Large Population Dataset Research Articles

Gait-CNN-ViT: Multi-Model Gait Recognition with Convolutional Neural Networks and Vision Transformer.

Gait recognition, the task of identifying an individual based on their unique walking style, can be difficult because walking styles can be influenced by external factors such as clothing, viewing angle, and carrying conditions. To address these challenges, this paper proposes a multi-model gait recognition system that integrates Convolutional Neural Networks (CNNs) and Vision Transformer. The first step in the process is to obtain a gait energy image, which is achieved by applying an averaging technique to a gait cycle. The gait energy image is then fed into three different models, DenseNet-201, VGG-16, and a Vision Transformer. These models are pre-trained and fine-tuned to encode the salient gait features that are specific to an individual's walking style. Each model provides prediction scores for the classes based on the encoded features, and these scores are then summed and averaged to produce the final class label. The performance of this multi-model gait recognition system was evaluated on three datasets, CASIA-B, OU-ISIR dataset D, and OU-ISIR Large Population dataset. The experimental results showed substantial improvement compared to existing methods on all three datasets. The integration of CNNs and ViT allows the system to learn both the pre-defined and distinct features, providing a robust solution for gait recognition even under the influence of covariates.

Batch Hard Contrastive Loss and Its Application to Cross-View Gait Recognition

Biometric person authentication comprises two tasks: the identification task (i.e., one-to-many matching) and the verification task (i.e., one-to-one matching). In this paper, we propose a loss function called batch hard contrastive loss (BHCn) for the deep learning-based verification task. For this purpose, we consider batch mining techniques developed in the identification task and translate them to the verification task. More specifically, inspired by batch mining triplet losses to learn a relative distance for the identification task, we propose BHCn to learn an absolute distance that better represents verification in general. Our method preserves the identity-agnostic nature of the contrastive loss by selecting the hardest pair of samples for each pair of identities in a batch instead of selecting the hardest pair for each sample. We validate the effectiveness of the proposed method in cross-view gait recognition using three networks: a lightweight input, structure, and output network we call GEI + CNN (Gait Energy Image Convolutional Neural Network) as well as the widely used GaitSet and GaitGL, which have sophisticated inputs, structures, and outputs. We trained these networks with the publicly available silhouette-based datasets, the OU-ISIR Gait Database Multi-View Large Population (OU-MVLP) dataset and the Institute of Automation Chinese Academy of Sciences Gait Database Multiview (CASIA-B) dataset. Experimental results show that the proposed BHCn outperforms other loss functions, such as a triplet loss with batch mining as well as the conventional contrastive loss.

Academic resilience from school entry to third grade: Child, parenting, and school factors associated with closing competency gaps.

There is substantial evidence confirming that children who begin school with strong developmental skills tend to maintain positive academic trajectories across the elementary school years. Much less is known about children who begin school with poorer developmental competencies yet go on to achieve academically on par with, or above, their initially more competent peers, demonstrating academic resilience. This study used a large population dataset, the Longitudinal Study of Australian Children (n = 2,118) to identify children who demonstrated academic resilience between school entry and third grade, and the child, parenting, and school characteristics associated with this resilience. Findings show that two in five children who were initially identified as academically vulnerable on a school entry measure of language and cognitive skills were classified as academically resilient by Grade 3. In multivariate analysis, higher attentional regulation and receptive vocabulary skills were key factors associated with academic resilience in reading and numeracy, along with paternal consistency (for reading resilience) and fewer sleep problems (for numeracy resilience). Bivariate relations (ANOVAs) showed that resilient children, when compared to children who remained vulnerable, also showed fewer peer problems, fewer behavioral sleep problems, higher levels of parenting consistency and lower levels of parenting anger by mothers and fathers, higher levels of parental engagement in children's school, and higher levels of teacher self-efficacy. Supporting resilient pathways for children who are identified as vulnerable at school entry should include a particular focus on vocabulary development and attentional regulation, along with social skills and peer relationships, school-based parental engagement, and positive parenting support.

VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron

Gait is a pattern of a person’s walking. The body movements of a person while walking makes the gait unique. Regardless of the uniqueness, the gait recognition process suffers under various factors, namely the viewing angle, carrying condition, and clothing. In this paper, a pre-trained VGG-16 model is incorporated with a multilayer perceptron to enhance the performance under various covariates. At first, the gait energy image is obtained by averaging the silhouettes over a gait cycle. Transfer learning and fine-tuning techniques are then applied on the pre-trained VGG-16 model to learn the gait features of the attained gait energy image. Subsequently, a multilayer perceptron is utilized to determine the relationship among the gait features and the corresponding subject. Lastly, the classification layer identifies the corresponding subject. Experiments are conducted to evaluate the performance of the proposed method on the CASIA-B dataset, the OU-ISIR dataset D, and the OU-ISIR large population dataset. The comparison with the state-of-the-art methods shows that the proposed method outperforms the methods on all the datasets.

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At present, the existing gait recognition systems are focusing on developing methods to extract robust gait feature from silhouette images and they indeed achieved great success. However, gait can be sensitive to appearance features such as clothing and carried items. Compared with appearance-based method, model-based gait recognition is promising due to the robustness against these variations. In recent years, with the development of human pose estimation, the difficulty of model-based gait recognition methods has been mitigated. In this paper, to resist the increase of subjects and views variation, local features are built and a siamese network is proposed to maximize the distance of samples from the same subject. We leverage recent advances in action recognition to embed human pose sequence to a vector and introduce Spatial-Temporal Graph Convolution Blocks (STGCB) which has been commonly used in action recognition for gait recognition. Experiments on the very large population dataset named OUMVLP-Pose and the popular dataset, CASIA-B, show that our method archives some state-of-the-art (SOTA) performances in model-based gait recognition. The code and models of our method are available at https://github.com/timelessnaive/Gait-for-Large-Dataset after being accepted.

A Regional Burden of Sequence-Level Variation in the 22q11.2 Region Influences Schizophrenia Risk and Educational Attainment

BackgroundGenomic loci where recurrent pathogenic copy number variants are associated with psychiatric phenotypes in the population may also be sensitive to the collective impact of multiple functional low-frequency single nucleotide variants (SNVs). MethodsWe examined the cumulative impact of low-frequency, functional SNVs within the 22q11.2 region on schizophrenia risk in a discovery cohort and an independent replication cohort (N = 1933 and N = 11,128, respectively), as well as the impact on educational attainment (EA) in a third, independent, general population cohort (N = 2081). In the discovery and EA cohorts, SNVs were identified using genotyping arrays; in the replication cohort, whole-exome sequencing was available. For verification, we compared the regional SNV count for schizophrenia cases in the discovery cohort with a normative count distribution derived from a large population dataset (N = 26,500) using bootstrap procedures. ResultsIn both schizophrenia cohorts, an increased regional SNV burden (≥4 low-frequency SNVs) in the 22q11.2 region was associated with schizophrenia (discovery cohort: odds ratio = 7.48, p = .039; replication cohort: odds ratio = 1.92, p = .004). In the EA cohort, an increased regional SNV burden at 22q11.2 was associated with decreased EA (odds ratio = 4.65, p = .049). Comparing the SNV count for schizophrenia cases with a normative distribution confirmed the unique nature of the distribution for schizophrenia cases (p = .002). ConclusionsIn the general population, an increased burden of low-frequency, functional SNVs in the 22q11.2 region is associated with schizophrenia risk and a decrease in EA. These findings suggest that in addition to structural variation, a cumulative regional burden of low-frequency, functional SNVs in the 22q11.2 region can also have a relevant phenotypic impact.

