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BMC Women's Health Jul 2024For women who have experienced recurrent pregnancy loss (RPL), it is crucial not only to treat them but also to evaluate the risk of recurrence. The study aimed to...
BACKGROUND
For women who have experienced recurrent pregnancy loss (RPL), it is crucial not only to treat them but also to evaluate the risk of recurrence. The study aimed to develop a risk predictive model to predict the subsequent early pregnancy loss (EPL) in women with RPL based on preconception data.
METHODS
A prospective, dynamic population cohort study was carried out at the Second Hospital of Lanzhou University. From September 2019 to December 2022, a total of 1050 non-pregnant women with RPL were participated. By December 2023, 605 women had subsequent pregnancy outcomes and were randomly divided into training and validation group by 3:1 ratio. In the training group, univariable screening was performed on RPL patients with subsequent EPL outcome. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were utilized to select variables, respectively. Subsequent EPL prediction model was constructed using generalize linear model (GLM), gradient boosting machine (GBM), random forest (RF), and deep learning (DP). The variables selected by LASSO regression and multivariate logistic regression were then established and compared using the best prediction model. The AUC, calibration curve, and decision curve (DCA) were performed to assess the prediction performances of the best model. The best model was validated using the validation group. Finally, a nomogram was established based on the best predictive features.
RESULTS
In the training group, the GBM model achieved the best performance with the highest AUC (0.805). The AUC between the variables screened by the LASSO regression (16-variables) and logistic regression (9-variables) models showed no significant difference (AUC: 0.805 vs. 0.777, P = 0.1498). Meanwhile, the 9-variable model displayed a well discrimination performance in the validation group, with an AUC value of 0.781 (95%CI 0.702, 0.843). The DCA showed the model performed well and was feasible for making beneficial clinical decisions. Calibration curves revealed the goodness of fit between the predicted values by the model and the actual values, the Hosmer-Lemeshow test was 7.427, and P = 0.505.
CONCLUSIONS
Predicting subsequent EPL in RPL patients using the GBM model has important clinical implications. Future prospective studies are needed to verify the clinical applicability.
TRIAL REGISTRATION
This study was registered in the Chinese Clinical Trial Registry with the registration number of ChiCTR2000039414 (27/10/2020).
Topics: Humans; Female; Pregnancy; Adult; Abortion, Habitual; Prospective Studies; Risk Assessment; Risk Factors; China; Cohort Studies; Logistic Models
PubMed: 38956627
DOI: 10.1186/s12905-024-03206-9 -
Nature Communications Jul 2024Olfaction is influenced by contextual factors, past experiences, and the animal's internal state. Whether this information is integrated at the initial stages of...
Olfaction is influenced by contextual factors, past experiences, and the animal's internal state. Whether this information is integrated at the initial stages of cortical odour processing is not known, nor how these signals may influence odour encoding. Here we revealed multiple and diverse non-olfactory responses in the primary olfactory (piriform) cortex (PCx), which dynamically enhance PCx odour discrimination according to behavioural demands. We performed recordings of PCx neurons from mice trained in a virtual reality task to associate odours with visual contexts to obtain a reward. We found that learning shifts PCx activity from encoding solely odours to a regime in which positional, contextual, and associative responses emerge on odour-responsive neurons that become mixed-selective. The modulation of PCx activity by these non-olfactory signals was dynamic, improving odour decoding during task engagement and in rewarded contexts. This improvement relied on the acquired mixed-selectivity, demonstrating how integrating extra-sensory inputs in sensory cortices can enhance sensory processing while encoding the behavioural relevance of stimuli.
Topics: Animals; Odorants; Mice; Smell; Reward; Male; Olfactory Cortex; Piriform Cortex; Mice, Inbred C57BL; Olfactory Perception; Neurons; Female; Discrimination, Psychological
PubMed: 38956072
DOI: 10.1038/s41467-024-49897-4 -
CMAJ : Canadian Medical Association... Jul 2024Transgender and nonbinary (TNB) people experience obstacles that create barriers to accessing health care, including stigmatization and health inequities. Our intention...
BACKGROUND
Transgender and nonbinary (TNB) people experience obstacles that create barriers to accessing health care, including stigmatization and health inequities. Our intention was to describe the lived experiences of TNB patients and identify potential gaps in the education of health care professionals.
METHODS
We conducted a qualitative descriptive study influenced by phenomenology by interviewing with TNB adults who underwent surgery in Canada within the previous 5 years. We recruited participants using purposeful and snowball sampling via online social networking sites. Audio recordings were transcribed. Two authors coded the transcripts and derived the themes.
