plants. Moreover, when comparing the proteolytic system activity between localizations/segments, the highest values were observed in tergite 5, regardless of what age the bees were and whether they were fed candy with added pollen. It is important to understand the impact of individual types of pollen in the context of potential future monodiets. Furthermore, the beneficial impact of Phacelia pollen to drive the rise of protease and protease inhibitor activities, helping to counteract negative environmental factors, can be supported by introducing, for example, flower mixtures for the insects or pollen-supplemented sugar candies for bees during periods without access to pollen.

Correction to “Magnetite Nanodiscs Activate Mechanotransductive Calcium Signaling in Diverse Cell Types”

Animal movement estimation and network-based epidemic modeling: Illustration for the swine industry in Iowa (US)

Animal movement plays a critical role in disease transmission between farms. However, in the United States, the lack of available animal shipment data, sometimes coupled with a lack of detailed information about farm demographics and characteristics, presents great challenges for epidemic modeling and prediction. In this study, we proposed a new method based on the maximum entropy to generate “synthetic” animal movement networks, considering available statistics about the premises operation type, operation size, and the distance between premises. We illustrated our method for the swine movement networks in Iowa and performed network analyses to gain insights into the swine industry. We then applied the generated networks to a network-based epidemic model to identify potential system vulnerabilities in terms of disease transmission. The model was parameterized for African Swine Fever (ASF) as the US swine industry is quite concerned about this disease. Results show that premises with a central role in the network are more vulnerable to disease outbreaks and play an important role in disease spread. Simulations with outbreaks starting from random farms reveal no significant large outbreaks, indicating the system’s relative robustness against arbitrary disease introductions. However, outbreaks originating from high out-degree farms can lead to large epidemic sizes. This underscores the importance for stakeholders and policymakers to continue improving animal movement records and traceability programs in the US and the value of making that data available to epidemiologists and modelers to better understand risk and inform strategies aimed to cost-effectively prevent and control disease transmission. Our approach could be easily adapted to estimate movement networks in other animal production systems and to inform disease spread models for various infectious diseases.

Correction: Psychological distress during the COVID-19 pandemic in Canada

Consecutive Multiphoton-Mediated Defluorinative Amination of Fluoroarenes

A comparative systematic review and meta-analysis of uterine artery resistance in pregnant women with and without previous history of cesarean section

Background Increased uterine artery resistance in pregnant women with a history of cesarean section has been suggested to contribute to adverse pregnancy outcomes. However, the literature presents conflicting reports on this association. In this comparative meta-analysis and systematic review, we aimed to evaluate the studies that reported uterine artery resistance using Color Doppler ultrasonography in pregnant women with and without a history of cesarean section. Methods We searched PubMed, Scopus, Web of Science, and Embase up to April 2024 using relevant keywords. Study selection was performed by two independent researchers, with conflicts resolved by a third. Risk of bias was assessed using the Newcastle-Ottawa Scale. The primary outcomes were the Pulsatility Index (PI) and Resistance Index (RI) Meta-analysis and meta-regression were conducted using STATA version 17. Results After screening 442 articles, the meta-analysis included six studies, encompassing 1,656 participants. We found a small but statistically significant increase in uterine artery resistance, based on PI, in women with a history of cesarean section (Hedges’s g = 0.15, 95% CI [0.03, 0.26], p = 0.01). Heterogeneity among studies was low (I² = 26.60%, p = 0.23), and no significant publication bias was detected (Egger’s test, p = 0.81). Analysis of the RI, based on two studies, showed a non-significant increase in the cesarean group (Hedges’s g = 0.19, 95% CI [−0.06, 0.43], p = 0.13). Conclusion A history of cesarean section may be associated with increased uterine artery resistance. These findings suggest a possible benefit in monitoring uterine artery resistance in subsequent pregnancies, mainly using Color Doppler ultrasonography, to better understand potential risks such as preeclampsia and intrauterine growth restriction. However, given the limited evidence, further studies are warranted to confirm these associations and clarify their clinical relevance.

Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference

Trajectories of motile cells represent a rich source of data that provide insights into the mechanisms of cell migration via mathematical modeling and statistical analysis. However, mechanistic models require cell type dependent parameter estimation, which in case of computational simulation is technically challenging due to the nonlinear and inherently stochastic nature of the models. Here, we employ simulation-based inference (SBI) to estimate cell specific model parameters from cell trajectories based on Bayesian inference. Using automated time-lapse image acquisition and image recognition large sets of 1D single cell trajectories are recorded from cells migrating on microfabricated lanes. A deep neural density estimator is trained via simulated trajectories generated from a previously published mechanical model of cell migration. The trained neural network in turn is used to infer the probability distribution of a limited number of model parameters that correspond to the experimental trajectories. Our results demonstrate the efficacy of SBI in discerning properties specific to non-cancerous breast epithelial cell line MCF-10A and cancerous breast epithelial cell line MDA-MB-231. Moreover, SBI is capable of unveiling the impact of inhibitors Latrunculin A and Y-27632 on the relevant elements in the model without prior knowledge of the effect of inhibitors. The proposed approach of SBI based data analysis combined with a standardized migration platform opens new avenues for the installation of cell motility libraries, including cytoskeleton drug efficacies, and may play a role in the evaluation of refined models.

A study protocol for a pragmatic pre-post trial to determine the feasibility and effectiveness of a novel co-designed service to support health and wellbeing of older carers of older people

Older carers (≥50 years) of older people (≥65 years) are an important sub-group of carers performing valuable roles in providing informal care but often do not have the time or give priority to supporting their own health and wellbeing. Current services supporting older carers’ health and wellbeing are fragmented and inadequate. Through previous research and co-design activity by our team, an innovative multidisciplinary Carer Health and Wellbeing Service (CHWS) has been developed. The purpose of this protocol paper is to describe the rationale for the CHWS and the methods proposed to evaluate its effectiveness and implementation outcomes. The CHWS commenced in March 2024 at Peninsula Health, Melbourne, Australia. Older carers of older people can be referred from multiple sources, including self-referral. A pre-post mixed methods study design is being utilised. Initial assessments include the Carer Support Assessment Needs Tool (CSNAT) and carer prioritisation of their needs, which guides further assessment and interventions. Assessments will occur at Service intake and 6 months later. The primary effectiveness outcome is the Preparedness for Caregiving Scale, and primary implementation outcomes are reach and adoption. Interviews of carers, referrers and staff, and a cost-utility analysis will be undertaken. The target sample size is 137 carers undertaking assessment and intervention over the 15 months data collection. Generalised linear regression will be used to compare pre- and post-continuous outcome measures. Qualitative data will be thematically analysed. Results will inform future scaling up of this innovative approach to optimising health and wellbeing of older carers of older people. Trial registration number: Australian New Zealand Clinical Trials Registry (ANZCTR) – ACTRN: 12625000245493.

Correction: Close to the border—Resilience in healthcare in a European border region: Findings of a needs analysis

Correction to “Copper Chelate Targeting Externalized Phosphatidylserine Inhibits PD-L1 Expression and Enhances Cancer Immunotherapy”

Enhancing stock timing predictions based on multimodal architecture: Leveraging large language models (LLMs) for text quality improvement

This study aims to enhance stock timing predictions by leveraging large language models (LLMs), specifically GPT-4, to filter and analyze online investor comment data. Recognizing challenges such as variable comment quality, redundancy, and authenticity issues, we propose a multimodal architecture that integrates filtered comment data with stock price dynamics and technical indicators. Using data from nine Chinese banks, we compare four filtering models and demonstrate that employing GPT-4 significantly improves financial metrics like profit-loss ratio, win rate, and excess return rate. The multimodal architecture outperforms baseline models by effectively preprocessing comment data and combining it with quantitative financial data. While focused on Chinese banks, the approach can be adapted to broader markets by modifying the prompts of large language models. Our findings highlight the potential of LLMs in financial forecasting and provide more reliable decision support for investors.

