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Objectives: The aim of this study was to characterise the first complete genome of Porphyromonas pogonae strain PP01-1 of human origin in China. Methods: The Illumina NovaSeq 6000 (200X coverage) and Nanopore MinION platforms (100× coverage) were used for genome sequencing. A de novo hybrid assembly of short Illumina reads and long MinION reads was performed using Unicycler (v.0.5.0). Genome annotation of PP01-1 was performed using the prokaryotic gene-prediction tool Prokka1.14.6. The genome was further analysed using several bioinformatics tools, including ResFinder, VFDB, VirulenceFinder, Type Strain Genome Server, AntiSMASH, PathogenFinder, MobileElementfinder, CRISPRFinder, and IslandViewer. Results: The assembled circular genome of P. pogonae strain PP01-1 was 2 916 423 bp in length, with a GC content of 41.0%, and no plasmid sequence was detected. A total of 2399 coding sequences were predicted by Prokka. PP01-1 harbours antimicrobial resistance genes blaOXA-347 (β-lactamase resistance), tet(Q) (tetracycline resistance), and floR (chloramphenicol and florfenicol resistance). Conclusions: Here, we are the first to report the complete genome of P. pogonae strain PP01-1 of human origin. In this strain, we first identified blaOXA-347 and tet(Q) in P. pogonae, which will pave the way for further analysis that could identify the potential mechanism of antibiotic resistance and virulence factors in P. pogonae.

Abstract Influenza A virus subtype H1N1 can cause severe acute respiratory distress syndrome and death in young children and elderly individuals. H1N1 initiates inflammatory responses that aim to contain and eliminate microbial invaders. Various lipid mediators (LMs) are biosynthesized and play a critical role in fighting viruses during inflammation; thus, by profiling the LMs in patients, researchers can obtain mechanistic insights into diseases, such as the pathways disrupted. To date, the relationship between molecular alterations in LMs and the pathogenesis of H1N1 influenza in children is poorly understood. Here, we employed a targeted liquid chromatography coupled with tandem mass spectrometry (LC‒MS/MS) to profile LMs in serum from children with H1N1 influenza (H1N1 children) and recovered children. We found that 22 LM species were altered in H1N1 children with mild symptoms. Analysis of the LM profiles of recovered children revealed a decrease in the levels of thromboxane B2 (TxB2) and thromboxane B3 (TxB3) and an increase in the levels of other 8 altered LM species associated with H1N1 influenza, including cytochrome P450 (CYP) enzyme-derived dihydroxyeicosatrienoic acids (DiHETrEs) and hydroxyeicosatetraenoic acids (HETEs) from arachidonic acid (AA), and epoxyoctadecamonoenoic acids (EpOMEs) from linoleic acid (LA). Taken together, the results of this study revealed that serum LMs change dynamically in H1N1 children with mild symptoms. The dramatically altered LMs in H1N1 children could serve as a basis for potential therapeutics or adjuvants against H1N1 influenza.

Abstract Studies have indicated that low high-density lipoprotein cholesterol (HDL-C) level is an important risk factor for diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D). However, whether higher HDL-C levels decrease the risk of developing DKD remains unclear. This study aimed to clarify the relationship between HDL-C levels and DKD risk in individuals with T2D in China. In total, 936 patients with T2D were divided into DKD and non-DKD groups. The association between HDL-C levels and DKD risk was evaluated using logistic regression analysis and restricted cubic spline curves adjusted for potential confounders. Threshold effect analysis of HDL-C for DKD risk was also performed. Higher HDL-C levels did not consistently decrease the DKD risk. Furthermore, a nonlinear association with threshold interval effects between HDL-C levels and the incidence of DKD was observed. Patients with HDL-C ≤ 0.94 mmol/L or HDL-C > 1.54 mmol/L had significantly higher DKD risk after adjusting for confounding factors. Interestingly, the association between high HDL-C levels and increased DKD risk was more significant in women. A U-shaped association between HDL-C levels and DKD risk was observed; therefore, low and high HDL-C levels may increase the DKD risk in patients with T2D.

