Category: 4. Technology

Scientists have demonstrated that a photonic qubit — a quantum bit powered by a particle of light — can detect and correct its own errors while running at room temperature. They say it is a foundational step toward scalable quantum processors.

In a new study published June 4 in the journal Nature, researchers at Canadian quantum computing startup Xanadu created a so-called “Gottesman–Kitaev–Preskill” (GKP) state directly on a silicon chip.

Samsung is getting ready to launch its new foldable phones — the Galaxy Z Fold 7 and Galaxy Z Flip 7 — at the Galaxy Unpacked event on July 9. But just before the big day, a new leak has caught everyone’s attention, and this one isn’t about the aforementioned Galaxy foldables. It’s about something new. According to a report by the Android Authority, an animation spotted inside the latest One UI 8 beta update shows what looks like a tri-fold Samsung phone, possibly called the Galaxy G Fold.

The animation is meant to show how to place the phone for NFC payments, but it clearly shows a phone with three folding parts and two hinges. This lines up with Samsung’s earlier tri-fold concept, which was shown off as the Flex G prototype. That version looked bulky and didn’t have a cover screen, but this new design looks much more polished and practical.

From the animation, we can see that the rear cameras are placed vertically in the top-left corner, just like on the Galaxy Z Fold series. There also seems to be a front camera on the middle screen, which could double as the cover screen when folded. The right panel looks like a regular display with very thin borders. When fully opened, the phone could offer a large, tablet-like experience.

This design is different from what Huawei has done with its foldables. Samsung’s version folds inward in a G shape, which could help protect the display when the phone is closed. While the animation doesn’t confirm the name or specs, it does suggest that Samsung is nearly ready to show off the device.

That said, the Galaxy G Fold is not expected to launch at the July 9 event. It might just be teased or mentioned briefly. The full reveal could happen later this year, possibly in October.

For now, the main focus of the Unpacked event will be the Galaxy Z Fold 7 and Galaxy Z Flip 7. Both phones have already leaked in renders and even in live photos. They are expected to come in two colours — Jet Black and Blue Shadow — and will feature a slimmer design, lighter build, and top-tier specs including the Snapdragon 8 Elite chip. Samsung may also launch a more affordable Galaxy Z Flip 7 FE, along with the Galaxy Watch 8 series.

With only a few days to go, we won’t have to wait long to know what’s really coming. Stay tuned to India Today Tech for all the latest on Samsung’s upcoming foldable smartphones.

– Ends

Introduction

The connection between cancer and microbial agents has undergone significant evolution in understanding over centuries. Symbiotic microbiota residing within the human body can affect metabolic pathways, growth dynamics, and neoplastic cell functions, thereby impacting the tumor microenvironment.¹ A comprehensive global analysis estimated that in 2018, around 13% of all cancer cases were linked to infectious agents, including viruses, bacteria, and parasites.² Bacteria are increasingly acknowledged for their contributions to the onset of various cancers and their influence on responses to treatments, such as immune checkpoint inhibitors.^3,4 This deepening insight highlights the microbiome’s potential to advance diagnostic and therapeutic strategies.^5,6

Dysbiosis, defined as an imbalance in microbial communities, is implicated in cancer development and progression through processes such as mutagenesis, epigenetic alterations, and immune modulation.⁷ For example, Fusobacterium nucleatum manipulates glucose metabolism to support colorectal cancer development,⁸ while other bacteria adjust immune responses and affect tumor prognosis.⁹ Strikingly, a randomized controlled trial showed that fecal microbiota transplantation could reverse resistance to immune checkpoint inhibitors in patients with treatment-resistant melanoma, reinforcing the pivotal role of commensal bacteria in tumor immunity.¹⁰

Multiple microbial niches exist within the human body, particularly at barrier surfaces. The interplay between these microbiota and tumor progression, such as the relationship between the gut microbiome and colorectal cancer, has been extensively studied.¹¹ However, these microbial sites have lower biomass than the gastrointestinal tract, and their roles in tumorigenesis are still being explored. The lungs, in particular, are exposed to local inflammation from infectious exposures, environmental allergens, pollutants, and cigarette smoke. Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is the leading cause of cancer-related deaths globally. Understanding the factors contributing to its development and treatment response is crucial for public health.¹² In NSCLC tissues, exposing airway epithelial cells to specific bacterial taxa such as Prevotella, Streptococcus, and Veillonella activates the PI3K and AKT signaling pathways, correlating with oncogenic transcriptome programs.¹³ Lung adenocarcinoma (LUAD), a major subtype of NSCLC, comprises approximately 40% of lung cancer cases and presents a poor prognosis, contributing significantly to the lung cancer burden.¹⁴ Recent studies have found that depleting the microbiota in a mouse model of lung adenocarcinoma with Kras mutation and p53 deletion significantly suppressed tumor growth.¹⁵

The precise influence of the lung microbiome on NSCLC remains poorly defined, partly because isolating viable microbial cells from healthy lung tissue is challenging due to low biomass or technical limitations.¹⁶ Chronic inflammation stands out as a key risk factor for NSCLC, underscoring the need for detailed mechanistic studies to clarify the microbiota’s role in cancer initiation and progression. Recent efforts analyzing whole-genome sequencing and transcriptomic data from The Cancer Genome Atlas (TCGA) with the SHOGUN algorithm have estimated commensal bacterial abundance across a wide array of tumor samples, offering a valuable means to investigate microbial roles in specific tumor contexts.¹⁷

This study aims to outline differences in microbial composition across various cancers, with a particular focus on lung adenocarcinoma. By examining how these microbial populations shape the oncogenic landscape, we aim to discover new possibilities for targeted therapeutic interventions.

Materials and Methods

Data Collection

Microbial composition data for lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), breast carcinoma (BRCA), and thyroid carcinoma (THCA) were sourced from the Cancer Microbiome database (http://cancermicrobiome.ucsd.edu/CancerMicrobiome_DataBrowser).¹⁷ Tumor mutational burden (TMB), immunophenoscore (IPS), and T-cell and B-cell receptor repertoire profiles were obtained from MF Portrait by BostonGene (https://science.bostongene.com/tumor-portrait).¹⁸ Multiple viral signatures for LUAD were derived using the VirusScan pipeline, with a threshold of 100 reads applied to determine positive or negative status.¹⁹ Additional datasets, including gene expression, miRNA profiles, DNA copy number variation (CNV), methylation, ATAC-seq peak-calling, and clinical data, were retrieved from UCSC Xena (https://xenabrowser.net/datapages).

Microbiome Data Analysis

Microbial abundance data from the Cancer Microbiome database were aligned with clinical data from UCSC Xena using sample identifiers. To minimize batch effects, microbial abundance data derived from RNA-seq were selected, encompassing both tumor tissue and adjacent normal solid tissue. These data were normalized to reads per million (RPM). Alpha diversity was assessed using the Shannon index, and beta diversity was evaluated with Bray-Curtis distances, both calculated using the “vegan” R package (v2.6.4).²⁰ Principal coordinates analysis (PCoA) was performed to visualize microbial community differences.²¹ Variations in microbial communities were tested using PERMANOVA and Mantel tests. Differentially abundant species were identified with the “DESeq2” package (v3.19), applying an adjusted P-value threshold of 0.05.²²

TME Signature and NMF-Based Clustering Analysis

Tumor microenvironment (TME) signatures for LUAD samples (n = 477) were acquired from BostonGene and normalized using median scaling. These signatures underwent non-negative matrix factorization (NMF) clustering (v0.27) with K-means, testing cluster numbers from 1 to 5.²³ Two distinct clusters were selected based on cophenetic, dispersion, and silhouette scores. Survival analysis between these TME clusters was conducted using the “survival” package (v3.6.4) in R.

