Comparative Analysis of the Potential Adaptability of Tibetan Dzo and Yellow Cattle Based on Blood Indices, Metabolites, and Fecal Microbiota.

This study aimed to investigate the differences in environmental adaptability between dzo and Tibetan yellow cattle by using corresponding assay kits to analyze blood indices, utilizing mass spectrometry for blood metabolite profiling, and performing 16S rDNA sequencing of fecal microbiota. Forty female cattle were randomly divided into a dzomo (female dzo) group (MG, n = 20) and a Tibetan-yellow-cattle group (HG, n = 20). After 150 days of uniform feeding, six cattle from each group were randomly picked for jugular blood sampling and collection of fecal microorganisms. The results showed that the serum albumin, creatinine, total protein, superoxide dismutase, IgG, and IgM concentrations in the MG group were higher (p < 0.05), whereas the serum triglyceride concentration was lower, compared to the HG group (p < 0.05). The higher level of phospholipids containing long-chain polyunsaturated fatty acids (PUFAs) (PC (18:5e/2:0), PC (20:5e/2:0), LPC 18:2, LPC 20:5) observed in the serum of the dzo suggests that they have an advantage in adapting to the challenging conditions of the plateau environment. The fecal microbiota analysis showed that Akkermansia was significantly enriched in the MG group; this might be the key bacterial genus leading to the strong adaptability of dzo. Our findings indicated the dzo's superior adaptation to the Tibetan Plateau's harsh environment.

Screening for moderate to severe obstructive sleep apnea by using heart rate variability features based on random forest algorithm.

PURPOSE: More than 80% of patients with moderate to severe obstructive sleep apnea (OSA) are still not diagnosed timely. The prediction model based on random forest (RF) algorithm was established by using heart rate variability (HRV), clinical and demographic features so as to screen for the patients with high risk of moderate and severe obstructive sleep apnea. METHODS: The sleep monitoring data of 798 patients were randomly divided into training set (n = 558) and test set (n = 240) in 7:3 proportion. Grid search was applied to determine the best parameters of the RF model. 10-fold cross validation was utilized to evaluate the predictive performance of the RF model, which was then compared to the performance of the Logistic regression model. RESULTS: Among the 798 patients, 638 were males and 160 were females, with the average age of 43.51 years old and the mean body mass index (BMI) of 25.92 kg/m2. The sensitivity, specificity, accuracy, F1 score and the area under receiver operating characteristic curve for RF model and Logistic regression model were 94.68% vs. 73.94%; 73.08% vs. 86.54%; 90.00% vs. 76.67%; 0.94 vs. 0.83 and 0.83 vs. 0.80 respectively. CONCLUSIONS: The RF prediction model can effectively distinguish patients with moderate to severe OSA, which is expected to carry out in a large-scale population in order to screening for high-risk patients, and helps to evaluate the effect of OSA treatment continuously.

Efficacy of Nasal Septal Complex in the Endoscopic Reconstruction of Medial Orbital Wall.

The aim of this study is to investigate the efficacy of nasal septal complex reconstructing the medial orbital wall under some specific circumstances. The authors performed a study that included 10 patients who underwent isolated medial orbital wall fracture (blow-out fracture). All the reconstruction surgery of the patient included in this study fixed defect of medial orbital wall using autologous nasal septal complex (composed of nasal septal cartilage and perpendicular plate of ethmoid). The authors observed postoperative improvements of diplopia or ocular motility disorders of the affected orbit. The surgeries had gained satisfactory results, and without severe postoperative complications. After long-term follow-up, postoperative CT indicated the implant-covered defect of medial orbital wall, and there's no incarceration of the medial rectus. Meanwhile, the authors found there is no crack in the implant, and the surgery using nasal septal complex achieved premorbid orbital form. Autologous nasal septal complex provides a better complement to endoscopic reconstruction of medial orbital wall; this strategy allows nasal septum to reconstruct isolated medial orbital wall defects of various degrees, such as nasal septum fracture and dysplasia of perpendicular plate of ethmoid.

Controlling factors of iron plaque formation and its adsorption of cadmium and arsenic throughout the entire life cycle of rice plants.

