Machine Learning K-Means Clustering Algorithm for Interpolative Separable Density Fitting to Accelerate Hybrid Functional Calculations with Numerical Atomic Orbitals

Multicenter integration analysis of TRP channels revealed potential mechanisms of immunosuppressive microenvironment activation and identified a machine learning-derived signature for improving outcomes in gliomas

AIM

This study aimed to explore the mechanisms of transient receptor potential (TRP) channels on the immune microenvironment and develop a TRP-related signature for predicting prognosis, immunotherapy response, and drug sensitivity in gliomas.

METHODS

Based on the unsupervised clustering algorithm, we identified novel TRP channel clusters and investigated their biological function, immune microenvironment, and genomic heterogeneity. In vitro and in vivo experiments revealed the association between TRPV2 and macrophages. Subsequently, based on 96 machine learning algorithms and six independent glioma cohorts, we constructed a machine learning-based TRP channel signature (MLTS). The performance of the MLTS in predicting prognosis, immunotherapy response, and drug sensitivity was evaluated.

RESULTS

Patients with high expression levels of TRP channel genes had worse prognoses, higher tumor mutation burden, and more activated immunosuppressive microenvironment. Meanwhile, TRPV2 was identified as the most essential regulator in TRP channels. TRPV2 activation could promote macrophages migration toward malignant cells and alleviate glioma prognosis. Furthermore, MLTS could work independently of common clinical features and present stable and superior prediction performance.

CONCLUSION

This study investigated the comprehensive effect of TRP channel genes in gliomas and provided a promising tool for designing effective, precise treatment strategies.

Integration analysis of cell division cycle-associated family genes revealed potential mechanisms of gliomagenesis and constructed an artificial intelligence-driven prognostic signature

Cell division cycle-associated (CDCA) gene family members are essential cell proliferation regulators and play critical roles in various cancers. However, the function of the CDCA family genes in gliomas remains unclear. This study aims to elucidate the role of CDCA family members in gliomas using in vitro and in vivo experiments and bioinformatic analyses. We included eight glioma cohorts in this study. An unsupervised clustering algorithm was used to identify novel CDCA gene family clusters. Then, we utilized multi-omics data to elucidate the prognostic disparities, biological functionalities, genomic alterations, and immune microenvironment among glioma patients. Subsequently, the scRNA-seq analysis and spatial transcriptomic sequencing analysis were carried out to explore the expression distribution of CDCA2 in glioma samples. In vivo and in vitro experiments were used to investigate the effects of CDCA2 on the viability, migration, and invasion of glioma cells. Finally, based on ten machine-learning algorithms, we constructed an artificial intelligence-driven CDCA gene family signature called the machine learning-based CDCA gene family score (MLCS). Our results suggested that patients with the higher expression levels of CDCA family genes had a worse prognosis, more activated RAS signaling pathways, and more activated immunosuppressive microenvironments. CDCA2 knockdown inhibited the proliferation, migration, and invasion of glioma cells. In addition, the MLCS had robust and favorable prognostic predictive ability and could predict the response to immunotherapy and chemotherapy drug sensitivity.

Machine Learning K-Means Clustering of Interpolative Separable Density Fitting Algorithm for Accurate and Efficient Cubic-Scaling Exact Exchange Plus Random Phase Approximation within Plane Waves

The exact-exchange plus random-phase approximation (EXX+RPA) method has emerged as a crucial tool for precisely characterizing electronic structures in molecular and solid systems. We present an accurate and efficient implementation of EXX+RPA calculations that scale cubically and are conducted within plane waves. Our approach incorporates the interpolative separable density fitting (ISDF) algorithm, effectively mitigating the computational challenges associated with the plane wave basis set. To overcome the constraints of the conventional ISDF algorithm, characterized by the exceptionally high prefactor in QR factorization for interpolation point selection, we introduce an enhanced machine learning K-means method. This method incorporates a novel empirical weight function called "SSM+" for more precise interpolation point selection, capturing physical information more accurately across diverse systems. Our machine learning approach offers a quasiquadratic scaling alternative, effectively replacing the computationally demanding cubic-scaling QRCP algorithm in plane-wave-based EXX+RPA calculations. Furthermore, we enhance the method's capabilities by optimizing GPU acceleration using MATLAB's integrated GPU toolkit. In particular, our approach reduces the computational scaling of χ0 from 3.80 to 2.13 and the overall computational scaling of EXX from 2.74 to 2.10. We achieve a remarkable GPU acceleration speedup of up to 35×. Regarding CPU computation time, the standard quartic-scaling method requires 22 h to compute Si128, while QRCP completes the calculation in only around 1 h, achieving a speedup up to 20×. However, the utilization of the K-means algorithm reduces the time to 800 s, a substantial improvement of 100× compared to the standard algorithm. By employing the K-means algorithm, the computational time for interpolative point calculation using QRCP decreases from 1 h to 1 min, resulting in a 55× speed increase. With this improved algorithm, we successfully computed the dissociation curve of H2 and the equilibrium polyynic geometry of C18 molecules.

