Communications medicine最新文献

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.

Background: Multiple Sulfatase Deficiency (MSD) is a rare inherited lysosomal storage disorder characterized by loss of function mutations in the SUMF1 gene that manifests as a severe pediatric neurological disease. There are no available targeted therapies for MSD.

Methods: We engineered a viral vector (AAV9/SUMF1) to deliver working copies of the SUMF1 gene and tested the vector in Sumf1 knock out mice that generally display a median lifespan of 10 days. Mice were injected as pre-symptomatic neonates via intracerebroventricular administration, or as post-symptomatic juveniles via intrathecal alone or combination intrathecal and intravenous delivery. Cohorts were assessed for survival, behavioral outcomes, and post-mortem for sulfatase activity.

Results: We show that treatment of neonates extends survival up to 1-year post-injection. Importantly, delivery of SUMF1 through cerebral spinal fluid at 7 days of age alleviates MSD symptoms. The treated mice show wide distribution of the SUMF1 gene, no signs of toxicity or neuropathy, improved vision and cardiac function, and no behavioral deficits. One-year post treatment, tissues show increased sulfatase activity, indicating functional SUMF1. Further, a GLP toxicology study conducted in rats demonstrates favorable overall safety of this approach.

Conclusions: These preclinical studies highlight the potential of our AAV9/SUMF1 vector, the design of which is directly translatable for clinical use, as a gene replacement therapy for MSD patients.

Background: Stargardt disease type 1 (STGD1) is a progressive retinal disorder caused by bi-allelic variants in the ABCA4 gene. A recurrent variant at the exon-intron junction of exon 6, c.768G>T, causes a 35-nt elongation of exon 6 that leads to premature termination of protein synthesis.

Methods: To correct this aberrant splicing, twenty-five 2'-O-methoxyethyl antisense oligonucleotides (AONs) were designed, spanning the entire exon elongation.

Results: Testing of these AONs in patient-derived photoreceptor precursor cells and retinal organoids allow the selection of a lead candidate AON (A7 21-mer) that rescues on average 52% and 50% expression of wild-type ABCA4 transcript and protein, respectively. In situ hybridization and probe-based ELISA demonstrate its distribution and stability in vitro and in vivo. No major safety concerns regarding off-targets, immunostimulation and toxicity are observed in transcriptomics analysis, cytokine stimulation assays in human primary immune cells, and cytotoxicity assays.

Conclusions: Additional optimization and in vivo studies will be performed to further investigate the lead candidate. Considering the high prevalence of this variant, a substantial number of patients are likely to benefit from a successful further development and implementation of this therapy.

Background: Routine screening to detect silent but deadly cancers such as pancreatic ductal adenocarcinoma (PDAC) can significantly improve survival, creating an important need for a convenient screening test. High-resolution proton (1H) magnetic resonance spectroscopy (MRS) of plasma identifies circulating metabolites that can allow detection of cancers such as PDAC that have highly dysregulated metabolism.

Methods: We first acquired 1H MR spectra of human plasma samples classified as normal, benign pancreatic disease and malignant (PDAC). We next trained a system of artificial neural networks (ANNs) to process and discriminate these three classes using the full spectrum range and resolution of the acquired spectral data. We then identified and ranked spectral regions that played a salient role in the discrimination to provide interpretability of the results. We tested the accuracy of the ANN performance using blinded plasma samples.

Results: We show that our ANN approach yields, in a cross validation-based training of 170 samples, a sensitivity and a specificity of 100% for malignant versus non-malignant (normal and disease combined) discrimination. The trained ANNs achieve a sensitivity and specificity of 87.5% and 93.1% respectively (AUC: ROC = 0.931, P-R = 0.854), with 45 blinded plasma samples. Further, we show that the salient spectral regions of the ANN discrimination correspond to metabolites of known importance for their role in cancers.

Conclusions: Our results demonstrate that the ANN approach presented here can identify PDAC from 1H MR plasma spectra to provide a convenient plasma-based assay for population-level screening of PDAC. The ANN approach can be suitably expanded to detect other cancers with metabolic dysregulation.

