Inter-rater variability and repeatability in the assessment of the Tanner-Whitehouse classification of hand radiographs for the estimation of bone age.

OBJECTIVE: To determine which bones and which grades had the highest inter-rater variability when employing the Tanner-Whitehouse (T-W) method. MATERIALS AND METHODS: Twenty-four radiologists were recruited and trained in the T-W classification of skeletal development. The consistency and skill of the radiologists in determining bone development status were assessed using 20 pediatric hand radiographs of children aged 1 to 18 years old. Four radiologists had a poor concordance rate and were excluded. The remaining 20 radiologists undertook a repeat reading of the radiographs, and their results were analyzed by comparing them with the mean assessment of two senior experts as the reference standard. Concordance rate, scoring, and Kendall's W were calculated to evaluate accuracy and consistency. RESULTS: Both the radius, ulna, and short finger (RUS) system (Kendall's W = 0.833) and the carpal (C) system (Kendall's W = 0.944) had excellent consistency, with the RUS system outperforming the C system in terms of scores. The repeatability analysis showed that the second rating test, performed after 2 months of further bone age assessment (BAA) practice, was more consistent and accurate than the first. The capitate had the lowest average concordance rate and scoring, as well as the lowest overall concordance rate for its D classification. Moreover, the G classifications of the seven carpal bones all had a concordance rate less than 0.6. The bones with lower Kendall's W were likewise those with lower scores and concordance rates. CONCLUSION: The D grade of the capitate showed the highest variation, and the use of the Tanner-Whitehouse 3rd edition (T-W3) to determine bone age (BA) was frequently inconsistent. A more comprehensive description with a focus on inaccuracy bones or ratings and a modification to the T-W3 approach would significantly advance BAA.

Bagging Improves the Performance of Deep Learning-Based Semantic Segmentation with Limited Labeled Images: A Case Study of Crop Segmentation for High-Throughput Plant Phenotyping.

Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolutional Neural Networks (DCNNs) have emerged as a powerful tool in FHTPP, particularly in crop segmentation-identifying crops from the background-crucial for trait analysis. However, the effectiveness of DCNNs often hinges on the availability of large, labeled datasets, which poses a challenge due to the high cost of labeling. In this study, a deep learning with bagging approach is introduced to enhance crop segmentation using high-resolution RGB images, tested on the NU-Spidercam dataset from maize plots. The proposed method outperforms traditional machine learning and deep learning models in prediction accuracy and speed. Remarkably, it achieves up to 40% higher Intersection-over-Union (IoU) than the threshold method and 11% over conventional machine learning, with significantly faster prediction times and manageable training duration. Crucially, it demonstrates that even small labeled datasets can yield high accuracy in semantic segmentation. This approach not only proves effective for FHTPP but also suggests potential for broader application in remote sensing, offering a scalable solution to semantic segmentation challenges. This paper is accompanied by publicly available source code.

Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms.

Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy (r2) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement.

A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.

Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (mean R2=0.688), with partial least-squares regression outperforming deep neural network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (mean R2=0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (mean R2=0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping, whilst extra-weight spiking improves model transferability and extends its utility.

Association Between Hip Bone Mineral Density and Mortality Risk After Hip Fracture: A Prospective Cohort Study.

Factors related to mortality after osteoporotic hip fracture (HF) have been investigated intensively, except for proximal femoral bone mineral density (BMD), which is also the primary cause of osteoporosis. In this study, we aimed to investigate the association of hip BMD with mortality risk after HF. Four hundred and eleven elderly patients with HF in Beijing, China, were included and prospectively followed up with a median time of 3 years. At baseline, quantitative CT technique (QCT) was used to measure areal BMD (aBMD) of the unaffected hip. Areal BMDs of the total hip (TH), femoral neck (FN), trochanter (TR), and intertrochanter were analyzed with postoperative mortality as the primary outcome. A total of 394 patients (78.59 ± 7.59 years, 75.4% female) were included in our final analysis, with 86 (82.23 ± 7.00 years, 81.4% female) dead. All hip bone densities demonstrated a significant association with mortality risks in the unadjusted model, but only TR aBMD remained significantly correlated after adjusting for all covariates. Compared to the lower TR aBMD group, the higher TR aBMD group yielded significantly lower death risks (HR 0.21 95% CI 0.05-0.9, P = 0.036). Higher survival probabilities were observed for higher TH and TR aBMD in survival analysis (P < 0.001). Hip BMD, especially TR BMD assessed by QCT, is an independent risk factor for postoperative mortality following HF. QCT may present a promising avenue for opportunistic analysis in immobilized patients, providing valuable information for early detection and personalized interventions to enhance patient outcomes.