Machine Learning K-Means Clustering in Interpolative Separable Density Fitting Algorithm: Advancing Accurate and Efficient Cubic-Scaling Density Functional Perturbation Theory Calculations within Plane Waves

Density functional perturbation theory (DFPT) is a crucial tool for accurately describing lattice dynamics. The adaptively compressed polarizability (ACP) method reduces the computational complexity of DFPT calculations from O(N4) to O(N3) by combining the interpolative separable density fitting (ISDF) algorithm. However, the conventional QR factorization with column pivoting (QRCP) algorithm, used for selecting the interpolation points in ISDF, not only incurs a high cubic-scaling computational cost, O(N3), but also leads to suboptimal convergence. This convergence issue is particularly pronounced when considering the complex interplay between the external potential and atomic displacement in ACP-based DFPT calculations. Here, we present a machine learning K-means clustering algorithm to select the interpolation points in ISDF, which offers a more efficient quadratic-scaling O(N2) alternative to the computationally intensive cubic-scaling O(N3) QRCP algorithm. We implement this efficient K-means-based ISDF algorithm to accelerate plane-wave DFPT calculations in KSSOLV, which is a MATLAB toolbox for performing Kohn-Sham density functional theory calculations within plane waves. We demonstrate that this K-means algorithm not only offers comparable accuracy to QRCP in ISDF but also achieves better convergence for ACP-based DFPT calculations. In particular, K-means can remarkably reduce the computational cost of selecting the interpolation points by nearly 2 orders of magnitude compared to QRCP in ISDF.

Complex-Valued K-Means Clustering of Interpolative Separable Density Fitting Algorithm for Large-Scale Hybrid Functional Enabled Ab Initio Molecular Dynamics Simulations within Plane Waves

K-means clustering, as a classic unsupervised machine learning algorithm, is the key step to select the interpolation sampling points in interpolative separable density fitting (ISDF) decomposition for hybrid functional electronic structure calculations. Real-valued K-means clustering for accelerating the ISDF decomposition has been demonstrated for large-scale hybrid functional enabled ab initio molecular dynamics (hybrid AIMD) simulations within plane-wave basis sets where the Kohn-Sham orbitals are real-valued. However, it is unclear whether such K-means clustering works for complex-valued Kohn-Sham orbitals. Here, we propose an improved weight function defined as the sum of the square modulus of complex-valued Kohn-Sham orbitals in K-means clustering for hybrid AIMD simulations. Numerical results demonstrate that the K-means algorithm with a new weight function yields smoother and more delocalized interpolation sampling points, resulting in smoother energy potential, smaller energy drift, and longer time steps for hybrid AIMD simulations compared to the previous weight function used in the real-valued K-means algorithm. In particular, we find that this improved algorithm can obtain more accurate oxygen-oxygen radial distribution functions in liquid water molecules and a more accurate power spectrum in crystal silicon dioxide compared to the previous K-means algorithm. Finally, we describe a massively parallel implementation of this ISDF decomposition to accelerate large-scale complex-valued hybrid AIMD simulations containing thousands of atoms (2,744 atoms), which can scale up to 5,504 CPU cores on modern supercomputers.

