Asymmetric winter warming reduces microbial carbon use efficiency and growth more than symmetric year-round warming in alpine soils.

Asymmetric seasonal warming trends are evident across terrestrial ecosystems, with winter temperatures rising more than summer ones. Yet, the impact of such asymmetric seasonal warming on soil microbial carbon metabolism and growth remains poorly understood. Using 18O isotope labeling, we examined the effects of a decade-long experimental seasonal warming on microbial carbon use efficiency (CUE) and growth in alpine grassland ecosystems. Moreover, the quantitative stable isotope probing with 18O-H2O was employed to evaluate taxon-specific bacterial growth in these ecosystems. Results show that symmetric year-round warming decreased microbial growth rate by 31% and CUE by 22%. Asymmetric winter warming resulted in a further decrease in microbial growth rate of 27% and microbial CUE of 59% compared to symmetric year-round warming. Long-term warming increased microbial carbon limitations, especially under asymmetric winter warming. Long-term warming suppressed the growth rates of most bacterial genera, with asymmetric winter warming having a stronger inhibition on the growth rates of specific genera (e.g., Gp10, Actinomarinicola, Bosea, Acidibacter, and Gemmata) compared to symmetric year-round warming. Bacterial growth was phylogenetically conserved, but this conservation diminished under warming conditions, primarily due to shifts in bacterial physiological states rather than the number of bacterial species and community composition. Overall, long-term warming escalated microbial carbon limitations, decreased microbial growth and CUE, with asymmetric winter warming having a more pronounced effect. Understanding these impacts is crucial for predicting soil carbon cycling as global warming progresses.

A novel RHH family transcription factor aCcr1 and its viral homologs dictate cell cycle progression in archaea.

Cell cycle regulation is of paramount importance for all forms of life. Here, we report that a conserved and essential cell cycle-specific transcription factor (designated as aCcr1) and its viral homologs control cell division in Sulfolobales. We show that the transcription level of accr1 reaches peak during active cell division (D-phase) subsequent to the expression of CdvA, an archaea-specific cell division protein. Cells over-expressing the 58-aa-long RHH (ribbon-helix-helix) family cellular transcription factor as well as the homologs encoded by large spindle-shaped viruses Acidianus two-tailed virus (ATV) and Sulfolobus monocaudavirus 3 (SMV3) display significant growth retardation and cell division failure, manifesting as enlarged cells with multiple chromosomes. aCcr1 over-expression results in downregulation of 17 genes (>4-fold), including cdvA. A conserved motif, aCcr1-box, located between the TATA-binding box and the translation initiation site of 13 out of the 17 highly repressed genes, is critical for aCcr1 binding. The aCcr1-box is present in the promoters and 5' UTRs of cdvA genes across Sulfolobales, suggesting that aCcr1-mediated cdvA repression is an evolutionarily conserved mechanism by which archaeal cells dictate cytokinesis progression, whereas their viruses take advantage of this mechanism to manipulate the host cell cycle.

Regulating Planarized Intramolecular Charge Transfer for Efficient Single-Phase White-Light Emission in Undoped Metal-Organic Framework Nanocrystals.

The technology of combining multiple emission centers to exploit white-light-emitting (WLE) materials by taking advantage of porous metal-organic frameworks (MOFs) is mature, but preparing undoped WLE MOFs remains a challenge. Herein, a pressure-treated strategy is reported to achieve efficient white photoluminescence (PL) in undoped [Zn(Tdc)(py)]n nanocrystals (NCs) at ambient conditions, where the Commission International del'Eclairage coordinates and color temperature reach (0.31, 0.37) and 6560 K, respectively. The initial [Zn(Tdc)(py)]n NCs exhibit weak-blue PL consisting of localized excited (LE) and planarized intramolecular charge transfer (PLICT) states. After pressure treatment, the emission contributions of LE and PLICT states are balanced by increasing the planarization of subunits, thereby producing white PL. Meanwhile, the reduction of nonradiative decay triggered by the planarized structure results in 5-fold PL enhancement. Phosphor-converted light-emitting diodes based on pressure-treated samples show favorable white-light characteristics. The finding provides a new platform for the development of undoped WLE MOFs.

Multiple spatial scales of bacterial and fungal structural and functional traits affect carbon mineralization.