Objective: To explore the intrinsic relationship between low-density lipoprotein cholesterol (LDL-C) and diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D) in China. Methods: This cross-sectional study included 1,313 patients with type 2 diabetes treated at the Affiliated Jinhua Hospital of Zhejiang University School of Medicine, located in Jinhua, China. The data were combined from two periods, 2017 and 2020–2021. Participants were categorized into groups with and without DKD. The relationship between LDL-C levels and DKD was evaluated employing logistics regression analysis and restricted cubic spline (RCS) curves. Results: Generally, there was no statistical difference in LDL-C levels between DKD and non-DKD groups, however, a significantly non-linear relationship (Pnon-linear = 0.011) was observed between LDL-C levels and DKD prevalence after adjusting for confounding factors according to the RCS analysis. Two optimal cut-points of 2.97 and 3.61 mmol/L were selected out using random forest algorithm. With the middle LDL-C concentration (2.97–3.61 mmol/L) as the reference, the odds ratios for low (3.61 mmol/L) concentrations were 1.45 (1.08–1.96) and 1.47 (1.01–2.15) respectively, after adjusting for confounding factors in the multivariate analyses. Notably, this association was more pronounced among female participants in the subgroup analyses. Conclusion: A non-linear association was observed between LDL-C levels and the risk of DKD in patients with T2D in China. LDL-C levels below 2.97 mmol/L may elevate the risk of DKD, particularly in female patients with T2D.

Abstract A simple and rapid electrochemical sensing method with high sensitivity and specificity of aptamers was developed for the detection of methylamphetamine (MAMP). A short anti-MAMP thiolated aptamer (Apt) with a methylene blue (MB) probe at 3ʹ-end was immobilized on the surface of a gold electrode (MB-Apt-S/GE). The electrochemical signal appeared when MAMP presenting in the sample solution competed with cDNA for binding with MB-Apt-S. Under optimized conditions, the liner range of this signal-on electrochemical aptasensor for the detection of MAMP achieved from 1.0 to 10.0 nmol/L and 10.0–400 nmol/L. LOD 0.88 nmol/L were obtained. Satisfactory spiked recoveries of saliva and urine were also obtained. In this method, only 5 min were needed to incubate before the square wave voltammetry (SWV) analysis, which was much more rapid than other electrochemical sensors, leading to a bright and broad prospect for the detection of MAMP in biological sample. This method can be used for on-site rapid detection on special occasions, such as drug driving scenes, entertainment venues suspected of drug use, etc.

ABSTRACTThe waning of maternal antibodies may cause infants to lose protection against measles before receiving measles-containing vaccine (MCV). The aim of this study is to investigate the changing characteristics and influencing factors of measles antibodies in preterm infants (PT), and to provide scientific basis for optimizing MCV vaccination strategy of the target population. Blood samples were collected from PT and full-term infants (FT) at the chronological age (CA) of 3, 6, and 12 months. Measles antibodies were quantitatively detected by enzyme-linked immunosorbent assay. Demographic and vaccination information were both collected. Kruskal–Wallis rank sum test was used to compare the measles antibodies among different gestation age (GA) groups, and multiple linear regression was performed to identify the correlative factors for the antibodies. Measles antibodies of PT decreased significantly with age increasing before MCV vaccination. The positive rates of antibodies of PT were 10.80% and 3.30% at the age of 3 and 6 months, respectively (p

BackgroundPrevious studies have compared the incidence of pertussis before and during the COVID-19 pandemic, finding that public health measures related to COVID-19 contributed to a temporary decline in reported pertussis cases during the pandemic. However, the post-pandemic period has seen a resurgence in respiratory infections, influenced by relaxed health measures and decreased public vigilance. This study investigates the epidemiological dynamics of pertussis among patients with acute respiratory tract infections (ARTI) in Zhejiang Province, China, providing essential reference information for ongoing public health strategies.MethodsThis study analyzed multicenter data from January 2023 to May 2024, involving 8,560 patients with ARTI from three hospitals in Zhejiang Province. Inclusion criteria included patients who presented with cough symptoms and were clinically diagnosed with either acute upper respiratory tract infections (URTI) or acute lower respiratory tract infections (LRTI), and who had undergone at least one Bordetella pertussis DNA test. The study analyzed the epidemiological changes of pertussis positivity rates and their associations with time, age, gender, and diagnosis types (URTI and LRTI).ResultsFrom January 2023 to May 2024, the positivity rate and testing number for pertussis among patients with ARTI generally showed a gradual increasing pattern. In March 2024, the positivity rate reached its peak at 31.58%, followed by a weekly decline. The overall positivity rate was 23.59%, with no significant differences observed between genders. Pertussis incidence was higher in patients with LRTI (24.49%) compared to those with URTI (18.63%, OR = 1.40, 95% CI: 1.20–1.63, p

