On the Consistency and Large-Scale Extension of Multiple Kernel Clustering.

Existing multiple kernel clustering (MKC) algorithms have two ubiquitous problems. From the theoretical perspective, most MKC algorithms lack sufficient theoretical analysis, especially the consistency of learned parameters, such as the kernel weights. From the practical perspective, the high complexity makes MKC unable to handle large-scale datasets. This paper tries to address the above two issues. We first make a consistency analysis of an influential MKC method named Simple Multiple Kernel k-Means (SimpleMKKM). Specifically, suppose that ∧γn are the kernel weights learned by SimpleMKKM from the training samples. We also define the expected version of SimpleMKKM and denote its solution as γ*. We establish an upper bound of ||∧γn-γ*||∞ in the order of ~O(1/√n), where n is the sample number. Based on this result, we also derive its excess clustering risk calculated by a standard clustering loss function. For the large-scale extension, we replace the eigen decomposition of SimpleMKKM with singular value decomposition (SVD). Consequently, the complexity can be decreased to O(n) such that SimpleMKKM can be implemented on large-scale datasets. We then deduce several theoretical results to verify the approximation ability of the proposed SVD-based method. The results of comprehensive experiments demonstrate the superiority of the proposed method. The code is publicly available at https://github.com/weixuan-liang/SVD-based-SimpleMKKM.

Longitudinal multi-omics analysis uncovers the altered landscape of gut microbiota and plasma metabolome in response to high altitude.

BACKGROUND: Gut microbiota is significantly influenced by altitude. However, the dynamics of gut microbiota in relation to altitude remains undisclosed. METHODS: In this study, we investigated the microbiome profile of 610 healthy young men from three different places in China, grouped by altitude, duration of residence, and ethnicity. We conducted widely targeted metabolomic profiling and clinical testing to explore metabolic characteristics. RESULTS: Our findings revealed that as the Han individuals migrated from low altitude to high latitude, the gut microbiota gradually converged towards that of the Tibetan populations but reversed upon returning to lower altitude. Across different cohorts, we identified 51 species specifically enriched during acclimatization and 57 species enriched during deacclimatization to high altitude. Notably, Prevotella copri was found to be the most enriched taxon in both Tibetan and Han populations after ascending to high altitude. Furthermore, significant variations in host plasma metabolome and clinical indices at high altitude could be largely explained by changes in gut microbiota composition. Similar to Tibetans, 41 plasma metabolites, such as lactic acid, sphingosine-1-phosphate, taurine, and inositol, were significantly elevated in Han populations after ascending to high altitude. Germ-free animal experiments demonstrated that certain species, such as Escherichia coli and Klebsiella pneumoniae, which exhibited altitude-dependent variations in human populations, might play crucial roles in host purine metabolism. CONCLUSIONS: This study provides insights into the dynamics of gut microbiota and host plasma metabolome with respect to altitude changes, indicating that their dynamics may have implications for host health at high altitude and contribute to host adaptation. Video Abstract.

Self-Supervised Temporal Graph Learning With Temporal and Structural Intensity Alignment.

Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast to static graphs, temporal graphs are typically organized as node interaction sequences over continuous time rather than an adjacency matrix. Most temporal graph learning methods model current interactions by incorporating historical neighborhood. However, such methods only consider first-order temporal information while disregarding crucial high-order structural information, resulting in suboptimal performance. To address this issue, we propose a self-supervised method called S2T for temporal graph learning, which extracts both temporal and structural information to learn more informative node representations. Notably, the initial node representations combine first-order temporal and high-order structural information differently to calculate two conditional intensities. An alignment loss is then introduced to optimize the node representations, narrowing the gap between the two intensities and making them more informative. Concretely, in addition to modeling temporal information using historical neighbor sequences, we further consider structural knowledge at both local and global levels. At the local level, we generate structural intensity by aggregating features from high-order neighbor sequences. At the global level, a global representation is generated based on all nodes to adjust the structural intensity according to the active statuses on different nodes. Extensive experiments demonstrate that the proposed model S2T achieves at most 10.13% performance improvement compared with the state-of-the-art competitors on several datasets.

HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification.

