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.

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.

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.

Burden of lung cancer attributed to particulate matter pollution in China: an epidemiological study from 1990 to 2019

OBJECTIVES

The aim of this study was to examine the disease burden of lung cancer attributable to particulate matter (PM2.5) pollution in China from 1990 to 2019.

STUDY DESIGN

Data from the Global Burden of Disease Study 2019 were used to estimate the disease burden of tracheal, bronchus and lung cancer attributed to PM2.5 over time in China.

METHODS

Joinpoint regression models were applied to disability-adjusted life years (DALYs) to assess the time trends and estimate the impact of PM2.5 on the overall disease burden of lung cancer. Furthermore, age-period-cohort models were conducted to assess the relationships between lung cancer DALYs attributed to PM2.5 exposure and age, calendar period and birth cohort trends in China from 1990 to 2019.

RESULTS

Lung cancer DALYs attributable to household air pollution from solid fuels decreased with an average annual percent change (AAPC) of 2.9 % per 100,000 population, while those attributable to ambient particular matter pollution (APE) increased (AAPC: -4.7 % per 100,000 population) over the past 30 years. The burden of lung cancer in terms of DALYs in males was higher than in females, and it demonstrated an age-dependent increase. The period and cohort effects also had significant impacts on the DALYs rates of lung cancer attributable to APE, indicating an overall increase in lung cancer DALYs for all age groups in each year.

CONCLUSIONS

This study highlights the need for effective strategies to reduce PM2.5 exposure in China, particularly from outdoor sources. Gender differences and age, period and cohort effects observed in the study provide valuable insights into long-term trends of lung cancer burden attributed to PM2.5.

Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models

Incorporating cover crops into cropping systems offers numerous potential benefits, including the reduction of soil erosion, suppression of weeds, decreased nitrogen requirements for subsequent crops, and increased carbon sequestration. The aboveground biomass (AGB) of cover crops strongly influences their performance in delivering these benefits. Despite the significance of AGB, a comprehensive field-based high-throughput phenotyping study to quantify AGB of multiple cover crops in the U.S. Midwest has not been found. This study presents a two-year field experiment carried out in Eastern Nebraska, USA, to estimate AGB of five different cover crop species [canola (Brassica napus L.), rye (Secale cereale L.), triticale (Triticale × Triticosecale L.), vetch (Vicia sativa L.), and wheat (Triticum aestivum L.)] using high-throughput phenotyping and Machine Learning (ML) models. Destructive AGB sampling was performed three times during each spring season in 2022 and 2023. An array of morphological, spectral, thermal, and environmental features from the sensors were utilized as feature inputs of ML models. Moderately strong linear correlations between AGB and the selected features were observed. Four ML models, namely Random Forests Regression (RFR), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Artificial Neural Network (ANN), were investigated. Among the four models, PLSR achieved the highest Coefficient of Determination (R2) of 0.84 and the lowest Root Mean Squared Error (RMSE) of 892 kg/ha (Normalized RMSE (NRMSE) = 8.87%), indicating that PLSR could be the most appropriate method for estimating AGB of multiple cover crop species. Feature importance analysis ranked spectral features like Normalized Difference Red Edge (NDRE), Solar-induced Fluorescence (SIF), Spectral Reflectance at 485 nm (R485), and Normalized Difference Vegetation Index (NDVI) as top model features using PLSR. When utilizing fewer feature inputs, ANN exhibited better prediction performance compared to other models. Using morphological and spectral parameters as input features alone led to a R2 of 0.80 and 0.77 for AGB prediction using ANN, respectively. This study demonstrated the feasibility of high-throughput phenotyping and ML techniques for accurately estimating AGB of multiple cover crop species. Further enhancement of model performance could be achieved through additional destructive sampling conducted across multiple locations and years.

Associations of Quantitative and Qualitative Muscle Parameters With Second Hip Fracture Risk in Older Women: A Prospective Cohort Study

