Correlation between serum sex hormone-binding globulin levels and nutrition indicators and malnutrition exposure risk in men and postmenopausal women with type 2 diabetes.

BACKGROUND: This study sought to investigate the correlation between serum sex hormone-binding globulin (SHBG) levels and nutrition indicators and the malnutrition exposure risk in men and postmenopausal women with type 2 diabetes mellitus (T2DM). METHODS: A cross-sectional analysis was conducted, involving patients diagnosed with T2DM at the Guangdong Provincial People's Hospital between May 2018 and December 2019. RESULTS: The study comprised 551 participants (363 men, mean age of 55.55 ± 11.57 years), among whom 167 (30.31%) were classified as with malnutrition exposure risk (GNRI ≤ 98). Multivariable logistic regression analysis revealed that SHBG (OR = 1.04, 95% CI: 1.02-1.05, P < 0.001), glycated hemoglobin (OR = 1.36, 95% CI: 1.22-1.51, P < 0.001), hemoglobin (OR = 0.96, 95% CI: 0.94-0.97, P < 0.001), and non-alcoholic fatty liver disease (OR = 0.41, 95% CI: 0.23-0.73, P < 0.003) were independently associated with the malnutrition exposure risk. SHBG was inversely correlated with body mass index (males: r = -0.34; postmenopausal females: r = -0.22), albumin (males: r = -0.30; postmenopausal females: r = -0.20), transferrin (males: r = -0.28; postmenopausal females: r = -0.19), and prealbumin (males: r = -0.35; postmenopausal females: r = -0.30) (all P < 0.05). CONCLUSIONS: Serum SHBG levels are correlated with nutritional indicators and the risk of malnutrition in men and postmenopausal women with T2DM. A multicenter prospective study is imperative to verify this result in the future.

An interpretable ensemble trees method with joint analysis of static and dynamic features for myocardial infarction detection.

OBJECTIVE: In recent years, artificial intelligence-based electrocardiogram (ECG) methods have been massively applied to myocardial infarction (MI). However, the joint analysis of static and dynamic features to achieve accurate and interpretable MI detection has not been comprehensively addressed. Approach. This paper proposes a simplified ensemble tree method with a joint analysis of static and dynamic features to solve this issue for MI detection. Initially, the dynamic features are extracted by modeling the intrinsic dynamics of ECG via dynamic learning in addition to extracting classical static features. Secondly, a two-stage feature selection strategy is designed to identify a few significant features, which substitute the original variables that are employed in constructing the ensemble tree. This approach enhances the discriminative ability by selecting significant static and dynamic features. Subsequently, this paper presents an interpretable classification method named StackTree by introducing a stacked ensemble scheme to modify the ensemble tree simplification algorithm. The representative rules of the raw ensemble trees are selected as the intermediate training data that is used to retrain a decision tree with performance close to that of the source ensemble model. Using this scheme, the significant precision and interpretability of MI detection are thus comprehensively addressed. Main results. The effectiveness of our method in detecting MI is evaluated using the PTB and clinical database. The findings suggest that our algorithm outperforms the traditional methods based on a single type of feature. Additionally, it is comparable to the conventional random forest, achieving 97.1% accuracy under the inter-patient framework on the PTB database. Furthermore, feature subsets trained on PTB are validated using the clinical database, resulting in an accuracy of 84.5%. The chosen important features demonstrate that both static and dynamic information have crucial roles in MI detection. Crucially, the proposed method provides clear internal workings in an easy-to-understand visual manner. .

Settling velocity of microplastics in turbulent open-channel flow.

The settling behavior of microplastics (MPs) plays a pivotal role in their transport and fate in aquatic environments, but the dominant mechanisms and physics governing the settling of MPs in rivers remain poorly understood. To gain mechanistic insights into the velocity lag of MPs in an open-channel flume under different turbulent flow conditions, an experimental study was conducted using three types of MPs: polystyrene, cellulose acetate, and acrylic, of sphere-shaped particles with diameters ranging from 1 mm to 5 mm. A particle tracking technique was employed to record and analyze the MPs velocity within turbulent flows. The results showed a variation in the vertical settling velocity of MPs ωMP ranging from -26 % to +16 %, when compared to their counterparts in still water (ωs). A new formula for the drag coefficient (Cd) of MP particles was developed by introducing the suspension number (u∗/ωs). The developed Cd formula was used to calculate the resultant velocity lag VMP, with a mean relative error of 16 % compared with the measured values. Further, the study highlighted that the MPs with large Stokes numbers are mainly driven by their own inertia and turbulence has less influence on their settling behavior. This study is crucial for understanding the settling behavior of MPs in turbulent flows and developing their transport and fate models for MPs in riverine systems.