Large Population Dataset Linkage to Understand Evolving Practice in Red Cell and Platelet Transfusion and Clinical Outcomes in Patients with Myelodysplastic Syndromes (MDS): A 15-Year Study

Aim: Transfusions are frequently administered for anemia and thrombocytopenia in MDS. However, evidence to guide transfusion remains sparse, along with data about real-world MDS transfusion practices, transfusion-related outcomes and changes over time with increasing access to disease-modifying therapies. In Australia, from 2011, 5-azacitidine was funded through the Pharmaceutical Benefit Scheme (PBS) for patients with IPSS classification intermediate-2 or high-risk MDS, or Chronic Myelomonocytic Leukemia-2 (CMML-2). Australian Patient Blood Management (PBM) guidelines were also first published in 2011. We aimed to characterise red blood cell (RBC) and platelet transfusion practices, and transfusion-related outcomes of MDS and CMML patients over a 15 year period, and explore whether access to disease-modifying therapies or the introduction of PBM guidelines impacted on transfusion requirements.Methods: Retrospective longitudinal cohort study including all patients with MDS/CMML admitted to hospitals in Victoria, Australia's second most populous state, from 2002-2017. Data linkage from the Victorian Admissions Episode Dataset (VAED) (contains data from all public and private hospital admissions in Victoria, including all transfusion episodes), the Victorian Cancer Registry and the Victorian Death Index was performed. We analysed transfusion episodes and outcome events (cardiac ischemia/failure, transfusion reactions, bleeding).Results: 6771 patients with a diagnosis of MDS/CMML reported to the Cancer Registry were included (5970 MDS; 801 CMML). The cohort was elderly (>50% aged 70y and over) and predominantly male (male 61%; female 39%). The majority of patients had a low number of comorbidities (Charlson Comorbidity Score: 60% low (0-2); 32% moderate (3-5); 8% high (≥6)).Of the 179 patients on 5-azacitidine, 38 (21.2%) commenced prior to 2011 and 141 (78.8%) from 2011 onwards. Patients on 5-azacitidine were more likely to be RBC transfusion dependent (TD) (defined as 2 or more RBC transfusions within 16 weeks) (77.7.1% vs 49.9%, p<0.001) and receive platelet transfusions (54.2% vs 28.6%, p<0.001).During the study period, the patients had a total of 142,765 hospital admissions. 66,068 (46.3%) admissions involved RBC transfusion, with median 3 admissions (IQR 1-10) per patient and median 14 days (IQR 14-33) between transfusions. 3433 (50.7%) of patients were RBC transfusion-dependent (TD). Comparing pre-and post-2011, the proportion of admissions involving RBC transfusion, median number of RBC transfusion admissions per patient, and rates of RBC-TD decreased (table 1). Cardiac events were common, and more frequent in RBC-TD patients (acute cardiac ischemia 14.6% vs 10.3%, p<0.001; acute cardiac failure 27.3% vs 14.1%,p<0.001).10,049 (7.0%) admissions involved platelet transfusion. Platelet transfusion admissions increased over time (table 1). Median time between platelet transfusions was 7 days (IQR 4-16). 2436 patients (36.0%) experienced bleeding, including 51.3% of platelet-transfused patients. The commonest bleeding site was gastrointestinal (n=1388, 20.5%). Intracranial bleeding occurred in 175 patients (2.6%). 300 patients (4.4%) died from bleeding complications, with higher bleeding-related mortality in platelet-transfused patients (7.0% vs 3.4%, p<0.001) and RBC-TD patients (5.2% vs 4.0%, p=0.001).Conclusion: This study highlights the high transfusion burden in MDS patients, and related adverse cardiac and bleeding outcomes and mortality. RBC transfusion requirements reduced over time but platelet transfusions increased. This may be related to changing availability or use of MDS therapies, or clinical transfusion decision-making practices, or both - although national PBM guidelines do not specify a particular hemoglobin threshold for chronically transfused MDS patients, clinicians may be extrapolating from recommendations for restrictive transfusion thresholds in other settings (e.g. critical care, perioperative transfusion). These data will help design future MDS transfusion trials, which should include quality-of-life and health economics outcomes, given the burden of transfusion on these elderly patients. [Display omitted] DisclosuresShortt: Amgen: Research Funding; Astex: Research Funding; BMS: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees. Wood: Amgen, Celgene, Gilead, Janssen, Novartis, Sanofi, Takeda: Research Funding; Abbvie, Amgen, Antengene, Bristol-Myers Squibb, Celgene, Gilead, GSK, Janssen, Novartis, Sanofi, and Takeda.: Other: The Australian and New Zealand Myeloma and Related Diseases Registry (MRDR) has received funding from Abbvie, Amgen, Antengene, Bristol-Myers Squibb, Celgene, Gilead, GSK, Janssen, Novartis, Sanofi, and Takeda. .