Iron (Fe) plaque, which forms on the surface of rice roots, plays a crucial role in immobilizing heavy metal(loids), thus reducing their accumulation in rice plants. However, the principal factors influencing Fe plaque formation and its adsorption capacity for heavy metal(loid)s throughout the rice plant's lifecycle remain poorly understood. Thus, this study investigated the dynamics of Fe plaque formation and its ability to adsorb cadmium (Cd) and arsenic (As) across different growth stages, aiming to identify the key drivers behind these processes. The findings reveal that the rate of radial oxygen loss (ROL) and the abundance of plaque-associated microbes are the primary drivers of Fe plaque formation, with their relative importance ranging from 1.4% to 81%. Similarly, the adsorption of As by Fe plaque is principally determined by the rate of ROL and the quantity of Fe plaque, with subsequent effects from the total Fe in rhizospheric soil, arsenate-reducing bacteria, and organic matter-degrading bacteria. The relative importance of these factors ranges from 6.0% to 11.7%. By contrast, the adsorption of Cd onto Fe plaque is primarily affected by competition for adsorption sites with ammonium in soils and the presence of organic matter-degrading bacteria, contributing 25.5% and 23.5% to the adsorption process, respectively. These findings provide significant insights into the development of Fe plaque and its absorption of heavy metal(loid)s throughout the lifecycle of rice plants.

A flexible strategy to fabricate trumpet-shaped porous PDMS membranes for organ-on-chip application.

Porous polydimethylsiloxane (PDMS) membrane is a crucial element in organs-on-chips fabrication, supplying a unique substrate that can be used for the generation of tissue-tissue interfaces, separate co-culture, biomimetic stretch application, etc. However, the existing methods of through-hole PDMS membrane production are largely limited by labor-consuming processes and/or expensive equipment. Here, we propose an accessible and low-cost strategy to fabricate through-hole PDMS membranes with good controllability, which is performed via combining wet-etching and spin-coating processes. The porous membrane is obtained by spin-coating OS-20 diluted PDMS on an etched glass template with a columnar array structure. The pore size and thickness of the PDMS membrane can be adjusted flexibly via optimizing the template structure and spinning speed. In particular, compared to the traditional vertical through-hole structure of porous membranes, the membranes prepared by this method feature a trumpet-shaped structure, which allows for the generation of some unique bionic structures on organs-on-chips. When the trumpet-shape faces upward, the endothelium spreads at the bottom of the porous membrane, and intestinal cells form a villous structure, achieving the same effect as traditional methods. Conversely, when the trumpet-shape faces downward, intestinal cells spontaneously form a crypt-like structure, which is challenging to achieve with other methods. The proposed approach is simple, flexible with good reproducibility, and low-cost, which provides a new way to facilitate the building of multifunctional organ-on-chip systems and accelerate their translational applications.

Correlation between Non-HDL-C/HDL-C and Aß1-42 levels in cerebral infarction-related cognitive dysfunction.

OBJECTIVE: Cerebral infarction treatments are most effective if used early after stroke symptoms occur. Also, early detection is crucial for delaying and improving cognitive impairment. This study investigated the relationship between the ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol (Non-HDL-C/HDL-C), which reflects the entire burden of the cholesterol transported in atherogenic lipoproteins, and the level of ß-amyloid 1-42 (Aß-1-42), a major component of cerebrovascular amyloid deposits, in peripheral blood and cognitive dysfunction secondary to cerebral infarction. METHODS: A total of 83 patients with cerebral infarction admitted to Bozhou People's Hospital between June 2019 and June 2022 were assessed. The patients were divided into two groups based on their Mini-Mental State Scale (MMSE) scores: cognitive dysfunction group (n = 30) and non-cognitive dysfunction group (n = 53). In addition, a control group comprising 34 patients with transient cerebral insufficiency or cerebrovascular stenosis was selected. The groups were compared in terms of various clinical factors, including gender, age, hypertension, hyperlipidemia, lipid indexes, Non-HDL-C/HDL-C, and Aß1-42 levels. Logistic regression analysis was used to identify the risk factors associated with cognitive dysfunction. RESULTS: The results showed that hypertensive patients with cognitive dysfunction secondary to cerebral infarction had a higher proportion of frontal lobe, temporal lobe, and thalamus involvement and lower scores on the MMSE compared to the non-cognitive impairment group and control group (p < 0.05). Additionally, the levels of homocysteine (HCY), Non-HDL-C/HDL-C, and Aß1-42 in peripheral blood were significantly higher in hypertensive patients with cognitive dysfunction compared to the other two groups (all p < 0.05) and were identified as risk factors for cognitive dysfunction secondary to cerebral infarction. Peripheral blood levels of Non-HDL-C/HDL-C and Aß1-42 are risk factors for secondary cognitive dysfunction following a cerebral infarction. CONCLUSION: These data have important clinical implications for understanding the mechanisms underlying cognitive dysfunction in individuals with cerebrovascular disorders, potentially leading to new early interventions for preventing or treating such diseases.

