High-level production of chitinase by multi-strategy combination optimization in Bacillus licheniformis.

In view of the extensive potential applications of chitinase (ChiA) in various fields such as agriculture, environmental protection, medicine, and biotechnology, the development of a high-yielding strain capable of producing chitinase with enhanced activity holds significant importance. The objective of this study was to utilize the extracellular chitinase from Bacillus thuringiensis as the target, and Bacillus licheniformis as the expression host to achieve heterologous expression of ChiA with enhanced activity. Initially, through structural analysis and molecular dynamics simulation, we identified key amino acids to improve the enzymatic performance of chitinase, and the specific activity of chitinase mutant D116N/E118N was 48% higher than that of the natural enzyme, with concomitant enhancements in thermostability and pH stability. Subsequently, the expression elements of ChiA(D116N/E118N) were screened and modified in Bacillus licheniformis, resulting in extracellular ChiA activity reached 89.31 U/mL. Further efforts involved the successful knockout of extracellular protease genes aprE, bprA and epr, along with the gene clusters involved in the synthesis of by-products such as bacitracin and lichenin from Bacillus licheniformis. This led to the development of a recombinant strain, DW2△abelA, which exhibited a remarkable improvement in chitinase activity, reaching 145.56 U/mL. To further improve chitinase activity, a chitinase expression frame was integrated into the genome of DW2△abelA, resulting in a significant increas to 180.26 U/mL. Optimization of fermentation conditions and medium components further boosted shake flask enzyme activity shake flask enzyme activity, achieving 200.28 U/mL, while scale-up fermentation experiments yielded an impressive enzyme activity of 338.79 U/mL. Through host genetic modification, expression optimization and fermentation optimization, a high-yielding ChiA strain was successfully constructed, which will provide a solid foundation for the extracellular production of ChiA.

Metagenomic next-generation sequencing for diagnosing severe leptospirosis in a patient suspected COVID-19: A case report.

Leptospirosis is a zoonotic and neglected waterborne disease caused by the pathogenic helical spirochetes. Early diagnosis of leptospirosis remains challenging due to non-specific symptoms and the limited availability of rapid point-of-care diagnostic tests. Herein, we present a case where a patient suspected of having COVID-19 was diagnosed with leptospirosis using metagenomic next-generation sequencing (mNGS). This case highlights the potential of mNGS to diagnose leptospirosis in the context of the COVID-19 pandemic.

Effects of storm runoff on the spatial-temporal variation and stratified water quality in Biliuhe Reservoir, a drinking water reservoir.

Stormflow runoff is an important non-point source of pollution in drinking water reservoirs. Storm runoff is usually very turbid and contains a high concentration of organic matter, therefore affecting water quality when it enters reservoirs. In order to investigate the impact of storm runoff on spatial-temporal variation and stratification of water quality during this rainstorm event, the inflow process of the storm runoff was studied through a combination of field investigation and simulation using the Delft3D-Flow model. Water samples were collected from Biliuhe Reservoir at four different periods: before storm runoff, storm runoff flood peak period, 1 week after storm runoff, and 5 weeks after storm runoff. The results showed that the input of storm runoff resulted in a significant increase in the nitrogen (N) and phosphorus (P) in the reservoir water, especially in the reservoir entrance. The concentrations of total nitrogen (TN) and total phosphorus (TP) gradually decreased after the flood peak period; however, the average concentrations of TN and TP in the entire reservoir remained higher than those before the storm runoff levels for an extended duration. The storm runoff will greatly contribute to the contamination of water quality in a reservoir, and the water quality cannot be quickly restored by self-purification in the short term. During the flood peak period, under the influence of density current, the electrical conductivity (EC) and turbidity increased significantly in the water depth of 10-15 m, so that the reservoir water had obvious stratification between 10 and 15 m. The form of pollutants in storm runoff was mostly in particle phosphorus. Total particulate phosphorus (TPP) concentration was 0.015 ± 0.011 mg/L, accounting for 44.12% of total phosphorus (TP) concentration in storm runoff flood peak period. The process of a rainstorm caused runoff, which carried high levels of turbidity, particulate phosphorus, and organic matter. The storm runoff disrupts the stratification of the reservoir water. In terms of vertical distribution, the turbidity in the reservoir area increased to 73.75 NTU. Therefore, the occurrence of significant turbidity density flow in the reservoir is frequently accompanied by intense rainfall events. Gaining insights into the impact of storm runoff on the vertical distribution of reservoir turbidity can help managers in selecting an appropriate inlet height to mitigate high turbidity outflow.

