bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimating 3D kinematics and kinetics from virtual inertial sensor data through musculoskeletal movement simulations
Authors: Nitschke Marlies , Dorschky Eva , Leyendecker Sigrid , Eskofier Bjoern M. , Koelewijn Anne D. 
Published: April 2024

Portable measurement systems using inertial sensors enable motion capture outside the lab, facilitating longitudinal and large-scale studies in natural environments. However, estimating 3D kinematics and kinetics from inertial data for a comprehensive biomechanical movement analysis is still challenging. Machine learning models or stepwise approaches performing Kalman filtering, inverse kinematics, and inverse dynamics can lead to inconsistencies between kinematics and kinetics. We investigated the reconstruction of 3D kinematics and kinetics of arbitrary running motions from inertial sensor data using optimal control simulations of full-body musculoskeletal models. To evaluate the feasibility of the proposed method, we used marker tracking simulations created from optical motion capture data as a reference and for computing virtual inertial data such that the desired solution was known exactly. We generated the inertial tracking simulations by formulating optimal control problems that tracked virtual acceleration and angular velocity while minimizing effort without requiring a task constraint or an initial state. To evaluate the proposed approach, we reconstructed three trials each of straight running, curved running, and a v-cut of 10 participants. We compared the estimated inertial signals and biomechanical variables of the marker and inertial tracking simulations. The inertial data was tracked closely, resulting in low mean root mean squared deviations for pelvis translation (≤20.2 mm), angles (≤1.8 deg), ground reaction forces (≤1.1 BW%), joint moments (≤0.1 BWBH%), and muscle forces (≤5.4 BW%) and high mean coefficients of multiple correlation for all biomechanical variables (≥0.99). Accordingly, our results showed that optimal control simulations tracking 3D inertial data could reconstruct the kinematics and kinetics of individual trials of all running motions. The simulations led to mutually and dynamically consistent kinematics and kinetics, which allows researching causal chains, for example, to analyze anterior cruciate ligament injury prevention. Our work proved the feasibility of the approach using virtual inertial data. When using the approach in the future with measured data, the sensor location and alignment on the segment must be estimated, and soft-tissue artifacts are potential error sources. Nevertheless, we demonstrated that optimal control simulation tracking inertial data is highly promising for estimating 3D kinematics and kinetics for a comprehensive biomechanical analysis.
Efficacy and classification of Sesamum indicum linn seeds with Rosa damascena mill oil in uncomplicated pelvic inflammatory disease using machine learning
Authors: Sumbul  , Sultana Arshiya , Heyat Md Belal Bin , Rahman Khaleequr , Akhtar Faijan , Parveen Saba , Urbano Mercedes Briones , Lipari Vivian , De la Torre Díez Isabel , Khan Azmat Ali , Malik Abdul 
Published: April 2024

