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1.
Sci Data ; 9(1): 532, 2022 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-36050327

RESUMO

Identifying relevant studies and harmonizing datasets are major hurdles for data reuse. Common Data Elements (CDEs) can help identify comparable study datasets and reduce the burden of retrospective data harmonization, but they have not been required, historically. The collaborative team at PhenX and dbGaP developed an approach to use PhenX variables as a set of CDEs to link phenotypic data and identify comparable studies in dbGaP. Variables were identified as either comparable or related, based on the data collection mode used to harmonize data across mapped datasets. We further added a CDE data field in the dbGaP data submission packet to indicate use of PhenX and annotate linkages in the future. Some 13,653 dbGaP variables from 521 studies were linked through PhenX variable mapping. These variable linkages have been made accessible for browsing and searching in the repository through dbGaP CDE-faceted search filter and the PhenX variable search tool. New features in dbGaP and PhenX enable investigators to identify variable linkages among dbGaP studies and reveal opportunities for cross-study analysis.


Assuntos
Coleta de Dados , Conjuntos de Dados como Assunto , Estudos Retrospectivos
2.
Nature ; 608(7922): 353-359, 2022 08.
Artigo em Inglês | MEDLINE | ID: mdl-35922509

RESUMO

Regulation of transcript structure generates transcript diversity and plays an important role in human disease1-7. The advent of long-read sequencing technologies offers the opportunity to study the role of genetic variation in transcript structure8-16. In this Article, we present a large human long-read RNA-seq dataset using the Oxford Nanopore Technologies platform from 88 samples from Genotype-Tissue Expression (GTEx) tissues and cell lines, complementing the GTEx resource. We identified just over 70,000 novel transcripts for annotated genes, and validated the protein expression of 10% of novel transcripts. We developed a new computational package, LORALS, to analyse the genetic effects of rare and common variants on the transcriptome by allele-specific analysis of long reads. We characterized allele-specific expression and transcript structure events, providing new insights into the specific transcript alterations caused by common and rare genetic variants and highlighting the resolution gained from long-read data. We were able to perturb the transcript structure upon knockdown of PTBP1, an RNA binding protein that mediates splicing, thereby finding genetic regulatory effects that are modified by the cellular environment. Finally, we used this dataset to enhance variant interpretation and study rare variants leading to aberrant splicing patterns.


Assuntos
Alelos , Perfilação da Expressão Gênica , Especificidade de Órgãos , RNA-Seq , Transcriptoma , Processamento Alternativo/genética , Linhagem Celular , Conjuntos de Dados como Assunto , Genótipo , Ribonucleoproteínas Nucleares Heterogêneas/deficiência , Ribonucleoproteínas Nucleares Heterogêneas/genética , Humanos , Especificidade de Órgãos/genética , Proteína de Ligação a Regiões Ricas em Polipirimidinas/deficiência , Proteína de Ligação a Regiões Ricas em Polipirimidinas/genética , Reprodutibilidade dos Testes , Transcriptoma/genética
3.
Sci Rep ; 12(1): 13878, 2022 Aug 16.
Artigo em Inglês | MEDLINE | ID: mdl-35974033

RESUMO

Compound mixtures represent an alternative, additional approach to DNA and synthetic sequence-defined macromolecules in the field of non-conventional molecular data storage, which may be useful depending on the target application. Here, we report a fast and efficient method for information storage in molecular mixtures by the direct use of commercially available chemicals and thus, zero synthetic steps need to be performed. As a proof of principle, a binary coding language is used for encoding words in ASCII or black and white pixels of a bitmap. This way, we stored a 25 × 25-pixel QR code (625 bits) and a picture of the same size. Decoding of the written information is achieved via spectroscopic (1H NMR) or chromatographic (gas chromatography) analysis. In addition, for a faster and automated read-out of the data, we developed a decoding software, which also orders the data sets according to an internal "ordering" standard. Molecular keys or anticounterfeiting are possible areas of application for information-containing compound mixtures.


