Optimization of resources in intelligent electronic health systems based on internet of things to predict heart diseases via artificial neural network.

As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention efforts. These systems acquire an in-depth understanding of patient health states through the real-time collection and analysis of medical data enabled by the Internet of Things (IoT) and machine learning. With the widespread use of cutting-edge artificial intelligence and machine learning techniques, predictive analytics in medicine can assist in making the shift from a reactive to a proactive healthcare strategy. With the ability to rapidly and precisely evaluate massive amounts of data, draw intelligent conclusions, and solve difficult issues, artificial neural networks could revolutionize several industries. Two cardiac illnesses were assessed in this study using a multilayer perceptron artificial neural network that incorporated a genetic algorithm and an error-back propagation mechanism. The ability of artificial neural networks to handle consecutive time series data is crucial for optimizing resources in smart electronic health systems, especially with the increasing volume of patient information and the broad use of electronic clinical records. This requires the creation of more accurate predictive models. Through the use of Internet of Things (IoT) sensors, the proposed system gathers data, which is then used to do predictive analytics on patient history-related electronic clinical data saved in the cloud. A smart healthcare system that uses Mu-LTM (multidirectional long-term memory) to accurately monitor and predict the risk of heart disease has a coverage error of 97.94 %, an accuracy of 97.89 %, a sensitivity of 97.96 %, and a specificity of 97.99 %. In comparison to other smart heart disease prediction systems, the F1-score of 97.95 % and precision of 97.71 % is very good.

Downregulation of miR-1388 Regulates the Expression of Antiviral Genes via Tumor Necrosis Factor Receptor (TNFR)-Associated Factor 3 Targeting Following poly(I:C) Stimulation in Silver Carp (Hypophthalmichthys molitrix).

MicroRNAs (miRNAs) are highly conserved endogenous single-stranded non-coding RNA molecules that play a crucial role in regulating gene expression to maintain normal physiological functions in fish. Nevertheless, the specific physiological role of miRNAs in lower vertebrates, particularly in comparison to mammals, remains elusive. Additionally, the mechanisms underlying the control of antiviral responses triggered by viral stimulation in fish are still not fully understood. In this study, we investigated the regulatory impact of miR-1388 on the signaling pathway mediated by IFN regulatory factor 3 (IRF3). Our findings revealed that following stimulation with the viral analog poly(I:C), the expression of miR-1388 was significantly upregulated in primary immune tissues and macrophages. Through a dual luciferase reporter assay, we corroborated a direct targeting relationship between miR-1388 and tumor necrosis factor receptor (TNFR)-associated factor 3 (TRAF3). Furthermore, our study demonstrated a distinct negative post-transcriptional correlation between miR-1388 and TRAF3. We observed a significant negative post-transcriptional regulatory association between miR-1388 and the levels of antiviral genes following poly(I:C) stimulation. Utilizing reporter plasmids, we elucidated the role of miR-1388 in the antiviral signaling pathway activated by TRAF3. By intervening with siRNA-TRAF3, we validated that miR-1388 regulates the expression of antiviral genes and the production of type I interferons (IFN-Is) through its interaction with TRAF3. Collectively, our experiments highlight the regulatory influence of miR-1388 on the IRF3-mediated signaling pathway by targeting TRAF3 post poly(I:C) stimulation. These findings provide compelling evidence for enhancing our understanding of the mechanisms through which fish miRNAs participate in immune responses.

[Deep transfer learning radiomics model based on temporal bone CT for assisting in the diagnosis of inner ear malformations].