Human Gait Recognition Based on Self-Adaptive Hidden Markov Model.

Human gait recognition has numerous challenges due to view angle changing, human dressing, bag carrying, and pedestrian walking speed, etc. In order to increase gait recognition accuracy under these circumstances, in this paper we propose a method for gait recognition based on a self-adaptive hidden Markov model (SAHMM). First, we present a feature extraction algorithm based on local gait energy image (LGEI) and construct an observation vector set. By using this set, we optimize parameters of the SAHMM-based method for gait recognition. Finally, the proposed method is evaluated extensively based on the CASIA Dataset B for gait recognition under various conditions such as cross view, human dressing, or bag carrying, etc. Furthermore, the generalization ability of this method is verified based on the OU-ISIR Large Population Dataset. Both experimental results show that the proposed method exhibits superior performance in comparison with those existing methods.

Non-local gait feature extraction and human identification

As a new human identification technology, gait recognition is receiving more and more attention in recent years. However, traditional gait recognition techniques are limited by the challenges of feature representation and extraction algorithms. In this paper, by utilizing the self-attention mechanism, we propose a novel gait-based human identification solution. Firstly, we utilize non-local neural networks (NLNN) to extract non-local features from a pair of randomly selected gait energy maps (GEIs). Secondly, based on the relationship between GEIs and various parts of the human body, the output of NLNN is horizontally segmented into three sections, i.e., strong-dynamic region, weak-dynamic region and micro-dynamic region, respectively. Thirdly, the segmented gait features are weighted ensembled by three two-class classifiers. Finally, two experiments are carried out with the OU-ISIR large population dataset and the CASIA dataset B to evaluate the proposed approach.

Evaluation of the Simultaneous Analysis of Organic and Inorganic Gunshot Residues Within a Large Population Data Set Using Electrochemical Sensors*, †.

The increasing demand for rapid methods to identify both inorganic and organic gunshot residues (IGSR and OGSR) makes electrochemical methods, an attractive screening tool to modernize current practice. Our research group has previously demonstrated that electrochemical screening of GSR samples delivers a simple, inexpensive, and sensitive analytical solution that is capable of detecting IGSR and OGSR in less than 10min per sample. In this study, we expand our previous work by increasing the number of GSR markers and applying machine learning classifiers to the interpretation of a larger population data set. Utilizing bare screen-printed carbon electrodes, the detection and resolution of seven markers (IGSR; lead, antimony, and copper, and OGSR; nitroglycerin, 2,4-dinitrotoluene, diphenylamine, and ethyl centralite) was achieved with limits of detection (LODs) below 1µg/mL. A large population data set was obtained from 395 authentic shooter samples and 350 background samples. Various statistical methods and machine learning algorithms, including critical thresholds (CT), naïve Bayes (NB), logistic regression (LR), and neural networks (NN), were utilized to calculate the performance and error rates. Neural networks proved to be the best predictor when assessing the dichotomous question of detection of GSR on the hands of shooter versus nonshooter groups. Accuracies for the studied population were 81.8 % (CT), 88.1% (NB), 94.7% (LR), and 95.4% (NN), respectively. The ability to detect both IGSR and OGSR simultaneously provides a selective testing platform for gunshot residues that can provide a powerful field-testing technique and assist with decisions in case management.

