medRxiv : the preprint server for health sciences最新文献

The standard of care for brain tumors is maximal safe surgical resection. Neuronavigation augments the surgeon's ability to achieve this but loses validity as surgery progresses due to brain shift. Moreover, gliomas are often indistinguishable from surrounding healthy brain tissue. Intraoperative magnetic resonance imaging (iMRI) and ultrasound (iUS) help visualize the tumor and brain shift. iUS is faster and easier to incorporate into surgical workflows but offers a lower contrast between tumorous and healthy tissues than iMRI. With the success of data-hungry Artificial Intelligence algorithms in medical image analysis, the benefits of sharing well-curated data cannot be overstated. To this end, we provide the largest publicly available MRI and iUS database of surgically treated brain tumors, including gliomas (n=92), metastases (n=11), and others (n=11). This collection contains 369 preoperative MRI series, 320 3D iUS series, 301 iMRI series, and 356 segmentations collected from 114 consecutive patients at a single institution. This database is expected to help brain shift and image analysis research and neurosurgical training in interpreting iUS and iMRI.

Objective: In the chronic phase after a stroke, limitations in activities of daily living (ADLs) and instrumental ADL (IADLs) initially plateau before steadily increasing. The benefits of pre-stroke physical activity on these limitations remain unclear. To clarify this relationship, we examined the effect of physical activity on the long-term evolution of functional limitations in a cohort of stroke survivors and compared it to a cohort of matched stroke-free adults.

Methods: Longitudinal data from 2,143 stroke survivors and 10,717 stroke-free adults aged 50 years and older were drawn from a prospective cohort study based on the Survey of Health, Ageing and Retirement in Europe (2004-2022; 8 data collection waves). Physical activity was assessed in the pre-stroke wave. Functional limitations were assessed in the post-stroke waves. Each stroke survivor was matched with 5 stroke-free adults who had similar propensity scores computed on the basis of key covariates, including baseline age, sex, body mass index, limitations in ADL and IADL, chronic conditions and country of residence, before any of the participants from either cohort had experienced a stroke.

Results: Results showed an interaction between stroke status and physical activity on ADL limitations (b = -0.076; 95% CI = -0.142 to -0.011), with the effect of physical activity being stronger in stroke survivors (b = -0.345, 95% CI = -0.438 to -0.252) than in stroke-free adults (b = -0.269, 95% CI = -0.269 to -0.241).

Conclusion: The beneficial effect of pre-stroke physical activity on ADL limitations after stroke is stronger than its effect in matched stroke-free adults followed for a similar number of years.

Impact: Physical activity, an intervention within the physical therapist's scope of practice, is effective in reducing the risk of functional dependence after stroke. Moreover, pre-stroke levels of physical activity can inform the prognosis of functional dependence in stroke survivors.

Publicly available audio data presents a unique opportunity for the development of digital health technologies with large language models (LLMs). In this study, YouTube was mined to collect audio data from individuals with self-declared positive COVID-19 tests as well as those with other upper respiratory infections (URI) and healthy subjects discussing a diverse range of topics. The resulting dataset was transcribed with the Whisper model and used to assess the capacity of LLMs for detecting self-reported COVID-19 cases and performing variant classification. Following prompt optimization, LLMs achieved accuracies of 0.89, 0.97, respectively, in the tasks of identifying self-reported COVID-19 cases and other respiratory illnesses. The model also obtained a mean accuracy of 0.77 at identifying the variant of self-reported COVID-19 cases using only symptoms and other health-related factors described in the YouTube videos. In comparison with past studies, which used scripted, standardized voice samples to capture biomarkers, this study focused on extracting meaningful information from public online audio data. This work introduced novel design paradigms for pandemic management tools, showing the potential of audio data in clinical and public health applications.

Background: Bipolar disorder (BD) presents with a wide range of symptoms that vary among relatives, casting doubt on categorical illness models. To address this uncertainly, we investigated the heritability and genetic relationships between categorical and dimensional models of BD in a family sample.

Methods: Participants in the Amish-Mennonite Bipolar Genetics (AMBiGen) study were assigned categorical mood disorder diagnoses by structured psychiatric interview and completed the Mood Disorder Questionnaire (MDQ), which assesses lifetime history of manic symptoms and associated impairment. Major MDQ dimensions were analyzed by Principal Component Analysis (PCA) in 726 participants. Heritability and genetic overlaps between categorical diagnoses and MDQ-derived dimensions were estimated with SOLAR-ECLIPSE within 432 genotyped participants.

