Communications medicine最新文献

Introduction: Current mitral annuloplasty rings fail to restrict the anteroposterior distance while allowing dynamic mitral annular changes. We designed and manufactured a mitral annuloplasty ring that demonstrated axis-specific, selective flexibility to meet this clinical need. The objectives were to evaluate ex vivo biomechanics of this ring and to validate the annular dynamics and safety after ring implantation in vivo.

Methods: Healthy human mitral annuli (n = 3) were tracked, and motions were isolated. Using the imaging data, we designed and manufactured our axis-specific mitral annuloplasty ring. An ex vivo annular dilation model was used to compare hemodynamics and chordal forces after repair using the axis-specific, rigid, and flexible rings in five porcine mitral valves. In vivo, axis-specific (n = 6), rigid (n = 6), or flexible rings (n = 6) were implanted into male Dorset sheep for annular motion analyses. Five additional animals receiving axis-specific rings survived for up to 6 months.

Results: Here we show the axis-specific, rigid, and flexible rings reduced regurgitation fraction to 4.7 ± 2.7%, 2.4 ± 3.2%, and 17.8 ± 10.0%, respectively. The axis-specific ring demonstrated lower average forces compared to the rigid ring (p = 0.046). Five animals receiving axis-specific rings survived for up to 6 months, with mitral annular motion preserved in vivo. Mature neoendocardial tissue coverage over the device was found to be complete with full endothelialization in all animals.

Conclusions: The axis-specific mitral annuloplasty ring we designed demonstrates excellent capability to repair mitral regurgitation while facilitating dynamic mitral annular motion. This ring has tremendous potential for clinical translatability, representing a promising surgical solution for mitral regurgitation.

Background: The human microbiome is an essential factor of physical and mental health, yet the general population has little knowledge about it. This survey explores public familiarity with the human microbiome and (potential) public preferences related to monitoring and improving one's microbiome health. The study also examines whether recognizing the importance of one's microbiome may promote a more ecosystem-aware perspective towards microorganisms.

Methods: We conducted an online survey with nationally representative samples from France, Germany, South Korea, and Taiwan (N = 2860). The results were interpreted using descriptive statistics and network analysis. We also performed a t-test to compare perceptions of microorganisms before and after a short reflection on the role of human microbiome for one's body and health.

Results: In our data, most respondents express willingness to monitor the health of their microbiome (especially, in the European countries) and to adjust their lifestyle such as diet and exercise to improve it. A paired samples t-test shows a slight positive shift in perceptions of microorganisms and the microbial world after the reflection exercise compared to baseline.

Conclusions: The study shows that the public recognize the essential role of the human microbiome in health and are willing to take care of it, which may have implications for public health policy. Our findings also suggest that stronger awareness of the human microbiome may promote lifestyle change and a more encompassing environmental outlook.

Background: Over the next 5 years, new breast cancer screening guidelines recommending magnetic resonance imaging (MRI) for certain patients will significantly increase the volume of imaging data to be analyzed. While this increase poses challenges for radiologists, artificial intelligence (AI) offers potential solutions to manage this workload. However, the development of AI models is often hindered by manual annotation requirements and strict data-sharing regulations between institutions.

Methods: In this study, we present an integrated pipeline combining weakly supervised learning-reducing the need for detailed annotations-with local AI model training via swarm learning (SL), which circumvents centralized data sharing. We utilized three datasets comprising 1372 female bilateral breast MRI exams from institutions in three countries: the United States (US), Switzerland, and the United Kingdom (UK) to train models. These models were then validated on two external datasets consisting of 649 bilateral breast MRI exams from Germany and Greece.

Results: Upon systematically benchmarking various weakly supervised two-dimensional (2D) and three-dimensional (3D) deep learning (DL) methods, we find that the 3D-ResNet-101 demonstrates superior performance. By implementing a real-world SL setup across three international centers, we observe that these collaboratively trained models outperform those trained locally. Even with a smaller dataset, we demonstrate the practical feasibility of deploying SL internationally with on-site data processing, addressing challenges such as data privacy and annotation variability.

Conclusions: Combining weakly supervised learning with SL enhances inter-institutional collaboration, improving the utility of distributed datasets for medical AI training without requiring detailed annotations or centralized data sharing.

Background: The lack of accurate and affordable monitoring of glycated hemoglobin (HbA1c) is a common issue among patients with diabetes in low- and middle-income countries. We aimed to test a tablet- and smartphone-based point-of-care (TSB POC) device against a local laboratory-based measure of HbA1c for monitoring diabetes under real-world conditions.

Methods: For this cross-sectional clinical method applicability study, capillary and venous blood was collected in duplicate and analyzed at local primary health care centers. For a heterogeneity test, the tests were performed by an expert, and by a team of local nurses. The study was conducted in a multicenter design in rural and urban Aceh, Indonesia in 2019, and included a total of 533 adults. We mainly used Bland-Altman plots to assess the number of readings within the 95%-limits of agreement (LoA) and Deming regressions.