Role of Allergy in Eustachian Tube Dysfunction.

Eustachian tube dysfunction (ETD) presents with symptoms of aural fullness and pressure, muffled hearing, tinnitus, and otalgia. When severe, it can lead to many common ear disorders such as otitis media with effusion, tympanic membrane retraction/perforation, and cholesteatoma. These diseases are prevalent in both the pediatric and adult population and significantly impact quality of life. The pathophysiology of ETD in the absence of an obstructive lesion has long been debated but is thought to be related to functional obstruction (i.e., inefficient tensor veli palatini muscle) and/or nasal cavity and nasopharyngeal inflammation. In the acute setting, the most common cause of nasal inflammation is an upper respiratory infection. When symptoms become chronic, however, the inflammation is thought to be associated with nasal irritants such as nasal allergens, i.e., allergic rhinitis (AR). The purpose of this review is to summarize our current understanding of the relationship between allergy and ETD. Past studies are either lacking or have reported equivocal findings regarding the relationship between allergy and ETD, and the 2018 International Consensus Statement on Allergy and Rhinology: Allergic Rhinitis has deemed the current level of evidence linking AR and ETD as low quality. A more recent study using a large adult population dataset did support an association between AR and ETD, but did not find that all cases of ETD are related to allergies. Furthermore, current evidence suggests that the use of medications that can decrease intranasal inflammation, such as intranasal corticosteroids or oral antihistamines, does not result in significant symptomatic improvement in patients with ETD. However, these studies included all patients with ETD, and evidence is lacking regarding the treatment effect of these medications on the subset of patients with concurrent AR and ETD. Current best evidence does support an association between AR and ETD; however, not all patients with ETD suffer from AR, and vice versa. Further research is necessary to understand the mechanism behind this positive association and to elucidate the cause of chronic ETD in patients without associated allergies.

Differences in DYF387S1 copy number distribution among haplogroups caused by haplogroup-specific ancestral Y-chromosome mutations

DYF387S1 is a major Y-chromosome short tandem repeat (Y-STR) used in forensic genetics that is included in the Y-chromosomal haplotype reference database (YHRD, https://yhrd.org) and it is known as a rapidly mutating Y-STR. DYF387S1 is a multi-locus marker and the two paralogs are within a palindromic sequence which is a region prone to structural chromosome mutation. In this study, we investigated DYF387S1 copy number distribution and separately typed the two DYF387S1 paralogs in a Japanese population. We found different DYF387S1 copy numbers among haplogroups indicating that the differences had been caused by haplogroup-specific ancestral Y-chromosomal mutations, such as deletion, duplication and non-allelic gene conversion. In haplogroup C, it is likely that gene conversion between two DYF387S1 paralogs had occurred in the common ancestral Y-chromosome for paragroup C-M130* and duplication of DYF387S1 had occurred in the common ancestral Y-chromosome for haplogroup C-M131. Meanwhile, in haplogroup D, deletion of the upstream DYF387S1 paralog is likely to have occurred in the common ancestral Y-chromosome for paragroup D-M57* and duplication of the remaining DYF387S1 paralog is indicated in the common ancestral Y-chromosome for haplogroup D-M125. In haplogroup O, structural mutations changing the DYF387S1 copy number had probably not occurred in the common ancestral Y-chromosome. We also suggest that deletion of one DYF387S1 paralog occurred in haplogroup N and that deletion of one DYF387S1 paralog or DYF387S1 gene conversion occurred in haplogroup Q. This is the first study that has separately typed the two DYF387S1 paralogs in a large population dataset. As haplogroups C, D, N, O and Q are also observed in other populations, the ancestral mutation events indicated by this study may have affected DYF387S1 polymorphism in other areas of the world.

