Yoxas/statistical_literacy huggingface dataset:
Hypothalamic obesity resulting from hypothalamic damage might affect melanocortin signalling. We investigated the melanocortin-4 receptor agonist setmelanotide for treatment of hypothalamic obesity. This phase 2, open-label, multicentre trial was done in five centres in the USA. Eligible patients were aged between 6 and 40 years with obesity and history of hypothalamic injury or diagnosis of a non-malignant tumour affecting the hypothalamus that was treated with surgery, chemotherapy, or radiation. Setmelanotide was titrated up to a dose of 3·0 mg and administered subcutaneously once a day for a total duration of 16 weeks. The primary endpoint was the proportion of patients with a reduction in BMI of at least 5% from baseline after 16 weeks, compared with a historic control rate of less than 5% in this population. The primary endpoint was analysed using the full analysis set, which includes all patients with baseline data who received at least one dose of setmelanotide. Safety was assessed in all patients who received at least one dose of study drug. This trial is registered with ClinicalTrials.gov (NCT04725240) and is complete. Between June 6, 2021, and Jan 13, 2022, 19 patients were screened for inclusion. One patient was excluded, and 18 were enrolled and received at least one dose of setmelanotide. Patients were primarily White (n=14 [78%]) and male (n=11 [61%]). Enrolled patients had a mean age of 15·0 years (SD 5·3) and a mean BMI of 38·0 kg/m2 (SD 6·5). Of 18 patients enrolled, 16 (89%) of 18 patients completed the study and met the primary endpoint of reduction in BMI of at least 5% from baseline after 16 weeks (p<0·0001). The mean reduction in BMI across all patients was 15% (SD 10). A composite proportion of patients had a clinically meaningful change (89%, 90% CI 69-98%; p<0·0001), comprising a reduction in BMI Z score of at least 0·2 points for patients younger than 18 years (92%, 68-100%; p<0·0001) and reduction in bodyweight of at least 5% for patients aged 18 years or older (80%, 34-99%; p<0·0001). Patients aged 12 years or older had a mean reduction in hunger score of 45%. Frequent adverse events included nausea (61%), vomiting (33%), skin hyperpigmentation (33%), and diarrhoea (22%). Of 14 patients who continued treatment in a long-term extension study (NCT03651765), 12 completed at least 12 months of treatment at the time of publication and had a mean change in BMI of -26% (SD 12) from index trial baseline. These findings support setmelanotide as a novel effective treatment of hypothalamic obesity. Rhythm Pharmaceuticals.
Hypoxia and hyperoxia (pulse oximetry [SpO2] > 96%) are associated with increased mortality in critically ill patients. However, provider practices regarding oxygenation in traumatic brain injury (TBI) patients are unknown. This study assesses views on oxygenation of critically ill trauma patients with and without TBI and how this varies between Neurological ICU (NeuroICU) and Surgical-Trauma ICU (STICU) providers. This is a cross-sectional survey of Level I trauma center's NeuroICU and STICU providers. We used Likert scales, yes-no questions, and multiple-choice case-based scenarios to characterize provider views on oxygenation with descriptive statistics to characterize responses. Significant differences regarding TBI and non-TBI patients or NeuroICU and STICU providers were determined using Fisher's exact test and a P-value of .05. A total of 83 providers initiated the survey, and 53 providers completed it. Most providers identified a threshold SpO2 < 92% for the administration of supplemental oxygen in critically ill TBI patients. A total of 9% of providers "somewhat or completely agreed" that they were more likely to give supplemental oxygen to a critically ill trauma patient with TBI than one without TBI and the same SpO2. A total of 48% of providers selected an SpO2 < 90% as the point at which supplemental oxygen should be initiated in patients without TBI, compared to 27% of providers in patients with TBI (P < .01). This threshold for supplemental oxygen use varied by provider type for non-TBI patients, but not for TBI patients (30% NeuroICU and 69% STICU providers selected SpO2 < 90% in non-TBI, P < .05; 30% NeuroICU and 35% STICU providers selected SpO2 < 90% in TBI, P = .85). Critical care providers at UCHealth University of Colorado Hospital approach the oxygenation of critically ill trauma patients with and without TBI differently. Specifically, critical care respondents accepted a different lower oxygen saturation threshold for TBI and non-TBI patients. NeuroICU and STICU respondents differed in their threshold for the down-titration of supplemental oxygen. Targeted education for critical care providers may reduce these discrepancies and optimize oxygen use.
