Tag: pubmed

Rev Med Inst Mex Seguro Soc. 2024 Jan 8;62(1):1-6. doi: 10.5281/zenodo.10278123.

ABSTRACT

BACKGROUND: Necrotizing fasciitis (NF) can affect any soft tissue and skin of the body. Its progression is rapid and it is associated with a high mortality rate. Therefore, the search for easily accessible and low-cost biomarkers that could predict the prognosis of patients with NF is necessary.

OBJECTIVE: To evaluate the role of neutrophil-lymphocyte ratio (NLR) as a predictor of mortality in patients with NF.

MATERIAL AND METHODS: Observational, cross-sectional, retrospective and analytical study of patients admitted between April and October 2020 in a tertiary-care hospital. The statistical tests used for the comparison of variables between the study groups were chi-square, Fisher’s exact, Student’s t and Mann-Whitney U. A receiver operating characteristic (ROC) curve was performed to determine the accuracy of NLR in predicting mortality in patients with NF.

RESULTS: A total of 25 patients were included and stratified into non-survivors and survivors. The non-survivor group had an elevated NLR value compared to survivors (15.57 [13.75] vs. 7.91 [4.13]; p = 0.065). The NLR had an area under the curve (AUC) of 0.729 (95% confidence interval [95% CI] 0.516-0.886; p = 0.044), sensitivity of 77.78% (40-97.2), and specificity of 75% (47.6-92.7). The optimal cut-off point obtained for NLR was > 9.21.

CONCLUSIONS: An NLR value > 9.21 could be a predictor of mortality in patients with NF.

PMID:39110816 | DOI:10.5281/zenodo.10278123

J Speech Lang Hear Res. 2024 Aug 7:1-13. doi: 10.1044/2024_JSLHR-23-00602. Online ahead of print.

ABSTRACT

PURPOSE: Our goal was to compare statistical learning abilities between preschoolers with developmental language disorder (DLD) and peers with typical development (TD) by assessing their learning of two artificial grammars.

METHOD: Four- and 5-year-olds with and without DLD were compared on their statistical learning ability using two artificial grammars. After learning an aX grammar, participants learned a relatively more complex abX grammar with a nonadjacent relationship between a and X. Participants were tested on their generalization of the grammatical pattern to new sequences with novel X elements that conformed to (aX, abX) or violated (Xa, baX) the grammars.

RESULTS: Results revealed an interaction between age and language group. Four-year-olds with and without DLD performed equivalently on the aX and abX grammar tests, and neither of the 4-year-old groups’ accuracy scores exceeded chance. In contrast, among 5-year-olds, TD participants scored significantly higher on aX tests compared to participants with DLD, but the groups’ abX scores did not differ. Five-year-old participants with DLD did not exceed chance on any test, whereas 5-year-old TD participants’ scores exceeded chance on all grammar learning outcomes. Regression analyses indicated that aX performance positively predicted learning outcomes on the subsequent abX grammar for TD participants.

CONCLUSION: These results indicate that preschool-age participants with DLD show deficits relative to typical peers in statistical learning, but group differences vary with participant age and type of grammatical structure being tested.

SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.26487376.

PMID:39110814 | DOI:10.1044/2024_JSLHR-23-00602

Sci Adv. 2024 Aug 9;10(32):eadn1607. doi: 10.1126/sciadv.adn1607. Epub 2024 Aug 7.

ABSTRACT

Glioblastoma (GBM) is the most prevalent and aggressive malignant primary brain tumor. GBM proximal to the lateral ventricles (LVs) is more aggressive, potentially because of subventricular zone contact. Despite this, cross-talk between GBM and neural stem/progenitor cells (NSC/NPCs) is not well understood. Using cell-specific proteomics, we show that LV-proximal GBM prevents neuronal maturation of NSCs through induction of senescence. In addition, GBM brain tumor-initiating cells (BTICs) increase expression of cathepsin B (CTSB) upon interaction with NPCs. Lentiviral knockdown and recombinant protein experiments reveal that both cell-intrinsic and soluble CTSB promote malignancy-associated phenotypes in BTICs. Soluble CTSB stalls neuronal maturation in NPCs while promoting senescence, providing a link between LV-tumor proximity and neurogenesis disruption. Last, we show LV-proximal CTSB up-regulation in patients, showing the relevance of this cross-talk in human GBM biology. These results demonstrate the value of proteomic analysis in tumor microenvironment research and provide direction for new therapeutic strategies in GBM.

PMID:39110807 | DOI:10.1126/sciadv.adn1607

PLoS One. 2024 Aug 7;19(8):e0308568. doi: 10.1371/journal.pone.0308568. eCollection 2024.

ABSTRACT

INTRODUCTION: The impact of SARS-CoV-2 infection among people living with HIV (PLWH) has been a matter of research. We evaluated the incidence and factors associated with SARS-CoV-2 diagnosis among PLWH. We also assessed factors related to vaccination coverage in the Balearic Islands.

