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Effectiveness of aspirin in preventing deep vein thrombosis following proximal femoral fracture surgery in Japan

Abstract Previous studies have shown that aspirin is effective as a prophylactic agent against venous thromboembolism (VTE) following proximal femoral fractures (PFF). In Japan, there is a lack of evidence regarding its efficacy and safety in this context. Consequently, aspirin is not covered by insurance for the prevention of venous thrombosis. This study aimed to investigate whether continued aspirin use in patients with PFF, who were already taking aspirin for cerebrovascular disease prevention before injury is effective as a prophylaxis for deep vein thrombosis (DVT). We retrospectively analyzed PFF patients (≥ 65 years) who underwent postoperative duplex ultrasonography from January 2010 to December 2023.The study compared patients taking aspirin alone (aspirin group) and those not taking antiplatelet agents or anticoagulants (control group), matched by propensity scores. We enrolled 1064 patients while 161 (15%) were in the aspirin group. After matching, 128 patients were analyzed. DVT incidence was not statistically significant between the aspirin (54) and control groups (60) (OR: 0.81; 95%CI: 0.49- 1.36; p = 0.44). Proximal DVT incidence was also similar (OR: 2; 95%CI: 0.50–7.00; p = 0.33). Additionally, since use of other postoperative antithrombotic prophylaxis (78%) is thought to have a significant impact on the incidence of DVT, a subgroup analysis was conducted to evaluate the effect of aspirin in patients who did not receive postoperative antithrombotic prophylaxis. Similarly, there was no statistically significant difference in either DVT (OR: 1.38; 95% CI: 0.55–3.42; p = 0.49) or proximal DVT (OR: 2.00; 95% CI: 0.37–10.92; p = 0.42). This study demonstrates that aspirin is not effective for preventing VTE in patients with PFF in Japan.

Basin-Size Mapping: Prediction of Metastable Polymorph Synthesizability Across TaC–TaN Alloys

Non-cancerous CT findings as predictors of survival outcome in advanced non-small cell lung cancer patients treated with first-generation EGFR-TKIs

Purpose To identify non-cancerous factors from baseline CT chest affecting survival in advanced non-small cell lung cancer (NSCLC) treated with first-generation Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitors (EGFR-TKIs). Methods Retrospective study of 172 advanced NSCLC patients treated with first-generation EGFR-TKIs as a first-line systemic treatment (January 2012 to September 2022). Baseline CT chest assessed visceral/subcutaneous fat (L1 level), sarcopenia, and myosteatosis (multiple levels), main pulmonary artery (MPA) size, MPA to aorta ratio, emphysema, and bone mineral density. Cox regression analyzed prognostic factors at 18-month outcome. Results Median overall survival was 17.57 months (14.87–20.10) with 76 (44.19%) patients died at 18 months. Deceased had lower baseline BMI (21.10 ± 3.44) vs. survived (23.25 ± 4.45) (p < 0.001). Univariable analysis showed 5 significant prognostic factors: low total adiposity with/without cutoff [HR 2.65 (1.68–4.18), p < 0.001; 1.00 (0.99–1.00), p = 0.006;], low subcutaneous adipose tissue (SAT) with/without cutoff [HR 1.95 (1.23–3.11), p = 0.005; 0.99 (0.98–0.99), p = 0.005], low SAT index (SATI) with/without cutoff [1.74 (1.10–2.78), p = 0.019; 0.98 (0.97–0.99), p = 0.003], high VSR [1.67 (1.06–2.62), p = 0.026], and high MPA size with/without cutoff [2.23 (1.23–4.04), p = 0.005; 1.09 (1.04–1.16), p = 0.001]. MPA size, MPA size > 29 mm, and total adiposity ≤85 cm2 remained significant in multivariable analysis, adjusted by BMI [HR 1.14 (1.07–1.21), p < 0.001; 3.10 (1.81–5.28), p < 0.001; 3.91 (1.63–9.40), p = 0.002]. There was no significant difference of sarcopenic and myosteatotic parameters between the two groups. Conclusion In advanced EGFR-mutated NSCLC patients, assessing pre-treatment prognosis is warranted to predict the survival outcome and guide decision regarding EGFR-TKI therapy. Enlarged MPA size, low total adiposity, and low subcutaneous fat (lower SAT, lower SATI, and higher VSR) are indicators of poor survival. Large MPA size (>29 mm) or low total adiposity (≤85 cm2) alone predict 18-month death.