Orthogeriatric co-managements lower early mortality in long-lived elderly hip fracture: a post-hoc analysis of a prospective study.

OBJECTIVE: To evaluate the clinical effectiveness of orthogeriatric co-management care in long-lived elderly hip fracture patients (age ≥ 90). METHODS: Secondary analysis was conducted in long-lived hip fracture patients between 2018 to 2019 in 6 hospitals in Beijing, China. Patients were divided into the orthogeriatric co-management group (CM group) and traditional consultation mode group (TC group) depending on the management mode. With 30-day mortality as the primary outcome, multivariate regression analyses were performed after adjusting for potential covariates. 30-day mobility and quality of life were compared between groups. RESULTS: A total of 233 patients were included, 223 of whom completed follow-up (125 in CM group, 98 in TC group). The average age was 92.4 ± 2.5 years old (range 90-102). The 30-day mortality in CM group was significantly lower than that in TC group after adjustments for (2.4% vs. 10.2%; OR = 0.231; 95% CI 0.059 ~ 0.896; P = 0.034). The proportion of patients undergoing surgery and surgery performed within 48 h also favored the CM group (97.6% vs. 85.7%, P = 0.002; 74.4% vs. 24.5%, P < 0.001; respectively). In addition, much more patients in CM group could walk with or without aids in postoperative 30 days than in the TC group (87.7% vs. 60.2%, P < 0.05), although differences were not found after 1-year follow-up. And there was no significant difference in total cost between the two groups (P > 0.05). CONCLUSIONS: For long-lived elderly hip fracture patients, orthogeriatric co-management care lowered early mortality, improved early mobility and compared with the traditional consultation mode.

Leaf-Counting in Monocot Plants Using Deep Regression Models.

Leaf numbers are vital in estimating the yield of crops. Traditional manual leaf-counting is tedious, costly, and an enormous job. Recent convolutional neural network-based approaches achieve promising results for rosette plants. However, there is a lack of effective solutions to tackle leaf counting for monocot plants, such as sorghum and maize. The existing approaches often require substantial training datasets and annotations, thus incurring significant overheads for labeling. Moreover, these approaches can easily fail when leaf structures are occluded in images. To address these issues, we present a new deep neural network-based method that does not require any effort to label leaf structures explicitly and achieves superior performance even with severe leaf occlusions in images. Our method extracts leaf skeletons to gain more topological information and applies augmentation to enhance structural variety in the original images. Then, we feed the combination of original images, derived skeletons, and augmentations into a regression model, transferred from Inception-Resnet-V2, for leaf-counting. We find that leaf tips are important in our regression model through an input modification method and a Grad-CAM method. The superiority of the proposed method is validated via comparison with the existing approaches conducted on a similar dataset. The results show that our method does not only improve the accuracy of leaf-counting, with overlaps and occlusions, but also lower the training cost, with fewer annotations compared to the previous state-of-the-art approaches.The robustness of the proposed method against the noise effect is also verified by removing the environmental noises during the image preprocessing and reducing the effect of the noises introduced by skeletonization, with satisfactory outcomes.

Variation in morpho-physiological and metabolic responses to low nitrogen stress across the sorghum association panel.