I would like to bring to the notice of academia and the public the plight of medical teachers (unjustly called "non-medical teachers") with MSc (Faculty of Medicine) and PhD (Faculty of Medicine) qualifications, who are being systematically excluded from teaching posts by the erstwhile Medical Council of India (MCI), and now, by the National Medical Commission (NMC).
ICD-10 has been widely used in statistical analysis of mortality rates and medical reimbursement. Automatic ICD-10 coding is desperately needed because manually assigning codes is expensive, time-consuming, and labor-intensive. Diagnoses described in medical records differ significantly from those used in ICD-10 classification, making it impossible for existing automatic coding techniques to perform well enough to support medical billing, resource allocation, and research requirements. Meanwhile, most of the current automatic coding approaches are oriented toward English ICD-10. This method for automatically assigning ICD-10 codes to diagnoses extracted from Chinese discharge records was provided in this paper. First, BERT creates word representations of the two texts. Second, the context representation layer incorporates contextual information into the representation of each time step of the word representations using a bidirectional Long Short-Term Memory. Third, the matching layer compares each contextual embedding of the uncoded diagnosis record against a weighted version of all contextual character embeddings of the manually coded diagnosis record. The matching strategy is element-wise subtraction and element-wise multiplication and then through a neural network layer. Fourth, the matching vectors are combined using a one-layer convolutional neural network. A sigmoid is then used to output matching results. To evaluate the proposed method, 1,003,558 manually coded primary diagnoses were gathered from the homepage of the discharge medical records. The experimental results showed that the proposed method outperformed popular deep semantic matching algorithms, such as DSSM, ConvNet, ESIM, and ABCNN, and demonstrated state-of-the-art results in a single text matching with an accuracy of 0.986, a precision of 0.979, a recall of 0.983, and an F1-score of 0.981. The automatic ICD-10 coding of Chinese diagnoses is successful when using the proposed deep semantic matching approach based on analogical reasoning.
Identification is the first step for treatment of hypertension. However, the awareness rate of hypertension was not high globally. This study aimed to examine the potential role of health insurance for early-identifying hypertension among urban older residents in China. In this cross-sectional study, urban residents aged 60+ years were chosen from Nanjing municipality of China in 2018. The outcome measure was hypertension status ("no hypertension," "diagnosed hypertension" or "un-diagnosed hypertension"). Independent variable was health insurance ("Urban Employee Basic Medical Insurance scheme, UEBMI" or "Urban Resident Basic Medical Insurance scheme, URBMI"). Logistic regression models were introduced to estimate odds ratio (OR) and 95% confidence interval (CI) to examine the association between health insurance and hypertension. Totally, 19,742 participants completed the study. Among overall, URBMI and UEBMI participants, 47.2% (95%CI = 46.5, 47.9%), 38.4% (95%CI = 37.3, 39.6%) and 52.1% (95%CI = 51.2, 53.0%), separately, were diagnosed with hypertension, while the prevalence of un-diagnosed hypertension was 12.7% (95%CI = 12.2, 13.2%), 18.5% (95%CI = 17.6, 19.4%) and 9.6% (95%CI = 9.1, 10.1%), respectively. For overall participants, those with UEBMI were more likely to have hypertension identified (OR = 1.20; 95%CI = 1.11, 1.29) and at lower odds to experience un-diagnosed hypertension (OR = 0.68; 95%CI = 0.61, 0.76) compared to their counterparts with URBMI after control for potential confounders. Moreover, such associations of health insurance with diagnosed and un-diagnosed hypertension were also observed among participants stratified by age and gender. Favorable health insurance may be a pathway for identifying hypertension among urban older residents in China. This study has important public health implications that hypertension may be identified early through favorable health insurance policies for older residents in China.
