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PyPeCT2S: Pythonic paediatric computed tomography to strength with automatic landmarking for the automation of bone strength analysis in children
Quantitative computed tomography (QCT) based finite element analysis (FEA) models have been used to accurately predict bone strength. However, the process is time-consuming and requires a trained professional to provide manual input during several steps. The aim of this work is to automate the FEA processes of the computed tomography to strength (CT2S) pipeline applied to the paediatric femur, so that use of the pipeline requires less training, is more user-friendly and can be run for a large cohort. A deployable application was built using Python and Qt to create a repeatable, extensible, automatic, and contained platform called pythonic paediatric computed tomography to strength (PyPeCT2S), with specific attention to the development of automatic landmarking for the paediatric cohort. In this study, the computed tomography (CT) scans of 69 children were included, and FEA models were created using the PyPeCT2S pipeline. The models were subjected to four-point bending for both landmarking methods (automatic versus manual). The FEA critical moment against age results showed comparable values to existing experimental research that utilises equivalent boundary conditions, with values from 0.19–167.94 Nm. The automatic landmarking methodology was shown to produce minimal differences in location and FEA results, compared to manual landmarking, but was substantially faster to operate. The overall pipeline performance showed a mean time reduction of 49–61% and a maximum of 70% against the native pipeline, reducing completion time from 35 to 13 min. Time savings came from both process optimisations and improved user interaction pathways. The work demonstrates that a pythonic approach is a step change to speed up the prediction of FE-based bone strength while still allowing interaction and substantially limiting the chance of human error. Overall, the pythonic approach provides simpler operation and time efficiencies, allowing the tool to be used by clinicians and deployed in the clinical setting in future.
Illumina and PacBio insights into the metataxonomic enrichment of fish pond sediment microbiota via Candida albicans co-culture
Using nominal group technique to select an HIV status disclosure decision aid for adaptation in Georgia
Introduction HIV status disclosure decision-making is a complex process influenced by stigma, relationship dynamics, and anticipated social consequences. Although decision aids can support individuals in navigating such decisions, no disclosure decision-support interventions have been adapted for use in the Georgian context. This study aimed to identify and prioritize an existing evidence-based intervention for adaptation to support HIV disclosure decision-making among people living with HIV (PLWH) in Georgia. Methods We used the Nominal Group Technique (NGT), a structured consensus method, to elicit and prioritize stakeholder perspectives. Two separate NGT sessions were conducted with HIV care providers (n = 12) and PLWH (n = 10), followed by a joint session to reach consensus. Prior to the sessions, nine evidence-based disclosure decision-support interventions from HIV, mental health, and substance use fields were identified through a desk review and grouped into session-based, paper-based, and digital formats. Participants generated ideas, discussed advantages and limitations, and ranked intervention formats and specific interventions. Descriptive content analysis was used to summarize discussion themes. Results Providers prioritized digital interventions, emphasizing accessibility and scalability, whereas PLWH preferred session-based interventions, highlighting the importance of trust, individualized support, and peer involvement. Within these formats, providers favored a structured digital program, while PLWH selected an individual session-based intervention focused on disclosure to family members. Adaptation priorities included incorporating peer educators, addressing disclosure to different social and healthcare contexts, and including locally relevant content on legal issues, treatment adherence, and available support services. In the joint session, consensus was reached to prioritize the intervention selected by PLWH. Conclusions This study identified a priority disclosure decision-support intervention for adaptation to the Georgian context, emphasizing the importance of patient-centered and contextually tailored approaches. Future research will focus on adapting the selected intervention and identifying appropriate implementation strategies to support pilot testing and integration into HIV care services.