BACKGROUND: Access to biologically available nitrogen is a key constraint on plant growth in both natural and agricultural settings. Variation in tolerance to nitrogen deficit stress and productivity in nitrogen limited conditions exists both within and between plant species. However, our understanding of changes in different phenotypes under long term low nitrogen stress and their impact on important agronomic traits, such as yield, is still limited. RESULTS: Here we quantified variation in the metabolic, physiological, and morphological responses of a sorghum association panel assembled to represent global genetic diversity to long term, nitrogen deficit stress and the relationship of these responses to grain yield under both conditions. Grain yield exhibits substantial genotype by environment interaction while many other morphological and physiological traits exhibited consistent responses to nitrogen stress across the population. Large scale nontargeted metabolic profiling for a subset of lines in both conditions identified a range of metabolic responses to long term nitrogen deficit stress. Several metabolites were associated with yield under high and low nitrogen conditions. CONCLUSION: Our results highlight that grain yield in sorghum, unlike many morpho-physiological traits, exhibits substantial variability of genotype specific responses to long term low severity nitrogen deficit stress. Metabolic response to long term nitrogen stress shown higher proportion of variability explained by genotype specific responses than did morpho-pysiological traits and several metabolites were correlated with yield. This suggest, that it might be possible to build predictive models using metabolite abundance to estimate which sorghum genotypes will exhibit greater or lesser decreases in yield in response to nitrogen deficit, however further research needs to be done to evaluate such model.

Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize.

Agriculture is considered a hotspot for wireless sensor network (WSN) facilities as they could potentially contribute towards improving on-farm management and food crop yields. This study proposes six designs of unmanned aerial system (UAS)-enabled data ferries with the intent of communicating with stationary sensor node stations in maize. Based on selection criteria and constraints, a proposed UAS data ferrying design was shortlisted from which a field experiment was conducted for two growing seasons to investigate the adoptability of the selected design along with an established WSN system. A data ferry platform comprised of a transceiver radio, a mini-laptop, and a battery was constructed and mounted on the UAS. Real-time monitoring of soil and temperature parameters was enabled through the node stations with data retrieved by the UAS data ferrying. The design was validated by establishing communication at different heights (31 m, 61 m, and 122 m) and lateral distances (0 m, 38 m, and 76 m) from the node stations. The communication success rate (CSR) was higher at a height of 31 m and within a lateral distance of 38 m from the node station. Lower communication was accredited to potential interference from the maize canopy and water losses from the maize canopy.

Modified chair method: an easy and efficient reduction method without medication for anterior shoulder dislocation.

BACKGROUND: Various maneuvers have been introduced to address anterior shoulder dislocations. Chair method allows the patient to sit comfortably and feel less pain during the reduction procedure. However, the rarity of comparative studies led to a lack of evidence to popularize. The present study aimed to introduce a modified chair (MOC) reduction method for anterior shoulder dislocation and explore its effectiveness compared with the traditional Hippocratic approach. METHODS: This is a single-center retrospective study of 257 patients with anterior shoulder dislocation from September 2020 and July 2021. Patients were divided into two groups according to the reduction method they received (either the Hippocratic method or the MOC method). Success rate, reduction time, visual analog scale (VAS) pain score, satisfaction level, and a new indicator, pain index (reduction time (s)* VAS/ 10), were compared. RESULTS: One hundred sixteen patients (43 females, 73 males) underwent the Hippocratic method, and 141 (65 females, 76 males) MOC method. A significantly higher success rate was seen in the MOC group (96.5%(136/141) vs. 84.5%(98/116) in the Hippocratic group; OR 5, 95%CI 1.79 ~ 13.91; p = 0.002). Pain index of the patients in the MOC group was much lower than that in the Hippocratic group (3.20 (2.10, 4.53) vs. 36.70 (22.40, 47.25), p <  0.001). The reduction time, VAS pain score, and satisfaction level also favored the MOC method. CONCLUSIONS: The MOC method is an easy and efficient reduction method with minimum assistance for anterior shoulder dislocations. Physicians can skillfully perform this procedure with the help of their body weight. The MOC method could be attempted for shoulder dislocations in the emergency department.

Midterm results of digastric trochanteric flip osteotomy for high acetabular posterior wall fracture.