Studying the functional heterogeneity of soil microorganisms at different spatial scales and linking it to soil carbon mineralization is crucial for predicting the response of soil carbon stability to environmental changes and human disturbance. Here, a total of 429 soil samples were collected from typical paddy fields in China, and the bacterial and fungal communities as well as functional genes related to carbon mineralization in the soil were analysed using MiSeq sequencing and GeoChip gene microarray technology. We postulate that CO2 emissions resulting from bacterial and fungal carbon mineralization are contingent upon their respective carbon consumption strategies, which rely on the regulation of interactions between biodiversity and functional genes. Our results showed that the spatial turnover of the fungal community was 2-4 times that of the bacterial community from hundreds of meters to thousands of kilometres. The effect of spatial scale exerted a greater impact on the composition rather than the functional characteristics of the microbial community. Furthermore, based on the establishment of functional networks at different spatial scales, we observed that both bacteria and fungi within the top 10 taxa associated with carbon mineralization exhibited a prevalence of generalist species at the regional scale. This study emphasizes the significance of spatial scaling patterns in soil bacterial and fungal carbon degradation functions, deepening our understanding of how the relationship between microbial decomposers and soil heterogeneity impacts carbon mineralization and subsequent greenhouse gas emissions.

The neglected roles of adjacent natural ecosystems in maintaining bacterial diversity in agroecosystems.

A central aim of community ecology is to understand how local species diversity is shaped. Agricultural activities are reshaping and filtering soil biodiversity and communities; however, ecological processes that structure agricultural communities have often overlooked the role of the regional species pool, mainly owing to the lack of large datasets across several regions. Here, we conducted a soil survey of 941 plots of agricultural and adjacent natural ecosystems (e.g., forest, wetland, grassland, and desert) in 38 regions across diverse climatic and soil gradients to evaluate whether the regional species pool of soil microbes from adjacent natural ecosystems is important in shaping agricultural soil microbial diversity and completeness. Using a framework of multiscales community assembly, we revealed that the regional species pool was an important predictor of agricultural bacterial diversity and explained a unique variation that cannot be predicted by historical legacy, large-scale environmental factors, and local community assembly processes. Moreover, the species pool effects were associated with microbial dormancy potential, where taxa with higher dormancy potential exhibited stronger species pool effects. Bacterial diversity in regions with higher agricultural intensity was more influenced by species pool effects than that in regions with low intensity, indicating that the maintenance of agricultural biodiversity in high-intensity regions strongly depends on species present in the surrounding landscape. Models for community completeness indicated the positive effect of regional species pool, further implying the community unsaturation and increased potential in bacterial diversity of agricultural ecosystems. Overall, our study reveals the indubitable role of regional species pool from adjacent natural ecosystems in predicting bacterial diversity, which has useful implication for biodiversity management and conservation in agricultural systems.

More sensitive microbial responses to the interactive effects of warming and altered precipitation in subsoil than topsoil of an alpine grassland ecosystem.

Subsoil is a large organic carbon reservoir, storing more than half of the total soil organic carbon (SOC) globally. Conventionally, subsoil is assumed to not be susceptible to climate change, but recent studies document that climate change could significantly alter subsoil carbon cycling. However, little is known about subsoil microbial responses to the interactive effects of climate warming and altered precipitation. Here, we investigated carbon cycling and associated microbial responses in both subsoil (30-40 cm) and topsoil (0-10 cm) in a Tibetan alpine grassland over 4 years of warming and altered precipitation. Compared to the unchanged topsoil carbon (ß = .55, p = .587), subsoil carbon exhibited a stronger response to the interactive effect of warming and altered precipitation (ß = 2.04, p = .021), that is, warming decreased subsoil carbon content by 28.20% under decreased precipitation while warming increased subsoil carbon content by 18.02% under increased precipitation.Furthermore, 512 metagenome-assembled genomes (MAGs) were recovered, including representatives of phyla with poor genomic representation. Compared to only one changed topsoil MAG, 16 subsoil MAGs were significantly affected by altered precipitation, and 5 subsoil MAGs were significantly affected by the interactive effect of warming and precipitation. More than twice as many populations whose MAG abundances correlated significantly with the variations of carbon content, components and fluxes were observed in the subsoil than topsoil, suggesting their potential contribution in mediating subsoil carbon cycling. Collectively, our findings highlight the more sensitive responses of specific microbial lineages to the interactive effects of warming and altered precipitation in the subsoil than topsoil, and provide key information for predicting subsoil carbon cycling under future climate change scenarios.