Background and objectives: As microbes are developing resistance to antibiotics, natural, botanical drugs or traditional herbal medicine are presently being studied with an eye of great curiosity and hope. Hence, complementary and alternative treatments for uncomplicated pelvic inflammatory disease (uPID) are explored for their efficacy. Therefore, this study determined the therapeutic efficacy and safety of Sesamum indicum Linn seeds with Rosa damascena Mill Oil in uPID with standard control. Additionally, we analyzed the data with machine learning.Materials and methods: We included 60 participants in a double-blind, double-dummy, randomized standard-controlled study. Participants in the Sesame and Rose oil group (SR group) (n = 30) received 14 days course of black sesame powder (5 gm) mixed with rose oil (10 mL) per vaginum at bedtime once daily plus placebo capsules orally. The standard group (SC), received doxycycline 100 mg twice and metronidazole 400 mg thrice orally plus placebo per vaginum for the same duration. The primary outcome was a clinical cure at post-intervention for visual analogue scale (VAS) for lower abdominal pain (LAP), and McCormack pain scale (McPS) for abdominal-pelvic tenderness. The secondary outcome included white blood cells (WBC) cells in the vaginal wet mount test, safety profile, and health-related quality of life assessed by SF-12. In addition, we used AdaBoost (AB), Naïve Bayes (NB), and Decision Tree (DT) classifiers in this study to analyze the experimental data.Results: The clinical cure for LAP and McPS in the SR vs SC group was 82.85% vs 81.48% and 83.85% vs 81.60% on Day 15 respectively. On Day 15, pus cells less than 10 in the SR vs SC group were 86.6% vs 76.6% respectively. No adverse effects were reported in both groups. The improvement in total SF-12 score on Day 30 for the SR vs SC group was 82.79% vs 80.04% respectively. In addition, our Naive Bayes classifier based on the leave-one-out model achieved the maximum accuracy (68.30%) for the classification of both groups of uPID.Conclusion: We concluded that the SR group is cost-effective, safer, and efficacious for curing uPID. Proposed alternative treatment (test drug) could be a substitute of standard drug used for Female genital tract infections.
Integration of Wnt-inhibitory activity and structural novelty scoring results to uncover novel bioactive natural products: new Bicyclo[3.3.1]non-3-ene-2,9-diones from the leaves of Hymenocardia punctata
Authors: Quiros-Guerrero Luis-Manuel , Marcourt Laurence , Chaiwangrach Nathareen , Koval Alexey , Ferreira Queiroz Emerson , David Bruno , Grondin Antonio , Katanaev Vladimir L. , Wolfender Jean-Luc 
Published: April 2024

In natural products (NPs) research, methods for the efficient prioritization of natural extracts (NEs) are key for discovering novel bioactive NPs. In this study a biodiverse collection of 1,600 NEs, previously analyzed by UHPLC-HRMS2 metabolite profiling was screened for Wnt pathway regulation. The results of the biological screening drove the selection of a subset of 30 non-toxic NEs with an inhibitory IC50 ≤ 5 μg/mL. To increase the chance of finding structurally novel bioactive NPs, Inventa, a computational tool for automated scoring of NEs based on structural novelty was used to mine the HRMS2 analysis and dereplication results. After this, four out of the 30 bioactive NEs were shortlisted by this approach. The most promising sample was the ethyl acetate extract of the leaves of Hymenocardia punctata (Phyllanthaceae). Further phytochemical investigations of this species resulted in the isolation of three known prenylated flavones (3, 5, 7) and ten novel bicyclo[3.3.1]non-3-ene-2,9-diones (1, 2, 4, 6, 8–13), named Hymenotamayonins. Assessment of the Wnt inhibitory activity of these compounds revealed that two prenylated flavones and three novel bicyclic compounds showed interesting activity without apparent cytotoxicity. This study highlights the potential of combining Inventa’s structural novelty scores with biological screening results to effectively discover novel bioactive NPs in large NE collections.
Francisella tularensis-specific antibody levels in sera from Swedish patients with suspected tularemia during a 13-year period
Authors: Lindgren Helena , Liu Xijia , Sjöstedt Anders 
Published: April 2024