Background: Increasing demands, such as the COVID-19 pandemic, have presented substantial challenges to global healthcare systems, resulting in staff shortages and overcrowded emergency rooms. Health kiosks have emerged as a promising solution to improve overall efficiency and healthcare accessibility. However, although kiosks are commonly used worldwide for access to information and financial services, the health kiosk industry, valued at $800 million, accounts for just 1.9% of the $42 billion global kiosk market. This review aims to bridge the research-to-practice gap by examining the development of health kiosk technology from 2013 to 2023.

Methods: We conducted a systematic search across PubMed, IEEE Xplore, and Google Scholar databases, identifying 5,537 articles, with 36 studies meeting inclusion criteria for detailed analysis. We evaluated each study based on kiosk purpose, targeted diseases, measured vital signs, and user demographics, along with an assessment of limitations in participant selection and data reporting.

Results: The findings reveal that blood pressure is the most frequently measured vital sign, utilized in 34% of the studies. Furthermore, cardiovascular disease detection emerges as the primary motivation in 56% of the included studies. The United States, India, and the United Kingdom are notable contributors, accounting for 43% of the reviewed articles. Our assessment reveals considerable limitations in participant selection and data reporting in many studies. Additionally, several research gaps remain, including a lack of performance testing, user experience evaluation, clinical intervention, development standardization, and inadequate sanitization protocols.

Conclusions: This review highlights health kiosks' potential to ease the burden on healthcare system and expand accessibility. However, widespread adoption is hindered by technical, regulatory, and financial challenges. Addressing these barriers could enable health kiosks to play a greater role in early disease detection and healthcare delivery.

Background: The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care and broadening access to medical knowledge. Despite the popularity of LLMs, there is a significant gap in systematized information on their use in patient care. Therefore, this systematic review aims to synthesize current applications and limitations of LLMs in patient care.

Methods: We systematically searched 5 databases for qualitative, quantitative, and mixed methods articles on LLMs in patient care published between 2022 and 2023. From 4349 initial records, 89 studies across 29 medical specialties were included. Quality assessment was performed using the Mixed Methods Appraisal Tool 2018. A data-driven convergent synthesis approach was applied for thematic syntheses of LLM applications and limitations using free line-by-line coding in Dedoose.

Results: We show that most studies investigate Generative Pre-trained Transformers (GPT)-3.5 (53.2%, n = 66 of 124 different LLMs examined) and GPT-4 (26.6%, n = 33/124) in answering medical questions, followed by patient information generation, including medical text summarization or translation, and clinical documentation. Our analysis delineates two primary domains of LLM limitations: design and output. Design limitations include 6 second-order and 12 third-order codes, such as lack of medical domain optimization, data transparency, and accessibility issues, while output limitations include 9 second-order and 32 third-order codes, for example, non-reproducibility, non-comprehensiveness, incorrectness, unsafety, and bias.

Conclusions: This review systematically maps LLM applications and limitations in patient care, providing a foundational framework and taxonomy for their implementation and evaluation in healthcare settings.

Background: High-field magnetic resonance imaging (MRI) is a powerful diagnostic tool but can induce unintended physiological effects, such as nystagmus and dizziness, potentially compromising the comfort and safety of individuals undergoing imaging. These effects likely result from the Lorentz force, which arises from the interaction between the MRI's static magnetic field and electrical currents in the inner ear. Yet, the Lorentz force hypothesis fails to explain observed eye movement patterns in healthy adults fully. This study explores these effects and tests whether the Lorentz force hypothesis adequately explains magnetic vestibular stimulation.

Methods: We developed a mathematical model integrating computational fluid dynamics, fluid-structure interaction solvers, and magnetohydrodynamic equations to simulate the biomechanical response of the cristae ampullares. Using high-resolution micro-CT data of the human membranous labyrinth, we ensured anatomical accuracy. Experimental validation involved measuring horizontal, vertical, and torsional slow-phase eye movements in healthy subjects exposed to varying magnetic field intensities and head positions.

Results: Our model accurately replicates observed nystagmus patterns, predicting slow-phase eye velocities that match experimental data. Results indicate that Lorentz force-induced stimulation of individual cupulae explains variability in eye movements across different magnetic field intensities and head orientations.

Conclusions: This study empirically supports the Lorentz force hypothesis as a valid explanation for magnetic vestibular stimulation, offering new insights into the effects of high-field MRI on the vestibular system. These findings provide a foundation for future research and improved clinical practices.