RESULTS
We interviewed 21 participants, with a median interview duration of 49 minutes. Participants described positive and negative health care encounters that led to stress, confusion, and feelings of vulnerability. Major themes included having to justify their need for health care in the face of structural discrimination; fear and previous traumatic experiences; community as a source of support and information; and the impact of interactions with health care professionals.
INTERPRETATION
Participants detailed barriers to accessing care, struggled to participate in shared decision-making, and desired trauma-informed care principles; they described strength in community and positive interactions with health care professionals, although barriers to accessing gender-affirming care often overshadowed other aspects of the perioperative experience. Additional research, increased education for health care professionals, and policy changes are necessary to improve access to competent care for TNB people.
Topics: Humans; Female; Male; Qualitative Research; Adult; Transgender Persons; Canada; Middle Aged; Health Services Accessibility; Aged; Social Stigma; Young Adult
PubMed: 38955410
DOI: 10.1503/cmaj.240061 -
Medical Image Analysis Jun 2024Accurate histopathological subtype prediction is clinically significant for cancer diagnosis and tumor microenvironment analysis. However, achieving accurate...
Accurate histopathological subtype prediction is clinically significant for cancer diagnosis and tumor microenvironment analysis. However, achieving accurate histopathological subtype prediction is a challenging task due to (1) instance-level discrimination of histopathological images, (2) low inter-class and large intra-class variances among histopathological images in their shape and chromatin texture, and (3) heterogeneous feature distribution over different images. In this paper, we formulate subtype prediction as fine-grained representation learning and propose a novel multi-instance selective transformer (MIST) framework, effectively achieving accurate histopathological subtype prediction. The proposed MIST designs an effective selective self-attention mechanism with multi-instance learning (MIL) and vision transformer (ViT) to adaptive identify informative instances for fine-grained representation. Innovatively, the MIST entrusts each instance with different contributions to the bag representation based on its interactions with instances and bags. Specifically, a SiT module with selective multi-head self-attention (S-MSA) is well-designed to identify the representative instances by modeling the instance-to-instance interactions. On the contrary, a MIFD module with the information bottleneck is proposed to learn the discriminative fine-grained representation for histopathological images by modeling instance-to-bag interactions with the selected instances. Substantial experiments on five clinical benchmarks demonstrate that the MIST achieves accurate histopathological subtype prediction and obtains state-of-the-art performance with an accuracy of 0.936. The MIST shows great potential to handle fine-grained medical image analysis, such as histopathological subtype prediction in clinical applications.
PubMed: 38954942
DOI: 10.1016/j.media.2024.103251 -
IEEE Transactions on Image Processing :... Jul 2024Conventional image set methods typically learn from small to medium-sized image set datasets. However, when applied to large-scale image set applications such as...
Conventional image set methods typically learn from small to medium-sized image set datasets. However, when applied to large-scale image set applications such as classification and retrieval, they face two primary challenges: 1) effectively modeling complex image sets, and 2) efficiently performing tasks. To address the above issues, we propose a novel Multiple Riemannian Kernel Hashing (MRKH) method that leverages the powerful capabilities of Riemannian manifold and Hashing on effective and efficient image set representation. MRKH considers multiple heterogeneous Riemannian manifolds to represent each image set. It introduces a multiple kernel learning framework designed to effectively combine statistics from multiple manifolds, and constructs kernels by selecting a small set of anchor points, enabling efficient scalability for large-scale applications. In addition, MRKH further exploits inter- and intra-modal semantic structure to enhance discrimination. Instead of employing continuous feature to represent each image set, MRKH suggests learning hash code for each image set, thereby achieving efficient computation and storage. We present an iterative algorithm with theoretical convergence guarantee to optimize MRKH, and the computational complexity is linear with the size of dataset. Extensive experiments on five image set benchmark datasets including three large-scale ones demonstrate the proposed method outperforms state-of-the-arts in accuracy and efficiency particularly in large-scale image set classification and retrieval.
PubMed: 38954580
DOI: 10.1109/TIP.2024.3419414 -
Orthopaedic Surgery Jul 2024Total hip arthroplasty (THA) remains the primary treatment option for femoral neck fractures in elderly patients. This study aims to explore the risk factors associated...
Development and Validation of a Machine Learning Algorithm to Predict the Risk of Blood Transfusion after Total Hip Replacement in Patients with Femoral Neck Fractures: A Multicenter Retrospective Cohort Study.