Identification of m6A methyltransferase-related WTAP and ZC3H13 predicts immune infiltrates in glioblastoma

An atlas of cells in the human brain’s control hub

Coherent Strain-Inhibiting Phase Construction of Lithium-Rich Manganese-Based Oxide Toward High Mechanochemical Stability

Pseudo label refining for semi-supervised temporal action localization

The training of temporal action localization models relies heavily on a large amount of manually annotated data. Video annotation is more tedious and time-consuming compared with image annotation. Therefore, the semi-supervised method that combines labeled and unlabeled data for joint training has attracted increasing attention from academics and industry. This study proposes a method called pseudo-label refining (PLR) based on the teacher-student framework, which consists of three key components. First, we propose pseudo-label self-refinement which features in a temporal region interesting pooling to improve the boundary accuracy of TAL pseudo label. Second, we design a module named boundary synthesis to further refined temporal interval in pseudo label with multiple inference. Finally, an adaptive weight learning strategy is tailored for progressively learning pseudo labels with different qualities. The method proposed in this study uses ActionFormer and BMN as the detector and achieves significant improvement on the THUMOS14 and ActivityNet v1.3 datasets. The experimental results show that the proposed method significantly improve the localization accuracy compared to other advanced SSTAL methods at a label rate of 10% to 60%. Further ablation experiments show the effectiveness of each module, proving that the PLR method can improve the accuracy of pseudo-labels obtained by teacher model reasoning.

Magnitude of self-reported non-fatal work-related injuries and associated factors among construction workers in Aleta Wondo, Sidama, Ethiopia

Why a standard method overlooks the real reason some antibiotics fail

Estimation of true dates of various flowering stages at a centennial scale by applying a Bayesian statistical state space model

Evaluation of long-term detailed cherry flowering phenology is required for a deep understanding of the sensitivity of spring phenology to climate change and its effect on cultural ecosystem services. Neodani Usuzumi-zakura (Cerasus itosakura) is a famous cherry tree in Gifu, Japan. On the basis of detailed decadal flowering phenology information published on the World Wide Web, we estimated the probability distributions of the year-to-year variability of the true dates of first flowering (FFL), first full bloom (FFB), last full bloom (LFB), and last flowering (LFL) from 1924 to 2024 by applying a Bayesian statistical state space model explained by air temperature data. We verified the estimated values against flowering phenology records of the tree from the literature and a private collection. The true dates of FFL and FFB could be explained by means of daily minimum air temperature from 1 December to 28/29 February and that of daily mean air temperature from 1 to 31 March, and those of LFB and LFL by means of daily mean air temperature from 1 to 10 April. Results were similar when we used air temperature data recorded at weather stations both 1 km and 29 km from the tree. These results indicated that our proposed Bayesian statistical state space model can estimate cherry flowering phenology that takes into account centennial-scale air temperature data recorded at a nearby weather station with a coarse temporal resolution.

Structural edge damage detection based on wavelet transform and immune genetic algorithm

A personalized cancer vaccine to prevent the return of high-risk kidney cancer

Diagnosis of carbon monoxide exposure in clinical research and practice: A scoping review

Objective To undertake a scoping review to identify methods and diagnostic levels used in determining unintentional, non-fire related carbon monoxide exposure. Design Online databases and grey literature were searched from 1946 to 2023 identifying 80 papers where carbon monoxide levels were reported. Results 80 papers were included; 71 research studies and 9 clinical guidelines. Four methods were described: blood carboxyhaemoglobin (arterial or venous blood analysis), carbon monoxide oximetry (SpO2), expired carbon monoxide, and ambient carbon monoxide sampling. Blood analysis methods predominated (60.0% of the papers). Multiple methods of measurement were used in 26 (32.5%) of the papers. Diagnostic levels for carboxyhaemoglobin were described in 54 (67.5%) papers, ranging between 2% and 15%. 26 (32.5%) papers reported diagnostic levels that were adjusted for the smoking status of the patient. Conclusions Four methods were found for use in different settings. Variability in diagnostic thresholds impairs diagnostic accuracy. Agreement on standardised diagnostic levels is required to enable consistent diagnosis of unintentional, non-fire related carbon monoxide exposure.