Assuntos
Armazenamento e Recuperação da Informação , Software , DNA/genética , Conjuntos de Dados como Assunto/estatística & dados numéricos , Armazenamento e Recuperação da Informação/métodos , Armazenamento e Recuperação da Informação/normas , Espectroscopia de Ressonância Magnética
4.
Sci Data ; 9(1): 495, 2022 08 13.
Artigo em Inglês | MEDLINE | ID: mdl-35963862

RESUMO

Pullorum disease and fowl typhoid are among the most significant poultry diseases worldwide. However, the global burden of these diseases remains unknown. Most importantly, the parameters contributing to the prevalence of Salmonella Gallinarum variants are not well documented. Therefore, in this study, we present a systematic review and meta-analysis of the global prevalence of Salmonella Gallinarum during 1945-2021. In total, 201 studies were identified for qualitative analysis (>900 million samples). The meta-analysis was subjected to over 183 screened studies. The global prevalence of S. Gallinarum (percentage of positive samples in total samples) was 8.54% (95% CI: 8.43-8.65) and showed a V-shaped recovery over time. Pullorum disease is most common in Asia, particularly in eastern China. Further investigations on chicken origin samples revealed significant differences in S. Gallinarum prevalence by gender, breed, raising mode, economic use, and growth stage, indicating a critical role of vertical transmission. Together, this study offered an updated, evidence-based dataset and knowledge regarding S. Gallinarum epidemics, which might significantly impact decision-making policy with targeted interventions.


Assuntos
Salmonelose Animal , Salmonella , Animais , Conjuntos de Dados como Assunto , Prevalência , Salmonelose Animal/epidemiologia
5.
Int J Neural Syst ; 32(9): 2250044, 2022 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-35946944

RESUMO

Identifying brain abnormalities in autism spectrum disorder (ASD) is critical for early diagnosis and intervention. To explore brain differences in ASD and typical development (TD) individuals by detecting structural features using T1-weighted magnetic resonance imaging (MRI), we developed a deep learning-based approach, three-dimensional (3D)-ResNet with inception (I-ResNet), to identify participants with ASD and TD and propose a gradient-based backtracking method to pinpoint image areas that I-ResNet uses more heavily for classification. The proposed method was implemented in a preschool dataset with 110 participants and a public autism brain imaging data exchange (ABIDE) dataset with 1099 participants. An extra epilepsy dataset with 200 participants with clear degeneration in the parahippocampal area was applied as a verification and an extension. Among the datasets, we detected nine brain areas that differed significantly between ASD and TD. From the ROC in PASD and ABIDE, the sensitivity was 0.88 and 0.86, specificity was 0.75 and 0.62, and area under the curve was 0.787 and 0.856. In a word, I-ResNet with gradient-based backtracking could identify brain differences between ASD and TD. This study provides an alternative computer-aided technique for helping physicians to diagnose and screen children with an potential risk of ASD with deep learning model.


Assuntos
Transtorno do Espectro Autista/diagnóstico por imagem , Encéfalo/diagnóstico por imagem , Aprendizado Profundo , Adolescente , Transtorno do Espectro Autista/patologia , Encéfalo/patologia , Mapeamento Encefálico/métodos , Estudos de Casos e Controles , Criança , Pré-Escolar , Conjuntos de Dados como Assunto , Humanos , Imageamento Tridimensional , Imageamento por Ressonância Magnética/métodos
6.
Int J Neural Syst ; 32(9): 2250043, 2022 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-35912583

RESUMO

A practical problem in supervised deep learning for medical image segmentation is the lack of labeled data which is expensive and time-consuming to acquire. In contrast, there is a considerable amount of unlabeled data available in the clinic. To make better use of the unlabeled data and improve the generalization on limited labeled data, in this paper, a novel semi-supervised segmentation method via multi-task curriculum learning is presented. Here, curriculum learning means that when training the network, simpler knowledge is preferentially learned to assist the learning of more difficult knowledge. Concretely, our framework consists of a main segmentation task and two auxiliary tasks, i.e. the feature regression task and target detection task. The two auxiliary tasks predict some relatively simpler image-level attributes and bounding boxes as the pseudo labels for the main segmentation task, enforcing the pixel-level segmentation result to match the distribution of these pseudo labels. In addition, to solve the problem of class imbalance in the images, a bounding-box-based attention (BBA) module is embedded, enabling the segmentation network to concern more about the target region rather than the background. Furthermore, to alleviate the adverse effects caused by the possible deviation of pseudo labels, error tolerance mechanisms are also adopted in the auxiliary tasks, including inequality constraint and bounding-box amplification. Our method is validated on ACDC2017 and PROMISE12 datasets. Experimental results demonstrate that compared with the full supervision method and state-of-the-art semi-supervised methods, our method yields a much better segmentation performance on a small labeled dataset. Code is available at https://github.com/DeepMedLab/MTCL.