Identification of high-risk areas of cancer, referred to as spatial clusters, can inform targeted policies for cancer control. Although cancer cluster detection could be affected by various geographic characteristics including sociodemographic and environmental factors which impacts could also vary over time, studies accounting for such influence remain limited. This study aims to assess the role of geographic characteristics in the spatial cluster detection for lung and stomach cancer over an extended period. We obtained sex-specific age-standardized incidence and mortality rates of lung and stomach cancer as well as geographic characteristics across 233 districts in South Korea for three five-year periods between 1999 and 2013. We classified geographic characteristics of each district into four categories: demography, socioeconomic status, behaviors, and physical environments. Specifically, we quantified physical environments using measures of greenness, concentrations of particulate matter and nitrogen dioxide, and air pollution emissions. Finally, we conducted cluster detection analyses using weighted normal spatial scan statistics with the residuals from multiple regression analyses performed with the four progressive sets of geographic attributes. We found that the size of clusters reduced as we progressively adjusted for geographic covariates. Among the four categories, physical environments had the greatest impact on the reduction or disappearance of clusters particularly for lung cancer consistently over time. Whereas older population affected a decrease of lung cancer clusters in the early period, the contribution of education was large in the recent period. The impact was less clear in stomach cancer than lung cancer. Our findings highlight the importance of geographic characteristics in explaining the existing cancer clusters and identifying new clusters, which jointly provides practical guidance to cancer control.
Identification of novel chemotypes with biological activity similar to a known active molecule is an important challenge in drug discovery called 'scaffold hopping'. Small-, medium-, and large-step scaffold hopping efforts may lead to increasing degrees of chemical structure novelty with respect to the parent compound. In the present paper, we focus on the problem of large-step scaffold hopping. We assembled a high quality and well characterized dataset of scaffold hopping examples comprising pairs of active molecules and including a variety of protein targets. This dataset was used to build a benchmark corresponding to the setting of real-life applications: one active molecule is known, and the second active is searched among a set of decoys chosen in a way to avoid statistical bias. This allowed us to evaluate the performance of computational methods for solving large-step scaffold hopping problems. In particular, we assessed how difficult these problems are, particularly for classical 2D and 3D ligand-based methods. We also showed that a machine-learning chemogenomic algorithm outperforms classical methods and we provided some useful hints for future improvements.
Identification of sugarcane stem nodes is generally dependent on high-performance recognition equipment in sugarcane seed pre-cutting machines and inefficient. Accordingly, this study proposes a novel lightweight architecture for the detection of sugarcane stem nodes based on the YOLOv5 framework, named G-YOLOv5s-SS. Firstly, the study removes the CBS and C3 structures at the end of the backbone network to fully utilize shallow-level feature information. This enhances the detection performance of sugarcane stem nodes. Simultaneously, it eliminates the 32 times down-sampled branches in the neck structure and the 20x20 detection heads at the prediction end, reducing model complexity. Secondly, a Ghost lightweight module is introduced to replace the conventional convolution module in the BottleNeck structure, further reducing the model's complexity. Finally, the study incorporates the SimAM attention mechanism to enhance the extraction of sugarcane stem node features without introducing additional parameters. This improvement aims to enhance recognition accuracy, compensating for any loss in precision due to lightweight modifications. The experimental results showed that the average precision of the improved network for sugarcane stem node identification reached 97.6%, which was 0.6% higher than that of the YOLOv5 baseline network. Meanwhile, a model size of 2.6MB, 1,129,340 parameters, and 7.2G FLOPs, representing respective reductions of 82%, 84%, and 54.4%. Compared with mainstream one-stage target detection algorithms such as YOLOv4-tiny, YOLOv4, YOLOv5n, YOLOv6n, YOLOv6s, YOLOv7-tiny, and YOLOv7, G-YOLOv5s-SS achieved respective average precision improvements of 12.9%, 5.07%, 3.6%, 2.1%, 1.2%, 3%, and 0.4% in sugarcane stem nodes recognition. Meanwhile, the model size was compressed by 88.9%, 98.9%, 33.3%, 72%, 92.9%, 78.8% and 96.3%, respectively. Compared with similar studies, G-YOLOv5s-SS not only enhanced recognition accuracy but also considered model size, demonstrating an overall excellent performance that aligns with the requirements of sugarcane seed pre-cutting machines.