IntroductionFor a majority of tularemia patients, serology is the basis for the diagnosis. The aim of this study was to perform an analysis of the samples analyzed at a Swedish reference laboratory for the presence of Francisella tularensis-specific antibody levels in sera from individuals with suspected tularemia. Annual and monthly variations of the total number of samples and proportions of positive samples were analyzed, as well as the influence of age and gender.MethodsWe performed a retrospective analysis of the presence of F. tularensis-specific antibodies in serological samples from patients with suspected tularemia analyzed during the period 2010 - 2022 at the University Hospital of Umeå in Sweden, a national reference laboratory, by use of various statistical methods. In total, some 15,100 serum samples had been analyzed for the presence of IgG and IgM antibodies by ELISA during the 13-year period.ResultsOverall, there were higher number of samples with IgG positive or borderline titers, 2,522 and 921, respectively, than with IgM positive or borderline titers, 1,802 and 409, respectively. Repeated samples were obtained from some 1,930 individuals and approximately a third of the cases, which were initially seronegative, had seroconverted when resampled. Peak number of monthly samples were recorded in August and September, &gt; 3,000. Annual numbers varied greatly and peak numbers were observed in 2015 and 2019, 1,832 and 2,250, respectively, whereas some other years the numbers were 700 – 800. There was also much variation in the annual and monthly percentages of positive samples and they varied between less than 10% to greater than 20%. The highest percentages of positive samples were recorded in September and October. IgG and IgM titers declined with age and these differences were highly significant for IgG titers, with decreasing average titers for each 20-year interval.DiscussionCollectively, the data demonstrate the marked annual and seasonal variations in tularemia sampling occurring in Sweden. Also, the proportion of positive samples increased during months and years with peak number of samples. Another notable finding was that average antibody titers decreased with increased age.
Risk factors for erectile dysfunction in diabetes mellitus: a systematic review and meta-analysis
Authors: Dilixiati Diliyaer , Waili Alapati , Tuerxunmaimaiti Aizihaier , Tao Liwen , Zebibula Abudureheman , Rexiati Mulati 
Published: April 2024

BackgroundPrevious studies have established that diabetes mellitus (DM) markedly raises the risk of developing erectile dysfunction (ED). Despite extensive investigations, the risk factors associated with ED in diabetic men have yet to be unequivocally determined, owing to incongruent and inconclusive results reported in various studies.ObjectiveThe objective of this systematic review and meta-analysis was to assess the risk factors for ED in men with DM.MethodsA comprehensive systematic review was conducted, encompassing studies published in the PubMed, Scopus and Embase databases up to August 24th, 2023. All studies examining the risk factors of ED in patients with DM were included in the analysis. To identify significant variations among the risk factors, odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were employed. The risk of bias was evaluated using the Newcastle-Ottawa Scale(NOS) for longitudinal studies and the Agency for Healthcare Research and Quality Scale(AHRQ) for cross-sectional studies.ResultsA total of 58 studies, including a substantial participant pool of 66,925 individuals diagnosed with DM, both with or without ED, were included in the meta-analysis. Mean age (OR: 1.31, 95% CI=1.24-1.37), smoking status (OR: 1.32, 95% CI=1.18-1.47), HbA1C (OR: 1.44, 95% CI=1.28-1.62), duration of DM (OR: 1.39, 95% CI=1.29-1.50), diabetic neuropathy (OR: 3.47, 95% CI=2.16-5.56), diabetic retinopathy (OR: 3.01, 95% CI=2.02-4.48), diabetic foot (OR: 3.96, 95% CI=2.87-5.47), cardiovascular disease (OR: 1.92, 95% CI=1.71-2.16), hypertension (OR: 1.74, 95% CI=1.52-2.00), microvascular disease (OR: 2.14, 95% CI=1.61-2.85), vascular disease (OR: 2.75, 95% CI=2.35-3.21), nephropathy (OR: 2.67, 95% CI=2.06-3.46), depression (OR: 1.82, 95% CI=1.04-3.20), metabolic syndrome (OR: 2.22, 95% CI=1.98-2.49), and diuretic treatment (OR: 2.42, 95% CI=1.38-4.22) were associated with increased risk factors of ED in men with DM.ConclusionOur study indicates that in men with DM, several risk factors for ED have been identified, including mean age, HbA1C, duration of DM, diabetic neuropathy, diabetic retinopathy, diabetic foot, cardiovascular disease, hypertension, microvascular disease, vascular disease, nephropathy, depression, metabolic syndrome, and diuretic treatment. By clarifying the connection between these risk factors and ED, clinicians and scientific experts can intervene and address these risk factors, ultimately reducing the occurrence of ED and improving patient management.
A climate analog approach to evaluate seed transfer and vegetation transitions
Authors: Richardson Bryce A. , Rehfeldt Gerald E. , Sáenz-Romero Cuauhtémoc , Milano Elizabeth R. 
Published: April 2024