Therapeutic Options Targeting the Ataxia-Telangiectasia Mutated (ATM)-mediated DNA Damage Response, Macropinocytosis, and Adaptive Immunity in Ovarian Cancer.

Ataxia-telangiectasia mutated (ATM) is a pivotal protein with versatile kinase activity that responds to DNA damage. While its well-established role as a DNA repair protein is widely recognized, the understanding of its noncanonical functions in ovarian cancer remains limited. Numerous studies have investigated the potential of targeting ATM for ovarian cancer treatment. In addition to its involvement in homologous recombination repair (HRR), an increasing body of research suggests that ATM plays a role in cellular metabolism and adaptive immunity. This review focuses on the current evidence and provides a perspective on how targeting ATM in ovarian cancer can address HRR-deficient genotypes, influence macropinocytosis, and enhance immune checkpoint blockade (ICB) therapy. It underscores the diverse avenues through which targeting ATM is a potential tailored treatment for ovarian cancer.

An interpretable shapelets-based method for myocardial infarction detection using dynamic learning and deep learning.

Objective.Myocardial infarction (MI) is a prevalent cardiovascular disease that contributes to global mortality rates. Timely diagnosis and treatment of MI are crucial in reducing its fatality rate. Currently, electrocardiography (ECG) serves as the primary tool for clinical diagnosis. However, detecting MI accurately through ECG remains challenging due to the complex and subtle pathological ECG changes it causes. To enhance the accuracy of ECG in detecting MI, a more thorough exploration of ECG signals is necessary to extract significant features.Approach.In this paper, we propose an interpretable shapelet-based approach for MI detection using dynamic learning and deep learning. Firstly, the intrinsic dynamics of ECG signals are learned through dynamic learning. Then, a deep neural network is utilized to extract and select shapelets from ECG dynamics, which can capture locally specific ECG changes, and serve as discriminative features for identifying MI patients. Finally, the ensemble model for MI detection is built by integrating shapelets of multi-dimensional ECG dynamic signals.Main results.The performance of the proposed method is evaluated on the public PTB dataset with accuracy, sensitivity, and specificity of 94.11%, 94.97%, and 90.98%.Significance.The shapelets obtained in this study exhibit significant morphological differences between MI and healthy subjects.

Recurrent Cushing's Disease Caused by a TPIT-Lineage Densely Granulated Corticotroph Pituitary Neuroendocrine Tumor: A Case Report.

INTRODUCTION: Recurrent Cushing's disease (recurrent CD) is an uncommon and intricate clinical form of Cushing's syndrome. However, the connection between the pathological types of ACTH-secreting PitNETs and the clinical signs of recurrent CD remains uncertain. CASE DESCRIPTION: A 64-year-old woman, previously diagnosed with renal carcinoma, was admitted to our hospital due to recent weight gain. Previous endocrine tests indicated fluctuating hypercortisolemia and a recurrent pituitary tumor over the past six years. She underwent two transsphenoidal hypophysectomies, and histopathological analysis of the tumor revealed it as a densely granulated corticotroph tumor (DGCT), a subtype of TPIT-lineage PitNET, accompanied by tumor apoplexy. CONCLUSION: This case highlights the connection between recurrent CD and the pathological subtypes of TPIT-lineage DGCT-PitNETs.

Metabolomic assessment of African snail (Achatina fulica) meal on growth performance of giant river prawn (Macrobrachium rosenbergii).