SRGN promotes macrophage recruitment through CCL3 in osteoarthritis.

BACKGROUND: Osteoarthritis (OA) is a degenerative disease that affects synovial joints and leads to significant pain and disability, particularly in older adults. Infiltration of macrophages plays a key role in the progression of OA. However, the mechanisms underlying macrophage recruitment in OA are not fully understood. METHODS: The Serglycin (SRGN) expression pattern was analyzed, along with its association with macrophage infiltration in OA, using bioinformatic methods. SRGN expression in chondrocytes was altered by small interfering RNA (siRNA) and plasmids. Conditioned media (CM) was obtained from transfected chondrocytes to establish a co-culture model of chondrocytes and THP-1 derived macrophages. The impact of SRGN on macrophage recruitment was evaluated using a transwell assay. Furthermore, the regulatory effect of SRGN on CCL3 was validated through qPCR, WB, and ELISA experiments. RESULTS: In OA patients, the upregulation of SRGN positively correlated with K-L grade and macrophage infiltration. It was found that SRGN expression and secretion were up-regulated in OA and that it can promote macrophage migration in vitro. Further investigation showed that SRGN affects macrophage migration by regulating the expression of CCL3. CONCLUSION: SRGN in chondrocytes plays a role in promoting the recruitment of THP-1 derived macrophages in vitro by regulating production of CCL3.

S100A12 is involved in the pathology of osteoarthritis by promoting M1 macrophage polarization via the NF-κB pathway.

BACKGROUND: Osteoarthritis (OA) is a degenerative joint disease that affects millions worldwide. Synovitis and macrophage polarization are important factors in the development of OA. However, the specific components of synovial fluid (SF) responsible for promoting macrophage polarization remain unclear. METHODS: Semi-quantitative antibody arrays were used to outline the proteome of SF. Differential expression analysis and GO/KEGG were performed on the obtained data. Immunohistochemistry and ELISA were used to investigate the relationship between SF S100A12 levels and synovitis levels in clinalclinical samples. In vitro cell experiments were conducted to investigate the effect of S100A12 on macrophage polarization. Public databases were utilized to predict and construct an S100A12-centered lncRNA-miRNA-mRNA competing endogenous RNA network, which was preliminarily validated using GEO datasets. RESULTS: The study outlines the protein profile in OA and non-OA SF. The results showed that the S100A12 level was significantly increased in OA SF and inflammatory chondrocytes. The OA synovium had more severe synovitis and higher levels of S100A12 than non-OA synovium. Exogenous S100A12 upregulated the levels of M1 markers and phosphorylated p65 and promoted p65 nuclear translocation, while pretreatment with BAY 11-7082 reversed these changes. It was also discovered that LINC00894 was upregulated in OA and significantly correlated with S100A12, potentially regulating S100A12 expression by acting as a miRNA sponge. CONCLUSIONS: This study demonstrated that S100A12 promotes M1 macrophage polarization through the NF-κB pathway, and found that LINC00894 has the potential to regulate the expression of S100A12 as a therapeutic approach.

Low Carbon Loss from Long-Term Manure-Applied Soil during Abrupt Warming Is Realized through Soil and Microbiome Interplay.

Manure application is a global approach for enhancing soil organic carbon (SOC) sequestration. However, the response of SOC decomposition in manure-applied soil to abrupt warming, often occurring during diurnal temperature fluctuations, remains poorly understood. We examined the effects of long-term (23 years) continuous application of manure on SOC chemical composition, soil respiration, and microbial communities under temperature shifts (15 vs 25 °C) in the presence of plant residues. Compared to soil without fertilizer, manure application reduced SOC recalcitrance indexes (i.e., aliphaticity and aromaticity) by 17.45 and 21.77%, and also reduced temperature sensitivity (Q10) of native SOC decomposition, plant residue decomposition, and priming effect by 12.98, 15.98, and 52.83%, respectively. The relative abundances of warm-stimulated chemoheterotrophic bacteria and fungi were lower in the manure-applied soil, whereas those of chemoautotrophic Thaumarchaeota were higher. In addition, the microbial network of the manure-applied soil was more interconnected, with more negative connections with the warm-stimulated taxa than soils without fertilizer or with chemical fertilizer applied. In conclusion, our study demonstrated that the reduced loss of SOC to abrupt warming by manure application arises from C chemistry modification, less warm-stimulated microorganisms, a more complex microbial community, and the higher CO2 intercepting capability by Thaumarchaeota.