Network Analysis

Interaction networks incorporating TME features, immune checkpoints, viral abundance, and differentially abundant species were constructed using Spearman correlation analysis. The STRING database (https://string-db.org/) was employed to investigate potential interactions among differentially expressed genes (DEGs), generating protein interaction networks to illustrate regulatory relationships.²⁴ The MCODE algorithm was applied to detect densely connected regions within these networks.²⁵ Network co-presence and exclusivity were evaluated using Cytoscape’s Network Analyzer tool (v3.10.1).²⁶

ceRNA Network Analysis

The GDCRNATools pipeline (v1.24.0) was used to examine the lncRNA–mRNA competing endogenous RNA (ceRNA) network in LUAD TME clusters, based on expression data for 187 lncRNAs, miRNAs, and mRNAs.²⁷ The analysis targeted key genes, including BRAF and ISG15. Data were preprocessed, normalized, and analyzed with default parameters (Pearson’s r > 0.4, P < 0.05). Interactions were validated using the ENCORI platform. Priority was given to interactions exhibiting differential expression across TME clusters, high expression levels, and support from prior literature, such as the LCIIAR–miRNA–ISG15 axis. Databases such as miRBase v22 and starBase v2.0 were utilized to ensure robust and accurate network construction.

Web Resource Integration Archives

The gene mutation and expression profiles of immune-related genes in LUAD were analyzed using cBioPortal (https://www.cbioportal.org/). Survival analysis for ISG15 and LCIIAR was performed with the LnCeCell database (http://bio-bigdata.hrbmu.edu.cn/LnCeCell/).²⁸ The multi-omics landscape for ISG15 was explored using UCSC Xena, incorporating the GDC Pan-Cancer (PANCAN) database (n = 323), TCGA LUAD database, and TCGA LUSC database (n = 37). Survival analysis was conducted using the median as the cutoff point.

siRNA-Mediated Gene Knockdown and Quantitative PCR Analysis

Small interfering RNAs (siRNAs) targeting LCIIAR and ISG15 were designed and synthesized using BLOCK-iT RNAi Designer (Thermo Fisher Scientific, Carlsbad, CA). Lewis lung carcinoma cells were transfected with these siRNAs using Lipofectamine 3000 (L3000015, Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. After 24 hours, total RNA was extracted using an RNA isolation kit (RC101-01, Vazyme, Nanjing, China). cDNA was synthesized from 1 µg of total RNA with a reverse transcription kit (R223-01, Vazyme). Quantitative real-time PCR (qPCR) was conducted to measure LCIIAR and ISG15 expression using gene-specific primers and SYBR Green PCR Master Mix (Q711-02, Vazyme). Expression levels were normalized to the housekeeping gene GAPDH. Experiments were performed in triplicate, and relative expression levels were calculated using the 2^–ΔΔCt method (see Table S1 for sequences).

Luciferase Assay

To investigate the mechanism by which LCIIAR regulates ISG15 protein expression, a luciferase reporter plasmid (Figure S1) was constructed, connecting the CDS and 3′ UTR of ISG15 to luciferase mRNA. This plasmid was co-transfected with either an LCIIAR overexpression plasmid, miR-22-3p and miR-3127-5p mimics, or inhibitors. Luciferase activity was measured 48 hours post-transfection using the Luciferase Assay System (DD1203-03, Vazyme). The miRNA scramble, miR-22-3p mimic, miR-22-3p inhibitor, miR-3127-5p mimic, and miR-3127-5p inhibitor were sourced from RIBOBIO Corporation (Guangzhou, China).

Cell Proliferation Assay Using CCK-8

To assess the effects of LCIIAR or ISG15 knockdown on cell proliferation, a Cell Counting Kit-8 (CCK-8, A311-01, Vazyme) assay was conducted. Lewis lung carcinoma cells (LL/2 (LLC1), ATCC number CRL-1642) were cultured, frozen, and revived in our laboratory. Following transfection with siRNAs targeting LCIIAR or ISG15, cells were seeded into 96-well plates at a density of 5 × 10^3 cells per well. Cell proliferation was measured at 24, 48, and 72 hours post-transfection. At each time point, 10 µL of CCK-8 solution was added to each well, and plates were incubated at 37°C for 2 hours. Absorbance was recorded at 450 nm using a microplate reader.

Statistical Analysis

Statistical comparisons between two groups were performed using the Wilcoxon rank sum test. Linear associations between variables were evaluated with Spearman correlation analysis. Alpha diversity was assessed using the Mann–Whitney test, and principal coordinates analysis (PCoA) was visualized with Bray-Curtis distances. Variations in microbial compositions were tested using PERMANOVA or the Mantel test. Survival analyses were conducted with Log rank tests, and Cox regression was applied for multivariate analysis. Experimental data were analyzed using one-way ANOVA, with P < 0.05 considered statistically significant. The Benjamini-Hochberg method was used to control the false discovery rate in multiple testing scenarios. All statistical analyses were performed in R (v4.4.0).

Results

Distinct Microbial Diversity and Composition Across Cancer Types

We analyzed microbial characteristics in patients with lung adenocarcinoma (LUAD, n=528), lung squamous cell carcinoma (LUSC, n=442), breast carcinoma (BRCA, n=1166), and thyroid carcinoma (THCA, n=186) using the Cancer Microbiome “SHOGUN” RNA-seq dataset. These cancers encompassed those potentially exposed to the external environment (LUAD and LUSC) and contrast tumors not directly associated with lung microbiota (BRCA and THCA). At the phylum level, identified bacterial sequences predominantly belonged to Actinobacteria, Bacteroidetes, Candidatus, Chlamydiae, Firmicutes, and Proteobacteria. Notably, lung tumors (LUAD and LUSC) exhibited greater abundance of these bacterial phyla compared to BRCA and THCA (Figure 1A).

Figure 1 Distinct microbiome diversity and composition across various tumor types. (A) High-abundance bacterial sequences at the phylum level were analyzed in primary tumors (PT) and normal solid tissues (STN) from lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and control tumors not directly associated with the lung microbiota, including breast carcinoma (BRCA) and thyroid carcinoma (THCA). The distribution of sequence reads for six representative bacterial taxa is presented. (B) Bacterial sequence counts and (C) Shannon diversity indices were compared between PT and STN using Mann–Whitney test. (D) Principal coordinates analysis (PCoA) of Bray-Curtis distances calculated from bacterial sequence reads in PT and STN; each point represents a sample. (E) Differences in bacterial β-diversity among tumor samples were evaluated using PERMANOVA. (F) Volcano plot illustrating differences in bacterial abundance between each PT and the corresponding STN, identified using DESeq2 analysis. Upregulated taxa indicate enrichment in tumors (|Log2FC| > 1); statistical significance was considered at P < 0.05.

In LUAD tissues, bacterial counts differed significantly between primary tumor (PT) and solid tissue normal (STN) samples, with the latter serving as controls (Figure 1B). However, no significant differences in Shannon index values were observed among the cancer types (Figure 1C). Beta diversity analysis, based on Bray-Curtis principal coordinates analysis (PCoA), revealed distinct clustering differences in the microbiota across the four cancer types (Figure 1D). PERMANOVA confirmed significant beta diversity differences between LUAD and LUSC (P<0.001 and P=0.032, respectively), indicating distinct microbial communities in lung cancers (Figure 1E).

The volcano plots indicated differentially abundant bacterial genera between PT and STN tissues. In lung cancers, genera such as Cyanothece, Sulfolobus, and Alcanivorax were enriched, with Cylindrospermopsis specifically enriched in LUAD. Although Cylindrospermopsis showed higher abundance in LUAD tumor tissues than in normal tissues, its relative abundance varied across tumor subtypes, suggesting its role may depend on the tumor microenvironment and molecular subtype. In contrast, Lachnoclostridium and Ralstonia were enriched in BRCA and THCA, respectively (Figure 1F). Venn diagrams illustrated the overlap of differentially abundant genera between PT and STN across cancer types, revealing eight genera shared between the two lung cancers but fewer shared with non-lung cancers (Figure S2).