This study aimed to investigate the effects of replacing fishmeal (FM) with African giant snail (Achatina fulica) meal (SM) on the growth performance of giant river prawn (Macrobrachium rosenbergii), as well as to analyze the associated metabolomic changes. Six diets were formulated, replacing FM with SM at different inclusion levels ranging from 0 % to 100 %. Growth performance and feed conversion ratio of prawns fed diets with FM replaced by SM up to 80 % were not significantly different from control. In contrast, significantly decreased growth performance and higher feed conversion ratio (FCR) occurred with diets containing 100 % SM. To gain insights into the metabolic regulation of prawns fed different diets, a 1H NMR metabolomics approach was used to assess the metabolic changes in prawns fed diets containing 0 % and 80 % SM. The results revealed up-regulated metabolites significantly involved in several metabolic pathways, including alanine, aspartate, and glutamate metabolism; citrate cycle (TCA cycle); aminoacyl-tRNA biosynthesis; and valine, leucine, and isoleucine biosynthesis. These findings imply that including SM in the diet might modulate the regulation of muscle amino acids and tRNA synthesis, suggesting a potential impact on protein biosynthesis mechanisms. Additionally, alterations in the TCA cycle may reflect changes in carbon utilization, potentially contributing to the growth performance of giant river prawns when fishmeal is replaced with SM without adversely affecting their growth. In conclusion, this study demonstrated that SM could be a promising alternative protein source in aquafeed. The metabolomic approach provides valuable insights into the metabolic changes in prawns fed different diets, aiding in the development of more effective aquafeeds in the future. The study's limitations, such as the simplified diet formulation and the limited scope of the metabolomic analysis, were acknowledged and discussed, highlighting the need for further research to build upon these findings.

Deterministic learning-based neural identification and knowledge fusion.

Recent deterministic learning methods have achieved locally-accurate identification of unknown system dynamics. However, the locally-accurate identification means that the neural networks can only capture the local dynamics knowledge along the system trajectory. In order to capture a broader knowledge region, this article investigates the knowledge fusion problem of deterministic learning, that is, the integration of different knowledge regions along different individual trajectories. Specifically, two kinds of knowledge fusion schemes are systematically introduced: an online fusion scheme and an offline fusion scheme. The online scheme can be viewed as an extension of distributed cooperative learning control to cooperative neural identification for sampled-data systems. By designing an auxiliary information transmission strategy to enable the neural network to receive information learned from other tasks while learning its own task, it is proven that the weights of all localized RBF networks exponentially converge to their common true/ideal values. The offline scheme can be regarded as a knowledge distillation strategy, in which the fused network is obtained by offline training through the knowledge learned from all individual system trajectories via deterministic learning. A novel weight fusion algorithm with low computational complexity is proposed based on the least squares solution under subspace constraints. Simulation studies show that the proposed fusion schemes can successfully integrate the knowledge regions of different individual trajectories while maintaining the learning performance, thereby greatly expanding the knowledge region learned from deterministic learning.

Trifolium repens L.-Like Periodic Micronano Structured Superhydrophobic Surface with Ultralow Ice Adhesion for Efficient Anti-Icing/Deicing.

Wind turbine blades are often covered with ice and snow, which inevitably reduces their power generation efficiency and lifetime. Recently, a superhydrophobic surface has attracted widespread attention due to its potential values in anti-icing/deicing. However, the superhydrophobic surface can easily transition from Cassie-Baxter to Wenzel at low temperature, limiting its wide applications. Herein, inspired by the excellent water resistance and cold tolerance of Trifolium repens L. endowed by its micronano structure and low surface energy, a fresh structure was prepared by combining femtosecond laser processing technology and a boiling water treatment method. The prepared icephobic surface aluminum alloy (ISAl) mainly consists of a periodic microcrater array, nonuniform microclusters, and irregular nanosheets. This three-scale structure greatly promotes the stability of the Cassie-Baxter state. The critical Laplace pressure of ISAl is up to 1437 Pa, and the apparent water contact angle (CA) is higher than 150° at 0 °C. Those two factors contribute to its excellent anti-icing and deicing performances. The results show that the static icing delay time reaches 2577 s, and the ice adhesion strength is only 1.60 kPa. Furthermore, the anti-icing and deicing abilities of the proposed ISAl were examined under the environment of low temperature and high relative humidity to demonstrate its effectiveness. The dynamic anti-icing time of ISAl in extreme environments is up to 5 h, and ice can quickly fall with a speed of 34 r/min when it is in a horizontal rotational motion. Finally, ISAl has excellent reusability and mechanical durability, with the ice adhesion strength still being less than 6 kPa and the CA greater than 150° after 15 cycles of icing-deicing tests. The proposed structure would offer a promising strategy for the efficient anti-icing and deicing of wind turbine blades.