Cuproptosis facilitates immune activation but promotes immune escape, and a machine learning-based cuproptosis-related signature is identified for predicting prognosis and immunotherapy response of gliomas

AIMS

Cell death, except for cuproptosis, in gliomas has been extensively studied, providing novel targets for immunotherapy by reshaping the tumor immune microenvironment through multiple mechanisms. This study aimed to explore the effect of cuproptosis on the immune microenvironment and its predictive power in prognosis and immunotherapy response.

METHODS

Eight glioma cohorts were included in this study. We employed the unsupervised clustering algorithm to identify novel cuproptosis clusters and described their immune microenvironmental characteristics, mutation landscape, and altered signaling pathways. We verified the correlation among FDX1, SLC31A1, and macrophage infiltration in 56 glioma tissues. Next, based on multicenter cohorts and 10 machine learning algorithms, we constructed an artificial intelligence-driven cuproptosis-related signature named CuproScore.

RESULTS

Our findings suggested that glioma patients with high levels of cuproptosis had a worse prognosis owing to immunosuppression caused by unique immune escape mechanisms. Meanwhile, we experimentally validated the positive association between cuproptosis and macrophages and its tumor-promoting mechanism in vitro. Furthermore, our CuproScore exhibited powerful and robust prognostic predictive ability. It was also capable of predicting response to immunotherapy and chemotherapy drug sensitivity.

CONCLUSIONS

Cuproptosis facilitates immune activation but promotes immune escape. The CuproScore could predict prognosis and immunotherapy response in gliomas.

Benchmarking the accuracy of the separable resolution of the identity approach for correlated methods in the numeric atom-centered orbitals framework

Four-center two-electron Coulomb integrals routinely appear in electronic structure algorithms. The resolution-of-the-identity (RI) is a popular technique to reduce the computational cost for the numerical evaluation of these integrals in localized basis-sets codes. Recently, Duchemin and Blase proposed a separable RI scheme [J. Chem. Phys. 150, 174120 (2019)], which preserves the accuracy of the standard global RI method with the Coulomb metric and permits the formulation of cubic-scaling random phase approximation (RPA) and GW approaches. Here, we present the implementation of a separable RI scheme within an all-electron numeric atom-centered orbital framework. We present comprehensive benchmark results using the Thiel and the GW100 test set. Our benchmarks include atomization energies from Hartree-Fock, second-order Møller-Plesset (MP2), coupled-cluster singles and doubles, RPA, and renormalized second-order perturbation theory, as well as quasiparticle energies from GW. We found that the separable RI approach reproduces RI-free HF calculations within 9 meV and MP2 calculations within 1 meV. We have confirmed that the separable RI error is independent of the system size by including disordered carbon clusters up to 116 atoms in our benchmarks.

Low-Scaling Algorithm for the Random Phase Approximation Using Tensor Hypercontraction with k-point Sampling

We present a low-scaling algorithm for the random phase approximation (RPA) with k-point sampling in the framework of tensor hypercontraction (THC) for electron repulsion integrals (ERIs). The THC factorization is obtained via a revised interpolative separable density fitting (ISDF) procedure with a momentum-dependent auxiliary basis for generic single-particle Bloch orbitals. Our formulation does not require preoptimized interpolating points or auxiliary bases, and the accuracy is systematically controlled by the number of interpolating points. The resulting RPA algorithm scales linearly with the number of k-points and cubically with the system size without any assumption on sparsity or locality of orbitals. The errors of ERIs and RPA energy show rapid convergence with respect to the size of the THC auxiliary basis, suggesting a promising and robust direction to construct efficient algorithms of higher order many-body perturbation theories for large-scale systems.

Efficient implementation of analytical gradients for periodic hybrid functional calculations within fitted numerical atomic orbitals from NAO2GTO