A machine vision method for the evaluation of ship-to-ship collision risk.

The development of ship technology and information technology has been driving the continuous improvement of ship intelligence, with safety being an inevitable requirement in the shipping industry. A machine vision-based ship collision warning method is proposed for high monitoring system cost and limited information acquisition in safety design of autonomous ship navigation. The method combines machine learning with image algorithms. Firstly, the backbone of YOLOv7 detector is replaced by EfficientFormerV2 network to achieve model lightweight while ensuring detection accuracy. Public datasets SeaShips, Flow and self-made ship pictures are combined, and the network is trained on this dataset. StrongSORT is used for target tracking. Secondly, a data fusion algorithm is introduced to determine the target point at the bow-bottom of the ship based on the time-varying attitude of the camera and the time-series features of the bounding boxes. Ship navigation trajectory estimation is performed using image algorithms. Finally, a collision evaluation model is established to calculate the collision risk index. Experimental results demonstrate that the improved YOLOv7 network maintains similar mAP.5 and Recall compared to the original model, while reducing the parameters by 31.2 % and GFLOPs by 58.4 %. The accuracy of target ship trajectory estimation is high, with MAE values below 1.5 % and RMSE values below 2 % in experiments. In ship collision warning experiments, the proposed method accurately identifies navigating vessels, estimates the trajectories, and provides timely warnings for imminent collision accidents. Compared to traditional ship collision warning methods, this paper offers a more intelligent and lightweight solution.

A benchmark GaoFen-7 dataset for building extraction from satellite images.

Accurate building extraction is crucial for urban understanding, but it often requires a substantial number of building samples. While some building datasets are available for model training, there remains a lack of high-quality building datasets covering urban and rural areas in China. To fill this gap, this study creates a high-resolution GaoFen-7 (GF-7) Building dataset utilizing the Chinese GF-7 imagery from six Chinese cities. The dataset comprises 5,175 pairs of 512 × 512 image tiles, covering 573.17 km2. It contains 170,015 buildings, with 84.8% of the buildings in urban areas and 15.2% in rural areas. The usability of the GF-7 Building dataset has been proved with seven convolutional neural networks, all achieving an overall accuracy (OA) exceeding 93%. Experiments have shown that the GF-7 building dataset can be used for building extraction in urban and rural scenarios. The proposed dataset boasts high quality and high diversity. It supplements existing building datasets and will contribute to promoting new algorithms for building extraction, as well as facilitating intelligent building interpretation in China.

When Parents Are at Fault: Development and Validation of the Parental Guilt and Shame Proneness Scale.

Research has linked individuals' dispositional guilt and shame to their interpersonal processes. Although caregivers' guilt and shame proneness in the parenting context likely have important implications for their mental health and parenting, there is a lack of validated measures for such dispositions. In three studies with Chinese parents, we developed and validated the Parental Guilt and Shame Proneness scale (PGASP), which was based on the Guilt and Shame Proneness scale (GASP). The PGASP comprises two guilt subscales-negative behavior-evaluations (guilt-NBE) and repair action tendencies (guilt-repair)-and two shame subscales-negative self-evaluations (shame-NSE) and withdrawal action tendencies (shame-withdraw). Study 1 (N = 604) provided support for the four-factor structure of the PGASP, which was replicated in Study 2 (N = 451). The concurrent validity of the PGASP was examined in Study 2 and Study 3 (N = 455). The two guilt subscales were associated with better mental health and more positive parenting, whereas parents' shame-withdraw exhibited the opposite pattern; weak or no relations were found for shame-NSE. Findings highlight the need to differentiate between parents' shame-NSE and shame-withdraw. PGASP may be a useful tool for identifying parents at risk of engaging in negative parenting.

BMP-ACVR1 Axis is Critical for Efficacy of PRC2 Inhibitors in B-Cell Lymphoma.