Endocrine, genetic, and microbiome nexus of obesity and potential role of postbiotics: a narrative review.

Obesity is a public health crisis, presenting a huge burden on health care and the economic system in both developed and developing countries. According to the WHO's latest report on obesity, 39% of adults of age 18 and above are obese, with an increase of 18% compared to the last few decades. Metabolic energy imbalance due to contemporary lifestyle, changes in gut microbiota, hormonal imbalance, inherent genetics, and epigenetics is a major contributory factor to this crisis. Multiple studies have shown that probiotics and their metabolites (postbiotics) supplementation have an effect on obesity-related effects in vitro, in vivo, and in human clinical investigations. Postbiotics such as the SCFAs suppress obesity by regulating metabolic hormones such as GLP-1, and PPY thus reducing feed intake and suppressing appetite. Furthermore, muramyl di-peptides, bacteriocins, and LPS have been tested against obesity and yielded promising results in both human and mice studies. These insights provide an overview of targetable pharmacological sites and explore new opportunities for the safer use of postbiotics against obesity in the future.

Investigating the relationship between specific negative symptoms and metacognitive functioning in psychosis: A systematic review.

BACKGROUND: Disrupted metacognition is implicated in development and maintenance of negative symptoms, but more fine-grained analyses would inform precise treatment targeting for individual negative symptoms. AIMS: This systematic review identifies and examines datasets that test whether specific metacognitive capacities distinctly influence negative symptoms. MATERIALS & METHODS: PsycINFO, EMBASE, Medline and Cochrane Library databases plus hand searching of relevant articles, journals and grey literature identified quantitative research investigating negative symptoms and metacognition in adults aged 16+ with psychosis. Authors of included articles were contacted to identify unique datasets and missing information. Data were extracted for a risk of bias assessment using the Quality in Prognostic Studies tool. RESULTS: 85 published reports met criteria and are estimated to reflect 32 distinct datasets and 1623 unique participants. The data indicated uncertainty about the relationship between summed scores of negative symptoms and domains of metacognition, with significant findings indicating correlation coefficients from 0.88 to -0.23. Only eight studies investigated the relationship between metacognition and individual negative symptoms, with mixed findings. Studies were mostly moderate-to-low risk of bias. DISCUSSION: The relationship between negative symptoms and metacognition is rarely the focus of studies reviewed here, and negative symptom scores are often summed. This approach may obscure relationships between metacognitive domains and individual negative symptoms which may be important for understanding how negative symptoms are developed and maintained. CONLCLUSION: Methodological challenges around overlapping participants, variation in aggregation of negative symptom items and types of analyses used, make a strong case for use of Individual Participant Data Meta-Analysis to further elucidate these relationships.

Spontaneous Separation of Immiscible Organic Droplets on Asymmetric Wedge Channels with Hierarchical Microchannels.

Spontaneous separation of immiscible organic droplets has substantial research implications for environmental protection and resource regeneration. Compared to the widely explored separation of oil-water mixtures, there are fewer reports on separating mixed organic droplets on open surfaces due to the low surface tension differences. Efficient separation of mixed organic liquids by exploiting the rapid spontaneous transport of droplets on open surfaces remains a challenge. Here, through the fusion of inspiration from the fast droplet transport capability of Sarracenia trichome and the asymmetric wedge channel structure of shorebird beaks, this work proposes a spine with hierarchical microchannels and wedge channels (SHMW). Due to the synergistic effect of capillary force and asymmetric Laplace force, the SHMW can rapidly separate mixed organic droplets into two pure phases without requiring additional energy. In particular, the self-spreading of the oil solution on the open channel surface is utilized to amplify the surface energy difference between two droplets, and SHMW achieves the pickup of oil droplets floating on the surface of the organic solution. The maximum separation efficiency on 3-SHMW can reach 99.63%, and it can also realize the antigravity separation of mixed organic droplets with a surface tension difference as low as 0.87 mN·m-1. Furthermore, SHMW performs controllable separation, oil droplet pickup, and continuous separation and collection of mixed organic droplets. It is expected that this cooperative structure composed of hierarchical microchannels and wedge channels will be realized in resource recovery or chemical reactions in industrial production processes.