Older women with a first hip fracture exhibit heightened susceptibility and incidence of second fracture and potentially severe consequences. This prospective study was to compare the predictive power of qualitative and quantitative muscle parameters for a second hip fracture in older women with a first hip fracture. A total of 206 subjects were recruited from the longitudinal Chinese Second Hip Fracture Evaluation study. Hip computed tomography (CT) scans were obtained immediately after the first fracture. Muscle fat infiltration was assessed according to the Goutallier classification qualitatively. Quantitative parameters included cross-sectional area and density of gluteus maximus (G.MaxM) and gluteus medius and minimus (G.Med/MinM) muscles. CT X-ray absorptiometry was used to measure the areal bone mineral density (aBMD) of the contralateral femur. Cox proportional hazards models were used to compute hazard ratios (HR) of second hip fracture risk. The mean age of subjects was 74.9 (±9.5) years at baseline. After 4.5 years, 35 had a second hip fracture, 153 without a second hip fracture, and 18 died. Except for the combined G.MinM Goutallier grade 3 and 4 groups before adjustment for covariates (HR = 5.83; 95% confidence interval [CI] 1.49-22.83), there were no significant HRs for qualitative classification to predict a second hip fracture. Among quantitative metrics, after adjustment for covariates, G.Med/MinM density was significant in the original (HR = 1.44; CI 1.02-2.04) and competing risk analyses (HR = 1.46; CI 1.02-2.07). After additional adjustment for femoral neck (FN) aBMD, G.Med/MinM density remained borderline significant for predicting a second hip fracture in competing risk analysis (HR = 1.43; CI 0.99-2.06; p = 0.057). Our study revealed that Goutallier classification was less effective than quantitative muscle metrics for predicting hip second fracture in this elderly female cohort. After adjustment for FN aBMD, G.Med/MinM density is a borderline independent predictor of second hip fracture risk. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

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.

[Pay attention to the infectious complications in the clinical application of biological agents]

Biological agents have been widely used in the treatment of many clinical diseases by targeting specific immune cells or cytokines. In the course of clinical use, biological agents can lead to secondary immune deficiency, which increases the risk of infection. At present, there are no evidence-based guidelines or management opinions on the differences of infections caused by various biological agents, how to identify infectious complications in the course of treatment with different biological agents at an early stage, and how to take effective and targeted prevention. This paper summarizes the infection complications and their characteristics that need to be paid attention to in the clinical introduction of biological agents, aiming to help clinicians make reasonable decisions for infection complications in the process of using biological agents, reduce the incidence of infection, and improve the success rate of diagnosis and treatment.

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.

[Doublecortin-like kinase 1 activates Hippo pathway to promote migration, invasion and proliferation of pancreatic cancer cells]

Objective: To explore the mechanism of Doublecortin-like kinase 1 (DCLK1) in promoting cell migration, invasion and proliferation in pancreatic cancer. Methods: The correlation between DCLK1 and Hippo pathway was analyzed using TCGA and GTEx databases and confirmed by fluorescence staining of pancreatic cancer tissue microarrays. At the cellular level, immunofluorescence staining of cell crawls and western blot assays were performed to clarify whether DCLK1 regulates yes associated protein1 (YAP1), a downstream effector of the Hippo pathway. Reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) was used to analyze the expressions of YAP1 binding transcription factor TEA-DNA binding proteins (TEAD) and downstream malignant behavior-promoting molecules CYR61, EDN1, AREG, and CTGF. Transwell test of the DCLK1-overexpressing cells treated with the Hippo pathway inhibitor Verteporfin was used to examine whether the malignant behavior-promoting ability was blocked. Analysis of changes in the proliferation index of experimental cells used real-time label-free cells. Results: TCGA combined with GTEx data analysis showed that the expressions of DCLK1 and YAP1 molecules in pancreatic cancer tissues were significantly higher than those in adjacent tissues (P<0.05). Moreover, DCLK1was positively correlated with the expressions of many effectors in the Hippo pathway, including LATS1 (r=0.53, P<0.001), LATS2 (r=0.34, P<0.001), MOB1B (r=0.40, P<0.001). In addition, the tissue microarray of pancreatic cancer patients was stained with multicolor fluorescence, indicated that the high expression of DCLK1 in pancreatic cancer patients was accompanied by the up-regulated expression of YAP1. The expression of DCLK1 in pancreatic cancer cell lines was analyzed by the CCLE database. The results showed that the expression of DCLK1 in AsPC-1 and PANC-1 cells was low. Thus, we overexpressed DCLK1 in AsPC-1 and PANC-1 cell lines and found that DCLK1 overexpression in pancreatic cancer cell lines promoted YAP1 expression and accessible to the nucleus. In addition, DCLK1 up-regulated the expression of YAP1 binding transcription factor TEAD and increased the mRNA expression levels of downstream malignant behavior-promoting molecules. Finally, Verteporfin, an inhibitor of the Hippo pathway, could antagonize the cell's malignant behavior-promoting ability mediated by high expression of DCLK1. We found that the number of migrated cells with DCLK1 overexpressing AsPC-1 group was 68.33±7.09, which was significantly higher than 22.00±4.58 of DCLK1 overexpressing cells treated with Verteporfin (P<0.05). Similarly, the migration number of PANC-1 cells overexpressing DCLK1 was 65.66±8.73, which was significantly higher than 37.00±6.00 of the control group and 32.33±9.61 of Hippo pathway inhibitor-treated group (P<0.05). Meanwhile, the number of invasive cells in the DCLK1-overexpressed group was significantly higher than that in the DCLK1 wild-type group cells, while the Verteporfin-treated DCLK1-overexpressed cells showed a significant decrease. In addition, we monitored the cell proliferation index using the real-time cellular analysis (RTCA) assay, and the proliferation index of DCLK1-overexpressed AsPC-1 cells was 0.66±0.04, which was significantly higher than 0.38±0.01 of DCLK1 wild-type AsPC-1 cells (P<0.05) as well as 0.05±0.03 of DCLK1-overexpressed AsPC1 cells treated with Verteporfin (P<0.05). PANC-1 cells showed the same pattern, with a proliferation index of 0.77±0.04 for DCLK1-overexpressed PANC-1 cells, significantly higher than DCLK1-overexpressed PANC1 cells after Verteporfin treatment (0.14±0.05, P<0.05). Conclusion: The expression of DCLK1 is remarkably associated with the Hippo pathway, it promotes the migration, invasion, and proliferation of pancreatic cancer cells by activating the Hippo pathway.