The NAO2GTO scheme provides an efficient way to evaluate the electron repulsion integrals (ERIs) over numerical atomic orbitals (NAOs) with auxiliary Gaussian-type orbitals (GTOs). However, the NAO2GTO fitting will significantly impact the accuracy and convergence of hybrid functional calculations. To address this issue, here we propose to use the fitted orbitals as a new numerical basis to properly handle the mismatch between NAOs and fitted GTOs. We present an efficient and linear-scaling implementation of analytical gradients of Hartree-Fock exchange (HFX) energy for periodic HSE06 calculations with fitted NAOs in the HONPAS package. In our implementation, the ERIs and their derivatives for HFX matrix and forces are evaluated analytically with the auxiliary GTOs, while other terms are calculated using numerically discretized GTOs. Several integral screening techniques are employed to reduce the number of required ERI derivatives. We benchmark the accuracy and efficiency of our implementation and demonstrate that our results of lattice constants, bulk moduli, and band gaps of several typical semiconductors are in good agreement with the experimental values. We also show that the calculation of HFX forces based on a master-worker dynamic parallel scheme has a very high efficiency and scales linearly with respect to system size. Finally, we study the geometry optimization and polaron formation due to an excess electron in rutile TiO2 by means of HSE06 calculations to further validate the applicability of our implementation.

Pyroptosis patterns influence the clinical outcome and immune microenvironment characterization in HPV-positive head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous tumor with diverse molecular pathological profiles. Recent studies have suggested the vital role of pyroptosis in tumor microenvironment. However, the expression patterns of pyroptosis in HPV-positive HNSCC are still unclear.

METHODS

Unsupervised clustering analysis was used to identify the pyroptosis patterns based on the RNA-sequencing data of 27 pyroptosis-related genes (PRGs) in HPV-positive HNSCC samples. Random forest classifier and artificial neural network were performed to screen the signature genes associated with pyroptosis, which were verified in two independent external cohorts and qRT-PCR experiment. Principal component analysis was used to develop a scoring system, namely Pyroscore.

RESULTS

The expression variations of 27 PRGs in HPV-positive HNSCC patients were analyzed from genomic and transcriptional domains. Two pyroptosis-related subtypes with distinct clinical outcomes, enrichment pathways and immune characteristics were identified. Next, six signature genes (GZMB, LAG3, NKG7, PRF1, GZMA and GZMH) associated with pyroptosis were selected for prognostic prediction. Further, a Pyroscore system was constructed to determine the level of pyroptosis in each patient. A low Pyroscore was featured by better survival time, increased immune cell infiltration, higher expression of immune checkpoint molecules and T cell-inflamed genes, as well as elevated mutational burden. The Pyroscore was also related to the sensitivity of chemotherapeutic agents.

CONCLUSIONS

The pyroptosis-related signature genes and Pyroscore system may be reliable predictors of prognosis and serve as mediators of immune microenvironment in patients with HPV-positive HNSCC.

MLKL and other necroptosis-related genes promote the tumor immune cell infiltration, guiding for the administration of immunotherapy in bladder urothelial carcinoma

The involvement of necroptosis in the immunosuppressive tumor microenvironment has been established and has been shown to contribute to the growth of pancreatic ductal adenocarcinoma, indicating its role in promoting tumor development. However, the relationship between necroptosis and bladder urothelial carcinoma (BUC) has yet to be fully understood. To shed light on this issue, our study aimed to uncover the impact of necroptosis on immune cell infiltration and immunotherapy response in BUC patients. We conducted an analysis of 67 necroptosis genes to assess their expression and genomic changes across pan-cancer and identified 12 necroptosis genes that are prognostically relevant and associated with immune subtypes and tumor stemness in BUC. Using a public database of 1841 BUC samples, we then performed Unsupervised Cluster Analysis and discovered two distinct necroptotic phenotypes in BUC. These phenotypes showed significant differences in molecular subtypes, immune infiltration patterns, and gene mutation profiles. We confirmed this discovery in BUC through qPCR and WB experiments. To evaluate the impact of necroptosis on prognosis, chemotherapy sensitivity, and immunotherapy response (such as anti-PD-L1), we developed a principal component analysis model called NecroScore. Finally, we validated the effects of RIPK3 and MLKL through a nude mouse transplantation model for BUC. Our study has uncovered that necroptosis plays a role in shaping the tumor immune microenvironment in BUC. The high necroptosis phenotype (Cluster B) was characterized by a higher abundance of tumor immunosuppressive cells and more key biological processes driving tumor progression, while the low necroptosis group (Cluster A) had higher FGFR3 mutations. We found that the infiltration levels of immune cells, including CD8+ T cells, were significantly different between FGFR3 mutated and wild-type (WT) samples. Our results confirmed the reliability of NecroScore as a comprehensive assessment tool for evaluating the immunotherapeutic effect and prognosis of BUC patients, with high NecroScore values favoring basal-like differentiation and lower FGFR3 alterations. We also observed that high expression of MLKL had a significant inhibitory effect on tumor growth and increased neutrophil infiltration in vivo. In our study, we uncovered the regulation pattern of necroptosis in the tumor immune microenvironment of BUC. Additionally, we developed a scoring tool called NecroScore that can be utilized to predict the most suitable chemotherapy and immunotherapy strategy for bladder urothelial carcinoma patients. This tool can effectively guide the chemotherapy and immunotherapy regimens for patients with advanced BUC.