Investigating the relationship between negative symptoms and metacognitive functioning in psychosis: An individual participant data meta-analysis.

PURPOSE: Negative symptoms are a persistent, yet under-explored problem in psychosis. Disturbances in metacognition are a potential causal factor in negative symptom development and maintenance. This meta-analysis uses individual participant data (IPD) from existing research to assess the relationship between negative symptoms and metacognition treated as summed scores and domains. METHODS: Data sets containing individuals with negative symptoms and metacognition data, aged 16+ with psychosis, were identified according to pre-specific parameters. IPD integrity and completeness were checked and data were synthesized in two-stage meta-analyses of each negative symptoms cluster compared with metacognition in seemingly unrelated regression using restricted maximum likelihood estimation. Planned and exploratory sensitivity analyses were also conducted. RESULTS: Thirty-three eligible data sets were identified with 21 with sufficient similarity and availability to be included in meta-analyses, corresponding to 1301 participants. The strongest relationships observed were between summed scores of negative symptoms and metacognition. Metacognitive domains of self-reflectivity and understanding others' minds, and expressive negative symptoms emerged as significant in some meta-analyses. The uncertainty of several effect estimates increased significantly when controlling for covariates. CONCLUSIONS: This robust meta-analysis highlights the impact of using summed versus domain-specific scores of metacognition and negative symptoms, and relationships are not as clear-cut as once believed. Findings support arguments for further differentiation of negative symptom profiles and continued granular exploration of the relationship between metacognition and negative symptoms.

Interleukin-33 increases type 2 innate lymphoid cell count and their activation in eosinophilic asthma.

BACKGROUND: Interleukin-33 (IL-33) exacerbates asthma probably through type 2 innate lymphoid cells (ILC2s). Nevertheless, the association between eosinophilic asthma (EA) and ILC2s remains obscure, and the mechanisms by which IL-33 affects ILC2s are yet to be clarified. METHODS: ILC2s were evaluated in peripheral blood mononuclear cells, induced sputum, and bronchoalveolar lavage fluid obtained from patients with EA. Confocal microscopy was performed to locate ILC2s in lung tissue and the mRNA expression of ILC2-related genes was also evaluated in the EA model. The proliferation of ILC2s isolated from humans and mice was assessed following IL-33 or anti-IL-33 stimulation. RESULTS: The counts, activation, and mRNA expression of relevant genes in ILC2s were higher in PBMCs and airways of patients with EA. In addition, ILC2 cell counts correlated with Asthma control test, blood eosinophil count, Fractional exhaled nitric oxide level, and predicted eosinophilic airway inflammation. IL-33 induced stronger proliferation of ILC2s and increased their density around blood vessels in the lungs of mice with EA. Moreover, IL-33 treatment increased the counts and activation of ILC2s and lung inflammatory scores, whereas anti-IL-33 antibody significantly reversed these effects in EA mice. Finally, IL-33 enhanced PI3K and AKT protein expression in ILC2s, whereas inhibition of the PI3K/AKT pathway decreased IL-5 and IL-13 production by ILC2s in EA. CONCLUSIONS: ILC2s, especially activated ILC2s, might be critical markers of EA. IL-33 can induce and activate ILC2s in the lungs via the PI3K/AKT pathway in EA. Thus, using anti-IL-33 antibody could be a part of an effective treatment strategy for EA.

Establishment and validation of an AI-aid method in the diagnosis of myocardial perfusion imaging.

BACKGROUND: This study aimed to develop and validate an AI (artificial intelligence)-aid method in myocardial perfusion imaging (MPI) to differentiate ischemia in coronary artery disease. METHODS: We retrospectively selected 599 patients who had received gated-MPI protocol. Images were acquired using hybrid SPECT-CT systems. A training set was used to train and develop the neural network and a validation set was used to test the predictive ability of the neural network. We used a learning technique named "YOLO" to carry out the training process. We compared the predictive accuracy of AI with that of physician interpreters (beginner, inexperienced, and experienced interpreters). RESULTS: Training performance showed that the accuracy ranged from 66.20% to 94.64%, the recall rate ranged from 76.96% to 98.76%, and the average precision ranged from 80.17% to 98.15%. In the ROC analysis of the validation set, the sensitivity range was 88.9 ~ 93.8%, the specificity range was 93.0 ~ 97.6%, and the AUC range was 94.1 ~ 96.1%. In the comparison between AI and different interpreters, AI outperformed the other interpreters (most P-value < 0.05). CONCLUSION: The AI system of our study showed excellent predictive accuracy in the diagnosis of MPI protocols, and therefore might be potentially helpful to aid radiologists in clinical practice and develop more sophisticated models.