Muscle size and density are independently associated with death after hip fracture: A prospective cohort study

BACKGROUND

Mortality following hip fracture is high and incompletely understood. We hypothesize that hip musculature size and quality are related to mortality following hip fracture. This study aims to investigate the associations of hip muscle area and density from hip CT with death following hip fracture as well as assess the dependence of this association on time after hip fracture.

METHODS

In this secondary analysis of the prospectively collected CT images and data from the Chinese Second Hip Fracture Evaluation, 459 patients were enrolled between May 2015 and June 2016 and followed up for a median of 4.5 years. 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. The Goutallier classification (GC) was used for qualitatively assessing muscle fat infiltration. Separate Cox models were used to predict mortality risk adjusted for covariates.

RESULTS

At the end of the follow-up, 85 patients were lost, 81 patients (64% women) had died, and 293 (71% women) survived. The mean age of non-surviving patients at death (82.0 ± 8.1 years) was higher than that of the surviving patients (74.4 ± 9.9 years). The Parker Mobility Score and the American Society of Anesthesiologists scores of the patients that died were respectively lower and higher compared to the surviving patients. Hip fracture patients received different surgical procedures, and no significant difference in the percentage of hip arthroplasty was observed between the dead and the surviving patients (P = 0.11). The cumulative survival was significantly lower for patients with low G.MaxM area and density and low G.Med/MinM density, independent of age and clinical risk scores. The GC grades were not associated with the mortality after hip fracture. Muscle density of both G.MaxM (adj. HR 1.83; 95% CI, 1.06-3.17) and G.Med/MinM (adj. HR 1.98; 95% CI, 1.14-3.46) was associated with mortality in the 1st year after hip fracture. G.MaxM area (adj. HR 2.11; 95% CI, 1.08-4.14) was associated with mortality in the 2nd and later years after hip fracture.

CONCLUSION

Our results for the first time show that hip muscle size and density are associated with mortality in older hip fracture patients, independent of age and clinical risk scores. This is an important finding to better understand the factors contributing to the high mortality in older hip fracture patients and to develop better future risk prediction scores that include muscle parameters.

Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage

The number of leaves at a given time is important to characterize plant growth and development. In this work, we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB images. The digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages (150,000 images with over 2 million labels). The realism of the images was then improved using domain adaptation methods before training deep learning models. The results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset, collecting measurements from 5 countries obtained under different environments, growth stages, and lighting conditions with different cameras (450 images with over 2,162 labels). Among the 6 combinations of deep learning models and domain adaptation techniques, the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance (R² = 0.94, root mean square error = 8.7). Complementary studies show that it is essential to simulate images with sufficient realism (background, leaf texture, and lighting conditions) before applying domain adaptation techniques. Furthermore, the spatial resolution should be better than 0.6 mm per pixel to identify leaf tips. The method is claimed to be self-supervised since no manual labeling is required for model training. The self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping problems. The trained networks are available at https://github.com/YinglunLi/Wheat-leaf-tip-detection.

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: (i) assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum, (ii) evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), and (iii) 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 (average R 2 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 (average R 2 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 (average R 2 0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping; whereas extra-weight spiking improves model transferability and extends its utility.

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.