BackgroundPrevious studies have confirmed that the triglyceride glucose (TyG) index, recognized as a reliable marker of insulin resistance, is an important risk factor for diabetic kidney disease (DKD). However, it is still unclear whether the DKD risk continues to increase linearly with the elevation of TyG index. This study aimed to thoroughly investigated the intrinsic relationship between TyG index and DKD risk in type 2 diabetes (T2D).MethodsThis cross-sectional study included 933 patients with T2D in China, who were categorized into DKD and non-DKD groups and stratified by TyG index levels. Logistic regression analysis identified the independent risk factors for DKD. The association between DKD risk and TyG index was evaluated using the restricted cubic spline (RCS) curves analysis. The R package ‘CatPredi’ was utilized to determine the optimal cut-off point for the relationship between DKD risk and TyG index, followed by threshold effect analysis.ResultsThe prevalence of DKD was 33.01%. After adjusting for confounding factors, TyG index was identified as a prominent clinical risk factor for DKD, showing the highest odds ratio (OR 1.57 (1.26 - 1.94), P 9.35, DKD risk increased gradually with rising TyG index. Among patients with TyG index > 9.35, each 1-unit increase was associated with a 1.94-fold increased DKD risk (OR=1.94 (1.10 - 3.43), P=0.022).ConclusionThe DKD risk presented a threshold effect with the increase of TyG index, initially stable at a low level, and then gradually rising when the TyG index is above 9.35.

Abstract We demonstrate a novel flat-field, dual-optic imaging EUV—soft X-ray spectrometer and monochromator that attains an unprecedented throughput efficiency exceeding 60% by design, along with a superb spectral resolution of λ/Δλ > 200 accomplished without employing variable line spacing gratings. Exploiting the benefits of the conical diffraction geometry, the optical system is globally optimized in multidimensional parameter space to guarantee optimal imaging performance over a broad spectral range while maintaining circular and elliptical polarization states at the first, second, and third diffraction orders. Moreover, our analysis indicates minimal temporal dispersion, with pulse broadening confined within 80 fs tail-to-tail and an FWHM value of 29 fs, which enables ultrafast spectroscopic and pump-probe studies with femtosecond accuracy. Furthermore, the spectrometer can be effortlessly transformed into a monochromator spanning the EUV—soft X-ray spectral region using a single grating with an aberration-free spatial profile. Such capability allows coherent diffractive imaging applications to be conducted with highly monochromatic light in a broad spectral range and extended to the soft X-ray region with minimal photon loss, thus facilitating state-of-the-art imaging of intricate nano- and bio-systems, with a significantly enhanced spatiotemporal resolution, down to the nanometer–femtosecond level.

Abstract Photoreceptor apoptosis is an important pathogenesis of retinal degeneration and a primary cause of vision loss with limited treatment methods. Mesenchymal stem/stromal cells-derived small extracellular vesicles (MSC-sEVs) have shown therapeutic value in various ocular disorders. Recent studies have revealed that hypoxic preconditioning can improve the effectiveness of MSC-sEVs in tissue regeneration. However, whether hypoxic preconditioned MSC-sEVs (Hyp-sEVs) exert superior effects on photoreceptor protection relative to normoxic conditioned MSC-sEVs (Nor-sEVs) remains unclear. Here, we reported that Hyp-sEVs further improved retinal structure, recovered retinal function, and suppressed photoreceptor apoptosis in N-methyl-N-nitrosourea (MNU)-induced mouse model compared with Nor-sEVs. Hyp-sEVs also exhibited enhanced anti-apoptotic roles in MNU-provoked 661 W cell injury in vitro. We then analyzed the protein profiles of Nor-sEVs and Hyp-sEVs by LC-MS/MS and found that growth-associated protein 43 (GAP43) was enriched in Hyp-sEVs. The knockdown of GAP43 abolished the retinal therapeutic effects of Hyp-sEVs. Mechanistically, hypoxic stimulation-induced hypoxia-inducible factor-1α (HIF-1α) activation was responsible for preventing tripartite motif-containing protein 25 (TRIM25)-mediated GAP43 ubiquitination and degradation, leading to the upregulation of GAP43 in Hyp-sEVs. Together, our findings uncover the efficacy and mechanism of Hyp-sEVs-based photoreceptor protection and highlight the potential of Hyp-sEVs as optimized therapeutics for retinal degeneration. Graphical abstract

