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Metatarsal fractures rank among the ten most common fractures.Comprehensive studies on postoperative functional recovery remain limited. A reliable predictive model for recovery outcomes is essential for optimizing patient care. To develop and validate a predictive model for postoperative functional recovery in metatarsal fracture patients and implement it as an interactive web-based calculator. This retrospective study included 555 metatarsal fracture patients (2018-2022), with 425 in the training cohort and 130 in the validation cohort. The outcome variable was postoperative recovery as assessed by the AOFAS midfoot scoring system. LASSO regression identified significant predictors of recovery,the selected variables underwent binary logistic regression analysis to identify independent risk factors. A prediction model was constructed using the training cohort and visualized through a nomogram. Model validation was performed internally through bootstrapping and externally using the validation cohort. The model was implemented as an interactive web calculator using R Shiny. At final follow-up, 71.71% of patients achieved good recovery (AOFAS score >80). The model identified ten independent risk factors, including residence location, smoking status, obesity, rehabilitation training, educational level, age, injury mechanism, infection, and anemia. The model demonstrated robust discrimination (c-index: 0.832 training, 0.838 validation) and calibration (H-L test: P = 0.994 training, P = 0.648 validation). DCA showed optimal clinical utility within 0.72-1.00 threshold probability. Protective factors included hilly areas (OR = 0.183), smoking (OR = 0.4), obesity (OR = 0.270), and undergoing rehabilitation training (OR = 0.237),while risk factors included low educational level (OR = 3.884), advanced age (OR = 2.751), high-energy injury (OR = 3.003), residence in mountainous regions (OR = 4.671), infection (OR = 16.946), and anemia (OR = 5.787). The interactive web calculator is accessible at https://metarecoverypredictor.shinyapps.io/DynNomapp/. The validated prediction model effectively identifies risk factors for postoperative recovery in metatarsal fractures. This tool can aid clinicians in developing personalized treatment strategies and improving patient outcomes. The web-based calculator provides easy access for clinical application.

Closed femoral shaft fracture is caused by high-energy injuries, and non-union exists after operation, which can significantly damage patients’ body and mind. This study aimed to explore the factors influencing postoperative non-union of closed femoral shaft fractures and establish a predictive nomogram. Patients with closed femoral shaft fractures treated at Hebei Medical University Third Hospital between January 2015 and December 2021 were retrospectively enrolled. A total of 729 patients met the inclusion criteria; of them, those treated in 2015–2019 comprised the training cohort (n = 617), while those treated in 2020–2021 comprised the external validation cohort (n = 112). According to multivariate logistic regression analysis, complex fractures, bone defects, smoking, and postoperative infection were independent risk factors. Based on the factors, a predictive nomogram was constructed and validated. The C-indices in training and external validation cohorts were 0.818 and 0.781, respectively; and the C-index of internal validation via bootstrap resampling was 0.804. The Hosmer–Lemeshow test showed good fit of the nomogram ( P  > 0.05) consistent with the calibration plot results. The clinical effectiveness was best at a threshold probability of 0.10–0.40 in decision curve analysis. The risk prediction for patients with fractures using this nomogram may aid targeted prevention and rehabilitation programs.

To investigate the population distribution characteristics of hospitalized patients with postoperative fracture infections in Hebei Province and Xinjiang Uygur Autonomous Region, and to analyze the effects of air pollutants on postoperative fracture infections within the two regions. Data on orthopedic postoperative infection cases were retrospectively collected from representative hospitals in Hebei Province and the Xinjiang Uygur Autonomous Region from 2018 to 2022. Their distribution characteristics were analyzed using descriptive epidemiological methods. The lagged effects of air pollutants on postoperative infections were also evaluated using distributed lag nonlinear modeling, combined with air quality data from the same period. The rate of postoperative infections after orthopedic surgery in the Xinjiang Uygur Autonomous Region (3.06%) was significantly higher than that in the Hebei Region (0.47%) in this study. A total of 1338 patients with postoperative infections were collected from the two regions, with a mean age of 51.41 ± 17.34 years. The most affected age group was 41–60 years (521 cases, 39%), and there was a male predominance (875 cases, 65.40%). Using the air pollutant P50 as the reference concentration, the greatest cumulative 3-day increase in the risk of postoperative infection was observed for each 0.1 mg/m 3 increase in CO concentration (RR = 1.069, 95% CI 1.029, 1.110). The greatest cumulative 12-day effect was observed for each 10 μg/m 3 increase in NO 2 concentration (RR = 1.67, 95% CI 1.369, 2.037). CO and NO 2 showed reduced effects at very low concentrations and elevated effects at very high concentrations. The rate of postoperative infections after orthopedic surgery in the Xinjiang Uygur Autonomous Region was significantly higher than that in the Hebei Region. In Xinjiang, postoperative infections were predominantly observed in males aged 41–60 years. Exposure to air pollutants such as CO and NO 2 increased the risk of postoperative orthopedic infections to varying degrees, with both short-term and cumulative lagged effects.