Effect of deep margin elevation with different base materials on periodontal health in a clinical study
Abstract This prospective clinical investigation evaluated the effects of proximal box elevation using three base materials on periodontal tissues in premolar teeth treated with direct composite restorations. In endodontically treated maxillary premolars, gingival crevicular fluid (GCF) samples were collected after restoring cavities using three base materials: resin-modified glass ionomer (RMGI), flowable composite (Tetric N-Flow), and injectable hybrid composite (Beautifil Flow Plus). Periodontal health was assessed by measuring interleukin-1 (IL-1) and tumor necrosis factor (TNF) concentrations, bleeding on probing (BOP), and pocket depth (PD) immediately and at 3 months postoperatively. Data were statistically analyzed using one-way ANOVA to compare groups, Levene’s test to assess homogeneity of variances, and paired t-tests and chi-square tests were used to assess differences within groups. the results revealed that the levels of IL-1, TNF, and PD differed significantly among the three base materials at different time points. The Beautifil Flow Plus group showed the highest TNF levels, the most substantial absolute reduction, and the lowest reduction in IL-1 levels. However, there were no significant differences in BOP among the three materials at any time interval. Within the limitations of this three-month study, all materials were clinically acceptable, though Tetric N-Flow and RM-GI showed a more favorable short-term inflammatory profile than Beautifil Flow Plus. However, due to the limited sample size and short follow-up, no definitive claims regarding long-term periodontal compatibility or clinical superiority can be made.
Efficient and lightweight long-range modeling for 3d point cloud classification and segmentation
3D point clouds, with their compact structural representation and rich geometric information, have become fundamental data sources for visual understanding tasks in computer vision, robotics, and intelligent systems.Despite extensive progress, many existing methods still place strong emphasis on local geometric modeling while exhibiting limited ability to capture global long-range contextual dependencies. Moreover, the increasing architectural complexity of modern models often leads to high computational cost and memory consumption. In this paper, we propose Point BiLSTM, an efficient and lightweight framework for 3D point cloud classification and segmentation. The core of the proposed method is a bidirectional long short-term memory (BiLSTM)-based sequencer module, which models long-range contextual dependencies among points with linear computational complexity, enabling effective global feature learning at a low cost. Considering the unordered nature of point clouds, we further propose a Mixed Sequence Soft Cross-Entropy Loss that jointly supervises fixed-order and randomly permuted point sequences during training. This design explicitly enhances robustness to permutation ambiguity and improves training stability. Extensive experiments conducted on three widely used benchmarks—ModelNet40, ScanObjectNN, and ShapeNet Part—demonstrate that Point BiLSTM achieves highly competitive performance. In particular, the proposed method attains the fastest inference speed on both idealized and real-world datasets, outperforming current state-of-the-art methods by 30.2% and 54.2%, respectively. In addition, Point BiLSTM significantly reduces computational complexity and memory consumption, providing an effective solution for efficient point cloud learning. Our code will be available at https://github.com/wendaodao04/PointBiLSTM .
Spatiotemporal patterns and influencing factors of taxi supply-demand imbalance: A case study of Urumqi, China
Projected climate change effects on suitable habitat for Pseudosuccinea columella and Radix natalensis in uMgungundlovu, South Africa
Introduction Climate change impacts oceanic and atmospheric circulation patterns, precipitation, air and sea surface temperatures leading to altered intermediate host vector distribution, reproduction, and maturation. With temperatures predicted to rise by 1.5°C or more, understanding the influence of environmental characteristics and climatic conditions on distribution of intermediate host vectors, occurrence and habitat suitability is vital in vector borne diseases and their control policies. Hence, the study focused on the predicted changes in habitat suitability of Pseudosuccinea columella and Radix natalensis in uMgungundlovu district under climate change scenarios. Methods Freshwater Pseudosuccinea columella and Radix natalensis were collected from seven localities in uMgungundlovu using stratified random cluster sampling. Snail samples were collected at 45 sites. The sites were selected based on local knowledge and accessibility. The ensemble model assessed climate variability, while Representative Concentration Pathways (RCP) predicted habitat appropriateness for P. columella and R. natalensis . Maps were created using the MaxEnt model, and the model’s performance was assessed using the Area Under the Curve (AUC) of the receiver operating characteristic (ROC) curve. Results Our study indicates that P. columella and R. natalensis under current climatic conditions have suitable habitats in the Mpofana, Impendle, Richmond, and Mkhambathini municipalities within the uMgungundlovu district, but the central and northern regions are considered unsuitable. Furthermore, under the RCP4.5 and RCP8.5 climate scenarios, an estimated 22.31% and 22.57% of areas that are presently suitable are expected to become unsuitable for P. columella in uMgungundlovu, with fragmented areas and unsuitable areas becoming suitable for R. natalensis distribution by 2085. Conclusion Climate change could influence the distribution of fasciola intermediate host snails, potentially shifting the hotspots of fascioliasis transmission. Future studies should take into account shifts in land use, evolving agricultural techniques, and the impact of human activities to ensure effective management strategies.