Identify and describe diet patterns of children during early childhood using confirmatory factor analysis (CFA). Longitudinal data were drawn from the STRONG Kids 2 program. Mothers were surveyed about their child's diet at 24 (n = 337), 36 (n = 317), and 48 (n = 289) months old. The Block Food Frequency Questionnaire for children aged 2-7 years was used to derive diet patterns; 23 food groups were created for analyses. Principal component analysis was used to obtain preliminary factor loadings, and loadings were used to form a priori hypotheses for CFA-derived diet patterns. Independent samples t tests were used to compare food groups, nutrient intakes, and child and family characteristics by CFA pattern scores above vs at/below the median. Three diet patterns consistently emerged: (1) processed meats, sweets, and fried foods; (2) vegetables, legumes, and starchy vegetables; and (3) grains, nuts/seeds, and condiments (only 24 and 36 months). Patterns were related to differences in added sugars, dietary fiber and potassium intakes, maternal education, and household income. Opposing healthful vs Western patterns, extant in child and adult literature, were observed across all ages. The third pattern differed between 24/36 and 48 months, representing a potential shift in food choices or offerings as children age.
Identifying a patient's dominant language hemisphere is an important evaluation performed prior to epilepsy surgery and is commonly assessed using functional magnetic resonance imaging (fMRI). However, the lack of standardization and resultant heterogeneity of fMRI paradigms used in clinical practice limits the ability of cross-center comparisons to be made regarding language laterality results. Through surveying Canadian Epilepsy Centres in combination with reviewing supporting literature, current fMRI language lateralization practices for the clinical evaluation of patients with epilepsy were assessed. To encourage standardization of this practice, we outlined a two-part paradigm series that demonstrates widespread acceptance, reliability and accessibility in lateralizing various aspects of language functioning in individuals with average or near-average IQ and normal literacy skills. The collected data confirm a lack of standardization in fMRI laterality assessments leading to clinical heterogeneity in stimulation and control tasks, paradigm design and timing, laterality index calculations, thresholding values and analysis software and technique. We suggest a Sentence Completion (SC) and Word Generation (WG) paradigm series as it was most commonly employed across Canada, demonstrated reliability in lateralizing both receptive and expressive language areas in supporting literature, and could be readily intelligible to an inclusive population. Through providing recommendations for a two-part paradigm series, we hope to contribute to the standardization of this practice across Canada to reduce clinical heterogeneity, encourage communicability between institutions, and enhance methodologies for the surgical treatment of epilepsy for the benefit of all individuals living with epilepsy in Canada.
Identifying associations between phenotype and genotype is the fundamental basis of genetic analyses. Inspired by frequentist probability and the work of R.A. Fisher, genome-wide association studies (GWAS) extract information using averages and variances from genotype-phenotype datasets. Averages and variances are legitimated upon creating distribution density functions obtained through the grouping of data into categories. However, as data from within a given category cannot be differentiated, the investigative power of such methodologies is limited. Genomic Informational Field Theory (GIFT) is a method specifically designed to circumvent this issue. The way GIFT proceeds is opposite to that of GWAS. Whilst GWAS determines the extent to which genes are involved in phenotype formation (bottom-up approach), GIFT determines the degree to which the phenotype can select microstates (genes) for its subsistence (top-down approach). Doing so requires dealing with new genetic concepts, a.k.a. genetic paths, upon which significance levels for genotype-phenotype associations can be determined. By using different datasets obtained in ovis aries related to bone growth (Dataset-1) and to a series of linked metabolic and epigenetic pathways (Dataset-2), we demonstrate that removing the informational barrier linked to categories enhances the investigative and discriminative powers of GIFT, namely that GIFT extracts more information than GWAS. We conclude by suggesting that GIFT is an adequate tool to study how phenotypic plasticity and genetic assimilation are linked.