PURPOSE: Kocher-Langenbeck (K-L) approach is widely used in surgery of posterior wall fracture of acetabulum. However, challenges are frequently encountered in fractures involving the superior dome due to its short of view. We aimed to evaluate the efficacy of digastric trochanteric flip osteotomy (DTFO) in the K-L approach for high posterior wall acetabular fracture (HPWF). METHODS: From January 2014 to December 2016, 39 patients who suffered high posterior wall fracture (HPWF) were included in this retrospective study. All the patients were divided into two groups according to surgery type (17 standard K-L approach (control group), 22 with DTFO (DTFO group)). The Matta criterion was used to evaluate the accuracy of reduction according to post-operative CT image, while modified Harris hip score and 12-item short-form health survey (SF-12) were applied to measure the clinical outcomes. RESULTS: The median follow-up period was 55 (45 to 62.5) months. Blood loss and operation time were similar between the two groups. DTFO group achieved much more anatomical reduction than the control group (54.6% vs. 35.3%; OR, 2.2; 95% CI, 0.6 ~ 8.08). Significantly better functional outcomes were found in the DTFO group (10% higher points than the control group, p < 0.05). All the patients receiving good-to-anatomical reduction ended with good or excellent outcomes. The total incidence of complications in the DTFO group was much lower than in the control group (40.9% vs. 70.6%, p = 0.07). CONCLUSIONS: Compared with the traditional K-L approach, the intraoperative DTFO technique enabled better quality of reduction in patients with HPWF, thus ensuring superior clinical outcomes.

A Two-Photon Fluorescent Probe for the Visual Detection of Peroxynitrite in Living Cells and Zebrafish.

Peroxynitrite (ONOO-), as an important reactive oxygen species (ROS), holds great potential to react with a variety of biologically active substances, leading to the occurrence of various diseases such as cancer and neurodegenerative diseases. In this work, we developed a novel mitochondria-localized fluorescent probe, HDBT-ONOO-, which was designed as a mitochondria-targeting two-photon fluorescence probe based on 1,8-naphthylimide fluorophore and the reactive group of 4-(bromomethyl)-benzene boronic acid pinacol ester. More importantly, the probe exhibited good biocompatibility, sensitivity, and selectivity, enabling its successful application in imaging the generation of intracellular and extracellular ONOO-. Furthermore, exogenous and endogenous ONOO- products in live zebrafish were visualized. It is greatly expected that the designed probe can serve as a useful imaging tool for clarifying the distribution and pathophysiological functions of ONOO- in cells and zebrafish.

High-resolution phenotyping of sorghum genotypic and phenotypic responses to low nitrogen and synthetic microbial communities.

Much effort has been placed on developing microbial inoculants to replace or supplement fertilizers to improve crop productivity and environmental sustainability. However, many studies ignore the dynamics of plant-microbe interactions and the genotypic specificity of the host plant on the outcome of microbial inoculation. Thus, it is important to study temporal plant responses to inoculation in multiple genotypes within a single species. With the implementation of high-throughput phenotyping, the dynamics of biomass and nitrogen (N) accumulation of four sorghum genotypes with contrasting N-use efficiency were monitored upon the inoculation with synthetic microbial communities (SynComs) under high and low-N. Five SynComs comprising bacteria isolated from field grown sorghum were designed based on the overall phylar composition of bacteria and the enriched host compartment determined from a field-based culture independent study of the sorghum microbiome. We demonstrated that the growth response of sorghum to SynCom inoculation is genotype-specific and dependent on plant N status. The sorghum genotypes that were N-use inefficient were more susceptible to the colonization from a diverse set of inoculated bacteria as compared to the N-use efficient lines especially under low-N. By integrating high-throughput phenotyping with sequencing data, our findings highlight the roles of host genotype and plant nutritional status in determining colonization by bacterial synthetic communities.

Design of Plant Protection UAV Variable Spray System Based on Neural Networks.

Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized.

A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum.