IntroductionThere is growing recognition that restoring species diversity is crucial to maintaining ecological functions and services. Increasing the diversity of species used in restoration programs has placed greater emphasis on determining the seed transfer needs for a wider array of plants. However, many plants, outside of commercial forestry, lack information that would provide guidance on seed transfer for current or future climates. Generalized seed transfer approaches use climate partitioning to approximate adaptive differentiation among populations and provide an estimation of seed transfer distance for such species.MethodsHerein, we describe a generalized seed transfer approach that uses Euclidean distance of 19 climate variables within North America (from northern Honduras to the Arctic). Euclidean distances are used to identify climate analogs from vegetation databases of about 685,000 plots, an average density of 1 plot per 32 km2. Analogs are classified into three thresholds (strong, moderate, and weak) that correspond to altitudinal climate gradients and are guided by the scientific literature of observed adaptive variation of natural tree populations and seed transfer limits.ResultsFor strong threshold observations, about 97% of the analogs had climate distances equivalent to ≤300 m elevation, whereas for the weak threshold observations, 53% had an elevation equivalence of ≤300 m. On average 120, 267, and 293 m elevation separated two points under strong, moderate, and weak thresholds, respectively. In total, threshold classification errors were low at 13.9%.DiscussionWe use examples of plot data identified from a reference period (1961–1990) and mid-century (2056–2065) analogs across North American biomes to compare and illustrate the outcomes of projected vegetation change and seed transfer. These examples showcase that mid-century analogs may be located in any cardinal direction and vary greatly in spatial distance and abundance from no analog to hundreds depending on the site. The projected vegetative transitions will have substantial impacts on conservation programs and ecosystem services. Our approach highlights the complexity that climate change presents to managing ecosystems, and the need for predictive tools in guiding land management decisions to mitigate future impacts caused by climate change.
PyPop: a mature open-source software pipeline for population genomics
Authors: Lancaster Alexander K. , Single Richard M. , Mack Steven J. , Sochat Vanessa , Mariani Michael P. , Webster Gordon D. 
Published: April 2024

Python for Population Genomics (PyPop) is a software package that processes genotype and allele data and performs large-scale population genetic analyses on highly polymorphic multi-locus genotype data. In particular, PyPop tests data conformity to Hardy-Weinberg equilibrium expectations, performs Ewens-Watterson tests for selection, estimates haplotype frequencies, measures linkage disequilibrium, and tests significance. Standardized means of performing these tests is key for contemporary studies of evolutionary biology and population genetics, and these tests are central to genetic studies of disease association as well. Here, we present PyPop 1.0.0, a new major release of the package, which implements new features using the more robust infrastructure of GitHub, and is distributed via the industry-standard Python Package Index. New features include implementation of the asymmetric linkage disequilibrium measures and, of particular interest to the immunogenetics research communities, support for modern nomenclature, including colon-delimited allele names, and improvements to meta-analysis features for aggregating outputs for multiple populations.Code available at: https://zenodo.org/records/10080668 and https://github.com/alexlancaster/pypop
Value of carotid corrected flow time or changes value of FTc could be more useful in predicting fluid responsiveness in patients undergoing robot-assisted gynecologic surgery: a prospective observational study
Authors: Tang Xixi , Liang Jingqiu , Tan Dongling , Chen Qi , Zhou Chengfu , Yang Tingjun , Liu Hongliang 
Published: April 2024

BackgroundThe aim of this study was to evaluate the ability of point-of-care Doppler ultrasound measurements of carotid corrected flow time and its changes induced by volume expansion to predict fluid responsiveness in patients undergoing robot-assisted gynecological surgery.MethodsIn this prospective study, carotid corrected flow time was measured using Doppler images of the common carotid artery before and after volume expansion. The stroke volume index at each time point was recorded using noninvasive cardiac output monitoring with MostCare. Of the 52 patients enrolled, 26 responded.ResultsThe areas under the receiver operating characteristic curves of the carotid corrected flow time and changes in carotid corrected flow time induced by volume expansion were 0.82 and 0.67, respectively. Their optimal cut-off values were 357 and 19.5 ms, respectively.ConclusionCarotid corrected flow time was superior to changes in carotid corrected flow time induced by volume expansion for predicting fluid responsiveness in this population.
Roles of gut microbiota in androgenetic alopecia: insights from Mendelian randomization analysis
Authors: Fu Haijing , Xu Tianyi , Zhao Wumei , Jiang Leiwei , Shan Shijun 
Published: April 2024