[Efficacy and safety of daptomycin in the treatment of gram-positive infective endocarditis: a meta-analysis]

Objective: To assess the efficacy and safety of daptomycin in the treatment of gram-positive infective endocarditis (IE) systematically. Methods: China Biology Medicine Database (CBM), China National Knowledge Internet (CNKI), Wanfang Data, VIP Database, PubMed, Embase, the Cochrane Library, and Web of Science were searched from the time of establishing databases to April 2022 to obtain relevant controlled and uncontrolled studies of daptomycin for gram-positive infective endocarditis, using key search terms ("daptomycin","gram-positive bacterial infections","endocarditis"). We performed literature screening according to inclusion/exclusion criteria, data extraction, and quality assessment, and performed random-effects meta-analyses for pooled results data using R software. Results: A total of 11 studies (including 13 articles) were included. The findings in the three controlled studies showed that in the treatment of staphylococcus aureus endocarditis, there was no statistically significant differences in in-hospital death risk (RR=0.66, 95%CI: 0.24-1.84, P=0.427) and 6-month death risk (RR=1.27, 95%CI: 0.75-2.14, P=0.374) for daptomycin versus anti-staphylococcal penicillin or vancomycin; in the treatment of enterococcal endocarditis, there was no statistically significant difference in death risk (both P>0.05) for daptomycin versus ampicillin combined with ceftriaxone (RR=0.39, 95%CI: 0.06-2.49) and ampicillin or vancomycin plus or minus gentamicin (RR=0.42, 95%CI: 0.05-3.36); and for daptomycin versus ampicillin or vancomycin combined with an aminoglycoside antibiotic, the differences in in-hospital death risk (RR=0.80, 95%CI: 0.11-5.83) and 6-month death risk (RR=0.47, 95%CI: 0.07-3.21) were not statistically significant(both P>0.05). In a cost-effectiveness study, daptomycin as first-line treatment could save the medical cost of 4 037 pounds per patient compared with vancomycin over a longer period of patient treatment. The results of the meta-analysis of uncontrolled studies showed that the mean clinical success rate of daptomycin for left-side endocarditis was 77% (95%CI: 70% to 83%; I²=28%), for MSSA-infective right-side endocarditis was 87% (95%CI: 73%-95%), and for MRSA-infective right-side endocarditis was 78% (95%CI: 38%-95%; I²=49%); while the mortality rate [mean mortality rate for left-side endocarditis was 13% (95%CI: 11%-17%; I²=0); the mortality rate for right-side endocarditis was reported in only 2 studies, 3% and 27%, respectively] or the rate of daptomycin-related adverse events (4%) was within the acceptable ranges for clinical practice. Conclusions: The death risk in the treatment of infective endocarditis with dattomycin is comparable to that of other antibiotics, and the clinical success rate is higher. Some efficacy may be achieved with daptomycin while other treatments are not effective in treating IE.

Low-volume precision spray for plant pest control using profile variable rate spraying and ultrasonic detection

Spraying chemical pesticides is one of the important means to control plant pest, and the profile variable spraying is an important technology to achieve precise pesticide application. A profiling tracking control method and an improved algorithm based on CMAC-PID (Cerebellar Model Articulation Controller- Potential Induced Degradation) were proposed in the paper. The test results of the sprayer profiling tracking of the tree canopies showed that the profiling control system using the improved algorithm had significantly better dynamic tracking performance, and the overall mean tracking error was reduced by 35.0%, compared with the traditional CMAC-PID. A spray flow calculation method based on tree canopy volume and leaf area density was proposed. Outdoor testing of the profile variable spraying and conventional spraying was carried out. There was no significant difference between the two spraying methods in terms of droplet coverage, VMD (Volume Median Diameter), NMD (Number Median Diameter), spray quality parameter and relative span coefficient, as well as droplet deposition density. The spray coefficient of variation was reduced by 25.9% and 21.9% inside and outside the tree canopy, respectively. The mean value of the ground deposition coverage of the profile variable spraying and the traditional spray was 13.0% and 33.2%, respectively, indicating a significant impact on the ground droplet deposition coverage by the two spraying methods. The spray flow rate of the profile variable spraying could be decreased by 32.1% compared to the conventional spraying. Profile variable spraying would reduce the cost associated with pesticide use and environmental pollution.

Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography

BACKGROUND

The technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI.

RESULTS

In this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with [Formula: see text] and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with [Formula: see text] and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the [Formula: see text] and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively.

CONCLUSIONS

Dense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density.

Rapid online plant leaf area change detection with high-throughput plant image data

High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis.

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.

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.

