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A machine learning model to predict the risk of perinatal depression: Psychosocial and sleep-related factors in the Life-ON study cohort.
Get PDFPerinatal depression (PND) is a common complication of pregnancy associated with serious health consequences for both mothers and their babies. Identifying risk factors for PND is key to early detect women at increased risk of developing this condition. We applied a machine learning (ML) approach to data from a multicenter cohort study on sleep and mood changes during the perinatal period ("Life-ON") to derive models for PND risk prediction in a cross-validation setting. A wide range of sociodemographic variables, blood-based biomarkers, sleep, medical, and psychological data collected from 439 pregnant women, as well as polysomnographic parameters recorded from 353 women, were considered for model building. These covariates were correlated with the risk of future depression, as assessed by regularly administering the Edinburgh Postnatal Depression Scale across the perinatal period. The ML model indicated the mood status of pregnant women in the first trimester, previous depressive episodes and marital status, as the most important predictors of PND. Sleep quality, insomnia symptoms, age, previous miscarriages, and stressful life events also added to the model performance. Besides other predictors, sleep changes during early pregnancy should therefore assessed to identify women at higher risk of PND and support them with appropriate therapeutic strategies
A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin.
Get PDFThe massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary
Letter to the editor: Comment on Wang et al., "Effects of different interventions on insomnia in adults: Systematic review and network meta-analysis".
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Individual cerebellar metabolic connectome in patients with MTLE and NTLE associated with surgical prognosis.
No full textPURPOSE
This study aimed to comprehensively explore the different metabolic connectivity topological changes in MTLE and NTLE, as well as their association with surgical outcomes.
METHODS
This study enrolled a cohort of patients with intractable MTLE and NTLE. Each individual's metabolic connectome, as determined by Kullback-Leibler divergence similarity estimation for the [18F]FDG PET image, was employed to conduct a comprehensive analysis of the cerebral metabolic network. Alterations in network connectivity were assessed by extracting and evaluating the strength of edge and weighted connectivity. By utilizing these two connectivity strength metrics with the cerebellum, we explored the network properties of connectivity and its association with prognosis in surgical patients.
RESULTS
Both MTLE and NTLE patients exhibited substantial alterations in the connectivity of the metabolic network at the edge and nodal levels (p < 0.01, FDR corrected). The key disparity between MTLE and NTLE was observed in the cerebellum. In MTLE, there was a predominance of increased connectivity strength in the cerebellum. Whereas, a decrease in cerebellar connectivity was identified in NTLE. It was found that in MTLE, higher edge connectivity and weighted connectivity strength in the contralateral cerebellar hemisphere correlated with improved surgical outcomes. Conversely, in NTLE, a higher edge metabolic connectivity strength in the ipsilateral cerebellar hemisphere suggested a worse surgical prognosis.
CONCLUSION
The cerebellum exhibits distinct topological characteristics in the metabolic networks between MTLE and NTLE. The hyper- or hypo-metabolic connectivity in the cerebellum may be a prognostic biomarker of surgical prognosis, which might aid in therapeutic decision-making for TLE individuals
Biodegradable Temporising Matrix: The Rising Star in Synthetic Skin Substitutes for the Hand?
Get PDFEdoxaban and/or colchicine for patients with coronavirus disease 2019 managed in the out-of-hospital setting (CONVINCE): a randomized clinical trial.
No full textThe optimal pharmacological management of patients with Coronavirus disease 2019 (COVID-19) managed outside the hospital remains largely unsettled. In the investigator-initiated, open-label CONVINCE trial, 59 outpatients with COVID-19 were randomized (2 × 2 factorial design) to colchicine versus no treatment (anti-inflammatory comparison) or edoxaban versus no treatment (anticoagulation comparison). The study had two co-primary outcomes (one for each randomization): major vascular thrombotic events (MVTE, the composite of asymptomatic proximal deep vein thrombosis [DVT], symptomatic proximal or distal DVT, symptomatic pulmonary embolism or thrombosis, myocardial infarction, ischemic stroke, non-central nervous system embolism and death) at 25 ± 3 days for the anticoagulation comparison and the composite of SARS-CoV-2 detection rates or freedom from death or hospitalizations at 14 ± 3 days for the anti-inflammatory comparison. The trial was prematurely halted due to slow recruitment and availability of effective vaccines. Overall, 16 patients were randomized to edoxaban plus colchicine, 13 to edoxaban, 14 to colchicine and 16 to standard of care. The study showed no significant difference in the two co-primary outcomes with edoxaban and/or colchicine versus standard of care. However, these results should be interpreted in light of the low-risk profile of included patients and the premature termination of the trial
Pneumothorax risk reduction during CT-guided lung biopsy - Effect of fluid application to the pleura before lung puncture and the gravitational effect of pleural pressure.
Get PDFPURPOSE
This study investigated strategies to reduce pneumothorax risk in CT-guided lung biopsy. The approach involved administering 10 ml of 1 % lidocaine fluid in the subpleural or pleural space before lung puncture and utilizing the gravitational effect of pleural pressure with specific patient positioning.
METHOD
We retrospectively analyzed 72 percutaneous CT-guided lung biopsies performed at a single center between January 2020 and April 2023. These were grouped based on fluid administration during the biopsy and whether the biopsies were conducted in dependent or non-dependent lung regions. Confounding factors like patient demographics, lesion characteristics, and procedural details were assessed. Patient characteristics and the occurrence of pneumothoraces were compared using a Kurskal-Wallis test for continuous variables and a Fisher's exact test for categorical variables. Multivariable logistic regression was used to identify potential confounders.
RESULTS
Subpleural or pleural fluid administration and performing biopsies in dependent lung areas were significantly linked to lower peri-interventional pneumothorax incidence (n = 15; 65 % without fluid in non-dependent areas, n = 5; 42 % without fluid in dependent areas, n = 5; 36 % with fluid in non-dependent areas,n = 0; 0 % with fluid in dependent areas; p = .001). Even after adjusting for various factors, biopsy in dependent areas and fluid administration remained independently associated with reduced pneumothorax risk (OR 0.071, p<=.01 for lesions with fluid administration; OR 0.077, p = .016 for lesions in dependent areas).
CONCLUSIONS
Pre-puncture fluid administration to the pleura and consideration of gravitational effects during patient positioning can effectively decrease pneumothorax occurrences in CT-guided lung biopsy
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