BackgroundThere is an incomplete understanding of fluctuations in vitamin D (VitD) concentration during pregnancy among Chinese women. Furthermore, previous research has yielded conflicting results in this area. This study aims to investigate the changes in VitD status and deficiency in Chinese pregnant women across various age groups, gestational weeks, and as well as seasonal variations through conducting a large-scale survey.MethodsA toal of 11,220 Chinese pregnant women between 2021 and April 2023 were included in this study. Generalized additive models (GAM), stratified analysis, and restricted cubic splines (RCS) were used to analyze changes in VitD status and deficiency risk during pregnancy.ResultsOf the participants, 45.2% had deficient concentration of 25-hydroxyvitamin D. VitD concentration and deficiency rate do not show linear changes with age and gestational weeks. With increasing gestational weeks, VitD concentration rapidly increased in women with gestational age < 20 weeks, remained stable between 20 and 30 weeks, and decreased beyond 30 weeks; however, the odds of VitD deficiency showed three different patterns: a rapid decline, a stable period, and a mild increase, respectively. Based on the stratified regression analysis, VitD deficiency odds increased by 16% with each additional week of gestation in pregnant women with gestational age > 30 weeks, OR = 1.16 (1.10–1.22), p

AbstractThe cost of living has shown a gradual upward trend in recent years, which has seriously impacted the quality of life and well-being of residents. This study empirically examines the impact of the digital economy on residents’ cost of living and the mechanisms behind it by measuring the digital economy and cost of living indicators in 160 cities in China from 2010 to 2020. The results show that the digital economy can significantly reduce the cost of living for the population. The “marginal effect” of the digital economy on the cost of living is found to be non-linearly decreasing as the level of the digital economy increases, and the more severe the air pollution, the weaker the impact of the digital economy on the cost of living, using a threshold variable of the digital economy and air pollution. Finally, the analysis of mechanisms shows that environmental regulation and healthcare are important ways in which the digital economy can play a role in reducing the cost of living for the population. Meanwhile, this study reveals the intrinsic link between the digital economy and the cost of living, which is important for alleviating the cost of living and enhancing people’s well-being.

BackgroundOur previous cross-sectional study has demonstrated the independently non-linear relationship between fasting C-peptide with renal dysfunction odds in patients with type 2 diabetes (T2D) in China. This longitudinal observational study aims to explore the role of serum C-peptide in risk prediction of new-onset renal dysfunction, then construct a predictive model based on serum C-peptide and other clinical parameters.MethodsThe patients with T2D and normal renal function at baseline were recruited in this study. The LASSO algorithm was performed to filter potential predictors from the baseline variables. Logistic regression (LR) was performed to construct the predictive model for new-onset renal dysfunction risk. Power analysis was performed to assess the statistical power of the model.ResultsDuring a 2-year follow-up period, 21.08% (35/166) of subjects with T2D and normal renal function at baseline progressed to renal dysfunction. Six predictors were determined using LASSO regression, including baseline albumin-to-creatinine ratio, glycated hemoglobin, hypertension, retinol-binding protein-to-creatinine ratio, quartiles of fasting C-peptide, and quartiles of fasting C-peptide to 2h postprandial C-peptide ratio. These 6 predictors were incorporated to develop model for renal dysfunction risk prediction using LR. Finally, the LR model achieved a high efficiency, with an AUC of 0.83 (0.76 - 0.91), an accuracy of 75.80%, a sensitivity of 88.60%, and a specificity of 70.80%. According to the power analysis, the statistical power of the LR model was found to be 0.81, which was at a relatively high level. Finally, a nomogram was developed to make the model more available for individualized prediction in clinical practice.ConclusionOur results indicated that the baseline level of serum C-peptide had the potential role in the risk prediction of new-onset renal dysfunction. The LR model demonstrated high efficiency and had the potential to guide individualized risk assessments for renal dysfunction in clinical practice.