Flexible Self-Powered Weak Light Detectors Based on ZnO/CsPbBr3/γ-CuI Heterojunctions

Halide perovskites (HPs) with marvelous optical and electrical properties are regarded as one of the competitive candidates for building next-generation photodetectors (PDs). However, combining their excellent properties with satisfactory long-term robustness is still challenging, ultimately limiting the practical applications of HP-based PDs. Herein, a high vacuum deposition system is employed to fabricate flexible self-powered PDs with a ZnO/CsPbBr₃/γ-CuI structure, which shows excellent stability and outstanding performance in weak light detection. Benefiting from the improved crystallinity and optimized device structure, a high detectivity of 8.1 × 10¹³ Jones and a rapid response speed (rise/decay time of 3.9/1.8 μs) are obtained in this self-powered device. Furthermore, the unencapsulated device exhibits intriguing environmental stability and mechanical flexibility. The photocurrent remains unchanged after 7000 s of continuous operation or 100 bending cycles. Furthermore, a 15 × 15 PD array is fabricated as an image sensor. A high contrast image of the target object can be obtained owing to the high sensitivity and uniformity of the self-powered PDs. These results demonstrate the feasibility and practicality of the ZnO/CsPbBr₃/γ-CuI heterojunction for applications in weak light detection and image formation.

The role of booster vaccination and ongoing viral evolution in seasonal circulation of SARS-CoV-2

Periodic resurgences of COVID-19 in the coming years can be expected, while public health interventions may be able to reduce their intensity. We used a transmission model to assess how the use of booster doses and non-pharmaceutical interventions (NPIs) amid ongoing pathogen evolution might influence future transmission waves. We find that incidence is likely to increase as NPIs relax, with a second seasonally driven surge expected in autumn 2022. However, booster doses can greatly reduce the intensity of both waves and reduce cumulative deaths by 20% between 7 January 2022 and 7 January 2023. Reintroducing NPIs during the autumn as incidence begins to increase again could also be impactful. Combining boosters and NPIs results in a 30% decrease in cumulative deaths, with potential for greater impacts if variant-adapted boosters are used. Reintroducing these NPIs in autumn 2022 as transmission rates increase provides similar benefits to sustaining NPIs indefinitely (307 000 deaths with indefinite NPIs and boosters compared with 304 000 deaths with transient NPIs and boosters). If novel variants with increased transmissibility or immune escape emerge, deaths will be higher, but vaccination and NPIs are expected to remain effective tools to decrease both cumulative and peak health system burden, providing proportionally similar relative impacts.

Association analyses of host genetics, root-colonizing microbes, and plant phenotypes under different nitrogen conditions in maize

The root-associated microbiome (rhizobiome) affects plant health, stress tolerance, and nutrient use efficiency. However, it remains unclear to what extent the composition of the rhizobiome is governed by intraspecific variation in host plant genetics in the field and the degree to which host plant selection can reshape the composition of the rhizobiome. Here we quantify the rhizosphere microbial communities associated with a replicated diversity panel of 230 maize (Zea mays L.) genotypes grown in agronomically relevant conditions under high N (+N) and low N (-N) treatments. We analyze the maize rhizobiome in terms of 150 abundant and consistently reproducible microbial groups and we show that the abundance of many root-associated microbes is explainable by natural genetic variation in the host plant, with a greater proportion of microbial variance attributable to plant genetic variation in -N conditions. Population genetic approaches identify signatures of purifying selection in the maize genome associated with the abundance of several groups of microbes in the maize rhizobiome. Genome-wide association study was conducted using the abundance of microbial groups as rhizobiome traits, and identified n = 622 plant loci that are linked to the abundance of n = 104 microbial groups in the maize rhizosphere. In 62/104 cases, which is more than expected by chance, the abundance of these same microbial groups was correlated with variation in plant vigor indicators derived from high throughput phenotyping of the same field experiment. We provide comprehensive datasets about the three-way interaction of host genetics, microbe abundance, and plant performance under two N treatments to facilitate targeted experiments towards harnessing the full potential of root-associated microbial symbionts in maize production.

Quasi-two-dimensional α-molybdenum oxide thin film prepared by magnetron sputtering for neuromorphic computing

Two-dimensional (2D) layered materials have attracted intensive attention in recent years due to their rich physical properties, and shown great promise due to their low power consumption and high integration density in integrated electronics. However, mostly limited to mechanical exfoliation, large scale preparation of the 2D materials for application is still challenging. Herein, quasi-2D α-molybdenum oxide (α-MoO3) thin film with an area larger than 100 cm2 was fabricated by magnetron sputtering, which is compatible with modern semiconductor industry. An all-solid-state synaptic transistor based on this α-MoO3 thin film is designed and fabricated. Interestingly, by proton intercalation/deintercalation, the α-MoO3 channel shows a reversible conductance modulation of about four orders. Several indispensable synaptic behaviors, such as potentiation/depression and short-term/long-term plasticity, are successfully demonstrated in this synaptic device. In addition, multilevel data storage has been achieved. Supervised pattern recognition with high recognition accuracy is demonstrated in a three-layer artificial neural network constructed on this α-MoO3 based synaptic transistor. This work can pave the way for large scale production of the α-MoO3 thin film for practical application in intelligent devices.