EZH2 is the catalytic subunit of the histone methyltransferase Polycomb Repressive Complex 2 (PRC2), and its somatic activating mutations drive lymphoma, particularly the germinal center B-cell type. Although PRC2 inhibitors, such as tazemetostat, have demonstrated anti-lymphoma activity in patients, the clinical efficacy is not limited to EZH2-mutant lymphoma. In this study, Activin A Receptor Type 1 (ACVR1), a type I Bone Morphogenetic Protein (BMP) receptor, is identified as critical for the anti-lymphoma efficacy of PRC2 inhibitors through a whole-genome CRISPR screen. BMP6, BMP7, and ACVR1 are repressed by PRC2-mediated H3K27me3, and PRC2 inhibition upregulates their expression and signaling in cell and patient-derived xenograft models. Through BMP-ACVR1 signaling, PRC2 inhibitors robustly induced cell cycle arrest and B cell lineage differentiation in vivo. Remarkably, blocking ACVR1 signaling using an inhibitor or genetic depletion significantly compromised the in vitro and in vivo efficacy of PRC2 inhibitors. Furthermore, high levels of BMP6 and BMP7, along with ACVR1, are associated with longer survival in lymphoma patients, underscoring the clinical relevance of this study. Altogether, BMP-ACVR1 exhibits anti-lymphoma function and represents a critical PRC2-repressed pathway contributing to the efficacy of PRC2 inhibitors.

Glycine recalibrates iron homeostasis of lens epithelial cells by blocking lysosome-dependent ferritin degradation.

One of the major pathological processes in cataracts has been identified as ferroptosis. However, studies on the iron metabolism mechanism in lens epithelial cells (LECs) and the methods of effectively alleviating ferroptosis in LECs are scarce. Along these lines, we found that in the ultraviolet radiation b (UVB) induced cataract model in vitro and in vivo, the ferritin of LECs is over-degraded by lysosomes, resulting in the occurrence of iron homeostasis disorder. Glycine can affect the ferritin degradation through the proton-coupled amino acid transporter (PAT1) on the lysosome membrane, further upregulating the content of nuclear factor erythrocyte 2 related factor 2 (Nrf2) to reduce the damage of LECs from two aspects of regulating iron homeostasis and alleviating oxidative stress. By co-staining, we further demonstrate that there is a more sensitive poly-(rC)-binding protein 2 (PCBP2) transportation of iron ions in LECs after UVB irradiation. Additionally, this study illustrated the increased expression of nuclear receptor coactivator 4 (NCOA4) in NRF2-KO mice, indicating that Nrf2 may affect ferritin degradation by decreasing the expression of NCOA4. Collectively, glycine can effectively regulate cellular iron homeostasis by synergistically affecting the lysosome-dependent ferritin degradation and PCBP2-mediated ferrous ion transportation, ultimately delaying the development of cataracts.

Diagnostic performances of Nonhyperemic Pressure Ratios and Coronary Angiography-Based Fractional Flow Reserve against conventional Wire-Based Fractional Flow Reserve.

BACKGROUND: Nonhyperemic pressure ratios (NHPRs) have been proposed as alternatives to fractional flow reserve (FFR) without induction of hyperemia. More recently, imaging based-FFR estimation, especially coronary angiography-derived FFR (Angio-FFR) measurement, is proposed to estimate wire-based FFR. However, little is known about the diagnostic performance of these indices against conventional FFR. AIMS: We aimed to assess and compare the diagnostic performance of both NHPRs and coronary Angio-FFR against wire-based conventional FFR. METHODS: PubMed and Embase databases were systematically searched for peer-reviewed original articles up to 08/2022. The primary outcomes were the pooled sensitivity and specificity as well as the area under the curve (AUC) of the summary receiver-operating characteristic curve of those indices. RESULTS: A total of 6693 records were identified after a literature search, including 37 reports for NHPRs and 34 for Angio-FFR. Overall, NHPRs have a lower diagnostic performance in estimating wire-based FFR with an AUC of 0.85 (0.81, 0.88) when compared with Angio-FFR of 0.95 (0.93, 0.97). When all four modalities of NHPRs (iFR, Pd/Pa, DPR, RFR) were compared, those had overlapping AUCs without major differences among each other. Similarly, when the two most commonly used Angio-FFR (QFR, FFR angio ) were compared, those had overlapping AUCs without major differences among each other. CONCLUSION: Angio-FFR may offer a better estimation of wire-based FFR than NHPRs. Our results support a wider use of Angio-FFR in the cardiac catheterization laboratory to streamline our workflow for coronary physiologic assessment. CLASSIFICATIONS: FFR,, stable ischemic disease and non-ST elevation acute coronary syndrome.

Mechanism of Intestinal Epithelial Absorption and Electrophysiological Regulation of the Shrimp Peptide QMDDQ.

