Linguistic intuitionistic fuzzy multi-attribute bilateral matching considering satisfaction and fairness degree.

Aiming at the multi-attribute bilateral matching problem with unknown attribute weights under a linguistic intuitionistic fuzzy environment, a decision method based on TODIM considering satisfaction and fairness degrees is proposed. First, the theories of linguistic intuitionistic fuzzy sets and bilateral matching are given, and the multi-attribute bilateral matching problem under a linguistic intuitionistic fuzzy environment is described. To solve this problem, according to linguistic intuitionistic fuzzy preference matrices, the overall attribute dominances are calculated based on TODIM; considering group consensus, a new method is proposed to calculate attribute weights based on linguistic intuitionistic fuzzy induced ordered weighted averaging (LIFIOWA) operator; then, the overall dominances of bilateral subjects are obtained by aggregating the overall attribute dominances and attribute weights. Furthermore, the overall dominances are standardized to calculate the satisfaction degrees of bilateral subjects; the fairness degrees of bilateral subjects are calculated considering the loss attenuation coefficient. Based on satisfaction degree matrices, fairness degree matrices and bilateral matching matrices, multiple bilateral matching models are established and then solved to obtain the optimal bilateral matching scheme. Finally, an example shows the effectiveness, reliability and accuracy of the proposed method. The research results indicate the following main characteristics of the proposed method: (1) A new method for calculating the unknown attribute weights using LIFIOWA operator is proposed. (2) According to the TODIM idea, a calculation method for fairness degree considering the loss attenuation coefficient is proposed. (3) Considering satisfaction and fairness degrees, multiple bilateral matching models under a linguistic intuitionistic fuzzy environment are established.

AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding.

Protein function annotation has been one of the longstanding issues in biological sciences, and various computational methods have been developed. However, the existing methods suffer from a serious long-tail problem, with a large number of GO families containing few annotated proteins. Herein, an innovative strategy named AnnoPRO was therefore constructed by enabling sequence-based multi-scale protein representation, dual-path protein encoding using pre-training, and function annotation by long short-term memory-based decoding. A variety of case studies based on different benchmarks were conducted, which confirmed the superior performance of AnnoPRO among available methods. Source code and models have been made freely available at: https://github.com/idrblab/AnnoPRO and https://zenodo.org/records/10012272.

[Predictive value of lipoproteins on progression to chronic critical illness in intensive care unit patients].

OBJECTIVE: To explore the predictive value of lipoproteins on the progression of critically ill patients to chronic critical illness (CCI). METHODS: A retrospective cohort study was conducted to analyze clinical data of patients admitted to the intensive care unit (ICU) of Nanjing Drum Tower Hospital from January 1, 2020, to December 31, 2022. The levels of high-density lipoprotein (HDL), low-density lipoprotein (LDL) and apolipoproteins (ApoA-I, ApoB) at 1, 3, 7, 14 and 21 days after admission to ICU were collected. The progression to CCI was recorded. CCI was defined as the length of ICU stay ≥14 days with sustained organ dysfunction [sequential organ failure assessment (SOFA) score ≥2]. Differences in lipoprotein levels between the patients with and without CCI were compared. Multivariate Logistic regression was used to analyze risk factors for critically ill patients progressing to CCI. Receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of lipoproteins on critically ill patients progressing to CCI. RESULTS: A total of 200 patients were enrolled in the final analysis. 137 patients (68.5%) progressed to CCI, and 63 patients (31.5%) did not. The lipoprotein indicators in the CCI group showed a decrease after the acute phase, while the lipoprotein indicators in the non-CCI group showed an increase. The levels of HDL, LDL, ApoA-I, and ApoB at various time points in the CCI group were significantly lower than those in the non-CCI group. HDL at 7 days in the CCI group was significantly lower than that in the non-CCI group [mmol/L: 0.44 (0.31, 0.61) vs. 0.67 (0.49, 0.75), P < 0.01]. Multivariate Logistic regression analysis showed that 7-day HDL was an independent risk factor for critically ill patients progressing to CCI [odds ratio (OR) = 0.033, 95% confidence interval (95%CI) was 0.004-0.282, P = 0.002]. ROC curve analysis showed that the area under the ROC curve (AUC) of 7-day HDL for predicting critically ill patients progressing to CCI was 0.702, with a 95%CI of 0.625-0.779, P < 0.001. When the optimal cut-off value was 0.59 mmol/L, the sensitivity was 69.8%, and the specificity was 72.4%. CONCLUSIONS: The low level of lipoproteins is closely related to the progression of critically ill patients, and 7-day HDL has a certain predictive value for critically ill patients progressing to CCI. Continuously observation of the change trend of lipoprotein level is helpful to judge the progression of CCI in critically ill patients.