Immune Clustering Distinguishes Prognosis in LUAD

We investigated the associations between microbial diversity, composition, and both clinical and immune characteristics in tumor tissues. Analyzed variables included gender, stage, tumor mutational burden (TMB), molecular functional portrait (MFP) signature, and immune features such as immune subtype, immune cell infiltration score (IPS). In LUAD, bacterial read counts and diversity correlated significantly with several clinical and immune characteristics, notably the Shannon indices of BCR and TCR, T and N stages, and tumor purity (Figure 2A). Specifically, we observed significant differences in the Shannon index across different T stages, and significant differences in microbial community structure across different N stages, as measured by Bray-Curtis distance. These findings suggest a robust association between commensal bacteria and the immunosuppressive microenvironment in LUAD. By contrast, LUSC displayed fewer associations between bacterial flora and immune features.

Figure 2 Clustering of immunological features significantly distinguishes LUAD prognosis. (A) The left heatmap shows correlations between bacterial sequence reads, Shannon α-diversity index (analyzed by Mann–Whitney test), and Bray-Curtis β-diversity distance (evaluated by PERMANOVA) with tumor and immune-related categorical variables. The right heatmap displays correlations with continuous variables, using Spearman regression for α-diversity and Mantel regression for β-diversity. Statistical significance is indicated by * P < 0.05, ** P < 0.01, *** P < 0.001. (B) Kaplan-Meier survival curves showing differences in survival times between groups defined by the non-negative matrix factorization (NMF) algorithm based on tumor immune microenvironment characteristics; significance assessed using the Log rank test. (C) Differences in bacterial sequence reads and Shannon α-diversity index between the two groups were analyzed using Student’s t-test (* P < 0.05). (D) PCA of Bray-Curtis distances between the two groups; differences in bacterial β-diversity were assessed using PERMANOVA. (E) Volcano plot showing differences in bacterial abundance between the groups, identified using DESeq2 analysis. Upregulated taxa indicate enrichment in tumors (|Log2FC| > 0.5); P < 0.05 was considered statistically significant.

Using non-negative matrix factorization (NMF) based on tumor microenvironment (TME) signatures, we classified LUAD patients into two clusters (Figure 2B and Table S2). Survival analysis revealed significant differences between these clusters, with Cluster 1 associated with a better prognosis (Figure 2B). Cluster 1 exhibited lower bacterial read counts than Cluster 2 (Figure 2C), though no significant differences in alpha diversity were observed between the clusters (Figure 2C). Beta diversity analysis indicated significant differences between the clusters (PERMANOVA, P=0.037) (Figure 2D). DESeq2 analysis showed that genera such as Cylindrospermopsis and Saccharibacter were enriched in Cluster 1, which correlated with improved prognosis (Figure 2E).

Tumor Commensal Bacteria Correlate with an Inflammatory TME in LUAD

Building on these observations, we further analyzed TME signatures and found that Cluster 1 was characterized by lower proliferation rates and heightened immune responses, including elevated expression of MHC class I molecules, natural killer (NK) cells, and effector cells (Figure 3A). Immune checkpoint molecules such as CTLA4, LAG3, CD80, and CD86 were also significantly increased in Cluster 1 (Figure 3B). Immune infiltration analysis using Cibersortx revealed enrichment of pro-inflammatory cells, including effector T cells, in Cluster 1, suggesting a stronger immune response (Figures 3C and S4).

Figure 3 Significant correlations between tumor-associated microbiota, tumor immune microenvironment, and TME signature genes. (A) Heatmap displaying clustering of tumor microenvironment (TME) signature genes across different clusters. (B) Differential expression of immune checkpoint-related genes between clusters, assessed using Student’s t-test (**** P < 0.0001). (C) Immune infiltration analysis by Cibersortx suggests differences in immune cells (* P < 0.05). (D) Multi-omics interaction network constructed among clusters for TME signature genes, immune checkpoint genes, and viral and bacterial abundances using Spearman correlation analysis, visualized with Cytoscape. Node color indicates type, node size reflects weight, and line color intensity represents interaction strength (intra-group interactions not shown). (E) Volcano plot showing differential gene expression between clusters calculated using DESeq2 analysis (|Log2FC| > 0.5); P < 0.05 was considered statistically significant. (F) Interaction network and clustering of differentially expressed genes between clusters, constructed using STRING and MCODE algorithms.

We constructed a multi-omics interaction network incorporating TME features, immune checkpoint genes, viral abundances, and differentially abundant bacterial genera (Figure 3D). Within this network, commensal bacteria such as Sulfolobus, Cylindrospermopsis, and Cyanothece were prominent and strongly correlated with immune microenvironment characteristics, including effector cell presence and NK cell activity. Viral interactions within the network were relatively weak.

Differential gene expression analysis between the clusters identified significant differences in protein-coding genes, pseudogenes (eg, TLK2P2), and non-coding RNAs (eg, TTC3-AS1) (Figure 3E). A protein-protein interaction (PPI) network, constructed from genes enriched in Cluster 2 and analyzed with minimal common oncology data elements (MCODE), identified a core set of interacting genes comprising typical inflammatory factors (Figure 3F). These findings emphasize the pivotal role of the microbiota in immunoregulation in LUAD and indicate that commensal bacterial enrichment is closely tied to inflammatory characteristics within the TME. Given these strong associations between bacterial presence and inflammatory signaling, we proceeded to investigate the mutation landscapes of inflammation-associated genes in LUAD.

Mutation Landscapes of Microbiota-Associated Inflammatory Genes in LUAD

Following our characterization of TME inflammatory profiles and bacterial compositions, we explored the genetic architecture of immune-related genes within the identified clusters. Analysis of the ImmPort dataset revealed differential expression of key inflammatory regulators: IFNGR1, CD40, and ISG15 were upregulated in Cluster 2 compared to Cluster 1, whereas BRAF, IKBKB, and IRF9 were downregulated (Figure 4A). These genes may serve as potential mechanistic links between bacterial presence and inflammatory responses in the TME. The differential expression patterns of immune-related genes, in the context of varying bacterial abundances, suggested possible genetic mechanisms underlying these associations.

Figure 4 Microbiome-associated inflammatory gene expression and mutation profiles at varying levels of immune activation in LUAD. (A) Differences in expression of immune-related genes between clusters; upregulated genes are above the x-axis, downregulated genes below. Different colors represent various immune cell types. (B) Correlation analysis between significant commensal bacteria and differentially expressed immune-related genes. In the network diagram, rectangles represent bacteria, circles represent genes, lines indicate correlations, and line opacity reflects interaction strength (intra-group interactions not shown). (C) Visualization of mutations and expression profiles of immune-related genes with significant mutations across clusters using cBioPortal. The eight genes with significant mutations are indicated in the legend.

We developed an interaction network between enriched bacterial species and differentially expressed immune genes, which showed that bacterial species were positively correlated with genes upregulated in Cluster 2 and negatively correlated with those downregulated (Figure 4B). This indicates a coordinated regulation of immune responses by the microbiota. Analysis of the TCGA dataset revealed that, for genes such as ISG15 exhibiting abnormal expression in over 5% of cases, these changes could not be attributed to genetic mutations (Figure 4C), suggesting that alternative regulatory mechanisms may mediate the microbiota’s influence on immune gene expression.