To investigate the population distribution characteristics of elderly osteoporosis fracture patients in Hebei Province and analyze the effects of air pollutants on elderly osteoporosis fractures, We retrospectively collected 18,933 cases of elderly osteoporosis fractures from January 1, 2019, to December 31, 2022, from four hospitals in Hebei Province. The average age was 76.44 ± 7.58 years, predominantly female (13,189 patients, 69.66%). The number of hospitalized patients increased progressively from 2019 to 2022. The Distribution Lag Nonlinear Model (DLNM) showed that the cumulative lagged effects of PM2.5 and PM10 on the number of hospitalized elderly osteoporosis fracture patients exhibited a bimodal distribution, with the Relative Risk (RR) reaching its peak at a 1-day lag (PM2.5: RR = 1.032, 95% CI: 1.019, 1.045; PM10: RR = 1.022, 95% CI: 1.014, 1.029). Similarly, the cumulative lagged effect of NO 2 displayed a bimodal pattern, with the RR peaking at a 12-day lag (RR = 1.138, 95% CI: 1.101, 1.187). The single-day lag effect of SO 2 was statistically significant from day 9 to day 12, reaching its maximum at day 11 (RR = 1.054, 95% CI: 1.032, 1.71). PM2.5, PM10, NO 2 , and SO 2 increase the risk of osteoporosis fractures in the elderly, including single-day and cumulative lag effects. Further studies are needed to explore the molecular mechanisms behind this relationship.

Key messageWe identified and validated two stable grain filling rate (GFR) quantitative trait loci (QTL) in wheat that positively influenced several yield-related traits. Among them, QGfr.sicau-7D.1 was a novel GFR QTL.The grain filling rate (GFR) plays a crucial role in determining grain yield. To advance the current understanding of the genetic characteristics underlying the GFR in common wheat, three recombinant inbred line populations were used to map and validate GFR quantitative trait loci (QTL). Using a high-density genetic linkage map, 10 GFR QTL were detected. They were located on chromosomes 2D, 4A, 4B, 5B, 6D, 7A and 7D, explained 4.99–12.62% of the phenotypic variation. Two of them, QGfr.sicau-6D and QGfr.sicau-7D.1, were detected in all four environments tested and their genetic effect was validated by closely linked kompetitive allele specific PCR (KASP) markers in different genetic backgrounds. The effects of these two GFR QTL on other yield-related traits were also estimated. QGfr.sicau-6D had a significant positive influence (p < 0.01) on thousand kernel weight, kernel width, kernel volume, and kernel surface area. QGfr.sicau-7D.1 had a significant positive influence (p < 0.01) on thousand kernel weight and kernel length. Furthermore, QGfr.sicau-7D.1 was a completely novel QTL for GFR; several genes associated with grain growth and development were predicted in its physical interval. These results will facilitate molecular marker-assisted selection of wheat with high-confidence QTL for GFR and fine mapping of genes associated with GFR, thereby contributing to yield improvement.