Impact of pulmonary infection after radical esophagectomy on serum inflammatory markers, pulmonary function indices, and prognosis.

OBJECTIVES: To analyze the influence of pulmonary infection after radical esophagectomy on serum inflammatory markers, pulmonary function, and prognosis. METHODS: We enrolled 278 esophageal cancer patients who underwent radical esophagectomy. Patients were split into the infected (n=51) and uninfected groups (n=227). The inflammatory parameters, complications, and prognosis were compared. RESULTS: In the infected group, interleukin (IL)-6 was 16.19±2.63 ng/L, tumor necrosis factor-α was 19.64±3.07 µg/L, and IL-1ß was 22.49±5.13 ng/L at 7 days postoperatively; white blood cell counts was 12.65±2.14 ×109/L, percentage of neutrophils (NEU%) was 67.04±10.48%, and platelet (PLT) counts was 249.82±63.26 ×109/L; the increasing ranges of the above factors after the operation were much raised compared with the uninfected group (p<0.05). Compared with the uninfected group, forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and FEV1/FVC were greater declines in ranges (p<0.05), and the arrhythmia incidence and the mortality within 60 days postoperatively were greater in the infected group (p<0.05). CONCLUSION: Postoperative pulmonary infection can lead to pulmonary function damage, proinflammatory factor overexpression, and an increased risk of early death.

Tissue Segmentation of Thick-Slice Fetal Brain MR Scans With Guidance From High-Quality Isotropic Volumes.

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development. However, this task is challenging due to the use of thick-slice scans in clinically-acquired fetal brain data. To address this issue, we propose to leverage high-quality isotropic fetal brain MR volumes (and also their corresponding annotations) as guidance for segmentation of thick-slice scans. Due to existence of significant domain gap between high-quality isotropic volume (i.e., source data) and thick-slice scans (i.e., target data), we employ a domain adaptation technique to achieve the associated knowledge transfer (from high-quality "source" volumes to thick-slice "target" scans). Specifically, we first register the available high-quality isotropic fetal brain MR volumes across different gestational weeks to construct longitudinally-complete source data. To capture domain-invariant information, we then perform Fourier decomposition to extract image content and style codes. Finally, we propose a novel Cycle-Consistent Domain Adaptation Network (C 2DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C 2DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans. Extensive experiments on a large-scale dataset of 372 clinically acquired thick-slice MR scans demonstrate that our C 2DA-Net achieves much better performance than cutting-edge methods quantitatively and qualitatively.

SingPro: a knowledge base providing single-cell proteomic data.

Single-cell proteomics (SCP) has emerged as a powerful tool for detecting cellular heterogeneity, offering unprecedented insights into biological mechanisms that are masked in bulk cell populations. With the rapid advancements in AI-based time trajectory analysis and cell subpopulation identification, there exists a pressing need for a database that not only provides SCP raw data but also explicitly describes experimental details and protein expression profiles. However, no such database has been available yet. In this study, a database, entitled 'SingPro', specializing in single-cell proteomics was thus developed. It was unique in (a) systematically providing the SCP raw data for both mass spectrometry-based and flow cytometry-based studies and (b) explicitly describing experimental detail for SCP study and expression profile of any studied protein. Anticipating a robust interest from the research community, this database is poised to become an invaluable repository for OMICs-based biomedical studies. Access to SingPro is unrestricted and does not mandate a login at: http://idrblab.org/singpro/.

CellSTAR: a comprehensive resource for single-cell transcriptomic annotation.

Large-scale studies of single-cell sequencing and biological experiments have successfully revealed expression patterns that distinguish different cell types in tissues, emphasizing the importance of studying cellular heterogeneity and accurately annotating cell types. Analysis of gene expression profiles in these experiments provides two essential types of data for cell type annotation: annotated references and canonical markers. In this study, the first comprehensive database of single-cell transcriptomic annotation resource (CellSTAR) was thus developed. It is unique in (a) offering the comprehensive expertly annotated reference data for annotating hundreds of cell types for the first time and (b) enabling the collective consideration of reference data and marker genes by incorporating tens of thousands of markers. Given its unique features, CellSTAR is expected to attract broad research interests from the technological innovations in single-cell transcriptomics, the studies of cellular heterogeneity & dynamics, and so on. It is now publicly accessible without any login requirement at: https://idrblab.org/cellstar.