Tumor-Associated Microbiota Influence Gene Expression and Prognosis via ceRNA Networks and Chromatin Accessibility

We extended our analysis to investigate how microbiota might regulate gene expression and affect prognosis in LUAD. Differential expression analysis identified the long non-coding RNA (lncRNA) LCIIAR as significantly upregulated in LUAD clusters (|Log²FC|>1; P<0.001), with greater significance than other genes. Additional lncRNAs associated with the differentially expressed genes are presented in Figure 5A and Table S3. In LUAD tumor tissues, ISG15 and LCIIAR exhibited a significant positive correlation within the competing endogenous RNA (ceRNA) network (P=7.18×10⁻³) (Figure 5B and Figure S3). Spearman correlation analysis further confirmed significant associations among LCIIAR expression, ISG15 mRNA levels, and Cylindrospermopsis abundance (Figure 5C and Table S4).

Figure 5 Tumor-associated microbiota influence gene expression and prognosis in multiple cancers through ceRNA networks and chromatin accessibility. (A) Volcano plot showing differential expression of lncRNA LCIIAR in LUAD clusters, with higher significance than other genes (|Log2FC| > 1); P < 0.001 was considered statistically significant. (B) In LUAD tumor tissues, ISG15 and LCIIAR in the ceRNA network exhibit a significant positive correlation (P = 7.18 × 10⁻³). (C) Significant Spearman correlations among the expression of lncRNA LCIIAR, mRNA ISG15, and the abundance of Cylindrospermopsis. (D) Survival analysis showing the association between the expression of ceRNA network gene LCIIAR and LUAD patient survival. (E) Survival analysis showing the association between the expression of ceRNA network gene LIMD1 and LUAD patient survival. (F) The lncRNA-mediated ceRNA pathway (LCIIAR, hsa-miR-22-3p, hsa-miR-3127-5p, ISG15) was identified based on calculations using the R starBase database. (G) Visualization from the TCGA-Xena database showing the relationships among overall survival, expression and methylation, and chromatin accessibility of ISG15 in TCGA pan-cancer samples.

Survival analyses indicated that higher expression levels of the ceRNA network genes LCIIAR and ISG15 were associated with poorer survival in LUAD patients (Figures 5D, E and S3). Using the R starBase database, we identified an lncRNA-mediated ceRNA pathway involving LCIIAR, hsa-miR-22-3p, hsa-miR-3127-5p, and ISG15 (Figure 5F). Data from the TCGA-Xena database highlighted relationships among overall survival, ISG15 gene expression, methylation, and chromatin accessibility across TCGA pan-cancer samples (Figure 5G). Tumor survival time appeared somewhat associated with ISG15 expression and chromatin accessibility in the ISG15 coding region, but not with DNA methylation in this region. Comparable data for LCIIAR are currently unavailable.

Cell Experiments Confirm the Role of LCIIAR and ISG15 in Enhancing Lung Cancer Cell Proliferation

To evaluate the effect of Cylindrospermopsis on lung adenocarcinoma cells, we co-cultured its metabolite, Cylindrospermopsin (CYN), with Lewis lung carcinoma cells. CYN reduced the expression of LCIIAR and ISG15 at low concentrations and LCIIAR expression at high concentrations (Figure 6A and B). To confirm the functional significance of LCIIAR and ISG15 in lung cancer, we conducted experiments using the Lewis lung carcinoma cell line. Transfection with siRNAs targeting LCIIAR significantly reduced its expression (Figure 6C), and siRNA-mediated knockdown of ISG15 similarly decreased its expression (Figure 6D). Furthermore, LCIIAR knockdown led to reduced ISG15 mRNA and protein levels via the ceRNA network (Figures 6E, F and S5).

Figure 6 Cell experiments confirm that the role of bacterial metabolites and ceRNA network genes LCIIAR—ISG15 significantly influence lung cancer cell proliferation. (A) Cylindrospermopsin (CYN) regulates the expression of LCIIAR and (B) ISG15 in Lewis cell-line. (C) LCIIAR-siRNA reduces LCIIAR expression in Lewis cell-line. (D) ISG15-siRNA reduces ISG15 expression. (E) LCIIAR-siRNA reduces ISG15 mRNA expression via the ceRNA network in Lewis cell-line. (F) LCIIAR-siRNA reduces ISG15-protein expression in Lewis cell-line. (G) LCIIAR increases the expression level of ISG15 through post-transcriptional regulation. (H and I) CCK-8 cell proliferation assays indicate that altering the expression levels of ISG15 and LCIIAR significantly regulates the proliferation of lung cancer cell lines, with the most pronounced effect observed at 72 hours. (*P < 0.05, **P < 0.01, ***P < 0.001 ****P < 0.0001).

Luciferase experiments demonstrated that LCIIAR increased the translation of luciferase-ISG15CDS-3’UTR, thereby enhancing ISG15 expression through post-transcriptional regulation. This effect was counteracted by miR-22-3p and miR-3127-5p mimics, while the miR-22-3p inhibitor amplified it (Figures 6G and S6). Cell proliferation assays using CCK-8 showed that altering LCIIAR and ISG15 expression significantly affected lung cancer cell proliferation, with the most pronounced effects observed 72 hours post-transfection. These results indicate that both genes play a critical role in promoting lung cancer cell proliferation (Figure 6H and I).

Discussion

In this study, we performed an in-depth analysis of microbial diversity and composition across various cancer types, focusing particularly on lung adenocarcinoma (LUAD). Our results revealed that LUAD tissues exhibit a distinct microbiota compared to other cancers, such as lung squamous cell carcinoma (LUSC), breast carcinoma (BRCA), and thyroid carcinoma (THCA). Notably, bacterial genera including Cylindrospermopsis, Cyanothece, and Sulfolobus were significantly enriched in LUAD tissues, pointing to a unique microbial signature associated with this malignancy. These findings align with growing evidence that the lung microbiome contributes to tumorigenesis and tumor progression.^9,29 By identifying significant correlations between specific bacteria, immune characteristics, and clinical outcomes in LUAD, our study expands upon this foundation.

The elevated bacterial counts and distinct microbial profiles observed in LUAD suggest that the lung microbiota exerts a substantial influence on the tumor microenvironment (TME). By clustering LUAD patients based on immunological features, we identified two groups with markedly different prognoses. Cluster 1, linked to improved survival, displayed lower bacterial read counts and enrichment of genera such as Cylindrospermopsis and Saccharibacter. This cluster also exhibited robust immune responses, characterized by increased expression of MHC class I molecules, effector cells, and immune cell infiltration. Cibersortx analysis further confirmed higher proportions of effector T cells and M1 macrophages in Cluster 1, reinforcing the presence of a stronger immune response in this group. These data imply that specific commensal bacteria may foster an immune-activated TME, bolstering antitumor immunity and enhancing patient prognosis.

Our experimental findings provide deeper insight into the role of Cylindrospermopsis. We found that Cylindrospermopsin (CYN), a toxin produced by this bacterium, modulates the LCIIAR–ISG15 axis in a concentration-dependent manner. At low concentrations, CYN activates this axis and upregulates ISG15 expression, whereas at high concentrations, it suppresses ISG15 expression. Additionally, CYN exerts toxic effects on human cells, negatively regulating cell proliferation. Existing literature indicates that CYN can significantly impair lymphocyte proliferation and immune function,³⁰ suggesting a complex interplay with tumor proliferation and the immune microenvironment. This dual role may represent a mechanism by which Cylindrospermopsis contributes to tumor development. In contrast, current research on Cyanothece reveals no evidence of its influence on immune function. For Sulfolobus, some studies propose that it may promote immune evasion via CRISPR-Cas and CRISPR-Cmr systems, potentially enhancing tumor growth within the TME.^31,32 The increased abundance of Sulfolobus in LUAD tissues could thus contribute to an immunosuppressive microenvironment, facilitating tumor progression.

To elucidate the molecular pathways connecting the microbiota to gene expression and prognosis in LUAD, we investigated downstream mechanisms. Our analysis identified the long non-coding RNA (lncRNA) LCIIAR and the gene ISG15 as significantly upregulated in LUAD, both correlating with poorer survival. We delineated a competing endogenous RNA (ceRNA) network involving LCIIAR, hsa-miR-22-3p, hsa-miR-3127-5p, and ISG15. The positive correlation between LCIIAR and ISG15 expression, alongside their association with Cylindrospermopsis abundance, suggests that the microbiota may regulate gene expression through ceRNA-mediated pathways.