Assuntos
Currículo , Aprendizado de Máquina Supervisionado , Curadoria de Dados/métodos , Curadoria de Dados/normas , Conjuntos de Dados como Assunto/normas , Conjuntos de Dados como Assunto/provisão & distribuição , Processamento de Imagem Assistida por Computador/métodos , Aprendizado de Máquina Supervisionado/classificação , Aprendizado de Máquina Supervisionado/estatística & dados numéricos , Aprendizado de Máquina Supervisionado/tendências
7.
Sci Rep ; 12(1): 14626, 2022 Aug 26.
Artigo em Inglês | MEDLINE | ID: mdl-36028547

RESUMO

Polyp segmentation has accomplished massive triumph over the years in the field of supervised learning. However, obtaining a vast number of labeled datasets is commonly challenging in the medical domain. To solve this problem, we employ semi-supervised methods and suitably take advantage of unlabeled data to improve the performance of polyp image segmentation. First, we propose an encoder-decoder-based method well suited for the polyp with varying shape, size, and scales. Second, we utilize the teacher-student concept of training the model, where the teacher model is the student model's exponential average. Third, to leverage the unlabeled dataset, we enforce a consistency technique and force the teacher model to generate a similar output on the different perturbed versions of the given input. Finally, we propose a method that upgrades the traditional pseudo-label method by learning the model with continuous update of pseudo-label. We show the efficacy of our proposed method on different polyp datasets, and hence attaining better results in semi-supervised settings. Extensive experiments demonstrate that our proposed method can propagate the unlabeled dataset's essential information to improve performance.


Assuntos
Pólipos/patologia , Aprendizado de Máquina Supervisionado , Conjuntos de Dados como Assunto/normas , Conjuntos de Dados como Assunto/tendências , Humanos , Processamento de Imagem Assistida por Computador , Pólipos/diagnóstico por imagem
8.
Nature ; 608(7921): 108-121, 2022 08.
Artigo em Inglês | MEDLINE | ID: mdl-35915342

RESUMO

Social capital-the strength of an individual's social network and community-has been identified as a potential determinant of outcomes ranging from education to health1-8. However, efforts to understand what types of social capital matter for these outcomes have been hindered by a lack of social network data. Here, in the first of a pair of papers9, we use data on 21 billion friendships from Facebook to study social capital. We measure and analyse three types of social capital by ZIP (postal) code in the United States: (1) connectedness between different types of people, such as those with low versus high socioeconomic status (SES); (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analysing their associations with economic mobility across areas. The share of high-SES friends among individuals with low SES-which we term economic connectedness-is among the strongest predictors of upward income mobility identified to date10,11. Other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality12-14. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org .


Assuntos
Status Econômico , Amigos , Renda , Capital Social , Mobilidade Social , Adulto , Criança , Relações Comunidade-Instituição , Conjuntos de Dados como Assunto , Status Econômico/estatística & dados numéricos , Mapeamento Geográfico , Humanos , Renda/estatística & dados numéricos , Pobreza/estatística & dados numéricos , Racismo , Mídias Sociais/estatística & dados numéricos , Mobilidade Social/estatística & dados numéricos , Apoio Social , Estados Unidos , Voluntários
9.
Nature ; 608(7921): 122-134, 2022 08.
Artigo em Inglês | MEDLINE | ID: mdl-35915343