Development and internal validation of a risk prediction model for stress urinary incontinence throughout pregnancy: A multicenter retrospective longitudinal study in Indonesia.

BACKGROUND: This study aimed to develop a risk prediction model for stress urinary incontinence (SUI) throughout pregnancy in Indonesian women. METHODS: We conducted a multicenter retrospective longitudinal study involving pregnant women in Indonesia, who sought care at obstetrics clinics from January 2023 to March 2023, encompassing all stages of pregnancy. We collected data on their predictive factors and SUI outcome. SUI was diagnosed based on responses to the "leaks when you are physically active/exercising" criterion in the ICIQ-UI-SF questionnaire during our investigation of the participants. The models underwent internal validation using a bootstrapping method with 1000 resampling iterations to assess discrimination and calibration. RESULTS: A total of 660 eligible pregnant women were recruited from the two study centers, with an overall SUI prevalence of 39% (258/660). The final model incorporated three predictive factors: BMI during pregnancy, constipation, and previous delivery mode. The area under the curve (AUROC) was 0.787 (95% CI: 0.751-0.823). According to the max Youden index, the optimal cut-off point was 44.6%, with a sensitivity of 79.9% and specificity of 65.9%. A discrimination slope of 0.213 was found. CONCLUSION: The developed risk prediction model for SUI in pregnant women offers a valuable tool for early identification and intervention among high-risk SUI populations in Indonesian pregnant women throughout their pregnancies. These findings challenge the assumption that a high BMI and multiple previous deliveries are predictors of SUI in Indonesian women. Further research is recommended to validate the model in diverse populations and settings.

RVvictor: Virus RNA-directed molecular interactions for RNA virus infection.

RNA viruses are major human pathogens that cause seasonal epidemics and occasional pandemic outbreaks. Due to the nature of their RNA genomes, it is anticipated that virus's RNA interacts with host protein (INTPRO), messenger RNA (INTmRNA), and non-coding RNA (INTncRNA) to perform their particular functions during their transcription and replication. In other words, thus, it is urgently needed to have such valuable data on virus RNA-directed molecular interactions (especially INTPROs), which are highly anticipated to attract broad research interests in the fields of RNA virus translation and replication. In this study, a new database was constructed to describe the virus RNA-directed interaction (INTPRO, INTmRNA, INTncRNA) for RNA virus (RVvictor). This database is unique in a) unambiguously characterizing the interactions between viruses RNAs and host proteins, b) providing, for the first time, the most systematic RNA-directed interaction data resources in providing clues to understand the molecular mechanisms of RNA viruses' translation, and replication, and c) in RVvictor, comprehensive enrichment analysis is conducted for each virus RNA based on its associated target genes/proteins, and the enrichment results were explicitly illustrated using various graphs. We found significant enrichment of a suite of pathways related to infection, translation, and replication, e.g., HIV infection, coronavirus disease, regulation of viral genome replication, and so on. Due to the devastating and persistent threat posed by the RNA virus, RVvictor constructed, for the first time, a possible network of cross-talk in RNA-directed interaction, which may ultimately explain the pathogenicity of RNA virus infection. The knowledge base might help develop new anti-viral therapeutic targets in the future. It's now free and publicly accessible at: https://idrblab.org/rvvictor/.

Outcomes of early fiberoptic bronchoscopic sputum aspiration and lavage after thoracoscopic and laparoscopic esophageal cancer surgery: a randomized clinical trial.