Cell-based experiments validated the functional roles of LCIIAR and ISG15 in driving lung cancer cell proliferation. Knockdown of LCIIAR reduced its own expression and concurrently decreased ISG15 levels via the ceRNA network. Modulating the expression of these genes significantly altered cancer cell proliferation, with the most notable effects observed 72 hours post-transfection. These findings highlight the pivotal roles of LCIIAR and ISG15 in tumor growth, positioning them as potential therapeutic targets.

Analysis of TCGA-Xena data further revealed that tumor survival time correlates to some extent with ISG15 expression and chromatin accessibility in its coding region, though not with DNA methylation in this area. Comparable epigenetic data for LCIIAR remain unavailable, marking a gap for future investigation. Together, these results illuminate the intricate regulatory networks linking the microbiota, non-coding RNAs, and epigenetic modifications in LUAD.

Our findings add to the expanding evidence base implicating the tumor microbiota in cancer development and progression. While much of the prior research has centered on the gut microbiome, particularly in colorectal cancer,¹¹ our study underscores the significance of the lung microbiota in LUAD. Analogous to gut microbiota–ceRNA interactions, which modulate gene expression and tumor progression,³³ our data suggest that lung microbiota exert similar effects via ceRNA networks. However, the distinct bacterial genera and molecular pathways involved reflect the unique microbial and tissue contexts of the lung, distinguishing our observations from those in gut-focused studies.

The delineation of regulatory networks involving LCIIAR and ISG15 provides novel insights into how the microbiota shapes tumor biology. These discoveries open avenues for developing diagnostic markers and therapeutic strategies targeting the lung microbiome and its associated pathways in LUAD. For example, interventions aimed at modulating the microbiota or inhibiting the LCIIAR–ISG15 axis could enhance antitumor immunity and improve clinical outcomes.

Nevertheless, our study has limitations. Although sourced from a reliable database, the microbial data may contain errors or contamination. Additionally, the observational design limits our ability to establish causality. Future studies, including in vivo models, are essential to clarify the mechanistic contributions of specific bacteria to LUAD progression.

In conclusion, our comprehensive analysis demonstrates that the tumor-associated microbiota in LUAD is intricately tied to immune characteristics, gene expression profiles, and patient prognosis. By elucidating microbial drivers and downstream molecular mechanisms, particularly the LCIIAR–ISG15 axis, this study lays the groundwork for future efforts to leverage the microbiome for therapeutic advancements in lung adenocarcinoma.

Data Sharing Statement

All raw data and code are available upon request.

Ethics Statement

This study was exempt from ethical review approval based on item 1-4 of Article 32 of the Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects, dated February 18, 2023, China.

Author Contributions

Shipu Liu: data analysis; writing – original draft. Zijian Zhang: investigation; project administration; writing – review and editing. All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was supported by the Beijing Natural Science Foundation (7242056).

Disclosure

The authors have no conflicts of interest in this work.

References

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3. Riquelme E, Zhang Y, Zhang L, et al. Tumor microbiome diversity and composition influence pancreatic cancer outcomes. Cell. 2019;178(4):795–806e12. doi:10.1016/j.cell.2019.07.008

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6. Iida N, Dzutsev A, Stewart CA, et al. Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment. Science. 2013;342(6161):967–970. doi:10.1126/science.1240527

7. Sepich-Poore GD, Zitvogel L, Straussman R, et al. The microbiome and human cancer. Science. 2021;371(6536). doi:10.1126/science.abc4552

8. Hong J, Guo F, Lu SY, et al. F. nucleatum targets lncRNA ENO1-IT1 to promote glycolysis and oncogenesis in colorectal cancer. Gut. 2021;70(11):2123–2137. doi:10.1136/gutjnl-2020-322780

9. Fidelle M, Rauber C, Alves Costa Silva C, et al. A microbiota-modulated checkpoint directs immunosuppressive intestinal T cells into cancers. Science. 2023;380(6649):eabo2296. doi:10.1126/science.abo2296

10. Routy B, Lenehan JG, Miller WH Jr, et al. Fecal microbiota transplantation plus anti-PD-1 immunotherapy in advanced melanoma: a Phase I trial. Nat Med. 2023;29(8):2121–2132. doi:10.1038/s41591-023-02453-x

11. Louis P, Hold GL, Flint HJ. The gut microbiota, bacterial metabolites and colorectal cancer. Nat Rev Microbiol. 2014;12(10):661–672. doi:10.1038/nrmicro3344

12. Dickson RP, Erb-Downward JR, Martinez FJ, et al. The microbiome and the respiratory tract. Annu Rev Physiol. 2016;78:481–504. doi:10.1146/annurev-physiol-021115-105238

13. Tsay JJ, Wu BG, Badri MH, et al. Airway microbiota is associated with upregulation of the PI3K pathway in lung cancer. Am J Respir Crit Care Med. 2018;198(9):1188–1198. doi:10.1164/rccm.201710-2118OC

14. Nicholson AG, Tsao MS, Beasley MB, et al. The 2021 WHO classification of lung tumors: impact of advances since 2015. J Thorac Oncol. 2022;17(3):362–387. doi:10.1016/j.jtho.2021.11.003

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Samsung is taking background noise cancellation to the next level. With the upcoming One UI 8.0 update, the Audio Eraser feature is getting faster and expanding beyond the Gallery app. Previously, Audio Eraser was limited to the Gallery in One UI 7.0, where it could detect up to six types of audio in videos and let users adjust their volume manually. However, the process involved tapping a Galaxy AI button and entering a separate editing screen, making it a bit slow and clunky.

Now, One UI 8.0 changes that. Samsung has not only improved the speed of Audio Eraser but also integrated it into more stock apps, including Samsung Notes and Voice Recorder. When you record audio in either app, a Galaxy AI button appears below the clip. Just tap it, and the app will instantly remove background noise.

This enhancement means users can now clean up voice memos or lecture recordings with a single tap, making note-taking and audio capture much clearer.

In the Gallery app, things are now more seamless too. Instead of opening a dedicated screen, users can just tap the Audio Eraser icon in the corner while playing a video. The noise reduction happens instantly.

This improved version of Audio Eraser has appeared in the latest internal beta build of One UI 8.0 for the Galaxy S25 series. It’s not yet part of the public beta, but sources suggest it will be included in the next public beta release for supported Galaxy devices.

As Samsung continues to integrate Galaxy AI deeper into everyday apps, features like Audio Eraser are becoming more practical and accessible.

Also read:

Samsung’s Galaxy AI Wants to Be More Than Just a Tool—It Wants to Know You

Introduction

Chronic obstructive pulmonary disease (COPD) is a major public health concern, as it remains one of the leading causes of death worldwide.¹ Patients with COPD often experience reduced musculoskeletal mass due to chronic inflammation, malnutrition, and inactivity resulting from dyspnea.^2,3 This musculoskeletal loss contributes to sarcopenia and osteoporosis, increasing fracture risk and further inactivity, which worsens prognosis.^4,5 Exercise plays a vital role in maintaining healthy body composition.⁶ The Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2025 report emphasizes the importance of pulmonary rehabilitation.⁷ However, its availability is limited due to high costs and a shortage of therapists.⁸ Encouraging patients with COPD to establish their exercise habits may promote physical activity (PA) and help reduce healthcare costs.

Musculoskeletal mass reaches a peak in young adulthood and gradually declines with age.⁹ Higher levels of PA during youth are associated with increased lean mass, and maintaining PA in later life helps preserve it.⁶ However, to the best of our knowledge, no study has clarified the associations of adolescent versus current exercise habits on body composition in patients with COPD. Moreover, the association of adolescent exercise on current PA levels and pulmonary function in patients with COPD has not been thoroughly investigated.