Multicenter investigation of preoperative distinction between primary central nervous system lymphomas and glioblastomas through interpretable artificial intelligence models.

OBJECTIVE: Research into the effectiveness and applicability of deep learning, radiomics, and their integrated models based on Magnetic Resonance Imaging (MRI) for preoperative differentiation between Primary Central Nervous System Lymphoma (PCNSL) and Glioblastoma (GBM), along with an exploration of the interpretability of these models. MATERIALS AND METHODS: A retrospective analysis was performed on MRI images and clinical data from 261 patients across two medical centers. The data were split into a training set (n = 153, medical center 1) and an external test set (n = 108, medical center 2). Radiomic features were extracted using Pyradiomics to build the Radiomics Model. Deep learning networks, including the transformer-based MobileVIT Model and Convolutional Neural Networks (CNN) based ConvNeXt Model, were trained separately. By applying the "late fusion" theory, the radiomics model and deep learning model were fused to produce the optimal Max-Fusion Model. Additionally, Shapley Additive exPlanations (SHAP) and Grad-CAM were employed for interpretability analysis. RESULTS: In the external test set, the Radiomics Model achieved an Area under the receiver operating characteristic curve (AUC) of 0.86, the MobileVIT Model had an AUC of 0.91, the ConvNeXt Model demonstrated an AUC of 0.89, and the Max-Fusion Model showed an AUC of 0.92. The Delong test revealed a significant difference in AUC between the Max-Fusion Model and the Radiomics Model (P = 0.02). CONCLUSION: The Max-Fusion Model, combining different models, presents superior performance in distinguishing PCNSL and GBM, highlighting the effectiveness of model fusion for enhanced decision-making in medical applications. CLINICAL RELEVANCE STATEMENT: The preoperative non-invasive differentiation between PCNSL and GBM assists clinicians in selecting appropriate treatment regimens and clinical management strategies.

Predicting Outcome of Patients With Cerebral Hemorrhage Using a Computed Tomography-Based Interpretable Radiomics Model: A Multicenter Study.

OBJECTIVE: The aim of this study was to develop and validate an interpretable and highly generalizable multimodal radiomics model for predicting the prognosis of patients with cerebral hemorrhage. METHODS: This retrospective study involved 237 patients with cerebral hemorrhage from 3 medical centers, of which a training cohort of 186 patients (medical center 1) was selected and 51 patients from medical center 2 and medical center 3 were used as an external testing cohort. A total of 1762 radiomics features were extracted from nonenhanced computed tomography using Pyradiomics, and the relevant macroscopic imaging features and clinical factors were evaluated by 2 experienced radiologists. A radiomics model was established based on radiomics features using the random forest algorithm, and a radiomics-clinical model was further trained by combining radiomics features, clinical factors, and macroscopic imaging features. The performance of the models was evaluated using area under the curve (AUC), sensitivity, specificity, and calibration curves. Additionally, a novel SHAP (SHAPley Additive exPlanations) method was used to provide quantitative interpretability analysis for the optimal model. RESULTS: The radiomics-clinical model demonstrated superior predictive performance overall, with an AUC of 0.88 (95% confidence interval, 0.76-0.95; P < 0.01). Compared with the radiomics model (AUC, 0.85; 95% confidence interval, 0.72-0.94; P < 0.01), there was a 0.03 improvement in AUC. Furthermore, SHAP analysis revealed that the fusion features, rad score and clinical rad score, made significant contributions to the model's decision-making process. CONCLUSION: Both proposed prognostic models for cerebral hemorrhage demonstrated high predictive levels, and the addition of macroscopic imaging features effectively improved the prognostic ability of the radiomics-clinical model. The radiomics-clinical model provides a higher level of predictive performance and model decision-making basis for the risk prognosis of cerebral hemorrhage.

A Novel Interpretable Radiomics Model to Distinguish Nodular Goiter From Malignant Thyroid Nodules.