The discharge of sediment plumes, which occurs mainly in the two depth zones, has a critical impact on assessing the deep-sea environment. Therefore, it is necessary to establish the corresponding physical oceanography for the evolution of these sediment plumes. For a more accurate evolution estimation of the plumes, the model in this research is concerned with the dynamic interaction between the deep-sea mining vehicle (DSMV) and the sediment plumes on small scales (t ≤ 2 s), contributing to a focus on the vital physical mechanics of controlling the extent of these plumes. The sediment concentration and particle trajectories of the plume emissions were determined using the Lagrangian discrete phase model (DPM). The results show that (1) the wake structure of the DSMV wraps the plume vortex discharged from the rear of the vehicle and inhibits the lateral diffusion of the plume, (2) the length of the entire wake (Lw) increases exponentially as the relative discharge velocity of the plume (U*) increases, where U* is defined as the dimensionless difference between the traveling velocity of the DSMV and the discharge velocity of the plume, and (3) at the same traveling speed of the DSMV and U* less than 0.75, the dispersion of the sediment particles in the early discharge stage of the plume does not vary with the plume discharge rate. This will be beneficial for the more accurate monitoring of ecological changes in deep-sea mining activities and provide theoretical guidance for the green design of DSMVs.

Photoperiod is acknowledged as a crucial environmental factor for plant flowering. According to different responses to photoperiod, plants were divided into short-day plants (SDPs), long-day plants (LDPs), and day-neutral plants (DNPs). The day length measurement system of SDPs is different from LDPs. Many SDPs, such as rice, have a critical threshold for day length (CDL) and can even detect changes of 15 minutes for flowering decisions. Over the last 20 years, molecular mechanisms of flowering time in SDP rice and LDP Arabidopsis have gradually clarified, which offers a chance to elucidate the differences in day length measurement between the two types of plants. In Arabidopsis, CO is a pivotal hub in integrating numerous internal and external signals for inducing photoperiodic flowering. By contrast, Hd1 in rice, the homolog of CO , promotes and prevents flowering under SD and LD, respectively. Subsequently, numerous dual function regulators, such as phytochromes, Ghd7 , DHT8 , OsPRR37 , OsGI , OsLHY , and OsELF3 , were gradually identified. This review assesses the relationship among these regulators and a proposed regulatory framework for the reversible mechanism, which will deepen our understanding of the CDL regulation mechanism and the negative response to photoperiod between SDPs and LDPs.

In plants, carbohydrates are central products of photosynthesis. Rice is a staple that contributes to the daily calorie intake for over half of the world’s population. Hence, the primary objective of rice cultivation is to maximize carbohydrate production. The “source-sink” theory is proposed as a valuable principle for guiding crop breeding. However, the “flow” research lag, especially in sugar transport, has hindered high-yield rice breeding progress. This review concentrates on the genetic and molecular foundations of sugar transport and its regulation, enhancing the fundamental understanding of sugar transport processes in plants. We illustrate that the apoplastic pathway is predominant over the symplastic pathway during phloem loading in rice. Sugar transport proteins, such as SUTs and SWEETs, are essential carriers for sugar transportation in the apoplastic pathway. Additionally, we have summarized a regulatory pathway for sugar transport genes in rice, highlighting the roles of transcription factors (OsDOF11, OsNF-YB1, OsNF-YC12, OsbZIP72, Nhd1), OsRRM (RNA Recognition Motif containing protein), and GFD1 (Grain Filling Duration 1). Recognizing that the research shortfall in this area stems from a lack of advanced research methods, we discuss cutting-edge analytical techniques such as Mass Spectrometry Imaging and single-cell RNA sequencing, which could provide profound insights into the dynamics of sugar distribution and the associated regulatory mechanisms. In summary, this comprehensive review serves as a valuable guide, directing researchers toward a deep understanding and future study of the intricate mechanisms governing sugar transport.