We hypothesized that adolescent and current exercise habits would independently contribute to an improved clinical profile of COPD. In the present study, we aimed to clarify the associations of adolescent and current exercise habits with body composition, PA, and pulmonary function. Furthermore, we determined whether disease severity influences these effects.

Materials and Methods

Participants

Outpatients with COPD or pre-COPD at our university hospital between October 2021 and December 2023 were enrolled in this cross-sectional study, as part of exploratory research. Participants with a smoking history of more than 10 pack-years and no exacerbation of respiratory symptoms within 4 weeks prior to enrollment were included. The exclusion criteria included severe heart failure, progressive malignant diseases, or other chronic pulmonary diseases, except for stable asthma. COPD and pre-COPD were diagnosed according to the GOLD recommendations.⁷ In this study, Pre-COPD and GOLD 1 were defined as mild COPD (forced expiratory volume in 1 s [FEV₁] % predicted ≥ 80%), and GOLD 2 to 4 were defined as moderate-to-severe COPD (FEV₁ % predicted < 80%). This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. This study was approved by the Ethics Committee of Shiga University of Medical Science (registration number: R2021-026), and all participants provided written informed consent before participation.

Exercise Habit Questionnaire

Self-report questionnaires were used to ask the participants about their exercise habits at different stages of their lives (Figure S1). We defined exercise habit as engaging in any sport or exercise at least twice a week for a minimum of 30 min per session.¹⁰ Participants were categorized as “adolescent exercisers” if they had exercise habits between the ages of 16 and 22 years for at least 1 continuous year,¹¹ and as “current exercisers” if they had maintained exercise habits for at least 1 year.

PA Assessment

PA was measured using a triaxial accelerometer (Active Style Pro HJA-750C; Omron Healthcare, Kyoto, Japan). The participants wore the device during the daytime, except while bathing, for 14 days. Rainy days were excluded, and the data were validated as previously described.¹² The mean durations of PA based on metabolic equivalents (METs) and step counts were analyzed. The intensity of PA was defined as moderate to vigorous PA (moderate to vigorous physical activity [MVPA] ≥ 3.0 METs), light PA (1.6 to 2.9 METs), and sedentary behavior (1.0 to 1.5 METs), based on a previous report.¹³

Body Composition Analysis

Body composition was assessed using direct segmental multifrequency bioelectrical impedance analysis (BIA) (InBody S10; InBody Co., Ltd., Seoul, South Korea).⁶ Multifrequency measurements were performed for each body segment in the supine position. Fat-free mass index (FFMI) and fat mass index (FMI) were calculated by dividing the respective mass values by height squared. Phase angle (PhA), a raw BIA variable, reflects the relationship between reactance and resistance in the body as a conductor, and a smaller value indicates a worse cellular condition.¹⁴ The PhA at 50 kHz provides information about muscle quality.^14,15

Handgrip Strength Test

Handgrip strength was measured twice per hand, and the maximum value was analyzed.

6-minute Walk Test

A 6-minute walk test was performed according to the guidelines.¹⁶

Pulmonary Function Tests

Pulmonary function tests were performed after the inhalation of 20 μg procaterol using a spirometer (FUDAC77; Fukuda Denshi, Tokyo, Japan), according to the ATS/ERS guidelines.¹⁷ Carbon monoxide diffusing capacity was measured using the single-breath washout technique. Predicted spirometry values were calculated according to the Japanese Respiratory Society guidelines.¹⁸

Computed Tomography Imaging

Chest computed tomography (CT) was performed in the supine position using a 320-detector row CT scanner (Aquilion ONE; Canon Medical Systems Corporation, Tochigi, Japan) with full inspiration. The 20 μg procaterol inhalation was given 1 hour before CT scan. Emphysematous lesions were assessed as the percentage of low attenuation volume (LAV%), which is defined as the percentage of lung volume exhibiting CT attenuation values below –950 Hounsfield units. Small airway lesions were assessed by plotting the square root of the wall area (√Aaw) of each visible bronchial segment against its internal perimeter, and estimating the √Aaw for a hypothetical airway with an internal perimeter of 10 mm using linear regression (√Aaw at Pi10). All parameters were quantified using the Apollo software version 1.2 (VIDA, Coralville, IA, USA), based on previous reports.^19,20

Statistical Analysis

Statistical analyses were performed using JMP Pro 17 software (SAS Institute, Cary, NC, USA). Differences between exercisers and non-exercisers were evaluated using the Wilcoxon rank-sum and Fisher’s exact tests. The correlations between PA and body composition were assessed using Spearman’s rank correlation coefficients. Statistical significance was set at p < 0.05.

Results

A total of 86 participants (81 men and 5 women) were enrolled in this study. Seventy-two patients were diagnosed with COPD, and 36 patients were diagnosed with moderate-to-severe COPD (Table 1). As presented in Figure S2, there was no relationship between the presence or absence of adolescent exercise habits and current exercise habits. Adolescence and current exercise habits were not related to exercise habits in the 30s to 40s.

Table 1 Patient Characteristics

Association Between Adolescent Exercise Habits and Current Conditions

The demographic and clinical characteristics of the participants were not significantly different between adolescent exercisers and non-exercisers (Table S1). Adolescent exercise habits did not influence step count, PA duration at any intensity, (Figure 1A–D), or body composition parameters (Figure 1E–H). When pulmonary function and CT imaging were determined, only vital capacity was significantly higher in adolescent exercisers than in non-exercisers (p = 0.038; Table 2).

Table 2 Associations of Adolescent Exercise Habits with Pulmonary Functions and CT Imaging Biomarkers

Figure 1 Associations of adolescent exercise habits with physical activity and body composition parameters. (A) Comparison of step counts per day. (B) Comparison of MVPA per day. (C) Comparison of light PA per day. (D) Comparison of sedentary behavior per day. (E) Comparison of fat-free mass index. (F) Comparison of bone mineral content. (G) Comparison of phase angle at 50 kHz. (H) Comparison of fat mass index. Abbreviations: MVPA, moderate to vigorous physical activity; PA, physical activity.

Association Between Current Exercise Habits and Current Conditions

Table S2 presents the demographic and clinical characteristics of current exercisers and non-exercisers. Current non-exercisers were more likely to be current smokers or female sex. They also showed a non-significant trend toward lower grip strength. Current exercisers were more physically active (p < 0.001; Figure 2A, and p < 0.001; Figure 2B) throughout the day. The duration of light PA tended to be longer in current exercisers than in non-exercisers, but this difference was not significant (p = 0.059; Figure 2C). Current exercisers spent less time engaging in sedentary behaviors (p = 0.021; Figure 2D). Body composition measures, including FFMI (p = 0.002; Figure 2E), bone mineral content (BMC, p = 0.009; Figure 2F), and PhA (p = 0.017; Figure 2G), were significantly higher in current exercisers than in non-exercisers, whereas the FMI (p = 0.52; Figure 2H) and body mass index (BMI; p = 0.42; Table S2) showed no significant differences. Multiple linear regression analysis, adjusted for age, sex, FEV₁ % predicted, and both adolescent and current exercise habits, revealed that current exercise habits were independent factors affecting FFMI, BMC, and PhA (Table 3). The results remained consistent when an interaction term between adolescents and their current exercise habits was incorporated into the multivariate analysis (data not shown). Although CT imaging parameters were not related to current exercise habits, diffusing capacity was higher in current exercisers than in non-exercisers (p = 0.006; Table 4).