Results: The results show a mean difference between capillary HbA1c on the test device and the reference method of -0.54 [CI_0.95 = -1.6933; 0.6048] with 5.21% of measurements outside the LoA and a Pearson's r = 0.91 in the Deming Regression. There is no significant difference in test concordance between local nurses and the expert (4.23% versus 5.13% results outside the LoA [CI_0.95 = -0.0331; 0.0511]).

Conclusions: TSB POC for analysis of HbA1c is an acceptable alternative for accessible monitoring of diabetes patients under these conditions. This method could provide access to high-quality diagnostic decisions through regular and cost-effective HbA1c monitoring directly in healthcare facilities, thus providing better access to essential health services.

Background: Effective communication during a health crisis is critical as it directly influences psychological and behavioral responses that will shape the further progression of the crisis. Past research has suggested that one type of cognitive mechanism that is likely to be affected by the framing of public health messages relates to counterfactual thinking.

Methods: Based on 6731 incentivized daily forecasts collected over 377 days (from April 2020-May 2021), we investigate the role of triggering counterfactual thinking when interpreting public information regarding the daily US death toll from COVID-19.

Results: Here we show that individuals who engaged in thinking about "interventions that could have led to an alternative evolution of the death toll" prior to making forecasts exhibit greater judgmental bias in their predictions compared to the control group. Specifically, subjects in the treatment group tend to generate upward counterfactuals and underestimate the death toll, potentially due to anchoring on more favorable scenarios and insensitivity to trend changes. Interestingly, this behavior is also observed among individuals who had recovered from COVID-19 (or someone in their close social circle).

Conclusions: Our findings underscore the importance of using debiasing strategies and neutral communication during health crises to mitigate the generation of upward counterfactuals, thus reducing the likelihood of systematic misperceptions and flawed decision-making.

Background: The role of diet in aging is crucial, yet research findings on how specific diets influence human aging remain inconsistent. Understanding the relationship between dietary factors and aging could inform interventions to promote healthier aging outcomes.

Methods: We analyzed data from the UK Biobank baseline survey and a 24-hour dietary assessment survey to investigate the association between diet and aging. The study examined 18 individual food intakes, 6 dietary patterns, 3 macronutrient intakes, and 3 dietary quality scores. High-dimensional Fixed Effects (HDFE) models were used to assess associations between dietary factors and aging measures, including telomere length, phenotypic age, and brain grey/white matter volumes. Multivariable Mendelian Randomization (MVMR) was employed to explore causal links between macronutrient consumption and aging outcomes.

Results: Our results show that healthier diets are generally associated with improved aging outcomes from HDFE analyses. Plant-based food consumption correlates with increased telomere length and reduced phenotypic age, while animal-based food intake is linked to adverse aging effects. MVMR results confirm the causal benefits of carbohydrate intake, including reductions in phenotypic age (β = -0.0025; 95% CI = [-0.0047, -0.0003]; p = 0.0253) and increases in whole-brain grey matter volume (β = 0.0262; 95% CI = [0.007, 0.046]; p = 0.0087). The latter association remains significant after multiple testing correction.

Conclusions: This study underscores the significant role of diet in biological aging and provides robust evidence for the benefits of carbohydrate intake in promoting healthier aging. These findings highlight the potential of dietary interventions to improve aging-related outcomes.

Background: Circulating tumor cells (CTCs) are rare (a few cells per milliliter of blood) and mostly isolated as single-cell CTCs (scCTCs). CTC clusters (cCTCs), even rarer, are of growing interest, notably because of their higher metastatic potential, but very difficult to isolate.

Method: We introduce gravity-based microfiltration (GµF) for facile isolation of cCTCs using in-house fabricated microfilters and 3D printed cartridges. Optimal flow rate and pore size for cCTC isolation are determined by GµF of cultured ovarian single cells and cell clusters spiked in healthy blood. We perform GµF of blood from orthotopic ovarian cancer mouse models and characterize the morphological features of scCTCs and cCTCs, and the expression of molecular markers for aggressiveness. Finally, we analyze blood from 17 epithelial ovarian cancer patients with either localized or metastatic disease, and from 13 colorectal cancer liver metastasis patients.

Results: Here, we show that GµF optimized for cell cluster isolation captures cCTCs from blood while minimizing unwanted cluster disaggregation, with ~85% capture efficiency. We detect cCTCs in every patient, with between 2-100+ cells. We find cCTCs represent between 5-30% of all CTC capture events, and 10-80% of the CTCs are clustered; remarkably, in 10 patients, most CTCs are circulating not as scCTCs, but as cCTCs.