OBJECTIVES: The purpose of this study is to inquire about the potential association between radiomics features and the pathological nature of thyroid nodules (TNs), and to propose an interpretable radiomics-based model for predicting the risk of malignant TN. METHODS: In this retrospective study, computed tomography (CT) imaging and pathological data from 141 patients with TN were collected. The data were randomly stratified into a training group (n = 112) and a validation group (n = 29) at a ratio of 4:1. A total of 1316 radiomics features were extracted by using the pyradiomics tool. The redundant features were removed through correlation testing, and the least absolute shrinkage and selection operator (LASSO) or the minimum redundancy maximum relevance standard was used to select features. Finally, 4 different machine learning models (RF Hybrid Feature, SVM Hybrid Feature, RF, and LASSO) were constructed. The performance of the 4 models was evaluated using the receiver operating characteristic curve. The calibration curve, decision curve analysis, and SHapley Additive exPlanations method were used to evaluate or explain the best radiomics machine learning model. RESULTS: The optimal radiomics model (RF Hybrid Feature model) demonstrated a relatively high degree of discrimination with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI, 0.70-0.97; P < 0.001) for the validation cohort. Compared with the commonly used LASSO model (AUC, 0.78; 95% CI, 0.60-0.91; P < 0.01), there is a significant improvement in AUC in the validation set, net reclassification improvement, 0.79 (95% CI, 0.13-1.46; P < 0.05), and integrated discrimination improvement, 0. 20 (95% CI, 0.10-0.30; P < 0.001). CONCLUSION: The interpretable radiomics model based on CT performs well in predicting benign and malignant TNs by using quantitative radiomics features of the unilateral total thyroid. In addition, the data preprocessing method incorporating different layers of features has achieved excellent experimental results. CLINICAL RELEVANCE STATEMENT: As the detection rate of TNs continues to increase, so does the diagnostic burden on radiologists. This study establishes a noninvasive, interpretable and accurate machine learning model to rapidly identify the nature of TN found in CT.

An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.

OBJECTIVES: To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset. MATERIALS AND METHODS: Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions. RESULTS: The Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort. CONCLUSION: The multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model. CLINICAL RELEVANCE STATEMENT: Predicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.

Virus-induced cell gigantism and asymmetric cell division in archaea.

Archaeal viruses represent one of the most mysterious parts of the global virosphere, with many virus groups sharing no evolutionary relationship to viruses of bacteria or eukaryotes. How these viruses interact with their hosts remains largely unexplored. Here we show that nonlytic lemon-shaped virus STSV2 interferes with the cell cycle control of its host, hyperthermophilic and acidophilic archaeon Sulfolobus islandicus, arresting the cell cycle in the S phase. STSV2 infection leads to transcriptional repression of the cell division machinery, which is homologous to the eukaryotic endosomal sorting complexes required for transport (ESCRT) system. The infected cells grow up to 20-fold larger in size, have 8,000-fold larger volume compared to noninfected cells, and accumulate massive amounts of viral and cellular DNA. Whereas noninfected Sulfolobus cells divide symmetrically by binary fission, the STSV2-infected cells undergo asymmetric division, whereby giant cells release normal-sized cells by budding, resembling the division of budding yeast. Reinfection of the normal-sized cells produces a new generation of giant cells. If the CRISPR-Cas system is present, the giant cells acquire virus-derived spacers and terminate the virus spread, whereas in its absence, the cycle continues, suggesting that CRISPR-Cas is the primary defense system in Sulfolobus against STSV2. Collectively, our results show how an archaeal virus manipulates the cell cycle, transforming the cell into a giant virion-producing factory.

A case study showing highly traceable sources of bacteria on surfaces of university buildings.

University students predominantly spend their time indoors, where prolonged exposure raises the risk of contact with microorganisms of concern. However, our knowledge about the microbial community characteristics on university campus and their underpinnings is limited. To address it, we characterized bacterial communities from the surfaces of various built environments typical of a university campus, including cafeterias, classrooms, dormitories, offices, meeting rooms, and restrooms, in addition to human skin. The classrooms harbored the highest α-diversity, while the cafeterias had the lowest α-diversity. The bacterial community composition varied significantly across different building types. Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes, and Cyanobacteria were common phyla in university buildings, accounting for more than 90 % of total abundance. Staphylococcus aureus was the most abundant potential pathogen in classrooms, dormitories, offices, restrooms, and on human skin, indicating a potential risk for skin disease infections in these buildings. We further developed a new quantitative pathogenic risk assessment method according to the threat of pathogens to humans and found that classrooms exhibited the highest potential risk. The fast expectation-maximization algorithm identified 59 %-86 % of bacterial sources in buildings, with the human skin as the largest bacterial source for most buildings. As the sources of bacteria were highly traceable, we showed that homogeneous selection, dispersal limitation, and ecological drift were major ecological forces that drove community assembly. Our findings have important implications for predicting the distribution and sources of indoor dust bacterial communities on university campus.