Interpolative Separable Density Fitting for Accelerating Two-Electron Integrals: A Theoretical Perspective

Low-rank approximations have long been considered an efficient way to accelerate electronic structure calculations associated with the evaluation of electron repulsion integrals (ERIs). As an accurate and efficient algorithm for compressing the ERI tensor, the interpolative separable density fitting (ISDF) decomposition has recently attracted great attention in this context. In this perspective, we introduce the ISDF decomposition from the theoretical aspects and technique details. The ISDF decomposition can construct a fully separable low-rank approximation (tensor hypercontraction factorization) of ERIs in real space with a cubic cost, offering great flexibility for accelerating high-scaling electronic structure calculations. We review the typical applications of ISDF in hybrid functionals, time-dependent density functional theory, and GW approximation. Finally, we discuss the promising directions for future development of ISDF.

Anatomical mining method of cervical nerve root syndrome under visual sensing technology

INTRODUCTION:  The gray resolution of anatomical image of cervical nerve root syndrome is low, that can not be mined accurately. OBJECTIVES: Aiming at the defect of low gray resolution of anatomical images, an image mining method using visual perception technology was studied. METHODS: According to the visual perception technology, the internal parameter matrix and external parameter matrix of binocular visual camera were determined by coordinate transformation, and the anatomical images of cervical nerve root syndrome were collected. The collected images are smoothed and enhanced by nonlinear smoothing algorithm and multi-scale nonlinear contrast enhancement method. The directional binary simple descriptor method is selected to extract the features of the enhanced image; Using K-means clustering algorithm, the anatomical image mining of cervical nerve root syndrome is completed by obtaining the initial clustering center and image mining. RESULTS: Experimental results show that the information entropy of the images mined by the proposed method is higher than 5, the average gradient is greater than 7, the edge information retention is greater than 0.7, the peak signal-to-noise ratio is higher than 30 dB, and the similarity of the same category of images is greater than 0.9. CONCLUSIONS: This method can effectively mine the anatomical images of cervical nerve root syndrome and provide an important basis for the diagnosis and treatment of cervical nerve root syndrome.

Comprehensive analysis of the prognosis and immune infiltration landscape of RNA methylation-related subtypes in pancreatic cancer

Background

RNA methylation refers to a form of methyl modification in RNA that modulates various epigenetic alterations. Mounting studies have focused on its potential mechanisms in cancer initiation and progression. However, the prognostic value and potential role of RNA methylation in the immune microenvironment of pancreatic cancer remain unclear.

Methods

Comprehensive bioinformatics analysis was performed to illuminate the expression profiles of RNA methylation modulators. In addition, the ConsensusClusterPlus algorithm was utilized to identify two remarkably different subtypes, and a feasible risk stratification method was established to accurately estimate prognosis. In addition, we validated our signature at the cytology and histology levels and conducted functional experiments to explore the biological functions of our key genes.

Results

Two subtypes with remarkable survival differences were identified by the consensus clustering algorithm. Cluster 2 tended to have higher expression levels of RNA methylation regulators and to be the high RNA methylation group. In addition, cluster 1 exhibited a significantly higher abundance of almost all immune cells and increased immune checkpoint expression compared to cluster 2. Chemotherapeutic sensitivity analysis indicated that there were significant differences in the sensitivity of four of the six drugs between different subgroups. Mutation investigation revealed a higher mutation burden and a higher number of mutations in cluster 2. An accurate and feasible risk stratification method was established based on the expression of key genes of each subtype. Patients with low risk scores exhibited longer survival times in one training (TCGA) and two validation cohorts (ICGC, GSE57495), with p values of 0.001, 0.0081, and 0.0042, respectively. In addition, our signature was further validated in a cohort from Fudan University Shanghai Cancer Center. The low-risk group exhibited higher immune cell abundance and immune checkpoint levels than the high-risk group. The characteristics of the low-risk group were consistent with those of cluster 1: higher stromal score, estimate score, and immune score and lower tumor purity. Additionally, cell function investigations suggested that knockdown of CDKN3 remarkably inhibited the proliferation and migration of pancreatic cancer cells.