Results: MDQ scores were significantly higher among individuals diagnosed with BD and related disorders, as expected, but varied widely among relatives. PCA suggested a three-component model for the MDQ. Heritability of the MDQ score was 30% (p<0.001), evenly distributed across its three principal components. Strong and significant genetic correlations were found between categorical diagnoses and most MDQ measures.

Limitations: Recruitment through probands with BD resulted in increased prevalence of BD in this sample, limiting generalizability. Unavailable genetic data reduced sample size for some analyses.

Conclusion: heritability and high genetic correlations between categorical diagnoses and MDQ measures support a genetic continuity between dimensional and categorical models of BD.

Little is known regarding the relationship between common comorbidities in persons with tuberculosis (TB) (including human immunodeficiency virus [HIV], diabetes, and hepatitis C virus [HCV]) with post-TB mortality. We conducted a retrospective cohort study among persons who initiated treatment for rifampicin-resistant and multi/extensively drug-resistant (RR and M/XDR) TB reported to the country of Georgia's TB surveillance during 2009-2017. Exposures included HIV serologic status, diabetes, and HCV status. Our outcome was all-cause post-TB mortality determined by cross-validating vital status with Georgia's death registry through November 2019. We estimated adjusted hazard rate ratios (aHR) and 95% confidence intervals (CI) of post-TB mortality among participants with and without comorbidities using cause-specific hazard regressions. Among 1032 eligible participants, 34 (3.3%) died during treatment and 87 (8.7%) died post-TB treatment. Among those who died post-TB treatment, the median time to death was 21 months (interquartile range 7-39) post-TB treatment. After adjusting for confounders, the hazard rates of post-TB mortality were higher among participants with HIV co-infection (aHR=3.74, 95%CI 1.77-7.91) compared to those without HIV co-infection. In our cohort, post-TB mortality occurred most commonly in the first three years post-TB treatment. Linkage to care for common TB comorbidities post-treatment may reduce post-TB mortality rates.

Introduction: Detecting voice disorders from voice recordings could allow for frequent, remote, and low-cost screening before costly clinical visits and a more invasive laryngoscopy examination. Our goals were to detect unilateral vocal fold paralysis (UVFP) from voice recordings using machine learning, to identify which acoustic variables were important for prediction to increase trust, and to determine model performance relative to clinician performance.

Methods: Patients with confirmed UVFP through endoscopic examination (N=77) and controls with normal voices matched for age and sex (N=77) were included. Voice samples were elicited by reading the Rainbow Passage and sustaining phonation of the vowel "a". Four machine learning models of differing complexity were used. SHapley Additive explanations (SHAP) was used to identify important features.

Results: The highest median bootstrapped ROC AUC score was 0.87 and beat clinician's performance (range: 0.74 - 0.81) based on the recordings. Recording durations were different between UVFP recordings and controls due to how that data was originally processed when storing, which we can show can classify both groups. And counterintuitively, many UVFP recordings had higher intensity than controls, when UVFP patients tend to have weaker voices, revealing a dataset-specific bias which we mitigate in an additional analysis.

Conclusion: We demonstrate that recording biases in audio duration and intensity created dataset-specific differences between patients and controls, which models used to improve classification. Furthermore, clinician's ratings provide further evidence that patients were over-projecting their voices and being recorded at a higher amplitude signal than controls. Interestingly, after matching audio duration and removing variables associated with intensity in order to mitigate the biases, the models were able to achieve a similar high performance. We provide a set of recommendations to avoid bias when building and evaluating machine learning models for screening in laryngology.

The authors have withdrawn their manuscript due to becoming aware of methodology issues related to the curation of the training set used to determine cut-off values for Biotyper cluster assignation and lack of replicate measurements on different days for the isolates analysed. It is therefore unclear whether the conclusions of the manuscript are founded and no further work is possible to correct these issues as the instrument is no longer available to the authors. If you have any questions, please contact the corresponding author.

Context: The body has evolved homeostatic mechanisms to maintain free levels of Ca⁺² and 1,25-dihydroxyvitamin D [1,25(OH)₂D] within narrow physiological ranges. Clinical guidelines emphasize important contributions of PTH in maintaining this homeostasis.

Objective: To investigate mechanisms of homeostatic regulation of vitamin D (VitD) metabolism and to apply mechanistic insights to improve clinical assessment of VitD status.

Design: Crossover clinical trial studying participants before and after VitD3-supplementation.

Setting: Community.

Participants: 11 otherwise healthy individuals with VitD-deficiency (25-hydroxyvitamin D [25(OH)D] ≤20 ng/mL).

Interventions: VitD3-supplements (50,000 IU once or twice a week depending on BMI, for 4-6 weeks) were administered to achieve 25(OH)D≥30 ng/mL.

