Tag: nevin manimala

Hum Mol Genet. 2023 Sep 6:ddad142. doi: 10.1093/hmg/ddad142. Online ahead of print.

ABSTRACT

The myocyte enhancer factor 2 C (MEF2C) gene encodes a transcription factor important for neurogenesis and synapse development and contains common variants associated with intelligence (IQ) and educational attainment (EA). Here, we took gene expression data from the mouse cortex of a Mef2c mouse model with a heterozygous DNA binding-deficient mutation of Mef2c (Mef2c-het) and combined these data with MEF2C ChIP-seq data from cortical neurons and single-cell data from the mouse brain. This enabled us to create a set of genes that were differentially regulated in Mef2c-het mice, represented direct target genes of MEF2C and had elevated in expression in cortical neurons. We found this gene-set to be enriched for genes containing common genetic variation associated with IQ and EA. Genes within this gene-set that were down-regulated, i.e. have reduced expression in Mef2c-het mice versus controls, were specifically significantly enriched for both EA and IQ associated genes. These down-regulated genes were enriched for functionality in the adenylyl cyclase signalling system, which is known to positively regulate synaptic transmission and has been linked to learning and memory. Within the adenylyl cyclase signalling system, three genes regulated by MEF2C, CRHR1, RGS6, and GABRG3, are associated at genome-wide significant levels with IQ and/or EA. Our results indicate that genetic variation in MEF2C and its direct target genes within cortical neurons contribute to variance in cognition within the general population, and the molecular mechanisms involved include the adenylyl cyclase signalling system’s role in synaptic function.

PMID:37672226 | DOI:10.1093/hmg/ddad142

Clin Pharmacokinet. 2023 Sep 6. doi: 10.1007/s40262-023-01282-y. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: Vericiguat is a soluble guanylate cyclase stimulator indicated to reduce the risk of cardiovascular death and hospitalization due to heart failure. A dedicated QTc study in patients with chronic coronary syndromes demonstrated no clinically relevant QTc effect of vericiguat for exposures across the therapeutic dose range (2.5-10 mg). Interval prolongation concentration-QTc (C-QTc) modeling was performed to complement the statistical evaluations of QTc in the dedicated QTc study.

METHODS: Individual time-matched, baseline- and placebo-corrected Fridericia-corrected QT interval values (ΔΔQTcF) were derived. Two approaches for ΔΔQTcF calculation were investigated: (1) ΔΔQTcF correction with data from a single baseline (as in the primary statistical analysis); and (2) ΔΔQTcF correction with a modeled baseline (considering all available individual non-treatment baselines). The ΔΔQTcF values were related to observed vericiguat concentrations with linear mixed-effects modeling.

RESULTS: For both modeling approaches, a positive relationship was found between ΔΔQTcF and vericiguat concentration; however, the slope for the single-baseline approach was not statistically significant, whereas the slope from the modeled-baseline approach was statistically significant. The upper bound of the two-sided 90% confidence interval for model-derived QTc was < 10 ms at the highest observed exposure (745 μg/L; investigated dose range 2.5-10 mg).

CONCLUSION: By applying a single-baseline approach and a modeled-baseline approach that integrated all available QTc data across doses to characterize the QTc prolongation potential, this study showed that vericiguat 2.5-10 mg is not associated with clinically relevant QTc effects, in line with the conclusion from the primary statistical analysis.

CLINICAL TRIALS REGISTRATION NUMBER: ClinicalTrials.gov NCT03504982.

PMID:37672197 | DOI:10.1007/s40262-023-01282-y

J Racial Ethn Health Disparities. 2023 Sep 6. doi: 10.1007/s40615-023-01755-7. Online ahead of print.

ABSTRACT

OBJECTIVE: To quantify racial disparities in mortality and post-hospitalization outcomes among incarcerated individuals that were hospitalized during their incarceration period.

METHODS: We designed a retrospective cohort study using administrative and hospital data collected from a preferred healthcare referral center for all Massachusetts jails and prisons between January 2011 and December 2018 with linkage to Massachusetts Vital Records and Statistics. We identified 4260 incarcerated individuals with complete data on race/ethnicity that were hospitalized during the study period. The primary study indicators were age, race, ethnicity, length of hospital stay, Elixhauser comorbidity score, incarceration facility type, and number of hospital admissions. The primary outcome was time to death.