Hazard ratios of second primary malignancy after radioiodine for differentiated thyroid carcinoma: a large-cohort retrospective study.

INTRODUCTION: The objective of this study is to evaluate the benefits of radioactive iodine (RAI) treatment and the risk of second primary malignancy (SPM) in RAI-treated patients. MATERIAL AND METHODS: The cohort for this analysis consisted of individuals diagnosed with a first primary differentiated thyroid carcinoma (DTC), reported by the Surveillance, Epidemiology, and End Results (SEER) database in 1988-2016. Overall survival (OS) difference was estimated by Kaplan-Meier curves and log-rank test, and hazard ratios (HR) were obtained by Cox proportional-hazards model to evaluate the association between RAI and SPM. RESULTS: Among 130,902 patients, 61,210 received RAI and 69,692 did not, and a total of 8604 patients developed SPM. We found that OS was significantly higher in patients who received RAI than in those who did not (p < 0.001). DTC survivors treated with RAI had increased risk of SPM in females (p = 0.043), particularly for SPM occurring in the ovary (p = 0.039) and leukaemia (p < 0.0001). The risk of developing SPM was higher in the RAI group than in the non-RAI group and the general population, and the incidence increased with age. CONCLUSIONS: Increased risk of SPM occurs in female DTC survivors treated with RAI, which become more obvious with increasing age. Our research findings were beneficial to the formulation of RAI treatment strategies and the prediction of SPM for patients with thyroid cancer of different genders and different ages.

Impacts of different reconstruction methods on the image quality of cadmium-zinc-telluride-based single photon emission computed tomography/computed tomography pulmonary perfusion imaging.

OBJECTIVE: The objective was to evaluate the impacts of different reconstruction methods [filtered back projection (FBP) and ordered subset expectation maximization (OSEM)] and different filters (Butterworth filter and Gaussian filter) on the image quality in cadmium-zinc-telluride (CZT)-based single photon emission computed tomography (SPECT)/computed tomography (CT) pulmonary perfusion imaging. METHODS: A combinations including FBP with Butterworth filter, OSEM with Butterworth filter (OSEM + Butterworth filter ), and OSEM with Gaussian filter (OSEM + Gaussian filter) were used during SPECT image reconstruction. Visual and quantitative parameters [root mean square (RMS) noise, contrast and contrast-to-noise ratio (CNR)] were used to evaluate image quality. RESULTS: The OSEM + Gaussian filter had better RMS noise and CNR than those of the FBP + Butterworth filter or OSEM + Butterworth filter, while the OSEM + Butterworth filter had the best contrast. The highest visual scores were obtained by OSEM + Gaussian filter ( P  < 0.0001). In the lesion size <2 cm group, the contrast ( P < 0.01) and visual scores ( P < 0.001) of OSEM + Butterworth filter were better than those of the other two groups. In the lesion size ≥2 cm group, the RMS noise and visual scores of OSEM + Gaussian filter were better than those of the other two groups. CONCLUSION: In CZT SPECT/CT pulmonary perfusion imaging, this study recommended the clinical use of the OSEM + Gaussian filter combination for reconstruction in both conventional and larger lesions, the OSEM + Butterworth filter image postprocessing method might be advantageous in small lesions.

Development and validation of a pulmonary complications prediction model based on the Yang's index.