BackgroundPrevious studies had showed divergent findings on the associations of C-peptide and/or uric acid (UA) with renal dysfunction odds in patients with type 2 diabetes mellitus (T2DM). We hypothesized that there were non-linear relationships between C-peptide, UA and renal dysfunction odds. This study aimed to further investigate the relationships of different stratification of C-peptide and UA with renal dysfunction in patients with T2DM.MethodWe conducted a cross-sectional real-world observational study of 411 patients with T2DM. The levels of fasting C-peptide, 2h postprandial C-peptide, the ratio of fasting C-peptide to 2h postprandial C-peptide (C0/C2 ratio), UA and other characteristics were recorded. Restricted cubic spline (RCS) curves was performed to evaluated the associations of stratified C-peptide and UA with renal dysfunction odds.ResultsFasting C-peptide, C0/C2 ratio and UA were independently and significantly associated with renal dysfunction in patients with T2DM as assessed by multivariate analyses (p < 0.05). In especial, non-linear relationships with threshold effects were observed among fasting C-peptide, UA and renal dysfunction according to RCS analyses. Compared with patients with 0.28 ≤ fasting C-peptide ≤ 0.56 nmol/L, patients with fasting C-peptide < 0.28 nmol/L (OR = 1.38, p = 0.246) or fasting C-peptide > 0.56 nmol/L (OR = 1.85, p = 0.021) had relatively higher renal dysfunction odds after adjusting for confounding factors. Similarly, compared with patients with 276 ≤ UA ≤ 409 μmol/L, patients with UA < 276 μmol/L (OR = 1.32, p = 0.262) or UA > 409 μmol/L (OR = 6.24, p < 0.001) had relatively higher odds of renal dysfunction.ConclusionThe renal dysfunction odds in patients with T2DM was non-linearly associated with the levels of serum fasting C-peptide and UA. Fasting C-peptide and UA might have the potential role in odds stratification of renal dysfunction.

ABSTRACT The increased carbapenem resistance among Pseudomonas aeruginosa has become a serious health issue worldwide. We reported an extensively drug-resistant (XDR) P. aeruginosa PA30 isolate which belonged to sequence type ST463 and contained an IncP-2 plasmid (pPA30_1) carrying two genes, namely, blaIMP-45 and blaAFM-1, which encoded the metallo-β-lactamases AFM-1 and IMP-45, respectively. Additionally, the strain had a plasmid (pPA30_2) with two copies of the blaKPC-2 genes embedded. The plasmid pPA30_1 was highly similar to the previously reported plasmid pHS17-127, which has the same genetic architecture. This plasmid contained blaIMP-45, located in a second gene cassette of the integron In786, carried by a Tn1403-derivative transposon acquiring an ISCR27n3-blaAFM-1 structure. Interestingly, the transposon in pPA30_1 acquired an extra ISCR1-qnrVC6 module and formed a novel transposon, which was subsequently annotated as Tn6485f. The blaKPC-2 genes in pPA30_2 underwent duplication due to the inversion of the IS26-blaKPC-2-IS26 element, which resulted in two copies of blaKPC-2. IMPORTANCE The ST463 clone is an emerging high-risk sequence type that is spreading with blaKPC-2-containing plasmids. The core blaKPC-2 genetic platform is ISKpn27-blaKPC-2-ISKpn6 in almost all samples, and the adjacent region beyond the core platform varies by IS26-mediated inversion or duplication events, amplifying the blaKPC-2 gene copies. The ST463 P. aeruginosa strain PA30 in our study contains another two metallo-β-lactamase genes, namely, blaIMP-45 and blaAFM-1, in a novel transposon Tn6485f that is harbored by the IncP-2 megaplasmid. The pPA30_1 carrying blaIMP-45 and blaAFM-1 is highly related to pHS17-127 from the ST369 P. aeruginosa strain, indicating the putative dissemination of the megaplasmid between different clones.

In the field of computer vision, the detection of multiple objects with different scales within a single image is challenging. To target this problem, feature pyramids are a basic component commonly found in multi-scale object detectors. In the construction of standard feature pyramids, different semantic features are simply connected to rebuild a new feature map, regardless of whether these features have a positive effect to the output or not. In order to avoid introducing too many redundant features within the feature fusion stage, a new feature fusion module called the Feature Selection Module (FSM) was proposed in this paper, which can automatically detect the most representative features for the rebuilding of feature maps. The channel attention mechanism in FSM is able to process and score each channel, filtering out irrelevant features while focusing on features with high contribution. Moreover, FSM can be easily embedded within feature pyramids. Simply adding a small number of trainable parameters to the network can significantly improve the ability of feature extraction. We validated our FSM with the VOC 2007 object detection dataset, based on Yolo series detectors. Findings from the present study demonstrates that for a small computational cost, our method is able to consistently improve the performance of Yolo detectors.