RESUMO

Low levels of social interaction across class lines have generated widespread concern1-4 and are associated with worse outcomes, such as lower rates of upward income mobility4-7. Here we analyse the determinants of cross-class interaction using data from Facebook, building on the analysis in our companion paper7. We show that about half of the social disconnection across socioeconomic lines-measured as the difference in the share of high-socioeconomic status (SES) friends between people with low and high SES-is explained by differences in exposure to people with high SES in groups such as schools and religious organizations. The other half is explained by friending bias-the tendency for people with low SES to befriend people with high SES at lower rates even conditional on exposure. Friending bias is shaped by the structure of the groups in which people interact. For example, friending bias is higher in larger and more diverse groups and lower in religious organizations than in schools and workplaces. Distinguishing exposure from friending bias is helpful for identifying interventions to increase cross-SES friendships (economic connectedness). Using fluctuations in the share of students with high SES across high school cohorts, we show that increases in high-SES exposure lead low-SES people to form more friendships with high-SES people in schools that exhibit low levels of friending bias. Thus, socioeconomic integration can increase economic connectedness in communities in which friending bias is low. By contrast, when friending bias is high, increasing cross-SES interactions among existing members may be necessary to increase economic connectedness. To support such efforts, we release privacy-protected statistics on economic connectedness, exposure and friending bias for each ZIP (postal) code, high school and college in the United States at https://www.socialcapital.org .


Assuntos
Status Econômico , Amigos , Mapeamento Geográfico , Instituições Acadêmicas , Capital Social , Classe Social , Estudantes , Conjuntos de Dados como Assunto , Status Econômico/estatística & dados numéricos , Humanos , Renda/estatística & dados numéricos , Preconceito/estatística & dados numéricos , Instituições Acadêmicas/estatística & dados numéricos , Mídias Sociais/estatística & dados numéricos , Estudantes/estatística & dados numéricos , Estados Unidos , Universidades/estatística & dados numéricos
10.
Nature ; 608(7921): 80-86, 2022 08.
Artigo em Inglês | MEDLINE | ID: mdl-35922501

RESUMO

Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change3.


Assuntos
Secas , Clima Extremo , Inundações , Gestão de Riscos , Mudança Climática/estatística & dados numéricos , Conjuntos de Dados como Assunto , Secas/prevenção & controle , Secas/estatística & dados numéricos , Inundações/prevenção & controle , Inundações/estatística & dados numéricos , Humanos , Hidrologia , Internacionalidade , Gestão de Riscos/métodos , Gestão de Riscos/estatística & dados numéricos , Gestão de Riscos/tendências
11.
J Chem Inf Model ; 62(17): 3982-3992, 2022 Sep 12.
Artigo em Inglês | MEDLINE | ID: mdl-35971760

RESUMO

Adverse events are a serious issue in drug development, and many prediction methods using machine learning have been developed. The random split cross-validation is the de facto standard for model building and evaluation in machine learning, but care should be taken in adverse event prediction because this approach does not strictly match the real-world situation. The time split, which uses the time axis, is considered suitable for real-world prediction. However, the differences in model performance obtained using the time and random splits are not clear due to the lack of comparable studies. To understand the differences, we compared the model performance between the time and random splits using nine types of compound information as input, eight adverse events as targets, and six machine learning algorithms. The random split showed higher area under the curve values than did the time split for six of eight targets. The chemical spaces of the training and test datasets of the time split were similar, suggesting that the concept of applicability domain is insufficient to explain the differences derived from the splitting. The area under the curve differences were smaller for the protein interaction than for the other datasets. Subsequent detailed analyses suggested the danger of confounding in the use of knowledge-based information in the time split. These findings indicate the importance of understanding the differences between the time and random splits in adverse event prediction and suggest that appropriate use of the splitting strategies and interpretation of results are necessary for the real-world prediction of adverse events. We provide the analysis code and datasets used in the present study at https://github.com/mizuno-group/AE_prediction.