RESULTS: Of the incarcerated individuals that were hospitalized, 2606 identified as White, 1214 identified as Black, and 411 people who identified as some other race. The hazard of death significantly increased by 3% (OR: 1.03; 95% CI: 1.02-1.03) for each additional yearly increase in age. After adjusting for the interaction between race and age, Black race was significantly associated with 3.01 increased hazard (95% CI: 1.75-5.19) of death for individuals hospitalized while incarcerated compared to White individuals hospitalized while incarcerated. Hispanic ethnicity and being incarcerated in a prison facility was not associated with time to mortality, while increased mean Elixhauser score (HR: 1.07; 95% CI: 1.06-1.08) and ≥ 3 hospital admissions (HR: 2.47; 95% CI: 2.07-2.95) increased the hazard of death.

CONCLUSIONS: Our findings suggest disparities exist in the mortality outcomes among Black and White individuals who are hospitalized during incarceration, with an increased rate of death among Black individuals. Despite hypothesized equal access to healthcare within correctional facilities, our findings suggest that incarcerated and hospitalized Black individuals may experience worse disparities than their White counterparts, which has not been previously explored or reported in the literature. In addition to decarceration, advocacy, and political efforts, increased efforts to support research access to datasets of healthcare outcomes, including hospitalization and death, for incarcerated people should be encouraged. Further research is needed to identify and address the implicit and explicit sources of these racial health disparities across the spectrum of healthcare provision.

PMID:37672188 | DOI:10.1007/s40615-023-01755-7

Qual Life Res. 2023 Sep 6. doi: 10.1007/s11136-023-03506-x. Online ahead of print.

ABSTRACT

PURPOSE: Tuberculosis (TB) has far-reaching effects on the social, mental, and emotional well-being of patients and consequently, their health-related quality of life (HRQOL). Few studies in Nigeria have examined changes in quality of life over the course of treatment. changes in (PTB) and factors associated with HRQOL.

METHODS: A prospective cohort study was conducted with patients recruited from health facilities in Lagos State. The World Health Organization Quality of Life Instrument, Short-Form (WHOQOL-BREF) was used to assess HRQOL. A semi-structured questionnaire was also administered to elicit information on socio-demographic characteristics and the medical and social history of the respondents. Data were analysed using Statistical Package for the Social Sciences (SPSS) version 23. A repeated measures analysis of variance (ANOVA) test with polynomial contrasts was used to assess how domain scores varied over time. Multivariable analysis was conducted using generalized estimating equations (GEE) to assess change in HRQOL and its predictors.

RESULTS: Two hundred and ten patients, predominantly male [108 (63.3%)] were recruited. The mean age was 36.7 ± 12.3 years. The HRQOL was impaired in all four domains at baseline. However, HRQOL scores increased over the treatment period with the largest improvement being in the ‘environment’ domain, where mean scores increased from 45.27 ± 14.59 to 61.28 ± 15.86. The proportion of respondents that expressed satisfaction with their health increased from 13.5% at baseline to 55.7% at the end of treatment. Low socio-economic status, delay in presentation, and an HIV-positive status were found to be significantly associated with reduced HRQOL at baseline (p < 0.05). In the multivariable longitudinal analysis, patients who were employed had higher HRQOL scores while persistent symptoms and a delay in presentation (≥ 4 weeks) were negatively associated with change in HRQOL scores over the course of treatment.

CONCLUSION: The HRQOL of respondents progressively improved over the six-month treatment period. However, change in HRQOL was influenced by a delay in presentation and persistence of symptoms. The study also highlights the need for increased recognition of patient-reported outcomes as an adjunct outcome measure.

PMID:37672154 | DOI:10.1007/s11136-023-03506-x

Environ Monit Assess. 2023 Sep 6;195(10):1153. doi: 10.1007/s10661-023-11609-8.

ABSTRACT

Predicting crop yields, and especially anomalously low yields, is of special importance for food insecure countries. In this study, we investigate a flexible deep learning approach to forecast crop yield at the provincial administrative level based on deep 1D and 2D convolutional neural networks using limited data. This approach meets the operational requirements-public and global records of satellite data in an application ready format with near real time updates-and can be transferred to any country with reliable yield statistics. Three-dimensional histograms of normalized difference vegetation index (NDVI) and climate data are used as input to the 2D model, while simple administrative-level time series averages of NDVI and climate data to the 1D model. The best model architecture is automatically identified during efficient and extensive hyperparameter optimization. To demonstrate the relevance of this approach, we hindcast (2002-2018) the yields of Algeria’s three main crops (barley, durum and soft wheat) and contrast the model’s performance with machine learning algorithms and conventional benchmark models used in a previous study. Simple benchmarks such as peak NDVI remained challenging to outperform while machine learning models were superior to deep learning models for all forecasting months and all tested crops. We attribute the poor performance of deep learning to the small size of the dataset available.