MOTIVATION: The rapid development of high-throughput biomedical technologies can provide researchers with detailed multi-omics data. The multi-omics integrated analysis approach based on machine learning contributes a more comprehensive perspective to human disease research. However, there are still significant challenges in representing single-omics data and integrating multi-omics information. RESULTS: This article presents HyperTMO, a Trusted Multi-Omics integration framework based on Hypergraph convolutional network for patient classification. HyperTMO constructs hypergraph structures to represent the association between samples in single-omics data, then evidence extraction is performed by hypergraph convolutional network, and multi-omics information is integrated at an evidence level. Last, we experimentally demonstrate that HyperTMO outperforms other state-of-the-art methods in breast cancer subtype classification and Alzheimer's disease classification tasks using multi-omics data from TCGA (BRCA) and ROSMAP datasets. Importantly, HyperTMO is the first attempt to integrate hypergraph structure, evidence theory, and multi-omics integration for patient classification. Its accurate and robust properties bring great potential for applications in clinical diagnosis. AVAILABILITY AND IMPLEMENTATION: HyperTMO and datasets are publicly available at https://github.com/ippousyuga/HyperTMO.

Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data.

In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting the fact that the scRNA mode holds significantly richer information compared to the scATAC. This disregard hinders the model benefits from the insights derived from multiple modalities, compromising the overall clustering performance. To this end, we propose an effective multi-modal clustering model scEMC for parallel scRNA and Assay of Transposase Accessible Chromatin data. Concretely, we have devised a skip aggregation network to simultaneously learn global structural information among cells and integrate data from diverse modalities. To safeguard the quality of integrated cell representation against the influence stemming from sparse scATAC data, we connect the scRNA data with the aggregated representation via skip connection. Moreover, to effectively fit the real distribution of cells, we introduced a Zero Inflated Negative Binomial-based denoising autoencoder that accommodates corrupted data containing synthetic noise, concurrently integrating a joint optimization module that employs multiple losses. Extensive experiments serve to underscore the effectiveness of our model. This work contributes significantly to the ongoing exploration of cell subpopulations and tumor microenvironments, and the code of our work will be public at https://github.com/DayuHuu/scEMC.

The Associations of Two Novel Inflammation Biomarkers, SIRI and SII, with Mortality Risk in Patients with Chronic Heart Failure.

Background: The associations of two novel inflammation biomarkers, systemic inflammation response index (SIRI) and systemic immune inflammation index (SII), with mortality risk in patients with chronic heart failure (CHF) are not well-characterized. Methods: This retrospective cohort study included patients with CHF in two medical centers of Chinese People's Liberation Army General Hospital, Beijing, China. The outcomes of this study included in-hospital mortality and long-term mortality. Associations of SIRI and SII with mortality were assessed using multivariable regressions and receiver operating characteristic (ROC) analyses. Results: A total of 6232 patients with CHF were included in the present study. We documented 97 cases of in-hospital mortality and 1738 cases of long-term mortality during an average 5.01-year follow-up. Compared with patients in the lowest quartile of SIRI, those in the highest quartile exhibited 134% higher risk of in-hospital mortality (adjusted odds ratio, 2.34; 95% confidence interval [CI], 1.16-4.72) and 45% higher risk of long-term mortality (adjusted hazard ratio, 1.45; 95% CI, 1.25-1.67). Compared with patients in the lowest quartile of SII, those in the highest quartile exhibited 27% higher risk of long-term mortality (adjusted hazard ratio, 1.27; 95% CI, 1.11-1.46). In ROC analyses, SIRI showed better prognostic discrimination than C-reactive protein (area under the curve: 69.39 vs 60.91, P = 0.01, for in-hospital mortality; 61.82 vs 58.67, P = 0.03, for 3-year mortality), whereas SII showed similar prognostic value with C-reactive protein. Conclusion: SIRI and SII were significantly associated with mortality risk in patients with CHF. SIRI may provide better prognostic discrimination than C-reactive protein.

Chronic diseases spectrum and multimorbidity in elderly inpatients based on a 12-year epidemiological survey in China.