Table 3 Factors Associated with Body Composition Parameters Based on the Multiple Linear Regression Test

Table 4 Associations of Current Exercise Habits with Pulmonary Functions and CT Imaging Biomarkers

Figure 2 Associations of current exercise habits with physical activity and body composition parameters. *is significant at the P < 0.05 level. (A) Comparison of step counts per day. (B) Comparison of MVPA per day. (C) Comparison of light PA per day. (D) Comparison of sedentary behavior per day. (E) Comparison of fat-free mass index. (F) Comparison of bone mineral content. (G) Comparison of phase angle at 50 kHz. (H) Comparison of fat mass index. Abbreviations: MVPA, moderate to vigorous physical activity; PA, physical activity.

Associations Between MVPA per Day and Body Composition Parameters by COPD Severity

As presented in Table 5 and Figure 3, MVPA per day was positively correlated with the FFMI and PhA, especially in moderate-to-severe COPD (rho = 0.51, p = 0.003; rho = 0.45, p = 0.011, respectively). In contrast, in mild COPD, MVPA per day was associated with the PhA and FMI (rho = 0.32, p = 0.024; rho = −0.29, p = 0.042, respectively).

Table 5 Associations Between MVPA per Day and Body Composition Parameters by COPD Severity

Figure 3 Correlations between MVPA per day and muscle quantity or quality indicators in moderate-to-severe COPD. (A) Correlation between MVPA per day and fat-free mass index. (B) Correlation between MVPA per day and phase angle at 50 kHz. Abbreviation: MVPA, moderate to vigorous physical activity.

Discussion

In patients with COPD, adolescent exercise habits showed no significant association with daily PA levels or body composition, although they were associated with increased lung volume. In contrast, current exercise habits were associated with prolonged engagement in higher PA, with a reduction in sedentary behavior, improved body composition, and enhanced diffusing capacity. In addition, the correlation between PA and musculoskeletal parameters varied with COPD severity and was more pronounced in patients with moderate-to-severe COPD than in those with mild COPD.

Previous studies have suggested that in healthy older adults, youth sports participation is associated with elevated PA levels and improved body composition in later life.^21,22 Contrary to our hypothesis, adolescent exercisers neither showed a tendency to maintain a high level of PA nor a better body composition in later life. Prolonged chronic inflammation, disease-related inactivity, particularly due to breathlessness, and COPD comorbidities may negate any long-term benefits of adolescent exercise.^2,5 Although adolescent exercise habits did not significantly affect COPD features, a notable finding was a higher lung volume in adolescent exercisers than in non-exercisers. Our results align with those of earlier studies in healthy adults;^23,24 however, to the best of our knowledge, our study is the first to describe this association in patients with COPD. Although the mechanisms underlying lung development through exercise are not fully understood, regular exercise during adolescence may be associated with lung development.^23,24 As impaired lung development reportedly contributes to the future onset of obstructive lung diseases, early life exercise may play a beneficial role in preventing the development or progression of COPD.²⁵

In the present study, current exercisers were physically active regularly and did not exhibit sedentary behaviors such as prolonged periods of television viewing. Increased PA is associated with improved body composition in COPD.^26,27 Consistently, our study demonstrated that current exercisers had significantly higher musculoskeletal measures than non-exercisers. Furthermore, these associations persisted even after adjusting for confounders, including adolescent exercise habits. It is possible that patients with well-controlled COPD are more likely to maintain exercise routines. However, regular exercise, even after the onset of COPD, may be more important than exercise during adolescence for achieving optimal body composition, leading to a good prognosis and indirectly contributing to reduced healthcare costs.⁴ Moreover, a low level of PA is associated with muscle wasting and reduced exercise performance.²⁷ Although the difference in grip strength was not statistically significant—possibly due to the relatively small sample size—grip strength, a reported prognostic factor, tended to be higher in current exercisers.^28,29

Diffusing capacity was significantly higher in current exercisers, which is consistent with the findings of a previous observational study.³⁰ However, there were no statistically significant differences in CT imaging factors. Individuals with a higher diffusing capacity may engage in higher levels of PA. However, regular exercise may promote pulmonary circulation at the capillary level and prevent a decrease in diffusing capacity,³⁰ regardless of morphological changes. Further studies are required to investigate these causal relationships.

In addition, we observed correlations between the duration of MVPA and musculoskeletal parameters in patients with moderate-to-severe COPD, consistent with a previous study.³¹ Patients with more severe COPD reportedly have lower musculoskeletal mass and experience faster muscle loss than those with mild disease.^2,3,20 Notably, exercise-induced changes in the muscle are not impaired in patients with severe COPD, and exercise may have an even greater impact on body composition in this population.³² Our finding further suggests that maintaining daily PA levels is crucial for preventing musculoskeletal mass loss, particularly in patients with more severe COPD.

This study had some limitations. This was a single-center study conducted in Japan with a relatively small sample size. As a cross-sectional study, it cannot establish causal relationships, and recall bias may have occurred. Moreover, nutritional supplementation, which may be related to body composition, was not evaluated. However, no participant reported anorexia at the time of examination. Further prospective studies are needed to confirm our findings and explore the mechanisms underlying these associations.

Conclusion

Exercise during adolescence may be associated with increased lung volume. However, even after the onset of COPD, regular exercise routines can help maintain PA, improve body composition, and diffusing capacity, particularly if the disease has progressed. The results of this study effectively underscore the importance of exercise habits in patients with COPD.

Acknowledgments

The authors would like to thank Yasutaka Horii, Yukie Miyatake, and Yoko Naito for their assistance throughout this study.

Disclosure

The authors report no conflicts of interest in this work.

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32. Mølmen KS, Hammarström D, Falch GS, et al. Chronic obstructive pulmonary disease does not impair responses to resistance training. J Transl Med. 2021;19(1):292. doi:10.1186/s12967-021-02969-1

Mecha BREAK, a sci-fi mecha shooter game developed by Chinese studio Seasun Games, launched globally on Wednesday across PC, PlayStation 5, and Xbox platforms. Touted as a beacon of hope for AAA-quality Chinese mecha games, the title saw a peak of over 130,000 concurrent players on Steam in the past two days.

Despite (or perhaps because of) the hype, Mecha BREAK has received a tepid reception from players so far, holding a mixed rating on Steam with over 6,000 reviews and a modest 63% approval rate.

Diverse mecha designs and gameplay modes

Set in a near-future world ravaged by the carbon-silicon substance EIC, Mecha BREAK follows elite mech pilots fighting to save humanity from an escalating existential threat. The game features three core gameplay modes: 6v6 Edge Battlefield (strategy-focused team combat), 3v3 Ace Sequence (death-match), and PvPvE Marsh Mark (loot-and-extract survival mode).

Mecha BREAK is free-to-play but offers in-game purchases for game skins, season passes, gears, extra bonuses, and other premium content. The current version offers 12 free mechs. They are divided into five roles: assault, melee, sniper, defense, and support. Each mech also falls into a weight class of light, medium, or heavy, which affects its movement speed, armor durability, and skill cooldowns.

UI issues disrupt the experience

Many players on Steam have criticized the user interface, describing it as cluttered, confusing, and poorly organized. Key functions are buried in deep menu layers, while overlapping prompts create an overwhelming experience, especially for first-time players.

Poor color contrast and low icon recognizability, combined with interaction logic that ignores typical PC game conventions, have led some players to complain that the game “feels like a mobile UI ported directly to PC.” 

Monetization discomfort and unsatisfying combat feedback

Early Steam reviews have also voiced strong dissatisfaction with the game’s monetization approach, particularly the instant pop-up of a RMB 288 ($40) limited-time offer immediately after the tutorial. Some players argued that the early emphasis on spending detracts from the gameplay experience and breaks immersion.

In an interview with TechNode, an online gamer known as Phantom Core criticized the game’s combat compared to titles such as Armored Core VI. He described the hit feedback as “plastic”, saying that the sound and visual effects are not properly matched and that the attack impacts are underwhelming. 