Muscle density is an independent risk factor of second hip fracture: a prospective cohort study

BACKGROUND

Patients with a first hip fracture are at high risk of fracturing their other hip. Despite this, preventive therapy is often not given. Because little is known about specific risk factors of a second hip fracture, we investigated the association with areal bone mineral density (aBMD), muscle size, and density. We also investigated whether muscle parameters predict the risk of a contralateral fracture independently of aBMD.

METHODS

Three groups were included, one without hip fracture (a subcohort of the China Action on Spine and Hip Status study), one with a first, and one with a second hip fracture. Subjects with fractures were recruited from the longitudinal Chinese Second Hip Fracture Evaluation (CSHFE). Computed tomography scans of CSHFE patients, which were obtained immediately following their first fracture, were used to measure cross-sectional area and density of the gluteus maximus (G.MaxM) and gluteus medius and minimus (G.Med/MinM) muscles. Computed tomography X-ray absorptiometry was used to measure aBMD of the contralateral femur. Median follow-up time to second fracture was 4.5 years. Cox proportional hazards models were used to compute hazard ratios (HR) of second hip fracture risk in subjects with a first hip fracture. Multivariate logistic regressions were used to compare odds ratios (OR) for the risk of a first and second hip fracture.

RESULTS

Three hundred and one participants (68.4 ± 6.1 years, 64% female) without and 302 participants (74.6 ± 9.9 years, 71% female) with a first hip fracture were included in the analysis. Among the latter, 45 (79.2 ± 7.1 years) sustained a second hip fracture. ORs for first hip fracture were significant for aBMD and muscle size and density. ORs for a second fracture were smaller by a factor of 3 to 4 and no longer significant for femoral neck (FN) aBMD. HRs for predicting second hip fracture confirmed the results. G.Med/MinM density (HR, 1.68; CI, 1.20-2.35) and intertrochanter aBMD (HR, 1.62; CI, 1.13-2.31) were the most significant. FN aBMD was not significant. G.Med/MinM density remained significant for predicting second hip fracture after adjustment for FN (HR, 1.66; Cl, 1.18-2.30) or total hip aBMD (HR, 1.50; 95% Cl, 1.04-2.15).

CONCLUSIONS

Density of the G.Med/MinM muscle is an aBMD independent predictor of the risk of second hip fracture. Intertrochanteric aBMD is a better predictor of second hip fracture than FN and total hip aBMD. These results may trigger a paradigm shift in the assessment of second hip fracture risk and prevention strategies.

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

BACKGROUND

Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features.

RESULTS

The models with a single color feature from RGB images predicted chlorophyll content with R² ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R² ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R² of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R² of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy.

CONCLUSION

All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum.

727 BOOSTING BONE HEALTH: IMPROVING JUNIOR DOCTORS’ CONFIDENCE IN ASSESSING AND MANAGING FRAGILITY FRACTURES

Introduction

Fragility fractures are a major disease burden in the UK. With an ageing population and number of fragility fractures predicted to double in 50 years, prevention in this high-risk population needs to be addressed. This audit aimed to examine the assessment of fracture risk in patients presenting with fragility fractures and improve awareness amongst trainee doctors through education.

Methods

A retrospective study was conducted on patients over 65 years admitted with fragility fractures, excluding neck of femur, from January to March 2021 (n = 51). Data was collected on Fracture Risk Assessment Tool (FRAX) scores, dual energy X-ray absorptiometry (DEXA) scans, and risk factors including body mass index (BMI), previous fragility fracture, smoking, alcohol intake, and serum calcium and vitamin D. A teaching seminar for junior doctors was delivered to increase confidence in assessing and managing fragility fractures.

Results

The mean age of patients was 79, with most common presentations being proximal humerus, distal femur and ankle fractures. 46% of patients had a previous fragility fracture. Smoking and alcohol history were documented in 72% and 60% of patients respectively, and 29% had BMIs calculated. 68% had calcium and 45% had vitamin D checked. DEXA scans occurred in 12%, all of whom had osteopenia or osteoporosis. Over half of patients were already on bone protection and 28% were subsequently started on bisphosphonates. A teaching session was delivered to junior doctors (n = 10), leading to improved confidence in assessing fracture risk by 30%, and improved confidence in managing fragility fractures by 35%. Knowledge of FRAX score increased from 62% to 100%.

Conclusion

A significant proportion of the over-65 population are likely to present with fragility fractures. Improving awareness and confidence amongst junior doctors can lead to identification of risk factors and help better prevent and manage fragility fractures in this high-risk population.

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.