Objective:To evaluate the diagnostic efficacy of traditional radiomics, deep learning, and deep learning radiomics in differentiating normal and inner ear malformations on temporal bone computed tomography（CT）. Methods:A total of 572 temporal bone CT data were retrospectively collected, including 201 cases of inner ear malformation and 371 cases of normal inner ear, and randomly divided into a training cohort（n=458） and a test cohort（n=114） in a ratio of 4∶1. Deep transfer learning features and radiomics features were extracted from the CT images and feature fusion was performed to establish the least absolute shrinkage and selection operator. The CT results interpretated by two chief otologists from the National Clinical Research Center for Otorhinolaryngological Diseases served as the gold standard for diagnosis. The model performance was evaluated using receiver operating characteristic（ROC）, and the accuracy, sensitivity, specificity, and other indicators of the models were calculated. The predictive power of each model was compared using the Delong test. Results:1 179 radiomics features were obtained from traditional radiomics, 2 048 deep learning features were obtained from deep learning, and 137 features fusion were obtained after feature screening and fusion of the two. The area under the curve（AUC） of the deep learning radiomics model on the test cohort was 0.964 0（95%CI 0.931 4-0.996 8）, with an accuracy of 0.922, sensitivity of 0.881, and specificity of 0.945. The AUC of the radiomics features alone on the test cohort was 0.929 0（95%CI 0.882 2-0.974 9）, with an accuracy of 0.878, sensitivity of 0.881, and specificity of 0.877. The AUC of the deep learning features alone on the test cohort was 0.947 0（95%CI 0.898 2-0.994 8）, with an accuracy of 0.913, sensitivity of 0.810, and specificity of 0.973. The results indicated that the prediction accuracy and AUC of the deep learning radiomics model are the highest. The Delong test showed that the differences between any two models did not reach statistical significance. Conclusion:The feature fusion model can be used for the differential diagnosis of normal and inner ear malformations, and its diagnostic performance is superior to radiomics or deep learning models alone.

Poly (I:C)-Induced microRNA-30b-5p Negatively Regulates the JAK/STAT Signaling Pathway to Mediate the Antiviral Immune Response in Silver Carp (Hypophthalmichthys molitrix) via Targeting CRFB5.

In aquaculture, viral diseases pose a significant threat and can lead to substantial economic losses. The primary defense against viral invasion is the innate immune system, with interferons (IFNs) playing a crucial role in mediating the immune response. With advancements in molecular biology, the role of non-coding RNA (ncRNA), particularly microRNAs (miRNAs), in gene expression has gained increasing attention. While the function of miRNAs in regulating the host immune response has been extensively studied, research on their immunomodulatory effects in teleost fish, including silver carp (Hyphthalmichthys molitrix), is limited. Therefore, this research aimed to investigate the immunomodulatory role of microRNA-30b-5p (miR-30b-5p) in the antiviral immune response of silver carp (Hypophthalmichthys molitrix) by targeting cytokine receptor family B5 (CRFB5) via the JAK/STAT signaling pathway. In this study, silver carp were stimulated with polyinosinic-polycytidylic acid (poly (I:C)), resulting in the identification of an up-regulated miRNA (miR-30b-5p). Through a dual luciferase assay, it was demonstrated that CRFB5, a receptor shared by fish type I interferon, is a novel target of miR-30b-5p. Furthermore, it was found that miR-30b-5p can suppress post-transcriptional CRFB5 expression. Importantly, this study revealed for the first time that miR-30b-5p negatively regulates the JAK/STAT signaling pathway, thereby mediating the antiviral immune response in silver carp by targeting CRFB5 and maintaining immune system stability. These findings not only contribute to the understanding of how miRNAs act as negative feedback regulators in teleost fish antiviral immunity but also suggest their potential therapeutic measures to prevent an excessive immune response.

DDHD2 promotes lipid droplet catabolism by acting as a TAG lipase and a cargo receptor for lipophagy.

Mutations in the DDHD2 (DDHD domain containing 2) gene cause autosomal recessive spastic paraplegia type 54 (SPG54), a rare neurodegenerative disorder characterized by the early childhood onset of progressive spastic paraplegia. DDHD2 is reported as the principal brain triacylglycerol (TAG) lipase whose dysfunction causes massive lipid droplet (LD) accumulation in the brains of SPG54 patients. However, the precise functions of DDHD2 in regulating LD catabolism are not yet fully understood. In a recent study, we demonstrate that DDHD2 interacts with multiple members of the Atg8-family proteins (MAP1LC3/LC3s, GABARAPs), which play crucial roles in lipophagy. DDHD2 possesses two LC3-interacting region (LIR) motifs that contribute to its LD-eliminating activity. Moreover, DDHD2 enhances the colocalization between LC3B and LDs to promote lipophagy. LD·ATTEC, a compound that tethers LC3 to LDs to enhance their macroautophagic/autophagic clearance, effectively counteracts DDHD2 deficiency-induced LD accumulation. These findings provide insights into the dual functions of DDHD2 as a TAG lipase and cargo receptor for lipophagy in neuronal LD catabolism, and also suggest a potential therapeutic approach for treating SPG54 patients.