Analysis of allele frequencies of 15 STR loci in a large population dataset from Ukraine at the regional level

ABSTRACT We aimed to improve the reference allele frequency database for Ukraine and to investigate whether any population genetic structure can be detected at the local regional level. The AmpFlSTR®Identifiler®Plus kit was used to genotype 4035 unrelated individuals from 24 administrative regions (oblast) and 6 major cities in Ukraine. All STR markers except one were in Hardy–Weinberg Equilibrium in the entire dataset, except for aslight excess of heterozygotes at TPOX locus (p = 0.01). Subsequent statistical tests for the presence of local structure among the 30 subpopulations were not significant. Comparisons of allele frequencies with an earlier study from Ukraine did not reveal any significant difference. The estimates of several forensic genetic indices (PE, PD, PIC) are improved on much larger data compared to an earlier study and the ordered list of the most informative loci is given separately for each region. The 15 investigated STR loci are highly polymorphic at the individual level, but at the same time do not capture population differences at the regional level.

Identifying biometrics in the wild – A time, erosion and neural inspired framework for gait identification

This paper evaluates the performance of contemporary gait identification systems. A time, erosion and neural inspired framework (TEN-FE) for gait identification was proposed to augment the performance of gait identification systems. Performance of TEN-FE framework was evaluated using CASIA and OU-ISIR large population dataset. Proposed framework relies on CNN and Reinforcement Learning to restrict the impact of confounding factors like baggage and bulky clothing on the accuracy of gait identification systems. Difference in gait signature due to time was also considered and normalized. The results observed a clear increase in system’s performance with minimal complexity and least hardware requirements.

Human Gait Recognition Based on Frame-by-Frame Gait Energy Images and Convolutional Long Short-Term Memory.

Human gait recognition is one of the most promising biometric technologies, especially for unobtrusive video surveillance and human identification from a distance. Aiming at improving recognition rate, in this paper we study gait recognition using deep learning and propose a novel method based on convolutional Long Short-Term Memory (Conv-LSTM). First, we present a variation of Gait Energy Images, i.e. frame-by-frame GEI (ff-GEI), to expand the volume of available Gait Energy Images (GEI) data and relax the constraints of gait cycle segmentation required by existing gait recognition methods. Second, we demonstrate the effectiveness of ff-GEI by analyzing the cross-covariance of one person's gait data. Then, making use of the temporality of our human gait, we design a novel gait recognition model using Conv-LSTM. Finally, the proposed method is evaluated extensively based on the CASIA Dataset B for cross-view gait recognition, furthermore the OU-ISIR Large Population Dataset is employed to verify its generalization ability. Our experimental results show that the proposed method outperforms other algorithms based on these two datasets. The results indicate that the proposed ff-GEI model using Conv-LSTM, coupled with the new gait representation, can effectively solve the problems related to cross-view gait recognition.

Adenocarcinoma with mixed subtypes is a rare but aggressive histologic subtype in colorectal cancer

BackgroundAlthough numerous studies have investigated the clinicopathologic and prognostic relevance of mucinous adenocarcinoma (MAC) and signet-ring cell carcinoma (SRCC) compared with classic adenocarcinoma (CA), little is known about the prognosis of adenocarcinoma with mixed subtypes (AM) and the differences among these four subtypes.MethodsThe statistics of colorectal cancer registered in the Surveillance, Epidemiology and End Results (SEER) database were retrieved and analyzed. We also compared the clinicopathologic and prognostic relevance between CA, SRCC, MAC, and AM.ResultsThe frequencies of these four subtypes were 69.9% (CA, n = 15,812), 25.1% (MAC, n = 5689), 3.6% (SRCC, n = 814) and 1.4% (AM, n = 321), respectively. All of MAC, SRCC, and AM were significantly related with aggressive features. Only SRCC and AM were identified as independent poor prognostic markers for overall survival by multivariate analysis. The aggressiveness of AM was between MAC and SRCC according to the clinicopathologic associations. The prognosis of AM was significantly worse than MAC but comparable with SRCC.ConclusionsWe confirmed the clinicopathologic relevance with aggressive features of MAC and SRCC, as well as poor prognostic relevance of SRCC by analyzing a large study population data set. Furthermore, we identified AM as a rare but aggressive histologic subtype in colorectal cancer, to which particular attention should be given in clinical practice.