BACKGROUND: This study aims to investigate the outcomes of patients who received early fiberoptic bronchoscopic sputum aspiration and lavage after thoracoscopic and laparoscopic esophagectomy due to esophageal cancer. METHODS: A prospective randomized clinical trial was performed between March 2020 and June 2022. Patients who were scheduled for thoracoscopic and laparoscopic esophagectomy due to esophageal cancer were enrolled. Then, these patients were assigned to the control group (traditional postoperative care) and study group (traditional postoperative care with early bronchoscopic sputum aspiration and lavage). The outcomes, which included the length of hospital stay and medical expenses, and postoperative complications, which included pulmonary infection, atelectasis, respiratory dysfunction and anastomotic leakage, were compared between these two groups. RESULTS: A total of 106 patients were enrolled for the present study, and 53 patients were assigned for the control and study groups. There were no statistically significant differences in gender, age, and location of the esophageal cancer between the two groups. Furthermore, the length of hospital stay was statistically significantly shorter and the medical expenses were lower during hospitalization in the study group, when compared to the control group (12.3 ± 1.2 vs. 18.8 ± 1.3 days, 5.5 ± 0.9 vs. 7.2 ± 1.2 Chinese Yuan, respectively; all, P < 0.05). Moreover, there were statistically significantly fewer incidences of overall complications in study group, when compared to the control group (20.7% vs.45.2%, P < 0.05). CONCLUSIONS: For patients with esophageal cancer, early fiberoptic bronchoscopic sputum aspiration and lavage after thoracoscopic and laparoscopic esophagectomy can shorten the length of hospital stay, and lower the medical expense and incidence of postoperative complications.

Photoinactivation of Escherichia coli by 405 nm and 450 nm light-emitting diodes: Comparison of continuous wave and pulsed light.

BACKGROUND: Antimicrobial blue light (ABL) therapy is one of the novel non-antibiotic approaches and recent studies showed the potential of pulsed ABL. PURPOSE: Comparing photoinactivation effect of continuous wave (CW) and pulsed blue light and investigating the impact of varying light parameters. METHODS: E. coli cells in planktonic were treated with CW and pulsed light (405 nm and 450 nm) at 60 mW/cm2, and the samples were taken to assess survival, reactive oxygen species (ROS) level, damage of cell membrane and metabolic activity. Further, a ROS scavenger was used to find the role of ROS played in ABL therapy. RESULTS: E. coli was more sensitive to 405 nm light and the photoinactivation was dose-dependent. Pulsed 405 nm light showed the better antimicrobial effect on E. coli and caused increasing damage of cell membrane. It might be attributed to the ROS production in bacteria. CONCLUSION: Pulsed light has a potential of improving the efficacy of ABL therapy and is worth to be explored deeply further.

Silicon-Nanomembrane-Based Broadband Synaptic Phototransistors for Neuromorphic Vision.

Neuromorphic vision has been attracting much attention due to its advantages over conventional machine vision (e.g., lower data redundancy and lower power consumption). Here we develop synaptic phototransistors based on the silicon nanomembrane (Si NM), which are coupled with lead sulfide quantum dots (PbS QDs) and poly(3-hexylthiophene) (P3HT) to form a heterostructure with distinct photogating. Synaptic phototransistors with optical stimulation have outstanding synaptic functionalities ranging from ultraviolet (UV) to near-infrared (NIR). The broadband synaptic functionalities enable an array of synaptic phototransistors to achieve the perception of brightness and color. In addition, an array of synaptic phototransistors is capable of simultaneous sensing, processing, and memory, which well mimics human vision.

RNAenrich: a web server for non-coding RNA enrichment.

MOTIVATION: With the rapid advances of RNA sequencing and microarray technologies in non-coding RNA (ncRNA) research, functional tools that perform enrichment analysis for ncRNAs are needed. On the one hand, because of the rapidly growing interest in circRNAs, snoRNAs, and piRNAs, it is essential to develop tools for enrichment analysis for these newly emerged ncRNAs. On the other hand, due to the key role of ncRNAs' interacting target in the determination of their function, the interactions between ncRNA and its corresponding target should be fully considered in functional enrichment. Based on the ncRNA-mRNA/protein-function strategy, some tools have been developed to functionally analyze a single type of ncRNA (the majority focuses on miRNA); in addition, some tools adopt predicted target data and lead to only low-confidence results. RESULTS: Herein, an online tool named RNAenrich was developed to enable the comprehensive and accurate enrichment analysis of ncRNAs. It is unique in (i) realizing the enrichment analysis for various RNA types in humans and mice, such as miRNA, lncRNA, circRNA, snoRNA, piRNA, and mRNA; (ii) extending the analysis by introducing millions of experimentally validated data of RNA-target interactions as a built-in database; and (iii) providing a comprehensive interacting network among various ncRNAs and targets to facilitate the mechanistic study of ncRNA function. Importantly, RNAenrich led to a more comprehensive and accurate enrichment analysis in a COVID-19-related miRNA case, which was largely attributed to its coverage of comprehensive ncRNA-target interactions. AVAILABILITY AND IMPLEMENTATION: RNAenrich is now freely accessible at https://idrblab.org/rnaenr/.