Identifying factors linked to autism traits in the general population may improve our understanding of the mechanisms underlying divergent neurodevelopment. In this study we assess whether factors increasing the likelihood of childhood autism are related to early autistic trait emergence, or if other exposures are more important. We used data from 536 toddlers from London (UK), collected at birth (gestational age at birth, sex, maternal body mass index, age, parental education, parental language, parental history of neurodevelopmental conditions) and at 18 months (parents cohabiting, measures of socio-economic deprivation, measures of maternal parenting style, and a measure of maternal depression). Autism traits were assessed using the Quantitative Checklist for Autism in Toddlers (Q-CHAT) at 18 months. A multivariable model explained 20% of Q-CHAT variance, with four individually significant variables (two measures of parenting style and two measures of socio-economic deprivation). In order to address variable collinearity we used principal component analysis, finding that a component which was positively correlated with Q-CHAT was also correlated to measures of parenting style and socio-economic deprivation. Our results show that parenting style and socio-economic deprivation correlate with the emergence of autism traits at age 18 months as measured with the Q-CHAT in a community sample.
Identifying potential cancer-associated genes and drug targets from omics data is challenging due to its diverse sources and analyses, requiring advanced skills and large amounts of time. To facilitate such analysis, we developed Cat-E (Cancer Target Explorer), a novel R/Shiny web tool designed for comprehensive analysis with evaluation according to cancer-related omics data. Cat-E is accessible at https://cat-e.bioinfo-wuerz.eu/. Cat-E compiles information on oncolytic viruses, cell lines, gene markers, and clinical studies by integrating molecular datasets from key databases such as OvirusTB, TCGA, DrugBANK, and PubChem. Users can use all datasets and upload their data to perform multiple analyses, such as differential gene expression analysis, metabolic pathway exploration, metabolic flux analysis, GO and KEGG enrichment analysis, survival analysis, immune signature analysis, single nucleotide variation analysis, dynamic analysis of gene expression changes and gene regulatory network changes, and protein structure prediction. Cancer target evaluation by Cat-E is demonstrated here on lung adenocarcinoma (LUAD) datasets. By offering a user-friendly interface and detailed user manual, Cat-E eliminates the need for advanced computational expertise, making it accessible to experimental biologists, undergraduate and graduate students, and oncology clinicians. It serves as a valuable tool for investigating genetic variations across diverse cancer types, facilitating the identification of novel diagnostic markers and potential therapeutic targets.
Identifying the population-level prevalence of a disability group is a prerequisite to monitoring their inclusion in society. The prevalence and sociodemographic characteristics of older adults with communication disabilities (CDs) are not well established in the literature. In this study we sought to describe the prevalence and sociodemographic characteristics of community-dwelling older adults experiencing difficulties with understanding others or being understand when communicating in their usual language. We conducted a cross-sectional analysis of the National Health and Aging Trends Survey (2015), a nationally representative survey of Medicare beneficiaries ages ≥ 65 years old (N = 7,029). We calculated survey weight-adjusted prevalence estimates by mutually exclusive subgroups of no, hearing only, expressive-only, cognitive only, multiple CDs, and an aggregate any-CD prevalence. We described race/ethnicity, age, gender, education, marital status, social network size, federal poverty status, and supplemental insurance for all groups. Pearson's chi-squared statistic was used to compare sociodemographic characteristics between the any-CD and no-CD groups. An estimated 25.3% (10.7 million) of community-dwelling older adults in the US experienced any-CDs in 2015; approximately 19.9% (8.4 million) experienced only one CD while 5.6% (2.4 million) had multiple. Older adults with CDs were more likely to be of Black race or Hispanic ethnicity as compared to older adults without CDs (Black 10.1 vs. 7.6%; Hispanic: 12.5 vs. 5.4%; P < 0.001). They also had lower educational attainment (Less than high school: 31.0 vs 12.4%; P < 0.001), lower poverty levels (<100% Federal poverty level: 23.5% vs. 11.1%; P < 0.001) and less social supports (Married: 51.3 vs. 61.0%; P < 0.001; Social network ≤ 1 person: 45.3 vs 36.0%; P < 0.001). The proportion of the older adult population experiencing any-CDs is large and disproportionately represented by underserved sociodemographic groups. These findings support greater inclusion of any-CDs into population-level efforts like national surveys, public health goals, health services, and community research aimed at understanding and addressing the access needs of older adults who have disabilities in communication.