Conclusions

RNA methylation has a close correlation with prognosis, immune infiltration and therapy in pancreatic cancer. Our subtypes and risk stratification method can accurately predict prognosis and the efficacy of immune therapy and chemotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09863-z.

Comparative Study of Physical Education Teaching in Middle Schools at Home and Abroad Using Clustering Algorithm

Physical education in middle school is very important for teenagers, so it is also crucial to understand the differences between PET (Physical Education Teaching) systems in middle schools at home and abroad. The frontier and hotspot of PET research in middle schools at home and abroad are examined in this paper using citation analysis, information visualization, and cluster analysis, as well as CiteSpace software. The findings show that PET method research in China is qualitative, whereas PET method research in middle schools around the world is quantitative evaluation and empirical research. Domestic research hotspots focus on classroom instructional design, whereas foreign countries focus on load identification theory's application in instructional design. Frontier research in the United States is dispersed and covers a wide range of topics, whereas research in other countries focuses on cognitive load theory. The classification time of this improved algorithm is reduced by 190.97 seconds when compared to the traditional KNN algorithm, and the total time is increased by more than 50%. According to the findings, nonsports or nonsports influencing factors should be given more consideration in the study of adolescent physical fitness decline in China.

Necroptosis is Related to Anti-PD-1 Treatment Response and Influences the Tumor Microenvironment in Head and Neck Squamous Cell Carcinoma

The latest research suggesting that necroptosis plays a vital role in immune response. However, the influence of necroptosis on tumor microenvironment (TME) remodeling and immunotherapy is still unclear. We analyzed the variations in the expression of 26 necroptosis-related molecules in HNSCC and the influence of genome changes. We investigated HNSCC samples and determined that there are two necroptosis phenotypes in HNSCC cancer, and there are significant differences in cell infiltration characteristics and survival in different necroptosis phenotypes. We used the single‐sample gene set enrichment analysis to measure the level of necroptosis in patients with NecroticScore, we confirmed that the NecroticScore can predict the prognosis of HNSCC patients and the response to immunotherapy. Patients with a high NecroticScore are more sensitive to immunotherapy and have a better prognosis. Our study suggests a significant correlation between the expression imbalance of necroptosis-related molecules and suggests necroptosis plays an important role in modeling the TME. In addition, we construct a risk prediction model which could stratify patients with HNSCC and predict patient prognosis according to this necroptosis-related molecules. In conclusion, evaluating necroptosis modification patterns contributes to enhancing our understanding of TME and can guide more effective immunotherapy strategies.

Turning up the heat on non-immunoreactive tumors: pyroptosis influences the tumor immune microenvironment in bladder cancer

The latest research confirms that cytotoxic lymphocytes rely on pyroptosis to kill tumor cells, suggesting that pyroptosis plays a vital role in immune response. However, the influence of pyroptosis on tumor microenvironment (TME) remodeling and immunotherapy is still unclear. We analyzed the variations in the expression of 28 pyroptosis-related molecules in pan-cancer tissues and normal tissues and the influence of genome changes. We investigated 2,214 bladder cancer samples and determined that there are three pyroptosis phenotypes in bladder cancer, and there are significant differences in cell infiltration characteristics in different pyroptosis phenotypes. Phenotypes with high expression of pyroptosis-related molecules are "hot tumors" with better immune function. We used a principal component analysis to measure the level of pyroptosis in patients with PyroScore, and confirmed that the PyroScore can predict the prognosis of bladder cancer patients, the sensitivity of the immune phenotype to chemotherapy, and the response to immunotherapy. Patients with a high PyroScore are more sensitive to chemotherapeutics such as cisplatin and gemcitabine, and have a better prognosis (HR = 0.7; 95%CI = 0.51-0.97, P = 0.041). Our study suggests a significant correlation between the expression imbalance of pyroptosis-related molecules and genome variation in various cancers and suggests pyroptosis plays an important role in modeling the TME. Evaluating pyroptosis modification patterns contributes to enhancing our understanding of TME infiltration and can guide more effective immunotherapy strategies.