1684P - Prevalence of chemotherapy use and its impact on overall survival in patients with bone and soft tissue sarcomas: A population-based analysis of 3746 patients

BackgroundThe aims of this study were to understand the prevalence of chemotherapy (chemo) use and its associated impact on survival in patients (pts) with either bone- and soft-tissue sarcomas (STS) through a large real-world population dataset. MethodsA population-based retrospective database was assembled to extract pts with sarcoma, as defined as ICD-9-CM codes of bone (170.x) or/and soft tissue (171.x) who have attended clinics or hospitals of the Hong Kong Hospital Authority (HA) between Jan 2004 and Mar 2018. Eligible pts with index presentation of bone or/and soft tissue sarcoma (STS) on or after Jan 2005 were analysed to allow 1-year window. Drug dispensing records of chemotherapy drugs (chemo) from the HA as categorized by the British National Formulary 8.x were extracted and correlated with all-cause and cancer specific mortality. ResultsOf 3746 pts identified, 3358 pts satisfied eligibility: bone: n=661, STS n=2576, both n=121. 32.8% of pts (n=1100) had chemo prescribed during the study period. The proportional use of chemo decreased with advancing age (<18y: 70.9%; 18-<40y: 42.2%; 40-65y: 36.1%, 65-80y: 19.7%;.>/=80y: 8.1%). Anthracyclines (AN) were the most commonly used class of chemo (19.8%), followed by alkylating drugs (17.3%), antimetabolites (11.9%) and vinca alkaloids & etoposide (8.7%). Comparing with pts who have not received chemo within the study period, the risk for death was higher in pts who have received AN either as first chemo (HR 1.51, 95%CI1.25-1.83; p<0.001) or later lines of therapy (HR 1.55; 95%CI1.10-2.17; p=0.002), and in pts who have had chemo but never received AN (HR 1.51; 95%CI1.27-1.80; p<0.001). AN use resulted in significant reduction in mortality over non-AN chemo use if used within 3months of index diagnosis. This significance was not apparent in pts who started chemo >3months post diagnosis (log-rank p<0.01 and p=0.14 respectively). The impact of chemo on mortality reduction appeared greater in pts with bone- vs. STS. ConclusionsThis is one of the largest assembled population-based real-world sarcoma cohort. Chemo use and choice of chemo has implications on survival. The positive effect on survival was especially prominent in pts with bone sarcomas. Legal entity responsible for the studyThe authors. FundingHas not received any funding. DisclosureAll authors have declared no conflicts of interest.

Biological, environmental and socioeconomic determinants of the human birth sex ratio in the Czech Republic.

The Trivers-Willard Hypothesis (TWH) states that parents in good conditions bias the sex ratio towards sons and parents in poor conditions bias the sex ratio towards daughters. This study used data from a large nationwide population dataset (N=1,401,851) from the Czech Republic - a modern contemporary society. The study included air pollution and property prices in the TWH estimation, and had a more detailed focus on stillbirths than previous studies. Using official natality microdata from the Czech Statistical Office for years between 1992 and 2010 and data on levels of air pollution in the country over the same period, the study assessed whether the biological and socioeconomic status of mothers and environmental factors affected the sex of children. The results were largely insignificant and not robust across specifications. The presented epidemiological evidence suggests that stillbirths are randomly distributed in the Czech Republic and that the sex ratio is not affected by the socioeconomic status of mothers or by environmental characteristics.

Peripheral trauma and risk of dystonia: What are the evidences and potential co-risk factors from a population insurance database?