Vocabulary mastery is critical to English as Foreign Language students. Mobile technologies enable students to learn vocabulary without space and time limitations. However, existing mobile-assisted vocabulary learning research often employed teacher-directed activities that increased instructors’ workload, undermined student motivation or targeted individual cognitive outcomes only. In this study, a Contribution-oriented Self-Directed Mobile Learning Ecology (CSDMLE) model is proposed for developing student-directed and motivational vocabulary learning activities in groups. Through a mixed-method design, we administered a survey and a vocabulary test to 55 freshmen students in a Chinese university, and conducted follow-up interviews. We found that students in the CSDMLE group outperformed those not in the group in the post-test vocabulary test, but there was no statistically significant difference between the two groups’ delayed vocabulary test or L2 motivation. However, the treatment group displayed a highly favorable attitude toward the learning approach and a strong intention to use it continuously. The findings have implications for technology-supported vocabulary learning activities.

Background. Studies in the past decade have reported many novel biomarkers for predicting the new-onset or progression risk of renal dysfunction in patients with type 2 diabetes (T2D) based on the genomic, metabolomic, and proteomic technologies. These novel predictive markers, however, are difficult to be widely used in clinical practice over the short term due to their high technology content, instability, and high cost. This study was aimed at evaluating the associations of clinical features and six traditional renal markers with the short-term risk of new-onset renal dysfunction in patients with T2D. Methods. This study involved 213 participants with T2D and normal renal function at baseline. The baseline levels of the albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), alpha-1-microglobulin-to-creatinine ratio (A1MCR), neutrophil gelatinase-associated lipocalin-to-creatinine ratio, transferrin-to-creatinine ratio (UTRF/Cr), and retinol-binding protein-to-creatinine ratio (URBP/Cr) were analyzed. Multivariate logistic models were established and validated. Results. During the two-year follow-up period, 23.01% participants progressed to renal dysfunction. The basal levels of ACR, A1MCR, UTRF/Cr, and URBP/Cr were the independent risk factors of new-onset renal dysfunction (P

The research of application models based on traditional convolutional neural networks has gradually entered the bottleneck period of performance improvement, and the improvement of chest X-ray image models has gradually become a difficult problem in the study. In this paper, the Swin Transformer is introduced into the application model of pneumonia recognition in chest X-ray images, and it is optimized according to the characteristics of chest X-ray images. The experimental results based on the model in this paper are compared with those of the model built with the traditional convolutional neural network as the backbone network, and the accuracy of the model is proved to be greatly improved. After the comparison experiments on two different datasets, the experimental results show that the accuracy of the model in this paper improves from 76.3% to 87.3% and from 92.8% to 97.2%, respectively. The experiments show that the accuracy of image enhancement based on the features of chest X-ray images in this model will be higher than the accuracy without image enhancement. In the experiments of this paper, the identification decision factors in the chest X-ray images were extracted by grad-cam combined with a transformer to find the corresponding approximate lesion regions.

Plant pests are the primary biological threats to agricultural and forestry production as well as forest ecosystem. Monitoring forest-pest damage via satellite images is crucial for the development of prevention and control strategies. Previous studies utilizing deep learning to monitor pest-infested damage in satellite imagery adopted RGB images, while multispectral imagery and vegetation indices were not used. Multispectral images and vegetation indices contain a wealth of useful information for detecting plant health, which can improve the precision of pest damage detection. The aim of the study is to further improve forest-pest infestation area segmentation by combining multispectral, vegetation indices and RGB information into deep learning. We also propose a new image segmentation method based on UNet++ with attention mechanism module for detecting forest damage induced by bark beetle and aspen leaf miner in Sentinel-2 images. The ResNeSt101 is used as the feature extraction backbone, and the attention mechanism scSE module is introduced in the decoding phase for improving the image segmentation results. We used Sentinel-2 imagery to produce a dataset based on forest health damage data gathered by the Ministry of Forests, Lands, Natural Resource Operations and Rural Development (FLNRORD) in British Columbia (BC), Canada, during aerial overview surveys (AOS) in 2020. The dataset contains the 11 original Sentinel-2 bands and 13 vegetation indices. The experimental results confirmed that the significance of vegetation indices and multispectral data in enhancing the segmentation effect. The results demonstrated that the proposed method exhibits better segmentation quality and more accurate quantitative indices with overall accuracy of 85.11%, in comparison with the state-of-the-art pest area segmentation methods.