Inhaling high THC cannabis acutely increases inflammation but not insulin sensitivity: a quasi-randomized trial
Epidemiology and clinical features of Hymenoptera stings in East Azerbaijan, northwestern Iran
Background Stings caused by Hymenoptera including honey bees, wasps, and hornets represent a growing global and national health concern with outcomes ranging from mild local reactions to severe systemic complications. Despite their significance, region-specific epidemiological data from Iran remain limited. This study aimed to investigate the demographic characteristics, anatomical distribution, seasonal patterns, clinical features, and hospitalization-related factors among Hymenoptera sting victims in northwestern Iran. Methods A retrospective descriptive-analytical study was conducted between 2021 and 2024 in East Azerbaijan Province, Iran, using medical records from hospitals affiliated with Tabriz University of Medical Sciences. A total of 225 eligible patients with confirmed or clinically suspected Hymenoptera stings were analyzed through a census-based approach. Demographic (including age, sex, and occupational status), epidemiological, and clinical variables were collected via structured forms. Descriptive statistics were applied. Chi-square or exact tests were used to assess associations between categorical variables, and multivariable logistic regression was performed to identify independent predictors of hospitalization. Statistical significance was set at p < 0.05. Results Among 225 cases, 71.6% were male, and the mean age was 30.99 ± 18.49 years, showing a bimodal distribution peaking in children aged <10 years and adults aged 30–49 years. A highly significant association was found between gender and occupation (p < 0.001); females were predominantly housewives, males were distributed mainly among self-employed individuals, students, farmers, and manual workers. Urban areas accounted for 53.3% of incidents, and summer was the peak season (52.4%). The head and neck were the most affected sites (44.9%), with a significant gender disparity: males were more frequently stung on the head/neck, whereas females were more frequently stung on the limbs (p < 0.001). Hospitalization was required in 28.0% of patients. In multivariable logistic regression, pain (OR = 40.43, 95% CI: 12.56–130.15), severe muscle pain (OR = 77.56, 95% CI: 12.97–463.89), and local swelling/redness (OR = 16.05, 95% CI: 3.96–65.06) were independently associated with hospitalization. Male gender showed a non-significant trend toward higher odds of hospitalization (OR = 3.06, p = 0.067). Conclusion Hymenoptera stings in northwestern Iran are shaped by a complex interplay of ecological, occupational, and socio-cultural factors. The distinct anatomical sting patterns may be influenced by regional dress codes and gender-specific activities, suggest that preventive strategies should address both domestic and occupational exposure settings. Public health interventions should focus on habitat management in residential areas, awareness among households, and protective clothing for outdoor workers to reduce the burden of envenomation.