METHODS: A retrospective analytical study was performed, including patients from the Balearic cohort (EVHIA) who were visited at least twice between 1st January 2020 and 31st March 2022. Chi-square test and Mann-Whitney U test were used to compare categorical and continuous variables respectively. Multivariable Cox proportional hazards regression models were estimated to identify risk factors.

RESULTS: A total of 3567 patients with HIV were included. The median age was 51 years (IQR 44-59). Most of them were male (77,3%), from Europe (82,1%) or South America (13,8%). During the study period 1036 patients were diagnosed with SARS-CoV-2 infection (29%). The incidence rate was 153,24 cases per 1000 person-year. After multivariable analysis, men who have sex with men (MSM) were associated with an increased risk of SARS-CoV-2 infection (adjusted hazard ratio 1,324, 95% CI 1,138-1,540), whereas African origin, tobacco use and complete or booster vaccination coverage were negatively related. Overall, complete vaccination or booster coverage was recorded in 2845 (79,75%) patients. When analysing vaccination uptake, older patients (adjusted hazard ratio 5,122, 95% CI 3,170-8,288) and those with a modified comorbidity index of 2-3 points (adjusted hazard ratio 1,492, 95% CI 1,056-2,107) had received more vaccine doses.

CONCLUSIONS: In our study no HIV related factor was associated with an increased risk of SARS-CoV-2 infection, except for differences in the transmission route. Possible confounding variables such as mask wearing or social interactions could not be measured. Vaccines were of utmost importance to prevent SARS-CoV-2 infection. Efforts should be made to encourage vaccination in those groups of PLWH with less coverage.

PMID:39110761 | DOI:10.1371/journal.pone.0308568

PLoS One. 2024 Aug 7;19(8):e0302920. doi: 10.1371/journal.pone.0302920. eCollection 2024.

ABSTRACT

While research involving pregnant women with HIV has largely focused on the antepartum and intrapartum periods, few studies in Nigeria have examined the clinical outcomes of these women postpartum. This study aimed to evaluate antiretroviral therapy retention, adherence, and viral suppression among postpartum women in Nigeria. This retrospective clinical data analysis included women with a delivery record at the antenatal HIV clinic at Jos University Teaching Hospital between 2013 and 2017. Descriptive statistics quantified proportions retained, adherent (≥95% medication possession ratio), and virally suppressed up to 24 months postpartum. Among 1535 included women, 1497 met the triple antiretroviral therapy eligibility criteria. At 24 months, 1342 (89.6%) women remained in care, 51 (3.4%) reported transferring, and 104 (7.0%) were lost to follow-up. The proportion of patients with ≥95% medication possession ratio decreased from 79.0% to 69.1% over the 24 months. Viral suppression among those with results was 88.7% at 24 months, but <62% of those retained had viral load results at each time point. In multiple logistic regression, predictors of loss to follow-up included having a more recent HIV diagnosis, higher gravidity, fewer antenatal care visits, and a non-hospital delivery. Predictors of viral non-suppression included poorer adherence, unsuppressed/missing baseline viral load, lower baseline CD4+ T-cell count, and higher gravidity. Loss to follow-up rates were lower and antiretroviral therapy adherence rates similar among postpartum women at our study hospital compared with other sub-Saharan countries. Longer follow-up time and inclusion of multiple facilities for a nationally representative sample would be beneficial in future studies.

PMID:39110750 | DOI:10.1371/journal.pone.0302920

PLoS One. 2024 Aug 7;19(8):e0308635. doi: 10.1371/journal.pone.0308635. eCollection 2024.

ABSTRACT

To date, apart from moderate hypothermia, there are almost no adequate interventions available for neuroprotection in cases of brain damage due to cardiac arrest. Affected persons often have severe limitations in their quality of life. The aim of this study was to investigate protective properties of the active compound of dimethyl fumarate, monomethyl fumarate (MMF), on distinct regions of the central nervous system after ischemic events. Dimethyl fumarate is an already established drug in neurology with known anti-inflammatory and antioxidant properties. In this study, we chose organotypic slice cultures of rat cerebellum and hippocampus as an ex vivo model. To simulate cardiac arrest and return of spontaneous circulation we performed oxygen-glucose-deprivation (OGD) followed by treatments with different concentrations of MMF (1-30 μM in cerebellum and 5-30 μM in hippocampus). Immunofluorescence staining with propidium iodide (PI) and 4′,6-diamidine-2-phenylindole (DAPI) was performed to analyze PI/DAPI ratio after imaging with a spinning disc confocal microscope. In the statistical analysis, the relative cell death of the different groups was compared. In both, the cerebellum and hippocampus, the MMF-treated group showed a significantly lower PI/DAPI ratio compared to the non-treated group after OGD. Thus, we showed for the first time that both cerebellar and hippocampal slice cultures treated with MMF after OGD are significantly less affected by cell death.