Differences in Hip Geometry Between Female Subjects With and Without Acute Hip Fracture: A Cross-Sectional Case-Control Study

BACKGROUND AND PURPOSE

Although it is widely recognized that hip BMD is reduced in patients with hip fracture, the differences in geometrical parameters such as cortical volume and thickness between subjects with and without hip fracture are less well known.

MATERIALS AND METHODS

Five hundred and sixty two community-dwelling elderly women with hip CT scans were included in this cross-sectional study, of whom 236 had an acute hip fracture. 326 age matched women without hip fracture served as controls. MIAF-Femur software was used for the measurement of the intact contralateral femur in patients with hip fracture and the left femur of the controls. Integral and cortical volumes (Vols) of the total hip (TH), femoral head (FH), femoral neck (FN), trochanter (TR) and intertrochanter (IT) were analyzed. In the FH and FN the volumes were further subdivided into superior anterior (SA) and posterior (SP) as well as inferior anterior (IA) and posterior (IP) quadrants. Cortical thickness (CortThick) was determined for all sub volumes of interest (VOIs) listed above.

RESULTS

The average age of the control and fracture groups was 71.7 and 72.0 years, respectively. The fracture patients had significantly lower CortThick and Vol of all VOIs except for TRVol. In the fracture patients, cortical thickness and volume at the FN were significantly lower in all quadrants except for cortical volume of quadrant SA (p= 0.635). Hip fracture patients had smaller integral FN volume and cross-sectional area (CSA) before and after adjustment of age, height and weight. With respect to hip fracture discrimination, cortical volume performed poorer than cortical thickness across the whole proximal femur. The ratio of Cort/TrabMass (RCTM), a measure of the internal distribution of bone, performed better than cortical thickness in discriminating hip fracture risk. The highest area under curve (AUC) value of 0.805 was obtained for the model that included THCortThick, FHVol, THRCTM and FNCSA.

CONCLUSION

There were substantial differences in total and cortical volume as well as cortical thickness between fractured and unfractured women across the proximal femur. A combination of geometric variables resulted in similar discrimination power for hip fracture risk as aBMD.

Prediction and verification of survival in patients with non-small-cell lung cancer based on an integrated radiomics nomogram

AIM

To develop and validate a nomogram to predict 1-, 2-, and 5-year survival in patients with non-small-cell lung cancer (NSCLC) by combining optimised radiomics features, clinicopathological factors, and conventional image features extracted from three-dimensional (3D) computed tomography (CT) images.

MATERIALS AND METHODS

A total of 172 patients with NSCLC were selected to construct the model, and 74 and 72 patients were selected for internal validation and external testing, respectively. A total of 828 radiomics features were extracted from each patient's 3D CT images. Univariable Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to select features and generate a radiomics signature (radscore). The performance of the nomogram was evaluated by calibration curves, clinical practicability, and the c-index. Kaplan-Meier (KM) analysis was used to compare the overall survival (OS) between the two subgroups.

RESULT

The radiomics features of the NSCLC patients correlated significantly with survival time. The c-indexes of the nomogram in the training cohort, internal validation cohort, and external test cohort were 0.670, 0.658, and 0.660, respectively. The calibration curves showed that the predicted survival time was close to the actual survival time. Decision curve analysis shows that the nomogram could be useful in the clinic. According to KM analysis, the 1-, 2- and 5-year survival rates of the low-risk group were higher than those of the high-risk group.

CONCLUSION

The nomogram, combining the radscore, clinicopathological factors, and conventional CT parameters, can improve the accuracy of survival prediction in patients with NSCLC.

Clinical outcomes of keratinized mucosa augmentation in jaws reconstructed with fibula or iliac bone flaps

This prospective study was undertaken to evaluate the treatment outcomes of keratinized mucosa augmentation (KMA) on the buccal and palatal/lingual sides of implants in jaws reconstructed after oncological surgery. Forty-two implants in 12 patients whose jaws had been reconstructed with a fibula or iliac bone flap were included. KMA was performed at 3 months after implant placement; this included an apically displaced partial-thickness flap and a free gingival graft (FGG) around the implants to increase the keratinized mucosa width (KMW). Patients were followed up for at least 6 months post-surgery. KMW, shrinkage, and patient pain and discomfort measured on a visual analogue scale were analysed. A histological analysis was performed of tissue epithelium from two patients. The results showed that KMW was >2 mm on both the buccal and palatal/lingual sides during follow-up. Before surgery, histological analysis showed epithelium with no epithelial spikes; normal keratinized epithelial spikes were observed at 8 weeks after KMA. Greater KMW was observed around implants in reconstructed maxillae than around those in reconstructed mandibles (P < 0.001). Patients felt more pain at the donor site than at the recipient site during the first 3 days post-surgery. KMA with FGG was predictable in reconstructed jaws and may help maintain the long-term stability of implants.