We investigated the absorption mechanism of the shrimp peptide QMDDQ in small intestines, explored its physiological function in inhibiting neuronal hyperactivity, and verified its entry into the brain in vivo to display functional activity. The everted rat sac model and a Caco-2 paracellular absorption monolayer model were used, indicating that QMDDQ has a good absorption capacity with an apparent permeability coefficient (Papp) > 1 × 10-6 cm/s and the absorption of QMDDQ was concentration-dependent. When the concentration of QMDDQ was 1 mM and the transport time was 180 min, the highest absorption concentration of QMDDQ was 41.17 ± 3.48 µM (P < 0.05). The myosin light-chain kinase (MLCK)-specific inhibitor ML-7 and activator MPA, Western blotting, and immunofluorescence results showed that QMDDQ absorption takes place by mediating the MLCK-p-MLCK-MLC signaling pathway, reversibly opening the zonula occludens-1 (ZO-1), occludin in tight junctions (TJs), upregulating claudin-2 expression, and reaching targets through blood to inhibit neuronal overactivity. Results of fluorescence imaging in vivo verified that QMDDQ could enter the brain 4 h after oral administration. The results provide a theoretical foundation for the mechanism of paracellular absorption of active peptides and a starting point for the development of functional foods for Alzheimer's disease intervention.

Effect of Natural Variation and Rootstock on Fruit Quality and Volatile Organic Compounds of 'Kiyomi tangor' (Citrus reticulata Blanco) Citrus.

In this study, we compared the fruit quality and color of 'Kiyomi' (WT) and its mutant (MT) grafted on Ziyang xiangcheng (Cj) (WT/Cj, MT/Cj), and the MT grafted on Trifoliate orange (Pt) (MT/Pt). The differences in sugar, organic acid, flavonoids, phenols, and volatile substances of the three materials were also analyzed by high performance liquid chromatography (HPLC) and headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). The results showed significant differences in the appearance of WT/Cj, MT/Cj, and MT/Pt. MT/Pt, compared to WT/Cj, MT/Cj, had lower sugar, acid, phenol and flavonoid contents in the pulp. However, MT/Pt pulp was higher in vitamin C (VC), and the peel had significantly higher total phenol and flavonoid contents. In terms of pulp, WT/Cj had the greatest diversity of volatile organic compounds (VOCs). 4-methyl-1-pentanol was significantly higher in MT/Cj pulp, while MT/Pt pulp had a unique octanoic acid, methyl ester. VOCs were more diverse in the peels of the three materials. ß-Myrcene and valencen were significantly higher in MT/Cj peels. In contrast, 16 unique VOCs were detected in MT/Pt, and D-limonene content was significantly higher than in WT/Cj and MT/Cj. The results suggest Trifoliate orange is a suitable rootstock for MT.

Exploring the role of SWI/SNF complex subunit BAF60c in lipid metabolism and inflammation in fish.

Chromatin remodeling plays an important role in regulating gene transcription, in which chromatin remodeling complex is a crucial aspect. Brg1/Brm-associated factor 60c (BAF60c) subunit forms a bridge between chromatin remodeling complexes and transcription factors in mammals; hence, it has received extensive attention. However, the roles of BAF60c in fish remain largely unexplored. In this study, we identified BAF60c-interacting proteins by using HIS-pull-down and LC-MS/MS analysis in fish. Subsequently, the RNA-seq analysis was performed to identify the overall effects of BAF60c. Then, the function of BAF60c was verified through BAF60c knockdown and overexpression experiments. We demonstrated for the first time that BAF60c interacts with glucose-regulated protein 78 (GRP78) and regulates lipid metabolism, endoplasmic reticulum (ER) stress, and inflammation. Knockdown of BAF60c reduces fatty acid biosynthesis, ER stress, and inflammation. In conclusion, the results enriched BAF60c-interacting protein network and explored the function of BAF60c in lipid metabolism and inflammation in fish.

Glial Cells of the Central Nervous System: A Potential Target in Chronic Prostatitis/Chronic Pelvic Pain Syndrome.