The Parameters Affecting Antimicrobial Efficiency of Antimicrobial Blue Light Therapy: A Review and Prospect.

Antimicrobial blue light (aBL) therapy is a novel non-antibiotic antimicrobial approach which works by generating reactive oxygen species. It has shown excellent antimicrobial ability to various microbial pathogens in many studies. However, due to the variability of aBL parameters (e.g., wavelength, dose), there are differences in the antimicrobial effect across different studies, which makes it difficult to form treatment plans for clinical and industrial application. In this review, we summarize research on aBL from the last six years to provide suggestions for clinical and industrial settings. Furthermore, we discuss the damage mechanism and protection mechanism of aBL therapy, and provide a prospect about valuable research fields related to aBL therapy.

RNAincoder: a deep learning-based encoder for RNA and RNA-associated interaction.

Ribonucleic acids (RNAs) involve in various physiological/pathological processes by interacting with proteins, compounds, and other RNAs. A variety of powerful computational methods have been developed to predict such valuable interactions. However, all these methods rely heavily on the 'digitalization' (also known as 'encoding') of RNA-associated interacting pairs into a computer-recognizable descriptor. In other words, it is urgently needed to have a powerful tool that can not only represent each interacting partner but also integrate both partners into a computer-recognizable interaction. Herein, RNAincoder (deep learning-based encoder for RNA-associated interactions) was therefore proposed to (a) provide a comprehensive collection of RNA encoding features, (b) realize the representation of any RNA-associated interaction based on a well-established deep learning-based embedding strategy and (c) enable large-scale scanning of all possible feature combinations to identify the one of optimal performance in RNA-associated interaction prediction. The effectiveness of RNAincoder was extensively validated by case studies on benchmark datasets. All in all, RNAincoder is distinguished for its capability in providing a more accurate representation of RNA-associated interactions, which makes it an indispensable complement to other available tools. RNAincoder can be accessed at https://idrblab.org/rnaincoder/.

Structural modification of (3E)-4,8-dimethyl-1,3,7-nontriene enhances its ability to kill Plutella xylostella insect pests.

BACKGROUND: Plant secondary metabolites and their modified derivatives play an important role in the discovery and development of novel insecticides. The natural plant product (3E)-4,8-dimethyl-1,3,7-nontriene (DMNT) has been proven to be able to effectively repel and kill the lepidopteran insect pest Plutella xylostella. RESULTS: In this study, four oxygenated derivatives of DMNT were synthesized by allylic hydroxylation and subsequent etherification or esterification. Bioassays on P. xylostella larvae showed that the compounds DMNT-OCH3 (2), DMNT-OCy (3) and DMNT-OAc (4) were more toxic to the larvae than DMNT alone. The most pronounced effect was observed for compound 2, which showed a 22.23% increase in lethality at a concentration of 0.25 µm. Moreover, the peritrophic matrix (PM) barrier in the insect midgut was more severely damaged by compounds 2, 3 and 4 than by DMNT. The median lethal concentration (LC50 , 48 h) of compounds 2, 3 and 4 on P. xylostella was determined to be 0.98, 1.13 and 1.11 mg mL-1 , respectively, which is much lower than the commercial insecticides eucalyptol (2.89 mg mL-1 ) and thymol (2.45 mg mL-1 ). CONCLUSION: These results suggested that oxygenated DMNT derivatives offer a significantly improved killing effect over DMNT on P. xylostella. This work has provided a basis for further design, structural modification and development of DMNT as botanical insecticides. © 2023 Society of Chemical Industry.

Combining quantitative trait locus mapping with multiomics profiling reveals genetic control of corn leaf aphid (Rhopalosiphum maidis) resistance in maize.