BACKGROUND: The number and proportion of the elderly population have been continuously increasing in China, leading to the elevated prevalence of chronic diseases and multimorbidity, which ultimately brings heavy burden to society and families. Meanwhile, the status of multimorbidity tends to be more complex in elderly inpatients than community population. In view of the above concerns, this study was designed to investigate the health status of elderly inpatients by analyzing clinical data in Chinese People's Liberation Army (PLA) General Hospital from 2008 to 2019, including the constitution of common diseases, comorbidities, the status of multimorbidity, in-hospital death and polypharmacy among elderly inpatients, so as to better understand the diseases spectrum and multimorbidity of elderly inpatients and also to provide supporting evidence for targeted management of chronic diseases in the elderly. METHODS: A clinical inpatients database was set up by collecting medical records of elderly inpatients from 2008 to 2019 in Chinese PLA General Hospital, focusing on diseases spectrum and characteristics of elderly inpatients. In this study, we collected data of inpatients aged ≥ 65 years old, and further analyzed the constitution of diseases, multimorbidity rates and mortality causes in the past decade. In addition, the prescriptions were also analyzed to investigate the status of polypharmacy in elderly inpatients. RESULTS: A total of 210,169 elderly patients were hospitalized from January 1st, 2008 to December 31st, 2019. The corresponding number of hospitalizations was 290,833. The average age of the study population was 72.67 years old. Of the total population, 73,493 elderly patients were re-admitted within one year, with the re-hospitalization rate of 25.27%. Malignant tumor, hypertension, ischemic heart disease, diabetes mellitus and cerebrovascular disease were the top 5 diseases. Among the study population, the number of patients with two or more long-term health conditions was 267,259, accounting for 91.89%, with an average of 4.68 diseases. In addition, the average number of medications taken by the study population was 5.4, among which, the proportion of patients taking more than 5 types of medications accounted for 55.42%. CONCLUSIONS: By analyzing the constitution of diseases and multimorbidity, we found that multimorbidity has turned out to be a prominent problem in elderly inpatients, greatly affecting the process of healthy aging and increasing the burden on families and society. Therefore, multidisciplinary treatment should be strengthened to make reasonable preventive and therapeutic strategies to improve the life quality of the elderly. Meanwhile, more attention should be paid to reasonable medications for elderly patients with multimorbidity to avoid preventable side effects caused by irrational medication therapy.

Impact of body mass index on long-term outcomes in patients undergoing percutaneous coronary intervention stratified by diabetes mellitus: a retrospective cohort study.

BACKGROUND: Patients with diabetes mellitus (DM) caused by obesity have increased in recent years. The impact of obesity on long-term outcomes in patients undergoing percutaneous coronary intervention (PCI) with or without DM remains unclear. METHODS: We retrospectively analysed data from 1918 patients who underwent PCI. Patients were categorized into four groups based on body mass index (BMI, normal weight: BMI < 25 kg/m2; overweight and obese: BMI ≥ 25 kg/m2) and DM status (presence or absence). The primary endpoint was the occurrence of major adverse cardiac and cerebrovascular events (MACCE; defined as all-cause death, myocardial infarction, stroke, and unplanned repeat revascularization). RESULTS: During a median follow-up of 7.0 years, no significant differences in MACCE, myocardial infarction, or stroke were observed among the four groups. Overweight and obese individuals exhibited lower all-cause mortality rates compared with normal-weight patients (without DM: hazard ratio [HR]: 0.54, 95% confidence interval [CI]: 0.37 to 0.78; with DM: HR: 0.57, 95% CI: 0.38 to 0.86). In non-diabetic patients, the overweight and obese group demonstrated a higher risk of unplanned repeat revascularization than the normal-weight group (HR:1.23, 95% CI:1.03 to 1.46). After multivariable adjustment, overweight and obesity were not significantly associated with MACCE, all-cause death, myocardial infarction, stroke, or unplanned repeat revascularization in patients with and without diabetes undergoing PCI. CONCLUSION: Overweight and obesity did not demonstrate a significant protective effect on long-term outcomes in patients with and without diabetes undergoing PCI.

Multi-omics reveals changed energy metabolism of liver and muscle by caffeine after mice swimming.