Core gameplay balance faces questions

The game’s 6v6 battlefield mode has drawn criticism for balance issues. Steam players report a clear disparity in mech performance, which makes fair competition difficult. Heavier defense-focused mechs offer disproportionately high firepower and survivability, whereas lighter units intended as assassins are under-powered and poorly tuned, Phantom Core said.

The PvPvE (Player vs Player vs Environment) mode also brought complaints on Steam for resource imbalances. Players who invest more time or money can quickly power up their mechs via boss drops and lootable upgrades, while average players fall behind in progression. This system translates directly into PvP combat power gaps, leading to a “grind (or spend) more, win more” experience that widens the divide between veteran and new players, Phantom Core explained.

Can Mecha BREAK defy the drop?

Despite ongoing controversy around the title, the development team is expected to continue refining the gameplay and system mechanics in response to player feedback. Whether the game can break away from the common pattern of early hype followed by rapid decline and disappointment remains to be seen.

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Sergey Kharitonov, whose work on small-scale liquid simulations we’ve featured before, has presented a new water simulation setup and shared insights into his approach.

Adding water droplets to close-up renders is a popular technique for boosting visual interest and making objects appear more dynamic and detailed. While creating static droplets on a surface is relatively straightforward, even for beginners, animating them to move realistically across surfaces is a much more complex challenge.

As Sergey mentioned, he personally considers two existing methods to be among the most realistic: a procedural tool developed by José Mauro Lobão and an X-Particles rig for Cinema4D created by Sam Tato. There’s also a built-in Houdini shelf tool located under Particle Fluids – Condensation, but he finds it relatively slow and difficult to control, likely due to its reliance on the FLIP solver at its core. The artist decided to take on the challenge and build a version entirely from scratch. Here’s the algorithm he followed:

Valve’s annual Steam Summer Sale is live and PC gamers can now take advantage of some of the deepest discounts of the year.

From critically acclaimed RPGs to blockbuster shooters and fan-favourite adventures, this year’s sale features major titles across different genres – with many up to 80% off.

The sale runs through July 10, giving players a limited chance to buy some of the most critically acclaimed titles across the last couple of years.

Here are some of the top offers:

🎮 Role-playing & fantasy:

1. Final Fantasy VII Rebirth – $59.99 → $35.99 (40% off)

The second chapter in Square Enix’s ambitious remake builds on the original’s legacy with modern mechanics and expanded narrative depth.

2. Elden Ring – $59.99 → $35.99 (40% off)

Still hailed as a genre-defining masterpiece, this dark fantasy epic from FromSoftware continues to top must-play lists for its design and atmosphere.

3. Baldur’s Gate 3 – $29.99 → $23.99 (20% off)

Larian’s award-winning RPG, rich with Dungeons & Dragons lore, offers exceptional replayability and character freedom.

4. God of War: Ragnarök – $49.99 → $39.99 (20% off)

The Norse saga continues as Kratos and Atreus face the coming of Ragnarök in a critically acclaimed, emotional action game based on Norse mythology.

5. Elder Scrolls IV: Oblivion Remastered – $34.99 → $27.99 (20% off)

Bethesda’s beloved 2006 RPG returns with updated visuals and renewed mod support.

6. Monster Hunter Wilds – $69.99 → $55.99 (20% off)

Capcom’s beast-slaying series evolves with open environments and deeper co-op systems.

7. Assassin’s Creed: Shadows – $50.99 → $38.24 (20% off)

Set in feudal Japan, Ubisoft’s latest entry focuses on stealth and dual protagonists in a visually rich world.

8. Hogwarts Legacy – $59.99 → $14.99 (75% off)

A sprawling magical adventure set decades before the Harry Potter series, allowing you to make your own mark on Hogwarts.

9. Black Myth: Wukong – $59.99 → $47.99 (20% off)

A visually arresting, mythology-driven action game inspired by Journey to the West, earning rave reviews.

🔫 Action & shooters:

10. Call of Duty: Black Ops 6 – $69.99 → $38.49 (45% off)

Fast-paced, cinematic, and packed with multiplayer content, this entry is one of the franchise’s most aggressive yet.

11. Helldivers 2 – $39.99 → $31.99 (20% off)

Known for its frenetic co-op chaos, Helldivers 2 delivers intense firefights against alien threats with signature tongue-in-cheek humour.

12. S.T.A.L.K.E.R. 2: Heart of Chornobyl – $42.99 → $30.09 (30% off)

A haunting survival experience blending shooter mechanics with horror in a radioactive wasteland.

13. Red Dead Redemption 2 – $59.99 → $14.99 (75% off)

Rockstar’s sweeping western remains one of the most immersive story-driven games in recent memory.

14. Spider-Man 2 – $59.99 → $47.99 (20% off)

Swing through an expanded New York City with both Peter Parker and Miles Morales in this thrilling sequel, taking on new threats.

15. Indiana Jones and the Great Circle – $42.99 → $34.39 (20% off)

A globe-trotting adventure filled with relics, puzzles and old-school action, perfect for fans of the classic films.

🕹️ Other must-buy picks:

16. EA FC25 – $69.99 → $13.99 (80% off)

A rebrand of the FIFA series, FC25 offers sleek football gameplay at its lowest price yet.

Whether you’re building your backlog or finally grabbing a title you’ve waited months to try, the 2025 Steam Summer Sale has something for everyone.

The sale ends Thursday, July 10 at 10am Pacific Time. Players can access deals through the Steam client or at store.steampowered.com.

The Fraunhofer Institute for Electron Beam and Plasma Technology (FEP) sees its results as “valuable basis for optimizing lithium-ion batteries” in industry. According to Fraunhofer FEP, the technology developed by its researchers enables “the precise application of copper and aluminum layers onto polymer films to produce current collectors with electrical conductivity and thickness comparable to conventional metal foil-based current collectors.” While current collectors are also simply referred to as electrodes, Fraunhofer FEP consistently uses the term ‘current collectors’ in its announcement.

Battery electrodes are produced by coating the active materials (such as a lithium, nickel, manganese, cobalt and binder mixture in an NMC cathode) onto a thin metal foil. This metal foil – typically aluminium for the cathode and copper for the anode – serves as the carrier, while the electrochemical reaction for energy storage takes place on the coating surface. The innovation here focuses precisely on replacing these carrier foils inside the electrodes.

The research team in Dresden has substituted the pure metal foil with a polymer film coated on both sides with a thin layer of aluminium or copper – offering similar conductivity properties to pure metal foils. Both copper and aluminium coatings are around one micrometre thick. According to Fraunhofer FEP, the coated polymer films remained “free of significant wrinkling – ideal for further processing in battery production.” The coatings themselves were applied using electron beam evaporation.

“The challenge was to design the polymer films and the coating process in such a way that the thickness of the current collector could be comparable to that of current metal films and the metal layer could have optimum electrical conductivity,” says technical project manager Claus Luber. The team succeeded and demonstrated the deposition of thick copper and aluminium layers onto 12-micrometre PET films. Deposition was carried out in a roll-to-roll process on web widths up to 60 centimetres.

Polymer films offer two main advantages. Firstly, they are lighter than pure metal foils due to the ultra-thin metal coating of just one micrometre per side. This reduces electrode weight and increases the battery cell’s energy density. More importantly, however, is the safety aspect. Should an internal short circuit occur, the polymer substrate melts, interrupting the current path. “ This prevents heat from continuing to build up and causing thermal runaway,” explain the researchers.

In the PolySafe project funded by Germany’s Federal Ministry of Education and Research (BMBF), the team demonstrated not only the production process but also a functional cell based on these electrodes. Project partner TU Braunschweig manufactured pouch cells using the metal-polymer current collectors. “These cells were tested for their electrochemical properties and compared with conventional reference cells. In these tests, the cells with metal-on-polymer current collectors performed similarly to the reference cells in terms of performance and cycle stability at different charging and discharging rates,” reports Fraunhofer FEP.

fraunhofer.de