Highly Pathogenic Avian Influenza A Virus in Wild Migratory Birds, Qinghai Lake, China, 2022.

In July 2022, an outbreak of highly pathogenic avian influenza A(H5N1) virus clade 2.3.4.4b occurred among migratory birds at Qinghai Lake in China. The virus circulated in June, and reassortants emerged after its introduction into the area. Surveillance in 2023 showed that the virus did not establish a stable presence in wild waterfowl.

Five commonly used traditional Chinese medicine formulas in the treatment of ulcerative colitis: A network meta-analysis.

BACKGROUND: Currently, traditional Chinese medicine (TCM) formulas are commonly being used as adjunctive therapy for ulcerative colitis in China. Network meta-analysis, a quantitative and comprehensive analytical method, can systematically compare the effects of different adjunctive treatment options for ulcerative colitis, providing scientific evidence for clinical decision-making. AIM: To evaluate the clinical efficacy and safety of commonly used TCM for the treatment of ulcerative colitis (UC) in clinical practice through a network meta-analysis. METHODS: Clinical randomized controlled trials of these TCM formulas used for the adjuvant treatment of UC were searched from the establishment of the databases to July 1, 2022. Studies that met the inclusion criteria were screened and evaluated for literature quality and risk of bias according to the Cochrane 5.1 standard. The methodological quality of the studies was assessed using ReviewManager (RevMan) 5.4, and a funnel plot was constructed to test for publication bias. ADDIS 1.16 statistical software was used to perform statistical analysis of the treatment measures and derive the network relationship and ranking diagrams of the various intervention measures. RESULTS: A total of 64 randomized controlled trials involving 5456 patients with UC were included in this study. The adjuvant treatment of UC using five TCM formulations was able to improve the clinical outcome of the patients. Adjuvant treatment with Baitouweng decoction (BTWT) showed a significant effect [mean difference = 36.22, 95% confidence interval (CI): 7.63 to 65.76]. For the reduction of tumor necrosis factor in patients with UC, adjunctive therapy with BTWT (mean difference = -9.55, 95%CI: -17.89 to -1.41), Shenlingbaizhu powder [SLBZS; odds ratio (OR) = 0.19, 95%CI: 0.08 to 0.39], and Shaoyao decoction (OR = -23.02, 95%CI: -33.64 to -13.14) was effective. Shaoyao decoction was more effective than BTWT (OR = 0.12, 95%CI: 0.03 to 0.39), SLBZS (OR = 0.19, 95%CI: 0.08 to 0. 39), and Xi Lei powder (OR = 0.34, 95%CI: 0.13 to 0.81) in reducing tumor necrosis factor and the recurrence rate of UC. CONCLUSION: TCM combined with mesalazine is more effective than mesalazine alone in the treatment of UC.

The Association Between Prolactin Levels and Cognitive Function in Female Patients With Severe Mental Disorders.