Vicarious post-traumatic growth in Chinese oncology nurses: A cross-sectional study

In the traditional Chinese culture, vicarious trauma poses a significant threat to oncology nurses who are frequently exposed to death and illness. This exposure can undermine both the physical and mental health of these nurses, potentially affecting team retention. Vicarious post-traumatic growth, a positive outcome of trauma exposure, has been shown to mitigate the adverse effects of vicarious trauma. As a result, fostering vicarious post-traumatic growth is an important area of focus. However, the prevalence of vicarious post-traumatic growth and its influencing factors is limited. The relationship between vicarious post-traumatic growth and vicarious trauma also remains unclear. This study used a cross-sectional survey design. A total of 445 questionnaire were collected between October and December 2023, with 401 valid responses retained for analysis. Participants completed questionnaires that included demographic and work-related variables questionnaire, the Vicarious Trauma Questionnaire (VTQ) and the Chinese-Post Traumatic Growth Inventory (C-PTGI). Descriptive statistics, Pearson correlation analysis, t-tests or ANOVA (F-tests) and multiple regression analysis were conducted to investigate the level of vicarious post-traumatic growth and the modifiable factors among oncology nurses. Multicollinearity diagnostics confirmed no significant collinearity among variables. This study found that Chinese oncology nurses developed high levels of vicarious post-traumatic growth (n = 401,68.67 ± 18.88) based on medium levels of vicarious trauma (n = 401,66.32 ± 22.50). In the multiple regression analysis, vicarious trauma (B = −0.129, 95%CI −0.211 ~ −0.046), social support (B = 7.963, 95%CI 4.680 ~ 11.247), and job satisfaction (B = 7.418, 95%CI 5.444 ~ 9.391) were independently associated with vicarious post-traumatic growth. These findings have important implications for the future implementation of effective interventions to improve the level of vicarious post-traumatic growth. Recommendations include death education, emotional labor strategies training, mindfulness therapy, psychological counseling, and Balint groups, which can improve the level of vicarious post-traumatic growth in Chinese oncology nurses.

Impact of serum phosphate levels during CRRT on extubation failure and hospital mortality in mechanically ventilated ICU patients—A study based on the MIMIC-IV database

Background Electrolyte imbalances, particularly phosphate depletion, are prevalent yet often underestimated complications in the Intensive Care Unit (ICU), notably among patients undergoing Continuous Renal Replacement Therapy (CRRT). Therefore, this study aims to examine the impact of serum phosphate levels during CRRT on the incidence of extubation failure and hospital mortality in mechanically ventilated patients. Methods Patients subjected to both CRRT and mechanical ventilation were extracted from the MIMIC-IV database. Cox regression analysis was employed to identify the potential risk factors for the extubation failure and hospital mortality rates. Patients were categorized into three groups based on their minimum serum phosphate level (Phosphate_min) during CRRT. Kaplan-Meier survival analysis and Receiver Operating Characteristic (ROC) curves were employed to assess differences in the primary outcomes among these groups. Additionally, a restricted cubic spline curve was utilized to explore potential nonlinear relationships between Phosphate_min and the primary outcomes. Results The analysis included 816 ICU patients undergoing CRRT and mechanical ventilation. Cox regression analysis identified Phosphate_min as a significant risk factor for both extubation failure (HR 1.29; 95% CI 1.22–1.36, p < 0.001) and hospital mortality (HR 1.48; 95% CI 1.37–1.60, p < 0.001). ROC curve analysis indicated that a Phosphate_min > 4.5 mg/dL was a moderate predictor for both extubation failure and hospital mortality. Kaplan-Meier analysis revealed significantly higher risks of the primary outcomes in the group with Phosphate_min > 4.5 mg/dL compared to the lower phosphate groups (log-rank p < 0.001). Additionally, restricted cubic spline analysis showed a J-shaped relationship between Phosphate_min and both primary outcomes, with nadirs at approximately 1.60 mg/dL for extubation failure and 1.98 mg/dL for hospital mortality. Conclusion Phosphate_min emerges as an independent risk factor for both extubation failure and hospital mortality. Maintaining serum phosphate levels within a therapeutic range may potentially mitigate these risks.