Appropriate livestock grazing alleviates the loss of plant diversity and maintains community resistance in alpine meadows.

Alpine meadows constitute one of the major ecosystems on the Qinghai-Tibetan Plateau, with livestock grazing exerting a considerable impact on their biodiversity. However, the degree to which plant diversity influences community stability under different grazing intensities remains unclear in this region. This study conducted controlled grazing experiments across four levels of grazing intensity (no-, low-, medium-, and high-grazing) based on herbage utilization rate to assess the influence of grazing intensities on plant community structure and diversity-stability relationships. We discovered that high-grazing reduced plant diversity and attenuated the temporal stability and resistance of above-ground biomass. No- and low-grazing could alleviate plant biomass loss, with community resistance being optimal under low-grazing. The direct effects of livestock grazing on temporal stability were found to be negligible. Plant characteristics and diversity accounted for a substantial proportion of livestock grazing effects on community resistance (R2 = 0.46), as revealed by piecewise structural equation model analysis. The presence of plant diversity enhances the resistance of alpine meadows against disturbance and accelerates the recovery after grazing. Our results suggest that low-grazing intensity may represent a judicious option for preserving species diversity and community stability on the Qinghai-Tibetan Plateau.

A systematic analysis on global epidemiology and burden of foot fracture over three decades.

PURPOSE: To comprehensively analyze the geographic and temporal trends of foot fracture, understand its health burden by age, sex, and sociodemographic index (SDI), and explore its leading causes from 1990 to 2019. METHODS: The datasets in the present study were generated from the Global Burden of Diseases Study 2019, which included foot fracture data from 1990 to 2019. We extracted estimates along with the 95% uncertainty interval (UI) for the incidence and years lived with disability (YLDs) of foot fracture by location, age, gender, and cause. The epidemiology and burden of foot fracture at the global, regional, and national level was exhibited. Next, we presented the age and sex patterns of foot fracture. The leading cause of foot fracture was another focus of this study from the viewpoint of age, sex, and location. Then, Pearson's correlations between age-standardized rate (ASR), SDI, and estimated annual percentage change were calculated. RESULTS: The age-standardized incidence rate was 138.68 (95% UI: 104.88 - 182.53) per 100,000 persons for both sexes, 174.24 (95% UI: 134.35 - 222.49) per 100,000 persons for males, and 102.19 (95% UI: 73.28 - 138.00) per 100,000 persons for females in 2019. The age-standardized YLDs rate was 5.91 (95% UI: 3.58 - 9.25) per 100,000 persons for both genders, 7.35 (95% UI: 4.45 - 11.50) per 100,000 persons for males, and 4.51 (95% UI: 2.75 - 7.03) per 100,000 persons for females in 2019. The global incidence and YLDs of foot fracture increased in number and decreased in ASR from 1990 to 2019. The global geographical distribution of foot fracture is uneven. The incidence rate for males peaked at the age group of 20 - 24 years, while that for females increased with advancing age. The incidence rate of older people was rising, as younger age incidence rate declined from 1990 to 2019. Falls, exposure to mechanical forces, and road traffic injuries were the 3 leading causes of foot fracture. Correlations were observed between ASR, estimated annual percentage change, and SDI. CONCLUSIONS: The burden of foot fracture remains high globally, and it poses an enormous public health challenge, with population ageing. It is necessary to allocate more resources to the high-risk populations. Targeted realistic intervention policies and strategies are warranted.

Research on breast cancer pathological image classification method based on wavelet transform and YOLOv8.

 Breast cancer is one of the cancers with high morbidity and mortality in the world, which is a serious threat to the health of women. With the development of deep learning, the recognition about computer-aided diagnosis technology is getting higher and higher. And the traditional data feature extraction technology has been gradually replaced by the feature extraction technology based on convolutional neural network which helps to realize the automatic recognition and classification of pathological images. In this paper, a novel method based on deep learning and wavelet transform is proposed to classify the pathological images of breast cancer. Firstly, the image flip technique is used to expand the data set, then the two-level wavelet decomposition and reconfiguration technology is used to sharpen and enhance the pathological images. Secondly, the processed data set is divided into the training set and the test set according to 8:2 and 7:3, and the YOLOv8 network model is selected to perform the eight classification tasks of breast cancer pathological images. Finally, the classification accuracy of the proposed method is compared with the classification accuracy obtained by YOLOv8 for the original BreaKHis dataset, and it is found that the algorithm can improve the classification accuracy of images with different magnifications, which proves the effectiveness of combining two-level wavelet decomposition and reconfiguration with YOLOv8 network model.