Background: Blunt chest trauma patients with pulmonary contusion are susceptible to pulmonary complications, and severe cases may develop respiratory failure. Some studies have suggested the extent of pulmonary contusion to be the main predictor of pulmonary complications. However, no simple and effective method to assess the severity of pulmonary contusion has been available yet. A reliable prognostic prediction model would facilitate the identification of high-risk patients, so that early intervention can be given to reduce pulmonary complications; however, no suitable model based on such an assumption has been available yet. Methods: In this study, a new method for assessing lung contusion by the product of the three dimensions of the lung window on the computed tomography (CT) image was proposed. We conducted a retrospective study on patients with both thoracic trauma and pulmonary contusion admitted to 8 trauma centers in China from January 2014 to June 2020. Using patients from 2 centers with a large number of patients as the training set and patients from the other 6 centers as the validation set, a prediction model for pulmonary complications was established with Yang's index and rib fractures, etc., being the predictors. The pulmonary complications included pulmonary infection and respiratory failure. Results: This study included 515 patients, among whom 188 developed pulmonary complications, including 92 with respiratory failure. Risk factors contributing to pulmonary complications were identified, and a scoring system and prediction model were constructed. Using the training set, models for adverse outcomes and severe adverse outcomes were developed, and area under the curve (AUC) of 0.852 and 0.788 were achieved in the validation set. In the model performance for predicting pulmonary complications, the positive predictive value of the model is 0.938, the sensitivity of the model is 0.563 and the specificity of the model is 0.958. Conclusions: The generated indicator, called Yang's index, was proven to be an easy-to-use method for the evaluation of pulmonary contusion severity. The prediction model based on Yang's index could facilitate early identification of patients at risk of pulmonary complications, yet the effectiveness of the model remains to be validated and its performance remains to be improved in further studies with larger sample sizes.

New Results on Rapid Dynamical Pattern Recognition via Deterministic Learning From Sampling Sequences.

Rapid dynamical pattern recognition based on the deterministic learning method (DLM-based RDPR) aims to rapidly recognize the most similar dynamical pattern pair from perspectives of differences in inherent system dynamics. The basic mechanism is to use available recognition errors to reflect the differences in the dynamics of dynamical pattern pairs and then to make a decision based on a minimal recognition error (MRE) principle. This article focuses on providing a rigorous theoretical analysis of the MRE principle in DLM-based RDPR under the sampled-data framework. Specifically, we seek a unified methodology from the similarity definition to the measure implementation and then to derive general sufficient conditions and necessary conditions for the MRE principle. The main idea is to: 1) from the average signal energy aspect, define a time-dependent dynamics-based similarity in dynamical pattern pairs and reestablish the measure of recognition errors generated from the DLM-based RDPR; 2) introduce the energy-based Lyapunov method to establish the interrelation between the dynamical distance and the recognition error; and 3) derive sufficient conditions and necessary conditions from two directions of the interrelation. The proposed conditions distinguish themselves from virtually all of the existing DLM-based RDPR works with only sufficient conditions in the sense that it is shown in a rigorous analysis that under what conditions, the pattern pair recognized based on the MRE principle is indeed the most similar one. Therefore, the proposed work makes the DLM-based RDPR possess good interpretability and provides strong theoretical guidance in engineering applications.

Prognosis of thyroid carcinoma patients with osseous metastases: an SEER-based study with machine learning.

OBJECTIVE: Osseous metastasis (OM) is the second most common site of thyroid cancer distant metastasis and presents a poor prognosis. Accurate prognostic estimation for OM has clinical significance. Ascertain the risk factors for survival and develop an effective model to predict the 3-year, 5-year overall survival (OS) and cancer-specific survival (CSS) for thyroid cancer patients with OM. METHODS: We retrieved the information of patients with OMs between 2010 and 2016 from the Surveillance, Epidemiology, and End Result Program. The Chi-square test, and univariate and multivariate Cox regression analyses were performed. Four machine learning (ML) algorithms, which were most commonly used in this field, were applied. RESULT: A total of 579 patients having OMs were eligible. Advanced age, tumor size ≥ 40 mm, combined with other distant metastasis were associated with worse OS in DTC OMs patients. Radioactive iodine (RAI) significantly improved CSS in both males and females. Among four ML models [logistic regression, support vector machines, extreme gradient boosting, and random forest (RF)], RF had the best performance [area under the receiver-operating characteristic curve: 0.9378 for 3-year CSS, 0.9105 for 5-year CSS, 0.8787 for 3-year OS, 0.8909 for 5-year OS]. The accuracy and specificity of RF were also the best. CONCLUSIONS: RF model shall be used to establish an accurate prognostic model for thyroid cancer patients with OM, not only from the SEER cohort but also intended for all thyroid cancer patients in the general population, which may be applicable in clinical practice in the future.