Using capnographic changes to assess pulmonary perfusion impairment during pulmonary surgery: animal experiments and clinical studies
A novel deep-learning approach for robust identification of plant diseases
Rising temperatures and changing weather conditions are accelerating the spread of plant diseases and increasing the threat to global food security. Reliable detection of leaf diseases is therefore essential to protect crop yields and ensure food quality. Deep learning has proven to be a powerful tool for classifying leaf diseases across various crops. Due to the natural variability of plants, plant diseases often appear in irregular structures. Surface unevenness, folds, or dirt particles are common in field images and can be mistakenly identified as important features by convolutional neural networks (CNNs). This is a challenge that has not been sufficiently addressed in previous studies. This study proposes a novel deep learning approach that takes into account both the specific visual characteristics of plant diseases and potential disturbances in the microstructure, such as surface irregularities or prominent leaf veins, which may mislead the model. Using stratified five-fold cross-validation on a peer-reviewed dataset, which comprises 2,801 images of radish leaves across five classes (healthy, three disease classes: mosaic virus, black leaf spot, and downy mildew, and one pest-affected class: flea beetle), the proposed method achieved an average and balanced accuracy of 99.86%, establishing a new dataset-level benchmark in the field and demonstrating its effectiveness. The results indicate that the proposed approach may provide a promising basis for future applications in agricultural field monitoring, automated sorting and post-harvest quality control, offering potential to reduce both food waste and associated costs.
Repeated heat pain threshold testing in pain-free individuals induces habituation and yields false-positive pre–post comparisons
Abstract Heat pain thresholds (HPTs) are used for clinical assessments, sensory phenotyping, and pre–post comparisons. This usage assumes that HPTs are stable, exchangeable, and unaffected by repetition. Twenty consecutive HPT measures were taken at each volar forearm during three daily sessions in pain-free individuals. Negative exponential growth models were used to characterise HPT series and estimate effects of session, side, and time between sessions. To assess suitability for within-session pre–post comparisons, the mean of the first n repetitions of a series was compared with the mean of the subsequent n repetitions for n = 1–10, and ICC(3,1) calculated for n = 2–20. From 23 participants, 2266 HPT measurements were recorded within 118 series. Strong habituation was observed (Cohen’s d = 1.9), with HPT increasing by a mean of 4.9 °C across all series (95% CI: 4.4 to 5.3 °C, p <0.001). Repeated HPTs followed an asymptotic trajectory; lower initial HPTs were associated with larger repetition-related increases, and right forearms adapted faster than left. Later sessions were associated with higher initial and asymptotic HPT values and faster adaptation. Using single or averaged HPTs for within-series pre–post comparisons consistently yielded false-positive findings, while ICC decreased as n increased.
Lifetime HIV testing frequency among women in Sub-Saharan Africa: A DHS-based analysis using zero-inflated negative binomial regression
Background HIV testing is an essential component of HIV prevention and care, yet lifetime testing frequency remains low in sub-Saharan Africa (SSA). This study examines the factors influencing lifetime HIV testing frequency among reproductive-age women in SSA. Methods We analyzed the most recent Demographic and Health Survey (DHS) data from nine sub-Saharan African countries, with an overall weighted sample of 158,722 women aged 15–49. The dependent variable was the lifetime number of HIV tests, and Zero-Inflated Negative Binomial (ZINB) regression was used. Model selection was based on Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC) and Vuong’s test, favoring the ZINB model over other count models. Adjusted incidence rate ratios (aIRRs) for the count and aOR for the inflation part with 95% confidence intervals (CIs) were reported, with statistical significance set at p < 0.05. Results The mean number of lifetime HIV tests was 2.49 (SD = 7.67), with a median of 0 (IQR = 0–3). Key predictors of lifetime HIV testing included: age (30–39 years: aIRR = 2.94; 40–49 years: aIRR = 3.14), rural residence (aIRR = 1.10), current or former union (aIRR = 1.37 and 1.30, respectively), education (primary: aIRR = 1.19; secondary: aIRR = 1.35; higher: aIRR = 1.81), wealth (richer: aIRR = 1.30; richest: aIRR = 1.34), and employment (manual labor: aIRR = 1.21; skilled work: aIRR = 1.18). Other significant factors included: comprehensive HIV knowledge (aIRR = 1.37), STI awareness (aIRR = 2.21), media exposure (aIRR = 1.25), access to health facilities (aIRR = 1.26), number of sexual partners (1 partner: aIRR = 4.82; 2–3 partners: aIRR = 5.67; 4 or more partners: aIRR = 6.13), early sexual debut (aIRR = 0.61), and region (East Africa: aIRR = 2.82; Southern Africa: aIRR = 7.25). The inflation model showed very low odds of never testing among women with primary and secondary education, lower odds with media exposure (OR=0.40), but higher odds with higher education (OR=5.26) and rural residence (OR=1.93). Conclusions This study indicated that over half of women of reproductive age in SSA have never tested for HIV. Addressing socio-economic, structural, and regional disparities, particularly in West Africa, is crucial, highlighting the need for targeted, equitable, and community-based strategies to expand testing coverage and promote repeat uptake.