Recently, imaged-based approaches have developed rapidly for high-throughput plant phenotyping (HTPP). Imaging reduces a 3D plant into 2D images, which makes the retrieval of plant morphological traits challenging. We developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants. The instrument combined a LiDAR scanner with a precision rotation stage on which an individual plant was placed. A LabVIEW program was developed to control the scanning and rotation motion, synchronize the measurements from both devices, and capture a 360° view point cloud. A data processing pipeline was developed for noise removal, voxelization, triangulation, and plant leaf surface reconstruction. Once the leaf digital surfaces were reconstructed, plant morphological traits, including individual and total leaf area, leaf inclination angle, and leaf angular distribution, were derived. The system was tested with maize and sorghum plants. The results showed that leaf area measurements by the instrument were highly correlated with the reference methods (R² > 0.91 for individual leaf area; R² > 0.95 for total leaf area of each plant). Leaf angular distributions of the two species were also derived. This instrument could fill a critical technological gap for indoor HTPP of plant morphological traits in 3D.

Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS.

As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R² of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R² of 0.91. Ultrasonic sensors did not perform well due to our static measurement style. In conclusion, we suggest LiDAR and UAS are reliable alternative methods for wheat height evaluation.

Muscle density, but not size, is independently associated with cognitive health in older adults with hip fractures.

Emerging evidence indicates a complex interplay between skeletal muscle and cognitive function. Despite the known differences between muscle quantity and quality, which can be measured via computed tomography (CT), the precise nature of their associations with cognitive performance remain underexplored. To investigate the links between muscle size and density and cognitive impairment (CI) in the older adults with hip fractures, we conducted a post hoc, cross-sectional analysis within a prospective cohort study on 679 patients with hip fractures over 65. Mini-Mental State Examination (MMSE) and routine hip CT imaging were utilized to assess cognition function and muscle characteristics in older adults with hip fractures. The CT scans provided data on cross-sectional area and attenuation for the gluteus maximus (G.MaxM) and the combined gluteus medius and minimus (G.Med/MinM). Participants were categorized into CI and non-CI groups based on education levels and MMSE scores. Multivariate logistic regressions, propensity score (PS) methods, and subgroup analysis were employed to analyze associations and validate findings. This study included 123 participants (81.6 ± 6.8 years, 74% female) with CI and 556 participants (78.5 ± 7.7 years, 72% female) without. Compared to the non-CI group, muscle parameters, especially density, were significantly lower in the CI group. Specifically, G.Med/Min muscle density, but not size was robustly associated with CI (odds ratio (OR) = 0.77, 95% confidence interval = 0.62-0.96, P = 0.02), independent of other medical situations. Sensitivity analysis corroborated that G.Med/Min muscle density was consistently lower in the CI group than the non-CI group, as evidenced in the PS matched (P = 0.024) and weighted cohort (P = 0.033). Enhanced muscle parameters, particularly muscle density in the G.Med/MinM muscle, correlate with a lower risk of CI. Muscle density demonstrates a stronger association with cognitive performance than muscle size, highlighting its potential as a key focus in future cognitive health research.

Sagittal support rather than medial cortical support matters in geriatric intertrochanteric fracture: A finite element analysis study.

Hip fracture, increasing exponentially with age, is osteoporosis's most severe clinical consequence. Intertrochanteric fracture, one of the main types of hip fracture, is associated with higher mortality and morbidity. The current research hotspots lay in improving the treatment effect and optimizing the secondary stability after intertrochanteric fracture surgery. Cortex buttress reduction is a widely accepted method for treating intertrochanteric fracture by allowing the head-neck fragment to slide and rigidly contact the femoral shaft's cortex. Medial cortical support is considered a more effective option in treating young patients. However, osteo-degenerations features, including bone weakness and cortical thickness thinning, affect the performance of cortex support in geriatric intertrochanteric fracture treatment. Literature focusing on the age-specific difference in cortex performance in the fractured hip is scarce. We hypothesized that this osteo-19 degenerative feature affects the performance of cortex support in treating intertrochanteric fractures between the young and the elderly. We established twenty models for the old and the young with intertrochanteric fractures and performed static and dynamic simulations under one-legged stance and walking cycle conditions. The von Mises stress and displacement on the femur, proximal femoral nail anti-rotation (PFNA) implant, fracture plane, and the cutting volume of cancellous bone of the femur were compared. It was observed that defects in the anterior and posterior cortical bone walls significantly increase the stress on the PFNA implant, the displacement of the fracture surface, and cause a greater volume of cancellous bone to be resected. We concluded that ensuring the integrity and alignment of the anterior and posterior cortical bones is essential for elderly patients, and sagittal support is recommended. This finding suggests that the treatment method for intertrochanteric fracture may differ, considering the patient's age difference.