BackgroundAndrogenetic alopecia (AGA) is the most common type of androgen-associated hair loss. Previous studies have indicated an association between the gut microbiota and AGA. To delve deeper, we executed a two-sample Mendelian randomization (MR) analysis to investigate the potential causal relationship between the gut microbiota and AGA.MethodsA two-sample MR investigation was utilized to delve into the intricate interplay between gut microbiota and AGA. Information regarding 211 gut microbial taxa was sourced from the MiBioGen consortium. The summary statistics of the genome-wide association studies (GWAS) for AGA were obtained from the FinnGen biobank, which included 195 cases and 201,019 controls. Various analytical approaches, including Inverse Variance Weighting (IVW), Weighted Median, MR-Egger, Weighted Mode, and Simple Mode were employed to evaluate the causal impact of gut microbiota on AGA. Sensitivity analyses were subsequently conducted to affirm the robustness of the findings.ResultsA two-sample MR investigation unveiled the genus Olsenella, genus Ruminococcaceae UCG-004, and genus Ruminococcaceae UCG-010 were identified as risk factors associated with AGA. In contrast, the family Acidaminococcaceae and genus Anaerofilum, along with the genus Ruminiclostridium 9, demonstrated a protective effect. The sensitivity analyses provided additional assurance that the findings of the current study were less susceptible to the influence of confounding variables and biases.ConclusionThe MR study has established a link between specific gut microbiota and AGA, offering evidence for the identification of more precisely targeted probiotics. This discovery has the potential to aid in the prevention, control, and reversal of AGA progression.
Global research trends and hotspots of artificial intelligence research in spinal cord neural injury and restoration—a bibliometrics and visualization analysis
Authors: Tao Guangyi , Yang Shun , Xu Junjie , Wang Linzi , Yang Bin 
Published: April 2024

BackgroundArtificial intelligence (AI) technology has made breakthroughs in spinal cord neural injury and restoration in recent years. It has a positive impact on clinical treatment. This study explores AI research’s progress and hotspots in spinal cord neural injury and restoration. It also analyzes research shortcomings related to this area and proposes potential solutions.MethodsWe used CiteSpace 6.1.R6 and VOSviewer 1.6.19 to research WOS articles on AI research in spinal cord neural injury and restoration.ResultsA total of 1,502 articles were screened, in which the United States dominated; Kadone, Hideki (13 articles, University of Tsukuba, JAPAN) was the author with the highest number of publications; ARCH PHYS MED REHAB (IF = 4.3) was the most cited journal, and topics included molecular biology, immunology, neurology, sports, among other related areas.ConclusionWe pinpointed three research hotspots for AI research in spinal cord neural injury and restoration: (1) intelligent robots and limb exoskeletons to assist rehabilitation training; (2) brain-computer interfaces; and (3) neuromodulation and noninvasive electrical stimulation. In addition, many new hotspots were discussed: (1) starting with image segmentation models based on convolutional neural networks; (2) the use of AI to fabricate polymeric biomaterials to provide the microenvironment required for neural stem cell-derived neural network tissues; (3) AI survival prediction tools, and transcription factor regulatory networks in the field of genetics were discussed. Although AI research in spinal cord neural injury and restoration has many benefits, the technology has several limitations (data and ethical issues). The data-gathering problem should be addressed in future research, which requires a significant sample of quality clinical data to build valid AI models. At the same time, research on genomics and other mechanisms in this field is fragile. In the future, machine learning techniques, such as AI survival prediction tools and transcription factor regulatory networks, can be utilized for studies related to the up-regulation of regeneration-related genes and the production of structural proteins for axonal growth.