Objective To investigate the impact of air pollutants on the incidence of hip fractures among older hospitalized patients in Hebei Province. Methods This epidemiological study was conducted across multiple hospitals in Hebei Province over a five-year period, focusing on older people with hip fractures. A generalized linear model (GLM) and a generalized additive model (GAM) were applied to identify potential factors influencing hospitalization risk. To evaluate the association between exposure to air pollutants (PM2.5, PM10, NO2 SO2, CO, and O3) and the incidence of hip fractures, a distributed lag nonlinear model (DLNM) based on the GLM framework was employed to account for lag effects. Air quality data were obtained from fixed monitoring stations within the environmental monitoring networks of each city. The selection of monitoring sites strictly followed national standards, ensuring locations were distant from major roads, industrial emission sources, and clusters of high-rise buildings, while avoiding direct residential coal-burning emissions. This ensured that the air pollutant measurements accurately reflected regional pollution levels. Natural cubic spline functions and categorical variables were incorporated to adjust for long-term trends, seasonality, and day-of-week effects. Sensitivity analyses were performed by varying the degrees of freedom (6–8 df per year) for the time trend to assess the robustness of the results. Results The study included 16,765 older hip fracture patients with an average age of 77.33 ± 7.60 years; the majority were female (7,216 cases, 68.50%). Using the P50 concentration of air pollutants as a reference, each 10 µg/m³ increase in PM2.5, PM10, and NO2 concentrations over 1 day (or a 0.1 mg/m³ increase in CO) was linked to a higher risk of hip fractures (PM2.5: RR = 1.02, 95% CI: 1.012, 1.027; PM10: RR = 1.017, 95% CI: 1.013, 1.023; NO2: RR = 1.068, 95% CI: 1.049, 1.088). Cumulative 12-day exposure to SO2 and CO increased hospitalization risk (SO2: RR = 1.19, 95% CI: 1.12, 1.28; CO: RR = 1.035, 95% CI: 1.027, 1.044). A 14-day cumulative lag showed that SO2 levels between 0 and 6 µg/m³ significantly increased the likelihood of hospitalization. Conclusion Short-term and cumulative exposure to air pollutants, including PM2.5, PM10, SO2, CO, and NO2 may be associated with an increased risk of hospitalization for hip fractures in older people. It should be noted that, as an observational study, the potential influence of unmeasured confounding factors cannot be excluded.

Objective Air pollution is increasing and threatening human health. The objective of this study is to investigate the population distribution characteristics of elderly osteoporosis fractures in Hebei Province and Xinjiang Uygur Autonomous Region and to analyze the effects of air pollutants on the number of elderly osteoporosis fracture inpatients in the two regions. Method A retrospective collection of elderly osteoporosis fracture cases was conducted in selected hospitals in Hebei Province and Xinjiang Uygur Autonomous Region from January 1, 2018 to December 31, 2022. The chi‐square test was used to compare the distributional characteristics of the population in the two regions. Additionally, we used a distributed lag nonlinear model (DLNM) in order to assess the effect of air pollutants on the number of daily hospital admissions of elderly osteoporosis fracture patients in different regions. Result A total of 19,203 elderly osteoporosis fracture patients were included in the study. The average age of these patients was 76.66 ± 7.55 years, and the majority of them were female (13,514 instances, 70.37%). The disparities in age distribution (χ2 = 133.9 p < 0.001), fracture site (χ2 = 62.0 p < 0.001), and hospitalization cost (Z = −15.635 p < 0.001) between the two regions were statistically significant. The lag effect curves of PM2.5, PM10, and NO2 on the number of elderly osteoporosis fracture hospitalizations in Xinjiang Uygur Autonomous Region exhibited a similar pattern resembling a “W”‐shaped curve. All three pollutants reached their highest values after a lag time of 14 days (PM2.5: RR = 1.053, 95% CI: 1.031, 1.074; PM10: RR = 1.031, 95% CI: 1.018, 1.043; NO2: RR = 1.125, 95% CI: 1.070, 1.182). In Hebei Province, the largest impacts of PM2.5 and PM10 were observed after a lag of 14 days (PM2.5: RR = 1.022, 95% CI: 1.013, 1.028; PM10: RR = 1.013, 95% CI: 1.008, 1.018). Similarly, the maximum effect of NO2 was observed after a lag of 11 days (RR = 1.020, 95% CI: 1.010, 1.028). Conclusion There were differences in the epidemiological characteristics of hospitalized patients with osteoporosis fractures between the two regions, PM2.5, PM10, and NO2 increased the number of hospitalizations for osteoporosis fractures. Exposure to air pollutants such as PM2.5 increases the risk of osteoporosis fractures in the elderly population. The aim of this study was to investigate the population distribution characteristics of osteoporosis fractures in the elderly in Hebei Province and Xinjiang Uygur Autonomous Region and the lag effects of air pollutants on them. The disparities in age distribution, fracture site, and hospitalization cost between the two regions were statistically significant. Older persons may face an elevated risk of osteoporosis fractures due to their exposure to air pollutants such as PM2.5, PM10, and NO2.