Transcript expression profiling of fibromelanosis-related genes in black-bone chickens

1. The aim of the present study was to identify differentially expressed genes (DEGs) and metabolic pathways involved in this phenotype. Fibromelanosis is the most striking feature of black-bone chickens, such as the Silkie and Dongxiang indigenous breeds. Due to the accumulation of eumelanin in connective tissues, fibromelanosis manifests as black colouration of the skin, muscles, gut, and periosteum. Studies on fibromelanosis can provide useful information pertaining to human diseases and offer commercial value to the poultry industry. However, the genetic basis of fibromelanosis remains unclear.2. Digital gene expression analysis was performed on black and white skin samples collected from the HW1 black-bone chicken line to detect differences in genome-wide expression patterns. A total of >30 billion bp were sequenced, and 2,707,926,466 bp and 2,948,782,964 bp of clean data obtained for creation of libraries for black and white skin, respectively. In total, 252 DEGs from 15,508 mapped genes were identified with 83 up-regulated in white skin and 169 up-regulated in black skin.3. Gene ontology analysis highlighted that genes from the extracellular region and associated components were abundant among the DEGs. Pathway analysis revealed that many DEGs were linked to amino acid metabolism and the immune system. qRT-PCR validation using 14 genes showed good conformity with the sequence analysis of fibromelanosis-related genes.4. The results showed that L-dopachrometautomerase precursor (DCT), tyrosine aminotransferase (TAT), 4-hydroxyphenylpyruvate dioxygenase (HPD) from the tyrosine metabolism pathway, coagulation factor II (F2), fibrinogen beta chain (FGB), plasminogen (PLG) and complement component 7 (C7) from the complement and coagulation cascades were important genes in the fibromelanosis process in black-bone chickens. These candidate genes require further correlation analysis and functional verification.

Plasma miR-146a and miR-365 expression and inflammatory factors in patients with osteoarthritis

OBJECTIVE

To investigate the expression levels of micro-ribonucleic acid (miR)-146a and miR-365 in the plasma of osteoarthritis (OA) patients, to study their expression with the inflammatory factors and the severity of disease in patients and to analyse their diagnostic significance.

MATERIALS AND METHODS

A total of 42 OA patients diagnosed with OA and treated in our hospital from January 2017 to January 2018 were selected as the subjects, and 28 healthy people were enrolled as controls. The expressions of interleukin-1 beta (IL-1β) and IL-6 in the plasma of OA patients were detected via immunohistochemical staining. Moreover, the knee joint function of OA patients was evaluated by Lysholm score, Western Ontario and McMaster Universities (WOMAC) score and Visual Analogue Scale (VAS) score. The expression levels of plasma miR-146a and miR-365 in OA patients were measured through RT-PCR. Besides, the significance of the expression levels of miR-146a and miR-365 for the diagnosis of OA was analysed by ROC curves.

RESULTS

As compared with healthy people, OA patients had elevated expression levels of plasma IL-1β and IL-6, decreased Lysholm score, increased WOMAC and VAS scores as well as significantly up-regulated levels of plasma miR-146a and miR-365, which were of important significance for diagnosis.

CONCLUSION

The expression levels of plasma miR-146a, miR-365 and inflammatory factors are notably higher, the disease is more severe, and the function of knee joint movement is weaker in OA patients than those in healthy controls. It can be concluded that the levels of both miR-146a and miR-365 can serve as biomarkers of OA diagnosis.

Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges

Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.

Factors associated with Scrub Typhus infection: A case-control study from Luhe, China

INTRODUCTION

Scrub typhus (ST) is an acute febrile infection and remains a significant health problem globally. This study aimed to determine the factors associated with ST infection in Luhe District, China.

MATERIAL AND METHODS

The case-control study was conducted among 116 cases identified through passive surveillance systems over three years.The control subjects were 232 living in the same village for more than six months without any history of ST infection were selected by matching to the age (within 5-years) and identified through active surveillance. Statistical analyses were performed using SPSS v. 25.0 for Windows (IBM SPSS, Chicago, IL, USA).

RESULTS

The mean age of confirmed persons was 58.1(SD=10.15) years, while control subjects were 56.14 (11.57).There is no significant difference in gender, age, education, and occupations between case and control. Farmers had the most significant number of cases among occupational groups. The three factors that were significantly associated with an increased odds of having ST infection are bundling or moving waste straw (OR: 1.94, 95%CI; 0.99,381), morning exercise in the park or field (OR: 4.74 95%CI; 1.19, 18.95), and working as labourer in the vegetable field (OR:1.02, 95%CI:1.02,3.19).

CONCLUSIONS

Our findings suggested establishing a prevention and control strategy for these groups to lower ST development risk.