Assuntos
Algoritmos , Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Aprendizado de Máquina , Conjuntos de Dados como Assunto , Previsões
12.
Nature ; 609(7925): 109-118, 2022 09.
Artigo em Inglês | MEDLINE | ID: mdl-36002572

RESUMO

Individual differences in brain functional organization track a range of traits, symptoms and behaviours1-12. So far, work modelling linear brain-phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants13,14. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain-phenotype relationships. To this end, here we related brain activity to phenotype using predictive models-trained and tested on independent data to ensure generalizability15-and examined model failure. We applied this data-driven approach to a range of neurocognitive measures in a new, clinically and demographically heterogeneous dataset, with the results replicated in two independent, publicly available datasets16,17. Across all three datasets, we find that models reflect not unitary cognitive constructs, but rather neurocognitive scores intertwined with sociodemographic and clinical covariates; that is, models reflect stereotypical profiles, and fail when applied to individuals who defy them. Model failure is reliable, phenotype specific and generalizable across datasets. Together, these results highlight the pitfalls of a one-size-fits-all modelling approach and the effect of biased phenotypic measures18-20 on the interpretation and utility of resulting brain-phenotype models. We present a framework to address these issues so that such models may reveal the neural circuits that underlie specific phenotypes and ultimately identify individualized neural targets for clinical intervention.


Assuntos
Encéfalo , Simulação por Computador , Individualidade , Fenótipo , Estereotipagem , Encéfalo/anatomia & histologia , Encéfalo/fisiologia , Conjuntos de Dados como Assunto , Humanos , Testes de Estado Mental e Demência , Modelos Biológicos
13.
Sci Data ; 9(1): 449, 2022 07 27.
Artigo em Inglês | MEDLINE | ID: mdl-35896564

RESUMO

Recent advances in fluorescence microscopy techniques and tissue clearing, labeling, and staining provide unprecedented opportunities to investigate brain structure and function. These experiments' images make it possible to catalog brain cell types and define their location, morphology, and connectivity in a native context, leading to a better understanding of normal development and disease etiology. Consistent annotation of metadata is needed to provide the context necessary to understand, reuse, and integrate these data. This report describes an effort to establish metadata standards for three-dimensional (3D) microscopy datasets for use by the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative and the neuroscience research community. These standards were built on existing efforts and developed with input from the brain microscopy community to promote adoption. The resulting 3D Microscopy Metadata Standards (3D-MMS) includes 91 fields organized into seven categories: Contributors, Funders, Publication, Instrument, Dataset, Specimen, and Image. Adoption of these metadata standards will ensure that investigators receive credit for their work, promote data reuse, facilitate downstream analysis of shared data, and encourage collaboration.


Assuntos
Metadados , Microscopia , Encéfalo/anatomia & histologia , Encéfalo/diagnóstico por imagem , Conjuntos de Dados como Assunto , Humanos , Microscopia/métodos , Microscopia/normas
14.
Proc Natl Acad Sci U S A ; 119(30): e2122593119, 2022 07 26.
Artigo em Inglês | MEDLINE | ID: mdl-35858413

RESUMO

Although political violence has been perpetrated on behalf of a wide range of political ideologies, it is unclear whether there are systematic differences between ideologies in the use of violence to pursue a political cause. Prior research on this topic is scarce and mostly restricted to self-reported measures or less extreme forms of political aggression. Moreover, it has generally focused on respondents in Western countries and has been limited to either comparisons of the supporters of left-wing and right-wing causes or examinations of only Islamist extremism. In this research we address these gaps by comparing the use of political violence by left-wing, right-wing, and Islamist extremists in the United States and worldwide using two unique datasets that cover real-world examples of politically motivated, violent behaviors. Across both datasets, we find that radical acts perpetrated by individuals associated with left-wing causes are less likely to be violent. In the United States, we find no difference between the level of violence perpetrated by right-wing and Islamist extremists. However, differences in violence emerge on the global level, with Islamist extremists being more likely than right-wing extremists to engage in more violent acts.


Assuntos
Islamismo , Política , Violência , Conjuntos de Dados como Assunto , Humanos , Estados Unidos , Violência/tendências
15.
Sensors (Basel) ; 22(13)2022 Jun 30.
Artigo em Inglês | MEDLINE | ID: mdl-35808449

RESUMO

In this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of augmenting continuous glucose monitor data from individual patients. We use these synthetic data with the aim of improving the overall performance of prediction models based on machine learning techniques. Experiments were performed on two cohorts of patients suffering from type 1 diabetes mellitus with significant differences in their clinical outcomes. In the first contribution, we have demonstrated that the chosen methodology is able to replicate the intrinsic characteristics of individual patients following the statistical distributions of the original data. Next, a second contribution demonstrates the potential of synthetic data to improve the performance of machine learning approaches by testing and comparing different prediction models for the problem of predicting nocturnal hypoglycemic events in type 1 diabetic patients. The results obtained for both generative and predictive models are quite encouraging and set a precedent in the use of generative techniques to train new machine learning models.