OBJECTIVE: Cognition impairments are considered as a fundamental characteristic of severe mental disorders (SMD). Recent studies suggest that hyperprolactinemia may exert a detrimental influence on cognitive performance in patients with SMD. The objective of this study was to investigate the correlation between serum prolactin levels and cognitive function in female individuals diagnosed with SMD. METHODS: We conducted a study on 294 patients with SMD and 195 healthy controls, aged between 14 to 55 years old. Cognitive function was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), while prolactin levels were measured in serum. Descriptive analysis and comparative analysis were performed to compare cognitive function and prolactin levels between groups, and linear regression models were used to explore the relationship between prolactin and cognitive function. RESULTS: Compared to the healthy control, individuals with SMD exhibited significantly higher levels of prolactin, while scoring lower on RBANS total and every index scores. Furthermore, a negative association between prolactin levels and cognitive function (RBANS total index score, attention, and delayed memory) was observed in SMD patients. Importantly, this inverse correlation between prolactin and cognition function (RBANS total index score, total scale score, and attention) persisted in patients who were not taking medications that could potentially influence serum prolactin levels. CONCLUSION: Our study reveals a significant correlation between elevated prolactin levels and cognitive impairment in female patients with SMD, underscoring the importance of monitoring prolactin levels in order to prevent cognitive deterioration among female SMD patients.

Identification of a Predictive Model for Schizophrenia Based on SNPs in a Chinese Population.

Background: Schizophrenia is a devastating mental disease with high heritability. A growing number of susceptibility genes associated with schizophrenia, as well as their corresponding SNPs loci, have been revealed by genome-wide association studies. However, using SNPs as predictors of disease and diagnosis remains difficult. Here, we aimed to uncover susceptibility SNPs in a Chinese population and to construct a prediction model for schizophrenia. Methods: A total of 210 participants, including 70 patients with schizophrenia, 70 patients with bipolar disorder, and 70 healthy controls, were enrolled in this study. We estimated 14 SNPs using published risk loci of schizophrenia, and used these SNPs to build a model for predicting schizophrenia via comparison of genotype frequencies and regression. We evaluated the efficacy of the diagnostic model in schizophrenia and control patients using ROC curves and then used the 70 patients with bipolar disorder to evaluate the model's differential diagnostic efficacy. Results: 5 SNPs were selected to construct the model: rs148415900, rs71428218, rs4666990, rs112222723 and rs1716180. Correlation analysis results suggested that, compared with the risk SNP of 0, the risk SNP of 3 was associated with an increased risk of schizophrenia (OR = 13.00, 95% CI: 2.35-71.84, p = 0.003). The ROC-AUC of this prediction model for schizophrenia was 0.719 (95% CI: 0.634-0.804), with the greatest sensitivity and specificity being 60% and 80%, respectively. The ROC-AUC of the model in distinguishing between schizophrenia and bipolar disorder was 0.591 (95% CI: 0.497-0.686), with the greatest sensitivity and specificity being 60% and 55.7%, respectively. Conclusion: The SNP risk score prediction model had good performance in predicting schizophrenia. To the best of our knowledge, previous studies have not applied SNP-based models to differentiate between cases of schizophrenia and other mental illnesses. It could have several potential clinical applications, including shaping disease diagnosis, treatment, and outcomes.

Functional analysis of a novel pathogenic variant in CREBBP associated with bone development.

BACKGROUND: CREBBP has been extensively studied in syndromic diseases associated with skeletal dysplasia. However, there is limited research on the molecular mechanisms through which CREBBP may impact bone development. We identified a novel pathogenic CREBBP variant (c.C3862T/p.R1288W, which is orthologous to mouse c.3789 C > T/p.R1289W) in a patient with non-syndromic polydactyly. METHODS: We created a homozygous Crebbp p.R1289W mouse model and compared their skeletal phenotypes to wild-type (WT) animals. Bone marrow stem cells (BMSCs) were isolated and assessed for their proliferative capacity, proportion of apoptotic cells in culture, and differentiation to chondrocytes and osteocytes. RESULTS: We observed a significant decrease in body length in 8-week-old homozygous Crebbp p.R1289W mice. The relative length of cartilage of the digits of Crebbp p.R1289W mice was significantly increased compared to WT mice. BMSCs derived from Crebbp p.R1289W mice had significantly decreased cell proliferation and an elevated rate of apoptosis. Consistently, cell proliferative capacity was decreased and the proportion of apoptotic cells was increased in the distal femoral growth plate of Crebbp p.R1289W compared to WT mice. Chemical induction of BMSCs indicated that Crebbp p.R1289W may promote chondrocyte differentiation. CONCLUSION: The Crebbp p.R1289W variant plays a pathogenic role in skeletal development in mice. IMPACT: CREBBP has been extensively studied in syndromic diseases characterized by skeletal dysplasia. There is limited research regarding the molecular mechanism through which CREBBP may affect bone development. To our knowledge, we generated the first animal model of a novel Crebbp variant, which is predicted to be pathogenic for skeletal diseases. Certain pathogenic variants, such as Crebbp p.R1289W, can independently lead to variant-specific non-syndromic skeletal dysplasia.