In recent years, numerous studies have investigated the effects of caffeine on exercise, and provide convincing evidence for its ergogenic effects on exercise performance. However, the precise mechanisms underlying these ergogenic effects remain unclear. In this study, an exercise swimming model was conducted to investigate the effects of orally administered with caffeine before swimming on the alterations of proteome and energy metabolome of liver and muscle after swimming. We found proteins in liver, such as S100a8, S100a9, Gabpa, Igfbp1 and Sdc4, were significantly up-regulated, while Rbp4 and Tf decreased after swimming were further down-regulated in caffeine group. The glycolysis and pentose phosphate pathways in liver and muscle were both significantly down-regulated in caffeine group. The pyruvate carboxylase and amino acid levels in liver, including cysteine, serine and tyrosine, were markedly up-regulated in caffeine group, exhibiting a strong correlation with the increased pyruvic acid and oxaloacetate levels in muscle. Moreover, caffeine significantly decreased the lactate levels in both liver and muscle after swimming, potentially benefiting exercise performance.

Methylation of NRIP3 Is a Synthetic Lethal Marker for Combined PI3K and ATR/ATM Inhibitors in Colorectal Cancer.

INTRODUCTION: The aim of this study was to investigate the epigenetic regulation and underlying mechanism of NRIP3 in colorectal cancer (CRC). METHODS: Eight cell lines (SW480, SW620, DKO, LOVO, HT29, HCT116, DLD1, and RKO), 187 resected margin samples from colorectal cancer tissue, 146 cases with colorectal adenomatous polyps, and 308 colorectal cancer samples were used. Methylation-specific PCR, Western blotting, RNA interference assay, and a xenograft mouse model were used. RESULTS: NRIP3 exhibited methylation in 2.7% (5/187) of resected margin samples from colorectal cancer tissue, 32.2% (47/146) of colorectal adenomatous polyps, and 50.6% (156/308) of CRC samples, and the expression of NRIP3 was regulated by promoter region methylation. The methylation of NRIP3 was found to be significantly associated with late onset (at age 50 years or older), poor tumor differentiation, lymph node metastasis, and poor 5-year overall survival in CRC (all P < 0.05). In addition, NRIP3 methylation was an independent poor prognostic marker ( P < 0.05). NRIP3 inhibited cell proliferation, colony formation, invasion, and migration, while induced G1/S arrest. NRIP3 suppressed CRC growth by inhibiting PI3K-AKT signaling both in vitro and in vivo . Methylation of NRIP3 sensitized CRC cells to combined PI3K and ATR/ATM inhibitors. DISCUSSION: NRIP3 was frequently methylated in both colorectal adenomatous polyps and CRC. The methylation of NRIP3 may potentially serve as an early detection, late-onset, and poor prognostic marker in CRC. NRIP3 is a potential tumor suppressor. NRIP3 methylation is a potential synthetic lethal marker for combined PI3K and ATR/ATM inhibitors.

An Energy Matching Vessel Segmentation Framework in 3-D Medical Images.

Accurate vascular segmentation from High Resolution 3-Dimensional (HR3D) medical scans is crucial for clinicians to visualize complex vasculature and diagnose related vascular diseases. However, a reliable and scalable vessel segmentation framework for HR3D scans remains a challenge. In this work, we propose a High-resolution Energy-matching Segmentation (HrEmS) framework that utilizes deep learning to directly process the entire HR3D scan and segment the vasculature to the finest level. The HrEmS framework introduces two novel components. Firstly, it uses the real-order total variation operator to construct a new loss function that guides the segmentation network to obtain the correct topology structure by matching the energy of the predicted segment to the energy of the manual label. This is different from traditional loss functions such as dice loss, which matches the pixels between predicted segment and manual label. Secondly, a curvature-based weight-correction module is developed, which directs the network to focus on crucial and complex structural parts of the vasculature instead of the easy parts. The proposed HrEmS framework was tested on three in-house multi-center datasets and three public datasets, and demonstrated improved results in comparison with the state-of-the-art methods using both topology-relevant and volumetric-relevant metrics. Furthermore, a double-blind assessment by three experienced radiologists on the critical points of the clinical diagnostic processes provided additional evidence of the superiority of the HrEmS framework.

SGT++: Improved Scene Graph-Guided Transformer for Surgical Report Generation.