Risk prediction of second hip fracture by bone and muscle density of the hip varies with time after first hip fracture: A prospective cohort study.

Purpose: Predictors of 'imminent' risk of second hip fracture are unknown. The aims of the study were to explore strength of hip areal bone mineral density (aBMD), and muscle area and density for predicting second hip fracture at different time intervals. Methods: Data of the Chinese Second Hip Fracture Evaluation were analyzed, a longitudinal study to evaluate the risk of second hip fracture (of the contralateral hip) by using CT images obtained immediately after first hip fracture. Muscle cross-sectional area and density were measured of the gluteus maximus (G.MaxM) and gluteus medius and minimus (G.Med/MinM) and aBMD of the proximal femur at the contralateral unfractured side. Patients were followed up for a median time of 4.5 years. Separate Cox models were used to predict second hip fracture risk at different time intervals after first event adjusted for age, sex, BMI and diabetes. Results: The mean age of subjects with imminent (within 1st or 2nd year) second hip fracture was 79.80 ± 5.16 and 81.56 ± 3.64 years. In the 1st year after the first hip fracture, femoral neck (FN) aBMD predicted second hip fracture (HR 5.88; 95 % CI, 1.32-26.09). In the remaining years of follow-up after 2nd year, muscle density predicted second hip fracture (G.MaxM HR 2.13; 95 % CI, 1.25-3.65,G.Med/MinM HR 2.10; 95 % CI, 1.32-3.34). Conclusions: Our results show that femoral neck aBMD is an important predictor for second hip fracture within the first year and therefore suggest supports the importance concept of early and rapid-acting bone-active drugs to increase hip BMD. In addition, the importance of muscle density predicting second hip fracture after the second year suggest post hip fracture rehabilitation and exercise programs could also be important to reduce muscle fatty infiltration.

FNDC5 prevents oxidative stress and neuronal apoptosis after traumatic brain injury through SIRT3-dependent regulation of mitochondrial quality control.

Mitochondrial dysfunction and oxidative stress are important mechanisms for secondary injury after traumatic brain injury (TBI), which result in progressive pathophysiological exacerbation. Although the Fibronectin type III domain-containing 5 (FNDC5) was reported to repress oxidative stress by retaining mitochondrial biogenesis and dynamics, its possible role in the secondary injury after TBI remain obscure. In present study, we observed that the level of serum irisin (the cleavage product of FNDC5) significantly correlated with the neurological outcomes of TBI patients. Knockout of FNDC5 increased the lesion volume and exacerbated apoptosis and neurological deficits after TBI in mice, while FNDC5 overexpression yielded a neuroprotective effect. Moreover, FNDC5 deficiency disrupted mitochondrial dynamics and function. Activation of Sirtuin 3 (SIRT3) alleviated FNDC5 deficiency-induced disruption of mitochondrial dynamics and bioenergetics. In neuron-specific SIRT3 knockout mice, FNDC5 failed to attenuate TBI-induced mitochondrial damage and brain injuries. Mechanically, FNDC5 deficiency led to reduced SIRT3 expression via enhanced ubiquitin degradation of transcription factor Nuclear factor erythroid 2-related factor 2 (NRF2), which contributed to the hyperacetylation and inactivation of key regulatory proteins of mitochondrial dynamics and function, including OPA1 and SOD2. Finally, engineered RVG29-conjugated nanoparticles were generated to selectively and efficiently deliver irisin to the brain of mice, which yielded a satisfactory curative effect against TBI. In conclusion, FNDC5/irisin exerts a protective role against acute brain injury by promoting SIRT3-dependent mitochondrial quality control and thus represents a potential target for neuroprotection after TBI.