The corn leaf aphid (Rhopalosiphum maidis) is a major maize pest that frequently causes substantial yield losses. Exploring the genetic basis of resistance to aphids is important for improving maize yield and quality. Here, we used a maize recombinant inbred line population derived from two parents with different susceptibility to aphids, B73 (susceptible) and Abe2 (resistant), and performed quantitative trait locus (QTL) mapping using aphid resistance scores as an indicator. We mapped a stable QTL, qRTA6, to chromosome 6 using data from 2 years of field trials, which explained 40.12-55.17% of the phenotypic variation. To further investigate the mechanism of aphid resistance in Abe2, we constructed transcriptome and metabolome libraries from Abe2 and B73 leaves with or without aphid infestation at different time points. Integrating QTL mapping and transcriptome data revealed three aphid resistance candidate genes (Zm00001d035736, Zm00001d035751, and Zm00001d035767) associated with the hypersensitive response, the jasmonic acid pathway, and protein ubiquitination. Integrated transcriptomic and metabolomic analysis revealed that the differentially expressed genes and metabolites were enriched in flavonoid biosynthesis. These findings extend our understanding of the molecular mechanisms controlling aphid resistance in maize, and the QTL and candidate genes are valuable resources for increasing this resistance.

N-Doped Carbon Confined NiCo Alloy Hollow Spheres as an Efficient and Durable Oxygen Electrocatalyst for Zinc-Air Batteries.

Exploring cost-efficient/durability bifunctional electrocatalysts are of upmost importance for the practical application of metal-air batteries. However, preparing bifunctional electrocatalysts with the above three advantages remains conceptually challenging. This work reports the preparation of N-doped carbon confined NiCo alloy hollow spheres (NiCo@N-C HS) as bifunctional oxygen electrocatalyst for Zn-air battery with a higher energy density (788.7 mWh gZn -1 ) and outstanding cycling stability (over 200 h), which are more durable than the commercialized Pt/C+RuO2 -based device. Electrochemical results and theoretical calculation demonstrate that the synergy in the NiCo@N-C accelerates the electronic transmission for improving activation of O2 * and OH* intermediates and optimizing reacted free energy pathways, while the hollow structures exposure more active sites for improving the reaction kinetics and enhancing the activity of ORR/OER reaction. This work provides crucial understanding for constructing low-cost transition metal-based catalyst to overcome the efficiency and durability barriers of metal-air batteries for widespread applications.

MOSFET Physics-Based Compact Model Mass-Produced: An Artificial Neural Network Approach.

The continued scaling-down of nanoscale semiconductor devices has made it very challenging to obtain analytic surface potential solutions from complex equations in physics, which is the fundamental purpose of the MOSFET compact model. In this work, we proposed a general framework to automatically derive analytical solutions for surface potential in MOSFET, by leveraging the universal approximation power of deep neural networks. Our framework incorporated a physical-relation-neural-network (PRNN) to learn side-by-side from a general-purpose numerical simulator in handling complex equations of mathematical physics, and then instilled the "knowledge'' from the simulation data into the neural network, so as to generate an accurate closed-form mapping between device parameters and surface potential. Inherently, the surface potential was able to reflect the numerical solution of a two-dimensional (2D) Poisson equation, surpassing the limits of traditional 1D Poisson equation solutions, thus better illustrating the physical characteristics of scaling devices. We obtained promising results in inferring the analytic surface potential of MOSFET, and in applying the derived potential function to the building of 130 nm MOSFET compact models and circuit simulation. Such an efficient framework with accurate prediction of device performances demonstrates its potential in device optimization and circuit design.

The pulse light mode enhances the effect of photobiomodulation on B16F10 melanoma cells through autophagy pathway.

Photobiomodulation (PBM) is the use of low irradiance light of specific wavelengths to generate physiological changes and therapeutic effects. However, there are few studies on the effects of PBM of different LED light modes on cells. Here, we investigated the difference of influence between continuous wave (CW) and pulse-PBM on B16F10 melanoma cells. Our results suggested that the pulse mode had a more significant PBM than the CW mode on B16F10 melanoma cells. Our study confirmed that ROS and Ca2+ levels in B16F10 melanoma cells treated with pulse-PBM were significantly higher than those in the control and CW-PBM groups. One mechanism that causes the difference in CW and pulse-PBM action is that pulse-PBM activates autophagy of melanoma cells through the ROS/OPN3/Ca2+ signaling pathway, and excessive autophagy activation inhibits proliferation and apoptosis of melanoma cells. Autophagy may be one of the reasons for the difference between pulse- and CW-PBM on melanoma cells. More importantly, melanoma cells responded to brief PBM pulses by increasing intracellular Ca2+ levels.