PMID:37672152 | DOI:10.1007/s10661-023-11609-8

Eur J Orthop Surg Traumatol. 2023 Sep 6. doi: 10.1007/s00590-023-03712-w. Online ahead of print.

ABSTRACT

PURPOSE: This systematic review and meta-analysis aimed to investigate the correlation between anthropometric measurements and graft size in anterior cruciate ligament (ACL) reconstruction.

METHODS: A systematic search of Ovid MEDLINE, Embase, and Cochrane Library databases was conducted for observational studies published until March 2023 that reported the relationship between anthropometric data [height, weight, body mass index (BMI), age, gender, thigh length, and circumference] and ACL graft size. Correlation coefficients (COR) and their associated 95% confidence intervals were used as the primary effect size. This review was conducted in line with PRISMA guidelines.

RESULTS: A total of 42 observational studies involving 7110 patients were included, with a mean age of 29.8 years. Statistically significant, moderately positive correlations were found between graft size and height (COR: 0.49; 95% CI: 0.41-0.57; p-value: < 0.001), weight (COR: 0.38; 95% CI: 0.31-0.44; p-value: < 0.001), thigh circumference (COR: 0.40; 95% CI: 0.19-0.58; p-value: < 0.001), and thigh length (COR: 0.35; 95% CI: 0.18-0.50; p-value: < 0.001). However, age and gender were insignificantly correlated with graft size (p-value: NS). A subanalysis based on graft type showed a significant positive correlation between height and graft diameter, which was more significant in the peroneus tendon than in hamstring grafts (COR: 0.76 vs. 0.45; p-value: 0.020).

CONCLUSION: This study demonstrated a moderate positive correlation between anthropometric measurements (height, weight, thigh circumference, and length) and ACL graft size, along with a weak positive correlation with BMI. Age and gender showed no significant correlation. These findings support the predictability and selection of ACL graft size based on pre-operative patient anthropometric data.

LEVEL OF EVIDENCE: Level of Evidence: IV. PROSPERO registration number: CRD42023416044.

PMID:37672150 | DOI:10.1007/s00590-023-03712-w

Med Biol Eng Comput. 2023 Sep 6. doi: 10.1007/s11517-023-02843-w. Online ahead of print.

ABSTRACT

Automatic seizure detection and prediction using clinical Electroencephalograms (EEGs) are challenging tasks due to factors such as low Signal-to-Noise Ratios (SNRs), high variance in epileptic seizures among patients, and limited clinical data constraints. To overcome these challenges, this paper presents two approaches for EEG signal classification. One of these approaches depends on Machine Learning (ML) tools. The used features are different types of entropy, higher-order statistics, and sub-band energies in the Hilbert Marginal Spectrum (HMS) domain. The classification is performed using Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbor (KNN) classifiers. Both seizure detection and prediction scenarios are considered. The second approach depends on spectrograms of EEG signal segments and a Convolutional Neural Network (CNN)-based residual learning model. We use 10000 spectrogram images for each class. In this approach, it is possible to perform both seizure detection and prediction in addition to a 3-state classification scenario. Both approaches are evaluated on the Children’s Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) dataset, which contains 24 EEG recordings for 6 males and 18 females. The results obtained for the HMS-based model showed an accuracy of 100%. The CNN-based model achieved accuracies of 97.66%, 95.59%, and 94.51% for Seizure (S) versus Pre-Seizure (PS), Non-Seizure (NS) versus S, and NS versus S versus PS classes, respectively. These results demonstrate that the proposed approaches can be effectively used for seizure detection and prediction. They outperform the state-of-the-art techniques for automatic seizure detection and prediction. Block diagram of proposed epileptic seizure detection method using HMS with different classifiers.

PMID:37672143 | DOI:10.1007/s11517-023-02843-w

Med Biol Eng Comput. 2023 Sep 6. doi: 10.1007/s11517-023-02919-7. Online ahead of print.