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is one of the most common diseases of the male urological system while the etiology and treatment of CP/CPPS remain a thorny issue. Cumulative research suggested a potentially important role of glial cells in CP/CPPS. This narrative review retrospected literature and grasped the research process about glial cells and CP/CPPS. Three types of glial cells showed a crucial connection with general pain and psychosocial symptoms. Microglia might also be involved in lower urinary tract symptoms. Only microglia and astrocytes have been studied in the animal model of CP/CPPS. Activated microglia and reactive astrocytes were found to be involved in both pain and psychosocial symptoms of CP/CPPS. The possible mechanism might be to mediate the production of some inflammatory mediators and their interaction with neurons. Glial cells provide a new insight to understand the cause of complex symptoms of CP/CPPS and might become a novel target to develop new treatment options. However, the activation and action mechanism of glial cells in CP/CPPS needs to be further explored.

Silicon photonic broadband polarization-insensitive switch based on polarization-mode diversity conversion.

We present a 2 × 2 polarization-insensitive switch on a 220-nm silicon-on-insulator platform, employing a balanced Mach-Zehnder interferometer (MZI) structure. This design incorporates polarization-insensitive adiabatic couplers, polarization rotators based on mode hybridization and evolution, and thermo-optic mode-insensitive phase shifters with wide waveguides. The switch exhibits broadband polarization-insensitive characteristics, with extinction ratios larger than 15 dB, insertion losses less than 2.3 dB, and polarization-dependent losses less than 1 dB for wavelengths ranging from 1500 nm to 1600 nm. The power consumption required for simultaneously switching the fundamental transverse electric (TE0) and transverse magnetic (TM0) polarized modes is 29.1 mW. These results highlight the potential of the switch as a building block for on-chip polarization-division-multiplexed optical interconnects.

A deep learning approach based on multi-omics data integration to construct a risk stratification prediction model for skin cutaneous melanoma.

PURPOSE: Skin cutaneous melanoma (SKCM) is a highly aggressive melanocytic carcinoma whose high heterogeneity and complex etiology make its prognosis difficult to predict. This study aimed to construct a risk subtype typing model for SKCM. METHODS: The study proposes a deep learning framework combining early fusion feature autoencoder (AE) and late fusion feature AE for risk subtype prediction of SKCM. The deep learning framework integrates mRNA, miRNA, and DNA methylation data of SKCM patients from The Cancer Genome Atlas (TCGA), and clusters the screened multi-omics features associated with survival prognosis to identify risk subtypes. Differential expression analysis and functional enrichment analysis were performed between risk subtypes, while SVM classifiers were constructed between differentially expressed genes (DEGs) obtained by Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression screening and risk subtype labels inferred from multi-omics data, and the predictive robustness of risk subtypes inferred from the risk subtype classification prediction model was validated using two independent datasets. RESULTS: The deep learning framework that combined early fusion feature AE with late fusion feature AE distinguished the two best risk subtypes compared to the multi-omics integration approach with single strategy AE or PCA. A promising C-index (C-index = 0.748) and a significant difference in survival (log-rank P value = 4.61 × 10-9) were found between the identified risk subtypes. The DEGs with the top significance values together with differentially expressed miRNAs provided the biological interpretation of risk subtypes on SKCM. Finally, the framework was applied to predict risk subtypes in two independent test datasets of SKCM patients, all of which showed good predictive power (C-index > 0.680) and significant survival differences (log-rank P value < 0.01). CONCLUSION: The SKCM risk subtypes identified by integrating multi-omics data based on deep learning can not only improve the understanding of the molecular mechanisms of SKCM, but also provide clinicians with assistance in treatment decisions.

A Case of Type V Hyperlipoproteinemia Resistant to Insulin Treatment.

Type V hyperlipoproteinemia or multifactorial chylomicronemia syndrome is a rare lipid disorder triggered mainly by uncontrolled diabetes, obesity, poor diet, or particular medications. It is associated with an increased risk of acute pancreatitis and accelerated coronary artery disease which may manifest in younger age groups. We present a case of a 42-year-old male who presented to the emergency department (ED) complaining of a non-healing hand injury. Upon laboratory workup, the patient was found to have an elevated total cholesterol (TC) of 1129 mg/dL, very low levels of high-density lipoprotein (HDL) and triglycerides (TG) > 4000 mg/dL with an inability to calculate low-density lipoprotein (LDL). Lipoprotein electrophoresis revealed an actual TG level of > 7000 mg/dL, increased chylomicrons, normal B and pre-B-lipoproteins, and increased L-lipoproteins with an elevated Apolipoprotein B. Despite these derangements, the patient did not exhibit any abdominal complaints, demonstrating a normal lipase level. The physical exam was indicative of bilateral arcus senilis and obesity. Insulin drip was initiated along with intravenous (IV) hydration and it required 12 days to bring triglycerides down to less than 1000 mg/dL. The total cholesterol was also seen to be down trending to around 500 mg/dL and the HDL improved to 22 mg/dL. We present this case as a unique presentation of asymptomatic chylomicronemia resistant to insulin treatment with an elevated ApoB but with no evidence of pancreatitis or coronary artery disease.