Automatically recording surgical procedures and generating surgical reports are crucial for alleviating surgeons' workload and enabling them to concentrate more on the operations. Despite some achievements, there still exist several issues for the previous works: 1) failure to model the interactive relationship between surgical instruments and tissue; and 2) neglect of fine-grained differences within different surgical images in the same surgery. To address these two issues, we propose an improved scene graph-guided Transformer, also named by SGT++, to generate more accurate surgical report, in which the complex interactions between surgical instruments and tissue are learnt from both explicit and implicit perspectives. Specifically, to facilitate the understanding of the surgical scene graph under a graph learning framework, a simple yet effective approach is proposed for homogenizing the input heterogeneous scene graph. For the homogeneous scene graph that contains explicit structured and fine-grained semantic relationships, we design an attention-induced graph transformer for node aggregation via an explicit relation-aware encoder. In addition, to characterize the implicit relationships about the instrument, tissue, and the interaction between them, the implicit relational attention is proposed to take full advantage of the prior knowledge from the interactional prototype memory. With the learnt explicit and implicit relation-aware representations, they are then coalesced to obtain the fused relation-aware representations contributing to generating reports. Some comprehensive experiments on two surgical datasets show that the proposed STG++ model achieves state-of-the-art results.

[Review of Research Advances in Medical Service Robotics].

With the progress of science and technology and the increase of clinical demand, medical robots have developed rapidly and played a important role in promoting the medical cause. Service robot is a branch of medical robot, which is mainly oriented to medical service and assistance needs, and has been applied in many medical scenarios and achieved demonstration effects. This research first describes the development of medical service robots, and then summarizes the key technologies and clinical applications of robots. Finally, it points out the challenges and directions that medical service robots face at present, and puts forward prospects for their further development in the medical field.

Structural variants involved in high-altitude adaptation detected using single-molecule long-read sequencing.

Structural variants (SVs), accounting for a larger fraction of the genome than SNPs/InDels, are an important pool of genetic variation, enabling environmental adaptations. Here, we perform long-read sequencing data of 320 Tibetan and Han samples and show that SVs are highly involved in high-altitude adaptation. We expand the landscape of global SVs, apply robust models of selection and population differentiation combining SVs, SNPs and InDels, and use epigenomic analyses to predict enhancers, target genes and biological functions. We reveal diverse Tibetan-specific SVs affecting the regulatory circuitry of biological functions, including the hypoxia response, energy metabolism and pulmonary function. We find a Tibetan-specific deletion disrupts a super-enhancer and downregulates EPAS1 using enhancer reporter, cellular knock-out and DNA pull-down assays. Our study expands the global SV landscape, reveals the role of gene-regulatory circuitry rewiring in human adaptation, and illustrates the diverse functional roles of SVs in human biology.

Design and application of a novel telemedicine system jointly driven by multinetwork integration and remote control: Practical experience from PLAGH, China.

In China, several problems were common in the telemedicine systems, such as the poor network stability and difficult interconnection. A new telemedicine system jointly driven by multinetwork integration and remote control has been designed to address these problems. A multilink aggregation algorithm and an overlay network for telemedicine system (ONTMS) were developed to improve network stability, and a non-intervention remote control method was designed for Internet of Things (IoT) devices/systems. The authors monitored the network parameters, and distributed the questionnaire to participants, for evaluating the telemedicine system and services. Under a detection bandwidth of 8 Mbps, the aggregation parameters of Unicom 4G, Telecom 4G, and China Mobile 4G were optimal, with an uplink bandwidth, delay, and packet loss ratio (PLR) of 7.93 Mbps, 58.80 ms, and 0.06%, respectively. These parameters were significantly superior to those of China Mobile 4G, the best single network (p < 0.001). Through the ONTMS, the mean round-trip transporting delay from Beijing to Sanya was 76 ms, and the PLR was 0 at vast majority of time. A total of 1988 participants, including 1920 patients and 68 doctors, completed the questionnaires. More than 97% of participants felt that the audio and video transmission and remote control were fluent and convenient. 96% of patients rated the telemedicine services with scores of 4 or 5. This system has shown robust network property and excellent interaction ability, and satisfied the needs of patients and doctors.

Circulating circRNA expression profile and its potential role in late recurrence of paroxysmal atrial fibrillation post catheter ablation.