OBJECTIVE
Total hip arthroplasty (THA) remains the primary treatment option for femoral neck fractures in elderly patients. This study aims to explore the risk factors associated with allogeneic blood transfusion after surgery and to develop a dynamic prediction model to predict post-operative blood transfusion requirements. This will provide more accurate guidance for perioperative humoral management and rational allocation of medical resources.
METHODS
We retrospectively analyzed data from 829 patients who underwent total hip arthroplasty for femoral neck fractures at three third-class hospitals between January 2017 and August 2023. Patient data from one hospital were used for model development, whereas data from the other two hospitals were used for external validation. Logistic regression analysis was used to screen the characteristic subsets related to blood transfusion. Various machine learning algorithms, including logistic regression, SVA (support vector machine), K-NN (k-nearest neighbors), MLP (multilayer perceptron), naive Bayes, decision tree, random forest, and gradient boosting, were used to process the data and construct prediction models. A 10-fold cross-validation algorithm facilitated the comparison of the predictive performance of the models, resulting in the selection of the best-performing model for the development of an open-source computing program.
RESULTS
BMI (body mass index), surgical duration, IBL (intraoperative blood loss), anticoagulant history, utilization rate of tranexamic acid, Pre-Hb, and Pre-ALB were included in the model as well as independent risk factors. The average area under curve (AUC) values for each model were as follows: logistic regression (0.98); SVA (0.91); k-NN (0.87) MLP, (0.96); naive Bayes (0.97); decision tree (0.87); random forest (0.96); and gradient boosting (0.97). A web calculator based on the best model is available at: (https://nomo99.shinyapps.io/dynnomapp/).
CONCLUSION
Utilizing a computer algorithm, a prediction model with a high discrimination accuracy (AUC > 0.5) was developed. The logistic regression model demonstrated superior differentiation and reliability, thereby successfully passing external validation. The model's strong generalizability and applicability have significant implications for clinicians, aiding in the identification of patients at high risk for postoperative blood transfusion.
PubMed: 38951965
DOI: 10.1111/os.14160 -
Microbiome Jun 2024Shotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and...
BACKGROUND
Shotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, the latter of which then helps retrieve their function based on annotated gene products, mRNA, protein, and metabolites. Within the context of a specific hypothesis, additional modalities are often included, to give host-microbiome interaction. For example, in human-associated microbiome projects, it has become increasingly common to include host immunology through flow cytometry. Whilst there are plenty of software approaches available, some that utilize marker-based and assembly-based approaches, for downstream statistical analyses, there is still a dearth of statistical tools that help consolidate all such information in a single platform. By virtue of stringent computational requirements, the statistical workflow is often passive with limited visual exploration.
RESULTS
In this study, we have developed a Java-based statistical framework ( https://github.com/KociOrges/cviewer ) to explore shotgun metagenomics data, which integrates seamlessly with conventional pipelines and offers exploratory as well as hypothesis-driven analyses. The end product is a highly interactive toolkit with a multiple document interface, which makes it easier for a person without specialized knowledge to perform analysis of multiomics datasets and unravel biologically relevant patterns. We have designed algorithms based on frequently used numerical ecology and machine learning principles, with value-driven from integrated omics tools which not only find correlations amongst different datasets but also provide discrimination based on case-control relationships.
CONCLUSIONS
CViewer was used to analyse two distinct metagenomic datasets with varying complexities. These include a dietary intervention study to understand Crohn's disease changes during a dietary treatment to include remission, as well as a gut microbiome profile for an obesity dataset comparing subjects who suffer from obesity of different aetiologies and against controls who were lean. Complete analyses of both studies in CViewer then provide very powerful mechanistic insights that corroborate with the published literature and demonstrate its full potential. Video Abstract.
Topics: Metagenomics; Humans; Software; Microbiota; Gastrointestinal Microbiome; Computational Biology; Metagenome; Crohn Disease
PubMed: 38951915
DOI: 10.1186/s40168-024-01834-9 -
Rhinology Jul 2024diabetic complications and olfactory dysfunction (OD) in patients with type 2 diabetes mellitus (T2DM) seem related. This study aims to evaluate the prevalence of OD in...
BACKGROUND
diabetic complications and olfactory dysfunction (OD) in patients with type 2 diabetes mellitus (T2DM) seem related. This study aims to evaluate the prevalence of OD in T2DM patients and to analyze its relationship with diabetic complications.