Assuntos
Simulação por Computador , Aprendizado Profundo , Diabetes Mellitus Tipo 1 , Glicemia/análise , Automonitorização da Glicemia , Estudos de Coortes , Conjuntos de Dados como Assunto , Diabetes Mellitus Tipo 1/diagnóstico , Humanos , Hipoglicemia/diagnóstico , Redes Neurais de Computação
16.
Sensors (Basel) ; 22(14)2022 Jul 12.
Artigo em Inglês | MEDLINE | ID: mdl-35890883

RESUMO

Cleaning is an important task that is practiced in every domain and has prime importance. The significance of cleaning has led to several newfangled technologies in the domestic and professional cleaning domain. However, strategies for auditing the cleanliness delivered by the various cleaning methods remain manual and often ignored. This work presents a novel domestic dirt image dataset for cleaning auditing application including AI-based dirt analysis and robot-assisted cleaning inspection. One of the significant challenges in an AI-based robot-aided cleaning auditing is the absence of a comprehensive dataset for dirt analysis. We bridge this gap by identifying nine classes of commonly occurring domestic dirt and a labeled dataset consisting of 3000 microscope dirt images curated from a semi-indoor environment. The dirt dataset gathered using the adhesive dirt lifting method can enhance the current dirt sensing and dirt composition estimation for cleaning auditing. The dataset's quality is analyzed by AI-based dirt analysis and a robot-aided cleaning auditing task using six standard classification models. The models trained with the dirt dataset were capable of yielding a classification accuracy above 90% in the offline dirt analysis experiment and 82% in real-time test results.


Assuntos
Solo , Conjuntos de Dados como Assunto
17.
J Clin Pathol ; 75(8): 514-518, 2022 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-35853656

RESUMO

In breast cancer, the quality of the pathology services is of paramount importance as inevitably, the pathologist makes the confirmatory diagnosis and provides prognostic and predictive information, informing treatment plans directly. Various national and international organisations provide a pathology reporting minimum dataset (MDS) to ensure consistency in reporting. While the use of MDS promotes clarity, there may be specific areas requiring the pathologist's input for individual patients and hence pathologists need to be aware of the clinical utility of pathology data to help tailor individualised patient treatment. In this article, we provide numerous examples of the role of pathology data in determining next steps in the patient pathway that are applicable to both the diagnostic and treatment pathways, including neoadjuvant treatment pathways. We also briefly discuss the important role and thereby the clinical utility of pathology data during the COVID-19 pandemic providing a template for the similar scenarios in the future if required.


Assuntos
Neoplasias da Mama , Mama , Conjuntos de Dados como Assunto , Mama/patologia , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/patologia , Neoplasias da Mama/terapia , COVID-19/epidemiologia , Feminino , Humanos , Pandemias , Patologistas
18.
Sci Rep ; 12(1): 11073, 2022 06 30.
Artigo em Inglês | MEDLINE | ID: mdl-35773438

RESUMO

Integrating data across institutions can improve learning efficiency. To integrate data efficiently while protecting privacy, we propose A one-shot, summary-statistics-based, Distributed Algorithm for fitting Penalized (ADAP) regression models across multiple datasets. ADAP utilizes patient-level data from a lead site and incorporates the first-order (ADAP1) and second-order gradients (ADAP2) of the objective function from collaborating sites to construct a surrogate objective function at the lead site, where model fitting is then completed with proper regularizations applied. We evaluate the performance of the proposed method using both simulation and a real-world application to study risk factors for opioid use disorder (OUD) using 15,000 patient data from the OneFlorida Clinical Research Consortium. Our results show that ADAP performs nearly the same as the pooled estimator but achieves higher estimation accuracy and better variable selection than the local and average estimators. Moreover, ADAP2 successfully handles heterogeneity in covariate distributions.