Wavelet-based selection-and-recalibration network for Parkinson's disease screening in OCT images.

BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is one of the most prevalent neurodegenerative brain diseases worldwide. Therefore, accurate PD screening is crucial for early clinical intervention and treatment. Recent clinical research indicates that changes in pathology, such as the texture and thickness of the retinal layers, can serve as biomarkers for clinical PD diagnosis based on optical coherence tomography (OCT) images. However, the pathological manifestations of PD in the retinal layers are subtle compared to the more salient lesions associated with retinal diseases. METHODS: Inspired by textural edge feature extraction in frequency domain learning, we aim to explore a potential approach to enhance the distinction between the feature distributions in retinal layers of PD cases and healthy controls. In this paper, we introduce a simple yet novel wavelet-based selection and recalibration module to effectively enhance the feature representations of the deep neural network by aggregating the unique clinical properties, such as the retinal layers in each frequency band. We combine this module with the residual block to form a deep network named Wavelet-based Selection and Recalibration Network (WaveSRNet) for automatic PD screening. RESULTS: The extensive experiments on a clinical PD-OCT dataset and two publicly available datasets demonstrate that our approach outperforms state-of-the-art methods. Visualization analysis and ablation studies are conducted to enhance the explainability of WaveSRNet in the decision-making process. CONCLUSIONS: Our results suggest the potential role of the retina as an assessment tool for PD. Visual analysis shows that PD-related elements include not only certain retinal layers but also the location of the fovea in OCT images.

Medical image registration and its application in retinal images: a review.

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, they have not systematically summarized the existing medical image registration methods. To this end, a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives, aiming to help audiences quickly understand the development of medical image registration. In particular, we review recent advances in retinal image registration, which has not attracted much attention. In addition, current challenges in retinal image registration are discussed and insights and prospects for future research provided.

Three new spider species of Belisana Thorell, 1898 (Araneae, Pholcidae) from karst caves, with a list of Belisana species from Guangxi, China.

Three new species of the genus Belisana Thorell, 1898 are described from karst caves in Guangxi, China: Belisanalangping Zhang, Li & Yao, sp. nov. (♂♀), B.lingui Zhang, Li & Yao, sp. nov. (♂♀), and B.tianyang Zhang, Li & Yao, sp. nov. (♂♀). In addition, a list of all Belisana species from Guangxi is also provided.

Effect of platelet indices on mortality and comorbidity in peritoneal dialysis: a cohort study.

BACKGROUND: There were limited data investigating platelet indices in predicting peritoneal dialysis (PD) outcomes on comorbidities. The aim of this study was to evaluate the association between platelet indices and new-onset comorbidity and all-cause mortality in PD patients. METHODS: A single-center, retrospective observational cohort study was conducted in incident PD patients from 28 December 2011 to 24 January 2018, and followed up until 31 December 2022. Time to the first new-onset cardiovascular disease (CVD) and time to the first new-onset infection event after PD were identified as the primary outcomes. All-cause mortality was identified as the secondary endpoint. The correlation between platelet indices and comorbidities and all-cause mortality were assessed by Cox model. Data of liver disease status was not collected and analyzed. Survival curves were performed by Kaplan-Meier method with log-rank tests. RESULTS: A total of 250 incident PD patients with a median follow-up of 6.79 (inter-quarter range 4.05, 8.89) years was included. A total of 81 and 139 patients experienced the first new-onset CVD and infection event respectively during the follow-up period. High mean platelet volume (MPV) was independently associated with high risk of time to the first new-onset CVD (HR 1.895, 95% CI 1.174-3.058, p = 0.009) and all-cause mortality (HR 1.710, 95% CI 1.155-2.531, p = 0.007). Patients with low mean platelet volume to platelet count ratio (MPV/PC) were prone to occur the new-onset infection events (log rank 5.693, p = 0.017). Low MPV/PC (HR 0.652, 95% CI 0.459-0.924, p = 0.016) was significantly associated with the time to the first new-onset infection event on PD. CONCLUSIONS: Platelet indices were associated with the new-onset CVD, infectious comorbidities and all-cause mortality on PD. Low MPV/PC was associated with time to the first new-onset infection event in PD patients. Moreover, high MPV was associated with new-onset CVD and all-cause mortality in the incident PD patients.