METHODS
130 T2DM patients and 100 comparable controls were enrolled. Olfaction was evaluated using the Extended Smell Test (TDI) and the Italian brief Questionnaire of Olfactory Disorders - Brief-IT-QOD. T2DM patients were divided into: "Group 1", patients with no complications, and "Group 2", patients with at least one diabetic complication. Non-parametric tests were used. Machine learning algorithms were applied to explore which variables were most important in predicting the presence of OD in T2DM.
RESULTS
The prevalence of OD was significantly higher in Group 2 than in controls (71.4% vs 30%) and in Group 1 (71.4% vs 43.3%). However, when comparing the TDI scores between Group 1 and 2 the only significant difference was found for the discrimination scale and not for the identification and threshold scales. Brief-IT-QOD scores were significantly higher in Group 2 than in controls. The Random Forest and variable importance algorithms highlighted the relevance of LDL, glycated hemoglobin, type of complication (macrovascular) and age in determining OD in T2DM. The last three variables were included in a nomogram for the prediction of OD risk in T2DM.
CONCLUSIONS
T2DM patients with diabetic complications are more frequently affected by OD. Poor glycemic control, LDL values, age and presence of macrovascular complications are the more important factors in determining OD in T2DM patients.
PubMed: 38950422
DOI: 10.4193/Rhin23.451 -
African Journal of Primary Health Care... Jun 2024Infection by human immunodeficiency virus (HIV) is a major disease in children, affecting an estimated 1.8 million children and adolescents worldwide. Eswatini has the...
BACKGROUND
Infection by human immunodeficiency virus (HIV) is a major disease in children, affecting an estimated 1.8 million children and adolescents worldwide. Eswatini has the highest prevalence of HIV in the world. Only 76% of children in Eswatini are on anti-retroviral treatment.
AIM
This study aimed to gain an in-depth understanding of the lived experience of school-going children with HIV in Eswatini. Being aware of these children's experiences can assist schools in supporting them.
SETTING
The study was conducted in four primary health care facilities in Eswatini.
METHODS
Employing a qualitative, exploratory, descriptive research design, 12 school-going children with HIV were interviewed through semi-structured face-to-face interviews. The data were coded, categorised and clustered into themes and sub-themes using Georgi's data analysis. Ethical considerations and measures to ensure trustworthiness were adhered to throughout the study.
RESULTS
The findings revealed three themes: Experiences after HIV disclosure, experience of disclosure and discrimination, and experience of desire to fulfil educational needs. Six sub-themes were identified: A feeling of sadness and worry relating to knowledge of HIV diagnosis, a desire to disclose their status to their teachers but not to their peers, a need for protection against discrimination, a desire to learn, illness affecting their learning and expectation for teachers to be supportive in their educational needs.Conclusion and contribution: The findings of the study guided recommendations that may assist, the Eswatini Ministry of Health, schools, parents and caregivers, and siblings to support school-going children with HIV.
Topics: Humans; HIV Infections; Male; Female; Qualitative Research; Child; Eswatini; Adolescent; Schools; Interviews as Topic; Social Stigma; Students
PubMed: 38949441
DOI: 10.4102/phcfm.v16i1.4472 -
Journal of Chemical Information and... Jul 2024This study addresses the challenge of accurately identifying stereoisomers in cheminformatics, which originates from our objective to apply machine learning to predict...
This study addresses the challenge of accurately identifying stereoisomers in cheminformatics, which originates from our objective to apply machine learning to predict the association constant between cyclodextrin and a guest. Identifying stereoisomers is indeed crucial for machine learning applications. Current tools offer various molecular descriptors, including their textual representation as Isomeric SMILES that can distinguish stereoisomers. However, such representation is text-based and does not have a fixed size, so a conversion is needed to make it usable to machine learning approaches. Word embedding techniques can be used to solve this problem. Mol2vec, a word embedding approach for molecules, offers such a conversion. Unfortunately, it cannot distinguish between stereoisomers due to its inability to capture the spatial configuration of molecular structures. This study proposes several approaches that use word embedding techniques to handle molecular discrimination using stereochemical information on molecules or considering Isomeric SMILES notation as a text in Natural Language Processing. Our aim is to generate a distinct vector for each unique molecule, correctly identifying stereoisomer information in cheminformatics. The proposed approaches are then compared to our original machine learning task: predicting the association constant between cyclodextrin and a guest molecule.
PubMed: 38949069
DOI: 10.1021/acs.jcim.4c00318