Results: VitD3-supplementation significantly increased mean 25(OH)D by 2.7-fold and 24,25-dihydroxyvitamin D [24,25(OH)₂D] by 4.3-fold. In contrast, mean levels of PTH, FGF23, and 1,25(OH)₂D did not change. Mathematical modeling suggested that 24-hydroxylase activity was maximal for 25(OH)D≥50 ng/mL and achieved a minimum (~90% suppression) with 25(OH)D<10-20 ng/mL. The 1,25(OH)₂D/24,25(OH)₂D ratio better predicted modeled 24-hydroxylase activity (h) (ρ=-0.85; p=0.001) compared to total plasma 25(OH)D (ρ=0.51; p=0.01) and the 24,25(OH)₂D/25(OH)D ratio (ρ=0.37; p=0.3).

Conclusions: Suppression of 24-hydroxylase provides a first line of defense against symptomatic VitD-deficiency by decreasing metabolic clearance of 1,25(OH)₂D. The 1,25(OH)₂D/24,25(OH)₂D ratio provides a useful index of VitD status since it incorporates 24,25(OH)₂D levels and therefore, provides insight into 24-hydroxylase activity. When VitD availability is limited, this suppresses 24-hydroxylase activity - thereby decreasing the level of 24,25(OH)₂D and increasing the 1,25(OH)₂D/24,25(OH)₂D ratio. Thus, an increased 1,25(OH)₂D/24,25(OH)₂D ratio signifies triggering of homeostatic regulation, which occurs at early stages of VitD-deficiency.

Objective: Postpartum psychosis, a mood disorder triggered by childbirth, is one of the most severe psychiatric conditions, with high risks of suicide and infanticide if untreated. While it is evident that genetic factors play a crucial role in disorder risk, the exact extent of their importance is yet to be determined.

Methods: This cohort study consisted of 1,648,759 women from the Swedish nationwide registers, of whom 2,514 (0.15%) experienced postpartum psychosis within three months of their first-ever childbirth. We estimated the relative recurrence risk of postpartum psychosis for female full siblings and cousins as a measure of familial, genetic, and environmental risk.

Results: Relative recurrence risk of postpartum psychosis in full siblings was 10.69 (95% CI=6.60-16.26) when adjusted for year of and age at childbirth. Although cousins showed an elevated relative recurrence risk, these results did not reach statistical significance (1.78, 95% CI=0.70-3.62). Despite the higher familial risk of postpartum psychosis among full siblings, the absolute risk for women with an affected sibling is relatively low, estimated at 1.55% within the entire population.

Conclusions: The observed increased risk of postpartum psychosis in full siblings suggests both genetic and shared environmental influences. However, the lack of significant results in cousins hampers a definitive distinction between these factors. Furthermore, despite increased relative recurrence risk in siblings, their overall likelihood of developing postpartum psychosis remains low. Our study underscores the need for further research to better understand the intricate interplay of genetics and environment in the development of postpartum psychosis.

Background: There has been an unprecedented effort to sequence the SARS-CoV-2 virus and examine its molecular evolution. This has been facilitated by the availability of publicly accessible databases, the Global Initiative on Sharing All Influenza Data (GISAID) and GenBank, which collectively hold millions of SARS-CoV-2 sequence records. Genomic epidemiology, however, seeks to go beyond phylogenetic analysis by linking genetic information to patient characteristics and disease outcomes, enabling a comprehensive understanding of transmission dynamics and disease impact.While these repositories include fields reflecting patient-related metadata for a given sequence, inclusion of these demographic and clinical details is scarce. The extent to which patient-related metadata is reported in published sequencing studies and its quality remains largely unexplored.

Methods: The NIH's LitCovid collection will be used for automated classification of articles reporting having deposited SARS-CoV-2 sequences in public repositories, while an independent search will be conducted in PubMed for validation. Data extraction will be conducted using Covidence. The extracted data will be synthesized and summarized to quantify the availability of patient metadata in the published literature of SARS-CoV-2 sequencing studies. For the bibliometric analysis, relevant data points, such as author affiliations and citation metrics will be extracted.

Discussion: This scoping review will report on the extent and types of patient-related metadata reported in genomic viral sequencing studies of SARS-CoV-2, identify gaps in this reporting, and make recommendations for improving the quality and consistency of reporting in this area. The bibliometric analysis will uncover trends and patterns in the reporting of patient-related metadata, including differences in reporting based on study types or geographic regions. Co-occurrence networks of author keywords will also be presented. The insights gained from this study may help improve the quality and consistency of reporting patient metadata, enhancing the utility of sequence metadata and facilitating future research on infectious diseases.