Evaluation of the Factors Influencing Blood Transfusion during Minimally Invasive Direct Coronary Artery Bypass Surgery.

INTRODUCTION: The objective of this study was to analyze the blood transfusion factors of minimally invasive direct coronary artery bypass (MIDCAB) surgery using artificial intelligence. METHODS: A retrospective analysis was performed for patients undergoing MIDCAB operations and no heart-lung machine was used from January 2017 to September 2022 in our hospital. The influencing factors of blood transfusion were used to build the artificial intelligence model. Eighty percent of the database was used as the training set, and twenty percent database was used as the testing set. To predict whether to use red blood cells during operation, we compared 104 artificial intelligence models. We aimed to assess whether which factors influence allogeneic transfusion in MIDCAB operations. RESULTS: Of the 104 machine learning algorithms, the XGBoost model delivered the best performance, with an AUC of 0.726 in the testing set and an accuracy of 0.854 in the testing set. The artificial intelligence model showed preoperative hemoglobin less than 120 g/L, prothrombin time greater than 13.75, body mass index less than 22.7 kg/m2, coronary heart disease with additional comorbidities, a history of percutaneous coronary intervention, weight lower than 67 kg were the six major risk factors of allogeneic transfusion. CONCLUSION: The XGBoost model can predict transfusion or not transfusion in MIDCBA surgery with high accuracy.

Identification of hub glycolysis-related genes in acute myocardial infarction and their correlation with immune infiltration using bioinformatics analysis.

PURPOSE: Glycolysis and immune metabolism play important roles in acute myocardial infarction (AMI). Therefore, this study aimed to identify and experimentally validate the glycolysis-related hub genes in AMI as diagnostic biomarkers, and further explore the association between hub genes and immune infiltration. METHODS: Differentially expressed genes (DEGs) from AMI peripheral blood mononuclear cells (PBMCs) were analyzed using R software. Glycolysis-related DEGs (GRDEGs) were identified and analyzed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) for functional enrichment. A protein-protein interaction network was constructed using the STRING database and visualized using Cytoscape software. Immune infiltration analysis between patients with AMI and stable coronary artery disease (SCAD) controls was performed using CIBERSORT, and correlation analysis between GRDEGs and immune cell infiltration was performed. We also plotted nomograms and receiver operating characteristic (ROC) curves to assess the predictive accuracy of GRDEGs for AMI occurrence. Finally, key genes were experimentally validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting using PBMCs. RESULTS: A total of 132 GRDEGs and 56 GRDEGs were identified on the first day and 4-6 days after AMI, respectively. Enrichment analysis indicated that these GRDEGs were mainly clustered in the glycolysis/gluconeogenesis and metabolic pathways. Five hub genes (HK2, PFKL, PKM, G6PD, and ALDOA) were selected using the cytoHubba plugin. The link between immune cells and hub genes indicated that HK2, PFKL, PKM, and ALDOA were significantly positively correlated with monocytes and neutrophils, whereas G6PD was significantly positively correlated with neutrophils. The calibration curve, decision curve analysis, and ROC curves indicated that the five hub GRDEGs exhibited high predictive value for AMI. Furthermore, the five hub GRDEGs were validated by RT-qPCR and western blotting. CONCLUSION: We concluded that HK2, PFKL, PKM, G6PD, and ALDOA are hub GRDEGs in AMI and play important roles in AMI progression. This study provides a novel potential immunotherapeutic method for the treatment of AMI.

Estimates of late Early Cretaceous atmospheric CO2 from Mongolia based on stomatal and isotopic analysis of Pseudotorellia.