Assuntos
Algoritmos , Transtornos Relacionados ao Uso de Opioides , Simulação por Computador , Conjuntos de Dados como Assunto , Humanos , Transtornos Relacionados ao Uso de Opioides/epidemiologia , Análise de Regressão , Fatores de Risco
19.
J Mol Biol ; 434(17): 167690, 2022 Sep 15.
Artigo em Inglês | MEDLINE | ID: mdl-35728652

RESUMO

Accurate development of allosteric modulators of GPCRs require a thorough assessment of their sequence, structure, and dynamics, toward gaining insights into their mechanisms of actions shared by family members, as well as dynamic features that distinguish subfamilies. Building on recent progress in the characterization of the signature dynamics of proteins, we analyzed here a dataset of 160 Class A GPCRs to determine their sequence similarities, structural landscape, and dynamic features across different species (human, bovine, mouse, squid, and rat), different activation states (active/inactive), and different subfamilies. The two dominant directions of variability across experimentally resolved structures, identified by principal component analysis of the dataset, shed light to cooperative mechanisms of activation, subfamily differentiation, and speciation of Class A GPCRs. The analysis reveals the functional significance of the conformational flexibilities of specific structural elements, including: the dominant role of the intracellular loop 3 (ICL3) together with the cytoplasmic ends of the adjoining helices TM5 and TM6 in enabling allosteric activation; the role of particular structural motifs at the extracellular loop 2 (ECL2) connecting TM4 and TM5 in binding ligands specific to different subfamilies; or even the differentiation of the N-terminal conformation across different species. Detailed analyses of the modes of motions accessible to the members of the dataset and their variations across members demonstrate how the active and inactive states of GPCRs obey distinct conformational dynamics. The collective fluctuations of the GPCRs are robustly defined in the active state, while the inactive conformers exhibit broad variance among members.


Assuntos
Receptores Acoplados a Proteínas G , Animais , Bovinos , Conjuntos de Dados como Assunto , Humanos , Ligantes , Camundongos , Simulação de Dinâmica Molecular , Conformação Proteica , Estrutura Secundária de Proteína , Ratos , Receptores Acoplados a Proteínas G/química
20.
Proc Natl Acad Sci U S A ; 119(27): e2200047119, 2022 07 05.
Artigo em Inglês | MEDLINE | ID: mdl-35759656

RESUMO

Adequate pain management is one of the biggest challenges of the modern healthcare system. Physician perception of patient subjective pain, which is crucial to pain management, is susceptible to a host of potential biases. Here we explore the timing of physicians' work as a previously unrecognized source of systematic bias in pain management. We hypothesized that during night shifts, sleep deprivation, fatigue, and stress would reduce physicians' empathy for others' pain, leading to underprescription of analgesics for patient pain relief. In study 1, 67 resident physicians, either following a night shift or not, performed empathy for pain assessment tasks and simulated patient scenarios in laboratory conditions. As predicted, following a night shift, physicians showed reduced empathy for pain. In study 2, we explored this phenomenon in medical decisions in the field. We analyzed three emergency department datasets from Israel and the United States that included discharge notes of patients arriving with pain complaints during 2013 to 2020 (n = 13,482). Across all datasets, physicians were less likely to prescribe an analgesic during night shifts (compared to daytime shifts) and prescribed fewer analgesics than generally recommended by the World Health Organization. This effect remained significant after adjusting for patient, physician, type of complaint, and emergency department characteristics. Underprescription for pain during night shifts was particularly prominent for opioids. We conclude that night shift work is an important and previously unrecognized source of bias in pain management, likely stemming from impaired perception of pain. We consider the implications for hospitals and other organizations employing night shifts.


Assuntos
Analgésicos , Prescrições de Medicamentos , Empatia , Relações Médico-Paciente , Médicos , Jornada de Trabalho em Turnos , Analgésicos/uso terapêutico , Conjuntos de Dados como Assunto , Humanos , Israel , Dor/tratamento farmacológico , Médicos/psicologia , Jornada de Trabalho em Turnos/psicologia , Privação do Sono , Estados Unidos
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