BackgroundDystonia is a neurological syndrome typically resulting in abnormal postures.ObjectivesWe tested the role of physical injury as potential risk factor for development of dystonia using The National Health Insurance Research Database of Taiwan.MethodsWe identified 65704 people who were coded in the database as having had peripheral traumatic injuries (ICD-9-CM 807–848 and 860–959) in the year 2000. Patients with traumatic brain or spine injuries were excluded from analysis. We matched them using purposive sampling with 65704 people in the database who had not suffered peripheral trauma. We looked then at the incidence of dystonia occurring at least 1 year from the date of the peripheral trauma until 2011. Psychiatric symptoms (depression and anxiety) and sleeps difficulties have been investigated as potential covariates.ResultsWe found 189 patients with dystonia (0.28%) in the trauma group, and 52 patients with dystonia (0.08%) in the non-trauma group. Trauma was independently associated with dystonia (adjusted HR = 3.12, 95% CI = 2.30–4.24). The incidence density of dystonia in the trauma group was 2.27 per 10000 person-years, while it was 0.71 per 10000 person-years in the non-trauma group Beyond the peripheral trauma, other variables associated to the incidence of dystonia included female sex, aged 40 years and above, depression and sleep disorders.ConclusionThese data from a large population dataset support traumatic injury as a risk factor for the development of dystonia.

Resolved threatened preterm labour: an opportunity for reducing future prematurity?

Prematurity remains the most significant obstetrical complication with respect to long-term newborn morbidity. Over the last few years, there has been some progress made in the prediction and reduction of risk of preterm birth (PTB). Prediction has focused on identification of maternal characteristics that increase risk, on the evaluation of cervical length and the use of biomarkers [fibronectin, PAPP A, inhibin, alpha fetoprotein (αFP)]. Of the preventive strategies, the use of progesterones in women perceived to be at risk is now commonplace and cervical cerclage or cervical pessaries are being used in selected cases. Many of the maternal characteristics that increase the risk of PTB are well established. Paramount to date have been the long recognised risk factors of a prior PTB or of prior premature membrane rupture (PROM) (Ekwo et al. Obstet Gynecol 1992;80:166–72). However, somewhat surprisingly, little attention has previously been paid to those women, who in earlier pregnancies did not in fact give birth preterm or suffer PROM, but who did present with episodes of threatened preterm labour (TPL) or premature uterine contractions. That is precisely the focus of this present study (Cho et al. BJOG 2019;126:901–5). From a large population dataset, the authors have demonstrated that, as expected, PTB in a first pregnancy is a risk factor, with an odds ratio (OR) of 8.15, for PTB in the second pregnancy. However, TPL in a pregnancy delivering at term was also associated with PTB, with an OR of 2.21. Both prior PTB and TPL with a term delivery present almost equal risks for women to experience PTL in their subsequent pregnancy, even if that pregnancy proceeds to term [OR 3.83 versus 4.61]. Although having less of an impact than prior PTB, given the larger proportion of women experiencing PTL followed by a term delivery, identification of these women as being an at-risk group identified almost as many of the subsequent PTBs as did prior PTB (6.1 versus 8.7% of all PTBs). Despite this modest benefit in detecting women at risk of PTB, considering all women with a combination of PTB and TPL with term delivery to be at risk of PTB identified a cohort of the study population comprising 4.5% of women, in whom just under 15% of all subsequent PTBs occurred. The follow-on question is how effective our preventive strategies might be in this cohort of women. This offers an ideal opportunity for future study and ideally this should be undertaken before we extend the use of current therapies to include this population of women without supportive evidence to justify that approach. It might be argued that the variable data regarding the mainstays of current preventive strategies, e.g. progesterone therapy (Jarde et al. BJOG 2017;124:1163–73; Norman et al. Lancet 2016;387:2106–16), as well as cerclage, exists because of the extrapolation of indications and the empiric use of treatment strategies in cases where robust data justifying these approaches is lacking. None declared. Completed disclosure of interest form is available to view online as supporting information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.