Oxygen Vacancy Mediation in SnO2 Electron Transport Layers Enables Efficient, Stable, and Scalable Perovskite Solar Cells.

Previous findings have suggested a close association between oxygen vacancies in SnO2 and charge carrier recombination as well as perovskite decomposition at the perovskite/SnO2 interface. Underlying the fundamental mechanism holds great significance in achieving a more favorable balance between the efficiency and stability. In this study, we prepared three SnO2 samples with different oxygen vacancy concentrations and observed that a low oxygen vacancy concentration is conducive to long-term device stability. Iodide ions were observed to easily diffuse into regions with high oxygen vacancies, thereby speeding up the deprotonation of FAI, as made evident by the detection of the decomposition product formamide. In contrast, a high oxygen vacancy concentration in SnO2 could prevent hole injection, leading to a decrease in interfacial recombination losses. To suppress this decomposition reaction and address the trade-off, we designed a bilayer SnO2 structure to ensure highly efficient carrier transport still while maintaining a chemically inert surface. As a result, an enhanced efficiency of 25.06% (certified at 24.55% with an active area of 0.09 cm2 under fast scan) was achieved, and the extended operational stability maintained 90% of their original efficiency (24.52%) after continuous operation for nearly 2000 h. Additionally, perovskite submodules with an active area of 14 cm2 were successfully assembled with a PCE of up to 22.96% (20.09% with an aperture area).

H/D Exchange of Aromatic Sulfones via Base Promotion and Silver Catalysis.

Aromatic sulfones are the prevailing scaffolds in pharmaceutical and material sciences. However, compared to their widespread application, the selective deuterium labeling of these structures is restricted due to their electron-deficient properties. This study presents two comprehensive strategies for the deuteration of aromatic sulfones. The base-promoted deuteration uses DMSO-d6 as the deuterium source, resulting in a rapid H/D exchange within 2 h. Meanwhile, a silver-catalyzed protocol offers a much milder option by using economical D2O to furnish the labeled sulfones.

Genome mining of sulfonated lanthipeptides reveals unique cyclic peptide sulfotransferases.

Although sulfonation plays crucial roles in various biological processes and is frequently utilized in medicinal chemistry to improve water solubility and chemical diversity of drug leads, it is rare and underexplored in ribosomally synthesized and post-translationally modified peptides (RiPPs). Biosynthesis of RiPPs typically entails modification of hydrophilic residues, which substantially increases their chemical stability and bioactivity, albeit at the expense of reducing water solubility. To explore sulfonated RiPPs that may have improved solubility, we conducted co-occurrence analysis of RiPP class-defining enzymes and sulfotransferase (ST), and discovered two distinctive biosynthetic gene clusters (BGCs) encoding both lanthipeptide synthetase (LanM) and ST. Upon expressing these BGCs, we characterized the structures of novel sulfonated lanthipeptides and determined the catalytic details of LanM and ST. We demonstrate that SslST-catalyzed sulfonation is leader-independent but relies on the presence of A ring formed by LanM. Both LanM and ST are promiscuous towards residues in the A ring, but ST displays strict regioselectivity toward Tyr5. The recognition of cyclic peptide by ST was further discussed. Bioactivity evaluation underscores the significance of the ST-catalyzed sulfonation. This study sets up the starting point to engineering the novel lanthipeptide STs as biocatalysts for hydrophobic lanthipeptides improvement.

mTORC1/S6K1 signaling promotes sustained oncogenic translation through modulating CRL3IBTK-mediated ubiquitination of eIF4A1 in cancer cells.

Enhanced protein synthesis is a crucial molecular mechanism that allows cancer cells to survive, proliferate, metastasize, and develop resistance to anti-cancer treatments, and often arises as a consequence of increased signaling flux channeled to mRNA-bearing eukaryotic initiation factor 4F (eIF4F). However, the post-translational regulation of eIF4A1, an ATP-dependent RNA helicase and subunit of the eIF4F complex, is still poorly understood. Here, we demonstrate that IBTK, a substrate-binding adaptor of the Cullin 3-RING ubiquitin ligase (CRL3) complex, interacts with eIF4A1. The non-degradative ubiquitination of eIF4A1 catalyzed by the CRL3IBTK complex promotes cap-dependent translational initiation, nascent protein synthesis, oncogene expression, and cervical tumor cell growth both in vivo and in vitro. Moreover, we show that mTORC1 and S6K1, two key regulators of protein synthesis, directly phosphorylate IBTK to augment eIF4A1 ubiquitination and sustained oncogenic translation. This link between the CRL3IBTK complex and the mTORC1/S6K1 signaling pathway, which is frequently dysregulated in cancer, represents a promising target for anti-cancer therapies.