Carnivore activity across landuse gradients in a Mexican biosphere reserve

Top universities warned against unfair research partnerships on their doorstep

Harnessing Large Language Models to Collect and Analyze Metal–Organic Framework Property Data Set

Impact and perceptions of Active Learning Classrooms on reducing sedentary behaviour and improving physical and mental health and academic indicators in children and adolescents: A scoping review

Prolonged sitting in school harms children’s physical and mental health and reduces the ability to focus on classroom tasks. ’Active Learning Classrooms’ (ALCs) aim to decrease sitting time, following current pedagogical trends, though research on the effects of ALCs on these aspects is still an emerging field. The aims of this review were to: (i) synthesise the available literature on the impact of ALCs on reducing sedentary behaviour, increasing physical activity (PA), physical and mental health, and academic indicators in children and adolescents; and (ii) describe the educational community’s perceptions and teaching practices used in ALCs. This scoping review followed Joanna Briggs Methods and PRISMA guidelines for scoping reviews. We searched for peer-reviewed quantitative and qualitative studies published in English that examined the impact of ALCs on movement patterns, physical or mental health, and academic indicators in children and adolescents, as well as those that explored the perceptions of members of the educational community and the teaching practices used in ALCs. Databases research included MEDLINE (PubMed), ERIC, SCOPUS and ProQuest Education. Nineteen studies were included, of which 11 were experimental, 4 were cross-sectional, and 4 were qualitative. The analysis revealed a predominantly positive influence of ALCs on children’s sedentary behaviour, learning engagement and psychological well-being; and mixed results on PA, physical health and academic performance. Our results also suggest that learning spaces are positively perceived and well accepted by the entire educational community, and that teachers teaching in ALCs are more prone to use student-centered and collaborative pedagogies than in traditional classrooms. Although this review shows a positive impact on key health and education variables, the evidence is limited and lacks depth. In addition, the small number of studies and their methodological weaknesses prevent robust conclusions, but the results still help to guide future decisions.

Author Correction: Pre-therapeutic efficacy of the CDK inhibitor dinaciclib in medulloblastoma cells

Two-Photon-Driven Photoprotection Mechanism in Echinenone-Functionalized Orange Carotenoid Protein

ECP-IEM: Enhancing seasonal crop productivity with deep integrated models

Accurate crop yield forecasting is vital for ensuring food security and making informed decisions. With the increasing population and global warming, addressing food security has become a priority, so accurate yield forecasting is very important. Artificial Intelligence (AI) has increased the yield accuracy significantly. The existing Machine Learning (ML) methods are using statistical measures as regression, correlation and chi square test for predicting crop yield, all such model’s leads to low accuracy when the number of factors (variables) such as the weather and soil conditions, the wind, fertilizer quantity, and the seed quality and climate are increased. The proposed methodology consists of different stages, like Data Collection, Preprocessing, Feature Extraction with Support Vector Machine (SVM), correlation with Normalized Google Distance (NGD), feature ranking with rising star. This study combines Bidirectional Gated Recurrent Unit (Bi-GRU) and Time Series CNN to predict crop yield and then recommendation for further improvement. The proposed model showed very good results in all datasets and showed significant improvement compared to baseline models. The ECP-IEM achieved an accuracy 96.34%, precision 94.56% and recall 95.23% on different datasets. Moreover, the proposed model was also evaluated based on MAE, MSE, and RMSE, which produced values of 0.191, 0.0674, and 0.238, respectively. This will help in improving production of crops by giving an early look about the yield of crops which will than help the farmer in improving the crops yield.