ABSTRACT

Efficient and reliable diagnosis of craniofacial patterns is critical to orthodontic treatment. Although machine learning (ML) is time-saving and high-precision, prior knowledge should validate its reliability. This study proposed a craniofacial ML diagnostic workflow base on a cephalometric geometric model through clinical verification. A cephalometric geometric model was established to determine the landmark location by analyzing 408 X-ray lateral cephalograms. Through geometric information and feature engineering, nine supervised ML algorithms were conducted for sagittal and vertical skeleton patterns. After dimension reduction, plane decision boundary and landmark contribution contours were depicted to demonstrate the diagnostic consistency and the consistency with clinical norms. As a result, multi-layer perceptron achieved 97.56% accuracy for sagittal, while linear support vector machine reached 90.24% for the vertical. Sagittal diagnoses showed average superiority (91.60 ± 5.43)% over the vertical (82.25 ± 6.37)%, where discriminative algorithms exhibited more steady performance (93.20 ± 3.29)% than the generative (85.98 ± 9.48)%. Further, the Kruskal-Wallis H test was carried out to explore statistical differences in diagnoses. Though sagittal patterns had no statistical difference in diagnostic accuracy, the vertical showed significance. All aspects of the tests indicated that the proposed craniofacial ML workflow was highly consistent with clinical norms and could supplement practical diagnosis.

PMID:37672141 | DOI:10.1007/s11517-023-02919-7

Ann Intensive Care. 2023 Sep 6;13(1):80. doi: 10.1186/s13613-023-01175-0.

ABSTRACT

AIMS: Impact of skin mottling has been poorly studied in patients admitted for cardiogenic shock. This study aimed to address this issue and identify determinants of 30-day and 1-year mortality in a large cardiogenic shock cohort of all etiologies.

METHODS AND RESULTS: FRENSHOCK is a prospective multicenter observational registry conducted in French critical care units between April and October, 2016. Among the 772 enrolled patients (mean age 65.7 ± 14.9 years; 71.5% male), 660 had skin mottling assessed at admission (85.5%) with almost 39% of patients in cardiogenic shock presenting mottling. The need for invasive respiratory support was significantly higher in patients with mottling (50.2% vs. 30.1%, p < 0.001) and likewise for the need for renal replacement therapy (19.9% vs. 12.4%, p = 0.09). However, the need for mechanical circulatory support was similar in both groups. Patients with mottling at admission presented a higher length of stay (19 vs. 16 days, p = 0.033), a higher 30-day mortality rate (31% vs. 23.3%, p = 0.031), and also showed significantly higher mortality at 1-year (54% vs. 42%, p = 0.003). The subgroup of patients in whom mottling appeared during the first 24 h after admission had the worst prognosis at 30 days.

CONCLUSION: Skin mottling at admission in patients with cardiogenic shock was statistically associated with prolonged length of stay and poor outcomes. As a perfusion-targeted resuscitation parameter, mottling is a simple, clinical-based approach and may thus help to improve and guide immediate goal-directed therapy to improve cardiogenic shock patients’ outcomes.

PMID:37672139 | DOI:10.1186/s13613-023-01175-0

Brain. 2023 Sep 6:awad297. doi: 10.1093/brain/awad297. Online ahead of print.

ABSTRACT

Parkinson’s disease is an age-related neurodegenerative disorder with a higher incidence in males than females. The causes for this sex difference are unknown. Genome-wide association studies (GWAS) have identified 90 Parkinson’s disease risk loci, but the genetic studies have not found sex-specific differences in allele frequency on autosomal chromosomes or sex chromosomes. Genetic variants, however, could exert sex-specific effects on gene function and regulation of gene expression. To identify genetic loci that might have sex-specific effects, we studied pleiotropy between Parkinson’s disease and sex-specific traits. Summary statistics from GWASs were acquired from large-scale consortia for Parkinson’s disease (n cases=13 708; n controls=95 282), age at menarche (n=368 888 women) and age at menopause (n=69 360 women). We applied the conditional/conjunctional false discovery rate (FDR) method to identify shared loci between Parkinson’s disease and these sex-specific traits. Next, we investigated sex-specific gene expression differences in the superior frontal cortex of both neuropathologically healthy individuals and Parkinson’s disease patients (n cases=61; n controls=23). To provide biological insights to the genetic pleiotropy, we performed sex-specific expression quantitative trait locus (eQTL) analysis and sex-specific age-related differential expression analysis for genes mapped to Parkinson’s disease risk loci. Through conditional/conjunctional FDR analysis we found 11 loci shared between Parkinson’s disease and the sex-specific traits age at menarche and age at menopause. Gene-set and pathway analysis of the genes mapped to these loci highlighted the importance of the immune response in determining an increased disease incidence in the male population. Moreover, we highlighted a total of nine genes whose expression or age-related expression in the human brain is influenced by genetic variants in a sex-specific manner. With these analyses we demonstrated that the lack of clear sex-specific differences in allele frequencies for Parkinson’s disease loci does not exclude a genetic contribution to differences in disease incidence. Moreover, further studies are needed to elucidate the role that the candidate genes identified here could have in determining a higher incidence of Parkinson’s disease in the male population.

PMID:37671566 | DOI:10.1093/brain/awad297