Extracellular polymeric substances altered ferrihydrite (trans)formation and induced arsenic mobilization.

The behavior of As is closely related to trans(formation) of ferrihydrite, which often coprecipitates with extracellular polymeric substances (EPS), forming EPS-mineral aggregates in natural environments. While the effect of EPS on ferrihydrite properity, mineralogy reductive transformation, and associated As fate in sulfate-reducing bacteria (SRB)-rich environments remains unclear. In this research, ferrihydrite-EPS aggregates were synthesized and batch experiments combined with spectroscopic, microscopic, and geochemical analyses were conducted to address these knowledge gaps. Results indicated that EPS blocked micropores in ferrihydrite, and altered mineral surface area and susceptibility. Although EPS enhanced Fe(III) reduction, it retarded ferrihydrite transformation to magnetite by inhibiting Fe atom exchange in systems with low SO42-. As a result, 16% of the ferrihydrite was converted into magnetite in the Fh-0.3 treatment, and no ferrihydrite transformation occurred in the Fh-EPS-0.3 treatment. In systems with high SO42-, however, EPS promoted mackinawite formation and increased As mobilization into the solution. Additionally, the coprecipitated EPS facilitated As(V) reduction to more mobilized As(III) and decreased conversion of As into the residual phase, enhancing the potential risk of As contamination. These findings advance our understanding on biogeochemistry of elements Fe, S, and As and are helpful for accurate prediction of As behavior.

Comprehensive analysis of autophagy associated genes and immune infiltrates in cervical cancer.

Objectives: Cervical cancer (CC) is the most common gynecological malignant tumor and the fourth leading cause of cancer-related death in women. The progression of CC is significantly affected by autophagy. Our objective was to use bioinformatics analysis to explore the expression, prognostic significance, and immune infiltration of autophagy-related genes in CC. Materials and Methods: We identified a set of autophagy-related differentially expressed genes (ARDEGs) from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. ARDEGs were further validated by The Human Protein Atlas (HPA), GSE52903, and GSE39001 dataset. Hub genes were found by the STRING network and Cytoscape. We performed Gene Set Enrichment Analysis (GSEA), Gene ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and immune infiltration analysis to further understand the functions of the hub genes. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) were used to check the hub genes. Results: A total of 10 up-regulated (CXCR4, BAX, SPHK1, EIF2AK2, TBK1, TNFSF10, ITGB4, CDKN2A, IL24, and BIRC5) and 19 down-regulated (PINK1, ATG16L2, ATG4D, IKBKE, MLST8, MAPK3, ERBB2, ULK3, TP53INP2, MTMR14, BNIP3, FOS, CCL2, FAS, CAPNS1, HSPB8, PTK6, FKBP1B , and DNAJB1) ARDEGs were identified. The ARDEGs were enriched in cell growth, apoptosis, human papillomavirus infection, and cytokine-mediated. Then, we found that low expression of MAPK3 was associated with poor prognosis in CC patients and was significantly enriched in immune pathways. In addition, the expression of MAPK3 was significantly positively correlated with the infiltration levels of macrophages, B cells, mast cell activation, and cancer-associated fibroblasts. Furthermore, MAPK3 was positively correlated with LGALS9, and negatively correlated with CTLA4 and CD40. Conclusion: Our results show that MAPK3 can be used as a new prognostic biomarker to predict the prognosis of patients with CC.

Sulfonamide modified chitosan oligosaccharide with high nematicidal activity against Meloidogyne incognita.

Chitosan oligosaccharide (COS) modification is a feasible way to develop novel green nematicides. This study involved the synthesis of various COS sulfonamide derivatives via hydroxylated protection and deprotection, which were then characterized using NMR, FTIR, MS, elemental analysis, XRD, and TG/DTG. In vitro experiments found that COS-alkyl sulfonamide derivatives (S6 and S11-S13) exhibited high mortality (>98 % at 1 mg/mL) against Meloidogyne incognita second-instar larvaes (J2s) among the derivatives. S6 can cause vacuole-like structures in the middle and tail regions of the nematode body and effectively inhibit egg hatching. In vivo tests have found that S6 has well control effects and low plant toxicity. Additionally, the structure-activity studies revealed that S6 with a high degree of substitution, a low molecular weight, and a sulfonyl bond on the amino group of the COS backbone exhibited increased nematicidal activity. The sulfonamide group is a potential active group for developing COS-based nematicides.