PMID:39110748 | DOI:10.1371/journal.pone.0308635

PLoS One. 2024 Aug 7;19(8):e0301301. doi: 10.1371/journal.pone.0301301. eCollection 2024.

ABSTRACT

Interrupted time series (ITS) designs are increasingly used for estimating the effect of shocks in natural experiments. Currently, ITS designs are often used in scenarios with many time points and simple data structures. This research investigates the performance of ITS designs when the number of time points is limited and with complex data structures. Using a Monte Carlo simulation study, we empirically derive the performance-in terms of power, bias and precision- of the ITS design. Scenarios are considered with multiple interventions, a low number of time points and different effect sizes based on a motivating example of the learning loss due to COVID school closures. The results of the simulation study show the power of the step change depends mostly on the sample size, while the power of the slope change depends on the number of time points. In the basic scenario, with both a step and a slope change and an effect size of 30% of the pre-intervention slope, the required sample size for detecting a step change is 1,100 with a minimum of twelve time points. For detecting a slope change the required sample size decreases to 500 with eight time points. To decide if there is enough power researchers should inspect their data, hypothesize about effect sizes and consider an appropriate model before applying an ITS design to their research. This paper contributes to the field of methodology in two ways. Firstly, the motivation example showcases the difficulty of employing ITS designs in cases which do not adhere to a single intervention. Secondly, models are proposed for more difficult ITS designs and their performance is tested.

PMID:39110741 | DOI:10.1371/journal.pone.0301301

Proc Natl Acad Sci U S A. 2024 Aug 13;121(33):e2320510121. doi: 10.1073/pnas.2320510121. Epub 2024 Aug 7.

ABSTRACT

Protein phase transitions (PPTs) from the soluble state to a dense liquid phase (forming droplets via liquid-liquid phase separation) or to solid aggregates (such as amyloids) play key roles in pathological processes associated with age-related diseases such as Alzheimer’s disease. Several computational frameworks are capable of separately predicting the formation of droplets or amyloid aggregates based on protein sequences, yet none have tackled the prediction of both within a unified framework. Recently, large language models (LLMs) have exhibited great success in protein structure prediction; however, they have not yet been used for PPTs. Here, we fine-tune a LLM for predicting PPTs and demonstrate its usage in evaluating how sequence variants affect PPTs, an operation useful for protein design. In addition, we show its superior performance compared to suitable classical benchmarks. Due to the “black-box” nature of the LLM, we also employ a classical random forest model along with biophysical features to facilitate interpretation. Finally, focusing on Alzheimer’s disease-related proteins, we demonstrate that greater aggregation is associated with reduced gene expression in Alzheimer’s disease, suggesting a natural defense mechanism.

PMID:39110734 | DOI:10.1073/pnas.2320510121

Proc Natl Acad Sci U S A. 2024 Aug 13;121(33):e2403771121. doi: 10.1073/pnas.2403771121. Epub 2024 Aug 7.

ABSTRACT

Complex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in an unsupervised way the microscopic dynamical events occurring in the system. However, decoupling statistically relevant fluctuations from the internal noise remains most often nontrivial. Here, we describe “Onion Clustering“: a simple, iterative unsupervised clustering method that efficiently detects and classifies statistically relevant fluctuations in noisy time-series data. We demonstrate its efficiency by analyzing simulation and experimental trajectories of various systems with complex internal dynamics, ranging from the atomic- to the microscopic-scale, in- and out-of-equilibrium. The method is based on an iterative detect-classify-archive approach. In a similar way as peeling the external (evident) layer of an onion reveals the internal hidden ones, the method performs a first detection/classification of the most populated dynamical environment in the system and of its characteristic noise. The signal of such dynamical cluster is then removed from the time-series data and the remaining part, cleared-out from its noise, is analyzed again. At every iteration, the detection of hidden dynamical subdomains is facilitated by an increasing (and adaptive) relevance-to-noise ratio. The process iterates until no new dynamical domains can be uncovered, revealing, as an output, the number of clusters that can be effectively distinguished/classified in a statistically robust way as a function of the time-resolution of the analysis. Onion Clustering is general and benefits from clear-cut physical interpretability. We expect that it will help analyzing a variety of complex dynamical systems and time-series data.

PMID:39110730 | DOI:10.1073/pnas.2403771121

Online J Public Health Inform. 2024 Aug 7;16:e57618. doi: 10.2196/57618.

ABSTRACT

BACKGROUND: Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care.

OBJECTIVE: This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings.

METHODS: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O’Malley’s methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria.

RESULTS: In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth.

CONCLUSIONS: All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested.

PMID:39110501 | DOI:10.2196/57618