Conclusions: GµF uncovers the unexpected prevalence and frequency of cCTCs including sometimes very large ones in epithelial ovarian cancer patients, and motivates additional studies to uncover their properties and role in disease progression.

Background: Although COVID-19 initially caused great concern about respiratory symptoms, mounting evidence shows that also the pancreas is productively infected by SARS-CoV-2. However, the severity of pancreatic SARS-CoV-2 infection and its pathophysiology is still under debate. Here, we investigate the consequences of SARS-CoV-2 pancreatic infection and the role of the host factor Placenta-associated protein (PLAC8).

Methods: We analyze plasma levels of pancreatic enzymes and inflammatory markers in a retrospective cohort study of 120 COVID-19 patients distributed in 3 severity-stratified groups. We study the expression of SARS-CoV-2 and PLAC8 in the pancreas of deceased COVID-19 patients as well as in non-infected donors. We perform pseudovirus infection experiments in PLAC8 knock-out PDAC and human beta cell-derived cell lines and validate results with SARS-CoV-2 virus.

Results: We find that analysis of circulating pancreatic enzymes aid the stratification of patients according to COVID-19 severity and predicts outcomes. Interestingly, we find an association between PLAC8 expression and SARS-CoV-2 infection in postmortem analysis of COVID-19 patients both in the pancreas and in other bonafide SARS-CoV-2 target tissues. Functional experiments demonstrate the requirement of PLAC8 in SARS-CoV-2 pancreatic productive infection by pseudovirus and full SARS-CoV-2 infectious virus inoculum from Wuhan-1 and BA.1 strains. Finally, we observe an overlap between PLAC8 and SARS-CoV-2 immunoreactivities in the pancreas of deceased patients.

Conclusions: Our data indicate the human pancreas as a SARS-CoV-2 target with plausible signs of injury and demonstrate that the host factor PLAC8 is required for SARS-CoV-2 pancreatic infection, thus defining new target opportunities for COVID-19-associated pancreatic pathogenesis.

Background: Deep learning methods on standard, 12-lead electrocardiograms (ECG) have resulted in the ability to identify individuals at high-risk for the development of atrial fibrillation. However, the process remains a "black box" and does not help clinicians in understanding the electrocardiographic changes at an individual level. we propose a nonparametric feature extraction approach to identify features that are associated with the development of atrial fibrillation (AF).

Methods: We apply functional principal component analysis to the raw ECG tracings collected in the Chronic Renal Insufficiency Cohort (CRIC) study. We define and select the features using ECGs from participants enrolled in Phase I (2003-2008) of the study. Cox proportional hazards models are used to evaluate the association of selected ECG features and their changes with the incident risk of AF during study follow-up. The findings are then validated in ECGs from participants enrolled in Phase III (2013-2015).

Results: We identify four features that are related to the P-wave amplitude, QRS complex and ST segment. Both their initial measurement and 3-year changes are associated with the development of AF. In particular, one standard deviation in the 3-year decline of the P-wave amplitude is independently associated with a 29% increased risk of incident AF in the multivariable model (HR: 1.29, 95% CI: [1.16, 1.43]).

Conclusions: Compared with deep learning methods, our features are intuitive and can provide insights into the longitudinal ECG changes at an individual level that precede the development of AF.

Background: Deep brain stimulation (DBS) has emerged as an important therapeutic intervention for neurological and neuropsychiatric disorders. After initial programming, clinicians are tasked with fine-tuning DBS parameters through repeated in-person clinic visits. We aimed to evaluate whether DBS patients achieve clinical benefit more rapidly by incorporating remote internet-based adjustment (RIBA) of stimulation parameters into the continuum of care.

Methods: We conducted a randomized controlled multicenter study (ClinicalTrails.gov NCT05269862) involving patients scheduled for de novo implantation with a DBS System to treat Parkinson's Disease. Eligibility criteria included the ability to incorporate RIBA as part of routine follow-up care. Ninety-six patients were randomly assigned in a 1:1 ratio using automated allocation, blocked into groups of 4, allocation concealed, and no stratification. After surgery and initial configuration of stimulation parameters, optimization of DBS settings occurred in the clinic alone (IC) or with additional access to RIBA. The primary outcome assessed differences in the average time to achieve a one-point improvement on the Patient Global Impression of Change score between groups. Patients, caregivers, and outcome assessors were not blinded to group assignment. Most of the data collection took place in the patient's home environment.

Results: Access to RIBA reduces the time to symptom improvement, with patients reporting 15.1 days faster clinical benefit (after 39.1 (SD 3.3) days in the RIBA group (n = 48) and after 54.2 (SD 3.7) days in the IC group (n = 48)). None of the reported adverse events are related to RIBA.

Conclusions: This study demonstrates safety and efficacy of internet-based adjustment of DBS therapy, while providing clinical benefit earlier than in-clinic optimization of stimulation parameters by increasing patient access to therapy adjustment.