Therapeutic mechanisms of modified Jiawei Juanbi decoction in early knee osteoarthritis: A multimodal analysis.

Modified Jiawei Juanbi decoction (MJD) is used for the treatment of early-stage knee osteoarthritis (KOA). Here, modified Jiawei Juanbi decoction (MJD) was employed for the treatment of early-stage knee osteoarthritis (KOA) and its mechanisms were assessed via metabonomics and network pharmacology. A total of 24 male Sprague-Dawley rats were randomly allocated into a normal control group, a model group, and an MJD group (n = 8 rats per group). Each rat group was further equally divided into two subgroups for investigation for either 14 or 28 days. A rat model of early-stage KOA was constructed and rats were treated with MJD. Effects were evaluated based on changes in knee circumference, mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL). We also analyzed histopathological changes in articular cartilage. High-resolution mass spectrometry was used to analyze the chemical profile of MJD, identifying 228 components. Using an LC-Q-TOF-MS metabonomics approach, 33 differential metabolites were identified. The relevant pathways significantly associated with MJD include arginine and proline metabolism, vitamin B6 metabolism, as well as the biosynthesis of phenylalanine, tyrosine and tryptophan. The system pharmacology paradigm revealed that MJD contains 1027 components and associates with 1637 genes, of which 862 disease genes are related to osteoarthritis. The construction of the MJD composition-target-KOA network revealed a total of 140 intersection genes. A total of 39 hub genes were identified via integration of betweenness centrality values greater than 100 using CytoHubba. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed several significantly affected signaling pathways including the HIF-1, AGE-RAGE (in diabetic complications), IL-17, rheumatoid arthritis and TNF pathways. Integrated-omics and network pharmacology approaches revealed a necessity for further detailed investigation focusing on two major targets, namely NOS2 and NOS3, along with their essential metabolite (arginine) and associated pathways (HIF-1 signaling and arginine and proline metabolism). Real-time PCR validated significantly greater downregulation of NOS2 and HIF-1ɑ in the MJD as compared to the model group. Molecular docking analysis further confirmed the binding of active MJD with key active components. Our findings elucidate the impact of MJD on relevant pathophysiological and metabolic networks relevant to KOA and assess the drug efficacy of MJD and its underlying mechanisms of action.

Raman Monitoring of the Electro-Optical Synergy-Induced Enhancements in Carbon-Bromine Bond Cleavage, Reaction Rate, and Product Selectivity of p-Bromothiophenol.

Electro-optical synergy has recently been targeted to improve the separation of hot carriers and thereby further improve the efficiency of plasmon-mediated chemical reactions (PMCRs). However, the electro-optical synergy in PMCRs needs to be more deeply understood, and its contribution to bond dissociation and product selectivity needs to be clarified. Herein, the electro-optical synergy in plasmon-mediated reduction of p-bromothiophenol (PBTP) was studied on a plasmonic nanostructured silver electrode using in situ Raman spectroscopy and theoretical calculations. It was found that the electro-optical synergy-induced enhancements in the cleavage of carbon-bromine bonds, reaction rate, and product selectivity (4,4'-biphenyl dithiol vs thiophenol) were largely affected by the applied bias, laser wavelength, and laser power. The theoretical simulation further clarified that the strong electro-optical synergy is attributed to the matching of energy band diagrams of the plasmonic silver with those of the adsorbed PBTP molecules. A deep understanding of the electro-optical synergy in PBTP reduction and the clarification of the mechanism will be highly beneficial for the development of other highly efficient PMCRs.

Exploring the Efficacy of Hydroxybenzoic Acid Derivatives in Mitigating Jellyfish Toxin-Induced Skin Damage: Insights into Protective and Reparative Mechanisms.