BACKGROUND: Catheter-based pulmonary vein isolation (PVI) is an effective and well-established intervention for symptomatic paroxysmal atrial fibrillation (PAF). Nevertheless, late recurrences of atrial fibrillation (LRAF) occurring during 3 to 12 months are common, and the underlying mechanisms remain elusive. Circular RNAs (circRNAs) in atrial tissue have been linked to the pathophysiological mechanisms and progression of PAF in a few studies. However, their expression patterns in peripheral blood and regulatory function in LRAF are not clear. METHODS: In the present study, the expression profile of circulating circRNAs in three paired nonvalvular PAF patients with or without LRAF was investigated by high-throughput sequencing and validated by quantitative real-time polymerase chain reaction (qRT-PCR). Bioinformatics analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and circRNA/miRNA regulatory network, were performed to predict the functions and potential regulatory roles of differentially expressed (DE) circRNAs. RESULTS: A total of 12,834 circRNAs, comprising 5,491 down-regulated and 7,343 up-regulated circRNAs, were found to be DE in blood smaples from the two groups in peripheral blood between LRAF and non-recurrence control individuals. The most enriched GO categories in terms of molecular function, biological process, and cellular component features were catalytic activity, cellular metabolic process, and intracellular part, respectively. The KEGG enrichment study revealed that the most important metabolic process controlled by DE circRNAs is endocytosis. In the circRNA/microRNAs interaction network, four up-regulated circRNAs (hsa_circ_0002665, hsa_circ_0001953, hsa_circ_0003831, and hsa_circ_0040533) and one down-regulated circRNA (hsa_circ_0041103) were predicted to play potential regulatory roles in the pathogenesis of LRAF. CONCLUSIONS: This investigation discovered the expression pattern of circulating circRNAs that is indicative of PAF late recurrence, which may serve as risk markers or therapeutic targets for LRAF after PVI.

Serum uric acid and outcome in hospitalized elderly patients with chronic heart failure through the whole spectrum of ejection fraction phenotypes.

INTRODUCTION: Elevated serum uric acid (SUA) levels have been associated with poor outcome in patients with heart failure (HF). Uric acid is associated with inflammation and microvascular dysfunction, which may differentially affect left ventricular ejection fraction (EF) phenotypes. We aimed to identify the role of SUA across EF phenotypes in hospitalized elderly patients with chronic HF. METHODS: We analyzed 1355 elderly patients who were diagnosed with chronic HF. All patients had SUA levels measured within the first 24 h following admission. Patients with left ventricle EF were categorized as having HF with reduced EF (HFrEF, EF < 40%), HF with mid-range EF (HFmrEF, 40%≦LVEF ≦ 49%) or HF with preserved EF (HFpEF, LVEF ≥ 50%). Endpoints were cardiovascular death, HF rehospitalization, and their composite. The median follow-up period was 18 months. RESULTS: Compared with the lowest SUA quartile, the highest SUA quartile was significantly associated with the endpoints (adjusted HR: 2.404, 95% CI: 1.178-4.906, P = 0.016; HR: 1.418, 95% CI: 1.021-1.971, P = 0.037; HR: 1.439, 95% CI: 1.049-1.972, P = 0.024, respectively). After model adjustment, a significant association of SUA with cardiovascular death and the composite endpoint persisted among HFrEF and HFmrEF patients in the highest SUA quartile (P < 0.05 for all). CONCLUSIONS: In hospitalized elderly patients with chronic HF, SUA is an independent predictor of adverse outcomes, which can be seen in HFrEF and HFmrEF patients.

Heart failure classifications via non-invasive pressure volume loops from echocardiography.