ARDS Patients Exhibiting a "Hyperinflammatory Anasarca" Phenotype Could Benefit From a Conservative Fluid Management Strategy

Object: The fluid management strategy in ARDS is not very clear. A secondary analysis of RCT data was conducted to identify patients with ARDS benefitting from a conservative strategy of fluid management.

Methods: The data of this study were downloaded from the ARDS network series of randomized controlled trials (Conservative Strategy vs. Liberal Strategy in 2006). Based on the clinical feature of patients, within the first 24 h after admission, clustering was performed using the k-means clustering algorithm to identify the phenotypes of ARDS. Survival was analyzed using the Kaplan-Meier survival analysis to assess the effect of the two fluid management strategies on the 90-day cumulative mortality. Categorical/dichotomic variables were analyzed by the chi-square test. Continuous variables were expressed as the mean and standard deviation and evaluated through a one-way ANOVA. A P-value < 0.05 was defined as the statistically significant cut-off value.

Results: A total of 1,000 ARDS patients were enrolled in this unsupervised clustering research study, of which 503 patients were treated with a conservative fluid-management strategy, and 497 patients were treated with a liberal fluid-management strategy. The first 7-day cumulative fluid balance in patients with the conservative strategy and liberal strategy were −136 ± 491 ml and 6,992 ± 502 ml, respectively (P < 0.001). Four phenotypes were found, and the conservative fluid-management strategy significantly improved the 90-day cumulative mortality compared with the liberal fluid-management strategy (HR = 0.532, P = 0.024) in patients classified as “hyperinflammatory anasarca” phenotype (phenotype II). The characteristics of this phenotype exhibited a higher WBC count (20487.51 ± 7223.86/mm³) with a higher incidence of anasarca (8.3%) and incidence of shock (26.6%) at baseline. The furthermore analysis found that the conservative fluid management strategy was superior to the liberal fluid management strategy in avoiding superinfection (10.10 vs. 14.40%, P = 0.037) and returned to assisted breathing (4.60 vs. 16.20%, P = 0.030) in patients classified as “hyperinflammatory anasarca” phenotype. In addition, patients with other phenotypes given the different fluid management strategies did not show significant differences in clinical outcomes.

Conclusion: Patients exhibiting a “hyperinflammatory anasarca” phenotype could benefit from a conservative fluid management strategy.

Realizing Effective Cubic-Scaling Coulomb Hole Plus Screened Exchange Approximation in Periodic Systems via Interpolative Separable Density Fitting with a Plane-Wave Basis Set

The GW approximation is an effective way to accurately describe the single-electron excitations of molecules and the quasiparticle energies of solids. However, a perceived drawback of the GW calculations is their high computational cost and large memory usage, which limit their applications to large systems. Herein, we demonstrate an accurate and effective low-rank approximation to accelerate non-self-consistent GW (G₀W₀) calculations under the static Coulomb hole plus screened exchange (COHSEX) approximation for periodic systems. Our approach is to adopt the interpolative separable density fitting (ISDF) decomposition and Cauchy's integral to construct low-rank representations of the dielectric matrix ϵ and self-energy matrix Σ. This approach reduces the number of floating point operations from O(Ne4) to O(Ne3) and requires a much smaller memory footprint. Two methods are used to select the interpolation points in ISDF, including the standard QR factorization with column pivoting (QRCP) procedure and the machine learning K-means clustering (K-means) algorithm. We demonstrate that these two methods can yield similar accuracy for both molecules and solids at much lower computational cost. In particular, K-means clustering can significantly reduce the computational cost of selecting the interpolation points by an order of magnitude compared to QRCP, resulting in an overall speedup factor of about ten times ISDF accelerated the static COHSEX calculations compared with conventional COHSEX approximation.