The escalation of jellyfish stings has drawn attention to severe skin reactions, underscoring the necessity for novel treatments. This investigation assesses the potential of hydroxybenzoic acid derivatives, specifically protocatechuic acid (PCA) and gentisic acid (DHB), for alleviating Nemopilema nomurai Nematocyst Venom (NnNV)-induced injuries. By employing an in vivo mouse model, the study delves into the therapeutic efficacy of these compounds. Through a combination of ELISA and Western blot analyses, histological examinations, and molecular assays, the study scrutinizes the inflammatory response, assesses skin damage and repair mechanisms, and investigates the compounds' ability to counteract venom effects. Our findings indicate that PCA and DHB significantly mitigate inflammation by modulating critical cytokines and pathways, altering collagen ratios through topical application, and enhancing VEGF and bFGF levels. Furthermore, both compounds demonstrate potential in neutralizing NnNV toxicity by inhibiting metalloproteinases and phospholipase-A2, showcasing the viability of small-molecule compounds in managing toxin-induced injuries.

The correlation between drivers' road familiarity and glance behavior using real vehicle experimental data and mathematical models.

OBJECTIVE: Road familiarity is an important factor affecting drivers' visual features. Analyzing the quantitative correlation between drivers' road familiarity and visual features in complex environment is of great help to improve driving safety. However, there are few relevant studies. This paper takes urban plane intersection as the environmental object to explore the correlation between drivers' glance behavior and road familiarity, and conducts research on the quantitative evaluation model of road familiarity based on this correlation. METHOD: First, a real vehicle experiment was carried out to record the eye movement data of 24 drivers with different road familiarity. The driver's visual field plane was divided into 10 areas of interest (AOIs) based on the driver's perspective. Three measures, including average glance duration, number of glances, and fixation transition probabilities between AOIs at urban plane intersections, were extracted. Finally, based on the experimental results, the driver road familiarity evaluation model was constructed using the factor analysis method. RESULTS: There are significant differences between unfamiliar and familiar drivers regarding the average glance duration toward the forward (FW) area, the left window (LW) area, the left rearview mirror (LVM) area and the left forward (LF) area, the number of glances toward the other (OT) area, and the fixation transition probabilities of LW→RF (right forward), LF→LF, LF→FW, FW→LW, FW→FW, FW→RVM (right rearview mirror). The comprehensive evaluation results show that the accuracy rate of the driver road familiarity evaluation model reached 83%. CONCLUSIONS: This paper revealed that there is a strong correlation between drivers' road familiarity and drivers' glance behavior. Based on this correlation, we can include road familiarity as a part of drivers' working status and establish a high accuracy evaluation model of driver road familiarity. The conclusion of this paper can provide some reference for the humanized design and improvement of advanced driving assistance system, which is of great significance for reducing the driving workload of drivers and improving the driving safety.

Gas concentration prediction by LSTM network combined with wavelet thresholding denoising and phase space reconstruction.

The Long Short-Term Memory neural network is a specialized architecture designed for handling time series data, extensively applied in the field of predicting gas concentrations. In the harsh conditions prevalent in coal mines, the time series data of gas concentrations collected by sensors are susceptible to noise interference. Directly inputting such noisy data into a neural network for training would significantly reduce predictive accuracy and lead to deviations from the actual values. The Empirical Mode Decomposition method, commonly employed in gas concentration prediction, faces challenges in practical engineering applications due to the substantial influence of newly acquired data on the initial decomposition subsequence values. Consequently, it is difficult to use this method as intended. Conversely, the Wavelet Threshold Denoising method does not encounter this issue. Furthermore, gas concentration sequences exhibit chaotic characteristics. Performing phase space reconstruction allows for the extraction of additional valuable hidden information. In light of these factors, a prediction model is proposed, integrating WTD, Phase Space Reconstruction, and LSTM neural networks. Initially, the gas concentration sequence itself is subjected to wavelet threshold denoising. Subsequently, phase space reconstruction is performed, and the resulting reconstructed phase space matrix serves as the input for the LSTM neural network. The outcomes from the final LSTM neural network reveal that the PS method indeed extracts more valuable information. The Mean Absolute Error and Root Mean Square Error are reduced by 35.1% and 25%, respectively. Additionally, when compared to the PS-LSTM model without utilizing the WTD method, the WTD-PS-LSTM predictive model showcases reductions of 77.1% and 80% in MAE and RMSE, respectively. Compared with the LSTM model, the MAE and RMSE of the WTD-PS-LSTM prediction model were reduced by 81.4% and 82.6%, respectively. This greatly improves the credibility of whether or not a response related to coal mine safety management is implemented.