What's the clinical significance of VAS, AOFAS, and SF-36 in progressive collapsing foot deformity.

BACKGROUND: This study aimed to ascertain the minimal clinically important difference (MCID), and substantial clinical benefit (SCB) of the American Orthopedic Foot and Ankle Society (AOFAS) scale, visual analog scale (VAS) for pain, and Short Form-36 Health Survey (SF-36) in progressive collapsing foot deformity (PCFD) surgery. METHODS: In this retrospective cohort study, a total of 84 patients with PCFD (84 feet) who underwent surgery between July 2015 and April 2021 were included. The study assessed the patients' subjective perception, as well as their VAS, AOFAS, and SF-36 scores at a minimum two-year follow-up, and these data were subjected to statistical analysis. The study utilized Spearman correlation analysis to determine the degree of correlation between patients' subjective perception and their VAS, AOFAS, and SF-36 scores. The minimal detectable change (MDC), MCID, and SCB for VAS, AOFAS, and SF-36 were calculated using both distribution- and anchor-based methods. The classification outcomes obtained from the distribution- and anchor-based methods were assessed using Cohen's kappa. RESULTS: Based on the subjective perception of the patients, a total of 84 individuals were categorized into three groups, with 7 in the no improvement group, 14 in the minimum improvement group, and 63 in the substantial improvement group. Spearman's correlation analysis indicated that the patients' subjective perception exhibited a moderate to strong association with VAS, AOFAS, SF-36 PCS, and SF-36 MCS, with all coefficients exceeding 0.4. The MCID of VAS, AOFAS, SF-36 PCS, and SF-36 MCS in PCFD surgery were determined to be 0.93, 5.84, 4.15, and 4.10 points using the distribution-based method and 1.50, 10.50, 8.34, and 3.03 points using the anchor-based method. The SCB of VAS, AOFAS, SF-36 PCS, and SF-36 MCS in PCFD surgery were 2.50, 18.50, 11.88, and 6.34 points, respectively. Moreover, the preliminary internal validation efforts have demonstrated the practical application and clinical utility of these findings. With the exception of the distribution-based MCID of SF-36 PCS, which showed fair agreement, all other measures demonstrated moderate to almost perfect agreement. CONCLUSIONS: The MDC, MCID, and SCB intuitively enhance the interpretation of VAS, AOFAS, and SF-36 in PCFD surgery, assisting all stakeholders to better understand the therapeutic benefits and limitations of clinical care, and thus to make a more rational decision. Each of these parameters has its own emphasis and complements the others. These parameters are recommended for evaluating the clinical relevance of the results, and their promotion should extend to other areas of foot and ankle surgery.

Long-term elevated precipitation induces grassland soil carbon loss via microbe-plant-soil interplay.

Global climate models predict that the frequency and intensity of precipitation events will increase in many regions across the world. However, the biosphere-climate feedback to elevated precipitation (eP) remains elusive. Here, we report a study on one of the longest field experiments assessing the effects of eP, alone or in combination with other climate change drivers such as elevated CO2 (eCO2 ), warming and nitrogen deposition. Soil total carbon (C) decreased after a decade of eP treatment, while plant root production decreased after 2 years. To explain this asynchrony, we found that the relative abundances of fungal genes associated with chitin and protein degradation increased and were positively correlated with bacteriophage genes, suggesting a potential viral shunt in C degradation. In addition, eP increased the relative abundances of microbial stress tolerance genes, which are essential for coping with environmental stressors. Microbial responses to eP were phylogenetically conserved. The effects of eP on soil total C, root production, and microbes were interactively affected by eCO2 . Collectively, we demonstrate that long-term eP induces soil C loss, owing to changes in microbial community composition, functional traits, root production, and soil moisture. Our study unveils an important, previously unknown biosphere-climate feedback in Mediterranean-type water-limited ecosystems, namely how eP induces soil C loss via microbe-plant-soil interplay.