PREMISE: The Aptian-Albian (121.4-100.5 Ma) was a greenhouse period with global temperatures estimated as 10-15°C warmer than pre-industrial conditions, so it is surprising that the most reliable CO2 estimates from this time are <1400 ppm. This low CO2 during a warm period implies a very high Earth-system sensitivity in the range of 6 to 9°C per CO2 doubling between the Aptian-Albian and today. METHODS: We applied a well-vetted paleo-CO2 proxy based on leaf gas-exchange principles (Franks model) to two Pseudotorellia species from three stratigraphically similar samples at the Tevshiin Govi lignite mine in central Mongolia (~119.7-100.5 Ma). RESULTS: Our median estimated CO2 concentration from the three respective samples was 2132, 2405, and 2770 ppm. The primary reason for the high estimated CO2 but with relatively large uncertainties is the very low stomatal density in both species, where small variations propagate to large changes in estimated CO2. Indeed, we found that at least 15 leaves are required before the aggregate estimated CO2 approaches that of the full data set. CONCLUSIONS: Our three CO2 estimates all exceeded 2000 ppm, translating to an Earth-system sensitivity (~3-5°C/CO2 doubling) that is more in keeping with the current understanding of the long-term climate system. Because of our large sample size, the directly measured inputs did not contribute much to the overall uncertainty in estimated CO2; instead, the inferred inputs were responsible for most of the overall uncertainty and thus should be scrutinized for their value choices.

Smart Cushions with Machine Learning-Enhanced Force Sensors for Pressure Injury Risk Assessment.

Prolonged sitting can easily result in pressure injury (PI) for certain people who have had strokes or spinal cord injuries. There are not many methods available for tracking contact surface pressure and shear force to evaluate the PI risk. Here, we propose a smart cushion that uses two-dimensional force sensors (2D-FSs) to measure the pressure and shear force in the buttocks. A machine learning algorithm is then used to compute the shear stresses in the gluteal muscles, which helps to determine the PI risk. The 2D-FS consists of a ferroelectret coaxial sensor (FCS) unit placed atop a ferroelectret film sensor (FFS) unit, allowing it to detect both vertical and horizontal forces simultaneously. To characterize and calibrate, two experimental approaches are applied: one involves simultaneously applying two perpendicular forces, and one involves applying a single force. To separate the two forces, the 2D-FS is decoupled using a deep neural network technique. Multiple FCSs are embedded to form a smart cushion, and a genetic algorithm-optimized backpropagation neural network is proposed and trained to predict the shear strain in the buttocks to prevent PI. By tracking the danger of PI, the smart cushion based on 2D-FSs may be further connected with home-based intelligent care platforms to increase patient equality for spinal cord injury patients and lower the expense of nursing or rehabilitation care.

Lung Cancer Biomarkers Associated with Increased Peripheral Arterial Stiffness in Middle-aged Chinese Adults.

AIMS: Previous evidence suggests that serum lung cancer biomarkers are associated with inflammatory conditions; however, their relationship with peripheral arterial stiffness remains unclear. Therefore, the present study investigated the relationship between serum lung cancer biomarkers and peripheral arterial stiffness in middle-aged Chinese adults. METHODS: In total, 3878 middle-aged Chinese adults were enrolled in this study. Increased peripheral arterial stiffness was assessed using the brachial-ankle pulse wave velocity and ankle-brachial index. Univariate and multivariate logistic regression analyses were used to determine the independent effects of serum lung cancer biomarkers on the risk of increased peripheral arterial stiffness. A receiver operating characteristic curve analysis was used to assess the diagnostic ability of serum lung cancer biomarkers in distinguishing increased peripheral arterial stiffness. RESULTS: Serum levels of carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin-19 fragment 21-1, and pro-gastrin-releasing peptide were higher in subjects with increased peripheral arterial stiffness than in those without (P＜0.05). After adjusting for other risk factors, serum CEA and NSE levels were found to be independently associated with increased peripheral arterial stiffness. The corresponding adjusted odds ratios (ORs) for increased peripheral arterial stiffness in CEA level quartiles were 1.00, 1.57, 2.15, and 6.13. The ORs for increased peripheral arterial stiffness in the quartiles of NSE levels were 1.00, 4.92, 6.65, and 8.01. CONCLUSIONS: Increased serum CEA and NSE levels are closely linked to increased peripheral arterial stiffness, and high serum CEA and NSE levels are potential risk markers for peripheral arterial stiffness in middle-aged Chinese adults.