Structural and functional characterization of a mycobacterial methylenetetrahydrofolate reductase utilizing NADH as the exclusive cofactor.

5,10-Methylenetetraydrofolate reductase (MTHFR) is a key enzyme in folate metabolism. MSMEG_6649, a non-canonical MTHFR from Mycobacterium smegmatis, was previously reported as a monomeric protein lacking the flavin coenzyme. However, the structural basis for its unique flavin-independent catalytic mechanism remains poorly understood. Here, we determined the crystal structures of apo MTHFR MSMEG_6649 and its complex with NADH from M. smegmatis. Structural analysis revealed that the groove formed by the loops 4 and 5 of non-canonical MSMEG_6649 interacting with FAD was significantly larger than that of canonical MTHFR. Meanwhile, the NADH-binding site in MSMEG_6649 is highly similar to the FAD binding site in canonical MTHFR, suggesting that NADH plays the same role (immediate hydride donor for methylenetetraydrofolate) as FAD in the catalytic reaction. Using biochemical analysis, molecular modeling, and site-directed mutagenesis, the critical residues participating in the binding of NADH and the substrate 5,10-methylenetetrahydrofolate as well as the product 5-methyltetrahydrofolate were identified and validated. Taken together, this work not only provides a good starting point for understanding the potential catalytic mechanism for MSMEG_6649, but also identifies an exploitable target for the development of anti-mycobacterial drugs.

Risk Stratification for Herpes Simplex Virus Pneumonia Using Elastic Net Penalized Cox Proportional Hazard Algorithm with Enhanced Explainability.

Herpes simplex virus (HSV) pneumonia is a serious and often fatal respiratory tract infection that occurs in immunocompromised individuals. The early detection of accurate risk stratification is essential in identifying patients who are at high risk of mortality and may benefit from more aggressive treatment. In this study, we developed and validated a risk stratification model for HSV bronchopneumonia using an elastic net penalized Cox proportional hazard algorithm. We analyzed data from a cohort of 104 critically ill patients with HSV bronchopneumonia identified in Chang Gung Memorial Hospital, Linkou, Taiwan: one of the largest tertiary medical centers in the world. A total of 109 predictors, both clinical and laboratory, were identified in this process to develop a risk stratification model that could accurately predict mortality in patients with HSV bronchopneumonia. This model was able to differentiate the risk of death and predict mortality in patients with HSV bronchopneumonia compared to the APACHE II score in the early stage of ICU admissions. Both hazard ratio coefficient and selection frequency were used as the metrics to enhance the explainability of the informative predictors. Our findings suggest that the elastic net penalized Cox proportional hazard algorithm is a promising tool for risk stratification in patients with HSV bronchopneumonia and could be useful in identifying those at high risk of mortality.

Improved multivariate multiscale sample entropy and its application in multi-channel data.

Entropy, as a nonlinear feature in information science, has drawn much attention for time series analysis. Entropy features have been used to measure the complexity behavior of time series. However, traditional entropy methods mainly focus on one-dimensional time series originating from single-channel transducers and are incapable of handling the multidimensional time series from multi-channel transducers. Previously, the multivariate multiscale sample entropy (MMSE) algorithm was introduced for multi-channel data analysis. Although MMSE generalizes multiscale sample entropy and provides a new method for multidimensional data analysis, it lacks necessary theoretical support and has shortcomings, such as missing cross-channel correlation information and having biased estimation results. This paper proposes an improved multivariate multiscale sample entropy (IMMSE) algorithm to overcome these shortcomings. This paper highlights the existing shortcomings in MMSE under the generalized algorithm. The rationality of IMMSE is theoretically proven using probability theory. Simulations and real-world data analysis have shown that IMMSE is capable of effectively extracting cross-channel correlation information and demonstrating robustness in practical applications. Moreover, it provides theoretical support for generalizing single-channel entropy methods to multi-channel situations.