Comet assay and antioxidant enzyme as blood biomarkers of low-dose radiation-induced adaptive response
Abstract This study investigated the ability of low doses to cause an in vivo radio-adaptive response (RAR) and examined the biological effects of acute low-dose total-body gamma irradiation in rats. Adult male rats were randomly divided into 8 groups. Acute gamma radiation doses of 0.25, 0.5, and 0.75 Gy for groups 2, 3, and 4, respectively, and a 2 Gy challenge dose for group 5 were administered 14 days later. then 0.25, 0.50, and 0.75 Gy, followed by 2 Gy to groups 6, 7, and 8. Twenty-four hours after irradiation, measurements were made of comet assay parameters and antioxidant indicators: Acute gamma exposure resulted in a significant rise in Comet Assay Score CAS (6–16%), Tail Length TL (4.7–7.45 μm), DNA in tail DNA% (11.82–17.76%), Tail Moment TM (1.17–3.78 μm), and Olive Tail Moment OTM (0.53–1.52 µmol/L), as well as dose-dependent increases in GSSG (4.6–9.58 µmol/L), and decrease in SOD (180.4–91.2 U/mL). These results demonstrate the rising detrimental consequences linked to higher acute gamma doses. Lower priming does encourage stronger adaptive responses, according to RAR analysis. The concentrations of GSSG, TL, OTM, and the ratio of Oxidized Glutathione/ Reduced Glutathione GSSG/GSH showed notable linear dose-response associations, indicating their potential as biomarkers for acute gamma radiation biodosimetry. These findings lay the groundwork for future research to verify these markers for dose evaluation and to examine the dynamics of RAR under various challenge doses and postirradiation circumstances.
Unraveling uneven urbanites’ expressed happiness across Chinese cities using geotagged social media data: Key predictors and future climate–happiness associations
Understanding inter-city disparities in urban expressed happiness (EH) and the key predictors for these differences is critical for advancing socially sustainable urban development. However, the key predictors for EH remain poorly understood, and existing studies have largely overlooked the potential association with future climate change. In this study, we analyzed 5,118,772 geotagged Weibo posts from 50 Chinese cities using SnowNLP for sentiment analysis, machine learning models, and LDA topic modeling to investigate the inter-city differences in EH, its underlying predictors, and the potential association with further climate change. Sentiment analysis revealed pronounced variations in EH across Chinese cities, with more positive emotions observed during weekends and holidays. Incorporating 17 potential predictors, we developed ten machine learning models. A random forest model achieved the best performance, with an R² that exceeded all other models by 1.05%–60.00% and an RMSE that was 7.41%–60.95% lower than the alternatives. SHAP analysis showed that landscape, socioeconomic, environmental, and geographic factors accounted for 24.58%–38.97%, 20.64%–40.12%, 11.96%–29.33%, and 11.47%–23.71% of the total feature importance in the EH prediction models, respectively. Among individual variables, the normalized difference vegetation index (NDVI) exhibited the highest feature importance, accounting for 18.56%–32.16% of the total importance, followed by per capita GDP, PM 2.5 concentration, AQI, and temperature. Scenario-based projections suggest an association between projected climate warming and potential changes in urbanites’ EH. Overall, this study identifies the key predictors associated with urbanites’ EH and highlights the potential association of future climate warming with EH, providing valuable evidence for urban planning and policy interventions.