BACKGROUND: Left ventricular pressure-volume (LV-PV) loops provide comprehensive characterization of cardiovascular system in both health and disease, which are the essential element of the hemodynamic evaluation of heart failure (HF). This study attempts to achieve more detailed HF classifications by non-invasive LV-PV loops from echocardiography and analyzes contribution of parameters to HF classifications. METHODS: Firstly, non-invasive PV loops are established by time-varying elastance model where LV volume curves were extracted from apical-four-chambers view of echocardiographic videos. Then, 16 parameters related to cardiac structure and functions are automatically acquired from PV loops. Next, we applied six machine learning (ML) methods to divide four categories. On this premise, we choose the best performing classifier among machine learning approaches for feature ranking. Finally, we compare the contributions of different parameters to HF classifications. RESULTS: By the experimental, the PV loops were successfully acquired in 1076 cases. When single left ventricular ejection fraction (LVEF) is used for HF classifications, the accuracy of the model is 91.67%. When added parameters extracted from ML-derived LV-PV loops, the classification accuracy is 96.57%, which improved by 5.1%. Especially, our parameters have a great improvement in the classification of non-HF controls and heart failure with preserved ejection fraction (HFpEF). CONCLUSIONS: We successfully presented the classification of HF by machine derived non-invasive LV-PV loops, which has the potential to improve the diagnosis and management of heart failure in clinic. Moreover, ventriculo-arterial (VA) coupling and ventricular efficiency were demonstrated important factors for ML-based HF classification model besides LVEF.

Inflammatory Markers Showed Significant Incremental Value for Predicting Post-Hepatectomy Liver Failure in Hepatocellular Carcinoma Patients.

BACKGROUND: Post-hepatectomy liver failure (PHLF) remains a complication with the potential risk of mortality for hepatocellular carcinoma (HCC) patients. The systemic inflammatory response (SIR) has been demonstrated to be associated with a bad prognosis of liver cirrhosis and tumors. This study aims to evaluate the incremental prognostic value of inflammatory markers in predicting PHLF in patients with HCC. METHODS: Clinical characteristics and variables were retrospectively collected in 2824 patients diagnosed with HCC who underwent radical hepatectomy from the First Medical Center of the General Hospital of the People's Liberation Army. A recently published prognostic model for PHLF was used as the reference model. The increase in AUC (ΔAUC), integrated discrimination improvement (IDI), and the continuous version of the net reclassification improvement (NRI) were applied for quantifying the incremental value of adding the inflammatory markers to the reference model. A p value < 0.05 was considered statistically significant. RESULTS: The reference PHLF model showed acceptable prediction performance in the current cohort, with an AUC of 0.7492 (95%CI, 0.7191-0.7794). The calculated ΔAUC associated with procalcitonin (PCT) was the only one that was statistically significant (p < 0.05), with a value of 0.0044, and demonstrated the largest magnitude of the increase in AUC. The continuous NRI value associated with the systemic immune-inflammation index (SII) was 35.79%, second only to GPS (46.07%). However, the inflammatory markers of the new models with statistically significant IDI only included WBC count, lymphocyte count, and SII. IDI associated with SII, meanwhile, was the maximum (0.0076), which was consistent with the performance of using the ΔAUC (0.0044) to assess the incremental value of each inflammatory variable. CONCLUSIONS: Among a wide range of inflammatory markers, only PCT and SII have potential incremental prognostic value for predicting PHLF in patients with radical resectable HCC.

Origin, evolution, and diversification of the wall-associated kinase gene family in plants.

KEY MESSAGE: The study of the origin, evolution, and diversification of the wall-associated kinase gene family in plants facilitates their functional investigations in the future. Wall-associated kinases (WAKs) make up one subfamily of receptor-like kinases (RLKs), and function directly in plant cell elongation and responses to biotic and abiotic stresses. The biological functions of WAKs have been extensively characterized in angiosperms; however, the origin and evolutionary history of the WAK family in green plants remain unclear. Here, we performed a comprehensive analysis of the WAK family to reveal its origin, evolution, and diversification in green plants. In total, 1061 WAK genes were identified in 37 species from unicellular algae to multicellular plants, and the results showed that WAK genes probably originated before bryophyte differentiation and were widely distributed in land plants, especially angiosperms. The phylogeny indicated that the land plant WAKs gave rise to five clades and underwent lineage-specific expansion after species differentiation. Cis-acting elements and expression patterns analyses of WAK genes in Arabidopsis and rice demonstrated the functional diversity of WAK genes in these two species. Many gene gains and losses have occurred in angiosperms, leading to an increase in the number of gene copies. The evolutionary trajectory of the WAK family during polyploidization was uncovered using Gossypium species. Our results provide insights into the evolution of WAK genes in green plants, facilitating their functional investigations in the future.